[MNT] fix formatting

Author: SimonBlanke

docs/source/_snippets/conftest.py

@@ -10,7 +10,7 @@
 
 @pytest.fixture
 def simple_search_space():
-    """Simple search space for basic examples."""
+    """Provide simple search space for basic examples."""
     return {
         "x": np.arange(-5, 5, 0.1),
         "y": np.arange(-5, 5, 0.1),
@@ -19,7 +19,7 @@ def simple_search_space():
 
 @pytest.fixture
 def simple_objective():
-    """Simple objective function for basic examples."""
+    """Provide simple objective function for basic examples."""
 
     def objective(params):
         x = params["x"]

docs/source/_snippets/examples/advanced_examples.py

@@ -4,17 +4,19 @@
 advanced functionality like warm starting and optimizer comparison.
 """
 
-import numpy as np
 from sklearn.datasets import load_wine
 from sklearn.ensemble import RandomForestClassifier
+
 from hyperactive.experiment.integrations import SklearnCvExperiment
 from hyperactive.opt.gfo import HillClimbing
 
 # Setup common fixtures for examples
 X, y = load_wine(return_X_y=True)
 experiment = SklearnCvExperiment(
     estimator=RandomForestClassifier(random_state=42),
-    X=X, y=y, cv=3,
+    X=X,
+    y=y,
+    cv=3,
 )
 search_space = {
     "n_estimators": list(range(10, 101, 10)),
@@ -24,7 +26,6 @@
 
 
 # [start:warm_starting]
-from hyperactive.opt.gfo import HillClimbing
 
 # Previous best parameters
 warm_start_points = [
@@ -43,10 +44,10 @@
 
 # [start:comparing_optimizers]
 from hyperactive.opt.gfo import (
-    HillClimbing,
-    RandomSearch,
     BayesianOptimizer,
+    HillClimbing,
     ParticleSwarmOptimizer,
+    RandomSearch,
 )
 
 optimizers = {

docs/source/_snippets/examples/basic_examples.py

@@ -7,7 +7,6 @@
 import numpy as np
 
 # [start:custom_function]
-import numpy as np
 from hyperactive.opt.gfo import HillClimbing
 
 
@@ -35,6 +34,7 @@ def objective(params):
 # [start:sklearn_tuning]
 from sklearn.datasets import load_wine
 from sklearn.ensemble import RandomForestClassifier
+
 from hyperactive.experiment.integrations import SklearnCvExperiment
 from hyperactive.opt.gfo import HillClimbing
 
@@ -44,7 +44,9 @@ def objective(params):
 # Create experiment
 experiment = SklearnCvExperiment(
     estimator=RandomForestClassifier(random_state=42),
-    X=X, y=y, cv=3,
+    X=X,
+    y=y,
+    cv=3,
 )
 
 # Define search space

docs/source/_snippets/getting_started/bayesian_optimizer.py

@@ -5,15 +5,15 @@
 """
 
 # [start:full_example]
-from hyperactive.opt.gfo import BayesianOptimizer
 # [end:full_example]
-
 # Need to define experiment and search_space for standalone execution
 import numpy as np
 
+from hyperactive.opt.gfo import BayesianOptimizer
+
 
 def experiment(params):
-    """Simple objective function."""
+    """Compute simple objective function."""
     x = params["x"]
     y = params["y"]
     return -(x**2 + y**2)

docs/source/_snippets/getting_started/index_bayesian.py

@@ -6,6 +6,7 @@
 
 # [start:full_example]
 import numpy as np
+
 from hyperactive.opt.gfo import BayesianOptimizer
 
 

docs/source/_snippets/getting_started/index_custom_function.py

@@ -6,6 +6,7 @@
 
 # [start:full_example]
 import numpy as np
+
 from hyperactive.opt.gfo import HillClimbing
 
 

docs/source/_snippets/getting_started/index_sklearn_tuning.py

@@ -5,9 +5,10 @@
 """
 
 # [start:full_example]
-from sklearn.svm import SVC
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+
 from hyperactive.integrations.sklearn import OptCV
 from hyperactive.opt.gfo import HillClimbing
 

docs/source/_snippets/getting_started/quick_start.py

@@ -6,6 +6,7 @@
 
 # [start:full_example]
 import numpy as np
+
 from hyperactive.opt.gfo import HillClimbing
 
 

docs/source/_snippets/getting_started/sklearn_optcv.py

@@ -5,9 +5,10 @@
 """
 
 # [start:full_example]
-from sklearn.svm import SVC
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+
 from hyperactive.integrations.sklearn import OptCV
 from hyperactive.opt.gfo import HillClimbing
 

docs/source/_snippets/getting_started/sklearn_random_forest.py

@@ -7,6 +7,7 @@
 # [start:full_example]
 from sklearn.datasets import load_iris
 from sklearn.ensemble import RandomForestClassifier
+
 from hyperactive.experiment.integrations import SklearnCvExperiment
 from hyperactive.opt.gfo import HillClimbing