[python] added plot_split_value_histogram function (#2043)

* added plot_split_value_histogram function

* updated init module

* added plot split value histogram example

* added plot_split_value_histogram to notebook

* added test

* fixed pylint

* updated API docs

* fixed grammar

* set y ticks to int value in more sufficient way

docs/Python-API.rst

@@ -62,6 +62,8 @@ Plotting
 
 .. autofunction:: lightgbm.plot_importance
 
+.. autofunction:: lightgbm.plot_split_value_histogram
+
 .. autofunction:: lightgbm.plot_metric
 
 .. autofunction:: lightgbm.plot_tree

examples/python-guide/README.md

@@ -57,5 +57,6 @@ Examples include:
     - Train and record eval results for further plotting
     - Plot metrics recorded during training
     - Plot feature importances
+    - Plot split value histogram
     - Plot one specified tree
     - Plot one specified tree with Graphviz

examples/python-guide/plot_example.py

@@ -50,6 +50,10 @@ print('Plotting feature importances...')
 ax = lgb.plot_importance(gbm, max_num_features=10)
 plt.show()
 
+print('Plotting split value histogram...')
+ax = lgb.plot_split_value_histogram(gbm, feature='f26', bins='auto')
+plt.show()
+
 print('Plotting 54th tree...')  # one tree use categorical feature to split
 ax = lgb.plot_tree(gbm, tree_index=53, figsize=(15, 15), show_info=['split_gain'])
 plt.show()

python-package/lightgbm/__init__.py

@@ -19,7 +19,8 @@ try:
 except ImportError:
     pass
 try:
-    from .plotting import plot_importance, plot_metric, plot_tree, create_tree_digraph
+    from .plotting import (plot_importance, plot_split_value_histogram, plot_metric,
+                           plot_tree, create_tree_digraph)
 except ImportError:
     pass
 
@@ -34,7 +35,7 @@ __all__ = ['Dataset', 'Booster',
            'train', 'cv',
            'LGBMModel', 'LGBMRegressor', 'LGBMClassifier', 'LGBMRanker',
            'print_evaluation', 'record_evaluation', 'reset_parameter', 'early_stopping',
-           'plot_importance', 'plot_metric', 'plot_tree', 'create_tree_digraph']
+           'plot_importance', 'plot_split_value_histogram', 'plot_metric', 'plot_tree', 'create_tree_digraph']
 
 # REMOVEME: remove warning after 2.3.0 version release
 if system() == 'Darwin':

python-package/lightgbm/plotting.py

@@ -141,6 +141,110 @@ def plot_importance(booster, ax=None, height=0.2,
     return ax
 
 
+def plot_split_value_histogram(booster, feature, bins=None, ax=None, width_coef=0.8,
+                               xlim=None, ylim=None,
+                               title='Split value histogram for feature with @index/name@ @feature@',
+                               xlabel='Feature split value', ylabel='Count',
+                               figsize=None, grid=True, **kwargs):
+    """Plot split value histogram for the specified feature of the model.
+
+    Parameters
+    ----------
+    booster : Booster or LGBMModel
+        Booster or LGBMModel instance of which feature split value histogram should be plotted.
+    feature : int or string
+        The feature name or index the histogram is plotted for.
+        If int, interpreted as index.
+        If string, interpreted as name.
+    bins : int, string or None, optional (default=None)
+        The maximum number of bins.
+        If None, the number of bins equals number of unique split values.
+        If string, it should be one from the list of the supported values by ``numpy.histogram()`` function.
+    ax : matplotlib.axes.Axes or None, optional (default=None)
+        Target axes instance.
+        If None, new figure and axes will be created.
+    width_coef : float, optional (default=0.8)
+        Coefficient for histogram bar width.
+    xlim : tuple of 2 elements or None, optional (default=None)
+        Tuple passed to ``ax.xlim()``.
+    ylim : tuple of 2 elements or None, optional (default=None)
+        Tuple passed to ``ax.ylim()``.
+    title : string or None, optional (default="Split value histogram for feature with @index/name@ @feature@")
+        Axes title.
+        If None, title is disabled.
+        @feature@ placeholder can be used, and it will be replaced with the value of ``feature`` parameter.
+        @index/name@ placeholder can be used,
+        and it will be replaced with ``index`` word in case of ``int`` type ``feature`` parameter
+        or ``name`` word in case of ``string`` type ``feature`` parameter.
+    xlabel : string or None, optional (default="Feature split value")
+        X-axis title label.
+        If None, title is disabled.
+    ylabel : string or None, optional (default="Count")
+        Y-axis title label.
+        If None, title is disabled.
+    figsize : tuple of 2 elements or None, optional (default=None)
+        Figure size.
+    grid : bool, optional (default=True)
+        Whether to add a grid for axes.
+    **kwargs
+        Other parameters passed to ``ax.bar()``.
+
+    Returns
+    -------
+    ax : matplotlib.axes.Axes
+        The plot with specified model's feature split value histogram.
+    """
+    if MATPLOTLIB_INSTALLED:
+        import matplotlib.pyplot as plt
+        from matplotlib.ticker import MaxNLocator
+    else:
+        raise ImportError('You must install matplotlib to plot split value histogram.')
+
+    if isinstance(booster, LGBMModel):
+        booster = booster.booster_
+    elif not isinstance(booster, Booster):
+        raise TypeError('booster must be Booster or LGBMModel.')
+
+    hist, bins = booster.get_split_value_histogram(feature=feature, bins=bins, xgboost_style=False)
+    if np.count_nonzero(hist) == 0:
+        raise ValueError('Cannot plot split value histogram, '
+                         'because feature {} was not used in splitting'.format(feature))
+    width = width_coef * (bins[1] - bins[0])
+    centred = (bins[:-1] + bins[1:]) / 2
+
+    if ax is None:
+        if figsize is not None:
+            _check_not_tuple_of_2_elements(figsize, 'figsize')
+        _, ax = plt.subplots(1, 1, figsize=figsize)
+
+    ax.bar(centred, hist, align='center', width=width, **kwargs)
+
+    if xlim is not None:
+        _check_not_tuple_of_2_elements(xlim, 'xlim')
+    else:
+        range_result = bins[-1] - bins[0]
+        xlim = (bins[0] - range_result * 0.2, bins[-1] + range_result * 0.2)
+    ax.set_xlim(xlim)
+
+    ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+    if ylim is not None:
+        _check_not_tuple_of_2_elements(ylim, 'ylim')
+    else:
+        ylim = (0, max(hist) * 1.1)
+    ax.set_ylim(ylim)
+
+    if title is not None:
+        title = title.replace('@feature@', str(feature))
+        title = title.replace('@index/name@', ('name' if isinstance(feature, string_type) else 'index'))
+        ax.set_title(title)
+    if xlabel is not None:
+        ax.set_xlabel(xlabel)
+    if ylabel is not None:
+        ax.set_ylabel(ylabel)
+    ax.grid(grid)
+    return ax
+
+
 def plot_metric(booster, metric=None, dataset_names=None,
                 ax=None, xlim=None, ylim=None,
                 title='Metric during training',

