ProfLogitClassifier

class empulse.models.ProfLogitClassifier(*, tp_cost=0.0, tn_cost=0.0, fn_cost=0.0, fp_cost=0.0, loss=None, C=1.0, fit_intercept=True, soft_threshold=False, l1_ratio=1.0, optimize_fn=None, optimizer_params=None, n_jobs=None)

Profit-driven logistic regression classifier.

Maximizing empirical cost-sensitive/value-driven metric by optimizing the regression coefficients of the logistic model through a Real-coded Genetic Algorithm (RGA).

Read more in the User Guide.

Parameters:

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.

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.

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 or None, default=None

Loss function to optimize.

If :class`~empulse.metrics.Metric`, metric parameters are passed as loss_params to the fit method.

If None, the loss is set to the Maximum Profit score.

Cfloat, default=1.0

Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.

fit_interceptbool, default=True

Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.

soft_thresholdbool, default=False

If True, apply soft-thresholding to the regression coefficients.

l1_ratiofloat, default=1.0

The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is a L2 penalty. For l1_ratio = 1 it is a L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.

optimize_fnCallable, optional

Optimization algorithm. Should be a Callable with signature optimize(objective, X). See Profit-Driven Logistic Regression (ProfLogit) for more information.

optimizer_paramsdict[str, Any], optional

Additional keyword arguments passed to optimize_fn.

By default, the optimizer is a Real-coded Genetic Algorithm (RGA) with the following parameters:

• max_iterint, default=1000

  Maximum number of iterations.

• patienceint, default=250

  Number of iterations with no improvement to wait before stopping the optimization.

• tolerancefloat, default=1e-4

  Relative tolerance to declare convergence.

• boundstuple[float, float], default=(-5, 5)

  Lower and upper bounds for the regression coefficients.

• all other parameters are passed to the Generation initializer.

n_jobsint, optional

Number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

Attributes:

classes_numpy.ndarray

Unique classes in the target found during fit.

result_scipy.optimize.OptimizeResult

Optimization result.

coef_numpy.ndarray

Coefficients of the logit model.

intercept_float

Intercept of the logit model. Only available when fit_intercept=True.

References

[1]

Stripling, E., vanden Broucke, S., Antonio, K., Baesens, B. and Snoeck, M. (2017). Profit Maximizing Logistic Model for Customer Churn Prediction Using Genetic Algorithms. Swarm and Evolutionary Computation.

[2]

Stripling, E., vanden Broucke, S., Antonio, K., Baesens, B. and Snoeck, M. (2015). Profit Maximizing Logistic Regression Modeling for Customer Churn Prediction. IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1–10). Paris, France.

Examples

from empulse.models import ProfLogitClassifier
from sklearn.datasets import make_classification

X, y = make_classification(n_features=4)

model = ProfLogitClassifier(C=0.1, l1_ratio=0.5, optimizer_params={'max_iter': 10})
model.fit(X, y, tp_cost=-200, fp_cost=10)

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:

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)

Compute predicted probabilities.

Parameters:

X2D array-like, shape=(n_samples, n_features)

Features.

Returns:

y_pred2D numpy.ndarray, shape=(n_samples, 2)

Predicted 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(*, 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_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.