xs, ys = DataUtil.get_dataset("mushroom", "../../_Data/mushroom.txt", tar_idx=0) nb = MultinomialNB() nb.feed_data(xs, ys) xs, ys = nb["x"].tolist(), nb["y"].tolist() train_num = 6000 x_train, x_test = xs[:train_num], xs[train_num:] y_train, y_test = ys[:train_num], ys[train_num:] learning_time = time.time() nb = GaussianNB() nb.fit(x_train, y_train) learning_time = time.time() - learning_time estimation_time = time.time() nb.estimate(x_train, y_train) nb.estimate(x_test, y_test) estimation_time = time.time() - estimation_time print( "Model building : {:12.6} s\n" "Estimation : {:12.6} s\n" "Total : {:12.6} s".format( learning_time, estimation_time, learning_time + estimation_time ) ) nb.show_timing_log() nb.visualize()
np.random.shuffle(_data) train_num = 6000 xs = _data ys = [xx.pop(0) for xx in xs] nb = MultinomialNB() nb.feed_data(xs, ys) xs, ys = nb["x"].tolist(), nb["y"].tolist() train_x, test_x = xs[:train_num], xs[train_num:] train_y, test_y = ys[:train_num], ys[train_num:] train_num = 6000 train_data = _data[:train_num] test_data = _data[train_num:] learning_time = time.time() nb = GaussianNB() nb.fit(train_x, train_y) learning_time = time.time() - learning_time estimation_time = time.time() nb.estimate(train_x, train_y) nb.estimate(test_x, test_y) estimation_time = time.time() - estimation_time print("Model building : {:12.6} s\n" "Estimation : {:12.6} s\n" "Total : {:12.6} s".format( learning_time, estimation_time, learning_time + estimation_time))