"""Optional preprocessing helpers for heterogeneous tabular data."""
import inspect
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
[docs]
def make_tabular_preprocessor(
categorical_features=(),
*,
scale_numeric=False,
add_missing_indicators=True,
):
"""Build a dense, leakage-safe preprocessor for use before SnapBoost.
Numeric columns are median-imputed, optionally augmented with missingness
indicators and standardized. Categorical columns are most-frequent-imputed
and one-hot encoded with unknown-category handling. The returned transformer
belongs in a normal scikit-learn ``Pipeline`` and does not alter SnapBoost's
internal training or model format.
"""
categorical_features = tuple(categorical_features)
numeric_steps = [
(
"impute",
SimpleImputer(
strategy="median",
add_indicator=add_missing_indicators,
),
)
]
if scale_numeric:
numeric_steps.append(("scale", StandardScaler()))
encoder_options = {"handle_unknown": "ignore"}
if "sparse_output" in inspect.signature(OneHotEncoder).parameters:
encoder_options["sparse_output"] = False
else:
encoder_options["sparse"] = False
categorical_pipeline = Pipeline([
("impute", SimpleImputer(strategy="most_frequent")),
(
"encode",
OneHotEncoder(**encoder_options),
),
])
transformers = []
if categorical_features:
transformers.append(
("categorical", categorical_pipeline, list(categorical_features))
)
return ColumnTransformer(
transformers=transformers,
remainder=Pipeline(numeric_steps),
sparse_threshold=0.0,
)
