[python] refine: solve several trivial issues (#753)

* refine python codes

* fix appveryor test

* add note to feature_importances

appveyor.yml

@@ -23,7 +23,7 @@ test_script:
   - conda info -a
   - conda install --yes numpy scipy scikit-learn pandas matplotlib
   - pip install pep8 pytest
-  - pytest tests/c_api_test/test.py
+  - pytest tests/c_api_test/test_.py
   - "set /p LGB_VER=< VERSION.txt"
   - cd python-package && python setup.py sdist --formats gztar
   - cd dist 

python-package/lightgbm/basic.py

@@ -224,10 +224,6 @@ def c_int_array(data):
 def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorical):
     if isinstance(data, DataFrame):
         if feature_name == 'auto' or feature_name is None:
-            if all([isinstance(name, integer_types + (np.integer, )) for name in data.columns]):
-                msg = """Using Pandas (default) integer column names, not column indexes. You can use indexes with DataFrame.values."""
-                warnings.filterwarnings('once')
-                warnings.warn(msg, stacklevel=5)
             data = data.rename(columns=str)
         cat_cols = data.select_dtypes(include=['category']).columns
         if pandas_categorical is None:  # train dataset
@@ -624,6 +620,8 @@ def _lazy_init(self, data, label=None, max_bin=255, reference=None,
         self.max_bin = max_bin
         self.predictor = predictor
         params["max_bin"] = max_bin
+        if "verbosity" in params:
+            params.setdefault("verbose", params.pop("verbosity"))
         if silent:
             params["verbose"] = 0
         elif "verbose" not in params:
@@ -642,8 +640,8 @@ def _lazy_init(self, data, label=None, max_bin=255, reference=None,
                 else:
                     raise TypeError("Wrong type({}) or unknown name({}) in categorical_feature"
                                     .format(type(name).__name__, name))
-
-            params['categorical_column'] = sorted(categorical_indices)
+            if categorical_indices:
+                params['categorical_column'] = sorted(categorical_indices)
 
         params_str = param_dict_to_str(params)
         """process for reference dataset"""
@@ -1222,6 +1220,8 @@ def __init__(self, params=None, train_set=None, model_file=None, silent=False):
         self.best_iteration = -1
         self.best_score = {}
         params = {} if params is None else params
+        if "verbosity" in params:
+            params.setdefault("verbose", params.pop("verbosity"))
         if silent:
             params["verbose"] = 0
         elif "verbose" not in params:

python-package/lightgbm/sklearn.py

@@ -454,9 +454,13 @@ def evals_result_(self):
 
     @property
     def feature_importances_(self):
-        """Get normailized feature importances."""
-        importace_array = self.booster_.feature_importance().astype(np.float32)
-        return importace_array / importace_array.sum()
+        """
+        Get feature importances.
+
+        Note: feature importance in sklearn interface used to normalize to 1,
+            it's deprecated after 2.0.4 and same as Booster.feature_importance() now
+        """
+        return self.booster_.feature_importance()
 
     @LGBMDeprecated('Use attribute booster_ instead.')
     def booster(self):