18754124332

Committed 23 Oct 2025 03:48PM UTC coverage: 92.865% (-0.2%) from 93.032%

Build # 18754124332

Build Type

Pull #431

github

Committed by

web-flow

Commit Message

Merge e4a9a83cb into 830c6bc23

Pull Request Pull Request #431: Product kernel explainer

Run Details

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

2 existing lines in 1 file now uncovered.

5375 of 5788 relevant lines covered (92.86%)

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71.43

/src/shapiq/explainer/product_kernel/conversion.py

"""Functions for converting scikit-learn models to a format used by shapiq."""

from __future__ import annotations

from typing import TYPE_CHECKING

import numpy as np

from shapiq.explainer.product_kernel.base import ProductKernelModel

if TYPE_CHECKING:
    from sklearn.gaussian_process import GaussianProcessRegressor
    from sklearn.svm import SVC, SVR


def convert_svm(model: SVC | SVR) -> ProductKernelModel:
    """Converts a scikit-learn SVM model to the product kernel format used by shapiq.

    Args:
        model: The scikit-learn SVM model to convert. Can be either a binary support vector classifier (SVC) or a support vector regressor (SVR).

    Returns:
        ProductKernelModel: The converted model in the product kernel format.

    """
    X_train = model.support_vectors_
    n, d = X_train.shape

    if hasattr(model, "kernel"):
        kernel_type = model.kernel  # pyright: ignore[reportAttributeAccessIssue]
        if kernel_type != "rbf":
            msg = "Currently only RBF kernel is supported for SVM models."
            raise ValueError(msg)
    else:
        msg = "Kernel type not found in the model. Ensure the model is a valid SVC or SVR."
        raise ValueError(msg)

    return ProductKernelModel(
        alpha=model.dual_coef_.flatten(),  # pyright: ignore[reportAttributeAccessIssue]
        X_train=X_train,
        n=n,
        d=d,
        gamma=model._gamma,  # pyright: ignore[reportArgumentType, reportAttributeAccessIssue] # noqa: SLF001
        kernel_type=kernel_type,
        intercept=model.intercept_[0],
    )


def convert_gp_reg(model: GaussianProcessRegressor) -> ProductKernelModel:
    """Converts a scikit-learn Gaussian Process Regression model to the product kernel format used by shapiq.

    Args:
        model: The scikit-learn Gaussian Process Regression model to convert.

    Returns:
        ProductKernelModel: The converted model in the product kernel format.

    """
    X_train = np.array(model.X_train_)
    n, d = X_train.shape

    if hasattr(model, "kernel"):
        kernel_type = model.kernel_.__class__.__name__.lower()  # Get the kernel type as a string
        if kernel_type != "rbf":
            msg = "Currently only RBF kernel is supported for Gaussian Process Regression models."
            raise ValueError(msg)
    else:
        msg = "Kernel type not found in the model. Ensure the model is a valid Gaussian Process Regressor."
        raise ValueError(msg)

    alphas = np.array(model.alpha_).flatten()
    parameters = (
        model.kernel_.get_params()  # pyright: ignore[reportAttributeAccessIssue]
    )
    if "length_scale" in parameters:
        length_scale = parameters["length_scale"]
    else:
        msg = "Length scale parameter not found in the kernel."
        raise ValueError(msg)

    return ProductKernelModel(
        alpha=alphas,
        X_train=X_train,
        n=n,
        d=d,
        gamma=(2 * (length_scale**2)) ** -1,
        kernel_type=kernel_type,
    )

1	"""Functions for converting scikit-learn models to a format used by shapiq."""
2
3	from __future__ import annotations	1✔
4
5	from typing import TYPE_CHECKING	1✔
6
7	import numpy as np	1✔
8
9	from shapiq.explainer.product_kernel.base import ProductKernelModel	1✔
10
11	if TYPE_CHECKING:	1✔
NEW 12	from sklearn.gaussian_process import GaussianProcessRegressor	×
NEW 13	from sklearn.svm import SVC, SVR	×
14
15
16	def convert_svm(model: SVC \| SVR) -> ProductKernelModel:	1✔
17	"""Converts a scikit-learn SVM model to the product kernel format used by shapiq.
18
19	Args:
20	model: The scikit-learn SVM model to convert. Can be either a binary support vector classifier (SVC) or a support vector regressor (SVR).
21
22	Returns:
23	ProductKernelModel: The converted model in the product kernel format.
24
25	"""
26	X_train = model.support_vectors_	1✔
27	n, d = X_train.shape	1✔
28
29	if hasattr(model, "kernel"):	1✔
30	kernel_type = model.kernel # pyright: ignore[reportAttributeAccessIssue]	1✔
31	if kernel_type != "rbf":	1✔
32	msg = "Currently only RBF kernel is supported for SVM models."	1✔
33	raise ValueError(msg)	1✔
34	else:
NEW 35	msg = "Kernel type not found in the model. Ensure the model is a valid SVC or SVR."	×
NEW 36	raise ValueError(msg)	×
37
38	return ProductKernelModel(	1✔
39	alpha=model.dual_coef_.flatten(), # pyright: ignore[reportAttributeAccessIssue]
40	X_train=X_train,
41	n=n,
42	d=d,
43	gamma=model._gamma, # pyright: ignore[reportArgumentType, reportAttributeAccessIssue] # noqa: SLF001
44	kernel_type=kernel_type,
45	intercept=model.intercept_[0],
46	)
47
48
49	def convert_gp_reg(model: GaussianProcessRegressor) -> ProductKernelModel:	1✔
50	"""Converts a scikit-learn Gaussian Process Regression model to the product kernel format used by shapiq.
51
52	Args:
53	model: The scikit-learn Gaussian Process Regression model to convert.
54
55	Returns:
56	ProductKernelModel: The converted model in the product kernel format.
57
58	"""
59	X_train = np.array(model.X_train_)	1✔
60	n, d = X_train.shape	1✔
61
62	if hasattr(model, "kernel"):	1✔
63	kernel_type = model.kernel_.__class__.__name__.lower() # Get the kernel type as a string	1✔
64	if kernel_type != "rbf":	1✔
NEW 65	msg = "Currently only RBF kernel is supported for Gaussian Process Regression models."	×
NEW 66	raise ValueError(msg)	×
67	else:
NEW 68	msg = "Kernel type not found in the model. Ensure the model is a valid Gaussian Process Regressor."	×
NEW 69	raise ValueError(msg)	×
70
71	alphas = np.array(model.alpha_).flatten()	1✔
72	parameters = (	1✔
73	model.kernel_.get_params() # pyright: ignore[reportAttributeAccessIssue]
74	)
75	if "length_scale" in parameters:	1✔
76	length_scale = parameters["length_scale"]	1✔
77	else:
NEW 78	msg = "Length scale parameter not found in the kernel."	×
NEW 79	raise ValueError(msg)	×
80
81	return ProductKernelModel(	1✔
82	alpha=alphas,
83	X_train=X_train,
84	n=n,
85	d=d,
86	gamma=(2 * (length_scale2)) -1,
87	kernel_type=kernel_type,
88	)

mmschlk / shapiq / 18754124332

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