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Merged
mfeurer merged 15 commits into from
Sep 13, 2019
Merged

Adding sklearn docstring to flow #756

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f1919e1
Using sklearn docstring as flow descriptions for sklearn flows
Neeratyoy Aug 5, 2019
0b5137f
Extracting parameter type and descriptions
Neeratyoy Aug 5, 2019
b0ad048
Handling certain edge cases
Neeratyoy Aug 6, 2019
d90f333
More robust failure checks + improved docstrings
Neeratyoy Aug 7, 2019
6dc4345
Trimming of all strings to be uploaded
Neeratyoy Aug 7, 2019
58a6609
Fixing edge cases to pass tests
Neeratyoy Aug 24, 2019
41549b0
Fixing PEP8
Neeratyoy Aug 25, 2019
235ded8
Leaner implementation for parameter docstring
Neeratyoy Aug 26, 2019
9b5d382
Making suggested changes
Neeratyoy Sep 2, 2019
43bf02d
Version handling and warning log
Neeratyoy Sep 5, 2019
579498a
Debugging
Neeratyoy Sep 5, 2019
52cbdb7
Debugging phase 2
Neeratyoy Sep 5, 2019
3b44e86
Fixing test cases
Neeratyoy Sep 9, 2019
6710b40
Handling different sklearn versions in unit testing
Neeratyoy Sep 9, 2019
7d685e1
Replace logging.info by logging.warning
mfeurer Sep 13, 2019
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Fixing test cases
  • Loading branch information
@Neeratyoy
Neeratyoy committed Sep 9, 2019
commit 3b44e86c5a1a8d10135cd8146111a7126e6e152e
2 changes: 1 addition & 1 deletion openml/extensions/sklearn/extension.py
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Expand Up @@ -514,7 +514,7 @@ def match_format(s):
return s.strip()
except ValueError:
logging.warning("'Read more' not found in descriptions. "
"Trying to trim till 'Parameters' if available in docstring.")
"Trying to trim till 'Parameters' if available in docstring.")
pass
Comment thread
mfeurer marked this conversation as resolved.
try:
# if 'Read more' doesn't exist, trim till 'Parameters'
Expand Down
72 changes: 36 additions & 36 deletions tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py
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Expand Up @@ -277,26 +277,26 @@ def test_serialize_pipeline(self):
fixture_short_name = 'sklearn.Pipeline(StandardScaler,DummyClassifier)'

if version.parse(sklearn.__version__) >= version.parse("0.21.0"):
fixture_description = "Pipeline of transforms with a final estimator.\n\nSequentially " \
"apply a list of transforms and a final estimator.\nIntermediate " \
"steps of the pipeline must be 'transforms', that is, they\nmust " \
"implement fit and transform methods.\nThe final estimator only " \
"needs to implement fit.\nThe transformers in the pipeline can be " \
"cached using ``memory`` argument.\n\nThe purpose of the pipeline is" \
" to assemble several steps that can be\ncross-validated together " \
"while setting different parameters.\nFor this, it enables setting " \
"parameters of the various steps using their\nnames and the " \
"parameter name separated by a '__', as in the example below.\nA " \
"step's estimator may be replaced entirely by setting the " \
"parameter\nwith its name to another estimator, or a transformer " \
"removed by setting\nit to 'passthrough' or ``None``."
fixture_description = "Pipeline of transforms with a final estimator.\n\nSequentially"\
" apply a list of transforms and a final estimator.\n"\
"Intermediate steps of the pipeline must be 'transforms', that "\
"is, they\nmust implement fit and transform methods.\nThe final "\
"estimator only needs to implement fit.\nThe transformers in "\
"the pipeline can be cached using ``memory`` argument.\n\nThe "\
"purpose of the pipeline is to assemble several steps that can "\
"be\ncross-validated together while setting different parameters"\
".\nFor this, it enables setting parameters of the various steps"\
" using their\nnames and the parameter name separated by a '__',"\
" as in the example below.\nA step's estimator may be replaced "\
"entirely by setting the parameter\nwith its name to another "\
"estimator, or a transformer removed by setting\nit to "\
"'passthrough' or ``None``."
else:
fixture_description = "Pipeline of transforms with a final estimator.\n\nSequentially"\
" apply a list of transforms and a final estimator.\nIntermediate"\
" steps of the pipeline must be 'transforms', that is, they\nmust "\
"implement fit and transform methods.\nThe final estimator only "\
"needs to implement fit.\nThe transformers in the pipeline can "\
"be cached using ``memory`` argument.\n\nThe purpose of the "\
" apply a list of transforms and a final estimator.\n"\
"Intermediate steps of the pipeline must be 'transforms', that "\
"is, they\nmust implement fit and transform methods.\nThe final"\
" estimator only needs to implement fit.\n\nThe purpose of the "\
"pipeline is to assemble several steps that can be\n"\
"cross-validated together while setting different parameters."\
"\nFor this, it enables setting parameters of the various steps"\
Expand Down Expand Up @@ -397,26 +397,26 @@ def test_serialize_pipeline_clustering(self):
fixture_short_name = 'sklearn.Pipeline(StandardScaler,KMeans)'

