def donations(self):
return load_donations()
import pytest
import lifetimes.estimation as estimation
import lifetimes.utils as utils
from lifetimes.datasets import load_cdnow, load_summary_data_with_monetary_value, load_donations,\
load_transaction_data
from lifetimes.generate_data import modified_beta_geometric_nbd_model
@pytest.fixture
def cdnow_customers():
return load_cdnow()
cdnow_customers_with_monetary_value = load_summary_data_with_monetary_value()
donations = load_donations()
class TestBaseFitter():
def test_repr(self):
base_fitter = estimation.BaseFitter()
assert repr(base_fitter) == '<lifetimes.BaseFitter>'
base_fitter.params_ = dict(x=12.3, y=42)
base_fitter.data = np.array([1, 2, 3])
assert repr(
base_fitter
) == '<lifetimes.BaseFitter: fitted with 3 subjects, x: 12.30, y: 42.00>'
def test_unload_params(self):
base_fitter = estimation.BaseFitter()
with pytest.raises(ValueError):
from __future__ import print_function
import numpy as np
import pandas as pd
import numpy.testing as npt
import lifetimes.estimation as estimation
import lifetimes.utils as utils
from lifetimes.datasets import load_cdnow, load_summary_data_with_monetary_value, load_donations
cdnow_customers = load_cdnow()
cdnow_customers_with_monetary_value = load_summary_data_with_monetary_value()
donations = load_donations()
class TestBetaGeoBetaBinomFitter():
def test_params_out_is_close_to_Hardie_paper(self):
bbtf = estimation.BetaGeoBetaBinomFitter()
bbtf.fit(
donations['frequency'],
donations['recency'],
donations['n'],
donations['n_custs'],
)
expected = np.array([1.204, 0.750, 0.657, 2.783])
npt.assert_array_almost_equal(expected, np.array(bbtf._unload_params('alpha','beta','gamma','delta')),
decimal=2)
def test_prob_alive_is_close_to_Hardie_paper_table_6(self):
"""Table 6: P(Alive in 2002) as a Function of Recency and Frequency"""
