finish the example

Author: Guillaume Lemaitre

examples/applications/plot_multi_class_under_sampling.py

@@ -32,7 +32,8 @@
                                                     random_state=RANDOM_STATE)
 
 # Create a pipeline
-pipeline = make_pipeline(NearMiss(version=2), LinearSVC())
+pipeline = make_pipeline(NearMiss(version=2, random_state=RANDOM_STATE),
+                         LinearSVC(random_state=RANDOM_STATE))
 pipeline.fit(X_train, y_train)
 
 # Classify and report the results

examples/evaluation/plot_classification_report.py

@@ -3,7 +3,7 @@
 Evaluate classification by compiling a report
 =============================================
 
-Specific metrics has been developed to evaluate classifier which has been
+Specific metrics have been developed to evaluate classifier which has been
 trained using imbalanced data. `imblearn` provides a classification
 report similar to `sklearn`, with additional metrics specific to imbalanced
 learning problem.
@@ -28,7 +28,8 @@
                                     n_clusters_per_class=4, n_samples=5000,
                                     random_state=RANDOM_STATE)
 
-pipeline = pl.make_pipeline(os.SMOTE(), LinearSVC())
+pipeline = pl.make_pipeline(os.SMOTE(random_state=RANDOM_STATE),
+                            LinearSVC(random_state=RANDOM_STATE))
 
 # Split the data
 X_train, X_test, y_train, y_test = train_test_split(X, y,

examples/evaluation/plot_metrics.py

@@ -0,0 +1,74 @@
+"""
+=======================================
+Metrics specific to imbalanced learning
+=======================================
+
+Specific metrics have been developed to evaluate classifier which
+has been trained using imbalanced data. `imblearn` provides mainly
+two additional metrics which are not implemented in `sklearn`: (i)
+geometric mean and (ii) indexed balanced accuracy.
+"""
+
+from sklearn import datasets
+from sklearn.svm import LinearSVC
+from sklearn.model_selection import train_test_split
+
+from imblearn import over_sampling as os
+from imblearn import pipeline as pl
+from imblearn.metrics import (geometric_mean_score,
+                              make_indexed_balanced_accuracy)
+
+print(__doc__)
+
+RANDOM_STATE = 42
+
+# Generate a dataset
+X, y = datasets.make_classification(n_classes=3, 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=RANDOM_STATE)
+
+pipeline = pl.make_pipeline(os.SMOTE(random_state=RANDOM_STATE),
+                            LinearSVC(random_state=RANDOM_STATE))
+
+# Split the data
+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)
+
+# Test the classifier and get the prediction
+y_pred_bal = pipeline.predict(X_test)
+
+###############################################################################
+# The geometric mean corresponds to the square root of the product of the
+# sensitivity and specificity. Combining the two metrics should account for
+# the balancing of the dataset.
+
+print('The geometric mean is {}'.format(geometric_mean_score(
+    y_test,
+    y_pred_bal)))
+
+###############################################################################
+# The indexed balanced accuracy can transform any metric to be used in
+# imbalanced learning problems.
+
+alpha = 0.1
+geo_mean = make_indexed_balanced_accuracy(alpha=alpha, squared=True)(
+    geometric_mean_score)
+
+print('The IBA using alpha = {} and the geometric mean: {}'.format(
+    alpha, geo_mean(
+        y_test,
+        y_pred_bal)))
+
+alpha = 0.5
+geo_mean = make_indexed_balanced_accuracy(alpha=alpha, squared=True)(
+    geometric_mean_score)
+
+print('The IBA using alpha = {} and the geometric mean: {}'.format(
+    alpha, geo_mean(
+        y_test,
+        y_pred_bal)))

examples/model_selection/plot_validation_curve.py

@@ -29,7 +29,7 @@
                                     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)
+                                    random_state=RANDOM_STATE)
 smote = os.SMOTE(random_state=RANDOM_STATE)
 cart = tree.DecisionTreeClassifier(random_state=RANDOM_STATE)
 pipeline = pl.make_pipeline(smote, cart)