ex_fuzzy.evolutionary_fit.BaseFuzzyRulesClassifier#

class ex_fuzzy.evolutionary_fit.BaseFuzzyRulesClassifier(nRules=30, nAnts=4, fuzzy_type=FUZZY_SETS.t1, tolerance=0.0, class_names=None, n_linguistic_variables=3, verbose=False, linguistic_variables=None, categorical_mask=None, domain=None, n_class=None, precomputed_rules=None, runner=1, ds_mode=0, fuzzy_modifiers=False, allow_unknown=False)[source]#

Bases: ClassifierMixin

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

__init__(nRules=30, nAnts=4, fuzzy_type=FUZZY_SETS.t1, tolerance=0.0, class_names=None, n_linguistic_variables=3, verbose=False, linguistic_variables=None, categorical_mask=None, domain=None, n_class=None, precomputed_rules=None, runner=1, ds_mode=0, fuzzy_modifiers=False, allow_unknown=False)[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 dominance score of the rules.

  • n_linguist_variables – number of linguistic variables per antecedent.

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

  • linguistic_variables (list[fuzzyVariable]) – list of fuzzyVariables type. If None (default) the optimization process will init+optimize them.

  • domain (list[float]) – list of the limits for each variable. If None (default) the classifier will compute them empirically.

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

  • precomputed_rules (MasterRuleBase) – MasterRuleBase object. If not None, the classifier will use the rules in the object and ignore the conflicting parameters.

  • runner (int) – number of threads to use. If None (default) the classifier will use 1 thread.

  • ds_mode (int) – mode for the dominance score. 0: normal dominance score, 1: rules without weights, 2: weights optimized for each rule based on the data.

  • fuzzy_modifiers (bool) – if True, the classifier will use the modifiers in the optimization process.

  • allow_unknown (bool) – if True, the classifier will allow the unknown class in the classification process. (Which would be a -1 value)

Methods

__init__([nRules, nAnts, fuzzy_type, ...])

Inits the optimizer with the corresponding parameters.

customized_loss(loss_function)

Function to customize the loss function used for the optimization.

explainable_predict(X[, out_class_names])

Returns the predicted class for each sample.

fit(X, y[, n_gen, pop_size, checkpoints, ...])

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

forward(X[, out_class_names])

Returns the predicted class for each sample.

get_rulebase()

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

load_master_rule_base(rule_base)

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

p_value_validation([bootstrap_size])

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

plot_fuzzy_variables()

Plot the fuzzy partitions in each fuzzy variable.

predict(X[, out_class_names])

Returns the predicted class for each sample.

predict_membership_class(X)

Returns the predicted class memberships for each sample.

predict_proba(X)

Returns the predicted class probabilities for each sample.

predict_proba_rules(X[, truth_degrees])

Returns the predicted class probabilities for each sample.

print_rule_bootstrap_results()

Prints the bootstrap results for each rule.

print_rules([return_rules, bootstrap_results])

Print the rules contained in the fitted rulebase.

rename_fuzzy_variables()

Renames the linguist labels so that high, low and so on are consistent.

reparametrice_loss(alpha, beta)

Changes the parameters in the loss function.

score(X, y[, sample_weight])

Return accuracy on provided data and labels.
__init__(nRules=30, nAnts=4, fuzzy_type=FUZZY_SETS.t1, tolerance=0.0, class_names=None, n_linguistic_variables=3, verbose=False, linguistic_variables=None, categorical_mask=None, domain=None, n_class=None, precomputed_rules=None, runner=1, ds_mode=0, fuzzy_modifiers=False, allow_unknown=False)[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 dominance score of the rules.

  • n_linguist_variables – number of linguistic variables per antecedent.

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

  • linguistic_variables (list[fuzzyVariable]) – list of fuzzyVariables type. If None (default) the optimization process will init+optimize them.

  • domain (list[float]) – list of the limits for each variable. If None (default) the classifier will compute them empirically.

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

  • precomputed_rules (MasterRuleBase) – MasterRuleBase object. If not None, the classifier will use the rules in the object and ignore the conflicting parameters.

  • runner (int) – number of threads to use. If None (default) the classifier will use 1 thread.

  • ds_mode (int) – mode for the dominance score. 0: normal dominance score, 1: rules without weights, 2: weights optimized for each rule based on the data.

  • fuzzy_modifiers (bool) – if True, the classifier will use the modifiers in the optimization process.

  • allow_unknown (bool) – if True, the classifier will allow the unknown class in the classification process. (Which would be a -1 value)

customized_loss(loss_function)[source]#

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

fit(X, y, n_gen=70, pop_size=30, checkpoints=0, candidate_rules=None, initial_rules=None, random_state=33, var_prob=0.3, sbx_eta=3.0, mutation_eta=7.0, tournament_size=3, bootstrap_size=1000, checkpoint_path='', p_value_compute=False, checkpoint_callback=None)[source]#

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

Parameters:

  • X (array) – numpy array samples x features

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

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

  • pop_size (int) – integer. Population size for each gneration.

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

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

  • random_state (int) – integer. Random seed for the optimization process.

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

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

  • checkpoint_path (str) – string. Path to save the checkpoints. If None (default) the checkpoints will be saved in the current directory.

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

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

  • checkpoint_callback (Callable[[int, MasterRuleBase], None]) – 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.

Return type:

None

print_rule_bootstrap_results()[source]#

Prints the bootstrap results for each rule.

p_value_validation(bootstrap_size=100)[source]#

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

Parameters:

bootstrap_size (int) – integer. Number of bootstraps samples to use.

load_master_rule_base(rule_base)[source]#

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

Parameters:

rule_base (MasterRuleBase) – ruleBase object.

Returns:

None

Return type:

None

explainable_predict(X, out_class_names=False)[source]#

Returns the predicted class for each sample.

forward(X, out_class_names=False)[source]#

Returns the predicted class for each sample.

Parameters:

  • X (array) – 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.

Return type:

array

predict(X, out_class_names=False)[source]#

Returns the predicted class for each sample.

Parameters:

  • X (array) – 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.

Return type:

array

predict_proba_rules(X, truth_degrees=True)[source]#

Returns the predicted class probabilities for each sample.

Parameters:

  • X (array) – np array samples x features.

  • truth_degrees (bool) – 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.

Return type:

array

predict_membership_class(X)[source]#

Returns the predicted class memberships for each sample.

Parameters:

X (array) – np array samples x features.

Returns:

np array samples x classes with the predicted class probabilities.

Return type:

array

predict_proba(X)[source]#

Returns the predicted class probabilities for each sample.

Parameters:

X (array) – np array samples x features.

Returns:

np array samples x classes with the predicted class probabilities.

Return type:

array

print_rules(return_rules=False, bootstrap_results=False)[source]#

Print the rules contained in the fitted rulebase.

plot_fuzzy_variables()[source]#

Plot the fuzzy partitions in each fuzzy variable.

rename_fuzzy_variables()[source]#

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.

Return type:

None

get_rulebase()[source]#

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

Returns:

a matrix format for the rulebase.

Return type:

list[array]

reparametrice_loss(alpha, beta)[source]#

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 (float) – controls the MCC term.

  • beta (float) – controls the average rule size loss.

__call__(X)[source]#

Returns the predicted class for each sample.

Parameters:

X (array) – np array samples x features.

Returns:

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

Return type:

array