def search_space(self):
space = {
"n_estimators":
hp.randint(self.n_estimators_range[0], self.n_estimators_range[1]),
"max_depth":
hp.randint(self.max_depth_range[0], self.max_depth_range[1]),
"min_child_weight":
self.min_child_weight,
"lr":
self.lr
}
return space
def get_past_seq_config(look_back):
"""
get_past_seq_config(look_back)
Generate pass sequence config based on look_back.
:param look_back: look_back configuration
:return: search configuration for past sequence
"""
if isinstance(look_back, tuple) and len(look_back) == 2 and isinstance(
look_back[0], int) and isinstance(look_back[1], int):
if look_back[1] < 2:
raise ValueError(
"The max look back value should be at least 2")
if look_back[0] < 2:
print("The input min look back value is smaller than 2. "
"We sample from range (2, {}) instead.".format(
look_back[1]))
past_seq_config = hp.randint(look_back[0], look_back[1] + 1)
elif isinstance(look_back, int):
if look_back < 2:
raise ValueError(
"look back value should not be smaller than 2. "
"Current value is ", look_back)
past_seq_config = look_back
else:
raise ValueError(
"look back is {}.\n "
"look_back should be either a tuple with 2 int values:"
" (min_len, max_len) or a single int".format(look_back))
return past_seq_config
def test_fit(self):
data, validation_data = get_data()
auto_arima = AutoARIMA(metric="mse",
p=hp.randint(0, 4),
q=hp.randint(0, 4),
seasonality_mode=hp.choice([True, False]),
P=hp.randint(5, 12),
Q=hp.randint(5, 12),
m=hp.choice([4, 7]))
auto_arima.fit(
data=data,
validation_data=validation_data,
epochs=1,
n_sampling=1,
)
best_model = auto_arima.get_best_model()
def test_num_channels(self):
auto_tcn = AutoTCN(input_feature_num=input_feature_dim,
output_target_num=output_feature_dim,
past_seq_len=past_seq_len,
future_seq_len=future_seq_len,
optimizer='Adam',
loss=torch.nn.MSELoss(),
metric="mse",
hidden_units=4,
levels=hp.randint(1, 3),
num_channels=[8] * 2,
kernel_size=hp.choice([2, 3]),
lr=hp.choice([0.001, 0.003, 0.01]),
dropout=hp.uniform(0.1, 0.2),
logs_dir="/tmp/auto_tcn",
cpus_per_trial=2,
name="auto_tcn")
auto_tcn.fit(
data=train_dataloader_creator,
epochs=1,
batch_size=hp.choice([32, 64]),
validation_data=valid_dataloader_creator,
n_sampling=1,
)
assert auto_tcn.get_best_model()
best_config = auto_tcn.get_best_config()
assert best_config['num_channels'] == [8] * 2
def test_fit_lstm_data_creator(self):
input_feature_dim = 4
output_feature_dim = 2 # 2 targets are generated in get_tsdataset
search_space = {
'hidden_dim': hp.grid_search([32, 64]),
'layer_num': hp.randint(1, 3),
'lr': hp.choice([0.001, 0.003, 0.01]),
'dropout': hp.uniform(0.1, 0.2)
}
auto_estimator = AutoTSEstimator(model='lstm',
search_space=search_space,
past_seq_len=7,
future_seq_len=1,
input_feature_num=input_feature_dim,
output_target_num=output_feature_dim,
selected_features="auto",
metric="mse",
loss=torch.nn.MSELoss(),
logs_dir="/tmp/auto_trainer",
cpus_per_trial=2,
name="auto_trainer")
auto_estimator.fit(data=get_data_creator(),
epochs=1,
batch_size=hp.choice([32, 64]),
validation_data=get_data_creator(),
