45a5b3ee5e0fed29012d3cf2e9c785304789b034,gplearn/genetic.py,BaseSymbolic,fit,#BaseSymbolic#Any#Any#Any#,921

Before Change

        if isinstance(self, TransformerMixin):
            // Find the best individuals in the final generation
            fitness = np.array([gp.raw_fitness_ for gp in self._programs[-1]])
            hall_of_fame = fitness.argsort()[:self.hall_of_fame]
            evaluation = np.array([gp.execute(X) for gp in
                                   [self._programs[-1][i] for
                                    i in hall_of_fame]])

After Change

                                       length, X, y, sample_weight)

            // Check for early stopping
            if self.metric in ("pearson", "spearman"):
                best_fitness = fitness[np.argmax(fitness)]
                if best_fitness >= self.stopping_criteria:
                    break
            else:
                best_fitness = fitness[np.argmin(fitness)]
                if best_fitness <= self.stopping_criteria:
                    break

        if isinstance(self, RegressorMixin):
            // Find the best individual in the final generation
            self._program = self._programs[-1][np.argmin(fitness)]

Instances