skpro.regression.compose.Pipeline#

class skpro.regression.compose.Pipeline(steps)[source]#

Pipeline for probabilistic supervised regression.

Pipeline is only applying the given transformers to X. The regressor can also be a TransformedTargetregressor containing transformers to transform y.

For a list t1, t2, …, tN, r

where t[i] are transformers, and r is an sktime regressor, the pipeline behaves as follows:

fit(X, y) - changes state by running t1.fit_transform with X=X, y=y

then t2.fit_transform on X= the output of t1.fit_transform, y=y, etc sequentially, with t[i] receiving the output of t[i-1] as X, then running r.fit with X being the output of t[N], and y=y

predict(X) - result is of executing r.predict, with X=X

being the result of the following process: running t1.fit_transform with X=X, then t2.fit_transform on X= the output of t1.fit_transform, etc sequentially, with t[i] receiving the output of t[i-1] as X, and returning th output of tN to pass to r.predict as X.

predict_interval(X), predict_quantiles(X) - as predict(X),

with predict_interval or predict_quantiles substituted for predict

predict_var, predict_proba - uses base class default to obtain

crude estimates from predict_quantiles.

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)

Pipeline can also be created by using the magic multiplication

on any regressor, i.e., if my_regressor inherits from BaseProbaRegressor,: and my_t1, my_t2, are an sklearn transformer, then, for instance, my_t1 * my_t2 * my_regressor will result in the same object as obtained from the constructor Pipeline([my_t1, my_t2, my_regressor])
magic multiplication can also be used with (str, transformer) pairs,: as long as one element in the chain is a regressor

Parameters:

stepslist of sktime transformers and regressors, or

list of tuples (str, estimator) of sktime transformers or regressors: the list must contain exactly one regressor
these are “blueprint” transformers resp regressors,: regressor/transformer states do not change when fit is called

Examples

>>> from skpro.regression.compose import Pipeline
>>> from skpro.regression.residual import ResidualDouble
>>> from sklearn.datasets import load_diabetes
>>> from sklearn.impute import SimpleImputer as Imputer
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.preprocessing import MinMaxScaler
>>>
>>> X, y = load_diabetes(return_X_y=True, as_frame=True)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y)
>>>
>>> reg_mean = LinearRegression()
>>> reg_proba = ResidualDouble(reg_mean)

Example 1: string/estimator pairs

>>> pipe = Pipeline(steps=[
...     ("imputer", Imputer()),
...     ("scaler", MinMaxScaler()),
...     ("regressor", reg_proba),
... ])
>>> pipe.fit(X_train, y_train)
Pipeline(...)
>>> y_pred = pipe.predict(X=X_test)
>>> y_pred_proba = pipe.predict_proba(X=X_test)

Example 2: without strings

>>> pipe = Pipeline([
...     Imputer(),
...     MinMaxScaler(),
...     ("regressor", reg_proba),
... ])

Example 3: using the dunder method (requires bracketing as sklearn does not support dunders)

>>> reg_proba = ResidualDouble(reg_mean)
>>> pipe = Imputer() * (MinMaxScaler() * reg_proba)

Attributes:

steps_list of tuples (str, estimator) of sktime transformers or regressors: clones of estimators in steps which are fitted in the pipeline is always in (str, estimator) 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
regressor_estimator, reference to the unique regressor in steps_: Return reference to the regressor in the pipeline.

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, y[, C])	Fit regressor to training data.
`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_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])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composite.
`predict`(X)	Predict labels for data from features.
`predict_interval`([X, coverage])	Compute/return interval predictions.
`predict_proba`(X)	Predict distribution over labels for data from features.
`predict_quantiles`([X, alpha])	Compute/return quantile predictions.
`predict_var`([X])	Compute/return variance predictions.
`reset`()	Reset the object to a clean post-init state.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**kwargs)	Set the object's direct parameters and the parameters of components.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set dynamic tags to given values.

property regressor_[source]#

Return reference to the regressor in the pipeline.

Valid after _fit.

check_is_fitted()[source]#

Check if the estimator has been fitted.

Inspects object’s _is_fitted attribute that should initialize to False during object construction, and be set to True in calls to an object’s fit method.

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.

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)[source]#

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')[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__}

fit(X, y, C=None)[source]#

Fit regressor to training data.

Writes to self:: Sets fitted model attributes ending in “_”.

Changes state to “fitted” = sets is_fitted flag to True

Parameters:

Xpandas DataFrame: feature instances to fit regressor to
ypd.DataFrame, must be same length as X: labels to fit regressor to
Cignored, optional (default=None): censoring information for survival analysis All probabilistic regressors assume data to be uncensored

Returns:

selfreference to self

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

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()[source]#

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()[source]#

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_fitted_params(deep=True)[source]#

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()[source]#

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()[source]#

Get object’s parameter names.

Returns:

param_names: list[str]: Alphabetically sorted list of parameter names of cls.

get_params(deep=True)[source]#

Get a dict of parameters values for this object.

This expands on get_params of standard BaseObject by also retrieving components parameters when deep=True a component’s follows the named object API (either sequence of str, BaseObject tuples or dict[str, BaseObject]).

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

Returns:

dict[str, Any]

Dictionary of parameter name and value pairs. Includes direct parameters and indirect parameters whose values implement get_params or follow the named object API (either sequence of str, BaseObject tuples or dict[str, BaseObject]).

