def test_prophet_hyperparameter_grid_exception():
"""Tests prophet_hyperparameter_grid exceptions"""
# unknown argument
with pytest.raises(ValueError, match=r"Unexpected key\(s\) found"):
model_components = ModelComponentsParam(
seasonality={"unknown_seasonality": ["additive"]})
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = model_components
template.get_hyperparameter_grid()
# regressor must be specified under `add_regressor_dict`, not directly
with pytest.raises(ValueError, match=r"Unexpected key\(s\) found"):
model_components = ModelComponentsParam(
regressors={
"regressor1": {
"prior_scale": 10,
"standardize": True,
"mode": "additive"
},
"regressor2": {
"prior_scale": 15,
"standardize": False,
"mode": "additive"
},
"regressor3": {}
})
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = model_components
template.get_hyperparameter_grid()
def test_prophet_hyperparameter_grid_seasonality_growth(default_holidays):
"""Tests get_hyperparameter_grid for basic seasonality, growth and other default params"""
seasonality = {"yearly_seasonality": [True], "weekly_seasonality": [False]}
growth = {"growth_term": ["linear"]}
model_components = ModelComponentsParam(seasonality=seasonality,
growth=growth)
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = model_components
hyperparameter_grid = template.get_hyperparameter_grid()
# Expected Values
expected_holidays = default_holidays
expected_grid = {
"estimator__growth": ["linear"],
"estimator__seasonality_mode": ["additive"],
"estimator__seasonality_prior_scale": [10.0],
"estimator__yearly_seasonality": [True],
"estimator__weekly_seasonality": [False],
"estimator__daily_seasonality": ["auto"],
"estimator__add_seasonality_dict": [None],
"estimator__holidays": [expected_holidays],
"estimator__holidays_prior_scale": [10.0],
"estimator__changepoint_prior_scale": [0.05],
"estimator__changepoints": [None],
"estimator__n_changepoints": [25],
"estimator__changepoint_range": [0.8],
"estimator__mcmc_samples": [0],
"estimator__uncertainty_samples": [1000],
"estimator__add_regressor_dict": [None]
}
# Assertions
assert_equal(actual=hyperparameter_grid, expected=expected_grid)
def test_prophet_hyperparameter_grid_default():
"""Tests get_hyperparameter_grid and apply_prophet_model_components_defaults"""
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
# both model_components, time_properties are None
hyperparameter_grid = template.get_hyperparameter_grid()
expected_holidays = template.get_prophet_holidays(year_list=list(
range(2015 - 1, 2030 + 2)),
countries="auto",
lower_window=-2,
upper_window=2)
expected_grid = {
"estimator__growth": ["linear"],
"estimator__seasonality_mode": ["additive"],
"estimator__seasonality_prior_scale": [10.0],
"estimator__yearly_seasonality": ["auto"],
"estimator__weekly_seasonality": ["auto"],
"estimator__daily_seasonality": ["auto"],
"estimator__add_seasonality_dict": [None],
"estimator__holidays": [expected_holidays],
"estimator__holidays_prior_scale": [10.0],
"estimator__changepoint_prior_scale": [0.05],
"estimator__changepoints": [None],
"estimator__n_changepoints": [25],
"estimator__changepoint_range": [0.8],
"estimator__mcmc_samples": [0],
"estimator__uncertainty_samples": [1000],
"estimator__add_regressor_dict": [None]
}
assert_equal(actual=hyperparameter_grid, expected=expected_grid)
def test_prophet_hyperparameter_grid_events():
"""Tests get_prophet_hyperparameter_grid for selected Countries" holidays"""
# holiday params
start_year = 2018
end_year = 2022
holiday_pre_num_days = [1]
holiday_post_num_days = [1]
holiday_lookup_countries = ["UnitedStates", "China", "India"]
holidays_prior_scale = [5.0, 10.0, 15.0]
events = {
"holiday_lookup_countries": holiday_lookup_countries,
"holiday_pre_num_days": holiday_pre_num_days,
