fix tests

pyproject.toml

@@ -113,7 +113,7 @@ unfixable = []
 # Allow unused variables when underscore-prefixed.
 dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
 
-[tool.ruff.lint.pydocstyle]
+[tool.ruff.pydocstyle]
 convention = "google"
 
 [tool.ruff.format]

src/catsim/initialization.py

@@ -37,9 +37,10 @@ def __init__(
     :type dist_params: tuple, optional
     """
     super().__init__()
+    assert isinstance(dist_type, InitializationDistribution), "dist_type must be an InitializationDistribution"
     self._dist_type = dist_type
     self._dist_params = dist_params
-    self.__rng = numpy.random.default_rng(1337)
+    self.__rng = numpy.random.default_rng()
 
   def initialize(self, index: int | None = None, **kwargs: dict[str, Any]) -> float:
     """Generates a value using the chosen distribution and parameters.

src/catsim/irt.py

@@ -1,5 +1,6 @@
 import math
 from enum import Enum
+from math import pi  # noqa: F401
 from typing import Any
 
 import numexpr

src/catsim/simulation.py

@@ -333,13 +333,14 @@ def __init__(
     self._estimator = estimator
     self._stopper = stopper
 
+    self.__rng = numpy.random.default_rng()
+
     # `examinees` is passed to its special setter
     self._examinees = self._to_distribution(examinees)
 
     self._estimations: list[list[int]] = [[] for _ in range(self.examinees.shape[0])]
     self._administered_items: list[list[int]] = [[] for _ in range(self.examinees.shape[0])]
     self._response_vectors: list[list[bool]] = [[] for _ in range(self.examinees.shape[0])]
-    self.__rng = numpy.random.default_rng()
 
   @property
   def items(self) -> numpy.ndarray:

src/catsim/stopping.py

@@ -1,3 +1,4 @@
+from typing import Any
 import numpy
 
 from . import irt
@@ -23,7 +24,7 @@ def __init__(self, max_itens: int) -> None:
     super().__init__()
     self._max_itens = max_itens
 
-  def stop(self, index: int | None = None, administered_items: numpy.ndarray = None) -> bool:
+  def stop(self, index: int | None = None, administered_items: numpy.ndarray = None, **kwargs: dict[str, Any]) -> bool:
     """Check whether the test reached its stopping criterion for the given user.
 
     :param index: the index of the current examinee

tests/test_main.py

@@ -5,7 +5,7 @@
 from catsim import cat, irt, plot
 from catsim.cat import generate_item_bank
 from catsim.estimation import NumericalSearchEstimator
-from catsim.initialization import FixedPointInitializer, RandomInitializer
+from catsim.initialization import FixedPointInitializer, InitializationDistribution, RandomInitializer
 from catsim.selection import (
   AStratBBlockSelector,
   AStratSelector,
@@ -37,7 +37,7 @@ def one_simulation(
 
 @pytest.mark.parametrize("examinees", [100])
 @pytest.mark.parametrize("bank_size", [500])
-@pytest.mark.parametrize("initializer", [RandomInitializer("uniform", (-5, 5))])
+@pytest.mark.parametrize("initializer", [RandomInitializer(InitializationDistribution.UNIFORM, (-5, 5))])
 @pytest.mark.parametrize("estimator", [NumericalSearchEstimator()])
 @pytest.mark.parametrize("stopper", [MaxItemStopper(30), MinErrorStopper(0.4)])
 def test_cism(
@@ -62,7 +62,7 @@ def test_cism(
 @pytest.mark.parametrize(
   "initializer",
   [
-    RandomInitializer("uniform", (-5, 5)),
+    RandomInitializer(InitializationDistribution.UNIFORM, (-5, 5)),
     FixedPointInitializer(0),
   ],
 )
@@ -77,8 +77,9 @@ def test_finite_selectors(
   initializer: Initializer,
   estimator: Estimator,
 ):
+  rng = np.random.default_rng()
   finite_selectors = [
-    LinearSelector(list(np.random.choice(bank_size, size=test_size, replace=False))),
+    LinearSelector(list(rng.choice(bank_size, size=test_size, replace=False))),
     AStratSelector(test_size),
     AStratBBlockSelector(test_size),
     MaxInfoStratSelector(test_size),
@@ -91,7 +92,7 @@ def test_finite_selectors(
   for selector in finite_selectors:
     items = generate_item_bank(bank_size, itemtype=logistic_model)
     responses = cat.random_response_vector(random.randint(1, test_size - 1))
-    administered_items = np.random.choice(bank_size, len(responses), replace=False)
+    administered_items = rng.choice(bank_size, len(responses), replace=False)
     est_theta = initializer.initialize()
     selector.select(
       items=items,
@@ -118,7 +119,7 @@ def test_finite_selectors(
 @pytest.mark.parametrize(
   "initializer",
   [
-    RandomInitializer("uniform", (-5, 5)),
+    RandomInitializer(InitializationDistribution.UNIFORM, (-5, 5)),
     FixedPointInitializer(0),
   ],
 )
@@ -149,10 +150,11 @@ def test_infinite_selectors(
   estimator: Estimator,
   stopper: Stopper,
 ):
+  rng = np.random.default_rng()
   items = generate_item_bank(bank_size, itemtype=logistic_model)
   max_administered_items = stopper.max_itens if type(stopper) == MaxItemStopper else bank_size
   responses = cat.random_response_vector(random.randint(1, max_administered_items))
-  administered_items = np.random.choice(bank_size, len(responses), replace=False)
+  administered_items = rng.choice(bank_size, len(responses), replace=False)
   est_theta = initializer.initialize()
   selector.select(
     items=items,