data.manydataset.ManyToManyDataset

data.manydataset.ManyToManyDataset(
    df_list,
    target,
    drop=None,
    dtype=torch.float32,
)

Sequence dataset with one target value per time step.

Each element of df_list is one variable-length sequence; item i is the pair (features_i, targets_i) with shapes (T_i, n_features) and (T_i,).

Parameters

Name	Type	Description	Default
df_list	List[pd.DataFrame]	List of pandas DataFrames, one per sequence.	required
target	str	The target column name.	required
drop	Optional[Union[str, List[str]]]	Column(s) to drop from the DataFrames before extracting features. If a listed column is missing, no column is dropped. Defaults to None.	`None`
dtype	`torch`.`dtype`	Data type for the tensors. Defaults to `torch.float32`.	`torch.float32`

Attributes

Name	Type	Description
data	List[pd.DataFrame]	DataFrames with the `drop` columns removed.
target	List[`torch`.`Tensor`]	Per-sequence target tensors, shape `(T_i,)`.
features	List[`torch`.`Tensor`]	Per-sequence feature tensors, shape `(T_i, n_features)`.

Examples

import pandas as pd
from spotoptim.data.manydataset import ManyToManyDataset

df1 = pd.DataFrame({"x": [1.0, 2.0, 3.0], "y": [2.0, 4.0, 6.0]})
df2 = pd.DataFrame({"x": [4.0, 5.0], "y": [8.0, 10.0]})
ds = ManyToManyDataset([df1, df2], target="y")
print(len(ds))
features, targets = ds[0]
print(features.shape, targets.shape)

2
torch.Size([3, 1]) torch.Size([3])