Fix unittest?, increase coverage (hopefully)

autosklearn/automl.py

@@ -2010,11 +2010,17 @@ def sprint_statistics(self) -> str:
             )
         )[0]
         if len(idx_success) > 0:
+            key = (
+                "mean_test_score"
+                if len(self._metrics) == 1
+                else f"mean_test_" f"{self._metrics[0].name}"
+            )
+
             if not self._metrics[0]._optimum:
-                idx_best_run = np.argmin(cv_results["mean_test_score"][idx_success])
+                idx_best_run = np.argmin(cv_results[key][idx_success])
             else:
-                idx_best_run = np.argmax(cv_results["mean_test_score"][idx_success])
-            best_score = cv_results["mean_test_score"][idx_success][idx_best_run]
+                idx_best_run = np.argmax(cv_results[key][idx_success])
+            best_score = cv_results[key][idx_success][idx_best_run]
             sio.write("  Best validation score: %f\n" % best_score)
         num_runs = len(cv_results["status"])
         sio.write("  Number of target algorithm runs: %d\n" % num_runs)

examples/40_advanced/example_metrics.py

@@ -81,7 +81,6 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho
 scorer = autosklearn.metrics.accuracy
 cls = autosklearn.classification.AutoSklearnClassifier(
     time_left_for_this_task=60,
-    per_run_time_limit=30,
     seed=1,
     metric=scorer,
 )
@@ -107,7 +106,6 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho
 )
 cls = autosklearn.classification.AutoSklearnClassifier(
     time_left_for_this_task=60,
-    per_run_time_limit=30,
     seed=1,
     metric=accuracy_scorer,
 )
@@ -133,7 +131,6 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho
 )
 cls = autosklearn.classification.AutoSklearnClassifier(
     time_left_for_this_task=60,
-    per_run_time_limit=30,
     seed=1,
     metric=error_rate,
 )
@@ -184,7 +181,6 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho
 )
 cls = autosklearn.classification.AutoSklearnClassifier(
     time_left_for_this_task=60,
-    per_run_time_limit=30,
     seed=1,
     metric=error_rate,
 )
@@ -217,10 +213,8 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho
 )
 cls = autosklearn.classification.AutoSklearnClassifier(
     time_left_for_this_task=60,
-    per_run_time_limit=30,
     seed=1,
     metric=accuracy_scorer,
-    ensemble_size=0,
 )
 cls.fit(X_train, y_train)
 
@@ -232,4 +226,4 @@ def metric_which_needs_x(solution, prediction, X_data, consider_col, val_thresho
     consider_col=1,
     val_threshold=18.8,
 )
-print(f"Error score {score:.3f} using {error_rate.name:s}")
+print(f"Error score {score:.3f} using {accuracy_scorer.name:s}")

test/test_automl/test_post_fit.py

@@ -86,7 +86,7 @@ def test__load_pareto_front(automl: AutoML) -> None:
     """
     # Check that the predict function works
     X = np.array([[1.0, 1.0, 1.0, 1.0]])
-    print(automl.predict(X))
+
     assert automl.predict_proba(X).shape == (1, 3)
     assert automl.predict(X).shape == (1,)
 
@@ -98,3 +98,9 @@ def test__load_pareto_front(automl: AutoML) -> None:
         assert y_pred.shape == (1, 3)
         y_pred = ensemble.predict(X)
         assert y_pred in ["setosa", "versicolor", "virginica"]
+
+    statistics = automl.sprint_statistics()
+    assert "Metrics" in statistics
+    assert ("Best validation score: 0.9" in statistics) or (
+        "Best validation score: 1.0" in statistics
+    ), statistics

test/test_ensemble_builder/test_ensemble_builder_real.py

@@ -78,7 +78,7 @@ def test_run_builds_valid_ensemble(builder: EnsembleBuilder) -> None:
 
     assert mock_fit.call_count == 1
     # Check that the ids of runs in the ensemble were all candidates
-    candidates = mock_fit.call_args.kwargs["candidates"]
+    candidates = mock_fit.call_args[1]["candidates"]
     candidate_ids = {run.id for run in candidates}
     assert ensemble_ids <= candidate_ids