increased test coverage

shapiq/approximator/_base.py

@@ -317,7 +317,7 @@ def __str__(self) -> str:
     def __eq__(self, other: object) -> bool:
         """Checks if two Approximator objects are equal."""
         if not isinstance(other, Approximator):
-            raise NotImplementedError("Cannot compare Approximator with other types.")
+            raise ValueError("Cannot compare Approximator with other types.")
         if (
             self.n != other.n
             or self.max_order != other.max_order
@@ -336,26 +336,6 @@ def __hash__(self) -> int:
         """Returns the hash of the Approximator object."""
         return hash((self.n, self.max_order, self.index, self.top_order, self._random_state))
 
-    def __copy__(self) -> "Approximator":
-        """Returns a copy of the Approximator object."""
-        return self.__class__(
-            n=self.n,
-            max_order=self.max_order,
-            index=self.index,
-            top_order=self.top_order,
-            random_state=self._random_state,
-        )
-
-    def __deepcopy__(self, memo) -> "Approximator":
-        """Returns a deep copy of the Approximator object."""
-        return self.__class__(
-            n=self.n,
-            max_order=self.max_order,
-            index=self.index,
-            top_order=self.top_order,
-            random_state=self._random_state,
-        )
-
 
 class ShapleySamplingMixin:
     """Mixin class for the computation of Shapley weights.

shapiq/approximator/permutation/sii.py

@@ -151,9 +151,3 @@ def approximate(
         result = np.divide(result, counts, out=result, where=counts != 0)
 
         return self._finalize_result(result, budget=used_budget, estimated=True)
-
-
-if __name__ == "__main__":
-    import doctest
-
-    doctest.testmod()

shapiq/approximator/permutation/sti.py

@@ -192,9 +192,3 @@ def _compute_lower_order_sti(
                 interaction_index = self._interaction_lookup[subset]
                 result[interaction_index] += update
         return result
-
-
-if __name__ == "__main__":
-    import doctest
-
-    doctest.testmod()

shapiq/approximator/regression/fsi.py

@@ -157,9 +157,3 @@ def _get_fsi_subset_representation(
         ):
             regression_subsets[:, interaction_index] = all_subsets[:, interaction].all(axis=1)
         return regression_subsets, num_players
-
-
-if __name__ == "__main__":
-    import doctest
-
-    doctest.testmod()

shapiq/approximator/shapiq/shapiq.py

@@ -246,9 +246,3 @@ def _init_discrete_derivative_weights(self) -> dict[int, np.ndarray[float]]:
                 for k in range(max(0, order + t - self.n), min(order, t) + 1):
                     weights[order][t, k] = (-1) ** (order - k) * self._weight_kernel(t - k, order)
         return weights
-
-
-if __name__ == "__main__":
-    import doctest
-
-    doctest.testmod()

shapiq/games/dummy.py

@@ -54,26 +54,3 @@ def __repr__(self):
 
     def __str__(self):
         return f"DummyGame(n={self.n}, interaction={self.interaction})"
-
-    def __eq__(self, other):
-        return self.n == other.n and self.interaction == other.interaction
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __hash__(self):
-        return hash((self.n, self.interaction))
-
-    def __copy__(self):
-        return DummyGame(n=self.n, interaction=self.interaction)
-
-    def __deepcopy__(self, memo):
-        return DummyGame(n=self.n, interaction=self.interaction)
-
-    def __getstate__(self):
-        return {"n": self.n, "interaction": self.interaction}
-
-    def __setstate__(self, state):
-        self.n = state["n"]
-        self.interaction = state["interaction"]
-        self.N = set(range(self.n))

tests/test_approximator_permutation_sii.py

@@ -1,4 +1,6 @@
 """This test module contains all tests regarding the SII permutation sampling approximator."""
+from copy import copy, deepcopy
+
 import numpy as np
 import pytest
 
@@ -27,6 +29,18 @@ def test_initialization(n, max_order, top_order, expected):
     assert approximator.iteration_cost == expected
     assert approximator.index == "SII"
 
+    approximator_copy = copy(approximator)
+    approximator_deepcopy = deepcopy(approximator)
+    approximator_deepcopy.index = "something"
+    assert approximator_copy == approximator  # check that the copy is equal
+    assert approximator_deepcopy != approximator  # check that the deepcopy is not equal
+    approximator_string = str(approximator)
+    assert repr(approximator) == approximator_string
+    assert hash(approximator) == hash(approximator_copy)
+    assert hash(approximator) != hash(approximator_deepcopy)
+    with pytest.raises(ValueError):
+        _ = approximator == 1
+
 
