Support multi-objective study for from_optuna (#216)

* clarify the supported study type

* revert docs change and support multi-objective

* add test for from_optuna with multi-objective value

* fix returned type

hiplot/experiment.py

@@ -3,6 +3,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import csv
+import enum
 import uuid
 import json
 import warnings
@@ -514,11 +515,18 @@ def from_optuna(study: "optuna.study.Study") -> "Experiment":  # No type hint to
 
         # Create a list of dictionary objects using study trials
         # All parameters are taken using params.copy()
-    
+
         hyper_opt_data = []
         for each_trial in study.trials:
             trial_params = {}
-            trial_params["value"] = each_trial.value # name = value, as it could be RMSE / accuracy, or any value that the user selects for tuning
+            num_objectives = len(each_trial.values)
+
+            if num_objectives == 1:
+                trial_params["value"] = each_trial.value # name = value, as it could be RMSE / accuracy, or any value that the user selects for tuning
+            else:
+                for objective_id, value in enumerate(each_trial.values):
+                    trial_params[f"value_{objective_id}"] = value
+
             trial_params["uid"] = each_trial.number
             trial_params.update(each_trial.params.copy())
             hyper_opt_data.append(trial_params)

hiplot/test_experiment.py

@@ -59,6 +59,26 @@ def objective(trial: "optuna.trial.Trial") -> float:
     xp._asdict()
 
 
+def test_from_optuna_multi_objective() -> None:
+
+    def objective(trial: "optuna.trial.Trial") -> tp.Tuple[float, float]:
+        x = trial.suggest_float("x", -1, 1)
+        y = trial.suggest_float("y", -1, 1)
+        return x ** 2, y
+
+    study = optuna.create_study(directions=["minimize", "minimize"])
+    study.optimize(objective, n_trials=3)
+
+    # Create a dataframe from the study.
+    df = study.trials_dataframe()
+    assert isinstance(df, pd.DataFrame)
+    assert df.shape[0] == 3  # n_trials.
+    xp = hip.Experiment.from_optuna(study)
+    assert len(xp.datapoints) == 3
+    xp.validate()
+    xp._asdict()
+
+
 def test_from_dataframe_nan_values() -> None:
     # Pandas automatically convert numeric-based columns None to NaN in dataframes
     # Pandas will also automatically convert columns with NaN from integer to floats, since NaN is considered a float