Remove doctest

optuna · Mar 11, 2024 · 3930c89 · 3930c89
1 parent 44c1a20
commit 3930c89
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diff --git a/.github/workflows/sphinx-build.yml b/.github/workflows/sphinx-build.yml
@@ -48,34 +48,3 @@ jobs:
         name: built-html
         path: |
             docs/build/html
-
-  doctest:
-
-    runs-on: ubuntu-latest
-
-    steps:
-
-    - uses: actions/checkout@v3
-
-    - uses: actions/setup-python@v4
-      with:
-        python-version: 3.8
-
-    - name: Install Dependencies
-      run: |
-        python -m pip install -U pip
-        pip install --progress-bar off -U .[document]
-
-    - name: Output installed packages
-      run: |
-        pip freeze --all
-
-    - name: Output dependency tree
-      run: |
-        pip install pipdeptree
-        pipdeptree
-
-    - name: Run Doctest
-      run: |
-        cd docs
-        make doctest
diff --git a/optuna_integration/cma/cma.py b/optuna_integration/cma/cma.py
@@ -42,22 +42,6 @@ class PyCmaSampler(BaseSampler):
 
         Optimize a simple quadratic function by using :class:`~optuna_integration.PyCmaSampler`.
 
-        .. testcode::
-
-            import optuna
-            import optuna_integration
-
-
-            def objective(trial):
-                x = trial.suggest_float("x", -1, 1)
-                y = trial.suggest_int("y", -1, 1)
-                return x**2 + y
-
-
-            sampler = optuna_integration.PyCmaSampler()
-            study = optuna.create_study(sampler=sampler)
-            study.optimize(objective, n_trials=20)
-
     Note that parallel execution of trials may affect the optimization performance of CMA-ES,
     especially if the number of trials running in parallel exceeds the population size.
 

diff --git a/optuna_integration/mlflow/mlflow.py b/optuna_integration/mlflow/mlflow.py
@@ -30,39 +30,6 @@ class MLflowCallback:
 
         Add MLflow callback to Optuna optimization.
 
-        .. testsetup::
-
-            import pathlib
-            import tempfile
-
-            tempdir = tempfile.mkdtemp()
-            YOUR_TRACKING_URI = pathlib.Path(tempdir).as_uri()
-
-        .. testcode::
-
-            import optuna
-            from optuna_integration.mlflow import MLflowCallback
-
-
-            def objective(trial):
-                x = trial.suggest_float("x", -10, 10)
-                return (x - 2) ** 2
-
-
-            mlflc = MLflowCallback(
-                tracking_uri=YOUR_TRACKING_URI,
-                metric_name="my metric score",
-            )
-
-            study = optuna.create_study(study_name="my_study")
-            study.optimize(objective, n_trials=10, callbacks=[mlflc])
-
-        .. testcleanup::
-
-            import shutil
-
-            shutil.rmtree(tempdir)
-
     Args:
         tracking_uri:
             The URI of the MLflow tracking server.
@@ -172,30 +139,6 @@ def track_in_mlflow(self) -> Callable:
 
             Add additional logging to MLflow.
 
-            .. testcode::
-
-                import optuna
-                import mlflow
-                from optuna_integration.mlflow import MLflowCallback
-
-                mlflc = MLflowCallback(
-                    tracking_uri=YOUR_TRACKING_URI,
-                    metric_name="my metric score",
-                )
-
-
-                @mlflc.track_in_mlflow()
-                def objective(trial):
-                    x = trial.suggest_float("x", -10, 10)
-                    mlflow.log_param("power", 2)
-                    mlflow.log_metric("base of metric", x - 2)
-
-                    return (x - 2) ** 2
-
-
-                study = optuna.create_study(study_name="my_other_study")
-                study.optimize(objective, n_trials=10, callbacks=[mlflc])
-
         Returns:
             Objective function with tracking to MLflow enabled.
         """

diff --git a/optuna_integration/sklearn/sklearn.py b/optuna_integration/sklearn/sklearn.py
@@ -517,23 +517,6 @@ class OptunaSearchCV(BaseEstimator):
 
     Examples:
 
-        .. testcode::
-
-            import optuna
-            import optuna_integration
-
-            from sklearn.datasets import load_iris
-            from sklearn.svm import SVC
-
-            clf = SVC(gamma="auto")
-            param_distributions = {
-                "C": optuna.distributions.FloatDistribution(1e-10, 1e10, log=True)
-            }
-            optuna_search = optuna_integration.OptunaSearchCV(clf, param_distributions)
-            X, y = load_iris(return_X_y=True)
-            optuna_search.fit(X, y)
-            y_pred = optuna_search.predict(X)
-
     .. note::
         By following the scikit-learn convention for scorers, the direction of optimization is
         ``maximize``. See https://scikit-learn.org/stable/modules/model_evaluation.html.