Merge pull request #184 from HideakiImamura/fix-mypy-errors-due-to-nu…

…mpy-2.2.0 Fix mypy errors due to numpy 2.2.0
optuna · Dec 16, 2024 · 4b12939 · 4b12939
2 parents b92c134 + 6e42848
commit 4b12939
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Showing 3 changed files with 8 additions and 6 deletions.
diff --git a/.github/workflows/checks_template.yml b/.github/workflows/checks_template.yml
@@ -19,6 +19,7 @@ on:
 jobs:
   checks:
     runs-on: ubuntu-latest
+    if: ${{ !inputs.deprecated }}
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
@@ -34,7 +35,6 @@ jobs:
           pip install --progress-bar off -U .[test]
 
       - name: Install Integration Dependencies
-        if: ${{ !inputs.deprecated }}
         run: |
           pip install --progress-bar off .[${{ inputs.integration_name }}]
 
@@ -79,6 +79,7 @@ jobs:
     strategy:
       matrix:
         python-version: ${{ fromJSON(inputs.python_matrix) }}
+    if: ${{ !inputs.deprecated }}
     steps:
     - uses: actions/checkout@v4
     - uses: actions/setup-python@v5
@@ -94,7 +95,6 @@ jobs:
         pip install --progress-bar off .[test]
 
     - name: Install Integration Dependencies
-      if: ${{ !inputs.deprecated }}
       run: |
         pip install --progress-bar off .[${{ inputs.integration_name }}]
 

diff --git a/optuna_integration/_lightgbm_tuner/optimize.py b/optuna_integration/_lightgbm_tuner/optimize.py
@@ -494,7 +494,7 @@ def sample_train_set(self) -> None:
 
     def tune_feature_fraction(self, n_trials: int = 7) -> None:
         param_name = "feature_fraction"
-        param_values = np.linspace(0.4, 1.0, n_trials).tolist()
+        param_values = [0.4 + 0.6 * i / (n_trials - 1) for i in range(n_trials)]
 
         sampler = optuna.samplers.GridSampler({param_name: param_values}, seed=self._optuna_seed)
         self._tune_params([param_name], len(param_values), sampler, "feature_fraction")

diff --git a/optuna_integration/sklearn/sklearn.py b/optuna_integration/sklearn/sklearn.py
@@ -263,7 +263,7 @@ def __call__(self, trial: Trial) -> float:
         self._store_scores(trial, scores)
 
         test_scores = scores["test_score"]
-        scores_list = test_scores if isinstance(test_scores, list) else test_scores.tolist()
+        scores_list = test_scores if isinstance(test_scores, list) else [v for v in test_scores]
         try:
             report_cross_validation_scores(trial, scores_list)
         except ValueError as e:
@@ -303,10 +303,12 @@ def _cross_validate_with_pruning(
 
         for step in range(self.max_iter):
             for i, (train, test) in enumerate(self.cv.split(self.X, self.y, groups=self.groups)):
-                out = self._partial_fit_and_score(estimators[i], train, test, partial_fit_params)
+                _out = self._partial_fit_and_score(estimators[i], train, test, partial_fit_params)
+                out = np.asarray(_out, dtype=np.float64)
 
                 if self.return_train_score:
-                    scores["train_score"][i] = out.pop(0)
+                    scores["train_score"][i] = out[0]
+                    out = out[1:]
 
                 scores["test_score"][i] = out[0]
                 scores["fit_time"][i] += out[1]