Fixed error when stacking data with no exogenous variables (#4275)

* Initial commit

* Updated release notes

* Refactored code structure.

* Updated error message and docstring

* Final nits

docs/source/release_notes.rst

@@ -5,6 +5,7 @@ Release Notes
         * Added support for prediction intervals for VARMAX regressor :pr:`4267`
         * Integrated multiseries time series into AutoMLSearch :pr:`4270`
     * Fixes
+        * Fixed error when stacking data with no exogenous variables :pr:`4275`
     * Changes
         * Updated ``ARIMARegressor`` to be compatible with sktime v0.22.0 and beyond :pr:`4283`
     * Documentation Changes

evalml/pipelines/utils.py

@@ -1417,8 +1417,11 @@ def unstack_multiseries(
                 X_unstacked_cols.append(new_column)
 
     # Concatenate all the single series to reform dataframes
-    X_unstacked = pd.concat(X_unstacked_cols, axis=1)
     y_unstacked = pd.concat(y_unstacked_cols, axis=1)
+    if len(X_unstacked_cols) == 0:
+        X_unstacked = pd.DataFrame(index=y_unstacked.index)
+    else:
+        X_unstacked = pd.concat(X_unstacked_cols, axis=1)
 
     # Reset the axes now that they've been unstacked, keep time info in X
     X_unstacked = X_unstacked.reset_index()
@@ -1477,7 +1480,7 @@ def stack_data(data, include_series_id=False, series_id_name=None, starting_inde
     return stacked_series
 
 
-def stack_X(X, series_id_name, time_index, starting_index=None):
+def stack_X(X, series_id_name, time_index, starting_index=None, series_id_values=None):
     """Restacks the unstacked features into a single DataFrame.
 
     Args:
@@ -1486,37 +1489,61 @@ def stack_X(X, series_id_name, time_index, starting_index=None):
         time_index (str): The name of the time index column.
         starting_index (int): The starting index to use for the stacked DataFrame. If None, the starting index
             will match that of the input data. Defaults to None.
+        series_id_values (set, list): The unique values of a series ID, used to generate the index. If None, values will
+            be generated from X column values. Required if X only has time index values and no exogenous values.
+            Defaults to None.
 
     Returns:
         pd.DataFrame: The restacked features.
     """
     original_columns = set()
-    series_ids = set()
-    for col in X.columns:
-        if col == time_index:
-            continue
-        separated_name = col.split("_")
-        original_columns.add("_".join(separated_name[:-1]))
-        series_ids.add(separated_name[-1])
-
-    restacked_X = []
-
-    for i, original_col in enumerate(original_columns):
-        # Only include the series id once (for the first column)
-        include_series_id = i == 0
-        subset_X = [col for col in X.columns if original_col in col]
-        restacked_X.append(
-            stack_data(
-                X[subset_X],
-                include_series_id=include_series_id,
-                series_id_name=series_id_name,
-                starting_index=starting_index,
-            ),
+    series_ids = series_id_values or set()
+    if series_id_values is None:
+        for col in X.columns:
+            if col == time_index:
+                continue
+            separated_name = col.split("_")
+            original_columns.add("_".join(separated_name[:-1]))
+            series_ids.add(separated_name[-1])
+
+    if len(series_ids) == 0:
+        raise ValueError(
+            "Series ID values need to be passed in X column values or as a set with the `series_id_values` parameter.",
         )
-    restacked_X = pd.concat(restacked_X, axis=1)
 
