16345920221

Committed 17 Jul 2025 01:05PM UTC coverage: 77.589% (-13.5%) from 91.075%

Build # 16345920221

Build Type

push

github

Committed by

web-flow

Commit Message

🔨 Refactors library into a src structure. (#415)

* moves shapiq into a src folder

* moves shapiq tests into tests_shapiq subfolder in tests

* refactors tests to work properly

* removes pickle support and closes #413

* changes unit tests to only run the unit tests

* adds workflow for running shapiq_games

* updates coverage to only run for shapiq

* update workflow to check for shapiq_games import

* update CHANGELOG.md

* fixes install-import.yml

* fixes version in docs

* moved deprecated tests out of the main test suite

* moves fixtures in the correct test suite

* installs libomp on macos runner (try bugfix)

* correct spelling

* removes libomp again

* moves os runs into individual workflows for easier debugging

* runs macOS on py3.13

* renames workflows

* installs libomp again on macOS

* downgraded to 3.11 and reinstall python

* try different uv version

* adds libomp

* changes skip to xfail in integration tests with wrong index/order combinations

* moves test out for debugging CI

* removes outdated test

* adds concurrency for quicker testsing

* re-adds randomly

* dont reset seed

* removed pytest-randomly again

* adds the tests back in

Run Details

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5536 of 7135 relevant lines covered (77.59%)

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91.89

/src/shapiq/explainer/tree/validation.py

"""Conversion functions for the tree explainer implementation."""

from __future__ import annotations

from typing import TYPE_CHECKING

from shapiq.utils.modules import safe_isinstance

from .base import TreeModel
from .conversion.lightgbm import convert_lightgbm_booster
from .conversion.sklearn import (
    convert_sklearn_forest,
    convert_sklearn_isolation_forest,
    convert_sklearn_tree,
)
from .conversion.xgboost import convert_xgboost_booster

if TYPE_CHECKING:
    from shapiq.typing import Model

SUPPORTED_MODELS = {
    "sklearn.tree.DecisionTreeRegressor",
    "sklearn.tree._classes.DecisionTreeRegressor",
    "sklearn.tree.DecisionTreeClassifier",
    "sklearn.tree._classes.DecisionTreeClassifier",
    "sklearn.ensemble.RandomForestClassifier",
    "sklearn.ensemble._forest.RandomForestClassifier",
    "sklearn.ensemble.ExtraTreesClassifier",
    "sklearn.ensemble._forest.ExtraTreesClassifier",
    "sklearn.ensemble.RandomForestRegressor",
    "sklearn.ensemble._forest.RandomForestRegressor",
    "sklearn.ensemble.ExtraTreesRegressor",
    "sklearn.ensemble._forest.ExtraTreesRegressor",
    "sklearn.ensemble.IsolationForest",
    "sklearn.ensemble._iforest.IsolationForest",
    "lightgbm.sklearn.LGBMRegressor",
    "lightgbm.sklearn.LGBMClassifier",
    "lightgbm.basic.Booster",
    "xgboost.sklearn.XGBRegressor",
    "xgboost.sklearn.XGBClassifier",
}


def validate_tree_model(
    model: Model,
    class_label: int | None = None,
) -> TreeModel | list[TreeModel]:
    """Validate the model.

    Args:
        model: The model to validate.
        class_label: The class label of the model to explain. Only used for classification models.

    Returns:
        The validated model and the model function.

