Improve testing

CHANGELOG.md

@@ -6,6 +6,8 @@
 - adds the `upset_plot` function to the `plot` module to visualize the interactions of higher-order [#290](https://github.com/mmschlk/shapiq/issues/290)
 - adds support for IsoForest models to explainer and tree explainer [#278](https://github.com/mmschlk/shapiq/issues/278)
 - adds support for sub-selection of players in the interaction values data class [#276](https://github.com/mmschlk/shapiq/issues/276) which allows retrieving interaction values for a subset of players
+- refactors game theory computations like `ExactComputer`, `MoebiusConverter`, `core`, among others to be more modular and flexible into the `game_theory` module [#258](https://github.com/mmschlk/shapiq/issues/258)
+- improves quality of the tests by adding many more semantic tests to the different interaction indices and computations [#285](https://github.com/mmschlk/shapiq/pull/285)
 
 ### v1.1.1 (2024-11-13)
 

requirements.txt

@@ -16,6 +16,8 @@ tqdm==4.67.1
 torch==2.5.1
 torchvision==0.20.1
 transformers==4.46.3
+tensorflow==2.18.0
+tf-keras==2.18.0
 xgboost==2.1.3
 numpy==1.26.4
 requests==2.32.3

shapiq/__init__.py

@@ -38,12 +38,12 @@
 # dataset functions
 from .datasets import load_adult_census, load_bike_sharing, load_california_housing
 
-# exact computer classes
-from .exact import ExactComputer
-
 # explainer classes
 from .explainer import Explainer, TabularExplainer, TreeExplainer
 
+# exact computer classes
+from .game_theory.exact import ExactComputer
+
 # game classes
 # imputer classes
 from .games import BaselineImputer, ConditionalImputer, Game, MarginalImputer

shapiq/approximator/_base.py

@@ -6,15 +6,15 @@
 
 import numpy as np
 
-from shapiq.approximator.sampling import CoalitionSampler
-from shapiq.indices import (
+from ..approximator.sampling import CoalitionSampler
+from ..game_theory.indices import (
     AVAILABLE_INDICES_FOR_APPROXIMATION,
     get_computation_index,
     is_empty_value_the_baseline,
     is_index_aggregated,
 )
-from shapiq.interaction_values import InteractionValues
-from shapiq.utils.sets import generate_interaction_lookup
+from ..interaction_values import InteractionValues
+from ..utils.sets import generate_interaction_lookup
 
 __all__ = [
     "Approximator",
@@ -318,7 +318,7 @@ def aggregate_interaction_values(
         Returns:
             The aggregated interaction values.
         """
-        from ..aggregation import aggregate_interaction_values
+        from shapiq.game_theory.aggregation import aggregate_interaction_values
 
         if player_set is not None:
             raise NotImplementedError(
@@ -339,6 +339,6 @@ def aggregate_to_one_dimension(
         Returns:
             tuple[np.ndarray, np.ndarray]: The positive and negative aggregated values.
         """
-        from ..aggregation import aggregate_to_one_dimension
+        from shapiq.game_theory.aggregation import aggregate_to_one_dimension
 
         return aggregate_to_one_dimension(interaction_values)

shapiq/approximator/marginals/owen.py

@@ -6,8 +6,8 @@
 
 import numpy as np
 
-from shapiq.approximator._base import Approximator
-from shapiq.interaction_values import InteractionValues
+from ...interaction_values import InteractionValues
+from .._base import Approximator
 
 
 class OwenSamplingSV(Approximator):

shapiq/approximator/marginals/stratified.py

@@ -6,8 +6,8 @@
 
 import numpy as np
 
-from shapiq.approximator._base import Approximator
-from shapiq.interaction_values import InteractionValues
+from ...interaction_values import InteractionValues
+from .._base import Approximator
 
 
 class StratifiedSamplingSV(Approximator):

shapiq/approximator/montecarlo/_base.py

@@ -5,10 +5,10 @@
 import numpy as np
 from scipy.special import binom, factorial
 
