17372021070

Committed 01 Sep 2025 08:26AM UTC coverage: 93.704% (-0.2%) from 93.906%

Build # 17372021070

Build Type

Pull #431

github

Committed by

web-flow

Commit Message

Merge 7d2c68dac into 68f21c1d6

Pull Request Pull Request #431: Product kernel explainer

Run Details

180 of 203 new or added lines in 14 files covered. (88.67%)

4 existing lines in 2 files now uncovered.

5090 of 5432 relevant lines covered (93.7%)

0.94 hits per line

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77.27

/src/shapiq/explainer/product_kernel/validation.py

"""Conversion functions for the product kernel explainer implementation."""

from __future__ import annotations

from typing import TYPE_CHECKING

from shapiq.utils.modules import safe_isinstance

from .conversion import convert_gp_reg, convert_svm

if TYPE_CHECKING:
    from shapiq.typing import Model

    from .base import ProductKernelModel

SUPPORTED_MODELS = {
    "sklearn.svm.SVR",
    "sklearn.svm.SVC",
    "sklearn.gaussian_process.GaussianProcessRegressor",
}


def validate_pk_model(
    model: Model,
    class_label: int | None = None,  # TODO(IsaH57): check how to use (Issue #425)
) -> ProductKernelModel:
    """Validate the product kernel model.

    Args:
        model: The model to validate.
        class_label: The class label to be used for validation. Defaults to None.

    Returns:
        The validated product kernel model.
    """
    class_label = class_label or 1  # default to 1 for classification models

    # product kernel model already in the correct format
    if type(model).__name__ == "ProductKernelModel":
        pk_model = model
    elif safe_isinstance(model, "sklearn.svm.SVR") or (
        safe_isinstance(model, "sklearn.svm.SVC") and model.classes_.shape[0] == 2
    ):
        pk_model = convert_svm(model)
    elif safe_isinstance(model, "sklearn.gaussian_process.GaussianProcessRegressor"):
        pk_model = convert_gp_reg(model)

    # unsupported model types
    elif safe_isinstance(model, "sklearn.svm.SVC") and model.classes_.shape[0] >= 2:
        msg = "Only binary SVM classification supported."
        raise TypeError(msg)
    else:
        msg = f"Unsupported model type.Supported models are: {SUPPORTED_MODELS}"
        raise TypeError(msg)
    return pk_model

1	"""Conversion functions for the product kernel explainer implementation."""
2
3	from __future__ import annotations	1✔
4
5	from typing import TYPE_CHECKING	1✔
6
7	from shapiq.utils.modules import safe_isinstance	1✔
8
9	from .conversion import convert_gp_reg, convert_svm	1✔
10
11	if TYPE_CHECKING:	1✔
NEW 12	from shapiq.typing import Model	×
13
NEW 14	from .base import ProductKernelModel	×
15
16	SUPPORTED_MODELS = {	1✔
17	"sklearn.svm.SVR",
18	"sklearn.svm.SVC",
19	"sklearn.gaussian_process.GaussianProcessRegressor",
20	}
21
22
23	def validate_pk_model(	1✔
24	model: Model,
25	class_label: int \| None = None, # TODO(IsaH57): check how to use (Issue #425)
26	) -> ProductKernelModel:
27	"""Validate the product kernel model.
28
29	Args:
30	model: The model to validate.
31	class_label: The class label to be used for validation. Defaults to None.
32
33	Returns:
34	The validated product kernel model.
35	"""
36	class_label = class_label or 1 # default to 1 for classification models	1✔
37
38	# product kernel model already in the correct format
39	if type(model).__name__ == "ProductKernelModel":	1✔
NEW 40	pk_model = model	×
41	elif safe_isinstance(model, "sklearn.svm.SVR") or (	1✔
42	safe_isinstance(model, "sklearn.svm.SVC") and model.classes_.shape[0] == 2
43	):
44	pk_model = convert_svm(model)	1✔
45	elif safe_isinstance(model, "sklearn.gaussian_process.GaussianProcessRegressor"):	1✔
46	pk_model = convert_gp_reg(model)	1✔
47
48	# unsupported model types
49	elif safe_isinstance(model, "sklearn.svm.SVC") and model.classes_.shape[0] >= 2:	1✔
NEW 50	msg = "Only binary SVM classification supported."	×
NEW 51	raise TypeError(msg)	×
52	else:
53	msg = f"Unsupported model type.Supported models are: {SUPPORTED_MODELS}"	1✔
54	raise TypeError(msg)	1✔
55	return pk_model	1✔

mmschlk / shapiq / 17372021070

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