TSInterpolator: {"length": 10},
RandomIntervalSpectralForest: {"n_estimators": 3, "acf_lag": 10, "min_interval": 5},
SFA: {"return_pandas_data_series": True},
ContractableBOSS: {"n_parameter_samples": 25, "max_ensemble_size": 5},
TemporalDictionaryEnsemble: {
"n_parameter_samples": 25,
"max_ensemble_size": 5,
"randomly_selected_params": 20,
},
TimeSeriesForest: {"n_estimators": 3},
TimeSeriesForestClassifier: {"n_estimators": 3},
TimeSeriesForestRegressor: {"n_estimators": 3},
SupervisedTimeSeriesForest: {"n_estimators": 3},
CanonicalIntervalForest: {"n_estimators": 3},
DrCIF: {"n_estimators": 3},
HCrystalBallForecaster: {"model": HoltSmoothingWrapper()},
BATS: {
"use_box_cox": False,
"use_trend": False,
"use_damped_trend": False,
"sp": [],
"use_arma_errors": False,
"n_jobs": 1,
},
TBATS: {
"use_box_cox": False,
"use_trend": False,
"use_damped_trend": False,
"sp": [],
"use_arma_errors": False,
"n_jobs": 1,
"n_estimators": 3
},
ComposableTimeSeriesForestRegressor: {
"n_estimators": 3
},
SupervisedTimeSeriesForest: {
"n_estimators": 3
},
CanonicalIntervalForest: {
"n_estimators": 3
},
DrCIF: {
"n_estimators": 3
},
HCrystalBallForecaster: {
"model": HoltSmoothingWrapper()
},
BATS: {
"use_box_cox": False,
"use_trend": False,
"use_damped_trend": False,
"sp": [],
"use_arma_errors": False,
"n_jobs": 1,
},
TBATS: {
"use_box_cox": False,
"use_trend": False,
"use_damped_trend": False,
"sp": [],
"use_arma_errors": False,
def get_gridsearch(
frequency,
horizon=10,
n_splits=5,
between_split_lag=None,
scoring="neg_mean_absolute_error",
country_code_column=None,
country_code=None,
sklearn_models=True,
sklearn_models_optimize_for_horizon=False,
autosarimax_models=False,
autoarima_dict=None,
prophet_models=False,
tbats_models=False,
exp_smooth_models=False,
average_ensembles=False,
stacking_ensembles=False,
stacking_ensembles_train_horizon=10,
stacking_ensembles_train_n_splits=20,
clip_predictions_lower=None,
clip_predictions_upper=None,
exog_cols=None,
):
"""Get grid search object based on selection criteria.
Parameters
----------
frequency : str
Frequency of timeseries. Pandas compatible frequncies
horizon : int
How many units of frequency (e.g. 4 quarters), should be used to find the best models
n_splits : int
How many cross-validation folds should be used in model selection
between_split_lag : int
How big lag of observations should cv_splits have
If kept as None, horizon is used resulting in non-overlaping cv_splits
scoring : str, callable
String of sklearn regression metric name, or hcrystalball compatible scorer. For creation
of hcrystalball compatible scorer use `make_ts_scorer` function.
country_code_column : str
Column in data, that contains country code in str (e.g. 'DE'). Used in holiday transformer.
Only one of `country_code_column` or `country_code` can be set.
country_code : str
Country code in str (e.g. 'DE'). Used in holiday transformer.
Only one of `country_code_column` or `country_code` can be set.
