AlignerDTW

AlignerDTW

class AlignerDTW(dist_method='euclidean', step_pattern='symmetric2', window_type='none', window_size=None, open_begin=False, open_end=False, variable_to_align=None)[source]

Aligner interface for dtw-python.

Behaviour: computes the full alignment between X[0] and X[1]

assumes pairwise alignment (only two series) and univariate if multivariate series are passed: alignment is computed on univariate series with variable_to_align; if this is not set, defaults to the first variable of X[0] raises an error if variable_to_align is not present in X[0] or X[1]

Parameters:

dist_methodstr, optional, default = “euclidean”

distance function to use, a distance on real n-space one of the functions in scipy.spatial.distance.cdist

step_patternstr, optional, 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_typestring, the chosen windowing function

“none”, “itakura”, “sakoechiba”, or “slantedband”

• “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)

variable_to_alignstring, default = first variable in X[0] as passed to fit

which variable to use for univariate alignment

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 AlignerDTW
>>> X = [
...     pd.DataFrame({'col1': np.random.randn(100)}),
...     pd.DataFrame({'col1': np.random.randn(100)})
... ]
>>> aligner = AlignerDTW(dist_method='euclidean', step_pattern='symmetric2')
>>> aligner.fit(X)
AlignerDTW(...)
>>> alignment_df = aligner.get_alignment()

Advanced usage example with open-ended alignment:

>>> aligner_advanced = AlignerDTW(
...     dist_method='cityblock',
...     window_type='sakoechiba',
...     window_size=10,
...     step_pattern='asymmetric',
...     open_begin=True,
...     open_end=True,
... )
>>> X_advanced = [
...     pd.DataFrame({'col1': np.random.randn(150)}),
...     pd.DataFrame({'col1': np.random.randn(150)})
... ]
>>> aligner_advanced.fit(X_advanced)
AlignerDTW(...)
>>> alignment_df_advanced = aligner_advanced.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.