Fixed a couple of pyright issues.

Author: mwever

.github/workflows/main.yml

@@ -47,7 +47,7 @@ jobs:
         run: uv run python -m pytest tests --cov --cov-config=pyproject.toml --cov-report=xml
 
       - name: Check typing
-        run: uv run mypy
+        run: uv run pyright
 
 
       - name: Upload coverage reports to Codecov with GitHub Action on Python 3.11

pyproject.toml

@@ -85,8 +85,17 @@ ignore = [
     "E501",
     # DoNotAssignLambda
     "E731",
+    # Argument name should be lowercase (conflicting with sklearn)
+    "N803",
+    # Dynamically typed expressions (setup protocol type)
+    "ANN401",
+    # Boolean-typed positional argument in function definition
+    "FBT001",
 ]
 
+[tool.ruff.lint.pep8-naming]
+ignore-names = ["X"]
+
 [tool.ruff.lint.isort]
 known-first-party = ["hypershap"]
 extra-standard-library = ["typing_extensions"]

src/hypershap/games/ablation.py

@@ -24,14 +24,10 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
-
 import numpy as np
 
 from hypershap.games.abstract import AbstractHPIGame
-
-if TYPE_CHECKING:
-    from hypershap.task import AblationExplanationTask
+from hypershap.task import AblationExplanationTask
 
 
 class AblationGame(AbstractHPIGame):
@@ -82,7 +78,13 @@ def evaluate_single_coalition(self, coalition: np.ndarray) -> float:
         baseline_cfg = self._get_explanation_task().baseline_config.get_array()
         cfg_of_interest = self._get_explanation_task().config_of_interest.get_array()
         blend = np.where(coalition == 0, baseline_cfg, cfg_of_interest)
-        return self._get_explanation_task().surrogate_model.evaluate(blend)
+        res = self._get_explanation_task().surrogate_model.evaluate(blend)
+
+        # validate that we do not get a list of floats by accident
+        if isinstance(res, list):
+            raise TypeError
+
+        return res
 
     def _get_explanation_task(self) -> AblationExplanationTask:
         """Retrieve the explanation task associated with this ablation game.
@@ -94,4 +96,6 @@ def _get_explanation_task(self) -> AblationExplanationTask:
             AblationExplanationTask: The explanation task associated with this ablation game.
 
         """
-        return self.explanation_task
+        if isinstance(self.explanation_task, AblationExplanationTask):
+            return self.explanation_task
+        raise ValueError

src/hypershap/games/optimizerbias.py

@@ -24,9 +24,8 @@
 if TYPE_CHECKING:
     import numpy as np
 
-    from hypershap.task import OptimizerBiasExplanationTask
-
 from hypershap.games import AbstractHPIGame
+from hypershap.task import OptimizerBiasExplanationTask
 
 
 class OptimizerBiasGame(AbstractHPIGame):
@@ -82,7 +81,9 @@ def __init__(
         super().__init__(explanation_task, n_workers=n_workers, verbose=verbose)
 
     def _get_explanation_task(self) -> OptimizerBiasExplanationTask:
-        return self.explanation_task
+        if isinstance(self.explanation_task, OptimizerBiasExplanationTask):
+            return self.explanation_task
+        raise ValueError
 
     def evaluate_single_coalition(self, coalition: np.ndarray) -> float:
         """Evaluate a single coalition by comparing against an optimizer ensemble.

