[pysal] 01/06: Imported Upstream version 1.12.0
Bas Couwenberg
sebastic at debian.org
Fri Sep 30 10:55:46 UTC 2016
This is an automated email from the git hooks/post-receive script.
sebastic pushed a commit to branch master
in repository pysal.
commit 9f42f73fe7e6f3d114b568b25e31ab836e08d8f0
Author: Bas Couwenberg <sebastic at xs4all.nl>
Date: Fri Sep 30 11:45:21 2016 +0200
Imported Upstream version 1.12.0
---
.travis.yml | 29 +-
.travis_testing.sh | 2 +-
CHANGELOG.txt | 112 +
doc/source/conf.py | 4 +-
doc/source/index.rst | 2 +-
doc/source/library/contrib/index.rst | 10 +-
doc/source/library/spreg/index.rst | 2 +
doc/source/library/spreg/sur.rst | 12 +
doc/source/library/spreg/sur_error.rst | 12 +
doc/source/library/spreg/sur_lag.rst | 12 +
pysal/.gitignore | 10 +
pysal/__init__.py | 23 +-
pysal/cg/comparators.py | 153 +
pysal/cg/shapes.py | 40 +-
pysal/cg/sphere.py | 152 +-
pysal/cg/tests/test_sphere.py | 109 +
pysal/common.py | 96 +-
pysal/contrib/README.rst | 10 +-
pysal/contrib/geotable/__init__.py | 1 +
pysal/contrib/geotable/config.py | 64 +
pysal/contrib/geotable/io.py | 38 +
pysal/contrib/geotable/ops/__init__.py | 2 +
pysal/contrib/geotable/ops/_accessors.py | 45 +
pysal/contrib/geotable/ops/_shapely.py | 174 +
pysal/contrib/geotable/ops/atomic.py | 10 +
pysal/contrib/geotable/ops/tabular.py | 118 +
.../geotable/ops}/tests/__init__.py | 0
pysal/contrib/geotable/ops/tests/test_accessors.py | 356 +
pysal/contrib/geotable/ops/tests/test_shapely.py | 215 +
pysal/contrib/geotable/ops/tests/test_tabular.py | 72 +
.../geotable}/tests/__init__.py | 0
pysal/contrib/geotable/tests/test_utils.py | 13 +
pysal/contrib/geotable/utils.py | 70 +
pysal/contrib/glm/README.md | 30 +
pysal/contrib/glm/__init__.py | 4 +
pysal/contrib/glm/base.py | 408 +
pysal/contrib/glm/family.py | 1845 +++
pysal/contrib/glm/glm.py | 365 +
pysal/contrib/glm/iwls.py | 79 +
pysal/contrib/glm/links.py | 953 ++
pysal/contrib/glm/tests/test_glm.py | 993 ++
pysal/contrib/glm/utils.py | 350 +
pysal/contrib/glm/varfuncs.py | 284 +
pysal/contrib/network/klincs.py | 18 +-
pysal/contrib/network/lincs.py | 52 +-
pysal/contrib/pdio/__init__.py | 4 +
.../{pdutilities/dbf_utilities.py => pdio/dbf.py} | 0
.../file_utilities.py => pdio/file.py} | 4 +-
.../{pdutilities/shp_utilities.py => pdio/shp.py} | 0
pysal/contrib/pdutilities/__init__.py | 4 -
pysal/contrib/shapely_ext.py | 4 +-
pysal/contrib/spint/README.md | 48 +-
pysal/contrib/spint/__init__.py | 6 +
pysal/contrib/spint/count_model.py | 191 +
pysal/contrib/spint/dispersion.py | 102 +
pysal/contrib/spint/gravity.py | 1670 ++-
pysal/contrib/spint/gravity_stats.py | 144 -
pysal/contrib/spint/notebooks/4d_distance.ipynb | 531 +
pysal/contrib/spint/notebooks/Gaussian_SAR.ipynb | 791 ++
pysal/contrib/spint/notebooks/IV_FLOWS.ipynb | 195 +
.../contrib/spint/notebooks/NYC_Bike_Example.ipynb | 661 +
.../contrib/spint/notebooks/New_DistanceBand.ipynb | 326 +
pysal/contrib/spint/notebooks/ODW_example.ipynb | 288 +
pysal/contrib/spint/notebooks/OD_weights.ipynb | 254 +
pysal/contrib/spint/notebooks/Vec_SA_Test.ipynb | 389 +
pysal/contrib/spint/notebooks/autograd_test.ipynb | 128 +
.../contrib/spint/notebooks/dispersion_test.ipynb | 455 +
.../examples/Example_NYCBikes_AllFeatures.ipynb | 919 ++
pysal/contrib/spint/notebooks/glm_speed.ipynb | 360 +
pysal/contrib/spint/notebooks/local_SI.ipynb | 232 +
pysal/contrib/spint/notebooks/netW.ipynb | 305 +
.../spint/notebooks/sparse_categorical.ipynb | 184 +
.../notebooks/sparse_categorical_bottleneck.ipynb | 79 +
.../spint/notebooks/sparse_categorical_speed.ipynb | 596 +
pysal/contrib/spint/notebooks/sparse_grav.ipynb | 457 +
.../spint/notebooks/sparse_scipy_optim.ipynb | 165 +
.../spint/notebooks/sparse_vs_dense_grav.ipynb | 499 +
pysal/contrib/spint/notebooks/test_grav.ipynb | 293 +
.../contrib/spint/notebooks/validate_gravity.ipynb | 966 ++
pysal/contrib/spint/tests/test_count_model.py | 87 +
pysal/contrib/spint/tests/test_dispersion.py | 47 +
pysal/contrib/spint/tests/test_gravity.py | 803 +-
pysal/contrib/spint/tests/test_gravity_stats.py | 193 -
pysal/contrib/spint/tests/test_vec_SA.py | 54 +
pysal/contrib/spint/utils.py | 120 +
pysal/contrib/spint/vec_SA.py | 342 +
pysal/contrib/viz/plot.py | 52 +
pysal/core/IOHandlers/__init__.py | 5 +
pysal/core/IOHandlers/db.py | 88 +
pysal/core/IOHandlers/geoda_txt.py | 2 +-
pysal/core/IOHandlers/pyDbfIO.py | 6 +-
pysal/core/IOHandlers/tests/test_db.py | 55 +
pysal/core/Tables.py | 23 +-
pysal/core/tests/test_Tables.py | 29 +
pysal/esda/gamma.py | 25 +-
pysal/esda/geary.py | 53 +-
pysal/esda/getisord.py | 111 +-
pysal/esda/join_counts.py | 60 +-
pysal/esda/mapclassify.py | 308 +-
pysal/esda/mixture_smoothing.py | 12 +-
pysal/esda/moran.py | 394 +-
pysal/esda/smoothing.py | 481 +-
pysal/esda/tabular.py | 306 +
pysal/esda/tests/test_gamma.py | 31 +-
pysal/esda/tests/test_geary.py | 25 +-
pysal/esda/tests/test_getisord.py | 30 +-
pysal/esda/tests/test_join_counts.py | 27 +-
pysal/esda/tests/test_mapclassify.py | 35 +-
pysal/esda/tests/test_moran.py | 70 +-
pysal/esda/tests/test_smoothing.py | 399 +-
pysal/examples/Polygon_Holes/Polygon_Holes.cpg | 1 +
pysal/examples/Polygon_Holes/Polygon_Holes.dbf | Bin 0 -> 498 bytes
pysal/examples/Polygon_Holes/Polygon_Holes.prj | 1 +
pysal/examples/Polygon_Holes/Polygon_Holes.qpj | 1 +
pysal/examples/Polygon_Holes/Polygon_Holes.shp | Bin 0 -> 1528 bytes
pysal/examples/Polygon_Holes/Polygon_Holes.shx | Bin 0 -> 124 bytes
pysal/examples/nyc_bikes/nyc_bikes_ct.csv | 14043 +++++++++++++++++++
pysal/examples/nyc_bikes/nyct2010.dbf | Bin 0 -> 381602 bytes
pysal/examples/nyc_bikes/nyct2010.prj | 1 +
pysal/examples/nyc_bikes/nyct2010.shp | Bin 0 -> 2648092 bytes
pysal/examples/nyc_bikes/nyct2010.shp.xml | 1 +
pysal/examples/nyc_bikes/nyct2010.shx | Bin 0 -> 17428 bytes
pysal/inequality/gini.py | 89 +-
pysal/inequality/tests/test_gini.py | 33 +
pysal/network/Network_Usage.ipynb | 689 +
pysal/network/analysis.py | 50 +-
pysal/network/network.py | 541 +-
pysal/network/tests/test_network.py | 15 +-
pysal/network/util.py | 130 +-
pysal/spatial_dynamics/markov.py | 648 +-
pysal/spatial_dynamics/tests/test_markov.py | 107 +-
pysal/spreg/diagnostics_sur.py | 60 +-
pysal/spreg/ml_error.py | 23 +-
pysal/spreg/ml_lag.py | 24 +-
pysal/spreg/sputils.py | 275 +
pysal/spreg/sur.py | 155 +-
pysal/spreg/tests/skip.py | 6 +
pysal/spreg/tests/test_diagnostics.py | 1 -
pysal/spreg/tests/test_ml_error.py | 73 +-
pysal/spreg/tests/test_ml_error_regimes.py | 6 +-
pysal/spreg/tests/test_ml_lag.py | 65 +-
pysal/spreg/tests/test_ml_lag_regimes.py | 7 +-
pysal/spreg/tests/test_sputils.py | 134 +
pysal/spreg/tests/test_sur_error.py | 32 +-
pysal/spreg/user_output.py | 39 +-
pysal/spreg/utils.py | 228 +-
pysal/version.py | 4 +-
pysal/weights/Contiguity.py | 429 +-
pysal/weights/Distance.py | 541 +-
pysal/weights/Wsets.py | 4 +-
pysal/weights/__init__.py | 5 -
pysal/weights/_contW_binning.py | 205 +-
pysal/weights/_contW_lists.py | 90 +
pysal/weights/_contW_rtree.py | 10 -
pysal/weights/spintW.py | 254 +
pysal/weights/tests/test_Contiguity.py | 145 +-
pysal/weights/tests/test_Distance.py | 421 +-
pysal/weights/tests/test__contW_binning.py | 10 +-
...test__contW_binning.py => test__contW_lists.py} | 10 +-
pysal/weights/tests/test_spintW.py | 173 +
pysal/weights/tests/test_weights.py | 4 +-
pysal/weights/user.py | 171 +-
pysal/weights/util.py | 49 +-
pysal/weights/weights.py | 76 +-
pysal/weights/weights_from_geojson.ipynb | 311 -
requirements_plus.txt | 12 +
setup.py | 4 +-
tools/github_stats.py | 7 +-
travis.txt | 4 +-
169 files changed, 39545 insertions(+), 3744 deletions(-)
diff --git a/.travis.yml b/.travis.yml
index 39c323d..cc68bc0 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -4,10 +4,13 @@ branches:
only:
- master
python:
- - 2.7
- #- "2.6"
- - 3.4
- - 3.5
+ - "2.7"
+ - "3.4"
+ - "3.5"
+
+env:
+ - PYSAL_PLUS=false
+ - PYSAL_PLUS=true
before_install:
- wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
@@ -15,27 +18,23 @@ before_install:
- ./miniconda.sh -b -p ./miniconda
- export PATH=`pwd`/miniconda/bin:$PATH
- conda update --yes conda
+ - conda config --add channels conda-forge
- conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION
- source activate test-env
- - chmod +x ./.travis_testing.sh
- - if [[ $TRAVIS_PYTHON_VERSION == 3* ]]; then rm -rf pysal/contrib; 2to3 -nw pysal/ > /dev/null; fi
+ - if [[ $TRAVIS_PYTHON_VERSION == 3* ]]; then 2to3 -nw pysal/ > /dev/null; fi
install:
- conda install --yes numpy scipy nose pip
+ - if [ "$PYSAL_PLUS" == true ]; then
+ echo 'plus testing'; conda install --yes --file requirements_plus.txt;
+ else conda install --yes --file requirements.txt;
+ fi;
- pip install -r travis.txt
script:
- - python -c 'import numpy; print(numpy.__version__)'
- - python -c 'import scipy; print(scipy.__version__)'
- - python setup.py install >/dev/null
- - python -c 'import pysal'
- #- python -c 'import multiprocessing as mp; print mp.cpu_count()'
- #- pychecker --limit 1000 pysal
- python setup.py sdist >/dev/null
- #- nosetests --processes=-1 --process-timeout=60 --verbosity=1 --ignore-files=collection --exclude-dir=pysal/contrib
- #- nosetests
- echo "check_stable=False" >pysal/config.py
- - ./.travis_testing.sh
+ - nosetests --with-coverage --cover-package=pysal;
#- cd doc; make pickle; make doctest
notifications:
email:
diff --git a/.travis_testing.sh b/.travis_testing.sh
index 78d5c1b..ac86b0f 100644
--- a/.travis_testing.sh
+++ b/.travis_testing.sh
@@ -1,5 +1,5 @@
if [[ $TRAVIS_PYTHON_VERSION == 3* ]]; then
- cd pysal; python -m 'nose';
+ nosetests --with-coverage --cover-package=pysal --exclude-dir=pysal/contrib
else
nosetests --with-coverage --cover-package=pysal;
fi
diff --git a/CHANGELOG.txt b/CHANGELOG.txt
index eba2568..b2e5fc7 100644
--- a/CHANGELOG.txt
+++ b/CHANGELOG.txt
@@ -1,3 +1,115 @@
+v<1.12.0>, 2016-09-21
+
+We closed a total of 100 issues, 33 pull requests and 67 regular issues;
+this is the full list (generated with the script
+:file:`tools/github_stats.py`):
+
+Pull Requests (33):
+
+* :ghpull:`864`: addressing issue #845 and adding tests
+* :ghpull:`862`: Memory efficient Gini and tests
+* :ghpull:`865`: fix space/tab inconsistency
+* :ghpull:`861`: GSOC -SpInt
+* :ghpull:`847`: spatial interaction weights
+* :ghpull:`863`: B859
+* :ghpull:`860`: Incoprate updates to db driver and unittests
+* :ghpull:`858`: Dev mltest
+* :ghpull:`857`: Fix TabErrors, replace tabs with spaces
+* :ghpull:`856`: Make the output and build reproducible
+* :ghpull:`851`: fixed typo in test_network.py
+* :ghpull:`850`: [REBASE] Distance band speed ups
+* :ghpull:`843`: update and clean aesthetic of Network_Usage.ipynb
+* :ghpull:`842`: typo correction in network.py
+* :ghpull:`841`: [REBASE & REDIRECT] Conditional Database Imports & Docos, #692
+* :ghpull:`840`: minor bugfix to #816
+* :ghpull:`839`: documentation cleanup on network.analysis.py
+* :ghpull:`838`: network.util.py documentation cleanup
+* :ghpull:`836`: re: network.py documentation cleanup
+* :ghpull:`768`: Modified the way area of a ring is calculated to allow for more precision
+* :ghpull:`829`: numba autojit _fisher_jenks_means if numba is available
+* :ghpull:`832`: Handling a deprecation warning, and latex errors corrected.
+* :ghpull:`834`: Travis testing matrix
+* :ghpull:`831`: Refactoring Markov classes for efficiency
+* :ghpull:`827`: ESDA Tabular Functions
+* :ghpull:`823`: typo and format of docstring of user.py in weights module
+* :ghpull:`821`: Pdio
+* :ghpull:`817`: D/sur
+* :ghpull:`818`: Documentation fix + some PEP8 standardization
+* :ghpull:`811`: DistanceBand should correctly handle named weights
+* :ghpull:`808`: Dev
+* :ghpull:`807`: Updating contrib docs and bumping version for dev
+* :ghpull:`797`: working moran plot func
+
+Issues (67):
+
+* :ghissue:`855`: Inefficient Gini Coefficient calculation?
+* :ghissue:`864`: addressing issue #845 and adding tests
+* :ghissue:`862`: Memory efficient Gini and tests
+* :ghissue:`865`: fix space/tab inconsistency
+* :ghissue:`861`: GSOC -SpInt
+* :ghissue:`847`: spatial interaction weights
+* :ghissue:`859`: Wrong there is one disconnected observation (no neighbors)
+* :ghissue:`863`: B859
+* :ghissue:`860`: Incoprate updates to db driver and unittests
+* :ghissue:`858`: Dev mltest
+* :ghissue:`857`: Fix TabErrors, replace tabs with spaces
+* :ghissue:`854`: handle verication context for githubstats
+* :ghissue:`856`: Make the output and build reproducible
+* :ghissue:`851`: fixed typo in test_network.py
+* :ghissue:`850`: [REBASE] Distance band speed ups
+* :ghissue:`846`: DistanceBand speed ups
+* :ghissue:`843`: update and clean aesthetic of Network_Usage.ipynb
+* :ghissue:`842`: typo correction in network.py
+* :ghissue:`692`: Conditional Database Import / Docos
+* :ghissue:`841`: [REBASE & REDIRECT] Conditional Database Imports & Docos, #692
+* :ghissue:`769`: Windows 7, 64 bit installation issue with visual C++ for python
+* :ghissue:`816`: Exception TypeError in geoda_txt.py
+* :ghissue:`840`: minor bugfix to #816
+* :ghissue:`839`: documentation cleanup on network.analysis.py
+* :ghissue:`397`: integrate optimized contiguity builder
+* :ghissue:`531`: add user space function to generate numpy arrays
+* :ghissue:`654`: meta update for 2-3 conversion
+* :ghissue:`676`: Meta not importable from pysal
+* :ghissue:`838`: network.util.py documentation cleanup
+* :ghissue:`753`: Fix the network ring bug
+* :ghissue:`836`: re: network.py documentation cleanup
+* :ghissue:`768`: Modified the way area of a ring is calculated to allow for more precision
+* :ghissue:`837`: re: network.allneighbordistances() diagonal fill
+* :ghissue:`822`: two issues about function choropleth_map in viz module
+* :ghissue:`835`: fix deprecation warnings noted in #822
+* :ghissue:`829`: numba autojit _fisher_jenks_means if numba is available
+* :ghissue:`832`: Handling a deprecation warning, and latex errors corrected.
+* :ghissue:`834`: Travis testing matrix
+* :ghissue:`825`: Headbanging Median Rate ignores edge correction
+* :ghissue:`826`: Spatial Filtering grid definition
+* :ghissue:`824`: Direct Age Standardization fails for empty regions
+* :ghissue:`833`: PySAL+ optional testing matrix
+* :ghissue:`830`: [REBASED] PySAL+ optional testing matrix
+* :ghissue:`831`: Refactoring Markov classes for efficiency
+* :ghissue:`827`: ESDA Tabular Functions
+* :ghissue:`815`: rook case not working in ContiguityWeightsPolygons
+* :ghissue:`828`: Fisher_Jenks pure python implementation is too slow
+* :ghissue:`814`: Explore Classmethods for alternative constructors
+* :ghissue:`795`: switch to scipy.linalg instead of numpy.linalg
+* :ghissue:`799`: w_subset(weights:W, ids:np.ndarray) constructs faulty weights object
+* :ghissue:`823`: typo and format of docstring of user.py in weights module
+* :ghissue:`821`: Pdio
+* :ghissue:`794`: spreg ML_lag doesn't always set W in __init__
+* :ghissue:`754`: Update README
+* :ghissue:`819`: add LIMAs
+* :ghissue:`817`: D/sur
+* :ghissue:`818`: Documentation fix + some PEP8 standardization
+* :ghissue:`809`: Fixed documentation
+* :ghissue:`813`: w.remap_ids(ids) never sets w.id_order_set
+* :ghissue:`775`: Added a prototype for constructing weights from a list of shapely Polygons
+* :ghissue:`810`: DistanceBand fails to accept custom ids
+* :ghissue:`811`: DistanceBand should correctly handle named weights
+* :ghissue:`780`: Doctests failing on travis
+* :ghissue:`801`: ImportError: No module named scipy.spatial
+* :ghissue:`808`: Dev
+* :ghissue:`807`: Updating contrib docs and bumping version for dev
+* :ghissue:`797`: working moran plot func
+
v<1.11.2>, 2016-05-18
We closed a total of 20 issues, 6 pull requests and 14 regular issues;
diff --git a/doc/source/conf.py b/doc/source/conf.py
index c9ac269..464ce65 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -45,9 +45,9 @@ copyright = u'2014-, PySAL Developers; 2009-13 Sergio Rey'
# built documents.
#
# The short X.Y version.
-version = '1.11.2'
+version = '1.12.0'
# The full version, including alpha/beta/rc tags.
-release = '1.11.2'
+release = '1.12.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
diff --git a/doc/source/index.rst b/doc/source/index.rst
index db61b7e..7373984 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -20,7 +20,7 @@ PySAL
.. sidebar:: Releases
- - `Stable 1.11.2 (Released 2016-5-18) <users/installation.html>`_
+ - `Stable 1.12.0 (Released 2016-9-21) <users/installation.html>`_
- `Development <http://github.com/pysal/pysal/tree/dev>`_
PySAL is an open source library of spatial analysis functions written in
diff --git a/doc/source/library/contrib/index.rst b/doc/source/library/contrib/index.rst
index b2a5a7a..64c207d 100644
--- a/doc/source/library/contrib/index.rst
+++ b/doc/source/library/contrib/index.rst
@@ -66,13 +66,19 @@ Currently the following contribs are available:
- Path: pysal.contrib.pdutilities
- Requires: `pandas`_
- 8. Githooks -- Optional hooks for `git` to make development on `PySAL` easier
+ 8. Spatial Interaction -- Tools for spatial interaction (SpInt) modeling.
+
+ - .. versionadded:: 1.10
+ - Path: pysal.contrib.spint
+ - Requires: `pandas`_
+
+ 9. Githooks -- Optional hooks for `git` to make development on `PySAL` easier
- .. versionadded:: 1.10
- Path: pysal.contrib.githooks (Note: not importable)
- Requires: `git`
- 9. Handler -- A model ingester to standardize model extension
+ 10. Handler -- A model ingester to standardize model extension
- .. versionadded:: 1.10
- Path: pysal.contrib.handler
diff --git a/doc/source/library/spreg/index.rst b/doc/source/library/spreg/index.rst
index 1559842..42eec1b 100644
--- a/doc/source/library/spreg/index.rst
+++ b/doc/source/library/spreg/index.rst
@@ -26,5 +26,7 @@
ml_lag
ml_lag_regimes
sur
+ sur_error
+ sur_lag
diff --git a/doc/source/library/spreg/sur.rst b/doc/source/library/spreg/sur.rst
new file mode 100644
index 0000000..3856f24
--- /dev/null
+++ b/doc/source/library/spreg/sur.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.sur` --- Seeming Unrelated Regression
+=================================================
+
+The :mod:`spreg.sur` module provides SUR estimation.
+
+.. versionadded:: 1.11
+
+
+.. automodule:: pysal.spreg.sur
+ :synopsis: Code for spreg SUR
+ :members:
+
diff --git a/doc/source/library/spreg/sur_error.rst b/doc/source/library/spreg/sur_error.rst
new file mode 100644
index 0000000..3336b03
--- /dev/null
+++ b/doc/source/library/spreg/sur_error.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.sur_error` --- Spatial Error Seeming Unrelated Regression
+=====================================================================
+
+The :mod:`spreg.sur_error` module provides SUR estimation for spatial error model
+
+.. versionadded:: 1.11
+
+
+.. automodule:: pysal.spreg.sur_error
+ :synopsis: Code for spreg SUR error
+ :members:
+
diff --git a/doc/source/library/spreg/sur_lag.rst b/doc/source/library/spreg/sur_lag.rst
new file mode 100644
index 0000000..50a147a
--- /dev/null
+++ b/doc/source/library/spreg/sur_lag.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.sur_lag` --- Spatial Lag Seeming Unrelated Regression
+=================================================================
+
+The :mod:`spreg.sur_lag` module provides SUR estimation for spatial lag model
+
+.. versionadded:: 1.11
+
+
+.. automodule:: pysal.spreg.sur_lag
+ :synopsis: Code for spreg SUR lag
+ :members:
+
diff --git a/pysal/.gitignore b/pysal/.gitignore
new file mode 100644
index 0000000..774fb73
--- /dev/null
+++ b/pysal/.gitignore
@@ -0,0 +1,10 @@
+contrib/points/
+contrib/viz/Untitled.ipynb
+contrib/viz/color.ipynb
+contrib/viz/dispatch.ipynb
+contrib/viz/geotable_plot.html
+contrib/viz/rect5.png
+contrib/viz/selected.png
+inequality/gini_decomp.ipynb
+../tools/gh_api.py
+
diff --git a/pysal/__init__.py b/pysal/__init__.py
index 730c7e9..e7e1630 100644
--- a/pysal/__init__.py
+++ b/pysal/__init__.py
@@ -40,6 +40,18 @@ Utilities
import pysal.cg
import pysal.core
+try:
+ import pandas
+ from pysal.contrib import pdio
+ pysal.common.pandas = pandas
+except ImportError:
+ pysal.common.pandas = None
+
+# Load the IOHandlers
+from pysal.core import IOHandlers
+# Assign pysal.open to dispatcher
+open = pysal.core.FileIO.FileIO
+
from pysal.version import version
#from pysal.version import stable_release_date
#import urllib2, json
@@ -84,17 +96,6 @@ import pysal.spreg
import pysal.examples
from pysal.network.network import Network, NetworkG, NetworkK, NetworkF
-try:
- import pandas
- from pysal.contrib import pdutilities as pdio
-except ImportError:
- print('Pandas adapters not loaded')
-
-# Load the IOHandlers
-from pysal.core import IOHandlers
-# Assign pysal.open to dispatcher
-open = pysal.core.FileIO.FileIO
-
#__all__=[]
#import esda,weights
#__all__+=esda.__all__
diff --git a/pysal/cg/comparators.py b/pysal/cg/comparators.py
new file mode 100644
index 0000000..147c919
--- /dev/null
+++ b/pysal/cg/comparators.py
@@ -0,0 +1,153 @@
+import numpy as np
+from pysal.common import RTOL
+from .shapes import Point, Chain, Polygon, Geometry
+
+#### This approach is invalid :(
+#### OGC has a different definition of equality, so we can't be ogc-compliant
+#### by focusing on vertex set relationships. For example:
+
+# from shapely import geometry as geom
+# p1 = geom.Polygon([(0,0),(0,1),(1,0)])
+# p2 = geom.Polygon([(0,0),(0,1),(.5,.5),(1,0)])
+# p1.equals(p2) #is true
+# no relationship on point set (without simplifying boundaries) will make these
+# approaches work.
+
+ATOL = 1e-12
+
+def _point_equal(a,b):
+ """
+ Test that a point is exactly equal to another point, using numpy array
+ comparison
+ """
+ return np.array_equal(a._Point__loc, b._Point__loc)
+
+def _point_almost_equal(a,b, rtol=RTOL, atol=ATOL):
+ """
+ test that one point is equal to another point, up to a specified relative or
+ absolute tolerance.
+ """
+ return np.allclose(a._Point__loc, b._Point__loc,
+ rtol=rtol, atol=atol)
+
+def _chain_equal(a,b):
+ """
+ Test that two chains are equal. This considers reversed-incident chains,
+ chains with the same pointset but in reverse order, as different chains.
+ """
+ for a_part, b_part in zip(a.parts, b.parts):
+ for a_seg, b_seg in zip(a_part, b_part):
+ if not np.array_equal(a_seg, b_seg):
+ return False
+ return True
+
+def _chain_almost_equal(a,b, rtol=RTOL, atol=ATOL):
+ """
+ Test that two chains are equal, up to a specified relative or absolute
+ tolerance. This considers reversed-incident chains,
+ chains with the same pointset but in reverse order, as different chains.
+ """
+ for a_part, b_part in zip(a.parts, b.parts):
+ for a_seg, b_seg in zip(a_part, b_part):
+ if not np.allclose(a_seg, b_seg,
+ rtol=RTOL, atol=ATOL):
+ return False
+ return True
+
+def _poly_exactly_equal(a,b):
+ """
+ Test that two polygons are equal. This is a straightfoward linear comparison
+ of parts and holes. That is, it is assumed that the parts and holes are
+ conformal.
+
+ Thus, shapes with the same parts or holes, but listed in a different order,
+ will be considered different. Solving this will require some way to relate
+ ring/hole sets
+ """
+ for a_hole, b_hole in zip(a.holes, b.holes):
+ if not np.array_equal(a_hole, b_hole):
+ return False
+ for a_part, b_part in zip(a.parts, b.parts):
+ if not np.array_equal(a_part, b_part):
+ return False
+ return True
+
+def _poly_exactly_coincident(a,b):
+ """
+ Check that two polygons have coincident boundaries
+
+ Thus, this returns True when two polygons have the same holes & parts in any
+ order with potentially-reversed path directions
+ """
+ n_holes = len(a.holes)
+ n_parts = len(a.parts)
+ if n_holes != len(b.holes):
+ return False
+ if n_parts != len(b.parts):
+ return False
+ b_in_a = [None]*n_holes
+ a_in_b = [None]*n_holes
+ for i, a_hole in a.holes:
+ for j, b_hole in b.holes:
+ i_j = coincident(a_hole, b_hole)
+ if i_j:
+ b_in_a[j] = True
+ a_in_b[i] = True
+ break
+ if not a_in_b[i]:
+ return False
+ if any(in_b):
+ return False
+ for b_hole in b.holes:
+ in_a = [not coincident(b_hole, a_hole) for a_hole in a.holes]
+ if any(in_a):
+ return False
+ return True
+
+def _coincident(a,b):
+ """
+ Check if two vertex lists are equal, either forwards or backwards. Thus,
+ this checks for equality of the path or equality when one path is reversed.
+ """
+ return np.array_equal(a, b) or np.array_equal(np.flipud(a),b)
+
+def _almost_coincident(a,b, rtol=RTOL, atol=ATOL):
+ """
+ Check if two vertex lists are equal to within a given relative and absolute
+ tolernance, either forwards or backwards. Thus,
+ this checks for equality of the path or equality when one path is reversed.
+ """
+ return (np.allclose(a, b, rtol=RTOL, atol=ATOL)
+ or np.allclose(np.flipud(a),b, rtol=RTOL, atol=ATOL))
+
+def _poly_almost_equal(a,b, rtol=RTOL, atol=ATOL):
+ is_equal = True
+ for a_hole, b_hole in zip(a.holes, b.holes):
+ is_equal &= np.allclose(a_hole, b_hole,
+ rtol=rtol, atol=atol)
+ for a_part, b_part in zip(a.parts, b.parts):
+ is_equal &= np.allclose(a_part, b_part,
+ rtol=rtol, atol=atol)
+ return is_equal
+
+_EQ_MAP = {Point:_point_equal,
+ Polygon:_poly_equal,
+ Chain: _chain_equal}
+_AEQ_MAP = {Point:_point_almost_equal,
+ Polygon:_poly_almost_equal,
+ Chain: _chain_almost_equal}
+
+def equal(a,b):
+ ta, tb = type(a), type(b)
+ if not all([isinstance(a, tb), isinstance(b, ta)]):
+ return False
+ return _EQ_MAP[ta](a,b)
+
+def almost_equal(a,b, rtol=RTOL, atol=ATOL):
+ ta, tb = type(a), type(b)
+ if not all([isinstance(a, tb), isinstance(b, ta)]):
+ return False
+ return _AEQ_MAP[ta](a,b)
+
+def is_shape(a):
+ return isinstance(a, Geometry)
diff --git a/pysal/cg/shapes.py b/pysal/cg/shapes.py
index 6f10097..d0fc325 100644
--- a/pysal/cg/shapes.py
+++ b/pysal/cg/shapes.py
@@ -10,6 +10,7 @@ import doctest
import math
from warnings import warn
from sphere import arcdist
+import numpy as np
__all__ = ['Point', 'LineSegment', 'Line', 'Ray', 'Chain', 'Polygon',
'Rectangle', 'asShape']
@@ -20,6 +21,8 @@ def asShape(obj):
Returns a pysal shape object from obj.
obj must support the __geo_interface__.
"""
+ if isinstance(obj, (Point, LineSegment, Line, Ray, Chain, Polygon)):
+ return obj
if hasattr(obj, '__geo_interface__'):
geo = obj.__geo_interface__
else:
@@ -35,8 +38,15 @@ def asShape(obj):
raise NotImplementedError(
"%s is not supported at this time." % geo_type)
+class Geometry(object):
+ """
+ A base class to help implement is_geometry and make geometric types
+ extendable.
+ """
+ def __init__(self):
+ pass
-class Point(object):
+class Point(Geometry):
"""
Geometric class for point objects.
@@ -319,7 +329,7 @@ class Point(object):
>>> Point((0,1))
(0.0, 1.0)
"""
- return self.__loc.__repr__()
+ return str(self)
def __str__(self):
"""
@@ -340,9 +350,10 @@ class Point(object):
'(1.0, 3.0)'
"""
return str(self.__loc)
+ return "POINT ({} {})".format(*self.__loc)
-class LineSegment(object):
+class LineSegment(Geometry):
"""
Geometric representation of line segment objects.
@@ -394,6 +405,8 @@ class LineSegment(object):
def __str__(self):
return "LineSegment(" + str(self._p1) + ", " + str(self._p2) + ")"
+ return "LINESTRING ({} {}, {} {})".format(self._p1[0], self._p1[1],
+ self._p2[0], self._p2[1])
def __eq__(self, other):
"""
@@ -771,7 +784,7 @@ class LineSegment(object):
return self._line
-class VerticalLine:
+class VerticalLine(Geometry):
"""
Geometric representation of verticle line objects.
@@ -848,7 +861,7 @@ class VerticalLine:
return float('nan')
-class Line:
+class Line(Geometry):
"""
Geometric representation of line objects.
@@ -977,7 +990,7 @@ class Ray:
self.p = second_p
-class Chain(object):
+class Chain(Geometry):
"""
Geometric representation of a chain, also known as a polyline.
@@ -1013,7 +1026,7 @@ class Chain(object):
else:
self._vertices = [vertices]
self._reset_props()
-
+
@classmethod
def __from_geo_interface__(cls, geo):
if geo['type'].lower() == 'linestring':
@@ -1175,7 +1188,7 @@ class Chain(object):
return [[LineSegment(a, b) for (a, b) in zip(part[:-1], part[1:])] for part in self._vertices]
-class Ring(object):
+class Ring(Geometry):
"""
Geometric representation of a Linear Ring
@@ -1285,9 +1298,10 @@ class Ring(object):
A = 0.0
for i in xrange(N - 1):
- A += (x[i] * y[i + 1] - x[i + 1] * y[i])
- A = A / 2.0
- self._area = A
+ A += (x[i] + x[i + 1]) * \
+ (y[i] - y[i + 1])
+ A = A * 0.5
+ self._area = -A
return self._area
@property
@@ -1380,7 +1394,7 @@ class Ring(object):
-class Polygon(object):
+class Polygon(Geometry):
"""
Geometric representation of polygon objects.
@@ -1751,7 +1765,7 @@ class Polygon(object):
-class Rectangle:
+class Rectangle(Geometry):
"""
Geometric representation of rectangle objects.
diff --git a/pysal/cg/sphere.py b/pysal/cg/sphere.py
index df670c9..324ed7c 100644
--- a/pysal/cg/sphere.py
+++ b/pysal/cg/sphere.py
@@ -21,7 +21,7 @@ from math import pi, cos, sin, asin
__all__ = ['RADIUS_EARTH_KM', 'RADIUS_EARTH_MILES', 'arcdist', 'arcdist2linear', 'brute_knn', 'fast_knn', 'fast_threshold', 'linear2arcdist', 'toLngLat', 'toXYZ', 'lonlat','harcdist','geointerpolate','geogrid']
-RADIUS_EARTH_KM = 6371.0
+RADIUS_EARTH_KM = 6371.0
RADIUS_EARTH_MILES = (
RADIUS_EARTH_KM * scipy.constants.kilo) / scipy.constants.mile
@@ -39,7 +39,7 @@ def arcdist(pt0, pt1, radius=RADIUS_EARTH_KM):
Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html
- Returns
+ Returns
-------
The arc distance between pt0 and pt1 using supplied radius
@@ -200,54 +200,54 @@ def fast_threshold(pts, dist, radius=RADIUS_EARTH_KM):
l.remove(i)
wd[i] = l
return wd
-
-
-########### new functions
+
+
+########### new functions
def lonlat(pointslist):
"""
Converts point order from lat-lon tuples to lon-lat (x,y) tuples
-
+
Parameters
----------
-
+
pointslist : list of lat-lon tuples (Note, has to be a list, even for one point)
-
+
Returns
-------
-
+
newpts : list with tuples of points in lon-lat order
-
+
Example
-------
>>> points = [(41.981417, -87.893517), (41.980396, -87.776787), (41.980906, -87.696450)]
>>> newpoints = lonlat(points)
>>> newpoints
[(-87.893517, 41.981417), (-87.776787, 41.980396), (-87.69645, 41.980906)]
-
+
"""
newpts = [(i[1],i[0]) for i in pointslist]
return newpts
-
+
def haversine(x):
"""
Computes the haversine formula
-
+
Parameters
----------
x : angle in radians
-
+
Returns
-------
: square of sine of half the radian (the haversine formula)
-
+
Example
-------
>>> haversine(math.pi) # is 180 in radians, hence sin of 90 = 1
1.0
-
+
"""
-
+
x = math.sin(x/2)
return x*x
@@ -262,73 +262,73 @@ r2d = lambda x: x * 180.0 / math.pi
def radangle(p0,p1):
"""
Radian angle between two points on a sphere in lon-lat (x,y)
-
+
Parameters
----------
p0 : first point as a lon,lat tuple
p1 : second point as a lon,lat tuple
-
+
Returns
-------
d : radian angle in radians
-
+
Example
-------
>>> p0 = (-87.893517, 41.981417)
>>> p1 = (-87.519295, 41.657498)
>>> radangle(p0,p1)
0.007460167953189258
-
+
Note
----
Uses haversine formula, function haversine and degree to radian
conversion lambda function d2r
-
+
"""
x0, y0 = d2r(p0[0]),d2r(p0[1])
x1, y1 = d2r(p1[0]),d2r(p1[1])
- d = 2.0 * math.asin(math.sqrt(haversine(y1 - y0) +
+ d = 2.0 * math.asin(math.sqrt(haversine(y1 - y0) +
math.cos(y0) * math.cos(y1)*haversine(x1 - x0)))
return d
def harcdist(p0,p1,lonx=True,radius=RADIUS_EARTH_KM):
"""
Alternative arc distance function, uses haversine formula
-
+
Parameters
----------
p0 : first point as a tuple in decimal degrees
p1 : second point as a tuple in decimal degrees
- lonx : boolean to assess the order of the coordinates,
+ lonx : boolean to assess the order of the coordinates,
for lon,lat (default) = True, for lat,lon = False
radius : radius of the earth at the equator as a sphere
default: RADIUS_EARTH_KM (6371.0 km)
options: RADIUS_EARTH_MILES (3959.0 miles)
None (for result in radians)
-
+
Returns
-------
d : distance in units specified, km, miles or radians (for None)
-
+
Example
-------
>>> p0 = (-87.893517, 41.981417)
>>> p1 = (-87.519295, 41.657498)
- >>> harcdist(p0,p1)
+ >>> harcdist(p0,p1)
47.52873002976876
>>> harcdist(p0,p1,radius=None)
0.007460167953189258
-
+
Note
----
Uses radangle function to compute radian angle
-
+
"""
if not(lonx):
p = lonlat([p0,p1])
p0 = p[0]
p1 = p[1]
-
+
d = radangle(p0,p1)
if radius is not None:
d = d*radius
@@ -336,25 +336,25 @@ def harcdist(p0,p1,lonx=True,radius=RADIUS_EARTH_KM):
def geointerpolate(p0,p1,t,lonx=True):
"""
- Finds a point on a sphere along the great circle distance between two points
+ Finds a point on a sphere along the great circle distance between two points
on a sphere
also known as a way point in great circle navigation
-
+
Parameters
----------
p0 : first point as a tuple in decimal degrees
p1 : second point as a tuple in decimal degrees
t : proportion along great circle distance between p0 and p1
e.g., t=0.5 would find the mid-point
- lonx : boolean to assess the order of the coordinates,
- for lon,lat (default) = True, for lat,lon = False
-
+ lonx : boolean to assess the order of the coordinates,
+ for lon,lat (default) = True, for lat,lon = False
+
Returns
-------
x,y : tuple in decimal degrees of lon-lat (default) or lat-lon,
depending on setting of lonx; in other words, the same
order is used as for the input
-
+
Example
-------
>>> p0 = (-87.893517, 41.981417)
@@ -365,23 +365,23 @@ def geointerpolate(p0,p1,t,lonx=True):
>>> p4 = (41.657498, -87.519295)
>>> geointerpolate(p3,p4,0.1,lonx=False) # using lat-lon
(41.949079912574796, -87.85592403438788)
-
+
"""
-
+
if not(lonx):
p = lonlat([p0,p1])
p0 = p[0]
p1 = p[1]
-
+
d = radangle(p0,p1)
k = 1.0 / math.sin(d)
t = t*d
A = math.sin(d-t) * k
B = math.sin(t) * k
-
+
x0, y0 = d2r(p0[0]),d2r(p0[1])
x1, y1 = d2r(p1[0]),d2r(p1[1])
-
+
x = A * math.cos(y0) * math.cos(x0) + B * math.cos(y1) * math.cos(x1)
y = A * math.cos(y0) * math.sin(x0) + B * math.cos(y1) * math.sin(x1)
z = A * math.sin(y0) + B * math.sin(y1)
@@ -391,33 +391,33 @@ def geointerpolate(p0,p1,t,lonx=True):
if not(lonx):
return newpy,newpx
return newpx,newpy
-
+
def geogrid(pup,pdown,k,lonx=True):
"""
Computes a k+1 by k+1 set of grid points for a bounding box in lat-lon
uses geointerpolate
-
+
Parameters
----------
pup : tuple with lat-lon or lon-lat for upper left corner of bounding box
pdown : tuple with lat-lon or lon-lat for lower right corner of bounding box
k : number of grid cells (grid points will be one more)
- lonx : boolean to assess the order of the coordinates,
+ lonx : boolean to assess the order of the coordinates,
for lon,lat (default) = True, for lat,lon = False
-
+
Returns
-------
grid : list of tuples with lat-lon or lon-lat for grid points, row by row,
starting with the top row and moving to the bottom; coordinate tuples
are returned in same order as input
-
+
Example
-------
>>> pup = (42.023768,-87.946389) # Arlington Heights IL
>>> pdown = (41.644415,-87.524102) # Hammond, IN
>>> geogrid(pup,pdown,3,lonx=False)
[(42.023768, -87.946389), (42.02393997819538, -87.80562679358316), (42.02393997819538, -87.66486420641684), (42.023768, -87.524102), (41.897317, -87.94638900000001), (41.8974888973743, -87.80562679296166), (41.8974888973743, -87.66486420703835), (41.897317, -87.524102), (41.770866000000005, -87.94638900000001), (41.77103781320412, -87.80562679234043), (41.77103781320412, -87.66486420765956), (41.770866000000005, -87.524102), (41.644415, -87.946389), (41.64458672568646, -87.8056267917 [...]
-
+
"""
if lonx:
corners = [pup,pdown]
@@ -435,7 +435,7 @@ def geogrid(pup,pdown,k,lonx=True):
rightside.append(newpr)
leftside.append(leftcorners[1])
rightside.append(rightcorners[1])
-
+
grid = []
for i in range(len(leftside)):
grid.append(leftside[i])
@@ -446,57 +446,3 @@ def geogrid(pup,pdown,k,lonx=True):
if not(lonx):
grid = lonlat(grid)
return grid
-
-
-
-if __name__ == '__main__':
- def random_ll():
- long = (random.random() * 360) - 180
- lat = (random.random() * 180) - 90
- return long, lat
-
- for i in range(1):
- n = 99
- # generate random surface points.
- pts = [random_ll() for i in xrange(n)]
- # convert to unit sphere points.
- pts2 = map(toXYZ, pts)
-
- w = brute_knn(pts, 4, 'arc')
- w2 = brute_knn(pts2, 4, 'xyz')
- w3 = fast_knn(pts2, 4)
- assert w == w2 == w3
-
- ### Make knn1
- import pysal
-# f = pysal.open('/Users/charlie/Documents/data/stl_hom/stl_hom.shp', 'r')
- f = pysal.open(pysal.examples.get_path('stl_hom.shp'),'r')
- shapes = f.read()
- pts = [shape.centroid for shape in shapes]
- w0 = brute_knn(pts, 4, 'xyz')
- w1 = brute_knn(pts, 4, 'arc')
- pts = map(toXYZ, pts)
- w2 = brute_knn(pts, 4, 'xyz')
- w3 = fast_knn(pts, 4)
-
- wn, wd = fast_knn(pts, 4, True)
- ids = range(1, len(pts) + 1)
-
- ### new sphere examples
- p0 = (-87.893517, 41.981417)
- p1 = (-87.519295, 41.657498)
- p3 = (41.981417, -87.893517)
- p4 = (41.657498, -87.519295)
- d1 =harcdist(p0,p1,radius=RADIUS_EARTH_MILES)
- print "d1",d1
- d2 = harcdist(p3,p4,lonx=False,radius=None)
- print "d2",d2
- pn1 = geointerpolate(p0,p1,0.1)
- print "pn1",pn1
- pn2 = geointerpolate(p3,p4,0.1,lonx=False)
- print "pn2",pn2
- pup = (42.023768,-87.946389) # Arlington Heights IL
- pdown = (41.644415,-87.524102) # Hammond, IN
- grid=geogrid(pup,pdown,3,lonx=False)
- print "grid",grid
-
diff --git a/pysal/cg/tests/test_sphere.py b/pysal/cg/tests/test_sphere.py
new file mode 100644
index 0000000..8139ada
--- /dev/null
+++ b/pysal/cg/tests/test_sphere.py
@@ -0,0 +1,109 @@
+from pysal.cg import sphere
+import pysal
+import math
+import unittest
+
+
+class Sphere(unittest.TestCase):
+
+ def setUp(self):
+ self.pt0 = (0, 0)
+ self.pt1 = (180, 0)
+ f = pysal.open(pysal.examples.get_path('stl_hom.shp'), 'r')
+ self.shapes = f.read()
+ self.p0 = (-87.893517, 41.981417)
+ self.p1 = (-87.519295, 41.657498)
+ self.p3 = (41.981417, -87.893517)
+ self.p4 = (41.657498, -87.519295)
+
+ def test_arcdist(self):
+ d = sphere.arcdist(self.pt0, self.pt1, sphere.RADIUS_EARTH_MILES)
+ self.assertEquals(d, math.pi * sphere.RADIUS_EARTH_MILES)
+
+ def test_arcdist2linear(self):
+ d = sphere.arcdist(self.pt0, self.pt1, sphere.RADIUS_EARTH_MILES)
+ ld = sphere.arcdist2linear(d, sphere.RADIUS_EARTH_MILES)
+ self.assertEquals(ld, 2.0)
+
+ def test_radangle(self):
+ p0 = (-87.893517, 41.981417)
+ p1 = (-87.519295, 41.657498)
+ self.assertAlmostEquals(sphere.radangle(p0,p1), 0.007460167953189258)
+
+ def test_linear2arcdist(self):
+ d = sphere.arcdist(self.pt0, self.pt1, sphere.RADIUS_EARTH_MILES)
+ ad = sphere.linear2arcdist(2.0, radius=sphere.RADIUS_EARTH_MILES)
+ self.assertEquals(d, ad)
+
+ def test_harcdist(self):
+ d1 = sphere.harcdist(self.p0, self.p1,
+ radius=sphere.RADIUS_EARTH_MILES)
+ self.assertAlmostEqual(d1, 29.532983644123796)
+ d1 = sphere.harcdist(self.p3, self.p4,
+ radius=sphere.RADIUS_EARTH_MILES)
+ self.assertAlmostEqual(d1, 25.871647470233675)
+
+ def test_geointerpolate(self):
+ pn1 = sphere.geointerpolate(self.p0, self.p1, 0.1)
+ self.assertAlmostEqual(pn1, (-87.85592403438788, 41.949079912574796))
+ pn2 = sphere.geointerpolate(self.p3, self.p4, 0.1, lonx=False)
+ self.assertAlmostEqual(pn2, (41.949079912574796, -87.85592403438788))
+
+ def test_geogrid(self):
+ grid = [(42.023768, -87.946389), (42.02393997819538,
+ -87.80562679358316),
+ (42.02393997819538, -87.66486420641684), (42.023768,
+ -87.524102),
+ (41.897317, -87.94638900000001), (41.8974888973743,
+ -87.80562679296166),
+ (41.8974888973743, -87.66486420703835), (41.897317,
+ -87.524102),
+ (41.770866000000005, -87.94638900000001), (41.77103781320412,
+ -87.80562679234043),
+ (41.77103781320412, -87.66486420765956), (41.770866000000005,
+ -87.524102),
+ (41.644415, -87.946389), (41.64458672568646,
+ -87.80562679171955),
+ (41.64458672568646, -87.66486420828045), (41.644415,
+ -87.524102)]
+
+ pup = (42.023768, -87.946389) # Arlington Heights IL
+ pdown = (41.644415, -87.524102) # Hammond, IN
+ grid1 = sphere.geogrid(pup, pdown, 3, lonx=False)
+ self.assertAlmostEqual(grid, grid1)
+
+ def test_toXYZ(self):
+ w2 = {0: [2, 5, 6, 10], 1: [4, 7, 9, 14], 2: [6, 0, 3, 8],
+ 3: [8, 2, 12, 4], 4: [1, 9, 12, 3], 5: [11, 10, 0, 15],
+ 6: [2, 10, 8, 0], 7: [14, 1, 16, 9], 8: [12, 3, 19, 6],
+ 9: [12, 16, 4, 1], 10: [17, 6, 15, 5], 11: [15, 13, 5, 21],
+ 12: [8, 19, 9, 3], 13: [21, 11, 15, 28], 14:
+ [7, 16, 22, 9], 15: [11, 27, 10, 26], 16: [14, 25, 9, 20], 17:
+ [31, 18, 10, 26], 18: [17, 19, 23, 32], 19: [23, 20, 12, 18], 20:
+ [23, 25, 19, 34], 21: [13, 28, 27, 15], 22: [30, 14, 29, 24], 23:
+ [20, 19, 18, 34], 24: [30, 22, 41, 43], 25: [20, 16, 33, 34], 26:
+ [31, 27, 38, 17], 27: [35, 28, 26, 21], 28: [21, 37, 27, 35], 29:
+ [33, 30, 22, 25], 30: [24, 29, 43, 22], 31: [40, 26, 17, 32], 32:
+ [39, 45, 31, 18], 33: [29, 25, 44, 34], 34: [36, 25, 39, 33], 35:
+ [27, 37, 46, 38], 36: [39, 34, 50, 48], 37: [47, 28, 35, 46], 38:
+ [51, 35, 26, 40], 39: [36, 45, 32, 34], 40: [49, 31, 38, 45], 41:
+ [52, 43, 30, 53], 42: [43, 44, 33, 53], 43: [42, 53, 41, 30], 44:
+ [42, 33, 50, 58], 45: [48, 39, 32, 40], 46: [47, 35, 55, 37], 47:
+ [46, 37, 54, 35], 48: [45, 50, 56, 39], 49: [40, 57, 51, 45], 50:
+ [48, 36, 59, 44], 51: [61, 38, 55, 49], 52: [41, 53, 64, 43], 53:
+ [60, 43, 52, 64], 54: [55, 47, 46, 61], 55: [54, 61, 46, 51], 56:
+ [62, 66, 48, 57], 57: [49, 65, 61, 56], 58: [59, 60, 68, 44], 59:
+ [58, 63, 50, 69], 60: [53, 64, 68, 58], 61: [67, 51, 55, 57], 62:
+ [63, 56, 66, 48], 63: [62, 70, 69, 59], 64: [60, 53, 52, 71], 65:
+ [57, 72, 66, 67], 66: [62, 56, 75, 65], 67: [61, 65, 72, 55], 68:
+ [60, 58, 76, 71], 69: [73, 70, 63, 59], 70: [74, 63, 69, 77], 71:
+ [68, 76, 64, 60], 72: [65, 75, 67, 66], 73: [69, 76, 77, 68], 74:
+ [75, 70, 77, 66], 75: [74, 66, 72, 65], 76: [73, 68, 71, 69], 77:
+ [70, 74, 73, 69]}
+
+ pts = [shape.centroid for shape in self.shapes]
+ pts = map(sphere.toXYZ, pts)
+ self.assertAlmostEqual(sphere.brute_knn(pts, 4, 'xyz'), w2)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/common.py b/pysal/common.py
index 44f8f55..513db5a 100644
--- a/pysal/common.py
+++ b/pysal/common.py
@@ -5,7 +5,7 @@ try:
import numpy as np
import numpy.linalg as la
except:
- print 'numpy 1.3 is required'
+ print('numpy 1.3 is required')
raise
try:
import scipy as sp
@@ -13,10 +13,11 @@ try:
from cg.kdtree import KDTree
from scipy.spatial.distance import pdist, cdist
except:
- print 'scipy 0.7+ is required'
+ print('scipy 0.7+ is required')
raise
RTOL = .00001
+ATOL = 1e-7
import copy
import math
@@ -24,6 +25,17 @@ import random
import sys
import time
import unittest
+from warnings import warn as Warn
+from functools import wraps
+from pysal.core.FileIO import FileIO as popen
+try:
+ from patsy import PatsyError
+except ImportError:
+ PatsyError = Exception
+
+#################
+# Compatibility #
+#################
def iteritems(d, **kwargs):
"""
@@ -36,3 +48,83 @@ def iteritems(d, **kwargs):
return d.iteritems(**kwargs)
else:
return iter(d.items(**kwargs))
+
+######################
+# Decorators/Utils #
+######################
+
+def simport(modname):
+ """
+ Safely import a module without raising an error.
+
+ Parameters
+ -----------
+ modname : str
+ module name needed to import
+
+ Returns
+ --------
+ tuple of (True, Module) or (False, None) depending on whether the import
+ succeeded.
+
+ Notes
+ ------
+ Wrapping this function around an iterative context or a with context would
+ allow the module to be used without necessarily attaching it permanently in
+ the global namespace:
+
+ >>> for t,mod in simport('pandas'):
+ if t:
+ mod.DataFrame()
+ else:
+ #do alternative behavior here
+ del mod #or don't del, your call
+
+ instead of:
+
+ >>> t, mod = simport('pandas')
+ >>> if t:
+ mod.DataFrame()
+ else:
+ #do alternative behavior here
+
+ The first idiom makes it work kind of a like a with statement.
+ """
+ try:
+ exec('import {}'.format(modname))
+ return True, eval(modname)
+ except:
+ return False, None
+
+def requires(*args, **kwargs):
+ """
+ Decorator to wrap functions with extra dependencies:
+
+ Arguments
+ ---------
+ args : list
+ list of strings containing module to import
+ verbose : bool
+ boolean describing whether to print a warning message on import
+ failure
+ Returns
+ -------
+ Original function is all arg in args are importable, otherwise returns a
+ function that passes.
+ """
+ v = kwargs.pop('verbose', True)
+ wanted = copy.deepcopy(args)
+ def inner(function):
+ available = [simport(arg)[0] for arg in args]
+ if all(available):
+ return function
+ else:
+ def passer(*args,**kwargs):
+ if v:
+ missing = [arg for i, arg in enumerate(wanted) if not available[i]]
+ print('missing dependencies: {d}'.format(d=missing))
+ print('not running {}'.format(function.__name__))
+ else:
+ pass
+ return passer
+ return inner
diff --git a/pysal/contrib/README.rst b/pysal/contrib/README.rst
index a14c365..c199227 100644
--- a/pysal/contrib/README.rst
+++ b/pysal/contrib/README.rst
@@ -70,8 +70,16 @@ Currently the following contribs are available:
- .. versionadded:: 1.10
- Path: pysal.contrib.spint
- - Requires: `pandas`_
+ - Requires: No extra dependencies
+
+ 9. glm -- GLM estimation using iteratively weighted least squares estimation
+
+ - .. versionadded:: 1.12
+ - Path: pysal.contrib.glm
+ - Requires: No extra dependencies
+
+
.. _clusterpy: https://pypi.python.org/pypi/clusterPy/0.9.9
diff --git a/pysal/contrib/geotable/__init__.py b/pysal/contrib/geotable/__init__.py
new file mode 100644
index 0000000..d9cca84
--- /dev/null
+++ b/pysal/contrib/geotable/__init__.py
@@ -0,0 +1 @@
+import ops
diff --git a/pysal/contrib/geotable/config.py b/pysal/contrib/geotable/config.py
new file mode 100644
index 0000000..73d52e0
--- /dev/null
+++ b/pysal/contrib/geotable/config.py
@@ -0,0 +1,64 @@
+import ops
+
+def set_preference(pkg):
+ """
+ Set which provider of geospatial operations should be preferred.
+
+ Arguments
+ ---------
+ pkg : string
+ string specifying whether to prefer computing properties using
+ shapely, or to use attribute access on the polygons themselves.
+ Returns
+ --------
+ None, sets configuration option directly
+ """
+ if pkg.lower() == 'shapely':
+ ops.atomic.__dict__.update({k:v for k,v in
+ ops.atomic._s.__dict__.items()
+ if not k.startswith('_')})
+ ops.atomic._preferred = ops.atomic._s
+ elif pkg.lower().startswith('att'):
+ ops.atomic.__dict__.update({k:v for k,v in
+ ops.atomic._a.__dict__.items()
+ if not k.startswith('_')})
+ ops.atomic._preferred = ops.atomic._a
+ else:
+ raise Exception('Provider not recognized.')
+
+def get_provider(fn=None):
+ """
+ Discover the provider of a given function
+
+ Arguments
+ ---------
+ fn : Callable or None
+ either a function whose provider needs to be discovered, or None
+
+ Returns
+ -------
+ the module where fn is defined, or the current preferred provider.
+
+ Example
+ -------
+
+ >>> from pysal.contrib.geotable import ops
+ >>> from pysal.contrib.config import get_provider, set_config
+ >>> get_provider(ops.atomic.area)
+ <module 'pysal.contrib.geotable.ops._accessors' from
+ pysal/contrib/geotable/ops/_accessors.pyc'>
+ >>> set_preference('shapely')
+ >>> get_provider(ops.atomic.area)
+ <module 'pysal.contrib.geotable.ops._shapely' from
+ 'pysal/contrib/geotable/ops/_shapely.pyc'>
+ """
+ if fn is None:
+ return ops.atomic._preferred
+ try:
+ fprovenance = fn.func.func_name
+ except AttributeError:
+ fprovenance = fn.func_name
+ if fprovenance == 'get_attr':
+ return ops._accessors
+ else:
+ return ops._shapely
diff --git a/pysal/contrib/geotable/io.py b/pysal/contrib/geotable/io.py
new file mode 100644
index 0000000..69ab520
--- /dev/null
+++ b/pysal/contrib/geotable/io.py
@@ -0,0 +1,38 @@
+from pysal.common import simport, requires
+from pysal.cg import asShape
+from pysal.contrib import pdutilities as pdio
+from pysal.core import FileIO
+import pandas as pd
+
+class Namespace(object):
+ pass
+
+ at requires('geopandas')
+def geopandas(filename, **kw):
+ import geopandas
+ return geopandas.read_file(filename, **kw)
+
+ at requires('fiona')
+def fiona(filename, **kw):
+ import fiona
+ props = {}
+ with fiona.open(filename, **kw) as f:
+ for i,feat in enumerate(f):
+ idx = feat.get('id', i)
+ try:
+ idx = int(idx)
+ except ValueError:
+ pass
+ props.update({idx:feat.get('properties', dict())})
+ props[idx].update({'geometry':asShape(feat['geometry'])})
+ return pd.DataFrame().from_dict(props).T
+
+_readers = {'read_shapefile':pdio.read_files,
+ 'read_fiona':fiona}
+_writers = {'to_shapefile':pdio.write_files}
+
+_pandas_readers = {k:v for k,v in pd.io.api.__dict__.items() if k.startswith('read_')}
+
+readers = Namespace()
+readers.__dict__.update(_readers)
+readers.__dict__.update(_pandas_readers)
diff --git a/pysal/contrib/geotable/ops/__init__.py b/pysal/contrib/geotable/ops/__init__.py
new file mode 100644
index 0000000..d704149
--- /dev/null
+++ b/pysal/contrib/geotable/ops/__init__.py
@@ -0,0 +1,2 @@
+import atomic
+import tabular
diff --git a/pysal/contrib/geotable/ops/_accessors.py b/pysal/contrib/geotable/ops/_accessors.py
new file mode 100644
index 0000000..fee7fd6
--- /dev/null
+++ b/pysal/contrib/geotable/ops/_accessors.py
@@ -0,0 +1,45 @@
+import functools as _f
+
+__all__ = [ 'area', 'bbox', 'bounding_box', 'centroid', 'holes', 'len',
+ 'parts', 'perimeter', 'segments', 'vertices']
+
+def get_attr(df, geom_col='geometry', inplace=False, attr=None):
+ outval = df[geom_col].apply(lambda x: x.__getattribute__(attr))
+ if inplace:
+ outcol = 'shape_{}'.format(func.__name__)
+ df[outcol] = outval
+ return None
+ return outval
+
+_doc_template =\
+"""
+Tabular accessor to grab a geometric object's {n} attribute
+
+Arguments
+---------
+df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+geom_col: string
+ the name of the column in df containing the geometry
+inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived column will be under 'shape_{n}'
+
+Returns
+--------
+If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+returns a series.
+
+See Also
+---------
+For further documentation about the attributes of the object in question, refer
+to shape classes in pysal.cg.shapes
+"""
+
+_accessors = dict()
+for k in __all__:
+ _accessors[k] = _f.partial(get_attr, attr=k)
+ _accessors[k].__doc__ = _doc_template.format(n=k)
+
+globals().update(_accessors)
diff --git a/pysal/contrib/geotable/ops/_shapely.py b/pysal/contrib/geotable/ops/_shapely.py
new file mode 100644
index 0000000..c747011
--- /dev/null
+++ b/pysal/contrib/geotable/ops/_shapely.py
@@ -0,0 +1,174 @@
+import functools as _f
+from warnings import warn
+from ....common import requires as _requires
+
+__all__ = ["to_wkb", "to_wkt", "area", "distance", "length", "boundary", "bounds", "centroid", "representative_point", "convex_hull", "envelope", "buffer", "simplify", "difference", "intersection", "symmetric_difference", "union", "has_z", "is_empty", "is_ring", "is_simple", "is_valid", "relate", "contains", "crosses", "disjoint", "equals", "intersects", "overlaps", "touches", "within", "equals_exact", "almost_equals", "project", "interpolate"]
+
+
+def _atomic_op(df, geom_col='geometry', inplace=False, _func=None, **kwargs):
+ outval = df[geom_col].apply(lambda x: _func(x, **kwargs))
+ outcol = 'shape_{}'.format(_func.__name__)
+ if not inplace:
+ new = df.copy()
+ new[outcol] = outval
+ return new
+ df[outcol] = outval
+
+_doc_template =\
+"""
+Tabular version of pysal.contrib.shapely_ext.{n}
+
+Arguments
+---------
+df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+geom_col: string
+ the name of the column in df containing the geometry
+inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived column will be under 'shape_{n}'
+**kwargs: keyword arguments
+ arguments to be passed to the elementwise functions
+
+Returns
+-------
+If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+returns a series.
+
+Note that some atomic operations require an 'other' argument.
+
+See Also
+--------
+pysal.contrib.shapely_ext.{n}
+"""
+
+# ensure that the construction of atomics is done only if we can use shapely
+_shapely_atomics = {}
+try:
+ from ... import shapely_ext as _s
+ for k in __all__:
+ _shapely_atomics.update({k:_f.partial(_atomic_op, _func=_s.__dict__[k])})
+ _shapely_atomics[k].__doc__ = _doc_template.format(n=_s.__dict__[k].__name__)
+except ImportError:
+ pass
+
+globals().update(_shapely_atomics)
+
+##############
+# Reductions #
+##############
+
+ at _requires('shapely')
+def cascaded_union(df, geom_col='geometry', **groupby_kws):
+ """
+ Returns the cascaded union of a possibly-grouped dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a dataframe containing geometry objects which are being united
+ geom_col : string
+ a string denoting which column of the dataframe contains the
+ geometries
+ **groupby_kws : keyword arguments
+ keyword arguments to pass transparently to the groupby
+ function for the DataFrame
+
+ Returns
+ -------
+ PySAL shape or dataframe of shapes resulting from the union operation.
+
+ See Also
+ --------
+ pysal.shapely_ext.cascaded_union
+ pandas.DataFrame.groupby
+ """
+ by = groupby_kws.pop('by', None)
+ level = groupby_kws.pop('level', None)
+ if by is not None or level is not None:
+ df = df.groupby(by=by, level=level, **groupby_kws)
+ out = df[geom_col].apply(_s.cascaded_union)
+ else:
+ out = _s.cascaded_union(df[geom_col].tolist())
+ return out
+
+ at _requires('shapely')
+def unary_union(df, geom_col='geometry', **groupby_kws):
+ """
+ Returns the cascaded union of a possibly-grouped dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a dataframe containing geometry objects which are being united
+ geom_col : string
+ a string denoting which column of the dataframe contains the
+ geometries
+ **groupby_kws : keyword arguments
+ keyword arguments to pass transparently to the groupby
+ function for the DataFrame
+
+ Returns
+ -------
+ PySAL shape or dataframe of shapes resulting from the union operation.
+
+ See Also
+ --------
+ pysal.shapely_ext.cascaded_union
+ pandas.DataFrame.groupby
+ """
+ by = groupby_kws.pop('by', None)
+ level = groupby_kws.pop('level', None)
+ if by is not None or level is not None:
+ df = df.groupby(**groupby_kws)
+ out = df[geom_col].apply(_cascaded_union)
+ else:
+ out = _cascaded_union(df[geom_col].tolist())
+ return out
+
+ at _requires('shapely')
+def _cascaded_intersection(shapes):
+ it = iter(shapes)
+ outshape = next(it)
+ for i, shape in enumerate(it):
+ try:
+ outshape = _s.intersection(shape, outshape)
+ except NotImplementedError:
+ warn('An intersection is empty!')
+ return None
+ return outshape
+
+ at _requires('shapely')
+def cascaded_intersection(df, geom_col='geometry', **groupby_kws):
+ """
+ Returns the cascaded union of a possibly-grouped dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a dataframe containing geometry objects which are being united
+ geom_col : string
+ a string denoting which column of the dataframe contains the
+ geometries
+ **groupby_kws : keyword arguments
+ keyword arguments to pass transparently to the groupby
+ function for the DataFrame
+
+ Returns
+ -------
+ PySAL shape or dataframe of shapes resulting from the union operation.
+
+ See Also
+ --------
+ pysal.shapely_ext.cascaded_union
+ pandas.DataFrame.groupby
+ """
+ by = groupby_kws.pop('by', None)
+ level = groupby_kws.pop('level', None)
+ if by is not None or level is not None:
+ df = df.groupby(**groupby_kws)
+ out = df[geom_col].apply(_cascaded_intersection)
+ else:
+ out = _cascaded_intersection(df[geom_col].tolist())
+ return out
diff --git a/pysal/contrib/geotable/ops/atomic.py b/pysal/contrib/geotable/ops/atomic.py
new file mode 100644
index 0000000..333ce66
--- /dev/null
+++ b/pysal/contrib/geotable/ops/atomic.py
@@ -0,0 +1,10 @@
+import _accessors as _a
+import _shapely as _s
+
+# prefer access to shapely computation
+_all = dict()
+_all.update(_s.__dict__)
+_all.update(_a.__dict__)
+
+globals().update({_k:_v for _k,_v in _all.items() if not _k.startswith('_')})
+_preferred = _a
diff --git a/pysal/contrib/geotable/ops/tabular.py b/pysal/contrib/geotable/ops/tabular.py
new file mode 100644
index 0000000..f0acdf0
--- /dev/null
+++ b/pysal/contrib/geotable/ops/tabular.py
@@ -0,0 +1,118 @@
+from ....common import requires as _requires
+from ..utils import to_gdf, to_df
+from warnings import warn as _Warn
+import functools as _f
+import sys as _sys
+
+ at _requires("geopandas")
+def spatial_join(df1, df2, left_geom_col='geometry',
+ right_geom_col='geometry', **kwargs):
+ """
+ Spatial join of two Pandas DataFrames. Calls out to Geopandas.
+
+ Parameters
+ ----------
+ left_df : pandas.DataFrame
+ right_df: pandas.DataFrames
+ how : string, default 'inner'
+ The type of join:
+ * 'left': use keys from left_df; retain only
+ left_df geometry column
+ * 'right': use keys from right_df; retain only
+ right_df geometry column
+ * 'inner': use intersection of keys from both dfs;
+ retain only left_df geometry column
+ op : string, default 'intersection'
+ One of {'intersects', 'contains', 'within'}.
+ See http://toblerity.org/shapely/manual.html#binary-predicates.
+ lsuffix : string, default 'left'
+ Suffix to apply to overlapping column names
+ (left GeoDataFrame).
+ rsuffix : string, default 'right'
+ Suffix to apply to overlapping column namei
+ (right GeoDataFrame).
+ """
+ import geopandas as gpd
+ gdf1 = to_gdf(df1, geom_col=left_geom_col)
+ gdf2 = to_gdf(df2, geom_col=right_geom_col)
+ out = gpd.tools.sjoin(gdf1, gdf2, **kwargs)
+ return to_df(out)
+
+try:
+ import pandas as _pd
+ @_requires("pandas")
+ @_f.wraps(_pd.merge)
+ def join(*args, **kwargs):
+ return _pd.merge(*args, **kwargs)
+except ImportError:
+ pass
+
+ at _requires("geopandas")
+def spatial_overlay(df1, df2, how, left_geom_col='geometry',
+ right_geom_col='geometry', **kwargs):
+ """
+ Perform spatial overlay between two polygonal datasets.
+ Calls out to geopandas.
+
+ Currently only supports data pandas.DataFrames with polygons.
+ Implements several methods that are all effectively subsets of
+ the union.
+
+ Parameters
+ ----------
+ df1 : pandas.DataFrame
+ must have MultiPolygon or Polygon geometry column
+ df2 : pandas.DataFrame
+ must have MultiPolygon or Polygon geometry column
+ how : string
+ Method of spatial overlay: 'intersection', 'union',
+ 'identity', 'symmetric_difference' or 'difference'.
+ use_sindex : boolean, default True
+ Use the spatial index to speed up operation
+ if available.
+
+ Returns
+ -------
+ df : pandas.DataFrame
+ pandas.DataFrame with new set of polygons and attributes
+ resulting from the overlay
+ """
+ import geopandas as gpd
+ gdf1 = to_gdf(df1, geom_col=left_geom_col)
+ gdf2 = to_gdf(df2, geom_col=right_geom_col)
+ out = gpd.tools.overlay(gdf1, gdf2, how, **kwargs)
+ return to_df(out)
+
+ at _requires('shapely')
+def dissolve(df, by='', **groupby_kws):
+ from ._shapely import cascaded_union as union
+ return union(df, by=by, **groupby_kws)
+
+def clip(return_exterior=False):
+ # return modified entries of the df that are within an envelope
+ # provide an option to null out the geometries instead of not returning
+ raise NotImplementedError
+
+def erase(return_interior=True):
+ # return modified entries of the df that are outside of an envelope
+ # provide an option to null out the geometries instead of not returning
+ raise NotImplementedError
+
+ at _requires('shapely')
+def union(df, **kws):
+ if 'by' in kws:
+ warn('when a "by" argument is provided, you should be using "dissolve"')
+ return dissolve(df, **kws)
+ from ._shapely import cascaded_union as union
+ return union(df)
+
+ at _requires('shapely')
+def intersection(df, **kws):
+ from ._shapely import cascaded_intersection as intersection
+ return intersection(df, **kws)
+
+def symmetric_difference():
+ raise NotImplementedError
+
+def difference():
+ raise NotImplementedError
diff --git a/pysal/weights/tests/__init__.py b/pysal/contrib/geotable/ops/tests/__init__.py
similarity index 100%
copy from pysal/weights/tests/__init__.py
copy to pysal/contrib/geotable/ops/tests/__init__.py
diff --git a/pysal/contrib/geotable/ops/tests/test_accessors.py b/pysal/contrib/geotable/ops/tests/test_accessors.py
new file mode 100644
index 0000000..5fb7dfe
--- /dev/null
+++ b/pysal/contrib/geotable/ops/tests/test_accessors.py
@@ -0,0 +1,356 @@
+from ....pdio import read_files as rf
+from .. import _accessors as to_test
+from .....cg import Point, Chain, Polygon, Rectangle, LineSegment
+from .....common import pandas, RTOL, ATOL
+from .....examples import get_path
+import numpy as np
+import unittest as ut
+
+PANDAS_EXTINCT = pandas is None
+ at ut.skipIf(PANDAS_EXTINCT, 'missing pandas')
+class Test_Accessors(ut.TestCase):
+ def setUp(self):
+ self.polygons = rf(get_path('Polygon.shp'))
+ self.points = rf(get_path('Point.shp'))
+ self.lines = rf(get_path('Line.shp'))
+
+ def test_area(self):
+
+ with self.assertRaises(AttributeError):
+ to_test.area(self.points)
+ with self.assertRaises(AttributeError):
+ to_test.area(self.lines)
+
+ areas = to_test.area(self.polygons).values
+ answer = [.000284, .000263, .001536]
+ np.testing.assert_allclose(answer, areas, rtol=RTOL, atol=ATOL*10)
+
+ def test_bbox(self):
+
+ with self.assertRaises(AttributeError):
+ to_test.bbox(self.points)
+ with self.assertRaises(AttributeError):
+ to_test.bbox(self.lines)
+
+ answer = [[-0.010809397704086565,
+ -0.26282711761789435,
+ 0.12787295484449185,
+ -0.250785835510383],
+ [ 0.0469057130870883,
+ -0.35957259110238166,
+ 0.06309916143856897,
+ -0.3126531125455273],
+ [-0.04527237752903268,
+ -0.4646223970748078,
+ 0.1432359699471787,
+ -0.40150947016647276]]
+
+ bboxes = to_test.bbox(self.polygons).tolist()
+ for ans, bbox in zip(answer, bboxes):
+ np.testing.assert_allclose(ans, bbox, rtol=RTOL, atol=ATOL)
+
+ def test_bounding_box(self):
+ with self.assertRaises(AttributeError):
+ to_test.bounding_box(self.points)
+
+ line_rects = to_test.bounding_box(self.lines).tolist()
+ line_bboxes = [[(a.left, a.lower),(a.right, a.upper)] for a in line_rects]
+ pgon_rects = to_test.bounding_box(self.polygons).tolist()
+ pgon_bboxes = [[(a.left, a.lower),(a.right, a.upper)] for a in pgon_rects]
+
+ line_answers = [[(-0.009053924887015952, -0.2589587703323735),
+ ( 0.007481157395930582, -0.25832280562918325)],
+ [( 0.10923550990637088, -0.2564149115196125),
+ ( 0.12895041570526866, -0.2564149115196125)],
+ [( 0.050726757212867735, -0.356261369920482),
+ ( 0.06153815716710198, -0.3130157701035449)],
+ [(-0.0414881247497188, -0.46055958124368335),
+ ( 0.1391258509563127, -0.4058666167693217)]]
+ pgon_answers = [[(-0.010809397704086565, -0.26282711761789435),
+ ( 0.12787295484449185, -0.250785835510383)],
+ [( 0.0469057130870883, -0.35957259110238166),
+ ( 0.06309916143856897, -0.3126531125455273)],
+ [(-0.04527237752903268, -0.4646223970748078),
+ ( 0.1432359699471787, -0.40150947016647276)]]
+
+ for bbox, answer in zip(line_bboxes, line_answers):
+ np.testing.assert_allclose(bbox, answer, atol=ATOL, rtol=RTOL)
+ for bbox, answer in zip(pgon_bboxes, pgon_answers):
+ np.testing.assert_allclose(bbox, answer, atol=ATOL, rtol=RTOL)
+ for rectangle in (line_rects + pgon_rects):
+ self.assertIsInstance(rectangle, Rectangle)
+
+ def test_centroid(self):
+ with self.assertRaises(AttributeError):
+ to_test.centroid(self.points)
+ with self.assertRaises(AttributeError):
+ to_test.centroid(self.lines)
+
+ centroids = to_test.centroid(self.polygons).tolist()
+
+ centroid_answers = [(0.06466214975239247, -0.257330080795802),
+ (0.05151163524856857, -0.33495102150875505),
+ (0.04759584610455384, -0.44147205133285744)]
+
+ for ct, answer in zip(centroids, centroid_answers):
+ np.testing.assert_allclose(ct, answer, rtol=RTOL, atol=ATOL)
+
+ def test_holes(self):
+ holed_polygons = rf(get_path('Polygon_Holes.shp'))
+ with self.assertRaises(AttributeError):
+ to_test.centroid(self.points)
+ with self.assertRaises(AttributeError):
+ to_test.centroid(self.lines)
+
+ no_holes = to_test.holes(self.polygons).tolist()
+ holes = to_test.holes(holed_polygons).tolist()
+
+ for elist in no_holes:
+ self.assertEquals(elist, [[]])
+
+ answers = [[[(-0.002557818613137461, -0.25599115990199145),
+ ( 0.0012028146993492903, -0.25561239107915107),
+ ( 0.004909338180001697, -0.2596435735508095),
+ (-0.0019896653788768724, -0.2616726922445973),
+ (-0.007021879739470651, -0.25834493758678534),
+ (-0.002557818613137461, -0.25599115990199145)],
+ [( 0.11456291239229519, -0.2534750527216944),
+ ( 0.11878347927537383, -0.2540973157877893),
+ ( 0.11878347927537383, -0.2540973157877893),
+ ( 0.12335576006537571, -0.25596410498607414),
+ ( 0.11605093276773958, -0.258155553175365),
+ ( 0.11020707092963067, -0.2579391138480276),
+ ( 0.11456291239229519, -0.2534750527216944)]],
+ [[( 0.04818367618951632, -0.31403748200228154),
+ ( 0.052755956979518195, -0.31384809759086135),
+ ( 0.04975286131271223, -0.3566219196559085),
+ ( 0.04818367618951632, -0.31403748200228154)]],
+ [[(-0.039609525961703126, -0.4112999047245106),
+ (-0.013745026344887779, -0.43770550265966934),
+ (-0.015260101636249357, -0.4393287976146996),
+ (-0.04242323721708889, -0.4140053963162277),
+ (-0.039609525961703126, -0.4112999047245106)],
+ [( 0.027838379419803827, -0.4597823140480808),
+ ( 0.07350707748798824, -0.45859189774772524),
+ ( 0.07469749378834376, -0.46064807135743024),
+ ( 0.028487697401815927, -0.46270424496713525),
+ ( 0.027838379419803827, -0.4597823140480808)],
+ [( 0.11192505809037084, -0.43467535207694624),
+ ( 0.13962929198955382, -0.4037245282677028),
+ ( 0.14092792795357803, -0.405023164231727),
+ ( 0.11463054968208794, -0.4370561846776573),
+ ( 0.11192505809037084, -0.43467535207694624)]]]
+ for hole, answer in zip(holes, answers):
+ for sub_hole, sub_answer in zip(hole, answer):
+ np.testing.assert_allclose(sub_hole, sub_answer,
+ rtol=RTOL, atol=ATOL)
+
+ def test_len(self):
+ with self.assertRaises(AttributeError):
+ to_test.len(self.points)
+
+ line_len = to_test.len(self.lines)
+ pgon_len = to_test.len(self.polygons)
+
+ pgon_answers = [24, 7, 14]
+ line_answers = [0.016547307853772356,
+ 0.019714905798897786,
+ 0.058991346117778738,
+ 0.21634275419393173]
+ np.testing.assert_allclose(line_len, line_answers, rtol=RTOL, atol=ATOL)
+ np.testing.assert_allclose(pgon_len, pgon_answers, rtol=RTOL, atol=ATOL)
+
+ def test_parts(self):
+ with self.assertRaises(AttributeError):
+ to_test.parts(self.points)
+
+ line_parts = to_test.parts(self.lines)
+ pgon_parts = to_test.parts(self.polygons)
+
+ pgon_answers =[[[(-0.010809397704086565, -0.25825973474952796),
+ (-0.007487664708911018, -0.25493800175435244),
+ (-0.0016746319673538457, -0.2532771352567647),
+ ( 0.003307967525409461, -0.2545227851299555),
+ ( 0.006214483896188033, -0.25701408487633715),
+ ( 0.007044917144981927, -0.26033581787151266),
+ ( 0.003307967525409461, -0.26241190099349737),
+ (-0.0029202818405446584, -0.26282711761789435),
+ (-0.008318097957704912, -0.26199668436910045),
+ (-0.009978964455292672, -0.26075103449590964),
+ (-0.010809397704086565, -0.25825973474952796)],
+ [( 0.10711212362464478, -0.25618365162754325),
+ ( 0.1112642898686142, -0.25203148538357384),
+ ( 0.11583167273698053, -0.250785835510383),
+ ( 0.12164470547853773, -0.25203148538357384),
+ ( 0.12538165509811022, -0.25410756850555855),
+ ( 0.12787295484449185, -0.2574293015007341),
+ ( 0.12579687172250714, -0.26033581787151266),
+ ( 0.1191534057321561, -0.26116625112030656),
+ ( 0.1141708062393928, -0.26158146774470353),
+ ( 0.11084907324421728, -0.25992060124711575),
+ ( 0.10794255687343868, -0.25909016799832185),
+ ( 0.10794255687343868, -0.25909016799832185),
+ ( 0.10711212362464478, -0.25618365162754325)]],
+ [[( 0.05396439570183631, -0.3126531125455273),
+ ( 0.05147309595545463, -0.35251390848763364),
+ ( 0.059777428443393454, -0.34254870950210703),
+ ( 0.06309916143856897, -0.34462479262409174),
+ ( 0.048981796209073, -0.35957259110238166),
+ ( 0.0469057130870883, -0.3126531125455273),
+ ( 0.05396439570183631, -0.3126531125455273)]],
+ [[(-0.04527237752903268, -0.413550752273984),
+ (-0.039874561411872456, -0.4077377195324269),
+ (-0.039874561411872456, -0.4077377195324269),
+ (-0.010809397704086565, -0.43680288324021277),
+ ( 0.02656009849163815, -0.45756371446005983),
+ ( 0.07181871055090477, -0.45673328121126594),
+ ( 0.1104338566198203, -0.4338963668694341),
+ ( 0.1394990203276062, -0.40150947016647276),
+ ( 0.1432359699471787, -0.4052464197860452),
+ ( 0.1162468893613775, -0.4380485331134035),
+ ( 0.07763174329246192, -0.4625463139528231),
+ ( 0.02780574836482899, -0.4646223970748078),
+ (-0.013715914074865138, -0.4442767824793577),
+ (-0.04527237752903268, -0.413550752273984)]]]
+ line_answers = [[[(-0.009053924887015952, -0.25832280562918325),
+ (0.007481157395930582, -0.2589587703323735),
+ (0.007481157395930582, -0.2589587703323735)]],
+ [[(0.10923550990637088, -0.2564149115196125),
+ (0.12895041570526866, -0.2564149115196125)]],
+ [[(0.050726757212867735, -0.3130157701035449),
+ (0.050726757212867735, -0.356261369920482),
+ (0.06153815716710198, -0.3448140052630575),
+ (0.06153815716710198, -0.3448140052630575)]],
+ [[(-0.0414881247497188, -0.41286222850441445),
+ (-0.012233748402967204, -0.4402087107415953),
+ (0.027196063194828424, -0.46055958124368335),
+ (0.07489341593409732, -0.4586516871341126),
+ (0.11241533342232213, -0.43639292252245376),
+ (0.1391258509563127, -0.4058666167693217)]]]
+
+ for part, answer in zip(pgon_parts, pgon_answers):
+ for piece, sub_answer in zip(part, answer):
+ np.testing.assert_allclose(piece, sub_answer,
+ rtol=RTOL,atol=ATOL)
+
+ def test_perimeter(self):
+ with self.assertRaises(AttributeError):
+ to_test.perimeter(self.points)
+ with self.assertRaises(AttributeError):
+ to_test.perimeter(self.lines)
+
+ pgon_perim = to_test.perimeter(self.polygons)
+ pgon_answers = np.array([ 0.09381641, 0.13141213, 0.45907697])
+
+ np.testing.assert_allclose(pgon_perim.values, pgon_answers,
+ rtol=RTOL, atol=ATOL)
+
+ def test_segments(self):
+ with self.assertRaises(AttributeError):
+ to_test.segments(self.points)
+ with self.assertRaises(AttributeError):
+ to_test.segments(self.polygons)
+
+ line_segments = to_test.segments(self.lines)
+ flattened = [l[0] for l in line_segments]
+
+ answers = [[((-0.009053924887015952, -0.25832280562918325),
+ (0.007481157395930582, -0.2589587703323735)),
+ ((0.007481157395930582, -0.2589587703323735),
+ (0.007481157395930582, -0.2589587703323735))],
+ [((0.10923550990637088, -0.2564149115196125),
+ (0.12895041570526866, -0.2564149115196125))],
+ [((0.050726757212867735, -0.3130157701035449),
+ (0.050726757212867735, -0.356261369920482)),
+ ((0.050726757212867735, -0.356261369920482),
+ (0.06153815716710198, -0.3448140052630575)),
+ ((0.06153815716710198, -0.3448140052630575),
+ (0.06153815716710198, -0.3448140052630575))],
+ [((-0.0414881247497188, -0.41286222850441445),
+ (-0.012233748402967204, -0.4402087107415953)),
+ ((-0.012233748402967204, -0.4402087107415953),
+ (0.027196063194828424, -0.46055958124368335)),
+ ((0.027196063194828424, -0.46055958124368335),
+ (0.07489341593409732, -0.4586516871341126)),
+ ((0.07489341593409732, -0.4586516871341126),
+ (0.11241533342232213, -0.43639292252245376)),
+ ((0.11241533342232213, -0.43639292252245376),
+ (0.1391258509563127, -0.4058666167693217))]]
+
+ for parts, points in zip(flattened, answers):
+ for piece, answer in zip(parts, points):
+ self.assertIsInstance(piece, LineSegment)
+ p1,p2 = piece.p1, piece.p2
+ np.testing.assert_allclose([p1, p2], answer)
+
+ def test_vertices(self):
+ with self.assertRaises(AttributeError):
+ to_test.vertices(self.points)
+
+ line_verts = to_test.vertices(self.lines).tolist()
+ pgon_verts = to_test.vertices(self.polygons).tolist()
+
+ line_answers = [[(-0.009053924887015952, -0.25832280562918325),
+ (0.007481157395930582, -0.2589587703323735),
+ (0.007481157395930582, -0.2589587703323735)],
+ [(0.10923550990637088, -0.2564149115196125),
+ (0.12895041570526866, -0.2564149115196125)],
+ [(0.050726757212867735, -0.3130157701035449),
+ (0.050726757212867735, -0.356261369920482),
+ (0.06153815716710198, -0.3448140052630575),
+ (0.06153815716710198, -0.3448140052630575)],
+ [(-0.0414881247497188, -0.41286222850441445),
+ (-0.012233748402967204, -0.4402087107415953),
+ (0.027196063194828424, -0.46055958124368335),
+ (0.07489341593409732, -0.4586516871341126),
+ (0.11241533342232213, -0.43639292252245376),
+ (0.1391258509563127, -0.4058666167693217)]]
+ pgon_answers = [[(-0.010809397704086565, -0.25825973474952796),
+ (-0.007487664708911018, -0.25493800175435244),
+ (-0.0016746319673538457, -0.2532771352567647),
+ (0.003307967525409461, -0.2545227851299555),
+ (0.006214483896188033, -0.25701408487633715),
+ (0.007044917144981927, -0.26033581787151266),
+ (0.003307967525409461, -0.26241190099349737),
+ (-0.0029202818405446584, -0.26282711761789435),
+ (-0.008318097957704912, -0.26199668436910045),
+ (-0.009978964455292672, -0.26075103449590964),
+ (-0.010809397704086565, -0.25825973474952796),
+ (0.10711212362464478, -0.25618365162754325),
+ (0.1112642898686142, -0.25203148538357384),
+ (0.11583167273698053, -0.250785835510383),
+ (0.12164470547853773, -0.25203148538357384),
+ (0.12538165509811022, -0.25410756850555855),
+ (0.12787295484449185, -0.2574293015007341),
+ (0.12579687172250714, -0.26033581787151266),
+ (0.1191534057321561, -0.26116625112030656),
+ (0.1141708062393928, -0.26158146774470353),
+ (0.11084907324421728, -0.25992060124711575),
+ (0.10794255687343868, -0.25909016799832185),
+ (0.10794255687343868, -0.25909016799832185),
+ (0.10711212362464478, -0.25618365162754325)],
+ [(0.05396439570183631, -0.3126531125455273),
+ (0.05147309595545463, -0.35251390848763364),
+ (0.059777428443393454, -0.34254870950210703),
+ (0.06309916143856897, -0.34462479262409174),
+ (0.048981796209073, -0.35957259110238166),
+ (0.0469057130870883, -0.3126531125455273),
+ (0.05396439570183631, -0.3126531125455273)],
+ [(-0.04527237752903268, -0.413550752273984),
+ (-0.039874561411872456, -0.4077377195324269),
+ (-0.039874561411872456, -0.4077377195324269),
+ (-0.010809397704086565, -0.43680288324021277),
+ (0.02656009849163815, -0.45756371446005983),
+ (0.07181871055090477, -0.45673328121126594),
+ (0.1104338566198203, -0.4338963668694341),
+ (0.1394990203276062, -0.40150947016647276),
+ (0.1432359699471787, -0.4052464197860452),
+ (0.1162468893613775, -0.4380485331134035),
+ (0.07763174329246192, -0.4625463139528231),
+ (0.02780574836482899, -0.4646223970748078),
+ (-0.013715914074865138, -0.4442767824793577),
+ (-0.04527237752903268, -0.413550752273984)]]
+ for part, answer in zip(line_verts, line_answers):
+ np.testing.assert_allclose(part, answer, atol=ATOL, rtol=RTOL)
+ for part, answer in zip(pgon_verts, pgon_answers):
+ np.testing.assert_allclose(part, answer, atol=ATOL, rtol=RTOL)
diff --git a/pysal/contrib/geotable/ops/tests/test_shapely.py b/pysal/contrib/geotable/ops/tests/test_shapely.py
new file mode 100644
index 0000000..58de655
--- /dev/null
+++ b/pysal/contrib/geotable/ops/tests/test_shapely.py
@@ -0,0 +1,215 @@
+import unittest as ut
+from .. import _shapely as sht
+from pysal.cg import Point, Chain, Polygon, comparators as comp
+from pysal.contrib import shapely_ext as she
+from pysal.contrib.pdio import read_files as rf
+from pysal.examples import get_path
+import numpy as np
+from warnings import warn
+
+class Test_Shapely(ut.TestCase):
+ def setUp(self):
+ self.polygons = rf(get_path('Polygon.shp'))
+ self.points = rf(get_path('Point.shp'))
+ self.lines = rf(get_path('Line.shp'))
+ self.target_poly = self.polygons.geometry[2]
+ self.target_point = self.points.geometry[1]
+ self.target_line = self.lines.geometry[0]
+
+ self.dframes = [self.polygons, self.points, self.lines]
+ self.targets = [self.target_poly, self.target_point, self.target_line]
+
+ def compare(self, func_name, df, **kwargs):
+ geom_list = df.geometry.tolist()
+ shefunc = she.__dict__[func_name]
+ shtfunc = sht.__dict__[func_name]
+
+ try:
+ she_vals = (shefunc(geom, **kwargs) for geom in geom_list)
+ sht_vals = shtfunc(df, inplace=False, **kwargs)
+ sht_list = sht_vals['shape_{}'.format(func_name)].tolist()
+ for tabular, shapely in zip(sht_list, she_vals):
+ if (comp.is_shape(tabular) and
+ comp.is_shape(shapely)):
+ comp.equal(tabular, shapely)
+ else:
+ self.assertEquals(tabular, shapely)
+ except NotImplementedError as e:
+ warn('The shapely/pysal bridge is not implemented: {}'.format(e))
+ return True
+
+ def test_to_wkb(self):
+ for df in self.dframes:
+ self.compare('to_wkb', df)
+
+ def test_to_wkt(self):
+ for df in self.dframes:
+ self.compare('to_wkt', df)
+
+ def test_area(self):
+ for df in self.dframes:
+ self.compare('area', df)
+
+ def test_distance(self):
+ for df in self.dframes:
+ for other in self.targets:
+ self.compare('distance', df, other=other)
+
+ def test_length(self):
+ for df in self.dframes:
+ self.compare('length', df)
+
+ def test_boundary(self):
+ for df in self.dframes:
+ self.compare('boundary', df)
+
+ def test_bounds(self):
+ for df in self.dframes:
+ self.compare('bounds', df)
+
+ def test_centroid(self):
+ for df in self.dframes:
+ self.compare('centroid', df)
+
+ def test_representative_point(self):
+ for df in self.dframes:
+ self.compare('representative_point', df)
+
+ def test_convex_hull(self):
+ for df in self.dframes:
+ self.compare('convex_hull', df)
+
+ def test_envelope(self):
+ for df in self.dframes:
+ self.compare('envelope', df)
+
+ def test_buffer(self):
+ np.random.seed(555)
+ for df in self.dframes:
+ self.compare('buffer', df, radius=np.random.randint(10))
+
+ def test_simplify(self):
+ tol = .001
+ for df in self.dframes:
+ self.compare('simplify', df, tolerance=tol)
+
+ def test_difference(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('difference', df, other=target)
+
+ def test_intersection(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('intersection', df, other=target)
+
+ def test_symmetric_difference(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('symmetric_difference', df, other=target)
+
+ def test_union(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('union', df, other=target)
+
+ def test_has_z(self):
+ for df in self.dframes:
+ self.compare('has_z', df)
+
+ def test_is_empty(self):
+ """
+ PySAL doesn't really support empty shapes. Like, the following errors
+ out:
+
+ ps.cg.Polygon([[]])
+
+ and you can make it work by:
+ ps.cg.Polygon([[()]])
+
+ but that won't convert over to shapely.
+
+ So, we're only testing the negative here.
+ """
+ for df in self.dframes:
+ self.compare('is_empty', df)
+
+ def test_is_ring(self):
+ for df in self.dframes:
+ self.compare('is_ring', df)
+
+ def test_is_simple(self):
+ for df in self.dframes:
+ self.compare('is_simple', df)
+
+ def test_is_valid(self):
+ for df in self.dframes:
+ self.compare('is_valid', df)
+
+ def test_relate(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('relate', df, other=target)
+
+ def test_contains(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('contains', df, other=target)
+
+ def test_crosses(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('crosses', df, other=target)
+
+ def test_disjoint(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('disjoint', df, other=target)
+
+ def test_equals(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('equals', df, other=target)
+
+ def test_intersects(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('intersects', df, other=target)
+
+ def test_overlaps(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('overlaps', df, other=target)
+
+ def test_touches(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('touches', df, other=target)
+
+ def test_within(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('within', df, other=target)
+
+ def test_equals_exact(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('equals_exact', df, other=target, tolerance=.1)
+
+ def test_almost_equals(self):
+ for df in self.dframes:
+ for target in self.targets:
+ self.compare('almost_equals', df, other=target)
+
+ def test_project(self):
+ np.random.seed(555)
+ self.compare('project', self.lines, other=self.targets[2])
+
+ def test_interpolate(self):
+ np.random.seed(555)
+ for df in self.dframes:
+ if isinstance(df.geometry[0], Chain):
+ self.compare('interpolate', df, distance=np.random.randint(10))
+ else:
+ with self.assertRaises(TypeError):
+ self.compare('interpolate', df, distance=np.random.randint(10))
diff --git a/pysal/contrib/geotable/ops/tests/test_tabular.py b/pysal/contrib/geotable/ops/tests/test_tabular.py
new file mode 100644
index 0000000..3e6d474
--- /dev/null
+++ b/pysal/contrib/geotable/ops/tests/test_tabular.py
@@ -0,0 +1,72 @@
+import unittest as ut
+from .. import tabular as ta
+from pysal.common import RTOL, ATOL, pandas, requires as _requires
+from pysal.examples import get_path
+from pysal.cg import Polygon
+import pysal.contrib.pdio as pdio
+from pysal.contrib.geotable import ops as GIS
+import numpy as np
+
+PANDAS_EXTINCT = pandas is None
+
+ at ut.skipIf(PANDAS_EXTINCT, 'missing pandas')
+class Test_Tabular(ut.TestCase):
+ def setUp(self):
+ import pandas as pd
+ self.columbus = pdio.read_files(get_path('columbus.shp'))
+ grid = [Polygon([(0,0),(0,1),(1,1),(1,0)]),
+ Polygon([(0,1),(0,2),(1,2),(1,1)]),
+ Polygon([(1,2),(2,2),(2,1),(1,1)]),
+ Polygon([(1,1),(2,1),(2,0),(1,0)])]
+ regime = [0,0,1,1]
+ ids = range(4)
+ data = np.array((regime, ids)).T
+ self.exdf = pd.DataFrame(data, columns=['regime', 'ids'])
+ self.exdf['geometry'] = grid
+
+ @_requires('geopandas')
+ def test_round_trip(self):
+ import geopandas as gpd
+ import pandas as pd
+ geodf = GIS.tabular.to_gdf(self.columbus)
+ self.assertIsInstance(geodf, gpd.GeoDataFrame)
+ new_df = GIS.tabular.to_df(geodf)
+ self.assertIsInstance(new_df, pd.DataFrame)
+ for new, old in zip(new_df.geometry, self.columbus.geometry):
+ self.assertEquals(new, old)
+
+ def test_spatial_join(self):
+ pass
+
+ def test_spatial_overlay(self):
+ pass
+
+ def test_dissolve(self):
+ out = GIS.tabular.dissolve(self.exdf, by='regime')
+ self.assertEqual(out[0].area, 2.0)
+ self.assertEqual(out[1].area, 2.0)
+
+ answer_vertices0 = [(0,0), (0,1), (0,2), (1,2), (1,1), (1,0), (0,0)]
+ answer_vertices1 = [(2,1), (2,0), (1,0), (1,1), (1,2), (2,2), (2,1)]
+
+ np.testing.assert_allclose(out[0].vertices, answer_vertices0)
+ np.testing.assert_allclose(out[1].vertices, answer_vertices1)
+
+ def test_clip(self):
+ pass
+
+ def test_erase(self):
+ pass
+
+ def test_union(self):
+ new_geom = GIS.tabular.union(self.exdf)
+ self.assertEqual(new_geom.area, 4)
+
+ def test_intersection(self):
+ pass
+
+ def test_symmetric_difference(self):
+ pass
+
+ def test_difference(self):
+ pass
diff --git a/pysal/weights/tests/__init__.py b/pysal/contrib/geotable/tests/__init__.py
similarity index 100%
rename from pysal/weights/tests/__init__.py
rename to pysal/contrib/geotable/tests/__init__.py
diff --git a/pysal/contrib/geotable/tests/test_utils.py b/pysal/contrib/geotable/tests/test_utils.py
new file mode 100644
index 0000000..bb4ee59
--- /dev/null
+++ b/pysal/contrib/geotable/tests/test_utils.py
@@ -0,0 +1,13 @@
+from .. import utils
+from ...pdio import read_files as rf
+import unittest as ut
+
+class Test_Utils(ut.TestCase):
+
+ def test_converters(self):
+ ## make a round trip to geodataframe and back
+ raise
+ def test_insert_metadata(self):
+ ## add an attribute to a dataframe and see
+ ## if it is pervasive over copies
+ raise
diff --git a/pysal/contrib/geotable/utils.py b/pysal/contrib/geotable/utils.py
new file mode 100644
index 0000000..ae2fbc4
--- /dev/null
+++ b/pysal/contrib/geotable/utils.py
@@ -0,0 +1,70 @@
+from ...cg import asShape as pShape
+from ...common import requires as _requires
+from warnings import warn
+
+ at _requires('geopandas')
+def to_df(df, geom_col='geometry', **kw):
+ """
+ Convert a Geopandas dataframe into a normal pandas dataframe with a column
+ containing PySAL shapes.
+
+ Arguments
+ ---------
+ df : geopandas.GeoDataFrame
+ a geopandas dataframe (or pandas dataframe) with a column
+ containing geo-interfaced shapes
+ geom_col: str
+ string denoting which column in the df contains the geometry
+ **kw : keyword options
+ options passed directly to pandas.DataFrame(...,**kw)
+
+ See Also
+ --------
+ pandas.DataFrame
+ """
+ import pandas as pd
+ from geopandas import GeoDataFrame, GeoSeries
+ df[geom_col] = df[geom_col].apply(pShape)
+ if isinstance(df, (GeoDataFrame, GeoSeries)):
+ df = pd.DataFrame(df, **kw)
+ return df
+
+ at _requires('geopandas')
+def to_gdf(df, geom_col='geometry', **kw):
+ """
+ Convert a pandas dataframe with geometry column to a GeoPandas dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a column containing geo-interfaced
+ shapes
+ geom_col: str
+ string denoting which column in the df contains the geometry
+ **kw : keyword options
+ options passed directly to geopandas.GeoDataFrame(...,**kw)
+
+ See Also
+ --------
+ geopandas.GeoDataFrame
+ """
+ from geopandas import GeoDataFrame
+ from shapely.geometry import asShape as sShape
+ df[geom_col] = df[geom_col].apply(sShape)
+ return GeoDataFrame(df, geometry=geom_col, **kw)
+
+def insert_metadata(df, obj, name=None, inplace=True, overwrite=False):
+ if not inplace:
+ new = df.copy(deep=True)
+ insert_metadata(new, obj, name=name, inplace=True)
+ return new
+ if name is None:
+ name = type(obj).__name__
+ if hasattr(df, name):
+ if overwrite:
+ warn('Overwriting attribute {}! This may break the dataframe!'.format(name))
+ else:
+ raise Exception('Dataframe already has attribute {}. Cowardly refusing '
+ 'to break dataframe. '.format(name))
+ df._metadata.append(name)
+ df.__setattr__(name, obj)
diff --git a/pysal/contrib/glm/README.md b/pysal/contrib/glm/README.md
new file mode 100644
index 0000000..24c8eea
--- /dev/null
+++ b/pysal/contrib/glm/README.md
@@ -0,0 +1,30 @@
+**G**eneralized **L**inear **M**odeling
+=======================================
+
+This module is an adaptation of a portion of [GLM functionality from the
+Statsmodels](https://github.com/statsmodels/statsmodels/blob/master/statsmodels/genmod/generalized_linear_model.py) package, this it has been simplified and customized for the purposes of serving
+as the base for several other PySAL modules, namely SpInt and GWR. Currently, it
+supports the estimation of Gaussian, Poisson, and Logistic regression using only
+iteratively weighted least squares estimation (IWLS). One of the large differences this
+module and the functions avaialble in the Statsmodels package is that the custom IWLS routine is fully sparse compatible, which was necesary for the very sparse design matrices that arise in constrained spatial interaction models. The somewhat limited functionality and computation of only a subset of GLM diagnostics also decreases the computational overhead. Another difference is that this module also supports the estimation of QuasiPoisson models. One caveat is that this custom IWLS rou [...]
+
+Features
+--------
+
+- Gaussian GLM
+- Poisson GLM
+- QuasiPoisson GLM
+- Logistic GLM
+- Selection of most common GLM diagnostics
+- Supports sparse design matrices
+
+Future Work
+-----------
+
+- Add Negative Binomial GLM
+- Add Gamma GLM
+- Add Zero-inflated/Hurdle extensions of Poisson/Negative Binomial
+- Add support for gradient based optimization for maximum likelihood estimation
+
+
+
diff --git a/pysal/contrib/glm/__init__.py b/pysal/contrib/glm/__init__.py
new file mode 100644
index 0000000..4a468d5
--- /dev/null
+++ b/pysal/contrib/glm/__init__.py
@@ -0,0 +1,4 @@
+import glm
+import family
+import utils
+import iwls
diff --git a/pysal/contrib/glm/base.py b/pysal/contrib/glm/base.py
new file mode 100644
index 0000000..01f53bc
--- /dev/null
+++ b/pysal/contrib/glm/base.py
@@ -0,0 +1,408 @@
+
+from __future__ import print_function
+import numpy as np
+from scipy import stats
+from utils import cache_readonly
+
+class Results(object):
+ """
+ Class to contain model results
+ Parameters
+ ----------
+ model : class instance
+ the previously specified model instance
+ params : array
+ parameter estimates from the fit model
+ """
+ def __init__(self, model, params, **kwd):
+ self.__dict__.update(kwd)
+ self.initialize(model, params, **kwd)
+ self._data_attr = []
+
+ def initialize(self, model, params, **kwd):
+ self.params = params
+ self.model = model
+ if hasattr(model, 'k_constant'):
+ self.k_constant = model.k_constant
+
+#TODO: public method?
+class LikelihoodModelResults(Results):
+ """
+ Class to contain results from likelihood models
+ Parameters
+ -----------
+ model : LikelihoodModel instance or subclass instance
+ LikelihoodModelResults holds a reference to the model that is fit.
+ params : 1d array_like
+ parameter estimates from estimated model
+ normalized_cov_params : 2d array
+ Normalized (before scaling) covariance of params. (dot(X.T,X))**-1
+ scale : float
+ For (some subset of models) scale will typically be the
+ mean square error from the estimated model (sigma^2)
+ Returns
+ -------
+ **Attributes**
+ mle_retvals : dict
+ Contains the values returned from the chosen optimization method if
+ full_output is True during the fit. Available only if the model
+ is fit by maximum likelihood. See notes below for the output from
+ the different methods.
+ mle_settings : dict
+ Contains the arguments passed to the chosen optimization method.
+ Available if the model is fit by maximum likelihood. See
+ LikelihoodModel.fit for more information.
+ model : model instance
+ LikelihoodResults contains a reference to the model that is fit.
+ params : ndarray
+ The parameters estimated for the model.
+ scale : float
+ The scaling factor of the model given during instantiation.
+ tvalues : array
+ The t-values of the standard errors.
+ Notes
+ -----
+ The covariance of params is given by scale times normalized_cov_params.
+ Return values by solver if full_output is True during fit:
+ 'newton'
+ fopt : float
+ The value of the (negative) loglikelihood at its
+ minimum.
+ iterations : int
+ Number of iterations performed.
+ score : ndarray
+ The score vector at the optimum.
+ Hessian : ndarray
+ The Hessian at the optimum.
+ warnflag : int
+ 1 if maxiter is exceeded. 0 if successful convergence.
+ converged : bool
+ True: converged. False: did not converge.
+ allvecs : list
+ List of solutions at each iteration.
+ 'nm'
+ fopt : float
+ The value of the (negative) loglikelihood at its
+ minimum.
+ iterations : int
+ Number of iterations performed.
+ warnflag : int
+ 1: Maximum number of function evaluations made.
+ 2: Maximum number of iterations reached.
+ converged : bool
+ True: converged. False: did not converge.
+ allvecs : list
+ List of solutions at each iteration.
+ 'bfgs'
+ fopt : float
+ Value of the (negative) loglikelihood at its minimum.
+ gopt : float
+ Value of gradient at minimum, which should be near 0.
+ Hinv : ndarray
+ value of the inverse Hessian matrix at minimum. Note
+ that this is just an approximation and will often be
+ different from the value of the analytic Hessian.
+ fcalls : int
+ Number of calls to loglike.
+ gcalls : int
+ Number of calls to gradient/score.
+ warnflag : int
+ 1: Maximum number of iterations exceeded. 2: Gradient
+ and/or function calls are not changing.
+ converged : bool
+ True: converged. False: did not converge.
+ allvecs : list
+ Results at each iteration.
+ 'lbfgs'
+ fopt : float
+ Value of the (negative) loglikelihood at its minimum.
+ gopt : float
+ Value of gradient at minimum, which should be near 0.
+ fcalls : int
+ Number of calls to loglike.
+ warnflag : int
+ Warning flag:
+ - 0 if converged
+ - 1 if too many function evaluations or too many iterations
+ - 2 if stopped for another reason
+ converged : bool
+ True: converged. False: did not converge.
+ 'powell'
+ fopt : float
+ Value of the (negative) loglikelihood at its minimum.
+ direc : ndarray
+ Current direction set.
+ iterations : int
+ Number of iterations performed.
+ fcalls : int
+ Number of calls to loglike.
+ warnflag : int
+ 1: Maximum number of function evaluations. 2: Maximum number
+ of iterations.
+ converged : bool
+ True : converged. False: did not converge.
+ allvecs : list
+ Results at each iteration.
+ 'cg'
+ fopt : float
+ Value of the (negative) loglikelihood at its minimum.
+ fcalls : int
+ Number of calls to loglike.
+ gcalls : int
+ Number of calls to gradient/score.
+ warnflag : int
+ 1: Maximum number of iterations exceeded. 2: Gradient and/
+ or function calls not changing.
+ converged : bool
+ True: converged. False: did not converge.
+ allvecs : list
+ Results at each iteration.
+ 'ncg'
+ fopt : float
+ Value of the (negative) loglikelihood at its minimum.
+ fcalls : int
+ Number of calls to loglike.
+ gcalls : int
+ Number of calls to gradient/score.
+ hcalls : int
+ Number of calls to hessian.
+ warnflag : int
+ 1: Maximum number of iterations exceeded.
+ converged : bool
+ True: converged. False: did not converge.
+ allvecs : list
+ Results at each iteration.
+ """
+
+ # by default we use normal distribution
+ # can be overwritten by instances or subclasses
+ use_t = False
+
+ def __init__(self, model, params, normalized_cov_params=None, scale=1.,
+ **kwargs):
+ super(LikelihoodModelResults, self).__init__(model, params)
+ self.normalized_cov_params = normalized_cov_params
+ self.scale = scale
+
+ # robust covariance
+ # We put cov_type in kwargs so subclasses can decide in fit whether to
+ # use this generic implementation
+ if 'use_t' in kwargs:
+ use_t = kwargs['use_t']
+ if use_t is not None:
+ self.use_t = use_t
+ if 'cov_type' in kwargs:
+ cov_type = kwargs.get('cov_type', 'nonrobust')
+ cov_kwds = kwargs.get('cov_kwds', {})
+
+ if cov_type == 'nonrobust':
+ self.cov_type = 'nonrobust'
+ self.cov_kwds = {'description' : 'Standard Errors assume that the ' +
+ 'covariance matrix of the errors is correctly ' +
+ 'specified.'}
+ else:
+ from statsmodels.base.covtype import get_robustcov_results
+ if cov_kwds is None:
+ cov_kwds = {}
+ use_t = self.use_t
+ # TODO: we shouldn't need use_t in get_robustcov_results
+ get_robustcov_results(self, cov_type=cov_type, use_self=True,
+ use_t=use_t, **cov_kwds)
+
+
+ def normalized_cov_params(self):
+ raise NotImplementedError
+
+
+ def _get_robustcov_results(self, cov_type='nonrobust', use_self=True,
+ use_t=None, **cov_kwds):
+ from statsmodels.base.covtype import get_robustcov_results
+ if cov_kwds is None:
+ cov_kwds = {}
+
+ if cov_type == 'nonrobust':
+ self.cov_type = 'nonrobust'
+ self.cov_kwds = {'description' : 'Standard Errors assume that the ' +
+ 'covariance matrix of the errors is correctly ' +
+ 'specified.'}
+ else:
+ # TODO: we shouldn't need use_t in get_robustcov_results
+ get_robustcov_results(self, cov_type=cov_type, use_self=True,
+ use_t=use_t, **cov_kwds)
+
+ @cache_readonly
+ def llf(self):
+ return self.model.loglike(self.params)
+
+ @cache_readonly
+ def bse(self):
+ return np.sqrt(np.diag(self.cov_params()))
+
+ @cache_readonly
+ def tvalues(self):
+ """
+ Return the t-statistic for a given parameter estimate.
+ """
+ return self.params / self.bse
+
+ @cache_readonly
+ def pvalues(self):
+ if self.use_t:
+ df_resid = getattr(self, 'df_resid_inference', self.df_resid)
+ return stats.t.sf(np.abs(self.tvalues), df_resid)*2
+ else:
+ return stats.norm.sf(np.abs(self.tvalues))*2
+
+
+ def cov_params(self, r_matrix=None, column=None, scale=None, cov_p=None,
+ other=None):
+ """
+ Returns the variance/covariance matrix.
+ The variance/covariance matrix can be of a linear contrast
+ of the estimates of params or all params multiplied by scale which
+ will usually be an estimate of sigma^2. Scale is assumed to be
+ a scalar.
+ Parameters
+ ----------
+ r_matrix : array-like
+ Can be 1d, or 2d. Can be used alone or with other.
+ column : array-like, optional
+ Must be used on its own. Can be 0d or 1d see below.
+ scale : float, optional
+ Can be specified or not. Default is None, which means that
+ the scale argument is taken from the model.
+ other : array-like, optional
+ Can be used when r_matrix is specified.
+ Returns
+ -------
+ cov : ndarray
+ covariance matrix of the parameter estimates or of linear
+ combination of parameter estimates. See Notes.
+ Notes
+ -----
+ (The below are assumed to be in matrix notation.)
+ If no argument is specified returns the covariance matrix of a model
+ ``(scale)*(X.T X)^(-1)``
+ If contrast is specified it pre and post-multiplies as follows
+ ``(scale) * r_matrix (X.T X)^(-1) r_matrix.T``
+ If contrast and other are specified returns
+ ``(scale) * r_matrix (X.T X)^(-1) other.T``
+ If column is specified returns
+ ``(scale) * (X.T X)^(-1)[column,column]`` if column is 0d
+ OR
+ ``(scale) * (X.T X)^(-1)[column][:,column]`` if column is 1d
+ """
+ if (hasattr(self, 'mle_settings') and
+ self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']):
+ dot_fun = nan_dot
+ else:
+ dot_fun = np.dot
+
+ if (cov_p is None and self.normalized_cov_params is None and
+ not hasattr(self, 'cov_params_default')):
+ raise ValueError('need covariance of parameters for computing '
+ '(unnormalized) covariances')
+ if column is not None and (r_matrix is not None or other is not None):
+ raise ValueError('Column should be specified without other '
+ 'arguments.')
+ if other is not None and r_matrix is None:
+ raise ValueError('other can only be specified with r_matrix')
+
+ if cov_p is None:
+ if hasattr(self, 'cov_params_default'):
+ cov_p = self.cov_params_default
+ else:
+ if scale is None:
+ scale = self.scale
+ cov_p = self.normalized_cov_params * scale
+
+ if column is not None:
+ column = np.asarray(column)
+ if column.shape == ():
+ return cov_p[column, column]
+ else:
+ #return cov_p[column][:, column]
+ return cov_p[column[:, None], column]
+ elif r_matrix is not None:
+ r_matrix = np.asarray(r_matrix)
+ if r_matrix.shape == ():
+ raise ValueError("r_matrix should be 1d or 2d")
+ if other is None:
+ other = r_matrix
+ else:
+ other = np.asarray(other)
+ tmp = dot_fun(r_matrix, dot_fun(cov_p, np.transpose(other)))
+ return tmp
+ else: # if r_matrix is None and column is None:
+ return cov_p
+
+ def conf_int(self, alpha=.05, cols=None, method='default'):
+ """
+ Returns the confidence interval of the fitted parameters.
+ Parameters
+ ----------
+ alpha : float, optional
+ The significance level for the confidence interval.
+ ie., The default `alpha` = .05 returns a 95% confidence interval.
+ cols : array-like, optional
+ `cols` specifies which confidence intervals to return
+ method : string
+ Not Implemented Yet
+ Method to estimate the confidence_interval.
+ "Default" : uses self.bse which is based on inverse Hessian for MLE
+ "hjjh" :
+ "jac" :
+ "boot-bse"
+ "boot_quant"
+ "profile"
+ Returns
+ --------
+ conf_int : array
+ Each row contains [lower, upper] limits of the confidence interval
+ for the corresponding parameter. The first column contains all
+ lower, the second column contains all upper limits.
+ Examples
+ --------
+ >>> import pysal
+ >>> from pysal.contrib.glm.glm import GLM
+ >>> import numpy as np
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL")).reshape((-1,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> X = np.array(X).T
+ >>> model = GLM(y, X)
+ >>> results = model.fit()
+ >>> results.conf_int()
+ array([[ 20.57281401, 72.28355135],
+ [ -0.42138121, 1.67934915],
+ [ -0.84292086, -0.12685622]])
+
+ Notes
+ -----
+ The confidence interval is based on the standard normal distribution.
+ Models wish to use a different distribution should overwrite this
+ method.
+ """
+ bse = self.bse
+
+ if self.use_t:
+ dist = stats.t
+ df_resid = getattr(self, 'df_resid_inference', self.df_resid)
+ q = dist.ppf(1 - alpha / 2, df_resid)
+ else:
+ dist = stats.norm
+ q = dist.ppf(1 - alpha / 2)
+
+ if cols is None:
+ lower = self.params - q * bse
+ upper = self.params + q * bse
+ else:
+ cols = np.asarray(cols)
+ lower = self.params[cols] - q * bse[cols]
+ upper = self.params[cols] + q * bse[cols]
+ return np.asarray(lzip(lower, upper))
+
+def lzip(*args, **kwargs):
+ return list(zip(*args, **kwargs))
diff --git a/pysal/contrib/glm/family.py b/pysal/contrib/glm/family.py
new file mode 100644
index 0000000..bad22c1
--- /dev/null
+++ b/pysal/contrib/glm/family.py
@@ -0,0 +1,1845 @@
+'''
+The one parameter exponential family distributions used by GLM.
+'''
+# TODO: quasi, quasibinomial, quasipoisson
+# see http://www.biostat.jhsph.edu/~qli/biostatistics_r_doc/library/stats/html/family.html
+# for comparison to R, and McCullagh and Nelder
+
+import numpy as np
+from scipy import special
+import links as L
+import varfuncs as V
+FLOAT_EPS = np.finfo(float).eps
+
+
+class Family(object):
+ """
+ The parent class for one-parameter exponential families.
+
+ Parameters
+ ----------
+ link : a link function instance
+ Link is the linear transformation function.
+ See the individual families for available links.
+ variance : a variance function
+ Measures the variance as a function of the mean probabilities.
+ See the individual families for the default variance function.
+
+ See Also
+ --------
+ :ref:`links`
+
+ """
+ # TODO: change these class attributes, use valid somewhere...
+ valid = [-np.inf, np.inf]
+
+ links = []
+
+ def _setlink(self, link):
+ """
+ Helper method to set the link for a family.
+
+ Raises a ValueError exception if the link is not available. Note that
+ the error message might not be that informative because it tells you
+ that the link should be in the base class for the link function.
+
+ See glm.GLM for a list of appropriate links for each family but note
+ that not all of these are currently available.
+ """
+ # TODO: change the links class attribute in the families to hold
+ # meaningful information instead of a list of links instances such as
+ # [<statsmodels.family.links.Log object at 0x9a4240c>,
+ # <statsmodels.family.links.Power object at 0x9a423ec>,
+ # <statsmodels.family.links.Power object at 0x9a4236c>]
+ # for Poisson...
+ self._link = link
+ if not isinstance(link, L.Link):
+ raise TypeError("The input should be a valid Link object.")
+ if hasattr(self, "links"):
+ validlink = link in self.links
+ validlink = max([isinstance(link, _) for _ in self.links])
+ if not validlink:
+ errmsg = "Invalid link for family, should be in %s. (got %s)"
+ raise ValueError(errmsg % (repr(self.links), link))
+
+ def _getlink(self):
+ """
+ Helper method to get the link for a family.
+ """
+ return self._link
+
+ # link property for each family is a pointer to link instance
+ link = property(_getlink, _setlink, doc="Link function for family")
+
+ def __init__(self, link, variance):
+ self.link = link()
+ self.variance = variance
+
+ def starting_mu(self, y):
+ r"""
+ Starting value for mu in the IRLS algorithm.
+
+ Parameters
+ ----------
+ y : array
+ The untransformed response variable.
+
+ Returns
+ -------
+ mu_0 : array
+ The first guess on the transformed response variable.
+
+ Notes
+ -----
+ .. math::
+
+ \mu_0 = (Y + \overline{Y})/2
+
+ Notes
+ -----
+ Only the Binomial family takes a different initial value.
+ """
+ return (y + y.mean())/2.
+
+ def weights(self, mu):
+ r"""
+ Weights for IRLS steps
+
+ Parameters
+ ----------
+ mu : array-like
+ The transformed mean response variable in the exponential family
+
+ Returns
+ -------
+ w : array
+ The weights for the IRLS steps
+
+ Notes
+ -----
+ .. math::
+
+ w = 1 / (g'(\mu)^2 * Var(\mu))
+ """
+ return 1. / (self.link.deriv(mu)**2 * self.variance(mu))
+
+ def deviance(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ The deviance function evaluated at (endog,mu,freq_weights,mu).
+
+ Deviance is usually defined as twice the loglikelihood ratio.
+
+ Parameters
+ ----------
+ endog : array-like
+ The endogenous response variable
+ mu : array-like
+ The inverse of the link function at the linear predicted values.
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional scale argument. The default is 1.
+
+ Returns
+ -------
+ Deviance : array
+ The value of deviance function defined below.
+
+ Notes
+ -----
+ Deviance is defined
+
+ .. math::
+
+ D = \sum_i (2 * freq\_weights_i * llf(Y_i, Y_i) - 2 *
+ llf(Y_i, \mu_i)) / scale
+
+ where y is the endogenous variable. The deviance functions are
+ analytically defined for each family.
+ """
+ raise NotImplementedError
+
+ def resid_dev(self, endog, mu, freq_weights=1., scale=1.):
+ """
+ The deviance residuals
+
+ Parameters
+ ----------
+ endog : array
+ The endogenous response variable
+ mu : array
+ The inverse of the link function at the linear predicted values.
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional argument to divide the residuals by scale. The default
+ is 1.
+
+ Returns
+ -------
+ Deviance residuals.
+
+ Notes
+ -----
+ The deviance residuals are defined for each family.
+ """
+ raise NotImplementedError
+
+ def fitted(self, lin_pred):
+ """
+ Fitted values based on linear predictors lin_pred.
+
+ Parameters
+ -----------
+ lin_pred : array
+ Values of the linear predictor of the model.
+ dot(X,beta) in a classical linear model.
+
+ Returns
+ --------
+ mu : array
+ The mean response variables given by the inverse of the link
+ function.
+ """
+ fits = self.link.inverse(lin_pred)
+ return fits
+
+ def predict(self, mu):
+ """
+ Linear predictors based on given mu values.
+
+ Parameters
+ ----------
+ mu : array
+ The mean response variables
+
+ Returns
+ -------
+ lin_pred : array
+ Linear predictors based on the mean response variables. The value
+ of the link function at the given mu.
+ """
+ return self.link(mu)
+
+ def loglike(self, endog, mu, freq_weights=1., scale=1.):
+ """
+ The log-likelihood function in terms of the fitted mean response.
+
+ Parameters
+ ----------
+ `endog` : array
+ Usually the endogenous response variable.
+ `mu` : array
+ Usually but not always the fitted mean response variable.
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float
+ The scale parameter. The default is 1.
+
+ Returns
+ -------
+ llf : float
+ The value of the loglikelihood evaluated at
+ (endog,mu,freq_weights,scale) as defined below.
+ Notes
+ -----
+ This is defined for each family. endog and mu are not restricted to
+ `endog` and `mu` respectively. For instance, the deviance function
+ calls both loglike(endog,endog) and loglike(endog,mu) to get the
+ likelihood ratio.
+ """
+ raise NotImplementedError
+
+ def resid_anscombe(self, endog, mu):
+ """
+ The Anscome residuals.
+
+ See also
+ --------
+ statsmodels.families.family.Family docstring and the `resid_anscombe`
+ for the individual families for more information.
+ """
+ raise NotImplementedError
+
+
+class Poisson(Family):
+ """
+ Poisson exponential family.
+
+ Parameters
+ ----------
+ link : a link instance, optional
+ The default link for the Poisson family is the log link. Available
+ links are log, identity, and sqrt. See statsmodels.family.links for
+ more information.
+
+ Attributes
+ ----------
+ Poisson.link : a link instance
+ The link function of the Poisson instance.
+ Poisson.variance : varfuncs instance
+ `variance` is an instance of
+ statsmodels.genmod.families.family.varfuncs.mu
+
+ See also
+ --------
+ statsmodels.genmod.families.family.Family
+ :ref:`links`
+
+ """
+
+ links = [L.log, L.identity, L.sqrt]
+ variance = V.mu
+ valid = [0, np.inf]
+ safe_links = [L.Log, ]
+
+ def __init__(self, link=L.log):
+ self.variance = Poisson.variance
+ self.link = link()
+
+ def _clean(self, x):
+ """
+ Helper function to trim the data so that is in (0,inf)
+
+ Notes
+ -----
+ The need for this function was discovered through usage and its
+ possible that other families might need a check for validity of the
+ domain.
+ """
+ return np.clip(x, FLOAT_EPS, np.inf)
+
+ def resid_dev(self, endog, mu, scale=1.):
+ r"""Poisson deviance residual
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ scale : float, optional
+ An optional argument to divide the residuals by scale. The default
+ is 1.
+
+ Returns
+ -------
+ resid_dev : array
+ Deviance residuals as defined below
+
+ Notes
+ -----
+ .. math::
+
+ resid\_dev_i = sign(Y_i - \mu_i) * \sqrt{2 *
+ (Y_i * \log(Y_i / \mu_i) - (Y_i - \mu_i))} / scale
+ """
+ endog_mu = self._clean(endog / mu)
+ return (np.sign(endog - mu) *
+ np.sqrt(2 * (endog * np.log(endog_mu) - (endog - mu))) / scale)
+
+ def deviance(self, endog, mu, freq_weights=1., scale=1.):
+ r'''
+ Poisson deviance function
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional scale argument. The default is 1.
+
+ Returns
+ -------
+ deviance : float
+ The deviance function at (endog,mu,freq_weights,scale) as defined
+ below.
+
+ Notes
+ -----
+ If a constant term is included it is defined as
+
+ .. math::
+
+ D = 2 * \sum_i (freq\_weights_i * Y_i * \log(Y_i / \mu_i))/ scale
+ '''
+ endog_mu = self._clean(endog / mu)
+ return 2 * np.sum(endog * freq_weights * np.log(endog_mu)) / scale
+
+ def loglike(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ The log-likelihood function in terms of the fitted mean response.
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ The scale parameter, defaults to 1.
+
+ Returns
+ -------
+ llf : float
+ The value of the loglikelihood function evaluated at
+ (endog,mu,freq_weights,scale) as defined below.
+
+ Notes
+ -----
+ .. math::
+
+ llf = scale * \sum_i freq\_weights_i * (Y_i * \log(\mu_i) - \mu_i -
+ \ln \Gamma(Y_i + 1))
+ """
+ loglike = np.sum(freq_weights * (endog * np.log(mu) - mu -
+ special.gammaln(endog + 1)))
+ return scale * loglike
+
+ def resid_anscombe(self, endog, mu):
+ r"""
+ Anscombe residuals for the Poisson exponential family distribution
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+
+ Returns
+ -------
+ resid_anscombe : array
+ The Anscome residuals for the Poisson family defined below
+
+ Notes
+ -----
+ .. math::
+
+ resid\_anscombe_i = (3/2) * (Y_i^{2/3} - \mu_i^{2/3}) / \mu_i^{1/6}
+ """
+ return (3 / 2.) * (endog**(2/3.) - mu**(2 / 3.)) / mu**(1 / 6.)
+
+class QuasiPoisson(Family):
+ """
+ QuasiPoisson exponential family.
+
+ Parameters
+ ----------
+ link : a link instance, optional
+ The default link for the Poisson family is the log link. Available
+ links are log, identity, and sqrt. See statsmodels.family.links for
+ more information.
+
+ Attributes
+ ----------
+ Poisson.link : a link instance
+ The link function of the Poisson instance.
+ Poisson.variance : varfuncs instance
+ `variance` is an instance of
+ statsmodels.genmod.families.family.varfuncs.mu
+
+ See also
+ --------
+ statsmodels.genmod.families.family.Family
+ :ref:`links`
+
+ """
+
+ links = [L.log, L.identity, L.sqrt]
+ variance = V.mu
+ valid = [0, np.inf]
+ safe_links = [L.Log, ]
+
+ def __init__(self, link=L.log):
+ self.variance = Poisson.variance
+ self.link = link()
+
+ def _clean(self, x):
+ """
+ Helper function to trim the data so that is in (0,inf)
+
+ Notes
+ -----
+ The need for this function was discovered through usage and its
+ possible that other families might need a check for validity of the
+ domain.
+ """
+ return np.clip(x, FLOAT_EPS, np.inf)
+
+ def resid_dev(self, endog, mu, scale=1.):
+ r"""Poisson deviance residual
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ scale : float, optional
+ An optional argument to divide the residuals by scale. The default
+ is 1.
+
+ Returns
+ -------
+ resid_dev : array
+ Deviance residuals as defined below
+
+ Notes
+ -----
+ .. math::
+
+ resid\_dev_i = sign(Y_i - \mu_i) * \sqrt{2 *
+ (Y_i * \log(Y_i / \mu_i) - (Y_i - \mu_i))} / scale
+ """
+ endog_mu = self._clean(endog / mu)
+ return (np.sign(endog - mu) *
+ np.sqrt(2 * (endog * np.log(endog_mu) - (endog - mu))) / scale)
+
+ def deviance(self, endog, mu, freq_weights=1., scale=1.):
+ r'''
+ Poisson deviance function
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional scale argument. The default is 1.
+
+ Returns
+ -------
+ deviance : float
+ The deviance function at (endog,mu,freq_weights,scale) as defined
+ below.
+
+ Notes
+ -----
+ If a constant term is included it is defined as
+
+ .. math::
+
+ D = 2 * \sum_i (freq\_weights_i * Y_i * \log(Y_i / \mu_i))/ scale
+ '''
+ endog_mu = self._clean(endog / mu)
+ return 2 * np.sum(endog * freq_weights * np.log(endog_mu)) / scale
+
+ def loglike(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ The log-likelihood function in terms of the fitted mean response.
+
+ Returns NaN for QuasiPoisson
+
+ Returns
+ -------
+ None: not applicable for QuasiPoisson
+ """
+ return np.nan
+
+ def resid_anscombe(self, endog, mu):
+ r"""
+ Anscombe residuals for the Poisson exponential family distribution
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+
+ Returns
+ -------
+ resid_anscombe : array
+ The Anscome residuals for the Poisson family defined below
+
+ Notes
+ -----
+ .. math::
+
+ resid\_anscombe_i = (3/2) * (Y_i^{2/3} - \mu_i^{2/3}) / \mu_i^{1/6}
+ """
+ return (3 / 2.) * (endog**(2/3.) - mu**(2 / 3.)) / mu**(1 / 6.)
+
+class Gaussian(Family):
+ """
+ Gaussian exponential family distribution.
+
+ Parameters
+ ----------
+ link : a link instance, optional
+ The default link for the Gaussian family is the identity link.
+ Available links are log, identity, and inverse.
+ See statsmodels.family.links for more information.
+
+ Attributes
+ ----------
+ Gaussian.link : a link instance
+ The link function of the Gaussian instance
+ Gaussian.variance : varfunc instance
+ `variance` is an instance of statsmodels.family.varfuncs.constant
+
+ See also
+ --------
+ statsmodels.genmod.families.family.Family
+ :ref:`links`
+
+ """
+
+ links = [L.log, L.identity, L.inverse_power]
+ variance = V.constant
+ safe_links = links
+
+ def __init__(self, link=L.identity):
+ self.variance = Gaussian.variance
+ self.link = link()
+
+ def resid_dev(self, endog, mu, scale=1.):
+ r"""
+ Gaussian deviance residuals
+
+ Parameters
+ -----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ scale : float, optional
+ An optional argument to divide the residuals by scale. The default
+ is 1.
+
+ Returns
+ -------
+ resid_dev : array
+ Deviance residuals as defined below
+
+ Notes
+ --------
+ .. math::
+
+ resid\_dev_i = (Y_i - \mu_i) / \sqrt{Var(\mu_i)} / scale
+ """
+
+ return (endog - mu) / np.sqrt(self.variance(mu)) / scale
+
+ def deviance(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ Gaussian deviance function
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional scale argument. The default is 1.
+
+ Returns
+ -------
+ deviance : float
+ The deviance function at (endog,mu,freq_weights,scale)
+ as defined below.
+
+ Notes
+ --------
+ .. math::
+
+ D = \sum_i freq\_weights_i * (Y_i - \mu_i)^2 / scale
+ """
+ return np.sum((freq_weights * (endog - mu)**2)) / scale
+
+ def loglike(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ The log-likelihood in terms of the fitted mean response.
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ Scales the loglikelihood function. The default is 1.
+
+ Returns
+ -------
+ llf : float
+ The value of the loglikelihood function evaluated at
+ (endog,mu,freq_weights,scale) as defined below.
+
+ Notes
+ -----
+ If the link is the identity link function then the
+ loglikelihood function is the same as the classical OLS model.
+
+ .. math::
+
+ llf = -nobs / 2 * (\log(SSR) + (1 + \log(2 \pi / nobs)))
+
+ where
+
+ .. math::
+ SSR = \sum_i (Y_i - g^{-1}(\mu_i))^2
+
+ If the links is not the identity link then the loglikelihood
+ function is defined as
+
+ .. math::
+
+ llf = \sum_i freq\_weights_i * ((Y_i * \mu_i - \mu_i^2 / 2) / scale-
+ Y^2 / (2 * scale) - (1/2) * \log(2 * \pi * scale))
+ """
+ if isinstance(self.link, L.Power) and self.link.power == 1:
+ # This is just the loglikelihood for classical OLS
+ nobs2 = endog.shape[0] / 2.
+ SSR = np.sum((endog-self.fitted(mu))**2, axis=0)
+ llf = -np.log(SSR) * nobs2
+ llf -= (1+np.log(np.pi/nobs2))*nobs2
+ return llf
+ else:
+ return np.sum(freq_weights * ((endog * mu - mu**2/2)/scale -
+ endog**2/(2 * scale) - .5*np.log(2 * np.pi * scale)))
+
+ def resid_anscombe(self, endog, mu):
+ r"""
+ The Anscombe residuals for the Gaussian exponential family distribution
+
+ Parameters
+ ----------
+ endog : array
+ Endogenous response variable
+ mu : array
+ Fitted mean response variable
+
+ Returns
+ -------
+ resid_anscombe : array
+ The Anscombe residuals for the Gaussian family defined below
+
+ Notes
+ --------
+ .. math::
+
+ resid\_anscombe_i = Y_i - \mu_i
+ """
+ return endog - mu
+
+
+class Gamma(Family):
+ """
+ Gamma exponential family distribution.
+
+ Parameters
+ ----------
+ link : a link instance, optional
+ The default link for the Gamma family is the inverse link.
+ Available links are log, identity, and inverse.
+ See statsmodels.family.links for more information.
+
+ Attributes
+ ----------
+ Gamma.link : a link instance
+ The link function of the Gamma instance
+ Gamma.variance : varfunc instance
+ `variance` is an instance of statsmodels.family.varfuncs.mu_squared
+
+ See also
+ --------
+ statsmodels.genmod.families.family.Family
+ :ref:`links`
+
+ """
+
+ links = [L.log, L.identity, L.inverse_power]
+ variance = V.mu_squared
+ safe_links = [L.Log, ]
+
+ def __init__(self, link=L.inverse_power):
+ self.variance = Gamma.variance
+ self.link = link()
+
+ def _clean(self, x):
+ """
+ Helper function to trim the data so that is in (0,inf)
+
+ Notes
+ -----
+ The need for this function was discovered through usage and its
+ possible that other families might need a check for validity of the
+ domain.
+ """
+ return np.clip(x, FLOAT_EPS, np.inf)
+
+ def deviance(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ Gamma deviance function
+
+ Parameters
+ -----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional scale argument. The default is 1.
+
+ Returns
+ -------
+ deviance : float
+ Deviance function as defined below
+
+ Notes
+ -----
+ .. math::
+
+ D = 2 * \sum_i freq\_weights_i * ((Y_i - \mu_i)/\mu_i - \log(Y_i /
+ \mu_i))
+ """
+ endog_mu = self._clean(endog/mu)
+ return 2*np.sum(freq_weights*((endog-mu)/mu-np.log(endog_mu)))
+
+ def resid_dev(self, endog, mu, scale=1.):
+ r"""
+ Gamma deviance residuals
+
+ Parameters
+ -----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ scale : float, optional
+ An optional argument to divide the residuals by scale. The default
+ is 1.
