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"""
=============================================
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.
"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT
from collections import Counter
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from imblearn.datasets import make_imbalance
from imblearn.metrics import classification_report_imbalanced
from imblearn.pipeline import make_pipeline
from imblearn.under_sampling import NearMiss
print(__doc__)
RANDOM_STATE = 42
# Create a folder to fetch the dataset
iris = load_iris()
X, y = make_imbalance(
iris.data,
iris.target,
sampling_strategy={0: 25, 1: 50, 2: 50},
random_state=RANDOM_STATE,
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=RANDOM_STATE)
print(f"Training target statistics: {Counter(y_train)}")
print(f"Testing target statistics: {Counter(y_test)}")
# Create a pipeline
pipeline = make_pipeline(NearMiss(version=2), StandardScaler(), LogisticRegression())
pipeline.fit(X_train, y_train)
# Classify and report the results
print(classification_report_imbalanced(y_test, pipeline.predict(X_test)))
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