inspection.importance.generate_imp

inspection.importance.generate_imp(
    X_train,
    X_test,
    y_train,
    y_test,
    random_state=42,
    n_repeats=10,
    use_test=True,
)

Generates permutation importances from a RandomForestRegressor.

Parameters

Name Type Description Default
X_train pd.DataFrame or np.ndarray The training feature set. required
X_test pd.DataFrame or np.ndarray The test feature set. required
y_train pd.Series or np.ndarray The training target variable. required
y_test pd.Series or np.ndarray The test target variable. required
random_state int Random state for the RandomForestRegressor. Defaults to 42. 42
n_repeats int Number of repeats for permutation importance. Defaults to 10. 10
use_test bool If True, computes permutation importance on the test set. If False, uses the training set. Defaults to True. True

Returns

Name Type Description
permutation_importance permutation_importance Permutation importances object.

Examples

>>> from spotoptim.sensitivity.importance import generate_imp
>>> import pandas as pd
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=42)
>>> X_train, X_test = X[:80], X[80:]
>>> y_train, y_test = y[:80], y[80:]
>>> X_train_df = pd.DataFrame(X_train)
>>> X_test_df = pd.DataFrame(X_test)
>>> y_train_series = pd.Series(y_train)
>>> y_test_series = pd.Series(y_test)
>>> perm_imp = generate_imp(X_train_df, X_test_df, y_train_series, y_test_series)
>>> print(perm_imp)