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Fix/handle categorical features #30798 #30799

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Fix/handle categorical features #30798 #30799

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2f224d2
FIX Fix average_precision_score for single sample case
Shyanil Jan 12, 2025
f43553f
FIX Fix average_precision_score for single sample case
Shyanil Jan 12, 2025
e114d15
FIX Fix average_precision_score for single sample case
Shyanil Jan 12, 2025
e58a18f
FIX Fix average_precision_score for single sample case
Shyanil Jan 12, 2025
7778785
Merge pull request #1 from Shyanil/fix/average-precision-score-single…
Shyanil Jan 12, 2025
11ee502
Merge branch 'scikit-learn:main' into main
Shyanil Feb 9, 2025
a2735b4
Fix: Handle categorical features properly in SequentialFeatureSelector
Feb 9, 2025
cf42700
Fix: Handle categorical features properly in SequentialFeatureSelector
Feb 9, 2025
acb30ab
Merge branch 'main' into fix/handle-categorical-features-1
Shyanil Feb 10, 2025
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6 changes: 5 additions & 1 deletion 6 sklearn/feature_selection/_sequential.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
from numbers import Integral, Real

import numpy as np
import pandas as pd

from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
from ..metrics import check_scoring, get_scorer_names
Expand Down Expand Up @@ -310,7 +311,10 @@ def _get_best_new_feature_score(self, estimator, X, y, cv, current_mask, **param
candidate_mask[feature_idx] = True
if self.direction == "backward":
candidate_mask = ~candidate_mask
X_new = X[:, candidate_mask]
if isinstance(X, pd.DataFrame):
X_new = X.iloc[:, candidate_mask]
else:
X_new = X[:, candidate_mask]
Comment on lines +314 to +317
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You should the function _safe_indexing instead.

However, here it is already to late because you already too late. We should avoid the call to validate_data or at least not validate X to not convert it to a NumPy array.

We also need to use n_features = _num_features(X) to compute the number of features.

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@glemaitre Should I modify the earlier validation step to prevent X from being converted to a NumPy array? If so, should I change how validate_data is called, or should we handle it differently?

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validate_data has a skip_check_array arg which would skip conversion to numpy.

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Ok @adrinjalali

scores[feature_idx] = cross_val_score(
estimator,
X_new,
Expand Down
7 changes: 7 additions & 0 deletions 7 sklearn/metrics/_ranking.py
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Expand Up @@ -220,7 +220,14 @@ def average_precision_score(

def _binary_uninterpolated_average_precision(
y_true, y_score, pos_label=1, sample_weight=None

):
if len(y_true) < 2:
raise ValueError(
f"Average precision requires at least 2 samples. Got {len(y_true)}."
" A single sample cannot form a precision-recall curve."
)

precision, recall, _ = precision_recall_curve(
y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
)
Expand Down
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