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Incorrect sample weight handling in KBinsDiscretizer #29906

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snath-xoc opened this issue Sep 22, 2024 · 1 comment · Fixed by #29907
Closed

Incorrect sample weight handling in KBinsDiscretizer #29906

snath-xoc opened this issue Sep 22, 2024 · 1 comment · Fixed by #29907
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@snath-xoc
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snath-xoc commented Sep 22, 2024 •
edited by lesteve
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Describe the bug

Sample weights are not properly passed through when specifying subsample within KBinsDiscretizer.

Steps/Code to Reproduce

from sklearn.datasets import make_blobs
from sklearn.preprocessing import KBinsDiscretizer
import numpy as np

rng = np.random.RandomState(42)

# Four centres 
centres = np.array([[0, 0], [0, 5], [3, 1], [2, 4], [8, 8]])
X, _ = make_blobs(
            n_samples=100,
            cluster_std=0.5,
            centers=centres,
            random_state=10,
        )

# Randomly generate sample weights
sample_weight = rng.randint(0, 10, size=X.shape[0])

est = KBinsDiscretizer(n_bins=4, strategy='quantile', subsample=20,
                                    random_state=10).fit(X, sample_weight=sample_weight)

Expected Results

No error is thrown

Actual Results

[253](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/preprocessing/_discretization.py:253) if sample_weight is not None:
--> [254](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/preprocessing/_discretization.py:254)     sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
    [256](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/preprocessing/_discretization.py:256) bin_edges = np.zeros(n_features, dtype=object)
    [257](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/preprocessing/_discretization.py:257) for jj in range(n_features):

File ~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2133, in _check_sample_weight(sample_weight, X, dtype, copy, ensure_non_negative)
   [2130](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2130)         raise ValueError("Sample weights must be 1D array or scalar")
   [2132](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2132)     if sample_weight.shape != (n_samples,):
-> [2133](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2133)         raise ValueError(
   [2134](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2134)             "sample_weight.shape == {}, expected {}!".format(
   [2135](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2135)                 sample_weight.shape, (n_samples,)
   [2136](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2136)             )
   [2137](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2137)         )
   [2139](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2139) if ensure_non_negative:
   [2140](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2140)     check_non_negative(sample_weight, "`sample_weight`")

ValueError: sample_weight.shape == (100,), expected (20,)!

Versions

System:
    python: 3.12.4 | packaged by conda-forge | (main, Jun 17 2024, 10:13:44) [Clang 16.0.6 ]
executable: /Users/shrutinath/micromamba/envs/scikit-learn/bin/python
   machine: macOS-14.3-arm64-arm-64bit

Python dependencies:
      sklearn: 1.6.dev0
          pip: 24.0
   setuptools: 70.1.1
        numpy: 2.0.0
        scipy: 1.14.0
       Cython: 3.0.10
       pandas: 2.2.2
   matplotlib: 3.9.0
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 8
         prefix: libopenblas
...
    num_threads: 8
         prefix: libomp
       filepath: /Users/shrutinath/micromamba/envs/scikit-learn/lib/libomp.dylib
        version: None
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
@snath-xoc snath-xoc added Bug Needs Triage Issue requires triage labels Sep 22, 2024
@snath-xoc snath-xoc mentioned this issue Sep 22, 2024
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@snath-xoc
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Cross linking this to the meta-issue as well #16298

@lesteve lesteve removed the Needs Triage Issue requires triage label Sep 23, 2024
@lesteve lesteve changed the title Incorrect sample weight handling in KbinsDisctretizer Incorrect sample weight handling in KbinsDiscretizer Sep 23, 2024
@ogrisel ogrisel changed the title Incorrect sample weight handling in KbinsDiscretizer Incorrect sample weight handling in KBinsDiscretizer Nov 5, 2024
@snath-xoc snath-xoc mentioned this issue Mar 13, 2025
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Fix sample weight passing in KBinsDiscretizer snath-xoc/scikit-learn
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@lesteve @snath-xoc