 @pytest.mark.parametrize(
     "n, max_order, top_order, budget, batch_size",

tests/test_approximator_permutation_sti.py

@@ -1,4 +1,6 @@
 """This test module contains all tests regarding the STI permutation sampling approximator."""
+from copy import copy, deepcopy
+
 import numpy as np
 import pytest
 
@@ -28,6 +30,18 @@ def test_initialization(n, max_order, iteration_cost):
     assert approximator.iteration_cost == iteration_cost
     assert approximator.index == "STI"
 
+    approximator_copy = copy(approximator)
+    approximator_deepcopy = deepcopy(approximator)
+    approximator_deepcopy.index = "something"
+    assert approximator_copy == approximator  # check that the copy is equal
+    assert approximator_deepcopy != approximator  # check that the deepcopy is not equal
+    approximator_string = str(approximator)
+    assert repr(approximator) == approximator_string
+    assert hash(approximator) == hash(approximator_copy)
+    assert hash(approximator) != hash(approximator_deepcopy)
+    with pytest.raises(ValueError):
+        _ = approximator == 1
+
 
 @pytest.mark.parametrize(
     "n, max_order, budget, batch_size",
@@ -59,3 +73,16 @@ def test_approximate(n, max_order, budget, batch_size):
     # check efficiency
     efficiency = np.sum(sti_estimates.values)
     assert efficiency == pytest.approx(2.0, 0.01)
+
+
+def test_small_budget_warning():
+    """Tests that a warning is raised if the budget is too small."""
+    n, max_order = 10, 3
+    interaction = (1, 2)
+    game = DummyGame(n, interaction)
+    approximator = PermutationSamplingSTI(n, max_order, random_state=42)
+    # lower_order_cost is 55
+    with pytest.warns(UserWarning):
+        _ = approximator.approximate(1, game)  # not even lower_order_cost
+    with pytest.warns(UserWarning):
+        _ = approximator.approximate(56, game)  # lower_order_cost but no iteration

tests/test_approximator_regression_fsi.py

@@ -1,4 +1,6 @@
 """This test module contains all tests regarding the FSI regression approximator."""
+from copy import deepcopy, copy
+
 import numpy as np
 import pytest
 
@@ -28,6 +30,18 @@ def test_initialization(n, max_order):
     assert approximator.iteration_cost == 1
     assert approximator.index == "FSI"
 
+    approximator_copy = copy(approximator)
+    approximator_deepcopy = deepcopy(approximator)
+    approximator_deepcopy.index = "something"
+    assert approximator_copy == approximator  # check that the copy is equal
+    assert approximator_deepcopy != approximator  # check that the deepcopy is not equal
+    approximator_string = str(approximator)
+    assert repr(approximator) == approximator_string
+    assert hash(approximator) == hash(approximator_copy)
+    assert hash(approximator) != hash(approximator_deepcopy)
+    with pytest.raises(ValueError):
+        _ = approximator == 1
+
 
 @pytest.mark.parametrize(
     "n, max_order, budget, batch_size", [(7, 2, 380, 100), (7, 2, 380, None), (7, 2, 100, None)]

tests/test_approximator_shapiq.py

@@ -1,4 +1,6 @@
 """This test module contains all tests regarding the shapiq approximator."""
+from copy import copy, deepcopy
+
 import numpy as np
 import pytest
 from approximator._base import InteractionValues
@@ -32,6 +34,20 @@ def test_initialization(n, max_order, index, top_order):
     assert approximator.iteration_cost == 1
     assert approximator.index == index
 
+    approximator_copy = copy(approximator)
+    approximator_deepcopy = deepcopy(approximator)
+    approximator_deepcopy.index = "something"
+    assert approximator_copy == approximator  # check that the copy is equal
+    assert approximator_deepcopy != approximator  # check that the deepcopy is not equal
+    approximator_string = str(approximator)
+    assert repr(approximator) == approximator_string
+    assert hash(approximator) == hash(approximator_copy)
+    assert hash(approximator) != hash(approximator_deepcopy)
+    with pytest.raises(ValueError):
+        _ = approximator == 1
+    with pytest.raises(ValueError):
+        approximator_deepcopy._weight_kernel(3, 2)
+
 
 @pytest.mark.parametrize("n, max_order, budget, batch_size", [(7, 2, 100, None), (7, 2, 100, 10)])
 def test_approximate_fsi(n, max_order, budget, batch_size):

tests/test_games_dummy.py

@@ -49,6 +49,9 @@ def test_dummy_game(n, interaction, expected):
         x_input[list(coalition)] = True
         assert game(x_input)[0] == expected[coalition]
 
+    string_game = str(game)
+    assert repr(game) == string_game
+
 
 def test_dummy_game_access_counts():
     """Test how often the game was called."""