def setup_models(years):
release_dates = [
s2d("2018-05-09"),
s2d("2018-06-26"),
s2d("2018-09-05"),
s2d("2018-10-23"),
s2d("2018-12-11"),
s2d("2019-01-29"),
s2d("2019-03-19"),
s2d("2019-05-21"),
s2d("2019-07-09"),
s2d("2019-09-03"),
s2d("2019-10-22"),
s2d("2019-12-03"),
s2d("2020-01-07"),
s2d("2020-02-11"),
s2d("2020-03-10"),
s2d("2020-04-07"),
s2d("2020-05-05"),
s2d("2020-06-02"),
s2d("2020-06-30"),
s2d("2020-07-28"),
s2d("2020-08-25"),
s2d("2020-09-22"),
s2d("2020-10-20"),
s2d("2020-11-17"),
s2d("2020-12-15"),
]
release_cycles_69_day = pd.DataFrame({
"ds": release_dates,
"holiday": "release",
"lower_window": 0,
"upper_window": 69
})
release_cycles_14_day = pd.DataFrame({
"ds": release_dates,
"holiday": "release",
"lower_window": 0,
"upper_window": 14
})
monitor_pushes = pd.DataFrame({
"ds": [s2d("2019-11-25")],
"holiday": "monitor_push",
"lower_window": 0,
"upper_window": 30
})
models = {}
models["Desktop Global MAU"] = Prophet(yearly_seasonality=20,
changepoint_range=0.7,
seasonality_mode='multiplicative',
changepoint_prior_scale=0.008,
seasonality_prior_scale=0.25,
holidays=get_holidays(years))
models["Desktop Tier1 MAU"] = Prophet(yearly_seasonality=20,
changepoint_range=0.7,
seasonality_mode='multiplicative',
changepoint_prior_scale=0.008,
seasonality_prior_scale=0.25,
holidays=get_holidays(years))
models["Mobile Global MAU"] = Prophet(
yearly_seasonality=20,
changepoint_range=0.7,
seasonality_mode='multiplicative',
changepoint_prior_scale=0.006,
seasonality_prior_scale=0.0002,
)
models["Mobile Tier1 MAU"] = Prophet(
yearly_seasonality=20,
changepoint_range=0.7,
seasonality_mode='multiplicative',
changepoint_prior_scale=0.006,
seasonality_prior_scale=0.0002,
)
models["FxA Global MAU"] = Prophet(
changepoint_range=0.9,
changepoint_prior_scale=0.02,
seasonality_prior_scale=0.00002,
holidays=pd.concat([release_cycles_69_day, monitor_pushes],
ignore_index=True),
seasonality_mode='multiplicative',
yearly_seasonality=10,
)
models["FxA Tier1 MAU"] = Prophet(
changepoint_range=0.9,
changepoint_prior_scale=0.02,
seasonality_prior_scale=0.00002,
holidays=pd.concat([release_cycles_69_day, monitor_pushes],
ignore_index=True),
seasonality_mode='multiplicative',
yearly_seasonality=10,
)
models["Fennec Android Global MAU"] = Prophet(
changepoint_prior_scale=0.0005,
seasonality_prior_scale=0.001,
seasonality_mode='multiplicative')
models["Focus iOS Global MAU"] = Prophet(changepoint_prior_scale=0.0005)
models["Focus Android Global MAU"] = Prophet(changepoint_prior_scale=0.005)
models["Fennec iOS Global MAU"] = Prophet(
changepoint_prior_scale=0.005,
seasonality_prior_scale=0.001,
seasonality_mode='multiplicative')
models["Fenix Global MAU"] = Prophet(changepoint_prior_scale=0.0005)
models["Firefox Lite Global MAU"] = Prophet(changepoint_prior_scale=0.0005)
models["FirefoxForFireTV Global MAU"] = Prophet(
changepoint_prior_scale=0.0005,
seasonality_prior_scale=0.005,
seasonality_mode='multiplicative',
yearly_seasonality=True)
models["FirefoxConnect Global MAU"] = Prophet(
changepoint_prior_scale=0.0005)
models["Lockwise Android Global MAU"] = Prophet( # Not validated
