ClassifierChain#

class sklearn.multioutput.ClassifierChain(base_estimator, *, order=None, cv=None, chain_method='predict', random_state=None, verbose=False)[source]#

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.

For an example of how to use ClassifierChain and benefit from its ensemble, see ClassifierChain on a yeast dataset example.

Read more in the User Guide.

Added in version 0.19.

Parameters:

base_estimatorestimator

The base estimator from which the classifier chain is built.

orderarray-like of shape (n_outputs,) or ‘random’, default=None

If None, 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.

cvint, cross-validation generator or an iterable, default=None

Determines whether to use cross validated predictions or true labels for the results of previous estimators in the chain. Possible inputs for cv are:

None, to use true labels when fitting,
integer, to specify the number of folds in a (Stratified)KFold,
CV splitter,
An iterable yielding (train, test) splits as arrays of indices.

chain_method{‘predict’, ‘predict_proba’, ‘predict_log_proba’, ‘decision_function’} or list of such str’s, default=’predict’

Prediction method to be used by estimators in the chain for the ‘prediction’ features of previous estimators in the chain.

if str, name of the method;
if a list of str, provides the method names in order of preference. The method used corresponds to the first method in the list that is implemented by base_estimator.

Added in version 1.5.

random_stateint, 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 Glossary.

verbosebool, default=False

If True, chain progress is output as each model is completed.

Added in version 1.2.

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.
chain_method_str: Prediction method used by estimators in the chain for the prediction features.
n_features_in_int: Number of features seen during fit. Only defined if the underlying base_estimator exposes such an attribute when fit.

Added in version 0.24.
feature_names_in_ndarray of shape (n_features_in_,): Names of features seen during fit. Defined only when X has feature names that are all strings.

Added in version 1.0.

See also

RegressorChain: Equivalent for regression.
MultiOutputClassifier: Classifies each output independently rather than chaining.

References

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

Examples

>>> from sklearn.datasets import make_multilabel_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.multioutput import ClassifierChain
>>> X, Y = make_multilabel_classification(
...    n_samples=12, n_classes=3, random_state=0
... )
>>> X_train, X_test, Y_train, Y_test = train_test_split(
...    X, Y, random_state=0
... )
>>> base_lr = LogisticRegression(solver='lbfgs', random_state=0)
>>> chain = ClassifierChain(base_lr, order='random', random_state=0)
>>> chain.fit(X_train, Y_train).predict(X_test)
array([[1., 1., 0.],
       [1., 0., 0.],
       [0., 1., 0.]])
>>> chain.predict_proba(X_test)
array([[0.8387..., 0.9431..., 0.4576...],
       [0.8878..., 0.3684..., 0.2640...],
       [0.0321..., 0.9935..., 0.0626...]])

decision_function(X)[source]#

Evaluate the decision_function of the models in the chain.

Parameters:

Xarray-like of shape (n_samples, n_features): The input data.

Returns:

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

fit(X, Y, **fit_params)[source]#

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.

Yarray-like of shape (n_samples, n_classes)

The target values.

**fit_paramsdict of string -> object

Parameters passed to the fit method of each step.

Only available if enable_metadata_routing=True. See the User Guide.

Added in version 1.3.

Returns:

selfobject: Class instance.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Added in version 1.3.

Returns:

routingMetadataRouter: A MetadataRouter encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

predict(X)[source]#

Predict on the data matrix X using the ClassifierChain model.

Parameters:

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

Returns:

Y_predarray-like of shape (n_samples, n_classes): The predicted values.

predict_log_proba(X)[source]#

Predict logarithm of probability estimates.

Parameters:

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

Returns:

Y_log_probarray-like of shape (n_samples, n_classes): The predicted logarithm of the probabilities.

predict_proba(X)[source]#

Predict probability estimates.

Parameters:

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

Returns:

Y_probarray-like of shape (n_samples, n_classes): The predicted probabilities.

score(X, y, sample_weight=None)[source]#

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:

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

Returns:

scorefloat: Mean accuracy of self.predict(X) w.r.t. y.

set_params(**params)[source]#

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:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') 192; ClassifierChain[source]#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
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.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

Gallery examples#

Multilabel classification using a classifier chain