Merge branch 'dev'

Author: SimonBlanke

.github/workflows/test.yml

@@ -146,12 +146,38 @@ jobs:
             echo "No changed files to check."
           fi
 
+  # ===========================================================================
+  # STAGE 1.5: API smoke tests (gate — blocks all further tests on failure)
+  # ===========================================================================
+  api-smoke-tests:
+    name: api-smoke-tests
+    needs: [code-quality]
+    runs-on: ubuntu-latest
+    timeout-minutes: 5
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --no-cache-dir --upgrade pip
+          python -m pip install --no-cache-dir -e .[test]
+
+      - name: Run API smoke tests
+        run: |
+          python -m pytest tests/test_smoke_api.py -v -p no:warnings
+
   # ===========================================================================
   # STAGE 2: Targeted tests (only for changed areas, fast feedback)
   # ===========================================================================
   targeted-core:
     name: targeted-core
-    needs: [code-quality, detect-changes]
+    needs: [api-smoke-tests, detect-changes]
     if: needs.detect-changes.outputs.core == 'true' || needs.detect-changes.outputs.any_src == 'false'
     runs-on: ubuntu-latest
     timeout-minutes: 10
@@ -175,7 +201,7 @@ jobs:
 
   targeted-sklearn:
     name: targeted-sklearn
-    needs: [code-quality, detect-changes]
+    needs: [api-smoke-tests, detect-changes]
     if: needs.detect-changes.outputs.sklearn == 'true'
     runs-on: ubuntu-latest
     timeout-minutes: 10
@@ -199,7 +225,7 @@ jobs:
 
   targeted-sktime:
     name: targeted-sktime
-    needs: [code-quality, detect-changes]
+    needs: [api-smoke-tests, detect-changes]
     if: needs.detect-changes.outputs.sktime == 'true'
     runs-on: ubuntu-latest
     timeout-minutes: 10
@@ -223,7 +249,7 @@ jobs:
 
   targeted-skpro:
     name: targeted-skpro
-    needs: [code-quality, detect-changes]
+    needs: [api-smoke-tests, detect-changes]
     if: needs.detect-changes.outputs.skpro == 'true'
     runs-on: ubuntu-latest
     timeout-minutes: 10
@@ -247,7 +273,7 @@ jobs:
 
   targeted-utils:
     name: targeted-utils
-    needs: [code-quality, detect-changes]
+    needs: [api-smoke-tests, detect-changes]
     if: needs.detect-changes.outputs.utils == 'true'
     runs-on: ubuntu-latest
     timeout-minutes: 10
@@ -272,18 +298,25 @@ jobs:
   # Gate job: passes if all targeted tests pass OR are skipped (not failed)
   targeted-tests-gate:
     name: targeted-tests-gate
-    needs: [targeted-core, targeted-sklearn, targeted-sktime, targeted-skpro, targeted-utils]
+    needs: [api-smoke-tests, targeted-core, targeted-sklearn, targeted-sktime, targeted-skpro, targeted-utils]
     if: always()
     runs-on: ubuntu-latest
     steps:
-      - name: Check targeted test results
+      - name: Check results
         run: |
+          echo "API smoke tests: ${{ needs.api-smoke-tests.result }}"
           echo "Core: ${{ needs.targeted-core.result }}"
           echo "Sklearn: ${{ needs.targeted-sklearn.result }}"
           echo "Sktime: ${{ needs.targeted-sktime.result }}"
           echo "Skpro: ${{ needs.targeted-skpro.result }}"
           echo "Utils: ${{ needs.targeted-utils.result }}"
 
+          # Fail if API smoke tests did not succeed
+          if [[ "${{ needs.api-smoke-tests.result }}" != "success" ]]; then
+            echo "API smoke tests failed — blocking pipeline"
+            exit 1
+          fi
+
           # Fail if any targeted test failed (not skipped)
           if [[ "${{ needs.targeted-core.result }}" == "failure" ]] || \
              [[ "${{ needs.targeted-sklearn.result }}" == "failure" ]] || \
@@ -293,7 +326,7 @@ jobs:
             echo "One or more targeted tests failed"
             exit 1
           fi
-          echo "All targeted tests passed or were skipped"
+          echo "All gates passed"
 
