import numpy as np import pickle import itertools from Orange.classification.rules import _RuleClassifier, GuardianValidator import orangecontrib.abml.abrules as rules from Orange.classification.rules import Rule class TrueCondValidator: """ Checks whether all conditions have positive values """ def __init__(self, max_rule_length, min_covered_examples): self.max_rule_length = max_rule_length self.min_covered_examples = min_covered_examples self.guardian = GuardianValidator(self.max_rule_length, self.min_covered_examples) def validate_rule(self, rule): for att, op, val in rule.selectors: if op == "!=" and rule.domain[att].values[int(val)] == "T" or \ op == "==" and rule.domain[att].values[int(val)] == "F": return False return self.guardian.validate_rule(rule) class PureAccuracyValidator: def __init__(self, negative, threshold): self.negative = negative self.threshold = threshold def validate_rule(self, rule): if (rule.target_class == self.negative and (rule.curr_class_dist[rule.target_class] != rule.curr_class_dist.sum() and rule.quality < self.threshold)): return False return True class RelativePureValidator: def __init__(self, target, threshold, covered, Y): self.target = target self.threshold = threshold self.covered = covered self.Y = Y def validate_rule(self, rule): if rule.target_class == self.target: rel_covered = rule.covered_examples & ~self.covered rel_Y = self.Y[rel_covered] rf = rel_Y[rel_Y == rule.target_class].sum() rf /= rel_covered.sum() if rf < self.threshold: return False return True class NegativeFirstClassifier(_RuleClassifier): """ Classificator from rules that first checks if a negative rule covers an example. If it does, it will automatically classify example as negative. If it doesnt, then it checks for positive rules and assigns this example best rule's class accuracy. """ def __init__(self, domain, rule_list): self.domain = domain self.rule_list = rule_list self.num_classes = len(self.domain.class_var.values) self.negative = self.domain.class_var.values.index("F") def coverage(self, data): self.predict(data.X) coverages = np.zeros((self.X.shape[0], len(self.rule_list)), dtype=bool) for ri, r in enumerate(self.rule_list): coverages[:, ri] = r.evaluate_data(self.X) return coverages def predict(self, X): self.X = X probabilities = np.zeros((X.shape[0], self.num_classes), dtype=float) # negative rules first neg_rules = [r for r in self.rule_list if r.target_class == self.negative] solved = np.zeros(X.shape[0], dtype=bool) for rule in neg_rules: covered = rule.evaluate_data(X) solved |= covered probabilities[solved, self.negative] = 1.0 # now positive class pos_rules = [r for r in self.rule_list if r.target_class != self.negative] for rule in pos_rules: covered = rule.evaluate_data(X) to_change = covered & ~solved probabilities[to_change, rule.target_class] = rule.quality probabilities[to_change, np.arange(self.num_classes) != rule.target_class] = (1-rule.quality)/(self.num_classes-1) solved |= covered probabilities[~solved] = np.ones(self.num_classes) / self.num_classes return probabilities class Rules4Prolog: def __init__(self, name, threshold): self.threshold = threshold self.learner = rules.ABRuleLearner(width=50, parent_alpha=0.05) self.learner.rule_finder.general_validator = TrueCondValidator(self.learner.rule_finder.general_validator.max_rule_length, self.learner.rule_finder.general_validator.min_covered_examples) self.learner.rule_validator = PureAccuracyValidator(0, self.threshold) self.learner.classifier = NegativeFirstClassifier self.learner.evds = pickle.load(open("data/{}/evds.pickle".format(name), "rb")) def __call__(self, data): # first learn rules for negative class (quality should be higher than # threshold or distribution should be pure) self.learner.target_class = "F" neg_rules = self.learner(data).rule_list # then create another data set and remove all examples that negative # rules cover coverage = np.zeros(len(data), dtype=bool) for r in neg_rules: coverage |= r.covered_examples # learn positive rules, however accept them only if relative frequency # of rules on the temporary data set is higher than threshold OR there # are no negative examples X, Y, W = data.X, data.Y, data.W if data.W else None Y = Y.astype(dtype=int) self.learner.target_class = "T" old_validator = self.learner.rule_validator self.learner.rule_validator = RelativePureValidator(1, self.threshold, coverage, Y) cls = self.learner(data) pos_rules = cls.rule_list # create sub rules that satisfy rule_validator's conditions """all_rules = [] all_dists = set() for r in pos_rules: covered = r.covered_examples.tostring() tc = r.target_class if (covered, tc) not in all_dists: all_dists.add((covered, tc)) all_rules.append(r) # add sub rules to all_rules s = r.selectors ps = itertools.chain.from_iterable(itertools.combinations(s, i) for i in range(len(s))) for p in ps: if not p: continue newr = Rule(selectors = p, domain=r.domain, initial_class_dist=r.initial_class_dist, prior_class_dist=r.prior_class_dist, quality_evaluator=r.quality_evaluator, complexity_evaluator=r.complexity_evaluator) newr.filter_and_store(X, Y, W, tc) newr.do_evaluate() covered = newr.covered_examples.tostring() if (covered, tc) not in all_dists and \ self.learner.rule_validator.validate_rule(newr): # such rule is not in the set yet all_dists.add((covered, tc)) all_rules.append(newr) newr.create_model()""" # restore old validator to self.learner self.learner.rule_validator = old_validator return self.learner.classifier(domain=cls.domain, rule_list=neg_rules+pos_rules) #all_rules) def create_learner(name, evds=True): rule_learner = rules.ABRuleLearner(width=50, parent_alpha=0.05) rule_learner.rule_finder.general_validator = TrueCondValidator(rule_learner.rule_finder.general_validator.max_rule_length, rule_learner.rule_finder.general_validator.min_covered_examples) rule_learner.rule_validator = PureAccuracyValidator(0) rule_learner.classifier = NegativeFirstClassifier if evds: rule_learner.evds = pickle.load(open("data/{}/evds.pickle".format(name), "rb")) return rule_learner