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from .base_automodel import BaseAutomodel
[docs]class AutoLSTM(BaseAutomodel):
def __init__(self,
input_feature_num,
output_target_num,
past_seq_len,
optimizer,
loss,
metric,
metric_mode=None,
hidden_dim=32,
layer_num=1,
lr=0.001,
dropout=0.2,
backend="torch",
logs_dir="/tmp/auto_lstm",
cpus_per_trial=1,
name="auto_lstm",
remote_dir=None,
):
"""
Create an AutoLSTM.
:param input_feature_num: Int. The number of features in the input
:param output_target_num: Int. The number of targets in the output
:param past_seq_len: Int or hp sampling function The number of historical
steps used for forecasting.
:param optimizer: String or pyTorch optimizer creator function or
tf.keras optimizer instance.
:param loss: String or pytorch/tf.keras loss instance or pytorch loss creator function.
:param metric: String or customized evaluation metric function.
If string, metric is the evaluation metric name to optimize, e.g. "mse".
If callable function, it signature should be func(y_true, y_pred), where y_true and
y_pred are numpy ndarray. The function should return a float value
as evaluation result.
:param metric_mode: One of ["min", "max"]. "max" means greater metric value is better.
You have to specify metric_mode if you use a customized metric function.
You don't have to specify metric_mode if you use the built-in metric in
bigdl.orca.automl.metrics.Evaluator.
:param hidden_dim: Int or hp sampling function from an integer space. The number of features
in the hidden state `h`. For hp sampling, see bigdl.chronos.orca.automl.hp for more
details. e.g. hp.grid_search([32, 64]).
:param layer_num: Int or hp sampling function from an integer space. Number of recurrent
layers. e.g. hp.randint(1, 3)
:param lr: float or hp sampling function from a float space. Learning rate.
e.g. hp.choice([0.001, 0.003, 0.01])
:param dropout: float or hp sampling function from a float space. Learning rate. Dropout
rate. e.g. hp.uniform(0.1, 0.3)
:param backend: The backend of the lstm model. support "keras" and "torch".
:param logs_dir: Local directory to save logs and results. It defaults to "/tmp/auto_lstm"
:param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.
:param name: name of the AutoLSTM. It defaults to "auto_lstm"
:param remote_dir: String. Remote directory to sync training results and checkpoints. It
defaults to None and doesn't take effects while running in local. While running in
cluster, it defaults to "hdfs:///tmp/{name}".
"""
# todo: support search for past_seq_len.
self.search_space = dict(
hidden_dim=hidden_dim,
layer_num=layer_num,
lr=lr,
dropout=dropout,
input_feature_num=input_feature_num,
output_feature_num=output_target_num,
past_seq_len=past_seq_len,
future_seq_len=1
)
self.metric = metric
self.metric_mode = metric_mode
self.backend = backend
self.optimizer = optimizer
self.loss = loss
self._auto_est_config = dict(logs_dir=logs_dir,
resources_per_trial={"cpu": cpus_per_trial},
remote_dir=remote_dir,
name=name)
if self.backend.startswith("torch"):
from bigdl.chronos.model.VanillaLSTM_pytorch import model_creator
elif self.backend.startswith("keras"):
from bigdl.chronos.model.tf2.VanillaLSTM_keras import model_creator
else:
from bigdl.nano.utils.common import invalidInputError
invalidInputError(False,
f"We only support keras and torch as backend,"
f" but got {self.backend}")
self._model_creator = model_creator
super().__init__()