import numpy as np
from sklearn.datasets import load_iris, load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.inspection import permutation_importance as skpermutation_importance

def accuracy_score(y_true, y_pred):
    return np.mean(y_true == y_pred)

def permutation_importance(estimator, X, y, n_repeats=5, random_state=0):
    baseline_score = accuracy_score(y, estimator.predict(X))
    importances = np.zeros((X.shape[1], n_repeats))
    rng = np.random.RandomState(random_state)
    for i in range(X.shape[1]):
        original_features = X[:, i].copy()
        for j in range(n_repeats):
            rng.shuffle(X[:, i])
            importances[i, j] = accuracy_score(y, estimator.predict(X))
        X[:, i] = original_features
    importances = baseline_score - importances
    return importances, importances.mean(axis=1), importances.std(axis=1)

X, y = load_iris(return_X_y=True)
clf = RandomForestClassifier(random_state=0).fit(X, y)
importance1, mean1, std1 = permutation_importance(clf, X, y, random_state=0)
importance2 = skpermutation_importance(clf, X, y, random_state=0)
assert np.allclose(importance1, importance2.importances)
assert np.allclose(mean1, importance2.importances_mean)
assert np.allclose(std1, importance2.importances_std)

X, y = load_breast_cancer(return_X_y=True)
clf = RandomForestClassifier(random_state=0).fit(X, y)
importance1, mean1, std1 = permutation_importance(clf, X, y, random_state=0)
importance2 = skpermutation_importance(clf, X, y, random_state=0)
assert np.allclose(importance1, importance2.importances)
assert np.allclose(mean1, importance2.importances_mean)
assert np.allclose(std1, importance2.importances_std)