def __init__(self,
train_dataset: DeepARDataset,
predictor_filepath: str,
mean: bool = True,
num_samples: int = 100,
quantiles: List[float] = [],
nan_padding: bool = True):
""" constructs DeepAR forecast object
if mean False, will return median point estimates
"""
self.train_dataset = train_dataset
self.train_frame = train_dataset.get_frame()
self.predictor = GluonPredictor.deserialize(Path(predictor_filepath))
self.mean = mean
self.prediction_length = train_dataset.get_pred_length()
self.context_length = train_dataset.get_context_length()
self.num_samples = num_samples
self.quantiles = quantiles
self.nan_padding = nan_padding
self.data = []
self.series_idxs = []
self.max_intervals = []
self.pre_pad_lens = []
self.total_in_samples = []
def __init__(
self,
train_dataset: NBEATSDataset,
predictor_filepath: str,
interpretable: bool = True,
mean: bool = True,
nan_padding: bool = True,
):
"""constructs NBEATS forecast object
if mean False, will return median point estimates
"""
self.train_dataset = train_dataset
self.train_frame = train_dataset.get_frame()
self.predictor = GluonPredictor.deserialize(Path(predictor_filepath))
self.interpretable = interpretable
if interpretable:
self.mean = True
else:
self.mean = mean
self.prediction_length = train_dataset.get_pred_length()
self.context_length = train_dataset.get_context_length()
self.nan_padding = nan_padding
self.data = []
self.series_idxs = []
self.max_intervals = []
self.pre_pad_lens = []
self.total_in_samples = []
def unserialize_all(
base_path: str,
) -> List[GluonPredictor]:
""" unserialize all predictors from directory
Arguments:
base_path {str} -- base dir name from which to load models
Returns:
List[GluonPredictor] -- list of unserialized GluonPredictor objects
"""
return [
GluonPredictor.deserialize(Path(base_dir))
for base_dir in glob(base_path + "-fold*")
]