def test_purchase_predictions_do_not_differ_much_if_looking_at_hourly_or_daily_frequencies(
self):
transaction_data = load_transaction_data(parse_dates=['date'])
daily_summary = utils.summary_data_from_transaction_data(
transaction_data,
'id',
'date',
observation_period_end=max(transaction_data.date),
freq='D')
hourly_summary = utils.summary_data_from_transaction_data(
transaction_data,
'id',
'date',
observation_period_end=max(transaction_data.date),
freq='h')
thirty_days = 30
hours_in_day = 24
mbfg = estimation.ModifiedBetaGeoFitter()
np.random.seed(0)
mbfg.fit(daily_summary['frequency'], daily_summary['recency'],
daily_summary['T'])
thirty_day_prediction_from_daily_data = mbfg.expected_number_of_purchases_up_to_time(
thirty_days)
np.random.seed(0)
mbfg.fit(hourly_summary['frequency'], hourly_summary['recency'],
hourly_summary['T'])
thirty_day_prediction_from_hourly_data = mbfg.expected_number_of_purchases_up_to_time(
thirty_days * hours_in_day)
npt.assert_almost_equal(thirty_day_prediction_from_daily_data,
thirty_day_prediction_from_hourly_data)
def test_plot_calibration_purchases_vs_holdout_purchases_time_since_last_purchase(self):
transaction_data = load_transaction_data()
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'])
plt.figure()
plotting.plot_calibration_purchases_vs_holdout_purchases(bgf, summary, kind='time_since_last_purchase')
return plt.gcf()
def test_plot_calibration_purchases_vs_holdout_purchases_time_since_last_purchase(self):
transaction_data = load_transaction_data()
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"])
plt.figure()
plotting.plot_calibration_purchases_vs_holdout_purchases(bgf, summary, kind="time_since_last_purchase")
return plt.gcf()
def test_plot_calibration_purchases_vs_holdout_purchases_time_since_last_purchase(self):
transaction_data = load_transaction_data()
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'])
plt.figure()
plotting.plot_calibration_purchases_vs_holdout_purchases(bgf, summary, kind='time_since_last_purchase')
return plt.gcf()
def test_plot_customer_alive_history(self):
plt.figure()
transaction_data = load_transaction_data()
# yes I know this is using the wrong data, but I'm testing plotting here.
id = 35
days_since_birth = 200
sp_trans = transaction_data.ix[transaction_data['id'] == id]
plotting.plot_history_alive(bgf, days_since_birth, sp_trans, 'date')
return plt.gcf()
def test_plot_customer_alive_history(self):
plt.figure()
transaction_data = load_transaction_data()
# yes I know this is using the wrong data, but I'm testing plotting here.
id = 35
days_since_birth = 200
sp_trans = transaction_data.ix[transaction_data['id'] == id]
plotting.plot_history_alive(bgf, days_since_birth, sp_trans, 'date')
return plt.gcf()
def test_plot_calibration_purchases_vs_holdout_purchases(self):
from matplotlib import pyplot as plt
transaction_data = load_transaction_data()
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'])
plt.figure()
plotting.plot_calibration_purchases_vs_holdout_purchases(bgf, summary)
plt.show()
def test_plot_customer_alive_history(self):
from matplotlib import pyplot as plt
transaction_data = load_transaction_data()
# yes I know this is using the wrong data, but I'm testing plotting here.
id = 35
days_since_birth = 200
sp_trans = transaction_data.ix[transaction_data['id'] == id]
plt.figure()
plotting.plot_history_alive(bgf, days_since_birth, sp_trans, 'date')
plt.show()
def test_plot_customer_alive_history(self):
from matplotlib import pyplot as plt
transaction_data = load_transaction_data()