    """
    # direct returns for base tree models and dict as model
    # tree model (is already in the correct format)
    if type(model).__name__ == "TreeModel":
        tree_model = model
    # direct return if list of tree models
    elif type(model).__name__ == "list":
        # check if all elements are TreeModel
        if all(type(tree).__name__ == "TreeModel" for tree in model):
            tree_model = model
    # dict as model is parsed to TreeModel (the dict needs to have the correct format and names)
    elif type(model).__name__ == "dict":
        tree_model = TreeModel(**model)
    # transformation of common machine learning libraries to TreeModel
    # sklearn decision trees
    elif (
        safe_isinstance(model, "sklearn.tree.DecisionTreeRegressor")
        or safe_isinstance(model, "sklearn.tree._classes.DecisionTreeRegressor")
        or safe_isinstance(model, "sklearn.tree.DecisionTreeClassifier")
        or safe_isinstance(model, "sklearn.tree._classes.DecisionTreeClassifier")
    ):
        tree_model = convert_sklearn_tree(model, class_label=class_label)
    # sklearn random forests
    elif (
        safe_isinstance(model, "sklearn.ensemble.RandomForestRegressor")
        or safe_isinstance(model, "sklearn.ensemble._forest.RandomForestRegressor")
        or safe_isinstance(model, "sklearn.ensemble.RandomForestClassifier")
        or safe_isinstance(model, "sklearn.ensemble._forest.RandomForestClassifier")
        or safe_isinstance(model, "sklearn.ensemble.ExtraTreesRegressor")
        or safe_isinstance(model, "sklearn.ensemble._forest.ExtraTreesRegressor")
        or safe_isinstance(model, "sklearn.ensemble.ExtraTreesClassifier")
        or safe_isinstance(model, "sklearn.ensemble._forest.ExtraTreesClassifier")
    ):
        tree_model = convert_sklearn_forest(model, class_label=class_label)
    elif safe_isinstance(model, "sklearn.ensemble.IsolationForest") or safe_isinstance(
        model,
        "sklearn.ensemble._iforest.IsolationForest",
    ):
        tree_model = convert_sklearn_isolation_forest(model)
    elif safe_isinstance(model, "lightgbm.sklearn.LGBMRegressor") or safe_isinstance(
        model,
        "lightgbm.sklearn.LGBMClassifier",
    ):
        tree_model = convert_lightgbm_booster(model.booster_, class_label=class_label)
    elif safe_isinstance(model, "lightgbm.basic.Booster"):
        tree_model = convert_lightgbm_booster(model, class_label=class_label)
    elif safe_isinstance(model, "xgboost.sklearn.XGBRegressor") or safe_isinstance(
        model,
        "xgboost.sklearn.XGBClassifier",
    ):
        tree_model = convert_xgboost_booster(model, class_label=class_label)
    # unsupported model
    else:
        msg = f"Unsupported model type.Supported models are: {SUPPORTED_MODELS}"
        raise TypeError(msg)

    # if single tree model put it in a list
    if not isinstance(tree_model, list):
        tree_model = [tree_model]

    if len(tree_model) == 1:
        tree_model = tree_model[0]

