- class darts.utils.multioutput.MultiOutputClassifier(estimator, eval_set_name=None, eval_weight_name=None, output_chunk_length=None, **kwargs)[source]¶
Bases:
MultiOutputMixin
,MultiOutputClassifier
sklearn.utils.multioutput.MultiOutputClassifier
with a modifiedfit()
method that also slices validation data correctly. The validation data has to be passed as parametereval_set
in**fit_params
.Attributes
Whether model supports sample weight for training.
Methods
fit
(X, y[, sample_weight])Fit the model to data, separately for each output variable.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
partial_fit
(X, y[, classes, sample_weight])Incrementally fit a separate model for each class output.
predict
(X)Predict multi-output variable using model for each target variable.
Return prediction probabilities for each class of each output.
score
(X, y)Return the mean accuracy on the given test data and labels.
set_fit_request
(*[, sample_weight])Configure whether metadata should be requested to be passed to the
fit
method.set_params
(**params)Set the parameters of this estimator.
set_partial_fit_request
(*[, classes, ...])Configure whether metadata should be requested to be passed to the
partial_fit
method.- fit(X, y, sample_weight=None, **fit_params)[source]¶
Fit the model to data, separately for each output variable.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input data.
y ({array-like, sparse matrix} of shape (n_samples, n_outputs)) – Multi-output targets. An indicator matrix turns on multilabel estimation.
sample_weight (array-like of shape (n_samples, n_outputs), default=None) – Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
**fit_params (dict of string -> object) –
Parameters passed to the
estimator.fit
method of each step.New in version 0.23.
- Returns
self – Returns a fitted instance.
- Return type
object
- get_metadata_routing()¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
New in version 1.3.
- Returns
routing – A
MetadataRouter
encapsulating routing information.- Return type
MetadataRouter
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- partial_fit(X, y, classes=None, sample_weight=None, **partial_fit_params)¶
Incrementally fit a separate model for each class output.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input data.
y ({array-like, sparse matrix} of shape (n_samples, n_outputs)) – Multi-output targets.
classes (list of ndarray of shape (n_outputs,), default=None) – Each array is unique classes for one output in str/int. Can be obtained via
[np.unique(y[:, i]) for i in range(y.shape[1])]
, where y is the target matrix of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.sample_weight (array-like of shape (n_samples,), default=None) – Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
**partial_fit_params (dict of str -> object) –
Parameters passed to the
estimator.partial_fit
method of each sub-estimator.Only available if enable_metadata_routing=True. See the User Guide.
New in version 1.3.
- Returns
self – Returns a fitted instance.
- Return type
object
- predict(X)¶
Predict multi-output variable using model for each target variable.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input data.
- Returns
y – Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.
- Return type
{array-like, sparse matrix} of shape (n_samples, n_outputs)
- predict_proba(X)[source]¶
Return prediction probabilities for each class of each output.
This method will raise a
ValueError
if any of the estimators do not havepredict_proba
.- Parameters
X (array-like of shape (n_samples, n_features)) – The input data.
- Returns
p – The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
Changed in version 0.19: This function now returns a list of arrays where the length of the list is
n_outputs
, and each array is (n_samples
,n_classes
) for that particular output.- Return type
array of shape (n_samples, n_classes), or a list of n_outputs such arrays if n_outputs > 1.
- score(X, y)[source]¶
Return the mean accuracy on the given test data and labels.
- Parameters
X (array-like of shape (n_samples, n_features)) – Test samples.
y (array-like of shape (n_samples, n_outputs)) – True values for X.
- Returns
scores – Mean accuracy of predicted target versus true target.
- Return type
float
- set_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') MultiOutputClassifier ¶
Configure whether metadata should be requested to be passed to the
fit
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config()
). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
- Parameters
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter infit
.
- Returns
- selfobject
The updated object.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_partial_fit_request(*, classes: Union[bool, None, str] = '$UNCHANGED$', sample_weight: Union[bool, None, str] = '$UNCHANGED$') MultiOutputClassifier ¶
Configure whether metadata should be requested to be passed to the
partial_fit
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config()
). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topartial_fit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it topartial_fit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
- Parameters
- classesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
classes
parameter inpartial_fit
.- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inpartial_fit
.
- Returns
- selfobject
The updated object.
- property supports_sample_weight: bool¶
Whether model supports sample weight for training.
- Return type
bool
- class darts.utils.multioutput.MultiOutputMixin(estimator, eval_set_name=None, eval_weight_name=None, output_chunk_length=None, **kwargs)[source]¶
Bases:
object
Mixin for
sklearn.utils.multioutput._MultiOutputEstimator
with a modifiedfit()
method that also slices validation data correctly. The validation data has to be passed as parametereval_set
in**fit_params
.Attributes
Whether model supports sample weight for training.
