MAINT update dependencies for CI (#1145)

* MAINT update dependencies

* fix scipy for older scikit-learn

* try downgrading tensorflow

* iter

* iter

* iter

Author: glemaitre

.github/workflows/linters.yml

@@ -15,9 +15,9 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-      - uses: prefix-dev/[email protected].8
+      - uses: prefix-dev/[email protected].14
         with:
-          pixi-version: v0.39.2
+          pixi-version: v0.51.0
           frozen: true
 
       - name: Run tests

.github/workflows/tests.yml

@@ -20,6 +20,7 @@ jobs:
             ci-py310-min-tensorflow,
             ci-py311-sklearn-1-4,
             ci-py311-sklearn-1-5,
+            ci-py312-sklearn-1-6,
             ci-py311-latest-keras,
             ci-py311-latest-tensorflow,
             ci-py313-latest-dependencies,
@@ -37,9 +38,9 @@ jobs:
     runs-on: ${{ matrix.os }}
     steps:
       - uses: actions/checkout@v4
-      - uses: prefix-dev/[email protected].8
+      - uses: prefix-dev/[email protected].14
         with:
-          pixi-version: v0.39.2
+          pixi-version: v0.51.0
           environments: ${{ matrix.environment }}
           # we can freeze the environment and manually bump the dependencies to the
           # latest version time to time.

imblearn/keras/tests/test_generator.py

@@ -24,7 +24,8 @@ def data():
     X, y = make_imbalance(
         iris.data, iris.target, sampling_strategy={0: 30, 1: 50, 2: 40}
     )
-    y = LabelBinarizer().fit_transform(y)
+    X = X.astype(np.float32)
+    y = LabelBinarizer().fit_transform(y).astype(np.int32)
     return X, y
 
 
@@ -103,7 +104,7 @@ def test_balanced_batch_generator_function_no_return_indices(data):
         (None, None),
         (RandomOverSampler(), None),
         (NearMiss(), None),
-        (None, np.random.uniform(size=120)),
+        (None, np.random.uniform(size=120).astype(np.float32)),
     ],
 )
 def test_balanced_batch_generator_function(data, sampler, sample_weight):
@@ -117,6 +118,7 @@ def test_balanced_batch_generator_function(data, sampler, sample_weight):
         batch_size=10,
         random_state=42,
     )
+    print(next(training_generator))
     model.fit(
         training_generator,
         steps_per_epoch=steps_per_epoch,