Bagging classifiers using sampler#

In this example, we show how 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__)

Generate an imbalanced dataset#

For this example, we will create a synthetic dataset using the function 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)
1    0.8977
0    0.1023
Name: proportion, dtype: float64

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 BalancedBaggingClassifier by passing different sampler.

from sklearn.ensemble import BaggingClassifier
from sklearn.model_selection import cross_validate

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}")
0.713 +/- 0.018

Exactly Balanced Bagging and Over-Bagging#

The BalancedBaggingClassifier can use in conjunction with a RandomUnderSampler or 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}")
0.757 +/- 0.009
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}")
0.702 +/- 0.012

SMOTE-Bagging#

Instead of using a RandomOverSampler that make a bootstrap, an alternative is to use 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}")
0.738 +/- 0.019

Roughly Balanced Bagging#

While using a RandomUnderSampler or RandomOverSampler will create exactly the desired number of samples, it does not follow the statistical spirit wanted in the bagging framework. The authors in [3] proposes to use a negative binomial distribution to compute the number of samples of the majority class to be selected and then perform a random under-sampling.

Here, we illustrate this method by implementing a function in charge of resampling and use the FunctionSampler to integrate it within a Pipeline and cross_validate.

from collections import Counter

import numpy as np

from imblearn import FunctionSampler


def roughly_balanced_bagging(X, y, replace=False):
    """Implementation of Roughly Balanced Bagging for binary problem."""
    # find the minority and majority classes
    class_counts = Counter(y)
    majority_class = max(class_counts, key=class_counts.get)
    minority_class = min(class_counts, key=class_counts.get)

    # compute the number of sample to draw from the majority class using
    # a negative binomial distribution
    n_minority_class = class_counts[minority_class]
    n_majority_resampled = np.random.negative_binomial(n=n_minority_class, p=0.5)

    # draw randomly with or without replacement
    majority_indices = np.random.choice(
        np.flatnonzero(y == majority_class),
        size=n_majority_resampled,
        replace=replace,
    )
    minority_indices = np.random.choice(
        np.flatnonzero(y == minority_class),
        size=n_minority_class,
        replace=replace,
    )
    indices = np.hstack([majority_indices, minority_indices])

    return X[indices], y[indices]


# Roughly Balanced Bagging
rbb = BalancedBaggingClassifier(
    sampler=FunctionSampler(func=roughly_balanced_bagging, kw_args={"replace": True})
)
cv_results = cross_validate(rbb, X, y, scoring="balanced_accuracy")

print(f"{cv_results['test_score'].mean():.3f} +/- {cv_results['test_score'].std():.3f}")
0.750 +/- 0.010

Total running time of the script: (0 minutes 27.072 seconds)

Estimated memory usage: 10 MB

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