#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from bigdl.chronos.forecaster.tf.base_forecaster import BaseTF2Forecaster
from bigdl.chronos.model.tf2.TCN_keras import model_creator, TemporalBlock, TemporalConvNet
[docs]class TCNForecaster(BaseTF2Forecaster):
"""
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.save({ckpt_name})
>>> forecaster.load({ckpt_name})
"""
def __init__(self,
past_seq_len,
future_seq_len,
input_feature_num,
output_feature_num,
num_channels=[16]*3,
kernel_size=3,
normalization=False,
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="ray"):
"""
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 [16]*3.
:param kernel_size: Specify convolutional layer filter height in TCN's
encoder. This value defaults to 3.
:param normalization: bool, Specify if to use normalization trick to
alleviate distribution shift. It first subtractes the last value
of the sequence and add back after the model forwarding.
This value defaults to False.
: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: Str or a tf.keras.losses.Loss instance, specify the loss function
used for training. This value defaults to "mse". You can choose
from "mse", "mae" and "huber_loss" or any customized loss instance
you want to use.
: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", "mae",
"rmse", "r2", "mape", "smape" or a callable function for a
non-distributed forecaster. If callable function, it signature
should be func(y_true, y_pred), where y_true and y_pred are numpy
ndarray.
: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 "ray" or
"horovod". The value defaults to "ray".
"""
# config setting
self.model_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,
"num_channels": num_channels,
"kernel_size": kernel_size,
"repo_initialization": repo_initialization,
"dropout": dropout,
"loss": loss,
"lr": lr,
"optim": optimizer,
"normalization": normalization,
}
# model creator settings
self.model_creator = model_creator
self.custom_objects_config = {"TemporalBlock": TemporalBlock,
"TemporalConvNet": TemporalConvNet}
# distributed settings
self.distributed = distributed
self.local_distributed_backend = "subprocess"
self.remote_distributed_backend = distributed_backend
self.workers_per_node = workers_per_node
# other settings
self.lr = lr
self.metrics = metrics
self.seed = seed
# nano setting
# current_num_threads = torch.get_num_threads()
# self.num_processes = max(1, current_num_threads//8) # 8 is a magic num
# self.use_ipex = False # TCN has worse performance on ipex
# self.onnx_available = True
self.quantize_available = True
# self.checkpoint_callback = False
super().__init__()