n_sampling=1)
config = auto_estimator.get_best_config()
assert config["past_seq_len"] == 7
def test_fit_third_party_feature(self):
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
tsdata_train = get_tsdataset().gen_dt_feature().scale(scaler, fit=True)
tsdata_valid = get_tsdataset().gen_dt_feature().scale(scaler,
fit=False)
search_space = {
'hidden_dim': hp.grid_search([32, 64]),
'dropout': hp.uniform(0.1, 0.2)
}
auto_estimator = AutoTSEstimator(model=model_creator,
search_space=search_space,
past_seq_len=hp.randint(4, 6),
future_seq_len=1,
selected_features="auto",
metric="mse",
loss=torch.nn.MSELoss(),
cpus_per_trial=2)
ts_pipeline = auto_estimator.fit(data=tsdata_train,
epochs=1,
batch_size=hp.choice([32, 64]),
validation_data=tsdata_valid,
n_sampling=1)
best_config = auto_estimator.get_best_config()
best_model = auto_estimator._get_best_automl_model()
assert 4 <= best_config["past_seq_len"] <= 6
assert isinstance(ts_pipeline, TSPipeline)
# use raw base model to predic and evaluate
tsdata_valid.roll(lookback=best_config["past_seq_len"],
horizon=0,
feature_col=best_config["selected_features"])
x_valid, y_valid = tsdata_valid.to_numpy()
y_pred_raw = best_model.predict(x_valid)
y_pred_raw = tsdata_valid.unscale_numpy(y_pred_raw)
# use tspipeline to predic and evaluate
eval_result = ts_pipeline.evaluate(tsdata_valid)
y_pred = ts_pipeline.predict(tsdata_valid)
# check if they are the same
np.testing.assert_almost_equal(y_pred, y_pred_raw)
# save and load
ts_pipeline.save("/tmp/auto_trainer/autots_tmp_model_3rdparty")
new_ts_pipeline = TSPipeline.load(
"/tmp/auto_trainer/autots_tmp_model_3rdparty")
# check if load ppl is the same as previous
eval_result_new = new_ts_pipeline.evaluate(tsdata_valid)
y_pred_new = new_ts_pipeline.predict(tsdata_valid)
np.testing.assert_almost_equal(eval_result[0], eval_result_new[0])
np.testing.assert_almost_equal(y_pred, y_pred_new)
# use tspipeline to incrementally train
new_ts_pipeline.fit(tsdata_valid)
def get_auto_estimator():
auto_lstm = AutoLSTM(input_feature_num=input_feature_dim,
output_target_num=output_feature_dim,
past_seq_len=5,
optimizer='Adam',
loss=torch.nn.MSELoss(),
metric="mse",
hidden_dim=hp.grid_search([32, 64]),
layer_num=hp.randint(1, 3),
lr=hp.choice([0.001, 0.003, 0.01]),
dropout=hp.uniform(0.1, 0.2),
logs_dir="/tmp/auto_lstm",
cpus_per_trial=2,
name="auto_lstm")
return auto_lstm
def _gen_sample_func(self, ranges, param_name):
if isinstance(ranges, tuple):
assert len(ranges) == 2, \
f"length of tuple {param_name} should be 2 while get {len(ranges)} instead."
assert param_name != "teacher_forcing", \
f"type of {param_name} can only be a list while get a tuple"
if param_name in ["lr"]:
return hp.loguniform(lower=ranges[0], upper=ranges[1])
if param_name in [
"lstm_hidden_dim", "lstm_layer_num", "batch_size"
]:
return hp.randint(lower=ranges[0], upper=ranges[1])
if param_name in ["dropout"]:
return hp.uniform(lower=ranges[0], upper=ranges[1])
if isinstance(ranges, list):
return hp.grid_search(ranges)
raise RuntimeError(f"{param_name} should be either a list or a tuple.")
