How to use the lifetimes.utils.calibration_and_holdout_data function in Lifetimes

def test_plot_calibration_purchases_vs_holdout_purchases(self, transaction_data, bgf):
        holdout_expected = [0.161, 0.233, 0.348, 0.544, 0.710, 0.704, 1.606]
        predictions_expected = [0.270, 0.294, 0.402, 0.422, 0.706, 0.809, 1.019]
        labels = ['frequency_holdout', 'model_predictions']

        summary = utils.calibration_and_holdout_data(transaction_data, 'id', 'date', '2014-09-01', '2014-12-31')
        bgf.fit(summary['frequency_cal'], summary['recency_cal'], summary['T_cal'])

        ax = plotting.plot_calibration_purchases_vs_holdout_purchases(bgf, summary)

        lines = ax.lines
        legend = ax.legend_
        holdout = lines[0].get_data()[1]
        predictions = lines[1].get_data()[1]

        assert_allclose(holdout, holdout_expected, atol=0.01)
        assert_allclose(predictions, predictions_expected, atol=0.01)
        assert_array_equal([e.get_text() for e in legend.get_texts()], labels)
        assert_equal(ax.title.get_text(), "Actual Purchases in Holdout Period vs Predicted Purchases")
        assert_equal(ax.xaxis.get_label().get_text(), "Purchases in calibration period")
        assert_equal(ax.yaxis.get_label().get_text(), "Average of Purchases in Holdout Period")
        plt.close()

def test_calibration_and_holdout_data(large_transaction_level_data):
    today = '2015-02-07'
    calibration_end = '2015-02-01'
    actual = utils.calibration_and_holdout_data(large_transaction_level_data, 'id', 'date', calibration_end, observation_period_end=today)
    assert actual.loc[1]['frequency_holdout'] == 1
    assert actual.loc[2]['frequency_holdout'] == 0

    with pytest.raises(KeyError):
        actual.loc[6]

[1, '2015-01-01'],
        [1, '2015-02-06'],  # excluded from both holdout and calibration
        [2, '2015-01-01'],
        [3, '2015-01-01'],
        [3, '2015-01-02'],
        [3, '2015-01-05'],
        [4, '2015-01-16'],
        [4, '2015-02-02'],
        [4, '2015-02-05'],  # excluded from both holdout and calibration
        [5, '2015-01-16'],
        [5, '2015-01-17'],
        [5, '2015-01-18'],
        [6, '2015-02-02'],
    ]
    transactions = pd.DataFrame(d, columns=['id', 'date'])
    actual = utils.calibration_and_holdout_data(transactions, 'id', 'date', calibration_period_end='2015-02-01', observation_period_end='2015-02-04')
    assert actual['frequency_holdout'].loc[1] == 0
    assert actual['frequency_holdout'].loc[4] == 1

def test_calibration_and_holdout_data_with_monetary_value(large_transaction_level_data_with_monetary_value):
    today = '2015-02-07'
    calibration_end = '2015-02-01'
    actual = utils.calibration_and_holdout_data(large_transaction_level_data_with_monetary_value,
                                                'id',
                                                'date',
                                                calibration_end,
                                                observation_period_end=today,
                                                monetary_value_col='monetary_value')
    assert (actual['monetary_value_cal'] == [0, 0, 3, 0, 4.5]).all()
    assert (actual['monetary_value_holdout'] == [2, 0, 0, 3, 0]).all()

def test_calibration_and_holdout_data_works_with_specific_frequency(large_transaction_level_data):
    today = '2015-02-07'
    calibration_end = '2015-02-01'
    actual = utils.calibration_and_holdout_data(large_transaction_level_data, 'id', 'date', calibration_end, observation_period_end=today, freq='W')
    expected_cols = ['id', 'frequency_cal', 'recency_cal', 'T_cal', 'frequency_holdout', 'duration_holdout']
    expected = pd.DataFrame([[1, 0., 0., 4., 1, 1],
                             [2, 0., 0., 4., 0, 1],
                             [3, 1., 1., 4., 0, 1],
                             [4, 0., 0., 2., 1, 1],
                             [5, 0., 0., 2., 0, 1]], columns=expected_cols).set_index('id')
    assert_frame_equal(actual, expected, check_dtype=False)