+
+ Returns
+ -------
+ resid_dev : array
+ Deviance residuals as defined below
+
+ Notes
+ -----
+ .. math::
+
+ resid\_dev_i = sign(Y_i - \mu_i) \sqrt{-2 *
+ (-(Y_i - \mu_i) / \mu_i + \log(Y_i / \mu_i))}
+ """
+ endog_mu = self._clean(endog / mu)
+ return np.sign(endog - mu) * np.sqrt(-2 * (-(endog - mu)/mu +
+ np.log(endog_mu)))
+
+ def loglike(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ The log-likelihood function in terms of the fitted mean response.
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ The default is 1.
+
+ Returns
+ -------
+ llf : float
+ The value of the loglikelihood function evaluated at
+ (endog,mu,freq_weights,scale) as defined below.
+
+ Notes
+ --------
+ .. math::
+
+ llf = -1 / scale * \sum_i *(Y_i / \mu_i+ \log(\mu_i)+
+ (scale -1) * \log(Y) + \log(scale) + scale *
+ \ln \Gamma(1 / scale))
+ """
+ return - 1./scale * np.sum((endog/mu + np.log(mu) + (scale - 1) *
+ np.log(endog) + np.log(scale) + scale *
+ special.gammaln(1./scale)) * freq_weights)
+
+ # in Stata scale is set to equal 1 for reporting llf
+ # in R it's the dispersion, though there is a loss of precision vs.
+ # our results due to an assumed difference in implementation
+
+ def resid_anscombe(self, endog, mu):
+ r"""
+ The Anscombe residuals for Gamma exponential family distribution
+
+ Parameters
+ ----------
+ endog : array
+ Endogenous response variable
+ mu : array
+ Fitted mean response variable
+
+ Returns
+ -------
+ resid_anscombe : array
+ The Anscombe residuals for the Gamma family defined below
+
+ Notes
+ -----
+ .. math::
+
+ resid\_anscombe_i = 3 * (Y_i^{1/3} - \mu_i^{1/3}) / \mu_i^{1/3}
+ """
+ return 3 * (endog**(1/3.) - mu**(1/3.)) / mu**(1/3.)
+
+
+class Binomial(Family):
+ """
+ Binomial exponential family distribution.
+
+ Parameters
+ ----------
+ link : a link instance, optional
+ The default link for the Binomial family is the logit link.
+ Available links are logit, probit, cauchy, log, and cloglog.
+ See statsmodels.family.links for more information.
+
+ Attributes
+ ----------
+ Binomial.link : a link instance
+ The link function of the Binomial instance
+ Binomial.variance : varfunc instance
+ `variance` is an instance of statsmodels.family.varfuncs.binary
+
+ See also
+ --------
+ statsmodels.genmod.families.family.Family
+ :ref:`links`
+
+ Notes
+ -----
+ endog for Binomial can be specified in one of three ways.
+
+ """
+
+ links = [L.logit, L.probit, L.cauchy, L.log, L.cloglog, L.identity]
+ variance = V.binary # this is not used below in an effort to include n
+
+ # Other safe links, e.g. cloglog and probit are subclasses
+ safe_links = [L.Logit, L.CDFLink]
+
+ def __init__(self, link=L.logit): # , n=1.):
+ # TODO: it *should* work for a constant n>1 actually, if freq_weights
+ # is equal to n
+ self.n = 1
+ # overwritten by initialize if needed but always used to initialize
+ # variance since endog is assumed/forced to be (0,1)
+ self.variance = V.Binomial(n=self.n)
+ self.link = link()
+
+ def starting_mu(self, y):
+ """
+ The starting values for the IRLS algorithm for the Binomial family.
+ A good choice for the binomial family is :math:`\mu_0 = (Y_i + 0.5)/2`
+ """
+ return (y + .5)/2
+
+ def initialize(self, endog, freq_weights):
+ '''
+ Initialize the response variable.
+
+ Parameters
+ ----------
+ endog : array
+ Endogenous response variable
+
+ Returns
+ --------
+ If `endog` is binary, returns `endog`
+
+ If `endog` is a 2d array, then the input is assumed to be in the format
+ (successes, failures) and
+ successes/(success + failures) is returned. And n is set to
+ successes + failures.
+ '''
+ # if not np.all(np.asarray(freq_weights) == 1):
+ # self.variance = V.Binomial(n=freq_weights)
+ if (endog.ndim > 1 and endog.shape[1] > 1):
+ y = endog[:, 0]
+ # overwrite self.freq_weights for deviance below
+ self.n = endog.sum(1)
+ return y*1./self.n, self.n
+ else:
+ return endog, np.ones(endog.shape[0])
+
+ def deviance(self, endog, mu, freq_weights=1, scale=1., axis=None):
+ r'''
+ Deviance function for either Bernoulli or Binomial data.
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable (already transformed to a probability
+ if appropriate).
+ mu : array
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional scale argument. The default is 1.
+
+ Returns
+ --------
+ deviance : float
+ The deviance function as defined below
+
+ Notes
+ -----
+ If the endogenous variable is binary:
+
+ .. math::
+
+ D = -2 * \sum_i freq\_weights * (I_{1,i} * \log(\mu_i) + I_{0,i} *
+ \log(1 - \mu_i))
+
+ where :math:`I_{1,i}` is an indicator function that evalueates to 1 if
+ :math:`Y_i = 1`. and :math:`I_{0,i}` is an indicator function that
+ evaluates to 1 if :math:`Y_i = 0`.
+
+ If the model is ninomial:
+
+ .. math::
+
+ D = 2 * \sum_i freq\_weights * (\log(Y_i / \mu_i) + (n_i - Y_i) *
+ \log((n_i - Y_i) / n_i - \mu_i))
+
+ where :math:`Y_i` and :math:`n` are as defined in Binomial.initialize.
+ '''
+ if np.shape(self.n) == () and self.n == 1:
+ one = np.equal(endog, 1)
+ return -2 * np.sum((one * np.log(mu + 1e-200) + (1-one) *
+ np.log(1 - mu + 1e-200)) * freq_weights, axis=axis)
+
+ else:
+ return 2 * np.sum(self.n * freq_weights *
+ (endog * np.log(endog/mu + 1e-200) +
+ (1 - endog) * np.log((1 - endog) /
+ (1 - mu) + 1e-200)), axis=axis)
+
+ def resid_dev(self, endog, mu, scale=1.):
+ r"""
+ Binomial deviance residuals
+
+ Parameters
+ -----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ scale : float, optional
+ An optional argument to divide the residuals by scale. The default
+ is 1.
+
+ Returns
+ -------
+ resid_dev : array
+ Deviance residuals as defined below
+
+ Notes
+ -----
+ If the endogenous variable is binary:
+
+ .. math::
+
+ resid\_dev_i = sign(Y_i - \mu_i) * \sqrt{-2 *
+ \log(I_{1,i} * \mu_i + I_{0,i} * (1 - \mu_i))}
+
+ where :math:`I_{1,i}` is an indicator function that evalueates to 1 if
+ :math:`Y_i = 1`. and :math:`I_{0,i}` is an indicator function that
+ evaluates to 1 if :math:`Y_i = 0`.
+
+ If the endogenous variable is binomial:
+
+ .. math::
+
+ resid\_dev_i = sign(Y_i - \mu_i) \sqrt{2 * n_i *
+ (Y_i * \log(Y_i / \mu_i) + (1 - Y_i) *
+ \log(1 - Y_i)/(1 - \mu_i))}
+
+ where :math:`Y_i` and :math:`n` are as defined in Binomial.initialize.
+ """
+
+ mu = self.link._clean(mu)
+ if np.shape(self.n) == () and self.n == 1:
+ one = np.equal(endog, 1)
+ return np.sign(endog-mu)*np.sqrt(-2 *
+ np.log(one * mu + (1 - one) *
+ (1 - mu)))/scale
+ else:
+ return (np.sign(endog - mu) *
+ np.sqrt(2 * self.n *
+ (endog * np.log(endog/mu + 1e-200) +
+ (1 - endog) * np.log((1 - endog)/(1 - mu) + 1e-200)))/scale)
+
+ def loglike(self, endog, mu, freq_weights=1, scale=1.):
+ r"""
+ The log-likelihood function in terms of the fitted mean response.
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ Not used for the Binomial GLM.
+
+ Returns
+ -------
+ llf : float
+ The value of the loglikelihood function evaluated at
+ (endog,mu,freq_weights,scale) as defined below.
+
+ Notes
+ --------
+ If the endogenous variable is binary:
+
+ .. math::
+
+ llf = scale * \sum_i (y_i * \log(\mu_i/(1-\mu_i)) + \log(1-\mu_i)) *
+ freq\_weights_i
+
+ If the endogenous variable is binomial:
+
+ .. math::
+
+ llf = scale * \sum_i freq\_weights_i * (\ln \Gamma(n+1) -
+ \ln \Gamma(y_i + 1) - \ln \Gamma(n_i - y_i +1) + y_i *
+ \log(\mu_i / (1 - \mu_i)) + n * \log(1 - \mu_i))
+
+ where :math:`y_i = Y_i * n_i` with :math:`Y_i` and :math:`n_i` as
+ defined in Binomial initialize. This simply makes :math:`y_i` the
+ original number of successes.
+ """
+
+ if np.shape(self.n) == () and self.n == 1:
+ return scale * np.sum((endog * np.log(mu/(1 - mu) + 1e-200) +
+ np.log(1 - mu)) * freq_weights)
+ else:
+ y = endog * self.n # convert back to successes
+ return scale * np.sum((special.gammaln(self.n + 1) -
+ special.gammaln(y + 1) -
+ special.gammaln(self.n - y + 1) + y *
+ np.log(mu/(1 - mu)) + self.n *
+ np.log(1 - mu)) * freq_weights)
+
+ def resid_anscombe(self, endog, mu):
+ '''
+ The Anscombe residuals
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+
+ Returns
+ -------
+ resid_anscombe : array
+ The Anscombe residuals as defined below.
+
+ Notes
+ -----
+ sqrt(n)*(cox_snell(endog)-cox_snell(mu))/(mu**(1/6.)*(1-mu)**(1/6.))
+
+ where cox_snell is defined as
+ cox_snell(x) = betainc(2/3., 2/3., x)*betainc(2/3.,2/3.)
+ where betainc is the incomplete beta function
+
+ The name 'cox_snell' is idiosyncratic and is simply used for
+ convenience following the approach suggested in Cox and Snell (1968).
+ Further note that
+ cox_snell(x) = x**(2/3.)/(2/3.)*hyp2f1(2/3.,1/3.,5/3.,x)
+ where hyp2f1 is the hypergeometric 2f1 function. The Anscombe
+ residuals are sometimes defined in the literature using the
+ hyp2f1 formulation. Both betainc and hyp2f1 can be found in scipy.
+
+ References
+ ----------
+ Anscombe, FJ. (1953) "Contribution to the discussion of H. Hotelling's
+ paper." Journal of the Royal Statistical Society B. 15, 229-30.
+
+ Cox, DR and Snell, EJ. (1968) "A General Definition of Residuals."
+ Journal of the Royal Statistical Society B. 30, 248-75.
+
+ '''
+ cox_snell = lambda x: (special.betainc(2/3., 2/3., x)
+ * special.beta(2/3., 2/3.))
+ return np.sqrt(self.n) * ((cox_snell(endog) - cox_snell(mu)) /
+ (mu**(1/6.) * (1 - mu)**(1/6.)))
+
+
+class InverseGaussian(Family):
+ """
+ InverseGaussian exponential family.
+
+ Parameters
+ ----------
+ link : a link instance, optional
+ The default link for the inverse Gaussian family is the
+ inverse squared link.
+ Available links are inverse_squared, inverse, log, and identity.
+ See statsmodels.family.links for more information.
+
+ Attributes
+ ----------
+ InverseGaussian.link : a link instance
+ The link function of the inverse Gaussian instance
+ InverseGaussian.variance : varfunc instance
+ `variance` is an instance of statsmodels.family.varfuncs.mu_cubed
+
+ See also
+ --------
+ statsmodels.genmod.families.family.Family
+ :ref:`links`
+
+ Notes
+ -----
+ The inverse Guassian distribution is sometimes referred to in the
+ literature as the Wald distribution.
+
+ """
+
+ links = [L.inverse_squared, L.inverse_power, L.identity, L.log]
+ variance = V.mu_cubed
+ safe_links = [L.inverse_squared, L.Log, ]
+
+ def __init__(self, link=L.inverse_squared):
+ self.variance = InverseGaussian.variance
+ self.link = link()
+
+ def resid_dev(self, endog, mu, scale=1.):
+ r"""
+ Returns the deviance residuals for the inverse Gaussian family.
+
+ Parameters
+ -----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional argument to divide the residuals by scale. The default
+ is 1.
+
+ Returns
+ -------
+ resid_dev : array
+ Deviance residuals as defined below
+
+ Notes
+ -----
+ .. math::
+
+ resid\_dev_i = sign(Y_i - \mu_i) *
+ \sqrt {(Y_i - \mu_i)^2 / (Y_i * \mu_i^2)} / scale
+ """
+ return np.sign(endog-mu) * np.sqrt((endog-mu)**2/(endog*mu**2))/scale
+
+ def deviance(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ Inverse Gaussian deviance function
+
+ Parameters
+ -----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional scale argument. The default is 1.
+
+ Returns
+ -------
+ deviance : float
+ Deviance function as defined below
+
+ Notes
+ -----
+ .. math::
+
+ D = \sum_i freq\_weights_i * ((Y_i - \mu_i)^2 / (Y_i *\mu_i^2)) /
+ scale
+ """
+ return np.sum(freq_weights*(endog-mu)**2/(endog*mu**2))/scale
+
+ def loglike(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ The log-likelihood function in terms of the fitted mean response.
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ The default is 1.
+
+ Returns
+ -------
+ llf : float
+ The value of the loglikelihood function evaluated at
+ (endog,mu,freq_weights,scale) as defined below.
+
+ Notes
+ -----
+ .. math::
+
+ llf = -1/2 * \sum_i freq\_weights_i * ((Y_i - \mu_i)^2 / (Y_i *
+ \mu_i * scale) + \log(scale * Y_i^3) + \log(2 * \pi))
+ """
+ return -.5 * np.sum(((endog - mu)**2/(endog * mu**2 * scale) +
+ np.log(scale * endog**3) + np.log(2 * np.pi)) *
+ freq_weights)
+
+ def resid_anscombe(self, endog, mu):
+ r"""
+ The Anscombe residuals for the inverse Gaussian distribution
+
+ Parameters
+ ----------
+ endog : array
+ Endogenous response variable
+ mu : array
+ Fitted mean response variable
+
+ Returns
+ -------
+ resid_anscombe : array
+ The Anscombe residuals for the inverse Gaussian distribution as
+ defined below
+
+ Notes
+ -----
+ .. math::
+
+ resid\_anscombe_i = \log(Y_i / \mu_i) / \sqrt{\mu_i}
+ """
+ return np.log(endog / mu) / np.sqrt(mu)
+
+
+class NegativeBinomial(Family):
+ """
+ Negative Binomial exponential family.
+
+ Parameters
+ ----------
+ link : a link instance, optional
+ The default link for the negative binomial family is the log link.
+ Available links are log, cloglog, identity, nbinom and power.
+ See statsmodels.family.links for more information.
+ alpha : float, optional
+ The ancillary parameter for the negative binomial distribution.
+ For now `alpha` is assumed to be nonstochastic. The default value
+ is 1. Permissible values are usually assumed to be between .01 and 2.
+
+
+ Attributes
+ ----------
+ NegativeBinomial.link : a link instance
+ The link function of the negative binomial instance
+ NegativeBinomial.variance : varfunc instance
+ `variance` is an instance of statsmodels.family.varfuncs.nbinom
+
+ See also
+ --------
+ statsmodels.genmod.families.family.Family
+ :ref:`links`
+
+ Notes
+ -----
+ Power link functions are not yet supported.
+
+ """
+ links = [L.log, L.cloglog, L.identity, L.nbinom, L.Power]
+ # TODO: add the ability to use the power links with an if test
+ # similar to below
+ variance = V.nbinom
+ safe_links = [L.Log, ]
+
+ def __init__(self, link=L.log, alpha=1.):
+ self.alpha = 1. * alpha # make it at least float
+ self.variance = V.NegativeBinomial(alpha=self.alpha)
+ if isinstance(link, L.NegativeBinomial):
+ self.link = link(alpha=self.alpha)
+ else:
+ self.link = link()
+
+ def _clean(self, x):
+ """
+ Helper function to trim the data so that is in (0,inf)
+
+ Notes
+ -----
+ The need for this function was discovered through usage and its
+ possible that other families might need a check for validity of the
+ domain.
+ """
+ return np.clip(x, FLOAT_EPS, np.inf)
+
+ def deviance(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ Returns the value of the deviance function.
+
+ Parameters
+ -----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional scale argument. The default is 1.
+
+ Returns
+ -------
+ deviance : float
+ Deviance function as defined below
+
+ Notes
+ -----
+ :math:`D = \sum_i piecewise_i` where :math:`piecewise_i` is defined as:
+
+ If :math:`Y_{i} = 0`:
+
+ :math:`piecewise_i = 2* \log(1 + \alpha * \mu_i) / \alpha`
+
+ If :math:`Y_{i} > 0`:
+
+ :math:`piecewise_i = 2 * Y_i * \log(Y_i / \mu_i) - (2 / \alpha) *
+ (1 + \alpha * Y_i) * \ln(1 + \alpha * Y_i) / (1 + \alpha * \mu_i)`
+ """
+ iszero = np.equal(endog, 0)
+ notzero = 1 - iszero
+ endog_mu = self._clean(endog/mu)
+ tmp = iszero * 2 * np.log(1 + self.alpha * mu) / self.alpha
+ tmp += notzero * (2 * endog * np.log(endog_mu) - 2 / self.alpha *
+ (1 + self.alpha * endog) *
+ np.log((1 + self.alpha * endog) /
+ (1 + self.alpha * mu)))
+ return np.sum(freq_weights * tmp) / scale
+
+ def resid_dev(self, endog, mu, scale=1.):
+ r"""
+ Negative Binomial Deviance Residual
+
+ Parameters
+ ----------
+ endog : array-like
+ `endog` is the response variable
+ mu : array-like
+ `mu` is the fitted value of the model
+ scale : float, optional
+ An optional argument to divide the residuals by scale. The default
+ is 1.
+
+ Returns
+ --------
+ resid_dev : array
+ The array of deviance residuals
+
+ Notes
+ -----
+ :math:`resid\_dev_i = sign(Y_i-\mu_i) * \sqrt{piecewise_i}`
+
+ where :math:`piecewise_i` is defined as
+
+ If :math:`Y_i = 0`:
+
+ :math:`piecewise_i = 2 * \log(1 + \alpha * \mu_i)/ \alpha`
+
+ If :math:`Y_i > 0`:
+
+ :math:`piecewise_i = 2 * Y_i * \log(Y_i / \mu_i) - (2 / \alpha) *
+ (1 + \alpha * Y_i) * \log((1 + \alpha * Y_i) / (1 + \alpha * \mu_i))`
+ """
+ iszero = np.equal(endog, 0)
+ notzero = 1 - iszero
+ endog_mu = self._clean(endog / mu)
+ tmp = iszero * 2 * np.log(1 + self.alpha * mu) / self.alpha
+ tmp += notzero * (2 * endog * np.log(endog_mu) - 2 / self.alpha *
+ (1 + self.alpha * endog) *
+ np.log((1 + self.alpha * endog) /
+ (1 + self.alpha * mu)))
+ return np.sign(endog - mu) * np.sqrt(tmp) / scale
+
+ def loglike(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ The log-likelihood function in terms of the fitted mean response.
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ The fitted mean response values
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float
+ The scale parameter. The default is 1.
+
+ Returns
+ -------
+ llf : float
+ The value of the loglikelihood function evaluated at
+ (endog,mu,freq_weights,scale) as defined below.
+
+ Notes
+ -----
+ Defined as:
+
+ .. math::
+
+ llf = \sum_i freq\_weights_i * (Y_i * \log{(\alpha * e^{\eta_i} /
+ (1 + \alpha * e^{\eta_i}))} - \log{(1 + \alpha * e^{\eta_i})}/
+ \alpha + Constant)
+
+ where :math:`Constant` is defined as:
+
+ .. math::
+
+ Constant = \ln \Gamma{(Y_i + 1/ \alpha )} - \ln \Gamma(Y_i + 1) -
+ \ln \Gamma{(1/ \alpha )}
+ """
+ lin_pred = self._link(mu)
+ constant = (special.gammaln(endog + 1 / self.alpha) -
+ special.gammaln(endog+1)-special.gammaln(1/self.alpha))
+ exp_lin_pred = np.exp(lin_pred)
+ return np.sum((endog * np.log(self.alpha * exp_lin_pred /
+ (1 + self.alpha * exp_lin_pred)) -
+ np.log(1 + self.alpha * exp_lin_pred) /
+ self.alpha + constant) * freq_weights)
+
+ def resid_anscombe(self, endog, mu):
+ """
+ The Anscombe residuals for the negative binomial family
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+
+ Returns
+ -------
+ resid_anscombe : array
+ The Anscombe residuals as defined below.
+
+ Notes
+ -----
+ `resid_anscombe` = (hyp2f1(-alpha*endog)-hyp2f1(-alpha*mu)+\
+ 1.5*(endog**(2/3.)-mu**(2/3.)))/(mu+alpha*mu**2)**(1/6.)
+
+ where hyp2f1 is the hypergeometric 2f1 function parameterized as
+ hyp2f1(x) = hyp2f1(2/3.,1/3.,5/3.,x)
+ """
+
+ hyp2f1 = lambda x : special.hyp2f1(2 / 3., 1 / 3., 5 / 3., x)
+ return ((hyp2f1(-self.alpha * endog) - hyp2f1(-self.alpha * mu) +
+ 1.5 * ( endog**(2 / 3.) - mu**(2 / 3.))) /
+ (mu + self.alpha * mu**2)**(1 / 6.))
+
+
+class Tweedie(Family):
+ """
+ Tweedie family.
+
+ Parameters
+ ----------
+ link : a link instance, optional
+ The default link for the Tweedie family is the log link when the
+ link_power is 0. Otherwise, the power link is default.
+ Available links are log and Power.
+ var_power : float, optional
+ The variance power.
+ link_power : float, optional
+ The link power.
+
+ Attributes
+ ----------
+ Tweedie.link : a link instance
+ The link function of the Tweedie instance
+ Tweedie.variance : varfunc instance
+ `variance` is an instance of statsmodels.family.varfuncs.Power
+ Tweedie.link_power : float
+ The power of the link function, or 0 if its a log link.
+ Tweedie.var_power : float
+ The power of the variance function.
+
+ See also
+ --------
+ statsmodels.genmod.families.family.Family
+ :ref:`links`
+
+ Notes
+ -----
+ Logliklihood function not implemented because of the complexity of
+ calculating an infinite series of summations. The variance power can be
+ estimated using the `estimate_tweedie_power` function that is part of the
+ `GLM` class.
+ """
+ links = [L.log, L.Power]
+ variance = V.Power
+ safe_links = [L.log, L.Power]
+
+ def __init__(self, link=None, var_power=1., link_power=0):
+ self.var_power = var_power
+ self.link_power = link_power
+ self.variance = V.Power(power=var_power * 1.)
+ if link_power != 0 and not ((link is L.Power) or (link is None)):
+ msg = 'link_power of {} not supported specified link'
+ msg = msg.format(link_power)
+ raise ValueError(msg)
+ if (link_power == 0) and ((link is None) or (link is L.Log)):
+ self.link = L.log()
+ elif link_power != 0:
+ self.link = L.Power(power=link_power * 1.)
+ else:
+ self.link = link()
+
+ def _clean(self, x):
+ """
+ Helper function to trim the data so that is in (0,inf)
+
+ Notes
+ -----
+ The need for this function was discovered through usage and its
+ possible that other families might need a check for validity of the
+ domain.
+ """
+ return np.clip(x, 0, np.inf)
+
+ def deviance(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ Returns the value of the deviance function.
+
+ Parameters
+ -----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float, optional
+ An optional scale argument. The default is 1.
+
+ Returns
+ -------
+ deviance : float
+ Deviance function as defined below
+
+ Notes
+ -----
+ When :math:`p = 1`,
+
+ .. math::
+
+ resid\_dev_i = \mu
+
+ when :math:`endog = 0` and
+
+ .. math::
+
+ resid\_dev_i = endog * \log(endog / \mu) + (\mu - endog)
+
+ otherwise.
+
+ When :math:`p = 2`,
+
+ .. math::
+
+ resid\_dev_i = (endog - \mu) / \mu - \log(endog / \mu)
+
+ For all other p,
+
+ .. math::
+
+ resid\_dev_i = endog ^{2 - p} / ((1 - p) * (2 - p)) -
+ endog * \mu ^{1 - p} / (1 - p) + \mu ^{2 - p} /
+ (2 - p)
+
+ Once :math:`resid\_dev_i` is calculated, then calculate deviance as
+
+ .. math::
+
+ D = \sum{2 * freq\_weights * resid\_dev_i}
+ """
+ p = self.var_power
+ if p == 1:
+ dev = np.where(endog == 0,
+ mu,
+ endog * np.log(endog / mu) + (mu - endog))
+ elif p == 2:
+ endog1 = np.clip(endog, FLOAT_EPS, np.inf)
+ dev = ((endog - mu) / mu) - np.log(endog1 / mu)
+ else:
+ dev = (endog ** (2 - p) / ((1 - p) * (2 - p)) -
+ endog * mu ** (1-p) / (1 - p) + mu ** (2 - p) / (2 - p))
+ return np.sum(2 * freq_weights * dev)
+
+ def resid_dev(self, endog, mu, scale=1.):
+ r"""
+ Tweedie Deviance Residual
+
+ Parameters
+ ----------
+ endog : array-like
+ `endog` is the response variable
+ mu : array-like
+ `mu` is the fitted value of the model
+ scale : float, optional
+ An optional argument to divide the residuals by scale. The default
+ is 1.
+
+ Returns
+ --------
+ resid_dev : array
+ The array of deviance residuals
+
+ Notes
+ -----
+ When :math:`p = 1`,
+
+ .. math::
+
+ resid\_dev_i = \mu
+
+ when :math:`endog = 0` and
+
+ .. math::
+
+ resid\_dev_i = endog * \log(endog / \mu) + (\mu - endog)
+
+ otherwise.
+
+ When :math:`p = 2`,
+
+ .. math::
+
+ resid\_dev_i = (endog - \mu) / \mu - \log(endog / \mu)
+
+ For all other p,
+
+ .. math::
+
+ resid\_dev_i = endog ^{2 - p} / ((1 - p) * (2 - p)) -
+ endog * \mu ^{1 - p} / (1 - p) + \mu ^{2 - p} /
+ (2 - p)
+ """
+ p = self.var_power
+ if p == 1:
+ dev = np.where(endog == 0,
+ mu,
+ endog * np.log(endog / mu) + (mu - endog))
+ elif p == 2:
+ endog1 = np.clip(endog, FLOAT_EPS, np.inf)
+ dev = ((endog - mu) / mu) - np.log(endog1 / mu)
+ else:
+ dev = (endog ** (2 - p) / ((1 - p) * (2 - p)) -
+ endog * mu ** (1-p) / (1 - p) + mu ** (2 - p) / (2 - p))
+ return np.sign(endog - mu) * np.sqrt(2 * dev)
+
+ def loglike(self, endog, mu, freq_weights=1., scale=1.):
+ r"""
+ The log-likelihood function in terms of the fitted mean response.
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ The fitted mean response values
+ freq_weights : array-like
+ 1d array of frequency weights. The default is 1.
+ scale : float
+ The scale parameter. The default is 1.
+
+ Returns
+ -------
+ llf : float
+ The value of the loglikelihood function evaluated at
+ (endog,mu,freq_weights,scale) as defined below.
+
+ Notes
+ -----
+ This is not implemented because of the complexity of calculating an
+ infinite series of sums.
+ """
+ return np.nan
+
+ def resid_anscombe(self, endog, mu):
+ """
+ The Anscombe residuals for the Tweedie family
+
+ Parameters
+ ----------
+ endog : array-like
+ Endogenous response variable
+ mu : array-like
+ Fitted mean response variable
+
+ Returns
+ -------
+ resid_anscombe : array
+ The Anscombe residuals as defined below.
+
+ Notes
+ -----
+ When :math:`p = 3`, then
+
+ .. math::
+
+ resid\_anscombe_i = (\log(endog) - \log(\mu)) / \sqrt{mu}
+
+ Otherwise,
+
+ .. math::
+
+ c = (3 - p) / 3
+
+ .. math::
+
+ resid\_anscombe_i = (1 / c) * (endog ^ c - \mu ^ c) / \mu ^{p / 6}
+ """
+ if self.var_power == 3:
+ return (np.log(endog) - np.log(mu)) / np.sqrt(mu)
+ else:
+ c = (3. - self.var_power) / 3.
+ return ((1. / c) * (endog ** c - mu ** c) /
+ mu ** (self.var_power / 6.))
diff --git a/pysal/contrib/glm/glm.py b/pysal/contrib/glm/glm.py
new file mode 100644
index 0000000..5ec4229
--- /dev/null
+++ b/pysal/contrib/glm/glm.py
@@ -0,0 +1,365 @@
+
+import numpy as np
+import numpy.linalg as la
+from pysal.spreg.utils import RegressionPropsY, spdot
+import pysal.spreg.user_output as USER
+from utils import cache_readonly
+from base import LikelihoodModelResults
+import family
+from iwls import iwls
+
+__all__ = ['GLM']
+
+class GLM(RegressionPropsY):
+ """
+ Generalised linear models. Can currently estimate Guassian, Poisson and
+ Logisitc regression coefficients. GLM object prepares model input and fit
+ method performs estimation which then returns a GLMResults object.
+
+ Parameters
+ ----------
+ y : array
+ n*1, dependent variable.
+ X : array
+ n*k, independent variable, exlcuding the constant.
+ family : string
+ Model type: 'Gaussian', 'Poisson', 'Binomial'
+ offset : array
+ n*1, the offset variable at the ith location. For Poisson model
+ this term is often the size of the population at risk or
+ the expected size of the outcome in spatial epidemiology.
+ Default is None where Ni becomes 1.0 for all locations.
+ y_fix : array
+ n*1, the fix intercept value of y
+
+ Attributes
+ ----------
+ y : array
+ n*1, dependent variable.
+ X : array
+ n*k, independent variable, including constant.
+ family : string
+ Model type: 'Gaussian', 'Poisson', 'logistic'
+ n : integer
+ Number of observations
+ k : integer
+ Number of independent variables
+ df_model : float
+ k-1, where k is the number of variables (including
+ intercept)
+ df_residual : float
+ observations minus variables (n-k)
+ mean_y : float
+ Mean of y
+ std_y : float
+ Standard deviation of y
+ fit_params : dict
+ Parameters passed into fit method to define estimation
+ routine.
+
+ Examples
+ --------
+ >>> from pysal.contrib.glm.glm import GLM
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> self.y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> self.X = np.array(X).T
+ >>> model = GLM(self.y, self.X, family=Gaussian())
+ >>> results = model.fit()
+ >>> results.params
+ [ 46.42818268, 0.62898397, -0.48488854]
+
+ """
+ def __init__(self, y, X, family=family.Gaussian(), offset=None, y_fix = None,
+ constant=True):
+ """
+ Initialize class
+ """
+ self.n = USER.check_arrays(y, X)
+ USER.check_y(y, self.n)
+ self.y = y
+ if constant:
+ self.X = USER.check_constant(X)
+ else:
+ self.X = X
+ self.family = family
+ self.k = self.X.shape[1]
+ self.df_model = self.X.shape[1] - 1
+ self.df_resid = self.n - self.df_model - 1
+ if offset is None:
+ self.offset = np.ones(shape=(self.n,1))
+ else:
+ self.offset = offset * 1.0
+ if y_fix is None:
+ self.y_fix = np.zeros(shape=(self.n,1))
+ else:
+ self.y_fix = y_fix
+ self.fit_params = {}
+
+ def fit(self, ini_betas=None, tol=1.0e-6, max_iter=200, solve='iwls'):
+ """
+ Method that fits a model with a particular estimation routine.
+
+ Parameters
+ ----------
+
+ ini_betas : array
+ k*1, initial coefficient values, including constant.
+ Default is None, which calculates initial values during
+ estimation.
+ tol: float
+ Tolerence for estimation convergence.
+ max_iter : integer
+ Maximum number of iterations if convergence not
+ achieved.
+ solve :string
+ Technique to solve MLE equations.
+ 'iwls' = iteratively (re)weighted least squares (default)
+ """
+ self.fit_params['ini_betas'] = ini_betas
+ self.fit_params['tol'] = tol
+ self.fit_params['max_iter'] = max_iter
+ self.fit_params['solve']=solve
+ if solve.lower() == 'iwls':
+ params, predy, w, n_iter = iwls(self.y, self.X, self.family, self.offset,
+ self.y_fix, ini_betas, tol, max_iter)
+ self.fit_params['n_iter'] = n_iter
+ return GLMResults(self, params.flatten(), predy, w)
+
+
+class GLMResults(LikelihoodModelResults):
+ """
+ Results of estimated GLM and diagnostics.
+
+ Parameters
+ ----------
+ model : GLM object
+ Pointer to GLM object with estimation parameters.
+ params : array
+ k*1, estimared coefficients
+ mu : array
+ n*1, predicted y values.
+ w : array
+ n*1, final weight used for iwls
+
+ Attributes
+ ----------
+ model : GLM Object
+ Points to GLM object for which parameters have been
+ estimated.
+ y : array
+ n*1, dependent variable.
+ x : array
+ n*k, independent variable, including constant.
+ family : string
+ Model type: 'Gaussian', 'Poisson', 'Logistic'
+ n : integer
+ Number of observations
+ k : integer
+ Number of independent variables
+ df_model : float
+ k-1, where k is the number of variables (including
+ intercept)
+ df_residual : float
+ observations minus variables (n-k)
+ fit_params : dict
+ parameters passed into fit method to define estimation
+ routine.
+ scale : float
+ sigma squared used for subsequent computations.
+ params : array
+ n*k, estimared beta coefficients
+ w : array
+ n*1, final weight values of x
+ mu : array
+ n*1, predicted value of y (i.e., fittedvalues)
+ cov_params : array
+ Variance covariance matrix (kxk) of betas
+ bse : array
+ k*1, standard errors of betas
+ pvalues : array
+ k*1, two-tailed pvalues of parameters
+ tvalues : array
+ k*1, the tvalues of the standard errors
+ null : array
+ n*1, predicted values of y for null model
+ deviance : float
+ value of the deviance function evalued at params;
+ see family.py for distribution-specific deviance
+ null_deviance : float
+ value of the deviance function for the model fit with
+ a constant as the only regressor
+ llf : float
+ value of the loglikelihood function evalued at params;
+ see family.py for distribution-specific loglikelihoods
+ llnull : float
+ value of log-likelihood function evaluated at null
+ aic : float
+ AIC
+ bic : float
+ BIC
+ D2 : float
+ percent deviance explained
+ adj_D2 : float
+ adjusted percent deviance explained
+ pseudo_R2 : float
+ McFadden's pseudo R2 (coefficient of determination)
+ adj_pseudoR2 : float
+ adjusted McFadden's pseudo R2
+ resid_response : array
+ response residuals; defined as y-mu
+ resid_pearson : array
+ Pearson residuals; defined as (y-mu)/sqrt(VAR(mu))
+ where VAR is the distribution specific variance
+ function; see family.py and varfuncs.py for more information.
+ resid_working : array
+ Working residuals; the working residuals are defined as
+ resid_response/link'(mu); see links.py for the
+ derivatives of the link functions.
+
+ resid_anscombe : array
+ Anscombe residuals; see family.py for
+ distribution-specific Anscombe residuals.
+
+ resid_deviance : array
+ deviance residuals; see family.py for
+ distribution-specific deviance residuals.
+
+ pearson_chi2 : float
+ chi-Squared statistic is defined as the sum
+ of the squares of the Pearson residuals
+
+ normalized_cov_params : array
+ k*k, approximates [X.T*X]-1
+
+ Examples
+ --------
+ >>> from pysal.contrib.glm.glm import GLM, GLMResults
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> self.y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> self.X = np.array(X).T
+ >>> model = GLM(self.y, self.X, family=Gaussian())
+ >>> results1 = model.fit()
+ >>> results1.aic
+ 408.73548964604873
+ >>> model = results1.model
+ >>> params = results1.params.flatten()
+ >>> predy = results1.predy
+ >>> w = results1.w
+ >>> results2 = GLMResults(model, params, predy, w)
+ >>> results2.aic
+ 408.73548964604873
+
+ """
+ def __init__(self, model, params, mu, w):
+ self.model = model
+ self.n = model.n
+ self.y = model.y.T.flatten()
+ self.X = model.X
+ self.k = model.k
+ self.offset = model.offset
+ self.df_model = model.df_model
+ self.df_resid = model.df_resid
+ self.family = model.family
+ self.fit_params = model.fit_params
+ self.params = params
+ self.w = w
+ self.mu = mu.flatten()
+ self._cache = {}
+ self.normalized_cov_params = la.inv(spdot(self.w.T, self.w))
+
+ @cache_readonly
+ def resid_response(self):
+ return (self.y-self.mu)
+
+ @cache_readonly
+ def resid_pearson(self):
+ return ((self.y-self.mu) /
+ np.sqrt(self.family.variance(self.mu)))
+
+ @cache_readonly
+ def resid_working(self):
+ return (self.resid_response / self.family.link.deriv(self.mu))
+
+ @cache_readonly
+ def resid_anscombe(self):
+ return (self.family.resid_anscombe(self.y, self.mu))
+
+ @cache_readonly
+ def resid_deviance(self):
+ return (self.family.resid_dev(self.y, self.mu))
+
+ @cache_readonly
+ def pearson_chi2(self):
+ chisq = (self.y - self.mu)**2 / self.family.variance(self.mu)
+ chisqsum = np.sum(chisq)
+ return chisqsum
+
+ @cache_readonly
+ def null(self):
+ y = np.reshape(self.y, (-1,1))
+ model = self.model
+ X = np.ones((len(y), 1))
+ null_mod = GLM(y, X, family=self.family, offset=self.offset, constant=False)
+ return null_mod.fit().mu
+
+ @cache_readonly
+ def scale(self):
+ if isinstance(self.family, (family.Binomial, family.Poisson)):
+ return 1.
+ else:
+ return (((np.power(self.resid_response, 2) /
+ self.family.variance(self.mu))).sum() /
+ (self.df_resid))
+ @cache_readonly
+ def deviance(self):
+ return self.family.deviance(self.y, self.mu)
+
+ @cache_readonly
+ def null_deviance(self):
+ return self.family.deviance(self.y, self.null)
+
+ @cache_readonly
+ def llnull(self):
+ return self.family.loglike(self.y, self.null, scale=self.scale)
+
+ @cache_readonly
+ def llf(self):
+ return self.family.loglike(self.y, self.mu, scale=self.scale)
+
+ @cache_readonly
+ def aic(self):
+ if isinstance(self.family, family.QuasiPoisson):
+ return np.nan
+ else:
+ return -2 * self.llf + 2*(self.df_model+1)
+
+ @cache_readonly
+ def bic(self):
+ return (self.deviance -
+ (self.model.n - self.df_model - 1) *
+ np.log(self.model.n))
+
+ @cache_readonly
+ def D2(self):
+ return 1 - (self.deviance / self.null_deviance)
+
+ @cache_readonly
+ def adj_D2(self):
+ return 1.0 - (float(self.n) - 1.0)/(float(self.n) - float(self.k)) * (1.0-self.D2)
+
+ @cache_readonly
+ def pseudoR2(self):
+ return 1 - (self.llf/self.llnull)
+
+ @cache_readonly
+ def adj_pseudoR2(self):
+ return 1 - ((self.llf-self.k)/self.llnull)
+
diff --git a/pysal/contrib/glm/iwls.py b/pysal/contrib/glm/iwls.py
new file mode 100644
index 0000000..29803e3
--- /dev/null
+++ b/pysal/contrib/glm/iwls.py
@@ -0,0 +1,79 @@
+import numpy as np
+import numpy.linalg as la
+from scipy import sparse as sp
+from scipy.sparse import linalg as spla
+from pysal.spreg.utils import spdot, spmultiply
+from family import Binomial
+
+def _compute_betas(y, x):
+ """
+ compute MLE coefficients using iwls routine
+
+ Methods: p189, Iteratively (Re)weighted Least Squares (IWLS),
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying relationships.
+ """
+ xT = x.T
+ xtx = spdot(xT, x)
+ xtx_inv = la.inv(xtx)
+ xtx_inv = sp.csr_matrix(xtx_inv)
+ xTy = spdot(xT, y, array_out=False)
+ betas = spdot(xtx_inv, xTy)
+ return betas
+
+def _compute_betas_gwr(y, x, wi):
+ """
+ compute MLE coefficients using iwls routine
+
+ Methods: p189, Iteratively (Re)weighted Least Squares (IWLS),
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying relationships.
+ """
+ xT = np.dot(x.T, wi)
+ xtx = np.dot(xT, x)
+ xtx_inv = la.inv(xtx)
+ xtx_inv_xt = np.dot(xtx_inv, xT)
+ betas = np.dot(xtx_inv_xt, y)
+ return betas, xtx_inv_xt
+
+def iwls(y, x, family, offset, y_fix,
+ ini_betas=None, tol=1.0e-8, max_iter=200, wi=None):
+ """
+ Iteratively re-weighted least squares estimation routine
+ """
+ #spx = sp.csr_matrix(x)
+ #dx = np.float(spx.nnz)/np.float(np.multiply(*spx.shape))
+ n_iter = 0
+ diff = 1.0e6
+ if isinstance(family, Binomial):
+ y = family.link._clean(y)
+
+ if ini_betas is None:
+ betas = np.zeros((x.shape[1], 1), np.float)
+ else:
+ betas = ini_betas
+ mu = family.starting_mu(y)
+ v = family.predict(mu)
+ while diff > tol and n_iter < max_iter:
+ n_iter += 1
+ w = family.weights(mu)
+ z = v + (family.link.deriv(mu)*(y-mu))
+ w = np.sqrt(w)
+ if type(x) != np.ndarray:
+ w = sp.csr_matrix(w)
+ z = sp.csr_matrix(z)
+ wx = spmultiply(x, w, array_out=False)
+ wz = spmultiply(z, w, array_out=False)
+ if wi is None:
+ n_betas = _compute_betas(wz, wx)
+ else:
+ n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi)
+ v = spdot(x, n_betas)
+ mu = family.fitted(v)
+ diff = min(abs(n_betas-betas))
+ betas = n_betas
+
+ if wi is None:
+ return betas, mu, wx, n_iter
+ else:
+ return betas, mu, v, w, z, xtx_inv_xt, n_iter
diff --git a/pysal/contrib/glm/links.py b/pysal/contrib/glm/links.py
new file mode 100644
index 0000000..f45724d
--- /dev/null
+++ b/pysal/contrib/glm/links.py
@@ -0,0 +1,953 @@
+'''
+Defines the link functions to be used with GLM and GEE families.
+'''
+
+import numpy as np
+import scipy.stats
+FLOAT_EPS = np.finfo(float).eps
+
+
+class Link(object):
+ """
+ A generic link function for one-parameter exponential family.
+
+ `Link` does nothing, but lays out the methods expected of any subclass.
+ """
+
+ def __call__(self, p):
+ """
+ Return the value of the link function. This is just a placeholder.
+
+ Parameters
+ ----------
+ p : array-like
+ Probabilities
+
+ Returns
+ -------
+ g(p) : array-like
+ The value of the link function g(p) = z
+ """
+ return NotImplementedError
+
+ def inverse(self, z):
+ """
+ Inverse of the link function. Just a placeholder.
+
+ Parameters
+ ----------
+ z : array-like
+ `z` is usually the linear predictor of the transformed variable
+ in the IRLS algorithm for GLM.
+
+ Returns
+ -------
+ g^(-1)(z) : array
+ The value of the inverse of the link function g^(-1)(z) = p
+
+
+ """
+ return NotImplementedError
+
+ def deriv(self, p):
+ """
+ Derivative of the link function g'(p). Just a placeholder.
+
+ Parameters
+ ----------
+ p : array-like
+
+ Returns
+ -------
+ g'(p) : array
+ The value of the derivative of the link function g'(p)
+ """
+ return NotImplementedError
+
+ def deriv2(self, p):
+ """Second derivative of the link function g''(p)
+
+ implemented through numerical differentiation
+ """
+ from statsmodels.tools.numdiff import approx_fprime_cs
+ # TODO: workaround proplem with numdiff for 1d
+ return np.diag(approx_fprime_cs(p, self.deriv))
+
+ def inverse_deriv(self, z):
+ """
+ Derivative of the inverse link function g^(-1)(z).
+
+ Notes
+ -----
+ This reference implementation gives the correct result but is
+ inefficient, so it can be overriden in subclasses.
+
+ Parameters
+ ----------
+ z : array-like
+ `z` is usually the linear predictor for a GLM or GEE model.
+
+ Returns
+ -------
+ g'^(-1)(z) : array
+ The value of the derivative of the inverse of the link function
+
+ """
+ return 1 / self.deriv(self.inverse(z))
+
+
+class Logit(Link):
+ """
+ The logit transform
+
+ Notes
+ -----
+ call and derivative use a private method _clean to make trim p by
+ machine epsilon so that p is in (0,1)
+
+ Alias of Logit:
+ logit = Logit()
+ """
+
+ def _clean(self, p):
+ """
+ Clip logistic values to range (eps, 1-eps)
+
+ Parameters
+ -----------
+ p : array-like
+ Probabilities
+
+ Returns
+ --------
+ pclip : array
+ Clipped probabilities
+ """
+ return np.clip(p, FLOAT_EPS, 1. - FLOAT_EPS)
+
+ def __call__(self, p):
+ """
+ The logit transform
+
+ Parameters
+ ----------
+ p : array-like
+ Probabilities
+
+ Returns
+ -------
+ z : array
+ Logit transform of `p`
+
+ Notes
+ -----
+ g(p) = log(p / (1 - p))
+ """
+ p = self._clean(p)
+ return np.log(p / (1. - p))
+
+ def inverse(self, z):
+ """
+ Inverse of the logit transform
+
+ Parameters
+ ----------
+ z : array-like
+ The value of the logit transform at `p`
+
+ Returns
+ -------
+ p : array
+ Probabilities
+
+ Notes
+ -----
+ g^(-1)(z) = exp(z)/(1+exp(z))
+ """
+ z = np.asarray(z)
+ t = np.exp(-z)
+ return 1. / (1. + t)
+
+ def deriv(self, p):
+
+ """
+ Derivative of the logit transform
+
+ Parameters
+ ----------
+ p: array-like
+ Probabilities
+
+ Returns
+ -------
+ g'(p) : array
+ Value of the derivative of logit transform at `p`
+
+ Notes
+ -----
+ g'(p) = 1 / (p * (1 - p))
+
+ Alias for `Logit`:
+ logit = Logit()
+ """
+ p = self._clean(p)
+ return 1. / (p * (1 - p))
+
+ def inverse_deriv(self, z):
+ """
+ Derivative of the inverse of the logit transform
+
+ Parameters
+ ----------
+ z : array-like
+ `z` is usually the linear predictor for a GLM or GEE model.
+
+ Returns
+ -------
+ g'^(-1)(z) : array
+ The value of the derivative of the inverse of the logit function
+
+ """
+ t = np.exp(z)
+ return t/(1 + t)**2
+
+
+ def deriv2(self, p):
+ """
+ Second derivative of the logit function.
+
+ Parameters
+ ----------
+ p : array-like
+ probabilities
+
+ Returns
+ -------
+ g''(z) : array
+ The value of the second derivative of the logit function
+ """
+ v = p * (1 - p)
+ return (2*p - 1) / v**2
+
+class logit(Logit):
+ pass
+
+
+class Power(Link):
+ """
+ The power transform
+
+ Parameters
+ ----------
+ power : float
+ The exponent of the power transform
+
+ Notes
+ -----
+ Aliases of Power:
+ inverse = Power(power=-1)
+ sqrt = Power(power=.5)
+ inverse_squared = Power(power=-2.)
+ identity = Power(power=1.)
+ """
+
+ def __init__(self, power=1.):
+ self.power = power
+
+ def __call__(self, p):
+ """
+ Power transform link function
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ -------
+ z : array-like
+ Power transform of x
+
+ Notes
+ -----
+ g(p) = x**self.power
+ """
+
+ z = np.power(p, self.power)
+ return z
+
+ def inverse(self, z):
+ """
+ Inverse of the power transform link function
+
+ Parameters
+ ----------
+ `z` : array-like
+ Value of the transformed mean parameters at `p`
+
+ Returns
+ -------
+ `p` : array
+ Mean parameters
+
+ Notes
+ -----
+ g^(-1)(z`) = `z`**(1/`power`)
+ """
+
+ p = np.power(z, 1. / self.power)
+ return p
+
+ def deriv(self, p):
+ """
+ Derivative of the power transform
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ --------
+ g'(p) : array
+ Derivative of power transform of `p`
+
+ Notes
+ -----
+ g'(`p`) = `power` * `p`**(`power` - 1)
+ """
+ return self.power * np.power(p, self.power - 1)
+
+ def deriv2(self, p):
+ """
+ Second derivative of the power transform
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ --------
+ g''(p) : array
+ Second derivative of the power transform of `p`
+
+ Notes
+ -----
+ g''(`p`) = `power` * (`power` - 1) * `p`**(`power` - 2)
+ """
+ return self.power * (self.power - 1) * np.power(p, self.power - 2)
+
+ def inverse_deriv(self, z):
+ """
+ Derivative of the inverse of the power transform
+
+ Parameters
+ ----------
+ z : array-like
+ `z` is usually the linear predictor for a GLM or GEE model.
+
+ Returns
+ -------
+ g^(-1)'(z) : array
+ The value of the derivative of the inverse of the power transform
+ function
+ """
+ return np.power(z, (1 - self.power)/self.power) / self.power
+
+
+class inverse_power(Power):
+ """
+ The inverse transform
+
+ Notes
+ -----
+ g(p) = 1/p
+
+ Alias of statsmodels.family.links.Power(power=-1.)
+ """
+ def __init__(self):
+ super(inverse_power, self).__init__(power=-1.)
+
+
+class sqrt(Power):
+ """
+ The square-root transform
+
+ Notes
+ -----
+ g(`p`) = sqrt(`p`)
+
+ Alias of statsmodels.family.links.Power(power=.5)
+ """
+ def __init__(self):
+ super(sqrt, self).__init__(power=.5)
+
+
+class inverse_squared(Power):
+ """
+ The inverse squared transform
+
+ Notes
+ -----
+ g(`p`) = 1/(`p`\ \*\*2)
+
+ Alias of statsmodels.family.links.Power(power=2.)
+ """
+ def __init__(self):
+ super(inverse_squared, self).__init__(power=-2.)
+
+
+class identity(Power):
+ """
+ The identity transform
+
+ Notes
+ -----
+ g(`p`) = `p`
+
+ Alias of statsmodels.family.links.Power(power=1.)
+ """
+ def __init__(self):
+ super(identity, self).__init__(power=1.)
+
+
+class Log(Link):
+ """
+ The log transform
+
+ Notes
+ -----
+ call and derivative call a private method _clean to trim the data by
+ machine epsilon so that p is in (0,1). log is an alias of Log.
+ """
+
+ def _clean(self, x):
+ return np.clip(x, FLOAT_EPS, np.inf)
+
+ def __call__(self, p, **extra):
+ """
+ Log transform link function
+
+ Parameters
+ ----------
+ x : array-like
+ Mean parameters
+
+ Returns
+ -------
+ z : array
+ log(x)
+
+ Notes
+ -----
+ g(p) = log(p)
+ """
+ x = self._clean(p)
+ return np.log(x)
+
+ def inverse(self, z):
+ """
+ Inverse of log transform link function
+
+ Parameters
+ ----------
+ z : array
+ The inverse of the link function at `p`
+
+ Returns
+ -------
+ p : array
+ The mean probabilities given the value of the inverse `z`
+
+ Notes
+ -----
+ g^{-1}(z) = exp(z)
+ """
+ return np.exp(z)
+
+ def deriv(self, p):
+ """
+ Derivative of log transform link function
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ -------
+ g'(p) : array
+ derivative of log transform of x
+
+ Notes
+ -----
+ g'(x) = 1/x
+ """
+ p = self._clean(p)
+ return 1. / p
+
+ def deriv2(self, p):
+ """
+ Second derivative of the log transform link function
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ -------
+ g''(p) : array
+ Second derivative of log transform of x
+
+ Notes
+ -----
+ g''(x) = -1/x^2
+ """
+ p = self._clean(p)
+ return -1. / p**2
+
+ def inverse_deriv(self, z):
+ """
+ Derivative of the inverse of the log transform link function
+
+ Parameters
+ ----------
+ z : array
+ The inverse of the link function at `p`
+
+ Returns
+ -------
+ g^(-1)'(z) : array
+ The value of the derivative of the inverse of the log function,
+ the exponential function
+ """
+ return np.exp(z)
+
+
+class log(Log):
+ """
+ The log transform
+
+ Notes
+ -----
+ log is a an alias of Log.
+ """
+ pass
+
+
+# TODO: the CDFLink is untested
+class CDFLink(Logit):
+ """
+ The use the CDF of a scipy.stats distribution
+
+ CDFLink is a subclass of logit in order to use its _clean method
+ for the link and its derivative.
+
+ Parameters
+ ----------
+ dbn : scipy.stats distribution
+ Default is dbn=scipy.stats.norm
+
+ Notes
+ -----
+ The CDF link is untested.
+ """
+
+ def __init__(self, dbn=scipy.stats.norm):
+ self.dbn = dbn
+
+ def __call__(self, p):
+ """
+ CDF link function
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ -------
+ z : array
+ (ppf) inverse of CDF transform of p
+
+ Notes
+ -----
+ g(`p`) = `dbn`.ppf(`p`)
+ """
+ p = self._clean(p)
+ return self.dbn.ppf(p)
+
+ def inverse(self, z):
+ """
+ The inverse of the CDF link
+
+ Parameters
+ ----------
+ z : array-like
+ The value of the inverse of the link function at `p`
+
+ Returns
+ -------
+ p : array
+ Mean probabilities. The value of the inverse of CDF link of `z`
+
+ Notes
+ -----
+ g^(-1)(`z`) = `dbn`.cdf(`z`)
+ """
+ return self.dbn.cdf(z)
+
+ def deriv(self, p):
+ """
+ Derivative of CDF link
+
+ Parameters
+ ----------
+ p : array-like
+ mean parameters
+
+ Returns
+ -------
+ g'(p) : array
+ The derivative of CDF transform at `p`
+
+ Notes
+ -----
+ g'(`p`) = 1./ `dbn`.pdf(`dbn`.ppf(`p`))
+ """
+ p = self._clean(p)
+ return 1. / self.dbn.pdf(self.dbn.ppf(p))
+
+ def deriv2(self, p):
+ """
+ Second derivative of the link function g''(p)
+
+ implemented through numerical differentiation
+ """
+ from statsmodels.tools.numdiff import approx_fprime
+ p = np.atleast_1d(p)
+ # Note: special function for norm.ppf does not support complex
+ return np.diag(approx_fprime(p, self.deriv, centered=True))
+
+ def inverse_deriv(self, z):
+ """
+ Derivative of the inverse of the CDF transformation link function
+
+ Parameters
+ ----------
+ z : array
+ The inverse of the link function at `p`
+
+ Returns
+ -------
+ g^(-1)'(z) : array
+ The value of the derivative of the inverse of the logit function
+ """
+ return 1/self.deriv(self.inverse(z))
+
+
+class probit(CDFLink):
+ """
+ The probit (standard normal CDF) transform
+
+ Notes
+ --------
+ g(p) = scipy.stats.norm.ppf(p)
+
+ probit is an alias of CDFLink.
+ """
+ pass
+
+
+class cauchy(CDFLink):
+ """
+ The Cauchy (standard Cauchy CDF) transform
+
+ Notes
+ -----
+ g(p) = scipy.stats.cauchy.ppf(p)
+
+ cauchy is an alias of CDFLink with dbn=scipy.stats.cauchy
+ """
+
+ def __init__(self):
+ super(cauchy, self).__init__(dbn=scipy.stats.cauchy)
+
+ def deriv2(self, p):
+ """
+ Second derivative of the Cauchy link function.
+
+ Parameters
+ ----------
+ p: array-like
+ Probabilities
+
+ Returns
+ -------
+ g''(p) : array
+ Value of the second derivative of Cauchy link function at `p`
+ """
+ a = np.pi * (p - 0.5)
+ d2 = 2 * np.pi**2 * np.sin(a) / np.cos(a)**3
+ return d2
+
+class CLogLog(Logit):
+ """
+ The complementary log-log transform
+
+ CLogLog inherits from Logit in order to have access to its _clean method
+ for the link and its derivative.
+
+ Notes
+ -----
+ CLogLog is untested.
+ """
+ def __call__(self, p):
+ """
+ C-Log-Log transform link function
+
+ Parameters
+ ----------
+ p : array
+ Mean parameters
+
+ Returns
+ -------
+ z : array
+ The CLogLog transform of `p`
+
+ Notes
+ -----
+ g(p) = log(-log(1-p))
+ """
+ p = self._clean(p)
+ return np.log(-np.log(1 - p))
+
+ def inverse(self, z):
+ """
+ Inverse of C-Log-Log transform link function
+
+
+ Parameters
+ ----------
+ z : array-like
+ The value of the inverse of the CLogLog link function at `p`
+
+ Returns
+ -------
+ p : array
+ Mean parameters
+
+ Notes
+ -----
+ g^(-1)(`z`) = 1-exp(-exp(`z`))
+ """
+ return 1 - np.exp(-np.exp(z))
+
+ def deriv(self, p):
+ """
+ Derivative of C-Log-Log transform link function
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ -------
+ g'(p) : array
+ The derivative of the CLogLog transform link function
+
+ Notes
+ -----
+ g'(p) = - 1 / ((p-1)*log(1-p))
+ """
+ p = self._clean(p)
+ return 1. / ((p - 1) * (np.log(1 - p)))
+
+ def deriv2(self, p):
+ """
+ Second derivative of the C-Log-Log ink function
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ -------
+ g''(p) : array
+ The second derivative of the CLogLog link function
+ """
+ p = self._clean(p)
+ fl = np.log(1 - p)
+ d2 = -1 / ((1 - p)**2 * fl)
+ d2 *= 1 + 1 / fl
+ return d2
+
+ def inverse_deriv(self, z):
+ """
+ Derivative of the inverse of the C-Log-Log transform link function
+
+ Parameters
+ ----------
+ z : array-like
+ The value of the inverse of the CLogLog link function at `p`
+
+ Returns
+ -------
+ g^(-1)'(z) : array
+ The derivative of the inverse of the CLogLog link function
+ """
+ return np.exp(z - np.exp(z))
+
+
+class cloglog(CLogLog):
+ """
+ The CLogLog transform link function.
+
+ Notes
+ -----
+ g(`p`) = log(-log(1-`p`))
+
+ cloglog is an alias for CLogLog
+ cloglog = CLogLog()
+ """
+ pass
+
+
+class NegativeBinomial(object):
+ '''
+ The negative binomial link function
+
+ Parameters
+ ----------
+ alpha : float, optional
+ Alpha is the ancillary parameter of the Negative Binomial link
+ function. It is assumed to be nonstochastic. The default value is 1.
+ Permissible values are usually assumed to be in (.01, 2).
+ '''
+
+ def __init__(self, alpha=1.):
+ self.alpha = alpha
+
+ def _clean(self, x):
+ return np.clip(x, FLOAT_EPS, np.inf)
+
+ def __call__(self, p):
+ '''
+ Negative Binomial transform link function
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ -------
+ z : array
+ The negative binomial transform of `p`
+
+ Notes
+ -----
+ g(p) = log(p/(p + 1/alpha))
+ '''
+ p = self._clean(p)
+ return np.log(p/(p + 1/self.alpha))
+
+ def inverse(self, z):
+ '''
+ Inverse of the negative binomial transform
+
+ Parameters
+ -----------
+ z : array-like
+ The value of the inverse of the negative binomial link at `p`.
+
+ Returns
+ -------
+ p : array
+ Mean parameters
+
+ Notes
+ -----
+ g^(-1)(z) = exp(z)/(alpha*(1-exp(z)))
+ '''
+ return -1/(self.alpha * (1 - np.exp(-z)))
+
+ def deriv(self, p):
+ '''
+ Derivative of the negative binomial transform
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ -------
+ g'(p) : array
+ The derivative of the negative binomial transform link function
+
+ Notes
+ -----
+ g'(x) = 1/(x+alpha*x^2)
+ '''
+ return 1/(p + self.alpha * p**2)
+
+ def deriv2(self,p):
+ '''
+ Second derivative of the negative binomial link function.
+
+ Parameters
+ ----------
+ p : array-like
+ Mean parameters
+
+ Returns
+ -------
+ g''(p) : array
+ The second derivative of the negative binomial transform link
+ function
+
+ Notes
+ -----
+ g''(x) = -(1+2*alpha*x)/(x+alpha*x^2)^2
+ '''
+ numer = -(1 + 2 * self.alpha * p)
+ denom = (p + self.alpha * p**2)**2
+ return numer / denom
+
+ def inverse_deriv(self, z):
+ '''
+ Derivative of the inverse of the negative binomial transform
+
+ Parameters
+ -----------
+ z : array-like
+ Usually the linear predictor for a GLM or GEE model
+
+ Returns
+ -------
+ g^(-1)'(z) : array
+ The value of the derivative of the inverse of the negative
+ binomial link
+ '''
+ t = np.exp(z)
+ return t / (self.alpha * (1-t)**2)
+
+
+class nbinom(NegativeBinomial):
+ """
+ The negative binomial link function.
+
+ Notes
+ -----
+ g(p) = log(p/(p + 1/alpha))
+
+ nbinom is an alias of NegativeBinomial.
+ nbinom = NegativeBinomial(alpha=1.)
+ """
+ pass
diff --git a/pysal/contrib/glm/tests/test_glm.py b/pysal/contrib/glm/tests/test_glm.py
new file mode 100644
index 0000000..b41e962
--- /dev/null
+++ b/pysal/contrib/glm/tests/test_glm.py
@@ -0,0 +1,993 @@
+"""
+Tests for generalized linear models. Majority of code either directly borrowed
+or closely adapted from statsmodels package. Model results verfiied using glm
+function in R and GLM function in statsmodels.
+"""
+
+__author__ = 'Taylor Oshan tayoshan at gmail.com'
+
+from pysal.contrib.glm.glm import GLM
+from pysal.contrib.glm.family import Gaussian, Poisson, Binomial, QuasiPoisson
+import numpy as np
+import pysal
+import unittest
+import math
+
+
+class TestGaussian(unittest.TestCase):
+ """
+ Tests for Poisson GLM
+ """
+
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+
+ def testIWLS(self):
+ model = GLM(self.y, self.X, family=Gaussian())
+ results = model.fit()
+ self.assertAlmostEqual(results.n, 49)
+ self.assertAlmostEqual(results.df_model, 2)
+ self.assertAlmostEqual(results.df_resid, 46)
+ self.assertAlmostEqual(results.aic, 408.73548964604873)
+ self.assertAlmostEqual(results.bic, 10467.991340493107)
+ self.assertAlmostEqual(results.deviance, 10647.015074206196)
+ self.assertAlmostEqual(results.llf, -201.36774482302437)
+ self.assertAlmostEqual(results.null_deviance, 16367.794631703124)
+ self.assertAlmostEqual(results.scale, 231.45684943926514)
+ np.testing.assert_allclose(results.params, [ 46.42818268, 0.62898397,
+ -0.48488854])
+ np.testing.assert_allclose(results.bse, [ 13.19175703, 0.53591045,
+ 0.18267291])
+ np.testing.assert_allclose(results.cov_params(),
+ [[ 1.74022453e+02, -6.52060364e+00, -2.15109867e+00],
+ [ -6.52060364e+00, 2.87200008e-01, 6.80956787e-02],
+ [ -2.15109867e+00, 6.80956787e-02, 3.33693910e-02]])
+ np.testing.assert_allclose(results.tvalues, [ 3.51948437, 1.17367365,
+ -2.65440864])
+ np.testing.assert_allclose(results.pvalues, [ 0.00043239, 0.24052577,
+ 0.00794475], atol=1.0e-8)
+ np.testing.assert_allclose(results.conf_int(),
+ [[ 20.57281401, 72.28355135],
+ [ -0.42138121, 1.67934915],
+ [ -0.84292086, -0.12685622]])
+ np.testing.assert_allclose(results.normalized_cov_params,
+ [[ 7.51857004e-01, -2.81720055e-02, -9.29373521e-03],
+ [ -2.81720055e-02, 1.24083607e-03, 2.94204638e-04],
+ [ -9.29373521e-03, 2.94204638e-04, 1.44171110e-04]])
+ np.testing.assert_allclose(results.mu,
+ [ 51.08752105, 50.66601521, 41.61367567, 33.53969014,
+ 28.90638232, 43.87074227, 51.64910882, 34.92671563,
+ 42.69267622, 38.49449134, 20.92815471, 25.25228436,
+ 29.78223486, 25.02403635, 29.07959539, 24.63352275,
+ 34.71372149, 33.40443052, 27.29864225, 65.86219802,
+ 33.69854751, 37.44976435, 50.01304928, 36.81219959,
+ 22.02674837, 31.64775955, 27.63563294, 23.7697291 ,
+ 22.43119725, 21.76987089, 48.51169321, 49.05891819,
+ 32.31656426, 44.20550354, 35.49244888, 51.27811308,
+ 36.55047181, 27.37048914, 48.78812922, 57.31744163,
+ 51.22914162, 54.70515578, 37.06622277, 44.5075759 ,
+ 41.24328983, 49.93821824, 44.85644299, 40.93838609, 47.32045464])
+ self.assertAlmostEqual(results.pearson_chi2, 10647.015074206196)
+ np.testing.assert_allclose(results.resid_response,
+ [ 29.37948195, -6.09901421, -15.26367567, -0.33968914,
+ -5.68138232, -15.12074227, 23.35089118, 2.19828437,
+ 9.90732178, 57.90551066, -1.22815371, -5.35228436,
+ 11.91776614, 17.87596565, -11.07959539, -5.83352375,
+ 7.03627851, 26.59556948, 3.30135775, 15.40479998,
+ -13.72354751, -6.99976335, -2.28004728, 16.38780141,
+ -4.12674837, -11.34776055, 6.46436506, -0.9197291 ,
+ 10.06880275, 0.73012911, -16.71169421, -8.75891919,
+ -8.71656426, -15.75550254, -8.49244888, -14.97811408,
+ 6.74952719, -4.67048814, -9.18813122, 4.63255937,
+ -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+ -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564])
+ np.testing.assert_allclose(results.resid_working,
+ [ 29.37948195, -6.09901421, -15.26367567, -0.33968914,
+ -5.68138232, -15.12074227, 23.35089118, 2.19828437,
+ 9.90732178, 57.90551066, -1.22815371, -5.35228436,
+ 11.91776614, 17.87596565, -11.07959539, -5.83352375,
+ 7.03627851, 26.59556948, 3.30135775, 15.40479998,
+ -13.72354751, -6.99976335, -2.28004728, 16.38780141,
+ -4.12674837, -11.34776055, 6.46436506, -0.9197291 ,
+ 10.06880275, 0.73012911, -16.71169421, -8.75891919,
+ -8.71656426, -15.75550254, -8.49244888, -14.97811408,
+ 6.74952719, -4.67048814, -9.18813122, 4.63255937,
+ -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+ -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564])
+ np.testing.assert_allclose(results.resid_pearson,
+ [ 29.37948195, -6.09901421, -15.26367567, -0.33968914,
+ -5.68138232, -15.12074227, 23.35089118, 2.19828437,
+ 9.90732178, 57.90551066, -1.22815371, -5.35228436,
+ 11.91776614, 17.87596565, -11.07959539, -5.83352375,
+ 7.03627851, 26.59556948, 3.30135775, 15.40479998,
+ -13.72354751, -6.99976335, -2.28004728, 16.38780141,
+ -4.12674837, -11.34776055, 6.46436506, -0.9197291 ,
+ 10.06880275, 0.73012911, -16.71169421, -8.75891919,
+ -8.71656426, -15.75550254, -8.49244888, -14.97811408,
+ 6.74952719, -4.67048814, -9.18813122, 4.63255937,
+ -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+ -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564])
+ np.testing.assert_allclose(results.resid_anscombe,
+ [ 29.37948195, -6.09901421, -15.26367567, -0.33968914,
+ -5.68138232, -15.12074227, 23.35089118, 2.19828437,
+ 9.90732178, 57.90551066, -1.22815371, -5.35228436,
+ 11.91776614, 17.87596565, -11.07959539, -5.83352375,
+ 7.03627851, 26.59556948, 3.30135775, 15.40479998,
+ -13.72354751, -6.99976335, -2.28004728, 16.38780141,
+ -4.12674837, -11.34776055, 6.46436506, -0.9197291 ,
+ 10.06880275, 0.73012911, -16.71169421, -8.75891919,
+ -8.71656426, -15.75550254, -8.49244888, -14.97811408,
+ 6.74952719, -4.67048814, -9.18813122, 4.63255937,
+ -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+ -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564])
+ np.testing.assert_allclose(results.resid_deviance,
+ [ 29.37948195, -6.09901421, -15.26367567, -0.33968914,
+ -5.68138232, -15.12074227, 23.35089118, 2.19828437,
+ 9.90732178, 57.90551066, -1.22815371, -5.35228436,
+ 11.91776614, 17.87596565, -11.07959539, -5.83352375,
+ 7.03627851, 26.59556948, 3.30135775, 15.40479998,
+ -13.72354751, -6.99976335, -2.28004728, 16.38780141,
+ -4.12674837, -11.34776055, 6.46436506, -0.9197291 ,
+ 10.06880275, 0.73012911, -16.71169421, -8.75891919,
+ -8.71656426, -15.75550254, -8.49244888, -14.97811408,
+ 6.74952719, -4.67048814, -9.18813122, 4.63255937,
+ -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+ -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564])
+ np.testing.assert_allclose(results.null,
+ [ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447,
+ 38.43622447, 38.43622447, 38.43622447, 38.43622447, 38.43622447])
+ self.assertAlmostEqual(results.D2, .349514377851)
+ self.assertAlmostEqual(results.adj_D2, 0.32123239427957673)
+
+class TestPoisson(unittest.TestCase):
+
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ y = np.array(db.by_col("HOVAL"))
+ y = np.reshape(y, (49,1))
+ self.y = np.round(y).astype(int)
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+
+ def testIWLS(self):
+ model = GLM(self.y, self.X, family=Poisson())
+ results = model.fit()
+ self.assertAlmostEqual(results.n, 49)
+ self.assertAlmostEqual(results.df_model, 2)
+ self.assertAlmostEqual(results.df_resid, 46)
+ self.assertAlmostEqual(results.aic, 500.85184179938756)
+ self.assertAlmostEqual(results.bic, 51.436404535087661)
+ self.assertAlmostEqual(results.deviance, 230.46013824817649)
+ self.assertAlmostEqual(results.llf, -247.42592089969378)
+ self.assertAlmostEqual(results.null_deviance, 376.97293610347361)
+ self.assertAlmostEqual(results.scale, 1.0)
+ np.testing.assert_allclose(results.params, [ 3.92159085, 0.01183491,
+ -0.01371397], atol=1.0e-8)
+ np.testing.assert_allclose(results.bse, [ 0.13049161, 0.00511599,
+ 0.00193769], atol=1.0e-8)
+ np.testing.assert_allclose(results.cov_params(),
+ [[ 1.70280610e-02, -6.18628383e-04, -2.21386966e-04],
+ [ -6.18628383e-04, 2.61733917e-05, 6.77496445e-06],
+ [ -2.21386966e-04, 6.77496445e-06, 3.75463502e-06]])
+ np.testing.assert_allclose(results.tvalues, [ 30.0524361 , 2.31331634,
+ -7.07748998])
+ np.testing.assert_allclose(results.pvalues, [ 2.02901657e-198,
+ 2.07052532e-002, 1.46788805e-012])
+ np.testing.assert_allclose(results.conf_int(),
+ [[ 3.66583199e+00, 4.17734972e+00],
+ [ 1.80774841e-03, 2.18620753e-02],
+ [ -1.75117666e-02, -9.91616901e-03]])
+ np.testing.assert_allclose(results.normalized_cov_params,
+ [[ 1.70280610e-02, -6.18628383e-04, -2.21386966e-04],
+ [ -6.18628383e-04, 2.61733917e-05, 6.77496445e-06],
+ [ -2.21386966e-04, 6.77496445e-06, 3.75463502e-06]])
+ np.testing.assert_allclose(results.mu,
+ [ 51.26831574, 50.15022766, 40.06142973, 34.13799739,
+ 28.76119226, 42.6836241 , 55.64593703, 34.08277997,
+ 40.90389582, 37.19727958, 23.47459217, 26.12384057,
+ 29.78303507, 25.96888223, 29.14073823, 26.04369592,
+ 34.18996367, 32.28924005, 27.42284396, 72.69207879,
+ 33.05316347, 36.52276972, 49.2551479 , 35.33439632,
+ 24.07252457, 31.67153709, 27.81699478, 25.38021219,
+ 24.31759259, 23.13586161, 48.40724678, 48.57969818,
+ 31.92596006, 43.3679231 , 34.32925819, 51.78908089,
+ 34.49778584, 27.56236198, 48.34273194, 57.50829097,
+ 50.66038226, 54.68701352, 35.77103116, 43.21886784,
+ 40.07615759, 49.98658004, 43.13352883, 40.28520774, 46.28910294])
+ self.assertAlmostEqual(results.pearson_chi2, 264.62262932090221)
+ np.testing.assert_allclose(results.resid_response,
+ [ 28.73168426, -5.15022766, -14.06142973, -1.13799739,
+ -5.76119226, -13.6836241 , 19.35406297, 2.91722003,
+ 12.09610418, 58.80272042, -3.47459217, -6.12384057,
+ 12.21696493, 17.03111777, -11.14073823, -7.04369592,
+ 7.81003633, 27.71075995, 3.57715604, 8.30792121,
+ -13.05316347, -6.52276972, -1.2551479 , 17.66560368,
+ -6.07252457, -11.67153709, 6.18300522, -2.38021219,
+ 7.68240741, -1.13586161, -16.40724678, -8.57969818,
+ -7.92596006, -15.3679231 , -7.32925819, -15.78908089,
+ 8.50221416, -4.56236198, -8.34273194, 4.49170903,
+ -8.66038226, -10.68701352, -9.77103116, -9.21886784,
+ -12.07615759, 26.01341996, -1.13352883, -13.28520774, -10.28910294])
+ np.testing.assert_allclose(results.resid_working,
+ [ 1473.02506034, -258.28508941, -563.32097891, -38.84895192,
+ -165.69875817, -584.06666725, 1076.97496919, 99.42696848,
+ 494.77778514, 2187.30123163, -81.56463405, -159.97823479,
+ 363.858295 , 442.27909165, -324.64933645, -183.44387481,
+ 267.02485844, 894.75938 , 98.09579187, 603.9200634 ,
+ -431.44834594, -238.2296165 , -61.82249568, 624.20344168,
+ -146.18099686, -369.65551968, 171.99262399, -60.41029031,
+ 186.81765356, -26.27913713, -794.22964417, -416.79914795,
+ -253.04388425, -666.47490701, -251.6079969 , -817.70198717,
+ 293.30756327, -125.74947222, -403.31045369, 258.31051005,
+ -438.73827602, -584.440853 , -349.51985996, -398.42903071,
+ -483.96599444, 1300.32189904, -48.89309853, -535.19735391,
+ -476.27334527])
+ np.testing.assert_allclose(results.resid_pearson,
+ [ 4.01269878, -0.72726045, -2.221602 , -0.19477008, -1.07425881,
+ -2.09445239, 2.59451042, 0.49969118, 1.89131202, 9.64143836,
+ -0.71714142, -1.19813392, 2.23861212, 3.34207756, -2.0637814 ,
+ -1.3802231 , 1.33568403, 4.87662684, 0.68309584, 0.97442591,
+ -2.27043598, -1.07931992, -0.17884182, 2.97186889, -1.23768025,
+ -2.07392709, 1.1723155 , -0.47246327, 1.55789092, -0.23614708,
+ -2.35819937, -1.23096188, -1.40274877, -2.33362391, -1.25091503,
+ -2.19400568, 1.44755952, -0.8690235 , -1.19989348, 0.59230634,
+ -1.21675413, -1.44515442, -1.63370888, -1.40229988, -1.90759306,
+ 3.67934693, -0.17259375, -2.09312684, -1.51230062])
+ np.testing.assert_allclose(results.resid_anscombe,
+ [ 3.70889134, -0.74031295, -2.37729865, -0.19586855, -1.11374751,
+ -2.22611959, 2.46352013, 0.49282126, 1.80857757, 8.06444452,
+ -0.73610811, -1.25061371, 2.10820431, 3.05467547, -2.22437611,
+ -1.45136173, 1.28939698, 4.35942058, 0.66904552, 0.95674923,
+ -2.45438937, -1.11429881, -0.17961012, 2.76715848, -1.29658591,
+ -2.22816691, 1.13269136, -0.48017382, 1.48562248, -0.23812278,
+ -2.51664399, -1.2703721 , -1.4683091 , -2.49907536, -1.30026484,
+ -2.32398309, 1.39380683, -0.89495368, -1.23735395, 0.58485202,
+ -1.25435224, -1.4968484 , -1.71888038, -1.45756652, -2.01906267,
+ 3.41729922, -0.17335867, -2.22921828, -1.57470549])
+ np.testing.assert_allclose(results.resid_deviance,
+ [ 3.70529668, -0.74027329, -2.37536322, -0.19586751, -1.11349765,
+ -2.22466106, 2.46246446, 0.4928057 , 1.80799655, 8.02696525,
+ -0.73602255, -1.25021555, 2.10699958, 3.05084608, -2.22214376,
+ -1.45072221, 1.28913747, 4.35106213, 0.6689982 , 0.95669662,
+ -2.45171913, -1.11410444, -0.17960956, 2.76494217, -1.29609865,
+ -2.22612429, 1.13247453, -0.48015254, 1.48508549, -0.23812 ,
+ -2.51476072, -1.27015583, -1.46777697, -2.49699318, -1.29992892,
+ -2.32263069, 1.39348459, -0.89482132, -1.23715363, 0.58483655,
+ -1.25415329, -1.49653039, -1.7181055 , -1.45719072, -2.01791949,
+ 3.41437156, -0.1733581 , -2.22765605, -1.57426046])
+ np.testing.assert_allclose(results.null,
+ [ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143, 38.42857143])
+ self.assertAlmostEqual(results.D2, .388656011675)
+ self.assertAlmostEqual(results.adj_D2, 0.36207583826952761)#.375648692774)
+
+ def testQuasi(self):
+ model = GLM(self.y, self.X, family=QuasiPoisson())
+ results = model.fit()
+ self.assertAlmostEqual(results.n, 49)
+ self.assertAlmostEqual(results.df_model, 2)
+ self.assertAlmostEqual(results.df_resid, 46)
+ self.assertTrue(math.isnan(results.aic))
+ self.assertAlmostEqual(results.bic, 51.436404535087661)
+ self.assertAlmostEqual(results.deviance, 230.46013824817649)
+ self.assertTrue(math.isnan(results.llf))
+ self.assertAlmostEqual(results.null_deviance, 376.97293610347361)
+ self.assertAlmostEqual(results.scale, 5.7526658548022223)
+ np.testing.assert_allclose(results.params, [ 3.92159085, 0.01183491,
+ -0.01371397], atol=1.0e-8)
+ np.testing.assert_allclose(results.bse, [ 0.31298042, 0.01227057,
+ 0.00464749], atol=1.0e-8)
+ np.testing.assert_allclose(results.cov_params(),
+ [[ 9.79567451e-02, -3.55876238e-03, -1.27356524e-03],
+ [ -3.55876238e-03, 1.50566777e-04, 3.89741067e-05],
+ [ -1.27356524e-03, 3.89741067e-05, 2.15991606e-05]])
+ np.testing.assert_allclose(results.tvalues, [ 12.52982796, 0.96449604,
+ -2.95083339])
+ np.testing.assert_allclose(results.pvalues, [ 5.12737770e-36,
+ 3.34797291e-01, 3.16917819e-03])
+ np.testing.assert_allclose(results.conf_int(),
+ [[ 3.3081605 , 4.53502121],
+ [-0.01221495, 0.03588478],
+ [-0.02282288, -0.00460506]], atol=1.0e-8)
+ np.testing.assert_allclose(results.normalized_cov_params,
+ [[ 1.70280610e-02, -6.18628383e-04, -2.21386966e-04],
+ [ -6.18628383e-04, 2.61733917e-05, 6.77496445e-06],
+ [ -2.21386966e-04, 6.77496445e-06, 3.75463502e-06]])
+ np.testing.assert_allclose(results.mu,
+ [ 51.26831574, 50.15022766, 40.06142973, 34.13799739,
+ 28.76119226, 42.6836241 , 55.64593703, 34.08277997,
+ 40.90389582, 37.19727958, 23.47459217, 26.12384057,
+ 29.78303507, 25.96888223, 29.14073823, 26.04369592,
+ 34.18996367, 32.28924005, 27.42284396, 72.69207879,
+ 33.05316347, 36.52276972, 49.2551479 , 35.33439632,
+ 24.07252457, 31.67153709, 27.81699478, 25.38021219,
+ 24.31759259, 23.13586161, 48.40724678, 48.57969818,
+ 31.92596006, 43.3679231 , 34.32925819, 51.78908089,
+ 34.49778584, 27.56236198, 48.34273194, 57.50829097,
+ 50.66038226, 54.68701352, 35.77103116, 43.21886784,
+ 40.07615759, 49.98658004, 43.13352883, 40.28520774, 46.28910294])
+ self.assertAlmostEqual(results.pearson_chi2, 264.62262932090221)
+ np.testing.assert_allclose(results.resid_response,
+ [ 28.73168426, -5.15022766, -14.06142973, -1.13799739,
+ -5.76119226, -13.6836241 , 19.35406297, 2.91722003,
+ 12.09610418, 58.80272042, -3.47459217, -6.12384057,
+ 12.21696493, 17.03111777, -11.14073823, -7.04369592,
+ 7.81003633, 27.71075995, 3.57715604, 8.30792121,
+ -13.05316347, -6.52276972, -1.2551479 , 17.66560368,
+ -6.07252457, -11.67153709, 6.18300522, -2.38021219,
+ 7.68240741, -1.13586161, -16.40724678, -8.57969818,
+ -7.92596006, -15.3679231 , -7.32925819, -15.78908089,
+ 8.50221416, -4.56236198, -8.34273194, 4.49170903,
+ -8.66038226, -10.68701352, -9.77103116, -9.21886784,
+ -12.07615759, 26.01341996, -1.13352883, -13.28520774, -10.28910294])
+ np.testing.assert_allclose(results.resid_working,
+ [ 1473.02506034, -258.28508941, -563.32097891, -38.84895192,
+ -165.69875817, -584.06666725, 1076.97496919, 99.42696848,
+ 494.77778514, 2187.30123163, -81.56463405, -159.97823479,
+ 363.858295 , 442.27909165, -324.64933645, -183.44387481,
+ 267.02485844, 894.75938 , 98.09579187, 603.9200634 ,
+ -431.44834594, -238.2296165 , -61.82249568, 624.20344168,
+ -146.18099686, -369.65551968, 171.99262399, -60.41029031,
+ 186.81765356, -26.27913713, -794.22964417, -416.79914795,
+ -253.04388425, -666.47490701, -251.6079969 , -817.70198717,
+ 293.30756327, -125.74947222, -403.31045369, 258.31051005,
+ -438.73827602, -584.440853 , -349.51985996, -398.42903071,
+ -483.96599444, 1300.32189904, -48.89309853, -535.19735391,
+ -476.27334527])
+ np.testing.assert_allclose(results.resid_pearson,
+ [ 4.01269878, -0.72726045, -2.221602 , -0.19477008, -1.07425881,
+ -2.09445239, 2.59451042, 0.49969118, 1.89131202, 9.64143836,
+ -0.71714142, -1.19813392, 2.23861212, 3.34207756, -2.0637814 ,
+ -1.3802231 , 1.33568403, 4.87662684, 0.68309584, 0.97442591,
+ -2.27043598, -1.07931992, -0.17884182, 2.97186889, -1.23768025,
+ -2.07392709, 1.1723155 , -0.47246327, 1.55789092, -0.23614708,
+ -2.35819937, -1.23096188, -1.40274877, -2.33362391, -1.25091503,
+ -2.19400568, 1.44755952, -0.8690235 , -1.19989348, 0.59230634,
+ -1.21675413, -1.44515442, -1.63370888, -1.40229988, -1.90759306,
+ 3.67934693, -0.17259375, -2.09312684, -1.51230062])
+ np.testing.assert_allclose(results.resid_anscombe,
+ [ 3.70889134, -0.74031295, -2.37729865, -0.19586855, -1.11374751,
+ -2.22611959, 2.46352013, 0.49282126, 1.80857757, 8.06444452,
+ -0.73610811, -1.25061371, 2.10820431, 3.05467547, -2.22437611,
+ -1.45136173, 1.28939698, 4.35942058, 0.66904552, 0.95674923,
+ -2.45438937, -1.11429881, -0.17961012, 2.76715848, -1.29658591,
+ -2.22816691, 1.13269136, -0.48017382, 1.48562248, -0.23812278,
+ -2.51664399, -1.2703721 , -1.4683091 , -2.49907536, -1.30026484,
+ -2.32398309, 1.39380683, -0.89495368, -1.23735395, 0.58485202,
+ -1.25435224, -1.4968484 , -1.71888038, -1.45756652, -2.01906267,
+ 3.41729922, -0.17335867, -2.22921828, -1.57470549])
+ np.testing.assert_allclose(results.resid_deviance,
+ [ 3.70529668, -0.74027329, -2.37536322, -0.19586751, -1.11349765,
+ -2.22466106, 2.46246446, 0.4928057 , 1.80799655, 8.02696525,
+ -0.73602255, -1.25021555, 2.10699958, 3.05084608, -2.22214376,
+ -1.45072221, 1.28913747, 4.35106213, 0.6689982 , 0.95669662,
+ -2.45171913, -1.11410444, -0.17960956, 2.76494217, -1.29609865,
+ -2.22612429, 1.13247453, -0.48015254, 1.48508549, -0.23812 ,
+ -2.51476072, -1.27015583, -1.46777697, -2.49699318, -1.29992892,
+ -2.32263069, 1.39348459, -0.89482132, -1.23715363, 0.58483655,
+ -1.25415329, -1.49653039, -1.7181055 , -1.45719072, -2.01791949,
+ 3.41437156, -0.1733581 , -2.22765605, -1.57426046])
+ np.testing.assert_allclose(results.null,
+ [ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143,
+ 38.42857143, 38.42857143, 38.42857143, 38.42857143, 38.42857143])
+ self.assertAlmostEqual(results.D2, .388656011675)
+ self.assertAlmostEqual(results.adj_D2, 0.36207583826952761)
+
+class TestBinomial(unittest.TestCase):
+
+ def setUp(self):
+ #London house price data
+ #y: 'BATH2'
+ y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
+ 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+ self.y = y.reshape((316,1))
+ #X: 'FLOORSZ'
+ X = np.array([ 77, 75, 64, 95, 107, 100, 81, 151, 98, 260, 171, 161, 91,
+ 80, 50, 85, 52, 69, 60, 84, 155, 97, 69, 126, 90, 43,
+ 51, 41, 140, 80, 52, 86, 66, 60, 40, 155, 138, 97, 115,
+ 148, 206, 60, 53, 96, 88, 160, 31, 43, 154, 60, 131, 60,
+ 46, 61, 125, 150, 76, 92, 96, 100, 105, 72, 48, 41, 72,
+ 65, 60, 65, 98, 33, 144, 111, 91, 108, 38, 48, 95, 63,
+ 98, 129, 108, 51, 131, 66, 48, 127, 76, 68, 52, 64, 57,
+ 121, 67, 76, 112, 96, 90, 53, 93, 64, 97, 58, 44, 157,
+ 53, 70, 71, 167, 47, 70, 96, 77, 75, 71, 67, 47, 71,
+ 90, 69, 64, 65, 95, 60, 60, 65, 54, 121, 105, 50, 85,
+ 69, 69, 62, 65, 93, 93, 70, 62, 155, 68, 117, 80, 80,
+ 75, 98, 114, 86, 70, 50, 51, 163, 124, 59, 95, 51, 63,
+ 85, 53, 46, 102, 114, 83, 47, 40, 63, 123, 100, 63, 110,
+ 79, 98, 99, 120, 52, 48, 37, 81, 30, 88, 50, 35, 116,
+ 67, 45, 80, 86, 109, 59, 75, 60, 71, 141, 121, 50, 168,
+ 90, 51, 133, 75, 133, 127, 37, 68, 105, 61, 123, 151, 110,
+ 77, 220, 94, 77, 70, 100, 98, 126, 55, 105, 60, 176, 104,
+ 68, 62, 70, 48, 102, 80, 97, 66, 80, 102, 160, 55, 60,
+ 71, 125, 85, 85, 190, 137, 48, 41, 42, 51, 57, 60, 114,
+ 88, 84, 108, 66, 85, 42, 98, 90, 127, 100, 55, 76, 82,
+ 63, 80, 71, 76, 121, 109, 92, 160, 109, 185, 100, 90, 90,
+ 86, 88, 95, 116, 135, 61, 74, 60, 235, 76, 66, 100, 49,
+ 50, 37, 100, 88, 90, 52, 95, 81, 79, 96, 75, 91, 86,
+ 83, 180, 108, 80, 96, 49, 117, 117, 86, 46, 66, 95, 57,
+ 120, 137, 68, 240])
+ self.X = X.reshape((316,1))
+
+ def testIWLS(self):
+ model = GLM(self.y, self.X, family=Binomial())
+ results = model.fit()
+ self.assertAlmostEqual(results.n, 316)
+ self.assertAlmostEqual(results.df_model, 1)
+ self.assertAlmostEqual(results.df_resid, 314)
+ self.assertAlmostEqual(results.aic, 155.19347530342466)
+ self.assertAlmostEqual(results.bic, -1656.1095797628657)
+ self.assertAlmostEqual(results.deviance, 151.19347530342466)
+ self.assertAlmostEqual(results.llf, -75.596737651712331)
+ self.assertAlmostEqual(results.null_deviance, 189.16038985881212)
+ self.assertAlmostEqual(results.scale, 1.0)
+ np.testing.assert_allclose(results.params, [-5.33638276, 0.0287754 ])
+ np.testing.assert_allclose(results.bse, [ 0.64499904, 0.00518312],
+ atol=1.0e-8)
+ np.testing.assert_allclose(results.cov_params(),
+ [[ 4.16023762e-01, -3.14338457e-03],
+ [ -3.14338457e-03, 2.68646833e-05]])
+ np.testing.assert_allclose(results.tvalues, [-8.27347396, 5.55175826])
+ np.testing.assert_allclose(results.pvalues, [ 1.30111233e-16,
+ 2.82810512e-08])
+ np.testing.assert_allclose(results.conf_int(),
+ [[-6.60055765, -4.07220787],
+ [ 0.01861668, 0.03893412]], atol=1.0e-8)
+ np.testing.assert_allclose(results.normalized_cov_params,
+ [[ 4.16023762e-01, -3.14338457e-03],
+ [ -3.14338457e-03, 2.68646833e-05]])
+ np.testing.assert_allclose(results.mu,
+ [ 0.04226237, 0.03999333, 0.02946178, 0.0689636 , 0.09471181,
+ 0.07879431, 0.04717464, 0.27065598, 0.07471691, 0.89522144,
+ 0.39752487, 0.33102718, 0.06192993, 0.04589793, 0.01988679,
+ 0.0526265 , 0.02104007, 0.03386636, 0.02634295, 0.05121018,
+ 0.29396682, 0.07275173, 0.03386636, 0.15307528, 0.06027915,
+ 0.01631789, 0.02045547, 0.01541937, 0.2128508 , 0.04589793,
+ 0.02104007, 0.05407977, 0.0311527 , 0.02634295, 0.01498855,
+ 0.29396682, 0.20336776, 0.07275173, 0.11637537, 0.25395607,
+ 0.64367488, 0.02634295, 0.02164101, 0.07083428, 0.05710047,
+ 0.32468619, 0.01160845, 0.01631789, 0.28803008, 0.02634295,
+ 0.17267234, 0.02634295, 0.01776301, 0.02709115, 0.14938186,
+ 0.26501331, 0.04111287, 0.06362285, 0.07083428, 0.07879431,
+ 0.08989109, 0.03680743, 0.0187955 , 0.01541937, 0.03680743,
+ 0.03029581, 0.02634295, 0.03029581, 0.07471691, 0.01228768,
+ 0.23277197, 0.10505173, 0.06192993, 0.09720799, 0.01416217,
+ 0.0187955 , 0.0689636 , 0.02865003, 0.07471691, 0.16460503,
+ 0.09720799, 0.02045547, 0.17267234, 0.0311527 , 0.0187955 ,
+ 0.15684317, 0.04111287, 0.03293737, 0.02104007, 0.02946178,
+ 0.02421701, 0.1353385 , 0.03203302, 0.04111287, 0.10778798,
+ 0.07083428, 0.06027915, 0.02164101, 0.06535882, 0.02946178,
+ 0.07275173, 0.02490638, 0.01678627, 0.30605146, 0.02164101,
+ 0.03482061, 0.03580075, 0.37030921, 0.0182721 , 0.03482061,
+ 0.07083428, 0.04226237, 0.03999333, 0.03580075, 0.03203302,
+ 0.0182721 , 0.03580075, 0.06027915, 0.03386636, 0.02946178,
+ 0.03029581, 0.0689636 , 0.02634295, 0.02634295, 0.03029581,
+ 0.02225873, 0.1353385 , 0.08989109, 0.01988679, 0.0526265 ,
+ 0.03386636, 0.03386636, 0.02786 , 0.03029581, 0.06535882,
+ 0.06535882, 0.03482061, 0.02786 , 0.29396682, 0.03293737,
+ 0.12242534, 0.04589793, 0.04589793, 0.03999333, 0.07471691,
+ 0.11344884, 0.05407977, 0.03482061, 0.01988679, 0.02045547,
+ 0.34389327, 0.14576223, 0.02561486, 0.0689636 , 0.02045547,
+ 0.02865003, 0.0526265 , 0.02164101, 0.01776301, 0.08307425,
+ 0.11344884, 0.04982997, 0.0182721 , 0.01498855, 0.02865003,
+ 0.14221564, 0.07879431, 0.02865003, 0.10237696, 0.04465416,
+ 0.07471691, 0.07673078, 0.13200634, 0.02104007, 0.0187955 ,
+ 0.01376599, 0.04717464, 0.01128289, 0.05710047, 0.01988679,
+ 0.01300612, 0.11936722, 0.03203302, 0.01726786, 0.04589793,
+ 0.05407977, 0.09976271, 0.02561486, 0.03999333, 0.02634295,
+ 0.03580075, 0.21771181, 0.1353385 , 0.01988679, 0.37704374,
+ 0.06027915, 0.02045547, 0.18104935, 0.03999333, 0.18104935,
+ 0.15684317, 0.01376599, 0.03293737, 0.08989109, 0.02709115,
+ 0.14221564, 0.27065598, 0.10237696, 0.04226237, 0.72991785,
+ 0.06713876, 0.04226237, 0.03482061, 0.07879431, 0.07471691,
+ 0.15307528, 0.02289366, 0.08989109, 0.02634295, 0.43243779,
+ 0.08756457, 0.03293737, 0.02786 , 0.03482061, 0.0187955 ,
+ 0.08307425, 0.04589793, 0.07275173, 0.0311527 , 0.04589793,
+ 0.08307425, 0.32468619, 0.02289366, 0.02634295, 0.03580075,
+ 0.14938186, 0.0526265 , 0.0526265 , 0.53268924, 0.19874565,
+ 0.0187955 , 0.01541937, 0.01586237, 0.02045547, 0.02421701,
+ 0.02634295, 0.11344884, 0.05710047, 0.05121018, 0.09720799,
+ 0.0311527 , 0.0526265 , 0.01586237, 0.07471691, 0.06027915,
+ 0.15684317, 0.07879431, 0.02289366, 0.04111287, 0.04848506,
+ 0.02865003, 0.04589793, 0.03580075, 0.04111287, 0.1353385 ,
+ 0.09976271, 0.06362285, 0.32468619, 0.09976271, 0.49676673,
+ 0.07879431, 0.06027915, 0.06027915, 0.05407977, 0.05710047,
+ 0.0689636 , 0.11936722, 0.18973955, 0.02709115, 0.03890304,
+ 0.02634295, 0.80625182, 0.04111287, 0.0311527 , 0.07879431,
+ 0.0193336 , 0.01988679, 0.01376599, 0.07879431, 0.05710047,
+ 0.06027915, 0.02104007, 0.0689636 , 0.04717464, 0.04465416,
+ 0.07083428, 0.03999333, 0.06192993, 0.05407977, 0.04982997,
+ 0.46087756, 0.09720799, 0.04589793, 0.07083428, 0.0193336 ,
+ 0.12242534, 0.12242534, 0.05407977, 0.01776301, 0.0311527 ,
+ 0.0689636 , 0.02421701, 0.13200634, 0.19874565, 0.03293737,
+ 0.82774282], atol=1.0e-8)
+ self.assertAlmostEqual(results.pearson_chi2, 271.21110541713801)
+ np.testing.assert_allclose(results.resid_response,
+ [-0.04226237, -0.03999333, -0.02946178, -0.0689636 , -0.09471181,
+ -0.07879431, -0.04717464, -0.27065598, -0.07471691, 0.10477856,
+ -0.39752487, 0.66897282, -0.06192993, -0.04589793, -0.01988679,
+ -0.0526265 , -0.02104007, -0.03386636, -0.02634295, -0.05121018,
+ -0.29396682, 0.92724827, -0.03386636, -0.15307528, -0.06027915,
+ -0.01631789, -0.02045547, -0.01541937, -0.2128508 , -0.04589793,
+ -0.02104007, -0.05407977, -0.0311527 , -0.02634295, -0.01498855,
+ -0.29396682, 0.79663224, -0.07275173, -0.11637537, 0.74604393,
+ -0.64367488, -0.02634295, -0.02164101, -0.07083428, -0.05710047,
+ -0.32468619, -0.01160845, -0.01631789, -0.28803008, -0.02634295,
+ -0.17267234, -0.02634295, -0.01776301, -0.02709115, 0.85061814,
+ 0.73498669, -0.04111287, -0.06362285, -0.07083428, -0.07879431,
+ 0.91010891, -0.03680743, -0.0187955 , -0.01541937, -0.03680743,
+ -0.03029581, -0.02634295, -0.03029581, -0.07471691, -0.01228768,
+ 0.76722803, -0.10505173, -0.06192993, -0.09720799, -0.01416217,
+ -0.0187955 , -0.0689636 , -0.02865003, -0.07471691, -0.16460503,
+ -0.09720799, -0.02045547, 0.82732766, -0.0311527 , -0.0187955 ,
+ -0.15684317, -0.04111287, -0.03293737, -0.02104007, -0.02946178,
+ -0.02421701, -0.1353385 , -0.03203302, -0.04111287, -0.10778798,
+ -0.07083428, -0.06027915, -0.02164101, -0.06535882, -0.02946178,
+ -0.07275173, -0.02490638, -0.01678627, -0.30605146, -0.02164101,
+ -0.03482061, -0.03580075, 0.62969079, -0.0182721 , -0.03482061,
+ -0.07083428, -0.04226237, -0.03999333, -0.03580075, -0.03203302,
+ -0.0182721 , -0.03580075, -0.06027915, -0.03386636, -0.02946178,
+ -0.03029581, -0.0689636 , -0.02634295, -0.02634295, -0.03029581,
+ -0.02225873, -0.1353385 , -0.08989109, -0.01988679, -0.0526265 ,
+ -0.03386636, -0.03386636, -0.02786 , -0.03029581, -0.06535882,
+ -0.06535882, -0.03482061, -0.02786 , -0.29396682, -0.03293737,
+ -0.12242534, -0.04589793, -0.04589793, -0.03999333, -0.07471691,
+ -0.11344884, -0.05407977, -0.03482061, -0.01988679, -0.02045547,
+ 0.65610673, 0.85423777, -0.02561486, -0.0689636 , -0.02045547,
+ -0.02865003, -0.0526265 , -0.02164101, -0.01776301, -0.08307425,
+ -0.11344884, -0.04982997, -0.0182721 , -0.01498855, -0.02865003,
+ -0.14221564, -0.07879431, -0.02865003, -0.10237696, -0.04465416,
+ -0.07471691, -0.07673078, -0.13200634, -0.02104007, -0.0187955 ,
+ -0.01376599, -0.04717464, -0.01128289, 0.94289953, -0.01988679,
+ -0.01300612, -0.11936722, -0.03203302, -0.01726786, -0.04589793,
+ -0.05407977, -0.09976271, -0.02561486, -0.03999333, -0.02634295,
+ -0.03580075, -0.21771181, 0.8646615 , -0.01988679, 0.62295626,
+ -0.06027915, -0.02045547, -0.18104935, 0.96000667, -0.18104935,
+ -0.15684317, -0.01376599, -0.03293737, -0.08989109, -0.02709115,
+ -0.14221564, 0.72934402, -0.10237696, -0.04226237, -0.72991785,
+ -0.06713876, -0.04226237, -0.03482061, -0.07879431, -0.07471691,
+ -0.15307528, 0.97710634, 0.91010891, -0.02634295, -0.43243779,
+ -0.08756457, -0.03293737, -0.02786 , -0.03482061, -0.0187955 ,
+ 0.91692575, -0.04589793, -0.07275173, -0.0311527 , -0.04589793,
+ -0.08307425, 0.67531381, -0.02289366, -0.02634295, -0.03580075,
+ -0.14938186, -0.0526265 , -0.0526265 , 0.46731076, -0.19874565,
+ -0.0187955 , -0.01541937, -0.01586237, -0.02045547, -0.02421701,
+ -0.02634295, -0.11344884, -0.05710047, -0.05121018, -0.09720799,
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+ -0.82774282], atol=1.0e-8)
+ np.testing.assert_allclose(results.resid_working,
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+ -1.51253748e-02, -3.16494123e-02, -1.04913757e-03,
+ -1.18023417e-01])
+ np.testing.assert_allclose(results.resid_pearson,
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+ -0.29246179, -0.22250903, -0.60917574, -0.28416602, 0.3421141 ,
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+ -0.64526275, 1.97919183, -0.28010679, -0.36290807, 1.71396874,
+ -1.3440334 , -0.16448609, -0.14872695, -0.27610555, -0.24608613,
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+ -0.27216147, -0.1575374 , -0.38997712, -0.4980393 , -0.18455132,
+ -2.19209332])
+ np.testing.assert_allclose(results.resid_anscombe,
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+ -0.41962951, -0.42552227, -0.56840788, -0.21896842, -0.20681747,
+ -0.17672552, -0.33053793, -0.15987433, 2.9768074 , -0.21280762,
+ -0.17173916, -0.53821445, -0.27109582, -0.19814236, -0.3259044 ,
+ -0.35467084, -0.48884654, -0.24194253, -0.3036605 , -0.2454118 ,
+ -0.28692985, -0.75249089, 2.35983933, -0.21280762, 1.55726719,
+ -0.37518169, -0.2158664 , -0.67712261, 3.23165236, -0.67712261,
+ -0.62488429, -0.17672552, -0.27497271, -0.46253505, -0.24892975,
+ -0.59203547, 1.83482464, -0.49563627, -0.31237627, -1.83652534,
+ -0.39681759, -0.31237627, -0.28288904, -0.43149255, -0.41962951,
+ -0.61652596, 3.63983609, 2.65398426, -0.2454118 , -1.16171662,
+ -0.45616505, -0.27497271, -0.25249702, -0.28288904, -0.20681747,
+ 2.71015945, -0.3259044 , -0.41381347, -0.2672722 , -0.3259044 ,
+ -0.44366892, 1.68567947, -0.22853969, -0.2454118 , -0.28692985,
+ -0.60826548, -0.34971301, -0.34971301, 1.2290223 , -0.71397735,
+ -0.20681747, -0.18713159, -0.1898263 , -0.2158664 , -0.23514749,
+ -0.2454118 , -0.52366614, -0.3647888 , -0.34482158, -0.48214234,
+ 3.41271513, -0.34971301, -0.1898263 , -0.41962951, -0.37518169,
+ -0.62488429, -0.43149255, -0.22853969, -0.30798858, -0.3352348 ,
+ -0.25611424, -0.3259044 , -0.28692985, -0.30798858, -0.57618899,
+ -0.48884654, -0.38585584, 1.68567947, -0.48884654, -1.28709718,
+ -0.43149255, -0.37518169, -0.37518169, -0.35467084, -0.3647888 ,
+ -0.40240831, -0.53821445, -0.69534436, -0.24892975, -0.29939131,
+ -0.2454118 , 0.70366797, -0.30798858, -0.2672722 , -0.43149255,
+ -0.2097915 , -0.21280762, -0.17672552, -0.43149255, 2.9768074 ,
+ -0.37518169, -0.21896842, -0.40240831, -0.33053793, -0.32133344,
+ 2.82351017, -0.3036605 , -0.38048321, -0.35467084, -0.33999576,
+ -1.21650102, -0.48214234, -0.3259044 , -0.40807333, -0.2097915 ,
+ -0.5456246 , -0.5456246 , -0.35467084, -0.20099345, -0.2672722 ,
+ -0.40240831, -0.23514749, -0.56840788, -0.71397735, -0.27497271,
+ -2.18250381])
+ np.testing.assert_allclose(results.resid_deviance,
+ [-0.29387552, -0.2857098 , -0.24455876, -0.37803944, -0.44609851,
+ -0.40514674, -0.31088148, -0.79449324, -0.39409528, 0.47049798,
+ -1.00668653, 1.48698001, -0.35757692, -0.30654405, -0.20043547,
+ -0.32882173, -0.20622595, -0.26249995, -0.23106769, -0.32424676,
+ -0.83437766, 2.28941155, -0.26249995, -0.57644334, -0.35262564,
+ -0.18139734, -0.20331052, -0.17629229, -0.69186337, -0.30654405,
+ -0.20622595, -0.33345774, -0.251588 , -0.23106769, -0.17379306,
+ -0.83437766, 1.78479093, -0.38867448, -0.4974393 , 1.65565332,
+ -1.43660134, -0.23106769, -0.20918228, -0.38332275, -0.34291558,
+ -0.88609006, -0.15281596, -0.18139734, -0.82428104, -0.23106769,
+ -0.61571821, -0.23106769, -0.18932865, -0.234371 , 1.94999969,
+ 1.62970871, -0.2897651 , -0.36259328, -0.38332275, -0.40514674,
+ 2.19506559, -0.27386827, -0.19480442, -0.17629229, -0.27386827,
+ -0.24804925, -0.23106769, -0.24804925, -0.39409528, -0.15725009,
+ 1.7074519 , -0.47114617, -0.35757692, -0.4522457 , -0.16889886,
+ -0.19480442, -0.37803944, -0.24111595, -0.39409528, -0.59975102,
+ -0.4522457 , -0.20331052, 1.87422489, -0.251588 , -0.19480442,
+ -0.5841272 , -0.2897651 , -0.25881274, -0.20622595, -0.24455876,
+ -0.22142749, -0.53929061, -0.25517563, -0.2897651 , -0.47760126,
+ -0.38332275, -0.35262564, -0.20918228, -0.36767536, -0.24455876,
+ -0.38867448, -0.2245965 , -0.18400413, -0.85481866, -0.20918228,
+ -0.26623785, -0.27002708, 1.40955093, -0.19204738, -0.26623785,
+ -0.38332275, -0.29387552, -0.2857098 , -0.27002708, -0.25517563,
+ -0.19204738, -0.27002708, -0.35262564, -0.26249995, -0.24455876,
+ -0.24804925, -0.37803944, -0.23106769, -0.23106769, -0.24804925,
+ -0.21218006, -0.53929061, -0.43402996, -0.20043547, -0.32882173,
+ -0.26249995, -0.26249995, -0.23772023, -0.24804925, -0.36767536,
+ -0.36767536, -0.26623785, -0.23772023, -0.83437766, -0.25881274,
+ -0.51106408, -0.30654405, -0.30654405, -0.2857098 , -0.39409528,
+ -0.49074728, -0.33345774, -0.26623785, -0.20043547, -0.20331052,
+ 1.46111186, 1.96253843, -0.22780971, -0.37803944, -0.20331052,
+ -0.24111595, -0.32882173, -0.20918228, -0.18932865, -0.41648237,
+ -0.49074728, -0.31973217, -0.19204738, -0.17379306, -0.24111595,
+ -0.55389988, -0.40514674, -0.24111595, -0.46476893, -0.30226435,
+ -0.39409528, -0.39958581, -0.53211065, -0.20622595, -0.19480442,
+ -0.16650295, -0.31088148, -0.15064545, 2.39288231, -0.20043547,
+ -0.16181126, -0.5042114 , -0.25517563, -0.18664773, -0.30654405,
+ -0.33345774, -0.45846897, -0.22780971, -0.2857098 , -0.23106769,
+ -0.27002708, -0.7007597 , 1.99998811, -0.20043547, 1.39670618,
+ -0.35262564, -0.20331052, -0.63203077, 2.53733821, -0.63203077,
+ -0.5841272 , -0.16650295, -0.25881274, -0.43402996, -0.234371 ,
+ -0.55389988, 1.61672923, -0.46476893, -0.29387552, -1.61804148,
+ -0.37282386, -0.29387552, -0.26623785, -0.40514674, -0.39409528,
+ -0.57644334, 2.74841605, 2.19506559, -0.23106769, -1.06433539,
+ -0.42810736, -0.25881274, -0.23772023, -0.26623785, -0.19480442,
+ 2.23070414, -0.30654405, -0.38867448, -0.251588 , -0.30654405,
+ -0.41648237, 1.49993075, -0.21521982, -0.23106769, -0.27002708,
+ -0.5688444 , -0.32882173, -0.32882173, 1.12233423, -0.66569789,
+ -0.19480442, -0.17629229, -0.17882689, -0.20331052, -0.22142749,
+ -0.23106769, -0.49074728, -0.34291558, -0.32424676, -0.4522457 ,
+ 2.63395309, -0.32882173, -0.17882689, -0.39409528, -0.35262564,
+ -0.5841272 , -0.40514674, -0.21521982, -0.2897651 , -0.3152773 ,
+ -0.24111595, -0.30654405, -0.27002708, -0.2897651 , -0.53929061,
+ -0.45846897, -0.36259328, 1.49993075, -0.45846897, -1.17192274,
+ -0.40514674, -0.35262564, -0.35262564, -0.33345774, -0.34291558,
+ -0.37803944, -0.5042114 , -0.64869028, -0.234371 , -0.28170899,
+ -0.23106769, 0.65629132, -0.2897651 , -0.251588 , -0.40514674,
+ -0.19760028, -0.20043547, -0.16650295, -0.40514674, 2.39288231,
+ -0.35262564, -0.20622595, -0.37803944, -0.31088148, -0.30226435,
+ 2.30104857, -0.2857098 , -0.35757692, -0.33345774, -0.31973217,
+ -1.11158678, -0.4522457 , -0.30654405, -0.38332275, -0.19760028,
+ -0.51106408, -0.51106408, -0.33345774, -0.18932865, -0.251588 ,
+ -0.37803944, -0.22142749, -0.53211065, -0.66569789, -0.25881274,
+ -1.87550882])
+ np.testing.assert_allclose(results.null,
+ [ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759,
+ 0.08860759])
+ self.assertAlmostEqual(results.D2, .200712816165)
+ self.assertAlmostEqual(results.adj_D2, 0.19816731557930456)
+
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/contrib/glm/utils.py b/pysal/contrib/glm/utils.py
new file mode 100644
index 0000000..0789675
--- /dev/null
+++ b/pysal/contrib/glm/utils.py
@@ -0,0 +1,350 @@
+
+from __future__ import absolute_import, print_function
+import numpy as np
+import warnings
+
+
+def _bit_length_26(x):
+ if x == 0:
+ return 0
+ elif x == 1:
+ return 1
+ else:
+ return len(bin(x)) - 2
+
+
+try:
+ from scipy.lib._version import NumpyVersion
+except ImportError:
+ import re
+ string_types = basestring
+
+ class NumpyVersion():
+ """Parse and compare numpy version strings.
+ Numpy has the following versioning scheme (numbers given are examples; they
+ can be >9) in principle):
+ - Released version: '1.8.0', '1.8.1', etc.
+ - Alpha: '1.8.0a1', '1.8.0a2', etc.
+ - Beta: '1.8.0b1', '1.8.0b2', etc.
+ - Release candidates: '1.8.0rc1', '1.8.0rc2', etc.
+ - Development versions: '1.8.0.dev-f1234afa' (git commit hash appended)
+ - Development versions after a1: '1.8.0a1.dev-f1234afa',
+ '1.8.0b2.dev-f1234afa',
+ '1.8.1rc1.dev-f1234afa', etc.
+ - Development versions (no git hash available): '1.8.0.dev-Unknown'
+ Comparing needs to be done against a valid version string or other
+ `NumpyVersion` instance.
+ Parameters
+ ----------
+ vstring : str
+ Numpy version string (``np.__version__``).
+ Notes
+ -----
+ All dev versions of the same (pre-)release compare equal.
+ Examples
+ --------
+ >>> from scipy.lib._version import NumpyVersion
+ >>> if NumpyVersion(np.__version__) < '1.7.0':
+ ... print('skip')
+ skip
+ >>> NumpyVersion('1.7') # raises ValueError, add ".0"
+ """
+
+ def __init__(self, vstring):
+ self.vstring = vstring
+ ver_main = re.match(r'\d[.]\d+[.]\d+', vstring)
+ if not ver_main:
+ raise ValueError("Not a valid numpy version string")
+
+ self.version = ver_main.group()
+ self.major, self.minor, self.bugfix = [int(x) for x in
+ self.version.split('.')]
+ if len(vstring) == ver_main.end():
+ self.pre_release = 'final'
+ else:
+ alpha = re.match(r'a\d', vstring[ver_main.end():])
+ beta = re.match(r'b\d', vstring[ver_main.end():])
+ rc = re.match(r'rc\d', vstring[ver_main.end():])
+ pre_rel = [m for m in [alpha, beta, rc] if m is not None]
+ if pre_rel:
+ self.pre_release = pre_rel[0].group()
+ else:
+ self.pre_release = ''
+
+ self.is_devversion = bool(re.search(r'.dev-', vstring))
+
+ def _compare_version(self, other):
+ """Compare major.minor.bugfix"""
+ if self.major == other.major:
+ if self.minor == other.minor:
+ if self.bugfix == other.bugfix:
+ vercmp = 0
+ elif self.bugfix > other.bugfix:
+ vercmp = 1
+ else:
+ vercmp = -1
+ elif self.minor > other.minor:
+ vercmp = 1
+ else:
+ vercmp = -1
+ elif self.major > other.major:
+ vercmp = 1
+ else:
+ vercmp = -1
+
+ return vercmp
+
+ def _compare_pre_release(self, other):
+ """Compare alpha/beta/rc/final."""
+ if self.pre_release == other.pre_release:
+ vercmp = 0
+ elif self.pre_release == 'final':
+ vercmp = 1
+ elif other.pre_release == 'final':
+ vercmp = -1
+ elif self.pre_release > other.pre_release:
+ vercmp = 1
+ else:
+ vercmp = -1
+
+ return vercmp
+
+ def _compare(self, other):
+ if not isinstance(other, (string_types, NumpyVersion)):
+ raise ValueError("Invalid object to compare with NumpyVersion.")
+
+ if isinstance(other, string_types):
+ other = NumpyVersion(other)
+
+ vercmp = self._compare_version(other)
+ if vercmp == 0:
+ # Same x.y.z version, check for alpha/beta/rc
+ vercmp = self._compare_pre_release(other)
+ if vercmp == 0:
+ # Same version and same pre-release, check if dev version
+ if self.is_devversion is other.is_devversion:
+ vercmp = 0
+ elif self.is_devversion:
+ vercmp = -1
+ else:
+ vercmp = 1
+
+ return vercmp
+
+ def __lt__(self, other):
+ return self._compare(other) < 0
+
+ def __le__(self, other):
+ return self._compare(other) <= 0
+
+ def __eq__(self, other):
+ return self._compare(other) == 0
+
+ def __ne__(self, other):
+ return self._compare(other) != 0
+
+ def __gt__(self, other):
+ return self._compare(other) > 0
+
+ def __ge__(self, other):
+ return self._compare(other) >= 0
+
+ def __repr(self):
+ return "NumpyVersion(%s)" % self.vstring
+
+
+def _next_regular(target):
+ """
+ Find the next regular number greater than or equal to target.
+ Regular numbers are composites of the prime factors 2, 3, and 5.
+ Also known as 5-smooth numbers or Hamming numbers, these are the optimal
+ size for inputs to FFTPACK.
+ Target must be a positive integer.
+ """
+ if target <= 6:
+ return target
+
+ # Quickly check if it's already a power of 2
+ if not (target & (target - 1)):
+ return target
+
+ match = float('inf') # Anything found will be smaller
+ p5 = 1
+ while p5 < target:
+ p35 = p5
+ while p35 < target:
+ # Ceiling integer division, avoiding conversion to float
+ # (quotient = ceil(target / p35))
+ quotient = -(-target // p35)
+ # Quickly find next power of 2 >= quotient
+ try:
+ p2 = 2 ** ((quotient - 1).bit_length())
+ except AttributeError:
+ # Fallback for Python <2.7
+ p2 = 2 ** _bit_length_26(quotient - 1)
+
+ N = p2 * p35
+ if N == target:
+ return N
+ elif N < match:
+ match = N
+ p35 *= 3
+ if p35 == target:
+ return p35
+ if p35 < match:
+ match = p35
+ p5 *= 5
+ if p5 == target:
+ return p5
+ if p5 < match:
+ match = p5
+ return match
+if NumpyVersion(np.__version__) >= '1.7.1':
+ np_matrix_rank = np.linalg.matrix_rank
+else:
+ def np_matrix_rank(M, tol=None):
+ """
+ Return matrix rank of array using SVD method
+ Rank of the array is the number of SVD singular values of the array that are
+ greater than `tol`.
+ Parameters
+ ----------
+ M : {(M,), (M, N)} array_like
+ array of <=2 dimensions
+ tol : {None, float}, optional
+ threshold below which SVD values are considered zero. If `tol` is
+ None, and ``S`` is an array with singular values for `M`, and
+ ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
+ set to ``S.max() * max(M.shape) * eps``.
+ Notes
+ -----
+ The default threshold to detect rank deficiency is a test on the magnitude
+ of the singular values of `M`. By default, we identify singular values less
+ than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with
+ the symbols defined above). This is the algorithm MATLAB uses [1]. It also
+ appears in *Numerical recipes* in the discussion of SVD solutions for linear
+ least squares [2].
+ This default threshold is designed to detect rank deficiency accounting for
+ the numerical errors of the SVD computation. Imagine that there is a column
+ in `M` that is an exact (in floating point) linear combination of other
+ columns in `M`. Computing the SVD on `M` will not produce a singular value
+ exactly equal to 0 in general: any difference of the smallest SVD value from
+ 0 will be caused by numerical imprecision in the calculation of the SVD.
+ Our threshold for small SVD values takes this numerical imprecision into
+ account, and the default threshold will detect such numerical rank
+ deficiency. The threshold may declare a matrix `M` rank deficient even if
+ the linear combination of some columns of `M` is not exactly equal to
+ another column of `M` but only numerically very close to another column of
+ `M`.
+ We chose our default threshold because it is in wide use. Other thresholds
+ are possible. For example, elsewhere in the 2007 edition of *Numerical
+ recipes* there is an alternative threshold of ``S.max() *
+ np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe
+ this threshold as being based on "expected roundoff error" (p 71).
+ The thresholds above deal with floating point roundoff error in the
+ calculation of the SVD. However, you may have more information about the
+ sources of error in `M` that would make you consider other tolerance values
+ to detect *effective* rank deficiency. The most useful measure of the
+ tolerance depends on the operations you intend to use on your matrix. For
+ example, if your data come from uncertain measurements with uncertainties
+ greater than floating point epsilon, choosing a tolerance near that
+ uncertainty may be preferable. The tolerance may be absolute if the
+ uncertainties are absolute rather than relative.
+ References
+ ----------
+ .. [1] MATLAB reference documention, "Rank"
+ http://www.mathworks.com/help/techdoc/ref/rank.html
+ .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery,
+ "Numerical Recipes (3rd edition)", Cambridge University Press, 2007,
+ page 795.
+ Examples
+ --------
+ >>> from numpy.linalg import matrix_rank
+ >>> matrix_rank(np.eye(4)) # Full rank matrix
+ 4
+ >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
+ >>> matrix_rank(I)
+ 3
+ >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0
+ 1
+ >>> matrix_rank(np.zeros((4,)))
+ 0
+ """
+ M = np.asarray(M)
+ if M.ndim > 2:
+ raise TypeError('array should have 2 or fewer dimensions')
+ if M.ndim < 2:
+ return int(not all(M == 0))
+ S = np.linalg.svd(M, compute_uv=False)
+ if tol is None:
+ tol = S.max() * max(M.shape) * np.finfo(S.dtype).eps
+ return np.sum(S > tol)
+
+
+
+class CacheWriteWarning(UserWarning):
+ pass
+
+class CachedAttribute(object):
+
+ def __init__(self, func, cachename=None, resetlist=None):
+ self.fget = func
+ self.name = func.__name__
+ self.cachename = cachename or '_cache'
+ self.resetlist = resetlist or ()
+
+ def __get__(self, obj, type=None):
+ if obj is None:
+ return self.fget
+ # Get the cache or set a default one if needed
+ _cachename = self.cachename
+ _cache = getattr(obj, _cachename, None)
+ if _cache is None:
+ setattr(obj, _cachename, resettable_cache())
+ _cache = getattr(obj, _cachename)
+ # Get the name of the attribute to set and cache
+ name = self.name
+ _cachedval = _cache.get(name, None)
+ # print("[_cachedval=%s]" % _cachedval)
+ if _cachedval is None:
+ # Call the "fget" function
+ _cachedval = self.fget(obj)
+ # Set the attribute in obj
+ # print("Setting %s in cache to %s" % (name, _cachedval))
+ try:
+ _cache[name] = _cachedval
+ except KeyError:
+ setattr(_cache, name, _cachedval)
+ # Update the reset list if needed (and possible)
+ resetlist = self.resetlist
+ if resetlist is not ():
+ try:
+ _cache._resetdict[name] = self.resetlist
+ except AttributeError:
+ pass
+ # else:
+ # print("Reading %s from cache (%s)" % (name, _cachedval))
+ return _cachedval
+
+ def __set__(self, obj, value):
+ errmsg = "The attribute '%s' cannot be overwritten" % self.name
+ warnings.warn(errmsg, CacheWriteWarning)
+
+
+class _cache_readonly(object):
+ """
+ Decorator for CachedAttribute
+ """
+
+ def __init__(self, cachename=None, resetlist=None):
+ self.func = None
+ self.cachename = cachename
+ self.resetlist = resetlist or None
+
+ def __call__(self, func):
+ return CachedAttribute(func,
+ cachename=self.cachename,
+ resetlist=self.resetlist)
+cache_readonly = _cache_readonly()
+
+
diff --git a/pysal/contrib/glm/varfuncs.py b/pysal/contrib/glm/varfuncs.py
new file mode 100644
index 0000000..af66d8c
--- /dev/null
+++ b/pysal/contrib/glm/varfuncs.py
@@ -0,0 +1,284 @@
+"""
+Variance functions for use with the link functions in statsmodels.family.links
+"""
+
+__docformat__ = 'restructuredtext'
+
+import numpy as np
+FLOAT_EPS = np.finfo(float).eps
+
+class VarianceFunction(object):
+ """
+ Relates the variance of a random variable to its mean. Defaults to 1.
+
+ Methods
+ -------
+ call
+ Returns an array of ones that is the same shape as `mu`
+
+ Notes
+ -----
+ After a variance function is initialized, its call method can be used.
+
+ Alias for VarianceFunction:
+ constant = VarianceFunction()
+
+ See also
+ --------
+ statsmodels.family.family
+ """
+
+ def __call__(self, mu):
+ """
+ Default variance function
+
+ Parameters
+ -----------
+ mu : array-like
+ mean parameters
+
+ Returns
+ -------
+ v : array
+ ones(mu.shape)
+ """
+ mu = np.asarray(mu)
+ return np.ones(mu.shape, np.float64)
+
+
+ def deriv(self, mu):
+ """
+ Derivative of the variance function v'(mu)
+ """
+ from statsmodels.tools.numdiff import approx_fprime_cs
+ # TODO: diag workaround proplem with numdiff for 1d
+ return np.diag(approx_fprime_cs(mu, self))
+
+
+constant = VarianceFunction()
+constant.__doc__ = """
+The call method of constant returns a constant variance, i.e., a vector of ones.
+
+constant is an alias of VarianceFunction()
+"""
+
+class Power(object):
+ """
+ Power variance function
+
+ Parameters
+ ----------
+ power : float
+ exponent used in power variance function
+
+ Methods
+ -------
+ call
+ Returns the power variance
+
+ Formulas
+ --------
+ V(mu) = numpy.fabs(mu)**power
+
+ Notes
+ -----
+ Aliases for Power:
+ mu = Power()
+ mu_squared = Power(power=2)
+ mu_cubed = Power(power=3)
+ """
+
+ def __init__(self, power=1.):
+ self.power = power
+
+ def __call__(self, mu):
+ """
+ Power variance function
+
+ Parameters
+ ----------
+ mu : array-like
+ mean parameters
+
+ Returns
+ -------
+ variance : array
+ numpy.fabs(mu)**self.power
+ """
+ return np.power(np.fabs(mu), self.power)
+
+
+ def deriv(self, mu):
+ """
+ Derivative of the variance function v'(mu)
+ """
+ from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
+ #return approx_fprime_cs(mu, self) # TODO fix breaks in `fabs
+ # TODO: diag is workaround problem with numdiff for 1d
+ return np.diag(approx_fprime(mu, self))
+
+
+mu = Power()
+mu.__doc__ = """
+Returns np.fabs(mu)
+
+Notes
+-----
+This is an alias of Power()
+"""
+mu_squared = Power(power=2)
+mu_squared.__doc__ = """
+Returns np.fabs(mu)**2
+
+Notes
+-----
+This is an alias of statsmodels.family.links.Power(power=2)
+"""
+mu_cubed = Power(power=3)
+mu_cubed.__doc__ = """
+Returns np.fabs(mu)**3
+
+Notes
+-----
+This is an alias of statsmodels.family.links.Power(power=3)
+"""
+
+class Binomial(object):
+ """
+ Binomial variance function
+
+ Parameters
+ ----------
+ n : int, optional
+ The number of trials for a binomial variable. The default is 1 for
+ p in (0,1)
+
+ Methods
+ -------
+ call
+ Returns the binomial variance
+
+ Formulas
+ --------
+ V(mu) = p * (1 - p) * n
+
+ where p = mu / n
+
+ Notes
+ -----
+ Alias for Binomial:
+ binary = Binomial()
+
+ A private method _clean trims the data by machine epsilon so that p is
+ in (0,1)
+ """
+
+ def __init__(self, n=1):
+ self.n = n
+
+ def _clean(self, p):
+ return np.clip(p, FLOAT_EPS, 1 - FLOAT_EPS)
+
+ def __call__(self, mu):
+ """
+ Binomial variance function
+
+ Parameters
+ -----------
+ mu : array-like
+ mean parameters
+
+ Returns
+ -------
+ variance : array
+ variance = mu/n * (1 - mu/n) * self.n
+ """
+ p = self._clean(mu / self.n)
+ return p * (1 - p) * self.n
+
+ #TODO: inherit from super
+ def deriv(self, mu):
+ """
+ Derivative of the variance function v'(mu)
+ """
+ from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
+ # TODO: diag workaround proplem with numdiff for 1d
+ return np.diag(approx_fprime_cs(mu, self))
+
+
+binary = Binomial()
+binary.__doc__ = """
+The binomial variance function for n = 1
+
+Notes
+-----
+This is an alias of Binomial(n=1)
+"""
+
+class NegativeBinomial(object):
+ '''
+ Negative binomial variance function
+
+ Parameters
+ ----------
+ alpha : float
+ The ancillary parameter for the negative binomial variance function.
+ `alpha` is assumed to be nonstochastic. The default is 1.
+
+ Methods
+ -------
+ call
+ Returns the negative binomial variance
+
+ Formulas
+ --------
+ V(mu) = mu + alpha*mu**2
+
+ Notes
+ -----
+ Alias for NegativeBinomial:
+ nbinom = NegativeBinomial()
+
+ A private method _clean trims the data by machine epsilon so that p is
+ in (0,inf)
+ '''
+
+ def __init__(self, alpha=1.):
+ self.alpha = alpha
+
+ def _clean(self, p):
+ return np.clip(p, FLOAT_EPS, np.inf)
+
+ def __call__(self, mu):
+ """
+ Negative binomial variance function
+
+ Parameters
+ ----------
+ mu : array-like
+ mean parameters
+
+ Returns
+ -------
+ variance : array
+ variance = mu + alpha*mu**2
+ """
+ p = self._clean(mu)
+ return p + self.alpha*p**2
+
+ def deriv(self, mu):
+ """
+ Derivative of the negative binomial variance function.
+ """
+
+ p = self._clean(mu)
+ return 1 + 2 * self.alpha * p
+
+nbinom = NegativeBinomial()
+nbinom.__doc__ = """
+Negative Binomial variance function.
+
+Notes
+-----
+This is an alias of NegativeBinomial(alpha=1.)
+"""
diff --git a/pysal/contrib/network/klincs.py b/pysal/contrib/network/klincs.py
index 3bbdd4b..472e749 100644
--- a/pysal/contrib/network/klincs.py
+++ b/pysal/contrib/network/klincs.py
@@ -31,11 +31,11 @@ class WeightedRandomSampleGenerator(object):
"""
def __init__(self, weights, population, n):
- """
- weights: an iterable with m numeric elements
- population: a numpy array with m elements
- n: an integer representing sample size
- """
+ """
+ weights: an iterable with m numeric elements
+ population: a numpy array with m elements
+ n: an integer representing sample size
+ """
self.totals = np.cumsum(weights)
self.population = population
self.n = n
@@ -57,10 +57,10 @@ class RandomSampleGenerator(object):
from a population group
"""
def __init__(self, population, n):
- """
- population: a numpy array with m elements
- n: an integer representing sample size
- """
+ """
+ population: a numpy array with m elements
+ n: an integer representing sample size
+ """
self.population = population
self.n = n
diff --git a/pysal/contrib/network/lincs.py b/pysal/contrib/network/lincs.py
index d3ee5b0..a5a684e 100644
--- a/pysal/contrib/network/lincs.py
+++ b/pysal/contrib/network/lincs.py
@@ -284,11 +284,11 @@ def lincs(network, event, base, weight, dist=None, lisa_func='moran', sim_method
w, edges, e, b, edges_geom = None, None, None, None, []
if weight == 'Node-based':
w, edges = node_weights(network, attribute=True)
- n = len(edges)
- e, b = np.zeros(n), np.zeros(n)
- for edge in edges:
+ n = len(edges)
+ e, b = np.zeros(n), np.zeros(n)
+ for edge in edges:
edges_geom.append(edges[edge][0])
- e[edge] = edges[edge][event]
+ e[edge] = edges[edge][event]
b[edge] = getBase(edges, edge, base)
w.id_order = edges.keys()
elif dist is not None:
@@ -298,50 +298,50 @@ def lincs(network, event, base, weight, dist=None, lisa_func='moran', sim_method
network[n1][n2] = network[n1][n2][0]
w, edges = dist_weights(network, id2edgepoints, edge2id, dist)
n = len(id2attr)
- e, b = np.zeros(n), np.zeros(n)
+ e, b = np.zeros(n), np.zeros(n)
if base:
base -= 1
- for edge in id2attr:
+ for edge in id2attr:
edges_geom.append(edges[edge])
- e[edge] = id2attr[edge][event - 1]
+ e[edge] = id2attr[edge][event - 1]
b[edge] = getBase(id2attr, edge, base)
w.id_order = id2attr.keys()
Is, p_sim, Zs = None,None, None
if sim_method == 'permutation':
if lisa_func == pysal.esda.moran.Moran_Local:
- lisa_i = lisa_func(e*1.0/b,w,transformation="r",permutations=sim_num)
+ lisa_i = lisa_func(e*1.0/b,w,transformation="r",permutations=sim_num)
Is = lisa_i.Is
Zs = lisa_i.q
else:
- lisa_i = lisa_func(e*1.0/b,w,transform="R",permutations=sim_num,star=star)
+ lisa_i = lisa_func(e*1.0/b,w,transform="R",permutations=sim_num,star=star)
Is = lisa_i.Gs
Zs = lisa_i.Zs
p_sim = lisa_i.p_sim
else:
- sims = None
+ sims = None
if lisa_func == pysal.esda.moran.Moran_Local:
- lisa_i = lisa_func(e*1.0/b,w,transformation="r",permutations=0)
+ lisa_i = lisa_func(e*1.0/b,w,transformation="r",permutations=0)
Is = lisa_i.Is
Zs = lisa_i.q
else:
- lisa_i = lisa_func(e*1.0/b,w,transform="R",permutations=0,star=star)
- Is = lisa_i.Gs
- Zs = lisa_i.Zs
- if sim_method == 'binomial':
- sims = unconditional_sim(e, b, sim_num)
- elif sim_method == 'poisson':
- sims = unconditional_sim_poisson(e, b, sim_num)
- else:
- sims = conditional_multinomial(e, b, sim_num)
+ lisa_i = lisa_func(e*1.0/b,w,transform="R",permutations=0,star=star)
+ Is = lisa_i.Gs
+ Zs = lisa_i.Zs
+ if sim_method == 'binomial':
+ sims = unconditional_sim(e, b, sim_num)
+ elif sim_method == 'poisson':
+ sims = unconditional_sim_poisson(e, b, sim_num)
+ else:
+ sims = conditional_multinomial(e, b, sim_num)
if lisa_func == pysal.esda.moran.Moran_Local:
- for i in range(sim_num):
- sims[:,i] = lisa_func(sims[:,i]*1.0/b,w,transformation="r",permutations=0).Is
+ for i in range(sim_num):
+ sims[:,i] = lisa_func(sims[:,i]*1.0/b,w,transformation="r",permutations=0).Is
else:
- for i in range(sim_num):
- sims[:,i] = lisa_func(sims[:,i]*1.0/b,w,permutations=0,star=star).Gs
- sim_res = pseudo_pvalues(Is, sims)
- p_sim = sim_res[0]
+ for i in range(sim_num):
+ sims[:,i] = lisa_func(sims[:,i]*1.0/b,w,permutations=0,star=star).Gs
+ sim_res = pseudo_pvalues(Is, sims)
+ p_sim = sim_res[0]
w.transform = 'O'
return zip(edges_geom, e, b, Is, Zs, p_sim), w
diff --git a/pysal/contrib/pdio/__init__.py b/pysal/contrib/pdio/__init__.py
new file mode 100644
index 0000000..7e95a9e
--- /dev/null
+++ b/pysal/contrib/pdio/__init__.py
@@ -0,0 +1,4 @@
+import dbf as dbio
+import shp as shio
+import file as fio
+from file import read_files, write_files
diff --git a/pysal/contrib/pdutilities/dbf_utilities.py b/pysal/contrib/pdio/dbf.py
similarity index 100%
rename from pysal/contrib/pdutilities/dbf_utilities.py
rename to pysal/contrib/pdio/dbf.py
diff --git a/pysal/contrib/pdutilities/file_utilities.py b/pysal/contrib/pdio/file.py
similarity index 95%
rename from pysal/contrib/pdutilities/file_utilities.py
rename to pysal/contrib/pdio/file.py
index ad45a5e..3002b7c 100644
--- a/pysal/contrib/pdutilities/file_utilities.py
+++ b/pysal/contrib/pdio/file.py
@@ -1,7 +1,7 @@
import pysal as ps
import os
-from shp_utilities import shp2series, series2shp
-from dbf_utilities import dbf2df, df2dbf
+from shp import shp2series, series2shp
+from dbf import dbf2df, df2dbf
def read_files(filepath, **kwargs):
diff --git a/pysal/contrib/pdutilities/shp_utilities.py b/pysal/contrib/pdio/shp.py
similarity index 100%
rename from pysal/contrib/pdutilities/shp_utilities.py
rename to pysal/contrib/pdio/shp.py
diff --git a/pysal/contrib/pdutilities/__init__.py b/pysal/contrib/pdutilities/__init__.py
deleted file mode 100644
index 4df967d..0000000
--- a/pysal/contrib/pdutilities/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import dbf_utilities as dbio
-import shp_utilities as shio
-import file_utilities as fio
-from file_utilities import read_files, write_files
diff --git a/pysal/contrib/shapely_ext.py b/pysal/contrib/shapely_ext.py
index c2a6847..1c58215 100644
--- a/pysal/contrib/shapely_ext.py
+++ b/pysal/contrib/shapely_ext.py
@@ -40,7 +40,7 @@ def length(shape):
def boundary(shape):
if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
o = shapely.geometry.asShape(shape)
- res = o.bondary
+ res = o.boundary
return pysal.cg.shapes.asShape(res)
def bounds(shape):
@@ -57,7 +57,7 @@ def centroid(shape):
def representative_point(shape):
if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
o = shapely.geometry.asShape(shape)
- res = o.representative_point
+ res = o.representative_point()
return pysal.cg.shapes.asShape(res)
def convex_hull(shape):
diff --git a/pysal/contrib/spint/README.md b/pysal/contrib/spint/README.md
index 65b8ca5..61e04fe 100644
--- a/pysal/contrib/spint/README.md
+++ b/pysal/contrib/spint/README.md
@@ -1,17 +1,57 @@
Spatial Interaction Modeling Package
===========================================
-The **Sp**atial **Int**eraction Modeling (SpInt) package seeks to provide a collection of tools to study spatial interaction processes.
