ProfSRClassifier#

class empulse.models.ProfSRClassifier(*, tp_cost=0.0, tn_cost=0.0, fn_cost=0.0, fp_cost=0.0, loss=None, generations=50, population_size=1000, parsimony_coefficient=0.01, random_state=None)[source]#

Profit-driven symbolic regression classifier.

Maximizes an empirical cost-sensitive/value-driven metric by evolving a population of mathematical expressions through genetic programming (symbolic regression). The predicted score of a program is squashed through the logistic function to obtain a probability estimate, which is used to evaluate the loss function.

Read more in the User Guide.

Note

This classifier requires the optional gplearn dependency. Install it with pip install empulse[symbolic] or pip install gplearn.

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.

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.

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.

lossempulse.metrics.BaseMetric or None, default=None

Fitness function for the genetic programming algorithm to optimize.

If BaseMetric, metric parameters are passed as loss_params to the fit method.

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

generationsint, default=50

Number of generations to evolve the population of programs.

population_sizeint, default=1000

Number of programs in each generation.

parsimony_coefficientfloat, default=0.01

Constant that penalizes large programs by adjusting their fitness to be less favorable for selection. Larger values penalize larger programs more severely.

random_stateint, numpy.random.RandomState or None, default=None

Controls the randomness of the estimator. To obtain a deterministic behaviour during fitting, random_state has to be fixed to an integer. See Sklearn Glossary for details.

Attributes:

classes_numpy.ndarray: Unique classes in the target found during fit.
model_gplearn:gplearn.genetic.SymbolicRegressor: Fitted symbolic regressor.
n_iter_int: Number of generations evolved.

References

[1]

Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.

Examples

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

X, y = make_classification(n_features=4)

model = ProfSRClassifier(generations=10, population_size=100, random_state=42)
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)[source]#

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.