CompatibleAdaBoostClassifier

class imbens.ensemble.CompatibleAdaBoostClassifier(estimator=None, n_estimators: int = 50, learning_rate: float = 1.0, algorithm: str = 'SAMME.R', early_termination: bool = False, random_state=None)[source]

AdaBoost classifier re-implemented in imbalanced-ensemble style.

An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.

This class implements the algorithm known as AdaBoost-SAMME [2].

Parameters:

estimatorobject, default=None: The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper classes_ and n_classes_ attributes. If None, then the base estimator is DecisionTreeClassifier
n_estimatorsint, default=50: The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.
learning_ratefloat, default=1.: Learning rate shrinks the contribution of each classifier by learning_rate. There is a trade-off between learning_rate and n_estimators.
algorithm{‘SAMME’, ‘SAMME.R’}, default=’SAMME.R’: If ‘SAMME.R’ then use the SAMME.R real boosting algorithm. estimator must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations.
early_terminationbool, default=False: Whether to enable early termination for AdaBoost training. If True, AdaBoost training can be terminated early when the error is zero or the sum of the sample weights is non-positive.
random_stateint, RandomState instance or None, default=None: Controls the random seed given at each estimator at each boosting iteration. Thus, it is only used when estimator exposes a random_state. Pass an int for reproducible output across multiple function calls.

Attributes:

estimator_estimator: The base estimator from which the ensemble is grown.
estimators_list of classifiers: The collection of fitted sub-estimators.
estimators_n_training_samples_list of ints: The number of training samples for each fitted base estimators.
classes_ndarray of shape (n_classes,): The classes labels.
n_classes_int: The number of classes.
estimator_weights_ndarray of floats: Weights for each estimator in the boosted ensemble.
estimator_errors_ndarray of floats: Classification error for each estimator in the boosted ensemble.
estimators_n_training_samples_list of ints: The number of training samples for each fitted base estimators.
feature_importances_ndarray of shape (n_features,): The impurity-based feature importances.

See also

CompatibleBaggingClassifier: Bagging re-implemented in imbalanced-ensemble style.

References

[1]

Y. Freund, R. Schapire, “A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting”, 1995.

[2]

Zhu, H. Zou, S. Rosset, T. Hastie, “Multi-class AdaBoost”, 2009.

Examples

>>> from imbens.ensemble import CompatibleAdaBoostClassifier
>>> from sklearn.datasets import make_classification
>>>
>>> X, y = make_classification(n_samples=1000, n_classes=3,
...                            n_informative=4, weights=[0.2, 0.3, 0.5],
...                            random_state=0)
>>> clf = CompatibleAdaBoostClassifier(random_state=0)
>>> clf.fit(X, y)  
CompatibleAdaBoostClassifier(...)
>>> clf.predict(X)  
array([...])

Methods

`decision_function`(X)	Compute the decision function of `X`.
`fit`(X, y, *[, sample_weight, eval_datasets, ...])	Build a boosted classifier from the training set (X, y).
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict classes for X.
`predict_log_proba`(X)	Predict class log-probabilities for X.
`predict_proba`(X)	Predict class probabilities for X.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_fit_request`(*[, eval_datasets, ...])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.
`staged_decision_function`(X)	Compute decision function of `X` for each boosting iteration.
`staged_predict`(X)	Return staged predictions for X.
`staged_predict_proba`(X)	Predict class probabilities for X.
`staged_score`(X, y[, sample_weight])	Return staged scores for X, y.

property base_estimator_: Estimator used to grow the ensemble.

decision_function(X)[source]

Compute the decision function of X.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns:

scorendarray of shape of (n_samples, k): The decision function of the input samples. The order of outputs is the same of that of the classes_ attribute. Binary classification is a special cases with k == 1, otherwise k==n_classes. For binary classification, values closer to -1 or 1 mean more like the first or second class in classes_, respectively.

property feature_importances_

The impurity-based feature importances.

The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.

Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See sklearn.inspection.permutation_importance() as an alternative.

Returns:

feature_importances_ndarray of shape (n_features,): The feature importances.

fit(X, y, *, sample_weight=None, eval_datasets: dict = None, eval_metrics: dict = None, train_verbose: bool = False)[source]

Build a boosted classifier from the training set (X, y).

Parameters:

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

The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

yarray-like of shape (n_samples,)

The target values (class labels).

sample_weightarray-like of shape (n_samples,), default=None

Sample weights. If None, the sample weights are initialized to 1 / n_samples.

eval_datasetsdict, default=None

Dataset(s) used for evaluation during the ensemble training process. The keys should be strings corresponding to evaluation datasets’ names. The values should be tuples corresponding to the input samples and target values.

