def da_rnn(train_data: TrainData, n_targs: int,
encoder_hidden_size=64, decoder_hidden_size=64,
T=10, learning_rate=0.01, batch_size=128):
train_cfg = TrainConfig(T, int(train_data.feats.shape[0] * 0.7), batch_size, nn.MSELoss())
logger.info(f"Training size: {train_cfg.train_size:d}.")
enc_kwargs = {"input_size": train_data.feats.shape[1], "hidden_size": encoder_hidden_size, "T": T}
encoder = Encoder(**enc_kwargs).to(device)
with open(os.path.join("data", "enc_kwargs.json"), "w") as f:
json.dump(enc_kwargs, f, indent=4)
dec_kwargs = {"encoder_hidden_size": encoder_hidden_size,
"decoder_hidden_size": decoder_hidden_size, "T": T, "out_feats": n_targs}
decoder = Decoder(**dec_kwargs).to(device)
with open(os.path.join("data", "dec_kwargs.json"), "w") as f:
json.dump(dec_kwargs, f, indent=4)
encoder_optimizer = optim.Adam(
params=[p for p in encoder.parameters() if p.requires_grad],
lr=learning_rate)
decoder_optimizer = optim.Adam(
params=[p for p in decoder.parameters() if p.requires_grad],
lr=learning_rate)
da_rnn_net = DaRnnNet(encoder, decoder, encoder_optimizer, decoder_optimizer)
return train_cfg, da_rnn_net
def set_params(train_data, device, **da_rnn_kwargs):
train_configs = TrainConfig(da_rnn_kwargs["time_step"],
int(train_data.shape[0] * 0.95),
da_rnn_kwargs["batch_size"],
nn.MSELoss())
enc_kwargs = {
"input_size": train_data.shape[1],
"hidden_size": da_rnn_kwargs["en_hidden_size"],
"time_step":
int(da_rnn_kwargs["time_step"] / self.predict_size)
}
dec_kwargs = {
"encoder_hidden_size": da_rnn_kwargs["en_hidden_size"],
"decoder_hidden_size": da_rnn_kwargs["de_hidden_size"],
"time_step":
int(da_rnn_kwargs["time_step"] / self.predict_size),
"out_feats": da_rnn_kwargs["target_cols"]
}
encoder = Encoder(**enc_kwargs).to(device)
decoder = Decoder(**dec_kwargs).to(device)
encoder_optimizer = optim.Adam(
params=[p for p in encoder.parameters() if p.requires_grad],
lr=da_rnn_kwargs["learning_rate"],
betas=(0.9, 0.999),
eps=1e-08)
decoder_optimizer = optim.Adam(
params=[p for p in decoder.parameters() if p.requires_grad],
lr=da_rnn_kwargs["learning_rate"],
betas=(0.9, 0.999),
eps=1e-08)
da_rnn_net = DaRnnNet(encoder, decoder, encoder_optimizer,
decoder_optimizer)
return train_configs, da_rnn_net
def da_rnn(train_data: TrainData,
n_targs: int,
learning_rate=0.01,
encoder_hidden_size=64,
decoder_hidden_size=64,
T=10,
batch_size=128):
# passed arguments are data, n_targs=len(targ_cols), learning_rate=.001, **da_rnn_kwargs
#here n_args : int means that this argument takes only an integer as its value
#train_data = TrainData means that this train_data argument takes only the datatype TrainData that we have defined as its value
training_data_size_out_of_total = train_data.feats.shape[0] * 0.7
training_configuration = TrainConfig(T,
int(training_data_size_out_of_total),
batch_size, nn.MSELoss())
'''
class TrainConfig(typing.NamedTuple):
T: int
train_size: int
batch_size: int
loss_func: typing.Callable
'''
logger.info(f"Training size: {training_configuration.train_size:d}.")
encoder_kwargs = {
"input_size": train_data.feats.shape[1],
"hidden_size": encoder_hidden_size,
"T": T
}
encoder = Encoder(**encoder_kwargs).to(device)
with open(os.path.join("data", "enc_kwargs.json"), "w") as fi:
json.dump(encoder_kwargs, fi, indent=4)
decoder_kwargs = {
"encoder_hidden_size": encoder_hidden_size,
"decoder_hidden_size": decoder_hidden_size,
"T": T,
"out_feats": n_targs
}
decoder = Decoder(**decoder_kwargs).to(device)
with open(os.path.join("data", "dec_kwargs.json"), "w") as fi:
json.dump(decoder_kwargs, fi, indent=4)
encoder_optimizer = optim.Adam(
params=[p for p in encoder.parameters() if p.requires_grad],
lr=learning_rate)
decoder_optimizer = optim.Adam(
params=[p for p in decoder.parameters() if p.requires_grad],
lr=learning_rate)
da_rnn_net = DaRnnNet(
encoder, decoder, encoder_optimizer, decoder_optimizer
) #-------------------------------return the DA-RNN network
return training_configuration, da_rnn_net