18697371034

Committed 21 Oct 2025 08:54PM UTC coverage: 92.858% (+0.06%) from 92.799%

Build # 18697371034

Build Type

Pull #431

github

Committed by

web-flow

Commit Message

Merge 435a34452 into ac5614648

Pull Request Pull Request #431: Product kernel explainer

Run Details

186 of 210 new or added lines in 13 files covered. (88.57%)

22 existing lines in 5 files now uncovered.

5214 of 5615 relevant lines covered (92.86%)

0.93 hits per line

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92.86

/src/shapiq/explainer/product_kernel/base.py

"""The base class for product kernel model conversion."""

from __future__ import annotations

from dataclasses import dataclass
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    import numpy as np


@dataclass
class ProductKernelModel:
    """A dataclass for storing the information of a product kernel model.

    Attributes:
         alpha: The alpha parameter of the product kernel model.
         X_train: The training data used to fit the product kernel model.
         n: The number of samples in the training data.
         d: The number of features in the training data.
         gamma: The gamma parameter of the product kernel model.
        intercept: The intercept term of the product kernel model. For Gaussian Processes this should be zero, but support vectors have often non-zero intercepts.
    """

    X_train: np.ndarray
    alpha: np.ndarray
    n: int
    d: int
    gamma: float | None = None
    kernel_type: str = "rbf"
    intercept: float = 0.0

1	"""The base class for product kernel model conversion."""
2
3	from __future__ import annotations	1✔
4
5	from dataclasses import dataclass	1✔
6	from typing import TYPE_CHECKING	1✔
7
8	if TYPE_CHECKING:	1✔
NEW 9	import numpy as np	×
10
11
12	@dataclass	1✔
13	class ProductKernelModel:	1✔
14	"""A dataclass for storing the information of a product kernel model.
15
16	Attributes:
17	alpha: The alpha parameter of the product kernel model.
18	X_train: The training data used to fit the product kernel model.
19	n: The number of samples in the training data.
20	d: The number of features in the training data.
21	gamma: The gamma parameter of the product kernel model.
22	intercept: The intercept term of the product kernel model. For Gaussian Processes this should be zero, but support vectors have often non-zero intercepts.
23	"""
24
25	X_train: np.ndarray	1✔
26	alpha: np.ndarray	1✔
27	n: int	1✔
28	d: int	1✔
29	gamma: float \| None = None	1✔
30	kernel_type: str = "rbf"	1✔
31	intercept: float = 0.0	1✔

mmschlk / shapiq / 18697371034

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