if version.parse(sklearn.__version__) >= version.parse("0.21.0"):
fixture_description = "Pipeline of transforms with a final estimator.\n\nSequentially " \
"apply a list of transforms and a final estimator.\nIntermediate " \
"steps of the pipeline must be 'transforms', that is, they\nmust " \
"implement fit and transform methods.\nThe final estimator only " \
"needs to implement fit.\nThe transformers in the pipeline can be " \
"cached using ``memory`` argument.\n\nThe purpose of the pipeline is" \
" to assemble several steps that can be\ncross-validated together " \
"while setting different parameters.\nFor this, it enables setting " \
"parameters of the various steps using their\nnames and the " \
"parameter name separated by a '__', as in the example below.\nA " \
"step's estimator may be replaced entirely by setting the " \
"parameter\nwith its name to another estimator, or a transformer " \
"removed by setting\nit to 'passthrough' or ``None``."
fixture_description = "Pipeline of transforms with a final estimator.\n\nSequentially"\
" apply a list of transforms and a final estimator.\n"\
"Intermediate steps of the pipeline must be 'transforms', that "\
"is, they\nmust implement fit and transform methods.\nThe final "\
"estimator only needs to implement fit.\nThe transformers in "\
"the pipeline can be cached using ``memory`` argument.\n\nThe "\
"purpose of the pipeline is to assemble several steps that can "\
"be\ncross-validated together while setting different parameters"\
".\nFor this, it enables setting parameters of the various steps"\
" using their\nnames and the parameter name separated by a '__',"\
" as in the example below.\nA step's estimator may be replaced "\
"entirely by setting the parameter\nwith its name to another "\
"estimator, or a transformer removed by setting\nit to "\
"'passthrough' or ``None``."
else:
fixture_description = "Pipeline of transforms with a final estimator.\n\nSequentially"\
" apply a list of transforms and a final estimator.\nIntermediate"\
" steps of the pipeline must be 'transforms', that is, they\nmust "\
"implement fit and transform methods.\nThe final estimator only "\
"needs to implement fit.\nThe transformers in the pipeline can "\
"be cached using ``memory`` argument.\n\nThe purpose of the "\
" apply a list of transforms and a final estimator.\n"\
"Intermediate steps of the pipeline must be 'transforms', that "\
"is, they\nmust implement fit and transform methods.\nThe final"\
" estimator only needs to implement fit.\n\nThe purpose of the "\
"pipeline is to assemble several steps that can be\n"\
"cross-validated together while setting different parameters."\
"\nFor this, it enables setting parameters of the various steps"\
Expand Down