Merge pull request scikit-learn#3078 from ndawe/examples

[MRG] plot_adaboost_multiclass.py: handle case where boosting terminated early

examples/ensemble/plot_adaboost_multiclass.py

@@ -35,7 +35,6 @@
 
 from sklearn.datasets import make_gaussian_quantiles
 from sklearn.ensemble import AdaBoostClassifier
-from sklearn.externals.six.moves import xrange
 from sklearn.metrics import accuracy_score
 from sklearn.tree import DecisionTreeClassifier
 
@@ -72,37 +71,49 @@
     discrete_test_errors.append(
         1. - accuracy_score(discrete_train_predict, y_test))
 
-n_trees = xrange(1, len(bdt_discrete) + 1)
+n_trees_discrete = len(bdt_discrete)
+n_trees_real = len(bdt_real)
+
+# Boosting might terminate early, but the following arrays are always
+# n_estimators long. We crop them to the actual number of trees here:
+discrete_estimator_errors = bdt_discrete.estimator_errors_[:n_trees_discrete]
+real_estimator_errors = bdt_real.estimator_errors_[:n_trees_real]
+discrete_estimator_weights = bdt_discrete.estimator_weights_[:n_trees_discrete]
 
 plt.figure(figsize=(15, 5))
 
 plt.subplot(131)
-plt.plot(n_trees, discrete_test_errors, c='black', label='SAMME')
-plt.plot(n_trees, real_test_errors, c='black',
-        linestyle='dashed', label='SAMME.R')
+plt.plot(range(1, n_trees_discrete + 1),
+         discrete_test_errors, c='black', label='SAMME')
+plt.plot(range(1, n_trees_real + 1),
+         real_test_errors, c='black',
+         linestyle='dashed', label='SAMME.R')
 plt.legend()
 plt.ylim(0.18, 0.62)
 plt.ylabel('Test Error')
 plt.xlabel('Number of Trees')
 
 plt.subplot(132)
-plt.plot(n_trees, bdt_discrete.estimator_errors_, "b", label='SAMME', alpha=.5)
-plt.plot(n_trees, bdt_real.estimator_errors_, "r", label='SAMME.R', alpha=.5)
+plt.plot(range(1, n_trees_discrete + 1), discrete_estimator_errors,
+         "b", label='SAMME', alpha=.5)
+plt.plot(range(1, n_trees_real + 1), real_estimator_errors,
+         "r", label='SAMME.R', alpha=.5)
 plt.legend()
 plt.ylabel('Error')
 plt.xlabel('Number of Trees')
 plt.ylim((.2,
-        max(bdt_real.estimator_errors_.max(),
-            bdt_discrete.estimator_errors_.max()) * 1.2))
+         max(real_estimator_errors.max(),
+             discrete_estimator_errors.max()) * 1.2))
 plt.xlim((-20, len(bdt_discrete) + 20))
 
 plt.subplot(133)
-plt.plot(n_trees, bdt_discrete.estimator_weights_, "b", label='SAMME')
+plt.plot(range(1, n_trees_discrete + 1), discrete_estimator_weights,
+         "b", label='SAMME')
 plt.legend()
 plt.ylabel('Weight')
 plt.xlabel('Number of Trees')
-plt.ylim((0, bdt_discrete.estimator_weights_.max() * 1.2))
-plt.xlim((-20, len(bdt_discrete) + 20))
+plt.ylim((0, discrete_estimator_weights.max() * 1.2))
+plt.xlim((-20, n_trees_discrete + 20))
 
 # prevent overlapping y-axis labels
 plt.subplots_adjust(wspace=0.25)

examples/ensemble/plot_adaboost_regression.py

@@ -14,6 +14,10 @@
 """
 print(__doc__)
 
+# Author: Noel Dawe <[email protected]>
+#
+# License: BSD 3 clause
+
 import numpy as np
 import matplotlib.pyplot as plt
 

examples/ensemble/plot_adaboost_twoclass.py

@@ -18,6 +18,10 @@
 """
 print(__doc__)
 
+# Author: Noel Dawe <[email protected]>
+#
+# License: BSD 3 clause
+
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -65,8 +69,8 @@
 for i, n, c in zip(range(2), class_names, plot_colors):
     idx = np.where(y == i)
     plt.scatter(X[idx, 0], X[idx, 1],
-               c=c, cmap=plt.cm.Paired,
-               label="Class %s" % n)
+                c=c, cmap=plt.cm.Paired,
+                label="Class %s" % n)
 plt.xlim(x_min, x_max)
 plt.ylim(y_min, y_max)
 plt.legend(loc='upper right')
@@ -78,11 +82,11 @@
 plt.subplot(122)
 for i, n, c in zip(range(2), class_names, plot_colors):
     plt.hist(twoclass_output[y == i],
-            bins=10,
-            range=plot_range,
-            facecolor=c,
-            label='Class %s' % n,
-            alpha=.5)
+             bins=10,
+             range=plot_range,
+             facecolor=c,
+             label='Class %s' % n,
+             alpha=.5)
 x1, x2, y1, y2 = plt.axis()
 plt.axis((x1, x2, y1, y2 * 1.2))
 plt.legend(loc='upper right')