Multioutput

ClassifierChain¶

Module Sklearn.Multioutput.ClassifierChain wraps Python class sklearn.multioutput.ClassifierChain.

type t

create¶

constructor and attributes create

val create :
  ?order:[`Arr of [>`ArrayLike] Np.Obj.t | `Random] ->
  ?cv:[`BaseCrossValidator of [>`BaseCrossValidator] Np.Obj.t | `I of int | `Arr of [>`ArrayLike] Np.Obj.t] ->
  ?random_state:int ->
  base_estimator:[>`BaseEstimator] Np.Obj.t ->
  unit ->
  t

A multi-label model that arranges binary classifiers into a chain.

Each model makes a prediction in the order specified by the chain using all of the available features provided to the model plus the predictions of models that are earlier in the chain.

Read more in the :ref:User Guide <classifierchain>.

.. versionadded:: 0.19

Parameters

base_estimator : estimator The base estimator from which the classifier chain is built.
order : array-like of shape (n_outputs,) or 'random', optional By default the order will be determined by the order of columns in the label matrix Y.::
```
order = [0, 1, 2, ..., Y.shape[1] - 1]
```
The order of the chain can be explicitly set by providing a list of integers. For example, for a chain of length 5.::
```
order = [1, 3, 2, 4, 0]
```
means that the first model in the chain will make predictions for column 1 in the Y matrix, the second model will make predictions for column 3, etc.

If order is 'random' a random ordering will be used.
cv : int, cross-validation generator or an iterable, optional (default=None) Determines whether to use cross validated predictions or true labels for the results of previous estimators in the chain. If cv is None the true labels are used when fitting. Otherwise possible inputs for cv are:
- integer, to specify the number of folds in a (Stratified)KFold,
- :term:CV splitter,
- An iterable yielding (train, test) splits as arrays of indices.
random_state : int, RandomState instance or None, optional (default=None) If order='random', determines random number generation for the chain order. In addition, it controls the random seed given at each base_estimator at each chaining iteration. Thus, it is only used when base_estimator exposes a random_state. Pass an int for reproducible output across multiple function calls.
See :term:Glossary <random_state>.

Attributes

classes_ : list A list of arrays of length len(estimators_) containing the class labels for each estimator in the chain.
estimators_ : list A list of clones of base_estimator.
order_ : list The order of labels in the classifier chain.

References

Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank, 'Classifier Chains for Multi-label Classification', 2009.

decision_function¶

method decision_function

val decision_function :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Evaluate the decision_function of the models in the chain.

Parameters

X : array-like, shape (n_samples, n_features)

Returns

Y_decision : array-like, shape (n_samples, n_classes ) Returns the decision function of the sample for each model in the chain.

fit¶

method fit

val fit :
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit the model to data matrix X and targets Y.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data.
Y : array-like, shape (n_samples, n_classes) The target values.

Returns

self : object

get_params¶

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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 : mapping of string to any Parameter names mapped to their values.

predict¶

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict on the data matrix X using the ClassifierChain model.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data.

Returns

Y_pred : array-like, shape (n_samples, n_classes) The predicted values.

predict_proba¶

method predict_proba

val predict_proba :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict probability estimates.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features)

Returns

Y_prob : array-like, shape (n_samples, n_classes)

score¶

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns

score : float Mean accuracy of self.predict(X) wrt. y.

set_params¶

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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 : object Estimator instance.

classes_¶

attribute classes_

val classes_ : t -> [>`ArrayLike] Np.Obj.t
val classes_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

estimators_¶

attribute estimators_

val estimators_ : t -> [>`ArrayLike] Np.Obj.t
val estimators_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

order_¶

attribute order_

val order_ : t -> [>`ArrayLike] Np.Obj.t
val order_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

MultiOutputClassifier¶

Module Sklearn.Multioutput.MultiOutputClassifier wraps Python class sklearn.multioutput.MultiOutputClassifier.

type t

create¶

constructor and attributes create

val create :
  ?n_jobs:int ->
  estimator:[>`BaseEstimator] Np.Obj.t ->
  unit ->
  t

Multi target classification

This strategy consists of fitting one classifier per target. This is a simple strategy for extending classifiers that do not natively support multi-target classification

Parameters

estimator : estimator object An estimator object implementing :term:fit, :term:score and :term:predict_proba.
n_jobs : int or None, optional (default=None) The number of jobs to use for the computation. It does each target variable in y in parallel. None means 1 unless in a :obj:joblib.parallel_backend context. -1 means using all processors. See :term:Glossary <n_jobs> for more details.

