refine plotting library (#282)

* refine plot

* use warnings

* refine  logic

* revert 'move to compat.py'

docs/Python-API.md

@@ -946,8 +946,8 @@ The methods of each Class is in alphabetical order.
 
     Parameters
     ----------
-    booster : Booster, LGBMModel or array
-        Booster or LGBMModel instance, or array of feature importances.
+    booster : Booster or LGBMModel
+        Booster or LGBMModel instance.
     ax : matplotlib Axes
         Target axes instance. If None, new figure and axes will be created.
     height : float

docs/Readme.md

@@ -9,5 +9,6 @@ Documents
 * [Parameters Tuning](./Parameters-tuning.md)
 * [Python API Reference](./Python-API.md)
 * [Parallel Learning Guide](https://github.com/Microsoft/LightGBM/wiki/Parallel-Learning-Guide)
+* [FAQ](./FAQ.md)
 * [Development Guide](./development.md)
 

python-package/lightgbm/basic.py

@@ -6,6 +6,7 @@ from __future__ import absolute_import
 
 import ctypes
 import os
+import warnings
 from tempfile import NamedTemporaryFile
 
 import numpy as np
@@ -223,6 +224,10 @@ PANDAS_DTYPE_MAPPER = {'int8': 'int', 'int16': 'int', 'int32': 'int',
 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

python-package/lightgbm/compat.py

@@ -14,7 +14,7 @@ is_py3 = (sys.version_info[0] == 3)
 if is_py3:
     string_type = str
     numeric_types = (int, float, bool)
-    integer_types = int
+    integer_types = (int, )
     range_ = range
 
     def argc_(func):

python-package/lightgbm/plotting.py

@@ -3,12 +3,13 @@
 """Plotting Library."""
 from __future__ import absolute_import
 
+import warnings
 from copy import deepcopy
 from io import BytesIO
 
 import numpy as np
 
-from .basic import Booster, is_numpy_1d_array
+from .basic import Booster
 from .sklearn import LGBMModel
 
 
@@ -27,8 +28,8 @@ def plot_importance(booster, ax=None, height=0.2,
 
     Parameters
     ----------
-    booster : Booster, LGBMModel or array
-        Booster or LGBMModel instance, or array of feature importances
+    booster : Booster or LGBMModel
+        Booster or LGBMModel instance
     ax : matplotlib Axes
         Target axes instance. If None, new figure and axes will be created.
     height : float
@@ -69,18 +70,17 @@ def plot_importance(booster, ax=None, height=0.2,
         raise ImportError('You must install matplotlib to plot importance.')
 
     if isinstance(booster, LGBMModel):
-        importance = booster.booster_.feature_importance(importance_type=importance_type)
-    elif isinstance(booster, Booster):
-        importance = booster.feature_importance(importance_type=importance_type)
-    elif is_numpy_1d_array(booster) or isinstance(booster, list):
-        importance = booster
-    else:
-        raise TypeError('booster must be Booster, LGBMModel or array instance.')
+        booster = booster.booster_
+    elif not isinstance(booster, Booster):
+        raise TypeError('booster must be Booster or LGBMModel.')
+
+    importance = booster.feature_importance(importance_type=importance_type)
+    feature_name = booster.feature_name()
 
     if not len(importance):
         raise ValueError('Booster feature_importances are empty.')
 
-    tuples = sorted(enumerate(importance), key=lambda x: x[1])
+    tuples = sorted(zip(feature_name, importance), key=lambda x: x[1])
     if ignore_zero:
         tuples = [x for x in tuples if x[1] > 0]
     if max_num_features is not None and max_num_features > 0:
@@ -196,7 +196,8 @@ def plot_metric(booster, metric=None, dataset_names=None,
     num_metric = len(metrics_for_one)
     if metric is None:
         if num_metric > 1:
-            print('Warning: more than one metric available, picking one to plot.')
+            msg = """more than one metric available, picking one to plot."""
+            warnings.warn(msg, stacklevel=2)
         metric, results = metrics_for_one.popitem()
     else:
         if metric not in metrics_for_one:

tests/python_package_test/test_plotting.py

@@ -46,8 +46,7 @@ class TestBasic(unittest.TestCase):
         for patch in ax1.patches:
             self.assertTupleEqual(patch.get_facecolor(), (1., 0, 0, 1.))  # red
 
-        ax2 = lgb.plot_importance(gbm0.feature_importance(),
-                                  color=['r', 'y', 'g', 'b'],
+        ax2 = lgb.plot_importance(gbm0, color=['r', 'y', 'g', 'b'],
                                   title=None, xlabel=None, ylabel=None)
         self.assertIsInstance(ax2, matplotlib.axes.Axes)
         self.assertEqual(ax2.get_title(), '')