sktime.series_as_features: Series-as-features tools

The sktime.series_as_features module contains algorithms and composition tools that are shared by the classification and regression modules.

Composition

FeatureUnion(transformer_list[, n_jobs, …])

Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer. Parameters of the transformations may be set using its name and the parameter name separated by a ‘__’. A transformer may be replaced entirely by setting the parameter with its name to another transformer, or removed by setting to ‘drop’ or None. :param transformer_list: List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer. :type transformer_list: list of (string, transformer) tuples :param n_jobs: Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. :type n_jobs: int or None, optional (default=None) :param transformer_weights: Multiplicative weights for features per transformer. Keys are transformer names, values the weights. :type transformer_weights: dict, optional.

Model selection

PresplitFilesCV([cv])

Cross-validation iterator over split predefined in files.

SingleSplit([test_size, train_size, …])

Helper class for orchestration that uses a single split for training and testing.