def setUp(self):
windows = [Window(7, 7, 2, 2, 0, 0),
Window(11, 11, 2, 2, 0, 0)]
rf1 = ExtraRandomForestConfig(n_estimators=40, min_samples_leaf=10)
rf2 = RandomForestConfig(n_estimators=40, min_samples_leaf=10)
est_for_windows = [[rf1, rf2],
[rf1, rf2]]
self.mgs = MultiGrainScanLayer(windows=windows,
est_for_windows=est_for_windows,
n_class=10)
pools = [[MaxPooling(), MaxPooling()],
[MaxPooling(), MaxPooling()]]
self.poolayer = PoolingLayer(pools=pools)
self.concat_layer = ConcatLayer()
self.est_configs = [
ExtraRandomForestConfig(n_estimators=40),
ExtraRandomForestConfig(n_estimators=40),
RandomForestConfig(n_estimators=40),
RandomForestConfig(n_estimators=40)
]
self.cascade = CascadeLayer(est_configs=self.est_configs,
n_classes=10,
keep_in_mem=True,
data_save_dir=osp.join(get_data_save_base(), 'test_layer', 'cascade'))
self.auto_cascade = AutoGrowingCascadeLayer(est_configs=self.est_configs,
early_stopping_rounds=3,
data_save_rounds=4,
stop_by_test=True,
n_classes=10,
data_save_dir=osp.join(get_data_save_base(),
'test_layer', 'auto_cascade'))
def _init(self, distribute=False):
self.est_configs = [
ExtraRandomForestConfig(n_estimators=20),
ExtraRandomForestConfig(n_estimators=20),
RandomForestConfig(n_estimators=20),
RandomForestConfig(n_estimators=20)
]
gc = CascadeLayer(est_configs=self.est_configs,
n_classes=2,
data_save_dir=osp.join(get_data_save_base(), 'test_layer', 'cascade'),
distribute=distribute)
agc = AutoGrowingCascadeLayer(est_configs=self.est_configs,
early_stopping_rounds=2,
stop_by_test=False,
data_save_rounds=4,
n_classes=2,
data_save_dir=osp.join(get_data_save_base(),
'test_layer', 'auto_cascade'),
distribute=distribute)
return gc, agc
def _init(self):
self.est_configs = [
ExtraRandomForestConfig(n_estimators=40),
ExtraRandomForestConfig(n_estimators=40),
RandomForestConfig(n_estimators=40),
RandomForestConfig(n_estimators=40)
]
windows = [Window(7, 7, 2, 2, 0, 0), Window(11, 11, 2, 2, 0, 0)]
rf1 = ExtraRandomForestConfig(min_samples_leaf=10)
rf2 = RandomForestConfig(min_samples_leaf=10)
est_for_windows = [[rf1, rf2], [rf1, rf2]]
mgs = MultiGrainScanLayer(dtype=np.float32,
windows=windows,
est_for_windows=est_for_windows,
n_class=10)
pools = [[Pooling(2, 2, "max"),
Pooling(2, 2, "max")],
[Pooling(2, 2, "max"),
Pooling(2, 2, "max")]]
poolayer = PoolingLayer(pools=pools)
concat_layer = ConcatLayer()
auto_cascade = AutoGrowingCascadeLayer(
est_configs=self.est_configs,
early_stopping_rounds=2,
stop_by_test=False,
data_save_rounds=4,
n_classes=10,
data_save_dir=osp.join(get_data_save_base(), 'test_graph',
'auto_cascade'))
return mgs, poolayer, concat_layer, auto_cascade
pool = PoolingLayer(pools=pools)
concatlayer = ConcatLayer()
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('x_train shape: {}'.format(x_train.shape))
print('x_test.shape: {}'.format(x_test.shape))
est_configs = [
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig()
]
data_save_dir = osp.join(get_data_save_base(), 'uci_yeast')
model_save_dir = osp.join(get_model_save_base(), 'uci_yeast')
auto_cascade = AutoGrowingCascadeLayer(est_configs=est_configs,
early_stopping_rounds=4,
n_classes=10,
data_save_dir=data_save_dir,
model_save_dir=model_save_dir,
distribute=False,
seed=0)
model = Graph()
model.add(auto_cascade)
model.fit_transform(x_train, y_train, x_test, y_test)
print("time cost: {}".format(time.time() - start_time))
pool = PoolingLayer(pools=pools)