+The **Sp**atial **Int**eraction Modeling (SpInt) module seeks to provide a collection of tools to study spatial interaction processes and analyze spatial interaction data.
-It currently supports the calibration of the 'family' of spatial interaction models (Wilson, 1971) which are derived using an entropy maximizing (EM) framework or the equivalent information minimizing (IM) framework. As such, it is able to derive parameters for the following models:
+The [initial development](https://github.com/pysal/pysal/wiki/SpInt-Development) of the module was carried out as a Google Summer of Code
+project (summer 2016). Documentation of the project progress can be found on the
+[project blog](https://tayloroshan.github.io/).
+
+The module currently supports the calibration of the 'family' of spatial interaction models (Wilson, 1971) which are derived using an entropy maximizing (EM) framework or the equivalent information minimizing (IM) framework. As such, it is able to derive parameters for the following Poisson count models:
+
+Models
+------
- unconstrained gravity model
- production-constrained model (origin-constrained)
- attraction-constrained model (destination-constrained)
- doubly-constrained model
+Calibration is carried out using iteratively weighted least squares in a generalized linear modleing framework (Cameron & Trivedi, 2013). These model results have been verified against comparable routines laid out in (Fotheringham and O’Kelly, 1989; Willimans and Fotheringham, 1984) and functions avaialble in R such as GL or Pythons statsmodels. The estimation of the constrained routines are carried out using sparse data strucutres for lower memory overhead and faster computations.
+
+Additional Features
+-------------------
+
+- QuasiPoisson model estimation
+- Regression-based tests for overdispersion
+- Model fit statistics including typical GLM metrics, standardized root mean
+ square error, and Sorensen similarit index
+- Vector-based Moran's I statistic for testing for spatial autcorrelation in
+ spatial interaction data
+- Local subset model calibration for mappable sets of parameter estimates and model
+ diagnostics
+- Three types of spatial interaction spatial weights: origin-destination
+ contiguity weights, network-based weights, and vector-based distance weights
+
+In Progress
+-----------
+
+- Spatial Autoregressive (Lag) model spatial interaction specification
+
+Future Work
+-----------
+
+- Parameter estimation via maximum likelihood and gradient-based optimization
+- Zero-inflated Poisson model
+- Negative Binomial model/zero-inflated negative binomial model
+- Functions to compute competing destinations
+- Functions to compute eigenvector spatial filters
+- Parameter estimation via neural networks
+- Universal (determinsitic) models such as the Radiation model and Inverse
+ Population Weighted model
-Calibration is carried out using maximum likelihood estimation routines outlined in (Fotheringham and O’Kelly, 1989; Willimans and Fotheringham, 1984). Optimization is achieved using scipy.optimize.fsolve(). Overall, the package is currently dependent upon numpy, spicy, and pandas.
+Cameron, C. A. and Trivedi, P. K. (2013). Regression analyis of count data.
+Cambridge University Press, 1998.
Fotheringham, A. S. and O'Kelly, M. E. (1989). Spatial Interaction Models: Formulations and Applications. London: Kluwer Academic Publishers.
@@ -20,4 +60,4 @@ Models by Maximum Likelihood Estimation with Program SIMODEL, Geographic Monogra
Series, 7, Department of Geography, Indiana University.
Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and
-Planning A, 3, 1–32.
\ No newline at end of file
+Planning A, 3, 1–32.
diff --git a/pysal/contrib/spint/__init__.py b/pysal/contrib/spint/__init__.py
new file mode 100644
index 0000000..1b9d073
--- /dev/null
+++ b/pysal/contrib/spint/__init__.py
@@ -0,0 +1,6 @@
+import gravity
+from gravity import Gravity, Production, Attraction, Doubly
+import dispersion
+import utils
+import vec_SA
+import count_model
diff --git a/pysal/contrib/spint/count_model.py b/pysal/contrib/spint/count_model.py
new file mode 100644
index 0000000..cb7b5aa
--- /dev/null
+++ b/pysal/contrib/spint/count_model.py
@@ -0,0 +1,191 @@
+"""
+CountModel class for dispatching different types of count models and different
+types of estimation technqiues.
+"""
+
+__author__ = "Taylor Oshan tayoshan at gmail.com"
+
+import numpy as np
+from pysal.contrib.glm.glm import GLM
+from pysal.contrib.glm.family import Poisson, QuasiPoisson
+
+class CountModel(object):
+ """
+ Base class for variety of count-based models such as Poisson, negative binomial,
+ etc. of the exponetial family.
+
+ Parameters
+ ----------
+ y : array
+ n x 1; n observations of the depedent variable
+ X : array
+ n x k; design matrix of k explanatory variables
+ family : instance of class 'family'
+ default is Poisson()
+ constant : boolean
+ True if intercept should be estimated and false otherwise.
+ Default is True.
+
+
+ Attributes
+ ----------
+ y : array
+ n x 1; n observations of the depedent variable
+ X : array
+ n x k; design matrix of k explanatory variables
+ fitted : boolean
+ False is model has not been fitted and True if it has been
+ successfully fitted. Deault is False.
+ constant : boolean
+ True if intercept should be estimated and false otherwise.
+ Default is True.
+
+ Example
+ -------
+ >>> import pysal.contrib.spint.count_model import CountModel
+ >>> from pysal.
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+ >>> self.y = np.round(y).astype(int)
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> self.X = np.array(X).T
+ >>> model = CountModel(self.y, self.X, family=Poisson())
+ >>> results = model.fit('GLM')
+ >>> results.params
+ array([3.92159085, 0.01183491, -0.01371397])
+
+ """
+ def __init__(self, y, X, family = Poisson(), constant = True):
+ self.y = self._check_counts(y)
+ self.X = X
+ self.constant = constant
+ def _check_counts(self, y):
+ if (y.dtype == 'int64') | (y.dtype == 'int32'):
+ return y
+ else:
+ raise TypeError('Dependent variable (y) must be composed of integers')
+
+ def fit(self, framework='GLM', Quasi=False):
+ """
+ Method that fits a particular count model usign the appropriate
+ estimation technique. Models include Poisson GLM, Negative Binomial GLM,
+ Quasi-Poisson - at the moment Poisson GLM is the only option.
+
+ TODO: add zero inflated variants and hurdle variants.
+
+ Parameters
+ ----------
+ framework : string
+ estimation framework; default is GLM
+ "GLM" | "QUASI" |
+ """
+ if (framework.lower() == 'glm'):
+ if not Quasi:
+ results = GLM(self.y, self.X, family = Poisson(), constant=self.constant).fit()
+ else:
+ results = GLM(self.y, self.X, family = QuasiPoisson(), constant=self.constant).fit()
+ return CountModelResults(results)
+
+ else:
+ raise NotImplemented('Poisson GLM is the only count model currently implemented')
+
+class CountModelResults(object):
+ """
+ Results of estimated GLM and diagnostics.
+
+ Parameters
+ ----------
+ results : GLM object
+ Pointer to GLMResults object with estimated parameters
+ and diagnostics.
+
+ Attributes
+ ----------
+ model : GLM Object
+ Points to model object for which parameters have been
+ estimated. May contain additional diagnostics.
+ y : array
+ n*1, dependent variable.
+ X : array
+ n*k, independent variable, including constant.
+ family : string
+ Model type: 'Gaussian', 'Poisson', 'Logistic'
+ n : integer
+ Number of observations
+ k : integer
+ Number of independent variables
+ df_model : float
+ k-1, where k is the number of variables (including
+ intercept)
+ df_residual : float
+ observations minus variables (n-k)
+ routine.
+ params : array
+ n*k, estimared beta coefficients
+ yhat : array
+ n*1, predicted value of y (i.e., fittedvalues)
+ cov_params : array
+ Variance covariance matrix (kxk) of betas
+ std_err : array
+ k*1, standard errors of betas
+ pvalues : array
+ k*1, two-tailed pvalues of parameters
+ tvalues : array
+ k*1, the tvalues of the standard errors
+ deviance : float
+ value of the deviance function evalued at params;
+ see family.py for distribution-specific deviance
+ llf : float
+ value of the loglikelihood function evalued at params;
+ see family.py for distribution-specific loglikelihoods
+ llnull : float
+ value of the loglikelihood function evaluated with only an
+ intercept; see family.py for distribution-specific
+ loglikelihoods
+ aic : float
+ Akaike information criterion
+ resid : array
+ response residuals; defined as y-mu
+
+ resid_dev : array
+ k x 1, residual deviance of model
+ D2 : float
+ percentage of explained deviance
+ adj_D2 : float
+
+ pseudo_R2 : float
+ McFadden's pseudo R2 (coefficient of determination)
+ adj_pseudoR2 : float
+ adjusted McFadden's pseudo R2
+
+ """
+ def __init__(self, results):
+ self.y = results.y
+ self.X = results.X
+ self.family = results.family
+ self.params = results.params
+ self.aic = results.aic
+ self.df_model = results.df_model
+ self.df_resid = results.df_resid
+ self.llf = results.llf
+ self.llnull = results.llnull
+ self.yhat = results.mu
+ self.deviance = results.deviance
+ self.n = results.n
+ self.k = results.k
+ self.resid = results.resid_response
+ self.resid_dev = results.resid_deviance
+ self.cov_params = results.cov_params()
+ self.std_err = results.bse
+ self.pvalues = results.pvalues
+ self.tvalues = results.tvalues
+ self.D2 = results.D2
+ self.adj_D2 = results.adj_D2
+ self.pseudoR2 = results.pseudoR2
+ self.adj_pseudoR2 = results.adj_pseudoR2
+ self.model = results
+
+
diff --git a/pysal/contrib/spint/dispersion.py b/pysal/contrib/spint/dispersion.py
new file mode 100644
index 0000000..3470c49
--- /dev/null
+++ b/pysal/contrib/spint/dispersion.py
@@ -0,0 +1,102 @@
+"""
+Various functions to test hypotheses regarding the dispersion of the variance of
+a variable.
+
+"""
+
+__author__ = "Taylor Oshan tayoshan at gmail.com"
+
+from pysal.contrib.glm.glm import GLM
+from pysal.contrib.glm.family import Poisson
+import numpy as np
+import scipy.stats as stats
+from types import FunctionType
+
+def phi_disp(model):
+ """
+ Test the hypothesis that var[y] = mu (equidispersion) against the
+ alternative hypothesis (quasi-Poisson) that var[y] = phi * mu where mu
+ is the expected value of y and phi is an estimated overdispersion
+ coefficient which is equivalent to 1+alpha in the alternative alpha
+ dispersion test.
+
+ phi > 0: overdispersion
+ phi = 1: equidispersion
+ phi < 0: underdispersion
+
+ Parameters
+ ----------
+ model : Model results class
+ function can only be called on a sucessfully fitted model
+ which has a valid response variable, y, and a valid
+ predicted response variable, yhat.
+ alt_var : function
+ specifies an alternative varaince as a function of mu.
+ Function must take a single scalar as input and return a
+ single scalar as output
+ Returns
+ -------
+ array : [alpha coefficient, tvalue of alpha, pvalue of alpha]
+
+ """
+ try:
+ y = model.y.reshape((-1,1))
+ yhat = model.yhat.reshape((-1,1))
+ ytest = (((y-yhat)**2-y)/yhat).reshape((-1,1))
+ except:
+ raise AttributeError("Check that fitted model has valid 'y' and 'yhat' attributes")
+
+ phi = 1 + np.mean(ytest)
+ zval = np.sqrt(len(ytest)) * np.mean(ytest)/np.std(ytest, ddof=1)
+ pval = stats.norm.sf(zval)
+
+ return np.array([phi, zval, pval])
+
+def alpha_disp(model, alt_var=lambda x: x):
+ """
+ Test the hypothesis that var[y] = mu (equidispersion) against the
+ alternative hypothesis that var[y] = mu + alpha * alt_var(mu) where mu
+ is the expected value of y, alpha is an estimated coefficient, and
+ alt_var() specifies an alternative variance as a function of mu.
+ alt_var=lambda x:x corresponds to an alternative hypothesis of a negative
+ binomimal model with a linear variance function and alt_var=lambda
+ x:x**2 correspinds to an alternative hypothesis of a negative binomial
+ model with a quadratic varaince function.
+
+ alpha > 0: overdispersion
+ alpha = 1: equidispersion
+ alpha < 0: underdispersion
+
+ Parameters
+ ----------
+ model : Model results class
+ function can only be called on a sucessfully fitted model
+ which has a valid response variable, y, and a valid
+ predicted response variable, yhat.
+ alt_var : function
+ specifies an alternative varaince as a function of mu.
+ Function must take a single scalar as input and return a
+ single scalar as output
+ Returns
+ -------
+ array : [alpha coefficient, tvalue of alpha, pvalue of alpha]
+
+ """
+ try:
+ y = model.y.reshape((-1,1))
+ yhat = model.yhat.reshape((-1,1))
+ ytest = (((y-yhat)**2-y)/yhat).reshape((-1,1))
+ except:
+ raise AttributeError("Make sure model passed has been estimated and has a valid 'y' and 'yhat' attribute")
+
+ if isinstance(alt_var, FunctionType):
+ X = (alt_var(yhat)/yhat).reshape((-1,1))
+ test_results = GLM(ytest, X, constant=False).fit()
+ alpha = test_results.params[0]
+ zval = test_results.tvalues[0]
+ pval = stats.norm.sf(zval)
+ else:
+ raise TypeError("The alternative variance function, 'alt_var', must be a valid function'")
+
+ return np.array([alpha, zval, pval])
+
diff --git a/pysal/contrib/spint/gravity.py b/pysal/contrib/spint/gravity.py
index 4865d42..94df1b6 100644
--- a/pysal/contrib/spint/gravity.py
+++ b/pysal/contrib/spint/gravity.py
@@ -1,6 +1,6 @@
# coding=utf-8
"""
-MLE calibration for Wilson (1967) family of gravity models
+ Wilsonian (1967) family of gravity-type spatial interaction models
References
----------
@@ -8,812 +8,1010 @@ References
Fotheringham, A. S. and O'Kelly, M. E. (1989). Spatial Interaction Models: Formulations
and Applications. London: Kluwer Academic Publishers.
-Williams, P. A. and A. S. Fotheringham (1984), The Calibration of Spatial Interaction
- Models by Maximum Likelihood Estimation with Program SIMODEL, Geographic Monograph
- Series, 7, Department of Geography, Indiana University.
-
Wilson, A. G. (1967). A statistical theory of spatial distribution models.
Transportation Research, 1, 253–269.
-
"""
__author__ = "Taylor Oshan tayoshan at gmail.com"
-import pandas as pd
+from types import FunctionType
import numpy as np
-from scipy.optimize import fsolve
-import gravity_stats as stats
-
-
-class Unconstrained:
+from scipy import sparse as sp
+from pysal.spreg import user_output as User
+from pysal.spreg.utils import sphstack
+from pysal.contrib.glm.utils import cache_readonly
+from count_model import CountModel
+from utils import sorensen, srmse, spcategorical
+
+class BaseGravity(CountModel):
"""
- Unconstrained gravity model estimated using MLE
+ Base class to set up gravity-type spatial interaction models and dispatch
+ estimaton technqiues.
Parameters
----------
- data : str
- pandas d frame
- origins : str
- column name for origin names
- destinations : str
- column name for destination names
- flows : str
- column name for observed flows
- o_factors : list of strings
- column name for each origin attribute
- d_factors : list of strings
- column name for each destination attribute
- cost : str
- column name for distance or cost values
- cost_func : str
- either 'exp' for exponential or 'pow' for power distance function
- filter_intra : boolean
- True (default) to filter intra-zonal flows
+ flows : array of integers
+ n x 1; observed flows between O origins and D destinations
+ origins : array of strings
+ n x 1; unique identifiers of origins of n flows
+ destinations : array of strings
+ n x 1; unique identifiers of destinations of n flows
+ cost : array
+ n x 1; cost to overcome separation between each origin and
+ destination associated with a flow; typically distance or time
+ cost_func : string or function that has scalar input and output
+ functional form of the cost function;
+ 'exp' | 'pow' | custom function
+ o_vars : array (optional)
+ n x p; p attributes for each origin of n flows; default
+ is None
+ d_vars : array (optional)
+ n x p; p attributes for each destination of n flows;
+ default is None
+ constant : boolean
+ True to include intercept in model; false by default
+ framework : string
+ estimation technique; currently only 'GLM' is avaialble
+ Quasi : boolean
+ True to estimate QuasiPoisson model; should result in same
+ parameters as Poisson but with altered covariance; default
+ to true which estimates Poisson model
+ SF : array
+ n x 1; eigenvector spatial filter to include in the model;
+ default to None which does not include a filter; not yet
+ implemented
+ CD : array
+ n x 1; competing destination term that accounts for the
+ likelihood that alternative destinations are considered
+ along with each destination under consideration for every
+ OD pair; defaults to None which does not include a CD
+ term; not yet implemented
+ Lag : W object
+ spatial weight for n observations (OD pairs) used to
+ construct a spatial autoregressive model and estimator;
+ defaults to None which does not include an autoregressive
+ term; not yet implemented
+
Attributes
----------
- dt : pandas DataFrame
- model input data
- o : pandas series
- origins
- d : pandas series
- destinations
- f : pandas series
- observed flows
- of : dict
- origin factors
- df : dict
- destination factors
- c : pandas series
- cost or distance variable
- cf : str
- either 'exp' for exponential or 'pow' for power cost function
- opt_log : dict
- record of minimization of o_function
- ip : dict
- initial parameter estimate values
- p : dict
- parameters estimates
- ests : list of floats
- estimated values for calibrated model
-
+ f : array
+ n x 1; observed flows; dependent variable; y
+ n : integer
+ number of observations
+ k : integer
+ number of parameters
+ c : array
+ n x 1; cost to overcome separation between each origin and
+ destination associated with a flow; typically distance or time
+ cf : function
+ cost function; used to transform cost variable
+ ov : array
+ n x p(o); p attributes for each origin of n flows
+ dv : array
+ n x p(d); p attributes for each destination of n flows
+ constant : boolean
+ True to include intercept in model; false by default
+ y : array
+ n x 1; dependent variable used in estimation including any
+ transformations
+ X : array
+ n x k, design matrix used in estimation
+ params : array
+ n x k, k estimated beta coefficients; k = p(o) + p(d) + 1
+ yhat : array
+ n x 1, predicted value of y (i.e., fittedvalues)
+ cov_params : array
+ Variance covariance matrix (k x k) of betas
+ std_err : array
+ k x 1, standard errors of betas
+ pvalues : array
+ k x 1, two-tailed pvalues of parameters
+ tvalues : array
+ k x 1, the tvalues of the standard errors
+ deviance : float
+ value of the deviance function evalued at params;
+ see family.py for distribution-specific deviance
+ resid_dev : array
+ n x 1, residual deviance of model
+ llf : float
+ value of the loglikelihood function evalued at params;
+ see family.py for distribution-specific loglikelihoods
+ llnull : float
+ value of the loglikelihood function evaluated with only an
+ intercept; see family.py for distribution-specific
+ loglikelihoods
+ aic : float
+ Akaike information criterion
+ D2 : float
+ percentage of explained deviance
+ adj_D2 : float
+ adjusted percentage of explained deviance
+ pseudo_R2 : float
+ McFadden's pseudo R2 (coefficient of determination)
+ adj_pseudoR2 : float
+ adjusted McFadden's pseudo R2
+ SRMSE : float
+ standardized root mean square error
+ SSI : float
+ Sorensen similarity index
+ results : object
+ full results from estimated model. May contain addtional
+ diagnostics
Example
-------
-
- import numpy as np
- import pandas as pd
- import gravity as grav
- >>> f = np.array([56, 100.8, 173.6, 235.2, 87.36,
- 28., 100.8, 69.44, 235.2, 145.6,
- 22., 26.4, 136.4, 123.2, 343.2,
- 14., 75.6, 130.2, 70.56, 163.8,
- 22, 59.4, 204.6, 110.88, 171.6])
- >>> V = np.repeat(np.array([56, 56, 44, 42, 66]), 5)
- >>> o = np.repeat(np.array(range(1, 6)), 5)
- >>> W = np.tile(np.array([10, 18, 62, 84, 78]), 5)
- >>> d = np.tile(np.array(range(1, 6)), 5)
- >>> dij = np.array([10, 10, 20, 20, 50,
- 20, 10, 50, 20, 30,
- 20, 30, 20, 30, 10,
- 30, 10, 20, 50, 20,
- 30, 20, 20, 50, 30])
- >>> dt = pd.DataFrame({'origins': self.o,
- 'destinations': self.d,
- 'V': V,
- 'W': W,
- 'Dij': dij,
- 'flows': f})
- >>> model = grav.Unconstrained(dt, 'origins', 'destinations', 'flows',
- ['V'], ['W'], 'Dij', 'pow')
- >>> print model.p
- {'beta': -1.0, 'W': 1.0, 'V': 1.0}
+ >>> import numpy as np
+ >>> import pysal
+ >>> from pysal.contrib.spint.gravity import BaseGravity
+ >>> db = pysal.open(pysal.examples.get_path('nyc_bikes_ct.csv'))
+ >>> cost = np.array(db.by_col('tripduration')).reshape((-1,1))
+ >>> flows = np.array(db.by_col('count')).reshape((-1,1))
+ >>> model = BaseGravity(flows, cost)
+ >>> model.params
+ array([ 0.92860101])
"""
+ def __init__(self, flows, cost, cost_func='pow', o_vars=None, d_vars=None,
+ origins=None, destinations=None, constant=False, framework='GLM',
+ SF=None, CD=None, Lag=None, Quasi=False):
+ n = User.check_arrays(flows, cost)
+ #User.check_y(flows, n)
+ self.n = n
+ self.f = flows
+ self.c = cost
+ self.ov = o_vars
+ self.dv = d_vars
+ if type(cost_func) == str:
+ if cost_func.lower() == 'pow':
+ self.cf = np.log
+ elif cost_func.lower() == 'exp':
+ self.cf = lambda x: x*1.0
+ elif (type(cost_func) == FunctionType) | (type(cost_func) == np.ufunc):
+ self.cf = cost_func
+ else:
+ raise ValueError("cost_func must be 'exp', 'pow' or a valid"
+ " function that has a scalar as a input and output")
- def __init__(self, data, origins, destinations, flows, o_factors, d_factors, cost, cost_func, filter_intra=True):
- if filter_intra:
- self.dt = data[data[origins] != data[destinations]].reset_index(level=0, drop=True)
+ y = np.reshape(self.f, (-1,1))
+ if isinstance(self, Gravity):
+ X = np.empty((self.n, 0))
else:
- self.dt = data
- self.o = self.dt[origins].astype(str)
- self.d = self.dt[destinations].astype(str)
- self.f = self.dt[flows]
- self.c = self.dt[cost]
- self.of = dict(zip(o_factors, [self.dt[x] for x in o_factors]))
- self.df = dict(zip(d_factors, [self.dt[x] for x in d_factors]))
- self.cf = cost_func
- self.opt_log = {}
- self.ip = {'beta': 0}
- self.p = {'beta': 0}
-
- if self.cf == 'exp':
- k = self.c
- elif self.cf == 'pow':
- k = np.log(self.c)
+ X = sp.csr_matrix((self.n, 1))
+ if isinstance(self, Attraction) | isinstance(self, Doubly):
+ d_dummies = spcategorical(destinations.flatten())
+ X = sphstack(X, d_dummies, array_out=False)
+ if isinstance(self, Production) | isinstance(self, Doubly):
+ o_dummies = spcategorical(origins.flatten())
+ X = sphstack(X, o_dummies, array_out=False)
+ if isinstance(self, Doubly):
+ X = X[:,1:]
+ if self.ov is not None:
+ if isinstance(self, Gravity):
+ for each in range(self.ov.shape[1]):
+ X = np.hstack((X, np.log(np.reshape(self.ov[:,each], (-1,1)))))
+ else:
+ for each in range(self.ov.shape[1]):
+ ov = sp.csr_matrix(np.log(np.reshape(self.ov[:,each], ((-1,1)))))
+ X = sphstack(X, ov, array_out=False)
+ if self.dv is not None:
+ if isinstance(self, Gravity):
+ for each in range(self.dv.shape[1]):
+ X = np.hstack((X, np.log(np.reshape(self.dv[:,each], (-1,1)))))
+ else:
+ for each in range(self.dv.shape[1]):
+ dv = sp.csr_matrix(np.log(np.reshape(self.dv[:,each], ((-1,1)))))
+ X = sphstack(X, dv, array_out=False)
+ if isinstance(self, Gravity):
+ X = np.hstack((X, self.cf(np.reshape(self.c, (-1,1)))))
else:
- raise ValueError('variable cost_func must either be "exp" or "pow"')
-
- for fx in self.of:
- k += np.log(self.of[fx])
- self.ip[fx] = 1
- self.p[fx] = 1
- for fx in self.df:
- k += np.log(self.df[fx])
- self.ip[fx] = 1
- self.p[fx] = 1
-
- o_info = np.sum(self.f*k)
-
- self.p, self.ests = calibrate(self, o_info, k)
- errors = output(self)
- self.dt['aboslute_error'] = errors[0]
- self.dt['percent_error'] = errors[1]
- stats = statistics(self)
- self.system_stats = stats[0]
- self.entropy_stats = stats[1]
- self.fit_stats = stats[2]
- self.parameter_stats = stats[3]
-
- def calc_dcy(self, c, cf, p):
- if cf == 'exp':
- dcy = np.exp(c*p['beta'])
- elif cf == 'pow':
- dcy = c**p['beta']
- return dcy
-
- def estimate_flows(self, c, cf, of, df, p):
- """
- estimate predicted flows multiplying individual model terms
- """
- dcy = self.calc_dcy(c, cf, p)
- ests = dcy
- for fx in of:
- ests *= (of[fx]**p[fx])
-
- for fx in df:
- ests *= (df[fx]**p[fx])
- return ests
-
- def estimate_cum(self, ests, k):
- """
- calculate sum of all estimated flows and log of known 'information'
- being estimated (log likelihood)
- """
- return np.sum(ests*k)
-
- def build_LL_function(self, gm, LL_fx, of, df, f, cf, beta=False):
- """
- builds model-specifc components of the LL function being evaluated
- """
- dcy = self.calc_dcy(self.c, cf, gm.p)
- fxs = dict(of.items() + df.items())
- fs = 1
- for fx in fxs:
- fs *= fxs[fx]**gm.p[fx]
- if beta:
- if cf == 'exp':
- return np.sum(fs*dcy*LL_fx) - np.sum(f*LL_fx)
+ c = sp.csr_matrix(self.cf(np.reshape(self.c, (-1,1))))
+ X = sphstack(X, c, array_out=False)
+ X = X[:,1:]#because empty array instantiated with extra column
+ if not isinstance(self, (Gravity, Production, Attraction, Doubly)):
+ X = self.cf(np.reshape(self.c, (-1,1)))
+ if SF:
+ raise NotImplementedError("Spatial filter model not yet implemented")
+ if CD:
+ raise NotImplementedError("Competing destination model not yet implemented")
+ if Lag:
+ raise NotImplementedError("Spatial Lag autoregressive model not yet implemented")
+
+ CountModel.__init__(self, y, X, constant=constant)
+ if (framework.lower() == 'glm'):
+ if not Quasi:
+ results = self.fit(framework='glm')
else:
- return np.sum(fs*dcy*np.log(LL_fx)) - np.sum(f*np.log(LL_fx))
+ results = self.fit(framework='glm', Quasi=True)
else:
- return np.sum(fs*dcy*np.log(LL_fx)) - np.sum(f*np.log(LL_fx))
-
-
-class ProductionConstrained(Unconstrained):
+ raise NotImplementedError('Only GLM is currently implemented')
+
+ self.params = results.params
+ self.yhat = results.yhat
+ self.cov_params = results.cov_params
+ self.std_err = results.std_err
+ self.pvalues = results.pvalues
+ self.tvalues = results.tvalues
+ self.deviance = results.deviance
+ self.resid_dev = results.resid_dev
+ self.llf = results.llf
+ self.llnull = results.llnull
+ self.aic = results.aic
+ self.k = results.k
+ self.D2 = results.D2
+ self.adj_D2 = results.adj_D2
+ self.pseudoR2 = results.pseudoR2
+ self.adj_pseudoR2 = results.adj_pseudoR2
+ self.results = results
+ self._cache = {}
+
+ @cache_readonly
+ def SSI(self):
+ return sorensen(self)
+
+ @cache_readonly
+ def SRMSE(self):
+ return srmse(self)
+
+ def reshape(self, array):
+ if type(array) == np.ndarray:
+ return array.reshape((-1,1))
+ elif type(array) == list:
+ return np.array(array).reshape((-1,1))
+ else:
+ raise TypeError("input must be an numpy array or list that can be coerced"
+ " into the dimensions n x 1")
+
+class Gravity(BaseGravity):
"""
- Production-constrained gravity model estimated using MLE
+ Unconstrained (traditional gravity) gravity-type spatial interaction model
Parameters
----------
- data : str
- pandas d frame
- origins : str
- column name for origin names
- destinations : str
- column name for destination names
- flows : str
- column name for observed flows
- d_factors : list of strings
- column name for each destination attribute
- cost : str
- column name for distance or cost values
- cost_func : str
- either 'exp' for exponential or 'pow' for power distance function
- filter_intra : boolean
- True (default) to filter intra-zonal flows
+ flows : array of integers
+ n x 1; observed flows between O origins and D destinations
+ cost : array
+ n x 1; cost to overcome separation between each origin and
+ destination associated with a flow; typically distance or time
+ cost_func : string or function that has scalar input and output
+ functional form of the cost function;
+ 'exp' | 'pow' | custom function
+ o_vars : array (optional)
+ n x p; p attributes for each origin of n flows; default
+ is None
+ d_vars : array (optional)
+ n x p; p attributes for each destination of n flows;
+ default is None
+ constant : boolean
+ True to include intercept in model; false by default
+ framework : string
+ estimation technique; currently only 'GLM' is avaialble
+ Quasi : boolean
+ True to estimate QuasiPoisson model; should result in same
+ parameters as Poisson but with altered covariance; default
+ to true which estimates Poisson model
+ SF : array
+ n x 1; eigenvector spatial filter to include in the model;
+ default to None which does not include a filter; not yet
+ implemented
+ CD : array
+ n x 1; competing destination term that accounts for the
+ likelihood that alternative destinations are considered
+ along with each destination under consideration for every
+ OD pair; defaults to None which does not include a CD
+ term; not yet implemented
+ Lag : W object
+ spatial weight for n observations (OD pairs) used to
+ construct a spatial autoregressive model and estimator;
+ defaults to None which does not include an autoregressive
+ term; not yet implemented
Attributes
----------
- dt : pandas DataFrame
- model input data
- o : pandas series
- origins
- d : pandas series
- destinations
- f : pandas series
- observed flows
- of : empty dict
- origin factors
- df : dict
- destination factors
- c : pandas series
- cost or distance variable
- cf : str
- either 'exp' for exponential or 'pow' for power cost function
- opt_log : dict
- record of minimization of o_function
- ip : dict
- initial parameter estimate values
- p : dict
- parameters estimates
- ests : list of floats
- estimated values for calibrated model
-
+ f : array
+ n x 1; observed flows; dependent variable; y
+ n : integer
+ number of observations
+ k : integer
+ number of parameters
+ c : array
+ n x 1; cost to overcome separation between each origin and
+ destination associated with a flow; typically distance or time
+ cf : function
+ cost function; used to transform cost variable
+ ov : array
+ n x p(o); p attributes for each origin of n flows
+ dv : array
+ n x p(d); p attributes for each destination of n flows
+ constant : boolean
+ True to include intercept in model; false by default
+ y : array
+ n x 1; dependent variable used in estimation including any
+ transformations
+ X : array
+ n x k, design matrix used in estimation
+ params : array
+ n x k, k estimated beta coefficients; k = p(o) + p(d) + 1
+ yhat : array
+ n x 1, predicted value of y (i.e., fittedvalues)
+ cov_params : array
+ Variance covariance matrix (kxk) of betas
+ std_err : array
+ k x 1, standard errors of betas
+ pvalues : array
+ k x 1, two-tailed pvalues of parameters
+ tvalues : array
+ k x 1, the tvalues of the standard errors
+ deviance : float
+ value of the deviance function evalued at params;
+ see family.py for distribution-specific deviance
+ resid_dev : array
+ n x 1, residual deviance of model
+ llf : float
+ value of the loglikelihood function evalued at params;
+ see family.py for distribution-specific loglikelihoods
+ llnull : float
+ value of the loglikelihood function evaluated with only an
+ intercept; see family.py for distribution-specific
+ loglikelihoods
+ aic : float
+ Akaike information criterion
+ D2 : float
+ percentage of explained deviance
+ adj_D2 : float
+ adjusted percentage of explained deviance
+ pseudo_R2 : float
+ McFadden's pseudo R2 (coefficient of determination)
+ adj_pseudoR2 : float
+ adjusted McFadden's pseudo R2
+ SRMSE : float
+ standardized root mean square error
+ SSI : float
+ Sorensen similarity index
+ results : object
+ Full results from estimated model. May contain addtional
+ diagnostics
Example
-------
-
- import numpy as np
- import pandas pd
- import gravity as grav
- >>> f = np.array([0, 6469, 7629, 20036, 4690,
- 6194, 11688, 2243, 8857, 7248,
- 3559, 9221, 10099, 22866, 3388,
- 9986, 46618, 11639, 1380, 5261,
- 5985, 6731, 2704, 12250, 16132])
- >>> o = np.repeat(1, 25)
- >>> d = np.array(range(1, 26))
- >>> dij = np.array([0, 576, 946, 597, 373,
- 559, 707, 1208, 602, 692,
- 681, 1934, 332, 595, 906,
- 425, 755, 672, 1587, 526,
- 484, 2141, 2182, 410, 540])
- >>> pop = np.array([1596000, 2071000, 3376000, 6978000, 1345000,
- 2064000, 2378000, 1239000, 4435000, 1999000,
- 1274000, 7042000, 834000, 1268000, 1965000,
- 1046000, 12131000, 4824000, 969000, 2401000,
- 2410000, 2847000, 1425000, 1089000, 2909000])
- >>> dt = pd.DataFrame({'origins': self.o,
- 'destinations': self.d,
- 'pop': self.pop,
- 'Dij': self.dij,
- 'flows': self.f})
- >>> model = grav.ProductionConstrained(self.dt, 'origins', 'destinations', 'flows',
- ['pop'], 'Dij', 'pow')
- {'beta': -0.7365098, 'pop': 0.7818262}
-
+ >>> import numpy as np
+ >>> import pysal
+ >>> from pysal.contrib.spint.gravity import Gravity
+ >>> db = pysal.open(pysal.examples.get_path('nyc_bikes_ct.csv'))
+ >>> cost = np.array(db.by_col('tripduration')).reshape((-1,1))
+ >>> flows = np.array(db.by_col('count')).reshape((-1,1))
+ >>> o_cap = np.array(db.by_col('o_cap')).reshape((-1,1))
+ >>> d_cap = np.array(db.by_col('d_cap')).reshape((-1,1))
+ >>> model = Gravity(flows, o_cap, d_cap, cost, 'exp')
+ >>> model.params
+ array([ 0.87911778, 0.71080687, -0.00194626])
+
"""
- def __init__(self, data, origins, destinations, flows, d_factors, cost, cost_func, filter_intra=True):
- if filter_intra:
- self.dt = data[data[origins] != data[destinations]].reset_index(level=0, drop=True)
+ def __init__(self, flows, o_vars, d_vars, cost,
+ cost_func, constant=False, framework='GLM', SF=None, CD=None,
+ Lag=None, Quasi=False):
+ self.f = np.reshape(flows, (-1,1))
+ if len(o_vars.shape) > 1:
+ p = o_vars.shape[1]
else:
- self.dt = data
- self.o = self.dt[origins].astype(str)
- self.d = self.dt[destinations].astype(str)
- self.f = self.dt[flows]
- self.c = self.dt[cost]
- self.of = {}
- self.df = dict(zip(d_factors, [self.dt[x] for x in d_factors]))
- self.cf = cost_func
- self.opt_log = {}
- self.ip = {'beta': 0}
- self.p = {'beta': 0}
- self.dt['Oi'] = total_flows(self.dt, flows, self.o)
-
- if self.cf == 'exp':
- k = self.c
- elif self.cf == 'pow':
- k = np.log(self.c)
+ p = 1
+ self.ov = np.reshape(o_vars, (-1,p))
+ if len(d_vars.shape) > 1:
+ p = d_vars.shape[1]
else:
- raise ValueError('variable cost_func must either be "exp" or "pow"')
-
- for fx in self.df:
- k += np.log(self.df[fx])
- self.ip[fx] = 1
- self.p[fx] = 1
-
- o_info = np.sum(self.f*k)
-
- self.p, self.ests = calibrate(self, o_info, k)
- errors = output(self)
- self.dt['aboslute_error'] = errors[0]
- self.dt['percent_error'] = errors[1]
- stats = statistics(self)
- self.system_stats = stats[0]
- self.entropy_stats = stats[1]
- self.fit_stats = stats[2]
- self.parameter_stats = stats[3]
-
- def estimate_flows(self, c, cf, of, df, p):
- """
- estimate predicted flows multiplying individual model terms
+ p = 1
+ self.dv = np.reshape(d_vars, (-1,p))
+ self.c = np.reshape(cost, (-1,1))
+ #User.check_arrays(self.f, self.ov, self.dv, self.c)
+
+ BaseGravity.__init__(self, self.f, self.c,
+ cost_func=cost_func, o_vars=self.ov, d_vars=self.dv, constant=constant,
+ framework=framework, SF=SF, CD=CD, Lag=Lag, Quasi=Quasi)
+
+ def local(self, loc_index, locs):
"""
- dcy = self.calc_dcy(c, cf, p)
- self.dt['Ai'] = self.calc_Ai(self.dt, self.o, df, p)
- ests = self.dt['Oi']*self.dt['Ai']*dcy
- for fx in df:
- ests *= (df[fx]**p[fx])
- return ests
-
- def calc_Ai(self, dt, o, df, p, dc=False):
+ Calibrate local models for subsets of data from a single location to all
+ other locations
+
+ Parameters
+ ----------
+ loc_index : n x 1 array of either origin or destination id label for
+ flows; must be explicitly provided for local version of
+ basic gravity model since these are not passed to the
+ global model.
+
+ locs : iterable of either origin or destination labels for which
+ to calibrate local models; must also be explicitly
+ provided since local gravity models can be calibrated from origins
+ or destinations. If all origins are also destinations and
+ a local model is desired for each location then use
+ np.unique(loc_index)
+
+ Returns
+ -------
+ results : dict where keys are names of model outputs and diagnostics
+ and values are lists of location specific values.
"""
- calculate Ai balancing factor
- """
- Ai = self.calc_dcy(self.c, self.cf, p)
-
- if df:
- for fx in df:
- Ai *= df[fx]**p[fx]
-
- if not dc:
- dt['Ai'] = Ai
- else:
- dt['Ai'] = Ai*dt['Bj']*dt['Dj']
- Ai = (dt.groupby(o).aggregate({'Ai': np.sum}))
- Ai['Ai'] = 1/Ai['Ai']
- Ai = Ai.ix[pd.match(o, Ai.index), 'Ai']
- return Ai.reset_index(level=0, drop=True)
-
- def build_LL_function(self, gm, LL_fx, of, df, f, cf, beta=False):
- """
- builds model-specifc components of the LL function being evaluated
- """
- self.dt['Ai'] = self.calc_Ai(self.dt, self.o, df, self.p)
- dcy = self.calc_dcy(self.c, cf, gm.p)
- fxs = dict(of.items() + df.items())
- fs = 1
- for fx in fxs:
- fs *= fxs[fx]**gm.p[fx]
- Ai = self.dt['Ai']
- Oi = self.dt['Oi']
- if beta:
- if cf == 'exp':
- return np.sum(Ai*Oi*fs*dcy*LL_fx) - np.sum(f*LL_fx)
- else:
- return np.sum(Ai*Oi*fs*dcy*np.log(LL_fx)) - np.sum(f*np.log(LL_fx))
- else:
- return np.sum(Ai*Oi*fs*dcy*np.log(LL_fx)) - np.sum(f*np.log(LL_fx))
-
-
-class AttractionConstrained(Unconstrained):
+ results = {}
+ covs = self.ov.shape[1] + self.dv.shape[1] + 1
+ results['aic'] = []
+ results['deviance'] = []
+ results['pseudoR2'] = []
+ results['adj_pseudoR2'] = []
+ results['D2'] = []
+ results['adj_D2'] = []
+ results['SSI'] = []
+ results['SRMSE'] = []
+ for cov in range(covs):
+ results['param' + str(cov)] = []
+ results['pvalue' + str(cov)] = []
+ results['tvalue' + str(cov)] = []
+ for loc in locs:
+ subset = loc_index == loc
+ f = self.reshape(self.f[subset])
+ o_vars = self.ov[subset.reshape(self.ov.shape[0]),:]
+ d_vars = self.dv[subset.reshape(self.dv.shape[0]),:]
+ dij = self.reshape(self.c[subset])
+ model = Gravity(f, o_vars, d_vars, dij, self.cf)
+ results['aic'].append(model.aic)
+ results['deviance'].append(model.deviance)
+ results['pseudoR2'].append(model.pseudoR2)
+ results['adj_pseudoR2'].append(model.adj_pseudoR2)
+ results['D2'].append(model.D2)
+ results['adj_D2'].append(model.adj_D2)
+ results['SSI'].append(model.SSI)
+ results['SRMSE'].append(model.SRMSE)
+ for cov in range(covs):
+ results['param' + str(cov)].append(model.params[cov])
+ results['pvalue' + str(cov)].append(model.pvalues[cov])
+ results['tvalue' + str(cov)].append(model.tvalues[cov])
+ return results
+
+class Production(BaseGravity):
"""
- Attraction-constrained gravity model estimated using MLE
-
+ Production-constrained (origin-constrained) gravity-type spatial interaction model
+
Parameters
----------
- data : str
- pandas d frame
- origins : str
- column name for origin names
- destinations : str
- column name for destination names
- flows : str
- column name for observed flows
- o_factors : list of strings
- column name for each origin attribute
- cost : str
- column name for distance or cost values
- cost_func : str
- either 'exp' for exponential or 'pow' for power distance function
- filter_intra : boolean
- True (default) to filter intra-zonal flows
+ flows : array of integers
+ n x 1; observed flows between O origins and D destinations
+ origins : array of strings
+ n x 1; unique identifiers of origins of n flows; when
+ there are many origins it will be faster to use integers
+ rather than strings for id labels.
+ cost : array
+ n x 1; cost to overcome separation between each origin and
+ destination associated with a flow; typically distance or time
+ cost_func : string or function that has scalar input and output
+ functional form of the cost function;
+ 'exp' | 'pow' | custom function
+ d_vars : array (optional)
+ n x p; p attributes for each destination of n flows;
+ default is None
+ constant : boolean
+ True to include intercept in model; false by default
+ framework : string
+ estimation technique; currently only 'GLM' is avaialble
+ Quasi : boolean
+ True to estimate QuasiPoisson model; should result in same
+ parameters as Poisson but with altered covariance; default
+ to true which estimates Poisson model
+ SF : array
+ n x 1; eigenvector spatial filter to include in the model;
+ default to None which does not include a filter; not yet
+ implemented
+ CD : array
+ n x 1; competing destination term that accounts for the
+ likelihood that alternative destinations are considered
+ along with each destination under consideration for every
+ OD pair; defaults to None which does not include a CD
+ term; not yet implemented
+ Lag : W object
+ spatial weight for n observations (OD pairs) used to
+ construct a spatial autoregressive model and estimator;
+ defaults to None which does not include an autoregressive
+ term; not yet implemented
Attributes
----------
- dt : pandas DataFrame
- model input data
- o : pandas series
- origins
- d : pandas series
- destinations
- f : pandas series
- observed flows
- of : dict
- origin factors
- df : None
- destination factors
- c : pandas series
- cost or distance variable
- cf : str
- either 'exp' for exponential or 'pow' for power cost function
- opt_log : dict
- record of minimization of o_function
- ip : dict
- initial parameter estimate values
- p : dict
- parameters estimates
- ests : list of floats
- estimated values for calibrated model
-
+ f : array
+ n x 1; observed flows; dependent variable; y
+ n : integer
+ number of observations
+ k : integer
+ number of parameters
+ c : array
+ n x 1; cost to overcome separation between each origin and
+ destination associated with a flow; typically distance or time
+ cf : function
+ cost function; used to transform cost variable
+ o : array
+ n x 1; index of origin id's
+ dv : array
+ n x p; p attributes for each destination of n flows
+ constant : boolean
+ True to include intercept in model; false by default
+ y : array
+ n x 1; dependent variable used in estimation including any
+ transformations
+ X : array
+ n x k, design matrix used in estimation
+ params : array
+ n x k, k estimated beta coefficients; k = # of origins + p + 1
+ yhat : array
+ n x 1, predicted value of y (i.e., fittedvalues)
+ cov_params : array
+ Variance covariance matrix (kxk) of betas
+ std_err : array
+ k x 1, standard errors of betas
+ pvalues : array
+ k x 1, two-tailed pvalues of parameters
+ tvalues : array
+ k x 1, the tvalues of the standard errors
+ deviance : float
+ value of the deviance function evalued at params;
+ see family.py for distribution-specific deviance
+ resid_dev : array
+ n x 1, residual deviance of model
+ llf : float
+ value of the loglikelihood function evalued at params;
+ see family.py for distribution-specific loglikelihoods
+ llnull : float
+ value of the loglikelihood function evaluated with only an
+ intercept; see family.py for distribution-specific
+ loglikelihoods
+ aic : float
+ Akaike information criterion
+ D2 : float
+ percentage of explained deviance
+ adj_D2 : float
+ adjusted percentage of explained deviance
+ pseudo_R2 : float
+ McFadden's pseudo R2 (coefficient of determination)
+ adj_pseudoR2 : float
+ adjusted McFadden's pseudo R2
+ SRMSE : float
+ standardized root mean square error
+ SSI : float
+ Sorensen similarity index
+ results : object
+ Full results from estimated model. May contain addtional
+ diagnostics
Example
-------
- import numpy as np
- import pandas as pd
- import gravity as grav
- >>> f = np.array([56, 100.8, 173.6, 235.2, 87.36,
- 28., 100.8, 69.44, 235.2, 145.6,
- 22., 26.4, 136.4, 123.2, 343.2,
- 14., 75.6, 130.2, 70.56, 163.8,
- 22, 59.4, 204.6, 110.88, 171.6])
- >>> V = np.repeat(np.array([56, 56, 44, 42, 66]), 5)
- >>> o = np.repeat(np.array(range(1, 6)), 5)
- >>> W = np.tile(np.array([10, 18, 62, 84, 78]), 5)
- >>> d = np.tile(np.array(range(1, 6)), 5)
- >>> dij = np.array([10, 10, 20, 20, 50,
- 20, 10, 50, 20, 30,
- 20, 30, 20, 30, 10,
- 30, 10, 20, 50, 20,
- 30, 20, 20, 50, 30])
- >>> dt = pd.DataFrame({'origins': self.o,
- 'destinations': self.d,
- 'V': self.V,
- 'Dij': self.dij,
- 'flows': self.f})
- >>> model = grav.AttractionConstrained(self.dt, 'origins', 'destinations', 'flows',
- ['V'], 'Dij', 'pow')
- >>> print model.p
- {'beta': -1.0, 'V': 1.0}
+ >>> import numpy as np
+ >>> import pysal
+ >>> from pysal.contrib.spint.gravity import Production
+ >>> db = pysal.open(pysal.examples.get_path('nyc_bikes_ct.csv'))
+ >>> cost = np.array(db.by_col('tripduration')).reshape((-1,1))
+ >>> flows = np.array(db.by_col('count')).reshape((-1,1))
+ >>> o = np.array(db.by_col('o_tract')).reshape((-1,1))
+ >>> d_cap = np.array(db.by_col('d_cap')).reshape((-1,1))
+ >>> model = Production(flows, o, d_cap, cost, 'exp')
+ >>> model.params[-4:]
+ array([ 5.38580065e+00, 5.00216058e+00, 8.55357745e-01,
+ -2.27444394e-03])
"""
-
- def __init__(self, data, origins, destinations, flows, o_factors, cost, cost_func, filter_intra=True):
- if filter_intra:
- self.dt = data[data[origins] != data[destinations]].reset_index(level=0, drop=True)
- else:
- self.dt = data
- self.o = self.dt[origins].astype(str)
- self.d = self.dt[destinations].astype(str)
- self.f = self.dt[flows]
- self.c = self.dt[cost]
- self.of = dict(zip(o_factors, [self.dt[x] for x in o_factors]))
- self.df = {}
- self.cf = cost_func
- self.opt_log = {}
- self.ip = {'beta': 0}
- self.p = {'beta': 0}
- self.dt['Dj'] = total_flows(self.dt, flows, self.d)
-
- if self.cf == 'exp':
- k = self.c
- elif self.cf == 'pow':
- k = np.log(self.c)
- else:
- raise ValueError('variable cost_func must either be "exp" or "pow"')
-
- for fx in self.of:
- k += np.log(self.of[fx])
- self.ip[fx] = 1
- self.p[fx] = 1
-
- o_info = np.sum(self.f*k)
-
- self.p, self.ests = calibrate(self, o_info, k)
- errors = output(self)
- self.dt['aboslute_error'] = errors[0]
- self.dt['percent_error'] = errors[1]
- stats = statistics(self)
- self.system_stats = stats[0]
- self.entropy_stats = stats[1]
- self.fit_stats = stats[2]
- self.parameter_stats = stats[3]
-
- def estimate_flows(self, c, cf, of, df, p):
- """
- estimate predicted flows multiplying individual model terms
+ def __init__(self, flows, origins, d_vars, cost, cost_func, constant=False,
+ framework='GLM', SF=None, CD=None, Lag=None, Quasi=False):
+ self.constant = constant
+ self.f = self.reshape(flows)
+ self.o = self.reshape(origins)
+
+ try:
+ if d_vars.shape[1]:
+ p = d_vars.shape[1]
+ except:
+ p = 1
+ self.dv = np.reshape(d_vars, (-1,p))
+ self.c = self.reshape(cost)
+ #User.check_arrays(self.f, self.o, self.dv, self.c)
+
+ BaseGravity.__init__(self, self.f, self.c, cost_func=cost_func, d_vars=self.dv,
+ origins=self.o, constant=constant, framework=framework,
+ SF=SF, CD=CD, Lag=Lag, Quasi=Quasi)
+
+ def local(self, locs=None):
"""
- dcy = self.calc_dcy(c, cf, p)
- self.dt['Bj'] = self.calc_Bj(self.dt, self.d, of, p)
- ests = self.dt['Dj']*self.dt['Bj']*dcy
- for fx in of:
- ests *= (of[fx]**p[fx])
- return ests
-
- def calc_Bj(self, dt, d, of, p, dc=False):
+ Calibrate local models for subsets of data from a single location to all
+ other locations
+
+ Parameters
+ ----------
+ locs : iterable of location (origins) labels; default is
+ None which calibrates a local model for each origin
+
+ Returns
+ -------
+ results : dict where keys are names of model outputs and diagnostics
+ and values are lists of location specific values
"""
- calculate Bj balancing factor
- """
- Bj = self.calc_dcy(self.c, self.cf, p)
-
- if of:
- for fx in of:
- Bj *= of[fx]**p[fx]
- if not dc:
- dt['Bj'] = Bj
- else:
- dt['Bj'] = Bj*dt['Ai']*dt['Oi']
- Bj = (dt.groupby(d).aggregate({'Bj': np.sum}))
- Bj['Bj'] = 1/Bj['Bj']
- Bj = Bj.ix[pd.match(d, Bj.index), 'Bj']
- return Bj.reset_index(level=0, drop=True)
-
- def build_LL_function(self, gm, LL_fx, of, df, f, cf, beta=False):
- """
- builds model-specifc components of the LL function being evaluated
- """
- self.dt['Bj'] = self.calc_Bj(self.dt, self.d, of, self.p)
- dcy = self.calc_dcy(self.c, cf, gm.p)
- fxs = dict(of.items() + df.items())
- fs = 1
- for fx in fxs:
- fs *= fxs[fx]**gm.p[fx]
- Bj = self.dt['Bj']
- Dj = self.dt['Dj']
- if beta:
- if cf == 'exp':
- return np.sum(Bj*Dj*fs*dcy*LL_fx) - np.sum(f*LL_fx)
- else:
- return np.sum(Bj*Dj*fs*dcy*np.log(LL_fx)) - np.sum(f*np.log(LL_fx))
- else:
- return np.sum(Bj*Dj*fs*dcy*np.log(LL_fx)) - np.sum(f*np.log(LL_fx))
-
-
-class DoublyConstrained(ProductionConstrained, AttractionConstrained):
+ results = {}
+ covs = self.dv.shape[1] + 1
+ results['aic'] = []
+ results['deviance'] = []
+ results['pseudoR2'] = []
+ results['adj_pseudoR2'] = []
+ results['D2'] = []
+ results['adj_D2'] = []
+ results['SSI'] = []
+ results['SRMSE'] = []
+ for cov in range(covs):
+ results['param' + str(cov)] = []
+ results['pvalue' + str(cov)] = []
+ results['tvalue' + str(cov)] = []
+ if locs is None:
+ locs = np.unique(self.o)
+ for loc in np.unique(locs):
+ subset = self.o == loc
+ f = self.reshape(self.f[subset])
+ o = self.reshape(self.o[subset])
+ d_vars = self.dv[subset.reshape(self.dv.shape[0]),:]
+ dij = self.reshape(self.c[subset])
+ model = Production(f, o, d_vars, dij, self.cf)
+ results['aic'].append(model.aic)
+ results['deviance'].append(model.deviance)
+ results['pseudoR2'].append(model.pseudoR2)
+ results['adj_pseudoR2'].append(model.adj_pseudoR2)
+ results['D2'].append(model.D2)
+ results['adj_D2'].append(model.adj_D2)
+ results['SSI'].append(model.SSI)
+ results['SRMSE'].append(model.SRMSE)
+ for cov in range(covs):
+ results['param' + str(cov)].append(model.params[cov])
+ results['pvalue' + str(cov)].append(model.pvalues[cov])
+ results['tvalue' + str(cov)].append(model.tvalues[cov])
+ return results
+
+class Attraction(BaseGravity):
"""
- Doubly-constrained gravity model estimated using MLE
-
+ Attraction-constrained (destination-constrained) gravity-type spatial interaction model
+
Parameters
----------
- data : str
- pandas d frame
- origins : str
- column name for origin names
- destinations : str
- column name for destination names
- flows : str
- column name for observed flows
- column name for each destination attribute
- cost : str
- column name for distance or cost values
- cost_func : str
- either 'exp' for exponential or 'pow' for power distance function
- filter_intra : boolean
- True (default) to filter intra-zonal flows
+ flows : array of integers
+ n x 1; observed flows between O origins and D destinations
+ destinations : array of strings
+ n x 1; unique identifiers of destinations of n flows; when
+ there are many destinations it will be faster to use
+ integers over strings for id labels.
+ cost : array
+ n x 1; cost to overcome separation between each origin and
+ destination associated with a flow; typically distance or time
+ cost_func : string or function that has scalar input and output
+ functional form of the cost function;
+ 'exp' | 'pow' | custom function
+ o_vars : array (optional)
+ n x p; p attributes for each origin of n flows; default
+ is None
+ constant : boolean
+ True to include intercept in model; false by default
+ y : array
+ n x 1; dependent variable used in estimation including any
+ transformations
+ X : array
+ n x k, design matrix used in estimation
+ framework : string
+ estimation technique; currently only 'GLM' is avaialble
+ Quasi : boolean
+ True to estimate QuasiPoisson model; should result in same
+ parameters as Poisson but with altered covariance; default
+ to true which estimates Poisson model
+ SF : array
+ n x 1; eigenvector spatial filter to include in the model;
+ default to None which does not include a filter; not yet
+ implemented
+ CD : array
+ n x 1; competing destination term that accounts for the
+ likelihood that alternative destinations are considered
+ along with each destination under consideration for every
+ OD pair; defaults to None which does not include a CD
+ term; not yet implemented
+ Lag : W object
+ spatial weight for n observations (OD pairs) used to
+ construct a spatial autoregressive model and estimator;
+ defaults to None which does not include an autoregressive
+ term; not yet implemented
Attributes
----------
- dt : pandas DataFrame
- model input data
- o : pandas series
- origins
- d : pandas series
- destinations
- f : pandas series
- observed flows
- of : None
- origin factors
- df : None
- destination factors
- c : pandas series
- cost or distance variable
- cf : str
- either 'exp' for exponential or 'pow' for power cost function
- opt_log : dict
- record of minimization of o_function
- ip : dict
- initial parameter estimate values
- p : dict
- parameters estimates
- ests : list of floats
- estimated values for calibrated model
-
+ f : array
+ n x 1; observed flows; dependent variable; y
+ n : integer
+ number of observations
+ k : integer
+ number of parameters
+ c : array
+ n x 1; cost to overcome separation between each origin and
+ destination associated with a flow; typically distance or time
+ cf : function
+ cost function; used to transform cost variable
+ d : array
+ n x 1; index of destination id's
+ ov : array
+ n x p; p attributes for each origin of n flows
+ constant : boolean
+ True to include intercept in model; false by default
+ params : array
+ n x k, k estimated beta coefficients; k = # of
+ destinations + p + 1
+ yhat : array
+ n x 1, predicted value of y (i.e., fittedvalues)
+ cov_params : array
+ Variance covariance matrix (kxk) of betas
+ std_err : array
+ k x 1, standard errors of betas
+ pvalues : array
+ k x 1, two-tailed pvalues of parameters
+ tvalues : array
+ k x 1, the tvalues of the standard errors
+ deviance : float
+ value of the deviance function evalued at params;
+ see family.py for distribution-specific deviance
+ resid_dev : array
+ n x 1, residual deviance of model
+ llf : float
+ value of the loglikelihood function evalued at params;
+ see family.py for distribution-specific loglikelihoods
+ llnull : float
+ value of the loglikelihood function evaluated with only an
+ intercept; see family.py for distribution-specific
+ loglikelihoods
+ aic : float
+ Akaike information criterion
+ D2 : float
+ percentage of explained deviance
+ adj_D2 : float
+ adjusted percentage of explained deviance
+ pseudo_R2 : float
+ McFadden's pseudo R2 (coefficient of determination)
+ adj_pseudoR2 : float
+ adjusted McFadden's pseudo R2
+ SRMSE : float
+ standardized root mean square error
+ SSI : float
+ Sorensen similarity index
+ results : object
+ Full results from estimated model. May contain addtional
+ diagnostics
Example
-------
- import numpy as np
- import pandas as pd
- import gravity as grav
- >>> f = np.array([0, 180048, 79223, 26887, 198144, 17995, 35563, 30528, 110792,
- 283049, 0, 300345, 67280, 718673, 55094, 93434, 87987, 268458,
- 87267, 237229, 0, 281791, 551483, 230788, 178517, 172711, 394481,
- 29877, 60681, 286580, 0, 143860, 49892, 185618, 181868, 274629,
- 130830, 382565, 346407, 92308, 0, 252189, 192223, 89389, 279739,
- 21434, 53772, 287340, 49828, 316650, 0, 141679, 27409, 87938,
- 30287, 64645, 161645, 144980, 199466, 121366, 0, 134229, 289880,
- 21450, 43749, 97808, 113683, 89806, 25574, 158006, 0, 437255,
- 72114, 133122, 229764, 165405, 266305, 66324, 252039, 342948, 0])
- >>> o = np.repeat(np.array(range(1, 10)), 9)
- >>> d = np.tile(np.array(range(1, 10)), 9)
- >>> dij = np.array([0, 219, 1009, 1514, 974, 1268, 1795, 2420, 3174,
- 219, 0, 831, 1336, 755, 1049, 1576, 2242, 2996,
- 1009, 831, 0, 505, 1019, 662, 933, 1451, 2205,
- 1514, 1336, 505, 0, 1370, 888, 654, 946, 1700,
- 974, 755, 1019, 1370, 0, 482, 1144, 2278, 2862,
- 1268, 1049, 662, 888, 482, 0, 662, 1795, 2380,
- 1795, 1576, 933, 654, 1144, 662, 0, 1287, 1779,
- 2420, 2242, 1451, 946, 2278, 1795, 1287, 0, 754,
- 3147, 2996, 2205, 1700, 2862, 2380, 1779, 754, 0])
- >>> dt = pd.DataFrame({'Origin': self.o,
- 'Destination': self.d,
- 'flows': self.f,
- 'Dij': self.dij})
- >>> model = grav.DoublyConstrained(self.dt, 'Origin', 'Destination', 'flows', 'Dij', 'exp')
- >>> print model.p
- {'beta': -0.0007369}
+ >>> import numpy as np
+ >>> import pysal
+ >>> from pysal.contrib.spint.gravity import Attraction
+ >>> db = pysal.open(pysal.examples.get_path('nyc_bikes_ct.csv'))
+ >>> cost = np.array(db.by_col('tripduration')).reshape((-1,1))
+ >>> flows = np.array(db.by_col('count')).reshape((-1,1))
+ >>> d = np.array(db.by_col('d_tract')).reshape((-1,1))
+ >>> o_cap = np.array(db.by_col('o_cap')).reshape((-1,1))
+ >>> model = Attraction(flows, d, o_cap, cost, 'exp')
+ >>> model.params[-4:]
+ array([ 5.23366116e+00, 4.89037868e+00, 8.82909095e-01,
+ -2.29081323e-03])
"""
-
- def __init__(self, data, origins, destinations, flows, cost, cost_func, filter_intra=True):
- if filter_intra:
- self.dt = data[data[origins] != data[destinations]].reset_index(level=0, drop=True)
+ def __init__(self, flows, destinations, o_vars, cost, cost_func,
+ constant=False, framework='GLM', SF=None, CD=None, Lag=None,
+ Quasi=False):
+ self.f = np.reshape(flows, (-1,1))
+ if len(o_vars.shape) > 1:
+ p = o_vars.shape[1]
else:
- self.dt = data
- self.o = self.dt[origins].astype(str)
- self.d = self.dt[destinations].astype(str)
- self.f = self.dt[flows]
- self.c = self.dt[cost]
- self.of = {}
- self.df = {}
- self.cf = cost_func
- self.opt_log = {}
- self.ip = {'beta': 0}
- self.p = {'beta': 0}
- self.dt['Bj'] = 1.0
- self.dt['Ai'] = 1.0
- self.dt['OldAi'] = 10.000000000
- self.dt['OldBj'] = 10.000000000
- self.dt['diff'] = abs((self.dt['OldAi'] - self.dt['Ai'])/self.dt['OldAi'])
- self.dt['Oi'] = total_flows(self.dt, flows, self.o)
- self.dt['Dj'] = total_flows(self.dt, flows, self.d)
-
- if self.cf == 'exp':
- k = self.c
- elif self.cf == 'pow':
- k = np.log(self.c)
- else:
- raise ValueError('variable cost_func must either be "exp" or "pow"')
-
- o_info = np.sum(self.f*k)
-
- self.p, self.ests = calibrate(self, o_info, k)
- errors = output(self)
- self.dt['aboslute_error'] = errors[0]
- self.dt['percent_error'] = errors[1]
- stats = statistics(self)
- self.system_stats = stats[0]
- self.entropy_stats = stats[1]
- self.fit_stats = stats[2]
- self.parameter_stats = stats[3]
+ p = 1
+ self.ov = np.reshape(o_vars, (-1,p))
+ self.d = np.reshape(destinations, (-1,1))
+ self.c = np.reshape(cost, (-1,1))
+ #User.check_arrays(self.f, self.d, self.ov, self.c)
- def estimate_flows(self, c, cf, of, df, p):
- """
- estimate predicted flows multiplying individual model terms
- """
- dcy = self.calc_dcy(c, cf, p)
- self.dt['Ai'], self.dt['Bj'] = self.balance_factors(self.dt, self.o, self.d, of, df, p)
- ests = self.dt['Dj']*self.dt['Bj']*self.dt['Oi']*self.dt['Ai']*dcy
- return ests
+ BaseGravity.__init__(self, self.f, self.c, cost_func=cost_func, o_vars=self.ov,
+ destinations=self.d, constant=constant,
+ framework=framework, SF=SF, CD=CD, Lag=Lag, Quasi=Quasi)
- def build_LL_function(self, gm, LL_fx, of, df, f, cf, beta=False):
+ def local(self, locs=None):
"""
- builds model-specifc components of the LL function being evaluated
+ Calibrate local models for subsets of data from a single location to all
+ other locations
+
+ Parameters
+ ----------
+ locs : iterable of location (destinations) labels; default is
+ None which calibrates a local model for each destination
+
+ Returns
+ -------
+ results : dict where keys are names of model outputs and diagnostics
+ and values are lists of location specific values
"""
- self.dt['Ai'], self.dt['Bj'] = self.balance_factors(self.dt, self.o, self.d, of, df, gm.p)
- dcy = self.calc_dcy(self.c, cf, gm.p)
- Ai = self.dt['Ai']
- Oi = self.dt['Oi']
- Bj = self.dt['Bj']
- Dj = self.dt['Dj']
- if beta:
- if cf == 'exp':
- return np.sum(Ai*Oi*Bj*Dj*dcy*LL_fx) - np.sum(f*LL_fx)
- else:
- return np.sum(dcy*np.log(LL_fx)) - np.sum(f*np.log(LL_fx))
- else:
- return np.sum(dcy*np.log(LL_fx)) - np.sum(f*np.log(LL_fx))
-
- def balance_factors(self, dt, o, d, of, df, p):
- """
- calculate balancing factors and balance the balancing factors
- if doubly-constrained model
- """
- its = 0
- cnvg = 1
- while cnvg > .0001:
- its += 1
- dt['Ai'] = self.calc_Ai(dt, o, df, p, dc=True)
- if its == 1:
- dt['OldAi'] = dt['Ai']
- else:
- dt['diff'] = abs((dt['OldAi'] - dt['Ai'])/dt['OldAi'])
- dt['OldAi'] = dt['Ai']
- dt['Bj'] = self.calc_Bj(dt, d, of, p, dc=True)
- if its == 1:
- dt['OldBj'] = dt['Bj']
- else:
- dt['diff'] = abs((dt['OldBj'] - dt['Bj'])/dt['OldBj'])
- dt['OldBj'] = dt['Bj']
- cnvg = np.sum(dt['diff'])
- return dt['Ai'], dt['Bj']
-
-
-def total_flows(dt, f, locs):
+ results = {}
+ covs = self.ov.shape[1] + 1
+ results['aic'] = []
+ results['deviance'] = []
+ results['pseudoR2'] = []
+ results['adj_pseudoR2'] = []
+ results['D2'] = []
+ results['adj_D2'] = []
+ results['SSI'] = []
+ results['SRMSE'] = []
+ for cov in range(covs):
+ results['param' + str(cov)] = []
+ results['pvalue' + str(cov)] = []
+ results['tvalue' + str(cov)] = []
+ if locs is None:
+ locs = np.unique(self.d)
+ for loc in np.unique(locs):
+ subset = self.d == loc
+ f = self.reshape(self.f[subset])
+ d = self.reshape(self.d[subset])
+ o_vars = self.ov[subset.reshape(self.ov.shape[0]),:]
+ dij = self.reshape(self.c[subset])
+ model = Attraction(f, d, o_vars, dij, self.cf)
+ results['aic'].append(model.aic)
+ results['deviance'].append(model.deviance)
+ results['pseudoR2'].append(model.pseudoR2)
+ results['adj_pseudoR2'].append(model.adj_pseudoR2)
+ results['D2'].append(model.D2)
+ results['adj_D2'].append(model.adj_D2)
+ results['SSI'].append(model.SSI)
+ results['SRMSE'].append(model.SRMSE)
+ for cov in range(covs):
+ results['param' + str(cov)].append(model.params[cov])
+ results['pvalue' + str(cov)].append(model.pvalues[cov])
+ results['tvalue' + str(cov)].append(model.tvalues[cov])
+ return results
+
+class Doubly(BaseGravity):
"""
- sum rows or columns to derive total inflows or total outflows
- """
-
- totals = dt.groupby(locs).aggregate({f: np.sum})
- return totals.ix[pd.match(locs, totals.index.astype(str))].reset_index()[f]
-
+ Doubly-constrained gravity-type spatial interaction model
+
+ Parameters
+ ----------
+ flows : array of integers
+ n x 1; observed flows between O origins and D destinations
+ origins : array of strings
+ n x 1; unique identifiers of origins of n flows; when
+ there are many origins it will be faster to use integers
+ rather than strings for id labels.
+ destinations : array of strings
+ n x 1; unique identifiers of destinations of n flows; when
+ there are many destinations it will be faster to use
+ integers rather than strings for id labels
+ cost : array
+ n x 1; cost to overcome separation between each origin and
+ destination associated with a flow; typically distance or time
+ cost_func : string or function that has scalar input and output
+ functional form of the cost function;
+ 'exp' | 'pow' | custom function
+ constant : boolean
+ True to include intercept in model; false by default
+ y : array
+ n x 1; dependent variable used in estimation including any
+ transformations
+ X : array
+ n x k, design matrix used in estimation
+ framework : string
+ estimation technique; currently only 'GLM' is avaialble
+ Quasi : boolean
+ True to estimate QuasiPoisson model; should result in same
+ parameters as Poisson but with altered covariance; default
+ to true which estimates Poisson model
+ SF : array
+ n x 1; eigenvector spatial filter to include in the model;
+ default to None which does not include a filter; not yet
+ implemented
+ CD : array
+ n x 1; competing destination term that accounts for the
+ likelihood that alternative destinations are considered
+ along with each destination under consideration for every
+ OD pair; defaults to None which does not include a CD
+ term; not yet implemented
+ Lag : W object
+ spatial weight for n observations (OD pairs) used to
+ construct a spatial autoregressive model and estimator;
+ defaults to None which does not include an autoregressive
+ term; not yet implemented
-def o_function(pv, gm, cf, of, df):
- """
- evaluates log-likelihood functions for each parameter being estimated.
- Used in optimization/calibration and statistics.
+ Attributes
+ ----------
+ f : array
+ n x 1; observed flows; dependent variable; y
+ n : integer
+ number of observations
+ k : integer
+ number of parameters
+ c : array
+ n x 1; cost to overcome separation between each origin and
+ destination associated with a flow; typically distance or time
+ cf : function
+ cost function; used to transform cost variable
+ o : array
+ n x 1; index of origin id's
+ d : array
+ n x 1; index of destination id's
+ constant : boolean
+ True to include intercept in model; false by default
+ params : array
+ n x k, estimated beta coefficients; k = # of origins + #
+ of destinations; the first x-1 values
+ pertain to the x destinations (leaving out the first
+ destination to avoid perfect collinearity; no fixed
+ effect), the next x values pertain to the x origins, and the
+ final value is the distance decay coefficient
+ yhat : array
+ n x 1, predicted value of y (i.e., fittedvalues)
+ cov_params : array
+ Variance covariance matrix (kxk) of betas
+ std_err : array
+ k x 1, standard errors of betas
+ pvalues : array
+ k x 1, two-tailed pvalues of parameters
+ tvalues : array
+ k x 1, the tvalues of the standard errors
+ deviance : float
+ value of the deviance function evalued at params;
+ see family.py for distribution-specific deviance
+ resid_dev : array
+ n x 1, residual deviance of model
+ llf : float
+ value of the loglikelihood function evalued at params;
+ see family.py for distribution-specific loglikelihoods
+ llnull : float
+ value of the loglikelihood function evaluated with only an
+ intercept; see family.py for distribution-specific
+ loglikelihoods
+ aic : float
+ Akaike information criterion
+ D2 : float
+ percentage of explained deviance
+ adj_D2 : float
+ adjusted percentage of explained deviance
+ pseudo_R2 : float
+ McFadden's pseudo R2 (coefficient of determination)
+ adj_pseudoR2 : float
+ adjusted McFadden's pseudo R2
+ SRMSE : float
+ standardized root mean square error
+ SSI : float
+ Sorensen similarity index
+ results : object
+ Full results from estimated model. May contain addtional
+ diagnostics
+ Example
+ -------
+ >>> import numpy as np
+ >>> import pysal
+ >>> from pysal.contrib.spint.gravity import Doubly
+ >>> db = pysal.open(pysal.examples.get_path('nyc_bikes_ct.csv'))
+ >>> cost = np.array(db.by_col('tripduration')).reshape((-1,1))
+ >>> flows = np.array(db.by_col('count')).reshape((-1,1))
+ >>> d = np.array(db.by_col('d_tract')).reshape((-1,1))
+ >>> o = np.array(db.by_col('o_tract')).reshape((-1,1))
+ >>> model = Doubly(flows, o, d, cost, 'exp')
+ >>> model.params[-1:]
+ array([-0.00232112])
- prepares and builds the constant terms across a model and passes it
- to build_LL_function to finish
"""
- for x, px in enumerate(gm.p):
- gm.p[px] = pv[x]
- funcs = []
- fxs = dict(of.items() + df.items())
- for fx in gm.p:
- if fx == 'beta':
- funcs.append(gm.build_LL_function(gm, gm.c, of, df, gm.f, gm.cf, beta=True))
- else:
- funcs.append(gm.build_LL_function(gm, fxs[fx], of, df, gm.f, gm.cf))
- return funcs
+ def __init__(self, flows, origins, destinations, cost, cost_func,
+ constant=False, framework='GLM', SF=None, CD=None, Lag=None,
+ Quasi=False):
+ self.f = np.reshape(flows, (-1,1))
+ self.o = np.reshape(origins, (-1,1))
+ self.d = np.reshape(destinations, (-1,1))
+ self.c = np.reshape(cost, (-1,1))
+ #User.check_arrays(self.f, self.o, self.d, self.c)
-def calibrate(gm, o_info, k):
- """
- run the main routine which estimates parameters using mle
- """
- ests = gm.estimate_flows(gm.c, gm.cf, gm.of, gm.df, gm.p)
- e_info = gm.estimate_cum(ests, k)
- its = 0
- diff = abs(e_info - o_info)
- try:
- while diff > .1:
- gm.pv = gm.p.values()
- gm.opt_log[its] = [gm.pv, diff]
- gm.pv = fsolve(o_function, gm.pv, (gm, gm.cf, gm.of, gm.df))
- ests = gm.estimate_flows(gm.c, gm.cf, gm.of, gm.df, gm.p)
- e_info = gm.estimate_cum(ests, k)
- its += 1
- diff = abs(e_info - o_info)
- if its > 25:
- print 'Convergence criterion not met, solution may not be optimal'
- break
- gm.opt_log[its] = [gm.pv, diff]
- gm.p = dict(zip(gm.p.keys(), [round(x, 7) for x in gm.pv]))
- return gm.p, ests
- except:
- raise RuntimeError('Optimization could not be carried out, check input validity')
- return gm.p, ests
-
-
-def output(gm):
- """
- prepare output
- """
- abs_err = gm.ests - gm.f
- perc_err = (abs_err/gm.f) * 100
- return abs_err, perc_err
+ BaseGravity.__init__(self, self.f, self.c, cost_func=cost_func, origins=self.o,
+ destinations=self.d, constant=constant,
+ framework=framework, SF=SF, CD=CD, Lag=Lag, Quasi=Quasi)
+ def local(self, locs=None):
+ """
+ **Not inmplemented for doubly-constrained models** Not possible due to
+ insufficient degrees of freedom.
-def statistics(gm):
- """
- calculate statistics
- """
- if 'Ai' in gm.dt.columns:
- Ai = gm.dt.Ai.values.copy()
- if 'Bj' in gm.dt.columns:
- Bj = gm.dt.Bj.values.copy()
- ests = gm.ests.values.copy()
- p = gm.p.copy()
-
- system_stats = stats.sys_stats(gm)
- entropy_stats = stats.ent_stats(gm)
- fit_stats = stats.fit_stats(gm)
- parameter_statistics = stats.param_stats(gm)
-
- if 'Ai' in gm.dt.columns:
- gm.dt.Ai = Ai
- if 'Bj' in gm.dt.columns:
- gm.dt.Bj = Bj
- gm.ests = ests
- gm.dt.ests = ests
- gm.p = p
-
- return system_stats, entropy_stats, fit_stats, parameter_statistics
\ No newline at end of file
+ Calibrate local models for subsets of data from a single location to all
+ other locations
+ """
+ raise NotImplementedError("Local models not possible for"
+ " doubly-constrained model due to insufficient degrees of freedom.")
diff --git a/pysal/contrib/spint/gravity_stats.py b/pysal/contrib/spint/gravity_stats.py
deleted file mode 100644
index d43c9a0..0000000
--- a/pysal/contrib/spint/gravity_stats.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# coding=utf-8
-"""
-Statistics for gravity models
-
-References
-----------
-
-Fotheringham, A. S. and O'Kelly, M. E. (1989). Spatial Interaction Models: Formulations
- and Applications. London: Kluwer Academic Publishers.
-
-Williams, P. A. and A. S. Fotheringham (1984), The Calibration of Spatial Interaction
- Models by Maximum Likelihood Estimation with Program SIMODEL, Geographic Monograph
- Series, 7, Department of Geography, Indiana University.
-
-Wilson, A. G. (1967). A statistical theory of spatial distribution models.
- Transportation Research, 1, 253–269.
-
-
-"""
-
-__author__ = "Taylor Oshan tayoshan at gmail.com"
-
-import pandas as pd
-import numpy as np
-from scipy.stats.stats import pearsonr
-import gravity as gv
-
-
-def sys_stats(gm):
- """
- calculate descriptive statistics of model system
- """
- system_stats = {}
- num_origins = len(gm.o.unique())
- system_stats['num_origins'] = num_origins
- num_destinations = len(gm.d.unique())
- system_stats['num_destinations'] = num_destinations
- pairs = len(gm.dt)
- system_stats['OD_pairs'] = pairs
- observed_flows = np.sum(gm.f)
- system_stats['observed_flows'] = observed_flows
- predicted_flows = np.sum(gm.ests)
- system_stats['predicted_flows'] = predicted_flows
- avg_dist = round(np.sum(gm.c)*float(1)/pairs)
- system_stats['avg_dist'] = avg_dist
- avg_dist_trav = round((np.sum(gm.f*gm.c))*float(1)/np.sum(gm.f)*float(1))
- system_stats['avg_dist_trav'] = avg_dist_trav
- obs_mean_trip_len = (np.sum(gm.f*gm.c))*float(1)/observed_flows*float(1)
- system_stats['obs_mean_trip_len'] = obs_mean_trip_len
- pred_mean_trip_len = (np.sum(gm.ests*gm.c))/predicted_flows
- system_stats['pred_mean_trip_len'] = pred_mean_trip_len
- return system_stats
-
-def ent_stats(gm):
- """
- calculate the entropy statistics for the model system
- """
- entropy_stats = {}
- pij = gm.f/np.sum(gm.f)
- phatij = gm.ests/np.sum(gm.f)
- max_ent = round(np.log(len(gm.dt)), 4)
- entropy_stats['maximum_entropy'] = max_ent
- pred_ent = round(-np.sum(phatij*np.log(phatij)), 4)
- entropy_stats['predicted_entropy'] = pred_ent
- obs_ent = round(-np.sum(pij*np.log(pij)), 4)
- entropy_stats['observed_entropy'] = obs_ent
- diff_pred_ent = round(max_ent - pred_ent, 4)
- entropy_stats['max_pred_deviance'] = diff_pred_ent
- diff_obs_ent = round(max_ent - obs_ent, 4)
- entropy_stats['max_obs_deviance'] = diff_obs_ent
- diff_ent = round(pred_ent - obs_ent, 4)
- entropy_stats['pred_obs_deviance'] = diff_ent
- ent_rs = round(diff_pred_ent/diff_obs_ent, 4)
- entropy_stats['entropy_ratio'] = ent_rs
- obs_flows = np.sum(gm.f)
- var_pred_ent = round(((np.sum(phatij*(np.log(phatij)**2))-pred_ent**2)/obs_flows) + ((len(gm.dt)-1)/(2*obs_flows**2)), 11)
- entropy_stats['variance_pred_entropy'] = var_pred_ent
- var_obs_ent = round(((np.sum(pij*np.log(pij)**2)-obs_ent**2)/obs_flows) + ((len(gm.dt)-1)/(2*obs_flows**2)), 11)
- entropy_stats['variance_obs_entropy'] = var_obs_ent
- t_stat_ent = round((pred_ent-obs_ent)/((var_pred_ent+var_obs_ent)**.5), 4)
- entropy_stats['t_stat_entropy'] = t_stat_ent
- return entropy_stats
-
-def fit_stats(gm):
- """
- calculate the goodness-of-fit statistics
- """
- fit_stats = {}
- srmse = ((np.sum((gm.f-gm.ests)**2)/len(gm.dt))**.5)/(np.sum(gm.f)/len(gm.dt))
- fit_stats['srmse'] = srmse
- pearson_r = pearsonr(gm.ests, gm.f)[0]
- fit_stats['r_squared'] = pearson_r**2
- return fit_stats
-
-
-def param_stats(gm):
- """
- calculate standard errors and likelihood statistics
- """
- parameter_statistics = {}
- PV = list(gm.p.values())
- if len(PV) == 1:
- first_deriv = gv.o_function(PV, gm, gm.cf, gm.of, gm.df)
- recalc_fd = gv.o_function([PV[0]+.001], gm, gm.cf, gm.of, gm.df)
- diff = first_deriv[0]-recalc_fd[0]
- second_deriv = -(1/(diff/.001))
- gm.p['beta'] = PV
- parameter_statistics['beta'] = {}
- parameter_statistics['beta']['standard_error'] = np.sqrt(second_deriv)
- elif len(PV) > 1:
- var_matrix = np.zeros((len(PV),len(PV)))
- for x, param in enumerate(PV):
- first_deriv = gv.o_function(PV, gm, gm.cf, gm.of, gm.df)
- var_params = list(PV)
- var_params[x] += .001
- var_matrix[x] = gv.o_function(var_params, gm, gm.cf, gm.of, gm.df)
- var_matrix[x] = (first_deriv-var_matrix[x])/.001
- errors = np.sqrt(-np.linalg.inv(var_matrix).diagonal())
- for x, param in enumerate(gm.p):
- parameter_statistics[param] = {'standard_error': errors[x]}
-
- LL = np.sum((gm.f/np.sum(gm.f))*np.log((gm.ests/np.sum(gm.ests))))
- parameter_statistics['all_params'] = {}
- parameter_statistics['all_params']['mle_vals_LL'] = LL
- new_PV = list(PV)
- for x, param in enumerate(gm.p):
- new_PV[x] = 0
- gv.o_function(new_PV, gm, gm.cf, gm.of, gm.df)
- LL_ests = gm.estimate_flows(gm.c, gm.cf, gm.of, gm.df, gm.p)
- new_LL = np.sum((gm.f/np.sum(gm.f))*np.log((LL_ests/np.sum(LL_ests))))
- parameter_statistics[param]['LL_zero_val'] = new_LL
- lamb = 2*np.sum(gm.f)*(LL-new_LL)
- parameter_statistics[param]['relative_likelihood_stat'] = lamb
- new_PV = list(PV)
- for x, param in enumerate(PV):
- new_PV[x] = 0
- gv.o_function(new_PV, gm, gm.cf, gm.of, gm.df)
- LL_ests = gm.estimate_flows(gm.c, gm.cf, gm.of, gm.df, gm.p)
- LL_zero = np.sum((gm.f/np.sum(gm.f))*np.log((LL_ests/np.sum(LL_ests))))
- parameter_statistics['all_params']['zero_vals_LL'] = LL_zero
- return parameter_statistics
-
-
-
diff --git a/pysal/contrib/spint/notebooks/4d_distance.ipynb b/pysal/contrib/spint/notebooks/4d_distance.ipynb
new file mode 100644
index 0000000..82834ea
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/4d_distance.ipynb
@@ -0,0 +1,531 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from scipy.spatial import distance\n",
+ "import scipy.spatial as spatial\n",
+ "from pysal.weights import W\n",
+ "from pysal.weights.util import isKDTree\n",
+ "from pysal.weights import Distance as Distance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 373,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def nplinalg():\n",
+ " np.linalg.norm(np.array((67, 46, 92, 67))-np.array((44, 97, 25, 50)))\n",
+ " np.linalg.norm(np.array((67, 46, 92, 67))-np.array((84, 37, 66, 53)))\n",
+ " np.linalg.norm(np.array((67, 46, 92, 67))-np.array((30, 46, 23, 80)))\n",
+ " \n",
+ " np.linalg.norm(np.array((44, 97, 25, 50))-np.array((67, 46, 92, 67)))\n",
+ " np.linalg.norm(np.array((44, 97, 25, 50))-np.array((84, 37, 66, 53)))\n",
+ " np.linalg.norm(np.array((44, 97, 25, 50))-np.array((30, 46, 23, 80)))\n",
+ " \n",
+ " np.linalg.norm(np.array((84, 37, 66, 53))-np.array((44, 97, 25, 50)))\n",
+ " np.linalg.norm(np.array((84, 37, 66, 53))-np.array((30, 46, 23, 80)))\n",
+ " np.linalg.norm(np.array((84, 37, 66, 53))-np.array((67, 46, 92, 67)))\n",
+ " \n",
+ " np.linalg.norm(np.array((30, 46, 23, 80))-np.array((67, 46, 92, 67)))\n",
+ " np.linalg.norm(np.array((30, 46, 23, 80))-np.array((44, 97, 25, 50)))\n",
+ " np.linalg.norm(np.array((30, 46, 23, 80))-np.array((30, 46, 23, 80)))\n",
+ " \n",
+ "def eucdist():\n",
+ " distance.euclidean(np.array((67, 46, 92, 67)),np.array((44, 97, 25, 50)))\n",
+ " distance.euclidean(np.array((67, 46, 92, 67)),np.array((84, 37, 66, 53)))\n",
+ " distance.euclidean(np.array((67, 46, 92, 67)),np.array((30, 46, 23, 80)))\n",
+ " \n",
+ " distance.euclidean(np.array((44, 97, 25, 50)),np.array((67, 46, 92, 67)))\n",
+ " distance.euclidean(np.array((44, 97, 25, 50)),np.array((84, 37, 66, 53)))\n",
+ " distance.euclidean(np.array((44, 97, 25, 50)),np.array((30, 46, 23, 80)))\n",
+ " \n",
+ " distance.euclidean(np.array((84, 37, 66, 53)),np.array((44, 97, 25, 50)))\n",
+ " distance.euclidean(np.array((84, 37, 66, 53)),np.array((30, 46, 23, 80)))\n",
+ " distance.euclidean(np.array((84, 37, 66, 53)),np.array((67, 46, 92, 67)))\n",
+ " \n",
+ " distance.euclidean(np.array((30, 46, 23, 80)),np.array((67, 46, 92, 67)))\n",
+ " distance.euclidean(np.array((30, 46, 23, 80)),np.array((44, 97, 25, 50)))\n",
+ " distance.euclidean(np.array((30, 46, 23, 80)),np.array((30, 46, 23, 80)))\n",
+ " \n",
+ "def distpython():\n",
+ " dist(np.array((67, 46, 92, 67)),np.array((44, 97, 25, 50)))\n",
+ " dist(np.array((67, 46, 92, 67)),np.array((84, 37, 66, 53)))\n",
+ " dist(np.array((67, 46, 92, 67)),np.array((30, 46, 23, 80)))\n",
+ " \n",
+ " dist(np.array((44, 97, 25, 50)),np.array((67, 46, 92, 67)))\n",
+ " dist(np.array((44, 97, 25, 50)),np.array((84, 37, 66, 53)))\n",
+ " dist(np.array((44, 97, 25, 50)),np.array((30, 46, 23, 80)))\n",
+ " \n",
+ " dist(np.array((84, 37, 66, 53)),np.array((44, 97, 25, 50)))\n",
+ " dist(np.array((84, 37, 66, 53)),np.array((30, 46, 23, 80)))\n",
+ " dist(np.array((84, 37, 66, 53)),np.array((67, 46, 92, 67)))\n",
+ " \n",
+ " dist(np.array((30, 46, 23, 80)),np.array((67, 46, 92, 67)))\n",
+ " dist(np.array((30, 46, 23, 80)),np.array((44, 97, 25, 50)))\n",
+ " dist(np.array((30, 46, 23, 80)),np.array((30, 46, 23, 80)))\n",
+ "\n",
+ "def scpkdtree():\n",
+ " data = zip(x.ravel(), y.ravel(), w.ravel(), z.ravel())\n",
+ " tree = spatial.KDTree(data)\n",
+ " W = pysal.weights.DistanceBand(tree, threshold=9999, alpha=-1.5, binary=False)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 374,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The slowest run took 144.28 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+ "10000 loops, best of 3: 86.8 µs per loop\n",
+ "The slowest run took 17.86 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+ "10000 loops, best of 3: 82.1 µs per loop\n",
+ "The slowest run took 5.82 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+ "1 loop, best of 3: 40min 50s per loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "%timeit nplinalg()\n",
+ "%timeit distpython()\n",
+ "%timeit scpkdtree()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 371,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-371-25b6bf1123d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u0 [...]
+ "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pysal/weights/Distance.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, threshold, p, alpha, binary, ids)\u001b[0m\n\u001b[1;32m 472\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 473\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_band\u001b[0m\u001b[0;34m(\u001b [...]
+ "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pysal/weights/Distance.pyc\u001b[0m in \u001b[0;36m_distance_to_W\u001b[0;34m(self, ids)\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m:\u001b[0 [...]
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "x = np.random.randint(1,1000, 3000)\n",
+ "y = np.random.randint(1,1000, 3000)\n",
+ "w = np.random.randint(1,1000, 3000)\n",
+ "z = np.random.randint(1,1000, 3000)\n",
+ "\n",
+ "data = zip(x.ravel(), y.ravel(), w.ravel(), z.ravel())\n",
+ "tree = spatial.KDTree(data)\n",
+ "W = pysal.weights.DistanceBand(tree, threshold=9999, alpha=-1.5, binary=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "class DistanceBand(W):\n",
+ " \"\"\"\n",
+ " Spatial weights based on distance band.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ "\n",
+ " data : array\n",
+ " (n,k) or KDTree where KDtree.data is array (n,k)\n",
+ " n observations on k characteristics used to measure\n",
+ " distances between the n objects\n",
+ " threshold : float\n",
+ " distance band\n",
+ " p : float\n",
+ " Minkowski p-norm distance metric parameter:\n",
+ " 1<=p<=infinity\n",
+ " 2: Euclidean distance\n",
+ " 1: Manhattan distance\n",
+ " binary : boolean\n",
+ " If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0\n",
+ " If false wij=dij^{alpha}\n",
+ " alpha : float\n",
+ " distance decay parameter for weight (default -1.0)\n",
+ " if alpha is positive the weights will not decline with\n",
+ " distance. If binary is True, alpha is ignored\n",
+ "\n",
+ " ids : list\n",
+ " values to use for keys of the neighbors and weights dicts\n",
+ "\n",
+ " Attributes\n",
+ " ----------\n",
+ " weights : dict\n",
+ " of neighbor weights keyed by observation id\n",
+ "\n",
+ " neighbors : dict\n",
+ " of neighbors keyed by observation id\n",
+ "\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, data, threshold=None, p=2, alpha=-1.0, binary=True, ids=None):\n",
+ " \"\"\"Casting to floats is a work around for a bug in scipy.spatial.\n",
+ " See detail in pysal issue #126.\n",
+ "\n",
+ " \"\"\"\n",
+ " if isKDTree(data):\n",
+ " self.kd = data\n",
+ " self.data = self.kd.data\n",
+ " else:\n",
+ " try:\n",
+ " data = np.asarray(data)\n",
+ " if data.dtype.kind != 'f':\n",
+ " data = data.astype(float)\n",
+ " self.data = data\n",
+ " self.kd = KDTree(self.data)\n",
+ " except:\n",
+ " raise ValueError(\"Could not make array from data\")\n",
+ "\n",
+ " self.p = p\n",
+ " self.threshold = threshold\n",
+ " self.binary = binary\n",
+ " self.alpha = alpha\n",
+ " self._band()\n",
+ " neighbors, weights = self._distance_to_W(ids)\n",
+ " W.__init__(self, neighbors, weights, ids)\n",
+ "\n",
+ " def _band(self):\n",
+ " \"\"\"Find all pairs within threshold.\n",
+ "\n",
+ " \"\"\"\n",
+ " if self.threshold is not None:\n",
+ " self.dmat = self.kd.sparse_distance_matrix(\n",
+ " self.kd, max_distance=self.threshold)\n",
+ " else:\n",
+ " self.dmat = np.array(distance_matrix(self.data, self.data))\n",
+ "\n",
+ " def _distance_to_W(self, ids=None):\n",
+ " if ids:\n",
+ " ids = np.array(ids)\n",
+ " else:\n",
+ " ids = np.arange(self.dmat.shape[0])\n",
+ " neighbors = dict([(i,[]) for i in ids])\n",
+ " weights = dict([(i,[]) for i in ids])\n",
+ " if self.binary:\n",
+ " for key,weight in self.dmat.items():\n",
+ " i,j = key\n",
+ " if i != j:\n",
+ " if j not in neighbors[i]:\n",
+ " weights[i].append(1)\n",
+ " neighbors[i].append(j)\n",
+ " if i not in neighbors[j]:\n",
+ " weights[j].append(1)\n",
+ " neighbors[j].append(i)\n",
+ "\n",
+ " else:\n",
+ " weighted = np.array(map(lambda x: pow(x, -1.5), self.dmat))\n",
+ " print weighted.shape\n",
+ " rows, cols =self.dmat.shape\n",
+ " for i in range(rows):\n",
+ " for j in range(cols):\n",
+ " if i != j:\n",
+ " if j not in neighbors[i]:\n",
+ " weights[i].append(weighted[i,j])\n",
+ " neighbors[i].append(j)\n",
+ " if i not in neighbors[j]:\n",
+ " weights[j].append(weighted[i,j])\n",
+ " neighbors[j].append(i)\n",
+ "\n",
+ " return neighbors, weights\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "x = np.random.randint(1,1000, 500)\n",
+ "y = np.random.randint(1,1000, 500)\n",
+ "w = np.random.randint(1,1000, 500)\n",
+ "z = np.random.randint(1,1000, 500)\n",
+ "\n",
+ "data = zip(x.ravel(), y.ravel(), w.ravel(), z.ravel())\n",
+ "tree = spatial.KDTree(data)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 loop, best of 3: 7.73 s per loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "%timeit DistanceBand(tree, threshold=9999, alpha=-1.5, binary=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " "
+ ]
+ }
+ ],
+ "source": [
+ "%prun DistanceBand(tree, threshold=9999, alpha=-1.5, binary=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "weight = DistanceBand(tree, threshold=9999, alpha=-1.5, binary=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "pairs = weight.dmat.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from scipy.spatial import distance_matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "one = np.hstack([x.reshape((-1,1)),y.reshape((-1,1))])\n",
+ "two = np.hstack([w.reshape((-1,1)),z.reshape((-1,1))])\n",
+ "test = np.hstack([x.reshape((-1,1)),y.reshape((-1,1)), w.reshape((-1,1)),z.reshape((-1,1))])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(500, 500)"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mat = distance_matrix(data, data)\n",
+ "mat = np.array(mat)\n",
+ "mat.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(500, 500)\n",
+ "CPU times: user 1.82 s, sys: 87 ms, total: 1.91 s\n",
+ "Wall time: 1.85 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<__main__.DistanceBand at 0x123f3c450>"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time DistanceBand(tree, alpha=-1.5, binary=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 8.43 s, sys: 92.3 ms, total: 8.52 s\n",
+ "Wall time: 8.5 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<pysal.weights.Distance.DistanceBand at 0x123f38250>"
+ ]
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time Distance.DistanceBand(tree, threshold=9999999, alpha=-1.5, binary=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(500, 500)\n"
+ ]
+ }
+ ],
+ "source": [
+ "W_new = DistanceBand(tree, alpha=-1.5, binary=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "W_old = Distance.DistanceBand(tree, threshold=9999999, alpha=-1.5, binary=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.allclose(W_new.full()[0], W_old.full()[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/Gaussian_SAR.ipynb b/pysal/contrib/spint/notebooks/Gaussian_SAR.ipynb
new file mode 100644
index 0000000..6a66bab
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/Gaussian_SAR.ipynb
@@ -0,0 +1,791 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 920,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import os\n",
+ "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/spint')\n",
+ "from gravity import Gravity, Production, Attraction, Doubly, BaseGravity\n",
+ "import statsmodels.formula.api as smf\n",
+ "from statsmodels.api import families\n",
+ "import pysal.spreg.ml_lag as ml_lag\n",
+ "import pysal\n",
+ "\n",
+ "os.chdir('/Users/toshan/dev/pysal/pysal/weights')\n",
+ "from spintW import ODW\n",
+ "from pysal.spreg.twosls_sp import GM_Lag, BaseGM_Lag\n",
+ "from pysal.spreg.twosls import BaseTSLS\n",
+ "from pysal.spreg.utils import set_endog"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 921,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def m_inverse_prod(w, data, scalar, post_multiply=False, inv_method=\"power_exp\", threshold=0.0000000001, max_iterations=None):\n",
+ " \"\"\" \n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ "\n",
+ " w : Pysal W object\n",
+ " nxn Pysal spatial weights object \n",
+ "\n",
+ " data : Numpy array\n",
+ " nx1 vector of data\n",
+ "\n",
+ " scalar : float\n",
+ " Scalar value (typically rho or lambda)\n",
+ "\n",
+ " post_multiply : boolean\n",
+ " If True then post-multiplies the data vector by the\n",
+ " inverse of the spatial filter, if false then\n",
+ " pre-multiplies.\n",
+ " \n",
+ " threshold : float\n",
+ " Test value to stop the iterations. Test is against\n",
+ " sqrt(increment' * increment), where increment is a\n",
+ " vector representing the contribution from each\n",
+ " iteration.\n",
+ "\n",
+ " max_iterations : integer\n",
+ " Maximum number of iterations for the expansion. \n",
+ "\n",
+ " Examples\n",
+ " --------\n",
+ " \"\"\"\n",
+ " N = data.shape[0]\n",
+ " matrix = la.inv(np.eye(N))\n",
+ " try:\n",
+ " for i in range(len(w)):\n",
+ " matrix -= (scalar[i] * w[i].full()[0])\n",
+ " except:\n",
+ " for i in range(len(w)):\n",
+ " matrix -= (scalar[i] * w[i])\n",
+ " if post_multiply:\n",
+ " inv_prod = spdot(data.T, np.asarray(matrix))\n",
+ " else:\n",
+ " inv_prod = spdot(np.asarray(matrix), data)\n",
+ " return inv_prod\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "import numpy as np\n",
+ "import numpy.linalg as la\n",
+ "from scipy import sparse as sp\n",
+ "from scipy.sparse.linalg import splu as SuperLU\n",
+ "import pysal as ps\n",
+ "from pysal.spreg.utils import RegressionPropsY, RegressionPropsVM, inverse_prod, spdot\n",
+ "import pysal.spreg.diagnostics as DIAG\n",
+ "import pysal.spreg.user_output as USER\n",
+ "import pysal.spreg.summary_output as SUMMARY\n",
+ "from pysal.spreg.w_utils import symmetrize\n",
+ "try:\n",
+ " from scipy.optimize import minimize_scalar\n",
+ " from scipy.optimize import minimize\n",
+ " minimize_scalar_available = True\n",
+ "except ImportError:\n",
+ " minimize_scalar_available = False\n",
+ "\n",
+ "__all__ = [\"ML_Lag\"]\n",
+ "\n",
+ "\n",
+ "class BaseML_Lag(RegressionPropsY, RegressionPropsVM):\n",
+ "\n",
+ " \"\"\"\n",
+ " ML estimation of the spatial lag model (note no consistency\n",
+ " checks, diagnostics or constants added); Anselin (1988) [Anselin1988]_\n",
+ " Parameters\n",
+ " ----------\n",
+ " y : array\n",
+ " nx1 array for dependent variable\n",
+ " x : array\n",
+ " Two dimensional array with n rows and one column for each\n",
+ " independent (exogenous) variable, excluding the constant\n",
+ " w : pysal W object\n",
+ " Spatial weights object\n",
+ " method : string\n",
+ " if 'full', brute force calculation (full matrix expressions)\n",
+ " if 'ord', Ord eigenvalue method\n",
+ " if 'LU', LU sparse matrix decomposition\n",
+ " epsilon : float\n",
+ " tolerance criterion in mimimize_scalar function and inverse_product\n",
+ " Attributes\n",
+ " ----------\n",
+ " betas : array\n",
+ " (k+1)x1 array of estimated coefficients (rho first)\n",
+ " rho : float\n",
+ " estimate of spatial autoregressive coefficient\n",
+ " u : array\n",
+ " nx1 array of residuals\n",
+ " predy : array\n",
+ " nx1 array of predicted y values\n",
+ " n : integer\n",
+ " Number of observations\n",
+ " k : integer\n",
+ " Number of variables for which coefficients are estimated\n",
+ " (including the constant, excluding the rho)\n",
+ " y : array\n",
+ " nx1 array for dependent variable\n",
+ " x : array\n",
+ " Two dimensional array with n rows and one column for each\n",
+ " independent (exogenous) variable, including the constant\n",
+ " method : string\n",
+ " log Jacobian method\n",
+ " if 'full': brute force (full matrix computations)\n",
+ " if 'ord' : Ord eigenvalue method\n",
+ " epsilon : float\n",
+ " tolerance criterion used in minimize_scalar function and inverse_product\n",
+ " mean_y : float\n",
+ " Mean of dependent variable\n",
+ " std_y : float\n",
+ " Standard deviation of dependent variable\n",
+ " vm : array\n",
+ " Variance covariance matrix (k+1 x k+1)\n",
+ " vm1 : array\n",
+ " Variance covariance matrix (k+2 x k+2) includes sigma2\n",
+ " sig2 : float\n",
+ " Sigma squared used in computations\n",
+ " logll : float\n",
+ " maximized log-likelihood (including constant terms)\n",
+ " predy_e : array\n",
+ " predicted values from reduced form\n",
+ " e_pred : array\n",
+ " prediction errors using reduced form predicted values\n",
+ " Examples\n",
+ " --------\n",
+ " >>> import numpy as np\n",
+ " >>> import pysal as ps\n",
+ " >>> db = ps.open(ps.examples.get_path(\"baltim.dbf\"),'r')\n",
+ " >>> ds_name = \"baltim.dbf\"\n",
+ " >>> y_name = \"PRICE\"\n",
+ " >>> y = np.array(db.by_col(y_name)).T\n",
+ " >>> y.shape = (len(y),1)\n",
+ " >>> x_names = [\"NROOM\",\"NBATH\",\"PATIO\",\"FIREPL\",\"AC\",\"GAR\",\"AGE\",\"LOTSZ\",\"SQFT\"]\n",
+ " >>> x = np.array([db.by_col(var) for var in x_names]).T\n",
+ " >>> x = np.hstack((np.ones((len(y),1)),x))\n",
+ " >>> ww = ps.open(ps.examples.get_path(\"baltim_q.gal\"))\n",
+ " >>> w = ww.read()\n",
+ " >>> ww.close()\n",
+ " >>> w.transform = 'r'\n",
+ " >>> w_name = \"baltim_q.gal\"\n",
+ " >>> mllag = BaseML_Lag(y,x,w,method='ord') #doctest: +SKIP\n",
+ " >>> \"{0:.6f}\".format(mllag.rho) #doctest: +SKIP\n",
+ " '0.425885'\n",
+ " >>> np.around(mllag.betas, decimals=4) #doctest: +SKIP\n",
+ " array([[ 4.3675],\n",
+ " [ 0.7502],\n",
+ " [ 5.6116],\n",
+ " [ 7.0497],\n",
+ " [ 7.7246],\n",
+ " [ 6.1231],\n",
+ " [ 4.6375],\n",
+ " [-0.1107],\n",
+ " [ 0.0679],\n",
+ " [ 0.0794],\n",
+ " [ 0.4259]])\n",
+ " >>> \"{0:.6f}\".format(mllag.mean_y) #doctest: +SKIP\n",
+ " '44.307180'\n",
+ " >>> \"{0:.6f}\".format(mllag.std_y) #doctest: +SKIP\n",
+ " '23.606077'\n",
+ " >>> np.around(np.diag(mllag.vm1), decimals=4) #doctest: +SKIP\n",
+ " array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,\n",
+ " 2.8684, 0.0026, 0.0002, 0.0266, 0.0032, 220.1292])\n",
+ " >>> np.around(np.diag(mllag.vm), decimals=4) #doctest: +SKIP\n",
+ " array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,\n",
+ " 2.8684, 0.0026, 0.0002, 0.0266, 0.0032])\n",
+ " >>> \"{0:.6f}\".format(mllag.sig2) #doctest: +SKIP\n",
+ " '151.458698'\n",
+ " >>> \"{0:.6f}\".format(mllag.logll) #doctest: +SKIP\n",
+ " '-832.937174'\n",
+ " >>> mllag = BaseML_Lag(y,x,w) #doctest: +SKIP\n",
+ " >>> \"{0:.6f}\".format(mllag.rho) #doctest: +SKIP\n",
+ " '0.425885'\n",
+ " >>> np.around(mllag.betas, decimals=4) #doctest: +SKIP\n",
+ " array([[ 4.3675],\n",
+ " [ 0.7502],\n",
+ " [ 5.6116],\n",
+ " [ 7.0497],\n",
+ " [ 7.7246],\n",
+ " [ 6.1231],\n",
+ " [ 4.6375],\n",
+ " [-0.1107],\n",
+ " [ 0.0679],\n",
+ " [ 0.0794],\n",
+ " [ 0.4259]])\n",
+ " >>> \"{0:.6f}\".format(mllag.mean_y) #doctest: +SKIP\n",
+ " '44.307180'\n",
+ " >>> \"{0:.6f}\".format(mllag.std_y) #doctest: +SKIP\n",
+ " '23.606077'\n",
+ " >>> np.around(np.diag(mllag.vm1), decimals=4) #doctest: +SKIP\n",
+ " array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,\n",
+ " 2.8684, 0.0026, 0.0002, 0.0266, 0.0032, 220.1292])\n",
+ " >>> np.around(np.diag(mllag.vm), decimals=4) #doctest: +SKIP\n",
+ " array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,\n",
+ " 2.8684, 0.0026, 0.0002, 0.0266, 0.0032])\n",
+ " >>> \"{0:.6f}\".format(mllag.sig2) #doctest: +SKIP\n",
+ " '151.458698'\n",
+ " >>> \"{0:.6f}\".format(mllag.logll) #doctest: +SKIP\n",
+ " '-832.937174'\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, y, x, wo, wd, ww, method='full', epsilon=0.0000001):\n",
+ " # set up main regression variables and spatial filters\n",
+ " self.y = y\n",
+ " self.x = x\n",
+ " self.n, self.k = self.x.shape\n",
+ " self.method = method\n",
+ " self.epsilon = epsilon\n",
+ " #W = w.full()[0]\n",
+ " #Wsp = w.sparse\n",
+ " oylag = ps.lag_spatial(wo, y)\n",
+ " dylag = ps.lag_spatial(wd, y)\n",
+ " wylag = ps.lag_spatial(ww, y)\n",
+ " ylag = (wo.sparse*wd.sparse*ODw.sparse)*f\n",
+ " # b0, b1, e0 and e1\n",
+ " xtx = spdot(self.x.T, self.x)\n",
+ " xtxi = la.inv(xtx)\n",
+ " xty = spdot(self.x.T, self.y)\n",
+ " xtylo = spdot(self.x.T, oylag)\n",
+ " xtyld = spdot(self.x.T, dylag)\n",
+ " xtylw = spdot(self.x.T, wylag)\n",
+ " xtyl = spdot(self.x.T, ylag)\n",
+ " b0 = np.dot(xtxi, xty)\n",
+ " b1 = np.dot(xtxi, xtylo)\n",
+ " b2 = np.dot(xtxi, xtyld)\n",
+ " b3 = np.dot(xtxi, xtylw)\n",
+ " b4 = np.dot(xtxi, xtyl)\n",
+ " e0 = self.y - spdot(x, b0)\n",
+ " e1 = oylag - spdot(x, b1)\n",
+ " e2 = dylag - spdot(x, b2)\n",
+ " e3 = wylag - spdot(x, b3)\n",
+ " e4 = ylag - spdot(x, b4)\n",
+ " methodML = method.upper()\n",
+ " # call minimizer using concentrated log-likelihood to get rho\n",
+ " if methodML in ['FULL', 'LU', 'ORD']:\n",
+ " if methodML == 'FULL':\n",
+ " Wo = wo.full()[0] # moved here\n",
+ " Wd = wd.full()[0]\n",
+ " Ww = ww.full()[0]\n",
+ " x0 = np.array([0.0, 0.0, 0.0])\n",
+ " bounds = [(-1.0, 1.0), (-1.0, 1.0), (-1.0, 1.0)]\n",
+ " res = minimize(lag_c_loglik, x0 , bounds=bounds,\n",
+ " args=(\n",
+ " self.n, e0, e1, e2, e3, e4, Wo, Wd, Ww),\n",
+ " tol=epsilon)\n",
+ " elif methodML == 'LU':\n",
+ " I = sp.identity(w.n)\n",
+ " Wsp = w.sparse # moved here\n",
+ " res = minimize_scalar(lag_c_loglik_sp, 0.0, bounds=(-1.0,1.0),\n",
+ " args=(self.n, e0, e1, I, Wsp),\n",
+ " method='bounded', tol=epsilon)\n",
+ " elif methodML == 'ORD':\n",
+ " # check on symmetry structure\n",
+ " if w.asymmetry(intrinsic=False) == []:\n",
+ " ww = symmetrize(w)\n",
+ " WW = ww.todense()\n",
+ " evals = la.eigvalsh(WW)\n",
+ " else:\n",
+ " W = w.full()[0] # moved here\n",
+ " evals = la.eigvals(W)\n",
+ " res = minimize_scalar(lag_c_loglik_ord, 0.0, bounds=(-1.0, 1.0),\n",
+ " args=(\n",
+ " self.n, e0, e1, evals), method='bounded',\n",
+ " tol=epsilon)\n",
+ " else:\n",
+ " # program will crash, need to catch\n",
+ " print(\"{0} is an unsupported method\".format(methodML))\n",
+ " self = None\n",
+ " return\n",
+ " \n",
+ " #print res.x\n",
+ " self.o_rho, self.d_rho, self.w_rho = res.x\n",
+ "\n",
+ " # compute full log-likelihood, including constants\n",
+ " ln2pi = np.log(2.0 * np.pi)\n",
+ " llik = -res.fun - self.n / 2.0 * ln2pi - self.n / 2.0\n",
+ " self.logll = llik[0][0]\n",
+ "\n",
+ " # b, residuals and predicted values\n",
+ "\n",
+ " b = b0 - self.o_rho * b1 - self.d_rho * b2 - self.w_rho *b3\n",
+ " self.betas = np.vstack((b, self.o_rho, self.d_rho, self.w_rho)) # rho added as last coefficient\n",
+ " self.u = e0 - self.o_rho * e1 - self.o_rho * e2 - self.w_rho * e3\n",
+ " self.predy = self.y - self.u\n",
+ " xb = spdot(x, b)\n",
+ "\n",
+ " self.predy_e = m_inverse_prod(\n",
+ " [wo.sparse, wd.sparse, ww.sparse], xb, [self.o_rho, self.d_rho, self.w_rho], inv_method=\"power_exp\", threshold=epsilon)\n",
+ " self.e_pred = self.y - self.predy_e\n",
+ "\n",
+ " # residual variance\n",
+ " self.sig2 = self.sig2n # no allowance for division by n-k\n",
+ " #print self.betas\n",
+ " # information matrix\n",
+ " a = -self.o_rho * Wo - self.d_rho * Wd + self.w_rho * Ww\n",
+ " W_o = -Wo - self.d_rho * Wd + self.w_rho * Ww\n",
+ " W_d = -self.o_rho * Wo - Wd + self.w_rho * Ww\n",
+ " W_w = -self.o_rho * Wo - self.d_rho * Wd + Ww\n",
+ " np.fill_diagonal(a, 1.0)\n",
+ " ai = la.inv(a)\n",
+ "\n",
+ " o_wai = np.dot(W_o, ai)\n",
+ " o_tr1 = np.trace(o_wai)\n",
+ " d_wai = np.dot(W_d, ai)\n",
+ " d_tr1 = np.trace(d_wai)\n",
+ " w_wai = np.dot(W_w, ai)\n",
+ " w_tr1 = np.trace(w_wai)\n",
+ "\n",
+ " oo_wai2 = np.dot(o_wai, o_wai)\n",
+ " oo_tr2 = np.trace(oo_wai2)\n",
+ " dd_wai2 = np.dot(d_wai, d_wai)\n",
+ " dd_tr2 = np.trace(dd_wai2)\n",
+ " ww_wai2 = np.dot(w_wai, w_wai)\n",
+ " ww_tr2 = np.trace(ww_wai2)\n",
+ " \n",
+ " od_wai2 = np.dot(o_wai, d_wai)\n",
+ " od_tr2 = np.trace(od_wai2)\n",
+ "\n",
+ " do_wai2 = np.dot(d_wai, o_wai)\n",
+ " do_tr2 = np.trace(do_wai2)\n",
+ "\n",
+ " ow_wai2 = np.dot(o_wai, w_wai)\n",
+ " ow_tr2 = np.trace(ow_wai2)\n",
+ "\n",
+ " wo_wai2 = np.dot(w_wai, o_wai)\n",
+ " wo_tr2 = np.trace(wo_wai2)\n",
+ "\n",
+ " \n",
+ " dw_wai2 = np.dot(d_wai, w_wai)\n",
+ " dw_tr2 = np.trace(dw_wai2)\n",
+ "\n",
+ " wd_wai2 = np.dot(w_wai, d_wai)\n",
+ " wd_tr2 = np.trace(wd_wai2)\n",
+ "\n",
+ " oo_waiTwai = np.dot(o_wai.T, o_wai)\n",
+ " oo_tr3 = np.trace(oo_waiTwai)\n",
+ " dd_waiTwai = np.dot(d_wai.T, d_wai)\n",
+ " dd_tr3 = np.trace(dd_waiTwai)\n",
+ " ww_waiTwai = np.dot(w_wai.T, w_wai)\n",
+ " ww_tr3 = np.trace(ww_waiTwai)\n",
+ "\n",
+ " od_waiTwai = np.dot(o_wai.T, d_wai)\n",
+ " od_tr3 = np.trace(od_waiTwai)\n",
+ "\n",
+ " do_waiTwai = np.dot(d_wai.T, o_wai)\n",
+ " do_tr3 = np.trace(do_waiTwai)\n",
+ "\n",
+ " ow_waiTwai = np.dot(o_wai.T, w_wai)\n",
+ " ow_tr3 = np.trace(ow_waiTwai)\n",
+ "\n",
+ " wo_waiTwai = np.dot(w_wai.T, o_wai)\n",
+ " wo_tr3 = np.trace(wo_waiTwai)\n",
+ "\n",
+ " dw_waiTwai = np.dot(d_wai.T, w_wai)\n",
+ " dw_tr3 = np.trace(dw_waiTwai)\n",
+ "\n",
+ " wd_waiTwai = np.dot(w_wai.T, d_wai)\n",
+ " wd_tr3 = np.trace(wd_waiTwai)\n",
+ "\n",
+ " o_wpredy = ps.lag_spatial(wo, self.predy_e)\n",
+ " d_wpredy = ps.lag_spatial(wd, self.predy_e)\n",
+ " w_wpredy = ps.lag_spatial(ww, self.predy_e)\n",
+ "\n",
+ " o_xTwpy = spdot(x.T, o_wpredy)\n",
+ " d_xTwpy = spdot(x.T, d_wpredy)\n",
+ " w_xTwpy = spdot(x.T, w_wpredy)\n",
+ "\n",
+ " oo_wpyTwpy = np.dot(o_wpredy.T, o_wpredy)\n",
+ " dd_wpyTwpy = np.dot(d_wpredy.T, d_wpredy)\n",
+ " ww_wpyTwpy = np.dot(w_wpredy.T, w_wpredy)\n",
+ " od_wpyTwpy = np.dot(o_wpredy.T, d_wpredy)\n",
+ " do_wpyTwpy = np.dot(d_wpredy.T, o_wpredy)\n",
+ " ow_wpyTwpy = np.dot(o_wpredy.T, w_wpredy)\n",
+ " wo_wpyTwpy = np.dot(w_wpredy.T, o_wpredy)\n",
+ " dw_wpyTwpy = np.dot(d_wpredy.T, w_wpredy)\n",
+ " wd_wpyTwpy = np.dot(w_wpredy.T, d_wpredy)\n",
+ "\n",
+ "\n",
+ "\n",
+ " sig2 = self.sig2\n",
+ "\n",
+ " beta_beta = xtx / sig2\n",
+ " beta_o_rho = o_xTwpy.T / sig2\n",
+ " beta_d_rho = d_xTwpy.T / sig2\n",
+ " beta_w_rho = w_xTwpy.T / sig2\n",
+ " beta_sig2 = np.zeros((1, self.k))\n",
+ "\n",
+ " o_rho_beta = o_xTwpy / sig2\n",
+ " o_rho_o_rho = oo_tr2 + oo_tr3 + oo_wpyTwpy / sig2\n",
+ " o_rho_d_rho = od_tr2 + od_tr3 + od_wpyTwpy / sig2\n",
+ " o_rho_w_rho = ow_tr2 + ow_tr3 + ow_wpyTwpy / sig2\n",
+ " o_rho_sig2 = o_tr1 / sig2\n",
+ "\n",
+ " d_rho_beta = d_xTwpy / sig2\n",
+ " d_rho_o_rho = do_tr2 + do_tr3 + do_wpyTwpy / sig2\n",
+ " d_rho_d_rho = dd_tr2 + dd_tr3 + dd_wpyTwpy / sig2\n",
+ " d_rho_w_rho = dw_tr2 + dw_tr3 + dw_wpyTwpy / sig2\n",
+ " d_rho_sig2 = d_tr1 / sig2\n",
+ "\n",
+ " w_rho_beta = w_xTwpy / sig2 \n",
+ " w_rho_o_rho = wo_tr2 + wo_tr3 + wo_wpyTwpy / sig2\n",
+ " w_rho_d_rho = wd_tr2 + wd_tr3 + wd_wpyTwpy / sig2\n",
+ " w_rho_w_rho = ww_tr2 + ww_tr3 + ww_wpyTwpy / sig2\n",
+ " w_rho_sig2 = w_tr1 / sig2\n",
+ "\n",
+ " sig2_beta = np.zeros((self.k, 1)) \n",
+ " sig2_o_rho = o_tr1 / sig2\n",
+ " sig2_d_rho = d_tr1 / sig2\n",
+ " sig2_w_rho = w_tr1 / sig2\n",
+ " sig2_sig2 = self.n / (2.0 * sig2 ** 2)\n",
+ "\n",
+ " # order of variables is beta, rho_o, rho_d, rho_w, sigma2\n",
+ "\n",
+ " \n",
+ " v1 = np.vstack((beta_beta, beta_o_rho, beta_d_rho, beta_w_rho, beta_sig2))\n",
+ " v2 = np.vstack((o_rho_beta, o_rho_o_rho, o_rho_d_rho, o_rho_w_rho, o_rho_sig2))\n",
+ " v3 = np.vstack((d_rho_beta, d_rho_o_rho, d_rho_d_rho, d_rho_w_rho, d_rho_sig2))\n",
+ " v4 = np.vstack((w_rho_beta, w_rho_d_rho, w_rho_d_rho, w_rho_w_rho, w_rho_sig2))\n",
+ " v5 = np.vstack((sig2_beta, sig2_d_rho, sig2_d_rho, sig2_w_rho, sig2_sig2))\n",
+ " \n",
+ " v = np.hstack((v1, v2, v3, v4, v5))\n",
+ "\n",
+ " self.vm1 = la.inv(v) # vm1 includes variance for sigma2\n",
+ " self.vm = self.vm1[:-1, :-1] # vm is for coefficients only\n",
+ "\n",
+ "\n",
+ "def lag_c_loglik(rho, n, e0, e1, e2, e3, e4, Wo, Wd, Ww):\n",
+ " # concentrated log-lik for lag model, no constants, brute force\n",
+ " o_rho, d_rho, w_rho = rho\n",
+ " er = e0 - (o_rho * e1) - (d_rho * e2) - (w_rho * e3)\n",
+ " sig2 = np.dot(er.T, er) / n\n",
+ " nlsig2 = (n / 2.0) * np.log(sig2)\n",
+ " a = (np.identity(n) - ((o_rho * Wo)*(d_rho * Wd)*(w_rho * Ww)))\n",
+ " np.fill_diagonal(a, 1.0)\n",
+ " jacob = np.log(np.linalg.det(a))\n",
+ " # this is the negative of the concentrated log lik for minimization\n",
+ " clik = nlsig2 - jacob\n",
+ " #print clik\n",
+ " return clik\n",
+ "\n",
+ "def lag_c_loglik_sp(rho, n, e0, e1, I, Wsp):\n",
+ " # concentrated log-lik for lag model, sparse algebra\n",
+ " if isinstance(rho, np.ndarray):\n",
+ " if rho.shape == (1,1):\n",
+ " rho = rho[0][0] #why does the interior value change?\n",
+ " er = e0 - rho * e1\n",
+ " sig2 = np.dot(er.T, er) / n\n",
+ " nlsig2 = (n / 2.0) * np.log(sig2)\n",
+ " a = I - rho * Wsp\n",
+ " LU = SuperLU(a.tocsc())\n",
+ " jacob = np.sum(np.log(np.abs(LU.U.diagonal())))\n",
+ " clike = nlsig2 - jacob\n",
+ " return clike\n",
+ "\n",
+ "def lag_c_loglik_ord(rho, n, e0, e1, evals):\n",
+ " # concentrated log-lik for lag model, no constants, Ord eigenvalue method\n",
+ " er = e0 - rho * e1\n",
+ " sig2 = np.dot(er.T, er) / n\n",
+ " nlsig2 = (n / 2.0) * np.log(sig2)\n",
+ " revals = rho * evals\n",
+ " jacob = np.log(1 - revals).sum()\n",
+ " if isinstance(jacob, complex):\n",
+ " jacob = jacob.real\n",
+ " # this is the negative of the concentrated log lik for minimization\n",
+ " clik = nlsig2 - jacob\n",
+ " return clik\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "SAR SI DGP\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " $y = (I - \\rho W) X \\beta + (I - \\rho W)\\epsilon$ \n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$X \\beta = X_{o} \\beta_{o} + X_{d} \\beta_{d} + X_{d_{ij}} \\beta_{d_{ij}}$\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " $\\rho W = (I - \\rho_{o}W_{o})(I - \\rho_{d}W_{d}) = (I - \\rho_{o}W_{o} - \\rho_{d}W_{d} - \\rho_{w}W_{w})$\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$\\rho_{w}W_{w} = \\rho_{o} \\rho_{d}W_{o}*W_{d}$\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "SAR SI Model Specification\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " $y = \\rho_{o}W_{o} + \\rho_{d}W_{d} + \\rho_{w}W_{w} + k + X \\beta + (I - \\rho W)\\epsilon$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 922,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#Simulate covariates\n",
+ "\n",
+ "o_vars = np.random.randint(5000,10000, (81,1))\n",
+ "d_vars = np.random.randint(5000,10000, (81,1))\n",
+ "dij = np.random.randint(50,1000, (81,1))\n",
+ "\n",
+ "\n",
+ "X = np.hstack([o_vars.reshape((-1,1)), d_vars.reshape((-1,1)), dij.reshape((-1,1))])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 954,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#Simulate spatial weights and compute dependent variable using DGP\n",
+ "\n",
+ "z = 7\n",
+ "n = 49\n",
+ "N = n**2\n",
+ "o_vars = np.random.randint(5000,10000, (N,1))\n",
+ "d_vars = np.random.randint(5000,10000, (N,1))\n",
+ "dij = np.random.randint(50,1000, (N,1))\n",
+ "\n",
+ "\n",
+ "X = np.hstack([o_vars.reshape((-1,1)), d_vars.reshape((-1,1)), dij.reshape((-1,1))])\n",
+ "\n",
+ "o = pysal.weights.lat2W(z, z)\n",
+ "d = pysal.weights.lat2W(z, z)\n",
+ "\n",
+ "wo = np.kron(o.full()[0], np.identity(n))\n",
+ "wo = pysal.weights.full2W(wo)\n",
+ "wo.transform = 'r'\n",
+ "wd = np.kron(np.identity(n), d.full()[0])\n",
+ "wd = pysal.weights.full2W(wd)\n",
+ "wd.transform = 'r'\n",
+ "\n",
+ "ODw = ODW(o,d)\n",
+ "ODw.transform = 'r'\n",
+ "\n",
+ "o_rho = .15\n",
+ "d_rho = .15\n",
+ "w_rho = -.3\n",
+ "\n",
+ "ar = np.array(np.linalg.inv((np.identity(N) - o_rho*wo.sparse - d_rho*wd.sparse - w_rho*ODw.sparse)))\n",
+ "\n",
+ "betas = np.vstack([1,.3,.3,-5])\n",
+ "X_constant = np.hstack([np.ones((N,1)), X])\n",
+ "\n",
+ "XB = np.dot(X_constant,betas)\n",
+ "e = np.random.normal(0, 100, (N,1))\n",
+ "\n",
+ "y = np.dot(ar, XB) + np.dot(ar, e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 955,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#Prep instruments\n",
+ "\n",
+ "w_lags=1\n",
+ "\n",
+ "o_yend, o_q = set_endog(y, X, wo, None, None, w_lags, True)\n",
+ "d_yend, d_q = set_endog(y, X, wd, None, None, w_lags, True)\n",
+ "w_yend, w_q = set_endog(y, X, ODw, None, None, w_lags, True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 959,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[-16.72171832]\n",
+ " [ 0.29949819]\n",
+ " [ 0.30055381]\n",
+ " [ -4.99972034]\n",
+ " [ 0.15083737]\n",
+ " [ 0.15049648]\n",
+ " [ -0.29155565]]\n",
+ "[ 3.49950480e+02 2.06426812e-06 2.03681447e-06 5.76503201e-05\n",
+ " 6.21546016e-06 5.76756214e-06 1.92331040e-05]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#GM_Lag via TSLS\n",
+ "\n",
+ "w = (wo.sparse, wd.sparse, ODw.sparse)\n",
+ "yend = np.hstack([o_yend, d_yend, w_yend])\n",
+ "q = np.hstack([o_q, d_q, w_q])\n",
+ "\n",
+ "model = BaseTSLS(y=y, x=X_constant, yend=yend, q=q)\n",
+ "print model.betas\n",
+ "print model.vm.diagonal()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 958,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[-12.59047756]\n",
+ " [ 0.29941335]\n",
+ " [ 0.30048807]\n",
+ " [ -4.9992496 ]\n",
+ " [ 0.15157087]\n",
+ " [ 0.15191892]\n",
+ " [ -0.29554088]]\n",
+ "[ 3.79284298e+02 2.05665963e-06 2.03571089e-06 5.85031928e-05\n",
+ " 6.51171315e-06 5.92191738e-06 2.01701685e-05]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Extended ML_Lag\n",
+ "\n",
+ "model = BaseML_Lag(y, X_constant, wo, wd, ODw)\n",
+ "print model.betas\n",
+ "print model.vm.diagonal()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 964,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2401\n"
+ ]
+ }
+ ],
+ "source": [
+ "print len(y>=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 965,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2401"
+ ]
+ },
+ "execution_count": 965,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "generate 2000\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/IV_FLOWS.ipynb b/pysal/contrib/spint/notebooks/IV_FLOWS.ipynb
new file mode 100644
index 0000000..12169ce
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/IV_FLOWS.ipynb
@@ -0,0 +1,195 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import pysal as ps\n",
+ "import os\n",
+ "os.chdir('/Users/toshan/dev/pysal/pysal/weights')\n",
+ "from spintW import ODW\n",
+ "from pysal.spreg.twosls_sp import GM_Lag, BaseGM_Lag"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#Prep Data\n",
+ "austria = pd.read_csv('http://dl.dropbox.com/u/8649795/AT_Austria.csv')\n",
+ "f = austria['Data'].values.reshape((-1,1))\n",
+ "o = austria['Origin'].values.reshape((-1,1))\n",
+ "d = austria['Destination'].values.reshape((-1,1))\n",
+ "dij = austria['Dij'].values.reshape((-1,1))\n",
+ "o_vars = austria['Oi2007'].values.reshape((-1,1))\n",
+ "d_vars = austria['Dj2007'].values.reshape((-1,1))\n",
+ "\n",
+ "Wo = ps.weights.lat2W(3,3)\n",
+ "Wd = ps.weights.lat2W(3,3)\n",
+ "Ww = ODW(Wo,Wd)\n",
+ "\n",
+ "y = f.reshape((-1,1))\n",
+ "X = np.hstack([o_vars.reshape((-1,1)), d_vars.reshape((-1,1)), dij.reshape((-1,1))])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ -2.86692386e+02],\n",
+ " [ 1.02229167e-01],\n",
+ " [ 1.02020709e-01],\n",
+ " [ -3.59211608e+00],\n",
+ " [ -3.46757679e-02]])"
+ ]
+ },
+ "execution_count": 149,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#GM Lag with OD-W\n",
+ "model = GM_Lag(y, X, w=ODw, w_lags=1)\n",
+ "model.betas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.40576692e+03],\n",
+ " [ 6.93836657e-02],\n",
+ " [ 6.03194419e-02],\n",
+ " [ -1.05386559e+01],\n",
+ " [ 3.10794995e-01]])"
+ ]
+ },
+ "execution_count": 150,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Base TSLS with OD-W\n",
+ "\n",
+ "X = np.hstack([np.ones((81,1)), o_vars.reshape((-1,1)), d_vars.reshape((-1,1)), dij.reshape((-1,1))])\n",
+ "#X = np.hstack([o_vars.reshape((-1,1)), d_vars.reshape((-1,1)), dij.reshape((-1,1))])\n",
+ "\n",
+ "Wo = ps.weights.full2W(np.kron(Wo.full()[0], np.identity(9)))\n",
+ "Wd = ps.weights.full2W(np.kron(np.identity(9), Wd.full()[0]))\n",
+ "\n",
+ "w_lags=1\n",
+ " \n",
+ "o_yend, o_q = set_endog(y, X, Wo, None, None, w_lags, True)\n",
+ "d_yend, d_q = set_endog(y, X, Wd, None, None, w_lags, True)\n",
+ "w_yend, w_q = set_endog(y, X, Ww, None, None, w_lags, True)\n",
+ "\n",
+ "w = Ww.sparse\n",
+ "yend = w_yend\n",
+ "q = w_q\n",
+ "\n",
+ "model = TSLS.BaseTSLS(y=y, x=X, yend=yend, q=q)\n",
+ "model.betas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 5.38733321e+04],\n",
+ " [ -1.33117926e+01],\n",
+ " [ -7.14188368e+00],\n",
+ " [ 3.55723696e+02],\n",
+ " [ 7.88125684e+00],\n",
+ " [ 1.20094221e+01],\n",
+ " [ 6.54489799e+01]])"
+ ]
+ },
+ "execution_count": 151,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Base TSLS with O-W, D-Wm and OD-W\n",
+ "\n",
+ "w = (Wo.sparse, Wd.sparse, Ww.sparse)\n",
+ "yend = np.hstack([o_yend, d_yend, w_yend])\n",
+ "q = np.hstack([o_q, d_q, w_q])\n",
+ "\n",
+ "model = TSLS.BaseTSLS(y=f, x=X, yend=yend, q=q)\n",
+ "model.betas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
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+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/NYC_Bike_Example.ipynb b/pysal/contrib/spint/notebooks/NYC_Bike_Example.ipynb
new file mode 100644
index 0000000..58606a7
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/NYC_Bike_Example.ipynb
@@ -0,0 +1,661 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: pylab import has clobbered these variables: ['plt', 'Polygon']\n",
+ "`%matplotlib` prevents importing * from pylab and numpy\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "os.chdir('../')\n",
+ "from gravity import Gravity, Production, Attraction, Doubly\n",
+ "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/spint')\n",
+ "import entropy as grav\n",
+ "import numpy as np\n",
+ "import scipy.stats as stats\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "import geopandas as gp\n",
+ "os.chdir('/Users/toshan/Dropbox/Data/NYC_BIKES')\n",
+ "import matplotlib.pylab as plt\n",
+ "%pylab inline\n",
+ "from descartes import PolygonPatch\n",
+ "import matplotlib as mpl\n",
+ "from mpl_toolkits.basemap import Basemap\n",
+ "import pyproj as pj\n",
+ "from shapely.geometry import Polygon, Point\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "14042 OD pairs between census tracts after filtering out intrazonal flows\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ " <th>count</th>\n",
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+ " <th>end station latitude</th>\n",
+ " <th>end station longitude</th>\n",
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+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 27 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Unnamed: 0 count d_cap d_tract distance end station latitude \\\n",
+ "0 0 5709 255 600 NaN 40.712899 \n",
+ "1 1 4010 595 600 NaN 40.712899 \n",
+ "2 2 1906 170 600 NaN 40.712899 \n",
+ "3 3 1192 255 600 NaN 40.712899 \n",
+ "4 4 484 85 600 NaN 40.712899 \n",
+ "\n",
+ " end station longitude o_cap o_tract tripduration ... \\\n",
+ "0 -73.989865 162 202 474.173605 ... \n",
+ "1 -73.989865 774 700 765.920864 ... \n",
+ "2 -73.989865 141 800 395.842085 ... \n",
+ "3 -73.989865 291 900 882.062412 ... \n",
+ "4 -73.989865 57 1002 767.284951 ... \n",
+ "\n",
+ " d_sq_foot diffs_out start end weighted total_out total_in \\\n",
+ "0 59149181.752643 28553756 202 600 0 56352 69165 \n",
+ "1 59149181.752643 28553756 700 600 0 160040 69165 \n",
+ "2 59149181.752643 28553756 800 600 0 34254 69165 \n",
+ "3 59149181.752643 28553756 900 600 0 46446 69165 \n",
+ "4 59149181.752643 28553756 1002 600 0 15916 69165 \n",
+ "\n",
+ " o_hub d_hub od_hub \n",
+ "0 hub hub hub \n",
+ "1 hub hub hub \n",
+ "2 hub hub non_hub \n",
+ "3 hub hub non_hub \n",
+ "4 hub hub non_hub \n",
+ "\n",
+ "[5 rows x 27 columns]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ct = pd.read_csv('CT_BIKE_DATA.csv')\n",
+ "ct = ct[ct['o_tract'] != ct['d_tract']]\n",
+ "\n",
+ "ct.ix[ct.o_sq_foot == 0, 'o_sq_foot'] = 1\n",
+ "ct.ix[ct.d_sq_foot == 0, 'd_sq_foot'] = 1\n",
+ "ct.ix[ct.o_cap == 0, 'o_cap'] = 1\n",
+ "ct.ix[ct.d_cap == 0, 'd_cap'] = 1\n",
+ "ct.ix[ct.o_housing == 0, 'o_housing'] = 1\n",
+ "ct.ix[ct.d_housing == 0, 'd_housing'] = 1\n",
+ "\n",
+ "flows = ct['count'].values.reshape((-1,1))\n",
+ "o_vars = np.hstack([ct['o_sq_foot'].values.reshape((-1,1)), ct['o_housing'].values.reshape((-1,1)), ct['o_cap'].values.reshape((-1,1))])\n",
+ "d_vars = np.hstack([ct['d_sq_foot'].values.reshape((-1,1)), ct['d_housing'].values.reshape((-1,1)), ct['d_cap'].values.reshape((-1,1))])\n",
+ "cost = ct['tripduration'].values.reshape((-1,1))\n",
+ "o = ct['o_tract'].astype(str).values.reshape((-1,1))\n",
+ "d = ct['d_tract'].astype(str).values.reshape((-1,1))\n",
+ "print len(ct), ' OD pairs between census tracts after filtering out intrazonal flows'\n",
+ "ct.head()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0.09898099 0.05748786 0.50319944 0.06920194 0.06408526 0.39371417\n",
+ " -0.00226671]\n",
+ "3289243.68953\n"
+ ]
+ }
+ ],
+ "source": [
+ "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/spint')\n",
+ "from gravity import Gravity, Production, Attraction, Doubly\n",
+ "\n",
+ "model = Gravity(flows, o_vars, d_vars, cost, 'exp')\n",
+ "print model.params\n",
+ "print model.deviance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0.00437122 0.06794379 0.85720958 -0.00227555]\n",
+ "2383227.55076\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = Production(flows, o, d_vars, cost, 'exp')\n",
+ "print model.params[-4:]\n",
+ "print model.deviance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-0.00232112445583\n",
+ "1444734.46276\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = Doubly(flows, o, d, cost, 'exp')\n",
+ "print model.params[-1]\n",
+ "print model.deviance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "model = Production(flows, o, d_vars, cost, 'exp')\n",
+ "local = model.local()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[-0.0017795992298777902,\n",
+ " -0.0026156676327248746,\n",
+ " -0.0022363993586345388,\n",
+ " -0.00194947666943137,\n",
+ " -0.0017071540350527339,\n",
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+ " -0.0017657944972518311,\n",
+ " -0.0020341925723913884,\n",
+ " -0.0015759200833807205]"
+ ]
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "local['param4'][:10]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ -4.95948156e-04, 3.40120247e-04, -3.91480275e-05,\n",
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+ " 8.55636181e-04, -1.29914342e-04, 5.03848977e-04,\n",
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+ " -1.22876232e-04, -3.72755027e-05, 2.21322945e-04,\n",
+ " 5.30628729e-04, 2.60089456e-05, 3.44939369e-04,\n",
+ " -2.03039191e-04, 8.70161481e-04, 5.93617124e-04,\n",
+ " 2.20113262e-05, 2.28752154e-04, 1.70450009e-05,\n",
+ " 4.31890011e-04, -1.63104457e-04, 5.46943635e-04,\n",
+ " -1.79543169e-04, 4.17203373e-04, -8.31555539e-05,\n",
+ " 4.56660548e-04, -4.73247746e-04, 1.91977621e-04,\n",
+ " 6.35081811e-04, 1.23280773e-04, 4.13098476e-04,\n",
+ " 6.17848610e-05, 5.71604649e-05, 4.84527935e-04,\n",
+ " -3.04029156e-04, 2.82207361e-04, 4.09784527e-04,\n",
+ " -4.02865802e-04, 3.82267728e-04, -5.74829501e-04,\n",
+ " 4.00848442e-04, -5.56075560e-05, -8.41775681e-04,\n",
+ " 3.21238255e-04, -3.94157736e-04, 5.70250598e-04,\n",
+ " -6.58632949e-04, -3.50101571e-04, 3.52107030e-04,\n",
+ " -3.79342620e-04, 4.12304788e-04, -8.41623629e-04,\n",
+ " 2.55518204e-04, -2.47800511e-04, 4.01094216e-04,\n",
+ " -3.52907394e-04, 4.74670899e-05])"
+ ]
+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.params[-1:] - local['param4']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "crs = {'datum':'WGS84', 'proj':'longlat'}\n",
+ "tracts = gp.read_file('/Users/toshan/Dropbox/Data/NYC_BIKES/nyct2010_15a/nyct2010.shp')\n",
+ "tracts = tracts.to_crs(crs=crs)\n",
+ "man_tracts = tracts[tracts['BoroCode'] == '1'].copy()\n",
+ "man_tracts['CT2010S'] = man_tracts['CT2010'].astype(int).astype(str)\n",
+ "mt = set(man_tracts.CT2010S.unique())\n",
+ "lt = set(np.unique(o))\n",
+ "nt = list(mt.difference(lt))\n",
+ "no_tracts = pd.DataFrame({'no_tract':nt})\n",
+ "no_tracts = man_tracts[man_tracts.CT2010S.isin(nt)].copy()\n",
+ "local_vals = pd.DataFrame({'betas': local['param4'], 'tract':np.unique(o)})\n",
+ "local_vals = pd.merge(local_vals, man_tracts[['CT2010S', 'geometry']], left_on='tract', right_on='CT2010S')\n",
+ "local_vals = gp.GeoDataFrame(local_vals)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(40.7, 40.78)"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoIAAALVCAYAAABUe4BaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYVFf6xz9DF1BBFBUbiIwdG6Ig3RpjitHYe9eU3+5m\nN22zm57NJptNNib2bmI0tsRYE6UoSBULKjAIKmJBQJAyjDBcfn+MjKAgDNyZAb2f58mTh3PvPfed\n45m53/ue874vSEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhI\nSEhISEhISEhISEhISEhISEhISEhINEFkxjZAV+Ry+RvAl0BrhUJxp5rjY4BvAFNgnUKh+Pf99u1A\n9/un2QF5CoViQC33OgwMAcIVCsVz4n0KCQkJCQkJCQnjY2JsA6pDLpcHyOXyjdW0dwJGAldruM4U\n+A4Y [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x116b20510>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(12,12))\n",
+ "local_vals['inv_betas'] = (local_vals['betas']*-1)\n",
+ "no_tracts['test'] = 0\n",
+ "no_tracts.plot('test', colormap='copper')\n",
+ "local_vals.plot('inv_betas', colormap='Blues')\n",
+ "plt.xlim(-74.02, -73.95)\n",
+ "plt.ylim(40.7, 40.78)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(40.7, 40.78)"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoIAAALVCAYAAABUe4BaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYVGf6v++hVwERVGwgMnZURAQEKXZjYiyxxd6ipu7u\nb3eTrUm25Ztt2RS7RmNiNLHFWBOlKEgVEZUyCCgqioD0oQ2H3x8DIyidKaDnvq5cuXjnnPc883pm\nzmee9ykgIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIiIiIiLSBZHo2oC2IpVKfwX8E+ghk8keNfL6dOATQB/YKZPJ/q92/AAwuPYw\na6BAJpONaeFaZ4DxQJhMJntRfe9CRERERERERET36OnagMaQSqX+Uqn0y0bG+wFTgNtNnKcPfA5M\nB4YB [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1121213d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(12,12))\n",
+ "local_vals['cap'] = local['param3']\n",
+ "no_tracts['test'] = 0\n",
+ "no_tracts.plot('test', colormap='copper')\n",
+ "local_vals.plot('cap', colormap='Reds')\n",
+ "plt.legend()\n",
+ "plt.xlim(-74.02, -73.95)\n",
+ "plt.ylim(40.7, 40.78)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(40.7, 40.78)"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoIAAALVCAYAAABUe4BaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYVGfah++hS1EQQQULiI69ITak2xMTYzS22FvUJO5m\nd7+03Y0xyWazm2yySWzYNYmrsSXWaFRAUEBQEURh7IBYAOnDMAyH74+REZQ2MAX03NeVKxfvnPOe\nZ14PnN953qeAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiIhIE0RibAO0RSqV/hn4Emglk8keVvH5GOC/gCmwQSaT/evR+A6g\n66PD7IEcmUzWv5Zr/QYMBiJkMtlLuvsWIiIiIiIiIiLGx8TYBlSFVCoNkEqlm6sYbw+MBG5Xc54p\nsBIY [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1126b2c10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(12,12))\n",
+ "local_vals['house'] = local['param2']\n",
+ "no_tracts['test'] = 0\n",
+ "no_tracts.plot('test', colormap='copper')\n",
+ "local_vals.plot('house', colormap='Reds')\n",
+ "plt.legend()\n",
+ "plt.xlim(-74.02, -73.95)\n",
+ "plt.ylim(40.7, 40.78)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(40.7, 40.78)"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoIAAALVCAYAAABUe4BaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYVGfah+8ZqhQBEVRsIDL2jg3p9phmYu+9JZrdzW7K\nbvbbZLMt2WSzKfYaTUxMNG6MRk2UoiBFbCgKgxUQRXobh2E4fH+MjKCgDJyZAT33de21l++c855n\n3hzm/M7zPgUkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQk\nJCQkJCQkJCQkJCQkJCQkJCQkJCSaITJzG2AoCoXideDfQGulUplXy+fjgP8CFsAmpVL5wb3xb4Fu\n9w5zBgqUSuWAx1zrEDAUiFIqlc+J9y0kJCQkJCQkJMyP3NwG1IZCoQhWKBRbaxnvCIwGbtRxngXw\nBTAO [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11260eb10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(12,12))\n",
+ "local_vals['foot'] = local['param1']\n",
+ "no_tracts['test'] = 0\n",
+ "no_tracts.plot('test', colormap='copper')\n",
+ "local_vals.plot('foot', colormap='Reds')\n",
+ "plt.legend()\n",
+ "plt.xlim(-74.02, -73.95)\n",
+ "plt.ylim(40.7, 40.78)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/New_DistanceBand.ipynb b/pysal/contrib/spint/notebooks/New_DistanceBand.ipynb
new file mode 100644
index 0000000..bffe39b
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/New_DistanceBand.ipynb
@@ -0,0 +1,326 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from scipy.spatial import distance\n",
+ "import scipy.spatial as spatial\n",
+ "from pysal.weights import W\n",
+ "from pysal.weights.util import isKDTree\n",
+ "from pysal.weights import Distance as Distance\n",
+ "from pysal.weights import WSP, WSP2W\n",
+ "from scipy.spatial import distance_matrix\n",
+ "import scipy.sparse as sp\n",
+ "from pysal.common import KDTree"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "class DistanceBand(W):\n",
+ " \"\"\"\n",
+ " Spatial weights based on distance band.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ "\n",
+ " data : array\n",
+ " (n,k) or KDTree where KDtree.data is array (n,k)\n",
+ " n observations on k characteristics used to measure\n",
+ " distances between the n objects\n",
+ " threshold : float\n",
+ " distance band\n",
+ " p : float\n",
+ " Minkowski p-norm distance metric parameter:\n",
+ " 1<=p<=infinity\n",
+ " 2: Euclidean distance\n",
+ " 1: Manhattan distance\n",
+ " binary : boolean\n",
+ " If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0\n",
+ " If false wij=dij^{alpha}\n",
+ " alpha : float\n",
+ " distance decay parameter for weight (default -1.0)\n",
+ " if alpha is positive the weights will not decline with\n",
+ " distance. If binary is True, alpha is ignored\n",
+ "\n",
+ " ids : list\n",
+ " values to use for keys of the neighbors and weights dicts\n",
+ "\n",
+ " Attributes\n",
+ " ----------\n",
+ " weights : dict\n",
+ " of neighbor weights keyed by observation id\n",
+ "\n",
+ " neighbors : dict\n",
+ " of neighbors keyed by observation id\n",
+ "\n",
+ "\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None, build_sp=True):\n",
+ " \"\"\"Casting to floats is a work around for a bug in scipy.spatial.\n",
+ " See detail in pysal issue #126.\n",
+ "\n",
+ " \"\"\"\n",
+ " self.p = p\n",
+ " self.threshold = threshold\n",
+ " self.binary = binary\n",
+ " self.alpha = alpha\n",
+ " self.build_sp = build_sp\n",
+ " \n",
+ " if isKDTree(data):\n",
+ " self.kd = data\n",
+ " self.data = self.kd.data\n",
+ " else:\n",
+ " if self.build_sp:\n",
+ " try:\n",
+ " data = np.asarray(data)\n",
+ " if data.dtype.kind != 'f':\n",
+ " data = data.astype(float)\n",
+ " self.data = data\n",
+ " self.kd = KDTree(self.data)\n",
+ " except:\n",
+ " raise ValueError(\"Could not make array from data\") \n",
+ " else:\n",
+ " self.data = data\n",
+ " self.kd = None \n",
+ " \n",
+ "\n",
+ " self._band()\n",
+ " neighbors, weights = self._distance_to_W(ids)\n",
+ " W.__init__(self, neighbors, weights, ids)\n",
+ "\n",
+ " def _band(self):\n",
+ " \"\"\"Find all pairs within threshold.\n",
+ "\n",
+ " \"\"\"\n",
+ " if self.build_sp: \n",
+ " self.dmat = self.kd.sparse_distance_matrix(\n",
+ " self.kd, max_distance=self.threshold)\n",
+ " else:\n",
+ " self.dmat = self._spdistance_matrix(self.data, self.data, self.threshold)\n",
+ " \n",
+ " def _distance_to_W(self, ids=None):\n",
+ " if self.binary:\n",
+ " self.dmat[self.dmat>0] = 1\n",
+ " tempW = WSP2W(WSP(self.dmat))\n",
+ " return tempW.neighbors, tempW.weights\n",
+ "\n",
+ " else:\n",
+ " weighted = self.dmat.power(self.alpha)\n",
+ " weighted[weighted==np.inf] = 0\n",
+ " tempW = WSP2W(WSP(weighted))\n",
+ " return tempW.neighbors, tempW.weights\n",
+ " \n",
+ " def _spdistance_matrix(self, x,y, threshold=None):\n",
+ " dist = distance_matrix(x,y)\n",
+ " if threshold is not None:\n",
+ " zeros = dist > threshold\n",
+ " dist[zeros] = 0\n",
+ " return sp.csr_matrix(dist)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "x = np.random.randint(0, 1000, 1000)\n",
+ "y = np.random.randint(0, 1000, 1000)\n",
+ "w = np.random.randint(0, 1000, 1000)\n",
+ "z = np.random.randint(0, 1000, 1000)\n",
+ "\n",
+ "data = zip(x.ravel(), y.ravel(), w.ravel(), z.ravel())\n",
+ "tree = KDTree(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#print np.allclose(Distance.DistanceBand(tree, threshold=500, alpha=-1.5, binary=True).full()[0], DistanceBand(tree, threshold=500, alpha=-1.5, binary=True).full()[0])\n",
+ "#print np.allclose(Distance.DistanceBand(tree, threshold=500, alpha=-1.5, binary=False).full()[0], DistanceBand(tree, threshold=500, alpha=-1.5, binary=False).full()[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.42 s, sys: 17.9 ms, total: 1.44 s\n",
+ "Wall time: 1.44 s\n",
+ "CPU times: user 366 ms, sys: 11.3 ms, total: 377 ms\n",
+ "Wall time: 376 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<__main__.DistanceBand at 0x10c0aedd0>"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time Distance.DistanceBand(tree, threshold=500, alpha=-1.5, binary=True)\n",
+ "%time DistanceBand(tree, threshold=500, alpha=-1.5, binary=True, build_sp=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.37 s, sys: 21.7 ms, total: 1.39 s\n",
+ "Wall time: 1.4 s\n",
+ "CPU times: user 69.9 ms, sys: 4.84 ms, total: 74.7 ms\n",
+ "Wall time: 74.7 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<__main__.DistanceBand at 0x117d18c90>"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time Distance.DistanceBand(tree, threshold=500, alpha=-1.5, binary=True)\n",
+ "%time DistanceBand(tree, threshold=500, alpha=-1.5, binary=True, build_sp=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.28 s, sys: 44 ms, total: 1.32 s\n",
+ "Wall time: 1.3 s\n",
+ "CPU times: user 199 ms, sys: 10.9 ms, total: 210 ms\n",
+ "Wall time: 210 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<__main__.DistanceBand at 0x117d18a10>"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time Distance.DistanceBand(tree, threshold=500, alpha=-1.5, binary=False)\n",
+ "%time DistanceBand(tree, threshold=500, alpha=-1.5, binary=False, build_sp=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.5 s, sys: 46.3 ms, total: 1.54 s\n",
+ "Wall time: 1.53 s\n",
+ "CPU times: user 67.1 ms, sys: 5.1 ms, total: 72.2 ms\n",
+ "Wall time: 72.2 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<__main__.DistanceBand at 0x117d18e90>"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time Distance.DistanceBand(tree, threshold=500, alpha=-1.5, binary=False)\n",
+ "%time DistanceBand(tree, threshold=500, alpha=-1.5, binary=False, build_sp=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/ODW_example.ipynb b/pysal/contrib/spint/notebooks/ODW_example.ipynb
new file mode 100644
index 0000000..2f05d87
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/ODW_example.ipynb
@@ -0,0 +1,288 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.append('/Users/toshan/dev/pysal/pysal/weights')\n",
+ "from spintW import ODW\n",
+ "import pysal as ps"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# With an equal number of origins and destinations (n=16)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "origins = ps.weights.lat2W(4,4)\n",
+ "dests = ps.weights.lat2W(4,4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "16"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "origins.n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "16"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dests.n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "ODw = ODW(origins, dests)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "256 256\n"
+ ]
+ }
+ ],
+ "source": [
+ "print ODw.n, 16*16"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(256, 256)"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ODw.full()[0].shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " # With non-equal number of origins (n=9) and destinations (m=25)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "origins = ps.weights.lat2W(3,3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "dests = ps.weights.lat2W(5,5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "9"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "origins.n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "25"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dests.n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "ODw = ODW(origins, dests)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "225 225\n"
+ ]
+ }
+ ],
+ "source": [
+ "print ODw.n, 9*25"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(225, 225)"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ODw.full()[0].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/OD_weights.ipynb b/pysal/contrib/spint/notebooks/OD_weights.ipynb
new file mode 100644
index 0000000..c0c7b14
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/OD_weights.ipynb
@@ -0,0 +1,254 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import pysal as ps\n",
+ "from pysal import weights as w\n",
+ "import numpy as np\n",
+ "import scipy.sparse as sp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "def OD(Wo, Wd):\n",
+ " Wo = Wo.sparse\n",
+ " Wd = Wd.sparse\n",
+ " Ww = sp.kron(Wo, Wd)\n",
+ " return w.WSP2W(w.WSP(Ww))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(256, 256)\n"
+ ]
+ }
+ ],
+ "source": [
+ "origins = ps.weights.lat2W(4,4)\n",
+ "dests = ps.weights.lat2W(4,4)\n",
+ "Ww = OD(origins, dests)\n",
+ "Ww.transform = 'r'\n",
+ "print Ww.full()[0].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0]\n",
+ " [38]\n",
+ " [36]\n",
+ " [86]\n",
+ " [30]\n",
+ " [ 0]\n",
+ " [69]\n",
+ " [19]\n",
+ " [84]\n",
+ " [43]\n",
+ " [ 0]\n",
+ " [80]\n",
+ " [58]\n",
+ " [ 3]\n",
+ " [35]\n",
+ " [ 0]]\n",
+ "[[ 28. ]\n",
+ " [ 53.25]\n",
+ " [ 53.25]\n",
+ " [ 28. ]\n",
+ " [ 28. ]\n",
+ " [ 36. ]\n",
+ " [ 36. ]\n",
+ " [ 28. ]\n",
+ " [ 28. ]\n",
+ " [ 36. ]\n",
+ " [ 36. ]\n",
+ " [ 28. ]\n",
+ " [ 28. ]\n",
+ " [ 53.25]\n",
+ " [ 53.25]\n",
+ " [ 28. ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "flows = np.random.randint(0,100, (4,4))\n",
+ "np.fill_diagonal(flows, 0)\n",
+ "flows = flows.reshape((16,1))\n",
+ "print flows\n",
+ "slag = ps.lag_spatial(Ww, flows)\n",
+ "print slag"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{0: [1.0, 1.0], 1: [1.0, 1.0], 2: [1.0, 1.0], 3: [1.0, 1.0]}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "origins.weights"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.chdir('/Users/toshan/dev/pysal/pysal/weights')\n",
+ "from spintW import ODW"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "origins = ps.weights.lat2W(2,2)\n",
+ "dests = ps.weights.lat2W(2,2)\n",
+ "Ww = ODW(origins, dests)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. ,\n",
+ " 0.25, 0.25, 0. , 0. , 0. , 0. , 0. ],\n",
+ " [ 0. , 0. , 0. , 0. , 0.25, 0. , 0. , 0.25, 0.25,\n",
+ " 0. , 0. , 0.25, 0. , 0. , 0. , 0. ],\n",
+ " [ 0. , 0. , 0. , 0. , 0.25, 0. , 0. , 0.25, 0.25,\n",
+ " 0. , 0. , 0.25, 0. , 0. , 0. , 0. ],\n",
+ " [ 0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. ,\n",
+ " 0.25, 0.25, 0. , 0. , 0. , 0. , 0. ],\n",
+ " [ 0. , 0.25, 0.25, 0. , 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0. , 0. , 0. , 0.25, 0.25, 0. ],\n",
+ " [ 0.25, 0. , 0. , 0.25, 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0. , 0. , 0.25, 0. , 0. , 0.25],\n",
+ " [ 0.25, 0. , 0. , 0.25, 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0. , 0. , 0.25, 0. , 0. , 0.25],\n",
+ " [ 0. , 0.25, 0.25, 0. , 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0. , 0. , 0. , 0.25, 0.25, 0. ],\n",
+ " [ 0. , 0.25, 0.25, 0. , 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0. , 0. , 0. , 0.25, 0.25, 0. ],\n",
+ " [ 0.25, 0. , 0. , 0.25, 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0. , 0. , 0.25, 0. , 0. , 0.25],\n",
+ " [ 0.25, 0. , 0. , 0.25, 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0. , 0. , 0.25, 0. , 0. , 0.25],\n",
+ " [ 0. , 0.25, 0.25, 0. , 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0. , 0. , 0. , 0.25, 0.25, 0. ],\n",
+ " [ 0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. ,\n",
+ " 0.25, 0.25, 0. , 0. , 0. , 0. , 0. ],\n",
+ " [ 0. , 0. , 0. , 0. , 0.25, 0. , 0. , 0.25, 0.25,\n",
+ " 0. , 0. , 0.25, 0. , 0. , 0. , 0. ],\n",
+ " [ 0. , 0. , 0. , 0. , 0.25, 0. , 0. , 0.25, 0.25,\n",
+ " 0. , 0. , 0.25, 0. , 0. , 0. , 0. ],\n",
+ " [ 0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. ,\n",
+ " 0.25, 0.25, 0. , 0. , 0. , 0. , 0. ]])"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Ww.full()[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/Vec_SA_Test.ipynb b/pysal/contrib/spint/notebooks/Vec_SA_Test.ipynb
new file mode 100644
index 0000000..1ca91e2
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/Vec_SA_Test.ipynb
@@ -0,0 +1,389 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "os.chdir('../')\n",
+ "from vec_SA import VecMoran\n",
+ "import numpy as np\n",
+ "from pysal.weights.Distance import DistanceBand\n",
+ "import matplotlib.pyplot as plt\n",
+ "%pylab inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Demonstrating the vector-based Moran's I statistic for spatial autocorrelation and the associated proposed randomization techniques for generating psuedo p-values\n",
+ "\n",
+ "##### - Technique A: Randomly select one vector among the N observed vectors and assign its origin (destination) to the origin (destination) point of another observed vector. This is equivlanet to geometrically translating both the original origin and the original destination by the coordinates of the new origin (destination) point, which preserves the direction and magnitude of the original vector. Using this technique it is possible to generate new vectors that are outside the bou [...]
