AlignerDTWfromDist

class AlignerDTWfromDist(dist_trafo, step_pattern='symmetric2', window_type='none', open_begin=False, open_end=False)

Aligner interface for dtw-python using pairwise transformer.

uses transformer for computation of distance matrix passed to alignment

Parameters:

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 optional, default = “none”

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

open_begin, 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.

Methods

check_is_fitted()	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters.
clone_tags(estimator[, tag_names])	Clone tags from another estimator as dynamic override.
create_test_instance([parameter_set])	Construct Estimator instance if possible.
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 a class tag's value.
get_class_tags()	Get class tags from the class and all its 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()	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 estimator class and dynamic tag overrides.
get_tags()	Get tags from estimator class and dynamic tag 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])	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_tags(**tag_dict)	Set dynamic tags to given values.

classmethod get_test_params(parameter_set='default')

Test parameters for AlignerDTWdist.

check_is_fitted()

Check if the estimator has been fitted.

Raises:

NotFittedError

If the estimator has not been fitted yet.

clone()

Obtain a clone of the object with same hyper-parameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self.

Raises:

RuntimeError if the clone is non-conforming, due to faulty __init__.

Notes

If successful, equal in value to type(self)(**self.get_params(deep=False)).

clone_tags(estimator, tag_names=None)

Clone tags from another estimator as dynamic override.

Parameters:

estimatorestimator inheriting from :class:BaseEstimator

tag_namesstr or list of str, default = None

Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns:

Self

Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')

Construct Estimator instance if possible.

Parameters:

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')

Create list of all test instances and a list of names for them.

Parameters:

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

objslist of instances of cls

i-th instance is cls(**cls.get_test_params()[i])

nameslist of str, same length as objs

i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

fit(X, Z=None)

Fit alignment given series/sequences to align.

State change:

Changes state to “fitted”.

Writes to self:

Sets self._is_fitted flag to True. Stores X and Z to self._X and self._Z, respectively. Sets fitted model attributes ending in “_”.

Parameters:

Xlist of pd.DataFrame (Series) of length n

collection of series to align

Zpd.DataFrame with n rows, optional

metadata, i-th row of Z corresponds to i-th element of X

get_aligned()

Return aligned version of sequences passed to fit.

Behaviour: returns aligned version of unaligned sequences in X passed to fit

model should be in fitted state, fitted model parameters read from self

State required:

Requires state to be “fitted”.

Accesses in self:

Fitted model attributes ending in “_”. self._is_fitted

Returns:

X_aligned_list: list of pd.DataFrame in sequence format

of length n, indices corresponding to indices of X passed to fit i-th element is re-indexed, aligned version of X[i]

get_alignment()

Return alignment for sequences/series passed in fit (iloc indices).

Behaviour: returns an alignment for sequences in X passed to fit

model should be in fitted state, fitted model parameters read from self

State required:

Requires state to be “fitted”.

Accesses in self:

Fitted model attributes ending in “_”. self._is_fitted

Returns:

pd.DataFrame in alignment format, with columns ‘ind’+str(i) for integer i

cols contain iloc index of X[i] mapped to alignment coordinate for alignment

get_alignment_loc()

Return alignment for sequences/series passed in fit (loc indices).

Behaviour: returns an alignment for sequences in X passed to fit

model should be in fitted state, fitted model parameters read from self

State required:

Requires state to be “fitted”.

Accesses in self:

Fitted model attributes ending in “_”. self._is_fitted

Returns:

pd.DataFrame in alignment format, with columns ‘ind’+str(i) for integer i

cols contain loc index of X[i] mapped to alignment coordinate for alignment

classmethod get_class_tag(tag_name, tag_value_default=None)

Get a class tag’s value.

Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Parameters:

tag_namestr

Name of tag value.

tag_value_defaultany

Default/fallback value if tag is not found.

Returns:

tag_value

Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()

Get class tags from the class and all its parent classes.

