def plot_expected_repeat_purchases(model, **kwargs):
from matplotlib import pyplot as plt
ax = kwargs.pop('ax', None) or plt.subplot(111)
label = kwargs.pop('label', None)
if plt.matplotlib.__version__ >= "1.5":
color_cycle = ax._get_lines.prop_cycler
color = coalesce(kwargs.pop('c', None), kwargs.pop('color', None), next(color_cycle)['color'])
else:
color_cycle = ax._get_lines.color_cycle
color = coalesce(kwargs.pop('c', None), kwargs.pop('color', None), next(color_cycle))
max_T = model.data['T'].max()
times = np.linspace(0, max_T, 100)
ax = plt.plot(times, model.expected_number_of_purchases_up_to_time(times), color=color, label=label, **kwargs)
times = np.linspace(max_T, 1.5 * max_T, 100)
plt.plot(times, model.expected_number_of_purchases_up_to_time(times), color=color, ls='--', **kwargs)
plt.title('Expected Number of Repeat Purchases per Customer')
plt.xlabel('Time Since First Purchase')
plt.legend(loc='lower right')
return ax
def plot_expected_repeat_purchases(model, **kwargs):
from matplotlib import pyplot as plt
ax = kwargs.pop('ax', None) or plt.subplot(111)
label = kwargs.pop('label', None)
if plt.matplotlib.__version__ >= "1.5":
color_cycle = ax._get_lines.prop_cycler
color = coalesce(kwargs.pop('c', None), kwargs.pop('color', None), next(color_cycle)['color'])
else:
color_cycle = ax._get_lines.color_cycle
color = coalesce(kwargs.pop('c', None), kwargs.pop('color', None), next(color_cycle))
max_T = model.data['T'].max()
times = np.linspace(0, max_T, 100)
ax = plt.plot(times, model.expected_number_of_sessions_up_to_time(times), color=color, label=label, **kwargs)
times = np.linspace(max_T, 1.5 * max_T, 100)
plt.plot(times, model.expected_number_of_sessions_up_to_time(times), color=color, ls='--', **kwargs)
plt.title('Expected Number of Repeat Purchases per Customer')
plt.xlabel('Time Since First Purchase')
plt.legend(loc='lower right')
return ax
def plot_expected_repeat_purchases(
model,
title='Expected Number of Repeat Purchases per Customer',
xlabel='Time Since First Purchase',
**kwargs):
"""
Plot expected repeat purchases on calibration period .
Parameters:
model: a fitted lifetimes model.
title: figure title
xlabel: figure xlabel
ylabel: figure ylabel
kwargs: passed into the matplotlib.pyplot.plot command.
"""
from matplotlib import pyplot as plt
ax = kwargs.pop('ax', None) or plt.subplot(111)
label = kwargs.pop('label', None)
if plt.matplotlib.__version__ >= "1.5":
color_cycle = ax._get_lines.prop_cycler
color = coalesce(kwargs.pop('c', None), kwargs.pop('color', None),
next(color_cycle)['color'])
else:
color_cycle = ax._get_lines.color_cycle
color = coalesce(kwargs.pop('c', None), kwargs.pop('color', None),
next(color_cycle))
max_T = model.data['T'].max()
times = np.linspace(0, max_T, 100)
ax = plt.plot(times,
model.expected_number_of_purchases_up_to_time(times),
color=color,
label=label,
**kwargs)
times = np.linspace(max_T, 1.5 * max_T, 100)
plt.plot(times,
model.expected_number_of_purchases_up_to_time(times),
color=color,
ls='--',
**kwargs)
plt.title(title)
plt.xlabel(xlabel)
plt.legend(loc='lower right')
return ax