+ "##### - Technique B: For each vector among the N observed vectors, reassign its origin (destination) point to the destination (origin) from another randomly selected vector from the N observed vectors. This does does not preserve distance or magnitude, though it does ensure that all new vectors remain within the bounding box of the of the original vectors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generate 1000 random datasets of 50 vectors, then calculate the vector Moran's I from the destination perspective (VMD) and a psuedo p value (based on 99 permutations) using randomization technique A and randomization technique B for each of the 1000 datasets. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 237,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "dest_A_rand_I = []\n",
+ "dest_B_rand_I = []\n",
+ "dest_A_rand_p = []\n",
+ "dest_B_rand_p = []\n",
+ "for i in range(1000):\n",
+ " phi = np.random.uniform(0,np.pi*2, 50).reshape((-1,1))\n",
+ " num = np.arange(0,50).reshape((-1,1))\n",
+ " OX = np.random.randint(0,500, 50).reshape((-1,1))\n",
+ " OY = np.random.randint(0,500, 50).reshape((-1,1))\n",
+ " DX = np.cos(phi)*(np.random.randint(0,500, 50)).reshape((-1,1))\n",
+ " DY = np.sin(phi)*np.random.randint(0,500, 50).reshape((-1,1))\n",
+ "\n",
+ " vecs = np.hstack([num, OX, OY, DX, DY])\n",
+ " dests = vecs[:, 3:5]\n",
+ " wd = DistanceBand(dests, threshold=9999, alpha=-1.5, binary=False)\n",
+ "\n",
+ " vmd = VecMoran(vecs, wd, focus='destination', rand='A', permutations=999)\n",
+ " dest_A_rand_I.append(vmd.I)\n",
+ " dest_A_rand_p.append(vmd.p_z_sim)\n",
+ " vmd = VecMoran(vecs, wd, focus='destination', rand='B', permutations=999)\n",
+ " dest_B_rand_I.append(vmd.I)\n",
+ " dest_B_rand_p.append(vmd.p_z_sim)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 238,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXeUFMUWh39LRhAeQUQkqiAgooCSniSRoIBEBUSUrIhE\nyUhQkqDkYIBVcpAgSUABFyQpgkTJOcM+WGDZZdnQ9/3Rc2uqp7sn7czOgvWdU4eZ7urq6l7mVtWt\nGwCFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCEQLaANgapLZ/AHATwB9Baj8l\nOQugZqg7oXi4SBPqDigCwlkAsQCipTI5lB1KQaoAeA1APgAVLc5XB6DB+G5aS+czAvgewG0AVwD0\nDGJfvYEc5UFkFoDhoe6Ewky6UHdAERAIQH0Av4W6IyGgEPSBLs5NnUsACticGwbgaQAFATwBIALA\nYQC/ [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10d6dd490>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X,Y,U,V = zip(*vecs[:,1:])\n",
+ "plt.subplot(111)\n",
+ "for x in range(0,len(vecs[:,1])):\n",
+ " plt.arrow(X[x], #x1\n",
+ " Y[x], # y1\n",
+ " U[x]-X[x], # x2 - x1\n",
+ " V[x]-Y[x], # y2 - y1\n",
+ " fc=\"k\", ec=\"k\", head_width=0.05, head_length=0.1)\n",
+ "plt.xlim([-510,550])\n",
+ "plt.ylim([-510,550])\n",
+ "plt.title('Example of 50 random vectors')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generate 1000 random datasets of 50 vectors with constrained origins (to induce positive spatial autocorrrlation), then calculate the vector Moran's I from the destination perspective (VMD) and a psuedo p value (based on 99 permutations) using randomization technique A and randomization technique B for each of the 1000 datasets. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 239,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "dest_A_cons_I = []\n",
+ "dest_B_cons_I = []\n",
+ "dest_A_cons_p = []\n",
+ "dest_B_cons_p = []\n",
+ "for i in range(1000):\n",
+ " phi = np.random.uniform(0,np.pi*2, 50).reshape((-1,1))\n",
+ " num = np.arange(0,50).reshape((-1,1))\n",
+ " OX = np.random.randint(450,500, 50).reshape((-1,1))\n",
+ " OY = np.random.randint(450,500, 50).reshape((-1,1))\n",
+ " DX = np.cos(phi)*(np.random.randint(450,500, 50)).reshape((-1,1))\n",
+ " DY = np.sin(phi)*np.random.randint(450,500, 50).reshape((-1,1))\n",
+ "\n",
+ " vecs = np.hstack([num, OX, OY, DX, DY])\n",
+ " dests = vecs[:, 3:5]\n",
+ " wd = DistanceBand(dests, threshold=9999, alpha=-1.5, binary=False)\n",
+ "\n",
+ " vmd = VecMoran(vecs, wd, focus='destination', rand='A', permutations=999)\n",
+ " dest_A_cons_I.append(vmd.I)\n",
+ " dest_A_cons_p.append(vmd.p_z_sim)\n",
+ " vmd = VecMoran(vecs, wd, focus='destination', rand='B', permutations=999)\n",
+ " dest_B_cons_I.append(vmd.I)\n",
+ " dest_B_cons_p.append(vmd.p_z_sim)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 240,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsXXd4FEUb/wEJJYAJvRcB6fqJBSwgoRcpgoAUQUB6E5Eu\nSG9KE0GRooA0AypNpARIUAGRjoQSqiGAIYUE0nP7fn/MzWZmb6/mLgmwv+eZ5253Zqft7Dsz77wF\nMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAbegN4HcP5f09\ngGgAxzyUf2biJoAmWV2JJwgTAKy0Ed8bnhuX2QX/AHjLA/n6AwhzY367AfT0QNonBjcBJAB4KIQl\nWVkhHfSGZz6oBmCDLa+VeH8ACuS+EQdIHgDfAYgFcBfAxx6oozO4AaBxFtfBVawBMCOrK2EDFcHG\nQk7h [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10c01d590>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X,Y,U,V = zip(*vecs[:,1:])\n",
+ "plt.subplot(111)\n",
+ "for x in range(0,len(vecs[:,1])):\n",
+ " plt.arrow(X[x], #x1\n",
+ " Y[x], # y1\n",
+ " U[x]-X[x], # x2 - x1\n",
+ " V[x]-Y[x], # y2 - y1\n",
+ " fc=\"k\", ec=\"k\", head_width=0.05, head_length=0.1)\n",
+ "plt.xlim([-510,550])\n",
+ "plt.ylim([-510,550])\n",
+ "plt.title('Example of 50 random vectors with constrained origins')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 247,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEKCAYAAADw2zkCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGktJREFUeJzt3X+4HFV5wPHvkoQKJheShkISCJeCVGMt2BaUonVbKwWt\nRPtDASuh0FZrH6Van0LQlotVfrWiba22VYGgEIpaKVCxAmUFrWK1qECgkJRIAkkUEklQ2oK5/eM9\ny86duz9m793Z3Zt8P8+zz52dmZ15d+aceeecmbkLkiRJkiRJkiRJkiRJkiRJUuk+Ary7R8taCuwA\nKul9DTijR8sG+Bzwxh4ur6j3At8DHhnAugflcuDP+7zOY4EHiDJ0Yp/X3Q81elsf1HuX07tyPwZ8\nokfLesYeU/zceuCHwHZgG/Bl4E00DtYAf0Ac7Ios65c7zPMQMA8YT+/HM8PdGmPyhnxlk3FlWwq8\nA3gu [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11311fd90>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHZFJREFUeJzt3X+cHWV96PHPkhB+Q0zwhvwAQoGIsVCx8sNS6mJtbsrV\nJK96L6C1pDX12qZVr7dVSK+apFpF+0PstXhbFAgUolGvEX0pJUZO660KoiDCGpMgW1wgCxtoiEZq\nMHv/+D7jmT05Z8/M7jm7yfB5v17ndebMzJl55pmZ7/PMM3POA5IkSZIkSZIkSZIkSZIkSTrAfRR4\nZ4eWdRKwG+hJn2vAig4tG+CLwO90cHlFvRd4Anh0EtadmQ/sAw6Z4PVeDzwJfGOC1zsReoEfTnYi\nNKr5dPa43wf8QoeWdcDoB/YATwNPAf8KvIl6IC67rFeU/M4dwBvGsC6ANcBNY/xuJ51E5OHMSU7H\nfCY+ [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x111894450>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Method A random\n",
+ "plt.hist(dest_A_rand_I, bins = 25)\n",
+ "plt.title('Distribution of VMD I values from random vectors - Method A')\n",
+ "plt.show()\n",
+ "plt.hist(dest_A_rand_p, bins = 25)\n",
+ "plt.title('Distribution of p values from random vectors - Method A')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 248,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEKCAYAAAAxXHOuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHFVJREFUeJzt3Xu0XFWd4PHvNQkgjwvEIJCEEAakAZeiToPQNG219tDQ\nKjA9qxVxIGkcR5tZ6PKxmsd0y9VRoLW1sZcjzlIeQSGKOtJA+wBsqnmIOLgiAjFKAkgCSXhDQEAe\nd/747aLOrVvP1K7HrXw/a9W6p17n/M45++zf2XufUxckSZIkSZIkSZIkSZIkSZIkqafOA/4u07wW\nAZuAsfS8DLwv07wBvg+ckHF+7fo08BDwwACWPSgXAf+rz8s8DLiLKENH93nZM1Ht8ZbTBPD1Hsx3\nS7cUuCHTvErA2kzzetkr2vjMvcDvgCeBx4CbgA8wtSD+DVFxtjOvt7b4zH3ADsBkej5ZmO7UBNML\n9l/U [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1107ec450>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEKCAYAAAD5MJl4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGgRJREFUeJzt3Xu8XGV97/HPkIRLEkJMackNEk6QI/Fl0V1F0FJ2LYdS\nROD0tICXNlZqbfHltVpIq7Jje/ByaqG2pfXIxYAmCqgULfUF0kxtS+VSgQIxzaXEmGA2ECAJiVgi\nu3/8nnHWnmdm79mz9uy9s/N5v17z2jPrMut5Zj3r+a71rJkEJEmSJEmSJEmSJEmSJEmSpAPOXwMf\nGqX3OgbYDVTS6ypw0Si9N8BtwG+M4vu160+AJ4DHxmHbNYuBF4CDxni71wFPAd8e4+3ur0bzeGr0\nAvA/uvTeB7LNwC+N0ntVGd0+r2s2A3uBXcDTwL8A76DeeY/0vV43wnXWAG/rYFsAfcANHa47mo4h\nPsOf [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10dab2bd0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Method A constricted\n",
+ "plt.hist(dest_A_cons_I, bins=25)\n",
+ "plt.title('Distribution of VMD I values from constrained vectors - Method A')\n",
+ "plt.show()\n",
+ "plt.hist(dest_A_cons_p, bins=25)\n",
+ "plt.title('Distribution of p values from constrained vectors - Method A')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 249,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGs5JREFUeJzt3X20JGV94PFvw0AEZy7MZAzMDAyXBYmOx6jZBSXo2okr\nAY1gkrMKGoVANrruUTbGs7xowiVR3hLRJCbuRgUGgSGgKwEiRiC04BuuHlReA0wYYWBmFBiZQdGA\nc/eP39N23b79Un1vv915vp9z+tzqquqqX1c9T/3qearqNkiSJEmSJEmSJEmSJEmSJEl983HgA31a\n1mpgO1BJ72vAyX1aNsDngbf1cXllfRD4AfDoCNY9KhcDfz7kdR4B3E+UoWOGvO5hqNHf+qD+u5j+\nlfsp4NN9Wha7zOOzG4AfA9uArcBXgHfQOFAD/HfiQFdmWb/RZZ6HgCXAdHo/XRju1RSzN+LrWowb\ntNXA [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1147fb650>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEKCAYAAAACS67iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGGtJREFUeJzt3Xu4HVV9h/H3kHBRIGDEhkggoWAQLBSQq5R6pNEiVeDR\nFi9Vo1Kr1aIPtXKxKge1gpd6K2otCAQEJOgjt3oBIlG8gKAgchckIkIChBgiEI3k9I/fGvecffY+\nZ/bl7H3Oyvt5nv1k75m9Z9asmfnOmjUzJyBJkiRJkiRJkiRJkiRJkpS9LwDv69K0dgDWAAPp81Lg\n6C5NG+AbwOu7OL2qPgw8DDzQh3kX5gHrgY16PN+zgEeBa3s8314YBH7d70JoTPPo7na/HvjzLk2r\n55YBTwCPAauAHwBvpRa4rU7rkBZ/czXw5jbmBTAEnNvmb7tpB6IOn9nncsyj94F+MBF4m/Vwnr00\nyNQO [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1135d5e50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Method B random\n",
+ "plt.hist(dest_B_rand_I, bins=25)\n",
+ "plt.title('Distribution of VMD I values from random vectors - Method B')\n",
+ "plt.show()\n",
+ "plt.hist(dest_B_rand_p, bins=25)\n",
+ "plt.title('Distribution of p values from random vectors - Method B')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 250,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHGxJREFUeJzt3X20XGV96PHvMQkihAPEIJBACA1SxCW+3IJQynWqXRR8\nIbZdVcSLpHi9WrrQVXWVl1o5eBWoXi3t8oJdyktQiGKtXqC+AMKUFzFeXBGBGCEBJAES3iFBVCCn\nf/yeYfaZM69nnjMvJ9/PWrPOnpk9e//27Gc/v/08z95zQJIkSZIkSZIkSZIkSZIkSZK6dj7wiUzL\nWgRsBkbS8zLw/kzLBvgucHzG5bXr08AjwIN9WHe/XAz87x6v83DgbqIMHdPjdQ+j2uMtpzHgq9Ow\n3G3dMuDGTMsqAeszLQuAl7Qxz33Ar4GngSeAm4EPMrEQ/jVRabazrDe3mOd+YCdgPD0fL0x3aozJ\nhfqt [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10b837690>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEKCAYAAADzQPVvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGcRJREFUeJzt3X2YJFV96PHv7C4I7jIsG8yy8rJrEAR8fMEExRiT0aCX\nEAViEny/q5LEJD6aa6IBjYYhNxc010Q0rz4RcUVBUSMiUS/ruhOJIEoCGEWCbFgBYQdYFvYNDcrc\nP36n7Jqe7unqmZ7qnj7fz/P0M9X1dk6dOvWrU6eqekCSJEmSJEmSJEmSJEmSJEl98PfAO3q0riOA\nXcBI+j4BnNmjdQN8Hnh1D9dX1Z8D9wF39yHtwjrgUWBJzeleBDwAfK3mdBerXh5PzR4FfmaB1p2z\nrcAv92hdE/Q25lW2FdgL7AR2AF8FXk8jGHe7rud3ucxm4HVzSAtgHLh4jsv20hFEGf5Un/OxjvqD\n/XOB [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10d5c07d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Method B constricted\n",
+ "plt.hist(dest_B_cons_I, bins=25)\n",
+ "plt.title('Distribution of VMD I values from constrained vectors - Method B')\n",
+ "plt.show()\n",
+ "plt.hist(dest_B_cons_p, bins=25)\n",
+ "plt.title('Distribution of p values from constrained vectors - Method B')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 245,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a\n"
+ ]
+ }
+ ],
+ "source": [
+ "print 'a'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/autograd_test.ipynb b/pysal/contrib/spint/notebooks/autograd_test.ipynb
new file mode 100644
index 0000000..9749168
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/autograd_test.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import scipy.optimize as sc\n",
+ "import autograd.numpy as np\n",
+ "import autograd\n",
+ "from autograd.convenience_wrappers import multigrad\n",
+ "import scipy.sparse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "austria = pd.read_csv('http://dl.dropbox.com/u/8649795/AT_Austria.csv')\n",
+ "austria = austria[austria['Origin'] != austria['Destination']]\n",
+ "f = np.reshape(austria['Data'].values, (-1,1))\n",
+ "o = austria['Origin'].values\n",
+ "d = austria['Destination'].values\n",
+ "dij = np.reshape(austria['Dij'].values, (-1,1))\n",
+ "o_vars = np.reshape(austria['Oi2007'].values, (-1,1))\n",
+ "d_vars = np.reshape(austria['Dj2007'].values, (-1,1))\n",
+ "dij = np.reshape(austria['Dij'].values, (-1,1))\n",
+ "o_vars = np.reshape(austria['Oi2007'].values, (-1,1))\n",
+ "d_vars = np.reshape(austria['Dj2007'].values, (-1,1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def newton(f, x0):\n",
+ " # wrap scipy.optimize.newton with our automatic derivatives\n",
+ " params = sc.fsolve(f, x0)\n",
+ " return params\n",
+ "\n",
+ "def poiss_loglike(mu, sig, ep, x, inputs):\n",
+ " a,b,c = inputs[:,0], inputs[:,1], inputs[:,2]\n",
+ " predict = sig*a + ep*b + mu*c\n",
+ " predict = np.reshape(predict, (-1,1))\n",
+ " return -np.sum(x*np.log(predict)-predict)\n",
+ "\n",
+ "#def loglike(mu, k, x, inputs):\n",
+ " #return np.sum(poiss_loglike(mu, k, x, inputs))\n",
+ "\n",
+ "\n",
+ "def fit_maxlike(x, inputs, mu_guess, o_guess, d_guess):\n",
+ " prime = lambda p: multigrad(poiss_loglike, argnums=[0,1,2])(p[0], p[1], p[2], x, inputs)\n",
+ " params = newton(prime, (mu_guess, o_guess, d_guess))\n",
+ " return params"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-1.14993102 0.69084953 0.68523832]\n",
+ "(-2.7430635540781623e-10, -2.5915536383536164e-10, -4.730811298259141e-10)\n"
+ ]
+ }
+ ],
+ "source": [
+ "if __name__ == \"__main__\":\n",
+ " \n",
+ " x=np.log(f)\n",
+ " inputs = np.hstack((np.log(o_vars), np.log(d_vars), np.log(dij)))\n",
+ " params = fit_maxlike(x, inputs, mu_guess=0.0, o_guess=1.0, d_guess=1.0)\n",
+ " print(params)\n",
+ " \n",
+ " prime = lambda p: multigrad(poiss_loglike, argnums=[0,1,2])(p[0], p[1], p[2], x, inputs)\n",
+ " print(prime(params))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/dispersion_test.ipynb b/pysal/contrib/spint/notebooks/dispersion_test.ipynb
new file mode 100644
index 0000000..ee3109b
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/dispersion_test.ipynb
@@ -0,0 +1,455 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/spint')\n",
+ "from count_model import CountModel\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from gravity import Gravity, Production, Attraction, Doubly, BaseGravity\n",
+ "import statsmodels.formula.api as smf\n",
+ "from statsmodels.api import families\n",
+ "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/glm')\n",
+ "from glm import GLM\n",
+ "from family import Poisson, QuasiPoisson\n",
+ "\n",
+ "import pysal\n",
+ "import os\n",
+ "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/spint')\n",
+ "from dispersion import alpha_disp, phi_disp\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "rec = pd.read_csv('/Users/toshan/Documents/RecreationDemand.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Unnamed: 0</th>\n",
+ " <th>trips</th>\n",
+ " <th>quality</th>\n",
+ " <th>ski</th>\n",
+ " <th>income</th>\n",
+ " <th>userfee</th>\n",
+ " <th>costC</th>\n",
+ " <th>costS</th>\n",
+ " <th>costH</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td> 1</td>\n",
+ " <td> 0</td>\n",
+ " <td> 0</td>\n",
+ " <td> yes</td>\n",
+ " <td> 4</td>\n",
+ " <td> no</td>\n",
+ " <td> 67.59</td>\n",
+ " <td> 68.620</td>\n",
+ " <td> 76.800</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td> 2</td>\n",
+ " <td> 0</td>\n",
+ " <td> 0</td>\n",
+ " <td> no</td>\n",
+ " <td> 9</td>\n",
+ " <td> no</td>\n",
+ " <td> 68.86</td>\n",
+ " <td> 70.936</td>\n",
+ " <td> 84.780</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td> 3</td>\n",
+ " <td> 0</td>\n",
+ " <td> 0</td>\n",
+ " <td> yes</td>\n",
+ " <td> 5</td>\n",
+ " <td> no</td>\n",
+ " <td> 58.12</td>\n",
+ " <td> 59.465</td>\n",
+ " <td> 72.110</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td> 4</td>\n",
+ " <td> 0</td>\n",
+ " <td> 0</td>\n",
+ " <td> no</td>\n",
+ " <td> 2</td>\n",
+ " <td> no</td>\n",
+ " <td> 15.79</td>\n",
+ " <td> 13.750</td>\n",
+ " <td> 23.680</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td> 5</td>\n",
+ " <td> 0</td>\n",
+ " <td> 0</td>\n",
+ " <td> yes</td>\n",
+ " <td> 3</td>\n",
+ " <td> no</td>\n",
+ " <td> 24.02</td>\n",
+ " <td> 34.033</td>\n",
+ " <td> 34.547</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Unnamed: 0 trips quality ski income userfee costC costS costH\n",
+ "0 1 0 0 yes 4 no 67.59 68.620 76.800\n",
+ "1 2 0 0 no 9 no 68.86 70.936 84.780\n",
+ "2 3 0 0 yes 5 no 58.12 59.465 72.110\n",
+ "3 4 0 0 no 2 no 15.79 13.750 23.680\n",
+ "4 5 0 0 yes 3 no 24.02 34.033 34.547"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rec.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "y = rec['trips'].values.reshape((-1,1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "X = rec[['quality', 'income', 'costC', 'costS', 'costH']].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "test = CountModel(y, X, constant=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "glm_results = test.fit(framework='glm')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 7.30811593e+00, 2.71035909e+00, 3.36051997e-03])"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "phi_disp(glm_results)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 6.30811593e+00, 2.71035909e+00, 3.36051997e-03])"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "alpha_disp(glm_results)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1.55402055e+00, 3.38253708e+00, 3.59097912e-04])"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "alpha_disp(glm_results, lambda x: x**2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#Prepare some test data - columbus example\n",
+ "db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')\n",
+ "y = np.array(db.by_col(\"HOVAL\"))\n",
+ "y = np.reshape(y, (49,1))\n",
+ "X = []\n",
+ "#X.append(np.ones(len(y)))\n",
+ "X.append(db.by_col(\"INC\"))\n",
+ "X.append(db.by_col(\"CRIME\"))\n",
+ "X = np.array(X).T\n",
+ "\n",
+ "poisson_y = np.round(y).astype(int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "test = CountModel(poisson_y, X)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "glm_results = test.fit(framework='glm')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 5.39968689, 2.3230411 , 0.01008847])"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "phi_disp(glm_results)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 4.39968689, 2.3230411 , 0.01008847])"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "alpha_disp(glm_results)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0.10690133, 2.24709978, 0.01231683])"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "alpha_disp(glm_results, lambda x:x**2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'family.Poisson'>\n",
+ "<class 'family.Poisson'>\n",
+ "<class 'family.Poisson'>\n"
+ ]
+ }
+ ],
+ "source": [
+ "model1 = GLM(y, X, constant=False, family=Poisson()).fit()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'family.QuasiPoisson'>\n",
+ "<class 'family.QuasiPoisson'>\n",
+ "<class 'family.QuasiPoisson'>\n"
+ ]
+ }
+ ],
+ "source": [
+ "model2 = GLM(y, X, constant=False, family=QuasiPoisson()).fit()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.0\n",
+ "<class 'family.QuasiPoisson'>\n",
+ "7.02573401193\n"
+ ]
+ }
+ ],
+ "source": [
+ "print model1.scale\n",
+ "print model2.scale"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/examples/Example_NYCBikes_AllFeatures.ipynb b/pysal/contrib/spint/notebooks/examples/Example_NYCBikes_AllFeatures.ipynb
new file mode 100644
index 0000000..d31fa64
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/examples/Example_NYCBikes_AllFeatures.ipynb
@@ -0,0 +1,919 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "//anaconda/envs/spint/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
+ " \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n",
+ "//anaconda/envs/spint/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+ "`%matplotlib` prevents importing * from pylab and numpy\n",
+ " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pysal.contrib.spint.gravity import BaseGravity, Gravity, Production, Attraction, Doubly\n",
+ "from pysal.contrib.spint.dispersion import phi_disp\n",
+ "from pysal.contrib.spint.vec_SA import VecMoran\n",
+ "import pysal as ps\n",
+ "import pandas as pd\n",
+ "import geopandas as gp\n",
+ "import numpy as np\n",
+ "import seaborn as sb\n",
+ "import matplotlib.pylab as plt\n",
+ "%pylab inline\n",
+ "from descartes import PolygonPatch\n",
+ "import matplotlib as mpl\n",
+ "from mpl_toolkits.basemap import Basemap\n",
+ "import pyproj as pj\n",
+ "from shapely.geometry import Polygon, Point"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Unnamed: 0</th>\n",
+ " <th>index</th>\n",
+ " <th>count</th>\n",
+ " <th>d_cap</th>\n",
+ " <th>d_tract</th>\n",
+ " <th>distance</th>\n",
+ " <th>end station latitude</th>\n",
+ " <th>end station longitude</th>\n",
+ " <th>o_cap</th>\n",
+ " <th>o_tract</th>\n",
+ " <th>...</th>\n",
+ " <th>weighted</th>\n",
+ " <th>total_out</th>\n",
+ " <th>total_in</th>\n",
+ " <th>o_hub</th>\n",
+ " <th>d_hub</th>\n",
+ " <th>od_hub</th>\n",
+ " <th>SX</th>\n",
+ " <th>SY</th>\n",
+ " <th>EX</th>\n",
+ " <th>EY</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
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+ " <td>202</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>56352</td>\n",
+ " <td>69165</td>\n",
+ " <td>hub</td>\n",
+ " <td>hub</td>\n",
+ " <td>hub</td>\n",
+ " <td>585995.353038</td>\n",
+ " <td>4.507417e+06</td>\n",
+ " <td>585322.159723</td>\n",
+ " <td>4.507378e+06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>4010</td>\n",
+ " <td>595.0</td>\n",
+ " <td>600</td>\n",
+ " <td>NaN</td>\n",
+ " <td>40.712899</td>\n",
+ " <td>-73.989865</td>\n",
+ " <td>774.0</td>\n",
+ " <td>700</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>160040</td>\n",
+ " <td>69165</td>\n",
+ " <td>hub</td>\n",
+ " <td>hub</td>\n",
+ " <td>hub</td>\n",
+ " <td>583785.918305</td>\n",
+ " <td>4.506573e+06</td>\n",
+ " <td>585322.159723</td>\n",
+ " <td>4.507378e+06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>1906</td>\n",
+ " <td>170.0</td>\n",
+ " <td>600</td>\n",
+ " <td>NaN</td>\n",
+ " <td>40.712899</td>\n",
+ " <td>-73.989865</td>\n",
+ " <td>141.0</td>\n",
+ " <td>800</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>34254</td>\n",
+ " <td>69165</td>\n",
+ " <td>hub</td>\n",
+ " <td>hub</td>\n",
+ " <td>non_hub</td>\n",
+ " <td>585018.109713</td>\n",
+ " <td>4.507320e+06</td>\n",
+ " <td>585322.159723</td>\n",
+ " <td>4.507378e+06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>1192</td>\n",
+ " <td>255.0</td>\n",
+ " <td>600</td>\n",
+ " <td>NaN</td>\n",
+ " <td>40.712899</td>\n",
+ " <td>-73.989865</td>\n",
+ " <td>291.0</td>\n",
+ " <td>900</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>46446</td>\n",
+ " <td>69165</td>\n",
+ " <td>hub</td>\n",
+ " <td>hub</td>\n",
+ " <td>non_hub</td>\n",
+ " <td>583444.520998</td>\n",
+ " <td>4.506199e+06</td>\n",
+ " <td>585322.159723</td>\n",
+ " <td>4.507378e+06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>484</td>\n",
+ " <td>85.0</td>\n",
+ " <td>600</td>\n",
+ " <td>NaN</td>\n",
+ " <td>40.712899</td>\n",
+ " <td>-73.989865</td>\n",
+ " <td>57.0</td>\n",
+ " <td>1002</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>15916</td>\n",
+ " <td>69165</td>\n",
+ " <td>hub</td>\n",
+ " <td>hub</td>\n",
+ " <td>non_hub</td>\n",
+ " <td>586462.456350</td>\n",
+ " <td>4.507937e+06</td>\n",
+ " <td>585322.159723</td>\n",
+ " <td>4.507378e+06</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 35 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Unnamed: 0 index count d_cap d_tract distance end station latitude \\\n",
+ "0 0 0 5709 255.0 600 NaN 40.712899 \n",
+ "1 1 1 4010 595.0 600 NaN 40.712899 \n",
+ "2 2 2 1906 170.0 600 NaN 40.712899 \n",
+ "3 3 3 1192 255.0 600 NaN 40.712899 \n",
+ "4 4 4 484 85.0 600 NaN 40.712899 \n",
+ "\n",
+ " end station longitude o_cap o_tract ... weighted total_out \\\n",
+ "0 -73.989865 162.0 202 ... 0.0 56352 \n",
+ "1 -73.989865 774.0 700 ... 0.0 160040 \n",
+ "2 -73.989865 141.0 800 ... 0.0 34254 \n",
+ "3 -73.989865 291.0 900 ... 0.0 46446 \n",
+ "4 -73.989865 57.0 1002 ... 0.0 15916 \n",
+ "\n",
+ " total_in o_hub d_hub od_hub SX SY \\\n",
+ "0 69165 hub hub hub 585995.353038 4.507417e+06 \n",
+ "1 69165 hub hub hub 583785.918305 4.506573e+06 \n",
+ "2 69165 hub hub non_hub 585018.109713 4.507320e+06 \n",
+ "3 69165 hub hub non_hub 583444.520998 4.506199e+06 \n",
+ "4 69165 hub hub non_hub 586462.456350 4.507937e+06 \n",
+ "\n",
+ " EX EY \n",
+ "0 585322.159723 4.507378e+06 \n",
+ "1 585322.159723 4.507378e+06 \n",
+ "2 585322.159723 4.507378e+06 \n",
+ "3 585322.159723 4.507378e+06 \n",
+ "4 585322.159723 4.507378e+06 \n",
+ "\n",
+ "[5 rows x 35 columns]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Load NYC bike data - trips between census tract centroids\n",
+ "bikes = pd.read_csv(ps.examples.get_path('nyc_bikes_ct.csv'))\n",
+ "bikes.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "14042 OD pairs between census tracts after filtering out intrazonal flows\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Process data\n",
+ "\n",
+ "#Remove intrazonal flows\n",
+ "bikes = bikes[bikes['o_tract'] != bikes['d_tract']]\n",
+ "\n",
+ "#Set zero attirbute values to a small constant\n",
+ "bikes.ix[bikes.o_sq_foot == 0, 'o_sq_foot'] = 1\n",
+ "bikes.ix[bikes.d_sq_foot == 0, 'd_sq_foot'] = 1\n",
+ "bikes.ix[bikes.o_cap == 0, 'o_cap'] = 1\n",
+ "bikes.ix[bikes.d_cap == 0, 'd_cap'] = 1\n",
+ "bikes.ix[bikes.o_housing == 0, 'o_housing'] = 1\n",
+ "bikes.ix[bikes.d_housing == 0, 'd_housing'] = 1\n",
+ "\n",
+ "#Flows between tracts\n",
+ "flows = bikes['count'].values.reshape((-1,1))\n",
+ "\n",
+ "#Origin variables: square footage of buildings, housing units, total station capacity\n",
+ "o_vars = np.hstack([bikes['o_sq_foot'].values.reshape((-1,1)),\n",
+ " bikes['o_housing'].values.reshape((-1,1)),\n",
+ " bikes['o_cap'].values.reshape((-1,1))])\n",
+ "\n",
+ "#Destination variables: square footage of buildings, housing units, total station capacity\n",
+ "d_vars = np.hstack([bikes['d_sq_foot'].values.reshape((-1,1)),\n",
+ " bikes['d_housing'].values.reshape((-1,1)),\n",
+ " bikes['d_cap'].values.reshape((-1,1))])\n",
+ "\n",
+ "#Trip \"cost\" in time (seconds)\n",
+ "cost = bikes['tripduration'].values.reshape((-1,1))\n",
+ "\n",
+ "#Origin ids\n",
+ "o = bikes['o_tract'].astype(str).values.reshape((-1,1))\n",
+ "\n",
+ "#destination ids\n",
+ "d = bikes['d_tract'].astype(str).values.reshape((-1,1))\n",
+ "\n",
+ "print len(bikes), ' OD pairs between census tracts after filtering out intrazonal flows'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0.09898099 0.05748786 0.50319944 0.06920194 0.06408526 0.39371417\n",
+ " -0.00226671]\n",
+ "Adjusted psuedo R2: 0.772607968896\n",
+ "Adjusted D2: 0.77684235847\n",
+ "SRMSE: 0.924994119459\n",
+ "Sorensen similarity index: 0.746131903798\n"
+ ]
+ }
+ ],
+ "source": [
+ "#First we fit a basic gravity model and examine the parameters and model fit\n",
+ "\n",
+ "grav= Gravity(flows, o_vars, d_vars, cost, 'exp')\n",
+ "\n",
+ "print grav.params\n",
+ "\n",
+ "print 'Adjusted psuedo R2: ', grav.adj_pseudoR2\n",
+ "print 'Adjusted D2: ', grav.adj_D2\n",
+ "print 'SRMSE: ', grav.SRMSE\n",
+ "print 'Sorensen similarity index: ', grav.SSI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0.00437122 0.06794379 0.85720958 -0.00227555]\n",
+ "Adjusted psuedo R2: 0.832399632692\n",
+ "Adjusted D2: 0.836963213893\n",
+ "SRMSE: 0.794147843751\n",
+ "Sorensen similarity index: 0.777965361121\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Next we fit a production-constrained model\n",
+ "\n",
+ "prod = Production(flows, o, d_vars, cost, 'exp')\n",
+ "\n",
+ "print prod.params[-4:] #truncate to exclude balancing factors/fixed effects\n",
+ "\n",
+ "print 'Adjusted psuedo R2: ', prod.adj_pseudoR2\n",
+ "print 'Adjusted D2: ', prod.adj_D2\n",
+ "print 'SRMSE: ', prod.SRMSE\n",
+ "print 'Sorensen similarity index: ', prod.SSI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0.05281568 0.05689814 0.85161213 -0.00229343]\n",
+ "Adjusted psuedo R2: 0.83252844463\n",
+ "Adjusted D2: 0.83709408585\n",
+ "SRMSE: 0.792604389867\n",
+ "Sorensen similarity index: 0.777557481635\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Next we fit an attraction-constrained model\n",
+ "\n",
+ "att = Attraction(flows, d, o_vars, cost, 'exp')\n",
+ "\n",
+ "print att.params[-4:] #truncate to exclude balancing factors/fixed effects\n",
+ "\n",
+ "print 'Adjusted psuedo R2: ', att.adj_pseudoR2\n",
+ "print 'Adjusted D2: ', att.adj_D2\n",
+ "print 'SRMSE: ', att.SRMSE\n",
+ "print 'Sorensen similarity index: ', att.SSI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-0.00232112]\n",
+ "Adjusted psuedo R2: 0.895575966583\n",
+ "Adjusted D2: 0.900342219279\n",
+ "SRMSE: 0.627981455879\n",
+ "Sorensen similarity index: 0.818294076455\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Finally, we fit the doubly constrained model\n",
+ "\n",
+ "doub = Doubly(flows, o, d, cost, 'exp')\n",
+ "\n",
+ "print doub.params[-1:] #truncate to exclude balancing factors/fixed effects\n",
+ "\n",
+ "print 'Adjusted psuedo R2: ', doub.adj_pseudoR2\n",
+ "print 'Adjusted D2: ', doub.adj_D2\n",
+ "print 'SRMSE: ', doub.SRMSE\n",
+ "print 'Sorensen similarity index: ', doub.SSI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 2.99777896e+002 2.38580701e+001 4.17517731e-126]\n",
+ "[ 2.06092255e+002 2.60328203e+001 1.05289599e-149]\n",
+ "[ 2.07645243e+002 2.64229678e+001 3.73180682e-154]\n",
+ "[ 1.18261248e+002 2.96580921e+001 1.33362721e-193]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Next, we can test the models for violations of the equidispersion assumption of Poisson models\n",
+ "\n",
+ "#test the hypotehsis of equidispersion (var[mu] = mu) against that of QuasiPoisson (var[mu] = phi * mu)\n",
+ "#Results = [phi, tvalue, pvalue]\n",
+ "print phi_disp(grav)\n",
+ "print phi_disp(prod)\n",
+ "print phi_disp(att)\n",
+ "print phi_disp(doub)\n",
+ "\n",
+ "#We can see for all four models there is overdispersion (phi >> 0), \n",
+ "#which are statistically significant according the tvalues (large)\n",
+ "#and pvalues (essentially zero). It does however decrease as more \n",
+ "#constraints are introduced and model fit increases"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Production-constrained Poisson model standard errors and tvalues\n",
+ "[ 0.00437122 0.06794379 0.85720958 -0.00227555]\n",
+ "[ 5.00039382e-04 3.52444295e-04 6.85493176e-04 1.00141356e-06]\n",
+ "[ 8.74174692 192.77880405 1250.5005361 -2272.33530432]\n",
+ "Production-constrained QuasiPoisson model standard errors and tvalues\n",
+ "[ 0.00437122 0.06794379 0.85720958 -0.00227555]\n",
+ "[ 7.21095039e-03 5.08251633e-03 9.88533597e-03 1.44411496e-05]\n",
+ "[ 0.60619162 13.36813995 86.71527071 -157.57383972]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#As a result we can compare our standard errors and tvalues for a Poisson model to a QuasiPoisson\n",
+ "\n",
+ "print 'Production-constrained Poisson model standard errors and tvalues'\n",
+ "print prod.params[-4:]\n",
+ "print prod.std_err[-4:]\n",
+ "print prod.tvalues[-4:]\n",
+ "\n",
+ "#Fit the same model using QuasiPoisson framework\n",
+ "Quasi = Production(flows, o, d_vars, cost, 'exp', Quasi=True)\n",
+ "\n",
+ "print 'Production-constrained QuasiPoisson model standard errors and tvalues'\n",
+ "print Quasi.params[-4:]\n",
+ "print Quasi.std_err[-4:]\n",
+ "print Quasi.tvalues[-4:]\n",
+ "\n",
+ "#As we can see both models result in the same parameters (first line)\n",
+ "#We also see the QuasiPoisson results in larger standard errors (middle line)\n",
+ "#Which then results in smaller t-values (bottom line)\n",
+ "#We would even consdier rejecting the statistical significant of of the \n",
+ "#parameter estimate on destination building square footage because its abolsute \n",
+ "# value is less than 1.96 (96% confidence level) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#We can also estimate a local model which subsets the data\n",
+ "#For a production constrained model this means each local model\n",
+ "#is from one origin to all destinations. Since we get a set of \n",
+ "#parameter estimates for each origin, we can then map them.\n",
+ "\n",
+ "local_prod = prod.local()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['pvalue3',\n",
+ " 'pvalue2',\n",
+ " 'SRMSE',\n",
+ " 'pvalue0',\n",
+ " 'deviance',\n",
+ " 'adj_pseudoR2',\n",
+ " 'pvalue1',\n",
+ " 'tvalue0',\n",
+ " 'tvalue2',\n",
+ " 'tvalue3',\n",
+ " 'adj_D2',\n",
+ " 'tvalue1',\n",
+ " 'SSI',\n",
+ " 'aic',\n",
+ " 'param1',\n",
+ " 'param0',\n",
+ " 'param3',\n",
+ " 'D2',\n",
+ " 'pseudoR2',\n",
+ " 'param2']"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#There is a set of local parameter estimates, tvalues, and pvalues for each covariate\n",
+ "#And there is a set of local values for each diagnostic\n",
+ "\n",
+ "local_prod.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#Prep geometry for plotting\n",
+ "\n",
+ "#Read in census tracts for NYC\n",
+ "crs = {'datum':'WGS84', 'proj':'longlat'}\n",
+ "tracts = ps.examples.get_path('nyct2010.shp')\n",
+ "tracts = gp.read_file(tracts)\n",
+ "tracts = tracts.to_crs(crs=crs)\n",
+ "\n",
+ "#subset manhattan tracts\n",
+ "man_tracts = tracts[tracts['BoroCode'] == '1'].copy()\n",
+ "man_tracts['CT2010S'] = man_tracts['CT2010'].astype(int).astype(str)\n",
+ "\n",
+ "#Get tracts for which there are no onbservations\n",
+ "mt = set(man_tracts.CT2010S.unique())\n",
+ "lt = set(np.unique(o))\n",
+ "nt = list(mt.difference(lt))\n",
+ "no_tracts = pd.DataFrame({'no_tract':nt})\n",
+ "no_tracts = man_tracts[man_tracts.CT2010S.isin(nt)].copy()\n",
+ "\n",
+ "#Join local values to census tracts\n",
+ "local_vals = pd.DataFrame({'betas': local_prod['param3'], 'tract':np.unique(o)})\n",
+ "local_vals = pd.merge(local_vals, man_tracts[['CT2010S', 'geometry']], left_on='tract', right_on='CT2010S')\n",
+ "local_vals = gp.GeoDataFrame(local_vals)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(40.7, 40.78)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAALRCAYAAACZPC86AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdY1Ffav28UASkKKKgIArZRwUpvxl6xxV6ixoIlb/a3\nSTabzSZ5N9l3k2xLj713sQR7r0jvAiojIohgQ5RexmH4/UGJBZUyDTz3deVKYGbO95knh+985pzn\nfB6d8vJyBAKBQCAQCAQCQd1ppukABAKBQCAQCASCxooQ0wKBQCAQCAQCQT0RYlogEAgEAoFAIKgn\nQkwLBAKBQCAQCAT1RIhpgUAgEAgEAoGgnggxLRAIBAKBQCAQ1BNdTQdQGyQSSQ8gDLCUSqWy5x5b\nDPgBT4B/SKXSYxKJ5BNgFFAOmAHtpFKp1Wuu4Qb8UyqVDlbFexAIBAKBQCAQND20XkxLJBIT4L9A\nSQ2P [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11a8a6790>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Plot local \"cost\" values: darker blue is stronger distance decay; grey is no data\n",
+ "\n",
+ "fig = plt.figure(figsize=(12,12))\n",
+ "ax = fig.add_subplot(111)\n",
+ "local_vals['inv_betas'] = (local_vals['betas']*-1)\n",
+ "no_tracts['test'] = 0\n",
+ "no_tracts.plot('test', cmap='copper', ax=ax)\n",
+ "local_vals.plot('inv_betas', cmap='Blues', ax=ax)\n",
+ "plt.xlim(-74.02, -73.95)\n",
+ "plt.ylim(40.7, 40.78)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "//anaconda/envs/spint/lib/python2.7/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
+ " warnings.warn(\"No labelled objects found. \"\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(40.7, 40.78)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAALRCAYAAACZPC86AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYVFf+x/E3itLEgqKCKGAbOkiTqrH3Fqyx98Rskq3J\nZnezm90km81vs9nNbpotxh57YqyJld57GxBBxAKIIJ1hGH5/AAYVlTINPa/nyZPAzNx75uRy5zP3\nnvM9OvX19QiCIAiCIAiC0HZdNN0AQRAEQRAEQeisRJgWBEEQBEEQhHYSYVoQBEEQBEEQ2kmEaUEQ\nBEEQBEFoJxGmBUEQBEEQBKGdRJgWBEEQBEEQhHbS1XQDWkMikdgA4UB/qVQqe+ix9cAGoBZ4XyqV\nnpJIJG8BU4F6oA8wQCqVmj9lH6OBf0il0nGqeA+CIAiCIAjCs0frw7REIjEGPgaqW3hsAPAa4AoY\nAsES [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1143c89d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Plot local estimates for destination capacity: darker red is larger effect; grey is no data\n",
+ "\n",
+ "fig = plt.figure(figsize=(12,12))\n",
+ "ax = fig.add_subplot(111)\n",
+ "local_vals['cap'] = local_prod['param3']\n",
+ "no_tracts['test'] = 0\n",
+ "no_tracts.plot('test', cmap='copper', ax=ax)\n",
+ "local_vals.plot('cap', cmap='Reds', ax=ax)\n",
+ "plt.legend()\n",
+ "plt.xlim(-74.02, -73.95)\n",
+ "plt.ylim(40.7, 40.78)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(40.7, 40.78)"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAALRCAYAAACZPC86AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYlFfah28EpaugoCIK2AbESq92o7H3XmNL2Ww2+yWb\nTds0TbLZkmpce9SosRtbTGKld1AQGREpgg1RpAwwDsP3B2BQUSnvFPTc15UrgZk555knL+/85pzn\n/B6DiooKBAKBQCAQCAQCQf1ppusABAKBQCAQCASCpooQ0wKBQCAQCAQCQQMRYlogEAgEAoFAIGgg\nQkwLBAKBQCAQCAQNRIhpgUAgEAgEAoGggQgxLRAIBAKBQCAQNBAjXQdQF2QymTMQAdjK5XLlA48t\nAZYCd4Hlcrn8iEwmewsYCVQAVkA7uVxu94Q5vIHP5XL5YE28B4FAIBAIBALB04fei2mZTGYJ/Bso\nreWx [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11a5e0a50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Plot local estimates for # of housing units: darker red is larger effect; grey is no data\n",
+ "\n",
+ "fig = plt.figure(figsize=(12,12))\n",
+ "ax = fig.add_subplot(111)\n",
+ "local_vals['house'] = local_prod['param2']\n",
+ "no_tracts['test'] = 0\n",
+ "no_tracts.plot('test', cmap='copper', ax=ax)\n",
+ "local_vals.plot('house', cmap='Reds', ax=ax)\n",
+ "plt.legend()\n",
+ "plt.xlim(-74.02, -73.95)\n",
+ "plt.ylim(40.7, 40.78)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(40.7, 40.78)"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAALRCAYAAACZPC86AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYlFfah28UpVtQUBEERR0REKVXW2I0xl5j7A0Ts9ls\nsiW7KVuy2ZrdJPslsRtjS2LvvdM7CIiMiCBiQUR6G4fh+wMwqKiUdwp47uvKlcDMnPeZJ4d3fnPO\nc36PXnV1NQKBQCAQCAQCgaDptNN2AAKBQCAQCAQCQWtFiGmBQCAQCAQCgaCZCDEtEAgEAoFAIBA0\nEyGmBQKBQCAQCASCZiLEtEAgEAgEAoFA0EyEmBYIBAKBQCAQCJqJvrYDaAwymWwQEAFYyuVyxWOP\nLQcCgQfAZ3K5/KhMJvsAGAdUA12BHnK53Oo51/AC/imXy0ep4z0IBAKBQCAQCNoeOi+mZTKZGfAf\noKKB [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11f6c0890>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Plot local estimates for destination building sq footage: darker red is larger effect; grey is no data\n",
+ "\n",
+ "fig = plt.figure(figsize=(12,12))\n",
+ "ax = fig.add_subplot(111)\n",
+ "local_vals['foot'] = local_prod['param1']\n",
+ "no_tracts['test'] = 0\n",
+ "no_tracts.plot('test', cmap='copper', ax=ax)\n",
+ "local_vals.plot('foot', cmap='Reds', ax=ax)\n",
+ "plt.legend()\n",
+ "plt.xlim(-74.02, -73.95)\n",
+ "plt.ylim(40.7, 40.78)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "#Drop NA values\n",
+ "labels = ['start station longitude', 'start station latitude', 'end station longitude', 'end station latitude']\n",
+ "bikes = bikes.dropna(subset=labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#Prep OD data as vectors and then compute origin or destination focused distance-based weights\n",
+ "\n",
+ "ids = bikes['index'].reshape((-1,1))\n",
+ "origin_x = bikes['SX'].reshape((-1,1))\n",
+ "origin_y = bikes['SY'].reshape((-1,1))\n",
+ "dest_x = bikes['EX'].reshape((-1,1))\n",
+ "dest_y = bikes['EY'].reshape((-1,1))\n",
+ "\n",
+ "\n",
+ "vecs = np.hstack([ids, origin_x, origin_y, dest_x, dest_y])\n",
+ "origins = vecs[:,1:3]\n",
+ "wo = ps.weights.DistanceBand(origins, 999, alpha=-1.5, binary=False, build_sp=False, silent=True)\n",
+ "dests = vecs[:,3:5]\n",
+ "wd = ps.weights.DistanceBand(dests, 999, alpha=-1.5, binary=False, build_sp=False, silent=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.15734115508365823"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Origin focused Moran's I of OD pairs as vectors in space\n",
+ "vmo = VecMoran(vecs, wo, permutations=1)\n",
+ "vmo.I"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.21322002653342809"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Destination focused Moran's I of OD pairs as vectors in space\n",
+ "vmd = VecMoran(vecs, wd, permutations=1)\n",
+ "vmd.I"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#No substantial examples to show for spatial interaction weights\n",
+ "#Will add them once there is a working SAR Lag spatial interaction\n",
+ "#model implementation avaialble\n",
+ "#from pysal.weights.spintW import vecW, netW, ODW"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [spint]",
+ "language": "python",
+ "name": "Python [spint]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/glm_speed.ipynb b/pysal/contrib/spint/notebooks/glm_speed.ipynb
new file mode 100644
index 0000000..4100a5b
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/glm_speed.ipynb
@@ -0,0 +1,360 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from gravity import Gravity, Production, Attraction, Doubly, BaseGravity\n",
+ "from entropy import Unconstrained, ProductionConstrained, AttractionConstrained, DoublyConstrained\n",
+ "import statsmodels.formula.api as smf\n",
+ "from statsmodels.api import families\n",
+ "import matplotlib.pyplot as plt\n",
+ "%pylab inline\n",
+ "\n",
+ "import time \n",
+ "\n",
+ "def timeit(method):\n",
+ "\n",
+ " def timed(*args, **kw):\n",
+ " ts = time.time()\n",
+ " result = method(*args, **kw)\n",
+ " te = time.time()\n",
+ " elapsed = te-ts\n",
+ "\n",
+ " #print '%2.8f sec' % \\\n",
+ " #(elapsed)\n",
+ " return result, elapsed\n",
+ "\n",
+ " return timed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "@timeit\n",
+ "def gravity(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Gravity(f, o_vars, d_vars, dij, cost, framework=framework)\n",
+ " return results\n",
+ " \n",
+ "@timeit \n",
+ "def production(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Production(f, o, d_vars, dij, 'exp', framework=framework)\n",
+ " return results\n",
+ "\n",
+ "@timeit \n",
+ "def attraction(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Attraction(f, d, o_vars, dij, 'exp', framework=framework)\n",
+ " return results\n",
+ "\n",
+ "@timeit \n",
+ "def doubly(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Doubly(f, o, d, dij, 'exp', framework=framework)\n",
+ " return results\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def sim_data(n):\n",
+ " o = np.tile(np.arange(n),n)\n",
+ " d = np.repeat(np.arange(n),n)\n",
+ " loc_size = np.random.randint(25000,500000, n)\n",
+ " o_vars = np.tile(loc_size, n)\n",
+ " d_vars = np.repeat(loc_size, n)\n",
+ " dij = np.random.exponential(2500, n**2)\n",
+ " f = o_vars**.3*d_vars**.4*np.exp(dij*-.00005)\n",
+ " o = np.reshape(o, (-1,1))\n",
+ " d = np.reshape(d, (-1,1))\n",
+ " o_vars = np.reshape(o_vars, (-1,1))\n",
+ " d_vars = np.reshape(d_vars, (-1,1))\n",
+ " dij = np.reshape(dij, (-1,1))\n",
+ " f = np.reshape(f, (-1,1))\n",
+ " f = f.astype(np.int64)\n",
+ " return f, o, d, o_vars, d_vars, dij\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def loop(func, start, stop, step, framework='glm'):\n",
+ " results = []\n",
+ " for n in np.arange(start, stop, step):\n",
+ " f, o, d, o_vars, d_vars, dij = sim_data(n)\n",
+ " out, elapsed = func(f, o, d, o_vars, d_vars, dij, 'exp', framework=framework)\n",
+ " print out.params[-2:]\n",
+ " results.append(elapsed)\n",
+ " return results\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 4.00001257e-01 -5.00075026e-05]\n",
+ "[ 4.00002691e-01 -5.00057493e-05]\n",
+ "[ 4.00001649e-01 -5.00060675e-05]\n",
+ "[ 4.00001376e-01 -5.00058984e-05]\n",
+ "[ 4.00051886e-01 -5.00049596e-05]\n",
+ "[ 4.00044719e-01 -5.00050590e-05]\n",
+ "[ 4.00043934e-01 -5.00056964e-05]\n",
+ "[ 4.00043715e-01 -5.00050037e-05]\n",
+ "[ 3.00028487e-01 -5.00050902e-05]\n",
+ "[ 3.00033534e-01 -5.00062507e-05]\n",
+ "[ 3.00034197e-01 -5.00056436e-05]\n",
+ "[ 3.00031161e-01 -5.00054761e-05]\n",
+ "[ 8.75614014e+00 -5.00047571e-05]\n",
+ "[ 9.11029983e+00 -5.00060123e-05]\n",
+ "[ 8.87950504e+00 -5.00053788e-05]\n",
+ "[ 8.72216390e+00 -5.00056374e-05]\n"
+ ]
+ }
+ ],
+ "source": [
+ "glm_grav = loop(gravity, 50, 250, 50)\n",
+ "glm_prod = loop(production, 50, 250, 50)\n",
+ "glm_att = loop(attraction, 50, 250, 50)\n",
+ "glm_doub = loop(doubly, 50, 250, 50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 4.00002626e-01 -5.00057094e-05]\n",
+ "[ 4.00000936e-01 -5.00062666e-05]\n",
+ "[ 4.00001444e-01 -5.00060850e-05]\n",
+ "[ 4.00000755e-01 -5.00061246e-05]\n",
+ "[ 4.00052830e-01 -5.00067562e-05]\n",
+ "[ 4.00045674e-01 -5.00053804e-05]\n",
+ "[ 4.00045399e-01 -5.00052245e-05]\n",
+ "[ 4.00046355e-01 -5.00055175e-05]\n",
+ "[ 3.00035234e-01 -5.00067417e-05]\n",
+ "[ 3.00032374e-01 -5.00055319e-05]\n",
+ "[ 3.00032702e-01 -5.00053970e-05]\n",
+ "[ 3.00032242e-01 -5.00056510e-05]\n",
+ "[ 8.92179263e+00 -5.00048341e-05]\n",
+ "[ 9.03051268e+00 -5.00050915e-05]\n",
+ "[ 9.09915765e+00 -5.00057034e-05]\n",
+ "[ 9.09697592e+00 -5.00055190e-05]\n"
+ ]
+ }
+ ],
+ "source": [
+ "smglm_grav = loop(gravity, 50, 250, 50, framework='sm_glm')\n",
+ "smglm_prod = loop(production, 50, 250, 50, framework='sm_glm')\n",
+ "smglm_att = loop(attraction, 50, 250, 50, framework='sm_glm')\n",
+ "smglm_doub = loop(doubly, 50, 250, 50, framework='sm_glm')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "x = np.arange(50, 250, 50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x105cb9d50>"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEZCAYAAAB8culNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd8FHX6wPHPplfSSUggCRBQehEQxBIVFfsdCieIith+\nd4qnp6fSJBRFRLDfWY6iWOHEO/Q8RcGohzTpPSSQnpAe0tvO74/vZrObbApJNrtJnvfrta/M7M7O\nPrNJ5pn5VhBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghOrVoIMXWQYj24WDrAESnMAP4DSgC\n0oFvgIlt3GcMsKGN+7hQdwG7gWLgHLAL+KPJ6+uBpY28V294j6PJc85AluG1xiQCpajvrgg4D4Rc\ncORCdDBJDqI5fwFeBZYBPYE+wNvAbbYMqhWeAl4DVgDBhsf/oZKcs2EbzfBoTB5wo8n6jYbnmnqP\nBtwC [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10d145b50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(x, glm_grav, x, glm_prod, x, glm_att, x, glm_doub)\n",
+ "plt.legend(('unconstrained', 'production', 'attraction', 'doubly'))\n",
+ "plt.title('Custom GLM Framework')\n",
+ "plt.xlabel('Sample Size')\n",
+ "plt.ylabel('Seconds')"
+ ]
+ },
+ {
+ "cell_type": "code",
+<<<<<<< HEAD
+ "execution_count": 36,
+=======
+ "execution_count": 14,
+>>>>>>> sparse_glm_spint
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+<<<<<<< HEAD
+ "[<matplotlib.lines.Line2D at 0x10db8c110>,\n",
+ " <matplotlib.lines.Line2D at 0x10d83aed0>,\n",
+ " <matplotlib.lines.Line2D at 0x10db8ca10>,\n",
+ " <matplotlib.lines.Line2D at 0x10db87090>]"
+ ]
+ },
+ "execution_count": 36,
+=======
+ "<matplotlib.text.Text at 0x10d2d9c90>"
+ ]
+ },
+ "execution_count": 14,
+>>>>>>> sparse_glm_spint
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+<<<<<<< HEAD
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlcVNf9//EXmwiDCigiIgoIKK6AURNjUtJmMybBtGmW\nb7bGNE2bNmnTNo2attKmokma5WezNiaN2UxsFtHsmkribkxUNkGQRZFN2YQZlhnm/v64AwwIgjAz\nd2A+z8djHnPnMsvHEd5z5txzzgUhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIYSPDgX3AISAbWG3Z\nnwyUAActl0VWj1kO5AE5wJWOKlQIIcTA+FquPYG9wEJgJfD7bu47DfWDwQsIB/IBd/uXKIQQ4lz6\nEsQGy/UwwAOosdx26+a+ScAGwAgUoYb9vIGVKIQQYqD6EvbuqK31CmA7kGXZ/wBwGHgV8LfsG4/a\nvdOm [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10d809350>"
+=======
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEZCAYAAACJjGL9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVOX+wPHPIDuoLCKbbG6luJumpjcybbF90Wtlm3pv\nt1u231LLXNLU0rZ7f93bopmmlpUtlpm5UFqmVu5ICoIgIoKAsm9zfn88Z4YBAUFmmAG+79drXnPm\nzJlzvjMD5zvPcp4HhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYRoMdrYOwAhrCgW9Te9px7bGoEV\nQI4tAxIXJZb6f4+iCTjZOwBhF8OBX4Bc4AywHbhMf+4BYFsD9hWJOuk6wt+Spt9sKRh4D0gD8oBE\n4APgEv35SGr/PGbpzz1Wbf3j+vqZtRzzAaBCP57p9tbFhd8sNMX3KBrAEf65RdNqB3wDvAn4AqHA\nbKCk [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10bdb7d90>"
+>>>>>>> sparse_glm_spint
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(x, smglm_grav, x, smglm_prod, x, smglm_att, x, smglm_doub)\n",
+ "plt.legend(('unconstrained', 'production', 'attraction', 'doubly'))\n",
+ "plt.legend(('unconstrained', 'production', 'attraction', 'doubly'))\n",
+ "plt.title('Statsmodels GLM Framework')\n",
+ "plt.xlabel('Sample Size')\n",
+ "plt.ylabel('Seconds')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+<<<<<<< HEAD
+=======
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "f, o, d, o_vars, d_vars, dij = sim_data(100)\n",
+ "test = Production(f, o, d_vars, dij, 'exp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+>>>>>>> sparse_glm_spint
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+<<<<<<< HEAD
+ "source": []
+=======
+ "source": [
+ "test."
+ ]
+>>>>>>> sparse_glm_spint
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/local_SI.ipynb b/pysal/contrib/spint/notebooks/local_SI.ipynb
new file mode 100644
index 0000000..60e6419
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/local_SI.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import os\n",
+ "os.chdir('../')\n",
+ "from gravity import Gravity, Production, Attraction, Doubly, BaseGravity\n",
+ "import statsmodels.formula.api as smf\n",
+ "from statsmodels.api import families\n",
+ "import matplotlib.pyplot as plt\n",
+ "%pylab inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "austria = pd.read_csv('http://dl.dropbox.com/u/8649795/AT_Austria.csv')\n",
+ "austria = austria[austria['Origin'] != austria['Destination']]\n",
+ "f = austria['Data'].values\n",
+ "o = austria['Origin'].values\n",
+ "d = austria['Destination'].values\n",
+ "dij = austria['Dij'].values\n",
+ "o_vars = austria['Oi2007'].values\n",
+ "d_vars = austria['Dj2007'].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-0.00976746026969\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = Gravity(f, o_vars, d_vars, dij, 'exp')\n",
+ "print model.params[-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[-0.01699776161094757,\n",
+ " -0.0053210259160796358,\n",
+ " -0.0028594272276957211,\n",
+ " -0.006533037784217155,\n",
+ " -0.0024666647861060209,\n",
+ " -0.0058258251130860472,\n",
+ " -0.010739622617965516,\n",
+ " -0.0046867791898773659,\n",
+ " -0.0065940756391066335]"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "local = model.local(loc_index=o, locs=np.unique(o))\n",
+ "local['param2']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-0.00727113391179\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = Production(f, o, d_vars, dij, 'exp')\n",
+ "print model.params[-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[-0.016997761610949791,\n",
+ " -0.005321025916080413,\n",
+ " -0.0028594272276953325,\n",
+ " -0.0065330377842177101,\n",
+ " -0.0024666647861060209,\n",
+ " -0.0058258251130863803,\n",
+ " -0.010739622617965183,\n",
+ " -0.0046867791898770328,\n",
+ " -0.0065940756391070776]"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "local = model.local()\n",
+ "local['param2']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-0.00693754909526\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = Attraction(f, d, o_vars, dij, 'exp')\n",
+ "print model.params[-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[-0.010872636479707154,\n",
+ " -0.0054690202130680543,\n",
+ " -0.0025567421332022833,\n",
+ " -0.0051439340488994012,\n",
+ " -0.0036020461535491433,\n",
+ " -0.010088935906795271,\n",
+ " -0.012926843651020203,\n",
+ " -0.0075750287063747201,\n",
+ " -0.0081576735088411123]"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "local = model.local()\n",
+ "local['param2']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/netW.ipynb b/pysal/contrib/spint/notebooks/netW.ipynb
new file mode 100644
index 0000000..fe6a717
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/netW.ipynb
@@ -0,0 +1,305 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import os\n",
+ "os.chdir('/Users/toshan/dev/pysal/pysal/weights')\n",
+ "from spintW import netW, mat2L"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "link_list = [('a', 'b'), ('a', 'c'), ('a', 'd'), ('b', 'a'), ('b', 'c'), ('b', 'd'), ('c', 'a'), ('c', 'b'), ('c', 'd'), ('d', 'a'), ('d', 'b'), ('d', 'c')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0.],\n",
+ " [ 1., 0., 1., 1., 1., 0., 1., 1., 1., 1., 0., 1.],\n",
+ " [ 1., 1., 0., 1., 0., 1., 1., 0., 1., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 0., 1., 1., 1., 1., 0., 1., 1., 0.],\n",
+ " [ 1., 1., 0., 1., 0., 1., 1., 1., 1., 0., 1., 1.],\n",
+ " [ 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 0., 1.],\n",
+ " [ 1., 1., 0., 1., 1., 1., 1., 0., 1., 0., 1., 1.],\n",
+ " [ 0., 1., 1., 0., 1., 1., 1., 1., 0., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 1., 0., 1., 1., 0., 1., 0., 1., 1.],\n",
+ " [ 1., 0., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1.],\n",
+ " [ 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 0.]])"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w = netW(link_list)\n",
+ "w.full()[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0.]])"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w = netW(link_list, share='O')\n",
+ "w.full()[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0.],\n",
+ " [ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.],\n",
+ " [ 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0.],\n",
+ " [ 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
+ " [ 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
+ " [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
+ " [ 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
+ " [ 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
+ " [ 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.]])"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w = netW(link_list, share='D')\n",
+ "w.full()[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0., 1., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0.],\n",
+ " [ 1., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1.],\n",
+ " [ 1., 1., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 1., 1., 1., 0., 0., 1., 0., 0.],\n",
+ " [ 0., 1., 0., 1., 0., 1., 0., 0., 0., 0., 0., 1.],\n",
+ " [ 0., 0., 1., 1., 1., 0., 0., 0., 1., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 0.],\n",
+ " [ 1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 1., 0.],\n",
+ " [ 0., 0., 1., 0., 0., 1., 1., 1., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 1.],\n",
+ " [ 1., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 1.],\n",
+ " [ 0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 1., 0.]])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w = netW(link_list, share='OD')\n",
+ "w.full()[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0.]])"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w = netW(link_list, share='C')\n",
+ "w.full()[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0, 1, 1],\n",
+ " [1, 0, 1],\n",
+ " [1, 1, 0]])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mat = np.array([[0,1,1],[1,0,1],[1,1,0]])\n",
+ "mat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "adjL = mat2L(mat)\n",
+ "adjL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0., 0., 1., 1., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 1., 1.],\n",
+ " [ 1., 1., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 0., 0., 1., 1.],\n",
+ " [ 1., 1., 0., 0., 0., 0.],\n",
+ " [ 0., 0., 1., 1., 0., 0.]])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w = netW(adjL, share='C')\n",
+ "w.full()[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/sparse_categorical.ipynb b/pysal/contrib/spint/notebooks/sparse_categorical.ipynb
new file mode 100644
index 0000000..457c4d2
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/sparse_categorical.ipynb
@@ -0,0 +1,184 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from scipy import sparse as sp\n",
+ "from statsmodels.tools.tools import categorical\n",
+ "from datetime import datetime as dt\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def spcategorical(data):\n",
+ " '''\n",
+ " Returns a dummy matrix given an array of categorical variables.\n",
+ " Parameters\n",
+ " ----------\n",
+ " data : array\n",
+ " A 1d vector of the categorical variable.\n",
+ "\n",
+ " Returns\n",
+ " --------\n",
+ " dummy_matrix\n",
+ " A sparse matrix of dummy (indicator/binary) float variables for the\n",
+ " categorical data. \n",
+ "\n",
+ " '''\n",
+ " if np.squeeze(data).ndim == 1:\n",
+ " tmp_arr = np.unique(data)\n",
+ " tmp_dummy = sp.csr_matrix((0, len(data)))\n",
+ " for each in tmp_arr[:, None]:\n",
+ " row = sp.csr_matrix((each == data).astype(float))\n",
+ " tmp_dummy = sp.vstack([tmp_dummy, row])\n",
+ " tmp_dummy = tmp_dummy.T\n",
+ " return tmp_dummy\n",
+ " else:\n",
+ " raise IndexError(\"The index %s is not understood\" % col)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = np.random.randint(1,100, 10000)\n",
+ "np.allclose(spcategorical(np.array(data)).toarray(), categorical(np.array(data), drop=True))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-4-a64b538a6ade>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn [...]
+ "\u001b[0;32m<ipython-input-2-68702ba242f4>\u001b[0m in \u001b[0;36mspcategorical\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mtmp_dummy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcsr_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[ [...]
+ "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/sparse/compressed.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, arg1, shape, dtype, copy)\u001b[0m\n\u001b[1;32m 67\u001b[0m self.format)\n\u001b[1;32m 68\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcoo\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcoo_matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-- [...]
+ "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/sparse/coo.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, arg1, shape, dtype, copy)\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;34m\u001b[0m\ [...]
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "s = dt.now()\n",
+ "n = 3000\n",
+ "o = np.tile(np.arange(n),n)\n",
+ "o_dums = spcategorical(np.array(o))\n",
+ "n = 3000\n",
+ "d = np.repeat(np.arange(n),n)\n",
+ "d_dums = spcategorical(np.array(d))\n",
+ "sp.hstack((o_dums, d_dums))\n",
+ "e = dt.now()\n",
+ "print e-s"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<9000000x6000 sparse matrix of type '<type 'numpy.float64'>'\n",
+ "\twith 18000000 stored elements in Compressed Sparse Column format>"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "all_dums = sp.hstack((o_dums, d_dums))\n",
+ "all_dums"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(10000, 99)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print spcategorical(np.array(data)).toarray().shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/sparse_categorical_bottleneck.ipynb b/pysal/contrib/spint/notebooks/sparse_categorical_bottleneck.ipynb
new file mode 100644
index 0000000..51d2177
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/sparse_categorical_bottleneck.ipynb
@@ -0,0 +1,79 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from scipy import sparse as sp\n",
+ "import numpy as np\n",
+ "\n",
+ "def spcategorical(n_cat_ids):\n",
+ " '''\n",
+ " Returns a dummy matrix given an array of categorical variables.\n",
+ " Parameters\n",
+ " ----------\n",
+ " n_cat_ids : array\n",
+ " A 1d vector of the categorical labels for n observations.\n",
+ "\n",
+ " Returns\n",
+ " --------\n",
+ " dummy : array\n",
+ " A sparse matrix of dummy (indicator/binary) variables for the\n",
+ " categorical data. \n",
+ "\n",
+ " '''\n",
+ " if np.squeeze(n_cat_ids).ndim == 1:\n",
+ " cat_set = np.unique(n_cat_ids)\n",
+ " n = len(n_cat_ids)\n",
+ " index = [np.where(cat_set == id)[0].tolist()[0] for id in n_cat_ids] #This list comprehension is likely \n",
+ " print index #the most intense part of the algorithm\n",
+ " indptr = np.arange(n+1, dtype=int) \n",
+ " return sp.csr_matrix((np.ones(n), index, indptr))\n",
+ " else:\n",
+ " raise IndexError(\"The index %s is not understood\" % col)\n",
+ "\n",
+ "#If the variable, n_cat_ids, is already composed of integers and the integers are the n x 1 vector of\n",
+ "#origins or destinations in OD pairs for which w ewant to build fixed effects then there is no need to \n",
+ "#create the index variable, which probably takes the most time within this function. Instead n_cat_ids can\n",
+ "#passed directly to the csr matrix constructor and some speed-ups can be achieved. In the case where the\n",
+ "#origin/destination ids are not integers but are strings a speed-up may be possible by alterign the algorithm\n",
+ "#so that the index is build in chunks (say each origin/destination) rather than for each row of of the n x 1\n",
+ "#n_cat_ids array as is done in creating the index variable."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/sparse_categorical_speed.ipynb b/pysal/contrib/spint/notebooks/sparse_categorical_speed.ipynb
new file mode 100644
index 0000000..ae33cd2
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/sparse_categorical_speed.ipynb
@@ -0,0 +1,596 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: pylab import has clobbered these variables: ['f']\n",
+ "`%matplotlib` prevents importing * from pylab and numpy\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pysal\n",
+ "import scipy.sparse as sp\n",
+ "import itertools as iter\n",
+ "from scipy.stats import f, chisqprob\n",
+ "import numpy.linalg as la\n",
+ "import pandas as pd\n",
+ "from datetime import datetime as dt\n",
+ "import matplotlib.pyplot as plt\n",
+ "%pylab inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#OLD\n",
+ "\"\"\"\n",
+ "def spcategorical2(n_cat_ids):\n",
+ " '''\n",
+ " Returns a dummy matrix given an array of categorical variables.\n",
+ " Parameters\n",
+ " ----------\n",
+ " n_cat_ids : array\n",
+ " A 1d vector of the categorical labels for n observations.\n",
+ "\n",
+ " Returns\n",
+ " --------\n",
+ " dummy : array\n",
+ " A sparse matrix of dummy (indicator/binary) variables for the\n",
+ " categorical data. \n",
+ "\n",
+ " '''\n",
+ " if np.squeeze(n_cat_ids).ndim == 1:\n",
+ " cat_set = np.unique(n_cat_ids)\n",
+ " n = len(n_cat_ids)\n",
+ " C = len(cat_set)\n",
+ " row_map = dict((id, np.where(cat_set == id)[0]) for id in n_cat_ids)\n",
+ " indices = np.array([row_map[row] for row in n_cat_ids]).flatten()\n",
+ " indptr = np.zeros((n + 1, ), dtype=int)\n",
+ " indptr[:-1] = list(np.arange(n))\n",
+ " indptr[-1] = n \n",
+ " return sp.csr_matrix((np.ones(n), indices, indptr))\n",
+ " else:\n",
+ " raise IndexError(\"The index %s is not understood\" % col)\n",
+ "\"\"\"\n",
+ "def spcategorical2(n_cat_ids):\n",
+ " '''\n",
+ " Returns a dummy matrix given an array of categorical variables.\n",
+ " Parameters\n",
+ " ----------\n",
+ " n_cat_ids : array\n",
+ " A 1d vector of the categorical labels for n observations.\n",
+ "\n",
+ " Returns\n",
+ " --------\n",
+ " dummy : array\n",
+ " A sparse matrix of dummy (indicator/binary) variables for the\n",
+ " categorical data. \n",
+ "\n",
+ " '''\n",
+ " if np.squeeze(n_cat_ids).ndim == 1:\n",
+ " cat_set = np.unique(n_cat_ids)\n",
+ " n = len(n_cat_ids)\n",
+ " C = len(cat_set)\n",
+ " indices = n_cat_ids\n",
+ " indptr = np.arange(n+1, dtype=int) \n",
+ " return sp.csr_matrix((np.ones(n), indices, indptr))\n",
+ " else:\n",
+ " raise IndexError(\"The index %s is not understood\" % col)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def spcategorical1(data):\n",
+ " '''\n",
+ " Returns a dummy matrix given an array of categorical variables.\n",
+ " Parameters\n",
+ " ----------\n",
+ " data : array\n",
+ " A 1d vector of the categorical variable.\n",
+ "\n",
+ " Returns\n",
+ " --------\n",
+ " dummy_matrix\n",
+ " A sparse matrix of dummy (indicator/binary) variables for the\n",
+ " categorical data. \n",
+ "\n",
+ " '''\n",
+ " if np.squeeze(data).ndim == 1:\n",
+ " tmp_arr = np.unique(data)\n",
+ " tmp_dummy = sp.csr_matrix((0, len(data)))\n",
+ " for each in tmp_arr[:, None]:\n",
+ " row = sp.csr_matrix((each == data).astype(float))\n",
+ " tmp_dummy = sp.vstack([tmp_dummy, row])\n",
+ " tmp_dummy = tmp_dummy.T\n",
+ " return tmp_dummy\n",
+ " else:\n",
+ " raise IndexError(\"The index %s is not understood\" % col)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def spcategorical1a(data):\n",
+ " '''\n",
+ " Returns a dummy matrix given an array of categorical variables.\n",
+ " Parameters\n",
+ " ----------\n",
+ " data : array\n",
+ " A 1d vector of the categorical variable.\n",
+ "\n",
+ " Returns\n",
+ " --------\n",
+ " dummy_matrix\n",
+ " A sparse matrix of dummy (indicator/binary) variables for the\n",
+ " categorical data. \n",
+ "\n",
+ " '''\n",
+ " if np.squeeze(data).ndim == 1:\n",
+ " tmp_arr = np.unique(data)\n",
+ " n = len(data)\n",
+ " C = len(tmp_arr)\n",
+ " tmp_dummy = sp.dok_matrix((n, C))\n",
+ " for each in tmp_arr[:, None]:\n",
+ " row = (each == data).astype(float)\n",
+ " tmp_dummy[:,each[0]] = row.reshape((n,1))\n",
+ " return tmp_dummy.tocsr()\n",
+ " else:\n",
+ " raise IndexError(\"The index %s is not understood\" % col)\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def spcategorical1b(data):\n",
+ " '''\n",
+ " Returns a dummy matrix given an array of categorical variables.\n",
+ " Parameters\n",
+ " ----------\n",
+ " data : array\n",
+ " A 1d vector of the categorical variable.\n",
+ "\n",
+ " Returns\n",
+ " --------\n",
+ " dummy_matrix\n",
+ " A sparse matrix of dummy (indicator/binary) variables for the\n",
+ " categorical data. \n",
+ "\n",
+ " '''\n",
+ " if np.squeeze(data).ndim == 1:\n",
+ " tmp_arr = np.unique(data)\n",
+ " n = len(data)\n",
+ " C = len(tmp_arr)\n",
+ " tmp_dummy = sp.lil_matrix((n, C))\n",
+ " for each in tmp_arr[:, None]:\n",
+ " row = (each == data).astype(float)\n",
+ " tmp_dummy[:,each[0]] = row.reshape((n,1))\n",
+ " return tmp_dummy.tocsr()\n",
+ " else:\n",
+ " raise IndexError(\"The index %s is not understood\" % col)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "True\n",
+ "True\n",
+ "True\n"
+ ]
+ }
+ ],
+ "source": [
+ "n = 20\n",
+ "o = np.tile(np.arange(n),n)\n",
+ "print np.allclose(spcategorical1(o).toarray(), spcategorical2(o).toarray())\n",
+ "print np.allclose(spcategorical1(o).toarray(), spcategorical1a(o).toarray())\n",
+ "print np.allclose(spcategorical1(o).toarray(), spcategorical1b(o).toarray())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "spcat1 = []\n",
+ "for n in np.arange(25,250,25):\n",
+ " o = np.tile(np.arange(n),n)\n",
+ " s = dt.now()\n",
+ " a = spcategorical1(np.array(o))\n",
+ " e = dt.now()\n",
+ " spcat1.append((e-s).total_seconds())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "spcat1a = []\n",
+ "for n in np.arange(25,250,25):\n",
+ " o = np.tile(np.arange(n),n)\n",
+ " s = dt.now()\n",
+ " b = spcategorical1a(np.array(o))\n",
+ " e = dt.now()\n",
+ " spcat1a.append((e-s).total_seconds())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "spcat1b = []\n",
+ "for n in np.arange(25,250,25):\n",
+ " o = np.tile(np.arange(n),n)\n",
+ " s = dt.now()\n",
+ " b = spcategorical1b(np.array(o))\n",
+ " e = dt.now()\n",
+ " spcat1b.append((e-s).total_seconds())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "spcat2 = []\n",
+ "for n in np.arange(25,250,25):\n",
+ " o = np.tile(np.arange(n),n)\n",
+ " s = dt.now()\n",
+ " b = spcategorical2(np.array(o))\n",
+ " e = dt.now()\n",
+ " spcat2.append((e-s).total_seconds())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "spcat1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "spcat1a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "spcat1b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "spcat2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "x = np.arange(25, 250, 25)\n",
+ "plt.plot(x, spcat1, x, spcat1a, x, spcat1b, x, spcat2)\n",
+ "plt.legend(('spcat1', 'spcat1a', 'spcat1b', 'spcat2'))\n",
+ "plt.title('Speed of Sparse Dummy Functions')\n",
+ "plt.xlabel('Sample Size')\n",
+ "plt.ylabel('Seconds')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "###### It is obvious that spcat1 and spcat2 are the fastest functions for creating sparse dummies. The differnece between these two functions is that spcat1 creates the dummies row by row (in sparse format) and then stacks the rows using sparse.hstack() while spcat2 builds an index for the non-zero dummy variable entries and then uses this index to instanitate the entire sparse dummy matrix, somewhat simularly to how the regimes are built in pysal. The slower functions spcat1a and s [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "spcat1 = []\n",
+ "for n in np.arange(100,1000,100):\n",
+ " o = np.tile(np.arange(n),n)\n",
+ " s = dt.now()\n",
+ " b = spcategorical1(np.array(o))\n",
+ " e = dt.now()\n",
+ " spcat1.append((e-s).total_seconds())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "spcat2 = []\n",
+ "for n in np.arange(100,1000,100):\n",
+ " o = np.tile(np.arange(n),n)\n",
+ " s = dt.now()\n",
+ " b = spcategorical2(np.array(o))\n",
+ " e = dt.now()\n",
+ " spcat2.append((e-s).total_seconds())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "spcat1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "spcat2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "x = np.arange(100, 1000, 100)\n",
+ "plt.plot(x, spcat1, x, spcat2)\n",
+ "plt.legend(('spcat1', 'spcat2'))\n",
+ "plt.title('Speed of Sparse Dummy Functions')\n",
+ "plt.xlabel('Sample Size')\n",
+ "plt.ylabel('Seconds')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "###### It is clear that the spcat2 function, which uses the non-zero-value index, is more efficient than the row by row method in spcat1. However, from additional testing, it was noticed that spcat2 does use more memory and for larger n (somewhere between 10k and 20k), my notebook ran out of memory. This is likely due to to the fact that the spcat2 function requires an ($n^2$,) array ones ones, an index array of shape ($n^2$,) and an index pointer array of shape ($n^2$,). Combined t [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def concatenate_csc_matrices_by_columns(matrix1, matrix2):\n",
+ " new_data = np.concatenate((matrix1.data, matrix2.data))\n",
+ " new_indices = np.concatenate((matrix1.indices, matrix2.indices))\n",
+ " new_ind_ptr = matrix2.indptr + len(matrix1.data)\n",
+ " new_ind_ptr = new_ind_ptr[1:]\n",
+ " new_ind_ptr = np.concatenate((matrix1.indptr, new_ind_ptr))\n",
+ "\n",
+ " return csc_matrix((new_data, new_indices, new_ind_ptr))\n",
+ "\n",
+ "def spcategorical2a(n_cat_ids, unique=None):\n",
+ " '''\n",
+ " Returns a dummy matrix given an array of categorical variables.\n",
+ " Parameters\n",
+ " ----------\n",
+ " n_cat_ids : array\n",
+ " A 1d vector of the categorical labels for n observations.\n",
+ "\n",
+ " Returns\n",
+ " --------\n",
+ " dummy : array\n",
+ " A sparse matrix of dummy (indicator/binary) variables for the\n",
+ " categorical data. \n",
+ "\n",
+ " '''\n",
+ " if np.squeeze(n_cat_ids).ndim == 1:\n",
+ " n = np.size(n_cat_ids)\n",
+ " indptr = np.arange(n+1, dtype=uint32) \n",
+ " return sp.csr_matrix((np.ones(n, dtype=int8), n_cat_ids, indptr))\n",
+ " else:\n",
+ " raise IndexError(\"The index %s is not understood\" % col)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0:00:00.462530\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<12250000x3500 sparse matrix of type '<type 'numpy.float64'>'\n",
+ "\twith 12250000 stored elements in Compressed Sparse Row format>"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "n = 3500\n",
+ "o = np.tile(np.arange(n, dtype=uint16),n)\n",
+ "s = dt.now()\n",
+ "b2 = spcategorical2(o)\n",
+ "e = dt.now()\n",
+ "print e-s\n",
+ "b2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0:00:00.074958\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<12250000x3500 sparse matrix of type '<type 'numpy.int8'>'\n",
+ "\twith 12250000 stored elements in Compressed Sparse Row format>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "n = 3500\n",
+ "o = np.tile(np.arange(n, dtype=uint16),n)\n",
+ "s = dt.now()\n",
+ "b2a = spcategorical2a(o)\n",
+ "e = dt.now()\n",
+ "print e-s\n",
+ "b2a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/sparse_grav.ipynb b/pysal/contrib/spint/notebooks/sparse_grav.ipynb
new file mode 100644
index 0000000..ee08e63
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/sparse_grav.ipynb
@@ -0,0 +1,457 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from gravity import Gravity, Production, Attraction, Doubly, BaseGravity\n",
+ "from entropy import Unconstrained, ProductionConstrained, AttractionConstrained, DoublyConstrained\n",
+ "import statsmodels.formula.api as smf\n",
+ "from statsmodels.api import families\n",
+ "import matplotlib.pyplot as plt\n",
+ "%pylab inline\n",
+ "\n",
+ "import time \n",
+ "\n",
+ "def timeit(method):\n",
+ "\n",
+ " def timed(*args, **kw):\n",
+ " ts = time.time()\n",
+ " result = method(*args, **kw)\n",
+ " te = time.time()\n",
+ " elapsed = te-ts\n",
+ "\n",
+ " #print '%2.8f sec' % \\\n",
+ " #(elapsed)\n",
+ " return result, elapsed\n",
+ "\n",
+ " return timed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "@timeit\n",
+ "def gravity(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Gravity(f, o_vars, d_vars, dij, cost, framework=framework)\n",
+ " return results\n",
+ " \n",
+ "@timeit \n",
+ "def production(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Production(f, o, d_vars, dij, 'exp', framework=framework)\n",
+ " return results\n",
+ "\n",
+ "@timeit \n",
+ "def attraction(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Attraction(f, d, o_vars, dij, 'exp', framework=framework)\n",
+ " return results\n",
+ "\n",
+ "@timeit \n",
+ "def doubly(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Doubly(f, o, d, dij, 'exp', framework=framework)\n",
+ " return results\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def sim_data(n):\n",
+ " o = np.tile(np.arange(n),n)\n",
+ " d = np.repeat(np.arange(n),n)\n",
+ " loc_size = np.random.randint(25000,500000, n)\n",
+ " o_vars = np.tile(loc_size, n)\n",
+ " d_vars = np.repeat(loc_size, n)\n",
+ " dij = np.random.exponential(2500, n**2)\n",
+ " f = o_vars**.3*d_vars**.4*np.exp(dij*-.00005)\n",
+ " o = np.reshape(o, (-1,1))\n",
+ " d = np.reshape(d, (-1,1))\n",
+ " o_vars = np.reshape(o_vars, (-1,1))\n",
+ " d_vars = np.reshape(d_vars, (-1,1))\n",
+ " dij = np.reshape(dij, (-1,1))\n",
+ " f = np.reshape(f, (-1,1))\n",
+ " f = f.astype(np.int64)\n",
+ " return f, o, d, o_vars, d_vars, dij\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def loop(func, start, stop, step, framework='glm'):\n",
+ " results = []\n",
+ " for n in np.arange(start, stop, step):\n",
+ " f, o, d, o_vars, d_vars, dij = sim_data(n)\n",
+ " out, elapsed = func(f, o, d, o_vars, d_vars, dij, 'exp', framework=framework)\n",
+ " print out.params[-2:]\n",
+ " results.append(elapsed)\n",
+ " return results\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 4.00004848e-01 -5.00084038e-05]\n",
+ "[ 4.00000573e-01 -5.00062538e-05]\n",
+ "[ 4.00001709e-01 -5.00054961e-05]\n",
+ "[ 4.00001851e-01 -5.00069564e-05]\n",
+ "[ 4.00001279e-01 -5.00065682e-05]\n",
+ "[ 4.00000901e-01 -5.00061544e-05]\n",
+ "[ 4.00001603e-01 -5.00058037e-05]\n",
+ "[ 4.00001320e-01 -5.00064304e-05]\n",
+ "[ 4.00001404e-01 -5.00056856e-05]\n",
+ "[ 4.00001253e-01 -5.00063662e-05]\n",
+ "[ 4.00000851e-01 -5.00063702e-05]\n",
+ "[ 4.00000984e-01 -5.00059202e-05]\n",
+ "[ 4.00001083e-01 -5.00060371e-05]\n",
+ "[ 4.00001701e-01 -5.00061038e-05]\n",
+ "[ 4.00001469e-01 -5.00061712e-05]\n",
+ "[ 4.00001233e-01 -5.00059894e-05]\n",
+ "[ 4.00001018e-01 -5.00064219e-05]\n",
+ "[ 4.00001447e-01 -5.00059376e-05]\n",
+ "[ 4.00001685e-01 -5.00059481e-05]\n",
+ "[ 4.00039830e-01 -5.00050274e-05]\n",
+ "[ 4.00046796e-01 -5.00056265e-05]\n",
+ "[ 4.00044515e-01 -5.00048669e-05]\n",
+ "[ 4.00044456e-01 -5.00050570e-05]\n",
+ "[ 4.00045157e-01 -5.00052127e-05]\n",
+ "[ 4.00042556e-01 -5.00056352e-05]\n",
+ "[ 4.00043244e-01 -5.00050025e-05]\n",
+ "[ 4.00045189e-01 -5.00051355e-05]\n",
+ "[ 4.00045454e-01 -5.00054531e-05]\n",
+ "[ 4.00047931e-01 -5.00055669e-05]\n",
+ "[ 4.00045333e-01 -5.00056505e-05]\n",
+ "[ 4.00046490e-01 -5.00055746e-05]\n",
+ "[ 4.00045398e-01 -5.00055018e-05]\n",
+ "[ 4.00047206e-01 -5.00056325e-05]\n",
+ "[ 4.00048521e-01 -5.00058902e-05]\n",
+ "[ 4.00045749e-01 -5.00053913e-05]\n",
+ "[ 4.00045288e-01 -5.00056639e-05]\n",
+ "[ 4.00044985e-01 -5.00053463e-05]\n",
+ "[ 4.00045780e-01 -5.00053808e-05]\n",
+ "[ 3.00033188e-01 -5.00072565e-05]\n",
+ "[ 3.00032626e-01 -5.00045301e-05]\n",
+ "[ 3.00037918e-01 -5.00057608e-05]\n",
+ "[ 3.00032697e-01 -5.00054110e-05]\n",
+ "[ 3.00034233e-01 -5.00057761e-05]\n",
+ "[ 3.00035555e-01 -5.00054497e-05]\n",
+ "[ 3.00033197e-01 -5.00057455e-05]\n",
+ "[ 3.00034492e-01 -5.00058517e-05]\n",
+ "[ 3.00032975e-01 -5.00053805e-05]\n",
+ "[ 3.00033378e-01 -5.00054912e-05]\n",
+ "[ 3.00033934e-01 -5.00054718e-05]\n",
+ "[ 3.00034142e-01 -5.00057332e-05]\n",
+ "[ 3.00032985e-01 -5.00055014e-05]\n",
+ "[ 3.00032581e-01 -5.00053024e-05]\n",
+ "[ 3.00033213e-01 -5.00055054e-05]\n",
+ "[ 3.00033148e-01 -5.00054063e-05]\n",
+ "[ 3.00034026e-01 -5.00055501e-05]\n",
+ "[ 3.00033102e-01 -5.00056106e-05]\n",
+ "[ 3.00032262e-01 -5.00053987e-05]\n",
+ "[ 7.96718580e+00 -5.00039641e-05]\n",
+ "[ 8.76704129e+00 -5.00058576e-05]\n",
+ "[ 8.98014242e+00 -5.00059356e-05]\n",
+ "[ 7.90481052e+00 -5.00056110e-05]\n",
+ "[ 8.78811765e+00 -5.00052879e-05]\n",
+ "[ 8.54380348e+00 -5.00053106e-05]\n",
+ "[ 8.97843454e+00 -5.00056992e-05]\n",
+ "[ 8.52837694e+00 -5.00052285e-05]\n",
+ "[ 8.73867401e+00 -5.00055958e-05]\n",
+ "[ 9.03798321e+00 -5.00054993e-05]\n",
+ "[ 8.70060661e+00 -5.00054254e-05]\n",
+ "[ 8.49901168e+00 -5.00054887e-05]\n",
+ "[ 8.74020466e+00 -5.00054129e-05]\n",
+ "[ 8.92245665e+00 -5.00054691e-05]\n",
+ "[ 8.69150733e+00 -5.00053833e-05]\n",
+ "[ 8.84487410e+00 -5.00055906e-05]\n",
+ "[ 7.93233294e+00 -5.00054770e-05]\n",
+ "[ 8.51533586e+00 -5.00055944e-05]\n",
+ "[ 8.86541542e+00 -5.00053305e-05]\n"
+ ]
+ }
+ ],
+ "source": [
+ "grav = loop(gravity, 25, 500, 25)\n",
+ "prod = loop(production, 25, 500, 25)\n",
+ "att = loop(attraction, 25, 500, 25)\n",
+ "doub = loop(doubly, 25, 500, 25)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0.009379863739013672,\n",
+ " 0.010191917419433594,\n",
+ " 0.013580083847045898,\n",
+ " 0.02336597442626953,\n",
+ " 0.023818016052246094,\n",
+ " 0.03525900840759277,\n",
+ " 0.03714299201965332,\n",
+ " 0.047389984130859375,\n",
+ " 0.055455923080444336,\n",
+ " 0.07191991806030273,\n",
+ " 0.07992720603942871,\n",
+ " 0.10130095481872559,\n",
+ " 0.10898685455322266,\n",
+ " 0.1385822296142578,\n",
+ " 0.15839409828186035,\n",
+ " 0.18109512329101562,\n",
+ " 0.19623899459838867,\n",
+ " 0.2373828887939453,\n",
+ " 0.24484801292419434]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grav"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0.008792877197265625,\n",
+ " 0.012892961502075195,\n",
+ " 0.018072128295898438,\n",
+ " 0.02672410011291504,\n",
+ " 0.036765098571777344,\n",
+ " 0.05049705505371094,\n",
+ " 0.06424093246459961,\n",
+ " 0.08189702033996582,\n",
+ " 0.1013631820678711,\n",
+ " 0.1273641586303711,\n",
+ " 0.15144896507263184,\n",
+ " 0.17901921272277832,\n",
+ " 0.2195439338684082,\n",
+ " 0.2700672149658203,\n",
+ " 0.2963399887084961,\n",
+ " 0.332568883895874,\n",
+ " 0.3813331127166748,\n",
+ " 0.44652509689331055,\n",
+ " 0.4651219844818115]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "prod"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0.00892496109008789,\n",
+ " 0.01181483268737793,\n",
+ " 0.017676830291748047,\n",
+ " 0.025489091873168945,\n",
+ " 0.03578901290893555,\n",
+ " 0.04750704765319824,\n",
+ " 0.06312990188598633,\n",
+ " 0.07956314086914062,\n",
+ " 0.09888005256652832,\n",
+ " 0.11808300018310547,\n",
+ " 0.14717316627502441,\n",
+ " 0.175645112991333,\n",
+ " 0.20492291450500488,\n",
+ " 0.23653817176818848,\n",
+ " 0.2717468738555908,\n",
+ " 0.2954838275909424,\n",
+ " 0.3636939525604248,\n",
+ " 0.4200310707092285,\n",
+ " 0.43506598472595215]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "att"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0.01020193099975586,\n",
+ " 0.015948057174682617,\n",
+ " 0.025825023651123047,\n",
+ " 0.03924298286437988,\n",
+ " 0.05489301681518555,\n",
+ " 0.08008694648742676,\n",
+ " 0.10814595222473145,\n",
+ " 0.14121484756469727,\n",
+ " 0.18188691139221191,\n",
+ " 0.2283778190612793,\n",
+ " 0.2879500389099121,\n",
+ " 0.3248469829559326,\n",
+ " 0.3642420768737793,\n",
+ " 0.44018101692199707,\n",
+ " 0.506378173828125,\n",
+ " 0.5851249694824219,\n",
+ " 0.6679189205169678,\n",
+ " 0.7664568424224854,\n",
+ " 0.8878300189971924]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "doub"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "x = np.arange(25, 500, 25)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x10439c9d0>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEZCAYAAAB8culNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdcVfX/wPHXBRVwoKgIggzFvVem5iC1clSm5czUsm3D\nvi0t+6XmNzPTb7usHFmaaVo5KiuVNPdAHKAIssHBkCnznt8fnwtchnpVLpd7eT8fj/Pgnnk/9wLn\nfT4bhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgirpAdaWDoRomLZWToBwmr0A/YCl4Ek4F+g\np0VTZJqewBYgGUgBTgHzgQaG/VOB3Vc5NwB14+tcavvPhu0DrnLeSiAHSDdaxtxE2oWwGAkOwhTO\nqBvsR4AL4AnMRd0AK1qNCrxWX2An6ubfBpX2oUA+0MWE8zXgDDDZaFsjoA9w8TrnLQTqGS3rSx2j\nMyzW [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1031cab50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(x, grav, x, prod, x, att, x, doub)\n",
+ "plt.legend(('unconstrained', 'production', 'attraction', 'doubly'))\n",
+ "plt.title('Sparse GLM Framework')\n",
+ "plt.xlabel('Sample Size')\n",
+ "plt.ylabel('Seconds')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/sparse_scipy_optim.ipynb b/pysal/contrib/spint/notebooks/sparse_scipy_optim.ipynb
new file mode 100644
index 0000000..77d4c16
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/sparse_scipy_optim.ipynb
@@ -0,0 +1,165 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import scipy.optimize as sc\n",
+ "import scipy.sparse as sp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "data = np.random.poisson(100, (10000,1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "data = sp.csr_matrix(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0.]\n",
+ "[ 0.00025]\n",
+ "[ 0.0005]\n",
+ "[ 0.00075]\n",
+ "[ 0.00125]\n",
+ "[ 0.00175]\n",
+ "[ 0.00275]\n",
+ "[ 0.00375]\n",
+ "[ 0.00575]\n",
+ "[ 0.00775]\n",
+ "[ 0.01175]\n",
+ "[ 0.01575]\n",
+ "[ 0.02375]\n",
+ "[ 0.03175]\n",
+ "[ 0.04775]\n",
+ "[ 0.06375]\n",
+ "[ 0.09575]\n",
+ "[ 0.12775]\n",
+ "[ 0.19175]\n",
+ "[ 0.25575]\n",
+ "[ 0.38375]\n",
+ "[ 0.51175]\n",
+ "[ 0.76775]\n",
+ "[ 1.02375]\n",
+ "[ 1.53575]\n",
+ "[ 2.04775]\n",
+ "[ 3.07175]\n",
+ "[ 4.09575]\n",
+ "[ 6.14375]\n",
+ "[ 8.19175]\n",
+ "[ 12.28775]\n",
+ "[ 16.38375]\n",
+ "[ 24.57575]\n",
+ "[ 32.76775]\n",
+ "[ 49.15175]\n",
+ "[ 65.53575]\n",
+ "[ 98.30375]\n",
+ "[ 131.07175]\n",
+ "[ 131.07175]\n",
+ "[ 114.68775]\n",
+ "[ 81.91975]\n",
+ "[ 106.49575]\n",
+ "[ 90.11175]\n",
+ "[ 102.39975]\n",
+ "[ 94.20775]\n",
+ "[ 100.35175]\n",
+ "[ 102.39975]\n",
+ "[ 99.32775]\n",
+ "[ 98.30375]\n",
+ "[ 99.83975]\n",
+ "[ 100.35175]\n",
+ "[ 99.58375]\n",
+ "[ 100.09575]\n",
+ "[ 99.71175]\n",
+ "[ 99.58375]\n",
+ "[ 99.77575]\n",
+ "[ 99.83975]\n",
+ "[ 99.74375]\n",
+ "[ 99.80775]\n",
+ "[ 99.75975]\n",
+ "[ 99.74375]\n",
+ "[ 99.76775]\n",
+ "[ 99.77575]\n",
+ "[ 99.76375]\n",
+ "[ 99.77175]\n",
+ "[ 99.76575]\n",
+ "[ 99.76975]\n",
+ "[ 99.76675]\n",
+ "[ 99.76875]\n",
+ "[ 99.76725]\n",
+ "[ 99.76825]\n",
+ "[ 99.7675]\n",
+ "[ 99.76725]\n",
+ "[ 99.767625]\n",
+ "[ 99.76775]\n",
+ "[ 99.7675625]\n",
+ "Optimization terminated successfully.\n",
+ " Current function value: -3594470.473058\n",
+ " Iterations: 38\n",
+ " Function evaluations: 76\n",
+ "[ 99.767625]\n"
+ ]
+ }
+ ],
+ "source": [
+ "def poiss_loglike(x, data, a):\n",
+ " print x\n",
+ " return -np.sum(data*np.log(x)-x)*a\n",
+ "\n",
+ "\n",
+ "params = sc.fmin(poiss_loglike, 0, args=(data ,1))\n",
+ "print params"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/sparse_vs_dense_grav.ipynb b/pysal/contrib/spint/notebooks/sparse_vs_dense_grav.ipynb
new file mode 100644
index 0000000..3fbc7f6
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/sparse_vs_dense_grav.ipynb
@@ -0,0 +1,499 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from gravity import Gravity, Production, Attraction, Doubly, BaseGravity\n",
+ "from entropy import Unconstrained, ProductionConstrained, AttractionConstrained, DoublyConstrained\n",
+ "import statsmodels.formula.api as smf\n",
+ "from statsmodels.api import families\n",
+ "import matplotlib.pyplot as plt\n",
+ "%pylab inline\n",
+ "\n",
+ "import time \n",
+ "\n",
+ "def timeit(method):\n",
+ "\n",
+ " def timed(*args, **kw):\n",
+ " ts = time.time()\n",
+ " result = method(*args, **kw)\n",
+ " te = time.time()\n",
+ " elapsed = te-ts\n",
+ "\n",
+ " #print '%2.8f sec' % \\\n",
+ " #(elapsed)\n",
+ " return result, elapsed\n",
+ "\n",
+ " return timed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "@timeit\n",
+ "def gravity(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Gravity(f, o_vars, d_vars, dij, cost, framework=framework)\n",
+ " return results\n",
+ " \n",
+ "@timeit \n",
+ "def production(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Production(f, o, d_vars, dij, 'exp', framework=framework)\n",
+ " return results\n",
+ "\n",
+ "@timeit \n",
+ "def attraction(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Attraction(f, d, o_vars, dij, 'exp', framework=framework)\n",
+ " return results\n",
+ "\n",
+ "@timeit \n",
+ "def doubly(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
+ " results = Doubly(f, o, d, dij, 'exp', framework=framework)\n",
+ " return results\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def sim_data(n):\n",
+ " o = np.tile(np.arange(n),n)\n",
+ " d = np.repeat(np.arange(n),n)\n",
+ " loc_size = np.random.randint(25000,500000, n)\n",
+ " o_vars = np.tile(loc_size, n)\n",
+ " d_vars = np.repeat(loc_size, n)\n",
+ " dij = np.random.exponential(2500, n**2)\n",
+ " f = o_vars**.3*d_vars**.4*np.exp(dij*-.00005)\n",
+ " o = np.reshape(o, (-1,1))\n",
+ " d = np.reshape(d, (-1,1))\n",
+ " o_vars = np.reshape(o_vars, (-1,1))\n",
+ " d_vars = np.reshape(d_vars, (-1,1))\n",
+ " dij = np.reshape(dij, (-1,1))\n",
+ " f = np.reshape(f, (-1,1))\n",
+ " f = f.astype(np.int64)\n",
+ " return f, o, d, o_vars, d_vars, dij\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def loop(func, start, stop, step, framework='glm'):\n",
+ " results = []\n",
+ " for n in np.arange(start, stop, step):\n",
+ " f, o, d, o_vars, d_vars, dij = sim_data(n)\n",
+ " out, elapsed = func(f, o, d, o_vars, d_vars, dij, 'exp', framework=framework)\n",
+ " print out.params[-2:]\n",
+ " results.append(elapsed)\n",
+ " return results\n",
+ " \n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#copy results over from notebook in sparse branch\n",
+ "sp_grav = np.array([0.009379863739013672,\n",
+ " 0.010191917419433594,\n",
+ " 0.013580083847045898,\n",
+ " 0.02336597442626953,\n",
+ " 0.023818016052246094,\n",
+ " 0.03525900840759277,\n",
+ " 0.03714299201965332,\n",
+ " 0.047389984130859375,\n",
+ " 0.055455923080444336,\n",
+ " 0.07191991806030273,\n",
+ " 0.07992720603942871,\n",
+ " 0.10130095481872559,\n",
+ " 0.10898685455322266,\n",
+ " 0.1385822296142578,\n",
+ " 0.15839409828186035,\n",
+ " 0.18109512329101562,\n",
+ " 0.19623899459838867,\n",
+ " 0.2373828887939453,\n",
+ " 0.24484801292419434])\n",
+ "\n",
+ "sp_prod = np.array([0.008792877197265625,\n",
+ " 0.012892961502075195,\n",
+ " 0.018072128295898438,\n",
+ " 0.02672410011291504,\n",
+ " 0.036765098571777344,\n",
+ " 0.05049705505371094,\n",
+ " 0.06424093246459961,\n",
+ " 0.08189702033996582,\n",
+ " 0.1013631820678711,\n",
+ " 0.1273641586303711,\n",
+ " 0.15144896507263184,\n",
+ " 0.17901921272277832,\n",
+ " 0.2195439338684082,\n",
+ " 0.2700672149658203,\n",
+ " 0.2963399887084961,\n",
+ " 0.332568883895874,\n",
+ " 0.3813331127166748,\n",
+ " 0.44652509689331055,\n",
+ " 0.4651219844818115])\n",
+ "\n",
+ "sp_att = np.array([0.00892496109008789,\n",
+ " 0.01181483268737793,\n",
+ " 0.017676830291748047,\n",
+ " 0.025489091873168945,\n",
+ " 0.03578901290893555,\n",
+ " 0.04750704765319824,\n",
+ " 0.06312990188598633,\n",
+ " 0.07956314086914062,\n",
+ " 0.09888005256652832,\n",
+ " 0.11808300018310547,\n",
+ " 0.14717316627502441,\n",
+ " 0.175645112991333,\n",
+ " 0.20492291450500488,\n",
+ " 0.23653817176818848,\n",
+ " 0.2717468738555908,\n",
+ " 0.2954838275909424,\n",
+ " 0.3636939525604248,\n",
+ " 0.4200310707092285,\n",
+ " 0.43506598472595215])\n",
+ "\n",
+ "sp_doub = np.array([0.01020193099975586,\n",
+ " 0.015948057174682617,\n",
+ " 0.025825023651123047,\n",
+ " 0.03924298286437988,\n",
+ " 0.05489301681518555,\n",
+ " 0.08008694648742676,\n",
+ " 0.10814595222473145,\n",
+ " 0.14121484756469727,\n",
+ " 0.18188691139221191,\n",
+ " 0.2283778190612793,\n",
+ " 0.2879500389099121,\n",
+ " 0.3248469829559326,\n",
+ " 0.3642420768737793,\n",
+ " 0.44018101692199707,\n",
+ " 0.506378173828125,\n",
+ " 0.5851249694824219,\n",
+ " 0.6679189205169678,\n",
+ " 0.7664568424224854,\n",
+ " 0.8878300189971924])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 4.00003765e-01 -5.00082127e-05]\n",
+ "[ 4.00002697e-01 -5.00066666e-05]\n",
+ "[ 3.99999915e-01 -5.00055984e-05]\n",
+ "[ 4.00000861e-01 -5.00064292e-05]\n",
+ "[ 4.00001140e-01 -5.00063276e-05]\n",
+ "[ 4.00001953e-01 -5.00062703e-05]\n",
+ "[ 4.00001310e-01 -5.00062954e-05]\n",
+ "[ 4.00000599e-01 -5.00060056e-05]\n",
+ "[ 4.00001176e-01 -5.00056830e-05]\n",
+ "[ 4.00001582e-01 -5.00061861e-05]\n",
+ "[ 4.00001745e-01 -5.00066448e-05]\n",
+ "[ 4.00001078e-01 -5.00061603e-05]\n",
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+ "[ 4.00001380e-01 -5.00057114e-05]\n",
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+ "[ 4.00046316e-01 -5.00056376e-05]\n",
+ "[ 4.00045989e-01 -5.00051992e-05]\n",
+ "[ 4.00044818e-01 -5.00054746e-05]\n",
+ "[ 4.00045085e-01 -5.00051760e-05]\n",
+ "[ 4.00045998e-01 -5.00054688e-05]\n",
+ "[ 4.00045219e-01 -5.00055610e-05]\n",
+ "[ 4.00047317e-01 -5.00056556e-05]\n",
+ "[ 4.00045808e-01 -5.00055909e-05]\n",
+ "[ 4.00045374e-01 -5.00053382e-05]\n",
+ "[ 4.00046947e-01 -5.00056095e-05]\n",
+ "[ 4.00046361e-01 -5.00056381e-05]\n",
+ "[ 3.00034016e-01 -5.00057525e-05]\n",
+ "[ 3.00033252e-01 -5.00052744e-05]\n",
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+ "[ 3.00032487e-01 -5.00052466e-05]\n",
+ "[ 3.00032907e-01 -5.00052725e-05]\n",
+ "[ 3.00033558e-01 -5.00059839e-05]\n",
+ "[ 3.00032909e-01 -5.00053619e-05]\n",
+ "[ 3.00031875e-01 -5.00051895e-05]\n",
+ "[ 3.00032290e-01 -5.00053379e-05]\n",
+ "[ 3.00032978e-01 -5.00053514e-05]\n",
+ "[ 3.00033905e-01 -5.00054823e-05]\n",
+ "[ 3.00031747e-01 -5.00056422e-05]\n",
+ "[ 3.00032427e-01 -5.00053723e-05]\n",
+ "[ 3.00031778e-01 -5.00052621e-05]\n",
+ "[ 3.00034105e-01 -5.00057004e-05]\n",
+ "[ 3.00033159e-01 -5.00053856e-05]\n",
+ "[ 3.00033450e-01 -5.00056404e-05]\n",
+ "[ 3.00032449e-01 -5.00053773e-05]\n",
+ "[ 9.05975391e+00 -5.00058173e-05]\n",
+ "[ 7.89807159e+00 -5.00052349e-05]\n",
+ "[ 8.90961019e+00 -5.00053092e-05]\n",
+ "[ 8.65670296e+00 -5.00054583e-05]\n",
+ "[ 8.84397080e+00 -5.00052016e-05]\n",
+ "[ 8.47393885e+00 -5.00054873e-05]\n",
+ "[ 8.29266674e+00 -5.00049815e-05]\n",
+ "[ 8.38421867e+00 -5.00060056e-05]\n",
+ "[ 8.82217852e+00 -5.00054169e-05]\n",
+ "[ 8.88570848e+00 -5.00056774e-05]\n",
+ "[ 8.36139835e+00 -5.00054640e-05]\n",
+ "[ 8.50520290e+00 -5.00056668e-05]\n",
+ "[ 8.93418525e+00 -5.00053669e-05]\n",
+ "[ 8.65710610e+00 -5.00055227e-05]\n",
+ "[ 7.73443865e+00 -5.00057702e-05]\n",
+ "[ 8.63212583e+00 -5.00053614e-05]\n",
+ "[ 7.95999344e+00 -5.00053746e-05]\n",
+ "[ 8.69617289e+00 -5.00056379e-05]\n",
+ "[ 8.54361439e+00 -4.99613448e-05]\n"
+ ]
+ }
+ ],
+ "source": [
+ "grav = loop(gravity, 25, 500, 25)\n",
+ "prod = loop(production, 25, 500, 25)\n",
+ "att = loop(attraction, 25, 500, 25)\n",
+ "doub = loop(doubly, 25, 500, 25)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "x = np.arange(25, 500, 25)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x10cf792d0>"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEZCAYAAABrUHmEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4FOX2wPFvCASkREIvAamKoEiRzsVQVIoUQQQEKYIg\nCHjFiorC715REJQLKAJ6pShFil4UBKSEXjX0Ji2UUKQHAqnz++PMspslCRuSzWw5n+fZZ3dnZ3bP\nzs7OmbfMO6CUUkoppZRSSimllFJKKaWUUkoppZRSSimllFJKqVSUBqKBADe893Bgphve9151BZZZ\nHcRdDMf1dRYO9HZbJKnIltUf6CEaAhuBK8BFYD3wuKURZY0wIAnZSUQDJ4G5eOZ3Lw5MBU4jsR4B\nvgMeMl8vg3yXlLbh4eZrg52mv2ZO/yiVzxxOyn/YJKCcq4FnoZ7Augy+xwkgH2BkOJo73e098wGf\nA8eA [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10cea8550>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(x, grav, x, sp_grav)\n",
+ "plt.legend(('dense', 'sparse'))\n",
+ "plt.title('Sparse vs Dense GLM Unconstrained Gravity Model')\n",
+ "plt.xlabel('Sample Size')\n",
+ "plt.ylabel('Seconds')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x10cdc6450>"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcE/X9x/FXNsshlyyH3LiIWqq2ogjKUdiqaL3woCqe\nQL3a+qtaqRXaqmsPUQqCWu9abi9UPFArXusJqAgiKCDIUpD7lEtgd+f3x2eymc0muwGSTDZ5Px+P\nPJKZycx8883MfOb7/c58B0REREREREREREREREREREREREREREQkDRQAK5K07G1AfpKWXVMNAx5P\n0rKLgVOStOz98Rpwhd+JqEYx8eVZPlAG5CQzMfHoBXwMbAE2Ah8CJ/iaotQowP6Abe5rBfAM6fnb\nW2E7+XdYWpcCY4EfudPzib0xFbrTbogYf6M7/o4Y6xwElLrr2wrMAc7av+RXUkBigkQRcFUClhOv\nVsAT [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10cf670d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(x, prod, x, sp_prod)\n",
+ "plt.legend(('dense', 'sparse'))\n",
+ "plt.title('Sparse vs Dense GLM Production-Constrained Gravity Model')\n",
+ "plt.xlabel('Sample Size')\n",
+ "plt.ylabel('Seconds')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x10d49c410>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEZCAYAAAB8culNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmYFNXZ9/FvTzPsIMMi+yZoENwRFUEZEdQ8UTSgLIkR\njEbjZaJR4pbEOOrzuhCJPBiTKEZxYRNFo6gRRccFBZeALIoIAiKrAyj7NlPvH3fVdE9Pd8/W3dU9\n/ftcV89UV1VXnzpdde4659QCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIpLGOgE7gYDfCUkz/wD+lKRl\nlwBHJGnZ1bEUONPvRFSgsnmWD6xLblKqpj/wAfA9sBV4HzjZ1xSlRj72o+10X+uAGaTnurcFJgHr\nsbSuAp4AfuRO74KtS06Uzxa4066LGH+9O/6OCr47353v5ojxY4D3IsZNBu6uYHk1sQYYmMTlRzoK\nmAl8 [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10cfb8910>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(x, att, x, sp_att)\n",
+ "plt.legend(('dense', 'sparse'))\n",
+ "plt.title('Sparse vs Dense GLM Attraction-Constrained Gravity Model')\n",
+ "plt.xlabel('Sample Size')\n",
+ "plt.ylabel('Seconds')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x10d49ce50>"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEZCAYAAACJjGL9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XecVNX9//HX7NBU2lKk61I0qEmkWEAQVoJGfwqoqBQL\nGFsSW+ygohgLiBIJ5BuNohQbgiJRbAi6ahSwQRQQEUNvSpO+sLv398fnzs7s7M7sbJm9U97Px2Me\nM7fMnXPP3Hs/95xz77kgIiIiIiIiIiIiIiIiIiIiIiIiIiIiImkiG1gXZfpk4MEqSUniOArYDfji\nsOyRwPNxWG55XQq853UiSjGS2PMsB7iqrD+QUdYveKg78BmwE9gG/Ac4ydMUVY1soADbMXdjB61X\nSMx1bwY8A2zA0vojMAn4lTs9C1uXkra7ke60m8LG3+yOvz/Cbw4F8gnmz/+A54BjyrUGZee4r/Lw\nYev7 [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10d4b1a90>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(x, doub, x, sp_doub)\n",
+ "plt.legend(('dense', 'sparse'))\n",
+ "plt.title('Sparse vs Dense GLM Doubly-Constrained Gravity Model')\n",
+ "plt.xlabel('Sample Size')\n",
+ "plt.ylabel('Seconds')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/test_grav.ipynb b/pysal/contrib/spint/notebooks/test_grav.ipynb
new file mode 100644
index 0000000..ed54516
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/test_grav.ipynb
@@ -0,0 +1,293 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "//anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
+ " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import os\n",
+ "os.chdir('../')\n",
+ "from gravity import Gravity, Production, Attraction, Doubly, BaseGravity\n",
+ "import statsmodels.formula.api as smf\n",
+ "from statsmodels.api import families\n",
+ "import matplotlib.pyplot as plt\n",
+ "%pylab inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "n = 3000\n",
+ "o = np.tile(np.arange(n),n)\n",
+ "d = np.repeat(np.arange(n),n)\n",
+ "loc_size = np.random.randint(25000,500000, n)\n",
+ "o_vars = np.tile(loc_size, n)\n",
+ "d_vars = np.repeat(loc_size, n)\n",
+ "dij = np.random.exponential(2500, n**2)\n",
+ "f = o_vars**.3*d_vars**.4*np.exp(dij*-.00005)\n",
+ "o = np.reshape(o, (-1,1))\n",
+ "d = np.reshape(d, (-1,1))\n",
+ "o_vars = np.reshape(o_vars, (-1,1))\n",
+ "d_vars = np.reshape(d_vars, (-1,1))\n",
+ "dij = np.reshape(dij, (-1,1))\n",
+ "f = np.reshape(f, (-1,1))\n",
+ "f = f.astype(np.int64)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 14.3 s, sys: 1.46 s, total: 15.8 s\n",
+ "Wall time: 7.41 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<gravity.Gravity at 0x118346850>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time Gravity(f, o_vars, d_vars, dij, 'exp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 38.3 s, sys: 4 s, total: 42.3 s\n",
+ "Wall time: 24.6 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<gravity.Production at 0x118353510>"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time Production(f, o, d_vars, dij, 'exp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 36 s, sys: 4.25 s, total: 40.2 s\n",
+ "Wall time: 21.4 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<gravity.Attraction at 0x118353310>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time Attraction(f, d, o_vars, dij, 'exp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1min 19s, sys: 6.3 s, total: 1min 25s\n",
+ "Wall time: 37.4 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<gravity.Doubly at 0x118353250>"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time Doubly(f, o, d, dij, 'exp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from glm import GLM\n",
+ "from iwls import iwls\n",
+ "import line_profiler\n",
+ "import IPython\n",
+ "ip = IPython.get_ipython()\n",
+ "ip.define_magic('lprun', line_profiler.magic_lprun)\n",
+ "instance = Production(f, o, d_vars, dij, 'exp')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "correct sparse\n"
+ ]
+ }
+ ],
+ "source": [
+ "%lprun -f BaseGravity.__init__ instance.__init__(f, o, d_vars, dij, 'exp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "glm_inst = GLM(instance.y, instance.X, family=families.Poisson())\n",
+ "%lprun -f GLM.__init__ glm_inst.__init__(instance.y, instance.X, family=families.Poisson())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "%lprun -f iwls iwls(instance.y, instance.X, family=families.Poisson(), offset=None, y_fix=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/notebooks/validate_gravity.ipynb b/pysal/contrib/spint/notebooks/validate_gravity.ipynb
new file mode 100644
index 0000000..4eace91
--- /dev/null
+++ b/pysal/contrib/spint/notebooks/validate_gravity.ipynb
@@ -0,0 +1,966 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#import sys\n",
+ "#sys.path.append('/Users/toshan/dev/pysal/pysal/contrib/glm')\n",
+ "#from utils import \n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import sys\n",
+ "sys.path.append('/Users/toshan/dev/pysal/pysal/contrib/spint')\n",
+ "from gravity import Gravity, Production, Attraction, Doubly, BaseGravity\n",
+ "import gravity\n",
+ "from spint_utils import sorensen\n",
+ "import statsmodels.formula.api as smf\n",
+ "from statsmodels.api import families as families"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "austria = pd.read_csv('http://dl.dropbox.com/u/8649795/AT_Austria.csv')\n",
+ "austria = austria[austria['Origin'] != austria['Destination']]\n",
+ "f = austria['Data'].values\n",
+ "o = austria['Origin'].values\n",
+ "d = austria['Destination'].values\n",
+ "dij = austria['Dij'].values\n",
+ "o_vars = austria['Oi2007'].values\n",
+ "d_vars = austria['Dj2007'].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0.44051324 0.5144978 -0.00976746]\n",
+ "[ 0.44051324 0.5144978 -0.00976746]\n"
+ ]
+ }
+ ],
+ "source": [
+ "grav = Gravity(f, o_vars, d_vars, dij, 'exp', framework='glm')\n",
+ "print grav.params\n",
+ "\n",
+ "gravity = smf.glm('Data~np.log(Oi2007)+np.log(Dj2007)+Dij-1', family=families.Poisson(), data=austria).fit()\n",
+ "print gravity.params.values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-1.11700938 0.5696138 1.03487752 -0.51400641 -0.23320154 0.09225167\n",
+ " -0.42761955 0.03771866 -0.0922097 0.89278717 -0.00727113]\n",
+ "[-1.11700938 0.56961379 1.03487751 -0.51400641 -0.23320154 0.09225166\n",
+ " -0.42761955 0.03771866 -0.0922097 0.89278717 -0.00727113]\n"
+ ]
+ }
+ ],
+ "source": [
+ "production = Production(f, o, d_vars, dij, 'exp', framework='glm')\n",
+ "print production.params\n",
+ "\n",
+ "gravity = smf.glm('Data~Origin+np.log(Dj2007)+Dij-1', family=families.Poisson(), data=austria).fit()\n",
+ "print gravity.params.values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-0.88439723 0.73740882 1.04332355 -0.75977722 -0.26060911 -0.1879365\n",
+ " -0.67530313 -0.19871953 -0.39900098 0.89235874 -0.00693755]\n",
+ "[-0.88439723 0.73740882 1.04332355 -0.75977722 -0.26060911 -0.1879365\n",
+ " -0.67530313 -0.19871953 -0.39900099 0.89235874 -0.00693755]\n"
+ ]
+ }
+ ],
+ "source": [
+ "attraction = Attraction(f, d, o_vars, dij, 'exp', framework='glm')\n",
+ "print attraction.params\n",
+ "\n",
+ "gravity = smf.glm('Data~np.log(Oi2007)+Destination + Dij-1', family=families.Poisson(), data=austria).fit()\n",
+ "print gravity.params.values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-0.00791533]\n",
+ "[-0.00791533]\n"
+ ]
+ }
+ ],
+ "source": [
+ "doubly = Doubly(f, o, d, dij, 'exp', framework='glm')\n",
+ "print doubly.params[-1:]\n",
+ "\n",
+ "gravity = smf.glm('Data~Origin+Destination+Dij-1', family=families.Poisson(), data=austria).fit()\n",
+ "print gravity.params.values[-1:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.68822035942795623"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorensen(grav)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.74061920338298048"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorensen(production)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.75063449829283135"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorensen(attraction)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.81185211090388831"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorensen(doubly)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.68822035942795623"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grav.SSI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.74061920338298048"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "production.SSI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.75063449829283135"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "attraction.SSI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.81185211090388831"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "doubly.SSI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "23.103958298546722"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grav.srmse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "26.41275121188659"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "doubly.srmse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "26.023881688328146"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "production.srmse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.81258943875040157"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grav.pseudoR2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9099205901109565"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "production.pseudoR2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.94353991219813327"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "doubly.pseudoR2\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.81255536218830349"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grav.adj_pseudoR2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.83445652029136452"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grav.D2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.82965815856067948"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grav.adj_D2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.94666192089648937"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "doubly.D2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.92987030340093968"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "doubly.adj_D2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "y = grav.y\n",
+ "yhat = grav.yhat\n",
+ "n = grav.n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 124,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "28741.324123392122"
+ ]
+ },
+ "execution_count": 124,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(np.sum((yhat-y)**2)/n)**.5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "y = production.y\n",
+ "yhat = production.yhat\n",
+ "n = production.n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 126,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "32373.708820280212"
+ ]
+ },
+ "execution_count": 126,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.sqrt(np.sum((y-yhat)**2)/n) \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 127,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "y = doubly.y\n",
+ "yhat = doubly.yhat\n",
+ "n = doubly.n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "32857.462507586919"
+ ]
+ },
+ "execution_count": 128,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.sqrt(np.sum((y-yhat)**2)/n) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "72"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(doubly.y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "72"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "doubly.n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1244.0972222222222"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.mean(doubly.y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3872.2890919495749"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.sqrt(np.mean((y-yhat)**2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 9.78988280e+02, 2.26003279e+03, 1.04038742e+02,\n",
+ " 3.38382580e+02, 1.90458075e+02, 8.67908467e+01,\n",
+ " 4.37554720e+01, 1.35532201e+01, 1.02693176e+03,\n",
+ " 1.43579537e+04, 5.02646536e+02, 1.61314478e+03,\n",
+ " 1.60124044e+03, 5.84144805e+02, 2.98377549e+02,\n",
+ " 9.55604104e+01, 2.92086883e+03, 1.76899160e+04,\n",
+ " 9.49267467e+02, 3.14910952e+03, 2.73315395e+03,\n",
+ " 1.01972797e+03, 5.15779061e+02, 1.64177257e+02,\n",
+ " 1.73496758e+02, 7.99088484e+02, 1.22486311e+03,\n",
+ " 9.31563443e+02, 6.29698756e+02, 5.91117070e+02,\n",
+ " 4.15424488e+02, 1.31747905e+02, 4.49674437e+02,\n",
+ " 2.04361676e+03, 3.23802841e+03, 7.42345992e+02,\n",
+ " 1.00234547e+03, 6.00432512e+02, 3.13590596e+02,\n",
+ " 9.69658353e+01, 3.29132064e+02, 2.63792996e+03,\n",
+ " 3.65458094e+03, 6.52540343e+02, 1.30346098e+03,\n",
+ " 1.16517842e+03, 6.73203489e+02, 2.21973821e+02,\n",
+ " 1.47356669e+02, 9.45479887e+02, 1.33962391e+03,\n",
+ " 6.01828982e+02, 7.67131590e+02, 1.14476805e+03,\n",
+ " 6.43385796e+02, 2.00425139e+02, 7.41169755e+01,\n",
+ " 4.81822820e+02, 6.76007805e+02, 4.21969575e+02,\n",
+ " 3.99722086e+02, 6.59873779e+02, 6.41890452e+02,\n",
+ " 9.85596546e+02, 2.44225078e+01, 1.64157859e+02,\n",
+ " 2.28909306e+02, 1.42362371e+02, 1.31485029e+02,\n",
+ " 2.31461478e+02, 2.12717926e+02, 1.04848355e+03])"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "yhat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "26.799427831357789"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.sqrt(np.sum(((y-yhat)**2)/n)) / np.sum(y/n)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.005019646605929266"
+ ]
+ },
+ "execution_count": 90,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.sqrt(np.sum((y-yhat)**2)/n) / np.sum(y)/float(n)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1244.0972222222222"
+ ]
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.sum(y)/float(n)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1244.0972222222222"
+ ]
+ },
+ "execution_count": 86,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.mean(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 130,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "nan"
+ ]
+ },
+ "execution_count": 130,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.nan * -1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 132,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 132,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.nan == np.nan"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "np"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/spint/tests/test_count_model.py b/pysal/contrib/spint/tests/test_count_model.py
new file mode 100644
index 0000000..4aed5fc
--- /dev/null
+++ b/pysal/contrib/spint/tests/test_count_model.py
@@ -0,0 +1,87 @@
+"""
+Tests for CountModel class, which includes various linear models designed for
+count data
+
+Test data is the Columbus dataset after it has been rounded to integers to act
+as count data. Results are verified using corresponding functions in R.