.. versionchanged:: v0.20 n_jobs default changed from 1 to None

Attributes

classes_ : array, shape = (n_classes,) Class labels.
estimators_ : list of n_output estimators Estimators used for predictions.

Examples

>>> import numpy as np
>>> from sklearn.datasets import make_multilabel_classification
>>> from sklearn.multioutput import MultiOutputClassifier
>>> from sklearn.neighbors import KNeighborsClassifier

>>> X, y = make_multilabel_classification(n_classes=3, random_state=0)
>>> clf = MultiOutputClassifier(KNeighborsClassifier()).fit(X, y)
>>> clf.predict(X[-2:])
array([[1, 1, 0], [1, 1, 1]])

fit¶

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit the model to data matrix X and targets Y.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data.
Y : array-like of shape (n_samples, n_classes) The target values.
sample_weight : array-like of shape (n_samples,) or None Sample weights. If None, then samples are equally weighted. Only supported if the underlying classifier supports sample weights.
**fit_params : dict of string -> object Parameters passed to the estimator.fit method of each step.

Returns

self : object

get_params¶

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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 : mapping of string to any Parameter names mapped to their values.

partial_fit¶

method partial_fit

val partial_fit :
  ?classes:[>`ArrayLike] Np.Obj.t list ->
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`Spmatrix] Np.Obj.t ->
  y:[>`Spmatrix] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Incrementally fit the model to data. Fit a separate model for each output variable.

Parameters

X : (sparse) array-like, shape (n_samples, n_features) Data.
y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets.
classes : list of numpy arrays, shape (n_outputs) Each array is unique classes for one output in str/int Can be obtained by 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.

Returns

self : object

predict¶

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict multi-output variable using a model trained for each target variable.

Parameters

X : (sparse) array-like, shape (n_samples, n_features) Data.

Returns

y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets predicted across multiple predictors.
Note: Separate models are generated for each predictor.

score¶

method score

val score :
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Returns the mean accuracy on the given test data and labels.

Parameters

X : array-like, shape [n_samples, n_features] Test samples
y : array-like, shape [n_samples, n_outputs] True values for X

Returns

scores : float accuracy_score of self.predict(X) versus y

set_params¶

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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 : object Estimator instance.

classes_¶

attribute classes_

val classes_ : t -> [>`ArrayLike] Np.Obj.t
val classes_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

estimators_¶

attribute estimators_

val estimators_ : t -> Py.Object.t
val estimators_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

MultiOutputEstimator¶

Module Sklearn.Multioutput.MultiOutputEstimator wraps Python class sklearn.multioutput.MultiOutputEstimator.

type t

fit¶

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:[>`Spmatrix] Np.Obj.t ->
  y:[>`Spmatrix] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit the model to data. Fit a separate model for each output variable.

Parameters

X : (sparse) array-like, shape (n_samples, n_features) Data.
y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets. An indicator matrix turns on multilabel estimation.
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.
**fit_params : dict of string -> object Parameters passed to the estimator.fit method of each step.

Returns

self : object

get_params¶

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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 : mapping of string to any Parameter names mapped to their values.

partial_fit¶

method partial_fit

val partial_fit :
  ?classes:[>`ArrayLike] Np.Obj.t list ->
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`Spmatrix] Np.Obj.t ->
  y:[>`Spmatrix] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Incrementally fit the model to data. Fit a separate model for each output variable.

Parameters

X : (sparse) array-like, shape (n_samples, n_features) Data.
y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets.
classes : list of numpy arrays, shape (n_outputs) Each array is unique classes for one output in str/int Can be obtained by 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.

Returns

self : object

predict¶

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict multi-output variable using a model trained for each target variable.

Parameters

X : (sparse) array-like, shape (n_samples, n_features) Data.

Returns

y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets predicted across multiple predictors.
Note: Separate models are generated for each predictor.

set_params¶

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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 : object Estimator instance.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

MultiOutputRegressor¶

Module Sklearn.Multioutput.MultiOutputRegressor wraps Python class sklearn.multioutput.MultiOutputRegressor.

type t

create¶

constructor and attributes create

val create :
  ?n_jobs:int ->
  estimator:[>`BaseEstimator] Np.Obj.t ->
  unit ->
  t

Multi target regression

This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.

