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KMeans-SMOTE algorithm
This example illustrates the data generation process and the performance of KMeans-SMOTE.
# Author: Georgios Douzas <gdouzas@icloud.com>
# Licence: MIT
import matplotlib.pyplot as plt
import pandas as pd
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import make_pipeline
from sklearn.base import clone
from sklearn.datasets import make_classification
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from imblearn_extra.clover.over_sampling import KMeansSMOTE
RANDOM_STATE = 2
OVERSAMPLERS = [
SMOTE(random_state=RANDOM_STATE),
KMeansSMOTE(random_state=RANDOM_STATE + 3),
]
def generate_imbalanced_data():
"""Generate imbalanced data."""
X, y = make_classification(
n_classes=3,
flip_y=0.05,
weights=[0.15, 0.6, 0.25],
n_informative=2,
n_redundant=0,
n_repeated=0,
n_features=2,
n_clusters_per_class=1,
n_samples=1000,
random_state=RANDOM_STATE,
)
return X, y
def plot_data(X, y, oversampler, ax):
"""Plot original or resampled data."""
if oversampler is None:
X_res, y_res = X, y
title = 'Original data'
else:
oversampler = clone(oversampler)
X_res, y_res = oversampler.fit_resample(X, y)
ovs_name = oversampler.__class__.__name__
title = f'Resampling using {ovs_name}'
ax.scatter(X_res[:, 0], X_res[:, 1], c=y_res, alpha=0.8, edgecolor='k')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
ax.set_title(title)
def compare_f1_scores(X_train, X_test, y_train, y_test, clf, oversampler):
"""Compare F1 scores of oversamplers."""
ovs_clf = make_pipeline(clone(oversampler), clf)
y_pred = ovs_clf.fit(X_train, y_train).predict(X_test)
ovs_name = oversampler.__class__.__name__
ovs_score = f1_score(y_test, y_pred, average='macro')
return (ovs_name, ovs_score)
Generate imbalanced data
We are generating an imbalanced multi-class data set, using
make_classification from scikit-learn.
X, y = generate_imbalanced_data()
_, ax = plt.subplots(1, 1, figsize=(15, 7))
plot_data(X, y, None, ax)
Plot resampled data
KMeans-SMOTE allows to identify areas of the input space which are appropriate to generate artificial data. Therefore, the generation of noisy samples is avoided and the within-classes imbalanced issue is also addressed. The next plots show the resampled data of KMeans-SMOTE vs SMOTE.
fig, axs = plt.subplots(1, 2, figsize=(15, 5))
for ax, oversampler in zip(axs, OVERSAMPLERS, strict=True):
plot_data(X, y, clone(oversampler), ax)
fig.tight_layout()
Performance evaluation
We are evaluating the performance of KMeans-SMOTE using F1-score as evaluation metric on a test set. SMOTE's performance is also included.
clf = GradientBoostingClassifier(random_state=RANDOM_STATE)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=RANDOM_STATE)
scores = []
for oversampler in OVERSAMPLERS:
scores.append(compare_f1_scores(X_train, X_test, y_train, y_test, clf, oversampler))
pd.DataFrame(scores).rename(columns={0: 'Oversamplers', 1: 'F-score'}).set_index('Oversamplers')
Total running time of the script: ( 0 minutes 4.867 seconds)
Download Python source code: plot_kmeans_smote.py