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import statsmodels.api as sm

logit_model = sm.Logit(train[target], X_train_scaler)
modelo_log_sm = logit_model.fit()

print(modelo_log_sm.summary2())


def calcular_pesos(modelo, new_cols_select):
    coef_model = pd.DataFrame(modelo.tvalues,columns=[&#039;t_value&#039;]).reset_index()
    coef_model[&#039;t_value2&#039;] = np.power(coef_model[&#039;t_value&#039;],2)
    coef_model[&#039;total&#039;] = sum(coef_model[&#039;t_value2&#039;])
    coef_model[&#039;part&#039;] = coef_model[&#039;t_value2&#039;] / coef_model[&#039;total&#039;]
    coef_model[&#039;pesos&#039;] = coef_model[&#039;part&#039;] * 100
    coef_model[&#039;variable&#039;] = coef_model[&#039;index&#039;].apply(lambda _: new_cols_select[int(_.replace(&#039;x&#039;, &#039;&#039;)) - 1])
    return coef_model.sort_values([&#039;pesos&#039;], ascending=False)

calcular_pesos(modelo_log_sm, seleccionadas)