Ex-Fuzzy 3.3.0

Ex-Fuzzy 3.3.0 changes what the genetic search optimizes and adds a Pareto search. Seeded fits differ from 3.2.0.

pip install --upgrade ex-fuzzy

Added

• Pareto search: BaseFuzzyRulesClassifier(algorithm='nsga2') runs
  pymoo's NSGA-II on two objectives, the macro F1 with the class-coverage
  penalty and the share of conditions used, and keeps the Pareto front in
  pareto_front_. fit selects its most accurate solution and
  select_pareto_solution switches to another. On 61 KEEL datasets, in a
  prototype comparison with the MCC objective, the most accurate NSGA-II
  solution had 46% fewer conditions and 37% fewer rules than the genetic
  algorithm's, for 0.010 lower test accuracy. It needs the pymoo backend and
  the built-in loss, and also selects from candidate rules

Changed

• Genetic objective: the search maximizes the macro F1 of the pruned rule
  base on the training data instead of its Matthews correlation coefficient,
  minus two small penalties: beta times the share of classes without rules
  (default 0.05) and alpha times the share of the possible antecedent
  conditions, nRules * nAnts, that the rule base uses (default 0.03).
  reparametrize_loss(alpha, beta) sets these two weights; the rule-length
  and effective-rule terms it used to add are gone. On the 43 KEEL datasets
  with at most 2,500 samples, optimizing macro F1 instead of MCC raised the
  mean test macro F1 by 0.020 and halved how often a class was left without
  rules, while the mean test MCC fell by 0.018. With the default penalties,
  rule bases on the same datasets had 21% fewer conditions and 14% fewer rules
  than without them, at the same test macro F1. Rule selection from mined
  candidates (fit with candidate_rules) optimizes the same objective and
  now prunes rules during the search. Seeded fits, fitted models and
  performance differ from earlier versions, and a rule base without rules
  scores -beta instead of 0
• Tournament selection: the pymoo backend runs tournaments of
  tournament_size candidates (default 3). pymoo's GA ignored the setting
  and always ran binary tournaments, so seeded fits of the classifier and of
  the regressor, which asks for tournaments of 3, change. Ties go to the first
  candidate of a tournament, whose candidates are drawn at random

Fixed

• The core concepts guide described a multi-objective search and a two-stage
  workflow through functions that do not exist; it now shows the objective,
  reparametrize_loss and rule selection through fit
• The extending guide named a new_loss method; it is customized_loss

PyPI: https://pypi.org/project/ex-fuzzy/3.3.0/