.. versionadded:: 0.18

Parameters

estimator : estimator object An estimator object implementing :term:fit and :term:predict.
n_jobs : int or None, optional (default=None) The number of jobs to run in parallel for :meth:fit. None means 1 unless in a :obj:joblib.parallel_backend context. -1 means using all processors. See :term:Glossary <n_jobs> for more details.

When individual estimators are fast to train or predict using n_jobs>1 can result in slower performance due to the overhead of spawning processes.

.. versionchanged:: v0.20 n_jobs default changed from 1 to None

Attributes

estimators_ : list of n_output estimators Estimators used for predictions.

Examples

>>> import numpy as np
>>> from sklearn.datasets import load_linnerud
>>> from sklearn.multioutput import MultiOutputRegressor
>>> from sklearn.linear_model import Ridge
>>> X, y = load_linnerud(return_X_y=True)
>>> clf = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y)
>>> clf.predict(X[[0]])
array([[176..., 35..., 57...]])

fit¶

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:[>`Spmatrix] Np.Obj.t ->
  y:[>`Spmatrix] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit the model to data. Fit a separate model for each output variable.

Parameters

X : (sparse) array-like, shape (n_samples, n_features) Data.
y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets. An indicator matrix turns on multilabel estimation.
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.
**fit_params : dict of string -> object Parameters passed to the estimator.fit method of each step.

Returns

self : object

get_params¶

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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 : mapping of string to any Parameter names mapped to their values.

partial_fit¶

method partial_fit

val partial_fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`Spmatrix] Np.Obj.t ->
  y:[>`Spmatrix] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Incrementally fit the model to data. Fit a separate model for each output variable.

Parameters

X : (sparse) array-like, shape (n_samples, n_features) Data.
y : (sparse) array-like, 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.

Returns

self : object

predict¶

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict multi-output variable using a model trained for each target variable.

Parameters

X : (sparse) array-like, shape (n_samples, n_features) Data.

Returns

y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets predicted across multiple predictors.
Note: Separate models are generated for each predictor.

score¶

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - 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, shape = (n_samples, n_samples_fitted), where n_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 : float R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of :func:~sklearn.metrics.r2_score. This influences the score method of all the multioutput regressors (except for :class:~sklearn.multioutput.MultiOutputRegressor).

set_params¶

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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 : object Estimator instance.

estimators_¶

attribute estimators_

val estimators_ : t -> Py.Object.t
val estimators_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

RegressorChain¶

Module Sklearn.Multioutput.RegressorChain wraps Python class sklearn.multioutput.RegressorChain.

type t

create¶

constructor and attributes create

val create :
  ?order:[`Arr of [>`ArrayLike] Np.Obj.t | `Random] ->
  ?cv:[`BaseCrossValidator of [>`BaseCrossValidator] Np.Obj.t | `I of int | `Arr of [>`ArrayLike] Np.Obj.t] ->
  ?random_state:int ->
  base_estimator:[>`BaseEstimator] Np.Obj.t ->
  unit ->
  t

A multi-label model that arranges regressions into a chain.

Each model makes a prediction in the order specified by the chain using all of the available features provided to the model plus the predictions of models that are earlier in the chain.

Read more in the :ref:User Guide <regressorchain>.

.. versionadded:: 0.20

Parameters

base_estimator : estimator The base estimator from which the classifier chain is built.
order : array-like of shape (n_outputs,) or 'random', optional By default the order will be determined by the order of columns in the label matrix Y.::
```
order = [0, 1, 2, ..., Y.shape[1] - 1]
```
The order of the chain can be explicitly set by providing a list of integers. For example, for a chain of length 5.::
```
order = [1, 3, 2, 4, 0]
```
means that the first model in the chain will make predictions for column 1 in the Y matrix, the second model will make predictions for column 3, etc.