Retrieves tag: value pairs from _tags class attribute. Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Returns:

collected_tagsdict

Dictionary of class tag name: tag value pairs. Collected from _tags class attribute via nested inheritance.

get_config()

Get config flags for self.

Returns:

config_dictdict

Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

get_distance()

Return overall distance of alignment.

Behaviour: returns overall distance corresponding to alignment

not all aligners will return or implement this (optional)

State required:

Requires state to be “fitted”.

Accesses in self:

Fitted model attributes ending in “_”. self._is_fitted

Returns:

distance: float - overall distance between all elements of X passed to fit

get_distance_matrix()

Return distance matrix of alignment.

Behaviour: returns pairwise distance matrix of alignment distances

not all aligners will return or implement this (optional)

State required:

Requires state to be “fitted”.

Accesses in self:

Fitted model attributes ending in “_”. self._is_fitted

Returns:

distmat: an (n x n) np.array of floats, where n is length of X passed to fit

[i,j]-th entry is alignment distance between X[i] and X[j] passed to fit

get_fitted_params(deep=True)

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:

deepbool, default=True

Whether to return fitted parameters of components.

• If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).

• If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns:

fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:

• always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object

• if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value

• if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

classmethod get_param_defaults()

Get object’s parameter defaults.

Returns:

default_dict: dict[str, Any]

Keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__.

classmethod get_param_names()

Get object’s parameter names.

Returns:

param_names: list[str]

Alphabetically sorted list of parameter names of cls.

get_params(deep=True)

Get a dict of parameters values for this object.

Parameters:

deepbool, default=True

Whether to return parameters of components.

• If True, will return a dict of parameter name : value for this object, including parameters of components (= BaseObject-valued parameters).

• If False, will return a dict of parameter name : value for this object, but not include parameters of components.

Returns:

paramsdict with str-valued keys

Dictionary of parameters, paramname : paramvalue keys-value pairs include:

• always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction

• if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value

• if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

get_tag(tag_name, tag_value_default=None, raise_error=True)

Get tag value from estimator class and dynamic tag overrides.

Parameters:

tag_namestr

Name of tag to be retrieved

tag_value_defaultany type, optional; default=None

Default/fallback value if tag is not found

raise_errorbool

whether a ValueError is raised when the tag is not found

Returns:

tag_valueAny

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError if raise_error is True i.e. if tag_name is not in

self.get_tags().keys()

get_tags()

Get tags from estimator class and dynamic tag overrides.

Returns:

collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

is_composite()

Check if the object is composed of other BaseObjects.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:

composite: bool

Whether an object has any parameters whose values are BaseObjects.

property is_fitted

Whether fit has been called.

classmethod load_from_path(serial)

Load object from file location.

Parameters:

serialresult of ZipFile(path).open(“object)

Returns:

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)

Load object from serialized memory container.

Parameters:

serial1st element of output of cls.save(None)

Returns:

deserialized self resulting in output serial, of cls.save(None)

reset()

Reset the object to a clean post-init state.

Using reset, runs __init__ with current values of hyper-parameters (result of get_params). This Removes any object attributes, except:

• hyper-parameters = arguments of __init__

• object attributes containing double-underscores, i.e., the string “__”

Class and object methods, and class attributes are also unaffected.

Returns:

self

Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

Notes

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

save(path=None)

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:

pathNone or file location (str or Path)

if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns:

if path is None - in-memory serialized self

if path is file location - ZipFile with reference to the file

set_config(**config_dict)

Set config flags to given values.

Parameters:

config_dictdict

Dictionary of config name : config value pairs.

Returns:

selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

set_params(**params)

Set the parameters of this object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

BaseObject parameters.

Returns:

self

Reference to self (after parameters have been set).

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters:

**tag_dictdict

Dictionary of tag name: tag value pairs.

Returns:

Self

Reference to self.

Notes

Changes object state by settting tag values in tag_dict as dynamic tags in self.