inspection.importance.plot_importances

inspection.importance.plot_importances(
    df_mdi,
    perm_imp,
    X_test,
    target_name=None,
    feature_names=None,
    k=10,
    figsize=(12, 8),
    show=True,
)

Plots the impurity-based and permutation-based feature importances for a given classifier.

Parameters

Name	Type	Description	Default
df_mdi	pd.DataFrame	DataFrame with Gini importances.	required
perm_imp	object	Permutation importances object.	required
X_test	pd.DataFrame	The test feature set for permutation importance.	required
target_name	str	Name of the target variable for labeling. Defaults to None.	None
feature_names	list	List of feature names for labeling. Defaults to None.	None
k	int	Number of top features to display based on importance. Default is 10.	10
figsize	tuple	Size of the figure (width, height) in inches. Default is (12, 8).	(12, 8)
show	bool	If True, displays the plot immediately. Default is True.	True

Returns

Name	Type	Description
	None	None

Examples

>>> from spotoptim.sensitivity.importance import generate_mdi, generate_imp, plot_importances
>>> 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)
>>> df_mdi = generate_mdi(X_train_df, y_train_series)
>>> perm_imp = generate_imp(X_train_df, X_test_df, y_train_series, y_test_series)
>>> plot_importances(df_mdi, perm_imp, X_test_df, figsize=(15, 10))