AlignerDTWfromDist

class AlignerDTWfromDist(dist_trafo, step_pattern='symmetric2', window_type='none', window_size=None, open_begin=False, open_end=False)[source]

Aligner interface for dtw-python using pairwise transformer.

Uses transformer for computation of distance matrix passed to alignment.

Parameters:

dist_trafo: estimator following the pairwise transformer template

i.e., instance of concrete class implementing template BasePairwiseTransformer

step_patternstr, optional, default = “symmetric2”,

or dtw_python stepPattern object, optional step pattern to use in time warping, one of: ‘symmetric1’, ‘symmetric2’ (default), ‘asymmetric’, and dozens of other more non-standard step patterns; list can be displayed by calling help(stepPattern) in dtw

window_type: str, “none” (default), “itakura”, “sakoechiba”, “slantedband”, optional

the chosen windowing function

“none” (default) - no windowing
“sakoechiba” - a band around main diagonal
“slantedband” - a band around slanted diagonal
“itakura” - Itakura parallelogram

window_size: int, optional, default=None

size of the window if a windowing function is used if None and window_type=”sakoechiba”, defaults to 10% of series length

open_beginboolean, optional, default=False

open_end: boolean, optional, default=False

whether to perform open-ended alignments open_begin = whether alignment open ended at start (low index) open_end = whether alignment open ended at end (high index)

Attributes:

is_fitted: Whether fit has been called.

Examples

Basic usage example:

>>> import numpy as np
>>> import pandas as pd
>>> from sktime.alignment.dtw_python import AlignerDTWfromDist
>>> from sktime.dists_kernels import ScipyDist
>>> X = [
...     pd.DataFrame({'col1': np.random.randn(100)}),
...     pd.DataFrame({'col1': np.random.randn(100)})
... ]
>>> dist_trafo = ScipyDist()
>>> aligner = AlignerDTWfromDist(dist_trafo=dist_trafo, step_pattern='symmetric2')
>>> aligner.fit(X)
AlignerDTWfromDist(...)
>>> alignment_df = aligner.get_alignment()

Advanced usage example with custom distance transformation: >>> dist_trafo_custom = ScipyDist(‘cityblock’) >>> aligner_custom = AlignerDTWfromDist( … dist_trafo=dist_trafo_custom, … window_type=’sakoechiba’, … window_size=10, … ) >>> X_custom = [ … pd.DataFrame({‘col1’: np.random.randn(200)}), … pd.DataFrame({‘col1’: np.random.randn(200)}) … ] >>> aligner_custom.fit(X_custom) AlignerDTWfromDist(…) >>> alignment_df_custom = aligner_custom.get_alignment()

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[, Z])	Fit alignment given series/sequences to align.
`get_aligned`()	Return aligned version of sequences passed to fit.
`get_alignment`()	Return alignment for sequences/series passed in fit (iloc indices).
`get_alignment_loc`()	Return alignment for sequences/series passed in fit (loc indices).
`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_distance`()	Return overall distance of alignment.
`get_distance_matrix`()	Return distance matrix of alignment.
`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])	Test parameters for AlignerDTWdist.
`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.