Fix: Implement hotfix from #1407, aimed at master (#1408)

* Fix: Implement hotfix from #1407, aimed at master

* Add: Argument to ExecuteTaFuncWithQueue

* Add: multi_objectives arg to tests

* Fix: Two more locations of `multi_objectives`

Author: eddiebergman

autosklearn/__version__.py

@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "0.14.7"
+__version__ = "0.14.6"

autosklearn/automl.py

@@ -487,6 +487,7 @@ def _do_dummy_prediction(self, datamanager: XYDataManager, num_run: int) -> int:
         ta = ExecuteTaFuncWithQueue(
             backend=self._backend,
             autosklearn_seed=self._seed,
+            multi_objectives=["cost"],
             resampling_strategy=self._resampling_strategy,
             initial_num_run=num_run,
             stats=stats,
@@ -1347,6 +1348,7 @@ def fit_pipeline(
             backend=self._backend,
             autosklearn_seed=self._seed,
             abort_on_first_run_crash=False,
+            multi_objectives=["cost"],
             cost_for_crash=get_cost_of_crash(kwargs["metric"]),
             port=self._logger_port,
             **kwargs,

autosklearn/evaluation/__init__.py

@@ -131,6 +131,7 @@ def __init__(
         abort_on_first_run_crash: bool,
         port: int,
         pynisher_context: str,
+        multi_objectives: List[str],
         initial_num_run: int = 1,
         stats: Optional[Stats] = None,
         run_obj: str = "quality",
@@ -146,7 +147,6 @@ def __init__(
         ta: Optional[Callable] = None,
         **resampling_strategy_args: Any,
     ):
-
         if resampling_strategy == "holdout":
             eval_function = autosklearn.evaluation.train_evaluator.eval_holdout
         elif resampling_strategy == "holdout-iterative-fit":

autosklearn/util/single_thread_client.py

@@ -1,4 +1,5 @@
 import typing
+from typing import Any
 
 from pathlib import Path
 from typing import Any

doc/releases.rst

@@ -9,16 +9,6 @@
 Releases
 ========
 
-
-Version 0.14.7
-==============
-
-* HOTFIX #1445: Locks `ConfigSpace` to `<0.5.0` and `smac` to `<1.3`. Adds upper bounds on `automl` packages to help prevent further issues.
-
-Contributors v0.14.7
-********************
-* Eddie Bergman
-
 Version 0.14.6
 ==============
 
@@ -38,39 +28,6 @@ Contributors v0.14.5
 ********************
 * Eddie Bergman
 
-Version 0.14.4
-==============
-
-* Fix #1356: SVR degree hyperparameter now only active with "poly" kernel.
-* Add #1311: Black format checking (non-strict).
-* Maint #1306: Run history is now saved every iteration
-* Doc #1309: Updated the doc faqs to include many use cases and the manual for early introductions
-* Doc #1322: Fix typo in contribution guide
-* Maint #1326: Add isort checker (non-strict)
-* Maint #1238, #1346, #1368, #1370: Update warnings in tests
-* Maint #1325: Test workflow can now be manually triggered
-* Maint #1332: Update docstring and typing of ``include`` and ``exclude`` params
-* Add #1260: Support for Python 3.10
-* Add #1318: First update to use the shared backend in a new submodule `automl_common <https://github.com/automl/automl_common>`_
-* Fix #1339: Resolve dependancy issues with ``sphinx_toolbox``
-* Fix #1335: Fix issue where some regression algorithm gave incorrect output dimensions as raised in #1297
-* Doc #1340: Update example for predefined splits
-* Fix #1329: Fix random state not being passed to the ConfigurationSpace
-* Maint #1348: Stop double triggering of github workflows
-* Doc #1349: Rename OSX to macOS in docs
-* Add #1321: Change ``show_models()`` to produce actual pipeline objects and not a ``str``
-* Maint #1361: Remove ``flaky`` dependency
-* Maint #1366: Make ``SimpleClassificationPipeline`` tests more deterministic
-* Maint #1367: Update test values for ``MLPRegressor`` with newer numpy
-
-Contributors v0.14.4
-********************
-
-* Eddie Bergman
-* Matthias Feurer
-* Katharina Eggensperger
-* UserFindingSelf
-* partev
 
