def test_customer_lifetime_value_with_bgf(self):
from collections import OrderedDict
ggf = estimation.GammaGammaFitter()
ggf.params_ = OrderedDict({'p': 6.25, 'q': 3.74, 'v': 15.44})
bgf = estimation.BetaGeoFitter()
bgf.fit(cdnow_customers_with_monetary_value['frequency'],
cdnow_customers_with_monetary_value['recency'],
cdnow_customers_with_monetary_value['T'],
iterative_fitting=3)
ggf_clv = ggf.customer_lifetime_value(
bgf, cdnow_customers_with_monetary_value['frequency'],
cdnow_customers_with_monetary_value['recency'],
cdnow_customers_with_monetary_value['T'],
cdnow_customers_with_monetary_value['monetary_value'])
utils_clv = utils.customer_lifetime_value(
bgf, cdnow_customers_with_monetary_value['frequency'],
cdnow_customers_with_monetary_value['recency'],
cdnow_customers_with_monetary_value['T'],
ggf.conditional_expected_average_profit(
cdnow_customers_with_monetary_value['frequency'],
cdnow_customers_with_monetary_value['monetary_value']))
npt.assert_equal(ggf_clv.values, utils_clv.values)
def test_params_out_is_close_to_Hardie_paper(self):
ggf = estimation.GammaGammaFitter()
ggf.fit(
cdnow_customers_with_monetary_value['frequency'],
cdnow_customers_with_monetary_value['monetary_value'],
iterative_fitting=3
)
expected = np.array([6.25, 3.74, 15.44])
npt.assert_array_almost_equal(expected, np.array(ggf._unload_params('p', 'q', 'v')), decimal=2)
def test_conditional_expected_average_profit(self):
ggf = estimation.GammaGammaFitter()
ggf.params_ = OrderedDict({'p':6.25, 'q':3.74, 'v':15.44})
summary = cdnow_customers_with_monetary_value.head(10)
estimates = ggf.conditional_expected_average_profit(summary['frequency'], summary['monetary_value'])
expected = np.array([24.65, 18.91, 35.17, 35.17, 35.17, 71.46, 18.91, 35.17, 27.28, 35.17]) # from Hardie spreadsheet http://brucehardie.com/notes/025/
npt.assert_allclose(estimates.values, expected, atol=0.1)
def test_negative_log_likelihood_is_inf_when_q_constraint_true_and_q_lt_one(
self):
frequency = 25
avg_monetary_value = 100
ggf = estimation.GammaGammaFitter()
assert np.isinf(
ggf._negative_log_likelihood([6.25, -3.75, 15.44],
frequency,
avg_monetary_value,
q_constraint=True))
def test_fit_with_index(self, cdnow_customers_with_monetary_value):
returning_cdnow_customers_with_monetary_value = cdnow_customers_with_monetary_value[
cdnow_customers_with_monetary_value['frequency'] > 0]
ggf = estimation.GammaGammaFitter()
index = range(len(returning_cdnow_customers_with_monetary_value), 0,
-1)
ggf.fit(
returning_cdnow_customers_with_monetary_value['frequency'],
returning_cdnow_customers_with_monetary_value['monetary_value'],
iterative_fitting=1,
index=index)
assert (ggf.data.index == index).all() == True
ggf = estimation.GammaGammaFitter()
ggf.fit(
returning_cdnow_customers_with_monetary_value['frequency'],
returning_cdnow_customers_with_monetary_value['monetary_value'],
iterative_fitting=1,
index=None)
assert (ggf.data.index == index).all() == False
def test_params_out_is_close_to_Hardie_paper_with_q_constraint(
self, cdnow_customers_with_monetary_value):
returning_cdnow_customers_with_monetary_value = cdnow_customers_with_monetary_value[
cdnow_customers_with_monetary_value['frequency'] > 0]
ggf = estimation.GammaGammaFitter()
ggf.fit(
returning_cdnow_customers_with_monetary_value['frequency'],
returning_cdnow_customers_with_monetary_value['monetary_value'],
iterative_fitting=3,
q_constraint=True)
expected = np.array([6.25, 3.74, 15.44])
npt.assert_array_almost_equal(expected,
np.array(
ggf._unload_params('p', 'q', 'v')),
decimal=2)