Fixed forecast period generation function for multiseries

docs/source/release_notes.rst

@@ -9,6 +9,7 @@ Release Notes
         * Extended DateTimeFormatCheck data check to support multiseries :pr:`4300`
         * Extended TimeSeriesRegularizer to support multiseries :pr:`4303`
     * Fixes
+        * Fixed forecast period generation function for multiseries :pr:`4320`
     * Changes
         * Updated ``split_data`` to call ``split_multiseries_data`` when passed stacked multiseries data :pr:`4312`
         * Pinned pandas version under 2.1.0 :pr:`4315`

evalml/pipelines/multiseries_regression_pipeline.py

@@ -1,4 +1,5 @@
 """Pipeline base class for time series regression problems."""
+import pandas as pd
 from woodwork.statistics_utils import infer_frequency
 
 from evalml.pipelines.time_series_regression_pipeline import (
@@ -137,3 +138,39 @@ def predict_in_sample(
         # Index will start at the unstacked index, so we need to reset it to the original index
         stacked_predictions.index = X.index
         return stacked_predictions
+
+    def get_forecast_period(self, X):
+        """Generates all possible forecasting time points based on latest data point in X.
+
+        For the multiseries case, each time stamp is duplicated for each unique value in `X`'s `series_id` column.
+
+        Args:
+            X (pd.DataFrame, np.ndarray): Data the pipeline was trained on of shape [n_samples_train, n_feautures].
+
+        Raises:
+            ValueError: If pipeline is not trained.
+
+        Returns:
+            pd.DataFrame: Dataframe containing a column with datetime periods from `gap` to `forecast_horizon + gap`
+            per unique `series_id` value.
+        """
+        dates = super().get_forecast_period(X)
+        dates.name = self.time_index
+        series_id_values = X[self.series_id].unique()
+
+        new_period_df = dates.to_frame().merge(
+            pd.Series(series_id_values, name=self.series_id),
+            how="cross",
+        )
+
+        # Generate new numeric index
+        num_idx = pd.Series(
+            range(
+                dates.index[0] + (self.gap * len(series_id_values)),
+                dates.index[0]
+                + (self.gap * len(series_id_values))
+                + len(new_period_df),
+            ),
+        )
+        new_period_df.index = num_idx
+        return new_period_df

evalml/pipelines/time_series_regression_pipeline.py

@@ -205,39 +205,83 @@ def get_prediction_intervals(
         Raises:
             MethodPropertyNotFoundError: If the estimator does not support Time Series Regression as a problem type.
         """
-        X_no_datetime, y_no_datetime = self._drop_time_index(X, y)
-        estimator_input = self.transform_all_but_final(
-            X_no_datetime,
-            y_no_datetime,
-            X_train=X_train,
-            y_train=y_train,
-        )
         has_stl = STLDecomposer.name in list(
             self.component_graph.component_instances.keys(),
         )
         if coverage is None:
             coverage = [0.95]
 
         if self.estimator.model_family in self.NO_PREDS_PI_ESTIMATORS and has_stl:
+            if self.problem_type == ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION:
+                from evalml.pipelines.utils import stack_data, unstack_multiseries
+
+                X, y = unstack_multiseries(
+                    X,
+                    y,
+                    self.series_id,
+                    self.time_index,
+                    self.input_target_name,
+                )
+
+            X_no_datetime, y_no_datetime = self._drop_time_index(X, y)
+
+            estimator_input = self.transform_all_but_final(
+                X_no_datetime,
+                y_no_datetime,
+                X_train=X_train,
+                y_train=y_train,
+            )
             pred_intervals = self.estimator.get_prediction_intervals(
                 X=estimator_input,
                 y=y,
                 coverage=coverage,
             )
-            trans_pred_intervals = {}
+            intervals_labels = list(list(pred_intervals.values())[0].keys())
+            interval_series_pred_intervals = {
+                interval: {} for interval in intervals_labels
+            }
             residuals = self.estimator.predict(
                 estimator_input,
-            )  # Get residual values
-            trend_pred_intervals = self.get_component(
-                "STL Decomposer",
-            ).get_trend_prediction_intervals(y, coverage=coverage)
-            for key, orig_pi_values in pred_intervals.items():
-                trans_pred_intervals[key] = pd.Series(
-                    (orig_pi_values.values - residuals.values)
-                    + trend_pred_intervals[key].values
-                    + y.values,
-                    index=orig_pi_values.index,
-                )
+            )
+            trans_pred_intervals = {}
+            if self.problem_type == ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION:
+                trend_pred_intervals = self.get_component(
+                    "STL Decomposer",
+                ).get_trend_prediction_intervals(y_no_datetime, coverage=coverage)
+                for series_id, intervals in pred_intervals.items():
+                    for key, orig_pi_values in intervals.items():
+                        series_id_target_name = (
+                            self.input_target_name + "_" + str(series_id)
+                        )
+                        interval_series_pred_intervals[key][
+                            series_id_target_name
+                        ] = pd.Series(
+                            (orig_pi_values.values - residuals[series_id].values)
+                            + trend_pred_intervals[series_id_target_name][key].values
+                            + y[series_id_target_name].values,
+                            index=orig_pi_values.index,
+                        )
+                for interval in intervals_labels:
+                    series_id_df = pd.DataFrame(
+                        interval_series_pred_intervals[interval],
+                    )
+                    stacked_pred_interval = stack_data(
+                        data=series_id_df,
+                        series_id_name=self.series_id,
+                    )
+                    trans_pred_intervals[interval] = stacked_pred_interval
+
+            else:
+                trend_pred_intervals = self.get_component(
+                    "STL Decomposer",
+                ).get_trend_prediction_intervals(y, coverage=coverage)
+                for key, orig_pi_values in pred_intervals.items():
+                    trans_pred_intervals[key] = pd.Series(
+                        (orig_pi_values.values - residuals.values)
+                        + trend_pred_intervals[key].values
+                        + y.values,
+                        index=orig_pi_values.index,
+                    )
             return trans_pred_intervals
         else:
             future_vals = self.predict(