-from shapiq.approximator._base import Approximator
-from shapiq.indices import AVAILABLE_INDICES_MONTE_CARLO
-from shapiq.interaction_values import InteractionValues
-from shapiq.utils.sets import powerset
+from ...game_theory.indices import AVAILABLE_INDICES_MONTE_CARLO
+from ...interaction_values import InteractionValues
+from ...utils.sets import powerset
+from .._base import Approximator
 
 
 class MonteCarlo(Approximator):

shapiq/approximator/permutation/stii.py

@@ -6,9 +6,9 @@
 import numpy as np
 import scipy as sp
 
-from shapiq.approximator._base import Approximator
-from shapiq.interaction_values import InteractionValues
-from shapiq.utils import get_explicit_subsets, powerset
+from ...interaction_values import InteractionValues
+from ...utils import get_explicit_subsets, powerset
+from .._base import Approximator
 
 
 class PermutationSamplingSTII(Approximator):

shapiq/approximator/permutation/sv.py

@@ -6,8 +6,8 @@
 
 import numpy as np
 
-from shapiq.approximator._base import Approximator
-from shapiq.interaction_values import InteractionValues
+from ...interaction_values import InteractionValues
+from .._base import Approximator
 
 
 class PermutationSamplingSV(Approximator):

shapiq/approximator/regression/_base.py

@@ -7,10 +7,10 @@
 import numpy as np
 from scipy.special import bernoulli, binom
 
-from shapiq.approximator._base import Approximator
-from shapiq.indices import AVAILABLE_INDICES_REGRESSION
-from shapiq.interaction_values import InteractionValues
-from shapiq.utils.sets import powerset
+from ...game_theory.indices import AVAILABLE_INDICES_REGRESSION
+from ...interaction_values import InteractionValues
+from ...utils.sets import powerset
+from .._base import Approximator
 
 
 class Regression(Approximator):

shapiq/approximator/sampling.py

@@ -7,7 +7,7 @@
 import numpy as np
 from scipy.special import binom
 
-from shapiq.utils.sets import powerset
+from ..utils.sets import powerset
 
 
 class CoalitionSampler:

shapiq/explainer/_base.py

@@ -4,8 +4,8 @@
 
 import numpy as np
 
-from shapiq.explainer.utils import get_explainers, get_predict_function_and_model_type, print_class
-from shapiq.interaction_values import InteractionValues
+from ..explainer.utils import get_explainers, get_predict_function_and_model_type, print_class
+from ..interaction_values import InteractionValues
 
 
 class Explainer:

shapiq/explainer/tabular.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from shapiq.approximator import (
+from ..approximator import (
     SHAPIQ,
     SVARMIQ,
     InconsistentKernelSHAPIQ,
@@ -17,9 +17,9 @@
     RegressionFSII,
     UnbiasedKernelSHAP,
 )
-from shapiq.approximator._base import Approximator
-from shapiq.explainer._base import Explainer
-from shapiq.interaction_values import InteractionValues
+from ..approximator._base import Approximator
+from ..explainer._base import Explainer
+from ..interaction_values import InteractionValues
 
 APPROXIMATOR_CONFIGURATIONS = {
     "regression": {

shapiq/explainer/tree/conversion/lightgbm.py

@@ -5,8 +5,7 @@
 
 import pandas as pd
 
-from shapiq.utils.types import Model
-
+from ....utils.types import Model
 from ..base import TreeModel
 
 

shapiq/explainer/tree/conversion/sklearn.py

@@ -4,11 +4,9 @@
 from typing import Optional
 
 import numpy as np
-from sklearn.ensemble._iforest import _average_path_length
-
-from shapiq.utils import safe_isinstance
-from shapiq.utils.types import Model
 
+from ....utils import safe_isinstance
+from ....utils.types import Model
 from ..base import TreeModel
 
 
@@ -77,11 +75,20 @@ def convert_sklearn_tree(
     )
 
 
-def average_path_length(isolation_forest):
+def average_path_length(isolation_forest: Model) -> float:
+    """Compute the average path length of the isolation forest.
+
+    Args:
+        isolation_forest: The isolation forest model.
+
+    Returns:
+        The average path length of the isolation forest.
+    """
+    from sklearn.ensemble._iforest import _average_path_length
+
     max_samples = isolation_forest._max_samples
-    average_path_length = _average_path_length(
-        [max_samples]
-    )  # NOTE: _average_path_length func is equivalent to equation 1 in Isolation Forest paper Lui2008
+    # NOTE: _average_path_length func is equivalent to equation 1 in Isolation Forest paper Lui2008
+    average_path_length = _average_path_length([max_samples])
     return average_path_length
 
 
@@ -99,33 +106,27 @@ def convert_sklearn_isolation_forest(
     scaling = 1.0 / len(tree_model.estimators_)
 
     return [
-        # convert_isolation_tree_shap_isotree(tree, features, scaling=scaling)
         convert_isolation_tree(tree, features, scaling=scaling)
         for tree, features in zip(tree_model.estimators_, tree_model.estimators_features_)
     ]
 
 
 def convert_isolation_tree(
     tree_model: Model,
-    tree_features,
-    class_label: Optional[int] = None,
+    tree_features: np.ndarray,
     scaling: float = 1.0,
-    average_path_length: float = 1.0,  # TODO fix default value
 ) -> TreeModel:
     """Convert a scikit-learn decision tree to the format used by shapiq.
 
