Improve testing

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

@@ -7,7 +7,7 @@
 import numpy as np
 
 from shapiq.approximator.sampling import CoalitionSampler
-from shapiq.indices import (
+from shapiq.game_theory.indices import (
     AVAILABLE_INDICES_FOR_APPROXIMATION,
     get_computation_index,
     is_empty_value_the_baseline,
@@ -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/montecarlo/_base.py

@@ -6,7 +6,7 @@
 from scipy.special import binom, factorial
 
 from shapiq.approximator._base import Approximator
-from shapiq.indices import AVAILABLE_INDICES_MONTE_CARLO
+from shapiq.game_theory.indices import AVAILABLE_INDICES_MONTE_CARLO
 from shapiq.interaction_values import InteractionValues
 from shapiq.utils.sets import powerset
 

shapiq/approximator/regression/_base.py

@@ -8,7 +8,7 @@
 from scipy.special import bernoulli, binom
 
 from shapiq.approximator._base import Approximator
-from shapiq.indices import AVAILABLE_INDICES_REGRESSION
+from shapiq.game_theory.indices import AVAILABLE_INDICES_REGRESSION
 from shapiq.interaction_values import InteractionValues
 from shapiq.utils.sets import powerset
 

shapiq/explainer/tree/explainer.py

@@ -77,6 +77,8 @@ 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

@@ -7,10 +7,11 @@
 import numpy as np
 import scipy as sp
 
-from ...aggregation import aggregate_interaction_values
-from ...indices import get_computation_index
-from ...interaction_values import InteractionValues
-from ...utils.sets import generate_interaction_lookup, powerset
+from shapiq.game_theory.aggregation import aggregate_interaction_values
+from shapiq.game_theory.indices import get_computation_index
+from shapiq.interaction_values import InteractionValues
+from shapiq.utils.sets import generate_interaction_lookup, powerset
+
 from .base import EdgeTree, TreeModel
 from .conversion.edges import create_edge_tree
 from .validation import validate_tree_model

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 shapiq.interaction_values import InteractionValues
+from shapiq.utils.sets import powerset
 
 
 def _change_index(index: str) -> str:

shapiq/exact.py → shapiq/game_theory/exact.py

@@ -2,21 +2,22 @@
 like interaction indices or generalized values."""
 
 import copy
-from typing import Callable, Union
+from typing import Callable, Optional, Union
 
 import numpy as np
 from scipy.special import bernoulli, binom
 
-from shapiq.indices import ALL_AVAILABLE_CONCEPTS
 from shapiq.interaction_values import InteractionValues
 from shapiq.utils import powerset
 
+from .indices import ALL_AVAILABLE_CONCEPTS
+
 __all__ = ["ExactComputer", "get_bernoulli_weights"]
 
 
 class ExactComputer:
     """Computes exact Shapley Interactions for specified game by evaluating the powerset of all
-        :math:`2^n` coalitions.
+    :math:`2^n` coalitions.
 
     The ExactComputer class computes a variety of game theoretic concepts like interaction indices
     or generalized values. Currently, the following indices and values are supported:
@@ -27,6 +28,7 @@ class ExactComputer:
         n_players: The number of players in the game.
         game_fun: A callable game that takes a binary matrix of shape ``(n_coalitions, n_players)``
             and returns a numpy array of shape ``(n_coalitions,)`` containing the game values.
+        evaluate_game: whether to compute the values at init (if True) or first call (False)
 
     Attributes:
         n: The number of players.
@@ -40,6 +42,7 @@ def __init__(
         self,
         n_players: int,
         game_fun: Callable[[np.ndarray], np.ndarray[float]],
+        evaluate_game: bool = False,
     ) -> None:
         # set parameter attributes
         self.n: int = n_players
@@ -52,12 +55,15 @@ def __init__(
         self._n_interactions: np.ndarray = self.get_n_interactions(self.n)
         self._computed: dict[tuple[str, int], InteractionValues] = {}  # will store all computations
         self._elc_stability_subsidy: float = -1
+        self._game_is_computed: bool = False
+
+        self._baseline_value: Optional[float] = None
+        self._game_values: Optional[np.ndarray] = None
+        self._coalition_lookup: Optional[dict[tuple[int], int]] = None
 
-        # evaluate the game on the powerset
-        computed_game = self.compute_game_values(game_fun)
-        self.baseline_value: float = computed_game[0]
-        self.game_values: np.ndarray[float] = computed_game[1]
-        self.coalition_lookup: dict[tuple[int], int] = computed_game[2]
+        if evaluate_game:
+            # evaluate the game on the powerset
+            self._evaluate_game()
 
         # setup callable mapping from index to computation
         self._index_mapping: dict[str, Callable[[str, int], InteractionValues]] = {
@@ -124,9 +130,32 @@ def __call__(self, index: str, order: int = None) -> InteractionValues:
         else:
             raise ValueError(f"Index {index} not supported.")
 