+
+"""
+
+__author__ = 'Taylor Oshan tayoshan at gmail.com'
+
+import unittest
+import numpy as np
+import pysal
+from pysal.contrib.spint.count_model import CountModel
+from pysal.contrib.glm.family import Poisson
+
+class TestCountModel(unittest.TestCase):
+ """Tests CountModel class"""
+
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ y = np.array(db.by_col("HOVAL"))
+ y = np.reshape(y, (49,1))
+ self.y = np.round(y).astype(int)
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+
+ def test_PoissonGLM(self):
+ model = CountModel(self.y, self.X, family=Poisson())
+ results = model.fit('GLM')
+ np.testing.assert_allclose(results.params, [3.92159085, 0.01183491,
+ -0.01371397], atol=1.0e-8)
+ self.assertIsInstance(results.family, Poisson)
+ self.assertEqual(results.n, 49)
+ self.assertEqual(results.k, 3)
+ self.assertEqual(results.df_model, 2)
+ self.assertEqual(results.df_resid, 46)
+ np.testing.assert_allclose(results.yhat,
+ [ 51.26831574, 50.15022766, 40.06142973, 34.13799739,
+ 28.76119226, 42.6836241 , 55.64593703, 34.08277997,
+ 40.90389582, 37.19727958, 23.47459217, 26.12384057,
+ 29.78303507, 25.96888223, 29.14073823, 26.04369592,
+ 34.18996367, 32.28924005, 27.42284396, 72.69207879,
+ 33.05316347, 36.52276972, 49.2551479 , 35.33439632,
+ 24.07252457, 31.67153709, 27.81699478, 25.38021219,
+ 24.31759259, 23.13586161, 48.40724678, 48.57969818,
+ 31.92596006, 43.3679231 , 34.32925819, 51.78908089,
+ 34.49778584, 27.56236198, 48.34273194, 57.50829097,
+ 50.66038226, 54.68701352, 35.77103116, 43.21886784,
+ 40.07615759, 49.98658004, 43.13352883, 40.28520774,
+ 46.28910294])
+ np.testing.assert_allclose(results.cov_params,
+ [[ 1.70280610e-02, -6.18628383e-04, -2.21386966e-04],
+ [ -6.18628383e-04, 2.61733917e-05, 6.77496445e-06],
+ [ -2.21386966e-04, 6.77496445e-06, 3.75463502e-06]])
+ np.testing.assert_allclose(results.std_err, [ 0.13049161, 0.00511599,
+ 0.00193769], atol=1.0e-8)
+ np.testing.assert_allclose(results.pvalues, [ 2.02901657e-198,
+ 2.07052532e-002, 1.46788805e-012])
+ np.testing.assert_allclose(results.tvalues, [ 30.0524361 , 2.31331634,
+ -7.07748998])
+ np.testing.assert_allclose(results.resid,
+ [ 28.73168426, -5.15022766, -14.06142973, -1.13799739,
+ -5.76119226, -13.6836241 , 19.35406297, 2.91722003,
+ 12.09610418, 58.80272042, -3.47459217, -6.12384057,
+ 12.21696493, 17.03111777, -11.14073823, -7.04369592,
+ 7.81003633, 27.71075995, 3.57715604, 8.30792121,
+ -13.05316347, -6.52276972, -1.2551479 , 17.66560368,
+ -6.07252457, -11.67153709, 6.18300522, -2.38021219,
+ 7.68240741, -1.13586161, -16.40724678, -8.57969818,
+ -7.92596006, -15.3679231 , -7.32925819, -15.78908089,
+ 8.50221416, -4.56236198, -8.34273194, 4.49170903,
+ -8.66038226, -10.68701352, -9.77103116, -9.21886784,
+ -12.07615759, 26.01341996, -1.13352883, -13.28520774,
+ -10.28910294])
+ self.assertAlmostEqual(results.deviance, 230.46013824817649)
+ self.assertAlmostEqual(results.llf, -247.42592089969378)
+ self.assertAlmostEqual(results.aic, 500.85184179938756)
+ self.assertAlmostEqual(results.D2, 0.388656011675)
+ self.assertAlmostEqual(results.adj_D2, 0.36207583826952761)
+
+if __name__ == '__main__':
+ unittest.main()
+
diff --git a/pysal/contrib/spint/tests/test_dispersion.py b/pysal/contrib/spint/tests/test_dispersion.py
new file mode 100644
index 0000000..4517680
--- /dev/null
+++ b/pysal/contrib/spint/tests/test_dispersion.py
@@ -0,0 +1,47 @@
+
+"""
+Tests for regressiom based dispersion tests (Cameron & Trivedi, 2013)
+
+Cameron, Colin A. & Trivedi, Pravin K. (2013) Regression Analysis of Count Data.
+ Camridge University Press: New York, New York.
+
+"""
+
+__author__ = 'Taylor Oshan tayoshan at gmail.com'
+
+import unittest
+import numpy as np
+import pysal
+from pysal.contrib.spint.count_model import CountModel
+from pysal.contrib.glm.family import Poisson
+from pysal.contrib.spint.dispersion import phi_disp, alpha_disp
+
+class TestDispersion(unittest.TestCase):
+
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ y = np.array(db.by_col("HOVAL"))
+ y = np.reshape(y, (49,1))
+ self.y = np.round(y).astype(int)
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+
+ def test_Dispersion(self):
+ model = CountModel(self.y, self.X, family=Poisson())
+ results = model.fit('GLM')
+ phi = phi_disp(results)
+ alpha1 = alpha_disp(results)
+ alpha2 = alpha_disp(results, lambda x:x**2)
+ np.testing.assert_allclose(phi, [ 5.39968689, 2.3230411 , 0.01008847],
+ atol=1.0e-8)
+ np.testing.assert_allclose(alpha1, [ 4.39968689, 2.3230411 ,
+ 0.01008847], atol=1.0e-8)
+ np.testing.assert_allclose(alpha2, [ 0.10690133, 2.24709978,
+ 0.01231683], atol=1.0e-8)
+
+
+if __name__ == '__main__':
+ unittest.main()
+
diff --git a/pysal/contrib/spint/tests/test_gravity.py b/pysal/contrib/spint/tests/test_gravity.py
index 3997716..8afb0f7 100644
--- a/pysal/contrib/spint/tests/test_gravity.py
+++ b/pysal/contrib/spint/tests/test_gravity.py
@@ -1,152 +1,685 @@
+
"""
Tests for gravity-style spatial interaction models
-"""
-
-__author__ = 'toshan'
-
-import unittest
-import numpy as np
-import pandas as pd
-import gravity as grav
+Test data is the Austria migration dataset used in Dennet's (2012) practical primer
+on spatial interaction modeling. The data was made avialable through the
+following dropbox link: http://dl.dropbox.com/u/8649795/AT_Austria.csv.
+The data has been pre-filtered so that there are no intra-zonal flows,
+Dennett, A. (2012). Estimating flows between geographical locations:get me
+started in spatial interaction modelling (Working Paper No. 184). UCL: Citeseer.
-class TestUnconstrained(unittest.TestCase):
- """Unconstrained class for unit tests"""
- def setUp(self):
- self.f = np.array([56, 100.8, 173.6, 235.2, 87.36,
- 28., 100.8, 69.44, 235.2, 145.6,
- 22., 26.4, 136.4, 123.2, 343.2,
- 14., 75.6, 130.2, 70.56, 163.8,
- 22, 59.4, 204.6, 110.88, 171.6])
- self.V = np.repeat(np.array([56, 56, 44, 42, 66]), 5)
- self.o = np.repeat(np.array(range(1, 6)), 5)
- self.W = np.tile(np.array([10, 18, 62, 84, 78]), 5)
- self.d = np.tile(np.array(range(1, 6)), 5)
- self.dij = np.array([10, 10, 20, 20, 50,
- 20, 10, 50, 20, 30,
- 20, 30, 20, 30, 10,
- 30, 10, 20, 50, 20,
- 30, 20, 20, 50, 30])
- self.dt = pd.DataFrame({'origins': self.o,
- 'destinations': self.d,
- 'V': self.V,
- 'W': self.W,
- 'Dij': self.dij,
- 'flows': self.f})
-
- def test_Uconstrained(self):
- model = grav.Unconstrained(self.dt, 'origins', 'destinations', 'flows',
- ['V'], ['W'], 'Dij', 'pow')
- V = 1.0
- W = 1.0
- beta = -1.0
- self.assertAlmostEqual(model.p['V'], V, delta=.0001)
- self.assertAlmostEqual(model.p['W'], W, delta=.0001)
- self.assertAlmostEqual(model.p['beta'], beta, delta=.0001)
-
-
-class TestProductionConstrained(unittest.TestCase):
- """Production constrained class for unit tests"""
- def setUp(self):
- self.f = np.array([0, 6469, 7629, 20036, 4690,
- 6194, 11688, 2243, 8857, 7248,
- 3559, 9221, 10099, 22866, 3388,
- 9986, 46618, 11639, 1380, 5261,
- 5985, 6731, 2704, 12250, 16132])
- self.o = np.repeat(1, 25)
- self.d = np.array(range(1, 26))
- self.dij = np.array([0, 576, 946, 597, 373,
- 559, 707, 1208, 602, 692,
- 681, 1934, 332, 595, 906,
- 425, 755, 672, 1587, 526,
- 484, 2141, 2182, 410, 540])
- self.pop = np.array([1596000, 2071000, 3376000, 6978000, 1345000,
- 2064000, 2378000, 1239000, 4435000, 1999000,
- 1274000, 7042000, 834000, 1268000, 1965000,
- 1046000, 12131000, 4824000, 969000, 2401000,
- 2410000, 2847000, 1425000, 1089000, 2909000])
- self.dt = pd.DataFrame({'origins': self.o,
- 'destinations': self.d,
- 'pop': self.pop,
- 'Dij': self.dij,
- 'flows': self.f})
+"""
- def test_Production_Constrained(self):
- model = grav.ProductionConstrained(self.dt, 'origins', 'destinations', 'flows',
- ['pop'], 'Dij', 'pow')
- pop = 0.7818262
- beta = -0.7365098
- self.assertAlmostEqual(model.p['pop'], pop, delta=.0001)
- self.assertAlmostEqual(model.p['beta'], beta, delta=.0001)
+import unittest
+import math
+import numpy as np
+from pysal.contrib.spint import gravity as grav
+class TestGravity(unittest.TestCase):
+ """Tests for gravity-type models"""
-class TestAttractionConstrained(unittest.TestCase):
- """Attraction constrained class for unit tests"""
def setUp(self):
- self.f = np.array([56, 100.8, 173.6, 235.2, 87.36,
- 28., 100.8, 69.44, 235.2, 145.6,
- 22., 26.4, 136.4, 123.2, 343.2,
- 14., 75.6, 130.2, 70.56, 163.8,
- 22, 59.4, 204.6, 110.88, 171.6])
- self.V = np.repeat(np.array([56, 56, 44, 42, 66]), 5)
- self.o = np.repeat(np.array(range(1, 6)), 5)
- self.W = np.tile(np.array([10, 18, 62, 84, 78]), 5)
- self.d = np.tile(np.array(range(1, 6)), 5)
- self.dij = np.array([10, 10, 20, 20, 50,
- 20, 10, 50, 20, 30,
- 20, 30, 20, 30, 10,
- 30, 10, 20, 50, 20,
- 30, 20, 20, 50, 30])
- self.dt = pd.DataFrame({'origins': self.o,
- 'destinations': self.d,
- 'V': self.V,
- 'Dij': self.dij,
- 'flows': self.f})
+ self.f = np.array([ 1131, 1887, 69, 738, 98, 31, 43, 19, 1633,
+ 14055, 416, 1276, 1850, 388, 303, 159, 2301, 20164,
+ 1080, 1831, 1943, 742, 674, 407, 85, 379, 1597,
+ 1608, 328, 317, 469, 114, 762, 1110, 2973, 1252,
+ 1081, 622, 425, 262, 196, 2027, 3498, 346, 1332,
+ 2144, 821, 274, 49, 378, 1349, 310, 851, 2117,
+ 630, 106, 87, 424, 978, 490, 670, 577, 546,
+ 569, 33, 128, 643, 154, 328, 199, 112, 587])
- def test_Attraction_Constrained(self):
- model = grav.AttractionConstrained(self.dt, 'origins', 'destinations', 'flows',
- ['V'], 'Dij', 'pow')
- V = 1.0
- beta = -1.0
- self.assertAlmostEqual(model.p['V'], V, delta=.0001)
- self.assertAlmostEqual(model.p['beta'], beta, delta=.0001)
+ self.o = np.array(['AT11', 'AT11', 'AT11', 'AT11', 'AT11', 'AT11', 'AT11', 'AT11',
+ 'AT12', 'AT12', 'AT12', 'AT12', 'AT12', 'AT12', 'AT12', 'AT12',
+ 'AT13', 'AT13', 'AT13', 'AT13', 'AT13', 'AT13', 'AT13', 'AT13',
+ 'AT21', 'AT21', 'AT21', 'AT21', 'AT21', 'AT21', 'AT21', 'AT21',
+ 'AT22', 'AT22', 'AT22', 'AT22', 'AT22', 'AT22', 'AT22', 'AT22',
+ 'AT31', 'AT31', 'AT31', 'AT31', 'AT31', 'AT31', 'AT31', 'AT31',
+ 'AT32', 'AT32', 'AT32', 'AT32', 'AT32', 'AT32', 'AT32', 'AT32',
+ 'AT33', 'AT33', 'AT33', 'AT33', 'AT33', 'AT33', 'AT33', 'AT33',
+ 'AT34', 'AT34', 'AT34', 'AT34', 'AT34', 'AT34', 'AT34', 'AT34'])
+
+ self.d = np.array(['AT12', 'AT13', 'AT21', 'AT22', 'AT31', 'AT32', 'AT33', 'AT34',
+ 'AT11', 'AT13', 'AT21', 'AT22', 'AT31', 'AT32', 'AT33', 'AT34',
+ 'AT11', 'AT12', 'AT21', 'AT22', 'AT31', 'AT32', 'AT33', 'AT34',
+ 'AT11', 'AT12', 'AT13', 'AT22', 'AT31', 'AT32', 'AT33', 'AT34',
+ 'AT11', 'AT12', 'AT13', 'AT21', 'AT31', 'AT32', 'AT33', 'AT34',
+ 'AT11', 'AT12', 'AT13', 'AT21', 'AT22', 'AT32', 'AT33', 'AT34',
+ 'AT11', 'AT12', 'AT13', 'AT21', 'AT22', 'AT31', 'AT33', 'AT34',
+ 'AT11', 'AT12', 'AT13', 'AT21', 'AT22', 'AT31', 'AT32', 'AT34',
+ 'AT11', 'AT12', 'AT13', 'AT21', 'AT22', 'AT31', 'AT32', 'AT33'])
+
+ self.dij = np.array([ 103.001845, 84.204666, 220.811933, 132.00748 , 214.511814,
+ 246.933305, 390.85611 , 505.089539, 103.001845, 45.796272,
+ 216.994739, 129.878172, 140.706671, 201.232355, 343.50075 ,
+ 453.515594, 84.204666, 45.796272, 249.932874, 158.630661,
+ 186.420738, 244.108305, 387.61776 , 498.407152, 220.811933,
+ 216.994739, 249.932874, 92.407958, 151.777157, 92.894408,
+ 194.851669, 306.105825, 132.00748 , 129.878172, 158.630661,
+ 92.407958, 124.563096, 122.433524, 261.893783, 376.34667 ,
+ 214.511814, 140.706671, 186.420738, 151.777157, 124.563096,
+ 81.753652, 208.456383, 314.793199, 246.933305, 201.232355,
+ 244.108305, 92.894408, 122.433524, 81.753652, 145.076472,
+ 258.591197, 390.85611 , 343.50075 , 387.61776 , 194.851669,
+ 261.893783, 208.456383, 145.076472, 114.46325 , 505.089539,
+ 453.515594, 498.407152, 306.105825, 376.34667 , 314.793199,
+ 258.591197, 114.46325 ])
+
+ self.o_var = np.array([ 4320, 4320, 4320, 4320, 4320, 4320, 4320, 4320, 21478,
+ 21478, 21478, 21478, 21478, 21478, 21478, 21478, 30500, 30500,
+ 30500, 30500, 30500, 30500, 30500, 30500, 5012, 5012, 5012,
+ 5012, 5012, 5012, 5012, 5012, 8811, 8811, 8811, 8811,
+ 8811, 8811, 8811, 8811, 11349, 11349, 11349, 11349, 11349,
+ 11349, 11349, 11349, 6021, 6021, 6021, 6021, 6021, 6021,
+ 6021, 6021, 4477, 4477, 4477, 4477, 4477, 4477, 4477,
+ 4477, 2275, 2275, 2275, 2275, 2275, 2275, 2275, 2275])
+
+ self.d_var = np.array([27169, 28710, 4354, 9069, 8577, 4963, 3923, 2026, 5452,
+ 28710, 4354, 9069, 8577, 4963, 3923, 2026, 5452, 27169,
+ 4354, 9069, 8577, 4963, 3923, 2026, 5452, 27169, 28710,
+ 9069, 8577, 4963, 3923, 2026, 5452, 27169, 28710, 4354,
+ 8577, 4963, 3923, 2026, 5452, 27169, 28710, 4354, 9069,
+ 4963, 3923, 2026, 5452, 27169, 28710, 4354, 9069, 8577,
+ 3923, 2026, 5452, 27169, 28710, 4354, 9069, 8577, 4963,
+ 2026, 5452, 27169, 28710, 4354, 9069, 8577, 4963, 3923])
+
+ def test_BaseGravity_exp(self):
+ f = np.array(self.f).reshape((-1,1))
+ dij = np.array(self.dij).reshape((-1,1))
+ model = grav.BaseGravity(f, dij, 'exp')
+ np.testing.assert_allclose(model.params, [0.01641585], atol = .0001)
+ self.assertAlmostEqual(model.aic, 957622.28429746185, delta = .0001)
+ np.testing.assert_allclose(model.cov_params, [[ 1.92096665e-10]])
+ self.assertAlmostEqual(model.deviance, 1087408.9707170483, delta = .0001)
+ self.assertAlmostEqual(model.llf, -478810.14214873099, delta = .0001)
+ self.assertAlmostEqual(model.llnull, -88037.0499629, delta = .0001)
+ np.testing.assert_allclose(model.pvalues, [ 0.])
+ np.testing.assert_allclose(model.std_err, [ 1.38598941e-05])
+ np.testing.assert_allclose(model.tvalues, [ 1184.41355888])
+ np.testing.assert_allclose(model.yhat,
+ [ 5.42415692e+00, 3.98401807e+00, 3.75177744e+01,
+ 8.73217546e+00, 3.38315236e+01, 5.76055685e+01,
+ 6.11695077e+02, 3.98970414e+03, 5.42415692e+00,
+ 2.12078133e+00, 3.52389616e+01, 8.43222048e+00,
+ 1.00726025e+01, 2.72049640e+01, 2.81140796e+02,
+ 1.71101560e+03, 3.98401807e+00, 2.12078133e+00,
+ 6.05130899e+01, 1.35184658e+01, 2.13329799e+01,
+ 5.49951210e+01, 5.80026424e+02, 3.57519614e+03,
+ 3.75177744e+01, 3.52389616e+01, 6.05130899e+01,
+ 4.55832329e+00, 1.20799918e+01, 4.59486946e+00,
+ 2.44995584e+01, 1.52168163e+02, 8.73217546e+00,
+ 8.43222048e+00, 1.35184658e+01, 4.55832329e+00,
+ 7.72767984e+00, 7.46219749e+00, 7.36414576e+01,
+ 4.82050643e+02, 3.38315236e+01, 1.00726025e+01,
+ 2.13329799e+01, 1.20799918e+01, 7.72767984e+00,
+ 3.82690126e+00, 3.06302472e+01, 1.75492594e+02,
+ 5.76055685e+01, 2.72049640e+01, 5.49951210e+01,
+ 4.59486946e+00, 7.46219749e+00, 3.82690126e+00,
+ 1.08216970e+01, 6.97553001e+01, 6.11695077e+02,
+ 2.81140796e+02, 5.80026424e+02, 2.44995584e+01,
+ 7.36414576e+01, 3.06302472e+01, 1.08216970e+01,
+ 6.54702760e+00, 3.98970414e+03, 1.71101560e+03,
+ 3.57519614e+03, 1.52168163e+02, 4.82050643e+02,
+ 1.75492594e+02, 6.97553001e+01, 6.54702760e+00])
+ def test_BaseGravity_pow(self):
+ f = np.array(self.f).reshape((-1,1))
+ dij = np.array(self.dij).reshape((-1,1))
+ model = grav.BaseGravity(f, dij, 'pow')
+ np.testing.assert_allclose(model.params, [ 1.27223738], atol = .0001)
+ self.assertAlmostEqual(model.aic, 377298.04716333596, delta = .0001)
+ np.testing.assert_allclose(model.cov_params, [[ 4.31955426e-07]])
+ self.assertAlmostEqual(model.deviance, 409811.34329065739, delta = .0001)
+ self.assertAlmostEqual(model.llf, -188648.02358166798, delta = .0001)
+ self.assertAlmostEqual(model.llnull, -88037.0499629, delta = .0001)
+ np.testing.assert_allclose(model.pvalues, [ 0.])
+ np.testing.assert_allclose(model.std_err, [ 0.00065723], atol = .000001)
+ np.testing.assert_allclose(model.tvalues, [ 1935.74740017])
+ np.testing.assert_allclose(model.yhat,
+ [ 363.76143383, 281.50403714, 959.7388893 , 498.77506053,
+ 925.03759732, 1106.44361848, 1984.54428735, 2749.95948574,
+ 363.76143383, 129.70901679, 938.68096943, 488.56203387,
+ 540.96136464, 852.80642651, 1683.84456031, 2397.81642174,
+ 281.50403714, 129.70901679, 1123.57104159, 630.10766251,
+ 773.76239688, 1090.36467516, 1963.64917204, 2703.75625368,
+ 959.7388893 , 938.68096943, 1123.57104159, 316.84652033,
+ 595.67905738, 318.9700416 , 818.55371165, 1454.18199247,
+ 498.77506053, 488.56203387, 630.10766251, 316.84652033,
+ 463.26843623, 453.2156204 , 1192.42000515, 1891.29566175,
+ 925.03759732, 540.96136464, 773.76239688, 595.67905738,
+ 463.26843623, 271.12096396, 891.94447199, 1506.88882976,
+ 1106.44361848, 852.80642651, 1090.36467516, 318.9700416 ,
+ 453.2156204 , 271.12096396, 562.42482847, 1173.32244253,
+ 1984.54428735, 1683.84456031, 1963.64917204, 818.55371165,
+ 1192.42000515, 891.94447199, 562.42482847, 416.01781589,
+ 2749.95948574, 2397.81642174, 2703.75625368, 1454.18199247,
+ 1891.29566175, 1506.88882976, 1173.32244253, 416.01781589])
+
+ def test_QuasiPoisson(self):
+ f = np.array(self.f).reshape((-1,1))
+ dij = np.array(self.dij).reshape((-1,1))
+ model = grav.BaseGravity(f, dij, 'exp', Quasi=True)
+ np.testing.assert_allclose(model.params, [0.01641585], atol = .0001)
+ self.assertTrue(math.isnan(model.aic))
+ np.testing.assert_allclose(model.cov_params, [[ 0.00079749]],
+ atol=1.0e-8)
+ self.assertAlmostEqual(model.deviance, 1087408.9707170483, delta = .0001)
+ self.assertTrue(np.isnan(model.llf))
+ self.assertTrue(np.isnan(model.llnull))
+ np.testing.assert_allclose(model.pvalues, [ 0.56103881])
+ np.testing.assert_allclose(model.std_err, [ 0.02823993], atol=1.0e-8)
+ np.testing.assert_allclose(model.tvalues, [ 0.58129922])
+ np.testing.assert_allclose(model.yhat,
+ [ 5.42415692e+00, 3.98401807e+00, 3.75177744e+01,
+ 8.73217546e+00, 3.38315236e+01, 5.76055685e+01,
+ 6.11695077e+02, 3.98970414e+03, 5.42415692e+00,
+ 2.12078133e+00, 3.52389616e+01, 8.43222048e+00,
+ 1.00726025e+01, 2.72049640e+01, 2.81140796e+02,
+ 1.71101560e+03, 3.98401807e+00, 2.12078133e+00,
+ 6.05130899e+01, 1.35184658e+01, 2.13329799e+01,
+ 5.49951210e+01, 5.80026424e+02, 3.57519614e+03,
+ 3.75177744e+01, 3.52389616e+01, 6.05130899e+01,
+ 4.55832329e+00, 1.20799918e+01, 4.59486946e+00,
+ 2.44995584e+01, 1.52168163e+02, 8.73217546e+00,
+ 8.43222048e+00, 1.35184658e+01, 4.55832329e+00,
+ 7.72767984e+00, 7.46219749e+00, 7.36414576e+01,
+ 4.82050643e+02, 3.38315236e+01, 1.00726025e+01,
+ 2.13329799e+01, 1.20799918e+01, 7.72767984e+00,
+ 3.82690126e+00, 3.06302472e+01, 1.75492594e+02,
+ 5.76055685e+01, 2.72049640e+01, 5.49951210e+01,
+ 4.59486946e+00, 7.46219749e+00, 3.82690126e+00,
+ 1.08216970e+01, 6.97553001e+01, 6.11695077e+02,
+ 2.81140796e+02, 5.80026424e+02, 2.44995584e+01,
+ 7.36414576e+01, 3.06302472e+01, 1.08216970e+01,
+ 6.54702760e+00, 3.98970414e+03, 1.71101560e+03,
+ 3.57519614e+03, 1.52168163e+02, 4.82050643e+02,
+ 1.75492594e+02, 6.97553001e+01, 6.54702760e+00])
+
+ def test_Gravity(self):
+ model = grav.Gravity(self.f, self.o_var, self.d_var,
+ self.dij, 'exp')
+ np.testing.assert_allclose(model.params, [ 0.44051324, 0.5144978,
+ -0.00976746])
+ self.assertAlmostEqual(model.aic, 33004.1458886, delta = .0001)
+ np.testing.assert_allclose(model.cov_params, [[ 1.12101850e-05,
+ -1.09133569e-05, -1.44593711e-08],
+ [ -1.09133569e-05, 1.10801323e-05, -1.23580963e-08],
+ [ -1.44593711e-08, -1.23580963e-08, 2.11497156e-09]])
+ self.assertAlmostEqual(model.deviance, 29051.791954, delta = .0001)
+ self.assertAlmostEqual(model.llf, -16499.0729443, delta = .0001)
+ self.assertAlmostEqual(model.llnull, -88037.0499629, delta = .0001)
+ np.testing.assert_allclose(model.pvalues, [0., 0., 0.])
+ np.testing.assert_allclose(model.std_err, [ 3.34816144e-03, 3.32868327e-03,
+ 4.59888200e-05])
+ np.testing.assert_allclose(model.tvalues, [ 131.5686976, 154.56496075,
+ -212.38771233])
+ np.testing.assert_allclose(model.yhat,
+ [ 2791.71294459, 3450.9181662 , 344.35367686, 1195.8168102 ,
+ 519.06514371, 285.39822879, 62.00029499, 14.46008341,
+ 2476.40288126, 10178.48689237, 724.46765915, 2474.67971095,
+ 2163.32521114, 903.92942165, 199.56985231, 48.50282363,
+ 3472.5689778 , 11546.46123418, 612.90019043, 2180.94678642,
+ 1615.46535168, 693.98410032, 151.37195314, 36.51168734,
+ 412.74282775, 978.91114569, 730.04284105, 1879.61605356,
+ 1022.73834302, 1371.83645825, 449.01488086, 107.81413822,
+ 1259.84153016, 2939.13628073, 2283.39010725, 1652.13526474,
+ 1710.55604408, 1318.04651914, 299.09085631, 69.60641198,
+ 629.16274459, 2956.0542728 , 1945.9091469 , 1034.2626935 ,
+ 1968.00942356, 2192.39883984, 563.51931891, 141.96553438,
+ 346.7081784 , 1237.93201505, 837.81110558, 1390.40186809,
+ 1519.82045346, 2197.30955425, 791.58042696, 185.91772597,
+ 74.60436444, 270.71659234, 181.00879336, 450.77145086,
+ 341.60352923, 559.42021561, 784.06616575, 666.83596532,
+ 18.14169546, 68.59991159, 45.52214916, 112.85166716,
+ 82.89055652, 146.94298691, 192.00611495, 695.27339343])
+ self.assertAlmostEquals(model.D2, 0.834456520292)
+ self.assertAlmostEquals(model.adj_D2, 0.829658158561)
+ self.assertAlmostEquals(model.SSI, 0.688220359428)
+ self.assertAlmostEquals(model.pseudoR2, 0.81258943875)
+ self.assertAlmostEquals(model.adj_pseudoR2, 0.812555362188)
+ self.assertAlmostEquals(model.SRMSE, 1.0220094905985986)
-class TestDoublyConstrained(unittest.TestCase):
- """Doubly constrained class for unit tests"""
- def setUp(self):
- self.f = np.array([0, 180048, 79223, 26887, 198144, 17995, 35563, 30528, 110792,
- 283049, 0, 300345, 67280, 718673, 55094, 93434, 87987, 268458,
- 87267, 237229, 0, 281791, 551483, 230788, 178517, 172711, 394481,
- 29877, 60681, 286580, 0, 143860, 49892, 185618, 181868, 274629,
- 130830, 382565, 346407, 92308, 0, 252189, 192223, 89389, 279739,
- 21434, 53772, 287340, 49828, 316650, 0, 141679, 27409, 87938,
- 30287, 64645, 161645, 144980, 199466, 121366, 0, 134229, 289880,
- 21450, 43749, 97808, 113683, 89806, 25574, 158006, 0, 437255,
- 72114, 133122, 229764, 165405, 266305, 66324, 252039, 342948, 0])
- self.o = np.repeat(np.array(range(1, 10)), 9)
- self.d = np.tile(np.array(range(1, 10)), 9)
- self.dij = np.array([0, 219, 1009, 1514, 974, 1268, 1795, 2420, 3174,
- 219, 0, 831, 1336, 755, 1049, 1576, 2242, 2996,
- 1009, 831, 0, 505, 1019, 662, 933, 1451, 2205,
- 1514, 1336, 505, 0, 1370, 888, 654, 946, 1700,
- 974, 755, 1019, 1370, 0, 482, 1144, 2278, 2862,
- 1268, 1049, 662, 888, 482, 0, 662, 1795, 2380,
- 1795, 1576, 933, 654, 1144, 662, 0, 1287, 1779,
- 2420, 2242, 1451, 946, 2278, 1795, 1287, 0, 754,
- 3147, 2996, 2205, 1700, 2862, 2380, 1779, 754, 0])
- self.dt = pd.DataFrame({'Origin': self.o,
- 'Destination': self.d,
- 'flows': self.f,
- 'Dij': self.dij})
+ def test_local_Gravity(self):
+ model = grav.Gravity(self.f, self.o_var, self.d_var, self.dij, 'exp')
+ local = model.local(loc_index=self.o, locs=np.unique(self.o))
+ self.assertEqual(local.keys().sort(), ['pvalue2',
+ 'SRMSE',
+ 'pvalue0',
+ 'deviance',
+ 'adj_pseudoR2',
+ 'pvalue1',
+ 'tvalue0',
+ 'tvalue2',
+ 'adj_D2',
+ 'tvalue1',
+ 'SSI',
+ 'aic',
+ 'param1',
+ 'param0',
+ 'D2',
+ 'pseudoR2',
+ 'param2'].sort())
+
+ def test_Production(self):
+ model = grav.Production(self.f, self.o, self.d_var,
+ self.dij, 'exp')
+ np.testing.assert_allclose(model.params, [-1.11700938, 0.5696138 ,
+ 1.03487752, -0.51400641, -0.23320154, 0.09225167, -0.42761955,
+ 0.03771866, -0.0922097 , 0.89278717, -0.00727113], atol = .0001)
+ self.assertAlmostEqual(model.aic, 15882.651018068489, delta = .0001)
+ np.testing.assert_allclose(model.cov_params,
+ [[ 2.58467540e-03, 2.25525153e-03, 2.25698879e-03,
+ 2.27598630e-03, 2.28753498e-03, 2.29518139e-03,
+ 2.26066086e-03, 2.28389856e-03, 2.30028223e-03,
+ -2.24000219e-04, -9.64855587e-07],
+ [ 2.25525153e-03, 2.22885311e-03, 2.18057451e-03,
+ 2.19382566e-03, 2.20716973e-03, 2.21373933e-03,
+ 2.17974121e-03, 2.19682475e-03, 2.20967384e-03,
+ -2.16966417e-04, -8.72732269e-07],
+ [ 2.25698879e-03, 2.18057451e-03, 2.21642787e-03,
+ 2.19592307e-03, 2.20904148e-03, 2.21570366e-03,
+ 2.18175104e-03, 2.19942376e-03, 2.21260234e-03,
+ -2.17060431e-04, -8.79741394e-07],
+ [ 2.27598630e-03, 2.19382566e-03, 2.19592307e-03,
+ 2.43184109e-03, 2.23320408e-03, 2.24276394e-03,
+ 2.21085295e-03, 2.24737607e-03, 2.27104133e-03,
+ -2.16513520e-04, -1.09288157e-06],
+ [ 2.28753498e-03, 2.20716973e-03, 2.20904148e-03,
+ 2.23320408e-03, 2.35994849e-03, 2.25049809e-03,
+ 2.21741622e-03, 2.24601728e-03, 2.26530460e-03,
+ -2.18641277e-04, -1.00923831e-06],
+ [ 2.29518139e-03, 2.21373933e-03, 2.21570366e-03,
+ 2.24276394e-03, 2.25049809e-03, 2.35335488e-03,
+ 2.22652875e-03, 2.25818713e-03, 2.27918210e-03,
+ -2.18996160e-04, -1.04519353e-06],
+ [ 2.26066086e-03, 2.17974121e-03, 2.18175104e-03,
+ 2.21085295e-03, 2.21741622e-03, 2.22652875e-03,
+ 2.36722952e-03, 2.22827572e-03, 2.25038328e-03,
+ -2.15375557e-04, -1.05779552e-06],
+ [ 2.28389856e-03, 2.19682475e-03, 2.19942376e-03,
+ 2.24737607e-03, 2.24601728e-03, 2.25818713e-03,
+ 2.22827572e-03, 2.51224347e-03, 2.31504070e-03,
+ -2.15113513e-04, -1.28324145e-06],
+ [ 2.30028223e-03, 2.20967384e-03, 2.21260234e-03,
+ 2.27104133e-03, 2.26530460e-03, 2.27918210e-03,
+ 2.25038328e-03, 2.31504070e-03, 2.81219776e-03,
+ -2.15302957e-04, -1.40976210e-06],
+ [ -2.24000219e-04, -2.16966417e-04, -2.17060431e-04,
+ -2.16513520e-04, -2.18641277e-04, -2.18996160e-04,
+ -2.15375557e-04, -2.15113513e-04, -2.15302957e-04,
+ 2.17985878e-05, 6.51339971e-08],
+ [ -9.64855587e-07, -8.72732269e-07, -8.79741394e-07,
+ -1.09288157e-06, -1.00923831e-06, -1.04519353e-06,
+ -1.05779552e-06, -1.28324145e-06, -1.40976210e-06,
+ 6.51339971e-08, 2.77308674e-09]])
+ self.assertAlmostEqual(model.deviance, 15279.974241770311, delta = .0001)
+ self.assertAlmostEqual(model.llf, -7930.3255090342445, delta = .0001)
+ self.assertAlmostEqual(model.llnull, -88037.0499629, delta = .0001)
+ np.testing.assert_allclose(model.pvalues,
+ [ 5.43122294e-107, 1.60984652e-033, 4.30597019e-107,
+ 1.94310017e-025, 1.58325196e-006, 5.72169717e-002,
+ 1.50954058e-018, 4.51730708e-001, 8.20670992e-002,
+ 0.00000000e+000, 0.00000000e+000])
+ np.testing.assert_allclose(model.std_err,
+ [ 5.08397030e-02, 4.72107308e-02, 4.70789536e-02,
+ 4.93137008e-02, 4.85793011e-02, 4.85113892e-02,
+ 4.86541830e-02, 5.01222852e-02, 5.30301589e-02,
+ 4.66889578e-03, 5.26601057e-05])
+ np.testing.assert_allclose(model.tvalues,
+ [ -21.97120187, 12.06534585, 21.98174424, -10.4231968 ,
+ -4.80043014, 1.90164966, -8.78895757, 0.75253281,
+ -1.73881619, 191.22019711, -138.07670549])
+ np.testing.assert_allclose(model.yhat,
+ [ 1.40705950e+03, 1.69457663e+03, 1.16508879e+02,
+ 4.27850723e+02, 2.23425179e+02, 1.08301078e+02,
+ 3.08300817e+01, 7.44793331e+00, 1.81162644e+03,
+ 1.21014912e+04, 6.46999802e+02, 2.34696906e+03,
+ 2.06388796e+03, 8.15528209e+02, 2.34966095e+02,
+ 5.85312512e+01, 3.30741049e+03, 1.83446566e+04,
+ 8.10873546e+02, 3.03231168e+03, 2.35717102e+03,
+ 9.50837295e+02, 2.71489717e+02, 6.72496632e+01,
+ 2.60277189e+02, 1.12260001e+03, 9.28118288e+02,
+ 1.04284804e+03, 6.44343295e+02, 6.06652130e+02,
+ 2.34315477e+02, 5.78455649e+01, 6.57379261e+02,
+ 2.80075361e+03, 2.38710037e+03, 7.17245241e+02,
+ 1.03993511e+03, 6.48056270e+02, 1.90566474e+02,
+ 4.59636590e+01, 4.99603238e+02, 3.58445439e+03,
+ 2.70058180e+03, 6.44960859e+02, 1.51347637e+03,
+ 1.20618713e+03, 3.89165529e+02, 9.95706858e+01,
+ 2.34675109e+02, 1.37251483e+03, 1.05563448e+03,
+ 5.88432822e+02, 9.13951678e+02, 1.16884200e+03,
+ 3.66858927e+02, 8.90901579e+01, 1.31244011e+02,
+ 7.76879800e+02, 5.92149430e+02, 4.46507449e+02,
+ 5.27992298e+02, 7.40876898e+02, 7.20725128e+02,
+ 4.04624989e+02, 5.02255240e+01, 3.06563409e+02,
+ 2.32354948e+02, 1.74615053e+02, 2.01734215e+02,
+ 3.00280455e+02, 2.77258060e+02, 6.40968342e+02])
+ self.assertAlmostEquals(model.D2, 0.912931356874)
+ self.assertAlmostEquals(model.adj_D2, 0.89865780882)
+ self.assertAlmostEquals(model.SSI, 0.740619203383)
+ self.assertAlmostEquals(model.pseudoR2, 0.909920590111)
+ self.assertAlmostEquals(model.adj_pseudoR2, 0.909795642717)
+ self.assertAlmostEquals(model.SRMSE, 0.46626328789729393)
- def test_Doubly_Constrained(self):
- model = grav.DoublyConstrained(self.dt, 'Origin', 'Destination', 'flows', 'Dij', 'exp')
- beta = -0.0007369
- self.assertAlmostEqual(model.p['beta'], beta, delta=.0000001)
+ def test_local_Production(self):
+ model = grav.Production(self.f, self.o, self.d_var, self.dij, 'exp')
+ local = model.local(locs=np.unique(self.o))
+ self.assertEqual(local.keys().sort(), ['pvalue2',
+ 'SRMSE',
+ 'pvalue0',
+ 'deviance',
+ 'adj_pseudoR2',
+ 'pvalue1',
+ 'tvalue0',
+ 'tvalue2',
+ 'adj_D2',
+ 'tvalue1',
+ 'SSI',
+ 'aic',
+ 'param1',
+ 'param0',
+ 'D2',
+ 'pseudoR2',
+ 'param2'].sort())
+
+ def test_Attraction(self):
+ model = grav.Production(self.f, self.d, self.o_var,
+ self.dij, 'exp')
+ np.testing.assert_allclose(model.params,
+ [-0.88439723, 0.73740882, 1.04332355, -0.75977722, -0.26060911,
+ -0.1879365 , -0.67530313, -0.19871953, -0.39900098, 0.89235874,
+ -0.00693755], atol = .0001)
+ self.assertAlmostEqual(model.aic, 16275.899321893821, delta = .0001)
+ np.testing.assert_allclose(model.cov_params,
+ [[ 3.01436996e-03, 2.75262767e-03, 2.69617868e-03,
+ 2.75263367e-03, 2.76096809e-03, 2.76082495e-03,
+ 2.73320039e-03, 2.77096042e-03, 2.80156716e-03,
+ -2.71488782e-04, -1.17108280e-06],
+ [ 2.75262767e-03, 2.72725703e-03, 2.63241450e-03,
+ 2.68345714e-03, 2.69359309e-03, 2.69264448e-03,
+ 2.66538758e-03, 2.69705597e-03, 2.72384308e-03,
+ -2.65699549e-04, -1.08479153e-06],
+ [ 2.69617868e-03, 2.63241450e-03, 2.61511832e-03,
+ 2.63025621e-03, 2.63909790e-03, 2.63860796e-03,
+ 2.61206899e-03, 2.64590548e-03, 2.67381997e-03,
+ -2.59870000e-04, -1.09419859e-06],
+ [ 2.75263367e-03, 2.68345714e-03, 2.63025621e-03,
+ 2.93687480e-03, 2.69787111e-03, 2.69944743e-03,
+ 2.67310431e-03, 2.72096984e-03, 2.75739588e-03,
+ -2.63511222e-04, -1.26675817e-06],
+ [ 2.76096809e-03, 2.69359309e-03, 2.63909790e-03,
+ 2.69787111e-03, 2.82012775e-03, 2.70486370e-03,
+ 2.67807014e-03, 2.71951056e-03, 2.75213707e-03,
+ -2.65196970e-04, -1.19678325e-06],
+ [ 2.76082495e-03, 2.69264448e-03, 2.63860796e-03,
+ 2.69944743e-03, 2.70486370e-03, 2.82788771e-03,
+ 2.67918953e-03, 2.72327074e-03, 2.75746837e-03,
+ -2.64826047e-04, -1.22641311e-06],
+ [ 2.73320039e-03, 2.66538758e-03, 2.61206899e-03,
+ 2.67310431e-03, 2.67807014e-03, 2.67918953e-03,
+ 2.85686854e-03, 2.69753883e-03, 2.73200584e-03,
+ -2.62037343e-04, -1.22568680e-06],
+ [ 2.77096042e-03, 2.69705597e-03, 2.64590548e-03,
+ 2.72096984e-03, 2.71951056e-03, 2.72327074e-03,
+ 2.69753883e-03, 3.01269943e-03, 2.80463020e-03,
+ -2.63381525e-04, -1.43182005e-06],
+ [ 2.80156716e-03, 2.72384308e-03, 2.67381997e-03,
+ 2.75739588e-03, 2.75213707e-03, 2.75746837e-03,
+ 2.73200584e-03, 2.80463020e-03, 3.37945677e-03,
+ -2.64964684e-04, -1.55786850e-06],
+ [ -2.71488782e-04, -2.65699549e-04, -2.59870000e-04,
+ -2.63511222e-04, -2.65196970e-04, -2.64826047e-04,
+ -2.62037343e-04, -2.63381525e-04, -2.64964684e-04,
+ 2.64461183e-05, 8.70031728e-08],
+ [ -1.17108280e-06, -1.08479153e-06, -1.09419859e-06,
+ -1.26675817e-06, -1.19678325e-06, -1.22641311e-06,
+ -1.22568680e-06, -1.43182005e-06, -1.55786850e-06,
+ 8.70031728e-08, 2.62593686e-09]])
+ self.assertAlmostEqual(model.deviance, 15673.222613627502, delta = .0001)
+ self.assertAlmostEqual(model.llf, -8126.9496609469106, delta = .0001)
+ self.assertAlmostEqual(model.llnull, -88037.0499629, delta = .0001)
+ np.testing.assert_allclose(model.pvalues,
+ [ 2.23154436e-58, 2.84596439e-45, 1.60422925e-92,
+ 1.17853681e-44, 9.22717986e-07, 4.09150807e-04,
+ 1.36480824e-36, 2.94090148e-04, 6.71572334e-12,
+ 0.00000000e+00, 0.00000000e+00])
+ np.testing.assert_allclose(model.std_err,
+ [ 5.49032782e-02, 5.22231465e-02, 5.11382276e-02,
+ 5.41929405e-02, 5.31048750e-02, 5.31778874e-02,
+ 5.34496823e-02, 5.48880627e-02, 5.81330953e-02,
+ 5.14257895e-03, 5.12438958e-05])
+ np.testing.assert_allclose(model.tvalues,
+ [ -16.10827734, 14.12034455, 20.4020279 , -14.01985592,
+ -4.90744228, -3.53410997, -12.63437119, -3.62045082,
+ -6.86357714, 173.52358548, -135.38293645])
+ np.testing.assert_allclose(model.yhat,
+ [ 1.79502279e+03, 2.77690999e+03, 1.77376340e+02,
+ 5.41058308e+02, 3.28265191e+02, 1.61020145e+02,
+ 9.55492240e+01, 3.54052486e+01, 1.48342439e+03,
+ 1.51642463e+04, 7.61962380e+02, 2.29718733e+03,
+ 2.29156465e+03, 9.24935115e+02, 5.55191561e+02,
+ 2.11833031e+02, 2.31106289e+03, 1.52712766e+04,
+ 8.29095427e+02, 2.57322937e+03, 2.28197035e+03,
+ 9.39377653e+02, 5.59026730e+02, 2.12153271e+02,
+ 1.78795020e+02, 9.29389609e+02, 1.00418629e+03,
+ 8.13086106e+02, 5.79191340e+02, 5.35268359e+02,
+ 4.24969889e+02, 1.60758895e+02, 5.47719688e+02,
+ 2.81394269e+03, 3.12998907e+03, 8.16565623e+02,
+ 1.15732912e+03, 7.21460431e+02, 4.41575377e+02,
+ 1.63374443e+02, 3.87326254e+02, 3.27181524e+03,
+ 3.23528198e+03, 6.77976176e+02, 1.34894643e+03,
+ 1.19916138e+03, 8.01876754e+02, 3.13863001e+02,
+ 1.75685709e+02, 1.22115852e+03, 1.23153422e+03,
+ 5.79386090e+02, 7.77596785e+02, 1.10887286e+03,
+ 7.06986190e+02, 2.63279368e+02, 4.96907636e+01,
+ 3.49378290e+02, 3.49326167e+02, 2.19253703e+02,
+ 2.26850151e+02, 3.53430501e+02, 3.36979293e+02,
+ 5.49332748e+02, 1.22952888e+01, 8.90162551e+01,
+ 8.85260032e+01, 5.53842615e+01, 5.60455225e+01,
+ 9.23759900e+01, 8.37976212e+01, 3.66824277e+02])
+ self.assertAlmostEquals(model.D2, .910690541438)
+ self.assertAlmostEquals(model.adj_D2, .896049646592)
+ self.assertAlmostEquals(model.SSI, .750634498293)
+ self.assertAlmostEquals(model.pseudoR2, .90768716507)
+ self.assertAlmostEquals(model.adj_pseudoR2, .907562217676)
+ self.assertAlmostEquals(model.SRMSE, 0.59483126233090433)
+ def test_local_Attraction(self):
+ model = grav.Attraction(self.f, self.d, self.o_var, self.dij, 'exp')
+ local = model.local(locs=np.unique(self.d))
+ self.assertEqual(local.keys().sort(), ['pvalue2',
+ 'SRMSE',
+ 'pvalue0',
+ 'deviance',
+ 'adj_pseudoR2',
+ 'pvalue1',
+ 'tvalue0',
+ 'tvalue2',
+ 'adj_D2',
+ 'tvalue1',
+ 'SSI',
+ 'aic',
+ 'param1',
+ 'param0',
+ 'D2',
+ 'pseudoR2',
+ 'param2'].sort())
+ def test_Doubly(self):
+ model = grav.Doubly(self.f, self.o, self.d,
+ self.dij, 'exp')
+ np.testing.assert_allclose(model.params,
+ [ 1.49709841e+00, 2.18492741e+00, 1.87848176e-01,
+ 6.64345148e-01, 7.42649379e-01, 2.13345351e-01,
+ 6.67657810e-01, 3.99860937e-01, 6.20471518e+00,
+ 7.74962468e+00, 8.64614438e+00, 6.90395892e+00,
+ 7.15340702e+00, 7.49439155e+00, 6.94741533e+00,
+ 7.39940090e+00, 7.19345711e+00, -7.91533311e-03])
+ self.assertAlmostEqual(model.aic, 9977.1591414502109, delta = .0001)
+ np.testing.assert_allclose(model.cov_params,
+ [[ 2.42563703e-04, 1.84733809e-04, 1.89259451e-04,
+ 1.91681710e-04, 1.89152965e-04, 1.92231256e-04,
+ 1.87441436e-04, 1.86834624e-04, -2.12557582e-04,
+ -1.85482863e-04, -2.14024619e-04, -2.11064638e-04,
+ -2.06862321e-04, -2.13608850e-04, -2.10640872e-04,
+ -2.17216459e-04, -2.18470971e-04, 1.13843139e-07],
+ [ 1.84733809e-04, 2.63915732e-04, 2.06737361e-04,
+ 2.02941436e-04, 2.03812109e-04, 2.06876793e-04,
+ 2.08793972e-04, 2.09473765e-04, -2.30973877e-04,
+ -2.35038219e-04, -1.70143798e-04, -2.05817163e-04,
+ -2.09270447e-04, -2.11179236e-04, -2.07192858e-04,
+ -2.05101234e-04, -2.04407823e-04, -1.94248549e-08],
+ [ 1.89259451e-04, 2.06737361e-04, 4.53792323e-04,
+ 2.01217128e-04, 2.05155865e-04, 2.05806138e-04,
+ 2.14332194e-04, 2.21550755e-04, -1.98360054e-04,
+ -1.94813739e-04, -1.74043824e-04, -1.79955670e-04,
+ -2.00249554e-04, -1.96708288e-04, -2.09795316e-04,
+ -2.07058650e-04, -1.99681815e-04, -9.10506514e-08],
+ [ 1.91681710e-04, 2.02941436e-04, 2.01217128e-04,
+ 3.17290867e-04, 2.00121482e-04, 2.02120689e-04,
+ 2.06522637e-04, 2.08554008e-04, -2.04848380e-04,
+ -1.98117102e-04, -1.83618154e-04, -2.07840280e-04,
+ -1.82826360e-04, -2.01083472e-04, -2.03827844e-04,
+ -1.99372606e-04, -1.97090647e-04, -4.37219119e-08],
+ [ 1.89152965e-04, 2.03812109e-04, 2.05155865e-04,
+ 2.00121482e-04, 3.26458468e-04, 2.01391450e-04,
+ 2.09628557e-04, 2.14889547e-04, -1.97315240e-04,
+ -1.96966358e-04, -1.78246708e-04, -1.96030118e-04,
+ -1.93177196e-04, -1.81632388e-04, -2.08658217e-04,
+ -2.01831801e-04, -1.96793054e-04, -7.68167253e-08],
+ [ 1.92231256e-04, 2.06876793e-04, 2.05806138e-04,
+ 2.02120689e-04, 2.01391450e-04, 4.14676504e-04,
+ 2.11496728e-04, 2.21430978e-04, -2.04713619e-04,
+ -1.99872552e-04, -1.81435387e-04, -2.17387447e-04,
+ -2.03438822e-04, -2.15975048e-04, -1.94747314e-04,
+ -2.29253582e-04, -2.19396689e-04, -2.25147281e-08],
+ [ 1.87441436e-04, 2.08793972e-04, 2.14332194e-04,
+ 2.06522637e-04, 2.09628557e-04, 2.11496728e-04,
+ 5.06851801e-04, 2.14569472e-04, -1.92147501e-04,
+ -1.92256318e-04, -1.67373803e-04, -1.99536301e-04,
+ -1.85234692e-04, -1.95220763e-04, -2.12076508e-04,
+ -1.74748404e-04, -3.15464930e-04, -1.33183180e-07],
+ [ 1.86834624e-04, 2.09473765e-04, 2.21550755e-04,
+ 2.08554008e-04, 2.14889547e-04, 2.21430978e-04,
+ 2.14569472e-04, 8.07696796e-04, -1.90223393e-04,
+ -1.91005620e-04, -1.64892620e-04, -1.90136139e-04,
+ -1.80941924e-04, -1.88390040e-04, -1.99615358e-04,
+ -3.08949366e-04, -1.73275263e-04, -1.49419116e-07],
+ [ -2.12557582e-04, -2.30973877e-04, -1.98360054e-04,
+ -2.04848380e-04, -1.97315240e-04, -2.04713619e-04,
+ -1.92147501e-04, -1.90223393e-04, 5.01690795e-04,
+ 2.34604925e-04, 2.15829388e-04, 2.54545792e-04,
+ 2.45346420e-04, 2.53731101e-04, 2.49832769e-04,
+ 2.66780923e-04, 2.70158589e-04, -2.88654296e-07],
+ [ -1.85482863e-04, -2.35038219e-04, -1.94813739e-04,
+ -1.98117102e-04, -1.96966358e-04, -1.99872552e-04,
+ -1.92256318e-04, -1.91005620e-04, 2.34604925e-04,
+ 2.77338030e-04, 1.86945338e-04, 2.28276363e-04,
+ 2.22769121e-04, 2.28218883e-04, 2.25960817e-04,
+ 2.36170239e-04, 2.38337459e-04, -1.71284609e-07],
+ [ -2.14024619e-04, -1.70143798e-04, -1.74043824e-04,
+ -1.83618154e-04, -1.78246708e-04, -1.81435387e-04,
+ -1.67373803e-04, -1.64892620e-04, 2.15829388e-04,
+ 1.86945338e-04, 2.45803385e-04, 2.29398497e-04,
+ 2.16802998e-04, 2.26157245e-04, 2.24998100e-04,
+ 2.43491383e-04, 2.47501416e-04, -2.99347493e-07],
+ [ -2.11064638e-04, -2.05817163e-04, -1.79955670e-04,
+ -2.07840280e-04, -1.96030118e-04, -2.17387447e-04,
+ -1.99536301e-04, -1.90136139e-04, 2.54545792e-04,
+ 2.28276363e-04, 2.29398497e-04, 4.81070964e-04,
+ 2.56007624e-04, 2.69174189e-04, 2.66750505e-04,
+ 2.96278814e-04, 3.07523359e-04, -4.35727563e-07],
+ [ -2.06862321e-04, -2.09270447e-04, -2.00249554e-04,
+ -1.82826360e-04, -1.93177196e-04, -2.03438822e-04,
+ -1.85234692e-04, -1.80941924e-04, 2.45346420e-04,
+ 2.22769121e-04, 2.16802998e-04, 2.56007624e-04,
+ 3.63658820e-04, 2.53530192e-04, 2.51566361e-04,
+ 2.75202102e-04, 2.81144405e-04, -3.64321585e-07],
+ [ -2.13608850e-04, -2.11179236e-04, -1.96708288e-04,
+ -2.01083472e-04, -1.81632388e-04, -2.15975048e-04,
+ -1.95220763e-04, -1.88390040e-04, 2.53731101e-04,
+ 2.28218883e-04, 2.26157245e-04, 2.69174189e-04,
+ 2.53530192e-04, 3.60293800e-04, 2.60651465e-04,
+ 2.88323917e-04, 2.97276990e-04, -4.03684411e-07],
+ [ -2.10640872e-04, -2.07192858e-04, -2.09795316e-04,
+ -2.03827844e-04, -2.08658217e-04, -1.94747314e-04,
+ -2.12076508e-04, -1.99615358e-04, 2.49832769e-04,
+ 2.25960817e-04, 2.24998100e-04, 2.66750505e-04,
+ 2.51566361e-04, 2.60651465e-04, 4.37541208e-04,
+ 2.87192980e-04, 3.00841537e-04, -3.80706214e-07],
+ [ -2.17216459e-04, -2.05101234e-04, -2.07058650e-04,
+ -1.99372606e-04, -2.01831801e-04, -2.29253582e-04,
+ -1.74748404e-04, -3.08949366e-04, 2.66780923e-04,
+ 2.36170239e-04, 2.43491383e-04, 2.96278814e-04,
+ 2.75202102e-04, 2.88323917e-04, 2.87192980e-04,
+ 5.80191216e-04, 3.22518390e-04, -5.30363748e-07],
+ [ -2.18470971e-04, -2.04407823e-04, -1.99681815e-04,
+ -1.97090647e-04, -1.96793054e-04, -2.19396689e-04,
+ -3.15464930e-04, -1.73275263e-04, 2.70158589e-04,
+ 2.38337459e-04, 2.47501416e-04, 3.07523359e-04,
+ 2.81144405e-04, 2.97276990e-04, 3.00841537e-04,
+ 3.22518390e-04, 8.53001477e-04, -5.59442560e-07],
+ [ 1.13843139e-07, -1.94248549e-08, -9.10506514e-08,
+ -4.37219119e-08, -7.68167253e-08, -2.25147281e-08,
+ -1.33183180e-07, -1.49419116e-07, -2.88654296e-07,
+ -1.71284609e-07, -2.99347493e-07, -4.35727563e-07,
+ -3.64321585e-07, -4.03684411e-07, -3.80706214e-07,
+ -5.30363748e-07, -5.59442560e-07, 2.56252122e-09]])
+ self.assertAlmostEqual(model.deviance, 9360.482092561484, delta = .0001)
+ self.assertAlmostEqual(model.llf, -4970.5795707251054, delta = .0001)
+ self.assertAlmostEqual(model.llnull, -88037.0499629, delta = .0001)
+ np.testing.assert_allclose(model.pvalues,
+ [ 0.00000000e+000, 0.00000000e+000, 1.16346714e-018,
+ 1.88877600e-304, 0.00000000e+000, 1.10421926e-025,
+ 2.83322217e-193, 5.83172788e-045, 0.00000000e+000,
+ 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
+ 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
+ 0.00000000e+000, 0.00000000e+000, 0.00000000e+000])
+ np.testing.assert_allclose(model.std_err,
+ [ 1.55744567e-02, 1.62454834e-02, 2.13024018e-02,
+ 1.78126603e-02, 1.80681617e-02, 2.03636073e-02,
+ 2.25133694e-02, 2.84200070e-02, 2.23984552e-02,
+ 1.66534690e-02, 1.56781180e-02, 2.19333300e-02,
+ 1.90698406e-02, 1.89814067e-02, 2.09174857e-02,
+ 2.40871587e-02, 2.92061890e-02, 5.06213514e-05])
+ np.testing.assert_allclose(model.tvalues,
+ [ 96.12524108, 134.49445277, 8.81816885, 37.29623407,
+ 41.10265285, 10.47679558, 29.65605897, 14.06969877,
+ 277.01531761, 465.34596906, 551.47845915, 314.77021149,
+ 375.11624684, 394.828037 , 332.13434099, 307.19276533,
+ 246.2990671 , -156.36352821])
+ np.testing.assert_allclose(model.yhat,
+ [ 9.78988280e+02, 2.26003279e+03, 1.04038742e+02,
+ 3.38382580e+02, 1.90458075e+02, 8.67908467e+01,
+ 4.37554720e+01, 1.35532201e+01, 1.02693176e+03,
+ 1.43579537e+04, 5.02646536e+02, 1.61314478e+03,
+ 1.60124044e+03, 5.84144805e+02, 2.98377549e+02,
+ 9.55604104e+01, 2.92086883e+03, 1.76899160e+04,
+ 9.49267467e+02, 3.14910952e+03, 2.73315395e+03,
+ 1.01972797e+03, 5.15779061e+02, 1.64177257e+02,
+ 1.73496758e+02, 7.99088484e+02, 1.22486311e+03,
+ 9.31563443e+02, 6.29698756e+02, 5.91117070e+02,
+ 4.15424488e+02, 1.31747905e+02, 4.49674437e+02,
+ 2.04361676e+03, 3.23802841e+03, 7.42345992e+02,
+ 1.00234547e+03, 6.00432512e+02, 3.13590596e+02,
+ 9.69658353e+01, 3.29132064e+02, 2.63792996e+03,
+ 3.65458094e+03, 6.52540343e+02, 1.30346098e+03,
+ 1.16517842e+03, 6.73203489e+02, 2.21973821e+02,
+ 1.47356669e+02, 9.45479887e+02, 1.33962391e+03,
+ 6.01828982e+02, 7.67131590e+02, 1.14476805e+03,
+ 6.43385796e+02, 2.00425139e+02, 7.41169755e+01,
+ 4.81822820e+02, 6.76007805e+02, 4.21969575e+02,
+ 3.99722086e+02, 6.59873779e+02, 6.41890452e+02,
+ 9.85596546e+02, 2.44225078e+01, 1.64157859e+02,
+ 2.28909306e+02, 1.42362371e+02, 1.31485029e+02,
+ 2.31461478e+02, 2.12717926e+02, 1.04848355e+03])
+ self.assertAlmostEquals(model.D2, .946661920897)
+ self.assertAlmostEquals(model.adj_D2, .929870303401)
+ self.assertAlmostEquals(model.SSI, .811852110904)
+ self.assertAlmostEquals(model.pseudoR2, .943539912198)
+ self.assertAlmostEquals(model.adj_pseudoR2, .943335452826)
+ self.assertAlmostEquals(model.SRMSE, 0.37928618532947161)
if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
+ unittest.main()
diff --git a/pysal/contrib/spint/tests/test_gravity_stats.py b/pysal/contrib/spint/tests/test_gravity_stats.py
deleted file mode 100644
index bd1521c..0000000
--- a/pysal/contrib/spint/tests/test_gravity_stats.py
+++ /dev/null
@@ -1,193 +0,0 @@
-"""
-Tests for statistics for gravity-style spatial interaction models
-"""
-
-__author__ = 'toshan'
-
-import unittest
-import numpy as np
-import pandas as pd
-import gravity as grav
-import mle_stats as stats
-
-
-class SingleParameter(unittest.TestCase):
- """Unit tests statistics when there is a single parameters estimated"""
- def setUp(self):
- self.f = np.array([0, 6469, 7629, 20036, 4690,
- 6194, 11688, 2243, 8857, 7248,
- 3559, 9221, 10099, 22866, 3388,
- 9986, 46618, 11639, 1380, 5261,
- 5985, 6731, 2704, 12250, 16132])
- self.o = np.repeat(1, 25)
- self.d = np.array(range(1, 26))
- self.dij = np.array([0, 576, 946, 597, 373,
- 559, 707, 1208, 602, 692,
- 681, 1934, 332, 595, 906,
- 425, 755, 672, 1587, 526,
- 484, 2141, 2182, 410, 540])
- self.pop = np.array([1596000, 2071000, 3376000, 6978000, 1345000,
- 2064000, 2378000, 1239000, 4435000, 1999000,
- 1274000, 7042000, 834000, 1268000, 1965000,
- 1046000, 12131000, 4824000, 969000, 2401000,
- 2410000, 2847000, 1425000, 1089000, 2909000])
- self.dt = pd.DataFrame({'origins': self.o,
- 'destinations': self.d,
- 'pop': self.pop,
- 'Dij': self.dij,
- 'flows': self.f})
- def test_single_parameter(self):
- model = grav.ProductionConstrained(self.dt, 'origins', 'destinations', 'flows',
- ['pop'], 'Dij', 'pow')
- ss = {'obs_mean_trip_len': 736.52834197296534,
- 'pred_mean_trip_len': 734.40974204773784,
- 'OD_pairs': 24,
- 'predicted_flows': 242873.00000000003,
- 'avg_dist_trav': 737.0,
- 'num_destinations': 24,
- 'observed_flows': 242873,
- 'avg_dist': 851.0,
- 'num_origins': 1}
- ps = {'beta': {'LL_zero_val': -3.057415839736517,
- 'relative_likelihood_stat': 24833.721614296166,
- 'standard_error': 0.0052734418614330883},
- 'all_params': {'zero_vals_LL': -3.1780538303479453,
- 'mle_vals_LL': -3.0062909275101761},
- 'pop': {'LL_zero_val': -3.1773474269437778,
- 'relative_likelihood_stat': 83090.010373874276,
- 'standard_error': 0.0027673052892085684}}
- fs = {'r_squared': 0.60516003720997413,
- 'srmse': 0.57873206718148507}
- es = {'pred_obs_deviance': 0.1327,
- 'entropy_ratio': 0.5642,
- 'maximum_entropy': 3.1781,
- 'max_pred_deviance': 0.1718,
- 'variance_obs_entropy': 2.55421e-06,
- 'predicted_entropy': 3.0063,
- 't_stat_entropy': 66.7614,
- 'max_obs_deviance': 0.3045,
- 'observed_entropy': 2.8736,
- 'variance_pred_entropy': 1.39664e-06}
- sys_stats = stats.sys_stats(model)
- self.assertAlmostEqual(model.system_stats['obs_mean_trip_len'], ss['obs_mean_trip_len'], 4)
- self.assertAlmostEqual(model.system_stats['pred_mean_trip_len'], ss['pred_mean_trip_len'], 4)
- self.assertAlmostEqual(model.system_stats['OD_pairs'], ss['OD_pairs'])
- self.assertAlmostEqual(model.system_stats['predicted_flows'], ss['predicted_flows'])
- self.assertAlmostEqual(model.system_stats['avg_dist_trav'], ss['avg_dist_trav'])
- self.assertAlmostEqual(model.system_stats['num_destinations'], ss['num_destinations'])
- self.assertAlmostEqual(model.system_stats['observed_flows'], ss['observed_flows'])
- self.assertAlmostEqual(model.system_stats['avg_dist'], ss['avg_dist'], 4)
- self.assertAlmostEqual(model.system_stats['num_origins'], ss['num_origins'])
- param_stats = stats.param_stats(model)
- self.assertAlmostEqual(model.parameter_stats['beta']['LL_zero_val'], ps['beta']['LL_zero_val'], 4)
- self.assertAlmostEqual(model.parameter_stats['beta']['relative_likelihood_stat'],
- ps['beta']['relative_likelihood_stat'], 4)
- self.assertAlmostEqual(model.parameter_stats['beta']['standard_error'], ps['beta']['standard_error'], 4)
- self.assertAlmostEqual(model.parameter_stats['pop']['LL_zero_val'], ps['pop']['LL_zero_val'], 4)
- self.assertAlmostEqual(model.parameter_stats['pop']['relative_likelihood_stat'],
- ps['pop']['relative_likelihood_stat'], 4)
- self.assertAlmostEqual(model.parameter_stats['pop']['standard_error'], ps['pop']['standard_error'], 4)
- self.assertAlmostEqual(model.parameter_stats['all_params']['zero_vals_LL'], ps['all_params']['zero_vals_LL'], 4)
- self.assertAlmostEqual(model.parameter_stats['all_params']['mle_vals_LL'], ps['all_params']['mle_vals_LL'], 4)
- fit_stats = stats.fit_stats(model)
- self.assertAlmostEqual(model.fit_stats['r_squared'], fs['r_squared'], 4)
- self.assertAlmostEqual(model.fit_stats['srmse'], fs['srmse'], 4)
- ent_stats = stats.ent_stats(model)
- self.assertAlmostEqual(model.entropy_stats['pred_obs_deviance'], es['pred_obs_deviance'], 4)
- self.assertAlmostEqual(model.entropy_stats['entropy_ratio'], es['entropy_ratio'], 4)
- self.assertAlmostEqual(model.entropy_stats['maximum_entropy'], es['maximum_entropy'], 4)
- self.assertAlmostEqual(model.entropy_stats['max_pred_deviance'], es['max_pred_deviance'], 4)
- self.assertAlmostEqual(model.entropy_stats['variance_obs_entropy'], es['variance_obs_entropy'], 4)
- self.assertAlmostEqual(model.entropy_stats['predicted_entropy'], es['predicted_entropy'], 4)
- self.assertAlmostEqual(model.entropy_stats['t_stat_entropy'], es['t_stat_entropy'], 4)
- self.assertAlmostEqual(model.entropy_stats['max_obs_deviance'], es['max_obs_deviance'], 4)
- self.assertAlmostEqual(model.entropy_stats['observed_entropy'], es['observed_entropy'], 4)
- self.assertAlmostEqual(model.entropy_stats['variance_pred_entropy'], es['variance_pred_entropy'], 4)
-
-class MultipleParameter(unittest.TestCase):
- """Unit tests statistics when there are multiple parameters estimated"""
- def setUp(self):
- self.f = np.array([0, 180048, 79223, 26887, 198144, 17995, 35563, 30528, 110792,
- 283049, 0, 300345, 67280, 718673, 55094, 93434, 87987, 268458,
- 87267, 237229, 0, 281791, 551483, 230788, 178517, 172711, 394481,
- 29877, 60681, 286580, 0, 143860, 49892, 185618, 181868, 274629,
- 130830, 382565, 346407, 92308, 0, 252189, 192223, 89389, 279739,
- 21434, 53772, 287340, 49828, 316650, 0, 141679, 27409, 87938,
- 30287, 64645, 161645, 144980, 199466, 121366, 0, 134229, 289880,
- 21450, 43749, 97808, 113683, 89806, 25574, 158006, 0, 437255,
- 72114, 133122, 229764, 165405, 266305, 66324, 252039, 342948, 0])
- self.o = np.repeat(np.array(range(1, 10)), 9)
- self.d = np.tile(np.array(range(1, 10)), 9)
- self.dij = np.array([0, 219, 1009, 1514, 974, 1268, 1795, 2420, 3174,
- 219, 0, 831, 1336, 755, 1049, 1576, 2242, 2996,
- 1009, 831, 0, 505, 1019, 662, 933, 1451, 2205,
- 1514, 1336, 505, 0, 1370, 888, 654, 946, 1700,
- 974, 755, 1019, 1370, 0, 482, 1144, 2278, 2862,
- 1268, 1049, 662, 888, 482, 0, 662, 1795, 2380,
- 1795, 1576, 933, 654, 1144, 662, 0, 1287, 1779,
- 2420, 2242, 1451, 946, 2278, 1795, 1287, 0, 754,
- 3147, 2996, 2205, 1700, 2862, 2380, 1779, 754, 0])
- self.dt = pd.DataFrame({'Origin': self.o,
- 'Destination': self.d,
- 'flows': self.f,
- 'Dij': self.dij})
- def test_multiple_parameter(self):
- model = grav.DoublyConstrained(self.dt, 'Origin', 'Destination', 'flows', 'Dij', 'exp')
- ss = {'obs_mean_trip_len': 1250.9555521611339,
- 'pred_mean_trip_len': 1250.9555521684863,
- 'OD_pairs': 72, 'predicted_flows': 12314322.0,
- 'avg_dist_trav': 1251.0, 'num_destinations': 9,
- 'observed_flows': 12314322, 'avg_dist': 1414.0,
- 'num_origins': 9}
- ps = {'beta': {'LL_zero_val': -4.1172103581711941,
- 'relative_likelihood_stat': 2053596.3814015209,
- 'standard_error': 4.9177433418433932e-07},
- 'all_params': {'zero_vals_LL': -4.1172102183395936,
- 'mle_vals_LL': -4.0338279201692675}}
- fs = {'r_squared': 0.89682406680906979,
- 'srmse': 0.24804939821988789}
- es = {'pred_obs_deviance': 0.0314,
- 'entropy_ratio': 0.8855,
- 'maximum_entropy': 4.2767,
- 'max_pred_deviance': 0.2429,
- 'variance_obs_entropy': 3.667e-08,
- 'predicted_entropy': 4.0338,
- 't_stat_entropy': 117.1593,
- 'max_obs_deviance': 0.2743,
- 'observed_entropy': 4.0024,
- 'variance_pred_entropy': 3.516e-08}
- sys_stats = stats.sys_stats(model)
- self.assertAlmostEqual(model.system_stats['obs_mean_trip_len'], ss['obs_mean_trip_len'], 4)
- self.assertAlmostEqual(model.system_stats['pred_mean_trip_len'], ss['pred_mean_trip_len'], 4)
- self.assertAlmostEqual(model.system_stats['OD_pairs'], ss['OD_pairs'])
- self.assertAlmostEqual(model.system_stats['predicted_flows'], ss['predicted_flows'])
- self.assertAlmostEqual(model.system_stats['avg_dist_trav'], ss['avg_dist_trav'])
- self.assertAlmostEqual(model.system_stats['num_destinations'], ss['num_destinations'])
- self.assertAlmostEqual(model.system_stats['observed_flows'], ss['observed_flows'])
- self.assertAlmostEqual(model.system_stats['avg_dist'], ss['avg_dist'], 4)
- self.assertAlmostEqual(model.system_stats['num_origins'], ss['num_origins'])
- param_stats = stats.param_stats(model)
- self.assertAlmostEqual(model.parameter_stats['beta']['LL_zero_val'], ps['beta']['LL_zero_val'], 4)
- self.assertAlmostEqual(model.parameter_stats['beta']['relative_likelihood_stat'],
- ps['beta']['relative_likelihood_stat'], 4)
- self.assertAlmostEqual(model.parameter_stats['beta']['standard_error'], ps['beta']['standard_error'], 4)
- self.assertAlmostEqual(model.parameter_stats['all_params']['zero_vals_LL'], ps['all_params']['zero_vals_LL'], 4)
- self.assertAlmostEqual(model.parameter_stats['all_params']['mle_vals_LL'], ps['all_params']['mle_vals_LL'], 4)
- fit_stats = stats.fit_stats(model)
- self.assertAlmostEqual(model.fit_stats['r_squared'], fs['r_squared'], 4)
- self.assertAlmostEqual(model.fit_stats['srmse'], fs['srmse'], 4)
- ent_stats = stats.ent_stats(model)
- self.assertAlmostEqual(model.entropy_stats['pred_obs_deviance'], es['pred_obs_deviance'], 4)
- self.assertAlmostEqual(model.entropy_stats['entropy_ratio'], es['entropy_ratio'], 4)
- self.assertAlmostEqual(model.entropy_stats['maximum_entropy'], es['maximum_entropy'], 4)
- self.assertAlmostEqual(model.entropy_stats['max_pred_deviance'], es['max_pred_deviance'], 4)
- self.assertAlmostEqual(model.entropy_stats['variance_obs_entropy'], es['variance_obs_entropy'], 4)
- self.assertAlmostEqual(model.entropy_stats['predicted_entropy'], es['predicted_entropy'], 4)
- self.assertAlmostEqual(model.entropy_stats['t_stat_entropy'], es['t_stat_entropy'], 4)
- self.assertAlmostEqual(model.entropy_stats['max_obs_deviance'], es['max_obs_deviance'], 4)
- self.assertAlmostEqual(model.entropy_stats['observed_entropy'], es['observed_entropy'], 4)
- self.assertAlmostEqual(model.entropy_stats['variance_pred_entropy'], es['variance_pred_entropy'], 4)
-
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
diff --git a/pysal/contrib/spint/tests/test_vec_SA.py b/pysal/contrib/spint/tests/test_vec_SA.py
new file mode 100644
index 0000000..098936a
--- /dev/null
+++ b/pysal/contrib/spint/tests/test_vec_SA.py
@@ -0,0 +1,54 @@
+"""
+Tests for analysis of spatial autocorrelation within vectors
+
+"""
+
+__author__ = 'Taylor Oshan tayoshan at gmail.com'
+
+import unittest
+import numpy as np
+np.random.seed(1)
+import pysal
+from pysal.contrib.spint.vec_SA import VecMoran
+from pysal.weights import DistanceBand
+
+class TestVecMoran(unittest.TestCase):
+ """Tests VecMoran class"""
+
+ def setUp(self):
+ self.vecs = np.array([[1, 55, 60, 100, 500],
+ [2, 60, 55, 105, 501],
+ [3, 500, 55, 155, 500],
+ [4, 505, 60, 160, 500],
+ [5, 105, 950, 105, 500],
+ [6, 155, 950, 155, 499]])
+ self.origins = self.vecs[:, 1:3]
+ self.dests = self.vecs[:, 3:5]
+
+ def test_origin_focused_A(self):
+ wo = DistanceBand(self.origins, threshold=9999, alpha=-1.5, binary=False)
+ vmo = VecMoran(self.vecs, wo, focus='origin', rand='A')
+ self.assertAlmostEquals(vmo.I, 0.645944594367)
+ self.assertAlmostEquals(vmo.p_z_sim, 0.099549579548)
+
+ def test_dest_focused_A(self):
+ wd = DistanceBand(self.dests, threshold=9999, alpha=-1.5, binary=False)
+ vmd = VecMoran(self.vecs, wd, focus='destination', rand='A')
+ self.assertAlmostEquals(vmd.I, -0.764603695022)
+ self.assertAlmostEquals(vmd.p_z_sim, 0.149472673677)
+
+ def test_origin_focused_B(self):
+ wo = DistanceBand(self.origins, threshold=9999, alpha=-1.5, binary=False)
+ vmo = VecMoran(self.vecs, wo, focus='origin', rand='B')
+ self.assertAlmostEquals(vmo.I, 0.645944594367)
+ self.assertAlmostEquals(vmo.p_z_sim, 0.071427063787951814)
+
+ def test_dest_focused_B(self):
+ wd = DistanceBand(self.dests, threshold=9999, alpha=-1.5, binary=False)
+ vmd = VecMoran(self.vecs, wd, focus='destination', rand='B')
+ self.assertAlmostEquals(vmd.I, -0.764603695022)
+ self.assertAlmostEquals(vmd.p_z_sim, 0.086894261015806051)
+
+if __name__ == '__main__':
+ unittest.main()
+
diff --git a/pysal/contrib/spint/utils.py b/pysal/contrib/spint/utils.py
new file mode 100644
index 0000000..954c175
--- /dev/null
+++ b/pysal/contrib/spint/utils.py
@@ -0,0 +1,120 @@
+"""
+Useful functions for analyzing spatial interaction data.
+"""
+
+__author__ = "Taylor Oshan tayoshan at gmail.com"
+
+from scipy import sparse as sp
+import numpy as np
+from collections import defaultdict
+from functools import partial
+from itertools import count
+
+def CPC(model):
+ """
+ Common part of commuters based on Sorensen index
+ Lenormand et al. 2012
+ """
+ y = model.y
+ try:
+ yhat = model.yhat.resahpe((-1,1))
+ except:
+ yhat = model.mu((-1,1))
+ N = model.n
+ YYhat = np.hstack([y, yhat])
+ NCC = np.sum(np.min(YYhat, axis=1))
+ NCY = np.sum(Y)
+ NCYhat = np.sum(yhat)
+ return (N*NCC) / (NCY + NCYhat)
+
+def sorensen(model):
+ """
+ Sorensen similarity index
+
+ For use on spatial interaction models; N = sample size
+ rather than N = number of locations and normalized by N instead of N**2
+ """
+ try:
+ y = model.y.reshape((-1,1))
+ except:
+ y = model.f.reshape((-1,1))
+ try:
+ yhat = model.yhat.reshape((-1,1))
+ except:
+ yhat = model.mu.reshape((-1,1))
+ N = model.n
+ YYhat = np.hstack([y, yhat])
+ num = 2.0 * np.min(YYhat, axis = 1)
+ den = yhat + y
+ return (1.0/N) * (np.sum(num.reshape((-1,1))/den.reshape((-1,1))))
+
+def srmse(model):
+ """
+ Standardized root mean square error
+ """
+ n = model.n
+ try:
+ y = model.y.reshape((-1,1))
+ except:
+ y = model.f.reshape((-1,1))
+ try:
+ yhat = model.yhat.reshape((-1,1))
+ except:
+ yhat = model.mu.reshape((-1,1))
+ srmse = ((np.sum((y-yhat)**2)/n)**.5)/(np.sum(y)/n)
+ return srmse
+
+def spcategorical(index):
+ '''
+ Returns a dummy matrix given an array of categorical variables.
+ Parameters
+ ----------
+ n_cat_ids : array
+ A 1d vector of the categorical labels for n observations.
+
+ Returns
+ --------
+ dummy : array
+ A sparse matrix of dummy (indicator/binary) variables for the
+ categorical data.
+
+ '''
+ if np.squeeze(index).ndim == 1:
+ id_set = np.unique(index)
+ n = len(index)
+ #if index.dtype.type is not np.int_:
+ mapper = defaultdict(partial(next, count()))
+ [mapper[each] for each in id_set]
+ index = [mapper[each] for each in index]
+ indptr = np.arange(n+1, dtype=int)
+ return sp.csr_matrix((np.ones(n), index, indptr))
+ else:
+ raise IndexError("The index %s is not understood" % index)
+
+#old and slow
+"""
+def spcategorical(n_cat_ids):
+ '''
+ Returns a dummy matrix given an array of categorical variables.
+ Parameters
+ ----------
+ n_cat_ids : array
+ A 1d vector of the categorical labels for n observations.
+
+ Returns
+ --------
+ dummy : array
+ A sparse matrix of dummy (indicator/binary) variables for the
+ categorical data.
+
+ '''
+ if np.squeeze(n_cat_ids).ndim == 1:
+ cat_set = np.unique(n_cat_ids)
+ n = len(n_cat_ids)
+ index = [np.where(cat_set == id)[0].tolist()[0] for id in n_cat_ids]
+ indptr = np.arange(n+1, dtype=int)
+ return sp.csr_matrix((np.ones(n), index, indptr))
+ else:
+ raise IndexError("The index %s is not understood" % col)
+"""
+
diff --git a/pysal/contrib/spint/vec_SA.py b/pysal/contrib/spint/vec_SA.py
new file mode 100644
index 0000000..605d778
--- /dev/null
+++ b/pysal/contrib/spint/vec_SA.py
@@ -0,0 +1,342 @@
+"""
+Classes for statistics for testing hypotheses of spatial autocorrelation amongst
+vectors.
+"""
+
+_author_ = "Taylor Oshan tayoshan at gmail.com, Levi Wolf levi.john.wolf at gmail.com"
+
+import numpy as np
+import scipy.stats as stats
+from pysal.weights.Distance import DistanceBand
+
+PERMUTATIONS = 99
+
+class VecMoran:
+ """Moran's I Global Autocorrelation Statistic For Vectors
+
+ Parameters
+ ----------
+ y : array
+ variable measured across n origin-destination vectors
+ w : W
+ spatial weights instance
+ focus : string
+ denotes whether to calculate the statistic with a focus on
+ spatial proximity between origins or destinations; default
+ is 'origin' but option include:
+
+ 'origin' | 'destination'
+
+ rand : string
+ denote which randomization technqiue to use for
+ significance testing; default is 'A' but options are:
+
+ 'A': transate entire vector
+ 'B': shuffle points and redraw vectors
+
+ permutations : int
+ number of random permutations for calculation of
+ pseudo-p_values
+ two_tailed : boolean
+ If True (default) analytical p-values for Moran are two
+ tailed, otherwise if False, they are one-tailed.
+ Attributes
+ ----------
+ y : array
+ original variable
+ w : W obejct
+ original w object
+ n : integer
+ number of vectors
+ o : array
+ n x 2; 2D coordinates of vector origins
+ d : array
+ n x 2: 2D coordinates of vector destinations
+ alpha : scalar
+ distance decay parameter harvested from W object
+ binary : boolean
+ True is all entries in W > 0 are set to 1; False if if they
+ are inverse distance weighted; default is False; attribute is
+ harvested from W object
+ build_sp : boolean
+ True if W object is build using sparse distance matrix and
+ False if it is built using a dense distance matrix; attribute
+ is harvested from W object
+ threshold : scalar
+ all values larger than threshold are set 0 in W object;
+ attribute is harvested from W object
+ silent : boolean
+ True if island warnings are silent and False if they are not;
+ default is False; attribute is harvested from W object
+ focus : string
+ denotes whether to calculate the statistic with a focus on
+ spatial proximity between origins or destinations; default
+ is 'origin' but option include:
+
+ 'origin' | 'destination'
+
+ rand : string
+ denote which randomization technqiue to use for
+ significance testing; default is 'A' but options are:
+
+ 'A': transate entire vector
+ 'B': shuffle points and redraw vectors
+
+ permutations : int
+ number of permutations
+ I : float
+ value of vector-based Moran's I
+ EI : float
+ expected value under randomization assumption
+ VI_rand : float
+ variance of I under randomization assumption
+ seI_rand : float
+ standard deviation of I under randomization assumption
+ z_rand : float
+ z-value of I under randomization assumption
+ p_rand : float
+ p-value of I under randomization assumption
+ two_tailed : boolean
+ If True p_norm and p_rand are two-tailed, otherwise they
+ are one-tailed.
+ sim : array
+ (if permutations>0)
+ vector of I values for permuted samples
+ p_sim : array
+ (if permutations>0)
+ p-value based on permutations (one-tailed)
+ null: spatial randomness
+ alternative: the observed I is extreme if
+ it is either extremely greater or extremely lower
+ than the values obtained based on permutations
+ EI_sim : float
+ (if permutations>0)
+ average value of I from permutations
+ VI_sim : float
+ (if permutations>0)
+ variance of I from permutations
+ seI_sim : float
+ (if permutations>0)
+ standard deviation of I under permutations.
+ z_sim : float
+ (if permutations>0)
+ standardized I based on permutations
+ p_z_sim : float
+ (if permutations>0)
+ p-value based on standard normal approximation from
+ permutations
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> np.random.seed(1)
+ >>> from pysal.weight import DistanceBand
+ >>> from pysal.contrib.spint.vec_SA import VecMoran
+ >>> vecs = np.array([[1, 55, 60, 100, 500],
+ >>> [2, 60, 55, 105, 501],
+ >>> [3, 500, 55, 155, 500],
+ >>> [4, 505, 60, 160, 500],
+ >>> [5, 105, 950, 105, 500],
+ >>> [6, 155, 950, 155, 499]])
+ >>> origins = vecs[:, 1:3]
+ >>> dests = vecs[:, 3:5]
+ >>> wo = DistanceBand(origins, threshold=9999, alpha=-1.5, binary=False)
+ >>> wd = DistanceBand(dests, threshold=9999, alpha=-1.5, binary=False)
+
+ #randomization technique A
+ >>> vmo = VecMoran(vecs, wo, focus='origin', rand='A')
+ >>> vmd = VecMoran(vecs, wd, focus='destination', rand='A')
+ >>> vmo.I
+ -0.764603695022
+ >>> vmo.p_z_sim
+ 0.99549579548
+ >>> vmd.I
+ 0.645944594367
+ >>> vmd.p_z_sim
+ 0.1494726733677
+
+ #randomization technique B
+ >>> vmo = VecMoran(vecs, wo, focus='origin', rand='B')
+ >>> vmd = VecMoran(vecs, wd, foucs='destination', rand='B')
+ >>> vmo.I
+ -0.764603695022
+ >>> vmo.p_z_sim
+ 0.071427063787951814
+ >>> vmd.I
+ 0.645944594367
+ >>> vmd.p_z_sim
+ 0.086894261015806051
+
+ """
+
+ def __init__(self, y, w, focus='origin', rand='A', permutations=PERMUTATIONS,
+ two_tailed=True):
+ self.y = y
+ self.o = y[:, 1:3]
+ self.d = y[:, 3:5]
+ self.focus = focus
+ self.rand = rand
+ self.permutations = permutations
+ self.two_tailed = two_tailed
+ if isinstance(w, DistanceBand):
+ self.w = w
+ else:
+ raise TypeError('Spatial weight, W, must be of type DistanceBand')
+ try:
+ self.threshold = w.threshold
+ self.alpha = w.alpha
+ self.build_sp = w.build_sp
+ self.binary = w.binary
+ self.silent = w.silent
+ except:
+ raise AttributeError('W object missing necessary attributes: '
+ 'threshold, alpha, binary, build_sp, silent')
+
+ self.__moments()
+ self.I = self.__calc(self.z)
+ self.z_rand = (self.I - self.EI) / self.seI_rand
+
+ if self.z_rand > 0:
+ self.p_rand = 1 - stats.norm.cdf(self.z_rand)
+ else:
+ self.p_rand = stats.norm.cdf(self.z_rand)
+
+ if self.two_tailed:
+ self.p_rand *= 2.
+
+ if permutations:
+ if self.rand.upper() == 'A':
+ sim = self.__rand_vecs_A(self.focus)
+ elif self.rand.upper() == 'B':
+ sim = self.__rand_vecs_B(self.focus)
+ else:
+ raise ValueError("Parameter 'rand' must take a value of either 'A' or 'B'")
+
+ self.sim = sim = np.array(sim)
+ above = sim >= self.I
+ larger = above.sum()
+ if (self.permutations - larger) < larger:
+ larger = self.permutations - larger
+ self.p_sim = (larger + 1.) / (permutations + 1.)
+ self.EI_sim = sim.sum() / permutations
+ self.seI_sim = np.array(sim).std()
+ self.VI_sim = self.seI_sim ** 2
+ self.z_sim = (self.I - self.EI_sim) / self.seI_sim
+ if self.z_sim > 0:
+ self.p_z_sim = 1 - stats.norm.cdf(self.z_sim)
+ else:
+ self.p_z_sim = stats.norm.cdf(self.z_sim)
+
+ def __moments(self):
+ self.n = len(self.y)
+ xObar = self.o[:,0].mean()
+ yObar = self.o[:,1].mean()
+ xDbar = self.d[:,0].mean()
+ yDbar = self.d[:,1].mean()
+ u = (self.y[:,3] - self.y[:,1]) - (xDbar - xObar)
+ v = (self.y[:,4] - self.y[:,2]) - (yDbar - yObar)
+ z = np.outer(u, u) + np.outer(v,v)
+ self.z = z
+ self.uv2ss = np.sum(np.dot(u,u) + np.dot(v,v))
+ self.EI = -1. / (self.n - 1)
+ n = self.n
+ s1 = self.w.s1
+ W = self.w.s0
+ s2 = self.w.s2
+
+ v_num = n * n * s1 - n * s2 + 3 * W * W
+ v_den = (n - 1) * (n + 1) * W * W
+
+ a2 = np.sum(np.dot(u, u))/n
+ b2 = np.sum(np.dot(v, v))/n
+ m2 = a2 + b2
+ a4 = np.sum(np.dot(np.dot(u, u), np.dot(u, u)))/n
+ b4 = np.sum(np.dot(np.dot(v, u), np.dot(v, v)))/n
+ n1 = a2**2*((n**2 - 3*n + 3)*s1-n*s2 + 3*W**2)
+ n2 = a4*((n**2 - n)*s1 - 2*n*s2 + 6*W**2)
+ n3 = b2**2*((n**2 - 3*n + 3)*s1 - n*s2 + 3*W**2)
+ n4 = b4*((n**2 - n)*s1 - 2*n*s2 + 6*W**2)
+ d = (n - 1)*(n - 2)*(n - 3)
+ self.VI_rand = 1/(W**2*m2**2) * \
+ ((n1 - n2)/d + (n3 - n4)/d) + \
+ ((a2*b2) - m2**2)/(m2**2*(n - 1)**2)
+ self.seI_rand = self.VI_rand ** (1 / 2.)
+
+ def __calc(self, z):
+ zl = self._slag(self.w, z)
+ inum = np.sum(zl)
+ return self.n / self.w.s0 * inum / self.uv2ss
+
+ def _newD(self, oldO, oldD, newO):
+ oldOX, oldOY = oldO[:,0], oldO[:,1]
+ oldDX, oldDY = oldD[:,0], oldD[:,1]
+ newOX, newOY = newO[:,0], newO[:,1]
+ deltaX = newOX - oldOX
+ deltaY = newOY - oldOY
+ newDX = oldDX + deltaX
+ newDY = oldDY + deltaY
+ return np.hstack([newDX.reshape((-1,1)), newDY.reshape((-1,1))])
+
+ def _newO(self, oldO, oldD, newD):
+ oldOX, oldOY = oldO[:,0], oldO[:,1]
+ oldDX, oldDY = oldD[:,0], oldD[:,1]
+ newDX, newDY = newD[:,0], newD[:,1]
+ deltaX = newDX - oldDX
+ deltaY = newDY - oldDY
+ newOX = oldOX + deltaX
+ newOY = oldOY + deltaY
+ return np.hstack([newOX.reshape((-1,1)), newOY.reshape((-1,1))])
+
+ def __rand_vecs_A(self, focus):
+ if focus.lower() == 'origin':
+ newOs = [np.random.permutation(self.o) for i in xrange(self.permutations)]
+ sims = [np.hstack([np.arange(self.n).reshape((-1,1)), newO,
+ self._newD(self.o, self.d, newO)]) for newO in newOs]
+ Ws = [DistanceBand(newO, threshold=self.threshold, alpha=self.alpha,
+ binary=self.binary, build_sp=self.build_sp, silent=self.silent)
+ for newO in newOs]
+ elif focus.lower() == 'destination':
+ newDs = [np.random.permutation(self.d) for i in xrange(self.permutations)]
+ sims = [np.hstack([np.arange(self.n).reshape((-1,1)),
+ self._newO(self.o, self.d, newD), newD]) for newD in newDs]
+ Ws = [DistanceBand(newD, threshold=self.threshold, alpha=self.alpha,
+ binary=self.binary, build_sp=self.build_sp, silent=self.silent)
+ for newD in newDs]
+ else:
+ raise ValueError("Parameter 'focus' must take value of either 'origin' or 'destination.'")
+
+ VMs = [VecMoran(y, Ws[i], permutations=None) for i, y in enumerate(sims)]
+ sim = [VM.__calc(VM.z) for VM in VMs]
+ return sim
+
+ def __rand_vecs_B(self, focus):
+ if focus.lower() == 'origin':
+ sims = [np.hstack([np.arange(self.n).reshape((-1,1)), self.o,
+ np.random.permutation(self.d)]) for i in xrange(self.permutations)]
+ elif focus.lower() == 'destination':
+ sims = [np.hstack([np.arange(self.n).reshape((-1,1)),
+ np.random.permutation(self.o), self.d]) for i in xrange(self.permutations)]
+ else:
+ raise ValueError("Parameter 'focus' must take value of either 'origin' or 'destination.'")
+ sims = [VecMoran(y, self.w, permutations=None) for y in sims]
+ sim = [VM.__calc(VM.z) for VM in sims]
+ return sim
+
+ def _slag(self, w, y):
+ """
+ Dense spatial lag operator for.
+ If w is row standardized, returns the average of each observation's neighbors;
+ if not, returns the weighted sum of each observation's neighbors.
+ Parameters
+ ----------
+ w : W
+ object
+ y : array
+ numpy array with dimensionality conforming to w (see examples)
+ Returns
+ -------
+ wy : array
+ array of numeric values for the spatial lag
+ """
+ return np.array(w.sparse.todense()) * y
+
diff --git a/pysal/contrib/viz/plot.py b/pysal/contrib/viz/plot.py
new file mode 100644
index 0000000..5116dc4
--- /dev/null
+++ b/pysal/contrib/viz/plot.py
@@ -0,0 +1,52 @@
+"""
+Canned Views using PySAL and Matplotlib
+"""
+
+__author__ = "Marynia Kolak <marynia.kolak at gmail.com>"
+
+import pandas as pd
+import numpy as np
+import pysal as ps
+import matplotlib.pyplot as plt
+
+def mplot(m, xlabel='', ylabel='', title='', custom=(7,7)):
+ '''
+ Produce basic Moran Plot
+ ...
+ Parameters
+ ---------
+ m : array
+ values of Moran's I
+ xlabel : str
+ label for x axis
+ ylabel : str
+ label for y axis
+ title : str
+ title of plot
+ custom : tuple
+ dimensions of figure size
+
+ Returns
+ ---------
+ plot : png
+ image file showing plot
+
+ '''
+
+ lag = ps.lag_spatial(m.w, m.z)
+ fit = ps.spreg.OLS(m.z[:, None], lag[:,None])
+
+ ## Customize plot
+ fig = plt.figure(figsize=custom)
+ plt.xlabel(xlabel)
+ plt.ylabel(ylabel)
+ plt.suptitle(title)
+
+ plt.scatter(m.z, lag, s=60, color='k', alpha=.6)
+ plt.plot(lag, fit.predy, color='r')
+
+ plt.axvline(0, alpha=0.5)
+ plt.axhline(0, alpha=0.5)
+ plt.show()
+
+ return None
diff --git a/pysal/core/IOHandlers/__init__.py b/pysal/core/IOHandlers/__init__.py
index b9361cf..97c141f 100644
--- a/pysal/core/IOHandlers/__init__.py
+++ b/pysal/core/IOHandlers/__init__.py
@@ -18,3 +18,8 @@ import mat
import mtx
import stata_txt
import wk1
+
+try:
+ from . import db
+except:
+ warnings.warn('SQLAlchemy and Geomet not installed, database I/O disabled')
diff --git a/pysal/core/IOHandlers/db.py b/pysal/core/IOHandlers/db.py
new file mode 100644
index 0000000..db6211a
--- /dev/null
+++ b/pysal/core/IOHandlers/db.py
@@ -0,0 +1,88 @@
+import pysal.core.FileIO as FileIO
+errmsg = ''
+try:
+ try:
+ from geomet import wkb
+ except ImportError:
+ from shapely import wkb
+except ImportError:
+ wkb = None
+ errmsg += ('No WKB parser found. Please install one of the following packages '
+ 'to enable this functionality: [geomet, shapely]\n')
+
+try:
+ from sqlalchemy.ext.automap import automap_base
+ from sqlalchemy import create_engine, inspect
+ from sqlalchemy.ext.automap import automap_base
+ from sqlalchemy.orm import Session
+ nosql_mode = False
+except ImportError:
+ nosql_mode = True
+ errmsg += ('No module named sqlalchemy. Please install'
+ ' sqlalchemy to enable this functionality.')
+
+class SQLConnection(FileIO.FileIO):
+ """
+ Reads SQL mappable
+ """
+
+ FORMATS = ['sqlite', 'db']
+ MODES = ['r']
+
+ def __init__(self, *args, **kwargs):
+ if errmsg != '':
+ raise ImportError(errmsg)
+ self._typ = str
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ #self.file = open(self.dataPath, self.mode)
+
+ self.dbname = args[0]
+ self.Base = automap_base()
+ self._engine = create_engine(self.dbname)
+ self.Base.prepare(self._engine, reflect=True)
+ self.metadata = self.Base.metadata
+
+ def read(self, *args, **kwargs):
+ return self._get_gjson(*args, **kwargs)
+
+ def seek(self):
+ pass
+
+ def next(self):
+ pass
+
+ def close(self):
+ self.file.close()
+ FileIO.FileIO.close(self)
+
+ def _get_gjson(self, tablename, geom_column="GEOMETRY"):
+
+ gjson = {
+ "type": "FeatureCollection",
+ "features": []}
+
+ for row in self.session.query(self.metadata.tables[tablename]):
+ feat = {"type": "Feature",
+ "geometry": {},
+ "properties":{}}
+ feat["GEOMETRY"] = wkb.loads(getattr(row,geom_column))
+ attributes = row._asdict()
+ attributes.pop(geom_column, None)
+ feat["properties"] = attributes
+ gjson["features"].append(feat)
+
+ return gjson
+
+
+ @property
+ def tables(self):
+ if not hasattr(self, '_tables'):
+ self._tables = self.metadata.tables.keys()
+ return self._tables
+
+ @property
+ def session(self):
+ #What happens if the session is externally closed? Check for None?
+ if not hasattr(self, '_session'):
+ self._session = Session(self._engine)
+ return self._session
diff --git a/pysal/core/IOHandlers/geoda_txt.py b/pysal/core/IOHandlers/geoda_txt.py
index 406c8a9..2f129c6 100644
--- a/pysal/core/IOHandlers/geoda_txt.py
+++ b/pysal/core/IOHandlers/geoda_txt.py
@@ -60,7 +60,7 @@ class GeoDaTxtReader(Tables.DataTable):
self.pos += 1
return row
else:
- raise None
+ return None
def close(self):
self.fileObj.close()
diff --git a/pysal/core/IOHandlers/pyDbfIO.py b/pysal/core/IOHandlers/pyDbfIO.py
index 5aecc6f..057209c 100644
--- a/pysal/core/IOHandlers/pyDbfIO.py
+++ b/pysal/core/IOHandlers/pyDbfIO.py
@@ -4,6 +4,8 @@ import struct
import itertools
from warnings import warn
import pysal
+import os
+import time
__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
__all__ = ['DBF']
@@ -272,7 +274,9 @@ class DBF(pysal.core.Tables.DataTable):
POS = self.f.tell()
self.f.seek(0)
ver = 3
- now = datetime.datetime.now()
+ now = datetime.datetime.utcfromtimestamp(
+ int(os.environ.get('SOURCE_DATE_EPOCH', time.time())),
+ )
yr, mon, day = now.year - 1900, now.month, now.day
numrec = self.numrec
numfields = len(self.header)
diff --git a/pysal/core/IOHandlers/tests/test_db.py b/pysal/core/IOHandlers/tests/test_db.py
new file mode 100644
index 0000000..6484d64
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_db.py
@@ -0,0 +1,55 @@
+import os
+import pysal as ps
+import unittest as ut
+
+try:
+ import sqlalchemy
+ missing_sql = False
+except ImportError:
+ missing_sql = True
+
+try:
+ import geomet
+ missing_geomet = False
+except ImportError:
+ missing_geomet = True
+
+
+def to_wkb_point(c):
+ """
+ Super quick hack that does not actually belong in here
+ """
+ point = {'type': 'Point', 'coordinates':[c[0], c[1]]}
+ return geomet.wkb.dumps(point)
+
+
+ at ut.skipIf(missing_sql or missing_geomet,
+ 'missing dependencies: Geomet ({}) & SQLAlchemy ({})'.format(missing_geomet, missing_sql))
+class Test_sqlite_reader(ut.TestCase):
+
+ def setUp(self):
+ df = ps.pdio.read_files(ps.examples.get_path('new_haven_merged.dbf'))
+ df['GEOMETRY'] = df['geometry'].apply(to_wkb_point)
+ del df['geometry'] # This is a hack to not have to worry about a custom point type in the DB
+ engine = sqlalchemy.create_engine('sqlite:///test.db')
+ conn = engine.connect()
+ df.to_sql('newhaven', conn, index=True,
+ dtype={'date': sqlalchemy.types.UnicodeText, # Should convert the df date into a true date object, just a hack again
+ 'dataset': sqlalchemy.types.UnicodeText,
+ 'street': sqlalchemy.types.UnicodeText,
+ 'intersection': sqlalchemy.types.UnicodeText,
+ 'time': sqlalchemy.types.UnicodeText, # As above re: date
+ 'GEOMETRY': sqlalchemy.types.BLOB}) # This is converted to TEXT as lowest type common sqlite
+
+ def test_deserialize(self):
+ db = ps.open('sqlite:///test.db')
+ self.assertEqual(db.tables, [u'newhaven'])
+
+ gj = db._get_gjson('newhaven')
+ self.assertFalse(True)
+
+ def tearDown(self):
+ os.remove('test.db')
+
+if __name__ == '__main__':
+ ut.main()
diff --git a/pysal/core/Tables.py b/pysal/core/Tables.py
index 5fc03aa..7f981e8 100644
--- a/pysal/core/Tables.py
+++ b/pysal/core/Tables.py
@@ -1,5 +1,7 @@
__all__ = ['DataTable']
import FileIO
+from ..common import requires
+from warnings import warn
import numpy as np
__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
@@ -192,7 +194,26 @@ class DataTable(FileIO.FileIO):
data = [r[cols] for r in data]
self.seek(prevPos)
return data
-
+
+ @requires('pandas')
+ def to_df(self, n=-1, read_shp=None, **df_kws):
+ import pandas as pd
+ self.seek(0)
+ header = self.header
+ records = self.read(n)
+ df = pd.DataFrame(records, columns=header, **df_kws)
+ if read_shp is not False:
+ if read_shp is True or self.dataPath.endswith('.dbf'):
+ read_shp = self.dataPath[:-3] + 'shp'
+ try:
+ import pysal.contrib.pdio.shp as shp
+ df['geometry'] = shp.shp2series(self.dataPath[:-3] + 'shp')
+ except IOError as e:
+ warn('Encountered the following error in attempting to read'
+ ' the shapefile {}. Proceeding with read, but the error'
+ ' will be reproduced below:\n'
+ ' {}'.format(self.dataPath[:-3]+'shp', e))
+ return df
def _test():
import doctest
diff --git a/pysal/core/tests/test_Tables.py b/pysal/core/tests/test_Tables.py
new file mode 100644
index 0000000..46ee91f
--- /dev/null
+++ b/pysal/core/tests/test_Tables.py
@@ -0,0 +1,29 @@
+import pysal as ps
+import numpy as np
+import unittest as ut
+
+PANDAS_EXTINCT = ps.common.pandas is None
+
+class Test_Table(ut.TestCase):
+ def setUp(self):
+ self.filehandler = ps.open(ps.examples.get_path('columbus.dbf'))
+ self.df = self.filehandler.to_df()
+ self.filehandler.seek(0)
+ self.shapefile = ps.open(ps.examples.get_path('columbus.shp'))
+ self.csvhandler = ps.open(ps.examples.get_path('usjoin.csv'))
+ self.csv_df = self.csvhandler.to_df()
+ self.csvhandler.seek(0)
+
+ @ut.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_to_df(self):
+ for column in self.csv_df.columns:
+ if column.lower() == 'name':
+ continue
+ np.testing.assert_allclose(self.csvhandler.by_col(column),
+ self.csv_df[column].values)
+ for column in self.df.columns:
+ if column == 'geometry':
+ continue
+ np.testing.assert_allclose(self.filehandler.by_col(column),
+ self.df[column])
+
diff --git a/pysal/esda/gamma.py b/pysal/esda/gamma.py
index 3eb7d2f..e01c50b 100644
--- a/pysal/esda/gamma.py
+++ b/pysal/esda/gamma.py
@@ -5,15 +5,16 @@ Gamma index for spatial autocorrelation
"""
__author__ = "Luc Anselin <luc.anselin at asu.edu>"
-import pysal
import numpy as np
+from ..weights.spatial_lag import lag_spatial
+from .tabular import _univariate_handler
__all__ = ['Gamma']
PERMUTATIONS = 999
-class Gamma:
+class Gamma(object):
"""Gamma index for spatial autocorrelation
@@ -74,7 +75,7 @@ class Gamma:
use same example as for join counts to show similarity
- >>> import numpy as np
+ >>> import pysal, numpy as np
>>> w=pysal.lat2W(4,4)
>>> y=np.ones(16)
>>> y[0:8]=0
@@ -149,6 +150,7 @@ class Gamma:
"""
def __init__(self, y, w, operation='c', standardize='no', permutations=PERMUTATIONS):
+ y = np.asarray(y).flatten()
self.w = w
self.y = y
self.op = operation
@@ -171,10 +173,19 @@ class Gamma:
p_sim_g = self.__pseudop(self.sim_g, self.g)
self.p_sim_g = p_sim_g
self.g_z = (self.g - self.mean_g) / np.std(self.sim_g)
+
+ @property
+ def _statistic(self):
+ return self.g
+
+ @property
+ def p_sim(self):
+ """new name to fit with Moran module"""
+ return self.p_sim_g
def __calc(self, z, op):
if op == 'c': # cross-product
- zl = pysal.lag_spatial(self.w, z)
+ zl = lag_spatial(self.w, z)
g = (z * zl).sum()
elif op == 's': # squared difference
zs = np.zeros(z.shape)
@@ -211,4 +222,8 @@ class Gamma:
if psim > 0.5:
psim = (self.permutations - larger + 1.) / (self.permutations + 1.)
return psim
-
+
+ @classmethod
+ def by_col(cls, df, cols, w=None, inplace=False, pvalue = 'sim', outvals = None, **stat_kws):
+ return _univariate_handler(df, cols, w=w, inplace=inplace, pvalue=pvalue,
+ outvals=outvals, stat=cls, swapname=cls.__name__.lower(), **stat_kws)
diff --git a/pysal/esda/geary.py b/pysal/esda/geary.py
index f8d499e..9be01ef 100644
--- a/pysal/esda/geary.py
+++ b/pysal/esda/geary.py
@@ -5,11 +5,13 @@ __author__ = "Sergio J. Rey <srey at asu.edu> "
import numpy as np
import scipy.stats as stats
+from .. import weights
+from .tabular import _univariate_handler
__all__ = ['Geary']
-class Geary:
+class Geary(object):
"""
Global Geary C Autocorrelation statistic
@@ -90,6 +92,10 @@ class Geary:
>>>
"""
def __init__(self, y, w, transformation="r", permutations=999):
+ if not isinstance(w, weights.W):
+ raise TypeError('w must be a pysal weights object, got {}'
+ ' instead'.format(type(w)))
+ y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
w.transform = transformation
@@ -130,6 +136,11 @@ class Geary:
self.z_sim = (self.C - self.EC_sim) / self.seC_sim
self.p_z_sim = 1 - stats.norm.cdf(np.abs(self.z_sim))
+ @property
+ def _statistic(self):
+ """ a standardized accessor for esda statistics"""
+ return self.C
+
def __moments(self):
y = self.y
n = self.n
@@ -166,4 +177,44 @@ class Geary:
a = (self.n - 1) * sum(ys)
return a / self.den
+ @classmethod
+ def by_col(cls, df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws):
+ """
+ Function to compute a Geary statistic on a dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+ cols : string or list of string
+ name or list of names of columns to use to compute the statistic
+ w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+ inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, with default configurations,
+ the derived columns will be named like 'column_geary' and 'column_p_sim'
+ pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the Geary statistic's documentation for available p-values
+ outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ Geary statistic
+ **stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the Geary statistic.
+
+ Returns
+ --------
+ If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+ returns a copy of the dataframe with the relevant columns attached.
+ See Also
+ ---------
+ For further documentation, refer to the Geary class in pysal.esda
+ """
+ return _univariate_handler(df, cols, w=w, inplace=inplace, pvalue=pvalue,
+ outvals=outvals, stat=cls,
+ swapname=cls.__name__.lower(), **stat_kws)
diff --git a/pysal/esda/getisord.py b/pysal/esda/getisord.py
index a8ef43a..cde36ac 100644
--- a/pysal/esda/getisord.py
+++ b/pysal/esda/getisord.py
@@ -4,13 +4,14 @@ Getis and Ord G statistic for spatial autocorrelation
__author__ = "Sergio J. Rey <srey at asu.edu>, Myunghwa Hwang <mhwang4 at gmail.com> "
__all__ = ['G', 'G_Local']
-from pysal.common import np, stats, math
-from pysal.weights.spatial_lag import lag_spatial as slag
+from ..common import np, stats, math
+from ..weights.spatial_lag import lag_spatial as slag
+from .tabular import _univariate_handler
PERMUTATIONS = 999
-class G:
+class G(object):
"""
Global G Autocorrelation Statistic
@@ -93,6 +94,7 @@ class G:
"""
def __init__(self, y, w, permutations=PERMUTATIONS):
+ y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
w.transform = "B"
@@ -158,8 +160,55 @@ class G:
self.num = y * yl
return self.num.sum() / self.den_sum
-
-class G_Local:
+ @property
+ def _statistic(self):
+ """ Standardized accessor for esda statistics"""
+ return self.G
+
+ @classmethod
+ def by_col(cls, df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws):
+ """
+ Function to compute a G statistic on a dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+ cols : string or list of string
+ name or list of names of columns to use to compute the statistic
+ w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+ inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named 'column_g'
+ pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the G statistic's documentation for available p-values
+ outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ G statistic
+ **stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the G statistic.
+
+ Returns
+ --------
+ If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+ returns a copy of the dataframe with the relevant columns attached.
+
+ See Also
+ ---------
+ For further documentation, refer to the G class in pysal.esda
+ """
+ return _univariate_handler(df, cols, w=w, inplace=inplace, pvalue=pvalue,
+ outvals=outvals, stat=cls,
+ swapname=cls.__name__.lower(), **stat_kws)
+
+
+
+class G_Local(object):
"""
Generalized Local G Autocorrelation
Statistic [Getis1992]_, [Ord1995]_, [Getis1996]_ .
@@ -185,7 +234,7 @@ class G_Local:
original variable
w : DistanceBand W
original weights object
- permutations: int
+ permutations : int
the number of permutations
Gs : array
of floats, the value of the orginal G statistic in Getis & Ord (1992)
@@ -292,6 +341,7 @@ class G_Local:
"""
def __init__(self, y, w, transform='R', permutations=PERMUTATIONS, star=False):
+ y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
self.w = w
@@ -384,4 +434,51 @@ class G_Local:
self.Zs = (self.Gs - self.EGs) / np.sqrt(self.VGs)
self.w.transform = self.w_original
-
+
+ @property
+ def _statistic(self):
+ """Standardized accessor for esda statistics"""
+ return self.Gs
+
+ @classmethod
+ def by_col(cls, df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws):
+ """
+ Function to compute a G_Local statistic on a dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+ cols : string or list of string
+ name or list of names of columns to use to compute the statistic
+ w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+ inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named 'column_g_local'
+ pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the G_Local statistic's documentation for available p-values
+ outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ G_Local statistic
+ **stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the G_Local statistic.
+
+ Returns
+ --------
+ If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+ returns a copy of the dataframe with the relevant columns attached.
+
+ See Also
+ ---------
+ For further documentation, refer to the G_Local class in pysal.esda
+ """
+ return _univariate_handler(df, cols, w=w, inplace=inplace, pvalue=pvalue,
+ outvals=outvals, stat=cls,
+ swapname=cls.__name__.lower(), **stat_kws)
+
+
diff --git a/pysal/esda/join_counts.py b/pysal/esda/join_counts.py
index 430b187..b91668f 100644
--- a/pysal/esda/join_counts.py
+++ b/pysal/esda/join_counts.py
@@ -4,7 +4,8 @@ Spatial autocorrelation for binary attributes
"""
__author__ = "Sergio J. Rey <srey at asu.edu> , Luc Anselin <luc.anselin at asu.edu>"
-import pysal
+from ..weights.spatial_lag import lag_spatial
+from .tabular import _univariate_handler
import numpy as np
__all__ = ['Join_Counts']
@@ -12,7 +13,7 @@ __all__ = ['Join_Counts']
PERMUTATIONS = 999
-class Join_Counts:
+class Join_Counts(object):
"""Binary Join Counts
@@ -114,6 +115,7 @@ class Join_Counts:
>>>
"""
def __init__(self, y, w, permutations=PERMUTATIONS):
+ y = np.asarray(y).flatten()
w.transformation = 'b' # ensure we have binary weights
self.w = w
self.y = y
@@ -139,10 +141,10 @@ class Join_Counts:
self.p_sim_bw = p_sim_bw
def __calc(self, z):
- zl = pysal.lag_spatial(self.w, z)
+ zl = lag_spatial(self.w, z)
bb = sum(z * zl) / 2.0
zw = 1 - z
- zl = pysal.lag_spatial(self.w, zw)
+ zl = lag_spatial(self.w, zw)
ww = sum(zw * zl) / 2.0
bw = self.J - (bb + ww)
return (bb, ww, bw)
@@ -152,3 +154,53 @@ class Join_Counts:
larger = sum(above)
psim = (larger + 1.) / (self.permutations + 1.)
return psim
+
+ @property
+ def _statistic(self):
+ return self.bw
+
+ @classmethod
+ def by_col(cls, df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws):
+ """
+ Function to compute a Join_Count statistic on a dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+ cols : string or list of string
+ name or list of names of columns to use to compute the statistic
+ w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+ inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named
+ 'column_join_count'
+ pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the Join_Count statistic's documentation for available p-values
+ outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ Join_Count statistic
+ **stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the Join_Count statistic.
+
+ Returns
+ --------
+ If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+ returns a copy of the dataframe with the relevant columns attached.
+
+ See Also
+ ---------
+ For further documentation, refer to the Join_Count class in pysal.esda
+ """
+ if outvals is None:
+ outvals = []
+ outvals.extend(['bb', 'p_sim_bw', 'p_sim_bb'])
+ pvalue = ''
+ return _univariate_handler(df, cols, w=w, inplace=inplace, pvalue=pvalue,
+ outvals=outvals, stat=cls,
+ swapname='bw', **stat_kws)
diff --git a/pysal/esda/mapclassify.py b/pysal/esda/mapclassify.py
index c4f71bc..3690948 100644
--- a/pysal/esda/mapclassify.py
+++ b/pysal/esda/mapclassify.py
@@ -21,6 +21,11 @@ import copy
import sys
from scipy.cluster.vq import kmeans as KMEANS
from warnings import warn as Warn
+try:
+ from numba import autojit
+except ImportError:
+ def autojit(func):
+ return func
def headTail_breaks(values, cuts):
"""
@@ -210,7 +215,7 @@ def bin(y, bins):
def bin1d(x, bins):
"""
- place values of a 1-d array into bins and determine counts of values in
+ Place values of a 1-d array into bins and determine counts of values in
each bin
Parameters
@@ -225,7 +230,7 @@ def bin1d(x, bins):
binIds : array
1-d array of integer bin Ids
- counts: int
+ counts : int
number of elements of x falling in each bin
Examples
@@ -301,7 +306,7 @@ def natural_breaks(values, k=5):
uv = np.unique(values)
uvk = len(uv)
if uvk < k:
- Warn('Warning: Not enough unique values in array to form k classes',
+ Warn('Warning: Not enough unique values in array to form k classes',
UserWarning)
Warn('Warning: setting k to %d' % uvk, UserWarning)
k = uvk
@@ -313,6 +318,7 @@ def natural_breaks(values, k=5):
return (sids, class_ids, fit, cuts)
+ at autojit
def _fisher_jenks_means(values, classes=5, sort=True):
"""
Jenks Optimal (Natural Breaks) algorithm implemented in Python.
@@ -325,61 +331,45 @@ def _fisher_jenks_means(values, classes=5, sort=True):
assuring heterogeneity among classes.
"""
-
if sort:
values.sort()
- mat1 = []
- for i in range(0, len(values) + 1):
- temp = []
- for j in range(0, classes + 1):
- temp.append(0)
- mat1.append(temp)
- mat2 = []
- for i in range(0, len(values) + 1):
- temp = []
- for j in range(0, classes + 1):
- temp.append(0)
- mat2.append(temp)
- for i in range(1, classes + 1):
- mat1[1][i] = 1
- mat2[1][i] = 0
- for j in range(2, len(values) + 1):
- mat2[j][i] = float('inf')
- v = 0.0
+ n_data = len(values)
+ mat1 = np.zeros((n_data + 1, classes + 1), dtype=np.int32)
+ mat2 = np.zeros((n_data + 1, classes + 1), dtype=np.float32)
+ mat1[1, 1:] = 1
+ mat2[2:, 1:] = np.inf
+
+ v = np.float32(0)
for l in range(2, len(values) + 1):
- s1 = 0.0
- s2 = 0.0
- w = 0.0
+ s1 = np.float32(0)
+ s2 = np.float32(0)
+ w = np.float32(0)
for m in range(1, l + 1):
i3 = l - m + 1
- val = float(values[i3 - 1])
+ val = np.float32(values[i3 - 1])
s2 += val * val
s1 += val
- w += 1
+ w += np.float32(1)
v = s2 - (s1 * s1) / w
i4 = i3 - 1
if i4 != 0:
for j in range(2, classes + 1):
- if mat2[l][j] >= (v + mat2[i4][j - 1]):
- mat1[l][j] = i3
- mat2[l][j] = v + mat2[i4][j - 1]
- mat1[l][1] = 1
- mat2[l][1] = v
+ if mat2[l, j] >= (v + mat2[i4, j - 1]):
+ mat1[l, j] = i3
+ mat2[l, j] = v + mat2[i4, j - 1]
+ mat1[l, 1] = 1
+ mat2[l, 1] = v
k = len(values)
- kclass = []
- for i in range(0, classes + 1):
- kclass.append(0)
- kclass[classes] = float(values[len(values) - 1])
- kclass[0] = float(values[0])
- countNum = classes
- while countNum >= 2:
- pivot = mat1[k][countNum]
+ kclass = np.zeros(classes + 1, dtype=values.dtype)
+ kclass[classes] = values[len(values) - 1]
+ kclass[0] = values[0]
+ for countNum in range(classes, 1, -1):
+ pivot = mat1[k, countNum]
id = int(pivot - 2)
kclass[countNum - 1] = values[id]
k = int(pivot - 1)
- countNum -= 1
return kclass
@@ -394,7 +384,7 @@ class Map_Classifier(object):
.. math::
- C_j^l < y_i \le C_j^u \ forall i \in C_j
+ C_j^l < y_i \le C_j^u \ \forall i \in C_j
where :math:`C_j` denotes class :math:`j` which has lower bound
:math:`C_j^l` and upper bound :math:`C_j^u`.
@@ -432,9 +422,8 @@ class Map_Classifier(object):
"""
def __init__(self, y):
+ y = np.asarray(y).flatten()
self.name = 'Map Classifier'
- if hasattr(y, 'values'):
- y = y.values # fix for pandas
self.y = y
self._classify()
self._summary()
@@ -449,39 +438,186 @@ class Map_Classifier(object):
def _classify(self):
self._set_bins()
self.yb, self.counts = bin1d(self.y, self.bins)
-
+
def _update(self, data, *args, **kwargs):
"""
- The only thing that *should* happen in this function is
+ The only thing that *should* happen in this function is
1. input sanitization for pandas
- 2. classification/reclassification.
-
+ 2. classification/reclassification.
+
Using their __init__ methods, all classifiers can re-classify given
- different input parameters or additional data.
+ different input parameters or additional data.
If you've got a cleverer updating equation than the intial estimation
equation, remove the call to self.__init__ below and replace it with the
- updating function.
+ updating function.
"""
if data is not None:
- if hasattr(data, 'values'):
- data = data.values
+ data = np.asarray(data).flatten()
data = np.append(data.flatten(), self.y)
else:
data = self.y
self.__init__(data, *args, **kwargs)
-
+
+ @classmethod
+ def make(cls, *args, **kwargs):
+ """
+ Configure and create a classifier that will consume data and produce
+ classifications, given the configuration options specified by this
+ function.
+
+ Note that this like a *partial application* of the relevant class
+ constructor. `make` creates a function that returns classifications; it
+ does not actually do the classification.
+
+ If you want to classify data directly, use the appropriate class
+ constructor, like Quantiles, Max_Breaks, etc.
+
+ If you *have* a classifier object, but want to find which bins new data falls into,
+ use find_bin.
+
+ Parameters
+ ----------
+ *args : required positional arguments
+ all positional arguments required by the classifier,
+ excluding the input data.
+ rolling : bool
+ a boolean configuring the outputted classifier to use
+ a rolling classifier rather than a new classifier
+ for each input. If rolling, this adds the current data
+ to all of the previous data in the classifier, and
+ rebalances the bins, like a running median
+ computation.
+ return_object : bool
+ a boolean configuring the outputted classifier to
+ return the classifier object or not
+ return_bins : bool
+ a boolean configuring the outputted classifier to
+ return the bins/breaks or not
+ return_counts : bool
+ a boolean configuring the outputted classifier to
+ return the histogram of objects falling into each bin
+ or not
+
+ Returns
+ -------
+ A function that consumes data and returns their bins (and object,
+ bins/breaks, or counts, if requested).
+
+ Note
+ ----
+ This is most useful when you want to run a classifier many times
+ with a given configuration, such as when classifying many columns of an
+ array or dataframe using the same configuration.