# yes I know this is using the wrong data, but I'm testing plotting here.
id = 35
days_since_birth = 200
sp_trans = transaction_data.ix[transaction_data['id'] == id]
plt.figure()
plotting.plot_history_alive(bgf, days_since_birth, sp_trans, 'date')
plt.show()
def test_plot_calibration_purchases_vs_holdout_purchases(self):
from matplotlib import pyplot as plt
transaction_data = load_transaction_data()
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'])
plt.figure()
plotting.plot_calibration_purchases_vs_holdout_purchases(bgf, summary)
plt.show()
def test_plot_customer_alive_history(self, bgf):
from datetime import datetime, timedelta
x_expected = np.arange(datetime(2014, 6, 30), datetime(2015, 1, 17),
timedelta(days=1))
y_expected = [
1.0, 1.0, 1.0, 0.75, 0.72, 0.69, 0.67, 0.64, 0.62, 0.59, 0.57,
0.55, 0.81, 0.79, 0.77, 0.75, 0.85, 0.87, 0.85, 0.82, 0.8, 0.89,
0.87, 0.84, 0.82, 0.79, 0.76, 0.73, 0.69, 0.66, 0.62, 0.59, 0.9,
0.89, 0.87, 0.85, 0.83, 0.81, 0.91, 0.9, 0.88, 0.87, 0.85, 0.83,
0.8, 0.78, 0.75, 0.73, 0.7, 0.67, 0.64, 0.61, 0.57, 0.54, 0.51,
0.48, 0.45, 0.42, 0.39, 0.37, 0.34, 0.32, 0.29, 0.27, 0.25, 0.23,
0.21, 0.2, 0.18, 0.92, 0.93, 0.92, 0.91, 0.9, 0.89, 0.88, 0.87,
0.85, 0.84, 0.82, 0.8, 0.79, 0.77, 0.75, 0.73, 0.71, 0.94, 0.93,
0.92, 0.91, 0.9, 0.89, 0.88, 0.87, 0.86, 0.85, 0.83, 0.82, 0.8,
0.78, 0.77, 0.94, 0.93, 0.93, 0.92, 0.91, 0.9, 0.89, 0.94, 0.94,
0.93, 0.92, 0.92, 0.91, 0.9, 0.89, 0.88, 0.87, 0.95, 0.94, 0.94,
0.95, 0.95, 0.94, 0.93, 0.95, 0.95, 0.94, 0.94, 0.93, 0.92, 0.91,
0.9, 0.89, 0.88, 0.87, 0.86, 0.96, 0.95, 0.95, 0.94, 0.93, 0.93,
0.92, 0.91, 0.9, 0.89, 0.88, 0.87, 0.85, 0.84, 0.83, 0.81, 0.79,
0.78, 0.76, 0.74, 0.72, 0.7, 0.68, 0.66, 0.63, 0.61, 0.59, 0.56,
0.54, 0.52, 0.49, 0.47, 0.44, 0.42, 0.4, 0.38, 0.36, 0.34, 0.32,
0.3, 0.28, 0.26, 0.24, 0.23, 0.21, 0.2, 0.18, 0.17, 0.16, 0.15,
0.14, 0.13, 0.12, 0.11, 0.1, 0.1, 0.09, 0.08, 0.08, 0.07, 0.07,
0.06, 0.06, 0.05
]
labels = ['P_alive', 'purchases']
transaction_data = load_transaction_data()
# yes I know this is using the wrong data, but I'm testing plotting here.
id_user = 35
days_since_birth = 200
sp_trans = transaction_data.loc[transaction_data['id'] == id_user]
ax = plotting.plot_history_alive(bgf, days_since_birth, sp_trans,
'date')
x, y = ax.lines[0].get_data()
legend = ax.legend_
assert_allclose([np.round(e, 5) for e in y], y_expected,
atol=0.01) # y has some weird array shapes
assert_array_equal(x, x_expected)
assert_array_equal([e.get_text() for e in legend.get_texts()], labels)
assert_equal(ax.title.get_text(), "History of P_alive")
assert_equal(ax.xaxis.get_label().get_text(), "")
assert_equal(ax.yaxis.get_label().get_text(), "P_alive")
plt.close()
def test_purchase_predictions_do_not_differ_much_if_looking_at_hourly_or_daily_frequencies(self):
transaction_data = load_transaction_data(parse_dates=['date'])
daily_summary = utils.summary_data_from_transaction_data(transaction_data, 'id', 'date', observation_period_end=max(transaction_data.date), freq='D')
hourly_summary = utils.summary_data_from_transaction_data(transaction_data, 'id', 'date', observation_period_end=max(transaction_data.date), freq='h')
thirty_days = 30
hours_in_day = 24
mbfg = estimation.ModifiedBetaGeoFitter()
np.random.seed(0)
mbfg.fit(daily_summary['frequency'], daily_summary['recency'], daily_summary['T'])
thirty_day_prediction_from_daily_data = mbfg.expected_number_of_purchases_up_to_time(thirty_days)
np.random.seed(0)
mbfg.fit(hourly_summary['frequency'], hourly_summary['recency'], hourly_summary['T'])
thirty_day_prediction_from_hourly_data = mbfg.expected_number_of_purchases_up_to_time(thirty_days * hours_in_day)
npt.assert_almost_equal(thirty_day_prediction_from_daily_data, thirty_day_prediction_from_hourly_data)
def transaction_data():
return load_transaction_data()
#plt.show()
#from lifetimes.plotting import plot_probability_alive_matrix
#plot_probability_alive_matrix(bgf)
#plt.show()
t = 1
data['predicted_purchases'] = data.apply(lambda r: bgf.conditional_expected_number_of_purchases_up_to_time(t, r['frequency'], r['recency'], r['T']), axis = 1)
print data.sort('predicted_purchases').tail(5)
from lifetimes.datasets import load_transaction_data
from lifetimes.utils import summary_data_from_transaction_data
transaction_data = load_transaction_data()
print transaction_data.head()
print type(transaction_data)
print transaction_data.columns
print data.columns
print data.head()
t = 10
data['predicted_purchases'] = data.apply(lambda r: bgf.conditional_expected_number_of_purchases_up_to_time(t, r['frequency'], r['recency'], r['T']), axis = 1)
print data
from pandas import DataFrame
d = [{'id': 1, 'R':23, 'F':12, 'M':12.5}, {'id': 2,'R':43, 'F':1, 'M':120.5}, {'id': 3,'R':203, 'F':2, 'M':19.5}]
test = DataFrame(d)
print test
