Classifiers Module#

The ex_fuzzy.classifiers module provides the main classification interface for the ex-fuzzy library.

Overview#

This module contains the high-level classifier that combines rule mining and genetic optimization for fuzzy classification tasks.

Classes#

RuleMineClassifier#

class ex_fuzzy.classifiers.RuleMineClassifier(nRules=30, nAnts=4, fuzzy_type=FUZZY_SETS.t1, tolerance=0.0, verbose=False, n_class=None, runner=1, linguistic_variables=None)[source]#

Bases: ClassifierMixin, BaseEstimator

A classifier that works by mining a set of candidate rules with a minimum support, confidence and lift, and then using a genetic algorithm that chooses the optimal combination of those rules.

The main classifier that mines candidate rules and then optimizes them using genetic algorithms.

__init__(nRules=30, nAnts=4, fuzzy_type=FUZZY_SETS.t1, tolerance=0.0, verbose=False, n_class=None, runner=1, linguistic_variables=None)[source]#

Inits the optimizer with the corresponding parameters.

Parameters:

  • nRules (int) – number of rules to optimize.

  • nAnts (int) – max number of antecedents to use.

  • type (fuzzy) – FUZZY_SET enum type in fuzzy_sets module. The kind of fuzzy set used.

  • tolerance (float) – tolerance for the support/dominance score of the rules.

  • verbose – if True, prints the progress of the optimization.

  • n_class (int) – number of classes in the problem. If None (default) the classifier will compute it empirically.

  • runner (int) – number of threads to use.

  • linguistic_variables (list[fuzzyVariable]) – linguistic variables per antecedent.

fit(X, y, n_gen=30, pop_size=50, **kwargs)[source]#

Trains the model with the given data.

Parameters:

  • X (array) – samples to train.

  • y (array) – labels for each sample.

  • n_gen (int) – number of generations to compute in the genetic optimization.

  • pop_size (int) – number of subjects per generation.

  • kwargs – additional parameters for the genetic optimization. See fit method in BaseRuleBaseClassifier.

predict(X)[source]#

Predict for each sample the corresponding class.

Parameters:

X (array) – samples to predict.

Returns:

a class for each sample.

Return type:

array

internal_classifier()[source]#
set_fit_request(*, n_gen='$UNCHANGED$', pop_size='$UNCHANGED$')#

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:

  • n_gen (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for n_gen parameter in fit.

  • pop_size (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for pop_size parameter in fit.

Returns:

self – The updated object.

Return type:

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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

Examples#

Basic Usage#

from ex_fuzzy.classifiers import RuleMineClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create and train classifier
classifier = RuleMineClassifier(nRules=20, nAnts=4, verbose=True)
classifier.fit(X_train, y_train)

# Make predictions
y_pred = classifier.predict(X_test)
accuracy = classifier.score(X_test, y_test)
print(f"Accuracy: {accuracy:.3f}")

See Also#

  • ex_fuzzy.evolutionary_fit : Underlying genetic optimization

  • ex_fuzzy.rule_mining : Rule mining functionality

  • ex_fuzzy.fuzzy_sets : Fuzzy set definitions