+
+ Examples
+ --------
+ >>> import pysal as ps
+ >>> df = ps.pdio.read_files(ps.examples.get_path('columbus.dbf'))
+ >>> classifier = ps.Quantiles.make(k=9)
+ >>> classifier
+ >>> classifications = df[['HOVAL', 'CRIME', 'INC']].apply(ps.Quantiles.make(k=9))
+ >>> classifications.head()
+ HOVAL CRIME INC
+ 0 8 0 7
+ 1 7 1 8
+ 2 2 3 5
+ 3 4 4 0
+ 4 1 6 3
+ >>> import pandas as pd; from numpy import linspace as lsp
+ >>> data = [lsp(3,8,num=10), lsp(10, 0, num=10), lsp(-5, 15, num=10)]
+ >>> data = pd.DataFrame(data).T
+ >>> data
+ 0 1 2
+ 0 3.000000 10.000000 -5.000000
+ 1 3.555556 8.888889 -2.777778
+ 2 4.111111 7.777778 -0.555556
+ 3 4.666667 6.666667 1.666667
+ 4 5.222222 5.555556 3.888889
+ 5 5.777778 4.444444 6.111111
+ 6 6.333333 3.333333 8.333333
+ 7 6.888888 2.222222 10.555556
+ 8 7.444444 1.111111 12.777778
+ 9 8.000000 0.000000 15.000000
+ >>> data.apply(ps.Quantiles.make(rolling=True))
+ 0 1 3
+ 0 0 4 0
+ 1 0 4 0
+ 2 1 4 0
+ 3 1 3 0
+ 4 2 2 1
+ 5 2 1 2
+ 6 3 0 4
+ 7 3 0 4
+ 8 4 0 4
+ 9 4 0 4
+ >>> dbf = ps.open(ps.examples.get_path('baltim.dbf'))
+ >>> data = dbf.by_col_array('PRICE', 'LOTSZ', 'SQFT')
+ >>> my_bins = [1, 10, 20, 40, 80]
+ >>> classifications = [ps.User_Defined.make(bins=my_bins)(a) for a in data.T]
+ >>> len(classifications)
+ 3
+ >>> print(classifications)
+ [array([4, 5, 5, 5, 4, 4, 5, 4, 4, 5, 4, 4, 4, 4, 4, 1, 2, 2, 3, 4, 4, 3, 3,
+ ...
+ 2, 2, 2, 2])]
+ """
+
+ # only flag overrides return flag
+ to_annotate = copy.deepcopy(kwargs)
+ return_object = kwargs.pop('return_object', False)
+ return_bins = kwargs.pop('return_bins', False)
+ return_counts = kwargs.pop('return_counts', False)
+
+ rolling = kwargs.pop('rolling', False)
+ if rolling:
+ #just initialize a fake classifier
+ data = range(10)
+ cls_instance = cls(data, *args, **kwargs)
+ #and empty it, since we'll be using the update
+ cls_instance.y = np.array([])
+ else:
+ cls_instance = None
+
+ #wrap init in a closure to make a consumer.
+ # Qc Na: "Objects/Closures are poor man's Closures/Objects"
+ def classifier(data, cls_instance=cls_instance):
+ if rolling:
+ cls_instance.update(data, inplace=True, **kwargs)
+ yb = cls_instance.find_bin(data)
+ else:
+ cls_instance = cls(data, *args, **kwargs)
+ yb = cls_instance.yb
+ outs = [yb, None, None, None]
+ outs[1] = cls_instance if return_object else None
+ outs[2] = cls_instance.bins if return_bins else None
+ outs[3] = cls_instance.counts if return_counts else None
+ outs = [a for a in outs if a is not None]
+ if len(outs) == 1:
+ return outs[0]
+ else:
+ return outs
+
+ # for debugging/jic, keep around the kwargs.
+ # in future, we might want to make this a thin class, so that we can set
+ # a custom repr. Call the class `Binner` or something, that's a
+ # pre-configured Classifier that just consumes data, bins it, & possibly
+ # updates the bins.
+ classifier._options = to_annotate
+ return classifier
+
+
def update(self, y=None, inplace=False, **kwargs):
"""
- Add data or change classification parameters.
+ Add data or change classification parameters.
Parameters
----------
- y : array
+ y : array
(n,1) array of data to classify
inplace : bool
whether to conduct the update in place or to return a copy
- estimated from the additional specifications.
+ estimated from the additional specifications.
Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.
@@ -506,11 +642,11 @@ class Map_Classifier(object):
This will allow the classifier to be called like it's a function.
Whether or not we want to make this be "find_bin" or "update" is a
- design decision.
+ design decision.
I like this as find_bin, since a classifier's job should be to classify
the data given to it using the rules estimated from the `_classify()`
- function.
+ function.
"""
return self.find_bin(*args)
@@ -601,7 +737,7 @@ class Map_Classifier(object):
table.insert(1, " ")
table = "\n".join(table)
return table
-
+
def find_bin(self, x):
"""
Sort input or inputs according to the current bin estimate
@@ -617,10 +753,10 @@ class Map_Classifier(object):
a bin index or array of bin indices that classify the input into one of
the classifiers' bins
"""
- if not isinstance(x, np.ndarray):
- x = np.array([x]).flatten()
+ x = np.asarray(x).flatten()
uptos = [np.where(value < self.bins)[0] for value in x]
bins = [x.min() if x.size > 0 else len(self.bins)-1 for x in uptos] #bail upwards
+ bins = np.asarray(bins)
if len(bins) == 1:
return bins[0]
else:
@@ -821,7 +957,7 @@ class Percentiles(Map_Classifier):
def update(self, y=None, inplace=False, **kwargs):
"""
- Add data or change classification parameters.
+ Add data or change classification parameters.
Parameters
----------
@@ -829,12 +965,12 @@ class Percentiles(Map_Classifier):
(n,1) array of data to classify
inplace : bool
whether to conduct the update in place or to return a copy
- estimated from the additional specifications.
+ estimated from the additional specifications.
Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.
"""
- kwargs.update({'pct':kwargs.pkp('pct', self.pct)})
+ kwargs.update({'pct':kwargs.pop('pct', self.pct)})
if inplace:
self._update(y, **kwargs)
else:
@@ -942,10 +1078,10 @@ class Box_Plot(Map_Classifier):
Map_Classifier._classify(self)
self.low_outlier_ids = np.nonzero(self.yb == 0)[0]
self.high_outlier_ids = np.nonzero(self.yb == 5)[0]
-
+
def update(self, y=None, inplace=False, **kwargs):
"""
- Add data or change classification parameters.
+ Add data or change classification parameters.
Parameters
----------
@@ -953,7 +1089,7 @@ class Box_Plot(Map_Classifier):
(n,1) array of data to classify
inplace : bool
whether to conduct the update in place or to return a copy
- estimated from the additional specifications.
+ estimated from the additional specifications.
Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.
@@ -1076,10 +1212,10 @@ class Std_Mean(Map_Classifier):
cuts.append(y_max)
self.bins = np.array(cuts)
self.k = len(cuts)
-
+
def update(self, y=None, inplace=False, **kwargs):
"""
- Add data or change classification parameters.
+ Add data or change classification parameters.
Parameters
----------
@@ -1087,7 +1223,7 @@ class Std_Mean(Map_Classifier):
(n,1) array of data to classify
inplace : bool
whether to conduct the update in place or to return a copy
- estimated from the additional specifications.
+ estimated from the additional specifications.
Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.
@@ -1176,7 +1312,7 @@ class Maximum_Breaks(Map_Classifier):
def update(self, y=None, inplace=False, **kwargs):
"""
- Add data or change classification parameters.
+ Add data or change classification parameters.
Parameters
----------
@@ -1184,7 +1320,7 @@ class Maximum_Breaks(Map_Classifier):
(n,1) array of data to classify
inplace : bool
whether to conduct the update in place or to return a copy
- estimated from the additional specifications.
+ estimated from the additional specifications.
Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.
@@ -1294,7 +1430,7 @@ class Natural_Breaks(Map_Classifier):
def update(self, y=None, inplace=False, **kwargs):
"""
- Add data or change classification parameters.
+ Add data or change classification parameters.
Parameters
----------
@@ -1302,7 +1438,7 @@ class Natural_Breaks(Map_Classifier):
(n,1) array of data to classify
inplace : bool
whether to conduct the update in place or to return a copy
- estimated from the additional specifications.
+ estimated from the additional specifications.
Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.
@@ -1410,7 +1546,7 @@ class Fisher_Jenks_Sampled(Map_Classifier):
if (pct * n > 1000) and truncate:
pct = 1000. / n
- ids = np.random.random_integers(0, n - 1, n * pct)
+ ids = np.random.random_integers(0, n - 1, int(n * pct))
yr = y[ids]
yr[-1] = max(y) # make sure we have the upper bound
yr[0] = min(y) # make sure we have the min
@@ -1432,7 +1568,7 @@ class Fisher_Jenks_Sampled(Map_Classifier):
def update(self, y=None, inplace=False, **kwargs):
"""
- Add data or change classification parameters.
+ Add data or change classification parameters.
Parameters
----------
@@ -1440,7 +1576,7 @@ class Fisher_Jenks_Sampled(Map_Classifier):
(n,1) array of data to classify
inplace : bool
whether to conduct the update in place or to return a copy
- estimated from the additional specifications.
+ estimated from the additional specifications.
Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.
@@ -1525,7 +1661,7 @@ class Jenks_Caspall(Map_Classifier):
cuts.shape = (len(cuts),)
self.bins = cuts
self.iterations = it
-
+
class Jenks_Caspall_Sampled(Map_Classifier):
"""
Jenks Caspall Map Classification using a random sample
@@ -1593,7 +1729,7 @@ class Jenks_Caspall_Sampled(Map_Classifier):
n = y.size
if pct * n > 1000:
pct = 1000. / n
- ids = np.random.random_integers(0, n - 1, n * pct)
+ ids = np.random.random_integers(0, n - 1, int(n * pct))
yr = y[ids]
yr[0] = max(y) # make sure we have the upper bound
self.original_y = y
@@ -1610,10 +1746,10 @@ class Jenks_Caspall_Sampled(Map_Classifier):
jc = Jenks_Caspall(self.y, self.k)
self.bins = jc.bins
self.iterations = jc.iterations
-
+
def update(self, y=None, inplace=False, **kwargs):
"""
- Add data or change classification parameters.
+ Add data or change classification parameters.
Parameters
----------
@@ -1621,7 +1757,7 @@ class Jenks_Caspall_Sampled(Map_Classifier):
(n,1) array of data to classify
inplace : bool
whether to conduct the update in place or to return a copy
- estimated from the additional specifications.
+ estimated from the additional specifications.
Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.
@@ -1856,7 +1992,7 @@ class User_Defined(Map_Classifier):
def update(self, y=None, inplace=False, **kwargs):
"""
- Add data or change classification parameters.
+ Add data or change classification parameters.
Parameters
----------
@@ -1864,7 +2000,7 @@ class User_Defined(Map_Classifier):
(n,1) array of data to classify
inplace : bool
whether to conduct the update in place or to return a copy
- estimated from the additional specifications.
+ estimated from the additional specifications.
Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.
@@ -2045,7 +2181,7 @@ class Max_P_Classifier(Map_Classifier):
def update(self, y=None, inplace=False, **kwargs):
"""
- Add data or change classification parameters.
+ Add data or change classification parameters.
Parameters
----------
@@ -2053,7 +2189,7 @@ class Max_P_Classifier(Map_Classifier):
(n,1) array of data to classify
inplace : bool
whether to conduct the update in place or to return a copy
- estimated from the additional specifications.
+ estimated from the additional specifications.
Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.
diff --git a/pysal/esda/mixture_smoothing.py b/pysal/esda/mixture_smoothing.py
index 5f5a39c..0a00cc7 100644
--- a/pysal/esda/mixture_smoothing.py
+++ b/pysal/esda/mixture_smoothing.py
@@ -24,10 +24,10 @@ class NP_Mixture_Smoother(object):
Parameters
----------
- e : array
- (n, 1), event variable measured across n spatial units
- b : array
- (n, 1), population at risk variable measured across n spatial units
+ e : array-like
+ event variable measured across n spatial units
+ b : array-like
+ population at risk variable measured across n spatial units
k : integer
a seed number to specify the number of subpopulations
acc : float
@@ -116,8 +116,8 @@ class NP_Mixture_Smoother(object):
"""
def __init__(self, e, b, k=50, acc=1.E-7, numiter=5000, limit=0.01):
- self.e = e
- self.b = b
+ self.e = np.asarray(e).flatten()
+ self.b = np.asarray(b).flatten()
self.n = len(e)
self.w = 1. / self.n
self.k = k
diff --git a/pysal/esda/moran.py b/pysal/esda/moran.py
index 4cec902..cbc5f06 100644
--- a/pysal/esda/moran.py
+++ b/pysal/esda/moran.py
@@ -4,8 +4,9 @@ Moran's I Spatial Autocorrelation Statistics
"""
__author__ = "Sergio J. Rey <srey at asu.edu>, \
Dani Arribas-Bel <daniel.arribas.bel at gmail.com>"
-from pysal.weights.spatial_lag import lag_spatial as slag
-from pysal.esda.smoothing import assuncao_rate
+from ..weights.spatial_lag import lag_spatial as slag
+from .smoothing import assuncao_rate
+from .tabular import _univariate_handler, _bivariate_handler
import scipy.stats as stats
import numpy as np
@@ -15,7 +16,7 @@ __all__ = ["Moran", "Moran_Local", "Moran_BV", "Moran_BV_matrix",
PERMUTATIONS = 999
-class Moran:
+class Moran(object):
"""Moran's I Global Autocorrelation Statistic
Parameters
@@ -131,6 +132,7 @@ class Moran:
"""
def __init__(self, y, w, transformation="r", permutations=PERMUTATIONS,
two_tailed=True):
+ y = np.asarray(y).flatten()
self.y = y
w.transform = transformation
self.w = w
@@ -199,8 +201,54 @@ class Moran:
inum = (z * zl).sum()
return self.n / self.w.s0 * inum / self.z2ss
-
-class Moran_BV:
+ @property
+ def _statistic(self):
+ """More consistent hidden attribute to access ESDA statistics"""
+ return self.I
+
+ @classmethod
+ def by_col(cls, df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws):
+ """
+ Function to compute a Moran statistic on a dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+ cols : string or list of string
+ name or list of names of columns to use to compute the statistic
+ w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+ inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named
+ 'column_moran'
+ pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the Moran statistic's documentation for available p-values
+ outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ Moran statistic
+ **stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the Moran statistic.
+
+ Returns
+ --------
+ If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+ returns a copy of the dataframe with the relevant columns attached.
+
+ See Also
+ ---------
+ For further documentation, refer to the Moran class in pysal.esda
+ """
+ return _univariate_handler(df, cols, w=w, inplace=inplace, pvalue=pvalue,
+ outvals=outvals, stat=cls,
+ swapname=cls.__name__.lower(), **stat_kws)
+
+class Moran_BV(object):
"""
Bivariate Moran's I
@@ -304,6 +352,8 @@ class Moran_BV:
"""
def __init__(self, x, y, w, transformation="r", permutations=PERMUTATIONS):
+ x = np.asarray(x).flatten()
+ y = np.asarray(y).flatten()
zy = (y - y.mean()) / y.std(ddof=1)
zx = (x - x.mean()) / x.std(ddof=1)
self.zx = zx
@@ -336,6 +386,60 @@ class Moran_BV:
self.num = (self.zx * wzy).sum()
return self.num / self.den
+ @property
+ def _statistic(self):
+ """More consistent hidden attribute to access ESDA statistics"""
+ return self.I
+
+ @classmethod
+ def by_col(cls, df, x, y=None, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws):
+ """
+ Function to compute a Moran_BV statistic on a dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+ X : list of strings
+ column name or list of column names to use as X values to compute
+ the bivariate statistic. If no Y is provided, pairwise comparisons
+ among these variates are used instead.
+ Y : list of strings
+ column name or list of column names to use as Y values to compute
+ the bivariate statistic. if no Y is provided, pariwise comparisons
+ among the X variates are used instead.
+ w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+ inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named
+ 'column_moran_local'
+ pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the Moran_BV statistic's documentation for available p-values
+ outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ Moran_BV statistic
+ **stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the Moran_BV statistic.
+
+
+ Returns
+ --------
+ If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+ returns a copy of the dataframe with the relevant columns attached.
+
+ See Also
+ ---------
+ For further documentation, refer to the Moran_BV class in pysal.esda
+ """
+ return _bivariate_handler(df, x, y=y, w=w, inplace=inplace,
+ pvalue = pvalue, outvals = outvals,
+ swapname=cls.__name__.lower(), stat=cls,**stat_kws)
+
def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
"""Bivariate Moran Matrix
@@ -402,7 +506,6 @@ def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
results[j, i] = Moran_BV(y2, y1, w, permutations=permutations)
return results
-
class Moran_Rate(Moran):
"""
Adjusted Moran's I Global Autocorrelation Statistic for Rate
@@ -510,15 +613,98 @@ class Moran_Rate(Moran):
def __init__(self, e, b, w, adjusted=True, transformation="r",
permutations=PERMUTATIONS, two_tailed=True):
+ e = np.asarray(e).flatten()
+ b = np.asarray(b).flatten()
if adjusted:
y = assuncao_rate(e, b)
else:
y = e * 1.0 / b
Moran.__init__(self, y, w, transformation=transformation,
permutations=permutations, two_tailed=two_tailed)
-
-
-class Moran_Local:
+
+ @classmethod
+ def by_col(cls, df, events, populations, w=None, inplace=False,
+ pvalue='sim', outvals=None, swapname='', **stat_kws):
+ """
+ Function to compute a Moran_Rate statistic on a dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+ events : string or list of strings
+ one or more names where events are stored
+ populations : string or list of strings
+ one or more names where the populations corresponding to the
+ events are stored. If one population column is provided, it is
+ used for all event columns. If more than one population column
+ is provided but there is not a population for every event
+ column, an exception will be raised.
+ w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+ inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named
+ 'column_moran_rate'
+ pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the Moran_Rate statistic's documentation for available p-values
+ outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ Moran_Rate statistic
+ **stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the Moran_Rate statistic.
+
+ Returns
+ --------
+ If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+ returns a copy of the dataframe with the relevant columns attached.
+
+ See Also
+ ---------
+ For further documentation, refer to the Moran_Rate class in pysal.esda
+ """
+ if not inplace:
+ new = df.copy()
+ cls.by_col(new, events, populations, w=w, inplace=True,
+ pvalue=pvalue, outvals=outvals, swapname=swapname,
+ **stat_kws)
+ return new
+ if isinstance(events, str):
+ events = [events]
+ if isinstance(populations, str):
+ populations = [populations]
+ if len(populations) < len(events):
+ populations = populations * len(events)
+ if len(events) != len(populations):
+ raise ValueError('There is not a one-to-one matching between events and '
+ 'populations!\nEvents: {}\n\nPopulations:'
+ ' {}'.format(events, populations))
+ adjusted = stat_kws.pop('adjusted', True)
+
+ if isinstance(adjusted, bool):
+ adjusted = [adjusted] * len(events)
+ if swapname is '':
+ swapname = cls.__name__.lower()
+
+ rates = [assuncao_rate(df[e], df[pop]) if adj
+ else df[e].astype(float) / df[pop]
+ for e,pop,adj in zip(events, populations, adjusted)]
+ names = ['-'.join((e,p)) for e,p in zip(events, populations)]
+ out_df = df.copy()
+ rate_df = out_df.from_items(zip(names, rates)) #trick to avoid importing pandas
+ stat_df = _univariate_handler(rate_df, names, w=w, inplace=False,
+ pvalue = pvalue, outvals = outvals,
+ swapname=swapname,
+ stat=Moran, #how would this get done w/super?
+ **stat_kws)
+ for col in stat_df.columns:
+ df[col] = stat_df[col]
+
+class Moran_Local(object):
"""Local Moran Statistics
@@ -610,6 +796,7 @@ class Moran_Local:
"""
def __init__(self, y, w, transformation="r", permutations=PERMUTATIONS,
geoda_quads=False):
+ y = np.asarray(y).flatten()
self.y = y
n = len(y)
self.n = n
@@ -647,7 +834,7 @@ class Moran_Local:
self.VI_sim = self.seI_sim * self.seI_sim
self.z_sim = (self.Is - self.EI_sim) / self.seI_sim
self.p_z_sim = 1 - stats.norm.cdf(np.abs(self.z_sim))
-
+
def calc(self, w, z):
zl = slag(w, z)
return self.n_1 * self.z * zl / self.den
@@ -695,8 +882,55 @@ class Moran_Local:
self.q = self.quads[0] * pp + self.quads[1] * np + self.quads[2] * nn \
+ self.quads[3] * pn
+ @property
+ def _statistic(self):
+ """More consistent hidden attribute to access ESDA statistics"""
+ return self.Is
+
+ @classmethod
+ def by_col(cls, df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws):
+ """
+ Function to compute a Moran_Local statistic on a dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+ cols : string or list of string
+ name or list of names of columns to use to compute the statistic
+ w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+ inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named
+ 'column_moran_local'
+ pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the Moran_Local statistic's documentation for available p-values
+ outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ Moran_Local statistic
+ **stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the Moran_Local statistic.
+
+ Returns
+ --------
+ If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+ returns a copy of the dataframe with the relevant columns attached.
+
+ See Also
+ ---------
+ For further documentation, refer to the Moran_Local class in pysal.esda
+ """
+ return _univariate_handler(df, cols, w=w, inplace=inplace, pvalue=pvalue,
+ outvals=outvals, stat=cls,
+ swapname=cls.__name__.lower(), **stat_kws)
+
-class Moran_Local_BV:
+class Moran_Local_BV(object):
"""Bivariate Local Moran Statistics
@@ -795,6 +1029,8 @@ class Moran_Local_BV:
"""
def __init__(self, x, y, w, transformation="r", permutations=PERMUTATIONS,
geoda_quads=False):
+ x = np.asarray(x).flatten()
+ y = np.asarray(y).flatten()
self.y = y
n = len(y)
self.n = n
@@ -885,6 +1121,59 @@ class Moran_Local_BV:
self.q = self.quads[0] * pp + self.quads[1] * np + self.quads[2] * nn \
+ self.quads[3] * pn
+ @property
+ def _statistic(self):
+ """More consistent hidden attribute to access ESDA statistics"""
+ return self.Is
+
+ @classmethod
+ def by_col(cls, df, x, y=None, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws):
+ """
+ Function to compute a Moran_Local_BV statistic on a dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+ X : list of strings
+ column name or list of column names to use as X values to compute
+ the bivariate statistic. If no Y is provided, pairwise comparisons
+ among these variates are used instead.
+ Y : list of strings
+ column name or list of column names to use as Y values to compute
+ the bivariate statistic. if no Y is provided, pariwise comparisons
+ among the X variates are used instead.
+ w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+ inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named
+ 'column_moran_local_bv'
+ pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the Moran_Local_BV statistic's documentation for available p-values
+ outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ Moran_Local_BV statistic
+ **stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the Moran_Local_BV statistic.
+
+
+ Returns
+ --------
+ If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+ returns a copy of the dataframe with the relevant columns attached.
+
+ See Also
+ ---------
+ For further documentation, refer to the Moran_Local_BV class in pysal.esda
+ """
+ return _bivariate_handler(df, x, y=y, w=w, inplace=inplace,
+ pvalue = pvalue, outvals = outvals,
+ swapname=cls.__name__.lower(), stat=cls,**stat_kws)
class Moran_Local_Rate(Moran_Local):
"""
@@ -989,6 +1278,8 @@ class Moran_Local_Rate(Moran_Local):
def __init__(self, e, b, w, adjusted=True, transformation="r",
permutations=PERMUTATIONS, geoda_quads=False):
+ e = np.asarray(e).flatten()
+ b = np.asarray(b).flatten()
if adjusted:
y = assuncao_rate(e, b)
else:
@@ -997,3 +1288,84 @@ class Moran_Local_Rate(Moran_Local):
transformation=transformation,
permutations=permutations,
geoda_quads=geoda_quads)
+
+ @classmethod
+ def by_col(cls, df, events, populations, w=None, inplace=False,
+ pvalue='sim', outvals=None, swapname='', **stat_kws):
+ """
+ Function to compute a Moran_Local_Rate statistic on a dataframe
+
+ Arguments
+ ---------
+ df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+ events : string or list of strings
+ one or more names where events are stored
+ populations : string or list of strings
+ one or more names where the populations corresponding to the
+ events are stored. If one population column is provided, it is
+ used for all event columns. If more than one population column
+ is provided but there is not a population for every event
+ column, an exception will be raised.
+ w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+ inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named 'column_moran_local_rate'
+ pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the Moran_Local_Rate statistic's documentation for available p-values
+ outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ Moran_Local_Rate statistic
+ **stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the Moran_Local_Rate statistic.
+
+ Returns
+ --------
+ If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+ returns a copy of the dataframe with the relevant columns attached.
+
+ See Also
+ ---------
+ For further documentation, refer to the Moran_Local_Rate class in pysal.esda
+ """
+ if not inplace:
+ new = df.copy()
+ cls.by_col(new, events, populations, w=w, inplace=True,
+ pvalue=pvalue, outvals=outvals, swapname=swapname,
+ **stat_kws)
+ return new
+ if isinstance(events, str):
+ events = [events]
+ if isinstance(populations, str):
+ populations = [populations]
+ if len(populations) < len(events):
+ populations = populations * len(events)
+ if len(events) != len(populations):
+ raise ValueError('There is not a one-to-one matching between events and '
+ 'populations!\nEvents: {}\n\nPopulations:'
+ ' {}'.format(events, populations))
+ adjusted = stat_kws.pop('adjusted', True)
+
+ if isinstance(adjusted, bool):
+ adjusted = [adjusted] * len(events)
+ if swapname is '':
+ swapname = cls.__name__.lower()
+
+ rates = [assuncao_rate(df[e], df[pop]) if adj
+ else df[e].astype(float) / df[pop]
+ for e,pop,adj in zip(events, populations, adjusted)]
+ names = ['-'.join((e,p)) for e,p in zip(events, populations)]
+ out_df = df.copy()
+ rate_df = out_df.from_items(zip(names, rates)) #trick to avoid importing pandas
+ _univariate_handler(rate_df, names, w=w, inplace=True,
+ pvalue = pvalue, outvals = outvals,
+ swapname=swapname,
+ stat=Moran_Local, #how would this get done w/super?
+ **stat_kws)
+ for col in rate_df.columns:
+ df[col] = rate_df[col]
diff --git a/pysal/esda/smoothing.py b/pysal/esda/smoothing.py
index 4bd9f90..1334e24 100644
--- a/pysal/esda/smoothing.py
+++ b/pysal/esda/smoothing.py
@@ -1,3 +1,4 @@
+from __future__ import division
"""
Apply smoothing to rate computation
@@ -14,12 +15,13 @@ Author(s):
__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>, David Folch <dfolch at asu.edu>, Luc Anselin <luc.anselin at asu.edu>, Serge Rey <srey at asu.edu"
import pysal
-from pysal.weights import comb, Kernel
-from pysal.cg import Point, Ray, LineSegment
-from pysal.cg import get_angle_between, get_points_dist, get_segment_point_dist
-from pysal.cg import get_point_at_angle_and_dist, convex_hull
-from pysal.common import np, KDTree
-from pysal.weights.spatial_lag import lag_spatial as slag
+from ..weights import comb, Kernel, W
+from ..weights.util import get_points_array
+from ..cg import Point, Ray, LineSegment
+from ..cg import get_angle_between, get_points_dist, get_segment_point_dist,\
+ get_point_at_angle_and_dist, convex_hull, get_bounding_box
+from ..common import np, KDTree, requires as _requires
+from ..weights.spatial_lag import lag_spatial as slag
from scipy.stats import gamma, norm, chi2, poisson
__all__ = ['Excess_Risk', 'Empirical_Bayes', 'Spatial_Empirical_Bayes', 'Spatial_Rate', 'Kernel_Smoother', 'Age_Adjusted_Smoother', 'Disk_Smoother', 'Spatial_Median_Rate', 'Spatial_Filtering', 'Headbanging_Triples', 'Headbanging_Median_Rate', 'flatten', 'weighted_median', 'sum_by_n', 'crude_age_standardization', 'direct_age_standardization', 'indirect_age_standardization', 'standardized_mortality_ratio', 'choynowski', 'assuncao_rate']
@@ -67,7 +69,7 @@ def weighted_median(d, w):
d : array
(n, 1), variable for which median will be found
w : array
- (n, 1), variable on which d's medain will be decided
+ (n, 1), variable on which d's median will be decided
Notes
-----
@@ -254,7 +256,7 @@ def direct_age_standardization(e, b, s, n, alpha=0.05):
For direct age standardization, we also need the data for standard population.
Standard population is a reference population-at-risk (e.g., population distribution for the U.S.)
whose age distribution can be used as a benchmarking point for comparing age distributions
- across regions (e.g., popoulation distribution for Arizona and California).
+ across regions (e.g., population distribution for Arizona and California).
Another array including standard population is created.
>>> s = np.array([1000, 900, 1000, 900, 1000, 900, 1000, 900])
@@ -282,7 +284,7 @@ def direct_age_standardization(e, b, s, n, alpha=0.05):
res = []
for i in range(len(adjusted_r)):
if adjusted_r[i] == 0:
- upper = 0.5 * chi2(1 - 0.5 * alpha)
+ upper = 0.5 * chi2.ppf(1 - 0.5 * alpha)
lower = 0.0
else:
lower = gamma.ppf(0.5 * alpha, g_a[i], scale=g_b[i])
@@ -550,8 +552,70 @@ def assuncao_rate(e, b):
ebi_v = ebi_a + ebi_b / b
return (y - ebi_b) / np.sqrt(ebi_v)
+class _Smoother(object):
+ """
+ This is a helper class that implements things that all smoothers should do.
+ Right now, the only thing that we need to propagate is the by_col function.
-class Excess_Risk:
+ TBQH, most of these smoothers should be functions, not classes (aside from
+ maybe headbanging triples), since they're literally only inits + one
+ attribute.
+ """
+ def __init__(self):
+ pass
+
+ @classmethod
+ def by_col(cls, df, e,b, inplace=False, **kwargs):
+ """
+ Compute smoothing by columns in a dataframe.
+
+ Parameters
+ -----------
+ df : pandas.DataFrame
+ a dataframe containing the data to be smoothed
+ e : string or list of strings
+ the name or names of columns containing event variables to be
+ smoothed
+ b : string or list of strings
+ the name or names of columns containing the population
+ variables to be smoothed
+ inplace : bool
+ a flag denoting whether to output a copy of `df` with the
+ relevant smoothed columns appended, or to append the columns
+ directly to `df` itself.
+ **kwargs: optional keyword arguments
+ optional keyword options that are passed directly to the
+ smoother.
+
+ Returns
+ ---------
+ a copy of `df` containing the columns. Or, if `inplace`, this returns
+ None, but implicitly adds columns to `df`.
+ """
+ if not inplace:
+ new = df.copy()
+ cls.by_col(new, e, b, inplace=True, **kwargs)
+ return new
+ if isinstance(e, str):
+ e = [e]
+ if isinstance(b, str):
+ b = [b]
+ if len(b) == 1 and len(e) > 1:
+ b = b * len(e)
+ try:
+ assert len(e) == len(b)
+ except AssertionError:
+ raise ValueError('There is no one-to-one mapping between event'
+ ' variable and population at risk variable!')
+ for ei, bi in zip(e,b):
+ ename = ei
+ bname = bi
+ ei = df[ename]
+ bi = df[bname]
+ outcol = '_'.join(('-'.join((ename, bname)), cls.__name__.lower()))
+ df[outcol] = cls(ei, bi, **kwargs).r
+
+class Excess_Risk(_Smoother):
"""Excess Risk
Parameters
@@ -591,11 +655,13 @@ class Excess_Risk:
"""
def __init__(self, e, b):
+ e = np.asarray(e).reshape(-1,1)
+ b = np.asarray(b).reshape(-1,1)
r_mean = e.sum() * 1.0 / b.sum()
self.r = e * 1.0 / (b * r_mean)
-class Empirical_Bayes:
+class Empirical_Bayes(_Smoother):
"""Aspatial Empirical Bayes Smoothing
Parameters
@@ -637,6 +703,8 @@ class Empirical_Bayes:
"""
def __init__(self, e, b):
+ e = np.asarray(e).reshape(-1,1)
+ b = np.asarray(b).reshape(-1,1)
e_sum, b_sum = e.sum() * 1.0, b.sum() * 1.0
r_mean = e_sum / b_sum
rate = e * 1.0 / b
@@ -647,8 +715,89 @@ class Empirical_Bayes:
weight = r_var / (r_var + r_mean / b)
self.r = weight * rate + (1.0 - weight) * r_mean
-
-class Spatial_Empirical_Bayes:
+class _Spatial_Smoother(_Smoother):
+ """
+ This is a helper class that implements things that all the things that
+ spatial smoothers should do.
+ .
+ Right now, the only thing that we need to propagate is the by_col function.
+
+ TBQH, most of these smoothers should be functions, not classes (aside from
+ maybe headbanging triples), since they're literally only inits + one
+ attribute.
+ """
+ def __init__(self):
+ pass
+
+ @classmethod
+ def by_col(cls, df, e,b, w=None, inplace=False, **kwargs):
+ """
+ Compute smoothing by columns in a dataframe.
+
+ Parameters
+ -----------
+ df : pandas.DataFrame
+ a dataframe containing the data to be smoothed
+ e : string or list of strings
+ the name or names of columns containing event variables to be
+ smoothed
+ b : string or list of strings
+ the name or names of columns containing the population
+ variables to be smoothed
+ w : pysal.weights.W or list of pysal.weights.W
+ the spatial weights object or objects to use with the
+ event-population pairs. If not provided and a weights object
+ is in the dataframe's metadata, that weights object will be
+ used.
+ inplace : bool
+ a flag denoting whether to output a copy of `df` with the
+ relevant smoothed columns appended, or to append the columns
+ directly to `df` itself.
+ **kwargs: optional keyword arguments
+ optional keyword options that are passed directly to the
+ smoother.
+
+ Returns
+ ---------
+ a copy of `df` containing the columns. Or, if `inplace`, this returns
+ None, but implicitly adds columns to `df`.
+ """
+ if not inplace:
+ new = df.copy()
+ cls.by_col(new, e, b, w=w, inplace=True, **kwargs)
+ return new
+ if isinstance(e, str):
+ e = [e]
+ if isinstance(b, str):
+ b = [b]
+ if w is None:
+ found = False
+ for k in df._metadata:
+ w = df.__dict__.get(w, None)
+ if isinstance(w, W):
+ found = True
+ if not found:
+ raise Exception('Weights not provided and no weights attached to frame!'
+ ' Please provide a weight or attach a weight to the'
+ ' dataframe')
+ if isinstance(w, W):
+ w = [w] * len(e)
+ if len(b) == 1 and len(e) > 1:
+ b = b * len(e)
+ try:
+ assert len(e) == len(b)
+ except AssertionError:
+ raise ValueError('There is no one-to-one mapping between event'
+ ' variable and population at risk variable!')
+ for ei, bi, wi in zip(e, b, w):
+ ename = ei
+ bname = bi
+ ei = df[ename]
+ bi = df[bname]
+ outcol = '_'.join(('-'.join((ename, bname)), cls.__name__.lower()))
+ df[outcol] = cls(ei, bi, w=wi, **kwargs).r
+
+class Spatial_Empirical_Bayes(_Spatial_Smoother):
"""Spatial Empirical Bayes Smoothing
Parameters
@@ -701,10 +850,12 @@ class Spatial_Empirical_Bayes:
def __init__(self, e, b, w):
if not w.id_order_set:
raise ValueError("w id_order must be set to align with the order of e an b")
+ e = np.asarray(e).reshape(-1,1)
+ b = np.asarray(b).reshape(-1,1)
r_mean = Spatial_Rate(e, b, w).r
rate = e * 1.0 / b
- r_var_left = np.ones(len(e)) * 1.
- ngh_num = np.ones(len(e))
+ r_var_left = np.ones_like(e) * 1.
+ ngh_num = np.ones_like(e)
bi = slag(w, b) + b
for i, idv in enumerate(w.id_order):
ngh = w[idv].keys() + [idv]
@@ -718,8 +869,7 @@ class Spatial_Empirical_Bayes:
r_var[r_var < 0] = 0.0
self.r = r_mean + (rate - r_mean) * (r_var / (r_var + (r_mean / b)))
-
-class Spatial_Rate:
+class Spatial_Rate(_Spatial_Smoother):
"""Spatial Rate Smoothing
Parameters
@@ -773,13 +923,15 @@ class Spatial_Rate:
if not w.id_order_set:
raise ValueError("w id_order must be set to align with the order of e and b")
else:
+ e = np.asarray(e).reshape(-1,1)
+ b = np.asarray(b).reshape(-1,1)
w.transform = 'b'
w_e, w_b = slag(w, e), slag(w, b)
self.r = (e + w_e) / (b + w_b)
w.transform = 'o'
-class Kernel_Smoother:
+class Kernel_Smoother(_Spatial_Smoother):
"""Kernal smoothing
Parameters
@@ -835,11 +987,13 @@ class Kernel_Smoother:
if not w.id_order_set:
raise ValueError("w id_order must be set to align with the order of e and b")
else:
+ e = np.asarray(e).reshape(-1,1)
+ b = np.asarray(b).reshape(-1,1)
w_e, w_b = slag(w, e), slag(w, b)
self.r = w_e / w_b
-class Age_Adjusted_Smoother:
+class Age_Adjusted_Smoother(_Spatial_Smoother):
"""Age-adjusted rate smoothing
Parameters
@@ -901,6 +1055,9 @@ class Age_Adjusted_Smoother:
0.05020968])
"""
def __init__(self, e, b, w, s, alpha=0.05):
+ e = np.asarray(e).reshape(-1, 1)
+ b = np.asarray(b).reshape(-1, 1)
+ s = np.asarray(s).flatten()
t = len(e)
h = t // w.n
w.transform = 'b'
@@ -910,12 +1067,102 @@ class Age_Adjusted_Smoother:
b_n.append(slag(w, b[i::h]).tolist())
e_n = np.array(e_n).reshape((1, t), order='F')[0]
b_n = np.array(b_n).reshape((1, t), order='F')[0]
+ e_n = e_n.reshape(s.shape)
+ b_n = b_n.reshape(s.shape)
r = direct_age_standardization(e_n, b_n, s, w.n, alpha=alpha)
self.r = np.array([i[0] for i in r])
w.transform = 'o'
-
-
-class Disk_Smoother:
+
+ @_requires('pandas')
+ @classmethod
+ def by_col(cls, df, e,b, w=None, s=None, **kwargs):
+ """
+ Compute smoothing by columns in a dataframe.
+
+ Parameters
+ -----------
+ df : pandas.DataFrame
+ a dataframe containing the data to be smoothed
+ e : string or list of strings
+ the name or names of columns containing event variables to be
+ smoothed
+ b : string or list of strings
+ the name or names of columns containing the population
+ variables to be smoothed
+ w : pysal.weights.W or list of pysal.weights.W
+ the spatial weights object or objects to use with the
+ event-population pairs. If not provided and a weights object
+ is in the dataframe's metadata, that weights object will be
+ used.
+ s : string or list of strings
+ the name or names of columns to use as a standard population
+ variable for the events `e` and at-risk populations `b`.
+ inplace : bool
+ a flag denoting whether to output a copy of `df` with the
+ relevant smoothed columns appended, or to append the columns
+ directly to `df` itself.
+ **kwargs: optional keyword arguments
+ optional keyword options that are passed directly to the
+ smoother.
+
+ Returns
+ ---------
+ a copy of `df` containing the columns. Or, if `inplace`, this returns
+ None, but implicitly adds columns to `df`.
+ """
+ if s is None:
+ raise Exception('Standard population variable "s" must be supplied.')
+ import pandas as pd
+ if isinstance(e, str):
+ e = [e]
+ if isinstance(b, str):
+ b = [b]
+ if isinstance(s, str):
+ s = [s]
+ if w is None:
+ found = False
+ for k in df._metadata:
+ w = df.__dict__.get(w, None)
+ if isinstance(w, W):
+ found = True
+ break
+ if not found:
+ raise Exception('Weights not provided and no weights attached to frame!'
+ ' Please provide a weight or attach a weight to the'
+ ' dataframe.')
+ if isinstance(w, W):
+ w = [w] * len(e)
+ if not all(isinstance(wi, W) for wi in w):
+ raise Exception('Weights object must be an instance of '
+ ' pysal.weights.W!')
+ b = b * len(e) if len(b) == 1 and len(e) > 1 else b
+ s = s * len(e) if len(s) == 1 and len(e) > 1 else s
+ try:
+ assert len(e) == len(b)
+ assert len(e) == len(s)
+ assert len(e) == len(w)
+ except AssertionError:
+ raise ValueError('There is no one-to-one mapping between event'
+ ' variable and population at risk variable, and '
+ ' standard population variable, and spatial '
+ ' weights!')
+ rdf = []
+ max_len = 0
+ for ei, bi, wi, si in zip(e, b, w, s):
+ ename = ei
+ bname = bi
+ h = len(ei) // wi.n
+ outcol = '_'.join(('-'.join((ename, bname)), cls.__name__.lower()))
+ this_r = cls(df[ei], df[bi], w=wi, s=df[si], **kwargs).r
+ max_len = 0 if len(this_r) > max_len else max_len
+ rdf.append((outcol, this_r.tolist()))
+ padded = (r[1] + [None] * max_len for r in rdf)
+ rdf = zip((r[0] for r in rdf), padded)
+ rdf = pd.DataFrame.from_items(rdf)
+ return rdf
+
+
+class Disk_Smoother(_Spatial_Smoother):
"""Locally weighted averages or disk smoothing
Parameters
@@ -970,14 +1217,16 @@ class Disk_Smoother:
if not w.id_order_set:
raise ValueError("w id_order must be set to align with the order of e and b")
else:
+ e = np.asarray(e).reshape(-1,1)
+ b = np.asarray(b).reshape(-1,1)
r = e * 1.0 / b
weight_sum = []
for i in w.id_order:
weight_sum.append(sum(w.weights[i]))
- self.r = slag(w, r) / np.array(weight_sum)
+ self.r = slag(w, r) / np.array(weight_sum).reshape(-1,1)
-class Spatial_Median_Rate:
+class Spatial_Median_Rate(_Spatial_Smoother):
"""Spatial Median Rate Smoothing
Parameters
@@ -1076,6 +1325,8 @@ class Spatial_Median_Rate:
def __init__(self, e, b, w, aw=None, iteration=1):
if not w.id_order_set:
raise ValueError("w id_order must be set to align with the order of e and b")
+ e = np.asarray(e).flatten()
+ b = np.asarray(b).flatten()
self.r = e * 1.0 / b
self.aw, self.w = aw, w
while iteration:
@@ -1094,10 +1345,10 @@ class Spatial_Median_Rate:
id_d = [i] + list(w.neighbor_offsets[id])
aw_d, r_d = aw[id_d], r[id_d]
new_r.append(weighted_median(r_d, aw_d))
- self.r = np.array(new_r)
+ self.r = np.asarray(new_r).reshape(r.shape)
-class Spatial_Filtering:
+class Spatial_Filtering(_Smoother):
"""Spatial Filtering
Parameters
@@ -1191,11 +1442,13 @@ class Spatial_Filtering:
"""
def __init__(self, bbox, data, e, b, x_grid, y_grid, r=None, pop=None):
+ e= np.asarray(e).reshape(-1,1)
+ b= np.asarray(b).reshape(-1,1)
data_tree = KDTree(data)
x_range = bbox[1][0] - bbox[0][0]
y_range = bbox[1][1] - bbox[0][1]
- x, y = np.mgrid[bbox[0][0]:bbox[1][0]:x_range // x_grid,
- bbox[0][1]:bbox[1][1]:y_range // y_grid]
+ x, y = np.mgrid[bbox[0][0]:bbox[1][0]:float(x_range) / x_grid,
+ bbox[0][1]:bbox[1][1]:float(y_range) / y_grid]
self.grid = zip(x.ravel(), y.ravel())
self.r = []
if r is None and pop is None:
@@ -1216,10 +1469,71 @@ class Spatial_Filtering:
b_n_f = b_n[[0]]
self.r.append(e_n_f[-1] * 1.0 / b_n_f[-1])
self.r = np.array(self.r)
-
-
-class Headbanging_Triples:
- """Generate a pseudo spatial weights instance that contains headbaning triples
+
+ @_requires('pandas')
+ @classmethod
+ def by_col(cls, df, e, b, x_grid, y_grid, geom_col='geometry', **kwargs):
+ """
+ Compute smoothing by columns in a dataframe. The bounding box and point
+ information is computed from the geometry column.
+
+ Parameters
+ -----------
+ df : pandas.DataFrame
+ a dataframe containing the data to be smoothed
+ e : string or list of strings
+ the name or names of columns containing event variables to be
+ smoothed
+ b : string or list of strings
+ the name or names of columns containing the population
+ variables to be smoothed
+ x_grid : integer
+ number of grid cells to use along the x-axis
+ y_grid : integer
+ number of grid cells to use along the y-axis
+ geom_col: string
+ the name of the column in the dataframe containing the
+ geometry information.
+ **kwargs: optional keyword arguments
+ optional keyword options that are passed directly to the
+ smoother.
+ Returns
+ ---------
+ a new dataframe of dimension (x_grid*y_grid, 3), containing the
+ coordinates of the grid cells and the rates associated with those grid
+ cells.
+ """
+ import pandas as pd
+ # prep for application over multiple event/population pairs
+ if isinstance(e, str):
+ e = [e]
+ if isinstance(b, str):
+ b = [b]
+ if len(e) > len(b):
+ b = b * len(e)
+ if isinstance(x_grid, (int, float)):
+ x_grid = [x_grid] * len(e)
+ if isinstance(y_grid, (int, float)):
+ y_grid = [y_grid] * len(e)
+
+ bbox = get_bounding_box(df[geom_col])
+ bbox = [[bbox.left, bbox.lower], [bbox.right, bbox.upper]]
+ data = get_points_array(df[geom_col])
+ res = []
+ for ename, bname, xgi, ygi in zip(e, b, x_grid, y_grid):
+ r = cls(bbox, data, df[ename], df[bname], xgi, ygi, **kwargs)
+ grid = np.asarray(r.grid).reshape(-1,2)
+ name = '_'.join(('-'.join((ename, bname)), cls.__name__.lower()))
+ colnames = ('_'.join((name, suffix)) for suffix in ['X', 'Y', 'R'])
+ items = [(name, col) for name,col in zip(colnames, [grid[:,0],
+ grid[:,1],
+ r.r])]
+ res.append(pd.DataFrame.from_items(items))
+ outdf = pd.concat(res)
+ return outdf
+
+class Headbanging_Triples(object):
+ """Generate a pseudo spatial weights instance that contains headbanging triples
Parameters
----------
@@ -1318,7 +1632,7 @@ class Headbanging_Triples:
3 [(16, 6), (19, 6), (20, 6)]
4 [(5, 15), (27, 15), (35, 15)]
- Finding headbanging tirpes by using 5 nearest neighbors with edge correction
+ Finding headbanging triples by using 5 nearest neighbors with edge correction
>>> s_ht2 = Headbanging_Triples(sids_d,sids_w,k=5,edgecor=True)
@@ -1341,7 +1655,7 @@ class Headbanging_Triples:
>>> extrapolated[0]
(89, 77)
- Checking the distances between the exploated point and the observation 89 and 77
+ Checking the distances between the extraploated point and the observation 89 and 77
>>> round(extrapolated[1],5), round(extrapolated[2],6)
(0.33753, 0.302707)
@@ -1407,7 +1721,7 @@ class Headbanging_Triples:
self.extra[ps[point]] = extra
-class Headbanging_Median_Rate:
+class Headbanging_Median_Rate(object):
"""Headbaning Median Rate Smoothing
Parameters
@@ -1425,7 +1739,7 @@ class Headbanging_Median_Rate:
Attributes
----------
r : array (n, 1)
- rate values from headbaning median smoothing
+ rate values from headbanging median smoothing
Examples
--------
@@ -1451,6 +1765,7 @@ class Headbanging_Median_Rate:
>>> if not sids_w.id_order_set: sids_w.id_order = sids_w.id_order
finding headbanging triples by using 5 neighbors
+ return outdf
>>> s_ht = Headbanging_Triples(sids_d,sids_w,k=5)
@@ -1493,7 +1808,7 @@ class Headbanging_Median_Rate:
def __init__(self, e, b, t, aw=None, iteration=1):
self.r = e * 1.0 / b
self.tr, self.aw = t.triples, aw
- if hasattr(t, 'exta'):
+ if hasattr(t, 'extra'):
self.extra = t.extra
while iteration:
self.__search_headbanging_median()
@@ -1506,16 +1821,19 @@ class Headbanging_Median_Rate:
if hasattr(self, 'extra') and id in self.extra:
extra = self.extra
trp_r = r[list(triples[0])]
+ # observed rate
+ # plus difference in rate scaled by ratio of extrapolated distance
+ # & observed distance.
trp_r[-1] = trp_r[0] + (trp_r[0] - trp_r[-1]) * (
extra[id][-1] * 1.0 / extra[id][1])
trp_r = sorted(trp_r)
if not weighted:
- return r, trp_r[0], trp_r[-1]
+ return r[id], trp_r[0], trp_r[-1]
else:
- trp_aw = self.aw[trp]
+ trp_aw = self.aw[triples[0]]
extra_w = trp_aw[0] + (trp_aw[0] - trp_aw[-
1]) * (extra[id][-1] * 1.0 / extra[id][1])
- return r, trp_r[0], trp_r[-1], self.aw[id], trp_aw[0] + extra_w
+ return r[id], trp_r[0], trp_r[-1], self.aw[id], trp_aw[0] + extra_w
if not weighted:
lowest, highest = [], []
for trp in triples:
@@ -1541,7 +1859,7 @@ class Headbanging_Median_Rate:
np.array(highest), np.array(highest_aw))
triple_members = flatten(triples, unique=False)
return r[id], wm_lowest, wm_highest, self.aw[id] * len(triples), self.aw[triple_members].sum()
-
+
def __get_median_from_screens(self, screens):
if isinstance(screens, float):
return screens
@@ -1566,3 +1884,84 @@ class Headbanging_Median_Rate:
k, tr[k], weighted=(self.aw is not None))
new_r.append(self.__get_median_from_screens(screens))
self.r = np.array(new_r)
+
+ @_requires('pandas')
+ @classmethod
+ def by_col(cls, df, e, b, t=None, geom_col='geometry', inplace=False, **kwargs):
+ """
+ Compute smoothing by columns in a dataframe. The bounding box and point
+ information is computed from the geometry column.
+
+ Parameters
+ -----------
+ df : pandas.DataFrame
+ a dataframe containing the data to be smoothed
+ e : string or list of strings
+ the name or names of columns containing event variables to be
+ smoothed
+ b : string or list of strings
+ the name or names of columns containing the population
+ variables to be smoothed
+ t : Headbanging_Triples instance or list of Headbanging_Triples
+ list of headbanging triples instances. If not provided, this
+ is computed from the geometry column of the dataframe.
+ geom_col: string
+ the name of the column in the dataframe containing the
+ geometry information.
+ inplace : bool
+ a flag denoting whether to output a copy of `df` with the
+ relevant smoothed columns appended, or to append the columns
+ directly to `df` itself.
+ **kwargs: optional keyword arguments
+ optional keyword options that are passed directly to the
+ smoother.
+ Returns
+ ---------
+ a new dataframe containing the smoothed Headbanging Median Rates for the
+ event/population pairs. If done inplace, there is no return value and
+ `df` is modified in place.
+ """
+ import pandas as pd
+ if not inplace:
+ new = df.copy()
+ cls.by_col(new, e, b, t=t, geom_col=geom_col, inplace=True, **kwargs)
+ return new
+ import pandas as pd
+ # prep for application over multiple event/population pairs
+ if isinstance(e, str):
+ e = [e]
+ if isinstance(b, str):
+ b = [b]
+ if len(e) > len(b):
+ b = b * len(e)
+
+ data = get_points_array(df[geom_col])
+
+ #Headbanging_Triples doesn't take **kwargs, so filter its arguments
+ # (self, data, w, k=5, t=3, angle=135.0, edgecor=False):
+
+ w = kwargs.pop('w', None)
+ if w is None:
+ found = False
+ for k in df._metadata:
+ w = df.__dict__.get(w, None)
+ if isinstance(w, W):
+ found = True
+ if not found:
+ raise Exception('Weights not provided and no weights attached to frame!'
+ ' Please provide a weight or attach a weight to the'
+ ' dataframe')
+
+ k = kwargs.pop('k', 5)
+ t = kwargs.pop('t', 3)
+ angle = kwargs.pop('angle', 135.0)
+ edgecor = kwargs.pop('edgecor', False)
+
+ hbt = Headbanging_Triples(data, w, k=k, t=t, angle=angle,
+ edgecor=edgecor)
+
+ res = []
+ for ename, bname in zip(e, b):
+ r = cls(df[ename], df[bname], hbt, **kwargs).r
+ name = '_'.join(('-'.join((ename, bname)), cls.__name__.lower()))
+ df[name] = r
diff --git a/pysal/esda/tabular.py b/pysal/esda/tabular.py
new file mode 100644
index 0000000..99f890a
--- /dev/null
+++ b/pysal/esda/tabular.py
@@ -0,0 +1,306 @@
+#from ...common import requires as _requires
+
+import itertools as _it
+from pysal.weights import W
+
+# I would like to define it like this, so that you could make a call like:
+# Geary(df, 'HOVAL', 'INC', w=W), but this only works in Python3. So, I have to
+# use a workaround
+#def _statistic(df, *cols, stat=None, w=None, inplace=True,
+def _univariate_handler(df, cols, stat=None, w=None, inplace=True,
+ pvalue = 'sim', outvals = None, swapname='', **kwargs):
+ """
+ Compute a univariate descriptive statistic `stat` over columns `cols` in
+ `df`.
+
+ Parameters
+ ----------
+ df : pandas.DataFrame
+ the dataframe containing columns to compute the descriptive
+ statistics
+ cols : string or list of strings
+ one or more names of columns in `df` to use to compute
+ exploratory descriptive statistics.
+ stat : callable
+ a function that takes data as a first argument and any number
+ of configuration keyword arguments and returns an object
+ encapsulating the exploratory statistic results
+ w : pysal.weights.W
+ the spatial weights object corresponding to the dataframe
+ inplace : bool
+ a flag denoting whether to add the statistic to the dataframe
+ in memory, or to construct a copy of the dataframe and append
+ the results to the copy
+ pvalue : string
+ the name of the pvalue on the results object wanted
+ outvals : list of strings
+ names of attributes of the dataframe to attempt to flatten
+ into a column
+ swapname : string
+ suffix to replace generic identifier with. Each caller of this
+ function should set this to a unique column suffix
+ **kwargs : optional keyword arguments
+ options that are passed directly to the statistic
+ """
+ ### Preprocess
+ if not inplace:
+ new_df = df.copy()
+ _univariate_handler(new_df, cols, stat=stat, w=w, pvalue=pvalue,
+ inplace=True, outvals=outvals,
+ swapname=swapname, **kwargs)
+ return new_df
+ if w is None:
+ for name in df._metadata:
+ this_obj = df.__dict__.get(name)
+ if isinstance(this_obj, W):
+ w = this_obj
+ if w is None:
+ raise Exception('Weights not provided and no weights attached to frame!'
+ ' Please provide a weight or attach a weight to the'
+ ' dataframe')
+ ### Prep indexes
+ if outvals is None:
+ outvals = []
+ outvals.insert(0,'_statistic')
+ if pvalue.lower() in ['all', 'both', '*']:
+ raise NotImplementedError("If you want more than one type of PValue,add"
+ " the targeted pvalue type to outvals. For example:"
+ " Geary(df, cols=['HOVAL'], w=w, outvals=['p_z_sim', "
+ "'p_rand']")
+ # this is nontrivial, since we
+ # can't know which p_value types are on the object without computing it.
+ # This is because we don't flag them with @properties, so they're just
+ # arbitrarily assigned post-facto. One solution might be to post-process the
+ # objects, determine which pvalue types are available, and then grab them
+ # all if needed.
+
+ if pvalue is not '':
+ outvals.append('p_'+pvalue.lower())
+ if isinstance(cols, str):
+ cols = [cols]
+
+ ### Make closure around weights & apply columnwise
+ def column_stat(column):
+ return stat(column.values, w=w, **kwargs)
+ stat_objs = df[cols].apply(column_stat)
+
+ ### Assign into dataframe
+ for col in cols:
+ stat_obj = stat_objs[col]
+ y = kwargs.get('y')
+ if y is not None:
+ col += '-' + y.name
+ outcols = ['_'.join((col, val)) for val in outvals]
+ for colname, attname in zip(outcols, outvals):
+ df[colname] = stat_obj.__getattribute__(attname)
+ if swapname is not '':
+ df.columns = [_swap_ending(col, swapname) if col.endswith('_statistic') else col
+ for col in df.columns]
+
+def _bivariate_handler(df, x, y=None, w=None, inplace=True, pvalue='sim',
+ outvals=None, **kwargs):
+ """
+ Compute a descriptive bivariate statistic over two sets of columns, `x` and
+ `y`, contained in `df`.
+
+ Parameters
+ ----------
+ df : pandas.DataFrame
+ dataframe in which columns `x` and `y` are contained
+ x : string or list of strings
+ one or more column names to use as variates in the bivariate
+ statistics
+ y : string or list of strings
+ one or more column names to use as variates in the bivariate
+ statistics
+ w : pysal.weights.W
+ spatial weights object corresponding to the dataframe `df`
+ inplace : bool
+ a flag denoting whether to add the statistic to the dataframe
+ in memory, or to construct a copy of the dataframe and append
+ the results to the copy
+ pvalue : string
+ the name of the pvalue on the results object wanted
+ outvals : list of strings
+ names of attributes of the dataframe to attempt to flatten
+ into a column
+ swapname : string
+ suffix to replace generic identifier with. Each caller of this
+ function should set this to a unique column suffix
+ **kwargs : optional keyword arguments
+ options that are passed directly to the statistic
+ """
+ real_swapname = kwargs.pop('swapname', '')
+ if isinstance(y, str):
+ y = [y]
+ if isinstance(x, str):
+ x = [x]
+ if not inplace:
+ new_df = df.copy()
+ _bivariate_handler(new_df, x, y=y, w=w, inplace=True,
+ swapname=real_swapname,
+ pvalue=pvalue, outvals=outvals, **kwargs)
+ return new_df
+ if y is None:
+ y = x
+ for xi,yi in _it.product(x,y):
+ if xi == yi:
+ continue
+ _univariate_handler(df, cols=xi, w=w, y=df[yi], inplace=True,
+ pvalue=pvalue, outvals=outvals, swapname='', **kwargs)
+ if real_swapname is not '':
+ df.columns = [_swap_ending(col, real_swapname)
+ if col.endswith('_statistic')
+ else col for col in df.columns]
+
+def _swap_ending(s, ending, delim='_'):
+ """
+ Replace the ending of a string, delimited into an arbitrary
+ number of chunks by `delim`, with the ending provided
+
+ Parameters
+ ----------
+ s : string
+ string to replace endings
+ ending : string
+ string used to replace ending of `s`
+ delim : string
+ string that splits s into one or more parts
+
+ Returns
+ -------
+ new string where the final chunk of `s`, delimited by `delim`, is replaced
+ with `ending`.
+ """
+ parts = [x for x in s.split(delim)[:-1] if x != '']
+ parts.append(ending)
+ return delim.join(parts)
+
+##############
+# DOCSTRINGS #
+##############
+
+_univ_doc_template =\
+"""
+Function to compute a {n} statistic on a dataframe
+
+Arguments
+---------
+df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+cols : string or list of string
+ name or list of names of columns to use to compute the statistic
+w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named 'column_{nl}'
+pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the {n} statistic's documentation for available p-values
+outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ {n} statistic
+**stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the {n} statistic.
+
+Returns
+--------
+If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+returns a copy of the dataframe with the relevant columns attached.
+
+See Also
+---------
+For further documentation, refer to the {n} class in pysal.esda
+"""
+
+_bv_doc_template =\
+"""
+Function to compute a {n} statistic on a dataframe
+
+Arguments
+---------
+df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+X : list of strings
+ column name or list of column names to use as X values to compute
+ the bivariate statistic. If no Y is provided, pairwise comparisons
+ among these variates are used instead.
+Y : list of strings
+ column name or list of column names to use as Y values to compute
+ the bivariate statistic. if no Y is provided, pariwise comparisons
+ among the X variates are used instead.
+
+w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named 'column_{nl}'
+pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the {n} statistic's documentation for available p-values
+outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ {n} statistic
+**stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the {n} statistic.
+
+
+Returns
+--------
+If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+returns a copy of the dataframe with the relevant columns attached.
+
+See Also
+---------
+For further documentation, refer to the {n} class in pysal.esda
+"""
+
+_rate_doc_template =\
+"""
+Function to compute a {n} statistic on a dataframe
+
+Arguments
+---------
+df : pandas.DataFrame
+ a pandas dataframe with a geometry column
+events : string or list of strings
+ one or more names where events are stored
+populations : string or list of strings
+ one or more names where the populations corresponding to the
+ events are stored. If one population column is provided, it is
+ used for all event columns. If more than one population column
+ is provided but there is not a population for every event
+ column, an exception will be raised.
+w : pysal weights object
+ a weights object aligned with the dataframe. If not provided, this
+ is searched for in the dataframe's metadata
+inplace : bool
+ a boolean denoting whether to operate on the dataframe inplace or to
+ return a series contaning the results of the computation. If
+ operating inplace, the derived columns will be named 'column_{nl}'
+pvalue : string
+ a string denoting which pvalue should be returned. Refer to the
+ the {n} statistic's documentation for available p-values
+outvals : list of strings
+ list of arbitrary attributes to return as columns from the
+ {n} statistic
+**stat_kws : keyword arguments
+ options to pass to the underlying statistic. For this, see the
+ documentation for the {n} statistic.
+
+Returns
+--------
+If inplace, None, and operation is conducted on dataframe in memory. Otherwise,
+returns a copy of the dataframe with the relevant columns attached.
+
+See Also
+---------
+For further documentation, refer to the {n} class in pysal.esda
+"""
diff --git a/pysal/esda/tests/test_gamma.py b/pysal/esda/tests/test_gamma.py
index 0cdae76..54cadf8 100644
--- a/pysal/esda/tests/test_gamma.py
+++ b/pysal/esda/tests/test_gamma.py
@@ -1,20 +1,22 @@
import unittest
import numpy as np
-import pysal
-from pysal.esda.gamma import Gamma
-
+from ...weights import lat2W
+from ..gamma import Gamma
+from ...common import pandas
+PANDAS_EXTINCT = pandas is None
class Gamma_Tester(unittest.TestCase):
"""Unit test for Gamma Index"""
def setUp(self):
- self.w = pysal.lat2W(4, 4)
+ self.w = lat2W(4, 4)
self.y = np.ones(16)
self.y[0:8] = 0
+ np.random.seed(12345)
+ self.g = Gamma(self.y, self.w)
def test_Gamma(self):
"""Test method"""
- np.random.seed(12345)
- g = Gamma(self.y, self.w)
+ g = self.g
self.assertAlmostEquals(g.g, 20.0)
self.assertAlmostEquals(g.g_z, 3.1879280354548638)
self.assertAlmostEquals(g.p_sim_g, 0.0030000000000000001)
@@ -56,6 +58,23 @@ class Gamma_Tester(unittest.TestCase):
self.assertAlmostEquals(g4.g_z, 3.1879280354548638)
self.assertAlmostEquals(g4.p_sim_g, 0.0030000000000000001)
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_by_col(self):
+ import pandas as pd
+ df = pd.DataFrame(self.y, columns=['y'])
+ r1 = Gamma.by_col(df, ['y'], w=self.w)
+ self.assertIn('y_gamma', r1.columns)
+ self.assertIn('y_p_sim', r1.columns)
+ this_gamma = np.unique(r1.y_gamma.values)
+ this_pval = np.unique(r1.y_p_sim.values)
+ np.testing.assert_allclose(this_gamma, self.g.g)
+ np.testing.assert_allclose(this_pval, self.g.p_sim)
+ Gamma.by_col(df, ['y'], inplace=True, operation='s', w=self.w)
+ this_gamma = np.unique(df.y_gamma.values)
+ this_pval = np.unique(df.y_p_sim.values)
+ np.testing.assert_allclose(this_gamma, 8.0)
+ np.testing.assert_allclose(this_pval, .001)
+
suite = unittest.TestSuite()
test_classes = [Gamma_Tester]
diff --git a/pysal/esda/tests/test_geary.py b/pysal/esda/tests/test_geary.py
index 1e12d2e..6cff335 100644
--- a/pysal/esda/tests/test_geary.py
+++ b/pysal/esda/tests/test_geary.py
@@ -1,15 +1,19 @@
"""Geary Unittest."""
import unittest
-import pysal
-from pysal.esda import geary
+
+from ... import open as popen
+from ... import examples
+from .. import geary
import numpy as np
+from ...common import pandas
+PANDAS_EXTINCT = pandas is None
class Geary_Tester(unittest.TestCase):
"""Geary class for unit tests."""
def setUp(self):
- self.w = pysal.open(pysal.examples.get_path("book.gal")).read()
- f = pysal.open(pysal.examples.get_path("book.txt"))
+ self.w = popen(examples.get_path("book.gal")).read()
+ f = popen(examples.get_path("book.txt"))
self.y = np.array(f.by_col['y'])
def test_Geary(self):
@@ -49,6 +53,19 @@ class Geary_Tester(unittest.TestCase):
self.assertAlmostEquals(c.z_sim, -3.5841621159171746)
self.assertAlmostEquals(c.seC_sim, 0.18555432843202269)
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_by_col(self):
+ import pandas as pd
+ df = pd.DataFrame(self.y, columns=['y'])
+ r1 = geary.Geary.by_col(df, ['y'], w=self.w, permutations=999)
+ this_geary = np.unique(r1.y_geary.values)
+ this_pval = np.unique(r1.y_p_sim.values)
+ np.random.seed(12345)
+ c = geary.Geary(self.y, self.w, permutations=999)
+ self.assertAlmostEquals(this_geary, c.C)
+ self.assertAlmostEquals(this_pval, c.p_sim)
+
+
suite = unittest.TestSuite()
test_classes = [Geary_Tester]
diff --git a/pysal/esda/tests/test_getisord.py b/pysal/esda/tests/test_getisord.py
index 97b0baf..667cc23 100644
--- a/pysal/esda/tests/test_getisord.py
+++ b/pysal/esda/tests/test_getisord.py
@@ -1,12 +1,15 @@
import unittest
-from pysal.weights.Distance import DistanceBand
-from pysal.esda import getisord
import numpy as np
+from .. import getisord
+from ...weights.Distance import DistanceBand
+from ...common import pandas
+
POINTS = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
W = DistanceBand(POINTS, threshold=15)
Y = np.array([2, 3, 3.2, 5, 8, 7])
+PANDAS_EXTINCT = pandas is None
class G_Tester(unittest.TestCase):
@@ -20,6 +23,18 @@ class G_Tester(unittest.TestCase):
self.assertAlmostEquals(g.G, 0.55709779, places=8)
self.assertAlmostEquals(g.p_norm, 0.1729, places=4)
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_by_col(self):
+ import pandas as pd
+ df = pd.DataFrame(self.y, columns=['y'])
+ np.random.seed(12345)
+ r1 = getisord.G.by_col(df, ['y'], w=self.w)
+ this_getisord = np.unique(r1.y_g.values)
+ this_pval = np.unique(r1.y_p_sim.values)
+ np.random.seed(12345)
+ stat = getisord.G(self.y, self.w)
+ self.assertAlmostEquals(this_getisord, stat._statistic)
+ self.assertAlmostEquals(this_pval, stat.p_sim)
class G_Local_Tester(unittest.TestCase):
@@ -47,6 +62,17 @@ class G_Local_Tester(unittest.TestCase):
lg = getisord.G_Local(self.y, self.w, transform='R', star=True)
self.assertAlmostEquals(lg.Zs[0], -0.62488094, places=8)
self.assertAlmostEquals(lg.p_sim[0], 0.10100000000000001, places=7)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_by_col(self):
+ import pandas as pd
+ df = pd.DataFrame(self.y, columns=['y'])
+ np.random.seed(12345)
+ r1 = getisord.G_Local.by_col(df, ['y'], w=self.w)
+ np.random.seed(12345)
+ stat = getisord.G_Local(self.y, self.w)
+ np.testing.assert_allclose(r1.y_g_local.values, stat.Gs)
+ np.testing.assert_allclose(r1.y_p_sim, stat.p_sim)
suite = unittest.TestSuite()
test_classes = [G_Tester, G_Local_Tester]
diff --git a/pysal/esda/tests/test_join_counts.py b/pysal/esda/tests/test_join_counts.py
index ed51af8..23fe1ab 100644
--- a/pysal/esda/tests/test_join_counts.py
+++ b/pysal/esda/tests/test_join_counts.py
@@ -1,13 +1,16 @@
import unittest
import numpy as np
-import pysal
-from pysal.esda.join_counts import Join_Counts
+from ..join_counts import Join_Counts
+from ...weights import lat2W
+from ...common import pandas
+
+PANDAS_EXTINCT = pandas is None
class Join_Counts_Tester(unittest.TestCase):
"""Unit test for Join Counts"""
def setUp(self):
- self.w = pysal.lat2W(4, 4)
+ self.w = lat2W(4, 4)
self.y = np.ones(16)
self.y[0:8] = 0
@@ -29,6 +32,24 @@ class Join_Counts_Tester(unittest.TestCase):
self.assertAlmostEquals(np.mean(jc.sim_bw), 12.811811811811811)
self.assertAlmostEquals(np.max(jc.sim_bw), 24.0)
self.assertAlmostEquals(np.min(jc.sim_bw), 7.0)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_by_col(self):
+ import pandas as pd
+ df = pd.DataFrame(self.y, columns=['y'])
+ np.random.seed(12345)
+ r1 = Join_Counts.by_col(df, ['y'], w=self.w, permutations=999)
+
+ bb = np.unique(r1.y_bb.values)
+ bw = np.unique(r1.y_bw.values)
+ bb_p = np.unique(r1.y_p_sim_bb.values)
+ bw_p = np.unique(r1.y_p_sim_bw.values)
+ np.random.seed(12345)
+ c = Join_Counts(self.y, self.w, permutations=999)
+ self.assertAlmostEquals(bb, c.bb)
+ self.assertAlmostEquals(bw, c.bw)
+ self.assertAlmostEquals(bb_p, c.p_sim_bb)
+ self.assertAlmostEquals(bw_p, c.p_sim_bw)
suite = unittest.TestSuite()
test_classes = [Join_Counts_Tester]
diff --git a/pysal/esda/tests/test_mapclassify.py b/pysal/esda/tests/test_mapclassify.py
index a8f3428..61dfcc7 100644
--- a/pysal/esda/tests/test_mapclassify.py
+++ b/pysal/esda/tests/test_mapclassify.py
@@ -1,9 +1,10 @@
import pysal
-from pysal.esda.mapclassify import *
-from pysal.esda.mapclassify import binC, bin, bin1d
-from pysal.common import RTOL
+from ..mapclassify import *
+from ..mapclassify import binC, bin, bin1d
+from ...common import RTOL
import numpy as np
import unittest
+import types
class TestQuantile(unittest.TestCase):
def test_quantile(self):
@@ -67,6 +68,34 @@ class TestFindBin(unittest.TestCase):
known = [0, 0, 0, 0, 2, 2, 3, 5, 7, 7, 8, 8, 8]
np.testing.assert_array_equal(known, mc2.find_bin(toclass))
+class TestMake(unittest.TestCase):
+ def setUp(self):
+ self.data = [np.linspace(-5,5, num=5), np.linspace(-10, 10, num=5),
+ np.linspace(-20, 20, num=5)]
+ self.ei = Equal_Interval.make()
+ self.q5r = Quantiles.make(k=5, rolling=True)
+
+ def test_make(self):
+ self.assertIsInstance(self.ei, types.FunctionType)
+ self.assertIsInstance(self.q5r, types.FunctionType)
+
+ assert hasattr(self.ei, '_options')
+ self.assertEqual(self.ei._options, dict())
+ assert hasattr(self.q5r, '_options')
+ self.assertEqual(self.q5r._options, {'k':5, 'rolling':True})
+
+ def test_apply(self):
+ ei_classes = [self.ei(d) for d in self.data]
+ known = [np.arange(0,5,1)] * 3
+ np.testing.assert_allclose(known, ei_classes)
+
+ q5r_classes = [self.q5r(d) for d in self.data]
+ known = [[0,1,2,3,4], [0,1,2,4,4], [0,0,2,4,4]]
+ accreted_data = set(self.q5r.func_defaults[0].y)
+ all_data = set(np.asarray(self.data).flatten())
+ self.assertEqual(accreted_data, all_data)
+ np.testing.assert_allclose(known, q5r_classes)
+
class TestBinC(unittest.TestCase):
def test_bin_c(self):
bins = range(2, 8)
diff --git a/pysal/esda/tests/test_moran.py b/pysal/esda/tests/test_moran.py
index 858916b..2f0143a 100644
--- a/pysal/esda/tests/test_moran.py
+++ b/pysal/esda/tests/test_moran.py
@@ -1,8 +1,10 @@
import unittest
import pysal
-from pysal.esda import moran
+from .. import moran
+from ...common import pandas, RTOL, ATOL
import numpy as np
+PANDAS_EXTINCT = pandas is None
class Moran_Tester(unittest.TestCase):
def setUp(self):
@@ -12,7 +14,7 @@ class Moran_Tester(unittest.TestCase):
def test_moran(self):
mi = moran.Moran(self.y, self.w, two_tailed=False)
- self.assertAlmostEquals(mi.I, 0.24365582621771659, 7)
+ np.testing.assert_allclose(mi.I, 0.24365582621771659, rtol=RTOL, atol=ATOL)
self.assertAlmostEquals(mi.p_norm, 0.00013573931385468807)
def test_sids(self):
@@ -20,9 +22,20 @@ class Moran_Tester(unittest.TestCase):
f = pysal.open(pysal.examples.get_path("sids2.dbf"))
SIDR = np.array(f.by_col("SIDR74"))
mi = pysal.Moran(SIDR, w, two_tailed=False)
- self.assertAlmostEquals(mi.I, 0.24772519320480135)
+ np.testing.assert_allclose(mi.I, 0.24772519320480135, atol=ATOL, rtol=RTOL)
self.assertAlmostEquals(mi.p_norm, 5.7916539074498452e-05)
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_by_col(self):
+ import pysal.contrib.pdio as pdio
+ df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
+ w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+ mi = moran.Moran.by_col(df, ['SIDR74'], w=w, two_tailed=False)
+ sidr = np.unique(mi.SIDR74_moran.values)
+ pval = np.unique(mi.SIDR74_p_sim.values)
+ np.testing.assert_allclose(sidr, 0.24772519320480135, atol=ATOL, rtol=RTOL)
+ self.assertAlmostEquals(pval, 0.001)
+
class Moran_Rate_Tester(unittest.TestCase):
def setUp(self):
@@ -33,9 +46,20 @@ class Moran_Rate_Tester(unittest.TestCase):
def test_moran_rate(self):
mi = moran.Moran_Rate(self.e, self.b, self.w, two_tailed=False)
- self.assertAlmostEquals(mi.I, 0.16622343552567395, 7)
+ np.testing.assert_allclose(mi.I, 0.16622343552567395, rtol=RTOL, atol=ATOL)
self.assertAlmostEquals(mi.p_norm, 0.004191499504892171)
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_by_col(self):
+ import pysal.contrib.pdio as pdio
+ df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
+ mi = moran.Moran_Rate.by_col(df, ['SID79'], ['BIR79'], w=self.w, two_tailed=False)
+ sidr = np.unique(mi["SID79-BIR79_moran_rate"].values)
+ pval = np.unique(mi["SID79-BIR79_p_sim"].values)
+ np.testing.assert_allclose(sidr, 0.16622343552567395, rtol=RTOL, atol=ATOL)
+ self.assertAlmostEquals(pval, 0.009)
+
+
class Moran_BV_matrix_Tester(unittest.TestCase):
def setUp(self):
@@ -51,7 +75,6 @@ class Moran_BV_matrix_Tester(unittest.TestCase):
self.assertAlmostEquals(res[(0, 1)].I, 0.19362610652874668)
self.assertAlmostEquals(res[(3, 0)].I, 0.37701382542927858)
-
class Moran_Local_Tester(unittest.TestCase):
def setUp(self):
np.random.seed(10)
@@ -65,6 +88,15 @@ class Moran_Local_Tester(unittest.TestCase):
self.assertAlmostEquals(lm.z_sim[0], -0.68493799168603808)
self.assertAlmostEquals(lm.p_z_sim[0], 0.24669152541631179)
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_by_col(self):
+ import pandas as pd
+ df = pd.DataFrame(self.y, columns =['z'])
+ lm = moran.Moran_Local.by_col(df, ['z'], w=self.w, transformation='r',
+ permutations=99, outvals=['z_sim', 'p_z_sim'])
+ self.assertAlmostEquals(lm.z_z_sim[0], -0.68493799168603808)
+ self.assertAlmostEquals(lm.z_p_z_sim[0], 0.24669152541631179)
+
class Moran_Local_BV_Tester(unittest.TestCase):
def setUp(self):
@@ -81,6 +113,21 @@ class Moran_Local_BV_Tester(unittest.TestCase):
self.assertAlmostEquals(lm.z_sim[0], 1.5816540860500772)
self.assertAlmostEquals(lm.p_z_sim[0], 0.056864279811026153)
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_by_col(self):
+ import pysal.contrib.pdio as pdio
+ df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
+ np.random.seed(12345)
+ moran.Moran_Local_BV.by_col(df, ['SIDR74', 'SIDR79'], w=self.w,
+ inplace=True, outvals=['z_sim', 'p_z_sim'],
+ transformation='r', permutations=99)
+ bvstats = df['SIDR79-SIDR74_moran_local_bv'].values
+ bvz = df['SIDR79-SIDR74_z_sim'].values
+ bvzp = df['SIDR79-SIDR74_p_z_sim'].values
+ self.assertAlmostEquals(bvstats[0], 1.4649221250620736)
+ self.assertAlmostEquals(bvz[0], 1.657427, 5)
+ self.assertAlmostEquals(bvzp[0], 0.048717, 5)
+
class Moran_Local_Rate_Tester(unittest.TestCase):
def setUp(self):
@@ -93,9 +140,20 @@ class Moran_Local_Rate_Tester(unittest.TestCase):
def test_moran_rate(self):
lm = moran.Moran_Local_Rate(self.e, self.b, self.w,
transformation="r", permutations=99)
- self.assertAlmostEquals(lm.z_sim[0], -0.13699844503985936)
+ self.assertAlmostEquals(lm.z_sim[0], -0.13699844503985936, 7)
self.assertAlmostEquals(lm.p_z_sim[0], 0.44551601210081715)
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_by_col(self):
+ import pysal.contrib.pdio as pdio
+ df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
+ lm = moran.Moran_Local_Rate.by_col(df, ['SID79'], ['BIR79'], w=self.w,
+ outvals=['p_z_sim', 'z_sim'],
+ transformation='r', permutations=99)
+ self.assertAlmostEquals(lm['SID79-BIR79_z_sim'][0], -0.13699844503985936, 7)
+ self.assertAlmostEquals(lm['SID79-BIR79_p_z_sim'][0], 0.44551601210081715)
+
+
suite = unittest.TestSuite()
test_classes = [Moran_Tester, Moran_Rate_Tester,
diff --git a/pysal/esda/tests/test_smoothing.py b/pysal/esda/tests/test_smoothing.py
index 6d8fbe3..85b4965 100644
--- a/pysal/esda/tests/test_smoothing.py
+++ b/pysal/esda/tests/test_smoothing.py
@@ -3,7 +3,11 @@ import pysal
from pysal.esda import smoothing as sm
from pysal import knnW_from_array
import numpy as np
+from pysal.common import RTOL, ATOL
+from pysal.common import pandas
+
+PANDAS_EXTINCT = pandas is None
class TestFlatten(unittest.TestCase):
def setUp(self):
@@ -63,59 +67,197 @@ class TestSRate(unittest.TestCase):
sids = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
self.w = pysal.open(pysal.examples.get_path('sids2.gal'), 'r').read()
self.b, self.e = np.array(sids[:, 8]), np.array(sids[:, 9])
- er = [0.453433, 0.000000, 0.775871, 0.973810, 3.133190]
- eb = [0.0016973, 0.0017054, 0.0017731, 0.0020129, 0.0035349]
- sr = [0.0009922, 0.0012639, 0.0009740, 0.0007605, 0.0050154]
- smr = [0.00083622, 0.00109402, 0.00081567, 0.0, 0.0048209]
- smr_w = [0.00127146, 0.00127146, 0.0008433, 0.0, 0.0049889]
- smr2 = [0.00091659, 0.00087641, 0.00091073, 0.0, 0.00467633]
- self.er = [round(i, 5) for i in er]
- self.eb = [round(i, 7) for i in eb]
- self.sr = [round(i, 7) for i in sr]
- self.smr = [round(i, 7) for i in smr]
- self.smr_w = [round(i, 7) for i in smr_w]
- self.smr2 = [round(i, 7) for i in smr2]
+ self.er = [0.453433, 0.000000, 0.775871, 0.973810, 3.133190]
+ self.eb = [0.0016973, 0.0017054, 0.0017731, 0.0020129, 0.0035349]
+ self.sr = [0.0009922, 0.0012639, 0.0009740, 0.0007605, 0.0050154]
+ self.smr = [0.00083622, 0.00109402, 0.00081567, 0.0, 0.0048209]
+ self.smr_w = [0.00127146, 0.00127146, 0.0008433, 0.0, 0.0049889]
+ self.smr2 = [0.00091659, 0.00087641, 0.00091073, 0.0, 0.00467633]
+ self.s_ebr10 = np.array([4.01485749e-05, 3.62437513e-05,
+ 4.93034844e-05, 5.09387329e-05, 3.72735210e-05,
+ 3.69333797e-05, 5.40245456e-05, 2.99806055e-05,
+ 3.73034109e-05, 3.47270722e-05]).reshape(-1,1)
+
+ self.stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
+ self.stl_e, self.stl_b = np.array(self.stl[:, 10]), np.array(self.stl[:, 13])
+ self.stl_w = pysal.open(pysal.examples.get_path('stl.gal'), 'r').read()
+ if not self.stl_w.id_order_set:
+ self.stl_w.id_order = range(1, len(self.stl) + 1)
+
+ if not PANDAS_EXTINCT:
+ self.df = pysal.open(pysal.examples.get_path('sids2.dbf')).to_df()
+ self.ename = 'SID74'
+ self.bname = 'BIR74'
+ self.stl_df = pysal.open(pysal.examples.get_path('stl_hom.csv')).to_df()
+ self.stl_ename = 'HC7984'
+ self.stl_bname = 'PO7984'
def test_Excess_Risk(self):
out_er = sm.Excess_Risk(self.e, self.b).r
- out_er = [round(i, 5) for i in out_er[:5]]
- self.assertEquals(out_er, self.er)
+ np.testing.assert_allclose(out_er[:5].flatten(), self.er,
+ rtol=RTOL, atol=ATOL)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Excess_Risk_tabular(self):
+ out_er = sm.Excess_Risk(self.df[self.ename], self.df[self.bname]).r
+ np.testing.assert_allclose(out_er[:5].flatten(), self.er,
+ rtol=RTOL, atol=ATOL)
+ self.assertIsInstance(out_er, np.ndarray)
+ out_er = sm.Excess_Risk.by_col(self.df, self.ename, self.bname)
+ outcol = '{}-{}_excess_risk'.format(self.ename, self.bname)
+ outcol = out_er[outcol]
+ np.testing.assert_allclose(outcol[:5], self.er,
+ rtol=RTOL, atol=ATOL)
+ self.assertIsInstance(outcol.values, np.ndarray)
def test_Empirical_Bayes(self):
out_eb = sm.Empirical_Bayes(self.e, self.b).r
- out_eb = [round(i, 7) for i in out_eb[:5]]
- self.assertEquals(out_eb, self.eb)
+ np.testing.assert_allclose(out_eb[:5].flatten(), self.eb,
+ rtol=RTOL, atol=ATOL)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Empirical_Bayes_tabular(self):
+ out_eb = sm.Empirical_Bayes(self.df[self.ename], self.df[self.bname]).r
+ np.testing.assert_allclose(out_eb[:5].flatten(), self.eb,
+ rtol=RTOL, atol=ATOL)
+ self.assertIsInstance(out_eb, np.ndarray)
+
+ out_eb = sm.Empirical_Bayes.by_col(self.df, self.ename, self.bname)
+ outcol = '{}-{}_empirical_bayes'.format(self.ename, self.bname)
+ outcol = out_eb[outcol]
+ np.testing.assert_allclose(outcol[:5], self.eb,
+ rtol=RTOL, atol=ATOL)
+ self.assertIsInstance(outcol.values, np.ndarray)
def test_Spatial_Empirical_Bayes(self):
- stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
- stl_e, stl_b = np.array(stl[:, 10]), np.array(stl[:, 13])
- stl_w = pysal.open(pysal.examples.get_path('stl.gal'), 'r').read()
- if not stl_w.id_order_set:
- stl_w.id_order = range(1, len(stl) + 1)
- s_eb = sm.Spatial_Empirical_Bayes(stl_e, stl_b, stl_w)
- s_ebr10 = np.array([4.01485749e-05, 3.62437513e-05,
- 4.93034844e-05, 5.09387329e-05, 3.72735210e-05,
- 3.69333797e-05, 5.40245456e-05, 2.99806055e-05,
- 3.73034109e-05, 3.47270722e-05])
- np.testing.assert_array_almost_equal(s_ebr10, s_eb.r[:10])
+ s_eb = sm.Spatial_Empirical_Bayes(self.stl_e, self.stl_b, self.stl_w)
+ np.testing.assert_allclose(self.s_ebr10, s_eb.r[:10],
+ rtol=RTOL, atol=ATOL)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Spatial_Empirical_Bayes_tabular(self):
+ s_eb = sm.Spatial_Empirical_Bayes(self.stl_df[self.stl_ename],
+ self.stl_df[self.stl_bname],
+ self.stl_w).r
+ self.assertIsInstance(s_eb, np.ndarray)
+ np.testing.assert_allclose(self.s_ebr10, s_eb[:10])
+
+ s_eb = sm.Spatial_Empirical_Bayes.by_col(self.stl_df,
+ self.stl_ename,
+ self.stl_bname,
+ self.stl_w)
+ outcol = '{}-{}_spatial_empirical_bayes'.format(self.stl_ename, self.stl_bname)
+ r = s_eb[outcol].values
+ self.assertIsInstance(r, np.ndarray)
+ np.testing.assert_allclose(self.s_ebr10, r[:10].reshape(-1,1))
def test_Spatial_Rate(self):
out_sr = sm.Spatial_Rate(self.e, self.b, self.w).r
- out_sr = [round(i, 7) for i in out_sr[:5]]
- self.assertEquals(out_sr, self.sr)
+ np.testing.assert_allclose(out_sr[:5].flatten(), self.sr,
+ rtol=RTOL, atol=ATOL)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Spatial_Rate_tabular(self):
+ out_sr = sm.Spatial_Rate(self.df[self.ename], self.df[self.bname], self.w).r
+ np.testing.assert_allclose(out_sr[:5].flatten(), self.sr,
+ rtol=RTOL, atol=ATOL)
+ self.assertIsInstance(out_sr, np.ndarray)
+
+ out_sr = sm.Spatial_Rate.by_col(self.df, self.ename, self.bname,w=self.w)
+ outcol = '{}-{}_spatial_rate'.format(self.ename, self.bname)
+ r = out_sr[outcol].values
+ self.assertIsInstance(r, np.ndarray)
+ np.testing.assert_allclose(r[:5], self.sr, rtol=RTOL, atol=ATOL)
def test_Spatial_Median_Rate(self):
out_smr = sm.Spatial_Median_Rate(self.e, self.b, self.w).r
out_smr_w = sm.Spatial_Median_Rate(self.e, self.b, self.w, aw=self.b).r
- out_smr2 = sm.Spatial_Median_Rate(
- self.e, self.b, self.w, iteration=2).r
- out_smr = [round(i, 7) for i in out_smr[:5]]
- out_smr_w = [round(i, 7) for i in out_smr_w[:5]]
- out_smr2 = [round(i, 7) for i in out_smr2[:5]]
- self.assertEquals(out_smr, self.smr)
- self.assertEquals(out_smr_w, self.smr_w)
- self.assertEquals(out_smr2, self.smr2)
-
+ out_smr2 = sm.Spatial_Median_Rate(self.e, self.b, self.w, iteration=2).r
+ np.testing.assert_allclose(out_smr[:5].flatten(), self.smr,
+ atol=ATOL, rtol=RTOL)
+ np.testing.assert_allclose(out_smr_w[:5].flatten(), self.smr_w,
+ atol=ATOL, rtol=RTOL)
+ np.testing.assert_allclose(out_smr2[:5].flatten(), self.smr2,
+ atol=ATOL, rtol=RTOL)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Spatial_Median_Rate_tabular(self):
+ out_smr = sm.Spatial_Median_Rate(self.df[self.ename],
+ self.df[self.bname],
+ self.w).r
+ out_smr_w = sm.Spatial_Median_Rate(self.df[self.ename],
+ self.df[self.bname],
+ self.w,
+ aw = self.df[self.bname]).r
+ out_smr2 = sm.Spatial_Median_Rate(self.df[self.ename],
+ self.df[self.bname],
+ self.w,
+ iteration=2).r
+
+ self.assertIsInstance(out_smr, np.ndarray)
+ self.assertIsInstance(out_smr_w, np.ndarray)
+ self.assertIsInstance(out_smr2, np.ndarray)
+
+ np.testing.assert_allclose(out_smr[:5].flatten(), self.smr,
+ atol=ATOL, rtol=RTOL)
+ np.testing.assert_allclose(out_smr_w[:5].flatten(), self.smr_w,
+ atol=ATOL, rtol=RTOL)
+ np.testing.assert_allclose(out_smr2[:5].flatten(), self.smr2,
+ atol=ATOL, rtol=RTOL)
+
+ out_smr = sm.Spatial_Median_Rate.by_col(self.df, self.ename,
+ self.bname, self.w)
+ out_smr_w = sm.Spatial_Median_Rate.by_col(self.df, self.ename,
+ self.bname, self.w,
+ aw = self.df[self.bname])
+ out_smr2 = sm.Spatial_Median_Rate.by_col(self.df, self.ename,
+ self.bname, self.w,
+ iteration=2)
+ outcol = '{}-{}_spatial_median_rate'.format(self.ename, self.bname)
+
+ np.testing.assert_allclose(out_smr[outcol].values[:5], self.smr,
+ rtol=RTOL, atol=ATOL)
+ np.testing.assert_allclose(out_smr_w[outcol].values[:5], self.smr_w,
+ rtol=RTOL, atol=ATOL)
+ np.testing.assert_allclose(out_smr2[outcol].values[:5], self.smr2,
+ rtol=RTOL, atol=ATOL)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Spatial_Smoother_multicol(self):
+ """
+ test that specifying multiple columns works correctly. Since the
+ function is shared over all spatial smoothers, we can only test one.
+ """
+ enames = [self.ename, 'SID79']
+ bnames = [self.bname, 'BIR79']
+ out_df = sm.Spatial_Median_Rate.by_col(self.df, enames, bnames, self.w)
+ outcols = ['{}-{}_spatial_median_rate'.format(e,b)
+ for e,b in zip(enames, bnames)]
+ smr79 = np.array([0.00122129, 0.00176924, 0.00176924,
+ 0.00240964, 0.00272035])
+ answers = [self.smr, smr79]
+ for col, answer in zip(outcols, answer):
+ self.assertIn(out_df.columns, col)
+ np.testing.assert_allclose(out_df[col].values[:5], answer,
+ rtol=RTOL, atol=ATOL)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Smoother_multicol(self):
+ """
+ test that non-spatial smoothers work with multicolumn queries
+ """
+ enames = [self.ename, 'SID79']
+ bnames = [self.bname, 'BIR79']
+ out_df = sm.Excess_Risk.by_col(self.df, enames, bnames)
+ outcols = ['{}-{}_excess_risk'.format(e,b)
+ for e,b in zip(enames, bnames)]
+ er79 = np.array([0.000000, 2.796607, 0.8383863,
+ 1.217479, 0.943811])
+ answers = [self.er, er79]
+ for col, answer in zip(outcols, answer):
+ self.assertIn(out_df.columns, col)
+ np.testing.assert_allclose(out_df[col].values[:5], answer,
+ rtol=RTOL, atol=ATOL)
class TestHB(unittest.TestCase):
def setUp(self):
@@ -127,6 +269,20 @@ class TestHB(unittest.TestCase):
self.w.id_order = self.w.id_order
sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
self.b, self.e = np.array(sids_db[:, 8]), np.array(sids_db[:, 9])
+ self.sids_hb_rr5 = np.array([0.00075586, 0.,
+ 0.0008285, 0.0018315, 0.00498891])
+ self.sids_hb_r2r5 = np.array([0.0008285, 0.00084331,
+ 0.00086896, 0.0018315, 0.00498891])
+ self.sids_hb_r3r5 = np.array([0.00091659, 0.,
+ 0.00156838, 0.0018315, 0.00498891])
+ if not PANDAS_EXTINCT:
+ self.df = sids_db.to_df()
+ self.ename = 'SID74'
+ self.bname = 'BIR74'
+ self.enames = [self.ename, 'SID79']
+ self.bnames = [self.bname, 'BIR79']
+ self.sids79r = np.array([.000563, .001659, .001879,
+ .002410, .002720])
def test_Headbanging_Triples(self):
ht = sm.Headbanging_Triples(self.d, self.w)
@@ -140,26 +296,46 @@ class TestHB(unittest.TestCase):
self.assertTrue(i is not None)
def test_Headbanging_Median_Rate(self):
- sids_d = np.array([i.centroid for i in self.sids])
- sids_w = pysal.knnW_from_array(sids_d, k=5)
- if not sids_w.id_order_set:
- sids_w.id_order = sids_w.id_order
- s_ht = sm.Headbanging_Triples(sids_d, sids_w, k=5)
- sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
- s_e, s_b = np.array(sids_db[:, 9]), np.array(sids_db[:, 8])
- sids_hb_r = sm.Headbanging_Median_Rate(s_e, s_b, s_ht)
- sids_hb_rr5 = np.array([0.00075586, 0.,
- 0.0008285, 0.0018315, 0.00498891])
- np.testing.assert_array_almost_equal(sids_hb_rr5, sids_hb_r.r[:5])
- sids_hb_r2 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, iteration=5)
- sids_hb_r2r5 = np.array([0.0008285, 0.00084331,
- 0.00086896, 0.0018315, 0.00498891])
- np.testing.assert_array_almost_equal(sids_hb_r2r5, sids_hb_r2.r[:5])
- sids_hb_r3 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, aw=s_b)
- sids_hb_r3r5 = np.array([0.00091659, 0.,
- 0.00156838, 0.0018315, 0.00498891])
- np.testing.assert_array_almost_equal(sids_hb_r3r5, sids_hb_r3.r[:5])
-
+ s_ht = sm.Headbanging_Triples(self.d, self.w, k=5)
+ sids_hb_r = sm.Headbanging_Median_Rate(self.e, self.b, s_ht)
+ np.testing.assert_array_almost_equal(self.sids_hb_rr5, sids_hb_r.r[:5])
+ sids_hb_r2 = sm.Headbanging_Median_Rate(self.e, self.b, s_ht, iteration=5)
+ np.testing.assert_array_almost_equal(self.sids_hb_r2r5, sids_hb_r2.r[:5])
+ sids_hb_r3 = sm.Headbanging_Median_Rate(self.e, self.b, s_ht, aw=self.b)
+ np.testing.assert_array_almost_equal(self.sids_hb_r3r5, sids_hb_r3.r[:5])
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Headbanging_Median_Rate_tabular(self):
+
+ # test that dataframe columns are treated correctly
+ s_ht = sm.Headbanging_Triples(self.d, self.w, k=5)
+ sids_hb_r = sm.Headbanging_Median_Rate(self.df[self.ename],
+ self.df[self.bname], s_ht)
+ self.assertIsInstance(sids_hb_r.r, np.ndarray)
+ np.testing.assert_array_almost_equal(self.sids_hb_rr5, sids_hb_r.r[:5])
+
+ sids_hb_r2 = sm.Headbanging_Median_Rate(self.df[self.ename],
+ self.df[self.bname], s_ht,
+ iteration=5)
+ self.assertIsInstance(sids_hb_r2.r, np.ndarray)
+ np.testing.assert_array_almost_equal(self.sids_hb_r2r5, sids_hb_r2.r[:5])
+
+ sids_hb_r3 = sm.Headbanging_Median_Rate(self.df[self.ename],
+ self.df[self.bname], s_ht,
+ aw=self.df[self.bname])
+ self.assertIsInstance(sids_hb_r3.r, np.ndarray)
+ np.testing.assert_array_almost_equal(self.sids_hb_r3r5, sids_hb_r3.r[:5])
+
+ #test that the by col on multiple names works correctly
+ sids_hr_df = sm.Headbanging_Median_Rate.by_col(self.df, self.enames,
+ self.bnames, w=self.w)
+ outcols = ['{}-{}_headbanging_median_rate'.format(e,b) for e,b in
+ zip(self.enames, self.bnames)]
+ for col, answer in zip(outcols, [self.sids_hb_rr5, self.sids79r]):
+ this_col = sids_hr_df[col].values
+ self.assertIsInstance(this_col, np.ndarray)
+ np.testing.assert_allclose(sids_hr_df[col][:5], answer,
+ rtol=RTOL, atol=ATOL*10)
class TestKernel_AgeAdj_SM(unittest.TestCase):
def setUp(self):
@@ -171,36 +347,115 @@ class TestKernel_AgeAdj_SM(unittest.TestCase):
self.points = [(
10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
self.kw = pysal.weights.Kernel(self.points)
+ self.points1 = np.array(self.points) + 5
+ self.points1 = np.vstack((self.points1, self.points))
+ self.kw1 = pysal.weights.Kernel(self.points1)
if not self.kw.id_order_set:
self.kw.id_order = range(0, len(self.points))
-
+ if not PANDAS_EXTINCT:
+ import pandas as pd
+ dfa = np.array([self.e, self.b]).T
+ dfb = np.array([self.e1, self.b1, self.s]).T
+ self.dfa = pd.DataFrame(dfa, columns=['e','b'])
+ self.dfb = pd.DataFrame(dfb, columns=['e', 'b', 's'])
+
+ #answers
+ self.kernel_exp = [0.10543301, 0.0858573, 0.08256196, 0.09884584,
+ 0.04756872, 0.04845298]
+ self.ageadj_exp = [0.10519625, 0.08494318, 0.06440072, 0.06898604,
+ 0.06952076, 0.05020968]
+ self.disk_exp = [0.12222222000000001, 0.10833333, 0.08055556,
+ 0.08944444, 0.09944444, 0.09351852]
+ self.sf_exp = np.array([ 0.111111, 0.111111, 0.085106, 0.076923])
+
def test_Kernel_Smoother(self):
kr = sm.Kernel_Smoother(self.e, self.b, self.kw)
- exp = [0.10543301, 0.0858573, 0.08256196, 0.09884584,
- 0.04756872, 0.04845298]
- self.assertEquals(list(kr.r.round(8)), exp)
+ np.testing.assert_allclose(kr.r.flatten(), self.kernel_exp)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Kernel_Smoother_tabular(self):
+ dfa, dfb = self.dfa, self.dfb
+ kr = sm.Kernel_Smoother(dfa['e'], dfa['b'], self.kw)
+ np.testing.assert_allclose(kr.r.flatten(), kernel_exp)
+
+ kr = sm.Kernel_Smoother.by_col(dfa, 'e', 'b', w=self.kw)
+ colname = 'e_b_kernel_smoother'
+ np.testing.assert_allclose(kr[colname].values, kernel_exp)
+
+ kr = sm.Kernel_Smoother.by_col(dfb, ['e', 's'], 'b', w=self.kw)
+ outcols = ['{}-b_kernel_smoother'.format(l) for l in ['e','s']]
+
+ exp_eb = np.array([ 0.08276363, 0.08096262, 0.03636364, 0.0704302 ,
+ 0.07996067, 0.1287226 , 0.09831286, 0.0952105 ,
+ 0.02857143, 0.06671039, 0.07129231, 0.08078792])
+ exp_sb = np.array([ 1.00575463, 0.99597005, 0.96363636, 0.99440132,
+ 0.98468399, 1.07912333, 1.03376267, 1.02759815,
+ 0.95428572, 0.99716186, 0.98277235, 1.03906155])
+ for name, answer in zip(outcols, [exp_eb, exp_sb]):
+ np.testing.assert_allclose(kr[name].values, answer, rtol=RTOL, atol=ATOL)
def test_Age_Adjusted_Smoother(self):
ar = sm.Age_Adjusted_Smoother(self.e1, self.b1, self.kw, self.s)
- exp = [0.10519625, 0.08494318, 0.06440072, 0.06898604,
- 0.06952076, 0.05020968]
- self.assertEquals(list(ar.r.round(8)), exp)
+ np.testing.assert_allclose(ar.r, self.ageadj_exp)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Age_Adjusted_Smoother_tabular(self):
+ dfb = self.dfb
+ kr = sm.Age_Adjusted_Smoother(dfb.e, dfb.b, s=dfb.s, w=self.kw)
+ self.assertIsInstance(kr.r, np.ndarray)
+ np.testing.assert_allclose(kr.r, self.ageadj_exp)
+
+ kr = sm.Age_Adjusted_Smoother.by_col(dfb, 'e', 'b', w=self.kw, s='s')
+ answer = np.array([ 0.10519625, 0.08494318, 0.06440072,
+ 0.06898604, 0.06952076, 0.05020968])
+ colname = 'e-b_age_adjusted_smoother'
+ np.testing.assert_allclose(kr[colname].values, answer, rtol=RTOL, atol=ATOL)
def test_Disk_Smoother(self):
self.kw.transform = 'b'
- exp = [0.12222222000000001, 0.10833333, 0.08055556,
- 0.08944444, 0.09944444, 0.09351852]
disk = sm.Disk_Smoother(self.e, self.b, self.kw)
- self.assertEqual(list(disk.r.round(8)), exp)
+ np.testing.assert_allclose(disk.r.flatten(), self.disk_exp)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Disk_Smoother_tabular(self):
+ self.kw.transform = 'b'
+ dfa = self.dfa
+ disk = sm.Disk_Smoother(dfa.e, dfa.b, self.kw).r
+ np.testing.assert_allclose(disk.flatten(), self.disk_exp)
+
+ disk = sm.Disk_Smoother.by_col(dfa, 'e', 'b', self.kw)
+ col = 'e-b_disk_smoother'
+ np.testing.assert_allclose(disk[col].values, self.disk_exp,
+ rtol=RTOL, atol=ATOL)
def test_Spatial_Filtering(self):
points = np.array(self.points)
bbox = [[0, 0], [45, 45]]
sf = sm.Spatial_Filtering(bbox, points, self.e, self.b, 2, 2, r=30)
- exp = [0.11111111, 0.11111111, 0.20000000000000001, 0.085106379999999995,
- 0.076923080000000005, 0.05789474, 0.052173909999999997, 0.066666669999999997, 0.04117647]
- self.assertEqual(list(sf.r.round(8)), exp)
-
+ np.testing.assert_allclose(sf.r, self.sf_exp, rtol=RTOL, atol=ATOL)
+
+ @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ def test_Kernel_Smoother_tabular(self):
+ point_array = np.array(self.points)
+ bbox = [[0,0] , [45,45]]
+ dfa = self.dfa
+ sf = sm.Spatial_Filtering(bbox, point_array, dfa.e, dfa.b, 2, 2, r=30)
+
+ np.testing.assert_allclose(sf.r, self.sf_exp, rtol=RTOL, atol=ATOL)
+
+ dfa['geometry'] = self.points
+ sf = sm.Spatial_Filtering.by_col(dfa, 'e', 'b', 3, 3, r=30)
+ r_answer = np.array([ 0.07692308, 0.07213115, 0.07213115, 0.07692308,
+ 0.07692308, 0.07692308, 0.07692308, 0.07692308,
+ 0.07692308])
+ x_answer = np.array([10.0, 10.0, 10.0, 20.0, 20.0, 20.0, 30.0, 30.0, 30.0])
+ y_answer = np.array([10.000000, 16.666667, 23.333333,
+ 10.000000, 16.666667, 23.333333,
+ 10.000000, 16.666667, 23.333333])
+ columns = ['e-b_spatial_filtering_{}'.format(name) for name in ['X', 'Y', 'R']]
+
+ for col, answer in zip(columns, [x_answer, y_answer, r_answer]):
+ np.testing.assert_allclose(sf[col].values, answer, rtol=RTOL, atol=ATOL)
class TestUtils(unittest.TestCase):
def test_sum_by_n(self):
diff --git a/pysal/examples/Polygon_Holes/Polygon_Holes.cpg b/pysal/examples/Polygon_Holes/Polygon_Holes.cpg
new file mode 100644
index 0000000..3ad133c
--- /dev/null
+++ b/pysal/examples/Polygon_Holes/Polygon_Holes.cpg
@@ -0,0 +1 @@
+UTF-8
\ No newline at end of file
diff --git a/pysal/examples/Polygon_Holes/Polygon_Holes.dbf b/pysal/examples/Polygon_Holes/Polygon_Holes.dbf
new file mode 100644
index 0000000..2f3f1c7
Binary files /dev/null and b/pysal/examples/Polygon_Holes/Polygon_Holes.dbf differ
diff --git a/pysal/examples/Polygon_Holes/Polygon_Holes.prj b/pysal/examples/Polygon_Holes/Polygon_Holes.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/Polygon_Holes/Polygon_Holes.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/pysal/examples/Polygon_Holes/Polygon_Holes.qpj b/pysal/examples/Polygon_Holes/Polygon_Holes.qpj
new file mode 100644
index 0000000..5fbc831
--- /dev/null
+++ b/pysal/examples/Polygon_Holes/Polygon_Holes.qpj
@@ -0,0 +1 @@
+GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]
diff --git a/pysal/examples/Polygon_Holes/Polygon_Holes.shp b/pysal/examples/Polygon_Holes/Polygon_Holes.shp
new file mode 100644
index 0000000..e11e281
Binary files /dev/null and b/pysal/examples/Polygon_Holes/Polygon_Holes.shp differ
diff --git a/pysal/examples/Polygon_Holes/Polygon_Holes.shx b/pysal/examples/Polygon_Holes/Polygon_Holes.shx
new file mode 100644
index 0000000..98824b8
Binary files /dev/null and b/pysal/examples/Polygon_Holes/Polygon_Holes.shx differ
diff --git a/pysal/examples/nyc_bikes/nyc_bikes_ct.csv b/pysal/examples/nyc_bikes/nyc_bikes_ct.csv
new file mode 100644
index 0000000..f326556
--- /dev/null
+++ b/pysal/examples/nyc_bikes/nyc_bikes_ct.csv
@@ -0,0 +1,14043 @@
+,index,count,d_cap,d_tract,distance,end station latitude,end station longitude,o_cap,o_tract,start station latitude,start station longitude,tripduration,o_pop,d_pop,o_housing,d_housing,tract_x,o_sq_foot,tract_y,d_sq_foot,tract,diffs_out,start,end,weighted,total_out,total_in,o_hub,d_hub,od_hub,SX,SY,EX,EY
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diff --git a/pysal/examples/nyc_bikes/nyct2010.dbf b/pysal/examples/nyc_bikes/nyct2010.dbf
new file mode 100644
index 0000000..dd24d1c
Binary files /dev/null and b/pysal/examples/nyc_bikes/nyct2010.dbf differ
diff --git a/pysal/examples/nyc_bikes/nyct2010.prj b/pysal/examples/nyc_bikes/nyct2010.prj
new file mode 100644
index 0000000..b68d851
--- /dev/null
+++ b/pysal/examples/nyc_bikes/nyct2010.prj
@@ -0,0 +1 @@
+PROJCS["NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",984250.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.0],PARAMETER["Standard_Parallel_1",40.66666666666666],PARAMETER["Standard_Parallel_2",41.03333333333333],PARAMETER["Lati [...]
\ No newline at end of file
diff --git a/pysal/examples/nyc_bikes/nyct2010.shp b/pysal/examples/nyc_bikes/nyct2010.shp
new file mode 100644
index 0000000..3d94972
Binary files /dev/null and b/pysal/examples/nyc_bikes/nyct2010.shp differ
diff --git a/pysal/examples/nyc_bikes/nyct2010.shp.xml b/pysal/examples/nyc_bikes/nyct2010.shp.xml
new file mode 100644
index 0000000..16a3fc6
--- /dev/null
+++ b/pysal/examples/nyc_bikes/nyct2010.shp.xml
@@ -0,0 +1 @@
+<metadata xml:lang="en"><Esri><CreaDate>20150211</CreaDate><CreaTime>11091800</CreaTime><ArcGISFormat>1.0</ArcGISFormat><ArcGISstyle>ISO 19139 Metadata Implementation Specification</ArcGISstyle><SyncOnce>FALSE</SyncOnce><DataProperties><itemProps><itemName Sync="TRUE">nyct2010</itemName><itemLocation><linkage Sync="FALSE">withheld</linkage><protocol Sync="TRUE">Local Area Network</protocol></itemLocation><imsContentType Sync="TRUE">002</imsContentType><nativeExtBox><westBL Sync="TRUE">91 [...]
diff --git a/pysal/examples/nyc_bikes/nyct2010.shx b/pysal/examples/nyc_bikes/nyct2010.shx
new file mode 100644
index 0000000..180b3d6
Binary files /dev/null and b/pysal/examples/nyc_bikes/nyct2010.shx differ
diff --git a/pysal/inequality/gini.py b/pysal/inequality/gini.py
index 733a287..83a2bb9 100644
--- a/pysal/inequality/gini.py
+++ b/pysal/inequality/gini.py
@@ -4,13 +4,43 @@ Gini based Inequality Metrics
__author__ = "Sergio J. Rey <srey at asu.edu> "
-#from pysal.common import *
import numpy as np
from scipy.stats import norm as NORM
__all__ = ['Gini', 'Gini_Spatial']
+def _gini(x):
+ """
+ Memory efficient calculation of Gini coefficient in relative mean difference form
+
+ Parameters
+ ----------
+
+ x : array-like
+
+ Attributes
+ ----------
+
+ g : float
+ Gini coefficient
+
+ Notes
+ -----
+ Based on http://www.statsdirect.com/help/default.htm#nonparametric_methods/gini.htm
+
+ """
+ n = len(x)
+ try:
+ x_sum = x.sum()
+ except AttributeError:
+ x = np.asarray(x)
+ x_sum = x.sum()
+ n_x_sum = n * x_sum
+ r_x = (2. * np.arange(1, len(x)+1) * x[np.argsort(x)]).sum()
+ return (r_x - n_x_sum - x_sum) / n_x_sum
+
+
class Gini:
"""
Classic Gini coefficient in absolute deviation form
@@ -31,13 +61,7 @@ class Gini:
def __init__(self, x):
- x.shape = (x.shape[0],)
- d = np.abs(np.array([x - xi for xi in x]))
- n = len(x)
- xbar = x.mean()
- den = xbar * 2 * n**2
- dtotal = d.sum()
- self.g = dtotal/den
+ self.g = _gini(x)
class Gini_Spatial:
@@ -46,7 +70,7 @@ class Gini_Spatial:
Provides for computationally based inference regarding the contribution of
spatial neighbor pairs to overall inequality across a set of regions. [Rey2013]_
-
+
Parameters
----------
@@ -109,17 +133,11 @@ class Gini_Spatial:
>>> np.random.seed(12345)
>>> gs = pysal.inequality.gini.Gini_Spatial(y[:,0],w)
>>> gs.p_sim
- 0.01
+ 0.040000000000000001
>>> gs.wcg
4353856.0
>>> gs.e_wcg
- 1067629.2525252525
- >>> gs.s_wcg
- 95869.167798782844
- >>> gs.z_wcg
- 34.2782442252145
- >>> gs.p_z_sim
- 0.0
+ 4170356.7474747472
Thus, the amount of inequality between pairs of states that are not in the
same regime (neighbors) is significantly higher than what is expected
@@ -127,34 +145,41 @@ class Gini_Spatial:
"""
def __init__(self, x, w, permutations=99):
- x.shape = (x.shape[0],)
- d = np.abs(np.array([x - xi for xi in x]))
+
+ x = np.asarray(x)
+ g = _gini(x)
+ self.g = g
n = len(x)
- xbar = x.mean()
- den = xbar * 2 * n**2
- wg = w.sparse.multiply(d).sum()
- self.wg = wg # spatial inequality component
- dtotal = d.sum()
- wcg = dtotal - wg # complement to spatial inequality component
+ den = x.mean() * 2 * n**2
+ d = g * den
+ wg = self._calc(x, w)
+ wcg = d - wg
+ self.g = g
self.wcg = wcg
- self.g = dtotal / den
- self.wcg_share = wcg / dtotal
- self.dtotal = dtotal
+ self.wg = wg
+ self.dtotal = d
self.den = den
+ self.wcg_share = wcg / den
if permutations:
ids = np.arange(n)
- wcgp = np.zeros((permutations, 1))
- for perm in xrange(permutations):
- # permute rows/cols of d
+ wcgp = np.zeros((permutations, ))
+ for perm in range(permutations):
np.random.shuffle(ids)
- wcgp[perm] = w.sparse.multiply(d[ids, :][:, ids]).sum()
+ wcgp[perm] = d - self._calc(x[ids], w)
above = wcgp >= self.wcg
larger = above.sum()
if (permutations - larger) < larger:
larger = permutations - larger
+ self.wcgp = wcgp
self.p_sim = (larger + 1.) / (permutations + 1.)
self.e_wcg = wcgp.mean()
self.s_wcg = wcgp.std()
self.z_wcg = (self.wcg - self.e_wcg) / self.s_wcg
self.p_z_sim = 1.0 - NORM.cdf(self.z_wcg)
+
+ def _calc(self, x, w):
+ sad_sum = 0.0
+ for i, js in w.neighbors.iteritems():
+ sad_sum += np.abs(x[i]-x[js]).sum()
+ return sad_sum
diff --git a/pysal/inequality/tests/test_gini.py b/pysal/inequality/tests/test_gini.py
new file mode 100644
index 0000000..e0c4b90
--- /dev/null
+++ b/pysal/inequality/tests/test_gini.py
@@ -0,0 +1,33 @@
+import unittest
+import pysal
+import numpy as np
+import pysal as ps
+from pysal.inequality.gini import Gini, Gini_Spatial
+
+
+class Gini_Tester(unittest.TestCase):
+
+ def setUp(self):
+ f = pysal.open(pysal.examples.get_path("mexico.csv"))
+ vnames = ["pcgdp%d" % dec for dec in range(1940, 2010, 10)]
+ y = np.transpose(np.array([f.by_col[v] for v in vnames]))
+ self.y = y[:, 0]
+ regimes = np.array(f.by_col('hanson98'))
+ self.w = ps.block_weights(regimes)
+
+ def test_Gini(self):
+ g = Gini(self.y)
+ np.testing.assert_almost_equal(g.g, 0.35372371173452849)
+
+ def test_Gini_Spatial(self):
+ np.random.seed(12345)
+ g = Gini_Spatial(self.y, self.w)
+ np.testing.assert_almost_equal(g.g, 0.35372371173452849)
+ np.testing.assert_almost_equal(g.wg, 884130.0)
+ np.testing.assert_almost_equal(g.wcg, 4353856.0)
+ np.testing.assert_almost_equal(g.p_sim, 0.040)
+ np.testing.assert_almost_equal(g.e_wcg, 4170356.7474747472)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/network/Network_Usage.ipynb b/pysal/network/Network_Usage.ipynb
new file mode 100644
index 0000000..82aae1e
--- /dev/null
+++ b/pysal/network/Network_Usage.ipynb
@@ -0,0 +1,689 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic Tutorial for `pysal.network`\n",
+ "\n",
+ "#### updated 07/2016"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pylab inline\n",
+ "import pysal as ps"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Instantiate a network"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "ntw = ps.Network(ps.examples.get_path('streets.shp'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Snap point patterns to the network"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Crimes\n",
+ "ntw.snapobservations(ps.examples.get_path('crimes.shp'), 'crimes', attribute=True)\n",
+ "# Schools\n",
+ "ntw.snapobservations(ps.examples.get_path('schools.shp'), 'schools', attribute=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### A network is composed of a single topological representation of a road and $n$ point patterns which are snapped to the network."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'crimes': <pysal.network.network.PointPattern instance at 0x118767cb0>,\n",
+ " 'schools': <pysal.network.network.PointPattern instance at 0x118767c20>}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ntw.pointpatterns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['__doc__',\n",
+ " '__init__',\n",
+ " '__module__',\n",
+ " 'dist_to_node',\n",
+ " 'npoints',\n",
+ " 'obs_to_edge',\n",
+ " 'obs_to_node',\n",
+ " 'points',\n",
+ " 'snapped_coordinates']"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dir(ntw.pointpatterns['crimes'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Attributes for every point pattern"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. `dist_to_node` dict keyed by pointid with the value being a dict in the form \n",
+ " {node: distance to node, node: distance to node}\n",
+ "2. `obs_to_edge` dict keyed by edge with the value being a dict in the form \n",
+ " {pointID:(x-coord, y-coord), pointID:(x-coord, y-coord), ... }\n",
+ "3. `obs_to_node`\n",
+ "4. `points` geojson like representation of the point pattern. Includes properties if read with attributes=True\n",
+ "5. `snapped_coordinates` dict keyed by pointid with the value being (x-coord, y-coord)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Counts per edge are important, but should not be precomputed since we have different representations of the network (digitized and graph currently). (Relatively) Uniform segmentation still needs to be done."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.6822429906542058"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "counts = ntw.count_per_edge(ntw.pointpatterns['crimes'].obs_to_edge,\n",
+ " graph=False)\n",
+ "sum(counts.values()) / float(len(counts.keys()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Segmentation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "n200 = ntw.segment_edges(200.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.0354609929078014"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "counts = n200.count_per_edge(n200.pointpatterns['crimes'].obs_to_edge, graph=False)\n",
+ "sum(counts.values()) / float(len(counts.keys()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Visualization of the shapefile derived, unsegmented network with nodes in a larger, semi-opaque form and the distance segmented network with small, fully opaque nodes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxoAAALxCAYAAADbtNCnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VPW9//HXrNkDJIGQGVaBYEDAQCCIQABlCVARtFqp\ndtG2t/eqtbd6b29vVTR0b++v7UWrUrX2gqJ1t2BYBAQREwIk7BCWrJOEEAJZZjKZ9fdHZsaZyUwy\nCZNF/TwfDx5ikvmez/nOl8l5n/M936NwOp1OhBBCCCGEECKMlH1dgBBCCCGEEOLLR4KGEEIIIYQQ\nIuwkaAghhBBCCCHCToKGEEIIIYQQIuwkaAghhBBCCCHCToKGEEIIIYQQIuwkaAghhBBCCCHCToKG\nEEIIIYQQIuwkaAghhBBCCCHCToKGEEIIIYQQIuwkaAghhBBCCCHCToKGEEIIIYQQIuwkaAghhBBC\nCCHC [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1194dfbd0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import networkx as nx\n",
+ "figsize(10,10)\n",
+ "\n",
+ "g = nx.Graph()\n",
+ "for e in ntw.edges:\n",
+ " g.add_edge(*e)\n",
+ "for n, p in ntw.node_coords.iteritems():\n",
+ " g.node[n] = p\n",
+ "nx.draw(g, ntw.node_coords, node_size=300, alpha=0.5)\n",
+ "\n",
+ "g = nx.Graph()\n",
+ "for e in n200.edges:\n",
+ " g.add_edge(*e)\n",
+ "for n, p in n200.node_coords.iteritems():\n",
+ " g.node[n] = p\n",
+ "nx.draw(g, n200.node_coords, node_size=25, alpha=1.0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Moran's I using the digitized network"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['EI', 'EI_sim', 'I', 'VI_norm', 'VI_rand', 'VI_sim', '_Moran__calc', '_Moran__moments', '__doc__', '__init__', '__module__', 'n', 'p_norm', 'p_rand', 'p_sim', 'p_z_sim', 'permutations', 'seI_norm', 'seI_rand', 'seI_sim', 'sim', 'w', 'y', 'z', 'z2ss', 'z_norm', 'z_rand', 'z_sim']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Binary Adjacency\n",
+ "#ntw.contiguityweights(graph=False)\n",
+ "w = ntw.contiguityweights(graph=False)\n",
+ "\n",
+ "# Build the y vector\n",
+ "#edges = ntw.w.neighbors.keys()\n",
+ "edges = w.neighbors.keys()\n",
+ "y = np.zeros(len(edges))\n",
+ "for i, e in enumerate(edges):\n",
+ " if e in counts.keys():\n",
+ " y[i] = counts[e]\n",
+ "\n",
+ "# Moran's I\n",
+ "#res = ps.esda.moran.Moran(y, ntw.w, permutations=99)\n",
+ "res = ps.esda.moran.Moran(y, w, permutations=99)\n",
+ "print dir(res)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Moran's I using the graph representation to generate the W\n",
+ "\n",
+ "* Note that we have to regenerate the counts per edge, since the graph will have less edges."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['EI', 'EI_sim', 'I', 'VI_norm', 'VI_rand', 'VI_sim', '_Moran__calc', '_Moran__moments', '__doc__', '__init__', '__module__', 'n', 'p_norm', 'p_rand', 'p_sim', 'p_z_sim', 'permutations', 'seI_norm', 'seI_rand', 'seI_sim', 'sim', 'w', 'y', 'z', 'z2ss', 'z_norm', 'z_rand', 'z_sim']\n"
+ ]
+ }
+ ],
+ "source": [
+ "counts = ntw.count_per_edge(ntw.pointpatterns['crimes'].obs_to_edge, graph=True)\n",
+ "\n",
+ "# Binary Adjacency\n",
+ "#ntw.contiguityweights(graph=True)\n",
+ "w = ntw.contiguityweights(graph=True)\n",
+ "\n",
+ "# Build the y vector\n",
+ "#edges = ntw.w.neighbors.keys()\n",
+ "edges = w.neighbors.keys()\n",
+ "y = np.zeros(len(edges))\n",
+ "for i, e in enumerate(edges):\n",
+ " if e in counts.keys():\n",
+ " y[i] = counts[e]\n",
+ "\n",
+ "# Moran's I\n",
+ "#res = ps.esda.moran.Moran(y, ntw.w, permutations=99)\n",
+ "res = ps.esda.moran.Moran(y, w, permutations=99)\n",
+ "\n",
+ "\n",
+ "print dir(res)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Moran's I using the segmented network and intensities instead of counts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['EI', 'EI_sim', 'I', 'VI_norm', 'VI_rand', 'VI_sim', '_Moran__calc', '_Moran__moments', '__doc__', '__init__', '__module__', 'n', 'p_norm', 'p_rand', 'p_sim', 'p_z_sim', 'permutations', 'seI_norm', 'seI_rand', 'seI_sim', 'sim', 'w', 'y', 'z', 'z2ss', 'z_norm', 'z_rand', 'z_sim']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Binary Adjacency\n",
+ "#n200.contiguityweights(graph=False)\n",
+ "w = n200.contiguityweights(graph=False)\n",
+ "\n",
+ "# Compute the counts\n",
+ "counts = n200.count_per_edge(n200.pointpatterns['crimes'].obs_to_edge, graph=False)\n",
+ "\n",
+ "# Build the y vector and convert from raw counts to intensities\n",
+ "#edges = n200.w.neighbors.keys()\n",
+ "edges = w.neighbors.keys()\n",
+ "y = np.zeros(len(edges))\n",
+ "for i, e in enumerate(edges):\n",
+ " if e in counts.keys():\n",
+ " length = n200.edge_lengths[e]\n",
+ " y[i] = counts[e] / length\n",
+ "\n",
+ "# Moran's I\n",
+ "#res = ps.esda.moran.Moran(y, n200.w, permutations=99)\n",
+ "res = ps.esda.moran.Moran(y, w, permutations=99)\n",
+ "\n",
+ "print dir(res)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Timings for distance based methods, e.g. G-function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.734544992447\n"
+ ]
+ }
+ ],
+ "source": [
+ "import time\n",
+ "t1 = time.time()\n",
+ "n0 = ntw.allneighbordistances(ntw.pointpatterns['crimes'])\n",
+ "print time.time()-t1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "4.1571199894\n"
+ ]
+ }
+ ],
+ "source": [
+ "import time\n",
+ "t1 = time.time()\n",
+ "n1 = n200.allneighbordistances(n200.pointpatterns['crimes'])\n",
+ "print time.time()-t1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Note that the first time these methods are called, the underlying node-to-node shortest path distance matrix has to be calculated. Subsequent calls will not require this, and will be much faster:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.14933681488\n"
+ ]
+ }
+ ],
+ "source": [
+ "import time\n",
+ "t1 = time.time()\n",
+ "n0 = ntw.allneighbordistances(ntw.pointpatterns['crimes'])\n",
+ "print time.time()-t1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.159301042557\n"
+ ]
+ }
+ ],
+ "source": [
+ "import time\n",
+ "t1 = time.time()\n",
+ "n1 = n200.allneighbordistances(n200.pointpatterns['crimes'])\n",
+ "print time.time()-t1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Simulate a point pattern on the network\n",
+ "\n",
+ "* Need to supply a count of the number of points and a distirbution (default is uniform). Generally, this will not be called by the user, since the simulation will be used for Monte Carlo permutation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<pysal.network.network.SimulatedPointPattern instance at 0x11bd21cb0>"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "npts = ntw.pointpatterns['crimes'].npoints\n",
+ "sim = ntw.simulate_observations(npts)\n",
+ "sim"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a nearest neighbor matrix using the crimes point pattern\n",
+ "\n",
+ "* Right now, both the G and K functions generate a full distance matrix. This is because, I know that the full generation is correct and I believe that the truncated generated, e.g. nearest neighbor, has a big."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### G-function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "gres = ps.NetworkG(ntw,\n",
+ " ntw.pointpatterns['crimes'],\n",
+ " permutations = 99)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x10236be10>"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUIAAAE4CAYAAAAuFPo7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd8lFXWwPHfDQQIVUgCoQQEQdoqRXqNooCCBV0FLCCL\norDqqvv6ygpo0F1RX1zRVWyAIkhTQRBcBcQgLOBSAgjSpIQSkJ5QU8/7x52EkEICmZlnyvl+PvNh\n5pmbuWcmk8N9ntuMiKCUUsEsxOkAlFLKaZoIlVJBTxOhUiroaSJUSgU9TYRKqaCniVApFfQKTYTG\nmInGmN+NMRsvUeYdY8wOY8x6Y0xz94aolFKeVZQW4SdAj4KeNMbcClwjIg2Ax4AP3BSbUkp5RaGJ\nUESWAycuUeRO4DNX2Z+BSsaYau4JTymlPM8d1whrAvtyPD7gOqaUUn7BHYnQ5HNM5+0ppfxGSTe8\nxn4g [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1192d5a50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "figsize(5,5)\n",
+ "plot(gres.xaxis, gres.observed, 'b-', linewidth=1.5, label='Observed')\n",
+ "plot(gres.xaxis, gres.upperenvelope, 'r--', label='Upper')\n",
+ "plot(gres.xaxis, gres.lowerenvelope, 'k--', label='Lower')\n",
+ "legend(loc='best')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### K-function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "kres = ps.NetworkK(ntw,\n",
+ " ntw.pointpatterns['crimes'],\n",
+ " permutations=99)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x11c1cc510>"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAE+CAYAAAAAreLRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmczfX+wPHXexhmhsiWbRhLKnSRpIliukpEi7pZKpV0\n+xWlqG7dujHaqJBKJSlrtlAksqQhMUKE7DuDiSxhBrN8fn98junMOLNxzvmeM/N+Ph7zcM73+51z\n3uc74z3v7/eziTEGpZRSnoU4HYBSSgUyTZJKKZUDTZJKKZUDTZJKKZUDTZJKKZUDTZJKKZUDvydJ\nEflcRBJFZG0ejh0iIqtF5FcR2SwiR/wRo1JKnSP+7icpIjcCJ4GxxpgG+fi+p4BGxpjHfBacUkpl\n4fdK0hizBDjqvk1EaonIHBFZISKLROQKD9/aBZjolyCVUsqlqNMBuIwA/s8Ys11EmgKfAK3O7RSR\n6kAN [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11b5ff6d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "figsize(5,5)\n",
+ "plot(kres.xaxis, kres.observed, 'b-', linewidth=1.5, label='Observed')\n",
+ "plot(kres.xaxis, kres.upperenvelope, 'r--', label='Upper')\n",
+ "plot(kres.xaxis, kres.lowerenvelope, 'k--', label='Lower')\n",
+ "legend(loc='best')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": [
+ "-----------"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/network/analysis.py b/pysal/network/analysis.py
index 9bbb2e0..f461ca2 100644
--- a/pysal/network/analysis.py
+++ b/pysal/network/analysis.py
@@ -22,16 +22,17 @@ class NetworkBase(object):
self.lowerbound = lowerbound
self.upperbound = upperbound
- #Compute Statistic
+ # Compute Statistic.
self.computeobserved()
self.computepermutations()
- #Compute the envelope vectors
+ # Compute the envelope vectors.
self.computeenvelope()
def validatedistribution(self):
valid_distributions = ['uniform', 'poisson']
- assert(self.distribution in valid_distributions),"Disstribution not in {}".format(valid_distributions)
+ assert(self.distribution in valid_distributions),"Disstribution not in {}"\
+ .format(valid_distributions)
def computeenvelope(self):
upper = 1.0 - self.threshold / 2.0
@@ -48,11 +49,7 @@ class NetworkBase(object):
class NetworkG(NetworkBase):
"""
- Compute a network constrained G statistic
-
- Attributes
- ==========
-
+ Compute a network constrained G statistic.
"""
def computeobserved(self):
@@ -78,7 +75,7 @@ class NetworkG(NetworkBase):
class NetworkK(NetworkBase):
"""
- Network constrained K Function
+ Compute a network constrained K statistic.
"""
def computeobserved(self):
@@ -107,7 +104,7 @@ class NetworkK(NetworkBase):
class NetworkF(NetworkBase):
"""
- Network constrained F Function
+ Compute a network constrained F statistic.
This requires the capability to compute a distance matrix between two
point patterns. In this case one will be observed and one will be simulated
@@ -115,10 +112,11 @@ class NetworkF(NetworkBase):
def computeobserved(self):
self.fsim = self.ntw.simulate_observations(self.npts)
- #Nearest neighbor distances from the simulated to the observed
- nearest = np.nanmin(self.ntw.allneighbordistances(self.fsim, self.pointpattern), axis=1)
+ # Compute nearest neighbor distances from the simulated to the observed.
+ nearest = np.nanmin(self.ntw.allneighbordistances(self.fsim,
+ self.pointpattern), axis=1)
self.setbounds(nearest)
- #Generate a random distribution of points
+ # Generate a random distribution of points.
observedx, observedy = ffunction(nearest, self.lowerbound, self.upperbound,
nsteps=self.nsteps, npts=self.npts)
self.observed = observedy
@@ -163,13 +161,23 @@ def gfunction(nearest, lowerbound, upperbound, nsteps = 10):
Parameters
----------
- nearest ndarray A vector of nearest neighbor distances
- nsteps int The number of distance bands
- permutations int The number of permutations to perform
- threshold int Upper and lower significance threshold
- envelope bool Return results of all permutations
- poisson bool Use a poisson distribution to
- determine the number of points
+ nearest: ndarray
+ A vector of nearest neighbor distances.
+
+ nsteps: int
+ The number of distance bands.
+
+ permutations: int
+ The number of permutations to perform.
+
+ threshold: int
+ Upper and lower significance threshold.
+
+ envelope: bool
+ Return results of all permutations.
+
+ poisson: bool
+ Use a poisson distribution to determine the number of points.
"""
nobs = len(nearest)
x = np.linspace(lowerbound, upperbound, nsteps)
@@ -183,4 +191,4 @@ def gfunction(nearest, lowerbound, upperbound, nsteps = 10):
else:
g = 0
y[i] = g
- return x, y
+ return x, y
\ No newline at end of file
diff --git a/pysal/network/network.py b/pysal/network/network.py
index e67fdae..0603ba9 100644
--- a/pysal/network/network.py
+++ b/pysal/network/network.py
@@ -11,7 +11,7 @@ from pysal.weights.util import get_ids
from analysis import NetworkG, NetworkK, NetworkF
import util
-__all__ = ["Network", "PointPattern", "NetworkG", "NetworkK", "NetworkF" ]
+__all__ = ["Network", "PointPattern", "NetworkG", "NetworkK", "NetworkF"]
class Network:
@@ -19,71 +19,74 @@ class Network:
"""
Spatially constrained network representation and analytical functionality.
+
Parameters
-----------
- in_shp : string
- input shapefile
+ in_shp: str
+ The input shapefile. This must be in .shp format.
- node_sig : int
- round the x and y coordinates of all nodes to node_sig
- significant digits (combined significant digits on left and right
- of decimal place); default is 11; set to None for no rounding
+ node_sig: int
+ Round the x and y coordinates of all nodes to node_sig significant
+ digits (combined significant digits on the left and right
+ of the decimal place)
+ -- Default is 11
+ -- Set to None for no rounding
- unique_segs : boolean
- if True (default), keep only unique segments (i.e., prune
- out any duplicated segments); if False keep all segments
+ unique_segs: bool
+ If True (default), keep only unique segments (i.e., prune out any
+ duplicated segments).
+ If False keep all segments.
Attributes
----------
- in_shp : string
- input shapefile name
+ in_shp: str
+ The input shapefile. This must be in .shp format.
- adjacencylist : list
- of lists storing node adjacency
+ adjacencylist: list
+ List of lists storing node adjacency.
- nodes : dict
- key are tuple of node coords and value is the node ID
+ nodes: dict
+ Keys are tuples of node coords and values are the node ID.
- edge_lengths : dict
- key is a tuple of sorted node IDs representing an edge
- value is the length
+ edge_lengths: dict
+ Keys are tuples of sorted node IDs representing an edge and values are
+ the length.
- pointpatterns : dict
- key is a string name of the pattern
- value is a point pattern class instance
+ pointpatterns: dict
+ Keys are a string name of the pattern and values are point pattern
+ class instances.
- node_coords : dict
- key is th node ID and value are the (x,y) coordinates
- inverse to nodes
+ node_coords: dict
+ Keys are the node ID and values are the (x,y) coordinates inverse
+ to nodes.
- edges : list
- of edges, where each edge is a sorted tuple of node IDs
+ edges: list
+ List of edges, where each edge is a sorted tuple of node IDs.
- node_list : list
- node IDs
+ node_list: list
+ List of node IDs.
- alldistances : dict
- key is the node ID
- value is a tuple with two elements, first is a list of the
- shortest path distances, second is a dict with
- the key being the id of the destination node and the value
- is a list of the shortest path.
+ alldistances: dict
+ Keys are the node IDs.
+ Values are tuples with two elements:
+ 1. A list of the shortest path distances
+ 2. A dict with the key being the id of the destination node and
+ the value being a list of the shortest path.
Examples
--------
- Instantiate an instance of a network
+ Instantiate an instance of a network.
>>> ntw = ps.Network(ps.examples.get_path('streets.shp'))
- Snap point observations to the network with attribute information
+ Snap point observations to the network with attribute information.
>>> ntw.snapobservations(ps.examples.get_path('crimes.shp'), 'crimes', attribute=True)
- And without attribute information
+ And without attribute information.
>>> ntw.snapobservations(ps.examples.get_path('schools.shp'), 'schools', attribute=False)
-
"""
def __init__(self, in_shp=None, node_sig=11, unique_segs=True):
@@ -102,32 +105,32 @@ class Network:
self._extractnetwork()
self.node_coords = dict((value, key) for key, value in self.nodes.iteritems())
- #This is a spatial representation of the network.
+ # This is a spatial representation of the network.
self.edges = sorted(self.edges)
- #Extract the graph
+ # Extract the graph.
self.extractgraph()
self.node_list = sorted(self.nodes.values())
def _round_sig(self, v):
"""
- Used internally to round vertex to a set number of significant
- digits. If sig is set to 4, then the following are some possible
- results for a coordinate: 0.0xxxx, 0.xxxx, x.xxx, xx.xx, xxx.x,
- xxxx.0, xxxx0.0
+ Used internally to round the vertex to a set number of significant digits. If sig
+ is set to 4, then the following are some possible results for a coordinate:
+ 0.0xxxx, 0.xxxx, x.xxx, xx.xx, xxx.x, xxxx.0, xxxx0.0
"""
sig = self.node_sig
if sig is None:
return v
out_v = [val if 0 \
- else round(val, -int(math.floor(math.log10(math.fabs(val)))) + (sig-1)) \
+ else round(val, -int(math.floor(math.log10(math.fabs(val)))) +\
+ (sig-1)) \
for val in v]
return tuple(out_v)
def _extractnetwork(self):
"""
- Used internally, to extract a network from a polyline shapefile
+ Used internally, to extract a network from a polyline shapefile.
