import time import os.path as osp start_time = time.time() (X_train, y_train, X_test, y_test) = uci_letter.load_data() est_configs = [ MultiClassXGBConfig(num_class=26), MultiClassXGBConfig(num_class=26), MultiClassXGBConfig(num_class=26), MultiClassXGBConfig(num_class=26) ] agc = AutoGrowingCascadeLayer(est_configs=est_configs, early_stopping_rounds=4, stop_by_test=True, n_classes=26, data_save_dir=osp.join(get_data_save_base(), 'uci_adult', 'auto_cascade'), keep_in_mem=False) model = Graph() model.add(agc) model.fit_transform(X_train, y_train, X_test, y_test) end_time = time.time() print("Time cost: {}".format(end_time - start_time))
est_configs = [ ExtraRandomForestConfig(), ExtraRandomForestConfig(), ExtraRandomForestConfig(), ExtraRandomForestConfig(), RandomForestConfig(), RandomForestConfig(), RandomForestConfig(), RandomForestConfig() ] data_save_dir = osp.join(get_data_save_base(), 'mnist') model_save_dir = osp.join(get_model_save_base(), 'mnist') auto_cascade = AutoGrowingCascadeLayer(est_configs=est_configs, early_stopping_rounds=4, stop_by_test=True, n_classes=10, data_save_dir=data_save_dir, model_save_dir=model_save_dir) model = Graph() model.add(mgs) model.add(pool) model.add(concatlayer) model.add(auto_cascade) model.fit_transform(x_train, y_train, x_test, y_test) print('time cost: {}'.format(time.time() - start_time))
windows = [Window(win_x=24, win_y=24, stride_x=2, stride_y=2, pad_x=0, pad_y=0), Window(34, 34, 2, 2), Window(48, 48, 2, 2)] est_for_windows = [[rf1, rf2], [rf1, rf2], [rf1, rf2]] data_save_dir = osp.join(get_data_save_base(), 'small_norb') model_save_dir = osp.join(get_model_save_base(), 'small_norb') mgs = MultiGrainScanLayer(windows=windows, est_for_windows=est_for_windows, n_class=10, distribute=False, keep_in_mem=False, data_save_dir=data_save_dir, cache_in_disk=True, seed=0) model = Graph() model.add(mgs) # model.add(pool) # model.add(concatlayer) # model.add(auto_cascade) model.fit_transform(x_train, y_train, x_test, y_test) print('time cost: {}'.format(time.time() - start_time))