Source code for zoo.chronos.autots.model.auto_lstm

# Copyright 2018 Analytics Zoo Authors.
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from zoo.orca.automl.model.base_pytorch_model import PytorchModelBuilder
from zoo.orca.automl.auto_estimator import AutoEstimator
from zoo.chronos.model.VanillaLSTM_pytorch import model_creator
from .base_automodel import BasePytorchAutomodel

[docs]class AutoLSTM(BasePytorchAutomodel): def __init__(self, input_feature_num, output_target_num, past_seq_len, optimizer, loss, metric, 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. The evaluation metric name to optimize. e.g. "mse" :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 zoo.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. We only support backend as "torch" for now. :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}". """ super().__init__() # todo: support backend = 'keras' if backend != "torch": raise ValueError(f"We only support backend as torch. Got {backend}") 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 model_builder = PytorchModelBuilder(model_creator=model_creator, optimizer_creator=optimizer, loss_creator=loss, ) self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={"cpu": cpus_per_trial}, remote_dir=remote_dir, name=name)