"""
nodecount = 0
shps = ps.open(self.in_shp)
@@ -150,29 +153,29 @@ class Network:
self.adjacencylist[vid].append(nvid)
self.adjacencylist[nvid].append(vid)
- #Sort the edges so that mono-directional keys can be stored.
+ # Sort the edges so that mono-directional keys can be stored.
edgenodes = sorted([vid, nvid])
edge = tuple(edgenodes)
self.edges.append(edge)
length = util.compute_length(v, vertices[i+1])
self.edge_lengths[edge] = length
if self.unique_segs == True:
- # remove duplicate edges and duplicate adjacent nodes
+ # Remove duplicate edges and duplicate adjacent nodes.
self.edges = list(set(self.edges))
for k, v in self.adjacencylist.iteritems():
self.adjacencylist[k] = list(set(v))
def extractgraph(self):
"""
- Using the existing network representation, create a graph based representation,
- by removing all nodes with neighbor incidence of two. That is, we assume these
+ Using the existing network representation, create a graph based representation by
+ removing all nodes with a neighbor incidence of two. That is, we assume these
nodes are bridges between nodes with higher incidence.
"""
self.graphedges = []
self.edge_to_graph = {}
self.graph_lengths = {}
- #Find all nodes with cardinality 2
+ # Find all nodes with cardinality 2.
segment_nodes = []
for k, v in self.adjacencylist.iteritems():
#len(v) == 1 #cul-de-sac
@@ -181,11 +184,12 @@ class Network:
if len(v) == 2:
segment_nodes.append(k)
- #Start with a copy of the spatial representation and iteratively
- # remove edges deemed to be segments
+ # Start with a copy of the spatial representation and iteratively remove edges
+ # deemed to be segments.
self.graphedges = copy.deepcopy(self.edges)
self.graph_lengths = copy.deepcopy(self.edge_lengths)
- self.graph_to_edges = {} #Mapping all the edges contained within a single graph represented edge
+ # Mapping all the edges contained within a single graph represented edge.
+ self.graph_to_edges = {}
bridges = []
for s in segment_nodes:
@@ -203,19 +207,19 @@ class Network:
if len(bridge) == 1:
n = self.adjacencylist[bridge[0]]
newedge = tuple(sorted([n[0], n[1]]))
- #Identify the edges to be removed
+ # Identify the edges to be removed.
e1 = tuple(sorted([bridge[0], n[0]]))
e2 = tuple(sorted([bridge[0], n[1]]))
- #Remove from the graph
+ # Remove them from the graph.
self.graphedges.remove(e1)
self.graphedges.remove(e2)
- #Remove from the edge lengths
+ # Remove from the edge lengths.
length_e1 = self.edge_lengths[e1]
length_e2 = self.edge_lengths[e2]
self.graph_lengths.pop(e1, None)
self.graph_lengths.pop(e2, None)
self.graph_lengths[newedge] = length_e1 + length_e2
- #Update the pointers
+ # Update the pointers.
self.graph_to_edges[e1] = newedge
self.graph_to_edges[e2] = newedge
else:
@@ -245,8 +249,8 @@ class Network:
def _yieldneighbor(self, node, segment_nodes, bridge):
"""
- Used internally, this method traverses a bridge segement
- to find the source and destination nodes.
+ Used internally, this method traverses a bridge segement to find the source and
+ destination nodes.
"""
n = []
for i in self.adjacencylist[node]:
@@ -260,17 +264,17 @@ class Network:
Parameters
----------
- graph : boolean
- {True, False } controls whether the W is generated using the spatial
- representation or the graph representation
+ graph: bool
+ {True, False} controls whether the W is generated using the spatial
+ representation or the graph representation.
- weightings : dict
- of lists of weightings for each edge
+ weightings: dict
+ Dict of lists of weightings for each edge.
Returns
-------
- : W
- A PySAL W Object representing the binary adjacency of the network
+ W: object
+ A PySAL W Object representing the binary adjacency of the network.
Examples
--------
@@ -292,7 +296,6 @@ class Network:
Next, a standard call ot Moran is made and the result placed into `res`
>>> res = ps.esda.moran.Moran(y, w, permutations=99)
-
"""
neighbors = {}
@@ -316,11 +319,13 @@ class Network:
for neigh in edges:
if key == neigh:
continue
- if key[0] == neigh[0] or key[0] == neigh[1] or key[1] == neigh[0] or key[1] == neigh[1]:
+ if key[0] == neigh[0] or key[0] == neigh[1] or key[1] == neigh[0]\
+ or key[1] == neigh[1]:
neighbors[key].append(neigh)
if weightings:
weights[key].append(weightings[neigh])
- #TODO: Add a break condition - everything is sorted, so we know when we have stepped beyond a possible neighbor.
+ # TODO: Add a break condition - everything is sorted, so we know when we
+ # have stepped beyond a possible neighbor.
#if key[1] > neigh[1]: #NOT THIS
#break
@@ -332,8 +337,8 @@ class Network:
Parameters
----------
- threshold : float
- Distance threshold value
+ threshold: float
+ Distance threshold value.
"""
try:
hasattr(self.alldistances)
@@ -351,27 +356,27 @@ class Network:
def snapobservations(self, shapefile, name, idvariable=None, attribute=None):
"""
- Snap a point pattern shapefile to this network object. The point pattern
- is the stored in the network.pointpattern['key'] attribute of the network
- object.
+ Snap a point pattern shapefile to this network object. The point pattern is
+ stored in the network.pointpattern['key'] attribute of the network object.
Parameters
----------
- shapefile : str
- The PATH to the shapefile
+ shapefile: str
+ The path to the shapefile.
- name : str
- Name to be assigned to the point dataset
+ name: str
+ Name to be assigned to the point dataset.
- idvariable : str
- Column name to be used as ID variable
+ idvariable: str
+ Column name to be used as ID variable.
- attribute : bool
- Defines whether attributes should be extracted
+ attribute: bool
+ Defines whether attributes should be extracted.
+ True for attribute extraction.
+ False for no attribute extraaction.
Returns
-------
-
"""
self.pointpatterns[name] = PointPattern(shapefile, idvariable=idvariable, attribute=attribute)
@@ -379,27 +384,29 @@ class Network:
def compute_distance_to_nodes(self, x, y, edge):
"""
- Given an observation on a network edge, return the distance to the two
- nodes that bound that end.
+ Given an observation on a network edge, return the distance to the two nodes that
+ bound that end.
Parameters
----------
- x : float
- x-coordinate of the snapped point
+ x: float
+ x-coordinate of the snapped point.
- y : float
- y-coordiante of the snapped point
+ y: float
+ y-coordiante of the snapped point.
- edge : tuple
- (node0, node1) representation of the network edge
+ edge: tuple
+ (node0, node1) representation of the network edge.
Returns
-------
- d1 : float
- the distance to node0, always the node with the lesser id
+ d1: float
+ The distance to node0.
+ - always the node with the lesser id
- d2 : float
- the distance to node1, always the node with the greater id
+ d2: float
+ The distance to node1.
+ - always the node with the greater id
"""
d1 = util.compute_length((x,y), self.node_coords[edge[0]])
@@ -412,21 +419,20 @@ class Network:
Parameters
-----------
- pointpattern : obj
- PySAL Point Pattern Object
+ pointpattern: object
+ PySAL Point Pattern Object
Returns
-------
- obs_to_edge : dict
- with edge as key and list of points as value
-
- edge_to_obs : dict
- with point id as key and edge tuple as value
+ obs_to_edge: dict
+ Dict with edges as keys and lists of points as values.
- dist_to_node : dict
- with point id as key and value as a dict with key for
- node id, and value distance from point to node
+ edge_to_obs: dict
+ Dict with point ids as keys and edge tuples as values.
+ dist_to_node: dict
+ Dict with point ids as keys and values as dicts with keys for
+ node ids and values as distances from point to node.
"""
obs_to_edge = {}
@@ -476,18 +482,19 @@ class Network:
Parameters
----------
- obs_on_network : dict
- of observations on the network
- {(edge): {pt_id: (coords)}} or {edge: [(coord), (coord), (coord)]}
+ obs_on_network: dict
+ Dict of observations on the network.
+ {(edge):{pt_id:(coords)}} or {edge:[(coord),(coord),(coord)]}
Returns
-------
- counts: dict {(edge):count}
+ counts: dict
+ {(edge):count}
Example
-------
- Note that this passes the obs_to_edge attribute of a point pattern
- snapped to the network.
+ Note that this passes the obs_to_edge attribute of a point pattern snapped to the
+ network.
>>> ntw = ps.Network(ps.examples.get_path('streets.shp'))
>>> ntw.snapobservations(ps.examples.get_path('crimes.shp'), 'crimes', attribute=True)
@@ -513,19 +520,19 @@ class Network:
def _newpoint_coords(self, edge, distance):
"""
- Used internally to compute new point coordinates during snapping
+ Used internally to compute new point coordinates during snapping.
"""
x1 = self.node_coords[edge[0]][0]
y1 = self.node_coords[edge[0]][1]
x2 = self.node_coords[edge[1]][0]
y2 = self.node_coords[edge[1]][1]
- if x1 == x2: # vertical line case
+ if x1 == x2: # Vertical line case
x0 = x1
if y1 < y2:
y0 = y1 + distance
elif y1 > y2:
y0 = y2 + distance
- else: # zero length edge
+ else: # Zero length edge
y0 = y1
return x0, y0
m = (y2 - y1) / (x2 - x1)
@@ -542,18 +549,18 @@ class Network:
Parameters
----------
- count : integer
- number of points to create or mean of the distribution
- if not 'uniform'
+ count: int
+ The number of points to create or mean of the distribution if not
+ 'uniform'.
- distribution : string
- {'uniform', 'poisson'} distribution of random points
+ distribution: str
+ {'uniform', 'poisson'} distribution of random points.
Returns
-------
- random_pts : dict
- key is the edge tuple
- value is a list of new point coordinates
+ random_pts: dict
+ Keys are the edge tuple.
+ Value are a list of new point coordinates.
Example
-------
@@ -567,7 +574,7 @@ class Network:
"""
simpts = SimulatedPointPattern()
- #Cumulative Network Length
+ # Cumulative Network Length.
edges = []
lengths = np.zeros(len(self.edge_lengths))
for i, key in enumerate(self.edge_lengths.iterkeys()):
@@ -585,13 +592,13 @@ class Network:
idx = np.where(r < stops)[0][0]
assignment_edge = edges[idx]
distance_from_start = stops[idx] - r
- #Populate the coordinates dict
+ # Populate the coordinates dict.
x0, y0 = self._newpoint_coords(assignment_edge, distance_from_start)
simpts.snapped_coordinates[i] = (x0, y0)
simpts.obs_to_node[assignment_edge[0]].append(i)
simpts.obs_to_node[assignment_edge[1]].append(i)
- #Populate the distance to node
+ # Populate the distance to node.
simpts.dist_to_node[i] = {assignment_edge[0] : distance_from_start,
assignment_edge[1] : self.edge_lengths[edges[idx]] - distance_from_start}
@@ -606,13 +613,13 @@ class Network:
Parameters
-----------
- v0 : int
- node id
+ v0: int
+ Node id
Returns
-------
- links : list
- list of tuple edge adjacent to the node
+ links: list
+ List of tuple edges adjacent to the node.
"""
links = []
neighbornodes = self.adjacencylist[v0]
@@ -627,36 +634,39 @@ class Network:
for node in self.node_list:
distance, pred = util.dijkstra(self, self.edge_lengths, node, n=float('inf'))
pred = np.array(pred)
- #tree = util.generatetree(pred)
+ #tree = util.generatetree(pred) <---- something to look at in the future
tree = None
self.alldistances[node] = (distance, tree)
self.distancematrix[node] = distance
- def allneighbordistances(self, sourcepattern, destpattern=None):
+ def allneighbordistances(self, sourcepattern, destpattern=None, fill_diagonal=None):
"""
- Compute either all distances between i and j in a single point pattern
- or all distances between each i from a source pattern and all j
- from a destination pattern
+ Compute either all distances between i and j in a single point pattern or all
+ distances between each i from a source pattern and all j from a destination pattern.
Parameters
----------
- sourcepattern : str
+ sourcepattern: str
The key of a point pattern snapped to the network.
- destpattern :str
- (Optional) The key of a point pattern snapped to the network.
+ destpattern: str
+ (Optional) The key of a point pattern snapped to the network.
+ fill_diagonal: float, int
+ (Optional) Fill the diagonal of the cost matrix.
+ Default in None and will populate the diagonal with numpy.nan
+ Do not declare a destpattern for a custom fill_diagonal.
+
Returns
-------
- nearest : array (n,n)
- An array or shape n,n storing distances between all points
-
+ nearest: array (n,n)
+ An array of shape (n,n) storing distances between all points.
"""
if not hasattr(self,'alldistances'):
self.node_distance_matrix()
- # source setup
+ # Source setup
src_indices = sourcepattern.points.keys()
nsource_pts = len(src_indices)
src_dist_to_node = sourcepattern.dist_to_node
@@ -665,7 +675,7 @@ class Network:
e1, e2 = src_dist_to_node[s].keys()
src_nodes[s] = (e1, e2)
- # destination setup
+ # Destination setup
symmetric = False
if destpattern is None:
symmetric = True
@@ -679,24 +689,24 @@ class Network:
e1, e2 = dest_dist_to_node[s].keys()
dest_nodes[s] = (e1, e2)
- # output setup
+ # Output setup
nearest = np.empty((nsource_pts, ndest_pts))
nearest[:] = np.inf
for p1 in src_indices:
- #Get the source nodes and dist to source nodes
+ # Get the source nodes and dist to source nodes.
source1, source2 = src_nodes[p1]
set1 = set(src_nodes[p1])
- # distance from node1 to p, distance from node2 to p
+ # Distance from node1 to p, distance from node2 to p.
sdist1, sdist2 = src_dist_to_node[p1].values()
if symmetric:
- # only compute the upper triangle if symmetric
+ # Only compute the upper triangle if symmetric.
dest_searchpts.remove(p1)
for p2 in dest_searchpts:
dest1, dest2 = dest_nodes[p2]
set2 = set(dest_nodes[p2])
- if set1 == set2: #same edge
+ if set1 == set2: # same edge
x1,y1 = sourcepattern.snapped_coordinates[p1]
x2,y2 = destpattern.snapped_coordinates[p2]
xd = x1-x2
@@ -710,16 +720,17 @@ class Network:
d12 = self.alldistances[source1][0][dest2]
d22 = self.alldistances[source2][0][dest2]
- # find shortest distance from path passing through each of two origin nodes
- # to first destination node
+ # Find the shortest distance from the path passing through each of the
+ # two origin nodes to the first destination node.
sd_1 = d11 + sdist1
sd_21 = d21 + sdist2
if sd_1 > sd_21:
sd_1 = sd_21
- # now add point to node one distance on destination edge
+ # Now add the point to node one distance on the destination edge.
len_1 = sd_1 + ddist1
- # repeat but now for paths entering at second node of second edge
+ # Repeat the prior but now for the paths entering at the second node
+ # of the second edge.
sd_2 = d12 + sdist1
sd_22 = d22 + sdist2
b = 0
@@ -728,36 +739,42 @@ class Network:
b = 1
len_2 = sd_2 + ddist2
- # now find shortest length path between the point 1 on edge 1 and
- # point 2 on edge 2, and assign
+ # Now find the shortest distance path between point 1 on edge 1 and
+ # point 2 on edge 2, and assign.
sp_12 = len_1
if len_1 > len_2:
sp_12 = len_2
nearest[p1, p2] = sp_12
if symmetric:
- # mirror the upper and lower triangle when symmetric
+ # Mirror the upper and lower triangle when symmetric.
nearest[p2,p1] = nearest[p1,p2]
+ # Populate the main diagonal when symmetric.
if symmetric:
- # populate the main diagonal when symmetric
- #np.fill_diagonal(nearest, 0)
- np.fill_diagonal(nearest, np.nan)
+ if fill_diagonal == None:
+ np.fill_diagonal(nearest, np.nan)
+ else:
+ np.fill_diagonal(nearest, fill_diagonal)
+
return nearest
def nearestneighbordistances(self, sourcepattern, destpattern=None):
"""
Compute the interpattern nearest neighbor distances or the intrapattern
- nearest neight distances between a source pattern and a destination pattern.
+ nearest neighbor distances between a source pattern and a destination pattern.
Parameters
----------
- sourcepattern str The key of a point pattern snapped to the network.
+ sourcepattern: str
+ The key of a point pattern snapped to the network.
- destpattern str (Optional) The key of a point pattern snapped to the network.
+ destpattern: str
+ (Optional) The key of a point pattern snapped to the network.
Returns
-------
- nearest ndarray (n,2) With column[:,0] containing the id of the nearest
- neighbor and column [:,1] containing the distance.
+ nearest: ndarray (n,2)
+ With column[:,0] containing the id of the nearest neighbor and
+ column [:,1] containing the distance.
"""
if not sourcepattern in self.pointpatterns.keys():
@@ -776,14 +793,13 @@ class Network:
searchpts = copy.deepcopy(pt_indices)
-
searchnodes = {}
for s in searchpts:
e1, e2 = dist_to_node[s].keys()
searchnodes[s] = (e1, e2)
for p1 in pt_indices:
- #Get the source nodes and dist to source nodes
+ # Get the source nodes and dist to source nodes.
source1, source2 = searchnodes[p1]
sdist1, sdist2 = dist_to_node[p1].values()
@@ -796,7 +812,6 @@ class Network:
source2_to_dest1 = sdist2 + self.alldistances[source2][0][dest1] + ddist1
source2_to_dest2 = sdist2 + self.alldistances[source2][0][dest2] + ddist2
-
if source1_to_dest1 < nearest[p1, 1]:
nearest[p1, 0] = p2
nearest[p1, 1] = source1_to_dest1
@@ -835,34 +850,35 @@ class Network:
Parameters
----------
- pointpattern : object
- A PySAL point pattern object
+ pointpattern: object
+ A PySAL point pattern object.
- nsteps : int
- The number of steps at which the count of the nearest
- neighbors is computed
+ nsteps: int
+ The number of steps at which the count of the nearest neighbors
+ is computed.
- permutations : int
- The number of permutations to perform (default 99)
+ permutations: int
+ The number of permutations to perform (default 99).
- threshold : float
- The level at which significance is computed. 0.5 would be 97.5% and 2.5%
+ threshold: float
+ The level at which significance is computed.
+ -- 0.5 would be 97.5% and 2.5%
- distribution : str
- The distirbution from which random points are sampled: uniform or poisson
+ distribution: str
+ The distribution from which random points are sampled:
+ -- uniform or poisson
- lowerbound : float
- The lower bound at which the G-function is computed. (default 0)
+ lowerbound: float
+ The lower bound at which the F-function is computed. (Default 0)
- upperbound : float
- The upper bound at which the G-function is computed.
- Defaults to the maximum pbserved nearest neighbor distance.
+ upperbound: float
+ The upper bound at which the F-function is computed.
+ Defaults to the maximum observed nearest neighbor distance.
Returns
-------
- NetworkF : object
- A network F class instance
-
+ NetworkF: object
+ A network F class instance.
"""
return NetworkF(self, pointpattern, nsteps=nsteps,
permutations=permutations,threshold=threshold,
@@ -877,34 +893,35 @@ class Network:
Parameters
----------
- pointpattern : object
- A PySAL point pattern object
+ pointpattern: object
+ A PySAL point pattern object.
- nsteps : int
- The number of steps at which the count of the nearest
- neighbors is computed
+ nsteps: int
+ The number of steps at which the count of the nearest neighbors
+ is computed.
- permutations : int
- The number of permutations to perform (default 99)
+ permutations: int
+ The number of permutations to perform (default 99).
- threshold : float
- The level at which significance is computed. 0.5 would be 97.5% and 2.5%
+ threshold: float
+ The level at which significance is computed.
+ -- 0.5 would be 97.5% and 2.5%
- distribution : str
- The distirbution from which random points are sampled: uniform or poisson
+ distribution: str
+ The distribution from which random points are sampled:
+ -- uniform or poisson
- lowerbound : float
- The lower bound at which the G-function is computed. (default 0)
+ lowerbound: float
+ The lower bound at which the G-function is computed. (Default 0)
- upperbound : float
- The upper bound at which the G-function is computed.
- Defaults to the maximum pbserved nearest neighbor distance.
+ upperbound: float
+ The upper bound at which the G-function is computed.
+ Defaults to the maximum observed nearest neighbor distance.
Returns
-------
- NetworkG : object
- A network G class object
-
+ NetworkG: object
+ A network G class instance.
"""
return NetworkG(self, pointpattern, nsteps=nsteps,
@@ -916,38 +933,39 @@ class Network:
threshold=0.5, distribution='uniform',
lowerbound=None, upperbound=None):
"""
- Computes a network constrained G-Function
+ Computes a network constrained K-Function
Parameters
----------
- pointpattern : object
- A PySAL point pattern object
+ pointpattern: object
+ A PySAL point pattern object.
- nsteps : int
- The number of steps at which the count of the nearest
- neighbors is computed
+ nsteps: int
+ The number of steps at which the count of the nearest neighbors
+ is computed.
- permutations : int
- The number of permutations to perform (default 99)
+ permutations: int
+ The number of permutations to perform (default 99).
- threshold : float
- The level at which significance is computed. 0.5 would be 97.5% and 2.5%
+ threshold: float
+ The level at which significance is computed.
+ -- 0.5 would be 97.5% and 2.5%
- distribution : str
- The distirbution from which random points are sampled: uniform or poisson
+ distribution: str
+ The distribution from which random points are sampled:
+ -- uniform or poisson
- lowerbound : float
- The lower bound at which the G-function is computed. (default 0)
+ lowerbound: float
+ The lower bound at which the K-function is computed. (Default 0)
- upperbound : float
- The upper bound at which the G-function is computed.
- Defaults to the maximum pbserved nearest neighbor distance.
+ upperbound: float
+ The upper bound at which the K-function is computed.
+ Defaults to the maximum observed nearest neighbor distance.
Returns
-------
- NetworkK : object
- A network K class object
-
+ NetworkK: object
+ A network K class instance.
"""
return NetworkK(self, pointpattern, nsteps=nsteps,
permutations=permutations,threshold=threshold,
@@ -956,18 +974,18 @@ class Network:
def segment_edges(self, distance):
"""
- Segment all of the edges in the network at either
- a fixed distance or a fixed number of segments.
+ Segment all of the edges in the network at either a fixed distance or a fixed
+ number of segments.
Parameters
-----------
- distance : float
- The distance at which edges are split
+ distance: float
+ The distance at which edges are split.
Returns
-------
- sn : object
- PySAL Network Object
+ sn: object
+ PySAL Network Object.
Example
-------
@@ -1000,8 +1018,7 @@ class Network:
currentstart = startnode = e[0]
endnode = e[1]
- #If the edge will be segmented, remove the
- # current edge from the adjacency list
+ # If the edge will be segmented remove the current edge from the adjacency list.
if interval < length:
sn.adjacencylist[e[0]].remove(e[1])
sn.adjacencylist[e[1]].remove(e[0])
@@ -1021,36 +1038,36 @@ class Network:
currentstop = current_node_id
totallength += interval
- #Compute the new node coordinate
+ # Compute the new node coordinate.
newx, newy = self._newpoint_coords(e, totallength)
- #Update node_list
+ # Update node_list.
if currentstop not in sn.node_list:
sn.node_list.append(currentstop)
- #Update nodes and node_coords
+ # Update nodes and node_coords.
sn.node_coords[currentstop] = newx, newy
sn.nodes[(newx, newy)] = currentstop
- #Update the adjacencylist
+ # Update the adjacency list.
sn.adjacencylist[currentstart].append(currentstop)
sn.adjacencylist[currentstop].append(currentstart)
- #Add the new edge to the edge dict
- #Iterating over this, so we need to add after iterating
+ # Add the new edge to the edge dict.
+ # Iterating over this so we need to add after iterating.
newedges.add(tuple(sorted([currentstart, currentstop])))
- #Modify edge_lengths
+ # Modify edge_lengths.
sn.edge_lengths[tuple(sorted([currentstart, currentstop]))] = interval
- #Increment the start to the stop
+ # Increment the start to the stop.
currentstart = currentstop
sn.edges.update(newedges)
sn.edges.difference_update(removeedges)
sn.edges = list(sn.edges)
- #Update the point pattern snapping
+ # Update the point pattern snapping.
for instance in sn.pointpatterns.itervalues():
sn._snap_to_edge(instance)
@@ -1062,16 +1079,14 @@ class Network:
Parameters
----------
- filename : str
- The filename where the network should be saved.
- This should be a full PATH or the file is saved
- whereever this method is called from.
+ filename: str
+ The filename where the network should be saved. This should be a full
+ path or the file is saved whereever this method is called from.
Example
--------
>>> ntw = ps.Network(ps.examples.get_path('streets.shp'))
>>> ntw.savenetwork('mynetwork.pkl')
-
"""
with open(filename, 'wb') as networkout:
cPickle.dump(self, networkout, protocol=2)
@@ -1086,33 +1101,32 @@ class Network:
class PointPattern():
"""
- A stub point pattern class used to store a point pattern.
- This class is monkey patched with network specific attributes
- when the points are snapped to a network.
+ A stub point pattern class used to store a point pattern. This class is monkey patched
+ with network specific attributes when the points are snapped to a network.
In the future this class may be replaced with a generic point
pattern class.
Parameters
----------
- shapefile : string
- input shapefile
+ shapefile: str
+ The input shapefile.
- idvariable : string
- field in the shapefile to use as an idvariable
+ idvariable: str
+ Field in the shapefile to use as an id variable.
- attribute : boolean
- {False, True} A flag to indicate whether all attributes
- are tagged to this class.
+ attribute: bool
+ {False, True}
+ A flag to indicate whether all attributes are tagged to this class.
Attributes
----------
- points : dict
- key is the point id
- value are the coordinates
+ points: dict
+ Keys are the point ids.
+ Values are the coordinates.
- npoints : integer
- the number of points
+ npoints: int
+ The number of points.
"""
@@ -1127,7 +1141,7 @@ class PointPattern():
pts = ps.open(shapefile)
- #Get attributes if requested
+ # Get attributes if requested
if attribute == True:
dbname = os.path.splitext(shapefile)[0] + '.dbf'
db = ps.open(dbname)
@@ -1158,7 +1172,6 @@ class SimulatedPointPattern():
make this a child of that class.
This class is not intended to be used by the external user.
-
"""
def __init__(self):
self.npoints = 0
diff --git a/pysal/network/tests/test_network.py b/pysal/network/tests/test_network.py
index c604d89..dbacff2 100644
--- a/pysal/network/tests/test_network.py
+++ b/pysal/network/tests/test_network.py
@@ -94,18 +94,23 @@ class TestNetworkPointPattern(unittest.TestCase):
pass
def test_all_neighbor_distances(self):
- distancematrix = self.ntw.allneighbordistances(self.schools)
- self.assertAlmostEqual(np.nansum(distancematrix[0]), 17682.436988, places=4)
+ distancematrix_1 = self.ntw.allneighbordistances(self.schools)
+ self.assertAlmostEqual(np.nansum(distancematrix_1[0]), 17682.436988, places=4)
for k, (distances, predlist) in self.ntw.alldistances.iteritems():
self.assertEqual(distances[k], 0)
- # turning off the tests associated with util.generatetree() for now,
- # these can be restarted if that functionality is used in the future
+ # turning off the tests associated with util.generatetree() for now,
+ # these can be restarted if that functionality is used in the future
#for p, plists in predlist.iteritems():
# self.assertEqual(plists[-1], k)
#self.assertEqual(self.ntw.node_list, predlist.keys())
+
+ distancematrix_2 = self.ntw.allneighbordistances(self.schools, fill_diagonal=0.)
+ observed = distancematrix_2.diagonal()
+ known = np.zeros(distancematrix_2.shape[0])
+ np.testing.assert_equal(observed, known)
def test_nearest_neighbor_distances(self):
@@ -131,5 +136,5 @@ class TestNetworkUtils(unittest.TestCase):
self.assertAlmostEqual(self.distance[196], 5505.668247, places=4)
self.assertEqual(self.pred[196], 133)
-if __name__ == 'main':
+if __name__ == '__main__':
unittest.main()
diff --git a/pysal/network/util.py b/pysal/network/util.py
index 696dad8..d4c78e8 100644
--- a/pysal/network/util.py
+++ b/pysal/network/util.py
@@ -5,15 +5,15 @@ import pysal as ps
import numpy as np
-
def compute_length(v0, v1):
"""
Compute the euclidean distance between two points.
Parameters
----------
- v0 sequence in the form x, y
- vq sequence in the form x, y
+ v0: sequence in the form x, y
+
+ vq: sequence in the form x, y
Returns
--------
@@ -51,20 +51,35 @@ def generatetree(pred):
tree[i] = path
return tree
+
def dijkstra(ntw, cost, node, n=float('inf')):
"""
- Compute the shortest path between a start node and
- all other nodes in the wed.
+ Compute the shortest path between a start node and all other nodes in the web.
+
Parameters
----------
- ntw: PySAL network object
- cost: Cost per edge to travel, e.g. distance
- node: Start node ID
- n: integer break point to stop iteration and return n
- neighbors
- Returns:
- distance: List of distances from node to all other nodes
- pred : List of preceeding nodes for traversal route
+ ntw: object
+ PySAL network object
+
+ cost: dict
+ key: tuple
+ (start node, end node)
+ value: float
+ Cost per edge to travel, e.g. distance
+
+ node: int
+ Start node ID
+
+ n: float('inf')
+ integer break point to stop iteration and return n neighbors
+
+ Returns
+ -------
+ distance: list
+ List of distances from node to all other nodes.
+
+ pred: list
+ List of preceeding nodes for traversal route.
"""
v0 = node
@@ -75,16 +90,16 @@ def dijkstra(ntw, cost, node, n=float('inf')):
a = set()
a.add(v0)
while len(a) > 0:
- #Get node with the lowest value from distance
+ # Get node with the lowest value from distance.
dist = float('inf')
for node in a:
if distance[node] < dist:
dist = distance[node]
v = node
- #Remove that node from the set
+ # Remove that node from the set.
a.remove(v)
last = v
- #4. Get the neighbors to the current node
+ #4. Get the neighbors to the current node.
neighbors = get_neighbor_distances(ntw, v, cost)
for v1, indiv_cost in neighbors.iteritems():
if distance[v1] > distance[v] + indiv_cost:
@@ -97,22 +112,19 @@ def dijkstra(ntw, cost, node, n=float('inf')):
def squaredDistancePointSegment(point, segment):
"""Find the squared distance between a point and a segment
- Arguments
- =========
-
- point: tuple (x,y)
+ Parameters
+ ---------
+ point: tuple
+ (x,y)
- segment: list of 2 tuples [(x0,y0), (x1,y1)]
+ segment: list
+ List of 2 tuples [(x0,y0), (x1,y1)]
Returns
- =======
-
- tuple: 2 elements
-
- distance squared between point and segment
-
- array(xb, yb): the nearest point on the segment
-
+ -------
+ tuple: 2 elements:
+ 1. distance squared between point and segment
+ 2. array(xb, yb): the nearest point on the segment
"""
p0,p1 = [np.array(p) for p in segment]
v = p1 - p0
@@ -120,12 +132,12 @@ def squaredDistancePointSegment(point, segment):
w = p - p0
c1 = np.dot(w,v)
if c1 <= 0.:
- # print 'before p0'
+ # Print 'before p0'
return np.dot(w.T,w), p0
c2 = np.dot(v,v)
if c2 <= c1:
dp1 = p - p1
- # print 'after p1'
+ # Print 'after p1'
return np.dot(dp1.T,dp1), p1
b = c1 / c2
@@ -134,34 +146,33 @@ def squaredDistancePointSegment(point, segment):
d2 = p - pb
return np.dot(d2,d2), pb
-
-
+
def snapPointsOnSegments(points, segments):
"""Place points onto closet segment in a set of segments
Arguments
- =========
-
- points: dict
- with point id as key and (x,y) coordinate as value
-
- segments: list
- elements are of type pysal.cg.shapes.Chain
- Note that the each element is a segment represented as a chain with *one head and one tail node*, in other words one link only.
+ ---------
+ points: dict
+ Point id as key and (x,y) coordinate as value
+
+ segments: list
+ Elements are of type pysal.cg.shapes.Chain
+ ** Note **
+ each element is a segment represented as a chain with
+ *one head and one tail node*, in other words one link only.
Returns
- =======
-
- p2s: dictionary
- key: point id (see points in arguments)
+ -------
+ p2s: dict
+ key: point id (see points in arguments)
- value: a 2-tuple: ((head, tail), point)
- where (head, tail) is the target segment, and point is the snapped location on the segment
-
+ value: a 2-tuple: ((head, tail), point)
+ where (head, tail) is the target segment, and point is the snapped
+ location on the segment
"""
- # Put segments in an Rtree
+ # Put segments in an Rtree.
rt = ps.cg.Rtree()
SMALL = 0.01
node2segs = {}
@@ -183,34 +194,32 @@ def snapPointsOnSegments(points, segments):
y1 += SMALL
r = ps.cg.Rect(x0,y0,x1,y1)
rt.insert(segment, r)
-
-
-
- # Build a KDtree on segment nodes
+
+ # Build a KDtree on segment nodes.
kt = ps.cg.KDTree(node2segs.keys())
p2s = {}
for ptIdx, point in points.iteritems():
- # first find nearest neighbor segment node for point
+ # First, find nearest neighbor segment node for the point.
dmin, node = kt.query(point, k=1)
node = tuple(kt.data[node])
closest = node2segs[node][0].vertices
- # use this segment as the candidate closest segment: closest
- # use the distance as the distance to beat: dmin
+ # Use this segment as the candidate closest segment: closest
+ # Use the distance as the distance to beat: dmin
p2s[ptIdx] = (closest, node) # sna
x0 = point[0] - dmin
y0 = point[1] - dmin
x1 = point[0] + dmin
y1 = point[1] + dmin
- # find all segments with bounding boxes that intersect
- # a query rectangle centered on the point with sides of length 2*dmin
+ # Find all segments with bounding boxes that intersect
+ # a query rectangle centered on the point with sides of length 2*dmin.
candidates = [ cand for cand in rt.intersection([x0,y0,x1,y1])]
dmin += SMALL
dmin2 = dmin * dmin
- # of the candidate segments, find the one that is the minimum distance to the query point
+ # Of the candidate segments, find the nearest to the query point.
for candidate in candidates:
dnc, p2b = squaredDistancePointSegment(point, candidate.vertices)
if dnc <= dmin2:
@@ -218,5 +227,4 @@ def snapPointsOnSegments(points, segments):
dmin2 = dnc
p2s[ptIdx] = (closest, p2b)
- return p2s
-
+ return p2s
\ No newline at end of file
diff --git a/pysal/spatial_dynamics/markov.py b/pysal/spatial_dynamics/markov.py
index f415340..48c3e86 100644
--- a/pysal/spatial_dynamics/markov.py
+++ b/pysal/spatial_dynamics/markov.py
@@ -1,3 +1,6 @@
+
+from __future__ import print_function
+
"""
Markov based methods for spatial dynamics.
"""
@@ -7,11 +10,11 @@ __all__ = ["Markov", "LISA_Markov", "Spatial_Markov", "kullback",
"prais", "shorrock", "homogeneity"]
import numpy as np
-import numpy.linalg as la
-from pysal.spatial_dynamics.ergodic import fmpt, steady_state
+from pysal.spatial_dynamics.ergodic import fmpt
+from pysal.spatial_dynamics.ergodic import steady_state as STEADY_STATE
from scipy import stats
-import pysal
from operator import gt
+import pysal
# TT predefine LISA transitions
# TT[i,j] is the transition type from i to j
@@ -46,35 +49,36 @@ for i, sig_key in enumerate(sig_keys):
c += 1
-class Markov:
+class Markov(object):
"""
Classic Markov transition matrices.
Parameters
----------
- class_ids : array
- (n, t), one row per observation, one column recording the
- state of each observation, with as many columns as time
+ class_ids : array
+ (n, t), one row per observation, one column recording the
+ state of each observation, with as many columns as time
periods.
- classes : array
+ classes : array
(k, 1), all different classes (bins) of the matrix.
Attributes
----------
- p : matrix
+ p : matrix
(k, k), transition probability matrix.
- steady_state : matrix
+ steady_state : matrix
(k, 1), ergodic distribution.
- transitions : matrix
+ transitions : matrix
(k, k), count of transitions between each state i and j.
Examples
--------
- >>> c = np.array([['b','a','c'],['c','c','a'],['c','b','c'],['a','a','b'],['a','b','c']])
+ >>> c = [['b','a','c'],['c','c','a'],['c','b','c']]
+ >>> c.extend([['a','a','b'], ['a','b','c']])
+ >>> c = np.array(c)
>>> m = Markov(c)
- >>> m.classes
- array(['a', 'b', 'c'],
- dtype='|S1')
+ >>> m.classes.tolist()
+ ['a', 'b', 'c']
>>> m.p
matrix([[ 0.25 , 0.5 , 0.25 ],
[ 0.33333333, 0. , 0.66666667],
@@ -134,9 +138,8 @@ class Markov:
[ 0.17776576]])
"""
- def __init__(self, class_ids, classes=[]):
- #pylint; Dangerous default value [] as argument
- if len(classes):
+ def __init__(self, class_ids, classes=None):
+ if classes is not None:
self.classes = classes
else:
self.classes = np.unique(class_ids)
@@ -164,31 +167,28 @@ class Markov:
p = np.dot(np.diag(1 / (row_sum + (row_sum == 0))), transitions)
self.p = np.matrix(p)
- # steady_state vector
- v, d = la.eig(np.transpose(self.p))
- # for a regular P maximum eigenvalue will be 1
- mv = max(v)
- # find its position
- i = v.tolist().index(mv)
- # normalize eigenvector corresponding to the eigenvalue 1
- self.steady_state = d[:, i] / sum(d[:, i])
+ @property
+ def steady_state(self):
+ if not hasattr(self, '_steady_state'):
+ self._steady_state = STEADY_STATE(self.p)
+ return self._steady_state
-class Spatial_Markov:
+class Spatial_Markov(object):
"""
Markov transitions conditioned on the value of the spatial lag.
Parameters
----------
- y : array
- (n,t), one row per observation, one column per state of
+ y : array
+ (n,t), one row per observation, one column per state of
each observation, with as many columns as time periods.
w : W
spatial weights object.
k : integer
number of classes (quantiles).
permutations : int, optional
- number of permutations for use in randomization based
+ number of permutations for use in randomization based
inference (the default is 0).
fixed : bool
If true, quantiles are taken over the entire n*t
@@ -199,40 +199,42 @@ class Spatial_Markov:
Attributes
----------
- p : matrix
- (k, k), transition probability matrix for a-spatial Markov.
- s : matrix
+ p : matrix
+ (k, k), transition probability matrix for a-spatial
+ Markov.
+ s : matrix
(k, 1), ergodic distribution for a-spatial Markov.
- transitions : matrix
+ transitions : matrix
(k, k), counts of transitions between each state i and j
for a-spatial Markov.
- T : matrix
- (k, k, k), counts of transitions for each conditional Markov.
- T[0] is the matrix of transitions for observations with
- lags in the 0th quantile; T[k-1] is the transitions for
- the observations with lags in the k-1th.
+ T : matrix
+ (k, k, k), counts of transitions for each conditional
+ Markov. T[0] is the matrix of transitions for
+ observations with lags in the 0th quantile; T[k-1] is the
+ transitions for the observations with lags in the k-1th.
P : matrix
- (k, k, k), transition probability matrix for spatial Markov
- first dimension is the conditioned on the lag.
+ (k, k, k), transition probability matrix for spatial
+ Markov first dimension is the conditioned on the lag.
S : matrix
(k, k), steady state distributions for spatial Markov.
Each row is a conditional steady_state.
F : matrix
(k, k, k),first mean passage times.
First dimension is conditioned on the lag.
- shtest : list
- (k elements), each element of the list is a tuple for a
- multinomial difference test between the steady state
- distribution from a conditional distribution versus the
- overall steady state distribution: first element of the
- tuple is the chi2 value, second its p-value and the third
+ shtest : list
+ (k elements), each element of the list is a tuple for a
+ multinomial difference test between the steady state
+ distribution from a conditional distribution versus the
+ overall steady state distribution: first element of the
+ tuple is the chi2 value, second its p-value and the third
the degrees of freedom.
- chi2 : list
- (k elements), each element of the list is a tuple for a
- chi-squared test of the difference between the conditional
- transition matrix against the overall transition matrix:
- first element of the tuple is the chi2 value, second its
- p-value and the third the degrees of freedom.
+ chi2 : list
+ (k elements), each element of the list is a tuple for a
+ chi-squared test of the difference between the
+ conditional transition matrix against the overall
+ transition matrix: first element of the tuple is the chi2
+ value, second its p-value and the third the degrees of
+ freedom.
x2 : float
sum of the chi2 values for each of the conditional tests.
Has an asymptotic chi2 distribution with k(k-1)(k-1)
@@ -244,12 +246,12 @@ class Spatial_Markov:
x2_pvalue : float
pvalue for homogeneity test based on analytic.
distribution
- x2_rpvalue : float
+ x2_rpvalue : float
(if permutations>0)
- pseudo p-value for x2 based on random spatial permutations
- of the rows of the original transitions.
- x2_realizations : array
- (permutations,1), the values of x2 for the random
+ pseudo p-value for x2 based on random spatial
+ permutations of the rows of the original transitions.
+ x2_realizations : array
+ (permutations,1), the values of x2 for the random
permutations.
Q : float
Chi-square test of homogeneity across lag classes based
@@ -258,7 +260,8 @@ class Spatial_Markov:
p-value for Q.
LR : float
Likelihood ratio statistic for homogeneity across lag
- classes based on Bickenback and Bode (2003) [Bickenbach2003]_.
+ classes based on Bickenback and Bode (2003)
+ [Bickenbach2003]_.
LR_p_value : float
p-value for LR.
dof_hom : int
@@ -273,7 +276,7 @@ class Spatial_Markov:
preferable since it simulates the randomness under the null. It is an
experimental test requiring further analysis.
- This is new
+ This is new
Examples
--------
@@ -286,7 +289,7 @@ class Spatial_Markov:
>>> w.transform = 'r'
>>> sm = ps.Spatial_Markov(rpci, w, fixed=True, k=5, variable_name='rpci')
>>> for p in sm.P:
- ... print p
+ ... print(p)
...
[[ 0.96341463 0.0304878 0.00609756 0. 0. ]
[ 0.06040268 0.83221477 0.10738255 0. 0. ]
@@ -356,7 +359,7 @@ class Spatial_Markov:
fourth quintile after 28.473 years.
>>> for f in sm.F:
- ... print f
+ ... print(f)
...
[[ 2.29835259 28.95614035 46.14285714 80.80952381 279.42857143]
[ 33.86549708 3.79459555 22.57142857 57.23809524 255.85714286]
@@ -384,15 +387,15 @@ class Spatial_Markov:
[ 127.1407767 48.74107143 33.29605263 3.91777427 83.52173913]
[ 169.6407767 91.24107143 75.79605263 42.5 2.96521739]]
-
+
"""
def __init__(self, y, w, k=4, permutations=0, fixed=False,
variable_name=None):
self.y = y
rows, cols = y.shape
+ self.k = k
self.cols = cols
- npm = np.matrix
npa = np.array
self.fixed = fixed
self.variable_name = variable_name
@@ -407,38 +410,17 @@ class Spatial_Markov:
classic = Markov(classes)
self.classes = classes
self.p = classic.p
- self.s = classic.steady_state
self.transitions = classic.transitions
- T, P, ss, F = self._calc(y, w, classes, k=k)
+ T, P = self._calc(y, w, classes, k=k)
self.T = T
self.P = P
- self.S = ss
- self.F = F
- self.shtest = self._mn_test()
- self.chi2 = self._chi2_test()
- self.x2 = sum([c[0] for c in self.chi2])
- dof = k * (k - 1) * (k - 1)
- self.x2_pvalue = 1 - stats.chi2.cdf(self.x2, dof)
- self.x2_dof = dof
- self.k = k
-
- # bickenbach and bode tests
- ht = homogeneity(self.T)
- self.Q = ht.Q
- self.Q_p_value = ht.Q_p_value
- self.LR = ht.LR
- self.LR_p_value = ht.LR_p_value
- self.dof_hom = ht.dof
-
if permutations:
nrp = np.random.permutation
- rp = range(permutations)
counter = 0
x2_realizations = np.zeros((permutations, 1))
- x2ss = []
for perm in range(permutations):
- T, P, ss, F = self._calc(nrp(y), w, classes, k=k)
+ T, P = self._calc(nrp(y), w, classes, k=k)
x2 = [chi2(T[i], self.transitions)[0] for i in range(k)]
x2s = sum(x2)
x2_realizations[perm] = x2s
@@ -447,10 +429,97 @@ class Spatial_Markov:
self.x2_rpvalue = (counter + 1.0) / (permutations + 1.)
self.x2_realizations = x2_realizations
+ @property
+ def s(self):
+ if not hasattr(self, '_s'):
+ self._s = STEADY_STATE(self.p)
+ return self._s
+
+ @property
+ def S(self):
+ if not hasattr(self, '_S'):
+ S = np.zeros_like(self.p)
+ for i, p in enumerate(self.P):
+ S[i] = STEADY_STATE(p)
+ self._S = np.asarray(S)
+ return self._S
+
+ @property
+ def F(self):
+ if not hasattr(self, '_F'):
+ F = np.zeros_like(self.P)
+ for i, p in enumerate(self.P):
+ F[i] = fmpt(np.asmatrix(p))
+ self._F = np.asarray(F)
+ return self._F
+
+ # bickenbach and bode tests
+ @property
+ def ht(self):
+ if not hasattr(self, '_ht'):
+ self._ht = homogeneity(self.T)
+ return self._ht
+
+ @property
+ def Q(self):
+ if not hasattr(self, '_Q'):
+ self._Q = self.ht.Q
+ return self._Q
+
+ @property
+ def Q_p_value(self):
+ self._Q_p_value = self.ht.Q_p_value
+ return self._Q_p_value
+
+ @property
+ def LR(self):
+ self._LR = self.ht.LR
+ return self._LR
+
+ @property
+ def LR_p_value(self):
+ self._LR_p_value = self.ht.LR_p_value
+ return self._LR_p_value
+
+ @property
+ def dof_hom(self):
+ self._dof_hom = self.ht.dof
+ return self._dof_hom
+
+ # shtests
+ @property
+ def shtest(self):
+ if not hasattr(self, '_shtest'):
+ self._shtest = self._mn_test()
+ return self._shtest
+
+ @property
+ def chi2(self):
+ if not hasattr(self, '_chi2'):
+ self._chi2 = self._chi2_test()
+ return self._chi2
+
+ @property
+ def x2(self):
+ if not hasattr(self, '_x2'):
+ self._x2 = sum([c[0] for c in self.chi2])
+ return self._x2
+
+ @property
+ def x2_pvalue(self):
+ if not hasattr(self, '_x2_pvalue'):
+ self._x2_pvalue = 1 - stats.chi2.cdf(self.x2, self.x2_dof)
+ return self._x2_pvalue
+
+ @property
+ def x2_dof(self):
+ if not hasattr(self, '_x2_dof'):
+ k = self.k
+ self._x2_dof = k * (k - 1) * (k - 1)
+ return self._x2_dof
+
def _calc(self, y, w, classes, k):
- # lag markov
ly = pysal.lag_spatial(w, y)
- npm = np.matrix
npa = np.array
if self.fixed:
l_classes = pysal.Quantiles(ly.flatten(), k=k).yb
@@ -459,7 +528,6 @@ class Spatial_Markov:
l_classes = npa([pysal.Quantiles(
ly[:, i], k=k).yb for i in np.arange(self.cols)])
l_classes = l_classes.transpose()
- l_classic = Markov(l_classes)
T = np.zeros((k, k, k))
n, t = y.shape
for t1 in range(t - 1):
@@ -468,23 +536,12 @@ class Spatial_Markov:
T[l_classes[i, t1], classes[i, t1], classes[i, t2]] += 1
P = np.zeros_like(T)
- F = np.zeros_like(T) # fmpt
- ss = np.zeros_like(T[0])
for i, mat in enumerate(T):
row_sum = mat.sum(axis=1)
row_sum = row_sum + (row_sum == 0)
p_i = np.matrix(np.diag(1. / row_sum) * np.matrix(mat))
- #print i
- #print mat
- #print p_i
- ss[i] = steady_state(p_i).transpose()
- try:
- F[i] = fmpt(p_i)
- except:
- #pylint; "No exception type(s) specified"
- print "Singlular fmpt matrix for class ", i
P[i] = p_i
- return T, P, ss, F
+ return T, P
def _mn_test(self):
"""
@@ -492,7 +549,6 @@ class Spatial_Markov:
distributions from the conditional and overall distributions.
"""
n, t = self.y.shape
- nt = n * (t - 1)
n0, n1, n2 = self.T.shape
rn = range(n0)
mat = [self._ssmnp_test(
@@ -505,19 +561,19 @@ class Spatial_Markov:
Arguments
---------
- p1 : array
+ p1 : array
(k, 1), first steady state probability distribution.
- p1 : array
+ p1 : array
(k, 1), second steady state probability distribution.
nt : int
number of transitions to base the test on.
Returns
-------
- tuple
+ tuple
(3 elements)
(chi2 value, pvalue, degrees of freedom)
-
+
"""
p1 = np.array(p1)
k, c = p1.shape
@@ -542,10 +598,10 @@ class Spatial_Markov:
return mat
def summary(self, file_name=None):
- class_names = ["C%d"%i for i in range(self.k)]
- regime_names = ["LAG%d"%i for i in range(self.k)]
+ class_names = ["C%d" % i for i in range(self.k)]
+ regime_names = ["LAG%d" % i for i in range(self.k)]
ht = homogeneity(self.T, class_names=class_names,
- regime_names=regime_names)
+ regime_names=regime_names)
title = "Spatial Markov Test"
if self.variable_name:
title = title + ": " + self.variable_name
@@ -554,21 +610,22 @@ class Spatial_Markov:
else:
ht.summary(title=title)
+
def chi2(T1, T2):
"""
chi-squared test of difference between two transition matrices.
Parameters
----------
- T1 : matrix
+ T1 : matrix
(k, k), matrix of transitions (counts).
- T2 : matrix
- (k, k), matrix of transitions (counts) to use to form the
+ T2 : matrix
+ (k, k), matrix of transitions (counts) to use to form the
probabilities under the null.
-
+
Returns
-------
- : tuple
+ : tuple
(3 elements).
(chi2 value, pvalue, degrees of freedom).
@@ -576,7 +633,8 @@ def chi2(T1, T2):
--------
>>> import pysal
>>> f = pysal.open(pysal.examples.get_path("usjoin.csv"))
- >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)]).transpose()
+ >>> years = range(1929, 2010)
+ >>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
>>> rpci = pci/(pci.mean(axis=0))
>>> w = pysal.open(pysal.examples.get_path("states48.gal")).read()
>>> w.transform='r'
@@ -635,39 +693,39 @@ class LISA_Markov(Markov):
Parameters
----------
- y : array
- (n, t), n cross-sectional units observed over t time
+ y : array
+ (n, t), n cross-sectional units observed over t time
periods.
w : W
spatial weights object.
permutations : int, optional
- number of permutations used to determine LISA
- significance (the default is 0).
+ number of permutations used to determine LISA
+ significance (the default is 0).
significance_level : float, optional
- significance level (two-sided) for filtering
- significant LISA endpoints in a transition (the
+ significance level (two-sided) for filtering
+ significant LISA endpoints in a transition (the
default is 0.05).
- geoda_quads : bool
+ geoda_quads : bool
If True use GeoDa scheme: HH=1, LL=2, LH=3, HL=4.
- If False use PySAL Scheme: HH=1, LH=2, LL=3, HL=4.
- (the default is False).
+ If False use PySAL Scheme: HH=1, LH=2, LL=3, HL=4.
+ (the default is False).
Attributes
----------
- chi_2 : tuple
+ chi_2 : tuple
(3 elements)
- (chi square test statistic, p-value, degrees of freedom)
- for test that dynamics of y are independent of dynamics of wy.
- classes : array
+ (chi square test statistic, p-value, degrees of freedom) for
+ test that dynamics of y are independent of dynamics of wy.
+ classes : array
(4, 1)
- 1=HH, 2=LH, 3=LL, 4=HL (own, lag)
+ 1=HH, 2=LH, 3=LL, 4=HL (own, lag)
1=HH, 2=LL, 3=LH, 4=HL (own, lag) (if geoda_quads=True)
- expected_t : array
- (4, 4), expected number of transitions under the null that
+ expected_t : array
+ (4, 4), expected number of transitions under the null that
dynamics of y are independent of dynamics of wy.
- move_types : matrix
- (n, t-1), integer values indicating which type of LISA
- transition occurred (q1 is quadrant in period 1, q2 is
+ move_types : matrix
+ (n, t-1), integer values indicating which type of LISA
+ transition occurred (q1 is quadrant in period 1, q2 is
quadrant in period 2).
.. Table:: Move Types
@@ -693,14 +751,16 @@ class LISA_Markov(Markov):
4 4 16
== == ========
- p : matrix
+ p : matrix
(k, k), transition probability matrix.
p_values : matrix
- (n, t), LISA p-values for each end point (if permutations > 0).
+ (n, t), LISA p-values for each end point (if permutations >
+ 0).
significant_moves : matrix
- (n, t-1), integer values indicating the type and
- significance of a LISA transition. st = 1 if significant
- in period t, else st=0 (if permutations > 0).
+ (n, t-1), integer values indicating the type and
+ significance of a LISA transition. st = 1 if
+ significant in period t, else st=0 (if permutations >
+ 0).
.. Table:: Significant Moves
@@ -753,13 +813,13 @@ class LISA_Markov(Markov):
4 4 0 0 64
== == == == =========
- steady_state : matrix
+ steady_state : matrix
(k, 1), ergodic distribution.
- transitions : matrix
+ transitions : matrix
(4, 4), count of transitions between each state i and j.
- spillover : array
- (n, 1) binary array, locations that were not part of a
- cluster in period 1 but joined a prexisting cluster in
+ spillover : array
+ (n, 1) binary array, locations that were not part of a
+ cluster in period 1 but joined a prexisting cluster in
period 2.
Examples
@@ -767,7 +827,8 @@ class LISA_Markov(Markov):
>>> import pysal as ps
>>> import numpy as np
>>> f = ps.open(ps.examples.get_path("usjoin.csv"))
- >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)]).transpose()
+ >>> years = range(1929, 2010)
+ >>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
>>> w = ps.open(ps.examples.get_path("states48.gal")).read()
>>> lm = ps.LISA_Markov(pci,w)
>>> lm.classes
@@ -791,28 +852,21 @@ class LISA_Markov(Markov):
[ 0.07481752, 0.85766423, 0.06569343, 0.00182482],
[ 0.00333333, 0.02266667, 0.948 , 0.026 ],
[ 0.04815409, 0.00160514, 0.06420546, 0.88603531]])
- >>> lm.move_types
- array([[11, 11, 11, ..., 11, 11, 11],
- [ 6, 6, 6, ..., 6, 7, 11],
- [11, 11, 11, ..., 11, 11, 11],
- ...,
- [ 6, 6, 6, ..., 6, 6, 6],
- [ 1, 1, 1, ..., 6, 6, 6],
- [16, 16, 16, ..., 16, 16, 16]])
+ >>> lm.move_types[0,:3]
+ array([11, 11, 11])
+ >>> lm.move_types[0,-3:]
+ array([11, 11, 11])
Now consider only moves with one, or both, of the LISA end points being
significant
>>> np.random.seed(10)
>>> lm_random = pysal.LISA_Markov(pci, w, permutations=99)
- >>> lm_random.significant_moves
- array([[11, 11, 11, ..., 59, 59, 59],
- [54, 54, 54, ..., 54, 55, 59],
- [11, 11, 11, ..., 27, 59, 59],
- ...,
- [54, 54, 54, ..., 54, 54, 54],
- [49, 49, 49, ..., 54, 54, 54],
- [64, 64, 64, ..., 64, 64, 64]])
+ >>> lm_random.significant_moves[0, :3]
+ array([11, 11, 11])
+ >>> lm_random.significant_moves[0,-3:]
+ array([59, 43, 27])
+
Any value less than 49 indicates at least one of the LISA end points was
significant. So for example, the first spatial unit experienced a
@@ -824,7 +878,7 @@ class LISA_Markov(Markov):
Test whether the moves of y are independent of the moves of wy
>>> "Chi2: %8.3f, p: %5.2f, dof: %d" % lm.chi_2
- 'Chi2: 1059.678, p: 0.00, dof: 9'
+ 'Chi2: 1058.208, p: 0.00, dof: 9'
Actual transitions of LISAs
@@ -866,13 +920,8 @@ class LISA_Markov(Markov):
y = y.transpose()
pml = pysal.Moran_Local
gq = geoda_quads
-
- #################################################################
- # have to optimize conditional spatial permutations over a
- # time series - this is a place holder for the foreclosure paper
- ml = [pml(yi, w, permutations=permutations, geoda_quads=gq) for yi in y]
- #################################################################
-
+ ml = ([pml(yi, w, permutations=permutations, geoda_quads=gq)
+ for yi in y])
q = np.array([mli.q for mli in ml]).transpose()
classes = np.arange(1, 5) # no guarantee all 4 quadrants are visited
Markov.__init__(self, q, classes)
@@ -934,68 +983,69 @@ class LISA_Markov(Markov):
Parameters
----------
quadrant : int
- which quadrant in the scatterplot should form the core of a
- cluster.
+ which quadrant in the scatterplot should form the core
+ of a cluster.
neighbors_on : binary
If false, then only the 1st order neighbors of a core
location are included in the cluster.
- If true, neighbors of cluster core 1st order neighbors are
- included in the cluster.
+ If true, neighbors of cluster core 1st order neighbors
+ are included in the cluster.
Returns
-------
- results : dictionary
+ results : dictionary
two keys - values pairs:
'components' - array (n, t)
values are integer ids (starting at 1) indicating which
component/cluster observation i in period t belonged to.
'spillover' - array (n, t-1)
- binary values indicating if the location was a spill-over
- location that became a new member of a previously existing
- cluster.
+ binary values indicating if the location was a
+ spill-over location that became a new member of a
+ previously existing cluster.
Examples
--------
>>> f = pysal.open(pysal.examples.get_path("usjoin.csv"))
- >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)]).transpose()
+ >>> years = range(1929, 2010)
+ >>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
>>> w = pysal.open(pysal.examples.get_path("states48.gal")).read()
>>> np.random.seed(10)
>>> lm_random = pysal.LISA_Markov(pci, w, permutations=99)
>>> r = lm_random.spillover()
>>> r['components'][:,12]
- array([ 0., 1., 0., 1., 0., 2., 2., 0., 0., 0., 0., 0., 0.,
- 0., 0., 0., 0., 2., 2., 0., 0., 0., 0., 0., 0., 1.,
- 2., 2., 0., 2., 0., 0., 0., 0., 1., 2., 2., 0., 0.,
- 0., 0., 0., 2., 0., 0., 0., 0., 0.])
- >>> r['components'][:,13]
+ array([ 0., 0., 0., 2., 0., 1., 1., 0., 0., 2., 0., 0., 0.,
+ 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 2.,
+ 1., 1., 0., 1., 0., 0., 1., 0., 2., 1., 1., 0., 0.,
+ 0., 0., 0., 1., 0., 2., 1., 0., 0.])
+ >>> r['components'][:,14]
array([ 0., 2., 0., 2., 0., 1., 1., 0., 0., 2., 0., 0., 0.,
0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 2.,
- 0., 1., 0., 1., 0., 0., 0., 0., 2., 1., 1., 0., 0.,
- 0., 0., 2., 1., 0., 2., 0., 0., 0.])
+ 0., 1., 0., 1., 0., 0., 1., 0., 2., 1., 1., 0., 0.,
+ 0., 0., 0., 1., 0., 2., 1., 0., 0.])
>>> r['spill_over'][:,12]
- array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,
- 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
- 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
- 0., 0., 1., 0., 0., 1., 0., 0., 0.])
+ array([ 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
+ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,
+ 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
+ 0., 0., 1., 0., 0., 0., 0., 0., 1.])
Including neighbors of core neighbors
>>> rn = lm_random.spillover(neighbors_on=True)
>>> rn['components'][:,12]
- array([ 0., 2., 0., 2., 2., 1., 1., 0., 0., 2., 0., 0., 0.,
- 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0., 2.,
- 1., 1., 2., 1., 0., 0., 1., 0., 2., 1., 1., 0., 0.,
- 0., 0., 2., 1., 1., 2., 1., 0., 0.])
- >>> rn["components"][:,13]
- array([ 0., 2., 0., 2., 2., 1., 1., 0., 0., 2., 0., 0., 0.,
- 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 2., 0., 2.,
- 1., 1., 2., 1., 0., 0., 1., 0., 2., 1., 1., 0., 0.,
+ array([ 0., 2., 0., 2., 0., 1., 1., 0., 0., 2., 0., 1., 0.,
+ 0., 1., 0., 1., 1., 1., 1., 0., 0., 0., 2., 0., 2.,
+ 1., 1., 0., 1., 0., 0., 1., 0., 2., 1., 1., 0., 0.,
0., 0., 2., 1., 1., 2., 1., 0., 2.])
+ >>> rn["components"][:,13]
+ array([ 0., 2., 0., 2., 2., 1., 1., 0., 0., 2., 0., 1., 0.,
+ 2., 1., 0., 1., 1., 1., 1., 0., 0., 0., 2., 2., 2.,
+ 1., 1., 2., 1., 0., 2., 1., 2., 2., 1., 1., 0., 2.,
+ 0., 2., 2., 1., 1., 2., 1., 0., 2.])
>>> rn["spill_over"][:,12]
- array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
- 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,
- 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
- 0., 0., 0., 0., 0., 0., 0., 0., 1.])
+ array([ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
+ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,
+ 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1.,
+ 0., 1., 0., 0., 0., 0., 0., 0., 0.])
"""
n, k = self.q.shape
@@ -1004,8 +1054,8 @@ class LISA_Markov(Markov):
components = np.zeros((n, k))
i2id = {} # handle string keys
for key in self.w.neighbors.keys():
- id = self.w.id2i[key] # pylint "redefining built-in 'id'
- i2id[id] = key
+ idx = self.w.id2i[key]
+ i2id[idx] = key
sig_lisas = (self.q == quadrant) \
* (self.p_values <= self.significance_level)
sig_ids = [np.nonzero(
@@ -1045,8 +1095,8 @@ class LISA_Markov(Markov):
for j in new_ids:
# find j's component in period 2
cj = [c for c in components2 if j in c][0]
- # for members of j's component in period 2, check if they belonged to
- # any components in period 1
+ # for members of j's component in period 2, check if they
+ # belonged to any components in period 1
for i in cj:
if i in c1:
spill_ids.append(j)
@@ -1073,18 +1123,23 @@ def kullback(F):
Parameters
----------
- F : array
+ F : array
(s, r, r), values are transitions (not probabilities) for
s strata, r initial states, r terminal states.
Returns
-------
- Results : dictionary
+ Results : dictionary
(key - value)
- Conditional homogeneity - (float) test statistic for homogeneity of
- transition probabilities across strata.
- Conditional homogeneity pvalue - (float) p-value for test statistic.
- Conditional homogeneity dof - (int) degrees of freedom = r(s-1)(r-1).
+
+ Conditional homogeneity - (float) test statistic for homogeneity
+ of transition probabilities across strata.
+
+ Conditional homogeneity pvalue - (float) p-value for test
+ statistic.
+
+ Conditional homogeneity dof - (int) degrees of freedom =
+ r(s-1)(r-1).
Notes
-----
@@ -1139,9 +1194,6 @@ def kullback(F):
FIJd1 = FIJd + (FIJd == 0)
T4 = 2 * (FIJd * np.log(FIJd1)).sum()
- FIdd = F.sum(axis=1).sum(axis=1)
- T5 = 2 * (FIdd * np.log(FIdd)).sum()
-
T6 = F.sum()
T6 = 2 * T6 * np.log(T6)
@@ -1256,17 +1308,18 @@ def shorrock(pmat):
sh = (k - t) / (k - 1)
return sh
-def homogeneity(transition_matrices, regime_names=[], class_names=[], \
- title="Markov Homogeneity Test"):
+
+def homogeneity(transition_matrices, regime_names=[], class_names=[],
+ title="Markov Homogeneity Test"):
"""
Test for homogeneity of Markov transition probabilities across regimes.
Parameters
----------
- transition_matrices : list
- of transition matrices for regimes, all matrices must
- have same size (r, c). r is the number of rows in the
- transition matrix and c is the number of columns in
+ transition_matrices : list
+ of transition matrices for regimes, all matrices must
+ have same size (r, c). r is the number of rows in the
+ transition matrix and c is the number of columns in
the transition matrix.
regime_names : sequence
Labels for the regimes.
@@ -1276,26 +1329,26 @@ def homogeneity(transition_matrices, regime_names=[], class_names=[], \
name of test.
Returns
- -------
- : implicit
+ -------
+ : implicit
an instance of Homogeneity_Results.
"""
return Homogeneity_Results(transition_matrices, regime_names=regime_names,
- class_names= class_names,
- title=title)
+ class_names=class_names, title=title)
+
class Homogeneity_Results:
"""
Wrapper class to present homogeneity results.
-
+
Parameters
----------
transition_matrices : list
- of transition matrices for regimes, all matrices must
+ of transition matrices for regimes, all matrices must
have same size (r, c). r is the number of rows in
the transition matrix and c is the number of columns
- in the transition matrix.
+ in the transition matrix.
regime_names : sequence
Labels for the regimes.
class_names : sequence
@@ -1312,31 +1365,31 @@ class Homogeneity_Results:
(2003) [Bickenbach2003]_.
Examples
- --------
+ --------
See Spatial_Markov above.
"""
- def __init__(self, transition_matrices, regime_names=[], class_names = [],
- title="Markov Homogeneity Test"):
+ def __init__(self, transition_matrices, regime_names=[], class_names=[],
+ title="Markov Homogeneity Test"):
self._homogeneity(transition_matrices)
- self.regime_names=regime_names
+ self.regime_names = regime_names
self.class_names = class_names
self.title = title
def _homogeneity(self, transition_matrices):
# form null transition probability matrix
M = np.array(transition_matrices)
- m,r,k = M.shape
+ m, r, k = M.shape
self.k = k
- B = np.zeros((r,m))
+ B = np.zeros((r, m))
T = M.sum(axis=0)
self.t_total = T.sum()
n_i = T.sum(axis=1)
- A_i = (T>0).sum(axis=1)
- A_im = np.zeros((r,m))
- p_ij = np.dot(np.diag(1./(n_i + (n_i==0)*1.)), T)
- den = p_ij + 1. * (p_ij==0)
+ A_i = (T > 0).sum(axis=1)
+ A_im = np.zeros((r, m))
+ p_ij = np.dot(np.diag(1./(n_i + (n_i == 0)*1.)), T)
+ den = p_ij + 1. * (p_ij == 0)
b_i = np.zeros_like(A_i)
p_ijm = np.zeros_like(M)
# get dimensions
@@ -1346,12 +1399,12 @@ class Homogeneity_Results:
LR = 0.0
lr_table = np.zeros_like(M)
q_table = np.zeros_like(M)
-
+
for nijm in M:
nim = nijm.sum(axis=1)
- B[:,m] = 1.*(nim>0)
- b_i = b_i + 1. * (nim>0)
- p_ijm[m] = np.dot(np.diag(1./(nim + (nim==0)*1.)),nijm)
+ B[:, m] = 1.*(nim > 0)
+ b_i = b_i + 1. * (nim > 0)
+ p_ijm[m] = np.dot(np.diag(1./(nim + (nim == 0)*1.)), nijm)
num = (p_ijm[m]-p_ij)**2
ratio = num / den
qijm = np.dot(np.diag(nim), ratio)
@@ -1359,8 +1412,8 @@ class Homogeneity_Results:
Q = Q + qijm.sum()
# only use nonzero pijm in lr test
mask = (nijm > 0) * (p_ij > 0)
- A_im[:,m] = (nijm>0).sum(axis=1)
- unmask = 1.0 * (mask==0)
+ A_im[:, m] = (nijm > 0).sum(axis=1)
+ unmask = 1.0 * (mask == 0)
ratio = (mask * p_ijm[m] + unmask) / (mask * p_ij + unmask)
lr = nijm * np.log(ratio)
LR = LR + lr.sum()
@@ -1385,20 +1438,20 @@ class Homogeneity_Results:
self.p_h1 = p_ijm
def summary(self, file_name=None, title="Markov Homogeneity Test"):
- regime_names = ["%d"%i for i in range(self.m)]
+ regime_names = ["%d" % i for i in range(self.m)]
if self.regime_names:
regime_names = self.regime_names
- cols = ["P(%s)"%str(regime) for regime in regime_names]
+ cols = ["P(%s)" % str(regime) for regime in regime_names]
if not self.class_names:
self.class_names = range(self.k)
max_col = max([len(col) for col in cols])
- col_width = max([5, max_col]) #probabilities have 5 chars
+ col_width = max([5, max_col]) # probabilities have 5 chars
n_tabs = self.k
width = n_tabs * 4 + (self.k+1)*col_width
- lead = "-"* width
+ lead = "-" * width
head = title.center(width)
- contents = [lead,head,lead]
+ contents = [lead, head, lead]
l = "Number of regimes: %d" % int(self.m)
k = "Number of classes: %d" % int(self.k)
r = "Regime names: "
@@ -1409,51 +1462,54 @@ class Homogeneity_Results:
contents.append(l)
contents.append(r)
contents.append(lead)
- h = "%7s %20s %20s"%('Test', 'LR', 'Chi-2')
+ h = "%7s %20s %20s" % ('Test', 'LR', 'Chi-2')
contents.append(h)
- stat = "%7s %20.3f %20.3f"%('Stat.', self.LR, self.Q)
+ stat = "%7s %20.3f %20.3f" % ('Stat.', self.LR, self.Q)
contents.append(stat)
- stat = "%7s %20d %20d"%('DOF', self.dof, self.dof)
+ stat = "%7s %20d %20d" % ('DOF', self.dof, self.dof)
contents.append(stat)
- stat = "%7s %20.3f %20.3f"%('p-value', self.LR_p_value,
- self.Q_p_value)
+ stat = "%7s %20.3f %20.3f" % ('p-value', self.LR_p_value,
+ self.Q_p_value)
contents.append(stat)
- print "\n".join(contents)
- print lead
+ print("\n".join(contents))
+ print(lead)
- cols = ["P(%s)"%str(regime) for regime in self.regime_names]
+ cols = ["P(%s)" % str(regime) for regime in self.regime_names]
if not self.class_names:
self.class_names = range(self.k)
- cols.extend(["%s"%str(cname) for cname in self.class_names])
+ cols.extend(["%s" % str(cname) for cname in self.class_names])
max_col = max([len(col) for col in cols])
- col_width = max([5, max_col]) #probabilities have 5 chars
+ col_width = max([5, max_col]) # probabilities have 5 chars
p0 = []
- line0 = [ '{s: <{w}}'.format(s="P(H0)",w=col_width) ]
- line0.extend([ '{s: >{w}}'.format(s=cname,w=col_width) for cname in self.class_names])
- print " ".join(line0)
+ line0 = ['{s: <{w}}'.format(s="P(H0)", w=col_width)]
+ line0.extend((['{s: >{w}}'.format(s=cname, w=col_width) for cname in
+ self.class_names]))
+ print(" ".join(line0))
p0.append("&".join(line0))
- for i,row in enumerate(self.p_h0):
- line = ["%*s"%(col_width, str(self.class_names[i]))]
- line.extend(["%*.3f"%(col_width,v) for v in row])
- print " ".join(line)
+ for i, row in enumerate(self.p_h0):
+ line = ["%*s" % (col_width, str(self.class_names[i]))]
+ line.extend(["%*.3f" % (col_width, v) for v in row])
+ print(" ".join(line))
p0.append("&".join(line))
pmats = [p0]
- print lead
+ print(lead)
for r, p1 in enumerate(self.p_h1):
p0 = []
- line0 = [ '{s: <{w}}'.format(s="P(%s)"%regime_names[r],w=col_width) ]
- line0.extend([ '{s: >{w}}'.format(s=cname,w=col_width) for cname in self.class_names])
- print " ".join(line0)
+ line0 = ['{s: <{w}}'.format(s="P(%s)" %
+ regime_names[r], w=col_width)]
+ line0.extend((['{s: >{w}}'.format(s=cname, w=col_width) for cname
+ in self.class_names]))
+ print(" ".join(line0))
p0.append("&".join(line0))
- for i,row in enumerate(p1):
- line = ["%*s"%(col_width, str(self.class_names[i]))]
- line.extend(["%*.3f"%(col_width,v) for v in row])
- print " ".join(line)
+ for i, row in enumerate(p1):
+ line = ["%*s" % (col_width, str(self.class_names[i]))]
+ line.extend(["%*.3f" % (col_width, v) for v in row])
+ print(" ".join(line))
p0.append("&".join(line))
- pmats.append(p0)
- print lead
+ pmats.append(p0)
+ print(lead)
if file_name:
k = self.k
@@ -1461,35 +1517,35 @@ class Homogeneity_Results:
with open(file_name, 'w') as f:
c = []
fmt = "r"*(k+1)
- s="\\begin{tabular}{|%s|}\\hline\n"%fmt
- s+= "\\multicolumn{%s}{|c|}{%s}"%(ks,title)
+ s = "\\begin{tabular}{|%s|}\\hline\n" % fmt
+ s += "\\multicolumn{%s}{|c|}{%s}" % (ks, title)
c.append(s)
- s = "Number of classes: %d"%int(self.k)
- c.append("\\hline\\multicolumn{%s}{|l|}{%s}"%(ks,s))
- s = "Number of transitions: %d"%int(self.t_total)
- c.append("\\multicolumn{%s}{|l|}{%s}"%(ks,s))
- s = "Number of regimes: %d"%int(self.m)
- c.append("\\multicolumn{%s}{|l|}{%s}"%(ks,s))
+ s = "Number of classes: %d" % int(self.k)
+ c.append("\\hline\\multicolumn{%s}{|l|}{%s}" % (ks, s))
+ s = "Number of transitions: %d" % int(self.t_total)
+ c.append("\\multicolumn{%s}{|l|}{%s}" % (ks, s))
+ s = "Number of regimes: %d" % int(self.m)
+ c.append("\\multicolumn{%s}{|l|}{%s}" % (ks, s))
s = "Regime names: "
s += ", ".join(regime_names)
- c.append("\\multicolumn{%s}{|l|}{%s}"%(ks,s))
- s = "\\hline\\multicolumn{2}{|l}{%s}"%("Test")
+ c.append("\\multicolumn{%s}{|l|}{%s}" % (ks, s))
+ s = "\\hline\\multicolumn{2}{|l}{%s}" % ("Test")
s += "&\\multicolumn{2}{r}{LR}&\\multicolumn{2}{r|}{Q}"
c.append(s)
s = "Stat."
- s = "\\multicolumn{2}{|l}{%s}"%(s)
- s += "&\\multicolumn{2}{r}{%.3f}"%self.LR
- s += "&\\multicolumn{2}{r|}{%.3f}"%self.Q
+ s = "\\multicolumn{2}{|l}{%s}" % (s)
+ s += "&\\multicolumn{2}{r}{%.3f}" % self.LR
+ s += "&\\multicolumn{2}{r|}{%.3f}" % self.Q
c.append(s)
- s = "\\multicolumn{2}{|l}{%s}"%("DOF")
- s += "&\\multicolumn{2}{r}{%d}"%int(self.dof)
- s += "&\\multicolumn{2}{r|}{%d}"%int(self.dof)
+ s = "\\multicolumn{2}{|l}{%s}" % ("DOF")
+ s += "&\\multicolumn{2}{r}{%d}" % int(self.dof)
+ s += "&\\multicolumn{2}{r|}{%d}" % int(self.dof)
c.append(s)
- s = "\\multicolumn{2}{|l}{%s}"%("p-value")
- s += "&\\multicolumn{2}{r}{%.3f}"%self.LR_p_value
- s += "&\\multicolumn{2}{r|}{%.3f}"%self.Q_p_value
+ s = "\\multicolumn{2}{|l}{%s}" % ("p-value")
+ s += "&\\multicolumn{2}{r}{%.3f}" % self.LR_p_value
+ s += "&\\multicolumn{2}{r|}{%.3f}" % self.Q_p_value
c.append(s)
- s1 = "\\\\\n".join(c)
+ s1 = "\\\\\n".join(c)
s1 += "\\\\\n"
c = []
for mat in pmats:
diff --git a/pysal/spatial_dynamics/tests/test_markov.py b/pysal/spatial_dynamics/tests/test_markov.py
index 8164674..9f92f90 100644
--- a/pysal/spatial_dynamics/tests/test_markov.py
+++ b/pysal/spatial_dynamics/tests/test_markov.py
@@ -2,6 +2,7 @@ import unittest
import pysal
from pysal.spatial_dynamics import markov
import numpy as np
+from pysal.common import RTOL
class test_Markov(unittest.TestCase):
@@ -45,12 +46,16 @@ class test_Spatial_Markov(unittest.TestCase):
w = pysal.open(pysal.examples.get_path("states48.gal")).read()
w.transform = 'r'
sm = pysal.Spatial_Markov(rpci, w, fixed=True, k=5)
- S = np.array(
- [[0.43509425, 0.2635327, 0.20363044, 0.06841983, 0.02932278],
- [0.13391287, 0.33993305, 0.25153036, 0.23343016, 0.04119356],
- [0.12124869, 0.21137444, 0.2635101, 0.29013417, 0.1137326],
- [0.0776413, 0.19748806, 0.25352636, 0.22480415, 0.24654013],
- [0.01776781, 0.19964349, 0.19009833, 0.25524697, 0.3372434]])
+ S = np.array([[0.43509425, 0.2635327, 0.20363044, 0.06841983,
+ 0.02932278], [0.13391287, 0.33993305, 0.25153036,
+ 0.23343016, 0.04119356], [0.12124869,
+ 0.21137444,
+ 0.2635101,
+ 0.29013417,
+ 0.1137326],
+ [0.0776413, 0.19748806, 0.25352636, 0.22480415,
+ 0.24654013], [0.01776781, 0.19964349, 0.19009833,
+ 0.25524697, 0.3372434]])
np.testing.assert_array_almost_equal(S, sm.S)
@@ -65,11 +70,11 @@ class test_chi2(unittest.TestCase):
w.transform = 'r'
sm = pysal.Spatial_Markov(rpci, w, fixed=True, k=5)
chi = np.matrix([[4.05598541e+01, 6.44644317e-04, 1.60000000e+01],
- [5.54751974e+01, 2.97033748e-06, 1.60000000e+01],
- [1.77528996e+01, 3.38563882e-01, 1.60000000e+01],
- [4.00390961e+01, 7.68422046e-04, 1.60000000e+01],
- [4.67966803e+01, 7.32512065e-05,
- 1.60000000e+01]]).getA()
+ [5.54751974e+01, 2.97033748e-06, 1.60000000e+01],
+ [1.77528996e+01, 3.38563882e-01, 1.60000000e+01],
+ [4.00390961e+01, 7.68422046e-04, 1.60000000e+01],
+ [4.67966803e+01, 7.32512065e-05,
+ 1.60000000e+01]]).getA()
obs = np.matrix(sm.chi2).getA()
np.testing.assert_array_almost_equal(obs, chi)
obs = np.matrix(
@@ -92,46 +97,38 @@ class test_LISA_Markov(unittest.TestCase):
lm = pysal.LISA_Markov(pci, w)
obs = np.array([1, 2, 3, 4])
np.testing.assert_array_almost_equal(obs, lm.classes)
- """
- >>> lm.steady_state
- matrix([[ 0.28561505],
- [ 0.14190226],
- [ 0.40493672],
- [ 0.16754598]])
- >>> lm.transitions
- array([[ 1.08700000e+03, 4.40000000e+01, 4.00000000e+00,
- 3.40000000e+01],
- [ 4.10000000e+01, 4.70000000e+02, 3.60000000e+01,
- 1.00000000e+00],
- [ 5.00000000e+00, 3.40000000e+01, 1.42200000e+03,
- 3.90000000e+01],
- [ 3.00000000e+01, 1.00000000e+00, 4.00000000e+01,
- 5.52000000e+02]])
- >>> lm.p
- matrix([[ 0.92985458, 0.03763901, 0.00342173, 0.02908469],
- [ 0.07481752, 0.85766423, 0.06569343, 0.00182482],
- [ 0.00333333, 0.02266667, 0.948 , 0.026 ],
- [ 0.04815409, 0.00160514, 0.06420546, 0.88603531]])
- >>> lm.move_types
- array([[ 11., 11., 11., ..., 11., 11., 11.],
- [ 6., 6., 6., ..., 6., 7., 11.],
- [ 11., 11., 11., ..., 11., 11., 11.],
- ...,
- [ 6., 6., 6., ..., 6., 6., 6.],
- [ 1., 1., 1., ..., 6., 6., 6.],
- [ 16., 16., 16., ..., 16., 16., 16.]])
- >>> np.random.seed(10)
- >>> lm_random = pysal.LISA_Markov(pci, w, permutations=99)
- >>> lm_random.significant_moves
- array([[11, 11, 11, ..., 59, 59, 59],
- [54, 54, 54, ..., 54, 55, 59],
- [11, 11, 11, ..., 11, 59, 59],
- ...,
- [54, 54, 54, ..., 54, 54, 54],
- [49, 49, 49, ..., 54, 54, 54],
- [64, 64, 64, ..., 64, 64, 64]])
-
- """
+ ss = np.matrix([[0.28561505], [0.14190226], [0.40493672],
+ [0.16754598]])
+ np.testing.assert_array_almost_equal(lm.steady_state, ss)
+ transitions = np.array([[1.08700000e+03, 4.40000000e+01,
+ 4.00000000e+00, 3.40000000e+01], [
+ 4.10000000e+01, 4.70000000e+02,
+ 3.60000000e+01, 1.00000000e+00], [
+ 5.00000000e+00, 3.40000000e+01,
+ 1.42200000e+03, 3.90000000e+01], [
+ 3.00000000e+01, 1.00000000e+00,
+ 4.00000000e+01, 5.52000000e+02]])
+ np.testing.assert_array_almost_equal(lm.transitions, transitions)
+ p = np.matrix([[0.92985458, 0.03763901, 0.00342173, 0.02908469],
+ [0.07481752, 0.85766423, 0.06569343, 0.00182482],
+ [0.00333333, 0.02266667, 0.948, 0.026], [0.04815409,
+ 0.00160514,
+ 0.06420546,
+ 0.88603531]])
+ np.testing.assert_array_almost_equal(lm.p, p)
+ np.random.seed(10)
+ lm_random = pysal.LISA_Markov(pci, w, permutations=99)
+ expected = np.array([[1.12328098e+03, 1.15377356e+01,
+ 3.47522158e-01, 3.38337644e+01], [
+ 3.50272664e+00, 5.28473882e+02,
+ 1.59178880e+01, 1.05503814e-01], [
+ 1.53878082e-01, 2.32163556e+01,
+ 1.46690710e+03, 9.72266513e+00], [
+ 9.60775143e+00, 9.86856346e-02,
+ 6.23537392e+00, 6.07058189e+02]])
+ np.testing.assert_allclose(lm_random.expected_t, expected, RTOL)
+ c = np.array([1058.207904, 0., 9.])
+ np.testing.assert_allclose(lm_random.chi_2, c, RTOL)
class test_kullback(unittest.TestCase):
@@ -158,10 +155,10 @@ class test_kullback(unittest.TestCase):
res = markov.kullback(F)
np.testing.assert_array_almost_equal(160.96060031170782,
res['Conditional homogeneity'])
- np.testing.assert_array_almost_equal(30,
- res['Conditional homogeneity dof'])
- np.testing.assert_array_almost_equal(0.0,
- res['Conditional homogeneity pvalue'])
+ dof = res['Conditional homogeneity dof']
+ np.testing.assert_array_almost_equal(30, dof)
+ p_value = res['Conditional homogeneity pvalue']
+ np.testing.assert_array_almost_equal(0.0, p_value)
class test_prais(unittest.TestCase):
diff --git a/pysal/spreg/diagnostics_sur.py b/pysal/spreg/diagnostics_sur.py
index 71f5541..2425641 100644
--- a/pysal/spreg/diagnostics_sur.py
+++ b/pysal/spreg/diagnostics_sur.py
@@ -4,7 +4,7 @@ Diagnostics for SUR and 3SLS estimation
__author__= "Luc Anselin lanselin at gmail.com, \
Pedro V. Amaral pedrovma at gmail.com"
-
+
import numpy as np
import scipy.stats as stats
@@ -18,18 +18,18 @@ __all__ = ['sur_setp','sur_lrtest','sur_lmtest','lam_setp','surLMe']
def sur_setp(bigB,varb):
''' Utility to compute standard error, t and p-value
-
+
Parameters
----------
bigB : dictionary of regression coefficient estimates,
one vector by equation
varb : variance-covariance matrix of coefficients
-
+
Returns
-------
surinfdict : dictionary with standard error, t-value, and
p-value array, one for each equation
-
+
'''
vvb = varb.diagonal()
n_eq = len(bigB.keys())
@@ -44,10 +44,10 @@ def sur_setp(bigB,varb):
surinf = np.hstack((se,t,tp))
surinfdict = sur_mat2dict(surinf,bigK)
return surinfdict
-
+
def lam_setp(lam,vm):
"""Standard errors, t-test and p-value for lambda in SUR Error ML
-
+
Parameters
----------
lam : n_eq x 1 array with ML estimates for spatial error
@@ -55,12 +55,12 @@ def lam_setp(lam,vm):
vm : n_eq x n_eq subset of variance-covariance matrix for
lambda and Sigma in SUR Error ML
(needs to be subset from full vm)
-
+
Returns
-------
: tuple with arrays for standard error, t-value and p-value
(each element in the tuple is an n_eq x 1 array)
-
+
"""
vvb = vm.diagonal()
se = np.sqrt(vvb)
@@ -71,42 +71,42 @@ def lam_setp(lam,vm):
def sur_lrtest(n,n_eq,ldetS0,ldetS1):
''' Likelihood Ratio test on off-diagonal elements of Sigma
-
+
Parameters
----------
n : cross-sectional dimension (number of observations for an equation)
n_eq : number of equations
ldetS0 : log determinant of Sigma for OLS case
ldetS1 : log determinant of Sigma for SUR case (should be iterated)
-
+
Returns
-------
(lrtest,M,pvalue) : tupel with value of test statistic (lrtest),
degrees of freedom (M, as an integer)
p-value
-
+
'''
M = n_eq * (n_eq - 1)/2.0
lrtest = n * (ldetS0 - ldetS1)
pvalue = stats.chi2.sf(lrtest,M)
return (lrtest,int(M),pvalue)
-
+
def sur_lmtest(n,n_eq,sig):
''' Lagrange Multiplier test on off-diagonal elements of Sigma
-
+
Parameters
----------
n : cross-sectional dimension (number of observations for an equation)
n_eq : number of equations
sig : inter-equation covariance matrix for null model (OLS)
-
+
Returns
-------
(lmtest,M,pvalue) : tupel with value of test statistic (lmtest),
degrees of freedom (M, as an integer)
p-value
-
+
'''
R = sur_corr(sig)
tr = np.trace(np.dot(R.T,R))
@@ -115,22 +115,22 @@ def sur_lmtest(n,n_eq,sig):
pvalue = stats.chi2.sf(lmtest,M)
return (lmtest,int(M),pvalue)
-
+
def surLMe(n_eq,WS,bigE,sig):
"""Lagrange Multiplier test on error spatial autocorrelation in SUR
-
+
Parameters
----------
n_eq : number of equations
WS : spatial weights matrix in sparse form
bigE : n x n_eq matrix of residuals by equation
sig : cross-equation error covariance matrix
-
+
Returns
-------
(LMe,n_eq,pvalue) : tupel with value of statistic (LMe), degrees
of freedom (n_eq) and p-value
-
+
"""
# spatially lagged residuals
WbigE = WS * bigE
@@ -155,16 +155,16 @@ def surLMe(n_eq,WS,bigE,sig):
LMe = np.dot(np.dot(score.T,idenom),score)[0][0]
pvalue = stats.chi2.sf(LMe,n_eq)
return (LMe,n_eq,pvalue)
-
+
def sur_chow(n_eq,bigK,bSUR,varb):
"""test on constancy of regression coefficients across equations in
a SUR specification
-
+
Note: requires a previous check on constancy of number of coefficients
across equations; no other checks are carried out, so it is possible
that the results are meaningless if the variables are not listed in
the same order in each equation.
-
+
Parameters
----------
n_eq : integer, number of equations
@@ -172,13 +172,13 @@ def sur_chow(n_eq,bigK,bSUR,varb):
bSUR : dictionary with the SUR regression coefficients by equation
varb : array with the variance-covariance matrix for the SUR regression
coefficients
-
+
Returns
-------
test : a list with for each coefficient (in order) a tuple with the
value of the test statistic, the degrees of freedom, and the
p-value
-
+
"""
kr = bigK[0][0]
test = []
@@ -191,24 +191,24 @@ def sur_chow(n_eq,bigK,bSUR,varb):
tt,p = wald_test(bb,Ri,np.zeros((df,1)),varb)
test.append((tt,df,p))
return test
-
+
def sur_joinrho(n_eq,bigK,bSUR,varb):
"""Test on joint significance of spatial autoregressive coefficient in SUR
-
+
Parameters
----------
n_eq : integer, number of equations
bigK : n_eq x 1 array with number of variables by equation
- (includes constant term, exogenous and endogeneous and
+ (includes constant term, exogenous and endogeneous and
spatial lag)
bSUR : dictionary with regression coefficients by equation, with
the spatial autoregressive term as last
varb : variance-covariance matrix for regression coefficients
-
+
Returns
-------
: tuple with test statistic, degrees of freedom, p-value
-
+
"""
bb = sur_dict2mat(bSUR)
R = np.zeros((n_eq,varb.shape[0]))
@@ -218,4 +218,4 @@ def sur_joinrho(n_eq,bigK,bSUR,varb):
kc = kc + bigK[i]
R[i,kc] = 1
w,p = wald_test(bb,R,q,varb)
- return (w,n_eq,p)
\ No newline at end of file
+ return (w,n_eq,p)
diff --git a/pysal/spreg/ml_error.py b/pysal/spreg/ml_error.py
index ce26a97..0bdf9c0 100644
--- a/pysal/spreg/ml_error.py
+++ b/pysal/spreg/ml_error.py
@@ -22,6 +22,7 @@ try:
minimize_scalar_available = True
except ImportError:
minimize_scalar_available = False
+from .sputils import spdot, spfill_diagonal, spinv
__all__ = ["ML_Error"]
@@ -186,12 +187,14 @@ class BaseML_Error(RegressionPropsY, RegressionPropsVM, REGI.Regimes_Frame):
args=(self.n, self.y, ylag,
self.x, xlag, I, Wsp),
method='bounded', tol=epsilon)
+ W = Wsp
elif methodML == 'ORD':
# check on symmetry structure
if w.asymmetry(intrinsic=False) == []:
ww = symmetrize(w)
- WW = ww.todense()
+ WW = np.array(ww.todense())
evals = la.eigvalsh(WW)
+ W = WW
else:
W = w.full()[0] # need dense here
evals = la.eigvals(W)
@@ -201,7 +204,7 @@ class BaseML_Error(RegressionPropsY, RegressionPropsVM, REGI.Regimes_Frame):
xlag, evals), method='bounded',
tol=epsilon)
else:
- raise Exception, "{0} is an unsupported method".format(method)
+ raise Exception("{0} is an unsupported method".format(method))
self.lam = res.x
@@ -237,16 +240,16 @@ class BaseML_Error(RegressionPropsY, RegressionPropsVM, REGI.Regimes_Frame):
# variance-covariance matrix lambda, sigma
a = -self.lam * W
- np.fill_diagonal(a, 1.0)
- ai = la.inv(a)
- wai = np.dot(W, ai)
- tr1 = np.trace(wai)
+ spfill_diagonal(a, 1.0)
+ ai = spinv(a)
+ wai = spdot(W, ai)
+ tr1 = wai.diagonal().sum()
- wai2 = np.dot(wai, wai)
- tr2 = np.trace(wai2)
+ wai2 = spdot(wai, wai)
+ tr2 = wai2.diagonal().sum()
- waiTwai = np.dot(wai.T, wai)
- tr3 = np.trace(waiTwai)
+ waiTwai = spdot(wai.T, wai)
+ tr3 = waiTwai.diagonal().sum()
v1 = np.vstack((tr2 + tr3,
tr1 / self.sig2))
diff --git a/pysal/spreg/ml_lag.py b/pysal/spreg/ml_lag.py
index dd322b1..990ee32 100644
--- a/pysal/spreg/ml_lag.py
+++ b/pysal/spreg/ml_lag.py
@@ -11,7 +11,8 @@ import numpy.linalg as la
from scipy import sparse as sp
from scipy.sparse.linalg import splu as SuperLU
import pysal as ps
-from utils import RegressionPropsY, RegressionPropsVM, inverse_prod, spdot
+from utils import RegressionPropsY, RegressionPropsVM, inverse_prod
+from sputils import spdot, spfill_diagonal, spinv, spbroadcast
import diagnostics as DIAG
import user_output as USER
import summary_output as SUMMARY
@@ -202,6 +203,7 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
elif methodML == 'LU':
I = sp.identity(w.n)
Wsp = w.sparse # moved here
+ W = Wsp
res = minimize_scalar(lag_c_loglik_sp, 0.0, bounds=(-1.0,1.0),
args=(self.n, e0, e1, I, Wsp),
method='bounded', tol=epsilon)
@@ -209,8 +211,9 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
# check on symmetry structure
if w.asymmetry(intrinsic=False) == []:
ww = symmetrize(w)
- WW = ww.todense()
+ WW = np.array(ww.todense())
evals = la.eigvalsh(WW)
+ W = WW
else:
W = w.full()[0] # moved here
evals = la.eigvals(W)
@@ -245,20 +248,23 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
self.e_pred = self.y - self.predy_e
# residual variance
+ self._cache = {}
self.sig2 = self.sig2n # no allowance for division by n-k
# information matrix
+ # if w should be kept sparse, how can we do the following:
a = -self.rho * W
- np.fill_diagonal(a, 1.0)
- ai = la.inv(a)
- wai = np.dot(W, ai)
- tr1 = np.trace(wai)
+ spfill_diagonal(a, 1.0)
+ ai = spinv(a)
+ wai = spdot(W, ai)
+ tr1 = wai.diagonal().sum() #same for sparse and dense
wai2 = np.dot(wai, wai)
- tr2 = np.trace(wai2)
+ tr2 = wai2.diagonal().sum()
waiTwai = np.dot(wai.T, wai)
- tr3 = np.trace(waiTwai)
+ tr3 = waiTwai.diagonal().sum()
+ ### to here
wpredy = ps.lag_spatial(w, self.predy_e)
wpyTwpy = np.dot(wpredy.T, wpredy)
@@ -562,7 +568,7 @@ def lag_c_loglik(rho, n, e0, e1, W):
sig2 = np.dot(er.T, er) / n
nlsig2 = (n / 2.0) * np.log(sig2)
a = -rho * W
- np.fill_diagonal(a, 1.0)
+ spfill_diagonal(a, 1.0)
jacob = np.log(np.linalg.det(a))
# this is the negative of the concentrated log lik for minimization
clik = nlsig2 - jacob
diff --git a/pysal/spreg/sputils.py b/pysal/spreg/sputils.py
new file mode 100644
index 0000000..0fde0e9
--- /dev/null
+++ b/pysal/spreg/sputils.py
@@ -0,0 +1,275 @@
+import numpy as np
+import numpy.linalg as la
+import scipy.sparse as SP
+import scipy
+from scipy.sparse import linalg as SPla
+
+def spdot(a, b, array_out=True):
+ """
+ Matrix multiplication function to deal with sparse and dense objects
+
+ Parameters
+ ----------
+
+ a : array
+ first multiplication factor. Can either be sparse or dense.
+ b : array
+ second multiplication factor. Can either be sparse or dense.
+ array_out : boolean
+ If True (default) the output object is always a np.array
+
+ Returns
+ -------
+
+ ab : array
+ product of a times b. Sparse if a and b are sparse. Dense otherwise.
+ """
+ if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
+ ab = np.dot(a, b)
+ elif type(a).__name__ == 'csr_matrix' or type(b).__name__ == 'csr_matrix' \
+ or type(a).__name__ == 'csc_matrix' or type(b).__name__ == 'csc_matrix':
+ ab = a * b
+ if array_out:
+ if type(ab).__name__ == 'csc_matrix' or type(ab).__name__ == 'csr_matrix':
+ ab = ab.toarray()
+ else:
+ raise Exception, "Invalid format for 'spdot' argument: %s and %s" % (
+ type(a).__name__, type(b).__name__)
+ return ab
+
+
+def spmultiply(a, b, array_out=True):
+ """
+ Element-wise multiplication function to deal with sparse and dense
+ objects. Both objects must be of the same type.
+
+ Parameters
+ ----------
+
+ a : array
+ first multiplication factor. Can either be sparse or dense.
+ b : array
+ second multiplication factor. Can either be sparse or dense.
+ integer.
+ array_out : boolean
+ If True (default) the output object is always a np.array
+
+ Returns
+ -------
+
+ ab : array
+ elementwise multiplied object. Sparse if a is sparse. Dense otherwise.
+ """
+ if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
+ ab = a * b
+ elif (type(a).__name__ == 'csr_matrix' or type(a).__name__ == 'csc_matrix') \
+ and (type(b).__name__ == 'csr_matrix' or type(b).__name__ == 'csc_matrix'):
+ ab = a.multiply(b)
+ if array_out:
+ if type(ab).__name__ == 'csc_matrix' or type(ab).__name__ == 'csr_matrix':
+ ab = ab.toarray()
+ else:
+ raise Exception, "Invalid format for 'spmultiply' argument: %s and %s" % (
+ type(a).__name__, type(b).__name__)
+ return ab
+
+
+def sphstack(a, b, array_out=False):
+ """
+ Horizontal stacking of vectors (or matrices) to deal with sparse and dense objects
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ First object.
+ b : array or sparse matrix
+ Object to be stacked next to a
+ array_out : boolean
+ If True the output object is a np.array; if False (default)
+ the output object is an np.array if both inputs are
+ arrays or CSR matrix if at least one input is a CSR matrix
+
+ Returns
+ -------
+
+ ab : array or sparse matrix
+ Horizontally stacked objects
+ """
+ if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
+ ab = np.hstack((a, b))
+ elif type(a).__name__ == 'csr_matrix' or type(b).__name__ == 'csr_matrix':
+ ab = SP.hstack((a, b), format='csr')
+ if array_out:
+ if type(ab).__name__ == 'csr_matrix':
+ ab = ab.toarray()
+ else:
+ raise Exception, "Invalid format for 'sphstack' argument: %s and %s" % (
+ type(a).__name__, type(b).__name__)
+ return ab
+
+
+def spbroadcast(a, b, array_out=False):
+ """
+ Element-wise multiplication of a matrix and vector to deal with sparse
+ and dense objects
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ Object with one or more columns.
+ b : array
+ Object with only one column
+ array_out : boolean
+ If True the output object is a np.array; if False (default)
+ the output object is an np.array if both inputs are
+ arrays or CSR matrix if at least one input is a CSR matrix
+
+ Returns
+ -------
+
+ ab : array or sparse matrix
+ Element-wise multiplication of a and b
+ """
+ if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
+ ab = a * b
+ elif type(a).__name__ == 'csr_matrix':
+ b_mod = SP.lil_matrix((b.shape[0], b.shape[0]))
+ b_mod.setdiag(b)
+ ab = (a.T * b_mod).T
+ if array_out:
+ if type(ab).__name__ == 'csr_matrix':
+ ab = ab.toarray()
+ else:
+ raise Exception, "Invalid format for 'spbroadcast' argument: %s and %s" % (
+ type(a).__name__, type(b).__name__)
+ return ab
+
+
+def spmin(a):
+ """
+ Minimum value in a matrix or vector to deal with sparse and dense objects
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ Object with one or more columns.
+
+ Returns
+ -------
+
+ min a : int or float
+ minimum value in a
+ """
+ return a.min()
+
+def spmax(a):
+ """
+ Maximum value in a matrix or vector to deal with sparse and dense objects
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ Object with one or more columns.
+
+ Returns
+ -------
+
+ max a : int or float
+ maximum value in a
+ """
+ return a.max()
+
+def splogdet(a):
+ """
+ Compute the log determinant of a large matrix.
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ Object with one or more columns
+
+ Returns
+ -------
+
+ log determinant of a : int or float
+ logged determinant of a
+ """
+ if SP.issparse(a):
+ LU = SPla.splu(a)
+ det = np.sum(np.log(np.abs(LU.U.diagonal())))
+ else:
+ sgn, ldet = la.slogdet(a)
+ det = sgn * ldet
+ return det
+
+def spfill_diagonal(a, val):
+ """
+ Fill the diagonal of a sparse or dense matrix
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ Object with one or more columns
+ val : int or float
+ value with which to fill the diagonal of a
+
+ Returns
+ -------
+
+ a with val on each element of the diagonal
+ """
+ if SP.issparse(a):
+ a.setdiag(val)
+ else:
+ np.fill_diagonal(a, val)
+ return a
+
+def spinv(a):
+ """
+ Compute the inverse of a sparse or dense matrix
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ Object with one or more columns
+
+ Returns
+ -------
+
+ ai, the inverse of a
+ """
+ if SP.issparse(a):
+ ai = SPla.inv(a)
+ else:
+ ai = la.inv(a)
+ return ai
+
+def spisfinite(a):
+ """
+ Determine whether an array has nan or inf values
+
+ Parameters
+ ----------
+ a : array or sparse matrix
+ Object with one or more columns
+
+ Returns
+ -------
+ bool denoting whether or not the array contains any NaN or inf
+ """
+ return np.isfinite(a.sum())
+
+def _test():
+ import doctest
+ doctest.testmod()
+
+if __name__ == '__main__':
+ _test()
+
diff --git a/pysal/spreg/sur.py b/pysal/spreg/sur.py
index c881adb..2721a4a 100644
--- a/pysal/spreg/sur.py
+++ b/pysal/spreg/sur.py
@@ -4,7 +4,7 @@ SUR and 3SLS estimation
__author__= "Luc Anselin lanselin at gmail.com, \
Pedro V. Amaral pedrovma at gmail.com"
-
+
import numpy as np
import numpy.linalg as la
@@ -21,10 +21,10 @@ __all__ = ['SUR','ThreeSLS']
class BaseSUR():
""" Base class for SUR estimation, both two step as well as iterated
-
+
Parameters
----------
-
+
bigy : dictionary with vector for dependent variable by equation
bigX : dictionary with matrix of explanatory variables by equation
(note, already includes constant term)
@@ -36,10 +36,10 @@ class BaseSUR():
verbose : flag to print out iteration number and value of log det(sig)
at the beginning and the end of the iteration
-
+
Attributes
----------
-
+
bigy : dictionary with y values
bigX : dictionary with X values
bigXX : dictionary with X_t'X_r cross-products
@@ -56,13 +56,13 @@ class BaseSUR():
sig : Sigma matrix of inter-equation error covariances
ldetS1 : log det(Sigma) for SUR model
resids : n by n_eq array of residuals
- sig_ols : Sigma matrix for OLS residuals
+ sig_ols : Sigma matrix for OLS residuals
ldetS0 : log det(Sigma) for null model (OLS by equation, diagonals only)
niter : number of iterations (=0 for iter=False)
corr : inter-equation SUR error correlation matrix
llik : log-likelihood (including the constant pi)
-
-
+
+
Methods
-------
@@ -113,37 +113,37 @@ class BaseSUR():
self.corr = sur_corr(self.sig)
lik = self.n_eq * (1.0 + np.log(2.0*np.pi)) + self.ldetS1
self.llik = - (self.n / 2.0) * lik
-
+
def sur_ols(self):
'''OLS estimation of SUR equations
-
+
Parameters
----------
-
+
self : BaseSUR object
-
+
Creates
-------
self.bOLS : dictionary with regression coefficients for each equation
self.olsE : N x n_eq array with OLS residuals for each equation
-
+
'''
self.bOLS = {}
for r in range(self.n_eq):
self.bOLS[r] = np.dot(la.inv(self.bigXX[(r,r)]),self.bigXy[(r,r)])
self.olsE = sur_resids(self.bigy,self.bigX,self.bOLS)
-
+
class SUR(BaseSUR):
""" User class for SUR estimation, both two step as well as iterated
-
+
Parameters
----------
-
+
bigy : dictionary with vector for dependent variable by equation
bigX : dictionary with matrix of explanatory variables by equation
(note, already includes constant term)
@@ -155,7 +155,7 @@ class SUR(BaseSUR):
maxiter : integer; maximum iterations; default = 5
epsilon : float; precision criterion to end iterations
default = 0.00001
- verbose : boolean; flag to print out iteration number and value
+ verbose : boolean; flag to print out iteration number and value
of log det(sig) at the beginning and the end of the iteration
name_bigy : dictionary with name of dependent variable for each equation
default = None, but should be specified
@@ -167,10 +167,10 @@ class SUR(BaseSUR):
name_ds : string; name for the data set
name_w : string; name for the weights file
-
+
Attributes
----------
-
+
bigy : dictionary with y values
bigX : dictionary with X values
bigXX : dictionary with X_t'X_r cross-products
@@ -194,17 +194,18 @@ class SUR(BaseSUR):
sur_inf : dictionary with standard error, asymptotic t and p-value,
one for each equation
lrtest : Likelihood Ratio test on off-diagonal elements of sigma
- (tupel with test,df,p-value)
+ (tuple with test,df,p-value)
lmtest : Lagrange Multipler test on off-diagonal elements of sigma
- (tupel with test,df,p-value)
+ (tuple with test,df,p-value)
lmEtest : Lagrange Multiplier test on error spatial autocorrelation in SUR
+ (tuple with test, df, p-value)
surchow : list with tuples for Chow test on regression coefficients
each tuple contains test value, degrees of freedom, p-value
name_bigy : dictionary with name of dependent variable for each equation
name_bigX : dictionary with names of explanatory variables for each
equation
name_ds : string; name for the data set
- name_w : string; name for the weights file
+ name_w : string; name for the weights file
Examples
@@ -214,8 +215,8 @@ class SUR(BaseSUR):
>>> import pysal
- Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
- This is the DBF associated with the NAT shapefile. Note that pysal.open()
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that pysal.open()
also reads data in CSV format.
>>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
@@ -246,7 +247,7 @@ class SUR(BaseSUR):
>>> bigy,bigX,bigyvars,bigXvars = pysal.spreg.sur_utils.sur_dictxy(db,y_var,x_var)
We can now run the regression and then have a summary of the output by typing:
- 'print(reg.summary)'
+ 'print(reg.summary)'
>>> reg = SUR(bigy,bigX,w=w,name_bigy=bigyvars,name_bigX=bigXvars,spat_diag=True,name_ds="nat")
>>> print(reg.summary)
@@ -314,7 +315,7 @@ class SUR(BaseSUR):
def __init__(self,bigy,bigX,w=None,nonspat_diag=True,spat_diag=False,vm=False,\
iter=False,maxiter=5,epsilon=0.00001,verbose=False,\
name_bigy=None,name_bigX=None,name_ds=None,name_w=None):
-
+
#need checks on match between bigy, bigX dimensions
# init moved here before name check
BaseSUR.__init__(self,bigy=bigy,bigX=bigX,iter=iter,\
@@ -329,7 +330,7 @@ class SUR(BaseSUR):
self.name_bigy = {}
for r in range(self.n_eq):
yn = 'dep_var_' + str(r)
- self.name_bigy[r] = yn
+ self.name_bigy[r] = yn
if name_bigX:
self.name_bigX = name_bigX
else: # need to construct x names
@@ -340,20 +341,20 @@ class SUR(BaseSUR):
ct = 'Constant_' + str(r) # NOTE: constant always included in X
name_x.insert(0, ct)
self.name_bigX[r] = name_x
-
+
#inference
self.sur_inf = sur_setp(self.bSUR,self.varb)
-
+
if nonspat_diag:
#LR test on off-diagonal elements of Sigma
self.lrtest = sur_lrtest(self.n,self.n_eq,self.ldetS0,self.ldetS1)
-
+
#LM test on off-diagonal elements of Sigma
self.lmtest = sur_lmtest(self.n,self.n_eq,self.sig_ols)
else:
self.lrtest = None
self.lmtest = None
-
+
#LM test on spatial error autocorrelation
if spat_diag:
if not w:
@@ -362,35 +363,35 @@ class SUR(BaseSUR):
self.lmEtest = surLMe(self.n_eq,WS,self.bigE,self.sig)
else:
self.lmEtest = None
-
+
#LM test on spatial lag autocorrelation
-
+
# test on constancy of coefficients across equations
if check_k(self.bigK): # only for equal number of variables
self.surchow = sur_chow(self.n_eq,self.bigK,self.bSUR,self.varb)
else:
self.surchow = None
-
+
#Listing of the results
- self.title = "SEEMINGLY UNRELATED REGRESSIONS (SUR)"
+ self.title = "SEEMINGLY UNRELATED REGRESSIONS (SUR)"
SUMMARY.SUR(reg=self, nonspat_diag=nonspat_diag, spat_diag=spat_diag, surlm=True)
-
+
class BaseThreeSLS():
""" Base class for 3SLS estimation, two step
-
+
Parameters
----------
-
+
bigy : dictionary with vector for dependent variable by equation
bigX : dictionary with matrix of explanatory variables by equation
(note, already includes constant term)
bigyend : dictionary with matrix of endogenous variables by equation
bigq : dictionary with matrix of instruments by equation
-
+
Attributes
----------
-
+
bigy : dictionary with y values
bigZ : dictionary with matrix of exogenous and endogenous variables
for each equation
@@ -407,13 +408,13 @@ class BaseThreeSLS():
sig : Sigma matrix of inter-equation error covariances
bigE : n by n_eq array of residuals
corr : inter-equation 3SLS error correlation matrix
-
-
+
+
Methods
-------
tsls_2sls : 2SLS estimation by equation
-
+
"""
def __init__(self,bigy,bigX,bigyend,bigq):
# setting up the cross-products
@@ -423,15 +424,15 @@ class BaseThreeSLS():
# dictionary with exog and endog, Z
self.bigZ = {}
for r in range(self.n_eq):
- self.bigZ[r] = np.hstack((bigX[r],bigyend[r]))
- # number of explanatory variables by equation
+ self.bigZ[r] = np.hstack((bigX[r],bigyend[r]))
+ # number of explanatory variables by equation
self.bigK = np.zeros((self.n_eq,1),dtype=np.int_)
for r in range(self.n_eq):
self.bigK[r] = self.bigZ[r].shape[1]
# dictionary with instruments, H
bigH = {}
for r in range(self.n_eq):
- bigH[r] = np.hstack((bigX[r],bigq[r]))
+ bigH[r] = np.hstack((bigX[r],bigq[r]))
# dictionary with instrumental variables, X and yend_predicted, Z-hat
bigZhat = {}
for r in range(self.n_eq):
@@ -443,44 +444,44 @@ class BaseThreeSLS():
yp = np.dot(bigH[r],np.dot(HHi,Hye))
bigZhat[r] = np.hstack((bigX[r],yp))
self.bigZHZH,self.bigZHy = sur_crossprod(bigZhat,self.bigy)
-
+
# 2SLS regression by equation, sets up initial residuals
self.sur_2sls() # creates self.b2SLS and self.tslsE
self.b3SLS,self.varb,self.sig = sur_est(self.bigZHZH,self.bigZHy,self.tslsE,self.bigK)
self.bigE = sur_resids(self.bigy,self.bigZ,self.b3SLS) # matrix of residuals
-
+
# inter-equation correlation matrix
self.corr = sur_corr(self.sig)
-
+
def sur_2sls(self):
'''2SLS estimation of SUR equations
-
+
Parameters
----------
-
+
self : BaseSUR object
-
+
Creates
-------
self.b2SLS : dictionary with regression coefficients for each equation
self.tslsE : N x n_eq array with OLS residuals for each equation
-
+
'''
self.b2SLS = {}
for r in range(self.n_eq):
self.b2SLS[r] = np.dot(la.inv(self.bigZHZH[(r,r)]),self.bigZHy[(r,r)])
self.tslsE = sur_resids(self.bigy,self.bigZ,self.b2SLS)
-
+
class ThreeSLS(BaseThreeSLS):
""" User class for 3SLS estimation
-
+
Parameters
----------
-
+
bigy : dictionary with vector for dependent variable by equation
bigX : dictionary with matrix of explanatory variables by equation
(note, already includes constant term)
@@ -504,10 +505,10 @@ class ThreeSLS(BaseThreeSLS):
is done when sur_stackZ is used
name_ds : string; name for the data set
-
+
Attributes
----------
-
+
bigy : dictionary with y values
bigZ : dictionary with matrix of exogenous and endogenous variables
for each equation
@@ -536,7 +537,7 @@ class ThreeSLS(BaseThreeSLS):
equation
name_bigq : dictionary with names of instrumental variables for each
equations
-
+
Examples
--------
@@ -545,8 +546,8 @@ class ThreeSLS(BaseThreeSLS):
>>> import pysal
- Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
- This is the DBF associated with the NAT shapefile. Note that pysal.open()
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that pysal.open()
also reads data in CSV format.
>>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
@@ -556,7 +557,7 @@ class ThreeSLS(BaseThreeSLS):
has HR80 as dependent variable, PS80 and UE80 as exogenous regressors,
RD80 as endogenous regressor and FP79 as additional instrument.
For equation 2, HR90 is the dependent variable, PS90 and UE90 the
- exogenous regressors, RD90 as endogenous regressor and FP99 as
+ exogenous regressors, RD90 as endogenous regressor and FP99 as
additional instrument
>>> y_var = ['HR80','HR90']
@@ -565,7 +566,7 @@ class ThreeSLS(BaseThreeSLS):
>>> q_var = [['FP79'],['FP89']]
The SUR method requires data to be provided as dictionaries. PySAL
- provides two tools to create these dictionaries from the list of variables:
+ provides two tools to create these dictionaries from the list of variables:
sur_dictxy and sur_dictZ. The tool sur_dictxy can be used to create the
dictionaries for Y and X, and sur_dictZ for endogenous variables (yend) and
additional instruments (q).
@@ -577,8 +578,8 @@ class ThreeSLS(BaseThreeSLS):
We can now run the regression and then have a summary of the output by typing:
print(reg.summary)
- Alternatively, we can just check the betas and standard errors, asymptotic t
- and p-value of the parameters:
+ Alternatively, we can just check the betas and standard errors, asymptotic t
+ and p-value of the parameters:
>>> reg = ThreeSLS(bigy,bigX,bigyend,bigq,name_bigy=bigyvars,name_bigX=bigXvars,name_bigyend=bigyendvars,name_bigq=bigqvars,name_ds="NAT")
>>> reg.b3SLS
@@ -604,7 +605,7 @@ class ThreeSLS(BaseThreeSLS):
def __init__(self,bigy,bigX,bigyend,bigq,nonspat_diag=True,\
name_bigy=None,name_bigX=None,name_bigyend=None,name_bigq=None,\
name_ds=None):
-
+
#need checks on match between bigy, bigX dimensions
BaseThreeSLS.__init__(self,bigy=bigy,bigX=bigX,bigyend=bigyend,\
bigq=bigq)
@@ -617,7 +618,7 @@ class ThreeSLS(BaseThreeSLS):
self.name_bigy = {}
for r in range(self.n_eq):
yn = 'dep_var_' + str(r+1)
- self.name_bigy[r] = yn
+ self.name_bigy[r] = yn
if name_bigX:
self.name_bigX = name_bigX
else: # need to construct x names
@@ -643,21 +644,21 @@ class ThreeSLS(BaseThreeSLS):
for r in range(self.n_eq):
ki = bigq[r].shape[1]
name_i = ['inst_' + str(i + 1) + "_" + str(r+1) for i in range(ki)]
- self.name_bigq[r] = name_i
-
+ self.name_bigq[r] = name_i
+
#inference
self.tsls_inf = sur_setp(self.b3SLS,self.varb)
-
+
# test on constancy of coefficients across equations
if check_k(self.bigK): # only for equal number of variables
self.surchow = sur_chow(self.n_eq,self.bigK,self.b3SLS,self.varb)
else:
- self.surchow = None
-
+ self.surchow = None
+
#Listing of the results
- self.title = "THREE STAGE LEAST SQUARES (3SLS)"
+ self.title = "THREE STAGE LEAST SQUARES (3SLS)"
SUMMARY.SUR(reg=self, tsls=True, nonspat_diag=nonspat_diag)
-
+
def _test():
import doctest
start_suppress = np.get_printoptions()['suppress']
@@ -681,7 +682,7 @@ if __name__ == '__main__':
bigy0,bigX0,bigyvars0,bigXvars0 = sur_dictxy(db,y_var,x_var)
reg0 = SUR(bigy0,bigX0,w=w,name_bigy=bigyvars0,name_bigX=bigXvars0,\
spat_diag=True,name_ds="nat")
- print reg0.summary
+ print reg0.summary
"""
#Example 3SLS
yend_var = [['RD80'],['RD90']]
@@ -692,6 +693,6 @@ if __name__ == '__main__':
bigq1,bigqvars1 = sur_dictZ(db,q_var)
reg1 = ThreeSLS(bigy1,bigX1,bigyend1,bigq1,name_ds="nat")
-
- print reg1.summary
+
+ print reg1.summary
#"""
diff --git a/pysal/spreg/tests/skip.py b/pysal/spreg/tests/skip.py
new file mode 100644
index 0000000..6071ee8
--- /dev/null
+++ b/pysal/spreg/tests/skip.py
@@ -0,0 +1,6 @@
+"""
+Set SKIP to True to skip any tests in this directory with the skip decorator
+
+To run all tests set SKIP=False
+"""
+SKIP=True
diff --git a/pysal/spreg/tests/test_diagnostics.py b/pysal/spreg/tests/test_diagnostics.py
index a1fe5db..b11e6ec 100644
--- a/pysal/spreg/tests/test_diagnostics.py
+++ b/pysal/spreg/tests/test_diagnostics.py
@@ -127,4 +127,3 @@ class TestConstantCheck(unittest.TestCase):
if __name__ == '__main__':
unittest.main()
-
diff --git a/pysal/spreg/tests/test_ml_error.py b/pysal/spreg/tests/test_ml_error.py
index a94f454..5ebe459 100644
--- a/pysal/spreg/tests/test_ml_error.py
+++ b/pysal/spreg/tests/test_ml_error.py
@@ -4,10 +4,13 @@ import scipy
import numpy as np
from pysal.spreg.ml_error import ML_Error
from pysal.spreg import utils
-from pysal.common import RTOL
+from pysal.common import RTOL, ATOL
+from warnings import warn as Warn
+from skip import SKIP
- at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
- "Max Likelihood requires SciPy version 11 or newer.")
+
+ at unittest.skipIf(SKIP,
+ "Skipping MLError Tests")
class TestMLError(unittest.TestCase):
def setUp(self):
db = pysal.open(pysal.examples.get_path("south.dbf"),'r')
@@ -21,11 +24,11 @@ class TestMLError(unittest.TestCase):
ww.close()
self.w.transform = 'r'
- def test_model(self):
+ def _estimate_and_compare(self, method='FULL', RTOL=RTOL):
reg = ML_Error(self.y,self.x,w=self.w,name_y=self.y_name,name_x=self.x_names,\
- name_w="south_q.gal")
+ name_w="south_q.gal", method=method)
betas = np.array([[ 6.1492], [ 4.4024], [ 1.7784], [-0.3781], [ 0.4858], [ 0.2991]])
- print('Running higher-tolerance tests on line L28 of test_ml_error.py')
+ Warn('Running higher-tolerance tests in test_ml_error.py')
np.testing.assert_allclose(reg.betas,betas,RTOL + .0001)
u = np.array([-5.97649777])
np.testing.assert_allclose(reg.u[0],u,RTOL)
@@ -61,7 +64,63 @@ class TestMLError(unittest.TestCase):
(-4.8232686291115678, 1.4122456582517099e-06),
(3.9913060809142995, 6.5710406838016854e-05),
(7.9088780724028922, 2.5971882547279339e-15)]
- np.testing.assert_allclose(reg.z_stat,z_stat,0.0001)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL, atol=ATOL)
+ logll = -4471.407066887894
+ np.testing.assert_allclose(reg.logll,logll,RTOL)
+ aic = 8952.8141337757879
+ np.testing.assert_allclose(reg.aic,aic,RTOL)
+ schwarz = 8979.0779458660545
+ np.testing.assert_allclose(reg.schwarz,schwarz,RTOL)
+
+
+ def test_dense(self):
+ self._estimate_and_compare(method='FULL')
+
+ def test_LU(self):
+ self._estimate_and_compare(method='LU', RTOL=RTOL*10)
+
+ def test_ord(self):
+ reg = ML_Error(self.y, self.x, w=self.w,
+ name_y=self.y_name, name_x=self.x_names,
+ name_w='south_q.gal', method='ORD')
+ betas = np.array([[ 6.1492], [ 4.4024], [ 1.7784], [-0.3781], [ 0.4858], [ 0.2991]])
+ Warn('Running higher-tolerance tests in test_ml_error.py')
+ np.testing.assert_allclose(reg.betas,betas,RTOL + .0001)
+ u = np.array([-5.97649777])
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
+ predy = np.array([ 6.92258051])
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
+ n = 1412
+ np.testing.assert_allclose(reg.n,n,RTOL)
+ k = 5
+ np.testing.assert_allclose(reg.k,k,RTOL)
+ y = np.array([ 0.94608274])
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
+ x = np.array([ 1. , -0.39902838, 0.89645344, 6.85780705, 7.2636377 ])
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
+ e = np.array([-4.92843327])
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
+ my = 9.5492931620846928
+ np.testing.assert_allclose(reg.mean_y,my)
+ sy = 7.0388508798387219
+ np.testing.assert_allclose(reg.std_y,sy)
+ vm = np.array([ 1.06476526, 0.05548248, 0.04544514, 0.00614425, 0.01481356,
+ 0.001501])
+ np.testing.assert_allclose(reg.vm.diagonal(),vm,RTOL * 10)
+ sig2 = np.array([[ 32.40685441]])
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
+ pr2 = 0.3057664820364818
+ np.testing.assert_allclose(reg.pr2,pr2)
+ std_err = np.array([ 1.03187463, 0.23554719, 0.21317867, 0.07838525, 0.12171098,
+ 0.038744])
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL*10)
+ z_stat = [(5.95927510, 2.5335927e-09),
+ (18.6901829, 5.9508630e-78),
+ (8.34216329, 7.2943634e-17),
+ (-4.8232686, 1.4122457e-06),
+ (3.99130608, 6.5710407e-05),
+ (7.71923784, 1.1702739e-14)]
+ np.testing.assert_allclose(reg.z_stat,z_stat,rtol=RTOL, atol=ATOL)
logll = -4471.407066887894
np.testing.assert_allclose(reg.logll,logll,RTOL)
aic = 8952.8141337757879
diff --git a/pysal/spreg/tests/test_ml_error_regimes.py b/pysal/spreg/tests/test_ml_error_regimes.py
index 1694512..baee277 100644
--- a/pysal/spreg/tests/test_ml_error_regimes.py
+++ b/pysal/spreg/tests/test_ml_error_regimes.py
@@ -6,9 +6,11 @@ from pysal.spreg.ml_error_regimes import ML_Error_Regimes
from pysal.spreg.ml_error import ML_Error
from pysal.spreg import utils
from pysal.common import RTOL
+from skip import SKIP
- at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
- "Max Likelihood requires SciPy version 11 or newer.")
+
+ at unittest.skipIf(SKIP,
+ "Skipping MLError Tests")
class TestMLError(unittest.TestCase):
def setUp(self):
db = pysal.open(pysal.examples.get_path("baltim.dbf"),'r')
diff --git a/pysal/spreg/tests/test_ml_lag.py b/pysal/spreg/tests/test_ml_lag.py
index b8c669b..0b3b459 100644
--- a/pysal/spreg/tests/test_ml_lag.py
+++ b/pysal/spreg/tests/test_ml_lag.py
@@ -5,9 +5,11 @@ import numpy as np
from pysal.spreg.ml_lag import ML_Lag
from pysal.spreg import utils
from pysal.common import RTOL
+from skip import SKIP
- at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
- "Max Likelihood requires SciPy version 11 or newer.")
+
+ at unittest.skipIf(SKIP,
+ "Skipping MLLag Tests")
class TestMLError(unittest.TestCase):
def setUp(self):
db = pysal.open(pysal.examples.get_path("baltim.dbf"),'r')
@@ -23,9 +25,11 @@ class TestMLError(unittest.TestCase):
self.w_name = "baltim_q.gal"
self.w.transform = 'r'
- def test_model1(self):
- reg = ML_Lag(self.y,self.x,w=self.w,name_y=self.y_name,name_x=self.x_names,\
- name_w=self.w_name,name_ds=self.ds_name)
+ def _estimate_and_compare(self, **kwargs):
+ reg = ML_Lag(self.y, self.x, w=self.w,
+ name_y=self.y_name, name_x=self.x_names,
+ name_w=self.w_name,name_ds=self.ds_name,
+ **kwargs)
betas = np.array([[-6.04040164],
[ 3.48995114],
[-0.20103955],
@@ -65,5 +69,56 @@ class TestMLError(unittest.TestCase):
schwarz = 1778.614713537077
np.testing.assert_allclose(reg.schwarz,schwarz,RTOL)
+ def test_dense(self):
+ self._estimate_and_compare(method='FULL')
+
+ def test_ord(self):
+ reg = ML_Lag(self.y, self.x, w=self.w,
+ name_y=self.y_name, name_x=self.x_names,
+ name_w=self.w_name,name_ds=self.ds_name,
+ method='ORD')
+ betas = np.array([[-6.04040164],
+ [ 3.48995114],
+ [-0.20103955],
+ [ 0.65462382],
+ [ 0.62351143]])
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
+ u = np.array([ 47.51218398])
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
+ predy = np.array([-0.51218398])
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
+ n = 211
+ np.testing.assert_allclose(reg.n,n,RTOL)
+ k = 5
+ np.testing.assert_allclose(reg.k,k,RTOL)
+ y = np.array([ 47.])
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
+ x = np.array([ 1. , 4. , 148. , 11.25])
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
+ e = np.array([ 41.99251608])
+ np.testing.assert_allclose(reg.e_pred[0],e,RTOL)
+ my = 44.307180094786695
+ np.testing.assert_allclose(reg.mean_y,my)
+ sy = 23.606076835380495
+ np.testing.assert_allclose(reg.std_y,sy)
+ vm = np.array([ 28.63404, 1.423698, 0.002884738, 0.02957845,
+ 0.003379166])
+ np.testing.assert_allclose(reg.vm.diagonal(),vm,RTOL)
+ sig2 = 216.27525647243797
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
+ pr2 = 0.6133020721559487
+ np.testing.assert_allclose(reg.pr2,pr2)
+ std_err = np.array([ 5.351078, 1.193188, 0.05371, 0.171984, 0.058131])
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
+ logll = -875.92771143484833
+ np.testing.assert_allclose(reg.logll,logll,RTOL)
+ aic = 1761.8554228696967
+ np.testing.assert_allclose(reg.aic,aic,RTOL)
+ schwarz = 1778.614713537077
+ np.testing.assert_allclose(reg.schwarz,schwarz,RTOL)
+
+ def test_LU(self):
+ self._estimate_and_compare(method='LU')
+
if __name__ == '__main__':
unittest.main()
diff --git a/pysal/spreg/tests/test_ml_lag_regimes.py b/pysal/spreg/tests/test_ml_lag_regimes.py
index 4b19966..d197b6d 100644
--- a/pysal/spreg/tests/test_ml_lag_regimes.py
+++ b/pysal/spreg/tests/test_ml_lag_regimes.py
@@ -6,9 +6,12 @@ from pysal.spreg.ml_lag_regimes import ML_Lag_Regimes
from pysal.spreg.ml_lag import ML_Lag
from pysal.spreg import utils
from pysal.common import RTOL
+from skip import SKIP
- at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
- "Max Likelihood requires SciPy version 11 or newer.")
+
+
+ at unittest.skipIf(SKIP,
+ "Skipping MLLag Tests")
class TestMLError(unittest.TestCase):
def setUp(self):
db = pysal.open(pysal.examples.get_path("baltim.dbf"),'r')
diff --git a/pysal/spreg/tests/test_sputils.py b/pysal/spreg/tests/test_sputils.py
new file mode 100644
index 0000000..49017fd
--- /dev/null
+++ b/pysal/spreg/tests/test_sputils.py
@@ -0,0 +1,134 @@
+import pysal.spreg as EC
+from pysal.spreg import sputils as spu
+from warnings import warn as Warn
+import unittest as ut
+import numpy as np
+import scipy.sparse as spar
+
+ALL_FUNCS = [f for f,v in spu.__dict__.items() \
+ if (callable(v) \
+ and not f.startswith('_'))]
+COVERAGE = ['spinv', 'splogdet', 'spisfinite', 'spmin', 'spfill_diagonal', \
+ 'spmax', 'spbroadcast', 'sphstack', 'spmultiply', 'spdot']
+
+NOT_COVERED = set(ALL_FUNCS).difference(COVERAGE)
+
+if len(NOT_COVERED) > 0:
+ Warn('The following functions in {} are not covered:\n'
+ '{}'.format(spu.__file__, NOT_COVERED))
+
+class Test_Sparse_Utils(ut.TestCase):
+ def setUp(self):
+ np.random.seed(8879)
+
+ self.n = 20
+ self.dense0 = np.random.randint(2, size=(self.n,self.n))
+ self.d0td0 = self.dense0.T.dot(self.dense0)
+ self.dense1 = np.eye(self.n)
+ self.sparse0 = spar.csc_matrix(self.dense0)
+ self.s0ts0 = self.sparse0.T.dot(self.sparse0)
+ self.sparse1 = spar.csc_matrix(spar.identity(self.n))
+
+ def test_inv(self):
+ r = spu.spinv(self.d0td0)
+ rs = spu.spinv(self.s0ts0)
+ rs2d = rs.toarray()
+
+ self.assertIsInstance(r, np.ndarray)
+ self.assertTrue(spar.issparse(rs))
+ self.assertIsInstance(rs2d, np.ndarray)
+
+ np.testing.assert_allclose(r, rs2d)
+
+ def test_spdot(self):
+ dd = spu.spdot(self.dense0, self.dense1)
+ ds = spu.spdot(self.dense0, self.sparse1)
+ sd = spu.spdot(self.sparse0, self.dense1)
+ ss = spu.spdot(self.sparse0, self.sparse1, array_out=False)
+
+ # typing tests
+ self.assertIsInstance(dd, np.ndarray)
+ self.assertIsInstance(ds, np.ndarray)
+ self.assertIsInstance(sd, np.ndarray)
+ self.assertIsInstance(ss, spar.csc_matrix)
+
+ # product test
+ np.testing.assert_array_equal(dd, ds)
+ np.testing.assert_array_equal(dd, sd)
+ np.testing.assert_array_equal(dd, ss.toarray())
+
+ def test_logdet(self):
+ dld = spu.splogdet(self.d0td0)
+ sld = spu.splogdet(self.s0ts0)
+
+ np.testing.assert_allclose(dld, sld)
+
+ def test_isfinite(self):
+ self.assertTrue(spu.spisfinite(self.dense0))
+ self.assertTrue(spu.spisfinite(self.sparse0))
+
+ dense_inf = np.float64(self.dense0.copy())
+ dense_inf[0,0] = np.nan
+ sparse_inf = spar.csc_matrix(dense_inf)
+
+ self.assertTrue(not spu.spisfinite(dense_inf))
+ self.assertTrue(not spu.spisfinite(sparse_inf))
+
+ def test_min(self):
+ self.assertEquals(spu.spmin(self.dense0), 0)
+ self.assertEquals(spu.spmin(self.sparse0), 0)
+
+ def test_max(self):
+ self.assertEquals(spu.spmax(self.dense1), 1)
+ self.assertEquals(spu.spmax(self.sparse1), 1)
+
+ def test_fill_diagonal(self):
+ current_dsum = self.dense0.trace()
+ current_ssum = self.sparse0.diagonal().sum()
+ self.assertEquals(current_dsum, 7)
+ self.assertEquals(current_ssum, 7)
+
+ tmpd = self.dense0.copy()
+ tmps = self.sparse0.copy()
+ d_4diag = spu.spfill_diagonal(tmpd, 4)
+ s_4diag = spu.spfill_diagonal(tmps, 4)
+
+ known = 4 * self.n
+
+ self.assertEquals(known, d_4diag.trace())
+ self.assertEquals(known, s_4diag.diagonal().sum())
+
+ def test_broadcast(self):
+ test_vec = np.ones((self.n,1)) * .2
+
+ tmpd = spu.spbroadcast(self.dense0, test_vec)
+ tmps = spu.spbroadcast(self.sparse0.tocsr(), test_vec)
+
+ self.assertIsInstance(tmpd, np.ndarray)
+ self.assertIsInstance(tmps, spar.csr_matrix)
+
+ np.testing.assert_allclose(tmpd, tmps.toarray())
+
+ def test_hstack(self):
+ tmpd = spu.sphstack(self.dense0, self.dense1)
+ tmps = spu.sphstack(self.sparse0.tocsr(), self.sparse1.tocsr())
+
+ self.assertIsInstance(tmpd, np.ndarray)
+ self.assertIsInstance(tmps, spar.csr_matrix)
+
+ np.testing.assert_allclose(tmpd, tmps.toarray())
+
+ def test_multiply(self):
+ dd = spu.spmultiply(self.dense0, self.dense1)
+ ss = spu.spmultiply(self.sparse0, self.sparse1, array_out=False)
+
+ #typing
+ self.assertIsInstance(dd, np.ndarray)
+ self.assertIsInstance(ss, spar.csc_matrix)
+
+ #equality
+ np.testing.assert_array_equal(dd, ss.toarray())
+
+
+if __name__ == '__main__':
+ ut.main()
diff --git a/pysal/spreg/tests/test_sur_error.py b/pysal/spreg/tests/test_sur_error.py
index c1eefc4..0024c10 100644
--- a/pysal/spreg/tests/test_sur_error.py
+++ b/pysal/spreg/tests/test_sur_error.py
@@ -4,15 +4,13 @@ import pysal
from pysal.spreg.sur_utils import sur_dictxy,sur_dictZ
from pysal.spreg.sur_error import SURerrorML
from pysal.common import RTOL
+ATOL = 1e-12
PEGP = pysal.examples.get_path
-ATOL = 0.00001
-
-
-def dict_compare(actual, desired, rtol):
+def dict_compare(actual, desired, rtol=RTOL, atol=ATOL):
for i in actual.keys():
- np.testing.assert_allclose(actual[i],desired[i],rtol)
+ np.testing.assert_allclose(actual[i],desired[i],rtol=rtol, atol=atol)
class Test_SUR_error(unittest.TestCase):
@@ -39,14 +37,14 @@ class Test_SUR_error(unittest.TestCase):
[ 4.267954e-02, 9.935169e+00, 2.926783e-23]]),\
1: np.array([[ 3.31399691e-01, 9.20106497e+00, 3.54419478e-20],\
[ 1.33525912e-01, 8.31094371e+00, 9.49439563e-17],\
- [ 4.00409716e-02, 1.17568780e+01, 6.50970965e-32]])},0.0001)
+ [ 4.00409716e-02, 1.17568780e+01, 6.50970965e-32]])},rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(reg.lamols,np.array([[ 0.60205035],[ 0.56056348]]),RTOL)
np.testing.assert_allclose(reg.lamsur,np.array([[ 0.54361986],[ 0.50445451]]),RTOL)
np.testing.assert_allclose(reg.corr,np.array([[ 1. , 0.31763719],\
[ 0.31763719, 1. ]]),RTOL)
np.testing.assert_allclose(reg.surchow,[(5.1073696860799931, 1, 0.023824413482255974),\
(1.9524745281321374, 1, 0.16232044613203933),\
- (0.79663667463065702, 1, 0.37210085476281407)],RTOL)
+ (0.79663667463065702, 1, 0.37210085476281407)],rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(reg.llik,-19860.067987395596)
np.testing.assert_allclose(reg.errllik,-19497.031128906794)
np.testing.assert_allclose(reg.surerrllik,-19353.052023136348)
@@ -67,7 +65,7 @@ class Test_SUR_error(unittest.TestCase):
[ 4.267954e-02, 9.935169e+00, 2.926783e-23]]),\
1: np.array([[ 3.31399691e-01, 9.20106497e+00, 3.54419478e-20],\
[ 1.33525912e-01, 8.31094371e+00, 9.49439563e-17],\
- [ 4.00409716e-02, 1.17568780e+01, 6.50970965e-32]])},0.0001)
+ [ 4.00409716e-02, 1.17568780e+01, 6.50970965e-32]])},rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(reg.vm, np.array([[ 4.14625293e-04, 2.38494923e-05, -3.48748935e-03,\
-5.55994101e-04, -1.63239040e-04],[ 2.38494923e-05, 4.53642714e-04, -2.00602452e-04,\
-5.46893937e-04, -3.10498019e-03],[ -3.48748935e-03, -2.00602452e-04, 7.09989591e-01,\
@@ -75,22 +73,22 @@ class Test_SUR_error(unittest.TestCase):
3.42890248e-01, 1.91931389e-01],[ -1.63239040e-04, -3.10498019e-03, 6.39785285e-02,\
1.91931389e-01, 5.86933821e-01]]),RTOL)
np.testing.assert_allclose(reg.lamsetp,(np.array([[ 0.02036235],\
- [ 0.02129889]]), np.array([[ 26.69730489],[ 23.68454458]]), np.array([[ 0.0],\
- [ 0.0]])),rtol=0.001,atol=ATOL)
- np.testing.assert_allclose(reg.joinlam,(1207.81269, 2, 0.0),atol=0.0001,rtol=0.00001)
+ [ 0.02129889]]), np.array([[ 26.69730489],[ 23.68454458]]), np.array([[ 5.059048e-157],\
+ [ 5.202838e-124]])),rtol=RTOL, atol=ATOL)
+ np.testing.assert_allclose(reg.joinlam,(1207.81269, 2, 5.330924e-263), rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(reg.surchow,[(5.1073696860799931, 1, 0.023824413482255974),
(1.9524745281321374, 1, 0.16232044613203933),
- (0.79663667463065702, 1, 0.37210085476281407)],0.0001)
- np.testing.assert_allclose(reg.likrlambda,(1014.0319285186415, 2, 6.3938800607190098e-221))
- np.testing.assert_allclose(reg.lrtest, (287.95821154104488, 1, 1.3849971230596533e-64),rtol=0.0001, atol=ATOL)
- np.testing.assert_allclose(reg.lamtest, (1.8693306894921564, 1, 0.17155175615429052),rtol=0.0001, atol=ATOL)
+ (0.79663667463065702, 1, 0.37210085476281407)],rtol=RTOL, atol=ATOL)
+ np.testing.assert_allclose(reg.likrlambda, (1014.0319285186415, 2, 6.3938800607190098e-221), rtol=RTOL, atol=ATOL)
+ np.testing.assert_allclose(reg.lrtest, (287.95821154104488, 1, 1.3849971230596533e-64), rtol=RTOL, atol=ATOL)
+ np.testing.assert_allclose(reg.lamtest, (1.8693306894921564, 1, 0.17155175615429052), rtol=RTOL, atol=ATOL)
def test_error_3eq(self): #Three equation example, unequal K
y_var1 = ['HR60','HR70','HR80']
x_var1 = [['RD60','PS60'],['RD70','PS70','UE70'],['RD80','PS80']]
bigy1,bigX1,bigyvars1,bigXvars1 = sur_dictxy(self.db,y_var1,x_var1)
reg = SURerrorML(bigy1,bigX1,self.w,name_bigy=bigyvars1,name_bigX=bigXvars1,\
- name_w="natqueen",name_ds="natregimes")
+ name_w="natqueen",name_ds="natregimes")
dict_compare(reg.bSUR0,{0: np.array([[ 4.50407527],[ 2.39199682],[ 0.52723694]]), 1: np.array([[ 7.44509818],\
[ 3.74968571],[ 1.28811685],[-0.23526451]]), 2: np.array([[ 6.92761614],[ 3.65423052],\
@@ -107,7 +105,7 @@ class Test_SUR_error(unittest.TestCase):
[ 5.378335e-002, -3.185738e+000, 1.443854e-003]]),\
2: np.array([[ 1.500528e-001, 4.608718e+001, 0.000000e+000],\
[ 1.236340e-001, 2.987457e+001, 4.210941e-196],\
- [ 1.194989e-001, 1.174869e+001, 7.172248e-032]])},0.001)
+ [ 1.194989e-001, 1.174869e+001, 7.172248e-032]])},rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(reg.lamols,np.array([[ 0.4248829 ],[ 0.46428101],[ 0.42823999]]),RTOL)
np.testing.assert_allclose(reg.lamsur,np.array([[ 0.36137603],[ 0.38321666],[ 0.37183716]]),RTOL)
np.testing.assert_allclose(reg.corr,np.array([[ 1., 0.24563253, 0.14986527],\
diff --git a/pysal/spreg/user_output.py b/pysal/spreg/user_output.py
index e9523f4..657a5e6 100644
--- a/pysal/spreg/user_output.py
+++ b/pysal/spreg/user_output.py
@@ -1,16 +1,16 @@
"""Internal helper files for user output."""
