TruncationTransformer

TruncationTransformer

class TruncationTransformer(lower=None, upper=None)[source]

Truncates unequal length panels between lower/upper length ranges.

Truncates each series in transform to iloc between integers lower (inclusive) and upper (exclusive).

If lower is None, it is set to 0.

If upper is None, it is set to the length of the shortest series in the panel passed to fit.

Parameters:

lowerint, optional (default=None) minimum length, inclusive

If None, will find the length of the shortest series and use instead.

upperint, optional (default=None) maximum length, exclusive

Cannot be less than the length of the shortest series in the panel. This is used to calculate the range between. If None, will find the length of the shortest series and use instead.

Attributes:

is_fitted

Whether fit has been called.

Examples

Truncate only unequal length panels in data: >>> from sktime.transformations.truncation import TruncationTransformer >>> from sktime.utils._testing.hierarchical import _make_hierarchical >>> X = _make_hierarchical(same_cutoff=False) >>> tt = TruncationTransformer() >>> tt.fit(X) TruncationTransformer(…) >>> X_transformed = tt.transform(X)

Truncate each panel to first 5 elements: >>> from sktime.transformations.truncation import TruncationTransformer >>> from sktime.utils._testing.hierarchical import _make_hierarchical >>> X = _make_hierarchical(same_cutoff=False) >>> tt = TruncationTransformer(upper=5) >>> tt.fit(X) TruncationTransformer(…) >>> X_transformed = tt.transform(X)

Pick range from index 1 (inclusively) to 3 (exclusively): >>> from sktime.transformations.truncation import TruncationTransformer >>> from sktime.utils._testing.hierarchical import _make_hierarchical >>> X = _make_hierarchical(same_cutoff=False) >>> tt = TruncationTransformer(lower=1, upper=3) >>> tt.fit(X) TruncationTransformer(…) >>> X_transformed = tt.transform(X)

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit transformer to X, optionally to y.
fit_transform(X[, y])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.