-    def compute_game_values(
-        self, game_fun: Callable[[np.ndarray], np.ndarray[float]]
-    ) -> tuple[float, np.ndarray[float], dict[tuple[int], int]]:
+    @property
+    def baseline_value(self) -> float:
+        if not self._game_is_computed:
+            self._evaluate_game()
+        return self._baseline_value
+
+    @property
+    def coalition_lookup(self) -> dict[tuple[int], int]:
+        if not self._game_is_computed:
+            self._evaluate_game()
+        return self._coalition_lookup
+
+    @property
+    def game_values(self) -> np.ndarray[float]:
+        if not self._game_is_computed:
+            self._evaluate_game()
+        return self._game_values
+
+    def _evaluate_game(self):
+        computed_game = self.compute_game_values()
+        self._baseline_value = computed_game[0]
+        self._game_values = computed_game[1]
+        self._coalition_lookup = computed_game[2]
+        self._game_is_computed = True
+
+    def compute_game_values(self) -> tuple[float, np.ndarray[float], dict[tuple[int], int]]:
         """Evaluates the game on the powerset of all coalitions.
 
         Args:
@@ -141,8 +170,9 @@ def compute_game_values(
         for i, T in enumerate(powerset(self._grand_coalition_set, min_size=0, max_size=self.n)):
             coalition_lookup[T] = i  # set lookup for the coalition
             coalition_matrix[i, T] = True  # one-hot-encode the coalition
-        game_values = game_fun(coalition_matrix)  # compute the game values
+        game_values = self.game_fun(coalition_matrix)  # compute the game values
         baseline_value = float(game_values[0])  # set the baseline value
+        coalition_lookup = coalition_lookup
         return baseline_value, game_values, coalition_lookup
 
     def moebius_transform(self, *args, **kwargs) -> InteractionValues:
@@ -158,6 +188,7 @@ def moebius_transform(self, *args, **kwargs) -> InteractionValues:
             return self._computed[("Moebius", self.n)]
         except KeyError:  # if not computed yet, just continue
             pass
+
         # compute the Moebius transform
         moebius_transform = np.zeros(2**self.n)
         coalition_lookup = {}
@@ -836,7 +867,7 @@ def probabilistic_value(self, index: str, *args, **kwargs) -> InteractionValues:
 
     def compute_egalitarian_least_core(self, *args, **kwargs):
 
-        from shapiq.core import egalitarian_least_core
+        from shapiq.game_theory.core import egalitarian_least_core
 
         order = 1
 
@@ -856,7 +887,7 @@ def compute_egalitarian_least_core(self, *args, **kwargs):
 
 def get_bernoulli_weights(order: int) -> np.ndarray:
     """Returns the bernoulli weights in the k-additive approximation via SII, e.g. used in
-        kADD-SHAP.
+    kADD-SHAP.
 
     Args:
         order: The highest order of interactions

shapiq/moebius_converter.py → shapiq/game_theory/moebius_converter.py

@@ -7,8 +7,8 @@
 import numpy as np
 from scipy.special import binom
 
-from .interaction_values import InteractionValues
-from .utils.sets import powerset
+from shapiq.interaction_values import InteractionValues
+from shapiq.utils.sets import powerset
 
 
 class MoebiusConverter:

shapiq/games/base.py

@@ -478,7 +478,7 @@ def exact_values(self, index: str, order: int) -> InteractionValues:
         Returns:
             InteractionValues: The exact interaction values.
         """
-        from ..exact import ExactComputer
+        from shapiq.game_theory.exact import ExactComputer
 
         # raise warning if the game is not precomputed and n_players > 16
         if not self.precomputed and self.n_players > 16:

shapiq/games/benchmark/synthetic/soum.py

@@ -94,7 +94,7 @@ def __init__(
         normalize: bool = False,
         verbose: bool = False,
     ):
-        from ....moebius_converter import MoebiusConverter
+        from shapiq.game_theory.moebius_converter import MoebiusConverter
 
         self._rng = np.random.default_rng(random_state)
 
@@ -160,7 +160,7 @@ def exact_values(self, index: str, order: int) -> InteractionValues:
         Returns:
             The exact values for the given index and order.
         """
-        from ....moebius_converter import MoebiusConverter
+        from shapiq.game_theory.moebius_converter import MoebiusConverter
 
         if self.converter is None:
             self.converter = MoebiusConverter(self.moebius_coefficients)

shapiq/interaction_values.py

@@ -11,7 +11,11 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from shapiq.indices import ALL_AVAILABLE_INDICES, index_generalizes_bv, index_generalizes_sv
+from shapiq.game_theory.indices import (
+    ALL_AVAILABLE_INDICES,
+    index_generalizes_bv,
+    index_generalizes_sv,
+)
 from shapiq.utils.sets import count_interactions, generate_interaction_lookup, powerset
 
 

shapiq/plot/stacked_bar.py

@@ -28,7 +28,7 @@ def stacked_bar_plot(
     This stacked bar plot can be used to visualize the amount of interaction between the features
     for a given instance. The n-SII values are plotted as stacked bars with positive and negative
     parts stacked on top of each other. The colors represent the order of the n-SII values. For a
-    detailed explanation of this plot, refer to `Bordt and von Luxburg (2023) <https://doi.org/10.48550/arXiv.2209.0401>`_.
+    detailed explanation of this plot, refer to `Bordt and von Luxburg (2023) <https://proceedings.mlr.press/v206/bordt23a.html>`_.
 
     An example of the plot is shown below.