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

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from .base_automodel import BaseAutomodel


[docs]class AutoTCN(BaseAutomodel): def __init__(self, input_feature_num, output_target_num, past_seq_len, future_seq_len, optimizer, loss, metric, metric_mode=None, hidden_units=None, levels=None, num_channels=None, kernel_size=7, lr=0.001, dropout=0.2, backend="torch", logs_dir="/tmp/auto_tcn", cpus_per_trial=1, name="auto_tcn", remote_dir=None, ): """ Create an AutoTCN. :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 hidden_units: Int or hp sampling function from an integer space. The number of hidden units or filters for each convolutional layer. It is similar to `units` for LSTM. It defaults to 30. We will omit the hidden_units value if num_channels is specified. For hp sampling, see bigdl.orca.automl.hp for more details. e.g. hp.grid_search([32, 64]). :param levels: Int or hp sampling function from an integer space. The number of levels of TemporalBlocks to use. It defaults to 8. We will omit the levels value if num_channels is specified. :param num_channels: List of integers. A list of hidden_units for each level. You could specify num_channels if you want different hidden_units for different levels. By default, num_channels equals to [hidden_units] * (levels - 1) + [output_target_num]. :param kernel_size: Int or hp sampling function from an integer space. The size of the kernel to use in each convolutional layer. :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 TCN model. support "keras" and "torch". :param logs_dir: Local directory to save logs and results. It defaults to "/tmp/auto_tcn" :param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1. :param name: name of the AutoTCN. It defaults to "auto_tcn" :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. # todo: add input check. 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, nhid=hidden_units, levels=levels, num_channels=num_channels, kernel_size=kernel_size, lr=lr, dropout=dropout, ) 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.tcn import model_creator elif self.backend.startswith("keras"): from bigdl.chronos.model.tf2.TCN_keras import 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__()