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TSCOptCV

API referenceSource

Tune an sktime classifier via any optimizer in the hyperactive toolbox.

Quickstart

python
from sktime.classification.model_selection import TSCOptCV

estimator = TSCOptCV(estimator, optimizer, cv=None, scoring=None, refit=True, error_score=nan, backend=None, backend_params=None)

Parameters(8)

estimatorsktime classifier, BaseClassifier instance or interface compatible
The classifier to tune, must implement the sktime classifier interface.
optimizerhyperactive BaseOptimizer
The optimizer to be used for hyperparameter search.
cvint, sklearn cross-validation generator or an iterable, default=3-fold CV

Determines the cross-validation splitting strategy. Possible inputs for cv are:

  • None = default = KFold(n_splits=3, shuffle=True)

  • integer, number of folds folds in a KFold splitter, shuffle=True

  • An iterable yielding (train, test) splits as arrays of indices.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. These splitters are instantiated with shuffle=False so the splits will be the same across calls.

scoringstr, callable, default=None

Strategy to evaluate the performance of the cross-validated model on the test set. Can be:

  • a single string resolvable to an sklearn scorer

  • a callable that returns a single value;

  • None = default = accuracy_score

refitbool, optional (default=True)
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.
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.