def fit(self,
data,
epochs=1,
batch_size=32,
validation_data=None,
metric_threshold=None,
n_sampling=1,
search_alg=None,
search_alg_params=None,
scheduler=None,
scheduler_params=None):
"""
fit using AutoEstimator
:param data: train data.
For backend of "torch", data can be a TSDataset or a function that takes a
config dictionary as parameter and returns a PyTorch DataLoader.
For backend of "keras", data can be a TSDataset.
:param epochs: Max number of epochs to train in each trial. Defaults to 1.
If you have also set metric_threshold, a trial will stop if either it has been
optimized to the metric_threshold or it has been trained for {epochs} epochs.
:param batch_size: Int or hp sampling function from an integer space. Training batch size.
It defaults to 32.
:param validation_data: Validation data. Validation data type should be the same as data.
:param metric_threshold: a trial will be terminated when metric threshold is met.
:param n_sampling: Number of times to sample from the search_space. Defaults to 1.
If hp.grid_search is in search_space, the grid will be repeated n_sampling of times.
If this is -1, (virtually) infinite samples are generated
until a stopping condition is met.
:param search_alg: str, all supported searcher provided by ray tune
(i.e."variant_generator", "random", "ax", "dragonfly", "skopt",
"hyperopt", "bayesopt", "bohb", "nevergrad", "optuna", "zoopt" and
"sigopt")
:param search_alg_params: extra parameters for searcher algorithm besides search_space,
metric and searcher mode
:param scheduler: str, all supported scheduler provided by ray tune
:param scheduler_params: parameters for scheduler
:return: a TSPipeline with the best model.
"""
is_third_party_model = isinstance(self.model, AutoEstimator)
# generate data creator from TSDataset (pytorch base require validation data)
if isinstance(data, TSDataset) and isinstance(validation_data,
TSDataset):
train_d, val_d = self._prepare_data_creator(
search_space=self.search_space
if is_third_party_model else self.model.search_space,
train_data=data,
val_data=validation_data,
)
self._scaler = data.scaler
self._scaler_index = data.scaler_index
else:
train_d, val_d = data, validation_data
if is_third_party_model:
self.search_space.update({"batch_size": batch_size})
self.model.fit(
data=train_d,
epochs=epochs,
validation_data=val_d,
metric=self.metric,
metric_threshold=metric_threshold,
n_sampling=n_sampling,
search_space=self.search_space,
search_alg=search_alg,
search_alg_params=search_alg_params,
scheduler=scheduler,
scheduler_params=scheduler_params,
)
if not is_third_party_model:
self.model.fit(data=train_d,
epochs=epochs,
batch_size=batch_size,
validation_data=val_d,
metric_threshold=metric_threshold,
n_sampling=n_sampling,
search_alg=search_alg,
search_alg_params=search_alg_params,
scheduler=scheduler,
scheduler_params=scheduler_params)
return TSPipeline(best_model=self._get_best_automl_model(),
best_config=self.get_best_config(),
scaler=self._scaler,
scaler_index=self._scaler_index)
def test_fit_tcn_feature(self):
input_feature_dim = 11 # This param will not be used
output_feature_dim = 2 # 2 targets are generated in get_tsdataset
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_units': hp.grid_search([32, 64]),
'levels': hp.randint(4, 6),
'kernel_size': hp.randint(3, 5),
'dropout': hp.uniform(0.1, 0.2),
'lr': hp.loguniform(0.001, 0.01)
}
auto_trainer = AutoTSTrainer(model='tcn',
search_space=search_space,
past_seq_len=hp.randint(4, 6),
future_seq_len=1,
input_feature_num=input_feature_dim,
output_target_num=output_feature_dim,
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_trainer.fit(data=tsdata_train,
epochs=1,
batch_size=hp.choice([32, 64]),
validation_data=tsdata_valid,
n_sampling=1)
best_config = auto_trainer.get_best_config()
best_model = auto_trainer.get_best_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_tcn")
new_ts_pipeline = TSPipeline.load(
"/tmp/auto_trainer/autots_tmp_model_tcn")
# 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)