 Version 0.14.3
 ==============

test/test_evaluation/test_evaluation.py

@@ -91,6 +91,7 @@ def test_eval_with_limits_holdout(self, pynisher_mock):
             autosklearn_seed=1,
             port=self.logger_port,
             resampling_strategy="holdout",
+            multi_objectives=["cost"],
             stats=self.stats,
             memory_limit=3072,
             metric=accuracy,
@@ -121,6 +122,7 @@ def test_zero_or_negative_cutoff(self, pynisher_mock):
             backend=self.backend,
             autosklearn_seed=1,
             port=self.logger_port,
+            multi_objectives=["cost"],
             resampling_strategy="holdout",
             stats=self.stats,
             metric=accuracy,
@@ -150,6 +152,7 @@ def test_cutoff_lower_than_remaining_time(self, pynisher_mock):
             backend=self.backend,
             autosklearn_seed=1,
             port=self.logger_port,
+            multi_objectives=["cost"],
             resampling_strategy="holdout",
             stats=self.stats,
             metric=accuracy,
@@ -181,6 +184,7 @@ def test_eval_with_limits_holdout_fail_silent(self, pynisher_mock):
             backend=self.backend,
             autosklearn_seed=1,
             port=self.logger_port,
+            multi_objectives=["cost"],
             resampling_strategy="holdout",
             stats=self.stats,
             memory_limit=3072,
@@ -251,6 +255,7 @@ def test_eval_with_limits_holdout_fail_memory_error(self, pynisher_mock):
             backend=self.backend,
             autosklearn_seed=1,
             port=self.logger_port,
+            multi_objectives=["cost"],
             resampling_strategy="holdout",
             stats=self.stats,
             memory_limit=3072,
@@ -292,6 +297,7 @@ def test_eval_with_limits_holdout_fail_timeout(self, pynisher_mock):
             backend=self.backend,
             autosklearn_seed=1,
             port=self.logger_port,
+            multi_objectives=["cost"],
             resampling_strategy="holdout",
             stats=self.stats,
             memory_limit=3072,
@@ -341,6 +347,7 @@ def side_effect(**kwargs):
             backend=self.backend,
             autosklearn_seed=1,
             port=self.logger_port,
+            multi_objectives=["cost"],
             resampling_strategy="holdout",
             stats=self.stats,
             memory_limit=3072,
@@ -376,6 +383,7 @@ def side_effect(**kwargs):
             backend=self.backend,
             autosklearn_seed=1,
             port=self.logger_port,
+            multi_objectives=["cost"],
             resampling_strategy="holdout",
             stats=self.stats,
             memory_limit=3072,
@@ -419,6 +427,7 @@ def side_effect(*args, **kwargs):
             backend=self.backend,
             autosklearn_seed=1,
             port=self.logger_port,
+            multi_objectives=["cost"],
             resampling_strategy="holdout",
             stats=self.stats,
             memory_limit=3072,
@@ -454,6 +463,7 @@ def test_exception_in_target_function(self, eval_holdout_mock):
             backend=self.backend,
             autosklearn_seed=1,
             port=self.logger_port,
+            multi_objectives=["cost"],
             resampling_strategy="holdout",
             stats=self.stats,
             memory_limit=3072,
@@ -490,6 +500,7 @@ def test_silent_exception_in_target_function(self):
             port=self.logger_port,
             autosklearn_seed=1,
             resampling_strategy="holdout",
+            multi_objectives=["cost"],
             stats=self.stats,
             memory_limit=3072,
             metric=accuracy,