changepoint_range=0.9,
changepoint_prior_scale=0.007,
seasonality_mode='multiplicative',
)
models["Fennec Android Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005,
seasonality_prior_scale=0.001,
seasonality_mode='multiplicative')
models["Focus iOS Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005)
models["Focus Android Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.005)
models["Fennec iOS Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.005,
seasonality_prior_scale=0.001,
seasonality_mode='multiplicative')
models["Fenix Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005)
models["Firefox Lite Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005)
models["FirefoxForFireTV Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005,
seasonality_prior_scale=0.005,
seasonality_mode='multiplicative',
yearly_seasonality=True)
models["FirefoxConnect Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005)
models["Lockwise Android Tier1 MAU"] = Prophet(
changepoint_range=0.9,
changepoint_prior_scale=0.007,
seasonality_mode='multiplicative',
)
models["FxA Registration with Subscription Tier1 DAU"] = Prophet(
seasonality_mode='additive',
changepoint_prior_scale=0.015,
holidays=release_cycles_14_day,
)
return models
def setup_models(years):
release_dates = [
s2d("2018-05-09"),
s2d("2018-06-26"),
s2d("2018-09-05"),
s2d("2018-10-23"),
s2d("2018-12-11"),
s2d("2019-01-29"),
s2d("2019-03-19"),
s2d("2019-05-21"),
s2d("2019-07-09"),
s2d("2019-09-03"),
s2d("2019-10-22"),
s2d("2019-12-03"),
s2d("2020-01-07"),
s2d("2020-02-11"),
s2d("2020-03-10"),
s2d("2020-04-07"),
s2d("2020-05-05"),
s2d("2020-06-02"),
s2d("2020-06-30"),
s2d("2020-07-28"),
s2d("2020-08-25"),
s2d("2020-09-22"),
s2d("2020-10-20"),
s2d("2020-11-17"),
s2d("2020-12-15"),
]
release_cycles_69_day = pd.DataFrame({
"ds": release_dates,
"holiday": "release",
"lower_window": 0,
"upper_window": 69
})
release_cycles_14_day = pd.DataFrame({
"ds": release_dates,
"holiday": "release",
"lower_window": 0,
"upper_window": 14
})
monitor_pushes = pd.DataFrame({
"ds": [s2d("2019-11-25")],
"holiday": "monitor_push",
"lower_window": 0,
"upper_window": 30
})
models = {}
models["Desktop Global MAU"] = Prophet(yearly_seasonality=20,
changepoint_range=0.7,
seasonality_mode='multiplicative',
changepoint_prior_scale=0.008,
seasonality_prior_scale=0.25,
holidays=get_holidays(years))
models["Desktop Tier1 MAU"] = Prophet(yearly_seasonality=20,
changepoint_range=0.7,
seasonality_mode='multiplicative',
changepoint_prior_scale=0.008,
seasonality_prior_scale=0.25,
holidays=get_holidays(years))
models["Mobile Global MAU"] = Prophet(
changepoint_range=0.9,
changepoint_prior_scale=0.03
# change in Nov 2020 for better accuracy in forecasting w.r.t. Fennec to Fenix migration,
# details see https://colab.research.google.com/drive/10vfzTOjiwnXODh1zwUtPVmtR8VqAKAtr#scrollTo=GMT0vF34251n
)
models["Mobile Tier1 MAU"] = Prophet(
changepoint_range=0.9,
changepoint_prior_scale=0.03
