data.manydataset.load_sequence_data

data.manydataset.load_sequence_data(
    data,
    target,
    group_by,
    drop=None,
    input_features=None,
    feature_scaling=None,
    target_scaling=None,
    dataset_type='many_to_many',
)

Group a DataFrame into a variable-length sequence dataset.

Splits data by the values of group_by (e.g. one compressor speed line per group) and wraps the groups in a ManyToManyDataset or ManyToOneDataset. Ported from the schu25a study’s load_data helper (src/rnn/utils.py); the experiment-specific column defaults were removed.

Parameters

Name	Type	Description	Default
data	pd.DataFrame	Input data; one row per time step.	required
target	str	The target column name.	required
group_by	str	Column whose values define the sequences.	required
drop	Optional[Union[str, List[str]]]	Column(s) to drop from the groups before extracting features. Defaults to None.	`None`
input_features	Optional[List[str]]	Columns scaled by `feature_scaling`. Only used when `feature_scaling` is given; the dataset features are always all columns except `drop` and `target`. Defaults to None.	`None`
feature_scaling		Optional sklearn-style scaler; applied in place to `data[input_features]` via `fit_transform`. Defaults to None.	`None`
target_scaling		Optional sklearn-style scaler; applied in place to `data[target]` via `fit_transform`. Defaults to None.	`None`
dataset_type	str	Dataset flavor. Options: - “many_to_many”: one target value per time step. - “many_to_one”: one scalar target per sequence. Defaults to “many_to_many”.	`'many_to_many'`

Returns

Name	Type	Description
tuple	Tuple[`Dataset`, pd.DataFrame]	`(dataset, data)` — the sequence dataset and the (possibly scaled) DataFrame.

Raises

Name	Type	Description
	ValueError	If `dataset_type` is not “many_to_many” or “many_to_one”.

Examples

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

df = pd.DataFrame({
    "line": [1, 1, 1, 2, 2],
    "x": [0.1, 0.2, 0.3, 0.4, 0.5],
    "y": [1.0, 2.0, 3.0, 4.0, 5.0],
})
ds, df = load_sequence_data(df, target="y", group_by="line", drop="line")
print(len(ds), ds[0][0].shape)

2 torch.Size([3, 1])