fix warnings

Author: anwurl

octopus/modules/octo/core.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import copy
+import warnings
 from typing import TYPE_CHECKING
 
 import optuna
@@ -353,13 +354,16 @@ def _run_globalhp_optimization(
         )
 
         # multivariate sampler with group option
-        sampler = optuna.samplers.TPESampler(
-            multivariate=True,
-            group=True,
-            constant_liar=True,
-            seed=self.config.optuna_seed,
-            n_startup_trials=self.config.n_optuna_startup_trials,
-        )
+        # Suppress ExperimentalWarning — these parameters have been stable for 3+ years
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=optuna.exceptions.ExperimentalWarning)
+            sampler = optuna.samplers.TPESampler(
+                multivariate=True,
+                group=True,
+                constant_liar=True,
+                seed=self.config.optuna_seed,
+                n_startup_trials=self.config.n_optuna_startup_trials,
+            )
 
         # create study with in-memory storage
         study = optuna.create_study(

pyproject.toml

@@ -216,3 +216,10 @@ testpaths = ["tests"]
 markers = ["slow: mark test as slow.", "windows: mark test to run in windows."]
 addopts = ["--cov=octopus", "--durations=15"]
 tmp_path_retention_policy = "none"
+filterwarnings = [
+    "default",
+    # Ray: unclosed file handles in node management (third-party bug)
+    "ignore::ResourceWarning:ray",
+    # Ray subprocess cleanup (third-party)
+    "ignore:subprocess.*is still running:ResourceWarning",
+]

tests/study/test_validator.py

@@ -195,6 +195,9 @@ def test_validate_error_accumulation(validator_factory, sample_data):
     """Test that validate() accumulates multiple errors."""
     data_with_issues = sample_data.copy()
     data_with_issues["datasplit_group"] = 0
+    # Cast to object first to allow mixed types — direct assignment of a string
+    # into a float64 column is deprecated in pandas 2.x and will raise in a future version.
+    data_with_issues["feature1"] = data_with_issues["feature1"].astype(object)
     data_with_issues.loc[0, "feature1"] = "invalid_string"  # This will fail dtype validation
 
     validator = validator_factory(