concatlayer = ConcatLayer()
est_configs = [
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig()
]
data_save_dir = osp.join(get_data_save_base(), 'fashion_mnist')
model_save_dir = osp.join(get_model_save_base(), 'fashion_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('x_test.shape: {}'.format(x_test.shape))
start_time = time.time()
est_configs = [
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig()
]
data_save_dir = osp.join(get_data_save_base(), 'uci_iris')
model_save_dir = osp.join(get_model_save_base(), 'uci_iris')
auto_cascade = AutoGrowingCascadeLayer(est_configs=est_configs,
early_stopping_rounds=4,
n_classes=3,
stop_by_test=False,
data_save_dir=data_save_dir,
model_save_dir=model_save_dir,
distribute=False,
dis_level=0,
verbose_dis=False,
seed=0)
model = Graph()
model.add(auto_cascade)
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=8, win_y=8, stride_x=2, stride_y=2, pad_x=0, pad_y=0),
Window(11, 11, 2, 2),
Window(16, 16, 2, 2)
]
est_for_windows = [[rf1, rf2], [rf1, rf2], [rf1, rf2]]
data_save_dir = osp.join(get_data_save_base(), 'cifar10')
model_save_dir = osp.join(get_model_save_base(), 'cifar10')
mgs = MultiGrainScanLayer(windows=windows,
est_for_windows=est_for_windows,
n_class=10,
distribute=False,
dis_level=0,
keep_in_mem=False,
data_save_dir=data_save_dir,
cache_in_disk=False,
seed=0)
pools = [[MeanPooling(2, 2), MeanPooling(2, 2)],
[MeanPooling(2, 2), MeanPooling(2, 2)],
[MeanPooling(2, 2), MeanPooling(2, 2)]]
feat_dim_120 = auto_encoder()
# feat_dim_120 = PCA()
est_configs = [
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
RandomForestConfig(n_estimators=100),
RandomForestConfig(n_estimators=100),
GBDTConfig(n_estimators=100),
GBDTConfig(n_estimators=100),
# XGBRegressorConfig(),
# XGBRegressorConfig()
]
data_save_dir = osp.join(get_data_save_base(), 'tianchi', 'intelmanu')
agc = AutoGrowingCascadeLayer(task='regression',
est_configs=est_configs,
max_layers=1,
data_save_dir=data_save_dir,
keep_test_result=True)
agc.fit_transform(feat_dim_120[:500], label, feat_dim_120[500:])
result = agc.test_results
true_A = pd.read_csv(osp.join(get_dataset_dir(), 'tianchi', 'intelmanu',
'true_A_20180114.csv'),
header=None)
true = true_A.iloc[:, 1]
print('x_train.shape', x_train.shape, x_train.dtype)
print('x_test.shape', x_test.shape, x_test.dtype)
est_configs = [
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig()
]
data_save_dir = osp.join(get_data_save_base(), 'imdb')
model_save_dir = osp.join(get_model_save_base(), 'imdb')
cascade = AutoGrowingCascadeLayer(est_configs=est_configs,
early_stopping_rounds=4,
stop_by_test=True,
n_classes=2,
data_save_dir=data_save_dir,
model_save_dir=model_save_dir,
distribute=True,
dis_level=0,
seed=0)
cascade.fit_transform(x_train, y_train, x_test, y_test)
print("Time cost: {}".format(time.time() - start_time))
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print(x_train.shape[1], 'features')
est_configs = [
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
RandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig(),
ExtraRandomForestConfig()
]
data_save_dir = osp.join(get_data_save_base(), 'higgs', 'higgs-{}'.format(sz))
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=data_save_dir,
keep_in_mem=False,
distribute=False,
dis_level=2,
verbose_dis=True,
seed=0)
model = Graph()
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,
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)
