def create_predictor( self, transformation: Transformation, trained_network: nn.Module, device: torch.device, ) -> PTSPredictor: prediction_network = SimpleFeedForwardPredictionNetwork( num_hidden_dimensions=self.num_hidden_dimensions, prediction_length=self.prediction_length, context_length=self.context_length, distr_output=self.distr_output, batch_normalization=self.batch_normalization, mean_scaling=self.mean_scaling, num_parallel_samples=self.num_parallel_samples, ).to(device) copy_parameters(trained_network, prediction_network) return PTSPredictor( input_transform=transformation, prediction_net=prediction_network, batch_size=self.trainer.batch_size, freq=self.freq, prediction_length=self.prediction_length, device=device, )
def create_predictor( self, transformation: Transformation, trained_network: LSTNetTrain, device: torch.device, ) -> PTSPredictor: prediction_network = LSTNetPredict( num_series=self.num_series, channels=self.channels, kernel_size=self.kernel_size, rnn_cell_type=self.rnn_cell_type, rnn_num_cells=self.rnn_num_cells, skip_rnn_cell_type=self.skip_rnn_cell_type, skip_rnn_num_cells=self.skip_rnn_num_cells, skip_size=self.skip_size, ar_window=self.ar_window, context_length=self.context_length, horizon=self.horizon, prediction_length=self.prediction_length, dropout_rate=self.dropout_rate, output_activation=self.output_activation, scaling=self.scaling, ).to(device) copy_parameters(trained_network, prediction_network) return PTSPredictor( input_transform=transformation, prediction_net=prediction_network, batch_size=self.trainer.batch_size, freq=self.freq, prediction_length=self.horizon or self.prediction_length, device=device, )
def create_predictor( self, transformation: Transformation, trained_network: nn.Module, device: torch.device, ) -> Predictor: prediction_network = NBEATSPredictionNetwork( prediction_length=self.prediction_length, context_length=self.context_length, num_stacks=self.num_stacks, widths=self.widths, num_blocks=self.num_blocks, num_block_layers=self.num_block_layers, expansion_coefficient_lengths=self.expansion_coefficient_lengths, sharing=self.sharing, stack_types=self.stack_types).to(device) copy_parameters(trained_network, prediction_network) return PTSPredictor( input_transform=transformation, prediction_net=prediction_network, batch_size=self.trainer.batch_size, freq=self.freq, prediction_length=self.prediction_length, device=device, )
def create_predictor( self, transformation: Transformation, trained_network: nn.Module, device: torch.device, ) -> Predictor: prediction_network = DeepARPredictionNetwork( num_parallel_samples=self.num_parallel_samples, input_size=self.input_size, num_layers=self.num_layers, num_cells=self.num_cells, cell_type=self.cell_type, history_length=self.history_length, context_length=self.context_length, prediction_length=self.prediction_length, distr_output=self.distr_output, dropout_rate=self.dropout_rate, cardinality=self.cardinality, embedding_dimension=self.embedding_dimension, lags_seq=self.lags_seq, scaling=self.scaling, dtype=self.dtype, ).to(device) copy_parameters(trained_network, prediction_network) return PTSPredictor( input_transform=transformation, prediction_net=prediction_network, batch_size=self.trainer.batch_size, freq=self.freq, prediction_length=self.prediction_length, device=device, dtype=self.dtype, )
def create_predictor( self, transformation: Transformation, trained_network: TransformerTempFlowTrainingNetwork, device: torch.device, ) -> PTSPredictor: prediction_network = TransformerTempFlowPredictionNetwork( input_size=self.input_size, target_dim=self.target_dim, num_heads=self.num_heads, act_type=self.act_type, d_model=self.d_model, dim_feedforward_scale=self.dim_feedforward_scale, num_encoder_layers=self.num_encoder_layers, num_decoder_layers=self.num_decoder_layers, history_length=self.history_length, context_length=self.context_length, prediction_length=self.prediction_length, dropout_rate=self.dropout_rate, cardinality=self.cardinality, embedding_dimension=self.embedding_dimension, lags_seq=self.lags_seq, scaling=self.scaling, flow_type=self.flow_type, n_blocks=self.n_blocks, hidden_size=self.hidden_size, n_hidden=self.n_hidden, conditioning_length=self.conditioning_length, dequantize=self.dequantize, num_parallel_samples=self.num_parallel_samples, ).to(device) copy_parameters(trained_network, prediction_network) return PTSPredictor( input_transform=transformation, prediction_net=prediction_network, batch_size=self.trainer.batch_size, freq=self.freq, prediction_length=self.prediction_length, device=device, output_transform=None, )
def create_predictor( self, transformation: Transformation, trained_network: nn.Module, device: torch.device, ) -> PTSPredictor: prediction_network = ARPredictionNetwork( prediction_length=self.prediction_length, context_length=self.context_length, num_parallel_samples=self.num_parallel_samples, ).to(device) copy_parameters(trained_network, prediction_network) return PTSPredictor( input_transform=transformation, prediction_net=prediction_network, batch_size=self.trainer.batch_size, freq=self.freq, prediction_length=self.prediction_length, device=device, )