def get_auto_estimator():
auto_tcn = AutoTCN(input_feature_num=input_feature_dim,
output_target_num=output_feature_dim,
past_seq_len=past_seq_len,
future_seq_len=future_seq_len,
optimizer='Adam',
loss=torch.nn.MSELoss(),
metric="mse",
hidden_units=8,
levels=hp.randint(1, 3),
kernel_size=hp.choice([2, 3]),
lr=hp.choice([0.001, 0.003, 0.01]),
dropout=hp.uniform(0.1, 0.2),
logs_dir="/tmp/auto_tcn",
cpus_per_trial=2,
name="auto_tcn")
return auto_tcn
def get_auto_estimator():
auto_seq2seq = AutoSeq2Seq(input_feature_num=input_feature_dim,
output_target_num=output_feature_dim,
past_seq_len=past_seq_len,
future_seq_len=future_seq_len,
optimizer='Adam',
loss=torch.nn.MSELoss(),
metric="mse",
lr=hp.choice([0.001, 0.003, 0.01]),
lstm_hidden_dim=hp.grid_search([32, 64, 128]),
lstm_layer_num=hp.randint(1, 4),
dropout=hp.uniform(0.1, 0.3),
teacher_forcing=False,
logs_dir="/tmp/auto_seq2seq",
cpus_per_trial=2,
name="auto_seq2seq")
return auto_seq2seq
def test_select_feature(self):
sample_num = np.random.randint(100, 200)
df = pd.DataFrame({
"datetime":
pd.date_range('1/1/2019', periods=sample_num),
"value":
np.random.randn(sample_num),
"id":
np.array(['00'] * sample_num)
})
train_ts, val_ts, _ = TSDataset.from_pandas(df,
target_col=['value'],
dt_col='datetime',
id_col='id',
with_split=True,
val_ratio=0.1)
search_space = {
'hidden_dim': hp.grid_search([32, 64]),
'layer_num': hp.randint(1, 3),
'lr': hp.choice([0.001, 0.003, 0.01]),
'dropout': hp.uniform(0.1, 0.2)
}
input_feature_dim, output_feature_dim = 1, 1
auto_estimator = AutoTSEstimator(model='lstm',
search_space=search_space,
past_seq_len=6,
future_seq_len=1,
input_feature_num=input_feature_dim,
output_target_num=output_feature_dim,
selected_features="auto",
metric="mse",
loss=torch.nn.MSELoss(),
cpus_per_trial=2,
name="auto_trainer")
auto_estimator.fit(data=train_ts,
epochs=1,
batch_size=hp.choice([32, 64]),
validation_data=val_ts,
n_sampling=1)
config = auto_estimator.get_best_config()
assert config['past_seq_len'] == 6
args = parser.parse_args()
num_nodes = 1 if args.cluster_mode == "local" else args.num_workers
init_orca_context(cluster_mode=args.cluster_mode,
cores=args.cores,
memory=args.memory,
num_nodes=num_nodes,
init_ray_on_spark=True)
tsdata_train, tsdata_valid, tsdata_test = get_tsdata()
auto_lstm = AutoLSTM(input_feature_num=1,
output_target_num=1,
past_seq_len=20,
hidden_dim=hp.grid_search([32, 64]),
layer_num=hp.randint(1, 3),
lr=hp.choice([0.01, 0.03, 0.1]),
dropout=hp.uniform(0.1, 0.2),
optimizer='Adam',
loss=torch.nn.MSELoss(),
metric="mse")
x_train, y_train = tsdata_train.roll(lookback=20, horizon=1).to_numpy()
x_val, y_val = tsdata_test.roll(lookback=20, horizon=1).to_numpy()
x_test, y_test = tsdata_test.roll(lookback=20, horizon=1).to_numpy()
auto_lstm.fit(data=(x_train, y_train),
epochs=args.epochs,
validation_data=(x_val, y_val))
yhat = auto_lstm.predict(x_test)
def test_fit_seq2seq_feature(self):
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
tsdata_train = get_tsdataset().gen_dt_feature().scale(scaler, fit=True)
tsdata_valid = get_tsdataset().gen_dt_feature().scale(scaler,
fit=False)
auto_estimator = AutoTSEstimator(model='seq2seq',
search_space="minimal",
past_seq_len=hp.randint(4, 6),
future_seq_len=1,
selected_features="auto",
metric="mse",
optimizer="Adam",
loss=torch.nn.MSELoss(),
logs_dir="/tmp/auto_trainer",
