iter

Author: glemaitre

examples/ensemble/plot_bagging_classifier.py

@@ -0,0 +1,113 @@
+"""
+=================================
+Bagging classifiers using sampler
+=================================
+
+In this example, we show how
+:class:`~imblearn.ensemble.BalancedBaggingClassifier` can be used to create a
+large variety of classifiers by giving different samplers.
+
+We will give several examples that have been published in the passed year.
+"""
+
+# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
+# License: MIT
+
+# %%
+print(__doc__)
+
+# %% [markdown]
+# Generate an imbalanced dataset
+# ------------------------------
+#
+# For this example, we will create a synthetic dataset using the function
+# :func:`~sklearn.datasets.make_classification`. The problem will be a toy
+# classification problem with a ratio of 1:9 between the two classes.
+
+# %%
+from sklearn.datasets import make_classification
+
+X, y = make_classification(
+    n_samples=10_000,
+    n_features=10,
+    weights=[0.1, 0.9],
+    class_sep=0.5,
+    random_state=0,
+)
+
+# %%
+import pandas as pd
+
+pd.Series(y).value_counts(normalize=True)
+
+# %% [markdown]
+# In the following sections, we will show a couple of algorithms that have
+# been proposed over the years. We intend to illustrate how one can reuse the
+# :class:`~imblearn.ensemble.BalancedBaggingClassifier` by passing different
+# sampler.
+
+# %%
+from sklearn.model_selection import cross_validate
+from sklearn.ensemble import BaggingClassifier
+
+ebb = BaggingClassifier()
+cv_results = cross_validate(ebb, X, y, scoring="balanced_accuracy")
+
+print(f"{cv_results['test_score'].mean():.3f} +/- {cv_results['test_score'].std():.3f}")
+
+# %% [markdown]
+# Exactly Balanced Bagging and Over-Bagging
+# -----------------------------------------
+#
+# The :class:`~imblearn.ensemble.BalancedBaggingClassifier` can use in
+# conjunction with a :class:`~imblearn.under_sampling.RandomUnderSampler` or
+# :class:`~imblearn.over_sampling.RandomOverSampler`. These methods are
+# referred as Exactly Balanced Bagging and Over-Bagging, respectively and have
+# been proposed first in [1]_.
+
+# %%
+from imblearn.ensemble import BalancedBaggingClassifier
+from imblearn.under_sampling import RandomUnderSampler
+
+# Exactly Balanced Bagging
+ebb = BalancedBaggingClassifier(sampler=RandomUnderSampler())
+cv_results = cross_validate(ebb, X, y, scoring="balanced_accuracy")
+
+print(f"{cv_results['test_score'].mean():.3f} +/- {cv_results['test_score'].std():.3f}")
+
+# %%
+from imblearn.over_sampling import RandomOverSampler
+
+# Over-bagging
+over_bagging = BalancedBaggingClassifier(sampler=RandomOverSampler())
+cv_results = cross_validate(over_bagging, X, y, scoring="balanced_accuracy")
+
+print(f"{cv_results['test_score'].mean():.3f} +/- {cv_results['test_score'].std():.3f}")
+
+# %% [markdown]
+# SMOTE-Bagging
+# -------------
+#
+# Instead of using a :class:`~imblearn.over_sampling.RandomOverSampler` that
+# make a bootstrap, an alternative is to use
+# :class:`~imblearn.over_sampling.SMOTE` as an over-sampler. This is known as
+# SMOTE-Bagging [2]_.
+
+# %%
+from imblearn.over_sampling import SMOTE
+
+# SMOTE-Bagging
+smote_bagging = BalancedBaggingClassifier(sampler=SMOTE())
+cv_results = cross_validate(smote_bagging, X, y, scoring="balanced_accuracy")
+
+print(f"{cv_results['test_score'].mean():.3f} +/- {cv_results['test_score'].std():.3f}")
+
+# %% [markdown]
+# .. topic:: References:
+#
+#    .. [1] R. Maclin, and D. Opitz. "An empirical evaluation of bagging and
+#           boosting." AAAI/IAAI 1997 (1997): 546-551.
+#
+#    .. [2] S. Wang, and X. Yao. "Diversity analysis on imbalanced data sets by
+#           using ensemble models." 2009 IEEE symposium on computational
+#           intelligence and data mining. IEEE, 2009.

examples/ensemble/plot_comparison_ensemble_classifier.py

@@ -3,15 +3,14 @@
 Compare ensemble classifiers using resampling
 =============================================
 
-Ensembling classifiers have shown to improve classification performance compare
+Ensemble classifiers have shown to improve classification performance compare
 to single learner. However, they will be affected by class imbalance. This
 example shows the benefit of balancing the training set before to learn
 learners. We are making the comparison with non-balanced ensemble methods.
 
 We make a comparison using the balanced accuracy and geometric mean which are
 metrics widely used in the literature to evaluate models learned on imbalanced
 set.
-
 """
 
 # Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>