7dbb4a5a8b537c89ff89397c6f6875bf885d0cd4,tpot/tpot.py,TPOT,fit,#TPOT#Any#Any#,194

Before Change

            self.hof = tools.ParetoFront(similar=pareto_eq)

            stats = tools.Statistics(lambda ind: ind.fitness.values[1])
            stats.register("Minimum score", np.min)
            stats.register("Average score", np.mean)
            stats.register("Maximum score", np.max)

            verbose = (self.verbosity == 2)

            // Start the progress bar

After Change

            self.pbar = tqdm(total=num_evaluations, unit="pipeline",
                             disable=(not verbose), desc="GP Progress")

            with warnings.catch_warnings():
                // Ignore warnings
                warnings.simplefilter("ignore", category=UserWarning)

                pop, _ = algorithms.eaSimple(population=pop, toolbox=self._toolbox, cxpb=self.crossover_rate,
                                         mutpb=self.mutation_rate, ngen=self.generations,
                                         halloffame=self.hof, verbose=False)

            // Close the progress bar
            self.pbar.close()
            self.gp_generation = 0

            // Store the pipeline with the highest internal testing accuracy

Instances