Evolutionary Algorithms to Fit the rules

This is a the source file that contains the class to train/fit the rulebase using a genetic algorithm.

class ex_fuzzy.evolutionary_fit.BaseFuzzyRulesClassifier(nRules: int = 30, nAnts: int = 4, fuzzy_type: FUZZY_SETS = FUZZY_SETS.t1, tolerance: float = 0.0, class_names: list[str] = None, n_linguistic_variables: list[int] | int = 3, verbose=False, linguistic_variables: list[fuzzyVariable] = None, categorical_mask: list[int] = None, domain: list[float] = None, n_class: int = None, precomputed_rules: MasterRuleBase = None, runner: int = 1, ds_mode: int = 0, fuzzy_modifiers: bool = False, allow_unknown: bool = False)

Bases: ClassifierMixin

Class that is used as a classifier for a fuzzy rule based system. Supports precomputed and optimization of the linguistic variables.

customized_loss(loss_function)

Function to customize the loss function used for the optimization.

Parameters:

loss_function – function that takes as input the true labels and the predicted labels and returns a float.

Returns:

None

explainable_predict(X: array, out_class_names=False) → array

Returns the predicted class for each sample.

fit(X: array, y: array, n_gen: int = 70, pop_size: int = 30, checkpoints: int = 0, candidate_rules: MasterRuleBase = None, initial_rules: MasterRuleBase = None, random_state: int = 33, var_prob: float = 0.3, sbx_eta: float = 3.0, mutation_eta=7.0, tournament_size=3, bootstrap_size=1000, p_value_compute=False, checkpoint_callback: Callable[[int, MasterRuleBase], None] = None) → None

Fits a fuzzy rule based classifier using a genetic algorithm to the given data.

Parameters:

• X – numpy array samples x features

• y – labels. integer array samples (x 1)

• n_gen – integer. Number of generations to run the genetic algorithm.

• pop_size – integer. Population size for each gneration.

• checkpoints – integer. Number of checkpoints to save the best rulebase found so far.

• candidate_rules – if these rules exist, the optimization process will choose the best rules from this set. If None (default) the rules will be generated from scratch.

• initial_rules – if these rules exist, the optimization process will start from this set. If None (default) the rules will be generated from scratch.

• random_state – integer. Random seed for the optimization process.

• var_prob – float. Probability of crossover for the genetic algorithm.

• sbx_eta – float. Eta parameter for the SBX crossover.

• mutation_eta – float. Eta parameter for the polynomial mutation.

• tournament_size – integer. Size of the tournament for the genetic algorithm.

• checkpoint_callback – function. Callback function that get executed at each checkpoint (‘checkpoints’ must be greater than 0), its arguments are the generation number and the rule_base of the checkpoint.

Returns:

None. The classifier is fitted to the data.

forward(X: array, out_class_names=False) → array

Returns the predicted class for each sample.

Parameters:

• X – np array samples x features.

• out_class_names – if True, the output will be the class names instead of the class index.

Returns:

np array samples (x 1) with the predicted class.

get_rulebase() → list[array]

Get the rulebase obtained after fitting the classifier to the data.

Returns:

a matrix format for the rulebase.

load_master_rule_base(rule_base: MasterRuleBase) → None

Loads a master rule base to be used in the prediction process.

Parameters:

rule_base – ruleBase object.

Returns:

None

p_value_validation(bootstrap_size: int = 100)

Computes the permutation and bootstrapping p-values for the classifier and its rules.

Parameters:

bootstrap_size – integer. Number of bootstraps samples to use.

plot_fuzzy_variables() → None

Plot the fuzzy partitions in each fuzzy variable.

predict(X: array, out_class_names=False) → array

Returns the predicted class for each sample.

Parameters:

• X – np array samples x features.

• out_class_names – if True, the output will be the class names instead of the class index.

Returns:

np array samples (x 1) with the predicted class.

predict_proba(X: array, truth_degrees: bool = True) → array

Returns the predicted class probabilities for each sample.

Parameters:

• X – np array samples x features.

