[python] improved sklearn interface

.travis/test.sh

@@ -40,7 +40,7 @@ if [[ ${TASK} == "if-else" ]]; then
     exit 0
 fi
 
-conda install --yes numpy scipy scikit-learn pandas matplotlib
+conda install --yes numpy nose scipy scikit-learn pandas matplotlib
 pip install pytest
 
 if [[ ${TASK} == "sdist" ]]; then

appveyor.yml

@@ -21,7 +21,7 @@ test_script:
   - conda config --set always_yes yes --set changeps1 no
   - conda update -q conda
   - conda info -a
-  - conda install --yes numpy scipy scikit-learn pandas matplotlib
+  - conda install --yes numpy nose scipy scikit-learn pandas matplotlib
   - pip install pep8 pytest
   - pytest tests/c_api_test/test_.py
   - "set /p LGB_VER=< VERSION.txt"

examples/python-guide/sklearn_example.py

@@ -28,7 +28,7 @@
 
 print('Start predicting...')
 # predict
-y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
+y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
 # eval
 print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)
 

python-package/lightgbm/compat.py

@@ -64,23 +64,38 @@ class DataFrame(object):
     from sklearn.base import RegressorMixin, ClassifierMixin
     from sklearn.preprocessing import LabelEncoder
     from sklearn.utils import deprecated
+    from sklearn.utils.multiclass import check_classification_targets
+    from sklearn.utils.validation import check_X_y, check_array, check_consistent_length
     try:
         from sklearn.model_selection import StratifiedKFold, GroupKFold
+        from sklearn.exceptions import NotFittedError
     except ImportError:
         from sklearn.cross_validation import StratifiedKFold, GroupKFold
+        from sklearn.utils.validation import NotFittedError
     SKLEARN_INSTALLED = True
-    LGBMModelBase = BaseEstimator
-    LGBMRegressorBase = RegressorMixin
-    LGBMClassifierBase = ClassifierMixin
-    LGBMLabelEncoder = LabelEncoder
+    _LGBMModelBase = BaseEstimator
+    _LGBMRegressorBase = RegressorMixin
+    _LGBMClassifierBase = ClassifierMixin
+    _LGBMLabelEncoder = LabelEncoder
     LGBMDeprecated = deprecated
-    LGBMStratifiedKFold = StratifiedKFold
-    LGBMGroupKFold = GroupKFold
+    LGBMNotFittedError = NotFittedError
+    _LGBMStratifiedKFold = StratifiedKFold
+    _LGBMGroupKFold = GroupKFold
+    _LGBMCheckXY = check_X_y
+    _LGBMCheckArray = check_array
+    _LGBMCheckConsistentLength = check_consistent_length
+    _LGBMCheckClassificationTargets = check_classification_targets
 except ImportError:
-    SKLEARN_INSTALLED = False
-    LGBMModelBase = object
-    LGBMClassifierBase = object
-    LGBMRegressorBase = object
-    LGBMLabelEncoder = None
-    LGBMStratifiedKFold = None
-    LGBMGroupKFold = None
+    _SKLEARN_INSTALLED = False
+    _LGBMModelBase = object
+    _LGBMClassifierBase = object
+    _LGBMRegressorBase = object
+    _LGBMLabelEncoder = None
+    LGBMDeprecated = None
+    LGBMNotFittedError = ValueError
+    _LGBMStratifiedKFold = None
+    _LGBMGroupKFold = None
+    _LGBMCheckXY = None
+    _LGBMCheckArray = None
+    _LGBMCheckConsistentLength = None
+    _LGBMCheckClassificationTargets = None

python-package/lightgbm/engine.py

@@ -11,7 +11,7 @@
 
 from . import callback
 from .basic import Booster, Dataset, LightGBMError, _InnerPredictor
-from .compat import (SKLEARN_INSTALLED, LGBMGroupKFold, LGBMStratifiedKFold,
+from .compat import (SKLEARN_INSTALLED, _LGBMGroupKFold, _LGBMStratifiedKFold,
                      integer_types, range_, string_type)
 
 
@@ -264,12 +264,12 @@ def _make_n_folds(full_data, folds, nfold, params, seed, fpreproc=None, stratifi
             # lambdarank task, split according to groups
             group_info = full_data.get_group().astype(int)
             flatted_group = np.repeat(range(len(group_info)), repeats=group_info)
-            group_kfold = LGBMGroupKFold(n_splits=nfold)
+            group_kfold = _LGBMGroupKFold(n_splits=nfold)
             folds = group_kfold.split(X=np.zeros(num_data), groups=flatted_group)
         elif stratified:
             if not SKLEARN_INSTALLED:
                 raise LightGBMError('Scikit-learn is required for stratified cv.')
-            skf = LGBMStratifiedKFold(n_splits=nfold, shuffle=shuffle, random_state=seed)
+            skf = _LGBMStratifiedKFold(n_splits=nfold, shuffle=shuffle, random_state=seed)
             folds = skf.split(X=np.zeros(num_data), y=full_data.get_label())
         else:
             if shuffle: