EditDist#

class EditDist(distance: str = 'lcss', window: Optional[int] = None, itakura_max_slope: Optional[float] = None, bounding_matrix: Optional[numpy.ndarray] = None, epsilon: float = 1.0, g: float = 0.0, lmbda: float = 1.0, nu: float = 0.001, p: int = 2)[source]#

Interface to sktime native edit distances.

Interface to the following edit distances: LCSS - longest common subsequence distance ERP - Edit distance for real penalty EDR - Edit distance for real sequences TWE - Time warp edit distance

LCSS [1] attempts to find the longest common sequence between two time series and returns a value that is the percentage that longest common sequence assumes. LCSS is computed by matching indexes that are similar up until a defined threshold (epsilon).

The value returned will be between 0.0 and 1.0, where 0.0 means the two time series are exactly the same and 1.0 means they are complete opposites.

EDR [2] computes the minimum number of elements (as a percentage) that must be removed from x and y so that the sum of the distance between the remaining signal elements lies within the tolerance (epsilon).

The value returned will be between 0 and 1 per time series. The value will represent as a percentage of elements that must be removed for the time series to be an exact match.

ERP [3] attempts align time series by better considering how indexes are carried forward through the cost matrix. Usually in the dtw cost matrix, if an alignment can’t be found the previous value is carried forward. ERP instead proposes the idea of gaps or sequences of points that have no matches. These gaps are then punished based on their distance from ‘g’.

TWE [4] is a distance measure for discrete time series matching with time ‘elasticity’. In comparison to other distance measures, (e.g. DTW (Dynamic Time Warping) or LCS (Longest Common Subsequence Problem)), TWE is a metric. Its computational time complexity is O(n^2), but can be drastically reduced in some specific situation by using a corridor to reduce the search space. Its memory space complexity can be reduced to O(n).

Parameters
distance: str, one of [“lcss”, “edr”, “erp”, “twe”], optional, default = “lcss”

name of the distance that is calculated

window: float, default = None

Float that is the radius of the sakoe chiba window (if using Sakoe-Chiba lower bounding). Value must be between 0. and 1.

itakura_max_slope: float, default = None

Gradient of the slope for itakura parallelogram (if using Itakura Parallelogram lower bounding)

bounding_matrix: 2D np.ndarray, optional, default = None

if passed, must be of shape (len(X), len(X2)) for X, X2 in transform Custom bounding matrix to use. If defined then other lower_bounding params are ignored. The matrix should be structure so that indexes considered in bound should be the value 0. and indexes outside the bounding matrix should be infinity.

epsilonfloat, defaults = 1.

Used in LCSS, EDR, ERP, otherwise ignored Matching threshold to determine if two subsequences are considered close enough to be considered ‘common’.

g: float, defaults = 0.

Used in ERP, otherwise ignored. The reference value to penalise gaps.

lmbda: float, optional, default = 1.0

Used in TWE, otherwise ignored. A constant penalty that punishes the editing efforts. Must be >= 1.0.

nu: float optional, default = 0.001

Used in TWE, otherwise ignored. A non-negative constant which characterizes the stiffness of the elastic twe measure. Must be > 0.

p: int optional, default = 2

Used in TWE, otherwise ignored. Order of the p-norm for local cost.

Attributes
`is_fitted`

Whether fit has been called.

References

1

M. Vlachos, D. Gunopoulos, and G. Kollios. 2002. “Discovering Similar Multidimensional Trajectories”, In Proceedings of the 18th International Conference on Data Engineering (ICDE ‘02). IEEE Computer Society, USA, 673.

2

Lei Chen, M. Tamer Özsu, and Vincent Oria. 2005. Robust and fast similarity search for moving object trajectories. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data (SIGMOD ‘05). Association for Computing Machinery, New York, NY, USA, 491–502. DOI:https://doi.org/10.1145/1066157.1066213

3

Lei Chen and Raymond Ng. 2004. On the marriage of Lp-norms and edit distance. In Proceedings of the Thirtieth international conference on Very large data bases - Volume 30 (VLDB ‘04). VLDB Endowment, 792–803.