"holiday_post_num_days": holiday_post_num_days,
"start_year": start_year,
"end_year": end_year,
"holidays_prior_scale": holidays_prior_scale
}
model_components = ModelComponentsParam(events=events)
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = model_components
hyperparameter_grid = template.get_hyperparameter_grid()
# Expected Values
# Holidays df, based on given holidays params
expected_holidays = template.get_prophet_holidays(
year_list=list(range(start_year - 1, end_year + 2)),
countries=holiday_lookup_countries,
lower_window=-holiday_pre_num_days[0],
upper_window=holiday_post_num_days[0])
expected_grid = {
"estimator__growth": ["linear"],
"estimator__seasonality_mode": ["additive"],
"estimator__seasonality_prior_scale": [10.0],
"estimator__yearly_seasonality": ["auto"],
"estimator__weekly_seasonality": ["auto"],
"estimator__daily_seasonality": ["auto"],
"estimator__add_seasonality_dict": [None],
"estimator__holidays": [expected_holidays],
"estimator__holidays_prior_scale": [5.0, 10.0, 15.0],
"estimator__changepoint_prior_scale": [0.05],
"estimator__changepoints": [None],
"estimator__n_changepoints": [25],
"estimator__changepoint_range": [0.8],
"estimator__mcmc_samples": [0],
"estimator__uncertainty_samples": [1000],
"estimator__add_regressor_dict": [None]
}
# Assertions
assert_equal(actual=hyperparameter_grid, expected=expected_grid)
def test_property():
"""Tests properties"""
assert ProphetTemplate().allow_model_template_list is False
assert ProphetTemplate().allow_model_components_param_list is False
template = ProphetTemplate()
assert template.DEFAULT_MODEL_TEMPLATE == "PROPHET"
assert isinstance(template.estimator, ProphetEstimator)
assert template.estimator.coverage == 0.80
assert template.apply_forecast_config_defaults(
).model_template == "PROPHET"
estimator = ProphetEstimator(coverage=0.99)
template = ProphetTemplate(estimator=estimator)
assert template.estimator is estimator
def test_hyperparameter_override(default_holidays):
"""Tests the hyperparameter_override functionality.
Use hyperparameter_override to override parameters and
create multiple sets of grids.
"""
model_components = ModelComponentsParam(
seasonality={
"yearly_seasonality": [True, False],
"weekly_seasonality": False,
},
hyperparameter_override=[{
"input__response__null__max_frac": 0.1,
"estimator__yearly_seasonality": True,
"estimator__growth": ["logistic"],
}, {}])
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = model_components
hyperparameter_grid = template.get_hyperparameter_grid()
expected_grid = {
"estimator__growth": ["linear"],
"estimator__seasonality_mode": ["additive"],
"estimator__seasonality_prior_scale": [10.0],
"estimator__yearly_seasonality": [True, False],
"estimator__weekly_seasonality": [False],
"estimator__daily_seasonality": ["auto"],
"estimator__add_seasonality_dict": [None],
"estimator__holidays": [default_holidays],
"estimator__holidays_prior_scale": [10.0],
"estimator__changepoint_prior_scale": [0.05],
"estimator__changepoints": [None],
"estimator__n_changepoints": [25],
"estimator__changepoint_range": [0.8],
"estimator__mcmc_samples": [0],
"estimator__uncertainty_samples": [1000],
"estimator__add_regressor_dict": [None]
}
updated_grid = expected_grid.copy()
updated_grid["input__response__null__max_frac"] = [0.1]
updated_grid["estimator__yearly_seasonality"] = [True]
updated_grid["estimator__growth"] = ["logistic"]
assert_equal(hyperparameter_grid, [updated_grid, expected_grid])
def test_prophet_hyperparameter_grid_auto_list(default_holidays):
"""Tests `get_prophet_hyperparameter_grid` automatic list conversion
via `dictionaries_values_to_lists`. Holidays are tested separately
because they are not directly passed to ProphetEstimator."""
