src/gluonts/model/n_beats/_ensemble.py [323:413]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def __init__( self, freq: str, prediction_length: int, meta_context_length: Optional[List[int]] = None, meta_loss_function: Optional[List[str]] = None, meta_bagging_size: int = 10, trainer: Trainer = Trainer(), num_stacks: int = 30, widths: Optional[List[int]] = None, num_blocks: Optional[List[int]] = None, num_block_layers: Optional[List[int]] = None, expansion_coefficient_lengths: Optional[List[int]] = None, sharing: Optional[List[bool]] = None, stack_types: Optional[List[str]] = None, **kwargs, ) -> None: super().__init__() assert ( prediction_length > 0 ), "The value of `prediction_length` should be > 0" self.freq = freq self.prediction_length = prediction_length assert meta_loss_function is None or all( [ loss_function in VALID_LOSS_FUNCTIONS for loss_function in meta_loss_function ] ), f"Each loss function has to be one of the following: {VALID_LOSS_FUNCTIONS}." assert meta_context_length is None or all( [context_length > 0 for context_length in meta_context_length] ), "The value of each `context_length` should be > 0" assert ( meta_bagging_size is None or meta_bagging_size > 0 ), "The value of each `context_length` should be > 0" self.meta_context_length = ( meta_context_length if meta_context_length is not None else [multiplier * prediction_length for multiplier in range(2, 8)] ) self.meta_loss_function = ( meta_loss_function if meta_loss_function is not None else VALID_LOSS_FUNCTIONS ) self.meta_bagging_size = meta_bagging_size # The following arguments are validated in the NBEATSEstimator: self.trainer = trainer print(f"TRAINER:{str(trainer)}") self.num_stacks = num_stacks self.widths = widths self.num_blocks = num_blocks self.num_block_layers = num_block_layers self.expansion_coefficient_lengths = expansion_coefficient_lengths self.sharing = sharing self.stack_types = stack_types # Actually instantiate the different models self.estimators = self._estimator_factory(**kwargs) def _estimator_factory(self, **kwargs): estimators = [] for context_length, loss_function, init_id in product( self.meta_context_length, self.meta_loss_function, list(range(self.meta_bagging_size)), ): # So far no use for the init_id, models are by default always randomly initialized estimators.append( NBEATSEstimator( freq=self.freq, prediction_length=self.prediction_length, context_length=context_length, trainer=copy.deepcopy(self.trainer), 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, loss_function=loss_function, **kwargs, ) ) return estimators - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - src/gluonts/nursery/SCott/pts/model/n_beats/n_beats_ensemble.py [170:260]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def __init__( self, freq: str, prediction_length: int, meta_context_length: Optional[List[int]] = None, meta_loss_function: Optional[List[str]] = None, meta_bagging_size: int = 10, trainer: Trainer = Trainer(), num_stacks: int = 30, widths: Optional[List[int]] = None, num_blocks: Optional[List[int]] = None, num_block_layers: Optional[List[int]] = None, expansion_coefficient_lengths: Optional[List[int]] = None, sharing: Optional[List[bool]] = None, stack_types: Optional[List[str]] = None, **kwargs, ) -> None: super().__init__() assert ( prediction_length > 0 ), "The value of `prediction_length` should be > 0" self.freq = freq self.prediction_length = prediction_length assert meta_loss_function is None or all( [ loss_function in VALID_LOSS_FUNCTIONS for loss_function in meta_loss_function ] ), f"Each loss function has to be one of the following: {VALID_LOSS_FUNCTIONS}." assert meta_context_length is None or all( [context_length > 0 for context_length in meta_context_length] ), "The value of each `context_length` should be > 0" assert ( meta_bagging_size is None or meta_bagging_size > 0 ), "The value of each `context_length` should be > 0" self.meta_context_length = ( meta_context_length if meta_context_length is not None else [multiplier * prediction_length for multiplier in range(2, 8)] ) self.meta_loss_function = ( meta_loss_function if meta_loss_function is not None else VALID_LOSS_FUNCTIONS ) self.meta_bagging_size = meta_bagging_size # The following arguments are validated in the NBEATSEstimator: self.trainer = trainer print(f"TRAINER:{str(trainer)}") self.num_stacks = num_stacks self.widths = widths self.num_blocks = num_blocks self.num_block_layers = num_block_layers self.expansion_coefficient_lengths = expansion_coefficient_lengths self.sharing = sharing self.stack_types = stack_types # Actually instantiate the different models self.estimators = self._estimator_factory(**kwargs) def _estimator_factory(self, **kwargs): estimators = [] for context_length, loss_function, init_id in product( self.meta_context_length, self.meta_loss_function, list(range(self.meta_bagging_size)), ): # So far no use for the init_id, models are by default always randomly initialized estimators.append( NBEATSEstimator( freq=self.freq, prediction_length=self.prediction_length, context_length=context_length, trainer=copy.deepcopy(self.trainer), 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, loss_function=loss_function, **kwargs, ) ) return estimators - 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