     Args:
         tree_model: The scikit-learn decision tree model to convert.
-        class_label: The class label of the model to explain. Only used for classification models.
-            Defaults to ``1``.
+        tree_features: The features used in the tree.
         scaling: The scaling factor for the tree values.
 
     Returns:
         The converted decision tree model.
     """
     output_type = "raw"
-    tree_values = tree_model.tree_.value.copy()
-    tree_values = tree_values.flatten()
     features_updated, values_updated = isotree_value_traversal(
         tree_model.tree_, tree_features, normalize=False, scaling=1.0
     )
@@ -145,8 +146,24 @@ def convert_isolation_tree(
 
 
 def isotree_value_traversal(
-    tree, tree_features, normalize=False, scaling=1.0, data=None, data_missing=None
-):
+    tree: Model,
+    tree_features: np.ndarray,
+    normalize: bool = False,
+    scaling: float = 1.0,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Traverse the tree and calculate the average path length for each node.
+
+    Args:
+        tree: The tree to traverse.
+        tree_features: The features used in the tree.
+        normalize: Whether to normalize the values.
+        scaling: The scaling factor for the values.
+
+    Returns:
+        The updated features and values.
+    """
+    from sklearn.ensemble._iforest import _average_path_length
+
     features = tree.feature.copy()
     corrected_values = tree.value.copy()
     if safe_isinstance(tree, "sklearn.tree._tree.Tree"):

shapiq/explainer/tree/conversion/xgboost.py

@@ -7,8 +7,7 @@
 import numpy as np
 import pandas as pd
 
-from shapiq.utils.types import Model
-
+from ....utils.types import Model
 from ..base import TreeModel
 
 

shapiq/explainer/tree/explainer.py

@@ -7,9 +7,8 @@
 
 import numpy as np
 
-from shapiq.explainer._base import Explainer
-from shapiq.interaction_values import InteractionValues
-
+from ...interaction_values import InteractionValues
+from .._base import Explainer
 from .treeshapiq import TreeModel, TreeSHAPIQ
 from .validation import validate_tree_model
 
@@ -77,6 +76,8 @@ def __init__(
         self.baseline_value = self._compute_baseline_value()
 
     def explain(self, x: np.ndarray) -> InteractionValues:
+        if len(x.shape) != 1:
+            raise TypeError("explain expects a single instance, not a batch.")
         # run treeshapiq for all trees
         interaction_values: list[InteractionValues] = []
         for explainer in self._treeshapiq_explainers:

shapiq/explainer/tree/treeshapiq.py

@@ -7,8 +7,8 @@
 import numpy as np
 import scipy as sp
 
-from ...aggregation import aggregate_interaction_values
-from ...indices import get_computation_index
+from ...game_theory.aggregation import aggregate_interaction_values
+from ...game_theory.indices import get_computation_index
 from ...interaction_values import InteractionValues
 from ...utils.sets import generate_interaction_lookup, powerset
 from .base import EdgeTree, TreeModel

shapiq/explainer/tree/validation.py

@@ -29,6 +29,7 @@
     "lightgbm.sklearn.LGBMRegressor",
     "lightgbm.sklearn.LGBMClassifier",
     "lightgbm.basic.Booster",
+    # xboost?
 }
 
 

shapiq/game_theory/__init__.py

@@ -0,0 +1,30 @@
+"""conversions of interaction values to different indices
+"""
+
+from .aggregation import aggregate_interaction_values
+from .core import egalitarian_least_core
+from .exact import ExactComputer, get_bernoulli_weights
+from .indices import (
+    ALL_AVAILABLE_CONCEPTS,
+    get_computation_index,
+    index_generalizes_bv,
+    index_generalizes_sv,
+    is_empty_value_the_baseline,
+    is_index_aggregated,
+)
+from .moebius_converter import MoebiusConverter
+
+__all__ = [
+    "ExactComputer",
+    "aggregate_interaction_values",
+    "get_bernoulli_weights",
+    "ALL_AVAILABLE_CONCEPTS",
+    "index_generalizes_sv",
+    "index_generalizes_bv",
+    "get_computation_index",
+    "is_index_aggregated",
+    "is_empty_value_the_baseline",
+    "egalitarian_least_core",
+    "MoebiusConverter",
+]
+# todo complete list

shapiq/aggregation.py → shapiq/game_theory/aggregation.py

@@ -7,8 +7,8 @@
 import numpy as np
 import scipy as sp
 
-from .interaction_values import InteractionValues
-from .utils.sets import powerset
+from ..interaction_values import InteractionValues
+from ..utils.sets import powerset
 
 
 def _change_index(index: str) -> str: