"""
=======================================
Metrics specific to imbalanced learning
=======================================

Specific metrics have been developed to evaluate classifier which
has been trained using imbalanced data. `imblearn` provides mainly
two additional metrics which are not implemented in `sklearn`: (i)
geometric mean and (ii) index balanced accuracy.
"""

# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT

from sklearn import datasets
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split

from imblearn import over_sampling as os
from imblearn import pipeline as pl
from imblearn.metrics import geometric_mean_score, make_index_balanced_accuracy

print(__doc__)

RANDOM_STATE = 42

# Generate a dataset
X, y = datasets.make_classification(
    n_classes=3,
    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(
    os.SMOTE(random_state=RANDOM_STATE), LinearSVC(random_state=RANDOM_STATE)
)

# 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)

###############################################################################
# The geometric mean corresponds to the square root of the product of the
# sensitivity and specificity. Combining the two metrics should account for
# the balancing of the dataset.

print(f"The geometric mean is {geometric_mean_score(y_test, y_pred_bal)}")

###############################################################################
# The index balanced accuracy can transform any metric to be used in
# imbalanced learning problems.

alpha = 0.1
geo_mean = make_index_balanced_accuracy(alpha=alpha, squared=True)(geometric_mean_score)

print(
    f"The IBA using alpha = {alpha} and the geometric mean: "
    f"{geo_mean(y_test, y_pred_bal)}"
)

alpha = 0.5
geo_mean = make_index_balanced_accuracy(alpha=alpha, squared=True)(geometric_mean_score)

print(
    f"The IBA using alpha = {alpha} and the geometric mean: "
    f"{geo_mean(y_test, y_pred_bal)}"
)
