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)[source]#

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.

See also

CSThresholdClassifier : Sets a per-sample decision threshold instead of a fixed rate, for when there is no fixed capacity constraint.

Parameters:

estimatorobject

A binary classifier that implements fit and predict_proba.

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.

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.

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.

lossBaseMetric 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 BaseMetric, the optimal positive rate is computed based on the loss parameters provided to the fit or predict method.

pos_labelint, str, bool or None, default=None

The label of the positive class.

Attributes:

classes_numpy.ndarray of shape (n_classes,): The class labels, taken from the wrapped estimator.
estimator_Estimator: The fitted classifier.
rate_float: The optimal positive rate determined during fitting.

property classes_#: The class labels, taken from the wrapped estimator.

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, tn_cost=Parameter.UNCHANGED, fn_cost=Parameter.UNCHANGED, fp_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.
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.
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.
**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.
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.
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.
**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.

property rate_#: The optimal positive rate determined during fitting.

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.