[med-svn] [Git][med-team/umap-learn][master] 7 commits: Cleanup changelog
Andreas Tille (@tille)
gitlab at salsa.debian.org
Fri Sep 10 07:09:08 BST 2021
Andreas Tille pushed to branch master at Debian Med / umap-learn
Commits:
9ee805bb by Andreas Tille at 2021-09-10T08:04:37+02:00
Cleanup changelog
- - - - -
e08fb6e7 by Andreas Tille at 2021-09-10T08:05:03+02:00
routine-update: New upstream version
- - - - -
3fa8c8d1 by Andreas Tille at 2021-09-10T08:05:04+02:00
New upstream version 0.5.1+dfsg
- - - - -
3bce779b by Andreas Tille at 2021-09-10T08:05:06+02:00
Update upstream source from tag 'upstream/0.5.1+dfsg'
Update to upstream version '0.5.1+dfsg'
with Debian dir 2bce22555ac6392ec506eed0f802ba7cc6f1dab5
- - - - -
bc125ab5 by Andreas Tille at 2021-09-10T08:05:07+02:00
routine-update: Standards-Version: 4.6.0
- - - - -
30d4bb98 by Andreas Tille at 2021-09-10T08:05:11+02:00
Remove duplicate line from changelog.
Changes-By: lintian-brush
- - - - -
b41a9ab0 by Andreas Tille at 2021-09-10T08:08:37+02:00
Update to new upstream version, needs tensorflow
- - - - -
11 changed files:
- azure-pipelines.yml
- debian/changelog
- debian/control
- setup.py
- umap/__init__.py
- umap/layouts.py
- umap/plot.py
- umap/sparse.py
- umap/spectral.py
- umap/tests/test_umap_metrics.py
- umap/umap_.py
Changes:
=====================================
azure-pipelines.yml
=====================================
@@ -5,7 +5,6 @@
trigger:
- master
-- 0.5dev
jobs:
- job: Linux
=====================================
debian/changelog
=====================================
@@ -1,18 +1,19 @@
-umap-learn (0.5.0+dfsg-1) UNRELEASED; urgency=medium
+umap-learn (0.5.1+dfsg-1) UNRELEASED; urgency=medium
[ Andreas Tille ]
- * Drop unused lintian override
* New upstream version
- * Standards-Version: 4.5.1 (routine-update)
- * Rename binary package tp python3-umap-learn
+ * Drop unused lintian override
+ * Rename binary package to python3-umap-learn
Closes: #980509
* Test runs only on 64 bit architectures
- TODO: tensorflow
+ * Standards-Version: 4.6.0 (routine-update)
[ Steffen Moeller ]
* Fix watchfile to detect new versions on github (routine-update)
- -- Andreas Tille <tille at debian.org> Thu, 14 Jan 2021 21:58:17 +0100
+ TODO: Needs tensorflow
+
+ -- Andreas Tille <tille at debian.org> Fri, 10 Sep 2021 08:05:03 +0200
umap-learn (0.4.5+dfsg-2) unstable; urgency=medium
=====================================
debian/control
=====================================
@@ -15,7 +15,7 @@ Build-Depends: debhelper-compat (= 13),
python3-pynndescent,
python3-nose <!nocheck>,
python3-pytest
-Standards-Version: 4.5.1
+Standards-Version: 4.6.0
Vcs-Browser: https://salsa.debian.org/med-team/umap-learn
Vcs-Git: https://salsa.debian.org/med-team/umap-learn.git
Homepage: https://github.com/lmcinnes/umap
=====================================
setup.py
=====================================
@@ -15,7 +15,7 @@ def readme():
configuration = {
"name": "umap-learn",
- "version": "0.5.0",
+ "version": "0.5.1",
"description": "Uniform Manifold Approximation and Projection",
"long_description": readme(),
"long_description_content_type": "text/x-rst",
=====================================
umap/__init__.py
=====================================
@@ -6,7 +6,27 @@ try:
simplefilter("ignore")
from .parametric_umap import ParametricUMAP
except ImportError:
- warn("Tensorflow not installed; ParametricUMAP will be unavailable")
+ warn(
+ "Tensorflow not installed; ParametricUMAP will be unavailable",
+ category=ImportWarning
+ )
+ # Add a dummy class to raise an error
+ class ParametricUMAP (object):
+
+ def __init__(self, **kwds):
+ warn(
+ """The umap.parametric_umap package requires Tensorflow > 2.0 to be installed.
