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

print(__doc__)

RANDOM_STATE = 42

First, we will generate some imbalanced dataset.

from sklearn.datasets import make_classification

X, y = 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,
)

We will split the data into a training and testing set.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, stratify=y, random_state=RANDOM_STATE
)

We will create a pipeline made of a SMOTE over-sampler followed by a LogisticRegression classifier.

from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler

from imblearn.over_sampling import SMOTE

from imblearn.pipeline import make_pipeline

model = make_pipeline(
    StandardScaler(),
    SMOTE(random_state=RANDOM_STATE),
    LogisticRegression(max_iter=10_000, random_state=RANDOM_STATE),
)

Now, we will train the model on the training set and get the prediction associated with the testing set. Be aware that the resampling will happen only when calling fit: the number of samples in y_pred is the same than in y_test.

model.fit(X_train, y_train)
y_pred = model.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.

from imblearn.metrics import geometric_mean_score

print(f"The geometric mean is {geometric_mean_score(y_test, y_pred):.3f}")

The geometric mean is 0.940

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

from imblearn.metrics import make_index_balanced_accuracy

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):.3f}"
)

The IBA using alpha=0.1 and the geometric mean: 0.884

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):.3f}"
)

The IBA using alpha=0.5 and the geometric mean: 0.884

Estimated memory usage: 10 MB