Version handling and warning log

Author: Neeratyoy

openml/extensions/sklearn/extension.py

@@ -513,7 +513,7 @@ def match_format(s):
                 s = "{}...".format(s[:char_lim - 3])
             return s.strip()
         except ValueError:
-            logging.info("'Read more' not found in descriptions. "
+            logging.warning("'Read more' not found in descriptions. "
                          "Trying to trim till 'Parameters' if available in docstring.")
             pass
         try:
@@ -522,7 +522,7 @@ def match_format(s):
             index = s.index(match_format(pattern))
         except ValueError:
             # returning full docstring
-            logging.info("'Parameters' not found in docstring. Omitting docstring trimming.")
+            logging.warning("'Parameters' not found in docstring. Omitting docstring trimming.")
             index = len(s)
         s = s[:index]
         # trimming docstring to be within char_lim

tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py

@@ -8,6 +8,7 @@
 from collections import OrderedDict
 from unittest import mock
 import warnings
+from packaging import version
 
 import numpy as np
 import scipy.optimize
@@ -274,20 +275,35 @@ def test_serialize_pipeline(self):
                        'scaler=sklearn.preprocessing.data.StandardScaler,' \
                        'dummy=sklearn.dummy.DummyClassifier)'
         fixture_short_name = 'sklearn.Pipeline(StandardScaler,DummyClassifier)'
-        # str obtained from self.extension._get_sklearn_description(model)
-        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``."
+
+        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``."
+        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 "\
+                                  "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"\
+                                  " 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\nto None."
         fixture_structure = {
             fixture_name: [],
             'sklearn.preprocessing.data.StandardScaler': ['scaler'],