growth = {"growth_term": "linear"}
seasonality = {
"seasonality_mode": "multiplicative",
"seasonality_prior_scale": 10.0,
"yearly_seasonality": False,
"weekly_seasonality": False,
"daily_seasonality": True,
"add_seasonality_dict": {
"yearly": {
"period": 365.25,
"fourier_order": 20,
"prior_scale": 20.0
},
"quarterly": {
"period": 365.25 / 4,
"fourier_order": 15
},
"weekly": {
"period": 7,
"fourier_order": 35,
"prior_scale": 30.0
}
}
}
changepoints = {
"changepoint_prior_scale": 0.05,
"changepoints": ["2018-10-11", "2018-11-11", "2019-01-17"],
"n_changepoints": 25,
"changepoint_range": 0.8
}
uncertainty = {"mcmc_samples": 0, "uncertainty_samples": 1000}
regressors = {
"add_regressor_dict": {
"reg_col1": {
"prior_scale": 10.0,
"standardize": False,
"mode": "additive"
},
"reg_col2": {
"prior_scale": 20.0,
"standardize": True,
"mode": "multiplicative"
}
}
}
model_components = ModelComponentsParam(growth=growth,
seasonality=seasonality,
changepoints=changepoints,
regressors=regressors,
uncertainty=uncertainty)
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = model_components
hyperparameter_grid = template.get_hyperparameter_grid()
# Expected Values
expected_grid = {
"estimator__growth": ["linear"],
"estimator__seasonality_mode": ["multiplicative"],
"estimator__seasonality_prior_scale": [10.0],
"estimator__yearly_seasonality": [False],
"estimator__weekly_seasonality": [False],
"estimator__daily_seasonality": [True],
"estimator__add_seasonality_dict":
[seasonality["add_seasonality_dict"]],
"estimator__holidays": [default_holidays],
"estimator__holidays_prior_scale": [10.0],
"estimator__changepoint_prior_scale": [0.05],
"estimator__changepoints": [["2018-10-11", "2018-11-11",
"2019-01-17"]],
"estimator__n_changepoints": [25],
"estimator__changepoint_range": [0.8],
"estimator__mcmc_samples": [0],
"estimator__uncertainty_samples": [1000],
"estimator__add_regressor_dict": [{
"reg_col1": {
"prior_scale": 10.0,
"standardize": False,
"mode": "additive"
},
"reg_col2": {
"prior_scale": 20.0,
"standardize": True,
"mode": "multiplicative"
}
}]
}
assert_equal(actual=hyperparameter_grid, expected=expected_grid)
def test_prophet_hyperparameter_grid_changepoints_uncertainty_custom(
default_holidays):
"""Tests get_prophet_hyperparameter_grid for selected
changepoints, regressor, and uncertainty"""
changepoints = {
"changepoint_prior_scale": [0.05, 1.0, 5.0, 10.0, 15.0],
"changepoints": [None, ["2018-10-11", "2018-11-11", "2019-01-17"]],
"n_changepoints": [25, 50, 100],
"changepoint_range": [0.8, 0.9]
}
uncertainty = {
"mcmc_samples": [0, 1000],
"uncertainty_samples": [1000, 2000]
}
regressors = {
"add_regressor_dict": [{
"reg_col1": {
"prior_scale": 10.0,
"standardize": False,
"mode": "additive"
},
"reg_col2": {
"prior_scale": 20.0,
"standardize": True,
"mode": "multiplicative"
}
}, {
"reg_col1": {
"prior_scale": 20.0,
"standardize": True,
"mode": "additive"
},
"reg_col2": {
"prior_scale": 40.0,
"standardize": False,
"mode": "multiplicative"
}
}]
}
model_components = ModelComponentsParam(changepoints=changepoints,
regressors=regressors,
uncertainty=uncertainty)
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = model_components
hyperparameter_grid = template.get_hyperparameter_grid()
# Expected Values
expected_holidays = default_holidays
expected_grid = {
"estimator__growth": ["linear"],
"estimator__seasonality_mode": ["additive"],