tests/python_package_test/test_plotting.py

@@ -60,6 +60,45 @@ class TestBasic(unittest.TestCase):
         self.assertTupleEqual(ax2.patches[2].get_facecolor(), (0, .5, 0, 1.))  # g
         self.assertTupleEqual(ax2.patches[3].get_facecolor(), (0, 0, 1., 1.))  # b
 
+    @unittest.skipIf(not MATPLOTLIB_INSTALLED, 'matplotlib is not installed')
+    def test_plot_split_value_histogram(self):
+        gbm0 = lgb.train(self.params, self.train_data, num_boost_round=10)
+        ax0 = lgb.plot_split_value_histogram(gbm0, 27)
+        self.assertIsInstance(ax0, matplotlib.axes.Axes)
+        self.assertEqual(ax0.get_title(), 'Split value histogram for feature with index 27')
+        self.assertEqual(ax0.get_xlabel(), 'Feature split value')
+        self.assertEqual(ax0.get_ylabel(), 'Count')
+        self.assertLessEqual(len(ax0.patches), 2)
+
+        gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
+        gbm1.fit(self.X_train, self.y_train)
+
+        ax1 = lgb.plot_split_value_histogram(gbm1, gbm1.booster_.feature_name()[27], figsize=(10, 5),
+                                             title='Histogram for feature @index/name@ @feature@',
+                                             xlabel='x', ylabel='y', color='r')
+        self.assertIsInstance(ax1, matplotlib.axes.Axes)
+        self.assertEqual(ax1.get_title(),
+                         'Histogram for feature name {}'.format(gbm1.booster_.feature_name()[27]))
+        self.assertEqual(ax1.get_xlabel(), 'x')
+        self.assertEqual(ax1.get_ylabel(), 'y')
+        self.assertLessEqual(len(ax1.patches), 2)
+        for patch in ax1.patches:
+            self.assertTupleEqual(patch.get_facecolor(), (1., 0, 0, 1.))  # red
+
+        ax2 = lgb.plot_split_value_histogram(gbm0, 27, bins=10, color=['r', 'y', 'g', 'b'],
+                                             title=None, xlabel=None, ylabel=None)
+        self.assertIsInstance(ax2, matplotlib.axes.Axes)
+        self.assertEqual(ax2.get_title(), '')
+        self.assertEqual(ax2.get_xlabel(), '')
+        self.assertEqual(ax2.get_ylabel(), '')
+        self.assertEqual(len(ax2.patches), 10)
+        self.assertTupleEqual(ax2.patches[0].get_facecolor(), (1., 0, 0, 1.))  # r
+        self.assertTupleEqual(ax2.patches[1].get_facecolor(), (.75, .75, 0, 1.))  # y
+        self.assertTupleEqual(ax2.patches[2].get_facecolor(), (0, .5, 0, 1.))  # g
+        self.assertTupleEqual(ax2.patches[3].get_facecolor(), (0, 0, 1., 1.))  # b
+
+        self.assertRaises(ValueError, lgb.plot_split_value_histogram, gbm0, 0)  # was not used in splitting
+
     @unittest.skipIf(not MATPLOTLIB_INSTALLED or not GRAPHVIZ_INSTALLED, 'matplotlib or graphviz is not installed')
     def test_plot_tree(self):
         gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)