cpus_per_trial=2,
name="auto_trainer")
ts_pipeline = auto_estimator.fit(data=tsdata_train,
epochs=1,
batch_size=hp.choice([32, 64]),
validation_data=tsdata_valid,
n_sampling=1)
best_config = auto_estimator.get_best_config()
best_model = auto_estimator._get_best_automl_model()
assert 4 <= best_config["past_seq_len"] <= 6
assert isinstance(ts_pipeline, TSPipeline)
# use raw base model to predic and evaluate
tsdata_valid.roll(lookback=best_config["past_seq_len"],
horizon=0,
feature_col=best_config["selected_features"])
x_valid, y_valid = tsdata_valid.to_numpy()
y_pred_raw = best_model.predict(x_valid)
y_pred_raw = tsdata_valid.unscale_numpy(y_pred_raw)
# use tspipeline to predic and evaluate
eval_result = ts_pipeline.evaluate(tsdata_valid)
y_pred = ts_pipeline.predict(tsdata_valid)
# check if they are the same
np.testing.assert_almost_equal(y_pred, y_pred_raw)
# save and load
ts_pipeline.save("/tmp/auto_trainer/autots_tmp_model_seq2seq")
new_ts_pipeline = TSPipeline.load(
"/tmp/auto_trainer/autots_tmp_model_seq2seq")
# check if load ppl is the same as previous
eval_result_new = new_ts_pipeline.evaluate(tsdata_valid)
y_pred_new = new_ts_pipeline.predict(tsdata_valid)
np.testing.assert_almost_equal(eval_result[0], eval_result_new[0])
np.testing.assert_almost_equal(y_pred, y_pred_new)
# check if load ppl is the same as previous with onnx
try:
import onnx
import onnxruntime
eval_result_new_onnx = new_ts_pipeline.evaluate_with_onnx(
tsdata_valid)
y_pred_new_onnx = new_ts_pipeline.predict_with_onnx(tsdata_valid)
np.testing.assert_almost_equal(eval_result[0],
eval_result_new_onnx[0],
decimal=5)
np.testing.assert_almost_equal(y_pred, y_pred_new_onnx, decimal=5)
except ImportError:
pass
# use tspipeline to incrementally train
new_ts_pipeline.fit(tsdata_valid)
random_state=2)
config = {'random_state': 2}
recipe = None
num_rand_samples = 1
n_estimators_range = (800, 1000)
max_depth_range = (10, 15)
lr = (1e-4, 1e-1)
min_child_weight = [1, 2, 3]
if opt.mode == 'skopt':
search_space = {
"n_estimators":
hp.randint(n_estimators_range[0], n_estimators_range[1]),
"max_depth":
hp.randint(max_depth_range[0], max_depth_range[1]),
"lr":
hp.loguniform(lr[0], lr[-1]),
"min_child_weight":
hp.choice(min_child_weight),
}
search_alg = "skopt"
search_alg_params = None
scheduler = "AsyncHyperBand"
scheduler_params = dict(
max_t=50,
grace_period=1,
reduction_factor=3,
brackets=3,
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import bigdl.orca.automl.hp as hp
AUTO_MODEL_SUPPORT_LIST = ["lstm", "tcn", "seq2seq"]
AUTO_MODEL_DEFAULT_SEARCH_SPACE = {
"lstm": {"minimal": {"hidden_dim": hp.grid_search([16, 32]),
"layer_num": hp.randint(1, 2),
"lr": hp.loguniform(0.001, 0.005),
"dropout": hp.uniform(0.1, 0.2)},
"normal": {"hidden_dim": hp.grid_search([16, 32, 64]),
"layer_num": hp.grid_search([1, 2]),
"lr": hp.loguniform(0.0005, 0.01),
"dropout": hp.uniform(0, 0.2)},
"large": {"hidden_dim": hp.grid_search([16, 32, 64, 128]),
"layer_num": hp.grid_search([1, 2, 3, 4]),
"lr": hp.loguniform(0.0005, 0.01),
"dropout": hp.uniform(0, 0.3)}},
"tcn": {"minimal": {"hidden_units": hp.grid_search([16, 32]),
"levels": hp.randint(4, 6),
"kernel_size": 3,
"lr": hp.loguniform(0.001, 0.005),
def get_xgb_search_space():
return {
"n_estimators": hp.randint(5, 10),
"max_depth": hp.randint(2, 5),
"lr": hp.loguniform(1e-4, 1e-1),
}