    return tree_model

1	"""Conversion functions for the tree 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 .base import TreeModel	1✔
10	from .conversion.lightgbm import convert_lightgbm_booster	1✔
11	from .conversion.sklearn import (	1✔
12	convert_sklearn_forest,
13	convert_sklearn_isolation_forest,
14	convert_sklearn_tree,
15	)
16	from .conversion.xgboost import convert_xgboost_booster	1✔
17
18	if TYPE_CHECKING:	1✔
NEW 19	from shapiq.typing import Model	×
20
21	SUPPORTED_MODELS = {	1✔
22	"sklearn.tree.DecisionTreeRegressor",
23	"sklearn.tree._classes.DecisionTreeRegressor",
24	"sklearn.tree.DecisionTreeClassifier",
25	"sklearn.tree._classes.DecisionTreeClassifier",
26	"sklearn.ensemble.RandomForestClassifier",
27	"sklearn.ensemble._forest.RandomForestClassifier",
28	"sklearn.ensemble.ExtraTreesClassifier",
29	"sklearn.ensemble._forest.ExtraTreesClassifier",
30	"sklearn.ensemble.RandomForestRegressor",
31	"sklearn.ensemble._forest.RandomForestRegressor",
32	"sklearn.ensemble.ExtraTreesRegressor",
33	"sklearn.ensemble._forest.ExtraTreesRegressor",
34	"sklearn.ensemble.IsolationForest",
35	"sklearn.ensemble._iforest.IsolationForest",
36	"lightgbm.sklearn.LGBMRegressor",
37	"lightgbm.sklearn.LGBMClassifier",
38	"lightgbm.basic.Booster",
39	"xgboost.sklearn.XGBRegressor",
40	"xgboost.sklearn.XGBClassifier",
41	}
42
43
44	def validate_tree_model(	1✔
45	model: Model,
46	class_label: int \| None = None,
47	) -> TreeModel \| list[TreeModel]:
48	"""Validate the model.
49
50	Args:
51	model: The model to validate.
52	class_label: The class label of the model to explain. Only used for classification models.
53
54	Returns:
55	The validated model and the model function.
56
57	"""
58	# direct returns for base tree models and dict as model
59	# tree model (is already in the correct format)
60	if type(model).__name__ == "TreeModel":	1✔
61	tree_model = model	1✔
62	# direct return if list of tree models
63	elif type(model).__name__ == "list":	1✔
64	# check if all elements are TreeModel
65	if all(type(tree).__name__ == "TreeModel" for tree in model):	×
66	tree_model = model	×
67	# dict as model is parsed to TreeModel (the dict needs to have the correct format and names)
68	elif type(model).__name__ == "dict":	1✔
69	tree_model = TreeModel(**model)	1✔
70	# transformation of common machine learning libraries to TreeModel
71	# sklearn decision trees
72	elif (	1✔
73	safe_isinstance(model, "sklearn.tree.DecisionTreeRegressor")
74	or safe_isinstance(model, "sklearn.tree._classes.DecisionTreeRegressor")
75	or safe_isinstance(model, "sklearn.tree.DecisionTreeClassifier")
76	or safe_isinstance(model, "sklearn.tree._classes.DecisionTreeClassifier")
77	):
78	tree_model = convert_sklearn_tree(model, class_label=class_label)	1✔
79	# sklearn random forests
80	elif (	1✔
81	safe_isinstance(model, "sklearn.ensemble.RandomForestRegressor")
82	or safe_isinstance(model, "sklearn.ensemble._forest.RandomForestRegressor")
83	or safe_isinstance(model, "sklearn.ensemble.RandomForestClassifier")
84	or safe_isinstance(model, "sklearn.ensemble._forest.RandomForestClassifier")
85	or safe_isinstance(model, "sklearn.ensemble.ExtraTreesRegressor")
86	or safe_isinstance(model, "sklearn.ensemble._forest.ExtraTreesRegressor")
87	or safe_isinstance(model, "sklearn.ensemble.ExtraTreesClassifier")
88	or safe_isinstance(model, "sklearn.ensemble._forest.ExtraTreesClassifier")
89	):
90	tree_model = convert_sklearn_forest(model, class_label=class_label)	1✔
91	elif safe_isinstance(model, "sklearn.ensemble.IsolationForest") or safe_isinstance(	1✔
92	model,
93	"sklearn.ensemble._iforest.IsolationForest",
94	):
95	tree_model = convert_sklearn_isolation_forest(model)	1✔
96	elif safe_isinstance(model, "lightgbm.sklearn.LGBMRegressor") or safe_isinstance(	1✔
97	model,
98	"lightgbm.sklearn.LGBMClassifier",
99	):
100	tree_model = convert_lightgbm_booster(model.booster_, class_label=class_label)	1✔
101	elif safe_isinstance(model, "lightgbm.basic.Booster"):	1✔
102	tree_model = convert_lightgbm_booster(model, class_label=class_label)	1✔
103	elif safe_isinstance(model, "xgboost.sklearn.XGBRegressor") or safe_isinstance(	1✔
104	model,
105	"xgboost.sklearn.XGBClassifier",
106	):
107	tree_model = convert_xgboost_booster(model, class_label=class_label)	1✔
108	# unsupported model
109	else:
110	msg = f"Unsupported model type.Supported models are: {SUPPORTED_MODELS}"	1✔
111	raise TypeError(msg)	1✔
112
113	# if single tree model put it in a list
114	if not isinstance(tree_model, list):	1✔
115	tree_model = [tree_model]	1✔
116
117	if len(tree_model) == 1:	1✔
118	tree_model = tree_model[0]	1✔
119
120	return tree_model	1✔

mmschlk / shapiq / 16345920221

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