evalml/tests/pipeline_tests/regression_pipeline_tests/test_multiseries_regression_pipeline.py

@@ -1,7 +1,12 @@
+from unittest.mock import MagicMock, patch
+
+import numpy as np
 import pandas as pd
 import pytest
+from pandas._testing import assert_series_equal
 
 from evalml.pipelines import MultiseriesRegressionPipeline
+from evalml.pipelines.utils import unstack_multiseries
 from evalml.preprocessing import split_multiseries_data
 
 
@@ -157,3 +162,191 @@ def test_multiseries_pipeline_predict(
             dtype="float64",
         )
     pd.testing.assert_series_equal(y_pred, expected)
+
+
+@pytest.mark.parametrize("forecast_horizon,gap", [[3, 0], [5, 2], [2, 5]])
+@pytest.mark.parametrize("numeric_idx", [True, False])
+def test_time_series_get_forecast_period(
+    forecast_horizon,
+    gap,
+    numeric_idx,
+    multiseries_ts_data_stacked,
+    component_graph,
+    pipeline_parameters,
+):
+    X, y = multiseries_ts_data_stacked
+    if numeric_idx:
+        X = X.reset_index(drop=True)
+
+    pipeline_parameters["pipeline"]["forecast_horizon"] = forecast_horizon
+    pipeline_parameters["Time Series Featurizer"]["forecast_horizon"] = forecast_horizon
+    pipeline_parameters["Baseline Multiseries"]["forecast_horizon"] = forecast_horizon
+    pipeline_parameters["pipeline"]["gap"] = gap
+    pipeline_parameters["Time Series Featurizer"]["gap"] = gap
+    pipeline_parameters["Baseline Multiseries"]["gap"] = gap
+
+    clf = MultiseriesRegressionPipeline(component_graph, pipeline_parameters)
+
+    with pytest.raises(
+        ValueError,
+        match="Pipeline must be fitted before getting forecast.",
+    ):
+        clf.get_forecast_period(X)
+
+    clf.fit(X, y)
+    result = clf.get_forecast_period(X)
+
+    len_unique_series_id = len(X["series_id"].unique())
+
+    assert result.shape[0] == forecast_horizon * len_unique_series_id
+    assert all(
+        result.index
+        == range(
+            len(X) + (gap * len_unique_series_id),
+            len(X)
+            + (gap * len_unique_series_id)
+            + (forecast_horizon * len_unique_series_id),
+        ),
+    )
+    assert result.iloc[0]["date"] == X.iloc[-1]["date"] + np.timedelta64(
+        1 + gap,
+        clf.frequency,
+    )
+    assert np.issubdtype(result.dtypes["date"], np.datetime64)
+    assert list(result.columns) == ["date", "series_id"]
+
+
+@pytest.mark.parametrize("forecast_horizon,gap", [[3, 0], [5, 2], [2, 5]])
+def test_time_series_get_forecast_predictions(
+    forecast_horizon,
+    gap,
+    multiseries_ts_data_stacked,
+    component_graph,
+    pipeline_parameters,
+):
+    X, y = multiseries_ts_data_stacked
+
+    X_train, y_train = X.iloc[:25], y.iloc[:25]
+    X_validation = X.iloc[25 + (gap * 5) : 25 + (gap * 5) + (forecast_horizon * 5)]
+
+    pipeline_parameters["pipeline"]["forecast_horizon"] = forecast_horizon
+    pipeline_parameters["Time Series Featurizer"]["forecast_horizon"] = forecast_horizon
+    pipeline_parameters["Baseline Multiseries"]["forecast_horizon"] = forecast_horizon
+    pipeline_parameters["pipeline"]["gap"] = gap
+    pipeline_parameters["Time Series Featurizer"]["gap"] = gap
+    pipeline_parameters["Baseline Multiseries"]["gap"] = gap
+
+    clf = MultiseriesRegressionPipeline(component_graph, pipeline_parameters)
+
+    clf.fit(X_train, y_train)
+    forecast_preds = clf.get_forecast_predictions(X=X_train, y=y_train)
