Run black on all files

src/mlrose_ky/algorithms/decay/geometric_decay.py

@@ -68,9 +68,11 @@ def __repr__(self) -> str:
     def __eq__(self, other: object) -> bool:
         if not isinstance(other, GeometricDecay):
             return False
-        return (self.initial_temperature == other.initial_temperature
-                and self.decay_rate == other.decay_rate
-                and self.minimum_temperature == other.minimum_temperature)
+        return (
+            self.initial_temperature == other.initial_temperature
+            and self.decay_rate == other.decay_rate
+            and self.minimum_temperature == other.minimum_temperature
+        )
 
     def evaluate(self, time: int) -> float:
         """
@@ -86,7 +88,7 @@ def evaluate(self, time: int) -> float:
         float
             The temperature parameter at the given time, respecting the minimum temperature.
         """
-        return max(self.initial_temperature * (self.decay_rate ** time), self.minimum_temperature)
+        return max(self.initial_temperature * (self.decay_rate**time), self.minimum_temperature)
 
     def get_info(self, time: int | None = None, prefix: str = "") -> dict:
         """
@@ -127,9 +129,8 @@ def __new__(cls, *args, **kwargs):
         Please use 'GeometricDecay' instead.
         """
         warnings.warn(
-            "The class 'GeomDecay' is deprecated and will be removed in a future release. "
-            "Please use 'GeometricDecay' instead.",
+            "The class 'GeomDecay' is deprecated and will be removed in a future release. " "Please use 'GeometricDecay' instead.",
             DeprecationWarning,
-            stacklevel=2
+            stacklevel=2,
         )
         return super(GeomDecay, cls).__new__(cls)

src/mlrose_ky/decorators/short_name_decorator.py

@@ -51,8 +51,8 @@ def get_short_name(v: Any) -> str:
     str
         The short name of the variable, if assigned; otherwise, returns the full variable name or the variable itself as a fallback.
     """
-    if hasattr(v, '__short_name__'):
+    if hasattr(v, "__short_name__"):
         return v.__short_name__
-    elif hasattr(v, '__name__'):
+    elif hasattr(v, "__name__"):
         return v.__name__
     return v

src/mlrose_ky/runners/skmlp_runner.py

@@ -111,25 +111,25 @@ def _invoke_runner_callback(self):
             )
 
     def __init__(
-            self,
-            x_train,
-            y_train,
-            x_test,
-            y_test,
-            experiment_name,
-            seed,
-            iteration_list,
-            grid_search_parameters,
-            grid_search_scorer_method=skmt.balanced_accuracy_score,
-            early_stopping=True,
-            max_attempts=500,
-            n_jobs=1,
-            cv=5,
-            override_ctrl_c_handler=True,
-            generate_curves=True,
-            output_directory=None,
-            replay=False,
-            **kwargs,
+        self,
+        x_train,
+        y_train,
+        x_test,
+        y_test,
+        experiment_name,
+        seed,
+        iteration_list,
+        grid_search_parameters,
+        grid_search_scorer_method=skmt.balanced_accuracy_score,
+        early_stopping=True,
+        max_attempts=500,
+        n_jobs=1,
+        cv=5,
+        override_ctrl_c_handler=True,
+        generate_curves=True,
+        output_directory=None,
+        replay=False,
+        **kwargs,
     ):
 
         # take a copy of the grid search parameters

tests/globals.py

@@ -9,14 +9,7 @@
 @pytest.fixture
 def sample_data():
     """Return sample data for testing."""
-    X = np.array([
-        [0, 1, 0, 1],
-        [0, 0, 0, 0],
-        [1, 1, 1, 1],
-        [1, 1, 1, 1],
-        [0, 0, 1, 1],
-        [1, 0, 0, 0]
-    ])  # X.shape = (6, 4)
+    X = np.array([[0, 1, 0, 1], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1], [0, 0, 1, 1], [1, 0, 0, 0]])  # X.shape = (6, 4)
 
     y_classifier = np.reshape(np.array([1, 1, 0, 0, 1, 1]), (X.shape[0], 1))
     y_multiclass = np.array([[1, 1], [1, 0], [0, 0], [0, 0], [1, 0], [1, 1]])

tests/test_generators/test_flip_flop_generator.py

@@ -58,4 +58,3 @@ def test_generate_custom_size(self):
         problem = FlipFlopGenerator.generate(SEED, size=size)
 
         assert problem.length == size
-

tests/test_neural/test_neural_network.py

@@ -57,7 +57,13 @@ def test_fit_genetic_alg(self, sample_data):
         hidden_nodes = [2]
         bias = False
         network = NeuralNetwork(
-            hidden_nodes=hidden_nodes, activation="identity", algorithm="genetic_alg", bias=bias, learning_rate=1, clip_max=1, max_attempts=1
+            hidden_nodes=hidden_nodes,
+            activation="identity",
+            algorithm="genetic_alg",
+            bias=bias,
+            learning_rate=1,
+            clip_max=1,
+            max_attempts=1,
         )
 
         node_list = [X.shape[1], *hidden_nodes, 2 if bias else 1]
@@ -158,4 +164,3 @@ def test_learning_curve(self):
         train_sizes, train_scores, test_scores = learning_curve(network, X, y, train_sizes=train_sizes, cv=cv, scoring="accuracy")
 
         assert not np.isnan(train_scores).any() and not np.isnan(test_scores).any()
-

tests/test_opt_probs/test_discrete_opt.py

@@ -185,4 +185,3 @@ def test_sample_pop(self):
         problem.eval_node_probs()
         sample = problem.sample_pop(100)
         assert np.shape(sample)[0] == 100 and np.shape(sample)[1] == 5 and 0 < np.sum(sample) < 500
-

tests/test_runners/test_ga_runner.py

@@ -110,7 +110,7 @@ def test_garunner_initialization_with_additional_kwargs(self, problem, runner_kw
         runner = GARunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == 'ga'
+        assert runner.get_runner_name() == "ga"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]

tests/test_runners/test_mimic_runner.py

@@ -105,7 +105,7 @@ def test_mimicrunner_initialization_with_additional_kwargs(self, problem, runner
         runner = MIMICRunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == 'mimic'
+        assert runner.get_runner_name() == "mimic"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]

tests/test_runners/test_nngs_runner.py

@@ -38,7 +38,7 @@ def runner_kwargs(self, data):
             "max_iters": [1, 2],
             "learning_rate": [0.001, 0.002],
             "hidden_layer_sizes": [[2], [2, 2]],
-            "activation": [mlrose_ky.relu, mlrose_ky.sigmoid]
+            "activation": [mlrose_ky.relu, mlrose_ky.sigmoid],
         }
 
         return {

tests/test_runners/test_rhc_runner.py

@@ -77,7 +77,7 @@ def test_rhc_runner_initialization_with_additional_kwargs(self, problem, runner_
         runner = RHCRunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == 'rhc'
+        assert runner.get_runner_name() == "rhc"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]

tests/test_runners/test_sa_runner.py

@@ -91,7 +91,7 @@ def test_sarunner_initialization_with_additional_kwargs(self, problem, runner_kw
         runner = SARunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == 'sa'
+        assert runner.get_runner_name() == "sa"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]