src/hypershap/games/tunability.py

@@ -24,7 +24,12 @@
 if TYPE_CHECKING:
     import numpy as np
 
-    from hypershap.task import TunabilityExplanationTask
+    from hypershap.task import (
+        BaselineExplanationTask,
+        MistunabilityExplanationTask,
+        SensitivityExplanationTask,
+        TunabilityExplanationTask,
+    )
 
 from hypershap.games.abstract import AbstractHPIGame
 from hypershap.utils import ConfigSpaceSearcher, RandomConfigSpaceSearcher
@@ -37,8 +42,8 @@ class SearchBasedGame(AbstractHPIGame):
 
     def __init__(
         self,
-        explanation_task: TunabilityExplanationTask,
-        cs_searcher: ConfigSpaceSearcher = None,
+        explanation_task: BaselineExplanationTask,
+        cs_searcher: ConfigSpaceSearcher,
         n_workers: int | None = None,
         verbose: bool | None = None,
     ) -> None:
@@ -80,7 +85,7 @@ class TunabilityGame(SearchBasedGame):
     def __init__(
         self,
         explanation_task: TunabilityExplanationTask,
-        cs_searcher: ConfigSpaceSearcher = None,
+        cs_searcher: ConfigSpaceSearcher | None = None,
         n_workers: int | None = None,
         verbose: bool | None = None,
     ) -> None:
@@ -113,8 +118,8 @@ class SensitivityGame(SearchBasedGame):
 
     def __init__(
         self,
-        explanation_task: TunabilityExplanationTask,
-        cs_searcher: ConfigSpaceSearcher = None,
+        explanation_task: SensitivityExplanationTask,
+        cs_searcher: ConfigSpaceSearcher | None = None,
         n_workers: int | None = None,
         verbose: bool | None = None,
     ) -> None:
@@ -143,13 +148,13 @@ def __init__(
         super().__init__(explanation_task, cs_searcher, n_workers=n_workers, verbose=verbose)
 
 
-class MistunabilityGame(TunabilityGame):
+class MistunabilityGame(SearchBasedGame):
     """Game representing the mistunability of hyperparameters."""
 
     def __init__(
         self,
-        explanation_task: TunabilityExplanationTask,
-        cs_searcher: ConfigSpaceSearcher = None,
+        explanation_task: MistunabilityExplanationTask,
+        cs_searcher: ConfigSpaceSearcher | None = None,
         n_workers: int | None = None,
         verbose: bool | None = None,
     ) -> None:

src/hypershap/hypershap.py

@@ -16,6 +16,8 @@
 import logging
 
 import matplotlib.pyplot as plt
+import networkx as nx
+import numpy as np
 from shapiq import ExactComputer, InteractionValues
 
 from hypershap.games import (
@@ -144,7 +146,7 @@ def ablation(
 
     def tunability(
         self,
-        baseline_config: Configuration = None,
+        baseline_config: Configuration | None = None,
         index: str = "FSII",
         order: int = 2,
         n_samples: int = 10_000,
@@ -161,6 +163,9 @@ def tunability(
             InteractionValues: The computed interaction values.
 
         """
+        if baseline_config is None:
+            baseline_config = self.explanation_task.config_space.get_default_configuration()
+
         # setup explanation task
         tunability_task: TunabilityExplanationTask = TunabilityExplanationTask(
             config_space=self.explanation_task.config_space,
@@ -183,7 +188,7 @@ def tunability(
 
     def sensitivity(
         self,
-        baseline_config: Configuration = None,
+        baseline_config: Configuration | None = None,
         index: str = "FSII",
         order: int = 2,
         n_samples: int = 10_000,
@@ -200,18 +205,21 @@ def sensitivity(
             InteractionValues: The computed interaction values.
 
         """
+        if baseline_config is None:
+            baseline_config = self.explanation_task.config_space.get_default_configuration()
+
         # setup explanation task
-        tunability_task: SensitivityExplanationTask = SensitivityExplanationTask(
+        sensitivity_task: SensitivityExplanationTask = SensitivityExplanationTask(
             config_space=self.explanation_task.config_space,
             surrogate_model=self.explanation_task.surrogate_model,
             baseline_config=baseline_config,
         )
 
         # setup tunability game and get interaction values
         tg = SensitivityGame(
-            explanation_task=tunability_task,
+            explanation_task=sensitivity_task,
             cs_searcher=RandomConfigSpaceSearcher(
-                explanation_task=tunability_task,
+                explanation_task=sensitivity_task,
                 n_samples=n_samples,
                 mode="var",
             ),
@@ -222,7 +230,7 @@ def sensitivity(
 
     def mistunability(
         self,
-        baseline_config: Configuration = None,
+        baseline_config: Configuration | None = None,
         index: str = "FSII",
         order: int = 2,
         n_samples: int = 10_000,
@@ -239,18 +247,21 @@ def mistunability(
             InteractionValues: The computed interaction values.
 
