Update tests so that data contains metric_names that are on the corresponding experiments

ax/core/tests/test_observation.py

@@ -274,6 +274,7 @@ def test_ObservationsFromData(self) -> None:
         }
         type(experiment).arms_by_name = PropertyMock(return_value=arms)
         type(experiment).trials = PropertyMock(return_value=trials)
+        type(experiment).metrics = PropertyMock(return_value={"a": "a", "b": "b"})
 
         df = pd.DataFrame(truth)[
             ["arm_name", "trial_index", "mean", "sem", "metric_name"]
@@ -362,6 +363,7 @@ def test_ObservationsFromDataWithFidelities(self) -> None:
         }
         type(experiment).arms_by_name = PropertyMock(return_value=arms)
         type(experiment).trials = PropertyMock(return_value=trials)
+        type(experiment).metrics = PropertyMock(return_value={"a": "a", "b": "b"})
 
         df = pd.DataFrame(list(truth.values()))[
             ["arm_name", "trial_index", "mean", "sem", "metric_name", "fidelities"]
@@ -443,6 +445,7 @@ def test_ObservationsFromMapData(self) -> None:
         }
         type(experiment).arms_by_name = PropertyMock(return_value=arms)
         type(experiment).trials = PropertyMock(return_value=trials)
+        type(experiment).metrics = PropertyMock(return_value={"a": "a", "b": "b"})
 
         df = pd.DataFrame(list(truth.values()))[
             ["arm_name", "trial_index", "mean", "sem", "metric_name", "z", "timestamp"]
@@ -559,6 +562,7 @@ def test_ObservationsFromDataAbandoned(self) -> None:
         trials.get(2).mark_arm_abandoned(arm_name="2_1")
         type(experiment).arms_by_name = PropertyMock(return_value=arms)
         type(experiment).trials = PropertyMock(return_value=trials)
+        type(experiment).metrics = PropertyMock(return_value={"a": "a", "b": "b"})
 
         df = pd.DataFrame(list(truth.values()))[
             ["arm_name", "trial_index", "mean", "sem", "metric_name"]
@@ -635,6 +639,7 @@ def test_ObservationsFromDataWithSomeMissingTimes(self) -> None:
         }
         type(experiment).arms_by_name = PropertyMock(return_value=arms)
         type(experiment).trials = PropertyMock(return_value=trials)
+        type(experiment).metrics = PropertyMock(return_value={"a": "a", "b": "b"})
 
         df = pd.DataFrame(truth)[
             ["arm_name", "trial_index", "mean", "sem", "metric_name", "start_time"]
@@ -719,7 +724,7 @@ def test_ObservationsFromDataWithDifferentTimesSingleTrial(self) -> None:
         }
         type(experiment).arms_by_name = PropertyMock(return_value=arms_by_name)
         type(experiment).trials = PropertyMock(return_value=trials)
-
+        type(experiment).metrics = PropertyMock(return_value={"a": "a", "b": "b"})
         df = pd.DataFrame(truth)[
             [
                 "arm_name",
@@ -856,6 +861,7 @@ def test_ObservationsWithCandidateMetadata(self) -> None:
         }
         type(experiment).arms_by_name = PropertyMock(return_value=arms)
         type(experiment).trials = PropertyMock(return_value=trials)
+        type(experiment).metrics = PropertyMock(return_value={"a": "a", "b": "b"})
 
         df = pd.DataFrame(truth)[
             ["arm_name", "trial_index", "mean", "sem", "metric_name"]

ax/modelbridge/tests/test_alebo_strategy.py

@@ -32,7 +32,7 @@ def test_factory_functions(self) -> None:
             pd.DataFrame(
                 {
                     "arm_name": ["0_0", "0_1", "0_2"],
-                    "metric_name": "y",
+                    "metric_name": "branin",
                     "mean": [-1.0, 0.0, 1.0],
                     "sem": 0.1,
                 }

ax/modelbridge/tests/test_registry.py

@@ -380,7 +380,7 @@ def test_ALEBO(self) -> None:
             pd.DataFrame(
                 {
                     "arm_name": ["0_0", "0_1", "0_2"],
-                    "metric_name": "y",
+                    "metric_name": "branin",
                     "mean": [-1.0, 0.0, 1.0],
                     "sem": 0.1,
                 }

ax/plot/tests/test_fitted_scatter.py

@@ -14,7 +14,7 @@
 from ax.plot.base import AxPlotConfig
 from ax.plot.scatter import interact_fitted, interact_fitted_plotly
 from ax.utils.common.testutils import TestCase
-from ax.utils.testing.core_stubs import get_branin_experiment
+from ax.utils.testing.core_stubs import get_branin_experiment, get_branin_metric
 from ax.utils.testing.mock import fast_botorch_optimize
 