If order is 'random' a random ordering will be used.
cv : int, cross-validation generator or an iterable, optional (default=None) Determines whether to use cross validated predictions or true labels for the results of previous estimators in the chain. If cv is None the true labels are used when fitting. Otherwise possible inputs for cv are:
- integer, to specify the number of folds in a (Stratified)KFold,
- :term:CV splitter,
- An iterable yielding (train, test) splits as arrays of indices.
random_state : int, RandomState instance or None, optional (default=None) If order='random', determines random number generation for the chain order. In addition, it controls the random seed given at each base_estimator at each chaining iteration. Thus, it is only used when base_estimator exposes a random_state. Pass an int for reproducible output across multiple function calls.
See :term:Glossary <random_state>.

Attributes

estimators_ : list A list of clones of base_estimator.
order_ : list The order of labels in the classifier chain.

fit¶

method fit

val fit :
  ?fit_params:(string * Py.Object.t) list ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit the model to data matrix X and targets Y.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data.
Y : array-like, shape (n_samples, n_classes) The target values.
**fit_params : dict of string -> object Parameters passed to the fit method at each step of the regressor chain.

Returns

self : object

get_params¶

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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 : mapping of string to any Parameter names mapped to their values.

predict¶

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict on the data matrix X using the ClassifierChain model.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data.

Returns

Y_pred : array-like, shape (n_samples, n_classes) The predicted values.

score¶

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - 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, shape = (n_samples, n_samples_fitted), where n_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 : float R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of :func:~sklearn.metrics.r2_score. This influences the score method of all the multioutput regressors (except for :class:~sklearn.multioutput.MultiOutputRegressor).

set_params¶

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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 : object Estimator instance.

estimators_¶

attribute estimators_

val estimators_ : t -> [>`ArrayLike] Np.Obj.t
val estimators_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

order_¶

attribute order_

val order_ : t -> [>`ArrayLike] Np.Obj.t
val order_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

abstractmethod¶

function abstractmethod

val abstractmethod :
  Py.Object.t ->
  Py.Object.t

A decorator indicating abstract methods.

Requires that the metaclass is ABCMeta or derived from it. A class that has a metaclass derived from ABCMeta cannot be instantiated unless all of its abstract methods are overridden. The abstract methods can be called using any of the normal 'super' call mechanisms. abstractmethod() may be used to declare abstract methods for properties and descriptors.

Usage:

class C(metaclass=ABCMeta):
    @abstractmethod
    def my_abstract_method(self, ...):
        ...

check_X_y¶

function check_X_y

val check_X_y :
  ?accept_sparse:[`S of string | `StringList of string list | `Bool of bool] ->
  ?accept_large_sparse:bool ->
  ?dtype:[`Dtypes of Np.Dtype.t list | `S of string | `Dtype of Np.Dtype.t | `None] ->
  ?order:[`F | `C] ->
  ?copy:bool ->
  ?force_all_finite:[`Allow_nan | `Bool of bool] ->
  ?ensure_2d:bool ->
  ?allow_nd:bool ->
  ?multi_output:bool ->
  ?ensure_min_samples:int ->
  ?ensure_min_features:int ->
  ?y_numeric:bool ->
  ?estimator:[>`BaseEstimator] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  unit ->
  (Py.Object.t * Py.Object.t)

Input validation for standard estimators.

Checks X and y for consistent length, enforces X to be 2D and y 1D. By default, X is checked to be non-empty and containing only finite values. Standard input checks are also applied to y, such as checking that y does not have np.nan or np.inf targets. For multi-label y, set multi_output=True to allow 2D and sparse y. If the dtype of X is object, attempt converting to float, raising on failure.

Parameters

X : nd-array, list or sparse matrix Input data.
y : nd-array, list or sparse matrix Labels.
accept_sparse : string, boolean or list of string (default=False) String[s] representing allowed sparse matrix formats, such as 'csc', 'csr', etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error.
accept_large_sparse : bool (default=True) If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse will cause it to be accepted only if its indices are stored with a 32-bit dtype.

.. versionadded:: 0.20
dtype : string, type, list of types or None (default='numeric') Data type of result. If None, the dtype of the input is preserved. If 'numeric', dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list.
order : 'F', 'C' or None (default=None) Whether an array will be forced to be fortran or c-style.
copy : boolean (default=False) Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.
force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf, np.nan, pd.NA in X. This parameter does not influence whether y can have np.inf, np.nan, pd.NA values. The possibilities are:
- True: Force all values of X to be finite.
- False: accepts np.inf, np.nan, pd.NA in X.
- 'allow-nan': accepts only np.nan or pd.NA values in X. Values cannot be infinite.
.. versionadded:: 0.20 force_all_finite accepts the string 'allow-nan'.

