def fitness(self, classifier): ''' #TODO normalize diversity metric. ''' self.ensemble.add(classifier) y_pred = self.predict(self.validation_X) y_true = self.validation_y auc = evaluation.auc_score(y_true, y_pred) div = self.diversity.calculate(self.ensemble, self.validation_X, y_true) self.ensemble.classifiers.pop() # create interface for this later return self.alpha * auc + (1.0 - self.alpha) * div
def fitness(self, classifier): ''' #TODO normalize diversity metric. ''' self.ensemble.add(classifier) y_pred = self.predict(self.validation_X) y_true = self.validation_y auc = evaluation.auc_score(y_true, y_pred) div = self.diversity.calculate(self.ensemble, self.validation_X, y_true) self.ensemble.classifiers.pop() # create interface for this later return self.alpha * auc + (1.0 - self.alpha) * div
def fitness(self, classifier): ''' #TODO normalize diversity metric. ''' self.ensemble.add(classifier) out = self.ensemble.output(self.validation_X) y_pred = self.combiner.combine(out) y_true = self.validation_y auc = evaluation.auc_score(y_true, y_pred) div = self.diversity.calculate(self.ensemble, self.validation_X, self.validation_y) #diversity = entropy_measure_e(self.ensemble, # self.validation_X, self.validation_y) self.ensemble.classifiers.pop() return self.alpha * auc + (1.0 - self.alpha) * div
def fitness(self, classifier): ''' #TODO normalize diversity metric. ''' self.ensemble.add(classifier) out = self.ensemble.output(self.validation_X) y_pred = self.combiner.combine(out) y_true = self.validation_y auc = evaluation.auc_score(y_true, y_pred) div = self.diversity.calculate(self.ensemble, self.validation_X, self.validation_y) #diversity = entropy_measure_e(self.ensemble, # self.validation_X, self.validation_y) self.ensemble.classifiers.pop() return self.alpha * auc + (1.0 - self.alpha) * div