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"""
=============================================
Evaluate classification by compiling a report
=============================================
Specific metrics have been developed to evaluate classifier which has been
trained using imbalanced data. :mod:`imblearn` provides a classification report
similar to :mod:`sklearn`, with additional metrics specific to imbalanced
learning problem.
"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from imblearn import over_sampling as os
from imblearn import pipeline as pl
from imblearn.metrics import classification_report_imbalanced
print(__doc__)
RANDOM_STATE = 42
# Generate a dataset
X, y = datasets.make_classification(
n_classes=2,
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(
StandardScaler(),
os.SMOTE(random_state=RANDOM_STATE),
LogisticRegression(max_iter=10_000),
)
# 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)
# Show the classification report
print(classification_report_imbalanced(y_test, y_pred_bal))
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