# change in Nov 2020 for better accuracy in forecasting w.r.t. Fennec to Fenix migration,
# details see https://colab.research.google.com/drive/10vfzTOjiwnXODh1zwUtPVmtR8VqAKAtr#scrollTo=GMT0vF34251n
)
models["FxA Global MAU"] = Prophet(
changepoint_range=0.9,
changepoint_prior_scale=0.02,
seasonality_prior_scale=0.00002,
holidays=pd.concat([release_cycles_69_day, monitor_pushes],
ignore_index=True),
seasonality_mode='multiplicative',
yearly_seasonality=10,
)
models["FxA Tier1 MAU"] = Prophet(
changepoint_range=0.9,
changepoint_prior_scale=0.02,
seasonality_prior_scale=0.00002,
holidays=pd.concat([release_cycles_69_day, monitor_pushes],
ignore_index=True),
seasonality_mode='multiplicative',
yearly_seasonality=10,
)
models["Fennec Global MAU"] = Prophet(changepoint_prior_scale=0.0005,
seasonality_prior_scale=0.001,
seasonality_mode='multiplicative')
models["Focus iOS Global MAU"] = Prophet(changepoint_prior_scale=0.0005)
models["Focus Android Global MAU"] = Prophet(changepoint_prior_scale=0.005)
models["Firefox iOS Global MAU"] = Prophet(
changepoint_prior_scale=0.005,
seasonality_prior_scale=0.001,
seasonality_mode='multiplicative')
models["Fenix Global MAU"] = Prophet(changepoint_prior_scale=0.0005)
models["Firefox Lite Global MAU"] = Prophet(changepoint_prior_scale=0.0005)
models["Firefox Echo Global MAU"] = Prophet(changepoint_prior_scale=0.0005)
models["Lockwise Android Global MAU"] = Prophet( # Not validated
changepoint_range=0.9,
changepoint_prior_scale=0.007,
seasonality_mode='multiplicative',
)
models["Fennec Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005,
seasonality_prior_scale=0.001,
seasonality_mode='multiplicative')
models["Focus iOS Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005)
models["Focus Android Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.005)
models["Firefox iOS Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.005,
seasonality_prior_scale=0.001,
seasonality_mode='multiplicative')
models["Fenix Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005)
models["Firefox Lite Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005)
models["Firefox Echo Tier1 MAU"] = Prophet( # Not validated
changepoint_prior_scale=0.0005)
models["Lockwise Android Tier1 MAU"] = Prophet(
changepoint_range=0.9,
changepoint_prior_scale=0.007,
seasonality_mode='multiplicative',
)
models["FxA Registration with Subscription Tier1 DAU"] = Prophet(
seasonality_mode='additive',
changepoint_prior_scale=0.015,
holidays=release_cycles_14_day,
)
return models
def data_filter(data, product):
start_dates = {
"Desktop Global MAU": s2d('2016-04-08'),
"Desktop Tier1 MAU": s2d('2016-04-08'),
"Mobile Global MAU": s2d('2017-01-30'),
"Mobile Tier1 MAU": s2d('2017-01-30'),
"FxA Global MAU": s2d('2018-03-20'),
"FxA Tier1 MAU": s2d('2018-03-20'),
"Fennec Android Global MAU": s2d('2017-03-04'),
"Focus iOS Global MAU": s2d('2017-12-06'),
"Focus Android Global MAU": s2d('2017-07-17'),
"Fennec iOS Global MAU": s2d('2017-03-03'),
"Fenix Global MAU": s2d('2019-07-03'),
"Firefox Lite Global MAU": s2d('2017-03-04'),
"FirefoxForFireTV Global MAU": s2d('2018-02-04'),
"FirefoxConnect Global MAU": s2d('2018-10-10'),
