def config_save(self, config_file=None):
"""
save all configs to file.
:param config_file:
:return:
"""
config_file = config_file or os.path.join(DEFAULT_CONFIG_DIR, "{}_{}.json".
format(self.name, self.time))
save_config(config_file, self.config, replace=True)
return config_file
def save(self, file):
"""
save pipeline to file, contains feature transformer, model, trial config.
:param file:
:return:
"""
if not os.path.isdir(file):
os.mkdir(file)
model_path = os.path.join(file, "weights_tune.h5")
config_path = os.path.join(file, "all_config.json")
self.feature_transformers.save(config_path, replace=True)
self.model.save(model_path, config_path)
# check if ** is needed
save_config(config_path, self.config)
def save(self, file_path, replace=False):
"""
save the feature tools internal variables as well as the initialization args.
Some of the variables are derived after fit_transform, so only saving config is not enough.
:param: file : the file to be saved
:return:
"""
# for StandardScaler()
data_to_save = {"mean": self.scaler.mean_.tolist(),
"scale": self.scaler.scale_.tolist(),
"future_seq_len": self.future_seq_len,
"dt_col": self.dt_col,
"target_col": self.target_col,
"extra_features_col": self.extra_features_col,
"drop_missing": self.drop_missing
}
save_config(file_path, data_to_save, replace=replace)
def save(self, model_path, config_path):
self.model.save_weights(model_path)
config_to_save = {
"cnn_height": self.cnn_height,
"long_num": self.long_num,
"time_step": self.time_step,
"ar_window": self.ar_window,
"cnn_hid_size": self.cnn_hid_size,
"rnn_hid_sizes": self.rnn_hid_sizes,
"cnn_dropout": self.cnn_dropout,
"rnn_dropout": self.rnn_dropout,
"lr": self.lr,
"batch_size": self.batch_size,
# for fit eval
"epochs": self.epochs,
# todo: can not serialize metrics unless all elements are str
"metrics": self.metrics,
"mc": self.mc,
"feature_num": self.feature_num,
"output_dim": self.output_dim,
"loss": self.loss
}
assert set(config_to_save.keys()) == self.saved_configs, \
"The keys in config_to_save is not the same as self.saved_configs." \
"Please keep them consistent"
# if self.decay_epochs > 0:
# lr_decay_configs = {"min_lr": self.min_lr,
# "max_lr": self.max_lr}
# assert set(lr_decay_configs.keys()) == self.lr_decay_configs, \
# "The keys in lr_decay_configs is not the same as self.lr_decay_configs." \
# "Please keep them consistent"
# config_to_save.update(lr_decay_configs)
# else:
# lr_configs = {"lr": self.lr_value}
# assert set(lr_configs.keys()) == self.lr_configs, \
# "The keys in lr_configs is not the same as self.lr_configs." \
# "Please keep them consistent"
# config_to_save.update(lr_configs)
save_config(config_path, config_to_save)
def save(self, file_path, replace=False):
data_to_save = {
"feature_cols": self.feature_cols,
"target_col": self.target_col
}
save_config(file_path, data_to_save, replace=replace)