diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst index 718a536a55..c4fecb075c 100644 --- a/docs/source/release_notes.rst +++ b/docs/source/release_notes.rst @@ -19,6 +19,7 @@ Release Notes * Added stacking and unstacking utility functions to work with multiseries data :pr:`4250` * Added multiseries regression pipeline class :pr:`4256` * Added multiseries VARMAX regressor :pr:`4238` + * Added support for prediction intervals for VARMAX regressor :pr:`4267` * Fixes * Added support for pandas 2 :pr:`4216` * Fixed bug where time series pipelines would fail due to MASE needing `y_train` when scoring :pr:`4258` diff --git a/evalml/pipelines/components/estimators/regressors/exponential_smoothing_regressor.py b/evalml/pipelines/components/estimators/regressors/exponential_smoothing_regressor.py index 091f3fb1c2..1706927401 100644 --- a/evalml/pipelines/components/estimators/regressors/exponential_smoothing_regressor.py +++ b/evalml/pipelines/components/estimators/regressors/exponential_smoothing_regressor.py @@ -27,6 +27,8 @@ class ExponentialSmoothingRegressor(Estimator): random_seed (int): Seed for the random number generator. Defaults to 0. """ + _N_REPETITIONS = 400 + name = "Exponential Smoothing Regressor" hyperparameter_ranges = { "trend": [None, "additive"], @@ -167,7 +169,7 @@ def get_prediction_intervals( # anchor represents where the simulations should start from (forecasting is done from the "end") y_pred = self._component_obj._fitted_forecaster.simulate( nsimulations=X.shape[0], - repetitions=400, + repetitions=self._N_REPETITIONS, anchor="end", random_state=self.parameters["random_state"], ) diff --git a/evalml/pipelines/components/estimators/regressors/varmax_regressor.py b/evalml/pipelines/components/estimators/regressors/varmax_regressor.py index 81a4374de4..ecca230042 100644 --- a/evalml/pipelines/components/estimators/regressors/varmax_regressor.py +++ b/evalml/pipelines/components/estimators/regressors/varmax_regressor.py @@ -34,6 +34,8 @@ class VARMAXRegressor(Estimator): solely be based off of the datetimes and target values. Defaults to True. """ + _N_REPETITIONS = 400 + name = "VARMAX Regressor" hyperparameter_ranges = { "p": Integer(0, 10), @@ -215,11 +217,51 @@ def get_prediction_intervals( predictions (pd.Series): Not used for VARMAX regressor. Returns: - dict: Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper. + dict[dict]: A dict of prediction intervals, where the dict is in the format {series_id: {coverage}_lower or {coverage}_upper}. """ - raise NotImplementedError( - "VARMAX does not have prediction intervals implemented yet.", + if coverage is None: + coverage = [0.95] + + X, y = self._manage_woodwork(X, y) + use_exog = ( + # If exogenous variables were used during training + self._component_obj._fitted_forecaster.model.exog is not None + and self.use_covariates + ) + if use_exog: + X = X.ww.select(exclude=["Datetime"]) + X = convert_bool_to_double(X) + # Accesses the fitted statsmodels model within sktime + # nsimulations represents how many steps should be simulated + # repetitions represents the number of simulations that should be run (confusing, I know) + # anchor represents where the simulations should start from (forecasting is done from the "end") + y_pred = self._component_obj._fitted_forecaster.simulate( + nsimulations=X.shape[0], + repetitions=self._N_REPETITIONS, + anchor="end", + random_state=self.random_seed, + exog=X if use_exog else None, ) + prediction_interval_result = {} + # Access the target column names (i.e. the series_id values) that the VARMAX component obj was fitted on + for series in self._component_obj._fitted_forecaster.model.endog_names: + series_result = {} + series_preds = y_pred[[col for col in y_pred.columns if series in col]] + for conf_int in coverage: + prediction_interval_lower = series_preds.quantile( + q=round((1 - conf_int) / 2, 3), + axis="columns", + ) + prediction_interval_upper = series_preds.quantile( + q=round((1 + conf_int) / 2, 3), + axis="columns", + ) + prediction_interval_lower.index = X.index + prediction_interval_upper.index = X.index + series_result[f"{conf_int}_lower"] = prediction_interval_lower + series_result[f"{conf_int}_upper"] = prediction_interval_upper + prediction_interval_result[series] = series_result + return prediction_interval_result @property def feature_importance(self) -> np.ndarray: diff --git a/evalml/tests/component_tests/test_varmax_regressor.py b/evalml/tests/component_tests/test_varmax_regressor.py index 49627ccc3a..75efa173ae 100644 --- a/evalml/tests/component_tests/test_varmax_regressor.py +++ b/evalml/tests/component_tests/test_varmax_regressor.py @@ -260,26 +260,10 @@ def test_varmax_regressor_respects_use_covariates( assert "X" not in mock_predict.call_args.kwargs -def test_varmax_regressor_prediction_intervals_not_implemented_yet_error( - ts_multiseries_data, -): - X_train, X_test, y_train = ts_multiseries_data(n_series=2) - - clf = VARMAXRegressor() - - clf.fit(X_train, y_train) - with pytest.raises( - NotImplementedError, - match="VARMAX does not have prediction intervals implemented yet.", - ): - clf.get_prediction_intervals(X_test) - - def test_varmax_regressor_can_forecast_arbitrary_dates_no_covariates( ts_multiseries_data, ): X, _, y = ts_multiseries_data(n_series=2) - X_train, X_test, y_train, y_test = split_data( X, y, @@ -329,3 +313,46 @@ def test_varmax_regressor_can_forecast_arbitrary_dates_past_holdout( varmax.fit(X_train, y_train) varmax.predict(X_test) + + +@pytest.mark.parametrize("use_X_train", [True, False]) +@pytest.mark.parametrize("coverages", [None, [0.85], [0.95, 0.90, 0.85]]) +@pytest.mark.parametrize("use_covariates", [True, False]) +def test_varmax_regressor_prediction_intervals( + use_covariates, + coverages, + use_X_train, + ts_multiseries_data, +): + X_train, X_test, y_train = ts_multiseries_data(no_features=not use_covariates) + + clf = VARMAXRegressor(use_covariates=use_covariates) + clf.fit(X=X_train if use_X_train else None, y=y_train) + + # Check we are not using exogenous variables if use_covariates=False, even if X_test is passed. + if not use_covariates or not use_X_train: + with patch.object( + clf._component_obj._fitted_forecaster, + "simulate", + ) as mock_simulate: + clf.get_prediction_intervals(X_test, None, coverages) + assert mock_simulate.call_args[1]["exog"] is None + + results_coverage = clf.get_prediction_intervals(X_test, None, coverages) + predictions = clf.predict(X_test) + + series_id_targets = list(results_coverage.keys()) + for series in series_id_targets: + series_results_coverage = results_coverage[series] + conf_ints = list(series_results_coverage.keys()) + data = list(series_results_coverage.values()) + + assert len(conf_ints) == (len(coverages) if coverages is not None else 1) * 2 + assert len(data) == (len(coverages) if coverages is not None else 1) * 2 + + for interval in coverages if coverages is not None else [0.95]: + conf_int_lower = f"{interval}_lower" + conf_int_upper = f"{interval}_upper" + + assert (series_results_coverage[conf_int_upper] > predictions[series]).all() + assert (predictions[series] > series_results_coverage[conf_int_lower]).all()