 
@@ -27,6 +27,7 @@ def test_fitted_scatter(self) -> None:
         data = exp.fetch_data()
         df = deepcopy(data.df)
         df["metric_name"] = "branin_dup"
+        exp.add_tracking_metric(get_branin_metric(name="branin_dup"))
 
         model = Models.BOTORCH_MODULAR(
             # Model bridge kwargs
@@ -47,11 +48,12 @@ def test_fitted_scatter(self) -> None:
         self.assertIsInstance(plot, AxPlotConfig)
 
         # Make sure all parameters and metrics are displayed in tooltips
-        tooltips = list(exp.parameters.keys()) + list(exp.metrics.keys())
-        for d in plot.data["data"]:
+        metric_names = ["branin", "branin_dup", "branin:agg"]
+        tooltips = [list(exp.parameters.keys()) + [m_name] for m_name in metric_names]
+        for idata, d in enumerate(plot.data["data"]):
             # Only check scatter plots hoverovers
             if d["type"] != "scatter":
                 continue
             for text in d["text"]:
-                for tt in tooltips:
+                for tt in tooltips[idata]:
                     self.assertTrue(tt in text)

ax/service/tests/scheduler_test_utils.py

@@ -81,6 +81,7 @@
 from sqlalchemy.orm.exc import StaleDataError
 
 DUMMY_EXCEPTION = "test_exception"
+TEST_MEAN = 1.0
 
 
 class SyntheticRunnerWithStatusPolling(SyntheticRunner):
@@ -2147,28 +2148,26 @@ def test_it_does_not_overwrite_data_with_combine_fetch_kwarg(self) -> None:
                 df=pd.DataFrame(
                     {
                         "arm_name": ["0_0"],
-                        "metric_name": ["foo"],
-                        "mean": [1.0],
+                        "metric_name": ["branin"],
+                        "mean": [TEST_MEAN],
                         "sem": [0.1],
                         "trial_index": [0],
                     }
                 )
             )
         )
-        attached_metrics = (
-            self.branin_experiment.lookup_data().df["metric_name"].unique()
-        )
+
+        attached_means = self.branin_experiment.lookup_data().df["mean"].unique()
         # the attach has overwritten the data, so we can infer that
         # fetching happened in the next `run_n_trials()`
-        self.assertNotIn("branin", attached_metrics)
+        self.assertIn(TEST_MEAN, attached_means)
+        self.assertEqual(len(attached_means), 1)
 
         scheduler.run_n_trials(max_trials=1)
-        attached_metrics = (
-            self.branin_experiment.lookup_data().df["metric_name"].unique()
-        )
-        # it did fetch again, but kept "foo" because of the combine kwarg
-        self.assertIn("foo", attached_metrics)
-        self.assertIn("branin", attached_metrics)
+        attached_means = self.branin_experiment.lookup_data().df["mean"].unique()
+        # it did fetch again, but kept both rows because of the combine kwarg
+        self.assertIn(TEST_MEAN, attached_means)
+        self.assertEqual(len(attached_means), 2)
 
     @fast_botorch_optimize
     def test_it_works_with_multitask_models(

ax/service/tests/test_ax_client.py

@@ -1500,7 +1500,6 @@ def test_trial_completion(self) -> None:
         ax_client = get_branin_optimization()
         params, idx = ax_client.get_next_trial()
         ax_client.complete_trial(trial_index=idx, raw_data={"branin": (0, 0.0)})
-        ax_client.update_trial_data(trial_index=idx, raw_data={"m1": (1, 0.0)})
         metrics_in_data = ax_client.experiment.fetch_data().df["metric_name"].values
         self.assertNotIn("m1", metrics_in_data)
         self.assertIn("branin", metrics_in_data)