sklearn_models : bool
Whether to consider sklearn models
sklearn_models_optimize_for_horizon: bool
Whether to add to default sklearn behavior also models, that optimize predictions for each horizon
autosarimax_models : bool
Whether to consider auto sarimax models
autoarima_dict : dict
Specification of pmdautoarima search space
prophet_models : bool
Whether to consider FB prophet models
exp_smooth_models : bool
Whether to consider exponential smoothing models
average_ensembles : bool
Whether to consider average ensemble models
stacking_ensembles : bool
Whether to consider stacking ensemble models
stacking_ensembles_train_horizon : int
Which horizon should be used in meta model in stacking ensembles
stacking_ensembles_train_n_splits : int
Number of splits used in meta model in stacking ensembles
clip_predictions_lower : float, int
Minimal number allowed in the predictions
clip_predictions_upper : float, int
Maximal number allowed in the predictions
exog_cols : list
List of columns to be used as exogenous variables
Returns
-------
sklearn.model_selection.GridSearchCV
CV / Model selection configuration
"""
exog_cols = exog_cols if exog_cols is not None else []
# ensures only exogenous columns and country code column will be passed to model if provided
# and columns names will be stored in TSColumnTransformer
if exog_cols:
cols = exog_cols + [country_code_column] if country_code_column else exog_cols
exog_passthrough = TSColumnTransformer(transformers=[("raw_cols", "passthrough", cols)])
else:
exog_passthrough = "passthrough"
# ensures holiday transformer is added to the pipeline if requested
if country_code or country_code_column:
from hcrystalball.feature_extraction import HolidayTransformer
holiday = HolidayTransformer(country_code=country_code, country_code_column=country_code_column)
else:
holiday = "passthrough"
estimator = Pipeline(
[("exog_passthrough", exog_passthrough), ("holiday", holiday), ("model", "passthrough")]
)
scoring = get_scorer(scoring)
cv = FinerTimeSplit(n_splits=n_splits, horizon=horizon, between_split_lag=between_split_lag)
grid_search = GridSearchCV(
estimator=estimator, param_grid=[], scoring=scoring, cv=cv, refit=False, error_score=np.nan,
)
if autosarimax_models:
# adding autosarimax to param_grid might cause differently found models
# for different splits and raise inconsistency based errors.
# sarimax pipeline is added to new grid_search's attribute (`grid_search.autosarimax`)
# and handled in `hcrystalball.model_seleciton.select_model` function in following way
# 1. get best model for the data part on last split
# 2. append this best model to original `param_grid`
# 3. run full grid search with `param_grid` containing
# sarimax model selected from autosarimax in point 1
from hcrystalball.wrappers import SarimaxWrapper
if autoarima_dict is None:
autoarima_dict = {}
if "error_action" not in autoarima_dict:
autoarima_dict.update({"error_action": "raise"})
grid_search.autosarimax = Pipeline(estimator.steps[:-1])
grid_search.autosarimax.steps.append(
(
"model",
SarimaxWrapper(
init_with_autoarima=True,
autoarima_dict=autoarima_dict,
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
),
)
)
if stacking_ensembles or average_ensembles or sklearn_models:
from sklearn.linear_model import ElasticNet
from sklearn.ensemble import RandomForestRegressor
# TODO when scoring time is fixed, add HistGradientBoostingRegressor
# from sklearn.experimental import enable_hist_gradient_boosting
# from sklearn.ensemble import HistGradientBoostingRegressor
from hcrystalball.wrappers import get_sklearn_wrapper
from hcrystalball.feature_extraction import SeasonalityTransformer
sklearn_model = get_sklearn_wrapper(
RandomForestRegressor,
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
)
sklearn_model_pipeline = Pipeline(
[("seasonality", SeasonalityTransformer(auto=True, freq=frequency)), ("model", sklearn_model)]
)
# TODO make sure naming here works as expected
sklearn_model_pipeline.name = f"seasonality_{sklearn_model.name}"
if sklearn_models:
classes = [ElasticNet, RandomForestRegressor]
models = {
model_class.__name__: get_sklearn_wrapper(
model_class,
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
)
for model_class in classes
}
optimize_for_horizon = [False, True] if sklearn_models_optimize_for_horizon else [False]
