import time
"""Stand alone mode"""
ray.init()
"""Cluster mode"""
# ray.init(redis_address="192.168.x.x:6379")
(x_train, y_train, x_test, y_test) = uci_adult.load_data()
start_time = time.time()
est_configs = [
ExtraRandomForestConfig(n_jobs=-1),
ExtraRandomForestConfig(n_jobs=-1),
ExtraRandomForestConfig(n_jobs=-1),
ExtraRandomForestConfig(n_jobs=-1),
RandomForestConfig(n_jobs=-1),
RandomForestConfig(n_jobs=-1),
RandomForestConfig(n_jobs=-1),
RandomForestConfig(n_jobs=-1)
]
auto_cascade = AutoGrowingCascadeLayer(est_configs=est_configs,
early_stopping_rounds=4,
n_classes=2,
distribute=True,
seed=0)
model = Graph()
model.add(auto_cascade)
model.summary()
model.fit_transform(x_train, y_train, x_test, y_test)
# set_dataset_dir(osp.join(osp.expanduser('~'), 'forestlayer', 'data'))
(x_train, y_train, x_train_plus), (x_test, y_test, x_test_plus) = smallnorb.load_data(osp.join(get_dataset_dir(), "NORB"))
x_train = x_train[:100]
y_train = y_train[:100]
x_train_plus = x_train_plus[:100]
x_test = x_test[:50]
y_test = y_test[:50]
x_test_plus = x_test_plus[:50]
print('train shape and plus shape', x_train.shape, x_train_plus.shape)
print('test shape and plus shape', x_test.shape, x_test_plus.shape)
rf1 = ExtraRandomForestConfig(n_folds=3, n_jobs=-1, min_samples_leaf=10, max_features='auto')
rf2 = RandomForestConfig(n_folds=3, n_jobs=-1, min_samples_leaf=10)
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,
fl.init()
(x_train, y_train, x_test, y_test) = uci_adult.load_data()
start_time = time.time()
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print(x_train.shape[1], 'features')
est_configs = [
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
]
agc = AutoGrowingCascadeLayer(est_configs=est_configs,
early_stopping_rounds=4,
max_layers=0,
stop_by_test=True,
n_classes=2,
data_save_rounds=0,
data_save_dir=osp.join(get_data_save_base(),
'uci_adult',
'auto_cascade'),
keep_in_mem=False,
x_train = x_train.reshape(60000, -1, 28, 28)
x_test = x_test.reshape(10000, -1, 28, 28)
# small data for example.
x_train = x_train[:600, :, :, :]
x_test = x_test[:300, :, :, :]
y_train = y_train[:600]
y_test = y_test[:300]
print(x_train.shape, 'train', x_train.dtype, getmbof(x_train))
print(x_test.shape, 'test', x_test.dtype, getmbof(x_test))
rf1 = ExtraRandomForestConfig(n_jobs=-1,
min_samples_leaf=10,
max_features="auto")
rf2 = RandomForestConfig(n_jobs=-1, min_samples_leaf=10)
windows = [
Window(win_x=7, win_y=7, stride_x=2, stride_y=2, pad_x=0, pad_y=0),
Window(10, 10, 2, 2),
Window(13, 13, 2, 2)
]
est_for_windows = [[rf1, rf2], [rf1, rf2], [rf1, rf2]]
mgs = MultiGrainScanLayer(windows=windows,
est_for_windows=est_for_windows,
n_class=10,
distribute=True,
dis_level=2,
keep_in_mem=False)
