[python] bring pandas support to the sklearn wrapper back (#904)

* added test for sklearn handle categorical features

* use raw X, y in sklearn wrapper in case of pandas.DataFrame

* fixed probs

python-package/lightgbm/sklearn.py

@@ -5,6 +5,11 @@ from __future__ import absolute_import
 
 import numpy as np
 import warnings
+try:
+    import pandas as pd
+    _IS_PANDAS_INSTALLED = True
+except ImportError:
+    _IS_PANDAS_INSTALLED = False
 
 from .basic import Dataset, LightGBMError
 from .compat import (SKLEARN_INSTALLED, _LGBMClassifierBase, LGBMDeprecated,
@@ -332,7 +337,7 @@ class LGBMModel(_LGBMModelBase):
         categorical_feature : list of strings or int, or 'auto', optional (default="auto")
             Categorical features.
             If list of int, interpreted as indices.
-            If list of strings, interpreted as feature names (need to specify feature_name as well).
+            If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
             If 'auto' and data is pandas DataFrame, pandas categorical columns are used.
         callbacks : list of callback functions or None, optional (default=None)
             List of callback functions that are applied at each iteration.
@@ -407,8 +412,10 @@ class LGBMModel(_LGBMModelBase):
             feval = None
             params['metric'] = eval_metric
 
-        X, y = _LGBMCheckXY(X, y, accept_sparse=True, force_all_finite=False, ensure_min_samples=2)
-        _LGBMCheckConsistentLength(X, y, sample_weight)
+        if not _IS_PANDAS_INSTALLED or not isinstance(X, pd.DataFrame):
+            X, y = _LGBMCheckXY(X, y, accept_sparse=True, force_all_finite=False, ensure_min_samples=2)
+            _LGBMCheckConsistentLength(X, y, sample_weight)
+
         self._n_features = X.shape[1]
 
         def _construct_dataset(X, y, sample_weight, init_score, group, params):
@@ -482,7 +489,8 @@ class LGBMModel(_LGBMModelBase):
         """
         if self._n_features is None:
             raise LGBMNotFittedError("Estimator not fitted, call `fit` before exploiting the model.")
-        X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
+        if not _IS_PANDAS_INSTALLED or not isinstance(X, pd.DataFrame):
+            X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
         n_features = X.shape[1]
         if self._n_features != n_features:
             raise ValueError("Number of features of the model must "
@@ -508,7 +516,8 @@ class LGBMModel(_LGBMModelBase):
         """
         if self._n_features is None:
             raise LGBMNotFittedError("Estimator not fitted, call `fit` before exploiting the model.")
-        X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
+        if not _IS_PANDAS_INSTALLED or not isinstance(X, pd.DataFrame):
+            X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
         n_features = X.shape[1]
         if self._n_features != n_features:
             raise ValueError("Number of features of the model must "
@@ -686,7 +695,8 @@ class LGBMClassifier(LGBMModel, _LGBMClassifierBase):
         """
         if self._n_features is None:
             raise LGBMNotFittedError("Estimator not fitted, call `fit` before exploiting the model.")
-        X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
+        if not _IS_PANDAS_INSTALLED or not isinstance(X, pd.DataFrame):
+            X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
         n_features = X.shape[1]
         if self._n_features != n_features:
             raise ValueError("Number of features of the model must "

tests/python_package_test/test_engine.py

@@ -78,7 +78,7 @@ class TestEngine(unittest.TestCase):
         self.assertLess(ret, 0.25)
         self.assertAlmostEqual(evals_result['valid_0']['binary_logloss'][-1], ret, places=5)
 
-    def test_regreesion(self):
+    def test_regression(self):
         X, y = load_boston(True)
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
         params = {
@@ -444,7 +444,6 @@ class TestEngine(unittest.TestCase):
         gbm3 = lgb.train(params, lgb_train, num_boost_round=10, verbose_eval=False,
                          categorical_feature=['A', 'B', 'C', 'D'])
         pred3 = list(gbm3.predict(X_test))
-        lgb_train = lgb.Dataset(X, y)
         gbm3.save_model('categorical.model')
         gbm4 = lgb.Booster(model_file='categorical.model')
         pred4 = list(gbm4.predict(X_test))

tests/python_package_test/test_sklearn.py

@@ -19,6 +19,11 @@ try:
     sklearn_at_least_019 = True
 except ImportError:
     sklearn_at_least_019 = False
+try:
+    import pandas as pd
+    IS_PANDAS_INSTALLED = True
+except ImportError:
+    IS_PANDAS_INSTALLED = False
 
 
 def multi_error(y_true, y_pred):
@@ -40,7 +45,7 @@ class TestSklearn(unittest.TestCase):
         self.assertLess(ret, 0.15)
         self.assertAlmostEqual(ret, gbm.evals_result_['valid_0']['binary_logloss'][gbm.best_iteration_ - 1], places=5)
 
-    def test_regreesion(self):
+    def test_regression(self):
         X, y = load_boston(True)
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
         gbm = lgb.LGBMRegressor(n_estimators=50, silent=True)
@@ -194,3 +199,34 @@ class TestSklearn(unittest.TestCase):
                         check(name, estimator)
                     except SkipTest as message:
                         warnings.warn(message, SkipTestWarning)
+
+    @unittest.skipIf(not IS_PANDAS_INSTALLED, 'pandas not installed')
+    def test_pandas_categorical(self):
+        X = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'c', 'd'] * 75),  # str
+                          "B": np.random.permutation([1, 2, 3] * 100),  # int
+                          "C": np.random.permutation([0.1, 0.2, -0.1, -0.1, 0.2] * 60),  # float
+                          "D": np.random.permutation([True, False] * 150)})  # bool
+        y = np.random.permutation([0, 1] * 150)
+        X_test = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'e'] * 20),
+                               "B": np.random.permutation([1, 3] * 30),
+                               "C": np.random.permutation([0.1, -0.1, 0.2, 0.2] * 15),
+                               "D": np.random.permutation([True, False] * 30)})
+        for col in ["A", "B", "C", "D"]:
+            X[col] = X[col].astype('category')
+            X_test[col] = X_test[col].astype('category')
+        gbm0 = lgb.sklearn.LGBMClassifier().fit(X, y)
+        pred0 = list(gbm0.predict(X_test))
+        gbm1 = lgb.sklearn.LGBMClassifier().fit(X, y, categorical_feature=[0])
+        pred1 = list(gbm1.predict(X_test))
+        gbm2 = lgb.sklearn.LGBMClassifier().fit(X, y, categorical_feature=['A'])
+        pred2 = list(gbm2.predict(X_test))
+        gbm3 = lgb.sklearn.LGBMClassifier().fit(X, y, categorical_feature=['A', 'B', 'C', 'D'])
+        pred3 = list(gbm3.predict(X_test))
+        gbm3.booster_.save_model('categorical.model')
+        gbm4 = lgb.Booster(model_file='categorical.model')
+        pred4 = list(gbm4.predict(X_test))
+        pred_prob = list(gbm0.predict_proba(X_test)[:, 1])
+        np.testing.assert_almost_equal(pred0, pred1)
+        np.testing.assert_almost_equal(pred0, pred2)
+        np.testing.assert_almost_equal(pred0, pred3)
+        np.testing.assert_almost_equal(pred_prob, pred4)