.. versionchanged:: 0.23 Accepts pd.NA and converts it into np.nan
ensure_2d : boolean (default=True) Whether to raise a value error if X is not 2D.
allow_nd : boolean (default=False) Whether to allow X.ndim > 2.
multi_output : boolean (default=False) Whether to allow 2D y (array or sparse matrix). If false, y will be validated as a vector. y cannot have np.nan or np.inf values if multi_output=True.
ensure_min_samples : int (default=1) Make sure that X has a minimum number of samples in its first axis (rows for a 2D array).
ensure_min_features : int (default=1) Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when X has effectively 2 dimensions or is originally 1D and ensure_2d is True. Setting to 0 disables this check.
y_numeric : boolean (default=False) Whether to ensure that y has a numeric type. If dtype of y is object, it is converted to float64. Should only be used for regression algorithms.
estimator : str or estimator instance (default=None) If passed, include the name of the estimator in warning messages.

Returns

X_converted : object The converted and validated X.
y_converted : object The converted and validated y.

check_array¶

function check_array

val check_array :
  ?accept_sparse:[`S of string | `StringList of string list | `Bool of bool] ->
  ?accept_large_sparse:bool ->
  ?dtype:[`Dtypes of Np.Dtype.t list | `S of string | `Dtype of Np.Dtype.t | `None] ->
  ?order:[`F | `C] ->
  ?copy:bool ->
  ?force_all_finite:[`Allow_nan | `Bool of bool] ->
  ?ensure_2d:bool ->
  ?allow_nd:bool ->
  ?ensure_min_samples:int ->
  ?ensure_min_features:int ->
  ?estimator:[>`BaseEstimator] Np.Obj.t ->
  array:Py.Object.t ->
  unit ->
  Py.Object.t

Input validation on an array, list, sparse matrix or similar.

By default, the input is checked to be a non-empty 2D array containing only finite values. If the dtype of the array is object, attempt converting to float, raising on failure.

Parameters

array : object Input object to check / convert.
accept_sparse : string, boolean or list/tuple of strings (default=False) String[s] representing allowed sparse matrix formats, such as 'csc', 'csr', etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error.
accept_large_sparse : bool (default=True) If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse=False will cause it to be accepted only if its indices are stored with a 32-bit dtype.

.. versionadded:: 0.20
dtype : string, type, list of types or None (default='numeric') Data type of result. If None, the dtype of the input is preserved. If 'numeric', dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list.
order : 'F', 'C' or None (default=None) Whether an array will be forced to be fortran or c-style. When order is None (default), then if copy=False, nothing is ensured about the memory layout of the output array; otherwise (copy=True) the memory layout of the returned array is kept as close as possible to the original array.
copy : boolean (default=False) Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.
force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf, np.nan, pd.NA in array. The possibilities are:
- True: Force all values of array to be finite.
- False: accepts np.inf, np.nan, pd.NA in array.
- 'allow-nan': accepts only np.nan and pd.NA values in array. Values cannot be infinite.
.. versionadded:: 0.20 force_all_finite accepts the string 'allow-nan'.

.. versionchanged:: 0.23 Accepts pd.NA and converts it into np.nan
ensure_2d : boolean (default=True) Whether to raise a value error if array is not 2D.
allow_nd : boolean (default=False) Whether to allow array.ndim > 2.
ensure_min_samples : int (default=1) Make sure that the array has a minimum number of samples in its first axis (rows for a 2D array). Setting to 0 disables this check.
ensure_min_features : int (default=1) Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when the input data has effectively 2 dimensions or is originally 1D and ensure_2d is True. Setting to 0 disables this check.
estimator : str or estimator instance (default=None) If passed, include the name of the estimator in warning messages.

Returns

array_converted : object The converted and validated array.

check_classification_targets¶

function check_classification_targets

val check_classification_targets :
  [>`ArrayLike] Np.Obj.t ->
  Py.Object.t

Ensure that target y is of a non-regression type.

