Feature importance is zero!!!

[Gunnvant]

I am using a dataset to compute feature importance using permutation. Have checked results with R implementation, I am getting non zero var importance. What could be the reason? Here is my code

from rfpimp import *
from sklearn.ensemble.forest import _generate_unsampled_indices

# TODO: add arg for subsample size to compute oob score

def oob_classifier_accuracy(rf, X_train, y_train):
   X = X_train.values
    y = y_train.values

    n_samples = len(X)
    n_classes = len(np.unique(y))
    predictions = np.zeros((n_samples, n_classes))
    for tree in rf.estimators_:
        unsampled_indices = _generate_unsampled_indices(tree.random_state, n_samples)
        tree_preds = tree.predict_proba(X[unsampled_indices, :])
       predictions[unsampled_indices] += tree_preds

    predicted_class_indexes = np.argmax(predictions, axis=1)
    predicted_classes = [rf.classes_[i] for i in predicted_class_indexes]

    oob_score = np.mean(y == predicted_classes)
    return oob_score

def permutation_importances(rf, X_train, y_train, metric):
    """
    Return importances from pre-fit rf; metric is function
    that measures accuracy or R^2 or similar. This function
    works for regressors and classifiers.
    """
    baseline = metric(rf, X_train, y_train)
    imp = []
    for col in X_train.columns:
        save = X_train[col].copy()
        X_train[col] = np.random.permutation(X_train[col])
        m = metric(rf, X_train, y_train)
        X_train[col] = save
        imp.append(baseline - m)
    return np.array(imp)
rf = clone(base_rf)
rf.fit(X_train, y_train)
oob = oob_classifier_accuracy(rf, X_train, y_train)
print("oob accuracy",oob)

imp = permutation_importances(rf, X_train, y_train,
                              oob_classifier_accuracy)
imp

Gives an output of:

array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

I also computed the oob_classiifer_accuracy() by permuting all the variables, the accuracy reported doesn't change at all. The event rate is data is rather low around 5%.