adds new matplotlib module for abstracting matplotlib routines

examples/feature_importances.py

@@ -1,18 +1,26 @@
+"""
+Feature importances plot
+
+%load_ext autoreload
+%autoreload 2
+"""
 import matplotlib.pyplot as plt
 from sklearn import datasets
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 
 from sklearn_evaluation import plot
 
-data = datasets.make_classification(200, 10, 5, class_sep=0.65)
-X = data[0]
-y = data[1]
-
+X, y = datasets.make_classification(200, 20, 5, class_sep=0.65)
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
 
-est = RandomForestClassifier()
-est.fit(X_train, y_train)
+model = RandomForestClassifier(n_estimators=1)
+model.fit(X_train, y_train)
+
+# plot all features
+plot.feature_importances(model)
+plt.show()
 
-plot.feature_importances(est, top_n=5)
-plt.show()
+# only top 5
+plot.feature_importances(model, top_n=5)
+plt.show()

sklearn_evaluation/plot/matplotlib/__init__.py

@@ -0,0 +1,3 @@
+"""
+Matplotlib plotting code
+"""

sklearn_evaluation/plot/matplotlib/bar.py

@@ -0,0 +1,31 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def horizontal(values, labels=None, error=None):
+    """Horizontal bar plot
+
+    Examples
+    --------
+    >>> from sklearn_evaluation.plot.matplotlib import bar
+    >>> values = np.random.rand(10)
+    >>> bar.horizontal(values)
+    >>> plt.show()
+
+    Notes
+    -----
+    https://matplotlib.org/gallery/lines_bars_and_markers/barh.html
+
+    """
+    y_pos = np.arange(len(values))
+    ax = plt.gca()
+
+    if error is None:
+        ax.barh(y_pos, values)
+    else:
+        ax.barh(y_pos, values, xerr=error)
+
+    ax.set_yticks(y_pos if labels is None else labels)
+    ax.set_yticklabels(y_pos)
+    ax.invert_yaxis()
+    return ax