def boost_digits_show(n_trials, round_len, n_features): dct = cPickle.load( open(boost_digits_filename(n_trials, round_len, n_features))) trials = dct['trials'] bandit = BoostableDigits() algo = hyperopt.Random(bandit) boosting_algo = BoostingAlgo(algo, round_len=round_len) best_trials = boosting_algo.best_by_round(list(trials)) train_err_rates, test_err_rates = bandit.score_mixture_partial_svm( best_trials) train_err_rate_full, test_err_rate_full = bandit.score_mixture_full_svm( best_trials) hyperopt.plotting.main_plot_history(trials, bandit, do_show=False) best_times = range(round_len, len(trials) + round_len, round_len) matplotlib.pyplot.scatter(best_times, train_err_rates, c='g') matplotlib.pyplot.scatter(best_times, test_err_rates, c='r') matplotlib.pyplot.axhline(train_err_rate_full) matplotlib.pyplot.axhline(test_err_rate_full) matplotlib.pyplot.show()
def evaluate(self, config, ctrl): print 'FatFeatures.evaluate', config['feat_spec'] return BoostableDigits.evaluate(self, config, ctrl)