+    X_val_preds = clf.predict(X_validation, X_train=X_train, y_train=y_train)
+    assert_series_equal(forecast_preds, X_val_preds)
+
+
+@pytest.mark.parametrize("set_coverage", [True, False])
+@pytest.mark.parametrize("add_decomposer", [True, False])
+@pytest.mark.parametrize("ts_native_estimator", [True, False])
+def test_time_series_pipeline_get_prediction_intervals(
+    ts_native_estimator,
+    add_decomposer,
+    set_coverage,
+    multiseries_ts_data_stacked,
+):
+    X, y = multiseries_ts_data_stacked
+    y = pd.Series(np.random.rand(100), name="target")
+    component_graph = {
+        "Regressor": [
+            "VARMAX Regressor" if ts_native_estimator else "VARMAX Regressor",
+            "X" if not add_decomposer else "STL Decomposer.x",
+            "y" if not add_decomposer else "STL Decomposer.y",
+        ],
+    }
+    if add_decomposer:
+        component_graph.update(
+            {
+                "STL Decomposer": [
+                    "STL Decomposer",
+                    "X",
+                    "y",
+                ],
+            },
+        )
+
+    pipeline_parameters = {
+        "pipeline": {
+            "time_index": "date",
+            "max_delay": 10,
+            "forecast_horizon": 7,
+            "gap": 0,
+            "series_id": "series_id",
+        },
+    }
+
+    pipeline = MultiseriesRegressionPipeline(
+        component_graph=component_graph,
+        parameters=pipeline_parameters,
+    )
+    X_train, y_train = X[:65], y[:65]
+    X_validation, y_validation = X[65:], y[65:]
+    mock_X, _ = unstack_multiseries(
+        X_train,
+        y_train,
+        series_id="series_id",
+        time_index="date",
+        target_name="target",
+    )
+    mock_transform_return_value = (
+        mock_X,
+        pd.DataFrame(np.random.rand(13, 5)),
+    )
+    with patch(
+        "evalml.pipelines.components.transformers.preprocessing.stl_decomposer.STLDecomposer.transform",
+        MagicMock(return_value=mock_transform_return_value),
+    ):
+        pipeline.fit(X_train, y_train)
+
+    coverage = [0.75, 0.85, 0.95] if set_coverage else None
+
+    pl_intervals = pipeline.get_prediction_intervals(
+        X=X_validation,
+        y=y_validation,
+        X_train=X_train,
+        y_train=y_train,
+        coverage=coverage,
+    )
+
+    if set_coverage is False:
+        coverage = [0.95]
+
+    if set_coverage:
+        pairs = [(0.75, 0.85), (0.85, 0.95)]
+        for pair in pairs:
+            assert all(
+                [
+                    narrower >= broader
+                    for narrower, broader in zip(
+                        pl_intervals[f"{pair[0]}_lower"],
+                        pl_intervals[f"{pair[1]}_lower"],
+                    )
+                ],
+            )
+            assert all(
+                [
+                    narrower <= broader
+                    for narrower, broader in zip(
+                        pl_intervals[f"{pair[0]}_upper"],
+                        pl_intervals[f"{pair[1]}_upper"],
+                    )
+                ],
+            )
+    for cover_value in coverage:
+        assert all(
+            [
+                lower < upper
+                for lower, upper in zip(
+                    pl_intervals[f"{cover_value}_lower"],
+                    pl_intervals[f"{cover_value}_upper"],
+                )
+            ],
+        )