class LSTMForecaster(TFParkForecaster):
"""
Vanilla LSTM Forecaster
"""
def __init__(self,
target_dim=1,
feature_dim=1,
lstm_1_units=16,
dropout_1=0.2,
lstm_2_units=8,
dropout_2=0.2,
metric="mean_squared_error",
lr=0.001,
loss="mse",
uncertainty: bool = False):
"""
Build a LSTM Forecast Model.
:param target_dim: dimension of model output
:param feature_dim: dimension of input feature
:param lstm_1_units: num of units for the 1st LSTM layer
:param dropout_1: p for the 1st dropout layer
:param lstm_2_units: num of units for the 2nd LSTM layer
:param dropout_2: p for the 2nd dropout layer
:param metric: the metric for validation and evaluation
:param lr: learning rate
:param uncertainty: whether to return uncertainty
:param loss: the target function you want to optimize on
"""
#
self.target_dim = target_dim
self.check_optional_config = False
self.uncertainty = uncertainty
self.model_config = {
"lr": lr,
"lstm_1_units": lstm_1_units,
"dropout_1": dropout_1,
"lstm_2_units": lstm_2_units,
"dropout_2": dropout_2,
"metric": metric,
"feature_num": feature_dim,
"loss": loss
}
self.internal = None
super().__init__()
def _build(self):
"""
Build LSTM Model in tf.keras
"""
# build model with TF/Keras
self.internal = LSTMKerasModel(
check_optional_config=self.check_optional_config,
future_seq_len=self.target_dim)
return self.internal._build(mc=self.uncertainty, **self.model_config)
class LSTMForecaster(Forecaster):
"""
Vanilla LSTM Forecaster
"""
def __init__(self,
horizon=1,
feature_dim=1,
lstm_1_units=16,
dropout_1=0.2,
lstm_2_units=8,
dropout_2=0.2,
metric="mean_squared_error",
lr=0.001,
uncertainty: bool = False):
"""
Build a LSTM Forecast Model.
@param horizon: steps to look forward
@param feature_dim: dimension of input feature
@param lstm_1_units: num of units for the 1st LSTM layer
@param dropout_1: p for the 1st dropout layer
@param lstm_2_units: num of units for the 2nd LSTM layer
@param dropout_2: p for the 2nd dropout layer
@param metric: the metric for validation and evaluation
@param lr: learning rate
@param uncertainty: whether to return uncertainty
"""
#
self.horizon = horizon
self.check_optional_config = False
self.uncertainty = uncertainty
self.model_config = {
"lr": lr,
"lstm_1_units": lstm_1_units,
"dropout_1": dropout_1,
"lstm_2_units": lstm_2_units,
"dropout_2": dropout_2,
"metric": metric,
"feature_num": feature_dim
}
self.internal = None
super().__init__()
def _build(self):
"""
Build LSTM Model in tf.keras
"""
# build model with TF/Keras
self.internal = LSTMKerasModel(
check_optional_config=self.check_optional_config,
future_seq_len=self.horizon)
return self.internal._build(mc=self.uncertainty, **self.model_config)
