CSRateClassifier

class empulse.models.CSRateClassifier(estimator, *, tp_cost=0.0, tn_cost=0.0, fn_cost=0.0, fp_cost=0.0, loss=None, pos_label=None)

Binary Classifier that sets the positive rate to optimize the cost-sensitive metric.

This classifier classifies the top fraction of samples (by predicted probability) as positive, where the fraction is determined by the optimal rate computed during fitting.

Parameters:

estimatorobject

A binary classifier that implements fit and predict_proba.

pos_labelint, str, ‘boolean’ or None, default=None

The label of the positive class.

lossMetric or None, default=None

The cost-sensitive metric to optimize.

• If None, the optimal positive rate is computed based on tp_cost, tn_cost, fn_cost, and fp_cost.

• If a Metric, the optimal positive rate is computed based on the loss parameters provided to the fit or predict method.

tp_costfloat or array-like, shape=(n_samples,), default=0.0

Cost of true positives. If float, then all true positives have the same cost. If array-like, then it is the cost of each true positive classification. Is overwritten if another tp_cost is passed to the fit or predict method.

Note

It is not recommended to pass instance-dependent costs to the __init__ method. Instead, pass them to the fit or predict method.

fp_costfloat or array-like, shape=(n_samples,), default=0.0

Cost of false positives. If float, then all false positives have the same cost. If array-like, then it is the cost of each false positive classification. Is overwritten if another fp_cost is passed to the fit or predict method.

Note

It is not recommended to pass instance-dependent costs to the __init__ method. Instead, pass them to the fit or predict method.

tn_costfloat or array-like, shape=(n_samples,), default=0.0

Cost of true negatives. If float, then all true negatives have the same cost. If array-like, then it is the cost of each true negative classification. Is overwritten if another tn_cost is passed to the fit or predict method.

Note

It is not recommended to pass instance-dependent costs to the __init__ method. Instead, pass them to the fit or predict method.

fn_costfloat or array-like, shape=(n_samples,), default=0.0

Cost of false negatives. If float, then all false negatives have the same cost. If array-like, then it is the cost of each false negative classification. Is overwritten if another fn_cost is passed to the fit or predict method.

Note

It is not recommended to pass instance-dependent costs to the __init__ method. Instead, pass them to the fit or predict method.

Attributes:

classes_numpy.ndarray of shape (n_classes,)

The class labels.

estimator_Estimator

The fitted classifier.

rate_float

The optimal positive rate determined during fitting.

decision_function(X)

Decision function for samples in X using the fitted estimator.

Parameters:

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.

Returns:

decisionsndarray of shape (n_samples,)

The decision function computed the fitted estimator.

fit(X, y, *, tp_cost=Parameter.UNCHANGED, fp_cost=Parameter.UNCHANGED, tn_cost=Parameter.UNCHANGED, fn_cost=Parameter.UNCHANGED, **loss_params)

Fit the model according to the given training data.

Parameters:

Xarray-like of shape (n_samples, n_features)

Training data.

yarray-like of shape (n_samples,)

Target values.

tp_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$

Cost of true positives. If float, then all true positives have the same cost. If array-like, then it is the cost of each true positive classification.

fp_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$

Cost of false positives. If float, then all false positives have the same cost. If array-like, then it is the cost of each false positive classification.

tn_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$

Cost of true negatives. If float, then all true negatives have the same cost. If array-like, then it is the cost of each true negative classification.

fn_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$

Cost of false negatives. If float, then all false negatives have the same cost. If array-like, then it is the cost of each false negative classification.

loss_paramsAny

Additional parameter to be passed to the loss function.

Returns:

self

Fitted estimator.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRouter

A sklearn.utils.metadata_routing.MetadataRouter encapsulating routing information.

get_params(deep=True)

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, tp_cost=Parameter.UNCHANGED, tn_cost=Parameter.UNCHANGED, fn_cost=Parameter.UNCHANGED, fp_cost=Parameter.UNCHANGED, **loss_params)

Predict the target of new samples.

Parameters:

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

The samples, as accepted by estimator.predict.

tp_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$

Cost of true positives. If float, then all true positives have the same cost. If array-like, then it is the cost of each true positive classification.

fp_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$

Cost of false positives. If float, then all false positives have the same cost. If array-like, then it is the cost of each false positive classification.

tn_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$

Cost of true negatives. If float, then all true negatives have the same cost. If array-like, then it is the cost of each true negative classification.

fn_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$

Cost of false negatives. If float, then all false negatives have the same cost. If array-like, then it is the cost of each false negative classification.

loss_paramsdict

Additional keyword arguments to pass to the loss function if using a custom loss function.

Returns:

class_labelsndarray of shape (n_samples,)

The predicted class.

Notes

If all costs are zero, then fp_cost=1 and fn_cost=1 are used to avoid division by zero.

predict_log_proba(X)

Predict logarithm class probabilities for X using the fitted estimator.

Parameters:

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.

Returns:

log_probabilitiesndarray of shape (n_samples, n_classes)

The logarithm class probabilities of the input samples.

predict_proba(X)

Predict class probabilities for X using the fitted estimator.

Parameters:

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.

Returns:

probabilitiesndarray of shape (n_samples, n_classes)

The class probabilities of the input samples.

score(X, y, sample_weight=None)

Return accuracy on provided 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_fit_request(*, fn_cost='$UNCHANGED$', fp_cost='$UNCHANGED$', tn_cost='$UNCHANGED$', tp_cost='$UNCHANGED$')

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 (see sklearn.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 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.

Added in version 1.3.

Parameters:

fn_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for fn_cost parameter in fit.

fp_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for fp_cost parameter in fit.

tn_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for tn_cost parameter in fit.

tp_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for tp_cost parameter in fit.

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:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_predict_request(*, fn_cost='$UNCHANGED$', fp_cost='$UNCHANGED$', tn_cost='$UNCHANGED$', tp_cost='$UNCHANGED$')

Configure whether metadata should be requested to be passed to the predict 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 (see sklearn.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 to predict 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 predict.

• 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.

Parameters:

fn_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for fn_cost parameter in predict.

fp_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for fp_cost parameter in predict.

tn_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for tn_cost parameter in predict.

tp_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for tp_cost parameter in predict.

Returns:

selfobject

The updated object.

set_score_request(*, sample_weight='$UNCHANGED$')

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 (see sklearn.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 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.

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.