add an example for multiclass

Author: Guillaume Lemaitre

examples/applications/plot_multi_class_under_sampling.py

@@ -0,0 +1,39 @@
+"""
+=============================================
+Multiclass classification with under-sampling
+=============================================
+
+Some balancing methods allow for balancing dataset with multiples classes.
+We provide an example to illustrate the use of those methods which do
+not differ from the binary case.
+
+"""
+
+from sklearn.datasets import load_iris
+from sklearn.svm import LinearSVC
+from sklearn.model_selection import train_test_split
+
+from imblearn.under_sampling import NearMiss
+from imblearn.pipeline import make_pipeline
+from imblearn.metrics import classification_report_imbalanced
+
+print(__doc__)
+
+RANDOM_STATE = 42
+
+# Create a folder to fetch the dataset
+iris = load_iris()
+# Make the dataset imbalanced
+# Select only half of the first class
+iris.data = iris.data[25:-1, :]
+iris.target = iris.target[25:-1]
+
+X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
+                                                    random_state=RANDOM_STATE)
+
+# Create a pipeline
+pipeline = make_pipeline(NearMiss(version=2), LinearSVC())
+pipeline.fit(X_train, y_train)
+
+# Classify and report the results
+print(classification_report_imbalanced(y_test, pipeline.predict(X_test)))

examples/evaluation/plot_classification_report.py

@@ -14,7 +14,6 @@
 from sklearn.model_selection import train_test_split
 
 from imblearn import over_sampling as os
-from imblearn import under_sampling as us
 from imblearn import pipeline as pl
 from imblearn.metrics import classification_report_imbalanced
 
@@ -32,7 +31,8 @@
 pipeline = pl.make_pipeline(os.SMOTE(), LinearSVC())
 
 # Split the data
-X_train, X_test, y_train, y_test = train_test_split(X, y)
+X_train, X_test, y_train, y_test = train_test_split(X, y,
+                                                    random_state=RANDOM_STATE)
 
 # Train the classifier with balancing
 pipeline.fit(X_train, y_train)