FlatDist#

class FlatDist(transformer)[source]#

Panel distance from applying tabular distance to flattened time series.

Applies the wrapped tabular distance to flattened series. Flattening is done to a 2D numpy array of shape (n_instances, (n_vars, n_timepts))

Formal details (for real valued objects, mixed typed rows in analogy): Let $$d:\mathbb{R}^k \times \mathbb{R}^{k}\rightarrow \mathbb{R}$$ be the pairwise function in transformer, when applied to k-vectors. Let $$x_1, \dots, x_N\in \mathbb{R}^{n \times \ell}$$, $$y_1, \dots y_M \in \mathbb{R}^{n \times \ell}$$ be collections of matrices, representing time series panel valued inputs X and X2, as follows: $$x_i$$ is the i-th instance in X, and $$x_{i, j\ell}$$ is the j-th time point, ell-th variable of X. Analogous for $$y$$ and X2. Let $$f:\mathbb{R}^{n \times \ell} \rightarrow \mathbb{R}^{n \cdot \ell}$$ be the mapping that flattens matrices by column-first lexicographical ordering, and assume $$k = n \cdot \ell$$.

Then, transform(X, X2) returns the $$(N \times M)$$ matrix with $$(i, j)$$-th entry $$d\left(f(x_i), f(y_j)\right)$$.

Parameters
transformer: pairwise transformer of BasePairwiseTransformer scitype
Attributes
is_fitted

Whether fit has been called.

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 FlatDist. 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 FlatDist.

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]