If deep=False the name-value pairs for this object’s direct parameters (you can see these via get_param_names) are returned.
If deep=True then the parameter name-value pairs are returned for direct and component (indirect) parameters.
- When a BaseObject’s direct parameter value implements get_params the component parameters are returned as [direct_param_name]__[component_param_name] for 1st level components. Arbitrary levels of component recursion are supported (if the component has parameter’s whose values are objects that implement get_params). In this case, return parameters follow [direct_param_name]__[component_param_name]__[param_name] format.
- When a BaseObject’s direct parameter value is a sequence of (name, BaseObject) tuples or dict[str, BaseObject] the parameters name and value pairs of all component objects are returned. The parameter naming follows scikit-learn convention of treating named component objects like they are direct parameters; therefore, the names are assigned as [component_param_name]__[param_name].

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_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()[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.

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

is_composite()[source]#

Check if the object is composite.

A composite object is an object which contains objects as parameter values.

Returns:

bool: Whether self contains a parameter whose value is a BaseObject, list of (str, BaseObject) tuples or dict[str, BaseObject].

property is_fitted[source]#

Whether fit has been called.

Inspects object’s _is_fitted attribute that should initialize to False during object construction, and be set to True in calls to an object’s fit method.

Returns:

bool: Whether the estimator has been fit.

property name[source]#: Return the name of the object or estimator.

property named_steps[source]#: Map the steps to a dictionary.

predict(X)[source]#

Predict labels for data from features.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”

Parameters:

Xpandas DataFrame, must have same columns as X in fit: data to predict labels for

Returns:

ypandas DataFrame, same length as X: labels predicted for X

predict_interval(X=None, coverage=0.9)[source]#

Compute/return interval predictions.

If coverage is iterable, multiple intervals will be calculated.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”.

Parameters:

Xpandas DataFrame, must have same columns as X in fit: data to predict labels for
coveragefloat or list of float of unique values, optional (default=0.90): nominal coverage(s) of predictive interval(s)

Returns:

pred_intpd.DataFrame: Column has multi-index: first level is variable name from y in fit, second level coverage fractions for which intervals were computed, in the same order as in input coverage. Third level is string “lower” or “upper”, for lower/upper interval end. Row index is equal to row index of X. Entries are lower/upper bounds of interval predictions, for var in col index, at nominal coverage in second col index, lower/upper depending on third col index, for the row index. Upper/lower interval end are equivalent to quantile predictions at alpha = 0.5 - c/2, 0.5 + c/2 for c in coverage.

predict_proba(X)[source]#

Predict distribution over labels for data from features.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”

Parameters:

Xpandas DataFrame, must have same columns as X in fit: data to predict labels for

Returns:

yskpro BaseDistribution, same length as X: labels predicted for X

predict_quantiles(X=None, alpha=None)[source]#

Compute/return quantile predictions.

If alpha is iterable, multiple quantiles will be calculated.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”.

Parameters:

Xpandas DataFrame, must have same columns as X in fit: data to predict labels for
alphafloat or list of float of unique values, optional (default=[0.05, 0.95]): A probability or list of, at which quantile predictions are computed.

Returns:

quantilespd.DataFrame: Column has multi-index: first level is variable name from y in fit, second level being the values of alpha passed to the function. Row index is equal to row index of X. Entries are quantile predictions, for var in col index, at quantile probability in second col index, for the row index.

predict_var(X=None)[source]#

Compute/return variance predictions.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”.

Parameters:

Xpandas DataFrame, must have same columns as X in fit: data to predict labels for

Returns:

pred_varpd.DataFrame: Column names are exactly those of y passed in fit. Row index is equal to row index of X. Entries are variance prediction, for var in col index. A variance prediction for given variable and fh index is a predicted variance for that variable and index, given observed data.

reset()[source]#

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))

set_config(**config_dict)[source]#

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(**kwargs)[source]#

Set the object’s direct parameters and the parameters of components.

Valid parameter keys can be listed with get_params().

Like BaseObject implementation it allows values of indirect parameters of a component to be set when a parameter’s value is an object that implements set_params. This also also expands the functionality to allow parameter to allow the indirect parameters of components to be set when a parameter’s values follow the named object API (either sequence of str, BaseObject tuples or dict[str, BaseObject]).

Returns:

Self: Instance of self.

set_random_state(random_state=None, deep=True, self_policy='copy')[source]#

Set random_state pseudo-random seed parameters for self.

Finds random_state named parameters via estimator.get_params, and sets them to integers derived from random_state via set_params. These integers are sampled from chain hashing via sample_dependent_seed, and guarantee pseudo-random independence of seeded random generators.

Applies to random_state parameters in estimator depending on self_policy, and remaining component estimators if and only if deep=True.

Note: calls set_params even if self does not have a random_state, or none of the components have a random_state parameter. Therefore, set_random_state will reset any scikit-base estimator, even those without a random_state parameter.

Parameters:

random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.

deepbool, default=True

Whether to set the random state in sub-estimators. If False, will set only self’s random_state parameter, if exists. If True, will set random_state parameters in sub-estimators as well.

self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”

“copy” : estimator.random_state is set to input random_state
“keep” : estimator.random_state is kept as is
“new” : estimator.random_state is set to a new random state,

derived from input random_state, and in general different from it

Returns:

selfreference to 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 setting tag values in tag_dict as dynamic tags in self.