ForecastingOptCV

Quickstart

from sktime.forecasting.model_selection import ForecastingOptCV

estimator = ForecastingOptCV(forecaster, optimizer, cv, strategy='refit', update_behaviour='full_refit', scoring=None, refit=True, error_score=nan, cv_X=None, backend=None, backend_params=None)

Parameters(11)

forecastersktime forecaster, BaseForecaster instance or interface compatible

The forecaster to tune, must implement the sktime forecaster interface.

optimizerhyperactive BaseOptimizer

The optimizer to be used for hyperparameter search.

cvsktime BaseSplitter descendant

determines split of y and possibly X into test and train folds y is always split according to cv, see above

• if cv_X is not passed, X splits are subset to loc equal to y

• if cv_X is passed, X is split according to cv_X

strategy{“refit”, “update”, “no-update_params”}, optional, default=”refit”

data ingestion strategy in fitting cv, passed to evaluate internally defines the ingestion mode when the forecaster sees new data when window expands

• "refit" = a new copy of the forecaster is fitted to each training window

• "update" = forecaster is updated with training window data, in sequence provided

• "no-update_params" = fit to first training window, re-used without fit or update

update_behaviourstr, optional, default = “full_refit”

one of {“full_refit”, “inner_only”, “no_update”} behaviour of the forecaster when calling update

• "full_refit" = both tuning parameters and inner estimator refit on all data seen

• "inner_only" = tuning parameters are not re-tuned, inner estimator is updated

• "no_update" = neither tuning parameters nor inner estimator are updated

scoringsktime metric (BaseMetric), str, or callable, optional (default=None)

scoring metric to use in tuning the forecaster

• sktime metric objects (BaseMetric) descendants can be searched

with the registry.all_estimators search utility, for instance via all_estimators("metric", as_dataframe=True)

• If callable, must have signature

(y_true: 1D np.ndarray, y_pred: 1D np.ndarray) -> float, assuming np.ndarrays being of the same length, and lower being better. Metrics in sktime.performance_metrics.forecasting are all of this form.

• If str, uses registry.resolve_alias to resolve to one of the above. Valid strings are valid registry.craft specs, which include string repr-s of any BaseMetric object, e.g., “MeanSquaredError()”; and keys of registry.ALIAS_DICT referring to metrics.

• If None, defaults to MeanAbsolutePercentageError()

refitbool, optional (default=True)

Whether to refit the forecaster with the best parameters on the entire data.

• True = refit the forecaster with the best parameters on the entire data in fit

• False = no refitting takes place. The forecaster cannot be used to predict. This is to be used to tune the hyperparameters, and then use the estimator as a parameter estimator, e.g., via get_fitted_params or PluginParamsForecaster.

error_score“raise” or numeric, default=np.nan

Value to assign to the score if an exception occurs in estimator fitting. If set to “raise”, the exception is raised. If a numeric value is given, FitFailedWarning is raised.

cv_Xsktime BaseSplitter descendant, optional

determines split of X into test and train folds default is X being split to identical loc indices as y if passed, must have same number of splits as cv

backendstring, by default “None”.

Parallelization backend to use for runs. Runs parallel evaluate if specified and strategy="refit".

• “None”: executes loop sequentially, simple list comprehension

• “loky”, “multiprocessing” and “threading”: uses joblib.Parallel loops

• “joblib”: custom and 3rd party joblib backends, e.g., spark

• “dask”: uses dask, requires dask package in environment

• “dask_lazy”: same as “dask”, but changes the return to (lazy) dask.dataframe.DataFrame.

• “ray”: uses ray, requires ray package in environment

Recommendation: Use “dask” or “loky” for parallel evaluate. “threading” is unlikely to see speed ups due to the GIL and the serialization backend (cloudpickle) for “dask” and “loky” is generally more robust than the standard pickle library used in “multiprocessing”.

backend_paramsdict, optional

additional parameters passed to the backend as config. Directly passed to utils.parallel.parallelize. Valid keys depend on the value of backend:

• “None”: no additional parameters, backend_params is ignored

• “loky”, “multiprocessing” and “threading”: default joblib backends any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, with the exception of backend which is directly controlled by backend. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.

• “joblib”: custom and 3rd party joblib backends, e.g., spark. any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, backend must be passed as a key of backend_params in this case. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.

• “dask”: any valid keys for dask.compute can be passed, e.g., scheduler

• “ray”: The following keys can be passed:

  • “ray_remote_args”: dictionary of valid keys for ray.init

  • “shutdown_ray”: bool, default=True; False prevents ray from shutting

    down after parallelization.