pytoda.datasets.annotated_dataset module¶
Implementation of AnnotatedDataset class.
Reference¶
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class
AnnotatedDataset(annotations_filepath, dataset, annotation_index=- 1, label_columns=None, dtype=torch.float32, device=None, **kwargs)[source]¶ Bases:
Generic[torch.utils.data.dataset.T_co]Annotated samples in order of annotations csv, fetching data from passed dataset.
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__init__(annotations_filepath, dataset, annotation_index=- 1, label_columns=None, dtype=torch.float32, device=None, **kwargs)[source]¶ Initialize an annotated dataset via additional annotations dataframe. E.g. the dataset could be SMILES and the annotations could be single or multi task labels.
- Parameters
annotations_filepath (str) – path to the annotations of a dataset. Currently, the supported formats are column separated files. The default structure assumes that the last column contains an id that is also used in the dataset provided.
dataset (AnyBaseDataset) – instance of a AnyBaseDataset (supporting key lookup API of KeyDataset), e.g. a SMILESDataset.
annotation_index (Union[int, str]) – positional or string for the column containing the annotation index of keys to get items in the passed dataset. Defaults to -1, i.e. the last column.
label_columns (Union[List[int], List[str]]) – indexes (positional or strings) for the annotations. Defaults to None, a.k.a. all the columns, except the annotation index, are considered annotation labels.
dtype (torch.dtype) – torch data type for labels. Defaults to torch.float.
device (torch.device) – DEPRECATED
kwargs (dict) – additional parameter for pd.read_csv.
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