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shuffle as boolean while converting dataset to dataclass #1213

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shuffle as boolean while converting dataset to dataclass #1213

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6 changes: 5 additions & 1 deletion skrub/datasets/_fetching.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@
from sklearn import __version__ as sklearn_version
from sklearn.datasets import fetch_openml
from sklearn.datasets._base import _sha256
from sklearn.utils import Bunch
from sklearn.utils import Bunch, shuffle
from sklearn.utils.fixes import parse_version

from skrub._utils import import_optional_dependency
Expand Down Expand Up @@ -651,6 +651,7 @@ def _fetch_dataset_as_dataclass(
dataset_id: int | str,
target: str | None,
load_dataframe: bool,
shuffling: bool | bool = False,
data_directory: Path | str | None = None,
read_csv_kwargs: dict | None = None,
) -> DatasetAll | DatasetInfoOnly:
Expand Down Expand Up @@ -702,6 +703,8 @@ def _fetch_dataset_as_dataclass(
df = pd.read_parquet(info["path"])
else:
df = pd.read_csv(info["path"], **read_csv_kwargs)
if shuffling:
df = shuffle(df, random_state=42).reset_index(drop=True)
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I think the random state should be a function parameter, as it would allow for control for multiple seeds during testing and more fine-grained control on reproducibility.

I'm not 100% sure we should reset indices, because it could be surprising for users. WDYT?

y = df[target]
X = df.drop(target, axis="columns")
dataset = DatasetAll(
Expand Down Expand Up @@ -777,6 +780,7 @@ def fetch_employee_salaries(
dataset_name="Employee salaries",
dataset_id=EMPLOYEE_SALARIES_ID,
target="current_annual_salary",
shuffling=True,
read_csv_kwargs={
"quotechar": "'",
"escapechar": "\\",
Expand Down
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