Only the following target types (as defined in type_of_target) are allowed: 'binary', 'multiclass', 'multiclass-multioutput', 'multilabel-indicator', 'multilabel-sequences'

Parameters

y : array-like

check_is_fitted¶

function check_is_fitted

val check_is_fitted :
  ?attributes:[`Arr of [>`ArrayLike] Np.Obj.t | `S of string | `StringList of string list] ->
  ?msg:string ->
  ?all_or_any:[`Callable of Py.Object.t | `PyObject of Py.Object.t] ->
  estimator:[>`BaseEstimator] Np.Obj.t ->
  unit ->
  Py.Object.t

Perform is_fitted validation for estimator.

Checks if the estimator is fitted by verifying the presence of fitted attributes (ending with a trailing underscore) and otherwise raises a NotFittedError with the given message.

This utility is meant to be used internally by estimators themselves, typically in their own predict / transform methods.

Parameters

estimator : estimator instance. estimator instance for which the check is performed.
attributes : str, list or tuple of str, default=None Attribute name(s) given as string or a list/tuple of strings
Eg.: ['coef_', 'estimator_', ...], 'coef_'

If None, estimator is considered fitted if there exist an attribute that ends with a underscore and does not start with double underscore.
msg : string The default error message is, 'This %(name)s instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.'

For custom messages if '%(name)s' is present in the message string, it is substituted for the estimator name.
Eg. : 'Estimator, %(name)s, must be fitted before sparsifying'.
all_or_any : callable, {all, any}, default all Specify whether all or any of the given attributes must exist.

Returns

None

Raises

NotFittedError If the attributes are not found.

check_random_state¶

function check_random_state

val check_random_state :
  [`Optional of [`I of int | `None] | `RandomState of Py.Object.t] ->
  Py.Object.t

Turn seed into a np.random.RandomState instance

Parameters

seed : None | int | instance of RandomState If seed is None, return the RandomState singleton used by np.random. If seed is an int, return a new RandomState instance seeded with seed. If seed is already a RandomState instance, return it. Otherwise raise ValueError.

clone¶

function clone

val clone :
  ?safe:bool ->
  estimator:[>`BaseEstimator] Np.Obj.t ->
  unit ->
  Py.Object.t

Constructs a new estimator with the same parameters.

Clone does a deep copy of the model in an estimator without actually copying attached data. It yields a new estimator with the same parameters that has not been fit on any data.

Parameters

estimator : {list, tuple, set} of estimator objects or estimator object The estimator or group of estimators to be cloned.
safe : bool, default=True If safe is false, clone will fall back to a deep copy on objects that are not estimators.

cross_val_predict¶

function cross_val_predict

val cross_val_predict :
  ?y:[>`ArrayLike] Np.Obj.t ->
  ?groups:[>`ArrayLike] Np.Obj.t ->
  ?cv:[`BaseCrossValidator of [>`BaseCrossValidator] Np.Obj.t | `I of int | `Arr of [>`ArrayLike] Np.Obj.t] ->
  ?n_jobs:int ->
  ?verbose:int ->
  ?fit_params:[`Dict of Dict.t | `Defualt_None of Py.Object.t] ->
  ?pre_dispatch:[`S of string | `I of int] ->
  ?method_:string ->
  estimator:[>`BaseEstimator] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  unit ->
  [>`ArrayLike] Np.Obj.t

Generate cross-validated estimates for each input data point

The data is split according to the cv parameter. Each sample belongs to exactly one test set, and its prediction is computed with an estimator fitted on the corresponding training set.

Passing these predictions into an evaluation metric may not be a valid way to measure generalization performance. Results can differ from :func:cross_validate and :func:cross_val_score unless all tests sets have equal size and the metric decomposes over samples.

Read more in the :ref:User Guide <cross_validation>.

Parameters

estimator : estimator object implementing 'fit' and 'predict' The object to use to fit the data.
X : array-like of shape (n_samples, n_features) The data to fit. Can be, for example a list, or an array at least 2d.
y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None The target variable to try to predict in the case of supervised learning.
groups : array-like of shape (n_samples,), default=None Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a 'Group' :term:cv instance (e.g., :class:GroupKFold).
cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are:
- None, to use the default 5-fold cross validation,
- int, to specify the number of folds in a (Stratified)KFold,
- :term:CV splitter,
- An iterable yielding (train, test) splits as arrays of indices.
For int/None inputs, if the estimator is a classifier and y is either binary or multiclass, :class:StratifiedKFold is used. In all other cases, :class:KFold is used.
Refer :ref:User Guide <cross_validation> for the various cross-validation strategies that can be used here.

