TransformerPipeline#

class TransformerPipeline(steps)[source]#

Pipeline of transformers compositor.

The TransformerPipeline compositor allows to chain transformers. The pipeline is constructed with a list of sktime transformers, i.e. estimators following the BaseTransformer interface. The 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, the pipeline behaves as follows:

  • fit

    Changes state by running trafo1.fit_transform, trafo2.fit_transform` etc sequentially, with trafo[i] receiving the output of trafo[i-1]

  • transform

    Result is of executing trafo1.transform, trafo2.transform, etc with trafo[i].transform input = output of trafo[i-1].transform, and returning the output of trafoN.transform

  • inverse_transform

    Result is of executing trafo[i].inverse_transform, with trafo[i].inverse_transform input = output trafo[i-1].inverse_transform, and returning the output of trafoN.inverse_transform

  • update

    Changes state by chaining trafo1.update, trafo1.transform, trafo2.update, trafo2.transform, …, trafoN.update, where trafo[i].update and trafo[i].transform receive as input the output of trafo[i-1].transform

The get_params, set_params uses sklearn compatible nesting interface if list is unnamed, names are generated as names of classes if names are non-unique, f”_{str(i)}” is appended to each name string where i is the total count of occurrence of a non-unique string inside the list of names leading up to it (inclusive)

A TransformerPipeline can also be created by using the magic multiplication on any transformer, i.e., any estimator inheriting from BaseTransformer for instance, my_trafo1 * my_trafo2 * my_trafo3 will result in the same object as obtained from the constructor TransformerPipeline([my_trafo1, my_trafo2, my_trafo3]) A magic multiplication can also be used with (str, transformer) pairs, as long as one element in the chain is a transformer

Parameters
stepslist 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
steps_list of tuples (str, transformer) of sktime transformers

clones of transformers in steps which are fitted in the pipeline is always in (str, transformer) format, even if steps is just a list strings not passed in steps are replaced by unique generated strings i-th transformer in steps_ is clone of i-th in steps

Examples

>>> from sktime.transformations.series.exponent import ExponentTransformer
>>> t1 = ExponentTransformer(power=2)
>>> t2 = ExponentTransformer(power=0.5)

Example 1, option A: construct without strings (unique names are generated for the two components t1 and t2)

>>> pipe = TransformerPipeline(steps = [t1, t2])

Example 1, option B: construct with strings to give custom names to steps

>>> pipe = TransformerPipeline(
...         steps = [
...             ("trafo1", t1),
...             ("trafo2", t2),
...         ]
...     )

Example 1, option C: for quick construction, the * dunder method can be used

>>> pipe = t1 * t2

Example 2: sklearn transformers can be used in the pipeline. If applied to Series, sklearn transformers are applied by series instance. If applied to Table, sklearn transformers are applied to the table as a whole.

>>> from sklearn.preprocessing import StandardScaler
>>> from sktime.transformations.series.summarize import SummaryTransformer

This applies the scaler per series, then summarizes:

>>> pipe = StandardScaler() * SummaryTransformer()

This applies the sumamrization, then scales the full summary table:

>>> pipe = SummaryTransformer() * StandardScaler()

This scales the series, then summarizes, then scales the full summary table:

>>> pipe = StandardScaler() * SummaryTransformer() * StandardScaler()

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/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[, y])

Fit transformer to X, optionally to y.

fit_transform(X[, y])

Fit to data, then transform it.

get_class_tag(tag_name[, tag_value_default])

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

get_class_tags()

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

get_fitted_params()

Get fitted parameters.

get_param_defaults()

Get parameter defaults for the object.

get_param_names()

Get parameter names for the object.

get_params([deep])

Get parameters of estimator in _forecasters.

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])

Return testing parameter settings for the estimator.

inverse_transform(X[, y])

Inverse transform X and return an inverse transformed version.

is_composite()

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 _forecasters.

set_tags(**tag_dict)

Set dynamic tags to given values.

transform(X[, y])

Transform X and return a transformed version.

update(X[, y, update_params])

Update transformer with X, optionally y.

classmethod get_test_params(parameter_set='default')[source]#

Return testing parameter settings for the estimator.

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
paramsdict or list of dict, default={}

Parameters to create testing instances of the class. Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params.

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, y=None)[source]#

Fit transformer to X, optionally to y.