     time_index_col = X[time_index].repeat(len(series_ids)).reset_index(drop=True)
-    time_index_col.index = restacked_X.index
-    restacked_X[time_index] = time_index_col
+
+    if len(original_columns) == 0:
+        start_index = starting_index or X.index[0]
+        stacked_index = pd.RangeIndex(
+            start=start_index,
+            stop=start_index + len(time_index_col),
+        )
+        time_index_col.index = stacked_index
+        restacked_X = pd.DataFrame(
+            {
+                time_index: time_index_col,
+                series_id_name: sorted(list(series_ids)) * len(X),
+            },
+            index=stacked_index,
+        )
+    else:
+        restacked_X = []
+        for i, original_col in enumerate(original_columns):
+            # Only include the series id once (for the first column)
+            include_series_id = i == 0
+            subset_X = [col for col in X.columns if original_col in col]
+            restacked_X.append(
+                stack_data(
+                    X[subset_X],
+                    include_series_id=include_series_id,
+                    series_id_name=series_id_name,
+                    starting_index=starting_index,
+                ),
+            )
+
+        restacked_X = pd.concat(restacked_X, axis=1)
+        time_index_col.index = restacked_X.index
+        restacked_X[time_index] = time_index_col
 
     return restacked_X

evalml/preprocessing/utils.py

@@ -72,12 +72,22 @@ def split_multiseries_data(X, y, series_id, time_index, **kwargs):
         X_unstacked, y_unstacked, problem_type="time series regression", **kwargs
     )
 
-    X_train = stack_X(X_train_unstacked, series_id, time_index)
+    # Get unique series value from X if there is only the time_index column
+    # Otherwise, this information is generated in `stack_X` from the column values
+    series_id_values = set(X[series_id]) if len(X_unstacked.columns) == 1 else None
+
+    X_train = stack_X(
+        X_train_unstacked,
+        series_id,
+        time_index,
+        series_id_values=series_id_values,
+    )
     X_holdout = stack_X(
         X_holdout_unstacked,
         series_id,
         time_index,
         starting_index=X_train.index[-1] + 1,
+        series_id_values=series_id_values,
     )
     y_train = stack_data(y_train_unstacked)
     y_holdout = stack_data(y_holdout_unstacked, starting_index=y_train.index[-1] + 1)

evalml/tests/pipeline_tests/test_pipeline_utils.py

@@ -1472,9 +1472,13 @@ def test_stack_data_noop():
     pd.testing.assert_series_equal(stack_data(series_y), series_y)
 
 
+@pytest.mark.parametrize("series_id_values_type", [set, list])
+@pytest.mark.parametrize("no_features", [True, False])
 @pytest.mark.parametrize("starting_index", [None, 1, 132])
 def test_stack_X(
     starting_index,
+    no_features,
+    series_id_values_type,
     multiseries_ts_data_stacked,
     multiseries_ts_data_unstacked,
 ):
@@ -1484,7 +1488,28 @@ def test_stack_X(
     if starting_index is not None:
         X_expected.index = X_expected.index + starting_index
 
-    X_transformed = stack_X(X, "series_id", "date", starting_index=starting_index)
+    if no_features:
+        series_id_values = series_id_values_type(str(i) for i in range(0, 5))
+        X = pd.DataFrame(X["date"])
+        X_expected = X_expected[["date", "series_id"]]
+
+        with pytest.raises(
+            ValueError,
+            match="Series ID values need to be passed in X column values or as a set with the `series_id_values` parameter.",
+        ):
+            stack_X(X, "series_id", "date", starting_index=starting_index)
+
+        X_transformed = stack_X(
+            X,
+            "series_id",
+            "date",
+            starting_index=starting_index,
+            series_id_values=series_id_values,
+        )
+
+    else:
+        X_transformed = stack_X(X, "series_id", "date", starting_index=starting_index)
+
     pd.testing.assert_frame_equal(
         X_expected.sort_index(axis=1),
         X_transformed.sort_index(axis=1),

evalml/tests/preprocessing_tests/test_split_data.py

@@ -127,9 +127,13 @@ def test_split_data_ts(test, X_y_regression):
     assert len(y_test) == test_size
 
 
-def test_split_multiseries_data(multiseries_ts_data_stacked):
+@pytest.mark.parametrize("no_features", [True, False])
+def test_split_multiseries_data(no_features, multiseries_ts_data_stacked):
     X, y = multiseries_ts_data_stacked
 
+    if no_features:
+        X = X[["date", "series_id"]]
+
     X_train_expected, X_holdout_expected = X[:-10], X[-10:]
     y_train_expected, y_holdout_expected = y[:-10], y[-10:]