+ You can install Tensorflow at https://www.tensorflow.org/install
+
+ or you can install the CPU version of Tensorflow using
+
+ pip install umap-learn[parametric_umap]
+
+ """
+ )
+ raise ImportError(
+ "umap.parametric_umap requires Tensorflow >= 2.0") from None
+
from .aligned_umap import AlignedUMAP
# Workaround: https://github.com/numba/numba/issues/3341
=====================================
umap/layouts.py
=====================================
@@ -33,7 +33,7 @@ def clip(val):
locals={
"result": numba.types.float32,
"diff": numba.types.float32,
- "dim": numba.types.int32,
+ "dim": numba.types.intp,
},
)
def rdist(x, y):
=====================================
umap/plot.py
=====================================
@@ -42,6 +42,7 @@ from matplotlib.patches import Patch
from umap.utils import submatrix
from bokeh.plotting import show as show_interactive
+from bokeh.plotting import output_file, output_notebook
from bokeh.layouts import column
from bokeh.models import CustomJS, TextInput
from matplotlib.pyplot import show as show_static
@@ -1385,7 +1386,7 @@ def interactive(
tooltip_dict = {}
for col_name in hover_data:
data[col_name] = hover_data[col_name]
- tooltip_dict[col_name] = "@" + col_name
+ tooltip_dict[col_name] = "@{" + col_name + "}"
tooltips = list(tooltip_dict.items())
else:
tooltips = None
=====================================
umap/sparse.py
=====================================
@@ -288,15 +288,17 @@ def sparse_canberra(ind1, data1, ind2, data2):
@numba.njit()
def sparse_bray_curtis(ind1, data1, ind2, data2): # pragma: no cover
- abs_data1 = np.abs(data1)
- abs_data2 = np.abs(data2)
- denom_inds, denom_data = sparse_sum(ind1, abs_data1, ind2, abs_data2)
+ denom_inds, denom_data = sparse_sum(ind1, data1, ind2, data2)
+ denom_data = np.abs(denom_data)
if denom_data.shape[0] == 0:
return 0.0
denominator = np.sum(denom_data)
+ if denominator == 0:
+ return 0.0
+
numer_inds, numer_data = sparse_diff(ind1, data1, ind2, data2)
numer_data = np.abs(numer_data)
@@ -589,7 +591,7 @@ sparse_named_distances = {
# Other distances
"canberra": sparse_canberra,
"ll_dirichlet": sparse_ll_dirichlet,
- # 'braycurtis': sparse_bray_curtis,
+ 'braycurtis': sparse_bray_curtis,
# Binary distances
"hamming": sparse_hamming,
"jaccard": sparse_jaccard,
=====================================
umap/spectral.py
=====================================
@@ -207,7 +207,7 @@ def multi_component_layout(
distances = pairwise_distances([meta_embedding[label]], meta_embedding)
data_range = distances[distances > 0.0].min() / 2.0
- if component_graph.shape[0] < 2 * dim:
+ if component_graph.shape[0] < 2 * dim or component_graph.shape[0] <= dim + 1:
result[component_labels == label] = (
random_state.uniform(
low=-data_range,
=====================================
umap/tests/test_umap_metrics.py
=====================================
@@ -270,6 +270,9 @@ def test_sparse_correlation(sparse_spatial_data):
sparse_spatial_check("correlation", sparse_spatial_data)
+def test_sparse_braycurtis(sparse_spatial_data):
+ sparse_spatial_check("braycurtis", sparse_spatial_data)
+
# ---------------------------
# Sparse Binary Metric Tests
# ---------------------------
=====================================
umap/umap_.py
=====================================
@@ -1283,7 +1283,6 @@ def init_graph_transform(graph, embedding):
new_embedding: array of shape (n_new_samples, dim)
An initial embedding of the new sample points.
"""
- print("inside function\n", graph)
result = np.zeros((graph.shape[0], embedding.shape[1]), dtype=np.float32)
for row_index in range(graph.shape[0]):
View it on GitLab: https://salsa.debian.org/med-team/umap-learn/-/compare/2c8649a01f3fb20da2f5849ff7b80097e1b1458d...b41a9ab0b88ba6944d33f43c9d1c11e5b4526187
--
View it on GitLab: https://salsa.debian.org/med-team/umap-learn/-/compare/2c8649a01f3fb20da2f5849ff7b80097e1b1458d...b41a9ab0b88ba6944d33f43c9d1c11e5b4526187
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