"estimator__seasonality_prior_scale": [10.0],
"estimator__yearly_seasonality": ["auto"],
"estimator__weekly_seasonality": ["auto"],
"estimator__daily_seasonality": ["auto"],
"estimator__add_seasonality_dict": [None],
"estimator__holidays": [expected_holidays],
"estimator__holidays_prior_scale": [10.0],
"estimator__changepoint_prior_scale": [0.05, 1.0, 5.0, 10.0, 15.0],
"estimator__changepoints":
[None, ["2018-10-11", "2018-11-11", "2019-01-17"]],
"estimator__n_changepoints": [25, 50, 100],
"estimator__changepoint_range": [0.8, 0.9],
"estimator__mcmc_samples": [0, 1000],
"estimator__uncertainty_samples": [1000, 2000],
"estimator__add_regressor_dict": [{
"reg_col1": {
"prior_scale": 10.0,
"standardize": False,
"mode": "additive"
},
"reg_col2": {
"prior_scale": 20.0,
"standardize": True,
"mode": "multiplicative"
}
}, {
"reg_col1": {
"prior_scale": 20.0,
"standardize": True,
"mode": "additive"
},
"reg_col2": {
"prior_scale": 40.0,
"standardize": False,
"mode": "multiplicative"
}
}]
}
assert_equal(actual=hyperparameter_grid, expected=expected_grid)
def test_prophet_hyperparameter_grid_custom_seasonality(default_holidays):
"""Tests get_prophet_hyperparameter_grid for custom seasonality params, other params being defaults"""
seasonality = {
"seasonality_mode": ["additive", "multiplicative"],
"seasonality_prior_scale": [5.0, 10.0, 15.0],
"yearly_seasonality": [True, False],
"weekly_seasonality": [True, False],
"daily_seasonality": [True, False],
"add_seasonality_dict": [{
"yearly": {
"period": 365.25,
"fourier_order": 20,
"prior_scale": 20.0
},
"quarterly": {
"period": 365.25 / 4,
"fourier_order": 15
},
"weekly": {
"period": 7,
"fourier_order": 35,
"prior_scale": 30.0
}
}, {
"yearly": {
"period": 365.25,
"fourier_order": 10,
"prior_scale": 20.0
},
"quarterly": {
"period": 365.25 / 4,
"fourier_order": 3
},
"weekly": {
"period": 7,
"fourier_order": 5,
"prior_scale": 20.0
}
}, {
"yearly": {
"period": 365.25,
"fourier_order": 10,
"prior_scale": 30.0
},
"quarterly": {
"period": 365.25 / 4,
"fourier_order": 5
},
"weekly": {
"period": 7,
"fourier_order": 15,
"prior_scale": 20.0
}
}, {
"yearly": {
"period": 365.25,
"fourier_order": 15,
"prior_scale": 20.0
},
"quarterly": {
"period": 365.25 / 4,
"fourier_order": 10
},
"weekly": {
"period": 7,
"fourier_order": 25,
"prior_scale": 20.0
}
}]
}
model_components = ModelComponentsParam(seasonality=seasonality)
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = model_components
hyperparameter_grid = template.get_hyperparameter_grid()
# Expected Values
expected_holidays = default_holidays
expected_grid = {
"estimator__growth": ["linear"],
"estimator__seasonality_mode": ["additive", "multiplicative"],
"estimator__seasonality_prior_scale": [5.0, 10.0, 15.0],
"estimator__yearly_seasonality": [True, False],
"estimator__weekly_seasonality": [True, False],
"estimator__daily_seasonality": [True, False],
"estimator__add_seasonality_dict": [{
"yearly": {
"period": 365.25,
"fourier_order": 20,
"prior_scale": 20.0
},
"quarterly": {
"period": 365.25 / 4,
"fourier_order": 15
},
"weekly": {
"period": 7,
"fourier_order": 35,
"prior_scale": 30.0
}
}, {
"yearly": {
"period": 365.25,
"fourier_order": 10,
"prior_scale": 20.0
},
"quarterly": {
"period": 365.25 / 4,
"fourier_order": 3
},
"weekly": {
"period": 7,
"fourier_order": 5,
"prior_scale": 20.0
}
}, {
"yearly": {
"period": 365.25,
"fourier_order": 10,
"prior_scale": 30.0
},
"quarterly": {