         """
+        if baseline_config is None:
+            baseline_config = self.explanation_task.config_space.get_default_configuration()
+
         # setup explanation task
-        tunability_task: MistunabilityExplanationTask = MistunabilityExplanationTask(
+        mistunability_task: MistunabilityExplanationTask = MistunabilityExplanationTask(
             config_space=self.explanation_task.config_space,
             surrogate_model=self.explanation_task.surrogate_model,
             baseline_config=baseline_config,
         )
 
         # setup tunability game and get interaction values
         tg = MistunabilityGame(
-            explanation_task=tunability_task,
+            explanation_task=mistunability_task,
             cs_searcher=RandomConfigSpaceSearcher(
-                explanation_task=tunability_task,
+                explanation_task=mistunability_task,
                 n_samples=n_samples,
                 mode="min",
             ),
@@ -284,11 +295,10 @@ def optimizer_bias(
             surrogate_model=self.explanation_task.surrogate_model,
             optimizer_of_interest=optimizer_of_interest,
             optimizer_ensemble=optimizer_ensemble,
-            n_workers=self.n_workers,
         )
 
         # setup optimizer bias game and get interaction values
-        og = OptimizerBiasGame(explanation_task=optimizer_bias_task)
+        og = OptimizerBiasGame(explanation_task=optimizer_bias_task, n_workers=self.n_workers, verbose=self.verbose)
         return self.__get_interaction_values(game=og, index=index, order=order)
 
     def plot_si_graph(self, interaction_values: InteractionValues | None = None, save_path: str | None = None) -> None:
@@ -304,9 +314,11 @@ def plot_si_graph(self, interaction_values: InteractionValues | None = None, sav
 
         # if given interaction values use those, else use cached interaction values
         iv = interaction_values if interaction_values is not None else self.last_interaction_values
-        hyperparameter_names = self.explanation_task.get_hyperparameter_names()
 
-        import networkx as nx
+        if not isinstance(iv, InteractionValues):
+            raise TypeError
+
+        hyperparameter_names = self.explanation_task.get_hyperparameter_names()
 
         def get_circular_layout(n_players: int) -> dict:
             original_graph, graph_nodes = nx.Graph(), []
@@ -345,9 +357,17 @@ def plot_upset(self, interaction_values: InteractionValues | None = None, save_p
 
         # if given interaction values use those, else use cached interaction values
         iv = interaction_values if interaction_values is not None else self.last_interaction_values
+
+        if not isinstance(iv, InteractionValues):
+            raise TypeError
+
         hyperparameter_names = self.explanation_task.get_hyperparameter_names()
 
         fig = iv.plot_upset(feature_names=hyperparameter_names, show=False)
+
+        if fig is None:
+            raise TypeError
+
         ax = fig.get_axes()[0]
         ax.set_ylabel("Performance Gain")
         # also add "parameter" to the y-axis label
@@ -374,9 +394,13 @@ def plot_force(self, interaction_values: InteractionValues | None = None, save_p
 
         # if given interaction values use those, else use cached interaction values
         iv = interaction_values if interaction_values is not None else self.last_interaction_values
+
+        if not isinstance(iv, InteractionValues):
+            raise TypeError
+
         hyperparameter_names = self.explanation_task.get_hyperparameter_names()
 
-        iv.plot_force(feature_names=hyperparameter_names, show=False)
+        iv.plot_force(feature_names=np.array(hyperparameter_names), show=False)
         plt.tight_layout()
 
         if save_path is not None: