CombinedDistance

CombinedDistance

class CombinedDistance(pw_trafos, operation=None)[source]

Distances combined via arithmetic operation, e.g., addition, multiplication.

CombinedDistance creates a pairwise trafo from multiple other pairwise trafos, by performing an arithmetic operation (np.ufunc) on the multiple distance matrices.

For a list of transformers trafo1, trafo2, …, trafoN, ufunc operation, this compositor behaves as follows: transform(X, X2) - computes dist1 = trafo1.transform(X, X2),

dist2 = trafo2.transform(X, X2), …, distN = trafoN.transform(X, X2)`, all of shape (len(X), len(X2), then applies operation entry-wise, to obtain a single matrix dist of shape (len(X), len(X2) Example: if operation = np.sum, then dist is the entry-wise sum of dist1, dist2, …, distN

Parameters:

pw_trafoslist of sktime pairwise panel distances, or

list of tuples (str, transformer) of sktime pairwise panel distances distances combined to a single distance using the operation

operationNone, str, function, or numpy ufunc, optional, default = None = mean

if str, must be one of “mean”, “+” (add), “*” (multiply), “max”, “min” if func, must be of signature (1D iterable) -> float operation carried out on the distance matrices distances

Attributes:

is_fitted

Whether fit has been called.

Examples

>>> from sktime.dists_kernels.algebra import CombinedDistance
>>> from sktime.dists_kernels.dtw import DtwDist
>>> from sktime.datasets import load_unit_test
>>>
>>> X, _ = load_unit_test()
>>> X = X[0:3]
>>> sum_dist = CombinedDistance([DtwDist(), DtwDist(weighted=True)], "+")
>>> dist_mat = sum_dist.transform(X)

the same can also be done more compactly using dunders:

>>> sum_dist = DtwDist() + DtwDist(weighted=True)
>>> dist_mat = sum_dist(X)

Methods

__call__(X[, X2])	Compute distance/kernel matrix, call shorthand.
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, X2])	Fit method for interface compatibility (no logic inside).
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 parameters of estimator.
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.
is_composite()	Check if the object is composite.
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(**kwargs)	Set the parameters of estimator.
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[, X2])	Compute distance/kernel matrix.
transform_diag(X)	Compute diagonal of distance/kernel matrix.