State change:

Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. _X : X, coerced copy of X, if remember_data tag is True

possibly coerced to inner type or update_data compatible type by reference, when possible

model attributes (ending in “_”) : dependent on estimator

Parameters
XSeries or Panel, any supported mtype
Data to fit transform to, 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

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

Returns
selfa fitted instance of the estimator
fit_transform(X, y=None)[source]#

Fit to data, then transform it.

Fits the transformer to X and y and returns a transformed version of X.

State change:

Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. _X : X, coerced copy of X, if remember_data tag is True

possibly coerced to inner type or update_data compatible type by reference, when possible

model attributes (ending in “_”) : dependent on estimator

Parameters
XSeries or Panel, any supported mtype
Data to be transformed, 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

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

Returns
transformed version of X
type depends on type of X and scitype:transform-output tag:
X | tf-output | type of return |

|----------|————–|------------------------| | Series | Primitives | pd.DataFrame (1-row) | | Panel | Primitives | pd.DataFrame | | Series | Series | Series | | Panel | Series | Panel | | Series | Panel | Panel |

instances in return correspond to instances in X
combinations not in the table are currently not supported
Explicitly, with examples:
if X is Series (e.g., pd.DataFrame) and transform-output is Series

then the return is a single Series of the same mtype Example: detrending a single series

if X is Panel (e.g., pd-multiindex) and transform-output is Series
then the return is Panel with same number of instances as X

(the transformer is applied to each input Series instance)

Example: all series in the panel are detrended individually

if X is Series or Panel and transform-output is Primitives

then the return is pd.DataFrame with as many rows as instances in X Example: i-th row of the return has mean and variance of the i-th series

if X is Series and transform-output is Panel

then the return is a Panel object of type pd-multiindex Example: i-th instance of the output is the i-th window running over X

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.

Overrides BaseEstimator default in case of vectorization.

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 of estimator in _forecasters.

Parameters
deepboolean, optional

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.

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.

inverse_transform(X, y=None)[source]#

Inverse transform X and return an inverse transformed version.

Currently it is assumed that only transformers with tags

“scitype:transform-input”=”Series”, “scitype:transform-output”=”Series”,

have an inverse_transform.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : accessed by _inverse_transform

Parameters
XSeries or Panel, any supported mtype
Data to be inverse transformed, 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

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

Returns
inverse transformed version of X

of the same type as X, and conforming to mtype format specifications

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.

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_params(**kwargs)[source]#

Set the parameters of estimator in _forecasters.

Valid parameter keys can be listed with get_params().

Returns
selfreturns an instance of self.
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, y=None)[source]#

Transform X and return a transformed version.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : must be set, accessed by _transform

Parameters
XSeries or Panel, any supported mtype
Data to be transformed, 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

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

Returns
transformed version of X
type depends on type of X and scitype:transform-output tag:
| transform | |
X | -output | type of return |

|----------|————–|------------------------| | Series | Primitives | pd.DataFrame (1-row) | | Panel | Primitives | pd.DataFrame | | Series | Series | Series | | Panel | Series | Panel | | Series | Panel | Panel |

instances in return correspond to instances in X
combinations not in the table are currently not supported
Explicitly, with examples:
if X is Series (e.g., pd.DataFrame) and transform-output is Series

then the return is a single Series of the same mtype Example: detrending a single series

if X is Panel (e.g., pd-multiindex) and transform-output is Series
then the return is Panel with same number of instances as X

(the transformer is applied to each input Series instance)

Example: all series in the panel are detrended individually

if X is Series or Panel and transform-output is Primitives

then the return is pd.DataFrame with as many rows as instances in X Example: i-th row of the return has mean and variance of the i-th series

if X is Series and transform-output is Panel

then the return is a Panel object of type pd-multiindex Example: i-th instance of the output is the i-th window running over X

update(X, y=None, update_params=True)[source]#

Update transformer with X, optionally y.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : accessed by _update and by update_data, if remember_data tag is True fitted model attributes (ending in “_”) : must be set, accessed by _update

Writes to self: _X : updated by values in X, via update_data, if remember_data tag is True fitted model attributes (ending in “_”) : only if update_params=True

type and nature of update are dependent on estimator

Parameters
XSeries or Panel, any supported mtype
Data to fit transform to, 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

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

update_paramsbool, default=True

whether the model is updated. Yes if true, if false, simply skips call. argument exists for compatibility with forecasting module.

Returns
selfa fitted instance of the estimator