PwTrafoPanelPipeline#
- class PwTrafoPanelPipeline(pw_trafo, transformers)[source]#
Pipeline of transformers and a pairwise panel transformer.
PwTrafoPanelPipeline chains transformers and a pairwise transformer at the end. The pipeline is constructed with a list of sktime transformers (BaseTransformer),
plus a pairwise panel transformer, following BasePairwiseTransformerPanel.
- The transformer list can be unnamed - a simple list of transformers -
or string named - a list of pairs of string, estimator.
- For a list of transformers trafo1, trafo2, …, trafoN and an estimator est,
the pipeline behaves as follows:
- transform(X) - running trafo1.fit_transform on X,
them trafo2.fit_transform on the output of trafo1.fit_transform, etc sequentially, with trafo[i] receiving the output of trafo[i-1]. Then passes output of trafo[N] to pw_trafo.transform, as X. Same chain of transformers is run on X2 and passed, if not None.
- PwTrafoPanelPipeline can also be created by using the magic multiplication
- on any parameter estimator: if pw_t is BasePairwiseTransformerPanel,
and my_trafo1, my_trafo2 inherit from BaseTransformer, then, for instance, my_trafo1 * my_trafo2 * pw_t will result in the same object as obtained from the constructor PwTrafoPanelPipeline(pw_trafo=pw_t, transformers=[my_trafo1, my_trafo2])
- magic multiplication can also be used with (str, transformer) pairs,
as long as one element in the chain is a transformer
- Parameters
- pw_trafopairwise panel transformer,
i.e., estimator inheriting from BasePairwiseTransformerPane this is a “blueprint” estimator, state does not change when fit is called
- transformerslist of sktime transformers, or
list of tuples (str, transformer) of sktime transformers these are “blueprint” transformers, states do not change when fit is called
- Attributes
is_fitted
Whether fit has been called.
Examples
>>> from sktime.dists_kernels.compose import PwTrafoPanelPipeline >>> from sktime.dists_kernels.dtw import DtwDist >>> from sktime.transformations.series.exponent import ExponentTransformer >>> from sktime.datasets import load_unit_test >>> >>> X, _ = load_unit_test() >>> X = X[0:3] >>> pipeline = PwTrafoPanelPipeline(DtwDist(), [ExponentTransformer()]) >>> dist_mat = pipeline.transform(X)
Methods
__call__
(X[, X2])Compute distance/kernel matrix, call shorthand.
Check if the estimator has been fitted.
clone
()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 of estimator in transformers.
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 DistFromAligner.
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
()Reset the object to a clean post-init state.
save
([path])Save serialized self to bytes-like object or to (.zip) file.
set_params
(**kwargs)Set the parameters of estimator in transformers.
set_tags
(**tag_dict)Set dynamic tags to given values.
transform
(X[, X2])Compute distance/kernel matrix.
- get_params(deep=True)[source]#
Get parameters of estimator in transformers.
- Parameters
- deepboolean, optional, default=True
If True, will return the parameters for this estimator and contained sub-objects that are estimators.
- Returns
- paramsmapping of string to any
Parameter names mapped to their values.
- set_params(**kwargs)[source]#
Set the parameters of estimator in transformers.
Valid parameter keys can be listed with
get_params()
.- Returns
- selfreturns an instance of self.
- 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.
- 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_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
- classmethod load_from_path(serial)[source]#
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)[source]#
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_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]