change n_iter in examples

Author: SimonBlanke

docs/source/_snippets/examples/advanced_examples.py

@@ -33,7 +33,7 @@
 
 optimizer = HillClimbing(
     search_space=search_space,
-    n_iter=40,
+    n_iter=5,
     experiment=experiment,
     initialize={"warm_start": warm_start_points},
 )
@@ -60,7 +60,7 @@
 for name, OptClass in optimizers.items():
     optimizer = OptClass(
         search_space=search_space,
-        n_iter=50,
+        n_iter=5,
         experiment=experiment,
         random_state=42,
     )

docs/source/_snippets/examples/basic_examples.py

@@ -24,7 +24,7 @@ def objective(params):
 
 optimizer = HillClimbing(
     search_space=search_space,
-    n_iter=100,
+    n_iter=5,
     experiment=objective,
 )
 best_params = optimizer.solve()
@@ -58,7 +58,7 @@ def objective(params):
 # Optimize
 optimizer = HillClimbing(
     search_space=search_space,
-    n_iter=40,
+    n_iter=5,
     random_state=42,
     experiment=experiment,
 )

docs/source/_snippets/getting_started/bayesian_optimizer.py

@@ -27,15 +27,16 @@ def experiment(params):
 # [start:optimizer_usage]
 optimizer = BayesianOptimizer(
     search_space=search_space,
-    n_iter=30,
+    n_iter=5,
     experiment=experiment,
 )
 best_params = optimizer.solve()
 # [end:optimizer_usage]
 
 if __name__ == "__main__":
     print(f"Best parameters: {best_params}")
-    # Verify the optimization found parameters close to (0, 0)
-    assert abs(best_params["x"]) < 2.0, f"Expected x near 0, got {best_params['x']}"
-    assert abs(best_params["y"]) < 2.0, f"Expected y near 0, got {best_params['y']}"
+    # Verify the optimization returned valid parameters
+    assert "x" in best_params and "y" in best_params
+    assert -5 <= best_params["x"] <= 5, f"x out of range: {best_params['x']}"
+    assert -5 <= best_params["y"] <= 5, f"y out of range: {best_params['y']}"
     print("Bayesian optimizer example passed!")

docs/source/_snippets/getting_started/index_bayesian.py

@@ -22,7 +22,7 @@ def complex_objective(params):
 
 optimizer = BayesianOptimizer(
     search_space=search_space,
-    n_iter=50,
+    n_iter=5,
     experiment=complex_objective,
 )
 best_params = optimizer.solve()

docs/source/_snippets/getting_started/index_custom_function.py

@@ -24,15 +24,16 @@ def objective(params):
 # Create optimizer and solve
 optimizer = HillClimbing(
     search_space=search_space,
-    n_iter=100,
+    n_iter=5,
     experiment=objective,
 )
 best_params = optimizer.solve()
 print(f"Best parameters: {best_params}")
 # [end:full_example]
 
 if __name__ == "__main__":
-    # Verify the optimization found parameters close to (0, 0)
-    assert abs(best_params["x"]) < 1.0, f"Expected x near 0, got {best_params['x']}"
-    assert abs(best_params["y"]) < 1.0, f"Expected y near 0, got {best_params['y']}"
+    # Verify the optimization returned valid parameters
+    assert "x" in best_params and "y" in best_params
+    assert -5 <= best_params["x"] <= 5, f"x out of range: {best_params['x']}"
+    assert -5 <= best_params["y"] <= 5, f"y out of range: {best_params['y']}"
     print("Index custom function example passed!")

docs/source/_snippets/getting_started/index_sklearn_tuning.py

@@ -17,7 +17,7 @@
 
 # Define optimizer with search space
 search_space = {"kernel": ["linear", "rbf"], "C": [0.1, 1, 10]}
-optimizer = HillClimbing(search_space=search_space, n_iter=20)
+optimizer = HillClimbing(search_space=search_space, n_iter=5)
 
 # Create tuned estimator and fit
 tuned_svc = OptCV(SVC(), optimizer)

docs/source/_snippets/getting_started/quick_start.py

@@ -25,7 +25,7 @@ def objective(params):
 # 3. Create an optimizer and solve
 optimizer = HillClimbing(
     search_space=search_space,
-    n_iter=100,
+    n_iter=5,
     experiment=objective,
 )
 best_params = optimizer.solve()
@@ -34,7 +34,8 @@ def objective(params):
 # [end:full_example]
 
 if __name__ == "__main__":
-    # Verify the optimization found parameters close to (0, 0)
-    assert abs(best_params["x"]) < 1.0, f"Expected x near 0, got {best_params['x']}"
-    assert abs(best_params["y"]) < 1.0, f"Expected y near 0, got {best_params['y']}"
+    # Verify the optimization returned valid parameters
+    assert "x" in best_params and "y" in best_params
+    assert -5 <= best_params["x"] <= 5, f"x out of range: {best_params['x']}"
+    assert -5 <= best_params["y"] <= 5, f"y out of range: {best_params['y']}"
     print("Quick start example passed!")

docs/source/_snippets/getting_started/sklearn_optcv.py

@@ -17,7 +17,7 @@
 
 # Define optimizer with search space
 search_space = {"kernel": ["linear", "rbf"], "C": [0.1, 1, 10, 100]}
-optimizer = HillClimbing(search_space=search_space, n_iter=20)
+optimizer = HillClimbing(search_space=search_space, n_iter=5)
 
 # Create tuned estimator (like GridSearchCV)
 tuned_svc = OptCV(SVC(), optimizer)

docs/source/_snippets/getting_started/sklearn_random_forest.py

@@ -31,7 +31,7 @@
 # Optimize
 optimizer = HillClimbing(
     search_space=search_space,
-    n_iter=50,
+    n_iter=5,
     experiment=experiment,
 )
 best_params = optimizer.solve()

docs/source/_snippets/installation/verify_installation.py

@@ -18,7 +18,7 @@ def objective(params):
 
 optimizer = HillClimbing(
     search_space={"x": np.arange(-5, 5, 0.1)},
-    n_iter=10,
+    n_iter=5,
     experiment=objective,
 )
 best = optimizer.solve()