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# Copyright 2018 Analytics Zoo Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
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from zoo.chronos.forecaster.base_forecaster import BasePytorchForecaster
from zoo.chronos.forecaster.utils import set_pytorch_seed
from zoo.chronos.model.VanillaLSTM_pytorch import VanillaLSTMPytorch
from zoo.chronos.model.VanillaLSTM_pytorch import model_creator, optimizer_creator, loss_creator
[docs]class LSTMForecaster(BasePytorchForecaster):
"""
Example:
>>> #The dataset is split into x_train, x_val, x_test, y_train, y_val, y_test
>>> forecaster = LSTMForecaster(past_seq_len=24,
input_feature_num=2,
output_feature_num=2,
...)
>>> forecaster.fit((x_train, y_train))
>>> forecaster.to_local() # if you set distributed=True
>>> test_pred = forecaster.predict(x_test)
>>> test_eval = forecaster.evaluate((x_test, y_test))
>>> forecaster.save({ckpt_name})
>>> forecaster.restore({ckpt_name})
"""
def __init__(self,
past_seq_len,
input_feature_num,
output_feature_num,
hidden_dim=32,
layer_num=1,
dropout=0.1,
optimizer="Adam",
loss="mse",
lr=0.001,
metrics=["mse"],
seed=None,
distributed=False,
workers_per_node=1,
distributed_backend="torch_distributed"):
"""
Build a LSTM Forecast Model.
:param past_seq_len: Specify the history time steps (i.e. lookback).
:param input_feature_num: Specify the feature dimension.
:param output_feature_num: Specify the output dimension.
:param hidden_dim: int or list, Specify the hidden dim of each lstm layer.
The value defaults to 32.
:param layer_num: Specify the number of lstm layer to be used. The value
defaults to 1.
:param dropout: int or list, Specify the dropout close possibility
(i.e. the close possibility to a neuron). This value defaults to 0.1.
:param optimizer: Specify the optimizer used for training. This value
defaults to "Adam".
:param loss: Specify the loss function used for training. This value
defaults to "mse". You can choose from "mse", "mae" and
"huber_loss".
:param lr: Specify the learning rate. This value defaults to 0.001.
:param metrics: A list contains metrics for evaluating the quality of
forecasting. You may only choose from "mse" and "mae" for a
distributed forecaster. You may choose from "mse", "me", "mae",
"mse","rmse","msle","r2", "mpe", "mape", "mspe", "smape", "mdape"
and "smdape" for a non-distributed forecaster.
:param seed: int, random seed for training. This value defaults to None.
:param distributed: bool, if init the forecaster in a distributed
fashion. If True, the internal model will use an Orca Estimator.
If False, the internal model will use a pytorch model. The value
defaults to False.
:param workers_per_node: int, the number of worker you want to use.
The value defaults to 1. The param is only effective when
distributed is set to True.
:param distributed_backend: str, select from "torch_distributed" or
"horovod". The value defaults to "torch_distributed".
"""
# config setting
self.data_config = {
"past_seq_len": past_seq_len,
"future_seq_len": 1, # lstm model only supports 1 step prediction
"input_feature_num": input_feature_num,
"output_feature_num": output_feature_num
}
self.config = {
"lr": lr,
"loss": loss,
"hidden_dim": hidden_dim,
"layer_num": layer_num,
"optim": optimizer,
"dropout": dropout
}
# model creator settings
self.local_model = VanillaLSTMPytorch
self.model_creator = model_creator
self.optimizer_creator = optimizer_creator
self.loss_creator = loss_creator
# distributed settings
self.distributed = distributed
self.distributed_backend = distributed_backend
self.workers_per_node = workers_per_node
# other settings
self.lr = lr
self.metrics = metrics
self.seed = seed
super().__init__()