#
# 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.
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from bigdl.chronos.forecaster.tf.base_forecaster import BaseTF2Forecaster
from bigdl.chronos.model.tf2.VanillaLSTM_keras import model_creator, LSTMModel
[docs]class LSTMForecaster(BaseTF2Forecaster):
"""
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))
>>> test_pred = forecaster.predict(x_test)
>>> test_eval = forecaster.evaluate((x_test, y_test))
>>> forecaster.save({ckpt_dir_name})
>>> forecaster.load({ckpt_dir_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="ray"):
"""
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: 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 Keras 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".
"""
self.model_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,
"hidden_dim": hidden_dim,
"layer_num": layer_num,
"dropout": dropout,
"loss": loss,
"lr": lr,
"optim": optimizer,
}
# model creator settings
self.model_creator = model_creator
self.custom_objects_config = {"LSTMModel": LSTMModel}
# distributed settings
# self.distributed = distributed
# self.distributed_backend = distributed_backend
# self.workers_per_node = workers_per_node
from bigdl.nano.utils.log4Error import invalidInputError
if distributed:
invalidInputError(False,
"We will add distributed support in subsequent releases, "
"the feature is currently unavailable, "
"Please set distributed=False.")
# 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
# self.onnx_available = True
# self.quantize_available = True
# self.checkpoint_callback = False
super(LSTMForecaster, self).__init__()