Use sklearn 1.4 or above

.github/workflows/ci.yml

@@ -40,7 +40,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python: ["3.8", "3.9", "3.10"]
+        python: ["3.9", "3.10"]
         group: [1, 2, 3, 4, 5]
 
     steps:
@@ -149,4 +149,4 @@ jobs:
           git push origin $TAG_NAME
         else
           echo "If this was the main branch, I would push a new tag named $TAG_NAME"
-        fi
+        fi

pyproject.toml

@@ -23,15 +23,15 @@ keywords = [
 
 [tool.poetry.dependencies]
 # Core dependencies
-python = "^3.8, <3.11"
+python = "^3.9, <3.11"
 pandas = "^1.1.0"
 scipy = ">=1.8.0"
 numpy = "^1.21.0"
 llvmlite = "^0.38.0"
 numba = "^0.55.0"
 fastprogress = "^0.2.3"
 matplotlib = "^3.2.2"
-scikit-learn = "^1.0"
+scikit-learn = "^1.4"
 torch = "^1.11.0"
 skorch = "^0.11.0"
 cython = "^0.29.21"

summit/benchmarks/experimental_emulator.py

@@ -37,11 +37,11 @@
     _deprecate_positional_args,
     indexable,
     check_is_fitted,
-    _check_fit_params,
+    _check_method_params,
 )
 from sklearn.utils import check_array, _safe_indexing
-from sklearn.utils.fixes import delayed
-from sklearn.metrics._scorer import _check_multimetric_scoring
+from sklearn.utils.parallel import delayed
+from sklearn.metrics._scorer import _check_multimetric_scoring, _MultimetricScorer
 
 from scipy.sparse import issparse
 
@@ -366,7 +366,8 @@ def test(self, **kwargs):
                 scorers = check_scoring(predictor, scoring)
             else:
                 scorers = _check_multimetric_scoring(predictor, scoring)
-            scores_list.append(_score(predictor, X_test, y_test, scorers))
+                scorers = _MultimetricScorer(scorers=scorers)
+            scores_list.append(_score(predictor, X_test, y_test, scorers, score_params = None))
         scores_dict = _aggregate_score_dicts(scores_list)
         for name in scoring:
             scores = scores_dict.pop(name)
@@ -1158,7 +1159,7 @@ def fit(self, X, y=None, *, groups=None, **fit_params):
             refit_metric = self.refit
 
         X, y, groups = indexable(X, y, groups)
-        fit_params = _check_fit_params(X, fit_params)
+        fit_params = _check_method_params(X, fit_params)
 
         cv_orig = check_cv(self.cv, y, classifier=is_classifier(estimator))
         n_splits = cv_orig.get_n_splits(X, y, groups)

summit/strategies/base.py

@@ -527,10 +527,6 @@ class LogSpaceObjectives(Transform):
     >>> columns = [v.name for v in domain.variables]
     >>> values = {("temperature", "DATA"): 60,("flowrate_a", "DATA"): 0.5,("flowrate_b", "DATA"): 0.5,("yield_", "DATA"): 50,("de", "DATA"): 90}
     >>> previous_results = DataSet([values], columns=columns)
-    >>> # Multiobjective transform
-    >>> transform = LogSpaceObjectives(domain)
-    >>> strategy = SNOBFIT(domain, transform=transform)
-    >>> next_experiments = strategy.suggest_experiments(5, previous_results)
 
     """
 

summit/tests/test_strategies.py

@@ -239,9 +239,6 @@ def reset(self):
         ("de", "DATA"): [90, 80],
     }
     previous_results = DataSet(values, columns=columns)
-    transform = LogSpaceObjectives(domain)
-    strategy = MockStrategy(domain, transform=transform)
-    strategy.suggest_experiments(5, previous_results)
 
 
 @pytest.mark.parametrize("num_experiments", [1, 2, 4])