Note

Click here to download the full example code

# Metrics specific to imbalanced learningΒΆ

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 <[email protected]>
# 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)
```

Out:

```
/home/docs/checkouts/readthedocs.org/user_builds/imbalanced-learn/envs/stable/lib/python3.6/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
"the number of iterations.", ConvergenceWarning)
```

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('The geometric mean is {}'.format(geometric_mean_score(
y_test,
y_pred_bal)))
```

Out:

```
The geometric mean is 0.9262633940760341
```

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('The IBA using alpha = {} and the geometric mean: {}'.format(
alpha, geo_mean(
y_test,
y_pred_bal)))
alpha = 0.5
geo_mean = make_index_balanced_accuracy(alpha=alpha, squared=True)(
geometric_mean_score)
print('The IBA using alpha = {} and the geometric mean: {}'.format(
alpha, geo_mean(
y_test,
y_pred_bal)))
```

Out:

```
The IBA using alpha = 0.1 and the geometric mean: 0.8579638752052544
The IBA using alpha = 0.5 and the geometric mean: 0.8579638752052544
```

**Total running time of the script:** ( 0 minutes 0.606 seconds)