4

Marteau, P.; F. (2009). “Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31 (2): 306–318.

Methods

 `__call__`(X[, X2]) Compute distance/kernel matrix, call shorthand. Check if the estimator has been fitted. Obtain a clone of the object with same hyper-parameters. `clone_tags`(estimator[, tag_names]) clone/mirror 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, X2]) Fit method for interface compatibility (no logic inside). `get_class_tag`(tag_name[, tag_value_default]) Get tag value from estimator class (only class tags). Get class tags from estimator class and all its parent classes. Get fitted parameters. Get parameter defaults for the object. Get parameter names for the object. `get_params`([deep]) Get parameters for this estimator. `get_tag`(tag_name[, tag_value_default, …]) Get tag value from estimator class and dynamic tag overrides. Get tags from estimator class and dynamic tag overrides. `get_test_params`([parameter_set]) Test parameters for EditDist. 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 the object to a clean post-init state. `save`([path]) Save serialized self to bytes-like object or to (.zip) file. `set_params`(**params) Set the parameters of this object. `set_tags`(**tag_dict) Set dynamic tags to given values. `transform`(X[, X2]) Compute distance/kernel matrix.
classmethod get_test_params(parameter_set='default')[source]#

Test parameters for EditDist.

check_is_fitted()[source]#

Check if the estimator has been fitted.

Raises
NotFittedError

If the estimator has not been fitted yet.

clone()[source]#

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. Equal in value to type(self)(**self.get_params(deep=False)).

Returns
instance of type(self), clone of self (see above)
clone_tags(estimator, tag_names=None)[source]#

clone/mirror 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')[source]#

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')[source]#

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=None, X2=None)[source]#

Fit method for interface compatibility (no logic inside).

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get tag value from estimator class (only class tags).

Parameters
tag_namestr

Name of tag value.

tag_value_defaultany type

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()[source]#

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

Returns
collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_fitted_params()[source]#

Get fitted parameters.

State required:

Requires state to be “fitted”.

Returns
fitted_paramsdict of fitted parameters, keys are str names of parameters

parameters of components are indexed as [componentname]__[paramname]

classmethod get_param_defaults()[source]#

Get parameter defaults for the object.

Returns
default_dict: dict with str keys

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()[source]#

Get parameter names for the object.

Returns
param_names: list of str, alphabetically sorted list of parameter names of cls
get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsdict

Parameter names mapped to their values.

get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#

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_value

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()[source]#

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()[source]#

Check if the object is composite.

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 self contains a parameter which is BaseObject
property is_fitted[source]#

Whether fit has been called.

Load object from file location.

Parameters
serialresult of ZipFile(path).open(“object)
Returns
deserialized self resulting in output at path, of cls.save(path)

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()[source]#

Reset the object to a clean post-init state.

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

Detail behaviour: removes any object attributes, except:

hyper-parameters = arguments of __init__ object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)[source]#

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_params(**params)[source]#

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
selfreference to self (after parameters have been set)
set_tags(**tag_dict)[source]#

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.

transform(X, X2=None)[source]#

Compute distance/kernel matrix.

Behaviour: returns pairwise distance/kernel matrix

between samples in X and X2 (equal to X if not passed)

Parameters
XSeries or Panel, any supported mtype, of n instances
Data to transform, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

subject to sktime mtype format specifications, for further details see

examples/AA_datatypes_and_datasets.ipynb

X2Series or Panel, any supported mtype, of m instances

optional, default: X = X2

Data to transform, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

subject to sktime mtype format specifications, for further details see

examples/AA_datatypes_and_datasets.ipynb

X and X2 need not have the same mtype

Returns
distmat: np.array of shape [n, m]

(i,j)-th entry contains distance/kernel between X[i] and X2[j]