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_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]),
'dropout': hp.uniform(0.1, 0.2)
}
auto_estimator = AutoTSEstimator(model=model_creator,
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(),
cpus_per_trial=2)
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 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
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)