How to use the causalml.metrics.get_cumgain function in causalml

random_state=RANDOM_SEED)

    uplift_model = BaseTClassifier(learner=LogisticRegression())

    uplift_model.fit(X=df_train[x_names].values,
                     treatment=df_train['treatment_group_key'].values,
                     y=df_train[CONVERSION].values)

    y_pred = uplift_model.predict(X=df_test[x_names].values,
                                  treatment=df_test['treatment_group_key'].values)

    auuc_metrics = pd.DataFrame(
        np.c_[y_pred, df_test['treatment_group_key'].values, df_test[CONVERSION].values],
        columns=['y_pred', 'W', CONVERSION])

    cumgain = get_cumgain(auuc_metrics,
                          outcome_col=CONVERSION,
                          treatment_col='W')

    # Check if the cumulative gain when using the model's prediction is
    # higher than it would be under random targeting
    assert cumgain['y_pred'].sum() > cumgain['Random'].sum()

control_effect_learner=XGBRegressor(),
                                   treatment_outcome_learner=XGBClassifier(),
                                   treatment_effect_learner=XGBRegressor())

    uplift_model.fit(X=df_train[x_names].values,
                     treatment=df_train['treatment_group_key'].values,
                     y=df_train[CONVERSION].values)

    y_pred = uplift_model.predict(X=df_test[x_names].values,
                                  p=df_test['propensity_score'].values)

    auuc_metrics = pd.DataFrame(
        np.c_[y_pred, df_test['treatment_group_key'].values, df_test[CONVERSION].values],
        columns=['y_pred', 'W', CONVERSION])

    cumgain = get_cumgain(auuc_metrics,
                          outcome_col=CONVERSION,
                          treatment_col='W')

    # Check if the cumulative gain when using the model's prediction is
    # higher than it would be under random targeting
    assert cumgain['y_pred'].sum() > cumgain['Random'].sum()

random_state=RANDOM_SEED)

    uplift_model = BaseSClassifier(learner=XGBClassifier())

    uplift_model.fit(X=df_train[x_names].values,
                     treatment=df_train['treatment_group_key'].values,
                     y=df_train[CONVERSION].values)

    y_pred = uplift_model.predict(X=df_test[x_names].values,
                                  treatment=df_test['treatment_group_key'].values)

    auuc_metrics = pd.DataFrame(
        np.c_[y_pred, df_test['treatment_group_key'].values, df_test[CONVERSION].values],
        columns=['y_pred', 'W', CONVERSION])

    cumgain = get_cumgain(auuc_metrics,
                          outcome_col=CONVERSION,
                          treatment_col='W')

    # Check if the cumulative gain when using the model's prediction is
    # higher than it would be under random targeting
    assert cumgain['y_pred'].sum() > cumgain['Random'].sum()

uplift_model = BaseRClassifier(outcome_learner=XGBClassifier(),
                                   effect_learner=XGBRegressor())

    uplift_model.fit(X=df_train[x_names].values,
                     p=df_train['propensity_score'].values,
                     treatment=df_train['treatment_group_key'].values,
                     y=df_train[CONVERSION].values)

    y_pred = uplift_model.predict(X=df_test[x_names].values)

    auuc_metrics = pd.DataFrame(
        np.c_[y_pred, df_test['treatment_group_key'].values, df_test[CONVERSION].values],
        columns=['y_pred', 'W', CONVERSION])

    cumgain = get_cumgain(auuc_metrics,
                          outcome_col=CONVERSION,
                          treatment_col='W')

    # Check if the cumulative gain when using the model's prediction is
    # higher than it would be under random targeting
    assert cumgain['y_pred'].sum() > cumgain['Random'].sum()

df_test['treatment_group_key'] == best_treatment, 1, 0
    )
    actual_is_control = np.where(
        df_test['treatment_group_key'] == CONTROL_NAME, 1, 0
    )

    synthetic = (actual_is_best == 1) | (actual_is_control == 1)
    synth = result[synthetic]

    auuc_metrics = synth.assign(
        is_treated=1 - actual_is_control[synthetic],
        conversion=df_test.loc[synthetic, CONVERSION].values,
        uplift_tree=synth.max(axis=1)
    ).drop(columns=result.columns)

    cumgain = get_cumgain(auuc_metrics,
                          outcome_col=CONVERSION,
                          treatment_col='is_treated')

    # Check if the cumulative gain of UpLift Random Forest is higher than
    # random
    assert cumgain['uplift_tree'].sum() > cumgain['Random'].sum()

df_test['treatment_group_key'] == best_treatment, 1, 0
    )
    actual_is_control = np.where(
        df_test['treatment_group_key'] == CONTROL_NAME, 1, 0
    )

    synthetic = (actual_is_best == 1) | (actual_is_control == 1)
    synth = result[synthetic]

    auuc_metrics = synth.assign(
        is_treated=1 - actual_is_control[synthetic],
        conversion=df_test.loc[synthetic, CONVERSION].values,
        uplift_tree=synth.max(axis=1)
    ).drop(columns=list(uplift_model.classes_))

    cumgain = get_cumgain(auuc_metrics,
                          outcome_col=CONVERSION,
                          treatment_col='is_treated')

    # Check if the cumulative gain of UpLift Random Forest is higher than
    # random
    assert cumgain['uplift_tree'].sum() > cumgain['Random'].sum()

"""
    synthetic_preds_df = synthetic_preds.copy()
    generated_data = synthetic_preds_df.pop(KEY_GENERATED_DATA)
    synthetic_preds_df = pd.DataFrame(synthetic_preds_df)
    synthetic_preds_df = synthetic_preds_df.drop(drop_learners, axis=1)

    synthetic_preds_df['y'] = generated_data[outcome_col]
    synthetic_preds_df['w'] = generated_data[treatment_col]
    if treatment_effect_col in generated_data.keys():
        synthetic_preds_df['tau'] = generated_data[treatment_effect_col]

    assert ((outcome_col in synthetic_preds_df.columns) and
            (treatment_col in synthetic_preds_df.columns) or
            treatment_effect_col in synthetic_preds_df.columns)

    cumlift = get_cumgain(synthetic_preds_df, outcome_col='y', treatment_col='w',
                          treatment_effect_col='tau')
    auuc_df = pd.DataFrame(cumlift.columns)
    auuc_df.columns = ['Learner']
    auuc_df['cum_gain_auuc'] = [auc(cumlift.index.values/100, cumlift[learner].values) for learner in cumlift.columns]
    auuc_df = auuc_df.sort_values('cum_gain_auuc', ascending=False)

    if plot:
        plot_gain(synthetic_preds_df, outcome_col=outcome_col,
                  treatment_col=treatment_col, treatment_effect_col=treatment_effect_col)

    return auuc_df