[med-svn] [Git][med-team/q2-feature-classifier][upstream] New upstream version 2024.5.0
Michael R. Crusoe (@crusoe)
gitlab at salsa.debian.org
Wed Jun 26 14:19:08 BST 2024
Michael R. Crusoe pushed to branch upstream at Debian Med / q2-feature-classifier
Commits:
7f0ea9e4 by Michael R. Crusoe at 2024-06-26T14:40:56+02:00
New upstream version 2024.5.0
- - - - -
5 changed files:
- q2_feature_classifier/_skl.py
- q2_feature_classifier/_version.py
- q2_feature_classifier/classifier.py
- q2_feature_classifier/tests/test_classifier.py
- q2_feature_classifier/tests/test_custom.py
Changes:
=====================================
q2_feature_classifier/_skl.py
=====================================
@@ -61,7 +61,7 @@ _specific_fitters = [
{'__type__': 'feature_extraction.text.HashingVectorizer',
'analyzer': 'char_wb',
'n_features': 8192,
- 'ngram_range': [7, 7],
+ 'ngram_range': (7, 7),
'alternate_sign': False}],
['classify',
{'__type__': 'custom.LowMemoryMultinomialNB',
=====================================
q2_feature_classifier/_version.py
=====================================
@@ -23,9 +23,9 @@ def get_keywords():
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
- git_refnames = " (tag: 2024.2.0, Release-2024.2)"
- git_full = "5ce76be5b72482a7d033fb9d2c41446edd75851a"
- git_date = "2024-02-16 21:57:24 +0000"
+ git_refnames = " (tag: 2024.5.0, Release-2024.5)"
+ git_full = "d14730e4fc0447415705d0084b17f18f7e6b4d82"
+ git_date = "2024-05-29 04:15:35 +0000"
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
return keywords
=====================================
q2_feature_classifier/classifier.py
=====================================
@@ -86,6 +86,8 @@ def spec_from_pipeline(pipeline):
def pipeline_from_spec(spec):
def as_steps(obj):
+ if 'ngram_range' in obj:
+ obj['ngram_range'] = tuple(obj['ngram_range'])
if '__type__' in obj:
klass = _load_class(obj['__type__'])
return klass(**{k: v for k, v in obj.items() if k != '__type__'})
@@ -206,11 +208,8 @@ def classify_sklearn(reads: DNAFASTAFormat, classifier: Pipeline,
read_orientation: str = 'auto'
) -> pd.DataFrame:
- if n_jobs in (0, -1):
+ if n_jobs == 0:
n_jobs = get_available_cores()
- elif n_jobs < -1:
- n_less = abs(n_jobs + 1)
- n_jobs = get_available_cores(n_less=n_less)
try:
# autotune reads per batch
@@ -266,11 +265,9 @@ _parameter_descriptions = {
'reads_per_batch': 'Number of reads to process in each batch. If "auto", '
'this parameter is autoscaled to '
'min( number of query sequences / n_jobs, 20000).',
- 'n_jobs': 'The maximum number of concurrent worker processes. If -1 '
+ 'n_jobs': 'The maximum number of concurrent worker processes. If 0 '
'all CPUs are used. If 1 is given, no parallel computing '
- 'code is used at all, which is useful for debugging. For '
- 'n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for '
- 'n_jobs = -2, all CPUs but one are used.',
+ 'code is used at all, which is useful for debugging.',
'pre_dispatch': '"all" or expression, as in "3*n_jobs". The number of '
'batches (of tasks) to be pre-dispatched.'
}
@@ -332,6 +329,8 @@ def _register_fitter(name, spec):
kwargs[param] = json.loads(kwargs[param])
except (json.JSONDecodeError, TypeError):
pass
+ if param == 'feat_ext__ngram_range':
+ kwargs[param] = tuple(kwargs[param])
pipeline = pipeline_from_spec(spec)
pipeline.set_params(**kwargs)
if class_weight is not None:
=====================================
q2_feature_classifier/tests/test_classifier.py
=====================================
@@ -66,7 +66,7 @@ class ClassifierTests(FeatureClassifierTestPluginBase):
{'__type__': 'feature_extraction.text.HashingVectorizer',
'analyzer': 'char_wb',
'n_features': 8192,
- 'ngram_range': [8, 8],
+ 'ngram_range': (8, 8),
'alternate_sign': False}],
['classify',
{'__type__': 'naive_bayes.GaussianNB'}]]
@@ -117,7 +117,7 @@ class ClassifierTests(FeatureClassifierTestPluginBase):
{'__type__': 'feature_extraction.text.HashingVectorizer',
'analyzer': 'char_wb',
'n_features': 8192,
- 'ngram_range': [8, 8],
+ 'ngram_range': (8, 8),
'alternate_sign': False}],
['classify',
{'__type__': 'linear_model.LogisticRegression'}]]
=====================================
q2_feature_classifier/tests/test_custom.py
=====================================
@@ -39,7 +39,7 @@ class CustomTests(FeatureClassifierTestPluginBase):
{'__type__': 'feature_extraction.text.HashingVectorizer',
'analyzer': 'char',
'n_features': 8192,
- 'ngram_range': [8, 8],
+ 'ngram_range': (8, 8),
'alternate_sign': False}],
['classify',
{'__type__': 'custom.LowMemoryMultinomialNB',
@@ -68,7 +68,7 @@ class CustomTests(FeatureClassifierTestPluginBase):
params = {'analyzer': 'char',
'n_features': 8192,
- 'ngram_range': [8, 8],
+ 'ngram_range': (8, 8),
'alternate_sign': False}
hv = HashingVectorizer(**params)
unchunked = hv.fit_transform(X)
View it on GitLab: https://salsa.debian.org/med-team/q2-feature-classifier/-/commit/7f0ea9e45bb89d8970805e330fd00645c040be3a
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