Merge pull request #2 from eli5-org/new_sklearn

New sklearn enabling changes

docs/requirements.txt

@@ -4,5 +4,5 @@ sphinx_rtd_theme
 ipython
 scipy
 numpy > 1.9.0
-scikit-learn >= 0.18
+scikit-learn >= 0.20
 typing

eli5/base_utils.py

@@ -1,4 +1,8 @@
-import inspect
+try:
+    from inspect import getfullargspec
+except ImportError:
+    # python 2
+    from inspect import getargspec as getfullargspec  # type: ignore
 
 import attr
 
@@ -8,6 +12,7 @@
     from singledispatch import singledispatch  # type: ignore
 
 
+
 def attrs(class_):
     """ Like attr.s with slots=True,
     but with attributes extracted from __init__ method signature.
@@ -25,12 +30,12 @@ def attrs(class_):
         if method in class_.__dict__:
             # Allow to redefine a special method (or else attr.s will do it)
             attrs_kwargs[kw_name] = False
-    init_args = inspect.getargspec(class_.__init__)
+    init_args = getfullargspec(class_.__init__)
     defaults_shift = len(init_args.args) - len(init_args.defaults or []) - 1
     these = {}
     for idx, arg in enumerate(init_args.args[1:]):
         attrib_kwargs = {}
-        if idx >= defaults_shift:
+        if idx >= defaults_shift and init_args.defaults:
             attrib_kwargs['default'] = init_args.defaults[idx - defaults_shift]
         these[arg] = attr.ib(**attrib_kwargs)
     return attr.s(class_, these=these, init=False, slots=True, **attrs_kwargs)  # type: ignore

eli5/sklearn/permutation_importance.py

@@ -12,7 +12,7 @@
     clone,
     is_classifier
 )
-from sklearn.metrics.scorer import check_scoring
+from sklearn.metrics import check_scoring
 
 from eli5.permutation_importance import get_score_importances
 from eli5.sklearn.utils import pandas_available

eli5/sklearn/transform.py

@@ -3,8 +3,10 @@
 
 import numpy as np
 from sklearn.pipeline import Pipeline, FeatureUnion
-from sklearn.feature_selection.base import SelectorMixin
-
+try:
+    from sklearn.feature_selection import SelectorMixin
+except ImportError:
+    from sklearn.feature_selection.base import SelectorMixin
 from sklearn.preprocessing import (
     MinMaxScaler,
     StandardScaler,

requirements.txt

@@ -1,7 +1,7 @@
 numpy >= 1.9.0
 scipy
 singledispatch >= 3.4.0.3
-scikit-learn >= 0.18
+scikit-learn >= 0.20
 attrs > 16.0.0
 jinja2
 pip >= 8.1

setup.py

@@ -36,7 +36,7 @@ def get_long_description():
         'numpy >= 1.9.0',
         'scipy',
         'six',
-        'scikit-learn >= 0.18',
+        'scikit-learn >= 0.20',
         'graphviz',
         'tabulate>=0.7.7',
     ],