fixed imports (#1220)

python-package/lightgbm/basic.py

@@ -15,7 +15,7 @@
 
 from .compat import (DataFrame, Series, integer_types, json,
                      json_default_with_numpy, numeric_types, range_,
-                     string_type, LGBMDeprecationWarning)
+                     string_type)
 from .libpath import find_lib_path
 
 

python-package/lightgbm/libpath.py

@@ -1,7 +1,6 @@
 # coding: utf-8
 """Find the path to lightgbm dynamic library files."""
 import os
-import sys
 
 
 def find_lib_path():

python-package/lightgbm/sklearn.py

@@ -4,19 +4,13 @@
 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,
                      LGBMNotFittedError, _LGBMLabelEncoder, _LGBMModelBase,
                      _LGBMRegressorBase, _LGBMCheckXY, _LGBMCheckArray, _LGBMCheckConsistentLength,
                      _LGBMCheckClassificationTargets, _LGBMComputeSampleWeight,
-                     argc_, range_, LGBMDeprecationWarning)
+                     argc_, range_, DataFrame)
 from .engine import train
 
 
@@ -414,7 +408,7 @@ def fit(self, X, y,
             feval = None
             params['metric'] = eval_metric
 
-        if not _IS_PANDAS_INSTALLED or not isinstance(X, pd.DataFrame):
+        if not isinstance(X, DataFrame):
             X, y = _LGBMCheckXY(X, y, accept_sparse=True, force_all_finite=False, ensure_min_samples=2)
             _LGBMCheckConsistentLength(X, y, sample_weight)
 
@@ -504,7 +498,7 @@ def predict(self, X, raw_score=False, num_iteration=0):
         """
         if self._n_features is None:
             raise LGBMNotFittedError("Estimator not fitted, call `fit` before exploiting the model.")
-        if not _IS_PANDAS_INSTALLED or not isinstance(X, pd.DataFrame):
+        if not isinstance(X, DataFrame):
             X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
         n_features = X.shape[1]
         if self._n_features != n_features:
@@ -531,7 +525,7 @@ def apply(self, X, num_iteration=0):
         """
         if self._n_features is None:
             raise LGBMNotFittedError("Estimator not fitted, call `fit` before exploiting the model.")
-        if not _IS_PANDAS_INSTALLED or not isinstance(X, pd.DataFrame):
+        if not isinstance(X, DataFrame):
             X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
         n_features = X.shape[1]
         if self._n_features != n_features:
@@ -708,7 +702,7 @@ def predict_proba(self, X, raw_score=False, num_iteration=0):
         """
         if self._n_features is None:
             raise LGBMNotFittedError("Estimator not fitted, call `fit` before exploiting the model.")
-        if not _IS_PANDAS_INSTALLED or not isinstance(X, pd.DataFrame):
+        if not isinstance(X, DataFrame):
             X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
         n_features = X.shape[1]
         if self._n_features != n_features: