Merge pull request #555 from sinhrks/plot_sklearn

Allow plot function to handle XGBModel

python-package/xgboost/plotting.py

@@ -7,6 +7,7 @@ from __future__ import absolute_import
 import re
 import numpy as np
 from .core import Booster
+from .sklearn import XGBModel
 
 from io import BytesIO
 
@@ -19,8 +20,8 @@ def plot_importance(booster, ax=None, height=0.2,
 
     Parameters
     ----------
-    booster : Booster or dict
-        Booster instance, or dict taken by Booster.get_fscore()
+    booster : Booster, XGBModel or dict
+        Booster or XGBModel instance, or dict taken by Booster.get_fscore()
     ax : matplotlib Axes, default None
         Target axes instance. If None, new figure and axes will be created.
     height : float, default 0.2
@@ -46,12 +47,14 @@ def plot_importance(booster, ax=None, height=0.2,
     except ImportError:
         raise ImportError('You must install matplotlib to plot importance')
 
-    if isinstance(booster, Booster):
+    if isinstance(booster, XGBModel):
+        importance = booster.booster().get_fscore()
+    elif isinstance(booster, Booster):
         importance = booster.get_fscore()
     elif isinstance(booster, dict):
         importance = booster
     else:
-        raise ValueError('tree must be Booster or dict instance')
+        raise ValueError('tree must be Booster, XGBModel or dict instance')
 
     if len(importance) == 0:
         raise ValueError('Booster.get_fscore() results in empty')
@@ -142,8 +145,8 @@ def to_graphviz(booster, num_trees=0, rankdir='UT',
 
     Parameters
     ----------
-    booster : Booster
-        Booster instance
+    booster : Booster, XGBModel
+        Booster or XGBModel instance
     num_trees : int, default 0
         Specify the ordinal number of target tree
     rankdir : str, default "UT"
@@ -165,8 +168,11 @@ def to_graphviz(booster, num_trees=0, rankdir='UT',
     except ImportError:
         raise ImportError('You must install graphviz to plot tree')
 
-    if not isinstance(booster, Booster):
-        raise ValueError('booster must be Booster instance')
+    if not isinstance(booster, (Booster, XGBModel)):
+        raise ValueError('booster must be Booster or XGBModel instance')
+
+    if isinstance(booster, XGBModel):
+        booster = booster.booster()
 
     tree = booster.get_dump()[num_trees]
     tree = tree.split()
@@ -193,8 +199,8 @@ def plot_tree(booster, num_trees=0, rankdir='UT', ax=None, **kwargs):
 
     Parameters
     ----------
-    booster : Booster
-        Booster instance
+    booster : Booster, XGBModel
+        Booster or XGBModel instance
     num_trees : int, default 0
         Specify the ordinal number of target tree
     rankdir : str, default "UT"
@@ -216,7 +222,6 @@ def plot_tree(booster, num_trees=0, rankdir='UT', ax=None, **kwargs):
     except ImportError:
         raise ImportError('You must install matplotlib to plot tree')
 
-
     if ax is None:
         _, ax = plt.subplots(1, 1)
 

scripts/travis_script.sh

@@ -64,7 +64,7 @@ if [ ${TASK} == "python-package" -o ${TASK} == "python-package3" ]; then
         conda create -n myenv python=2.7
     fi
     source activate myenv
-    conda install numpy scipy pandas matplotlib nose
+    conda install numpy scipy pandas matplotlib nose scikit-learn
     python -m pip install graphviz
 
     make all CXX=${CXX} || exit -1

tests/python/test_basic.py

@@ -220,7 +220,6 @@ class TestBasic(unittest.TestCase):
         for p in ax.patches:
             assert p.get_facecolor() == (1.0, 0, 0, 1.0) # red
 
-
         ax = xgb.plot_importance(bst2, color=['r', 'r', 'b', 'b'],
                                  title=None, xlabel=None, ylabel=None)
         assert isinstance(ax, Axes)
@@ -235,5 +234,50 @@ class TestBasic(unittest.TestCase):
 
         g = xgb.to_graphviz(bst2, num_trees=0)
         assert isinstance(g, Digraph)
+
         ax = xgb.plot_tree(bst2, num_trees=0)
         assert isinstance(ax, Axes)
+
+    def test_sklearn_api(self):
+        from sklearn import datasets
+        from sklearn.cross_validation import train_test_split
+
+        np.random.seed(1)
+
+        iris = datasets.load_iris()
+        tr_d, te_d, tr_l, te_l = train_test_split(iris.data, iris.target, train_size=120)
+
+        classifier = xgb.XGBClassifier()
+        classifier.fit(tr_d, tr_l)
+
+        preds = classifier.predict(te_d)
+        labels = te_l
+        err = sum([1 for p, l in zip(preds, labels) if p != l]) / len(te_l)
+        # error must be smaller than 10%
+        assert err < 0.1
+
+    def test_sklearn_plotting(self):
+        from sklearn import datasets
+        iris = datasets.load_iris()
+
+        classifier = xgb.XGBClassifier()
+        classifier.fit(iris.data, iris.target)
+
+        import matplotlib
+        matplotlib.use('Agg')
+
+        from matplotlib.axes import Axes
+        from graphviz import Digraph
+
+        ax = xgb.plot_importance(classifier)
+        assert isinstance(ax, Axes)
+        assert ax.get_title() == 'Feature importance'
+        assert ax.get_xlabel() == 'F score'
+        assert ax.get_ylabel() == 'Features'
+        assert len(ax.patches) == 4
+
+        g = xgb.to_graphviz(classifier, num_trees=0)
+        assert isinstance(g, Digraph)
+
+        ax = xgb.plot_tree(classifier, num_trees=0)
+        assert isinstance(ax, Axes)