def test_customer_lifetime_value_with_known_values(fitted_bg):
"""
>>> print fitted_bg
<lifetimes.BetaGeoFitter: fitted with 5000 subjects, r: 0.16, alpha: 1.86, a: 1.85, b: 3.18>
>>> t = fitted_bg.data.head()
>>> t
frequency recency T
0 0 0 298
1 0 0 224
2 6 142 292
3 0 0 147
4 2 9 183
>>> print fitted_bg.predict(30, t['frequency'], t['recency'], t['T'])
0 0.016053
1 0.021171
2 0.030461
3 0.031686
4 0.001607
dtype: float64
"""
t = fitted_bg.data.head()
expected = np.array([0.016053, 0.021171, 0.030461, 0.031686, 0.001607])
# discount_rate=0 means the clv will be the same as the predicted
clv_d0 = utils._customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1, 1, 1, 1, 1]), time=1, discount_rate=0.)
assert_almost_equal(clv_d0.values, expected, decimal=5)
# discount_rate=1 means the clv will halve over a period
clv_d1 = utils._customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1, 1, 1, 1, 1]), time=1, discount_rate=1.)
assert_almost_equal(clv_d1.values, expected / 2., decimal=5)
# time=2, discount_rate=0 means the clv will be twice the initial
clv_t2_d0 = utils._customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1, 1, 1, 1, 1]), time=2, discount_rate=0)
assert_allclose(clv_t2_d0.values, expected * 2., rtol=0.1)
# time=2, discount_rate=1 means the clv will be twice the initial
clv_t2_d1 = utils._customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1, 1, 1, 1, 1]), time=2, discount_rate=1.)
assert_allclose(clv_t2_d1.values, expected / 2. + expected / 4., rtol=0.1)
def test_customer_lifetime_value_with_bgf(
self, cdnow_customers_with_monetary_value):
ggf = lt.GammaGammaFitter()
ggf.params_ = pd.Series({"p": 6.25, "q": 3.74, "v": 15.44})
bgf = lt.BetaGeoFitter()
bgf.fit(
cdnow_customers_with_monetary_value["frequency"],
cdnow_customers_with_monetary_value["recency"],
cdnow_customers_with_monetary_value["T"],
)
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)
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"],
freq="H",
)
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"]),
freq="H",
)
npt.assert_equal(ggf_clv.values, utils_clv.values)
def test_customer_lifetime_value_with_bgf(self):
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)