Fix GroupedPipeline bug selecting from pd.Series

When the target `y` is a pd.Series rather than a numpy array, and the
pd.Series has a numeric index that isn't consecutive, previously
GroupedPipeline will attempt to select targets based on the index. In
the newer version of pandas, any missing indices will also raise an
Exception.

This is because we selected from `y` assuming it's a numpy array:
`y[indices]`. If y has a consecutive index, it makes no difference. If y
has a non-numeric index, pandas falls back to selecting via integer
location like numpy. However, if the index for y is numeric _and_
non-consecutive, the pd.Index is incorrectly used rather than the
position, and an Exception can be raised if indices are missing.

This change adds a test for the bug and updates the `_iter_groups`
method to always select the correct targets regardless of their type
(pd.Series or np.array) or their index.

tests/test_pipeline/test_grouped_pipeline.py

@@ -191,10 +191,28 @@ def test_all_groups_missing_raises(input_df):
 )
 def test_iter_groups_non_consecutive_index(index):
     group = [1] * 2 + [2] * (len(index) - 2)
+    target = np.array(group)
     value = np.random.random(len(index))
     input_df = pd.DataFrame(
         [group, value], index=["group", "value"], columns=index
     ).T
     gp = GroupedPipeline(groupby=["group"], pipeline=None)
-    for key, sub_df, _ in gp._iter_groups(input_df):
+    for key, sub_df, sub_target in gp._iter_groups(input_df, target):
         assert (sub_df["group"] == key).all()
+        assert (sub_target == key).all()
+
+
+@pytest.mark.parametrize(
+    "index", [[1, 2, 3, 4], pd.date_range("2019-01-01", periods=4)]
+)
+def test_iter_groups_non_consecutive_index_target_series(index):
+    group = [1] * 2 + [2] * (len(index) - 2)
+    target = pd.Series(group, index=index)
+    value = np.random.random(len(index))
+    input_df = pd.DataFrame(
+        [group, value], index=["group", "value"], columns=index
+    ).T
+    gp = GroupedPipeline(groupby=["group"], pipeline=None)
+    for key, sub_df, sub_target in gp._iter_groups(input_df, target):
+        assert (sub_df["group"] == key).all()
+        assert (sub_target == key).all()

timeserio/pipeline/pipeline.py

@@ -127,7 +127,11 @@ def _iter_groups(self, df, y=None):
         groups = df.groupby(self.groupby).indices
         for key, sub_idx in groups.items():
             sub_df = df.iloc[sub_idx]
-            sub_y = y[sub_idx] if y is not None else None
+            if y is not None:
+                # y is either a numpy array or a pd.Series so index accordingly
+                sub_y = y.iloc[sub_idx] if type(y) is pd.Series else y[sub_idx]
+            else:
+                sub_y = None
             yield key, sub_df, sub_y
 
     def fit(self, df, y=None):