skpro.regression.residual.ResidualDouble#

class skpro.regression.residual.ResidualDouble(estimator, estimator_resid=None, residual_trafo='absolute', distr_type='Normal', distr_loc_scale_name=None, distr_params=None, use_y_pred=False, cv=None, min_scale=1e-10)[source]#

Residual double regressor.

Make a parametric probabilistic prediction using two tabular regressors, with one tabular regressor predicting the mean, and one the deviation from the mean.

The mean is predicted by estimator. The residual is predicted by estimator_resid. The residual is transformed by residual_trafo. The predicted mean and residual are passed to a distribution specified by distr_type, and possibly distr_params, distr_loc_scale_name.

The residuals predicted on the training data are used to fit estimator_resid. If cv is passed, the residuals are out-of-sample according to cv, otherwise in-sample.

use_y_pred determines whether the predicted mean is used as a feature in predicting the residual.

A formal description of the algorithm follows.

In fit, given training data X, y:

Fit clone estimator_ of estimator to predict y from X, i.e., fit(X, y).
Predict mean label y_pred for X using a clone of estimator. If cv is None, this is via plain estimator.predict(X). If cv is not None, out-of-sample predictions are obtained via cv. In this case, indices not appearing in cv are predicted in-sample.
Compute residual resid as residual_trafo(y - y_pred). If residual_trafo is a transformer, residual_trafo.fit_transform is used.
Fit clone estimator_resid_ of estimator_resid to predict resid from X, i.e., fit(X, resid). If use_y_pred is True, y_pred is used as a feature in predicting.

In predict, given test data X:

Predict mean label y_pred for X using estimator_.predict(X).
Return y_pred.

In predict_proba, given test data X:

Predict mean label y_pred for X using estimator_.predict(X).
Predict residual resid for X using estimator_resid_.predict(X). If use_y_pred is True, y_pred is used as a feature in predicting.
Predict distribution y_pred_proba for X as follows: The location parameter is y_pred. The scale parameter is resid. Further parameters can be specified via distr_params.
Return y_pred_proba.

Parameters:

estimatorsklearn regressor: estimator predicting the mean or location
estimator_residsklearn regressor: estimator predicting the scale of the residual default = sklearn DummyRegressor(strategy=”mean”)
residual_trafostr, or transformer, default=”absolute”: determines the labels predicted by estimator_resid absolute = absolute residuals squared = squared residuals if transformer, applies fit_transform to batch of signed residuals
distr_typestr or BaseDistribution, default = “Normal”: type of distribution to predict str options are “Normal”, “Laplace”, “Cauchy”, “t”
distr_loc_scale_nametuple of length two, default = (“loc”, “scale”): names of the parameters in the distribution to use for location and scale if distr_type is a string, this is overridden to the correct parameters if distr_type is a BaseDistribution, this is used to determine the location and scale parameters that the predictions are passed to
distr_paramsdict, default = {}: parameters to pass to the distribution must be valid parameters of distr_type, if BaseDistribution must be default or dict with key df, if t distribution
use_y_predbool, default=False: whether to use the predicted location in predicting the scale of the residual
cvoptional, sklearn cv splitter, default = None: if passed, will be used to obtain out-of-sample residuals according to cv instead of in-sample residuals in fit of this estimator
min_scalefloat, default=1e-10: minimum scale parameter. If smaller scale parameter is predicted by estimator_resid, will be clipped to this value

Attributes:

estimator_sklearn regressor, clone of estimator: fitted estimator predicting the mean or location
estimator_resid_sklearn regressor, clone of estimator_resid: fitted estimator predicting the scale of the residual

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)	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 composed of other BaseObjects.
`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`(**params)	Set the parameters of this object.
`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.

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)[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

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.

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 (= BaseObject-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include parameters of components.

Returns:

paramsdict with str-valued keys

Dictionary of parameters, paramname : paramvalue keys-value pairs include:

always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction
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

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 composed of other BaseObjects.

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 an object has any parameters whose values are BaseObjects.

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.

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(**params)[source]#

Set the parameters of this object.

The method works on simple estimators as well as on composite objects. Parameter key strings <component>__<parameter> can be used for composites, i.e., objects that contain other objects, to access <parameter> in the component <component>. The string <parameter>, without <component>__, can also be used if this makes the reference unambiguous, e.g., there are no two parameters of components with the name <parameter>.

Parameters:

**paramsdict: BaseObject parameters, keys must be <component>__<parameter> strings. __ suffixes can alias full strings, if unique among get_params keys.

Returns:

selfreference to self (after parameters have been set)

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.