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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, 0.8)
rule_learner.classifier = NegativeFirstClassifier
if evds:
rule_learner.evds = pickle.load(open("data/{}/evds.pickle".format(name), "rb"))
return rule_learner
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