CSBoostClassifier#

class empulse.models.CSBoostClassifier(estimator=None, *, tp_cost=0.0, tn_cost=0.0, fn_cost=0.0, fp_cost=0.0, loss=None)[source]#

Cost-sensitive gradient boosting classifier.

CSBoostClassifier supports xgboost.XGBClassifier, lightgbm.LGBMClassifier and catboost.CatBoostClassifier as base estimators. By default, it uses XGBoost classifier with default hyperparameters.

Read more in the User Guide.

See also

CSLogitClassifier : Cost-sensitive logistic regression classifier.

CSTreeClassifier : Cost-sensitive decision tree classifier.

CSForestClassifier : Cost-sensitive random forest classifier.

Parameters:
estimatorxgboost.XGBClassifier, lightgbm.LGBMClassifier or catboost.CatBoostClassifier, optional

XGBoost or LightGBM classifier to be fit with desired hyperparameters. If not provided, a XGBoost classifier with default hyperparameters is used.

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 method.

Note

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

Note

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

Note

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

Note

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

lossempulse.metrics.Metric, default=None
Loss function to optimize. Metric parameters are passed as loss_params

to the fit method.

Attributes:
classes_numpy.ndarray, shape=(n_classes,)

Unique classes in the target.

estimator_xgboost.XGBClassifier

Fitted XGBoost classifier.

References

[1]

Höppner, S., Baesens, B., Verbeke, W., & Verdonck, T. (2022). Instance-dependent cost-sensitive learning for detecting transfer fraud. European Journal of Operational Research, 297(1), 291-300.

Examples

import numpy as np
from empulse.models import CSBoostClassifier
from sklearn.datasets import make_classification

X, y = make_classification()
fn_cost = np.random.rand(y.size)  # instance-dependent cost
fp_cost = 5  # constant cost

model = CSBoostClassifier()
model.fit(X, y, fn_cost=fn_cost, fp_cost=fp_cost)
y_proba = model.predict_proba(X)

Example with passing instance-dependent costs through cross-validation:

import numpy as np
from empulse.models import CSBoostClassifier
from sklearn import set_config
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

set_config(enable_metadata_routing=True)

X, y = make_classification()
fn_cost = np.random.rand(y.size)
fp_cost = 5

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', CSBoostClassifier().set_fit_request(fn_cost=True, fp_cost=True))
])

cross_val_score(pipeline, X, y, params={'fn_cost': fn_cost, 'fp_cost': fp_cost})

Example with passing instance-dependent costs through a grid search:

import numpy as np
from empulse.metrics import expected_cost_loss
from empulse.models import CSBoostClassifier
from sklearn import set_config
from sklearn.datasets import make_classification
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from xgboost import XGBClassifier

set_config(enable_metadata_routing=True)

X, y = make_classification(n_samples=50)
fn_cost = np.random.rand(y.size)
fp_cost = 5

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', CSBoostClassifier(
        XGBClassifier(n_jobs=2, n_estimators=10)
    ).set_fit_request(fn_cost=True, fp_cost=True))
])
param_grid = {
    'model__estimator__learning_rate': np.logspace(-5, 0, 5),
}
scorer = make_scorer(
    expected_cost_loss,
    response_method='predict_proba',
    greater_is_better=False,
    normalize=True
)
scorer = scorer.set_score_request(fn_cost=True, fp_cost=True)

grid_search = GridSearchCV(pipeline, param_grid=param_grid, scoring=scorer)
grid_search.fit(X, y, fn_cost=fn_cost, fp_cost=fp_cost)
fit(X, y, *, tp_cost=Parameter.UNCHANGED, tn_cost=Parameter.UNCHANGED, fn_cost=Parameter.UNCHANGED, fp_cost=Parameter.UNCHANGED, fit_params=None, **loss_params)[source]#

Fit the model.

Parameters:
Xarray-like of shape (n_samples, n_features)
yarray-like of shape (n_samples,)
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.

fit_paramsdict

Additional keyword arguments to pass to the estimator’s fit method.

loss_paramsdict

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

Returns:
selfCSBoostClassifier

Fitted CSBoost model.

get_metadata_routing()#

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)#

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)#

Predict class labels for samples in X.

Parameters:
Xarray-like of shape (n_samples, n_features)

Features.

Returns:
y_predndarray of shape (n_samples,)

Predicted labels for each sample.

predict_proba(X)[source]#

Predict class probabilities for X.

Parameters:
X2D numpy.ndarray, shape=(n_samples, n_features)
Returns:
y_pred2D numpy.ndarray, shape=(n_samples, n_classes)

Predicted class probabilities.

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(*, fit_params='$UNCHANGED$', 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:
fit_paramsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for fit_params parameter in fit.

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_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.