grid_search.param_grid.append(
{
"model": [sklearn_model_pipeline],
"model__seasonality__weekly": [True, False],
"model__model": list(models.values()),
# TODO change add once HistGradientBoostingRegressor is back
# "model__model": list(models.values()) + [sklearn_model]
"model__model__optimize_for_horizon": optimize_for_horizon,
"model__model__lags": [3, 7, 10, 14],
}
)
grid_search.param_grid.append(
{
"model": [sklearn_model_pipeline],
"model__seasonality__weekly": [True, False],
"model__model__optimize_for_horizon": optimize_for_horizon,
"model__model": [sklearn_model],
"model__model__max_depth": [6],
}
)
if prophet_models:
from hcrystalball.wrappers import ProphetWrapper
extra_regressors = [None] if exog_cols is None else [None, exog_cols]
grid_search.param_grid.append(
{
"model": [
ProphetWrapper(
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
)
],
"model__seasonality_mode": ["multiplicative", "additive"],
"model__extra_regressors": extra_regressors,
}
)
grid_search.param_grid.append(
{
"model": [
ProphetWrapper(
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
)
],
"model__extra_seasonalities": [
[
{
"name": "quarterly",
"period": 90.0625,
"fourier_order": 5,
"prior_scale": 15.0,
"mode": None,
}
]
],
"model__extra_regressors": extra_regressors,
}
)
if exp_smooth_models:
from hcrystalball.wrappers import ExponentialSmoothingWrapper
from hcrystalball.wrappers import HoltSmoothingWrapper
from hcrystalball.wrappers import SimpleSmoothingWrapper
# commented options show non deterministic behavior
grid_search.param_grid.append(
{
"model": [
ExponentialSmoothingWrapper(
freq=frequency,
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
)
],
"model__trend": ["add"],
"model__seasonal": [None, "add"],
"model__damped": [True, False],
"model__fit_params": [
{"use_boxcox": True, "use_basinhopping": False},
# {'use_boxcox':True, 'use_basinhopping':True},
{"use_boxcox": False, "use_basinhopping": False},
# {'use_boxcox':False, 'use_basinhopping':True}
],
}
)
grid_search.param_grid.append(
{
"model": [
ExponentialSmoothingWrapper(
freq=frequency,
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
)
],
"model__trend": ["add"],
"model__seasonal": ["mul"],
"model__damped": [True, False],
"model__fit_params": [
{"use_boxcox": False, "use_basinhopping": False},
# {'use_boxcox':False, 'use_basinhopping':True}
],
}
)
grid_search.param_grid.append(
{
"model": [
ExponentialSmoothingWrapper(
freq=frequency,
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
)
],
"model__trend": [None],
"model__seasonal": [None, "add", "mul"],
"model__damped": [False],
"model__fit_params": [
{"use_boxcox": False, "use_basinhopping": False},
# {'use_boxcox':False, 'use_basinhopping':True}
],
}
)
grid_search.param_grid.append(
{
"model": [
SimpleSmoothingWrapper(
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
),
HoltSmoothingWrapper(
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
),
]
}
)
if tbats_models:
from hcrystalball.wrappers import TBATSWrapper
grid_search.param_grid.append(
{
"model": [
TBATSWrapper(
use_arma_errors=False,
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
)
]
}
)
if stacking_ensembles:
from hcrystalball.ensemble import StackingEnsemble
from hcrystalball.wrappers import ProphetWrapper
from sklearn.ensemble import RandomForestRegressor
grid_search.param_grid.append(
{
"model": [
StackingEnsemble(
train_n_splits=stacking_ensembles_train_n_splits,
train_horizon=stacking_ensembles_train_horizon,
meta_model=ElasticNet(),
horizons_as_features=True,
weekdays_as_features=True,
base_learners=[],
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
)
],
"model__meta_model": [ElasticNet(), RandomForestRegressor()],
"model__base_learners": [
[
ProphetWrapper(
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
),
sklearn_model_pipeline,
],
],
}
)
if average_ensembles:
from hcrystalball.ensemble import SimpleEnsemble
from hcrystalball.wrappers import ProphetWrapper
grid_search.param_grid.append(
{
"model": [
SimpleEnsemble(
base_learners=[],
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
)
],
"model__base_learners": [
[
ProphetWrapper(
clip_predictions_lower=clip_predictions_lower,
clip_predictions_upper=clip_predictions_upper,
),
sklearn_model_pipeline,
],
],
}
)
return grid_search