Migrate lit_nlp to sklearn v1.6.1

lit_nlp/components/curves_test.py

@@ -51,7 +51,7 @@ def input_spec(self) -> lit_types.Spec:
   def output_spec(self) -> lit_types.Spec:
     return {
         'pred': lit_types.MulticlassPreds(vocab=COLORS, parent='label'),
-        'aux_pred': lit_types.MulticlassPreds(vocab=COLORS, parent='label')
+        'aux_pred': lit_types.MulticlassPreds(vocab=COLORS, parent='label'),
     }
 
   def predict_minibatch(
@@ -64,10 +64,9 @@ def predict_example(ex: lit_types.JsonDict) -> tuple[float, float, float]:
       return TEST_DATA[x].prediction
 
     for example in inputs:
-      output.append({
-          'pred': predict_example(example),
-          'aux_pred': [1 / 3, 1 / 3, 1 / 3]
-      })
+      output.append(
+          {'pred': predict_example(example), 'aux_pred': [1 / 3, 1 / 3, 1 / 3]}
+      )
     return output
 
 
@@ -148,6 +147,43 @@ def test_model_output_is_missing_in_config(self):
           config={'Label': 'red'},
       )
 
+  @parameterized.named_parameters(
+      dict(
+          testcase_name='red',
+          label='red',
+          exp_roc=[(0.0, 0.0), (0.0, 0.5), (1.0, 0.5), (1.0, 1.0)],
+          exp_pr=[(0.5, 0.5), (2 / 3, 1.0), (1.0, 0.5), (1.0, 0.0)],
+      ),
+      dict(
+          testcase_name='blue',
+          label='blue',
+          exp_roc=[(0.0, 0.0), (0.0, 1.0), (1.0, 1.0)],
+          exp_pr=[
+              (0.3333333333333333, 1.0),
+              (0.5, 1.0),
+              (1.0, 1.0),
+              (1.0, 0.0),
+          ],
+      ),
+  )
+  def test_interpreter_honors_user_selected_label(
+      self, label: str, exp_roc: _Curve, exp_pr: _Curve
+  ):
+    """Tests a happy scenario when a user doesn't specify the class label."""
+    curves_data = self.ci.run(
+        inputs=self.dataset.examples,
+        model=self.model,
+        dataset=self.dataset,
+        config={
+            curves.TARGET_LABEL_KEY: label,
+            curves.TARGET_PREDICTION_KEY: 'pred',
+        },
+    )
+    self.assertIn(curves.ROC_DATA, curves_data)
+    self.assertIn(curves.PR_DATA, curves_data)
+    self.assertEqual(curves_data[curves.ROC_DATA], exp_roc)
+    self.assertEqual(curves_data[curves.PR_DATA], exp_pr)
+
   def test_config_spec(self):
     """Tests that the interpreter config has correct fields of correct type."""
     spec = self.ci.config_spec()

pyproject.toml

@@ -34,7 +34,7 @@ dependencies = [
   "rouge-score>=0.1.2",
   "sacrebleu>=2.3.1",
   "saliency>=0.1.3",
-  "scikit-learn>=1.0.2",
+  "scikit-learn>=1.6.1",
   "scipy>=1.10.1",
   "shap>=0.42.0,<0.46.0",
   "six>=1.16.0",

requirements.txt

@@ -31,7 +31,7 @@ requests>=2.31.0
 rouge-score>=0.1.2
 sacrebleu>=2.3.1
 saliency>=0.1.3
-scikit-learn>=1.0.2
+scikit-learn>=1.6.1
 scipy>=1.10.1
 shap>=0.42.0,<0.46.0
 six>=1.16.0