data.manydataset.load_pooled_sequence_data

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

Pool several DataFrames into one concatenated sequence dataset.

Applies load_sequence_data to every DataFrame (e.g. one compressor map each) and concatenates the resulting sequence datasets — the pooled training set of the schu25a “global training” workflow (ported from load_pretrain_data in src/rnn/utils.py).

Parameters

Name	Type	Description	Default
data_list	List[pd.DataFrame]	Input DataFrames; one per map.	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`; see `load_sequence_data`. Defaults to None.	`None`
feature_scaling		Optional sklearn-style scaler, applied per DataFrame. Defaults to None.	`None`
target_scaling		Optional sklearn-style scaler, applied per DataFrame. Defaults to None.	`None`
dataset_type	str	“many_to_many” or “many_to_one”. Defaults to “many_to_many”.	`'many_to_many'`

Returns

Name	Type	Description
ConcatDataset	`ConcatDataset`	The concatenation of the per-DataFrame sequence datasets.

Examples

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

df1 = pd.DataFrame({"line": [1, 1, 2], "x": [0.1, 0.2, 0.3], "y": [1.0, 2.0, 3.0]})
df2 = pd.DataFrame({"line": [1, 1], "x": [0.4, 0.5], "y": [4.0, 5.0]})
pooled = load_pooled_sequence_data([df1, df2], target="y", group_by="line", drop="line")
print(len(pooled))