• truth_degrees – if True, the output will be the truth degrees of the rules. If false, will return the association degrees i.e. the truth degree multiplied by the weights/dominance of the rules. (depending on the inference mode chosen)

Returns:

np array samples x classes with the predicted class probabilities.

predict_proba_class(X: array) → array

Returns the predicted class probabilities for each sample.

Parameters:

X – np array samples x features.

Returns:

np array samples x classes with the predicted class probabilities.

print_rule_bootstrap_results() → None

Prints the bootstrap results for each rule.

print_rules(return_rules: bool = False, bootstrap_results: bool = False) → None

Print the rules contained in the fitted rulebase.

rename_fuzzy_variables() → None

Renames the linguist labels so that high, low and so on are consistent. It does so usually after an optimization process.

Returns:

None. Names are sorted accorded to the central point of the fuzzy memberships.

reparametrice_loss(alpha: float, beta: float) → None

Changes the parameters in the loss function.

Note:

Does not check for convexity preservation. The user can play with these parameters as it wills.

Parameters:

• alpha – controls the MCC term.

• beta – controls the average rule size loss.

class ex_fuzzy.evolutionary_fit.ExploreRuleBases(X: array, y: array, nRules: int, n_classes: int, candidate_rules: MasterRuleBase, thread_runner: StarmapParallelization = None, tolerance: float = 0.01)

Bases: Problem

Class to model as pymoo problem the fitting of a rulebase to a set of data given a series of candidate rules for a classification problem using Evolutionary strategies Supports type 1 and t2.

fitness_func(ruleBase: RuleBase, X: array, y: array, tolerance: float, alpha: float = 0.0, beta: float = 0.0, precomputed_truth=None) → float

Fitness function for the optimization problem. :param ruleBase: RuleBase object :param X: array of train samples. X shape = (n_samples, n_features) :param y: array of train labels. y shape = (n_samples,) :param tolerance: float. Tolerance for the size evaluation. :return: float. Fitness value.

class ex_fuzzy.evolutionary_fit.FitRuleBase(X: array, y: array, nRules: int, nAnts: int, n_classes: int, thread_runner: StarmapParallelization = None, linguistic_variables: list[fuzzyVariable] = None, n_linguistic_variables: int = 3, fuzzy_type=FUZZY_SETS.t1, domain: list = None, categorical_mask: array = None, tolerance: float = 0.01, alpha: float = 0.0, beta: float = 0.0, ds_mode: int = 0, encode_mods: bool = False, allow_unknown: bool = False)

Bases: Problem

Class to model as pymoo problem the fitting of a rulebase for a classification problem using Evolutionary strategies. Supports type 1 and iv fs (iv-type 2)

encode_rulebase(rule_base: MasterRuleBase, optimize_lv: bool, encode_mods: bool = False) → array

Given a rule base, constructs the corresponding gene associated with that rule base.

GENE STRUCTURE

First: antecedents chosen by each rule. Size: nAnts * nRules (index of the antecedent) Second: Variable linguistics used. Size: nAnts * nRules Third: Parameters for the fuzzy partitions of the chosen variables. Size: nAnts * self.n_linguistic_variables * 8|4 (2 trapezoidal memberships if t2) Four: Consequent classes. Size: nRules

Parameters:

• rule_base – rule base object.

• optimize_lv – if True, the gene is prepared to optimize the membership functions.

• encode_mods – if True, the gene is prepared to encode the modifiers for the membership functions.

Returns:

np array of size self.single_gen_size.

fitness_func(ruleBase: RuleBase, X: array, y: array, tolerance: float, alpha: float = 0.0, beta: float = 0.0, precomputed_truth: array = None) → float

Fitness function for the optimization problem. :param ruleBase: RuleBase object :param X: array of train samples. X shape = (n_samples, n_features) :param y: array of train labels. y shape = (n_samples,) :param tolerance: float. Tolerance for the size evaluation. :param alpha: float. Weight for the accuracy term. :param beta: float. Weight for the average rule size term. :param precomputed_truth: np array. If given, it will be used as the truth values for the evaluation. :return: float. Fitness value.