Source code for bigdl.chronos.autots.model.auto_seq2seq

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from .base_automodel import BaseAutomodel


[docs]class AutoSeq2Seq(BaseAutomodel): def __init__(self, input_feature_num, output_target_num, past_seq_len, future_seq_len, optimizer, loss, metric, metric_mode=None, lr=0.001, lstm_hidden_dim=128, lstm_layer_num=2, dropout=0.25, teacher_forcing=False, backend="torch", logs_dir="/tmp/auto_seq2seq", cpus_per_trial=1, name="auto_seq2seq", remote_dir=None, ): """ Create an AutoSeq2Seq. :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. The number of historical steps used for forecasting. :param future_seq_len: Int. The number of future steps to forecast. :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 lr: float or hp sampling function from a float space. Learning rate. e.g. hp.choice([0.001, 0.003, 0.01]) :param lstm_hidden_dim: LSTM hidden channel for decoder and encoder. hp.grid_search([32, 64, 128]) :param lstm_layer_num: LSTM layer number for decoder and encoder. e.g. hp.randint(1, 4) :param dropout: float or hp sampling function from a float space. Learning rate. Dropout rate. e.g. hp.uniform(0.1, 0.3) :param teacher_forcing: If use teacher forcing in training. e.g. hp.choice([True, False]) :param backend: The backend of the Seq2Seq model. support "keras" and "torch". :param logs_dir: Local directory to save logs and results. It defaults to "/tmp/auto_seq2seq" :param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1. :param name: name of the AutoSeq2Seq. It defaults to "auto_seq2seq" :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( input_feature_num=input_feature_num, output_feature_num=output_target_num, past_seq_len=past_seq_len, future_seq_len=future_seq_len, lstm_hidden_dim=lstm_hidden_dim, lstm_layer_num=lstm_layer_num, lr=lr, dropout=dropout, teacher_forcing=teacher_forcing ) 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.Seq2Seq_pytorch import model_creator elif self.backend.startswith("keras"): from bigdl.chronos.model.tf2.Seq2Seq_keras import model_creator_auto as model_creator else: from bigdl.nano.utils.log4Error 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__()