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# Copyright 2018 Analytics Zoo Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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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.tcn import TCNPytorch
from zoo.chronos.model.tcn import model_creator, optimizer_creator, loss_creator
[docs]class TCNForecaster(BasePytorchForecaster):
"""
Example:
>>> #The dataset is split into x_train, x_val, x_test, y_train, y_val, y_test
>>> forecaster = TCNForecaster(past_seq_len=24,
future_seq_len=5,
input_feature_num=1,
output_feature_num=1,
...)
>>> 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,
future_seq_len,
input_feature_num,
output_feature_num,
num_channels=[30]*7,
kernel_size=3,
repo_initialization=True,
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 TCN Forecast Model.
TCN Forecast may fall into local optima. Please set repo_initialization
to False to alleviate the issue. You can also change a random seed to
work around.
:param past_seq_len: Specify the history time steps (i.e. lookback).
:param future_seq_len: Specify the output time steps (i.e. horizon).
:param input_feature_num: Specify the feature dimension.
:param output_feature_num: Specify the output dimension.
:param num_channels: Specify the convolutional layer filter number in
TCN's encoder. This value defaults to [30]*7.
:param kernel_size: Specify convolutional layer filter height in TCN's
encoder. This value defaults to 3.
:param repo_initialization: if to use framework default initialization,
True to use paper author's initialization and False to use the
framework's default initialization. The value defaults to True.
:param dropout: 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": future_seq_len,
"input_feature_num": input_feature_num,
"output_feature_num": output_feature_num
}
self.config = {
"lr": lr,
"loss": loss,
"num_channels": num_channels,
"kernel_size": kernel_size,
"repo_initialization": repo_initialization,
"optim": optimizer,
"dropout": dropout
}
# model creator settings
self.local_model = TCNPytorch
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__()