eda.py 文件源码

def feature_importance_regression(features, target, n_neighbors=3, random_state=None):

    cont = features.select_dtypes(include=[np.floating])
    disc = features.select_dtypes(include=[np.integer, np.bool])

    cont_imp = pd.DataFrame(index=cont.columns)
    disc_imp = pd.DataFrame(index=disc.columns)

    # Continuous features
    if cont_imp.index.size > 0:

        # Pearson correlation
        pearson = np.array([stats.pearsonr(feature, target) for _, feature in cont.iteritems()])
        cont_imp['pearson_r'] = pearson[:, 0]
        cont_imp['pearson_r_p_value'] = pearson[:, 1]

        # Mutual information
        mut_inf = feature_selection.mutual_info_regression(cont, target, discrete_features=False,
                                                           n_neighbors=n_neighbors,
                                                           random_state=random_state)
        cont_imp['mutual_information'] = mut_inf

    # Discrete features
    if disc_imp.index.size > 0:

        # F-test
        f_tests = defaultdict(dict)

        for feature in disc.columns:
            groups = [target[idxs] for idxs in disc.groupby(feature).groups.values()]
            statistic, p_value = stats.f_oneway(*groups)
            f_tests[feature]['f_statistic'] = statistic
            f_tests[feature]['f_p_value'] = p_value

        f_tests_df = pd.DataFrame.from_dict(f_tests, orient='index')
        disc_imp['f_statistic'] = f_tests_df['f_statistic']
        disc_imp['f_p_value'] = f_tests_df['f_p_value']

        # Mutual information
        mut_inf = feature_selection.mutual_info_regression(disc, target, discrete_features=True,
                                                           n_neighbors=n_neighbors,
                                                           random_state=random_state)
        disc_imp['mutual_information'] = mut_inf

    return cont_imp, disc_imp