Source code for bigdl.chronos.forecaster.tf.seq2seq_forecaster

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from bigdl.chronos.forecaster.tf.base_forecaster import BaseTF2Forecaster
from bigdl.chronos.model.tf2.Seq2Seq_keras import model_creator, LSTMSeq2Seq, model_creator_auto


[docs]class Seq2SeqForecaster(BaseTF2Forecaster): """ Example: >>> #The dataset is split into x_train, x_val, x_test, y_train, y_val, y_test >>> forecaster = Seq2SeqForecaster(past_seq_len=24, future_seq_len=2, input_feature_num=1, output_feature_num=1, ...) >>> 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, future_seq_len, input_feature_num, output_feature_num, lstm_hidden_dim=64, lstm_layer_num=2, teacher_forcing=False, 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 Seq2Seq Forecast Model. :param past_seq_len: Specify the history time steps (i.e. lookback). :param future_seq_len: Specify the output time steps (i.e. horizon). :param input_feature_num: Specify the feature dimension. :param output_feature_num: Specify the output dimension. :param lstm_hidden_dim: LSTM hidden channel for decoder and encoder. The value defaults to 64. :param lstm_layer_num: LSTM layer number for decoder and encoder. The value defaults to 2. :param teacher_forcing: If use teacher forcing in training. The value defaults to False. :param dropout: 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". """ # config setting self.model_config = { "past_seq_len": past_seq_len, "future_seq_len": future_seq_len, "input_feature_num": input_feature_num, "output_feature_num": output_feature_num, "lstm_hidden_dim": lstm_hidden_dim, "lstm_layer_num": lstm_layer_num, "teacher_forcing": teacher_forcing, "dropout": dropout, "loss": loss, "lr": lr, "optim": optimizer, } # model creator settings self.model_creator = model_creator_auto if distributed else model_creator self.custom_objects_config = {"LSTMSeq2Seq": LSTMSeq2Seq} # distributed settings self.distributed = distributed self.local_distributed_backend = "subprocess" self.remote_distributed_backend = distributed_backend self.workers_per_node = workers_per_node # 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.onnx_available = True # self.quantize_available = False # self.checkpoint_callback = False super(Seq2SeqForecaster, self).__init__()