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)