Example: eval_datasets = {'valid' : (X_valid, y_valid)}

eval_metricsdict, default=None

Metric(s) used for evaluation during the ensemble training process.

If None, use 3 default metrics:
- 'acc': sklearn.metrics.accuracy_score()
- 'balanced_acc': sklearn.metrics.balanced_accuracy_score()
- 'weighted_f1': sklearn.metrics.f1_score(average='weighted')
If dict, the keys should be strings corresponding to evaluation metrics’ names. The values should be tuples corresponding to the metric function (callable) and additional kwargs (dict).
- The metric function should at least take 2 named/keyword arguments, y_true and one of [y_pred, y_score], and returns a float as the evaluation score. Keyword arguments:
  - y_true, 1d-array of shape (n_samples,), true labels or binary label indicators corresponds to ground truth (correct) labels.
  - When using y_pred, input will be 1d-array of shape (n_samples,) corresponds to predicted labels, as returned by a classifier.
  - When using y_score, input will be 2d-array of shape (n_samples, n_classes,) corresponds to probability estimates provided by the predict_proba method. In addition, the order of the class scores must correspond to the order of labels, if provided in the metric function, or else to the numerical or lexicographical order of the labels in y_true.
- The metric additional kwargs should be a dictionary that specifies the additional arguments that need to be passed into the metric function.

Example: {'weighted_f1': (sklearn.metrics.f1_score, {'average': 'weighted'})}

train_verbosebool, int or dict, default=False

Controls the verbosity during ensemble training/fitting.

If bool: False means disable training verbose. True means print training information to sys.stdout use default setting:
- 'granularity' : int(n_estimators/10)
- 'print_distribution' : True
- 'print_metrics' : True
If int, print information per train_verbose rounds.
If dict, control the detailed training verbose settings. They are:
- 'granularity': corresponding value should be int, the training information will be printed per granularity rounds.
- 'print_distribution': corresponding value should be bool, whether to print the data class distribution after resampling. Will be ignored if the ensemble training does not perform resampling.
- 'print_metrics': corresponding value should be bool, whether to print the latest performance score. The performance will be evaluated on the training data and all given evaluation datasets with the specified metrics.

Warning

Setting a small 'granularity' value with 'print_metrics' enabled can be costly when the training/evaluation data is large or the metric scores are hard to compute. Normally, one can set 'granularity' to n_estimators/10 (this is used by default).

Returns:

selfobject

get_metadata_routing()[source]

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest 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 classes for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns:

yndarray of shape (n_samples,): The predicted classes.

predict_log_proba(X)[source]

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the weighted mean predicted class log-probabilities of the classifiers in the ensemble.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns:

pndarray of shape (n_samples, n_classes): The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

predict_proba(X)[source]

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns:

pndarray of shape (n_samples, n_classes): The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

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.

Request metadata passed to the fit 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 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 to 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.

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.Pipeline. Otherwise it has no effect.

Parameters:

eval_datasetsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for eval_datasets parameter in fit.
eval_metricsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for eval_metrics parameter in fit.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.
train_verbosestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for train_verbose parameter in fit.

Returns:

selfobject: The updated object.

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 = '$UNCHANGED$') → CompatibleAdaBoostClassifier[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.

New 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.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.

staged_decision_function(X)[source]

Compute decision function of X for each boosting iteration.

This method allows monitoring (i.e. determine error on testing set) after each boosting iteration.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Yields:

scoregenerator of ndarray of shape (n_samples, k): The decision function of the input samples. The order of outputs is the same of that of the classes_ attribute. Binary classification is a special cases with k == 1, otherwise k==n_classes. For binary classification, values closer to -1 or 1 mean more like the first or second class in classes_, respectively.

staged_predict(X)[source]

Return staged predictions for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.

This generator method yields the ensemble prediction after each iteration of boosting and therefore allows monitoring, such as to determine the prediction on a test set after each boost.

Parameters:

Xarray-like of shape (n_samples, n_features): The input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Yields:

ygenerator of ndarray of shape (n_samples,): The predicted classes.

staged_predict_proba(X)[source]

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

This generator method yields the ensemble predicted class probabilities after each iteration of boosting and therefore allows monitoring, such as to determine the predicted class probabilities on a test set after each boost.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Yields:

pgenerator of ndarray of shape (n_samples,): The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

staged_score(X, y, sample_weight=None)[source]

Return staged scores for X, y.

This generator method yields the ensemble score after each iteration of boosting and therefore allows monitoring, such as to determine the score on a test set after each boost.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
yarray-like of shape (n_samples,): Labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Yields:

zfloat