[MRG] Add an example using validation curves (#203)

* Add an example using validation cruves

* pep8

Author: chkoar

examples/model_selection/plot_validation_curve.py

@@ -0,0 +1,51 @@
+"""
+==========================
+Plotting Validation Curves
+==========================
+
+In this example the impact of the SMOTE's k_neighbors parameter is examined.
+In the plot you can see the validation scores of a SMOTE-CART classifier for
+different values of the SMOTE's k_neighbors parameter.
+"""
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn import model_selection as ms
+from sklearn import datasets, metrics, tree
+
+from imblearn import over_sampling as os
+from imblearn import pipeline as pl
+
+print(__doc__)
+
+LW = 2
+RANDOM_STATE = 42
+
+scorer = metrics.make_scorer(metrics.cohen_kappa_score)
+
+# Generate the dataset
+X, y = datasets.make_classification(n_classes=2, class_sep=2,
+                                    weights=[0.1, 0.9], n_informative=10,
+                                    n_redundant=1, flip_y=0, n_features=20,
+                                    n_clusters_per_class=4, n_samples=5000,
+                                    random_state=10)
+smote = os.SMOTE(random_state=RANDOM_STATE)
+cart = tree.DecisionTreeClassifier(random_state=RANDOM_STATE)
+pipeline = pl.make_pipeline(smote, cart)
+
+param_range = range(1, 11)
+train_scores, test_scores = ms.validation_curve(
+    pipeline, X, y, param_name="smote__k_neighbors", param_range=param_range,
+    cv=3, scoring=scorer, n_jobs=1)
+train_scores_mean = np.mean(train_scores, axis=1)
+train_scores_std = np.std(train_scores, axis=1)
+test_scores_mean = np.mean(test_scores, axis=1)
+test_scores_std = np.std(test_scores, axis=1)
+
+plt.title("Validation Curve with SMOTE-CART")
+plt.xlabel("k_neighbors")
+plt.ylabel("Cohen's kappa")
+plt.plot(param_range, test_scores_mean, color="navy", lw=LW)
+plt.legend(loc="best")
+plt.show()