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))