.. versionchanged:: 0.22 cv default value if None changed from 3-fold to 5-fold.
n_jobs : int, default=None The number of CPUs to use to do the computation. None means 1 unless in a :obj:joblib.parallel_backend context. -1 means using all processors. See :term:Glossary <n_jobs> for more details.
verbose : int, default=0 The verbosity level.
fit_params : dict, defualt=None Parameters to pass to the fit method of the estimator.

pre_dispatch : int or str, default='2*n_jobs' Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:

- None, in which case all the jobs are immediately
  created and spawned. Use this for lightweight and
  fast-running jobs, to avoid delays due to on-demand
  spawning of the jobs

- An int, giving the exact number of total jobs that are
  spawned

- A str, giving an expression as a function of n_jobs,
  as in '2*n_jobs'

method : str, default='predict' Invokes the passed method name of the passed estimator. For method='predict_proba', the columns correspond to the classes in sorted order.

Returns

predictions : ndarray This is the result of calling method

Notes

In the case that one or more classes are absent in a training portion, a default score needs to be assigned to all instances for that class if method produces columns per class, as in {'decision_function', 'predict_proba', 'predict_log_proba'}. For predict_proba this value is 0. In order to ensure finite output, we approximate negative infinity by the minimum finite float value for the dtype in other cases.

Examples

>>> from sklearn import datasets, linear_model
>>> from sklearn.model_selection import cross_val_predict
>>> diabetes = datasets.load_diabetes()
>>> X = diabetes.data[:150]
>>> y = diabetes.target[:150]
>>> lasso = linear_model.Lasso()
>>> y_pred = cross_val_predict(lasso, X, y, cv=3)

delayed¶

function delayed

val delayed :
  ?check_pickle:Py.Object.t ->
  function_:Py.Object.t ->
  unit ->
  Py.Object.t

Decorator used to capture the arguments of a function.

has_fit_parameter¶

function has_fit_parameter

val has_fit_parameter :
  estimator:[>`BaseEstimator] Np.Obj.t ->
  parameter:string ->
  unit ->
  bool

Checks whether the estimator's fit method supports the given parameter.

Parameters

estimator : object An estimator to inspect.
parameter : str The searched parameter.

Returns

is_parameter: bool Whether the parameter was found to be a named parameter of the estimator's fit method.

Examples

>>> from sklearn.svm import SVC
>>> has_fit_parameter(SVC(), 'sample_weight')
True

if_delegate_has_method¶

function if_delegate_has_method

val if_delegate_has_method :
  [`S of string | `StringList of string list] ->
  Py.Object.t

Create a decorator for methods that are delegated to a sub-estimator

This enables ducktyping by hasattr returning True according to the sub-estimator.

Parameters

delegate : string, list of strings or tuple of strings Name of the sub-estimator that can be accessed as an attribute of the base object. If a list or a tuple of names are provided, the first sub-estimator that is an attribute of the base object will be used.

is_classifier¶

function is_classifier

val is_classifier :
  [>`BaseEstimator] Np.Obj.t ->
  bool

Return True if the given estimator is (probably) a classifier.

Parameters

estimator : object Estimator object to test.

Returns

out : bool True if estimator is a classifier and False otherwise.

Multioutput

ClassifierChain¶

create¶

Parameters

Attributes

See also

References

decision_function¶

Parameters

Returns

fit¶

Parameters

Returns

get_params¶

Parameters

Returns

predict¶

Parameters

Returns

predict_proba¶

Parameters

Returns

score¶

Parameters

Returns

set_params¶

Parameters

Returns

classes_¶

estimators_¶

order_¶

to_string¶

show¶

pp¶

MultiOutputClassifier¶

create¶

Parameters

Attributes

Examples

fit¶

Parameters

Returns

get_params¶

Parameters

Returns

partial_fit¶

Parameters

Returns

predict¶

Parameters

Returns

score¶

Parameters

Returns

set_params¶

Parameters

Returns

classes_¶

estimators_¶

to_string¶

show¶

pp¶

MultiOutputEstimator¶

fit¶

Parameters

Returns

get_params¶

Parameters

Returns

partial_fit¶

Parameters

Returns

predict¶

Parameters

Returns

set_params¶

Parameters

Returns

to_string¶

show¶