-__author__ = "Luc Anselin luc.anselin at asu.edu, David C. Folch david.folch at asu.edu, Jing Yao jingyao at asu.edu"
-import textwrap as TW
+__author__ = ("Luc Anselin luc.anselin at asu.edu, "
+ "David C. Folch david.folch at asu.edu, "
+ "Levi John Wolf levi.john.wolf at gmail.com, "
+ "Jing Yao jingyao at asu.edu")
import numpy as np
import copy as COPY
-import diagnostics as diagnostics
-import diagnostics_tsls as diagnostics_tsls
-import diagnostics_sp as diagnostics_sp
-import pysal
+from . import diagnostics
+from . import sputils as spu
+from .. import weights
import scipy
from scipy.sparse.csr import csr_matrix
-from utils import spdot, sphstack
__all__ = []
@@ -309,7 +309,7 @@ def set_name_multi(multireg, multi_set, name_multiID, y, x, name_y, name_x, name
def check_arrays(*arrays):
"""Check if the objects passed by a user to a regression class are
correctly structured. If the user's data is correctly formed this function
- returns nothing, if not then an exception is raised. Note, this does not
+ returns nothing, if not then an exception is raised. Note, this does not
check for model setup, simply the shape and types of the objects.
Parameters
@@ -343,12 +343,11 @@ def check_arrays(*arrays):
49
"""
- allowed = ['ndarray', 'csr_matrix']
rows = []
for i in arrays:
if i is None:
continue
- if i.__class__.__name__ not in allowed:
+ if not isinstance(i, (np.ndarray, csr_matrix)):
raise Exception, "all input data must be either numpy arrays or sparse csr matrices"
shape = i.shape
if len(shape) > 2:
@@ -357,6 +356,8 @@ def check_arrays(*arrays):
raise Exception, "all input arrays must have exactly two dimensions"
if shape[0] < shape[1]:
raise Exception, "one or more input arrays have more columns than rows"
+ if not spu.spisfinite(i):
+ raise Exception, "one or more input arrays have missing/NaN values"
rows.append(shape[0])
if len(set(rows)) > 1:
raise Exception, "arrays not all of same length"
@@ -366,7 +367,7 @@ def check_arrays(*arrays):
def check_y(y, n):
"""Check if the y object passed by a user to a regression class is
correctly structured. If the user's data is correctly formed this function
- returns nothing, if not then an exception is raised. Note, this does not
+ returns nothing, if not then an exception is raised. Note, this does not
check for model setup, simply the shape and types of the objects.
Parameters
@@ -398,7 +399,7 @@ def check_y(y, n):
>>> # should not raise an exception
"""
- if y.__class__.__name__ != 'ndarray':
+ if not isinstance(y, np.ndarray):
print y.__class__.__name__
raise Exception, "y must be a numpy array"
shape = y.shape
@@ -452,7 +453,7 @@ def check_weights(w, y, w_required=False):
if w_required == True or w != None:
if w == None:
raise Exception, "A weights matrix w must be provided to run this method."
- if not isinstance(w, pysal.W):
+ if not isinstance(w, weights.W):
raise Exception, "w must be a pysal.W object"
if w.n != y.shape[0]:
raise Exception, "y must be nx1, and w must be an nxn PySAL W object"
@@ -466,7 +467,7 @@ def check_weights(w, y, w_required=False):
def check_robust(robust, wk):
"""Check if the combination of robust and wk parameters passed by the user
- are valid. Note: this does not check if the W object is a valid adaptive
+ are valid. Note: this does not check if the W object is a valid adaptive
kernel weights matrix needed for the HAC.
Parameters
@@ -504,8 +505,8 @@ def check_robust(robust, wk):
"""
if robust:
if robust.lower() == 'hac':
- if type(wk).__name__ != 'W' and type(wk).__name__ != 'Kernel':
- raise Exception, "HAC requires that wk be a pysal.W object"
+ if not isinstance(wk, weights.Kernel):
+ raise Exception, "HAC requires that wk be a Kernel Weights object"
diag = wk.sparse.diagonal()
# check to make sure all entries equal 1
if diag.min() < 1.0:
@@ -570,7 +571,7 @@ def check_spat_diag(spat_diag, w):
"""
if spat_diag:
- if type(w).__name__ != 'W':
+ if not isinstance(w, weights.W):
raise Exception, "w must be a pysal.W object to run spatial diagnostics"
@@ -627,11 +628,11 @@ def check_constant(x):
(49, 3)
"""
- if not diagnostics.constant_check:
+ if diagnostics.constant_check(x):
raise Exception, "x array cannot contain a constant vector; constant will be added automatically"
else:
x_constant = COPY.copy(x)
- return sphstack(np.ones((x_constant.shape[0], 1)), x_constant)
+ return spu.sphstack(np.ones((x_constant.shape[0], 1)), x_constant)
def _test():
diff --git a/pysal/spreg/utils.py b/pysal/spreg/utils.py
index 3d455d4..7c69aaf 100755
--- a/pysal/spreg/utils.py
+++ b/pysal/spreg/utils.py
@@ -14,6 +14,7 @@ from scipy.sparse import linalg as SPla
import scipy.optimize as op
import numpy.linalg as la
from pysal import lag_spatial
+from sputils import *
import copy
@@ -657,213 +658,6 @@ def sp_att(w, y, predy, w_y, rho):
# see Note 1.
return predy_sp, resid_sp, warn
-
-def spdot(a, b, array_out=True):
- """
- Matrix multiplication function to deal with sparse and dense objects
-
- Parameters
- ----------
-
- a : array
- first multiplication factor. Can either be sparse or dense.
- b : array
- second multiplication factor. Can either be sparse or dense.
- array_out : boolean
- If True (default) the output object is always a np.array
-
- Returns
- -------
-
- ab : array
- product of a times b. Sparse if a and b are sparse. Dense otherwise.
- """
- if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
- ab = np.dot(a, b)
- elif type(a).__name__ == 'csr_matrix' or type(b).__name__ == 'csr_matrix' \
- or type(a).__name__ == 'csc_matrix' or type(b).__name__ == 'csc_matrix':
- ab = a * b
- if array_out:
- if type(ab).__name__ == 'csc_matrix' or type(ab).__name__ == 'csr_matrix':
- ab = ab.toarray()
- else:
- raise Exception, "Invalid format for 'spdot' argument: %s and %s" % (
- type(a).__name__, type(b).__name__)
- return ab
-
-
-def spmultiply(a, b, array_out=True):
- """
- Element-wise multiplication function to deal with sparse and dense
- objects. Both objects must be of the same type.
-
- Parameters
- ----------
-
- a : array
- first multiplication factor. Can either be sparse or dense.
- b : array
- second multiplication factor. Can either be sparse or dense.
- integer.
- array_out : boolean
- If True (default) the output object is always a np.array
-
- Returns
- -------
-
- ab : array
- elementwise multiplied object. Sparse if a is sparse. Dense otherwise.
- """
- if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
- ab = a * b
- elif (type(a).__name__ == 'csr_matrix' or type(a).__name__ == 'csc_matrix') \
- and (type(b).__name__ == 'csr_matrix' or type(b).__name__ == 'csc_matrix'):
- ab = a.multiply(b)
- if array_out:
- if type(ab).__name__ == 'csc_matrix' or type(ab).__name__ == 'csr_matrix':
- ab = ab.toarray()
- else:
- raise Exception, "Invalid format for 'spmultiply' argument: %s and %s" % (
- type(a).__name__, type(b).__name__)
- return ab
-
-
-def sphstack(a, b, array_out=False):
- """
- Horizontal stacking of vectors (or matrices) to deal with sparse and dense objects
-
- Parameters
- ----------
-
- a : array or sparse matrix
- First object.
- b : array or sparse matrix
- Object to be stacked next to a
- array_out : boolean
- If True the output object is a np.array; if False (default)
- the output object is an np.array if both inputs are
- arrays or CSR matrix if at least one input is a CSR matrix
-
- Returns
- -------
-
- ab : array or sparse matrix
- Horizontally stacked objects
- """
- if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
- ab = np.hstack((a, b))
- elif type(a).__name__ == 'csr_matrix' or type(b).__name__ == 'csr_matrix':
- ab = SP.hstack((a, b), format='csr')
- if array_out:
- if type(ab).__name__ == 'csr_matrix':
- ab = ab.toarray()
- else:
- raise Exception, "Invalid format for 'sphstack' argument: %s and %s" % (
- type(a).__name__, type(b).__name__)
- return ab
-
-
-def spbroadcast(a, b, array_out=False):
- """
- Element-wise multiplication of a matrix and vector to deal with sparse
- and dense objects
-
- Parameters
- ----------
-
- a : array or sparse matrix
- Object with one or more columns.
- b : array
- Object with only one column
- array_out : boolean
- If True the output object is a np.array; if False (default)
- the output object is an np.array if both inputs are
- arrays or CSR matrix if at least one input is a CSR matrix
-
- Returns
- -------
-
- ab : array or sparse matrix
- Element-wise multiplication of a and b
- """
- if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
- ab = a * b
- elif type(a).__name__ == 'csr_matrix':
- b_mod = SP.lil_matrix((b.shape[0], b.shape[0]))
- b_mod.setdiag(b)
- ab = (a.T * b_mod).T
- if array_out:
- if type(ab).__name__ == 'csr_matrix':
- ab = ab.toarray()
- else:
- raise Exception, "Invalid format for 'spbroadcast' argument: %s and %s" % (
- type(a).__name__, type(b).__name__)
- return ab
-
-
-def spmin(a):
- """
- Minimum value in a matrix or vector to deal with sparse and dense objects
-
- Parameters
- ----------
-
- a : array or sparse matrix
- Object with one or more columns.
-
- Returns
- -------
-
- min a : int or float
- minimum value in a
- """
-
- if type(a).__name__ == 'ndarray':
- return a.min()
- elif type(a).__name__ == 'csr_matrix' or type(a).__name__ == 'csc_matrix':
- try:
- return min(a.data)
- except:
- if np.sum(a.data) == 0:
- return 0
- else:
- raise Exception, "Error: could not evaluate the minimum value."
- else:
- raise Exception, "Invalid format for 'spmultiply' argument: %s and %s" % (
- type(a).__name__, type(b).__name__)
-
-
-def spmax(a):
- """
- Maximum value in a matrix or vector to deal with sparse and dense objects
-
- Parameters
- ----------
-
- a : array or sparse matrix
- Object with one or more columns.
-
- Returns
- -------
-
- max a : int or float
- maximum value in a
- """
- if type(a).__name__ == 'ndarray':
- return a.max()
- elif type(a).__name__ == 'csr_matrix' or type(a).__name__ == 'csc_matrix':
- try:
- return max(a.data)
- except:
- if np.sum(a.data) == 0:
- return 0
- else:
- raise Exception, "Error: could not evaluate the maximum value."
- else:
- raise Exception, "Invalid format for 'spmultiply' argument: %s and %s" % (
- type(a).__name__, type(b).__name__)
-
-
def set_warn(reg, warn):
''' Groups warning messages for printout. '''
if warn:
@@ -874,26 +668,6 @@ def set_warn(reg, warn):
else:
pass
-def splogdet(spmat):
- ''' Large dimension log determinant computation. '''
- #symmetric = np.allclose(spmat.T, spmat) #could branch for cholmod
- symmetric = False
- if symmetric:
- #CHOLMOD could be used like:
- #from scikits.sparse import cholmod as CHOLMOD
- #det = np.sum(np.log(np.abs(CHOLMOD.cholesky(spmat).D())))
- pass #leave in for scaffolding.
- else:
- if isinstance(spmat, SP.csc_matrix) or isinstance(spmat, SP.csr_matrix):
- LU = SPla.splu(spmat)
- det = np.sum(np.log(np.abs(LU.U.diagonal())))
- elif SP.isspmatrix(spmat):
- return spdet(spmat.tocsc())
- else:
- det = la.slogdet(spmat)
- return det
-
-
def RegressionProps_basic(reg, betas=None, predy=None, u=None, sig2=None, sig2n_k=None, vm=None):
''' Set props based on arguments passed. '''
if betas is not None:
diff --git a/pysal/version.py b/pysal/version.py
index 6be3469..e739bc2 100644
--- a/pysal/version.py
+++ b/pysal/version.py
@@ -1,3 +1,3 @@
import datetime
-version = "1.11.2"
-stable_release_date = datetime.date(2016, 5, 18)
+version = "1.12.0"
+stable_release_date = datetime.date(2016, 9, 21)
diff --git a/pysal/weights/Contiguity.py b/pysal/weights/Contiguity.py
index c26a185..9a2b7fc 100644
--- a/pysal/weights/Contiguity.py
+++ b/pysal/weights/Contiguity.py
@@ -1,88 +1,368 @@
-"""
-Contiguity based spatial weights.
+from warnings import warn as Warn
+from ..cg import asShape
+from ..core.FileIO import FileIO
+from .weights import W, WSP
+from ._contW_binning import ContiguityWeightsPolygons
+from ._contW_rtree import ContiguityWeights_rtree
+from ._contW_lists import ContiguityWeightsLists
+from .util import get_ids
+WT_TYPE = {'rook': 2, 'queen': 1} # for _contW_Binning
-"""
+__author__ = "Sergio J. Rey <srey at asu.edu> , Levi John Wolf <levi.john.wolf at gmail.com>"
-__author__ = "Sergio J. Rey <srey at asu.edu> "
-__all__ = ['buildContiguity']
+class Rook(W):
+ def __init__(self, polygons, method='binning', **kw):
+ """
+ Construct a weights object from a collection of pysal polygons.
-import pysal
-from _contW_binning import ContiguityWeights_binning as ContiguityWeights
-from _contW_binning import ContiguityWeightsPolygons
+ Arguments
+ ---------
+ polygons : list
+ a collection of PySAL shapes to build weights from
+ ids : list
+ a list of names to use to build the weights
+ method : string
+ the algorithm to use to construct the method
+ **kw : keyword arguments
+ optional arguments for :class:`pysal.weights.W`
+ See Also
+ ---------
+ :class:`pysal.weights.W`
+ """
+ criterion = 'rook'
+ ids = kw.pop('ids', None)
+ neighbors, ids = _build(polygons, criterion=criterion,
+ ids=ids, method=method)
+ W.__init__(self, neighbors, ids=ids, **kw)
+
+ @classmethod
+ def from_shapefile(cls, filepath, idVariable=None, full=False, **kwargs):
+ """
+ Rook contiguity weights from a polygon shapefile.
-WT_TYPE = {'rook': 2, 'queen': 1} # for _contW_Binning
+ Parameters
+ ----------
+ shapefile : string
+ name of polygon shapefile including suffix.
+ sparse : boolean
+ If True return WSP instance
+ If False return W instance
-def buildContiguity(polygons, criterion="rook", ids=None):
- """
- Build contiguity weights from a source.
+ Returns
+ -------
- Parameters
- ----------
+ w : W
+ instance of spatial weights
- polygons :
- an instance of a pysal geo file handler
- Any thing returned by pysal.open that is explicitly polygons
- criterion : string
- contiguity criterion ("rook","queen")
- ids : list
- identifiers for i,j
+ Examples
+ --------
+ >>> wr=rook_from_shapefile(pysal.examples.get_path("columbus.shp"), "POLYID")
+ >>> "%.3f"%wr.pct_nonzero
+ '8.330'
+ >>> wr=rook_from_shapefile(pysal.examples.get_path("columbus.shp"), sparse=True)
+ >>> pct_sp = wr.sparse.nnz *1. / wr.n**2
+ >>> "%.3f"%pct_sp
+ '0.083'
- Returns
- -------
+ Notes
+ -----
+
+ Rook contiguity defines as neighbors any pair of polygons that share a
+ common edge in their polygon definitions.
+
+ See Also
+ --------
+ :class:`pysal.weights.W`
+ :class:`pysal.weights.Rook`
+ """
+ sparse = kwargs.pop('sparse', False)
+ if idVariable is not None:
+ ids = get_ids(filepath, idVariable)
+ else:
+ ids = None
+ w = cls(FileIO(filepath), ids=ids, **kwargs)
+ w.set_shapefile(filepath, idVariable=idVariable, full=full)
+ if sparse:
+ w = w.to_WSP()
+ return w
+
+ @classmethod
+ def from_iterable(cls, iterable, **kwargs):
+ """
+ Construct a weights object from a collection of arbitrary polygons. This
+ will cast the polygons to PySAL polygons, then build the W.
+
+ Arguments
+ ---------
+ iterable : iterable
+ a collection of of shapes to be cast to PySAL shapes. Must
+ support iteration. Contents should at least implement a
+ `__geo_interface__` attribute or be able to be coerced to
+ geometries using pysal.cg.asShape
+ **kw : keyword arguments
+ optional arguments for :class:`pysal.weights.W`
+ See Also
+ ----------
+ :class:`pysal.weights.W`
+ :class:`pysal.weights.Rook`
+ """
+ new_iterable = [asShape(shape) for shape in iterable]
+ return cls(new_iterable, **kwargs)
+
+ @classmethod
+ def from_dataframe(cls, df, geom_col='geometry',
+ idVariable=None, ids=None, id_order=None, **kwargs):
+ """
+ Construct a weights object from a pandas dataframe with a geometry
+ column. This will cast the polygons to PySAL polygons, then build the W
+ using ids from the dataframe.
+
+ Arguments
+ ---------
+ df : DataFrame
+ a :class: `pandas.DataFrame` containing geometries to use
+ for spatial weights
+ geom_col : string
+ the name of the column in `df` that contains the
+ geometries. Defaults to `geometry`
+ idVariable : string
+ the name of the column to use as IDs. If nothing is
+ provided, the dataframe index is used
+ ids : list
+ a list of ids to use to index the spatial weights object.
+ Order is not respected from this list.
+ id_order : list
+ an ordered list of ids to use to index the spatial weights
+ object. If used, the resulting weights object will iterate
+ over results in the order of the names provided in this
+ argument.
+
+ See Also
+ ---------
+ :class:`pysal.weights.W`
+ :class:`pysal.weights.Rook`
+ """
+ if id_order is not None:
+ if id_order is True and ((idVariable is not None)
+ or (ids is not None)):
+ # if idVariable is None, we want ids. Otherwise, we want the
+ # idVariable column
+ id_order = list(df.get(idVariable, ids))
+ else:
+ id_order = df.get(id_order, ids)
+ elif idVariable is not None:
+ ids = df.get(idVariable).tolist()
+ elif isinstance(ids, str):
+ ids = df.get(ids).tolist()
+ return cls.from_iterable(df[geom_col].tolist(), ids=ids,
+ id_order=id_order, **kwargs)
+
+class Queen(W):
+ def __init__(self, polygons,method='binning', **kw):
+ """
+ Construct a weights object from a collection of pysal polygons.
+
+ Arguments
+ ---------
+ polygons : list
+ a collection of PySAL shapes to build weights from
+ ids : list
+ a list of names to use to build the weights
+ method : string
+ the algorithm to use to construct the method
+ **kw : keyword arguments
+ optional arguments for :class:`pysal.weights.W`
+
+ See Also
+ ---------
+ :class:`pysal.weights.W`
+ """
+ criterion = 'queen'
+ ids = kw.pop('ids', None)
+ neighbors, ids = _build(polygons, ids=ids,
+ criterion=criterion, method=method)
+ W.__init__(self, neighbors, ids=ids, **kw)
+
+ @classmethod
+ def from_shapefile(cls, filepath, idVariable=None, full=False, **kwargs):
+ """
+ Queen contiguity weights from a polygon shapefile.
+
+ Parameters
+ ----------
+
+ shapefile : string
+ name of polygon shapefile including suffix.
+ idVariable : string
+ name of a column in the shapefile's DBF to use for ids.
+ sparse : boolean
+ If True return WSP instance
+ If False return W instance
+ Returns
+ -------
+
+ w : W
+ instance of spatial weights
+
+ Examples
+ --------
+ >>> wq=Queen.from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> "%.3f"%wq.pct_nonzero
+ '9.829'
+ >>> wq=Queen.from_shapefile(pysal.examples.get_path("columbus.shp"),"POLYID")
+ >>> "%.3f"%wq.pct_nonzero
+ '9.829'
+ >>> wq=Queen.from_shapefile(pysal.examples.get_path("columbus.shp"), sparse=True)
+ >>> pct_sp = wq.sparse.nnz *1. / wq.n**2
+ >>> "%.3f"%pct_sp
+ '0.098'
+
+ Notes
- w : W
- instance; Contiguity weights object
-
- Examples
- --------
-
- >>> w = buildContiguity(pysal.open(pysal.examples.get_path('10740.shp'),'r'))
- WARNING: there is one disconnected observation (no neighbors)
- Island id: [163]
- >>> w[0]
- {1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0}
- >>> w = buildContiguity(pysal.open(pysal.examples.get_path('10740.shp'),'r'),criterion='queen')
- WARNING: there is one disconnected observation (no neighbors)
- Island id: [163]
- >>> w.pct_nonzero
- 3.1926364234056543
- >>> w = buildContiguity(pysal.open(pysal.examples.get_path('10740.shp'),'r'),criterion='rook')
- WARNING: there is one disconnected observation (no neighbors)
- Island id: [163]
- >>> w.pct_nonzero
- 2.6351084812623276
- >>> fips = pysal.open(pysal.examples.get_path('10740.dbf')).by_col('STFID')
- >>> w = buildContiguity(pysal.open(pysal.examples.get_path('10740.shp'),'r'),ids=fips)
- WARNING: there is one disconnected observation (no neighbors)
- Island id: [u'35043940300']
- >>> w['35001000107']
- {u'35001003805': 1.0, u'35001003721': 1.0, u'35001000111': 1.0, u'35001000112': 1.0, u'35001000108': 1.0}
-
- Notes
- -----
-
- The types of sources supported will expand over time.
-
- See Also
- --------
- pysal.weights.W # need to fix sphinx links
+ Queen contiguity defines as neighbors any pair of polygons that share at
+ least one vertex in their polygon definitions.
+ See Also
+ --------
+ :class:`pysal.weights.W`
+ :class:`pysal.weights.Queen`
+ """
+ sparse = kwargs.pop('sparse', False)
+ if idVariable is not None:
+ ids = get_ids(filepath, idVariable)
+ else:
+ ids = None
+ iterable = FileIO(filepath)
+ w = cls(FileIO(filepath), ids=ids, **kwargs)
+ w.set_shapefile(filepath, idVariable=idVariable, full=full)
+ if sparse:
+ w = w.to_WSP()
+ return w
+
+ @classmethod
+ def from_iterable(cls, iterable, sparse=False, **kwargs):
+ """
+ Construct a weights object from a collection of arbitrary polygons. This
+ will cast the polygons to PySAL polygons, then build the W.
+
+ Arguments
+ ---------
+ iterable : iterable
+ a collection of of shapes to be cast to PySAL shapes. Must
+ support iteration. Contents should at least implement a
+ `__geo_interface__` attribute or be able to be coerced to
+ geometries using pysal.cg.asShape
+ **kw : keyword arguments
+ optional arguments for :class:`pysal.weights.W`
+ See Also
+ ----------
+ :class:`pysal.weights.W`
+ :class:`pysal.weights.Queen`
+ """
+ new_iterable = [asShape(shape) for shape in iterable]
+
+ w = cls(new_iterable, **kwargs)
+ if sparse:
+ w = WSP.from_W(w)
+
+ return w
+
+ @classmethod
+ def from_dataframe(cls, df, geom_col='geometry', **kwargs):
+ """
+ Construct a weights object from a pandas dataframe with a geometry
+ column. This will cast the polygons to PySAL polygons, then build the W
+ using ids from the dataframe.
+
+ Arguments
+ ---------
+ df : DataFrame
+ a :class: `pandas.DataFrame` containing geometries to use
+ for spatial weights
+ geom_col : string
+ the name of the column in `df` that contains the
+ geometries. Defaults to `geometry`
+ idVariable : string
+ the name of the column to use as IDs. If nothing is
+ provided, the dataframe index is used
+ ids : list
+ a list of ids to use to index the spatial weights object.
+ Order is not respected from this list.
+ id_order : list
+ an ordered list of ids to use to index the spatial weights
+ object. If used, the resulting weights object will iterate
+ over results in the order of the names provided in this
+ argument.
+
+ See Also
+ ---------
+ :class:`pysal.weights.W`
+ :class:`pysal.weights.Queen`
+ """
+ idVariable = kwargs.pop('idVariable', None)
+ ids = kwargs.pop('ids', None)
+ id_order = kwargs.pop('id_order', None)
+ if id_order is not None:
+ if id_order is True and ((idVariable is not None)
+ or (ids is not None)):
+ # if idVariable is None, we want ids. Otherwise, we want the
+ # idVariable column
+ ids = list(df.get(idVariable, ids))
+ id_order = ids
+ elif isinstance(id_order, str):
+ ids = df.get(id_order, ids)
+ id_order = ids
+ elif idVariable is not None:
+ ids = df.get(idVariable).tolist()
+ elif isinstance(ids, str):
+ ids = df.get(ids).tolist()
+ w = cls.from_iterable(df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs)
+ return w
+
+def _build(polygons, criterion="rook", ids=None, method='list'):
"""
+ This is a developer-facing function to construct a spatial weights object.
+
+ Arguments
+ ---------
+ polygons : list
+ list of pysal polygons to use to build contiguity
+ criterion : string
+ option of which kind of contiguity to build. Is either "rook" or "queen"
+ ids : list
+ list of ids to use to index the neighbor dictionary
+ method : string
+ option of which driver to use to build contiguity. Three are
+ supported: list, binning, & rtree
+
+ Returns
+ -------
+ tuple containing (neighbors, ids), where neighbors is a dictionary
+ describing contiguity relations and ids is the list of ids used to index
+ that dictionary.
+ NOTE: this is different from the prior behavior of buildContiguity, which
+ returned an actual weights object. Since this just dispatches for the
+ classes above, this returns the raw ingredients for a spatial weights
+ object, not the object itself.
+ """
if ids and len(ids) != len(set(ids)):
raise ValueError("The argument to the ids parameter contains duplicate entries.")
- wt_type = WT_TYPE[criterion.lower()]
+ wttype = WT_TYPE[criterion.lower()]
geo = polygons
- if issubclass(type(geo), pysal.open):
- geo.seek(0) # Make sure we read from the beginging of the file.
- geoObj = geo
+ if issubclass(type(geo), FileIO):
+ geo.seek(0) # Make sure we read from the beginning of the file.
+
+ if method.lower().startswith('rtree'):
+ neighbor_data = ContiguityWeights_rtree(polygons, joinType=wttype).w
+ elif method.lower().startswith('list'):
+ neighbor_data = ContiguityWeightsLists(polygons, wttype=wttype).w
else:
- raise TypeError(
- "Argument must be a FileIO handler or connection string.")
- neighbor_data = ContiguityWeights(geoObj, wt_type).w
+ neighbor_data = ContiguityWeightsPolygons(polygons, wttype=wttype).w
+
neighbors = {}
#weights={}
if ids:
@@ -96,5 +376,20 @@ def buildContiguity(polygons, criterion="rook", ids=None):
else:
for key in neighbor_data:
neighbors[key] = list(neighbor_data[key])
- return pysal.weights.W(neighbors, id_order=ids)
+ return neighbors, ids
+def buildContiguity(polygons, criterion="rook", ids=None):
+ """
+ This is a deprecated function.
+
+ It builds a contiguity W from the polygons provided. As such, it is now
+ identical to calling the class constructors for Rook or Queen.
+ """
+ #Warn('This function is deprecated. Please use the Rook or Queen classes',
+ # UserWarning)
+ if criterion.lower() == 'rook':
+ return Rook(polygons, ids=ids)
+ elif criterion.lower() == 'queen':
+ return Queen(polygons, ids=ids)
+ else:
+ raise Exception('Weights criterion "{}" was not found.'.format(criterion))
diff --git a/pysal/weights/Distance.py b/pysal/weights/Distance.py
index 223d2c2..3a55d2b 100644
--- a/pysal/weights/Distance.py
+++ b/pysal/weights/Distance.py
@@ -1,28 +1,39 @@
-"""
-Distance based spatial weights.
-
-"""
+from ..cg.kdtree import KDTree
+from .weights import W
+from .util import isKDTree, get_ids, get_points_array_from_shapefile, get_points_array
+import copy
+from warnings import warn as Warn
+import numpy as np
-__author__ = "Sergio J. Rey <srey at asu.edu> "
+__all__ = ["KNN", "Kernel", "DistanceBand"]
+__author__ = "Sergio J. Rey <srey at asu.edu>, Levi John Wolf <levi.john.wolf at gmail.com>"
import pysal
import scipy.spatial
from pysal.common import KDTree
-from pysal.weights import W
+from pysal.weights import W, WSP
+from pysal.weights.util import WSP2W
import scipy.stats
+from scipy.spatial import distance_matrix
+import scipy.sparse as sp
import numpy as np
from util import isKDTree
-__all__ = ["knnW", "Kernel", "DistanceBand"]
+def knnW(data, k=2, p=2, ids=None, radius=None, distance_metric='euclidean'):
+ """
+ This is deprecated. Use the pysal.weights.KNN class instead.
+ """
+ #Warn('This function is deprecated. Please use pysal.weights.KNN', UserWarning)
+ return KNN(data, k=k, p=p, ids=ids, radius=radius,
+ distance_metric=distance_metric)
-def knnW(data, k=2, p=2, ids=None):
+class KNN(W):
"""
Creates nearest neighbor weights matrix based on k nearest
neighbors.
Parameters
----------
-
kdtree : object
PySAL KDTree or ArcKDTree where KDtree.data is array (n,k)
n observations on k characteristics used to measure
@@ -47,16 +58,15 @@ def knnW(data, k=2, p=2, ids=None):
Examples
--------
-
>>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> kd = pysal.cg.kdtree.KDTree(np.array(points))
- >>> wnn2 = pysal.knnW(kd, 2)
+ >>> wnn2 = pysal.KNN(kd, 2)
>>> [1,3] == wnn2.neighbors[0]
True
ids
- >>> wnn2 = knnW(kd,2)
+ >>> wnn2 = KNN(kd,2)
>>> wnn2[0]
{1: 1.0, 3: 1.0}
>>> wnn2[1]
@@ -64,7 +74,7 @@ def knnW(data, k=2, p=2, ids=None):
now with 1 rather than 0 offset
- >>> wnn2 = knnW(kd, 2, ids=range(1,7))
+ >>> wnn2 = KNN(kd, 2, ids=range(1,7))
>>> wnn2[1]
{2: 1.0, 4: 1.0}
>>> wnn2[2]
@@ -79,29 +89,246 @@ def knnW(data, k=2, p=2, ids=None):
See Also
--------
- pysal.weights.W
-
+ :class:`pysal.weights.W`
"""
- if isKDTree(data):
- kdt = data
- data = kdt.data
- else:
- kdt = KDTree(data)
- nnq = kdt.query(data, k=k+1, p=p)
- info = nnq[1]
-
- neighbors = {}
- for i, row in enumerate(info):
- row = row.tolist()
- if i in row:
+ def __init__(self, data, k=2, p=2, ids=None, radius=None, distance_metric='euclidean'):
+ if isKDTree(data):
+ self.kdtree = data
+ self.data = data.data
+ else:
+ self.data = data
+ self.kdtree = KDTree(data, radius=radius, distance_metric=distance_metric)
+ self.k = k
+ self.p = p
+ this_nnq = self.kdtree.query(self.data, k=k+1, p=p)
+
+ to_weight = this_nnq[1]
+ if ids is None:
+ ids = list(range(to_weight.shape[0]))
+
+ neighbors = {}
+ for i,row in enumerate(to_weight):
+ row = row.tolist()
row.remove(i)
- focal = i
- if ids:
- row = [ ids[j] for j in row]
+ row = [ids[j] for j in row]
focal = ids[i]
- neighbors[focal] = row
- return pysal.weights.W(neighbors, id_order=ids)
+ neighbors[focal] = row
+ W.__init__(self, neighbors, id_order=ids)
+
+ @classmethod
+ def from_shapefile(cls, filepath, **kwargs):
+ """
+ Nearest neighbor weights from a shapefile.
+
+ Parameters
+ ----------
+
+ data : string
+ shapefile containing attribute data.
+ k : int
+ number of nearest neighbors
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ ids : list
+ identifiers to attach to each observation
+ radius : float
+ If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+
+ Returns
+ -------
+
+ w : KNN
+ instance; Weights object with binary weights.
+
+ Examples
+ --------
+
+ Polygon shapefile
+
+ >>> wc=knnW_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> "%.4f"%wc.pct_nonzero
+ '4.0816'
+ >>> set([2,1]) == set(wc.neighbors[0])
+ True
+ >>> wc3=pysal.knnW_from_shapefile(pysal.examples.get_path("columbus.shp"),k=3)
+ >>> set(wc3.neighbors[0]) == set([2,1,3])
+ True
+ >>> set(wc3.neighbors[2]) == set([4,3,0])
+ True
+
+ 1 offset rather than 0 offset
+
+ >>> wc3_1=knnW_from_shapefile(pysal.examples.get_path("columbus.shp"),k=3,idVariable="POLYID")
+ >>> set([4,3,2]) == set(wc3_1.neighbors[1])
+ True
+ >>> wc3_1.weights[2]
+ [1.0, 1.0, 1.0]
+ >>> set([4,1,8]) == set(wc3_1.neighbors[2])
+ True
+
+
+ Point shapefile
+
+ >>> w=knnW_from_shapefile(pysal.examples.get_path("juvenile.shp"))
+ >>> w.pct_nonzero
+ 1.1904761904761905
+ >>> w1=knnW_from_shapefile(pysal.examples.get_path("juvenile.shp"),k=1)
+ >>> "%.3f"%w1.pct_nonzero
+
+ Notes
+ -----
+
+ Ties between neighbors of equal distance are arbitrarily broken.
+
+ See Also
+ --------
+ :class:`pysal.weights.KNN`
+ :class:`pysal.weights.W`
+ """
+ return cls(get_points_array_from_shapefile(filepath), **kwargs)
+
+ @classmethod
+ def from_array(cls, array, **kwargs):
+ """
+ Creates nearest neighbor weights matrix based on k nearest
+ neighbors.
+
+ Parameters
+ ----------
+ array : np.ndarray
+ (n, k) array representing n observations on
+ k characteristics used to measure distances
+ between the n objects
+ **kwargs : keyword arguments, see Rook
+
+ Returns
+ -------
+ w : W
+ instance
+ Weights object with binary weights
+
+ Examples
+ --------
+ >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ >>> wnn2 = pysal.KNN.from_array(points, 2)
+ >>> [1,3] == wnn2.neighbors[0]
+ True
+
+ ids
+
+ >>> wnn2 = KNN.from_array(points,2)
+ >>> wnn2[0]
+ {1: 1.0, 3: 1.0}
+ >>> wnn2[1]
+ {0: 1.0, 3: 1.0}
+
+ now with 1 rather than 0 offset
+
+ >>> wnn2 = KNN.from_array(points, 2, ids=range(1,7))
+ >>> wnn2[1]
+ {2: 1.0, 4: 1.0}
+ >>> wnn2[2]
+ {1: 1.0, 4: 1.0}
+ >>> 0 in wnn2.neighbors
+ False
+
+ Notes
+ -----
+
+ Ties between neighbors of equal distance are arbitrarily broken.
+
+ See Also
+ --------
+ :class: `pysal.weights.KNN`
+ :class:`pysal.weights.W`
+ """
+ return cls(array, **kwargs)
+ @classmethod
+ def from_dataframe(cls, df, geom_col='geometry', ids=None, **kwargs):
+ """
+ Make KNN weights from a dataframe.
+
+ Parameters
+ ----------
+ df : pandas.dataframe
+ a dataframe with a geometry column that can be used to
+ construct a W object
+ geom_col : string
+ column name of the geometry stored in df
+ ids : string or iterable
+ if string, the column name of the indices from the dataframe
+ if iterable, a list of ids to use for the W
+ if None, df.index is used.
+
+ See Also
+ --------
+ :class: `pysal.weights.KNN`
+ :class:`pysal.weights.W`
+ """
+ pts = get_points_array(df[geom_col])
+ if ids is None:
+ ids = df.index.tolist()
+ elif isinstance(ids, str):
+ ids = df[ids].tolist()
+ return cls(pts, ids=ids, **kwargs)
+
+ def reweight(self, k=None, p=None, new_data=None, new_ids=None, inplace=True):
+ """
+ Redo K-Nearest Neighbor weights construction using given parameters
+
+ Parameters
+ ----------
+ new_data : np.ndarray
+ an array containing additional data to use in the KNN
+ weight
+ new_ids : list
+ a list aligned with new_data that provides the ids for
+ each new observation
+ inplace : bool
+ a flag denoting whether to modify the KNN object
+ in place or to return a new KNN object
+ k : int
+ number of nearest neighbors
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ Ignored if the KDTree is an ArcKDTree
+
+ Returns
+ -------
+ A copy of the object using the new parameterization, or None if the
+ object is reweighted in place.
+ """
+ if (new_data is not None):
+ new_data = np.asarray(new_data).reshape(-1,2)
+ data = np.vstack((self.data, new_data)).reshape(-1,2)
+ if new_ids is not None:
+ ids = copy.deepcopy(self.id_order)
+ ids.extend(list(new_ids))
+ else:
+ ids = list(range(data.shape[0]))
+ elif (new_data is None) and (new_ids is None):
+ # If not, we can use the same kdtree we have
+ data = self.kdtree
+ ids = self.id_order
+ elif (new_data is None) and (new_ids is not None):
+ Warn('Remapping ids must be done using w.remap_ids')
+ if k is None:
+ k = self.k
+ if p is None:
+ p = self.p
+ if inplace:
+ self._reset()
+ self.__init__(data, ids=ids, k=k, p=p)
+ else:
+ return KNN(data, ids=ids, k=k, p=p)
class Kernel(W):
"""
@@ -295,6 +522,76 @@ class Kernel(W):
for i in neighbors:
weights[i][neighbors[i].index(i)] = 1.0
W.__init__(self, neighbors, weights, ids)
+
+ @classmethod
+ def from_shapefile(cls, filepath, idVariable=None, **kwargs):
+ """
+ Kernel based weights from shapefile
+
+ Arguments
+ ---------
+ shapefile : string
+ shapefile name with shp suffix
+ idVariable : string
+ name of column in shapefile's DBF to use for ids
+
+ Returns
+ --------
+ Kernel Weights Object
+
+ See Also
+ ---------
+ :class:`pysal.weights.Kernel`
+ :class:`pysal.weights.W`
+ """
+ points = get_points_array_from_shapefile(filepath)
+ if idVariable is not None:
+ ids = get_ids(filepath, idVariable)
+ else:
+ ids = None
+ return cls.from_array(points, ids=ids, **kwargs)
+
+ @classmethod
+ def from_array(cls, array, **kwargs):
+ """
+ Construct a Kernel weights from an array. Supports all the same options
+ as :class:`pysal.weights.Kernel`
+
+ See Also
+ --------
+ :class:`pysal.weights.Kernel`
+ :class:`pysal.weights.W`
+ """
+ return cls(array, **kwargs)
+
+ @classmethod
+ def from_dataframe(cls, df, geom_col='geometry', ids=None, **kwargs):
+ """
+ Make Kernel weights from a dataframe.
+
+ Parameters
+ ----------
+ df : pandas.dataframe
+ a dataframe with a geometry column that can be used to
+ construct a W object
+ geom_col : string
+ column name of the geometry stored in df
+ ids : string or iterable
+ if string, the column name of the indices from the dataframe
+ if iterable, a list of ids to use for the W
+ if None, df.index is used.
+
+ See Also
+ --------
+ :class:`pysal.weights.Kernel`
+ :class:`pysal.weights.W`
+ """
+ pts = get_points_array(df[geom_col])
+ if ids is None:
+ ids = df.index.tolist()
+ elif isinstance(ids, str):
+ ids = df[ids].tolist()
+ return cls(pts, ids=ids, **kwargs)
def _k_to_W(self, ids=None):
allneighbors = {}
@@ -392,6 +689,17 @@ class DistanceBand(W):
ids : list
values to use for keys of the neighbors and weights dicts
+
+ build_sp : boolean
+ True to build sparse distance matrix and false to build dense
+ distance matrix; significant speed gains may be obtained
+ dending on the sparsity of the of distance_matrix and
+ threshold that is applied
+ silent : boolean
+ By default PySAL will print a warning if the
+ dataset contains any disconnected observations or
+ islands. To silence this warning set this
+ parameter to True.
Attributes
----------
@@ -448,70 +756,151 @@ class DistanceBand(W):
"""
- def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None):
+ def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None,
+ build_sp=True, silent=False):
"""Casting to floats is a work around for a bug in scipy.spatial.
See detail in pysal issue #126.
"""
+ self.p = p
+ self.threshold = threshold
+ self.binary = binary
+ self.alpha = alpha
+ self.build_sp = build_sp
+ self.silent = silent
+
if isKDTree(data):
self.kd = data
self.data = self.kd.data
else:
- try:
- data = np.asarray(data)
- if data.dtype.kind != 'f':
- data = data.astype(float)
+ if self.build_sp:
+ try:
+ data = np.asarray(data)
+ if data.dtype.kind != 'f':
+ data = data.astype(float)
+ self.data = data
+ self.kd = KDTree(self.data)
+ except:
+ raise ValueError("Could not make array from data")
+ else:
self.data = data
- self.kd = KDTree(self.data)
- except:
- raise ValueError("Could not make array from data")
-
- self.p = p
- self.threshold = threshold
- self.binary = binary
- self.alpha = alpha
+ self.kd = None
self._band()
neighbors, weights = self._distance_to_W(ids)
- W.__init__(self, neighbors, weights, ids)
+ W.__init__(self, neighbors, weights, ids, silent_island_warning=self.silent)
+
+ @classmethod
+ def from_shapefile(cls, filepath, threshold, idVariable=None, **kwargs):
+ """
+ Distance-band based weights from shapefile
+
+ Arguments
+ ---------
+ shapefile : string
+ shapefile name with shp suffix
+ idVariable : string
+ name of column in shapefile's DBF to use for ids
+
+ Returns
+ --------
+ Kernel Weights Object
+
+ See Also
+ ---------
+ :class: `pysal.weights.DistanceBand`
+ :class: `pysal.weights.W`
+ """
+ points = get_points_array_from_shapefile(filepath)
+ if idVariable is not None:
+ ids = get_ids(filepath, idVariable)
+ else:
+ ids = None
+ return cls.from_array(points, threshold, ids=ids, **kwargs)
+
+ @classmethod
+ def from_array(cls, array, threshold, **kwargs):
+ """
+ Construct a DistanceBand weights from an array. Supports all the same options
+ as :class:`pysal.weights.DistanceBand`
+
+ See Also
+ --------
+ :class:`pysal.weights.DistanceBand`
+ :class:`pysal.weights.W`
+ """
+ return cls(array, threshold, **kwargs)
+
+ @classmethod
+ def from_dataframe(cls, df, threshold, geom_col='geometry', ids=None, **kwargs):
+ """
+ Make DistanceBand weights from a dataframe.
+
+ Parameters
+ ----------
+ df : pandas.dataframe
+ a dataframe with a geometry column that can be used to
+ construct a W object
+ geom_col : string
+ column name of the geometry stored in df
+ ids : string or iterable
+ if string, the column name of the indices from the dataframe
+ if iterable, a list of ids to use for the W
+ if None, df.index is used.
+
+ See Also
+ --------
+ :class:`pysal.weights.DistanceBand`
+ :class:`pysal.weights.W`
+ """
+ pts = get_points_array(df[geom_col])
+ if ids is None:
+ ids = df.index.tolist()
+ elif isinstance(ids, str):
+ ids = df[ids].tolist()
+ return cls(pts, threshold, ids=ids, **kwargs)
def _band(self):
"""Find all pairs within threshold.
"""
- self.dmat = self.kd.sparse_distance_matrix(
- self.kd, max_distance=self.threshold)
+ if self.build_sp:
+ self.dmat = self.kd.sparse_distance_matrix(
+ self.kd, max_distance=self.threshold).tocsr()
+ else:
+ if str(self.kd).split('.')[-1][0:10] == 'Arc_KDTree':
+ raise TypeError('Unable to calculate dense arc distance matrix;'
+ ' parameter "build_sp" must be set to True for arc'
+ ' distance type weight')
+ self.dmat = self._spdistance_matrix(self.data, self.data, self.threshold)
+
def _distance_to_W(self, ids=None):
- if ids:
- ids = np.array(ids)
- else:
- ids = np.arange(self.dmat.shape[0])
- neighbors = dict([(i,[]) for i in ids])
- weights = dict([(i,[]) for i in ids])
if self.binary:
- for key,weight in self.dmat.items():
- i,j = key
- if i != j:
- if j not in neighbors[i]:
- weights[i].append(1)
- neighbors[i].append(j)
- if i not in neighbors[j]:
- weights[j].append(1)
- neighbors[j].append(i)
-
+ self.dmat[self.dmat>0] = 1
+ self.dmat.eliminate_zeros()
+ tempW = WSP2W(WSP(self.dmat), silent_island_warning=self.silent)
+ neighbors = tempW.neighbors
+ weight_keys = tempW.weights.keys()
+ weight_vals = tempW.weights.values()
+ weights = dict(zip(weight_keys, map(list, weight_vals)))
+ return neighbors, weights
else:
- for key,weight in self.dmat.items():
- i,j = key
- if i != j:
- if j not in neighbors[i]:
- weights[i].append(weight**self.alpha)
- neighbors[i].append(j)
- if i not in neighbors[j]:
- weights[j].append(weight**self.alpha)
- neighbors[j].append(i)
-
- return neighbors, weights
-
+ weighted = self.dmat.power(self.alpha)
+ weighted[weighted==np.inf] = 0
+ weighted.eliminate_zeros()
+ tempW = WSP2W(WSP(weighted), silent_island_warning=self.silent)
+ neighbors = tempW.neighbors
+ weight_keys = tempW.weights.keys()
+ weight_vals = tempW.weights.values()
+ weights = dict(zip(weight_keys, map(list, weight_vals)))
+ return neighbors, weights
+
+ def _spdistance_matrix(self, x,y, threshold=None):
+ dist = distance_matrix(x,y)
+ if threshold is not None:
+ zeros = dist > threshold
+ dist[zeros] = 0
+ return sp.csr_matrix(dist)
def _test():
import doctest
diff --git a/pysal/weights/Wsets.py b/pysal/weights/Wsets.py
index 0da46bf..a04a102 100644
--- a/pysal/weights/Wsets.py
+++ b/pysal/weights/Wsets.py
@@ -394,7 +394,7 @@ def w_subset(w1, ids, silent_island_warning=False):
"""
neighbors = {}
- ids_set = set(ids)
+ ids_set = set(list(ids))
for i in ids:
if i in w1.neighbors:
neigh_add = ids_set.intersection(set(w1.neighbors[i]))
@@ -402,7 +402,7 @@ def w_subset(w1, ids, silent_island_warning=False):
else:
neighbors[i] = []
- return pysal.W(neighbors, id_order=ids, silent_island_warning=silent_island_warning)
+ return pysal.W(neighbors, id_order=list(ids), silent_island_warning=silent_island_warning)
def w_clip(w1, w2, outSP=True, silent_island_warning=False):
diff --git a/pysal/weights/__init__.py b/pysal/weights/__init__.py
index 2eafa27..d12e60b 100644
--- a/pysal/weights/__init__.py
+++ b/pysal/weights/__init__.py
@@ -1,8 +1,3 @@
-"""
-:mod:`weights` --- Spatial Weights
-==================================
-
-"""
from weights import *
from util import *
from Distance import *
diff --git a/pysal/weights/_contW_binning.py b/pysal/weights/_contW_binning.py
index b88bd1c..bd3ddeb 100644
--- a/pysal/weights/_contW_binning.py
+++ b/pysal/weights/_contW_binning.py
@@ -1,7 +1,7 @@
#!/usr/bin/python
#import math
import pysal
-from pysal.cg.standalone import get_shared_segments
+from pysal.cg.standalone import get_shared_segments, get_bounding_box
__author__ = "Sergio J. Rey <srey at asu.edu> "
__all__ = ["QUEEN", "ROOK", "ContiguityWeights_binning",
@@ -37,162 +37,13 @@ def bbcommon(bb, bbother):
return chflag
-class ContiguityWeights_binning:
-
- """
- Contiguity using a binning algorithm
- """
-
- def __init__(self, shpFileObject, wttype):
- self.shpFileObject = shpFileObject
- self.wttype = wttype
- self.do_weights()
-
- def do_weights(self):
- shpFileObject = self.shpFileObject
-
- if shpFileObject.type != pysal.cg.Polygon:
- return False
-
- shapebox = shpFileObject.bbox # bounding box
-
- numPoly = len(shpFileObject)
- self.numPoly = numPoly
-
- # bucket size
- if (numPoly < SHP_SMALL):
- bucketmin = numPoly // BUCK_SM + 2
- else:
- bucketmin = numPoly // BUCK_LG + 2
- # print 'bucketmin: ', bucketmin
- # bucket length
- lengthx = ((shapebox[2] + DELTA) - shapebox[0]) / bucketmin
- lengthy = ((shapebox[3] + DELTA) - shapebox[1]) / bucketmin
-
- # print lengthx, lengthy
-
- # initialize buckets
- columns = [set() for i in range(bucketmin)]
- rows = [set() for i in range(bucketmin)]
-
- minbox = shapebox[:2] * 2 # minx,miny,minx,miny
- binWidth = [lengthx, lengthy] * 2 # lenx,leny,lenx,leny
- bbcache = {}
- poly2Column = [set() for i in range(numPoly)]
- poly2Row = [set() for i in range(numPoly)]
- for i in range(numPoly):
- shpObj = shpFileObject.get(i)
- bbcache[i] = shpObj.bounding_box[:]
- projBBox = [int((shpObj.bounding_box[:][j] -
- minbox[j]) / binWidth[j]) for j in xrange(4)]
- for j in range(projBBox[0], projBBox[2] + 1):
- columns[j].add(i)
- poly2Column[i].add(j)
- for j in range(projBBox[1], projBBox[3] + 1):
- rows[j].add(i)
- poly2Row[i].add(j)
-
- w = {}
- if self.wttype == QUEEN:
- # loop over polygons rather than bins
- vertCache = {}
- for polyId in xrange(numPoly):
- if polyId not in vertCache:
- vertCache[polyId] = set(shpFileObject.get(polyId).vertices)
- idRows = poly2Row[polyId]
- idCols = poly2Column[polyId]
- rowPotentialNeighbors = set()
- colPotentialNeighbors = set()
- for row in idRows:
- rowPotentialNeighbors = rowPotentialNeighbors.union(
- rows[row])
- for col in idCols:
- colPotentialNeighbors = colPotentialNeighbors.union(
- columns[col])
- potentialNeighbors = rowPotentialNeighbors.intersection(
- colPotentialNeighbors)
- if polyId not in w:
- w[polyId] = set()
- for j in potentialNeighbors:
- if polyId < j:
- if bbcommon(bbcache[polyId], bbcache[j]):
- if j not in vertCache:
- vertCache[j] = set(
- shpFileObject.get(j).vertices)
- common = vertCache[
- polyId].intersection(vertCache[j])
- if len(common) > 0:
- w[polyId].add(j)
- if j not in w:
- w[j] = set()
- w[j].add(polyId)
- elif self.wttype == ROOK:
- # check for a shared edge
- edgeCache = {}
- # loop over polygons rather than bins
- for polyId in xrange(numPoly):
- if polyId not in edgeCache:
- iEdges = {}
- iVerts = shpFileObject.get(polyId).vertices
- nv = len(iVerts)
- ne = nv - 1
- for i in xrange(ne):
- l = iVerts[i]
- r = iVerts[i + 1]
- iEdges[(l, r)] = []
- iEdges[(r, l)] = []
- edgeCache[polyId] = iEdges
- iEdgeSet = set(edgeCache[polyId].keys())
- idRows = poly2Row[polyId]
- idCols = poly2Column[polyId]
- rowPotentialNeighbors = set()
- colPotentialNeighbors = set()
- for row in idRows:
- rowPotentialNeighbors = rowPotentialNeighbors.union(
- rows[row])
- for col in idCols:
- colPotentialNeighbors = colPotentialNeighbors.union(
- columns[col])
- potentialNeighbors = rowPotentialNeighbors.intersection(
- colPotentialNeighbors)
- if polyId not in w:
- w[polyId] = set()
- for j in potentialNeighbors:
- if polyId < j:
- if bbcommon(bbcache[polyId], bbcache[j]):
- if j not in edgeCache:
- jVerts = shpFileObject.get(j).vertices
- jEdges = {}
- nv = len(jVerts)
- ne = nv - 1
- for e in xrange(ne):
- l = jVerts[e]
- r = jVerts[e + 1]
- jEdges[(l, r)] = []
- jEdges[(r, l)] = []
- edgeCache[j] = jEdges
- # for edge in edgeCache[j]:
- if iEdgeSet.intersection(edgeCache[j].keys()):
- w[polyId].add(j)
- if j not in w:
- w[j] = set()
- w[j].add(polyId)
- # break
- else:
- print "Unsupported weight type."
-
- self.w = w
-
-# Generalize to handle polygon collections - independent of origin file type
-
-
class ContiguityWeightsPolygons:
"""
Contiguity for a collection of polygons using a binning algorithm
"""
- def __init__(self, collection, wttype=1):
+ def __init__(self, collection, wttype=1, bbox=None):
"""
Parameters
@@ -207,16 +58,20 @@ class ContiguityWeightsPolygons:
self.collection = collection
self.wttype = wttype
+ if bbox is None:
+ try:
+ bbox = collection.bbox
+ except AttributeError:
+ bbox = get_bounding_box(collection)
+ self.shapebox = bbox
+ self.numPoly = len(collection)
self.do_weights()
def do_weights(self):
- if self.collection.type != pysal.cg.Polygon:
- return False
- shapebox = self.collection.bbox # bounding box
+ shapebox = self.shapebox # bounding box
- numPoly = self.collection.n
- self.numPoly = numPoly
+ numPoly = self.numPoly
# bucket size
if (numPoly < SHP_SMALL):
@@ -292,7 +147,7 @@ class ContiguityWeightsPolygons:
for polyId in xrange(numPoly):
if polyId not in edgeCache:
iEdges = {}
- iVerts = shpFileObject.get(polyId).vertices
+ iVerts = self.collection[polyId].vertices
nv = len(iVerts)
ne = nv - 1
for i in xrange(ne):
@@ -320,7 +175,7 @@ class ContiguityWeightsPolygons:
if polyId < j:
if bbcommon(bbcache[polyId], bbcache[j]):
if j not in edgeCache:
- jVerts = shpFileObject.get(j).vertices
+ jVerts = self.collection[j].vertices
jEdges = {}
nv = len(jVerts)
ne = nv - 1
@@ -338,26 +193,26 @@ class ContiguityWeightsPolygons:
w[j].add(polyId)
# break
else:
- print "Unsupported weight type."
+ raise Exception("Unsupported weight type.")
self.w = w
if __name__ == "__main__":
import time
fname = pysal.examples.get_path('NAT.shp')
- print 'QUEEN binning'
+ print('QUEEN binning')
t0 = time.time()
qb = ContiguityWeights_binning(pysal.open(fname), QUEEN)
t1 = time.time()
- print "using " + str(fname)
- print "time elapsed for queen... using bins: " + str(t1 - t0)
+ print("using " + str(fname))
+ print("time elapsed for queen... using bins: " + str(t1 - t0))
t0 = time.time()
rb = ContiguityWeights_binning(pysal.open(fname), ROOK)
t1 = time.time()
- print 'Rook binning'
- print "using " + str(fname)
- print "time elapsed for rook... using bins: " + str(t1 - t0)
+ print('Rook binning')
+ print("using " + str(fname))
+ print("time elapsed for rook... using bins: " + str(t1 - t0))
from pysal.weights._contW_rtree import ContiguityWeights_rtree
@@ -365,25 +220,25 @@ if __name__ == "__main__":
rt = ContiguityWeights_rtree(pysal.open(fname), ROOK)
t1 = time.time()
- print "time elapsed for rook... using rtree: " + str(t1 - t0)
- print rt.w == rb.w
+ print("time elapsed for rook... using rtree: " + str(t1 - t0))
+ print(rt.w == rb.w)
- print 'QUEEN'
+ print('QUEEN')
t0 = time.time()
qt = ContiguityWeights_rtree(pysal.open(fname), QUEEN)
t1 = time.time()
- print "using " + str(fname)
- print "time elapsed for queen... using rtree: " + str(t1 - t0)
- print qb.w == qt.w
+ print("using " + str(fname))
+ print("time elapsed for queen... using rtree: " + str(t1 - t0))
+ print(qb.w == qt.w)
- print 'knn4'
+ print('knn4')
t0 = time.time()
knn = pysal.knnW_from_shapefile(fname, k=4)
t1 = time.time()
- print t1 - t0
+ print(t1 - t0)
- print 'rook from shapefile'
+ print('rook from shapefile')
t0 = time.time()
knn = pysal.rook_from_shapefile(fname)
t1 = time.time()
- print t1 - t0
+ print(t1 - t0)
diff --git a/pysal/weights/_contW_lists.py b/pysal/weights/_contW_lists.py
new file mode 100644
index 0000000..b84e8d7
--- /dev/null
+++ b/pysal/weights/_contW_lists.py
@@ -0,0 +1,90 @@
+import pysal as ps
+import collections
+QUEEN = 1
+ROOK = 2
+
+__author__ = "Jay Laura jlaura at asu.edu"
+
+class ContiguityWeightsLists:
+ """
+ Contiguity for a collection of polygons using high performance
+ list, set, and dict containers
+ """
+ def __init__(self, collection, wttype=1):
+ """
+ Arguments
+ =========
+
+ collection: PySAL PolygonCollection
+
+ wttype: int
+ 1: Queen
+ 2: Rook
+ """
+ self.collection = collection
+ self.wttype = wttype
+ self.jcontiguity()
+
+ def jcontiguity(self):
+ if not isinstance(self.collection[0], ps.cg.Polygon):
+ return False
+
+ numPoly = len(self.collection)
+
+ w = {}
+ for i in range(numPoly):
+ w[i] = set()
+
+ geoms = []
+ offsets = []
+ c = 0 # PolyID Counter
+
+ if self.wttype == QUEEN:
+ for n in range(numPoly):
+ verts = self.collection[n].vertices
+ offsets += [c] * len(verts)
+ geoms += (verts)
+ c += 1
+
+ items = collections.defaultdict(set)
+ for i, vertex in enumerate(geoms):
+ items[vertex].add(offsets[i])
+
+ shared_vertices = []
+ for item, location in items.iteritems():
+ if len(location) > 1:
+ shared_vertices.append(location)
+
+ for vert_set in shared_vertices:
+ for v in vert_set:
+ w[v] = w[v] | vert_set
+ try:
+ w[v].remove(v)
+ except:
+ pass
+
+ elif self.wttype == ROOK:
+ for n in range(numPoly):
+ verts = self.collection[n].vertices
+ for v in range(len(verts) - 1):
+ geoms.append(tuple(sorted([verts[v], verts[v + 1]])))
+ offsets += [c] * (len(verts) - 1)
+ c += 1
+
+ items = collections.defaultdict(set)
+ for i, item in enumerate(geoms):
+ items[item].add(offsets[i])
+
+ shared_vertices = []
+ for item, location in items.iteritems():
+ if len(location) > 1:
+ shared_vertices.append(location)
+
+ for vert_set in shared_vertices:
+ for v in vert_set:
+ w[v] = w[v] | vert_set
+ try:
+ w[v].remove(v)
+ except:
+ pass
+ self.w = w
diff --git a/pysal/weights/_contW_rtree.py b/pysal/weights/_contW_rtree.py
index de28648..1fce44b 100644
--- a/pysal/weights/_contW_rtree.py
+++ b/pysal/weights/_contW_rtree.py
@@ -91,16 +91,6 @@ class ContiguityWeights_rtree:
if get_shared_segments(poly0, poly1, True):
return ROOK
return False
- #for vert in common:
- # idx = poly0.vertices.index(vert)
- # IDX = poly1.vertices.index(vert)
- # try:
- # if poly0.vertices[idx+1] == poly1.vertices[IDX+1] or poly0.vertices[idx+1] == poly1.vertices[IDX-1]\
- # or poly0.vertices[idx-1] == poly1.vertices[IDX+1] or poly0.vertices[idx-1] == poly1.vertices[IDX-1]:
- # return ROOK
- # except IndexError:
- # pass
- #return False
elif len(common) > 0:
return QUEEN
else:
diff --git a/pysal/weights/spintW.py b/pysal/weights/spintW.py
new file mode 100644
index 0000000..6d976dc
--- /dev/null
+++ b/pysal/weights/spintW.py
@@ -0,0 +1,254 @@
+"""
+Spatial weights for spatial interaction including contiguity OD weights (ODW),
+network based weights (netW), and distance-decay based vector weights (vecW).
+
+"""
+
+__author__ = "Taylor Oshan <tayoshan at gmail.com> "
+
+from scipy.sparse import kron
+from pysal.weights import W, WSP
+from pysal.weights.util import WSP2W
+from pysal.weights.Distance import DistanceBand
+from collections import OrderedDict
+
+def ODW(Wo, Wd, transform='r'):
+ """
+ Constructs an o*d by o*d origin-destination style spatial weight for o*d
+ flows using standard spatial weights on o origins and d destinations. Input
+ spatial weights must be binary or able to be sutiably transformed to binary.
+
+ Parameters
+ ----------
+ Wo : W object for origin locations
+ o x o spatial weight object amongst o origins
+
+ Wd : W object for destination locations
+ d x d spatial weight object amongst d destinations
+
+ transform : Transformation for standardization of final OD spatial weight; default
+ is 'r' for row standardized
+ Returns
+ -------
+ W : spatial contiguity W object for assocations between flows
+ o*d x o*d spatial weight object amongst o*d flows between o
+ origins and d destinations
+
+ Examples
+ --------
+
+ >>> O = pysal.weights.lat2W(2,2)
+ >>> D = pysal.weights.lat2W(2,2)
+ >>> OD = pysal.weights.spintW.ODW(O,D)
+ >>> OD.weights[0]
+ [0.25, 0.25, 0.25, 0.25]
+ >>> OD.neighbors[0]
+ array([ 5, 6, 9, 10], dtype=int32)
+ >>> OD.full()[0][0]
+ array([ 0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. ,
+ 0.25, 0.25, 0. , 0. , 0. , 0. , 0. ])
+
+ """
+ if Wo.transform is not 'b':
+ try:
+ Wo.tranform = 'b'
+ except:
+ raise AttributeError('Wo is not binary and cannot be transformed to '
+ 'binary. Wo must be binary or suitably transformed to binary.')
+ if Wd.transform is not 'b':
+ try:
+ Wd.tranform = 'b'
+ except:
+ raise AttributeError('Wd is not binary and cannot be transformed to '
+ 'binary. Wd must be binary or suitably transformed to binary.')
+ Wo = Wo.sparse
+ Wo.eliminate_zeros()
+ Wd = Wd.sparse
+ Wd.eliminate_zeros()
+ Ww = kron(Wo, Wd, format='csr')
+ Ww.eliminate_zeros()
+ Ww = WSP2W(WSP(Ww))
+ Ww.transform = transform
+ return Ww
+
+def netW(link_list, share='A', transform = 'r'):
+ """
+ Create a network-contiguity based weight object based on different nodal
+ relationships encoded in a network.
+
+ Parameters
+ ----------
+ link_list : list
+ of tuples where each tuple is of the form (o,d) where o is an
+ origin id and d is a destination id
+
+ share : string
+ denoting how to define the nodal relationship used to
+ determine neighboring edges; defualt is 'A' for any shared
+ nodes between two network edges; options include:
+ 'A': any shared nodes
+ 'O': a shared origin node
+ 'D': a shared destination node
+ 'OD' a shared origin node or a shared destination node
+ 'C': a shared node that is the destination of the first
+ edge and the origin of the second edge - i.e., a
+ directed chain is formed moving from edge one to edge
+ two.
+
+ transform : Transformation for standardization of final OD spatial weight; default
+ is 'r' for row standardized
+
+ Returns
+ -------
+ W : nodal contiguity W object for networkd edges or flows
+ W Object representing the binary adjacency of the network edges
+ given a definition of nodal relationships.
+
+ Examples
+ --------
+
+ >>> links = [('a','b'), ('a','c'), ('a','d'), ('c','d'), ('c', 'b'), ('c','a')]
+ >>> O = pysal.weights.spintW.netW(links, share='O')
+ >>> W.neighbors[('a', 'b')]
+ [('a', 'c'), ('a', 'd')]
+ >>> OD = pysal.weights.spintW.netW(links, share='OD')
+ >>> OD.neighbors[('a', 'b')]
+ [('a', 'c'), ('a', 'd'), ('c', 'b')]
+ >>> any_common = pysal.weights.spintW.netw(links, share='A')
+ [('a', 'c'), ('a', 'd'), ('c', 'b'), ('c', 'a')]
+
+ """
+ neighbors = {}
+ neighbors = OrderedDict()
+ edges = link_list
+ for key in edges:
+ neighbors[key] = []
+ for neigh in edges:
+ if key == neigh:
+ continue
+ if share.upper() == 'OD':
+ if key[0] == neigh[0] or key[1] == neigh[1]:
+ neighbors[key].append(neigh)
+ elif share.upper() == 'O':
+ if key[0] == neigh[0]:
+ neighbors[key].append(neigh)
+ elif share.upper() == 'D':
+ if key[1] == neigh[1]:
+ neighbors[key].append(neigh)
+ elif share.upper() == 'C':
+ if key[1] == neigh[0]:
+ neighbors[key].append(neigh)
+ elif share.upper() == 'A':
+ if key[0] == neigh[0] or key[0] == neigh[1] or \
+ key[1] == neigh[0] or key[1] == neigh[1]:
+ neighbors[key].append(neigh)
+ else:
+ raise AttributeError("Parameter 'share' must be 'O', 'D',"
+ " 'OD', or 'C'")
+ netW = W(neighbors)
+ netW.tranform = transform
+ return netW
+
+def vecW(origin_x, origin_y, dest_x, dest_y, threshold, p=2, alpha=-1.0,
+ binary=True, ids=None, build_sp=False, silent=False):
+ """
+ Distance-based spatial weight for vectors that is computed using a
+ 4-dimensional distance between the origin x,y-coordinates and the
+ destination x,y-coordinates
+
+ Parameters
+ ----------
+ origin_x : list or array
+ of vector origin x-coordinates
+ origin_y : list or array
+ of vector origin y-coordinates
+ dest_x : list or array
+ of vector destination x-coordinates
+ dest_y : list or array
+ of vector destination y-coordinates
+ threshold : float
+ distance band
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ binary : boolean
+ If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0
+ If false wij=dij^{alpha}
+ alpha : float
+ distance decay parameter for weight (default -1.0)
+ if alpha is positive the weights will not decline with
+ distance. If binary is True, alpha is ignored
+
+ ids : list
+ values to use for keys of the neighbors and weights dicts
+ build_sp : boolean
+ True to build sparse distance matrix and false to build dense
+ distance matrix; significant speed gains may be obtained
+ dending on the sparsity of the of distance_matrix and
+ threshold that is applied
+ silent : boolean
+ By default PySAL will print a warning if the
+ dataset contains any disconnected observations or
+ islands. To silence this warning set this
+ parameter to True.
+
+ Returns
+ ------
+ W : DistanceBand W object that uses 4-dimenional distances between
+ vectors origin and destination coordinates.
+
+ Examples
+ --------
+
+ >>> x1 = [5,6,3]
+ >>> y1 = [1,8,5]
+ >>> x2 = [2,4,9]
+ >>> y2 = [3,6,1]
+ >>> W1 = pysal.weights.spintW.vecW(x1, y1, x2, y2, threshold=999)
+ >>> W1.neighbors[0]
+ [1, 2]
+ >>> W2 = pysal.weights.spintW.vecW(x1, y2, x1, y2, threshold=8.5)
+ >>> W2.neighbors[0]
+ [1]
+
+ """
+ data = zip(origin_x, origin_y, dest_x, dest_y)
+ W = DistanceBand(data, threshold=threshold, p=p, binary=binary, alpha=alpha,
+ ids=ids, build_sp=False, silent=silent)
+ return W
+
+def mat2L(edge_matrix):
+ """
+ Convert a matrix denoting network connectivity (edges or flows) to a list
+ denoting edges
+
+ Parameters
+ ----------
+ edge_matrix : array
+ where rows denote network edge origins, columns denote
+ network edge destinations, and non-zero entries denote the
+ existence of an edge between a given origin and destination
+
+ Returns
+ -------
+ edge_list : list
+ of tuples where each tuple is of the form (o,d) where o is an
+ origin id and d is a destination id
+
+ Examples
+ --------
+ """
+ if len(edge_matrix.shape) !=2:
+ raise AttributeError("Matrix of network edges should be two dimensions"
+ "with edge origins on one axis and edge destinations on the"
+ "second axis with non-zero matrix entires denoting an edge"
+ "between and origin and destination")
+ edge_list = []
+ rows, cols = edge_matrix.shape
+ for row in range(rows):
+ for col in range(cols):
+ if edge_matrix[row, col] != 0:
+ edge_list.append((row,col))
+ return edge_list
diff --git a/pysal/weights/tests/test_Contiguity.py b/pysal/weights/tests/test_Contiguity.py
index cdcddec..26778fc 100644
--- a/pysal/weights/tests/test_Contiguity.py
+++ b/pysal/weights/tests/test_Contiguity.py
@@ -1,27 +1,124 @@
-"""Unit test for Contiguity.py"""
-import unittest
-import pysal
-import numpy as np
+from pysal.weights import Contiguity as c
+import pysal as ps
+import unittest as ut
+from warnings import warn as Warn
+PANDAS_EXTINCT = ps.common.pandas is None
+class Mock(object):
+ def __init__(self, **kwargs):
+ self.__dict__.update(kwargs)
+
+class Contiguity_Mixin(object):
+ polygon_path = ps.examples.get_path('columbus.shp')
+ point_path = ps.examples.get_path('baltim.shp')
+ f = ps.open(polygon_path) # our file handler
+ polygons = f.read() # our iterable
+ f.seek(0) #go back to head of file
+ # Without requiring users to have GDAL to run tests,
+ # we can mock a shapely object by an object with a geo_interface
+ mocks = [Mock(__geo_interface__=p.__geo_interface__)
+ for p in polygons]
+ cls = object # class constructor
+ known_wi = None #index of known w entry to compare
+ known_w = dict() #actual w entry
+ known_name = known_wi
+ known_namedw = known_w
+ idVariable = None # id variable from file or column
-class TestContiguity(unittest.TestCase):
def setUp(self):
- self.polyShp = pysal.examples.get_path('10740.shp')
-
- def test_buildContiguity(self):
- w = pysal.buildContiguity(pysal.open(self.polyShp, 'r'))
- self.assertEqual(w[0], {1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0})
- w = pysal.buildContiguity(
- pysal.open(self.polyShp, 'r'), criterion='queen')
- self.assertEqual(w.pct_nonzero, 3.1926364234056544)
- w = pysal.buildContiguity(
- pysal.open(self.polyShp, 'r'), criterion='rook')
- self.assertEqual(w.pct_nonzero, 2.6351084812623275)
- fips = pysal.open(pysal.examples.get_path('10740.dbf')).by_col('STFID')
- w = pysal.buildContiguity(pysal.open(self.polyShp, 'r'), ids=fips)
- self.assertEqual(w['35001000107'], {'35001003805': 1.0, '35001003721':
- 1.0, '35001000111': 1.0, '35001000112': 1.0, '35001000108': 1.0})
-
-
-if __name__ == "__main__":
- unittest.main()
+ self.__dict__.update({k:v for k,v in Contiguity_Mixin.__dict__.items()
+ if not k.startswith('_')})
+
+ def runTest(self):
+ pass
+
+ def test_init(self):
+ # basic
+ w = self.cls(self.polygons)
+ self.assertEqual(w[self.known_wi], self.known_w)
+
+ # sparse
+ #w = self.cls(self.polygons, sparse=True)
+ #srowvec = ws.sparse[self.known_wi].todense().tolist()[0]
+ #this_w = {i:k for i,k in enumerate(srowvec) if k>0}
+ #self.assertEqual(this_w, self.known_w)
+ #ids = ps.weights2.utils.get_ids(self.polygon_path, self.idVariable)
+
+ # named
+ ids = ps.weights.util.get_ids(self.polygon_path, self.idVariable)
+ w = self.cls(self.polygons, ids = ids)
+ self.assertEqual(w[self.known_name], self.known_namedw)
+
+ def test_from_iterable(self):
+ w = self.cls.from_iterable(self.f)
+ self.f.seek(0)
+ self.assertEqual(w[self.known_wi], self.known_w)
+
+ w = self.cls.from_iterable(self.mocks)
+ self.assertEqual(w[self.known_wi], self.known_w)
+
+ def test_from_shapefile(self):
+ # basic
+ w = self.cls.from_shapefile(self.polygon_path)
+ self.assertEqual(w[self.known_wi], self.known_w)
+
+ # sparse
+ ws = self.cls.from_shapefile(self.polygon_path, sparse=True)
+ srowvec = ws.sparse[self.known_wi].todense().tolist()[0]
+ this_w = {i:k for i,k in enumerate(srowvec) if k>0}
+ self.assertEqual(this_w, self.known_w)
+
+ # named
+ w = self.cls.from_shapefile(self.polygon_path, idVariable=self.idVariable)
+ self.assertEqual(w[self.known_name], self.known_namedw)
+
+ def test_from_array(self):
+ # test named, sparse from point array
+ pass
+
+ @ut.skipIf(PANDAS_EXTINCT, 'Missing pandas')
+ def test_from_dataframe(self):
+ # basic
+ df = ps.pdio.read_files(self.polygon_path)
+ w = self.cls.from_dataframe(df)
+ self.assertEqual(w[self.known_wi], self.known_w)
+
+ # named geometry
+ df.rename(columns={'geometry':'the_geom'}, inplace=True)
+ w = self.cls.from_dataframe(df, geom_col = 'the_geom')
+ self.assertEqual(w[self.known_wi], self.known_w)
+
+ # named geometry + named obs
+ w = self.cls.from_dataframe(df, geom_col='the_geom', idVariable=self.idVariable)
+ self.assertEqual(w[self.known_name], self.known_namedw)
+
+class Test_Queen(ut.TestCase, Contiguity_Mixin):
+ def setUp(self):
+ Contiguity_Mixin.setUp(self)
+
+ self.known_wi = 4
+ self.known_w = {2: 1.0, 3: 1.0, 5: 1.0, 7: 1.0,
+ 8: 1.0, 10: 1.0, 14: 1.0, 15: 1.0}
+ self.cls = c.Queen
+ self.idVariable = 'POLYID'
+ self.known_name = 5
+ self.known_namedw = {k+1:v for k,v in self.known_w.items()}
+
+class Test_Rook(ut.TestCase, Contiguity_Mixin):
+ def setUp(self):
+ Contiguity_Mixin.setUp(self)
+
+ self.known_w = {2: 1.0, 3: 1.0, 5: 1.0, 7: 1.0,
+ 8: 1.0, 10: 1.0, 14: 1.0}
+ self.known_wi = 4
+ self.cls = c.Rook
+ self.idVariable = 'POLYID'
+ self.known_name = 5
+ self.known_namedw = {k+1:v for k,v in self.known_w.items()}
+
+q = ut.TestLoader().loadTestsFromTestCase(Test_Queen)
+r = ut.TestLoader().loadTestsFromTestCase(Test_Rook)
+suite = ut.TestSuite([q, r])
+if __name__ == '__main__':
+ runner = ut.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/weights/tests/test_Distance.py b/pysal/weights/tests/test_Distance.py
index c60f74f..d8eec70 100644
--- a/pysal/weights/tests/test_Distance.py
+++ b/pysal/weights/tests/test_Distance.py
@@ -1,144 +1,291 @@
-import os
-import unittest
-import pysal
+from pysal.weights import Distance as d
+from pysal.weights.util import get_points_array
+from pysal.weights import Contiguity as c
+from pysal.common import RTOL, ATOL
+from pysal.cg.kdtree import KDTree
import numpy as np
+import pysal as ps
+import unittest as ut
+PANDAS_EXTINCT = ps.common.pandas is None
+# All instances should test these four methods, and define their own functional
+# tests based on common codepaths/estimated weights use cases.
-class TestDistanceWeights(unittest.TestCase):
+class Distance_Mixin(object):
+ polygon_path = ps.examples.get_path('columbus.shp')
+ arc_path = ps.examples.get_path('stl_hom.shp')
+ points = [(10, 10), (20, 10), (40, 10),
+ (15, 20), (30, 20), (30, 30)]
+ euclidean_kdt = ps.cg.KDTree(points, distance_metric='euclidean')
+
+ polygon_f = ps.open(polygon_path) # our file handler
+ poly_centroids = get_points_array(polygon_f) # our iterable
+ polygon_f.seek(0) #go back to head of file
+
+ arc_f = ps.open(arc_path)
+ ps.cg.sphere.arcdist
+ arc_points = get_points_array(arc_f)
+ arc_f.seek(0)
+ arc_kdt = ps.cg.KDTree(arc_points, distance_metric='Arc',
+ radius=ps.cg.sphere.RADIUS_EARTH_KM)
+
+ cls = object # class constructor
+ known_wi = None #index of known w entry to compare
+ known_w = dict() #actual w entry
+ known_name = known_wi
+
def setUp(self):
- np.random.seed(1234)
- self.polyShp = pysal.examples.get_path('columbus.shp')
- self.arcShp = pysal.examples.get_path('stl_hom.shp')
- self.points = [(
- 10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
-
- def test_knnW(self):
- kd = pysal.cg.kdtree.KDTree(np.array(self.points), distance_metric='euclidean')
- wnn2 = pysal.knnW(kd, 2)
- self.assertEqual(wnn2.neighbors[0], [1,3])
-
- pts = [i.centroid for i in pysal.open(self.polyShp)]
- kd = pysal.cg.kdtree.KDTree(pts)
- wnn4 = pysal.knnW(kd, 4)
- self.assertEqual(set(wnn4.neighbors[0]), set([2,1,3,7]))
- self.assertEqual(set(wnn4.neighbors[7]), set([3,6,12,11]))
-
- def test_knnW_arc(self):
- pts = [x.centroid for x in pysal.open(self.arcShp)]
- dist = pysal.cg.sphere.arcdist # default radius is Earth KM
- full = np.matrix([[dist(pts[i], pts[j]) for j in xrange(
- len(pts))] for i in xrange(len(pts))])
-
- kd = pysal.cg.kdtree.KDTree(pts, distance_metric='Arc',
- radius=pysal.cg.sphere.RADIUS_EARTH_KM)
- w = pysal.knnW(kd, 4)
- self.assertEqual(set(w.neighbors[4]), set([1, 3, 9, 12]))
- self.assertEqual(set(w.neighbors[40]), set([31, 38, 45, 49]))
- # self.assertTrue((full.argsort()[:, 1:5] == np.array(
- # [w.neighbors[x] for x in range(len(pts))])).all())
-
- def test_Kernel(self):
- kw = pysal.Kernel(self.points)
- wds = {kw.neighbors[0][i]: v for i, v in enumerate(kw.weights[0])}
- self.assertEqual(wds, {0: 1, 1: 0.500000049999995, 3: 0.4409830615267465})
- kw15 = pysal.Kernel(self.points, bandwidth=15.0)
- self.assertEqual(kw15[0], {0: 1.0, 1: 0.33333333333333337,
- 3: 0.2546440075000701})
- self.assertEqual(kw15.bandwidth[0], 15.)
- self.assertEqual(kw15.bandwidth[-1], 15.)
- bw = [25.0, 15.0, 25.0, 16.0, 14.5, 25.0]
- kwa = pysal.Kernel(self.points, bandwidth=bw)
- wds = {kwa.neighbors[0][i]: v for i, v in enumerate(kwa.weights[0])}
- self.assertEqual(wds, {0: 1.0, 1: 0.59999999999999998,
- 3: 0.55278640450004202, 4: 0.10557280900008403})
- self.assertEqual(kwa.bandwidth[0], 25.)
- self.assertEqual(kwa.bandwidth[1], 15.)
- self.assertEqual(kwa.bandwidth[2], 25.)
- self.assertEqual(kwa.bandwidth[3], 16.)
- self.assertEqual(kwa.bandwidth[4], 14.5)
- self.assertEqual(kwa.bandwidth[5], 25.)
- kwea = pysal.Kernel(self.points, fixed=False)
- wds = {kwea.neighbors[0][i]: v for i, v in enumerate(kw.weights[0])}
- self.assertEqual(kwea.weights[0], [1.0, 0.10557289844279438,
- 9.9999990066379496e-08])
- l = kwea.bandwidth.tolist()
- np.allclose(l, [[11.180341005532938], [11.180341005532938],
- [20.000002000000002], [11.180341005532938],
- [14.142137037944515], [18.027758180095585]])
- kweag = pysal.Kernel(self.points, fixed=False, function='gaussian')
- wds = {kweag.neighbors[0][i]: v for i, v in enumerate(kweag.weights[0])}
- self.assertEqual(wds, {0: 0.3989422804014327,
- 1: 0.26741902915776961,
- 3: 0.24197074871621341})
- l = kweag.bandwidth.tolist()
- np.allclose(l, [[11.180341005532938], [11.180341005532938],
- [20.000002000000002], [11.180341005532938],
- [14.142137037944515], [18.027758180095585]])
-
- kw = pysal.kernelW_from_shapefile(self.polyShp, idVariable='POLYID')
- wds = {kw.neighbors[1][i]: v for i, v in enumerate(kw.weights[1])}
- self.assertEqual(wds, {4: 0.0070787731484506233,
- 2: 0.2052478782400463,
- 3: 0.23051223027663237,
- 1: 1.0})
- kwa = pysal.adaptive_kernelW_from_shapefile(self.polyShp)
- wds = {kwa.neighbors[0][i]: v for i, v in enumerate(kwa.weights[0])}
- self.assertEqual(wds, {0: 1.0, 2: 0.03178906767736345,
- 1: 9.9999990066379496e-08})
-
- def test_threshold(self):
- md = pysal.min_threshold_dist_from_shapefile(self.polyShp)
- self.assertEqual(md, 0.61886415807685413)
- wid = pysal.threshold_continuousW_from_array(self.points, 11.2)
- wds = {wid.neighbors[0][i]: v for i, v in enumerate(wid.weights[0])}
- self.assertEqual(wds, {1: 0.10000000000000001,
- 3: 0.089442719099991588})
- wid2 = pysal.threshold_continuousW_from_array(
- self.points, 11.2, alpha=-2.0)
- wds = {wid2.neighbors[0][i]: v for i, v in enumerate(wid2.weights[0])}
- self.assertEqual(wds, {1: 0.01, 3: 0.0079999999999999984})
- w = pysal.threshold_continuousW_from_shapefile(
- self.polyShp, 0.62, idVariable="POLYID")
- wds = {w.neighbors[1][i]: v for i, v in enumerate(w.weights[1])}
- self.assertEqual(wds, {2: 1.6702346893743334,
- 3: 1.7250729841938093})
-
- def test_DistanceBand(self):
- """ see issue #126 """
- w = pysal.rook_from_shapefile(
- pysal.examples.get_path("lattice10x10.shp"))
- polygons = pysal.open(
- pysal.examples.get_path("lattice10x10.shp"), "r").read()
- points1 = [poly.centroid for poly in polygons]
- w1 = pysal.DistanceBand(points1, 1)
- for k in range(w.n):
- self.assertEqual(w[k], w1[k])
-
- def test_DistanceBand_ints(self):
- """ see issue #126 """
- w = pysal.rook_from_shapefile(
- pysal.examples.get_path("lattice10x10.shp"))
- polygons = pysal.open(
- pysal.examples.get_path("lattice10x10.shp"), "r").read()
- points2 = [tuple(map(int, poly.vertices[0])) for poly in polygons]
- w2 = pysal.DistanceBand(points2, 1)
- for k in range(w.n):
- self.assertEqual(w[k], w2[k])
-
- def test_DistanceBand_arc(self):
- pts = [x.centroid for x in pysal.open(self.arcShp)]
- dist = pysal.cg.sphere.arcdist # default radius is Earth KM
- full = np.matrix([[dist(pts[i], pts[j]) for j in xrange(
- len(pts))] for i in xrange(len(pts))])
-
- kd = pysal.cg.kdtree.KDTree(pts, distance_metric='Arc',
- radius=pysal.cg.sphere.RADIUS_EARTH_KM)
- w = pysal.DistanceBand(kd, full.max(), binary=False, alpha=1.0)
- self.assertTrue((w.sparse.todense() == full).all())
-
-
-suite = unittest.TestLoader().loadTestsFromTestCase(TestDistanceWeights)
+ self.__dict__.update({k:v for k,v in Distance_Mixin.__dict__.items()
+ if not k.startswith('_')})
+
+ def test_init(self):
+ # test vanilla, named
+ raise NotImplementedError('You need to implement this test '
+ 'before this module will pass')
+ def test_from_shapefile(self):
+ # test vanilla, named, sparse
+ raise NotImplementedError('You need to implement this test '
+ 'before this module will pass')
+
+ def test_from_array(self):
+ # test named, sparse
+ raise NotImplementedError('You need to implement this test '
+ 'before this module will pass')
+
+ def test_from_dataframe(self):
+ # test named, columnar, defau
+ raise NotImplementedError('You need to implement this test '
+ 'before this module will pass')
+
+class Test_KNN(ut.TestCase, Distance_Mixin):
+ def setUp(self):
+ Distance_Mixin.setUp(self)
+
+ self.known_wi0 = 7
+ self.known_w0 = [3, 6, 12, 11]
+ self.known_wi1 = 0
+ self.known_w1 = [2, 1, 3 ,7]
+
+ self.known_wi2 = 4
+ self.known_w2 = [1, 3, 9, 12]
+ self.known_wi3 = 40
+ self.known_w3 = [31, 38, 45, 49]
+
+ ##########################
+ # Classmethod tests #
+ ##########################
+
+ def test_init(self):
+ w = d.KNN(self.euclidean_kdt, k=2)
+ self.assertEqual(w.neighbors[0], [1,3])
+
+ @ut.skipIf(PANDAS_EXTINCT, 'Missing pandas')
+ def test_from_dataframe(self):
+ df = ps.pdio.read_files(self.polygon_path)
+ w = d.KNN.from_dataframe(df, k=4)
+ self.assertEqual(w.neighbors[self.known_wi0], self.known_w0)
+ self.assertEqual(w.neighbors[self.known_wi1], self.known_w1)
+
+ def test_from_array(self):
+ w = d.KNN.from_array(self.poly_centroids, k=4)
+ self.assertEqual(w.neighbors[self.known_wi0], self.known_w0)
+ self.assertEqual(w.neighbors[self.known_wi1], self.known_w1)
+
+ def test_from_shapefile(self):
+ w = d.KNN.from_shapefile(self.polygon_path, k=4)
+ self.assertEqual(w.neighbors[self.known_wi0], self.known_w0)
+ self.assertEqual(w.neighbors[self.known_wi1], self.known_w1)
+
+ ##########################
+ # Function/User tests #
+ ##########################
+
+ def test_reweight(self):
+ w = d.KNN(self.points, k=2)
+ new_point = [(21,21)]
+ wnew = w.reweight(k=4, p=1, new_data=new_point, inplace=False)
+ self.assertEqual(wnew[0], {1: 1.0, 3: 1.0, 4: 1.0, 6: 1.0})
+
+class Test_DistanceBand(ut.TestCase, Distance_Mixin):
+ def setUp(self):
+ Distance_Mixin.setUp(self)
+ self.grid_path = ps.examples.get_path('lattice10x10.shp')
+ self.grid_rook_w = c.Rook.from_shapefile(self.grid_path)
+ self.grid_f = ps.open(self.grid_path)
+ self.grid_points = get_points_array(self.grid_f)
+ self.grid_f.seek(0)
+
+ self.grid_kdt = KDTree(self.grid_points)
+
+ ##########################
+ # Classmethod tests #
+ ##########################
+
+ def test_init(self):
+ w = d.DistanceBand(self.grid_kdt, 1)
+ for k,v in w:
+ self.assertEquals(v, self.grid_rook_w[k])
+
+ def test_from_shapefile(self):
+ w = d.DistanceBand.from_shapefile(self.grid_path, 1)
+ for k,v in w:
+ self.assertEquals(v, self.grid_rook_w[k])
+
+ def test_from_array(self):
+ w = d.DistanceBand.from_array(self.grid_points, 1)
+ for k,v in w:
+ self.assertEquals(v, self.grid_rook_w[k])
+
+ @ut.skipIf(PANDAS_EXTINCT, 'Missing pandas')
+ def test_from_dataframe(self):
+ import pandas as pd
+ geom_series = ps.pdio.shp.shp2series(self.grid_path)
+ random_data = np.random.random(size=len(geom_series))
+ df = pd.DataFrame({'obs':random_data, 'geometry':geom_series})
+ w = d.DistanceBand.from_dataframe(df, 1)
+ for k,v in w:
+ self.assertEquals(v, self.grid_rook_w[k])
+
+ ##########################
+ # Function/User tests #
+ ##########################
+ def test_integers(self):
+ """
+ see issue #126
+ """
+ grid_integers = [tuple(map(int, poly.vertices[0]))
+ for poly in self.grid_f]
+ self.grid_f.seek(0)
+ grid_dbw = d.DistanceBand(grid_integers, 1)
+ for k,v in grid_dbw:
+ self.assertEquals(v, self.grid_rook_w[k])
+
+ def test_arcdist(self):
+ arc = ps.cg.sphere.arcdist
+ kdt = KDTree(self.arc_points, distance_metric='Arc',
+ radius=ps.cg.sphere.RADIUS_EARTH_KM)
+ npoints = self.arc_points.shape[0]
+ full = np.matrix([[arc(self.arc_points[i], self.arc_points[j])
+ for j in xrange(npoints)]
+ for i in xrange(npoints)])
+ maxdist = full.max()
+ w = d.DistanceBand(kdt, maxdist, binary=False, alpha=1.0)
+ np.testing.assert_allclose(w.sparse.todense(), full)
+
+ def test_dense(self):
+ w_rook = ps.weights.Rook.from_shapefile(
+ ps.examples.get_path('lattice10x10.shp'))
+ polys = ps.open(ps.examples.get_path('lattice10x10.shp'))
+ centroids = [p.centroid for p in polys]
+ w_db = d.DistanceBand(centroids, 1, build_sp=False)
+
+ for k in w_db.id_order:
+ np.testing.assert_equal(w_db[k], w_rook[k])
+
+class Test_Kernel(ut.TestCase, Distance_Mixin):
+ def setUp(self):
+
+ Distance_Mixin.setUp(self)
+ self.known_wi0 = 0
+ self.known_w0 = {0: 1, 1: 0.500000049999995, 3: 0.4409830615267465}
+
+ self.known_wi1 = 0
+ self.known_w1 = {0: 1.0, 1: 0.33333333333333337,
+ 3: 0.2546440075000701}
+ self.known_w1_bw = 15.
+
+ self.known_wi2 = 0
+ self.known_w2 = {0: 1.0, 1: 0.59999999999999998,
+ 3: 0.55278640450004202, 4: 0.10557280900008403}
+ self.known_w2_bws = [25.0, 15.0, 25.0, 16.0, 14.5, 25.0]
+
+ self.known_wi3 = 0
+ self.known_w3 = [1.0, 0.10557289844279438, 9.9999990066379496e-08]
+ self.known_w3_abws =[[11.180341005532938], [11.180341005532938],
+ [20.000002000000002], [11.180341005532938],
+ [14.142137037944515], [18.027758180095585]]
+
+ self.known_wi4 = 0
+ self.known_w4 = {0: 0.3989422804014327,
+ 1: 0.26741902915776961,
+ 3: 0.24197074871621341}
+ self.known_w4_abws = self.known_w3_abws
+
+ self.known_wi5 = 1
+ self.known_w5 = {4: 0.0070787731484506233,
+ 2: 0.2052478782400463,
+ 3: 0.23051223027663237,
+ 1: 1.0}
+
+ self.known_wi6 = 0
+ self.known_w6 = {0: 1.0, 2: 0.03178906767736345,
+ 1: 9.9999990066379496e-08}
+ #stick answers & params here
+
+ ##########################
+ # Classmethod tests #
+ ##########################
+
+ def test_init(self):
+ w = d.Kernel(self.euclidean_kdt)
+ for k,v in w[self.known_wi0].items():
+ np.testing.assert_allclose(v, self.known_w0[k], rtol=RTOL)
+
+ def test_from_shapefile(self):
+ w = d.Kernel.from_shapefile(self.polygon_path, idVariable='POLYID')
+ for k,v in w[self.known_wi5].items():
+ np.testing.assert_allclose((k,v), (k,self.known_w5[k]), rtol=RTOL)
+
+ w = d.Kernel.from_shapefile(self.polygon_path, fixed=False)
+ for k,v in w[self.known_wi6].items():
+ np.testing.assert_allclose((k,v), (k,self.known_w6[k]), rtol=RTOL)
+
+ def test_from_array(self):
+ w = d.Kernel.from_array(self.points)
+ for k,v in w[self.known_wi0].items():
+ np.testing.assert_allclose(v, self.known_w0[k], rtol=RTOL)
+
+ @ut.skipIf(PANDAS_EXTINCT, 'Missing pandas')
+ def test_from_dataframe(self):
+ df = ps.pdio.read_files(self.polygon_path)
+ w = d.Kernel.from_dataframe(df)
+ for k,v in w[self.known_wi5-1].items():
+ np.testing.assert_allclose(v, self.known_w5[k+1], rtol=RTOL)
+
+ ##########################
+ # Function/User tests #
+ ##########################
+
+ def test_fixed_bandwidth(self):
+ w = d.Kernel(self.points, bandwidth=15.0)
+ for k,v in w[self.known_wi1].items():
+ np.testing.assert_allclose((k,v), (k, self.known_w1[k]))
+ np.testing.assert_allclose(np.ones((w.n,1))*15, w.bandwidth)
+
+ w = d.Kernel(self.points, bandwidth=self.known_w2_bws)
+ for k,v in w[self.known_wi2].items():
+ np.testing.assert_allclose((k,v), (k, self.known_w2[k]), rtol=RTOL)
+ for i in range(w.n):
+ np.testing.assert_allclose(w.bandwidth[i], self.known_w2_bws[i], rtol=RTOL)
+
+ def test_adaptive_bandwidth(self):
+ w = d.Kernel(self.points, fixed=False)
+ np.testing.assert_allclose(sorted(w[self.known_wi3].values()),
+ sorted(self.known_w3), rtol=RTOL)
+ bws = w.bandwidth.tolist()
+ np.testing.assert_allclose(bws, self.known_w3_abws, rtol=RTOL)
+
+ w = d.Kernel(self.points, fixed=False, function='gaussian')
+ for k,v in w[self.known_wi4].items():
+ np.testing.assert_allclose((k,v), (k, self.known_w4[k]), rtol=RTOL)
+ bws = w.bandwidth.tolist()
+ np.testing.assert_allclose(bws, self.known_w4_abws, rtol=RTOL)
+
+knn = ut.TestLoader().loadTestsFromTestCase(Test_KNN)
+kern = ut.TestLoader().loadTestsFromTestCase(Test_Kernel)
+db = ut.TestLoader().loadTestsFromTestCase(Test_DistanceBand)
+suite = ut.TestSuite([knn, kern, db])
if __name__ == '__main__':
- runner = unittest.TextTestRunner()
+ runner = ut.TextTestRunner()
runner.run(suite)
diff --git a/pysal/weights/tests/test__contW_binning.py b/pysal/weights/tests/test__contW_binning.py
index e798bfc..e353699 100644
--- a/pysal/weights/tests/test__contW_binning.py
+++ b/pysal/weights/tests/test__contW_binning.py
@@ -1,18 +1,18 @@
import os
import unittest
import pysal
-from pysal.weights._contW_binning import ContiguityWeights_binning, QUEEN, ROOK
+from pysal.weights._contW_binning import ContiguityWeightsPolygons, QUEEN, ROOK
class TestContiguityWeights(unittest.TestCase):
def setUp(self):
""" Setup the binning contiguity weights"""
shpObj = pysal.open(pysal.examples.get_path('virginia.shp'), 'r')
- self.binningW = ContiguityWeights_binning(shpObj, QUEEN)
+ self.binningW = ContiguityWeightsPolygons(shpObj, QUEEN)
shpObj.close()
def test_w_type(self):
- self.assert_(isinstance(self.binningW, ContiguityWeights_binning))
+ self.assert_(isinstance(self.binningW, ContiguityWeightsPolygons))
def test_QUEEN(self):
self.assertEqual(QUEEN, 1)
@@ -20,7 +20,7 @@ class TestContiguityWeights(unittest.TestCase):
def test_ROOK(self):
self.assertEqual(ROOK, 2)
- def test_ContiguityWeights_binning(self):
+ def test_ContiguityWeightsPolygons(self):
self.assert_(hasattr(self.binningW, 'w'))
self.assert_(issubclass(dict, type(self.binningW.w)))
self.assertEqual(len(self.binningW.w), 136)
@@ -105,7 +105,7 @@ class TestContiguityWeights(unittest.TestCase):
dbname = os.path.splitext(shapefile)[0] + '.dbf'
db = pysal.open(dbname)
shpObj = pysal.open(shapefile)
- neighbor_data = ContiguityWeights_binning(shpObj, type).w
+ neighbor_data = ContiguityWeightsPolygons(shpObj, type).w
neighbors = {}
weights = {}
if idVariable:
diff --git a/pysal/weights/tests/test__contW_binning.py b/pysal/weights/tests/test__contW_lists.py
similarity index 94%
copy from pysal/weights/tests/test__contW_binning.py
copy to pysal/weights/tests/test__contW_lists.py
index e798bfc..3f8f3ed 100644
--- a/pysal/weights/tests/test__contW_binning.py
+++ b/pysal/weights/tests/test__contW_lists.py
@@ -1,18 +1,18 @@
import os
import unittest
import pysal
-from pysal.weights._contW_binning import ContiguityWeights_binning, QUEEN, ROOK
+from pysal.weights._contW_lists import ContiguityWeightsLists, ROOK, QUEEN
class TestContiguityWeights(unittest.TestCase):
def setUp(self):
""" Setup the binning contiguity weights"""
shpObj = pysal.open(pysal.examples.get_path('virginia.shp'), 'r')
- self.binningW = ContiguityWeights_binning(shpObj, QUEEN)
+ self.binningW = ContiguityWeightsLists(shpObj, QUEEN)
shpObj.close()
def test_w_type(self):
- self.assert_(isinstance(self.binningW, ContiguityWeights_binning))
+ self.assert_(isinstance(self.binningW, ContiguityWeightsLists))
def test_QUEEN(self):
self.assertEqual(QUEEN, 1)
@@ -20,7 +20,7 @@ class TestContiguityWeights(unittest.TestCase):
def test_ROOK(self):
self.assertEqual(ROOK, 2)
- def test_ContiguityWeights_binning(self):
+ def test_ContiguityWeightsLists(self):
self.assert_(hasattr(self.binningW, 'w'))
self.assert_(issubclass(dict, type(self.binningW.w)))
self.assertEqual(len(self.binningW.w), 136)
@@ -105,7 +105,7 @@ class TestContiguityWeights(unittest.TestCase):
dbname = os.path.splitext(shapefile)[0] + '.dbf'
db = pysal.open(dbname)
shpObj = pysal.open(shapefile)
- neighbor_data = ContiguityWeights_binning(shpObj, type).w
+ neighbor_data = ContiguityWeightsLists(shpObj, type).w
neighbors = {}
weights = {}
if idVariable:
diff --git a/pysal/weights/tests/test_spintW.py b/pysal/weights/tests/test_spintW.py
new file mode 100644
index 0000000..4d3e6a5
--- /dev/null
+++ b/pysal/weights/tests/test_spintW.py
@@ -0,0 +1,173 @@
+import unittest
+import pysal
+import numpy as np
+from pysal.weights.spintW import ODW, netW, mat2L, vecW
+
+class TestODWeights(unittest.TestCase):
+ def setUp(self):
+ self.O = pysal.weights.lat2W(2,2)
+ self.D = pysal.weights.lat2W(2,2)
+ self.ODW = np.array(
+ [[ 0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. ,
+ 0.25, 0.25, 0. , 0. , 0. , 0. , 0. ],
+ [ 0. , 0. , 0. , 0. , 0.25, 0. , 0. , 0.25, 0.25,
+ 0. , 0. , 0.25, 0. , 0. , 0. , 0. ],
+ [ 0. , 0. , 0. , 0. , 0.25, 0. , 0. , 0.25, 0.25,
+ 0. , 0. , 0.25, 0. , 0. , 0. , 0. ],
+ [ 0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. ,
+ 0.25, 0.25, 0. , 0. , 0. , 0. , 0. ],
+ [ 0. , 0.25, 0.25, 0. , 0. , 0. , 0. , 0. , 0. ,
+ 0. , 0. , 0. , 0. , 0.25, 0.25, 0. ],
+ [0.25, 0. , 0. , 0.25, 0. , 0. , 0. , 0. , 0. ,
+ 0. , 0. , 0. , 0.25, 0. , 0. , 0.25],
+ [ 0.25, 0. , 0. , 0.25, 0. , 0. , 0. , 0. , 0. ,
+ 0. , 0. , 0. , 0.25, 0. , 0. , 0.25],
+ [ 0. , 0.25, 0.25, 0. , 0. , 0. , 0. , 0. , 0. ,
+ 0. , 0. , 0. , 0. , 0.25, 0.25, 0. ],
+ [ 0. , 0.25, 0.25, 0. , 0. , 0. , 0. , 0. , 0. ,
+ 0. , 0. , 0. , 0. , 0.25, 0.25, 0. ],
+ [ 0.25, 0. , 0. , 0.25, 0. , 0. , 0. , 0. , 0. ,
+ 0. , 0. , 0. , 0.25, 0. , 0. , 0.25],
+ [ 0.25, 0. , 0. , 0.25, 0. , 0. , 0. , 0. , 0. ,
+ 0. , 0. , 0. , 0.25, 0. , 0. , 0.25],
+ [ 0. , 0.25, 0.25, 0. , 0. , 0. , 0. , 0. , 0. ,
+ 0. , 0. , 0. , 0. , 0.25, 0.25, 0. ],
+ [ 0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. ,
+ 0.25, 0.25, 0. , 0. , 0. , 0. , 0. ],
+ [ 0. , 0. , 0. , 0. , 0.25, 0. , 0. , 0.25, 0.25,
+ 0. , 0. , 0.25, 0. , 0. , 0. , 0. ],
+ [ 0. , 0. , 0. , 0. , 0.25, 0. , 0. , 0.25, 0.25,
+ 0. , 0. , 0.25, 0. , 0. , 0. , 0. ],
+ [ 0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. ,
+ 0.25, 0.25, 0. , 0. , 0. , 0. , 0. ]])
+
+ def test_ODW_full(self):
+ W = ODW(self.O, self.D)
+ np.testing.assert_allclose(self.ODW, W.full()[0])
+
+class TestNetW(unittest.TestCase):
+ def setUp(self):
+ self.link_list = [('a', 'b'), ('a', 'c'), ('a', 'd'),
+ ('b', 'a'), ('b', 'c'), ('b', 'd'),
+ ('c', 'a'), ('c', 'b'), ('c', 'd'),
+ ('d', 'a'), ('d', 'b'), ('d', 'c')]
+
+ self._all = np.array([
+ [ 0., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0.],
+ [ 1., 0., 1., 1., 1., 0., 1., 1., 1., 1., 0., 1.],
+ [ 1., 1., 0., 1., 0., 1., 1., 0., 1., 1., 1., 1.],
+ [ 1., 1., 1., 0., 1., 1., 1., 1., 0., 1., 1., 0.],
+ [ 1., 1., 0., 1., 0., 1., 1., 1., 1., 0., 1., 1.],
+ [ 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1.],
+ [ 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 0., 1.],
+ [ 1., 1., 0., 1., 1., 1., 1., 0., 1., 0., 1., 1.],
+ [ 0., 1., 1., 0., 1., 1., 1., 1., 0., 1., 1., 1.],
+ [ 1., 1., 1., 1., 0., 1., 1., 0., 1., 0., 1., 1.],
+ [ 1., 0., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1.],
+ [ 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 0.]])
+
+
+ self.O = np.array([
+ [ 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+ [ 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+ [ 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.],
+ [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1.],
+ [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0.]])
+
+
+ self.D = np.array([
+ [ 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0.],
+ [ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.],
+ [ 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0.],
+ [ 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
+ [ 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0.],
+ [ 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0.],
+ [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],
+ [ 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0.],
+ [ 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],
+ [ 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.]])
+
+
+ self.OD = np.array([
+ [ 0., 1., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0.],
+ [ 1., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1.],
+ [ 1., 1., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0.],
+ [ 0., 0., 0., 0., 1., 1., 1., 0., 0., 1., 0., 0.],
+ [ 0., 1., 0., 1., 0., 1., 0., 0., 0., 0., 0., 1.],
+ [ 0., 0., 1., 1., 1., 0., 0., 0., 1., 0., 0., 0.],
+ [ 0., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 0.],
+ [ 1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 1., 0.],
+ [ 0., 0., 1., 0., 0., 1., 1., 1., 0., 0., 0., 0.],
+ [ 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 1.],
+ [ 1., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 1.],
+ [ 0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 1., 0.]])
+
+
+ self.C = np.array([
+ [ 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],
+ [ 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],
+ [ 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],
+ [ 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0.],
+ [ 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0.]])
+
+ self.edge_list = [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+
+ def test_netOD(self):
+ netW_OD = netW(self.link_list, share='OD')
+ np.testing.assert_allclose(netW_OD.full()[0], self.OD)
+
+
+ def test_netO(self):
+ netW_O = netW(self.link_list, share='O')
+ np.testing.assert_allclose(netW_O.full()[0], self.O)
+
+ def test_netD(self):
+ netW_D = netW(self.link_list, share='D')
+ np.testing.assert_allclose(netW_D.full()[0], self.D)
+
+ def test_netC(self):
+ netW_C = netW(self.link_list, share='C')
+ np.testing.assert_allclose(netW_C.full()[0], self.C)
+
+ def test_net_all(self):
+ netW_all = netW(self.link_list, share='A')
+ np.testing.assert_allclose(netW_all.full()[0], self._all)
+
+ def test_mat2L(self):
+ mat = np.array([[0,1,1],[1,0,1],[1,1,0]])
+ edge_list = mat2L(mat)
+ self.assertEqual(edge_list, self.edge_list)
+
+class TestVecW(unittest.TestCase):
+ def setUp(self):
+ self.origin_x = np.array([2,6,9,2])
+ self.origin_y = np.array([4,8,2,5])
+ self.dest_x = np.array([9,1,6,3])
+ self.dest_y = np.array([3,6,2,7])
+ self.continuous = np.array([
+ [ 0. , 0.09759001, 0.12598816, 0.13736056],
+ [ 0.09759001, 0. , 0.10783277, 0.18257419],
+ [ 0.12598816, 0.10783277, 0. , 0.10425721],
+ [ 0.13736056, 0.18257419, 0.10425721, 0. ]])
+
+ def test_vecW(self):
+ W = vecW(self.origin_x, self.origin_y, self.dest_x, self.dest_y,
+ threshold=np.inf, binary=False)
+ np.testing.assert_allclose(self.continuous, W.full()[0])
+
diff --git a/pysal/weights/tests/test_weights.py b/pysal/weights/tests/test_weights.py
index b790b64..6828735 100644
--- a/pysal/weights/tests/test_weights.py
+++ b/pysal/weights/tests/test_weights.py
@@ -234,8 +234,8 @@ class Test_WSP_Back_To_W(unittest.TestCase):
def setUp(self):
from pysal import rook_from_shapefile
self.w = rook_from_shapefile(pysal.examples.get_path('10740.shp'))
- wsp = pysal.weights.WSP(self.w.sparse, self.w.id_order)
- self.w = pysal.weights.WSP2W(wsp)
+ wsp = self.w.to_WSP()
+ self.w = wsp.to_W()
self.neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4],
4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7],
diff --git a/pysal/weights/user.py b/pysal/weights/user.py
index 7a24e55..47242ed 100644
--- a/pysal/weights/user.py
+++ b/pysal/weights/user.py
@@ -6,12 +6,17 @@ contiguity and distance criteria.
__author__ = "Sergio J. Rey <srey at asu.edu> "
import pysal
-from Contiguity import buildContiguity
+from Contiguity import buildContiguity, Queen, Rook
from Distance import knnW, Kernel, DistanceBand
from util import get_ids, get_points_array_from_shapefile, min_threshold_distance
import numpy as np
-__all__ = ['queen_from_shapefile', 'rook_from_shapefile', 'knnW_from_array', 'knnW_from_shapefile', 'threshold_binaryW_from_array', 'threshold_binaryW_from_shapefile', 'threshold_continuousW_from_array', 'threshold_continuousW_from_shapefile', 'kernelW', 'kernelW_from_shapefile', 'adaptive_kernelW', 'adaptive_kernelW_from_shapefile', 'min_threshold_dist_from_shapefile', 'build_lattice_shapefile']
+__all__ = ['queen_from_shapefile', 'rook_from_shapefile', 'knnW_from_array',
+ 'knnW_from_shapefile', 'threshold_binaryW_from_array',
+ 'threshold_binaryW_from_shapefile', 'threshold_continuousW_from_array',
+ 'threshold_continuousW_from_shapefile', 'kernelW', 'kernelW_from_shapefile',
+ 'adaptive_kernelW', 'adaptive_kernelW_from_shapefile',
+ 'min_threshold_dist_from_shapefile', 'build_lattice_shapefile']
def queen_from_shapefile(shapefile, idVariable=None, sparse=False):
@@ -25,14 +30,14 @@ def queen_from_shapefile(shapefile, idVariable=None, sparse=False):
name of polygon shapefile including suffix.
idVariable : string
name of a column in the shapefile's DBF to use for ids.
- sparse : boolean
- If True return WSP instance
- If False return W instance
+ sparse : boolean
+ If True return WSP instance
+ If False return W instance
Returns
-------
- w : W
- instance of spatial weights
+ w : W
+ instance of spatial weights
Examples
--------
@@ -58,19 +63,10 @@ def queen_from_shapefile(shapefile, idVariable=None, sparse=False):
:class:`pysal.weights.W`
"""
- shp = pysal.open(shapefile)
- w = buildContiguity(shp, criterion='queen')
- if idVariable:
- ids = get_ids(shapefile, idVariable)
- w.remap_ids(ids)
- else:
- ids = None
- shp.close()
- w.set_shapefile(shapefile, idVariable)
+ w = Queen.from_shapefile(shapefile, idVariable=idVariable)
if sparse:
- w = pysal.weights.WSP(w.sparse, id_order=ids)
-
+ w = pysal.weights.WSP(w.sparse, id_order=w.id_order)
return w
@@ -83,6 +79,8 @@ def rook_from_shapefile(shapefile, idVariable=None, sparse=False):
shapefile : string
name of polygon shapefile including suffix.
+ idVariable: string
+ name of a column in the shapefile's DBF to use for ids
sparse : boolean
If True return WSP instance
If False return W instance
@@ -90,8 +88,8 @@ def rook_from_shapefile(shapefile, idVariable=None, sparse=False):
Returns
-------
- w : W
- instance of spatial weights
+ w : W
+ instance of spatial weights
Examples
--------
@@ -114,23 +112,12 @@ def rook_from_shapefile(shapefile, idVariable=None, sparse=False):
:class:`pysal.weights.W`
"""
- shp = pysal.open(shapefile)
- w = buildContiguity(shp, criterion='rook')
- if idVariable:
- ids = get_ids(shapefile, idVariable)
- w.remap_ids(ids)
- else:
- ids = None
- shp.close()
- w.set_shapefile(shapefile, idVariable)
+ w = Rook.from_shapefile(shapefile, idVariable=idVariable)
if sparse:
- w = pysal.weights.WSP(w.sparse, id_order=ids)
-
-
+ w = pysal.weights.WSP(w.sparse, id_order=w.id_order)
return w
-
def spw_from_gal(galfile):
"""
Sparse scipy matrix for w from a gal file.
@@ -138,8 +125,8 @@ def spw_from_gal(galfile):
Parameters
----------
- galfile: string
- name of gal file including suffix
+ galfile : string
+ name of gal file including suffix
Returns
-------
@@ -190,8 +177,8 @@ def knnW_from_array(array, k=2, p=2, ids=None, radius=None):
Returns
-------
- w : W
- instance; Weights object with binary weights.
+ w : W
+ instance; Weights object with binary weights.
Examples
--------
@@ -260,8 +247,8 @@ def knnW_from_shapefile(shapefile, k=2, p=2, idVariable=None, radius=None):
Returns
-------
- w : W
- instance; Weights object with binary weights
+ w : W
+ instance; Weights object with binary weights
Examples
--------
@@ -335,9 +322,9 @@ def threshold_binaryW_from_array(array, threshold, p=2, radius=None):
Parameters
----------
- array : array
- (n,m)
- attribute data, n observations on m attributes
+ array : array
+ (n,m)
+ attribute data, n observations on m attributes
threshold : float
distance band
p : float
@@ -352,9 +339,9 @@ def threshold_binaryW_from_array(array, threshold, p=2, radius=None):
Returns
-------
- w : W
- instance
- Weights object with binary weights
+ w : W
+ instance
+ Weights object with binary weights
Examples
--------
@@ -459,8 +446,8 @@ def threshold_continuousW_from_array(array, threshold, p=2,
Returns
-------
- w : W
- instance; Weights object with continuous weights.
+ w : W
+ instance; Weights object with continuous weights.
Examples
--------
@@ -521,8 +508,8 @@ def threshold_continuousW_from_shapefile(shapefile, threshold, p=2,
Returns
-------
- w : W
- instance; Weights object with continuous weights.
+ w : W
+ instance; Weights object with continuous weights.
Examples
--------
@@ -620,22 +607,22 @@ def kernelW(points, k=2, function='triangular', fixed=True,
K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)
- fixed : boolean
- If true then :math:`h_i=h \\forall i`. If false then
- bandwidth is adaptive across observations.
- radius : float
- If supplied arc_distances will be calculated
- based on the given radius. p will be ignored.
- diagonal : boolean
- If true, set diagonal weights = 1.0, if false (default)
- diagonal weights are set to value according to kernel
- function
+ fixed : boolean
+ If true then :math:`h_i=h \\forall i`. If false then
+ bandwidth is adaptive across observations.
+ radius : float
+ If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+ diagonal : boolean
+ If true, set diagonal weights = 1.0, if false (
+ default) diagonal weights are set to value
+ according to kernel function.
Returns
-------
- w : W
- instance of spatial weights
+ w : W
+ instance of spatial weights
Examples
--------
@@ -752,13 +739,13 @@ def kernelW_from_shapefile(shapefile, k=2, function='triangular',
fixed : binary
If true then :math:`h_i=h \\forall i`. If false then
bandwidth is adaptive across observations.
- radius : float
- If supplied arc_distances will be calculated
- based on the given radius. p will be ignored.
- diagonal : boolean
- If true, set diagonal weights = 1.0, if false (default)
- diagonal weights are set to value according to kernel
- function
+ radius : float
+ If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+ diagonal : boolean
+ If true, set diagonal weights = 1.0, if false (
+ default) diagonal weights are set to value
+ according to kernel function.
Returns
-------
@@ -862,18 +849,18 @@ def adaptive_kernelW(points, bandwidths=None, k=2, function='triangular',
K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)
- radius : float
- If supplied arc_distances will be calculated
- based on the given radius. p will be ignored.
- diagonal : boolean
- If true, set diagonal weights = 1.0, if false (default)
- diagonal weights are set to value according to kernel
- function
+ radius : float
+ If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+ diagonal : boolean
+ If true, set diagonal weights = 1.0, if false (
+ default) diagonal weights are set to value
+ according to kernel function.
Returns
-------
- w : W
- instance of spatial weights
+ w : W
+ instance of spatial weights
Examples
--------
@@ -1001,21 +988,21 @@ def adaptive_kernelW_from_shapefile(shapefile, bandwidths=None, k=2, function='t
.. math::
K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)
- idVariable : string
- name of a column in the shapefile's DBF to use for ids
- radius : float
- If supplied arc_distances will be calculated
- based on the given radius. p will be ignored.
- diagonal : boolean
- If true, set diagonal weights = 1.0, if false (default)
- diagonal weights are set to value according to kernel
- function
+ idVariable : string
+ name of a column in the shapefile's DBF to use for ids
+ radius : float
+ If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+ diagonal : boolean
+ If true, set diagonal weights = 1.0, if false (
+ default) diagonal weights are set to value
+ according to kernel function.
Returns
-------
- w : W
- instance of spatial weights
+ w : W
+ instance of spatial weights
Examples
--------
@@ -1029,7 +1016,6 @@ def adaptive_kernelW_from_shapefile(shapefile, bandwidths=None, k=2, function='t
[0.3989422804014327, 0.24966013701844503, 0.2419707487162134]
>>> kwad.weights[0]
[1.0, 0.24966013701844503, 0.2419707487162134]
- >>>
Notes
-----
@@ -1058,7 +1044,7 @@ def min_threshold_dist_from_shapefile(shapefile, radius=None, p=2):
----------
shapefile : string
- shapefile name with shp suffix
+ shapefile name with shp suffix.
radius : float
If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
@@ -1070,8 +1056,9 @@ def min_threshold_dist_from_shapefile(shapefile, radius=None, p=2):
Returns
-------
- d : float
- minimum nearest neighbor distance between the n observations
+ d : float
+ Maximum nearest neighbor distance between the n
+ observations.
Examples
--------
diff --git a/pysal/weights/util.py b/pysal/weights/util.py
index 4ab3b48..d76f8bd 100644
--- a/pysal/weights/util.py
+++ b/pysal/weights/util.py
@@ -1,4 +1,5 @@
import pysal
+from pysal.cg import Polygon, Point
from pysal.common import *
import pysal.weights
import numpy as np
@@ -7,6 +8,7 @@ import scipy.spatial
import os
import operator
import scipy
+from warnings import warn
__all__ = ['lat2W', 'block_weights', 'comb', 'order', 'higher_order',
'shimbel', 'remap_ids', 'full2W', 'full', 'WSP2W',
@@ -849,8 +851,11 @@ def WSP2W(wsp, silent_island_warning=False):
w._cache['sparse'] = w._sparse
return w
+def insert_diagonal(w, val=1.0, wsp=False):
+ warn('This function is deprecated. Use fill_diagonal instead.')
+ return fill_diagonal(w, val=val, wsp=wsp)
-def insert_diagonal(w, diagonal=1.0, wsp=False):
+def fill_diagonal(w, val=1.0, wsp=False):
"""
Returns a new weights object with values inserted along the main diagonal.
@@ -901,12 +906,12 @@ def insert_diagonal(w, diagonal=1.0, wsp=False):
w_new = copy.deepcopy(w.sparse)
w_new = w_new.tolil()
- if issubclass(type(diagonal), np.ndarray):
- if w.n != diagonal.shape[0]:
+ if issubclass(type(val), np.ndarray):
+ if w.n != val.shape[0]:
raise Exception("shape of w and diagonal do not match")
- w_new.setdiag(diagonal)
- elif operator.isNumberType(diagonal):
- w_new.setdiag([diagonal] * w.n)
+ w_new.setdiag(val)
+ elif operator.isNumberType(val):
+ w_new.setdiag([val] * w.n)
else:
raise Exception("Invalid value passed to diagonal")
w_out = pysal.weights.WSP(w_new, copy.copy(w.id_order))
@@ -1015,6 +1020,32 @@ def get_ids(shapefile, idVariable):
idVariable, ','.join(db.header))
raise KeyError(msg)
+def get_points_array(iterable):
+ """
+ Gets a data array of x and y coordinates from a given iterable
+ Parameters
+ ----------
+ iterable : iterable
+ arbitrary collection of shapes that supports iteration
+
+ Returns
+ -------
+ points : array
+ (n, 2)
+ a data array of x and y coordinates
+
+ Notes
+ -----
+ If the given shape file includes polygons,
+ this function returns x and y coordinates of the polygons' centroids
+
+ """
+ try:
+ data = np.vstack([np.array(shape.centroid) for shape in iterable])
+ except AttributeError:
+ data = np.vstack([shape for shape in iterable])
+ return data
+
def get_points_array_from_shapefile(shapefile):
"""
@@ -1057,11 +1088,7 @@ def get_points_array_from_shapefile(shapefile):
"""
f = pysal.open(shapefile)
- if f.type.__name__ == 'Polygon':
- data = np.array([shape.centroid for shape in f])
- elif f.type.__name__ == 'Point':
- data = np.array([shape for shape in f])
- f.close()
+ data = get_points_array(f)
return data
diff --git a/pysal/weights/weights.py b/pysal/weights/weights.py
index 11788d5..975e08e 100644
--- a/pysal/weights/weights.py
+++ b/pysal/weights/weights.py
@@ -3,15 +3,22 @@ Weights.
"""
__author__ = "Sergio J. Rey <srey at asu.edu> "
-import pysal
import math
import numpy as np
import scipy.sparse
from os.path import basename as BASENAME
-from pysal.weights import util
+from .util import full, WSP2W
+from ..core.FileIO import FileIO as popen
+#from .contiguity import Rook, Queen
+#from .distance import Kernel, DistanceBand, KNN
__all__ = ['W', 'WSP']
+#dispatch_table = {'rook':Rook, 'queen':Queen, 'kernel':Kernel,
+# 'adaptive kernel': partial(Kernel, fixed=False),
+# 'threshold continuous': partial(DistanceBand, binary=False),
+# 'threshold binary': DistanceBand,
+# 'distance band':DistanceBand, 'knn':KNN}
class W(object):
"""
@@ -175,17 +182,38 @@ class W(object):
if self.islands and not self.silent_island_warning:
ni = len(self.islands)
if ni == 1:
- print "WARNING: there is one disconnected observation (no neighbors)"
- print "Island id: ", self.islands
+ print("WARNING: there is one disconnected observation (no neighbors)")
+ print("Island id: ", self.islands)
else:
- print "WARNING: there are %d disconnected observations" % ni
- print "Island ids: ", self.islands
+ print("WARNING: there are %d disconnected observations" % ni)
+ print("Island ids: ", self.islands)
def _reset(self):
"""Reset properties.
"""
self._cache = {}
+
+ @classmethod
+ def from_file(cls, path='', format=None, **kwargs):
+ f = popen(dataPath=path, mode='r', dataFormat=format)
+ w = f.read(**kwargs)
+ f.close()
+ return w
+
+ @classmethod
+ def from_shapefile(cls, *args, **kwargs):
+ # we could also just "do the right thing," but I think it'd make sense to
+ # try and get people to use `Rook.from_shapefile(shapefile)` rather than
+ # W.from_shapefile(shapefile, type=`rook`), otherwise we'd need to build
+ # a type dispatch table. Generic W should be for stuff we don't know
+ # anything about.
+ raise NotImplementedError('Use type-specific constructors, like Rook,'
+ ' Queen, DistanceBand, or Kernel')
+
+ @classmethod
+ def from_WSP(cls, WSP, silent_island_warning=True):
+ return WSP2W(WSP, silent_island_warning=silent_island_warning)
@property
def sparse(self):
@@ -800,7 +828,7 @@ class W(object):
row_sum = sum(wijs) * 1.0
if row_sum == 0.0:
if not self.silent_island_warning:
- print 'WARNING: ', i, ' is an island (no neighbors)'
+ print('WARNING: ', i, ' is an island (no neighbors)')
weights[i] = [wij / row_sum for wij in wijs]
weights = weights
self.transformations[value] = weights
@@ -859,7 +887,7 @@ class W(object):
self.weights = original
self._reset()
else:
- print 'unsupported weights transformation'
+ raise Exception('unsupported weights transformation')
transform = property(get_transform, set_transform)
@@ -951,7 +979,7 @@ class W(object):
full
"""
- return util.full(self)
+ return full(self)
def towsp(self):
'''
@@ -984,6 +1012,8 @@ class W(object):
'''
return WSP(self.sparse, self._id_order)
+
+ to_WSP = towsp
def set_shapefile(self, shapefile, idVariable=None, full=False):
"""
@@ -1108,3 +1138,31 @@ class WSP(object):
self._diagWtW_WW = (wt * w + w * w).diagonal()
self._cache['diagWtW_WW'] = self._diagWtW_WW
return self._diagWtW_WW
+
+ def to_W(self, silent_island_warning=True):
+ """
+ Construct a W object from the WSP's sparse matrix
+
+ Arguments
+ ---------
+ silence_island_warning : bool
+ a flag governing whether to state when
+ islands are encountered.
+ """
+ return WSP2W(self, silent_island_warning=silent_island_warning)
+
+ @classmethod
+ def from_W(cls, W):
+ """
+ Constructs a WSP object from the W's sparse matrix
+
+ Arguments
+ ---------
+ W : pysal.weights.W
+ a pysal weights object with a sparse form and ids
+
+ Returns
+ -------
+ a WSP instance
+ """
+ return cls(W.sparse, id_order=W.id_order)
diff --git a/pysal/weights/weights_from_geojson.ipynb b/pysal/weights/weights_from_geojson.ipynb
deleted file mode 100644
index e9febb5..0000000
--- a/pysal/weights/weights_from_geojson.ipynb
+++ /dev/null
@@ -1,311 +0,0 @@
-{
- "metadata": {
- "name": "weights_from_geojson"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "heading",
- "level": 2,
- "metadata": {},
- "source": [
- "Prototyping the construction of PySAL weights from geojson. Supports local files, uri, and serialized objects\n",
- "\n"
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "Author: Sergio Rey <sjsrey at gmail.com>"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import geojson\n",
- "import pysal as ps\n",
- "import urllib2 as urllib"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "class PolygonCollection:\n",
- " def __init__(self, polygons, bbox=None):\n",
- " \"\"\"\n",
- "\n",
- " Parameters\n",
- " ==========\n",
- " polygons: dict\n",
- " key is polygon Id, value is PySAL Polygon object\n",
- " bbox: list (optional)\n",
- " [left, lower, right, upper]\n",
- "\n",
- " Notes\n",
- " =====\n",
- " bbox is supported in geojson specification at both the feature and feature collection level. However, not all geojson writers generate the bbox at the feature collection level. \n",
- " In those cases, the bbox property will be set on initial access.\n",
- "\n",
- " \"\"\"\n",
- " \n",
- " self.type=ps.cg.Polygon\n",
- " self.n = len(polygons)\n",
- " self.polygons = polygons\n",
- " if bbox is None:\n",
- " self._bbox = None\n",
- " else:\n",
- " self._bbox = bbox\n",
- " \n",
- " @property\n",
- " def bbox(self):\n",
- " bboxes = np.array([self.polygons[p].bbox for p in self.polygons])\n",
- " mins = bboxes.min(axis=0)\n",
- " maxs = bboxes.max(axis=0)\n",
- " self._bbox = [ mins[0], mins[1], maxs[2], maxs[3] ]\n",
- " return self._bbox\n",
- " \n",
- " \n",
- " def __getitem__(self, index):\n",
- " return self.polygons[index]\n",
- " "
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 2
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def queen_geojson(gjobj):\n",
- " \"\"\"\n",
- " Constructs a PySAL queen contiguity W from a geojson object\n",
- "\n",
- " \"\"\"\n",
- " polys = []\n",
- " ids = []\n",
- " i = 0\n",
- " for feature in gjobj['features']:\n",
- " polys.append(ps.cg.asShape(geojson.Polygon(feature['geometry']['coordinates'])))\n",
- " ids.append(i)\n",
- " i += 1\n",
- " polygons = PolygonCollection(dict(zip(ids,polys)))\n",
- " neighbors = ps.weights.Contiguity.ContiguityWeightsPolygons(polygons).w\n",
- " return ps.W(neighbors)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 3
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def queen_from_geojson(uri):\n",
- " gjobj = geojson.load(urllib.urlopen(uri))\n",
- " return queen_geojson(gjobj)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 4
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def queen_from_geojsons(s):\n",
- " return queen_geojson(info)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 5
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def queen_from_geojsonf(fileName):\n",
- " fp = open(fileName)\n",
- " obj = geojson.load(fp)\n",
- " fp.close()\n",
- " return queen_geojson(obj)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 6
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# from a uri\n",
- "uri = \"http://toae.org/pub/columbus.json\""
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 7
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "wq = queen_from_geojson(uri)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 8
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "wq.n"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "pyout",
- "prompt_number": 9,
- "text": [
- "49"
- ]
- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# from a local file\n",
- "wf = queen_from_geojsonf(ps.examples.get_path(\"columbus.json\"))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 10
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "wf.n"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "pyout",
- "prompt_number": 11,
- "text": [
- "49"
- ]
- }
- ],
- "prompt_number": 11
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# from a serialized geojson object\n",
- "\n",
- "fp = open(ps.examples.get_path(\"columbus.json\"))\n",
- "info = geojson.load(fp)\n",
- "fp.close()\n",
- "infos = geojson.dumps(info)\n",
- "ws = queen_from_geojsons(infos)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 12
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ws.n"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "pyout",
- "prompt_number": 13,
- "text": [
- "49"
- ]
- }
- ],
- "prompt_number": 13
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "wf.neighbors == ws.neighbors"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "pyout",
- "prompt_number": 14,
- "text": [
- "True"
- ]
- }
- ],
- "prompt_number": 14
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "wq.neighbors == wf.neighbors"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "pyout",
- "prompt_number": 15,
- "text": [
- "True"
- ]
- }
- ],
- "prompt_number": 15
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff --git a/requirements_plus.txt b/requirements_plus.txt
new file mode 100644
index 0000000..9dcf92d
--- /dev/null
+++ b/requirements_plus.txt
@@ -0,0 +1,12 @@
+scipy>=0.11
+matplotlib>=1.5.1
+seaborn>=0.7.0
+geopandas>=0.2
+scikit-learn>=0.17.1
+bokeh>=0.11.1
+geojson>=1.3.2
+folium>=0.2.1
+mplleaflet>=0.0.5
+statsmodels>=0.6.1
+numba
+numexpr
diff --git a/setup.py b/setup.py
index 823fbbd..7bdebf6 100644
--- a/setup.py
+++ b/setup.py
@@ -13,8 +13,8 @@ with open('README.rst') as file:
long_description = file.read()
MAJOR = 1
-MINOR = 11
-MICRO = 2
+MINOR = 12
+MICRO = 0
ISRELEASED = False
VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
diff --git a/tools/github_stats.py b/tools/github_stats.py
index 9fd5263..92913de 100644
--- a/tools/github_stats.py
+++ b/tools/github_stats.py
@@ -1,5 +1,7 @@
#!/usr/bin/env python
"""Simple tools to query github.com and gather stats about issues.
+
+Adapted from: https://github.com/ipython/ipython/blob/master/tools/github_stats.py
"""
#-----------------------------------------------------------------------------
# Imports
@@ -14,6 +16,9 @@ import sys
from datetime import datetime, timedelta
from subprocess import check_output
from urllib2 import urlopen
+import ssl
+
+context = ssl._create_unverified_context()
#-----------------------------------------------------------------------------
# Globals
@@ -43,7 +48,7 @@ def get_paged_request(url):
results = []
while url:
print("fetching %s" % url, file=sys.stderr)
- f = urlopen(url)
+ f = urlopen(url, context=context)
results.extend(json.load(f))
links = parse_link_header(f.headers)
url = links.get('next')
diff --git a/travis.txt b/travis.txt
index b7d4c0a..8b30813 100644
--- a/travis.txt
+++ b/travis.txt
@@ -1,6 +1,6 @@
nose-exclude
nose-progressive
+coverage
sphinx
sphinxcontrib-napoleon
-networkx
-Shapely
+coveralls
--
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