Write tests where we readd the data

Author: basnijholt

adaptive/learner/average_learner1D.py

@@ -150,7 +150,7 @@ def to_numpy(self, mean: bool = True) -> np.ndarray:
 
     def to_dataframe(
         self,
-        mean: bool = True,
+        mean: bool = False,
         with_default_function_args: bool = True,
         function_prefix: str = "function.",
         seed_name: str = "seed",

adaptive/learner/learnerND.py

@@ -401,7 +401,7 @@ def to_dataframe(
         with_default_function_args: bool = True,
         function_prefix: str = "function.",
         point_names: tuple[str, ...] = ("x", "y", "z"),
-        value_name: str = "y",
+        value_name: str = "value",
     ) -> pandas.DataFrame:
         if not with_pandas:
             raise ImportError("pandas is not installed.")
@@ -410,7 +410,7 @@ def to_dataframe(
                 f"point_names ({point_names}) should have the"
                 f" same length as learner.ndims ({self.ndim})"
             )
-        data = sorted((*x, y) for x, y in self.data.items())
+        data = list((*x, y) for x, y in self.data.items())
         df = pandas.DataFrame(data, columns=[*point_names, value_name])
         if with_default_function_args:
             assign_defaults(self.function, df, function_prefix)

adaptive/tests/test_learners.py

@@ -710,12 +710,34 @@ def test_to_dataframe(learner_type, f, learner_kwargs):
         kw = {"point_names": list("xyz")[: len(learner_kwargs["bounds"])]}
     else:
         kw = {}
+
     learner = learner_type(generate_random_parametrization(f), **learner_kwargs)
     simple_run(learner, 100)
     df = learner.to_dataframe(**kw)
     assert isinstance(df, pandas.DataFrame)
-    assert len(df) == learner.npoints
+    if learner_type is AverageLearner1D:
+        assert len(df) == learner.nsamples
+    else:
+        assert len(df) == learner.npoints
+
+    # Add points from the DataFrame to a new empty learner
+    learner2 = learner_type(generate_random_parametrization(f), **learner_kwargs)
+
+    if learner_type is Learner1D:
+        learner2.tell_many(df["x"], df["y"])
+    elif learner_type is Learner2D:
+        learner2.tell_many(df[["x", "y"]].values, df["z"])
+    elif learner_type is LearnerND:
+        point_names = list(kw["point_names"])
+        learner2.tell_many(df[point_names].values, df["value"])
+    elif learner_type is AverageLearner:
+        learner2.tell_many(df["seed"].values, df["y"])
+    elif learner_type is AverageLearner1D:
+        learner2.tell_many(df[["seed", "x"]].values, df["y"])
+    else:
+        raise NotImplementedError()
 
+    # Test this for a learner in a BalancingLearner
     learners = [
         learner_type(generate_random_parametrization(f), **learner_kwargs)
         for _ in range(2)
@@ -724,4 +746,8 @@ def test_to_dataframe(learner_type, f, learner_kwargs):
     simple_run(learner, 100)
     df = learner.to_dataframe(**kw)
     assert isinstance(df, pandas.DataFrame)
-    assert len(df) == learner.npoints
+
+    if learner_type is not AverageLearner1D:
+        assert len(df) == learner.npoints
+
+    # TODO: Test this for a learner in a DataSaver