   # ===========================================================================
   # STAGE 3: Full test matrix (only after targeted tests pass)
@@ -433,6 +466,7 @@ jobs:
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v5
         with:
+          token: ${{ secrets.CODECOV_TOKEN }}
           files: ./coverage.xml
           fail_ci_if_error: false
           verbose: true

pyproject.toml

@@ -11,7 +11,8 @@ version = "5.0.4"
 description = "An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models."
 readme = "README.md"
 requires-python = ">=3.10"
-license = {file = "LICENSE"}
+license = "MIT"
+license-files = ["LICENSE"]
 keywords = ["optimization",
   "hyperparameter-tuning",
   "bayesian-optimization",
@@ -38,7 +39,6 @@ classifiers=[
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
     "Programming Language :: Python :: 3.14",
-    "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent",
     "Topic :: Scientific/Engineering :: Information Analysis",
     "Topic :: Scientific/Engineering :: Mathematics",

src/hyperactive/integrations/sklearn/opt_cv.py

@@ -201,4 +201,4 @@ def score(self, X, y=None, **params):
     @property
     def fit_successful(self):
         """Fit Successful function."""
-        self._fit_successful
+        return self._fit_successful

src/hyperactive/opt/_adapters/_base_optuna_adapter.py

@@ -78,10 +78,12 @@ def _suggest_params(self, trial, param_space):
         dict
             The suggested parameters
         """
+        from optuna.distributions import BaseDistribution
+
         params = {}
         for key, space in param_space.items():
-            if hasattr(space, "suggest"):  # optuna distribution object
-                params[key] = trial._suggest(space, key)
+            if isinstance(space, BaseDistribution):
+                params[key] = trial._suggest(key, space)
             elif isinstance(space, tuple) and len(space) == 2:
                 # Tuples are treated as ranges (low, high)
                 low, high = space
@@ -110,7 +112,7 @@ def _objective(self, trial):
             The objective value
         """
         params = self._suggest_params(trial, self.param_space)
-        score = self.experiment(params)
+        score = self._experiment_wrapped(params)
 
         # Handle early stopping based on max_score
         if self.max_score is not None and score >= self.max_score:
@@ -130,19 +132,17 @@ def _setup_initial_positions(self, study):
             if isinstance(self.initialize, dict) and "warm_start" in self.initialize:
                 warm_start_points = self.initialize["warm_start"]
                 if isinstance(warm_start_points, list):
-                    # For warm start, we manually add trials to the study history
-                    # instead of using suggest methods to avoid distribution conflicts
                     for point in warm_start_points:
-                        self.experiment(point)
                         study.enqueue_trial(point)
 
     def _solve(self, experiment, param_space, n_trials, **kwargs):
         """Run the Optuna optimization.
 
         Parameters
         ----------
-        experiment : callable
-            The experiment to optimize
+        experiment : BaseExperiment
+            The experiment to optimize, already wrapped by
+            ``BaseOptimizer.get_experiment()``.
         param_space : dict
             The parameter space
         n_trials : int
@@ -157,29 +157,29 @@ def _solve(self, experiment, param_space, n_trials, **kwargs):
         """
         import optuna
 
-        # Create optimizer with random state if provided
+        self._experiment_wrapped = experiment
+
         optimizer = self._get_optimizer()
 
-        # Create study
         study = optuna.create_study(
-            direction="maximize",  # Assuming we want to maximize scores
+            direction="maximize",
             sampler=optimizer,
         )
 
-        # Setup initial positions
         self._setup_initial_positions(study)
 
-        # Setup early stopping callback
         callbacks = []
         if self.early_stopping is not None:
+            patience = self.early_stopping
 
             def early_stopping_callback(study, trial):
-                if len(study.trials) >= self.early_stopping:
+                if trial.number < patience:
+                    return
+                if trial.number - study.best_trial.number >= patience:
                     study.stop()
 
             callbacks.append(early_stopping_callback)
 
-        # Run optimization
         study.optimize(
             self._objective,
             n_trials=n_trials,