def load_ts_pipeline(file):
feature_transformers = TimeSequenceFeatureTransformer()
model = TimeSequenceModel(check_optional_config=False)
all_config = restore_zip(file, feature_transformers, model)
ts_pipeline = TimeSequencePipeline(
feature_transformers=feature_transformers,
model=model,
config=all_config)
print("Restore pipeline from", file)
return ts_pipeline
def fit_with_fixed_configs(self,
input_df,
validation_df=None,
mc=False,
**user_configs):
"""
Fit pipeline with fixed configs. The model will be trained from initialization
with the hyper-parameter specified in configs. The configs contain both identity configs
(Eg. "future_seq_len", "dt_col", "target_col", "metric") and automl tunable configs
(Eg. "past_seq_len", "batch_size").
We recommend calling get_default_configs to see the name and default values of configs you
you can specify.
:param input_df: one data frame or a list of data frames
:param validation_df: one data frame or a list of data frames
:param user_configs: you can overwrite or add more configs with user_configs. Eg. "epochs"
:return:
"""
# self._check_configs()
if self.config is None:
self.config = self.get_default_configs()
if user_configs is not None:
self.config.update(user_configs)
ft_id_config_set = {
'future_seq_len', 'dt_col', 'target_col', 'extra_features_col',
'drop_missing'
}
ft_id_configs = {a: self.config[a] for a in ft_id_config_set}
self.feature_transformers = TimeSequenceFeatureTransformer(
**ft_id_configs)
model_id_config_set = {'future_seq_len'}
ft_id_configs = {a: self.config[a] for a in model_id_config_set}
self.model = TimeSequenceModel(check_optional_config=False,
**ft_id_configs)
all_available_features = self.feature_transformers.get_feature_list(
input_df)
self.config.update({"selected_features": all_available_features})
(x_train, y_train) = self.feature_transformers.fit_transform(
input_df, **self.config)
if self._is_val_df_valid(validation_df):
validation_data = self.feature_transformers.transform(
validation_df)
else:
validation_data = None
self.model.fit_eval(x_train,
y_train,
validation_data=validation_data,
mc=mc,
verbose=1,
**self.config)
def create_model(self, resources_per_trial, config):
_model = TimeSequenceModel(
check_optional_config=False,
future_seq_len=self.future_seq_len)
return _model
def model_create_func():
# model = VanillaLSTM(check_optional_config=False)
_model = TimeSequenceModel(check_optional_config=False,
future_seq_len=self.future_seq_len)
return _model
def create_model():
_model = TimeSequenceModel(check_optional_config=False,
future_seq_len=future_seq_len)
return _model