"Lockwise Android Global MAU": s2d('2017-01-30'), # Not validated
"Lockwise Android Tier1 MAU": s2d('2017-01-30'),
"FxA Registration with Subscription Tier1 DAU": s2d('2019-06-04'),
}
anomalyDates = {
"Desktop Global MAU": [s2d('2019-05-16'),
s2d('2019-06-07')],
"Desktop Tier1 MAU": [s2d('2019-05-16'),
s2d('2019-06-07')],
"Focus Android Global MAU": [s2d('2018-09-01'),
s2d('2019-03-01')],
"Focus Android Tier1 MAU": [s2d('2018-09-01'),
s2d('2019-03-01')],
"Fennec iOS Global MAU": [s2d('2017-11-08'),
s2d('2017-12-31')],
"Fennec iOS Tier1 MAU": [s2d('2017-11-08'),
s2d('2017-12-31')],
"Mobile Global MAU": [s2d('2017-11-10'),
s2d('2018-03-11')],
"Mobile Tier1 MAU": [s2d('2017-11-10'),
s2d('2018-03-11')],
"FxA Registration with Subscription Tier1 DAU":
[s2d('2019-11-23'), s2d('2019-12-02')],
}
temp = data.copy()
if product in start_dates:
start_date = start_dates[product] # noqa: F841
temp = temp.query("ds >= @start_date")
if product in anomalyDates:
anomalystart_date = anomalyDates[product][0] # noqa: F841
anomalyend_date = anomalyDates[product][1] # noqa: F841
temp = temp.query(
"(ds < @anomalystart_date) | (ds > @anomalyend_date)")
return temp
def data_filter(data, product):
start_dates = {
"Desktop Global MAU": s2d('2016-04-08'),
"Desktop Tier1 MAU": s2d('2016-04-08'),
"Mobile Global MAU": s2d('2017-01-30'),
"Mobile Tier1 MAU": s2d('2017-01-30'),
"FxA Global MAU": s2d('2018-03-20'),
"FxA Tier1 MAU": s2d('2018-03-20'),
"Fennec Global MAU": s2d('2017-03-04'),
"Focus iOS Global MAU": s2d('2017-12-06'),
"Focus Android Global MAU": s2d('2017-07-17'),
"Firefox iOS Global MAU": s2d('2017-03-03'),
"Fenix Global MAU": s2d('2019-07-03'),
"Firefox Lite Global MAU": s2d('2017-03-04'),
"Firefox Echo Global MAU": s2d('2018-10-10'),
"Lockwise Android Global MAU": s2d('2017-01-30'), # Not validated
"Lockwise Android Tier1 MAU": s2d('2017-01-30'),
"FxA Registration with Subscription Tier1 DAU": s2d('2019-06-04'),
}
anomalyDates = {
"Desktop Global MAU": [[s2d('2019-05-16'),
s2d('2019-06-07')]],
"Desktop Tier1 MAU": [[s2d('2019-05-16'),
s2d('2019-06-07')]],
"Focus Android Global MAU": [[s2d('2018-09-01'),
s2d('2019-03-01')]],
"Focus Android Tier1 MAU": [[s2d('2018-09-01'),
s2d('2019-03-01')]],
"Firefox iOS Global MAU": [[s2d('2017-11-08'),
s2d('2017-12-31')]],
"Firefox iOS Tier1 MAU": [[s2d('2017-11-08'),
s2d('2017-12-31')]],
"Mobile Global MAU": [
[s2d('2017-11-10'), s2d('2018-03-11')],
[s2d('2019-12-04'),
s2d('2020-01-27')], # EoY 2019 paid UAC campaign
[s2d('2020-08-01'),
s2d('2020-10-08')], # Fennec-> Fenix transition
],
"Mobile Tier1 MAU": [
[s2d('2017-11-10'), s2d('2018-03-11')],
[s2d('2019-12-04'), s2d('2020-01-27')],
[s2d('2020-08-01'), s2d('2020-10-08')],
],
"FxA Registration with Subscription Tier1 DAU":
[[s2d('2019-11-23'), s2d('2019-12-02')]],
}
temp = data.copy()
if product in start_dates:
start_date = start_dates[product] # noqa: F841
temp = temp.query("ds >= @start_date")
if product in anomalyDates:
for anomalystart_date, anomalyend_date in anomalyDates[product]:
temp = temp.query(
"(ds < @anomalystart_date) | (ds > @anomalyend_date)")
return temp