Methods
fit
(X, y[, sample_weight])Fit the model to data, separately for each output variable.
- fit(X, y, sample_weight=None, **fit_params)[source]¶
Fit the model to data, separately for each output variable.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input data.
y ({array-like, sparse matrix} of shape (n_samples, n_outputs)) – Multi-output targets. An indicator matrix turns on multilabel estimation.
sample_weight (array-like of shape (n_samples, n_outputs), default=None) – Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
**fit_params (dict of string -> object) –
Parameters passed to the
estimator.fit
method of each step.New in version 0.23.
- Returns
self – Returns a fitted instance.
- Return type
object
- property supports_sample_weight: bool¶
Whether model supports sample weight for training.
- Return type
bool
- class darts.utils.multioutput.MultiOutputRegressor(estimator, eval_set_name=None, eval_weight_name=None, output_chunk_length=None, **kwargs)[source]¶
Bases:
MultiOutputMixin
,MultiOutputRegressor
sklearn.utils.multioutput.MultiOutputClassifier
with a modifiedfit()
method that also slices validation data correctly. The validation data has to be passed as parametereval_set
in**fit_params
.Attributes
Whether model supports sample weight for training.
Methods
fit
(X, y[, sample_weight])Fit the model to data, separately for each output variable.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
partial_fit
(X, y[, sample_weight])Incrementally fit the model to data, for each output variable.
predict
(X)Predict multi-output variable using model for each target variable.
score
(X, y[, sample_weight])Return coefficient of determination on test data.
set_fit_request
(*[, sample_weight])Configure whether metadata should be requested to be passed to the
fit
method.set_params
(**params)Set the parameters of this estimator.
set_partial_fit_request
(*[, sample_weight])Configure whether metadata should be requested to be passed to the
partial_fit
method.set_score_request
(*[, sample_weight])Configure whether metadata should be requested to be passed to the
score
method.- fit(X, y, sample_weight=None, **fit_params)¶
Fit the model to data, separately for each output variable.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input data.
y ({array-like, sparse matrix} of shape (n_samples, n_outputs)) – Multi-output targets. An indicator matrix turns on multilabel estimation.
sample_weight (array-like of shape (n_samples, n_outputs), default=None) – Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
**fit_params (dict of string -> object) –
Parameters passed to the
estimator.fit
method of each step.New in version 0.23.
- Returns
self – Returns a fitted instance.
- Return type
object
- get_metadata_routing()¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
New in version 1.3.
- Returns
routing – A
MetadataRouter
encapsulating routing information.- Return type
MetadataRouter
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- partial_fit(X, y, sample_weight=None, **partial_fit_params)[source]¶
Incrementally fit the model to data, for each output variable.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input data.
y ({array-like, sparse matrix} of shape (n_samples, n_outputs)) – Multi-output targets.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
**partial_fit_params (dict of str -> object) –
Parameters passed to the
estimator.partial_fit
method of each sub-estimator.Only available if enable_metadata_routing=True. See the User Guide.
New in version 1.3.
- Returns
self – Returns a fitted instance.
- Return type
object
- predict(X)¶
Predict multi-output variable using model for each target variable.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input data.
- Returns
y – Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.
- Return type
{array-like, sparse matrix} of shape (n_samples, n_outputs)
- score(X, y, sample_weight=None)¶
Return coefficient of determination on test data.
The coefficient of determination, \(R^2\), is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares
((y_true - y_pred)** 2).sum()
and \(v\) is the total sum of squares((y_true - y_true.mean()) ** 2).sum()
. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.- Parameters
X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.
- Returns
score – \(R^2\) of
self.predict(X)
w.r.t. y.- Return type
float
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') MultiOutputRegressor ¶
Configure whether metadata should be requested to be passed to the
fit
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config()
). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
- Parameters
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter infit
.
- Returns
- selfobject
The updated object.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_partial_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') MultiOutputRegressor ¶
Configure whether metadata should be requested to be passed to the
partial_fit
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config()
). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topartial_fit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it topartial_fit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
- Parameters
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inpartial_fit
.
- Returns
- selfobject
The updated object.
- set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') MultiOutputRegressor ¶
Configure whether metadata should be requested to be passed to the
score
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config()
). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
- Parameters
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns
- selfobject
The updated object.
- property supports_sample_weight: bool¶
Whether model supports sample weight for training.
- Return type
bool
- darts.utils.multioutput.get_multioutput_estimator_cls(model_type)[source]¶
- Return type
type
[MultiOutputMixin
]