"period": 365.25 / 4,
"fourier_order": 5
},
"weekly": {
"period": 7,
"fourier_order": 15,
"prior_scale": 20.0
}
}, {
"yearly": {
"period": 365.25,
"fourier_order": 15,
"prior_scale": 20.0
},
"quarterly": {
"period": 365.25 / 4,
"fourier_order": 10
},
"weekly": {
"period": 7,
"fourier_order": 25,
"prior_scale": 20.0
}
}],
"estimator__holidays": [expected_holidays],
"estimator__holidays_prior_scale": [10.0],
"estimator__changepoint_prior_scale": [0.05],
"estimator__changepoints": [None],
"estimator__n_changepoints": [25],
"estimator__changepoint_range": [0.8],
"estimator__mcmc_samples": [0],
"estimator__uncertainty_samples": [1000],
"estimator__add_regressor_dict": [None]
}
# Assertions
assert_equal(actual=hyperparameter_grid, expected=expected_grid)
def test_prophet_hyperparameter_grid_warn():
"""Tests get_prophet_hyperparameter_grid warnings"""
# holiday params
start_year = 2018
end_year = 2022
holiday_pre_num_days = [1, 2, 3, 4]
holiday_post_num_days = [1, 2, 3, 4]
holiday_lookup_countries = [["UnitedStates"],
["UnitedStates", "China", "India"]]
holidays_prior_scale = [5.0, 10.0, 15.0]
events = {
"holiday_lookup_countries": holiday_lookup_countries,
"holiday_pre_num_days": holiday_pre_num_days,
"holiday_post_num_days": holiday_post_num_days,
"start_year": start_year,
"end_year": end_year,
"holidays_prior_scale": holidays_prior_scale
}
model_components = ModelComponentsParam(events=events)
with pytest.warns(Warning) as record:
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = model_components
template.get_hyperparameter_grid()
assert f"`events['holiday_pre_num_days']` list has more than 1 element. "\
f"We currently support only 1 element. "\
f"Using 1." in record[0].message.args[0]
assert f"`events['holiday_post_num_days']` list has more than 1 element. " \
f"We currently support only 1 element. " \
f"Using 1." in record[1].message.args[0]
# Extra spaces for holidays to align with actual warning in the function, because of an extra tab.
assert f"`events['holiday_lookup_countries']` contains multiple options. "\
f"We currently support only 1 option. Using ['UnitedStates']." in record[2].message.args[0]
# other invalid holiday_lookup_countries
with pytest.warns(Warning) as record:
events["holiday_pre_num_days"] = [1]
events["holiday_post_num_days"] = [0]
events["holiday_lookup_countries"] = ["auto", None]
model_components = ModelComponentsParam(events=events)
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = model_components
template.get_hyperparameter_grid()
assert f"`events['holiday_lookup_countries']` contains multiple options. " \
f"We currently support only 1 option. Using auto." in record[0].message.args[0]
# no warning if only one list of holiday_lookup_countries is provided
with pytest.warns(None):
events["holiday_pre_num_days"] = [1]
events["holiday_post_num_days"] = [0]
events["holiday_lookup_countries"] = [[
"UnitedStates", "China", "UnitedKingdom", "India"
]]
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = ModelComponentsParam(
events=events)
hyp1 = template.get_hyperparameter_grid()
events["holiday_lookup_countries"] = [
"UnitedStates", "China", "UnitedKingdom", "India"
]
template = ProphetTemplate()
template.config = template.apply_forecast_config_defaults()
template.config.model_components_param = ModelComponentsParam(
events=events)
hyp2 = template.get_hyperparameter_grid()
assert_equal(hyp1, hyp2)
