[ci][python] fixes according to scikit-learn 0.20 release (#1707)

* fixed FutureWarning about cv default value

* fixed according to new check_estimator API

* fixed joblib warning

.appveyor.yml

@@ -26,10 +26,11 @@ install:
           default {$env:MINICONDA = """C:\Miniconda36-x64"""}
       }
   - set PATH=%MINICONDA%;%MINICONDA%\Scripts;%PATH%
+  - set SKLEARN_SITE_JOBLIB=true  # temp fix for joblib warning in examples
   - ps: $env:LGB_VER = (Get-Content VERSION.txt).trim()
   - conda config --set always_yes yes --set changeps1 no
   - conda update -q conda
-  - conda create -q -n test-env python=%PYTHON_VERSION% numpy nose scipy scikit-learn pandas matplotlib python-graphviz pytest
+  - conda create -q -n test-env python=%PYTHON_VERSION% numpy nose scipy scikit-learn pandas matplotlib python-graphviz pytest joblib
   - activate test-env
 
 build_script:

examples/python-guide/sklearn_example.py

@@ -66,7 +66,7 @@ def rmsle(y_true, y_pred):
     'n_estimators': [20, 40]
 }
 
-gbm = GridSearchCV(estimator, param_grid)
+gbm = GridSearchCV(estimator, param_grid, cv=3)
 
 gbm.fit(X_train, y_train)
 

python-package/lightgbm/compat.py

@@ -90,7 +90,8 @@ class DataFrame(object):
     from sklearn.preprocessing import LabelEncoder
     from sklearn.utils.class_weight import compute_sample_weight
     from sklearn.utils.multiclass import check_classification_targets
-    from sklearn.utils.validation import check_X_y, check_array, check_consistent_length
+    from sklearn.utils.validation import (assert_all_finite, check_X_y,
+                                          check_array, check_consistent_length)
     try:
         from sklearn.model_selection import StratifiedKFold, GroupKFold
         from sklearn.exceptions import NotFittedError
@@ -108,6 +109,7 @@ class DataFrame(object):
     _LGBMCheckXY = check_X_y
     _LGBMCheckArray = check_array
     _LGBMCheckConsistentLength = check_consistent_length
+    _LGBMAssertAllFinite = assert_all_finite
     _LGBMCheckClassificationTargets = check_classification_targets
     _LGBMComputeSampleWeight = compute_sample_weight
 except ImportError:
@@ -122,6 +124,7 @@ class DataFrame(object):
     _LGBMCheckXY = None
     _LGBMCheckArray = None
     _LGBMCheckConsistentLength = None
+    _LGBMAssertAllFinite = None
     _LGBMCheckClassificationTargets = None
     _LGBMComputeSampleWeight = None
 

python-package/lightgbm/sklearn.py

@@ -10,7 +10,7 @@
 from .compat import (SKLEARN_INSTALLED, _LGBMClassifierBase,
                      LGBMNotFittedError, _LGBMLabelEncoder, _LGBMModelBase,
                      _LGBMRegressorBase, _LGBMCheckXY, _LGBMCheckArray, _LGBMCheckConsistentLength,
-                     _LGBMCheckClassificationTargets, _LGBMComputeSampleWeight,
+                     _LGBMAssertAllFinite, _LGBMCheckClassificationTargets, _LGBMComputeSampleWeight,
                      argc_, range_, string_type, DataFrame, LGBMDeprecationWarning)
 from .engine import train
 
@@ -656,6 +656,7 @@ def fit(self, X, y,
             eval_class_weight=None, eval_init_score=None, eval_metric=None,
             early_stopping_rounds=None, verbose=True,
             feature_name='auto', categorical_feature='auto', callbacks=None):
+        _LGBMAssertAllFinite(y)
         _LGBMCheckClassificationTargets(y)
         self._le = _LGBMLabelEncoder().fit(y)
         _y = self._le.transform(y)

tests/python_package_test/test_sklearn.py

@@ -6,19 +6,16 @@
 
 import lightgbm as lgb
 import numpy as np
+from sklearn import __version__ as sk_version
 from sklearn.base import clone
 from sklearn.datasets import (load_boston, load_breast_cancer, load_digits,
                               load_iris, load_svmlight_file)
+from sklearn.exceptions import SkipTestWarning
 from sklearn.externals import joblib
 from sklearn.metrics import log_loss, mean_squared_error
 from sklearn.model_selection import GridSearchCV, train_test_split
 from sklearn.utils.estimator_checks import (_yield_all_checks, SkipTest,
                                             check_parameters_default_constructible)
-try:
-    from sklearn.utils.estimator_checks import check_no_fit_attributes_set_in_init
-    sklearn_at_least_019 = True
-except ImportError:
-    sklearn_at_least_019 = False
 
 
 def multi_error(y_true, y_pred):
@@ -180,17 +177,17 @@ def test_feature_importances_type(self):
         self.assertNotEqual(importance_split_top1, importance_gain_top1)
 
     # sklearn <0.19 cannot accept instance, but many tests could be passed only with min_data=1 and min_data_in_bin=1
-    @unittest.skipIf(not sklearn_at_least_019, 'scikit-learn version is less than 0.19')
+    @unittest.skipIf(sk_version < '0.19.0', 'scikit-learn version is less than 0.19')
     def test_sklearn_integration(self):
         # we cannot use `check_estimator` directly since there is no skip test mechanism
         for name, estimator in ((lgb.sklearn.LGBMClassifier.__name__, lgb.sklearn.LGBMClassifier),
                                 (lgb.sklearn.LGBMRegressor.__name__, lgb.sklearn.LGBMRegressor)):
             check_parameters_default_constructible(name, estimator)
-            check_no_fit_attributes_set_in_init(name, estimator)
             # we cannot leave default params (see https://github.com/Microsoft/LightGBM/issues/833)
             estimator = estimator(min_child_samples=1, min_data_in_bin=1)
             for check in _yield_all_checks(name, estimator):
-                if check.__name__ == 'check_estimators_nan_inf':
+                check_name = check.func.__name__ if hasattr(check, 'func') else check.__name__
+                if check_name == 'check_estimators_nan_inf':
                     continue  # skip test because LightGBM deals with nan
                 try:
                     check(name, estimator)