diff --git a/.github/workflows/merge.yml b/.github/workflows/merge.yml index e3dd873956..b74cd0a26f 100644 --- a/.github/workflows/merge.yml +++ b/.github/workflows/merge.yml @@ -80,7 +80,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - example-name: [00-quickstart.ipynb, 01-multi-time-series-and-covariates.ipynb, 02-data-processing.ipynb, 03-FFT-examples.ipynb, 04-RNN-examples.ipynb, 05-TCN-examples.ipynb, 06-Transformer-examples.ipynb, 07-NBEATS-examples.ipynb, 08-DeepAR-examples.ipynb, 09-DeepTCN-examples.ipynb, 10-Kalman-filter-examples.ipynb, 11-GP-filter-examples.ipynb, 12-Dynamic-Time-Warping-example.ipynb, 13-TFT-examples.ipynb, 15-static-covariates.ipynb, 16-hierarchical-reconciliation.ipynb, 18-TiDE-examples.ipynb, 19-EnsembleModel-examples.ipynb, 20-RegressionModel-examples.ipynb, 21-TSMixer-examples.ipynb, 22-anomaly-detection-examples.ipynb] + example-name: [00-quickstart.ipynb, 01-multi-time-series-and-covariates.ipynb, 02-data-processing.ipynb, 03-FFT-examples.ipynb, 04-RNN-examples.ipynb, 05-TCN-examples.ipynb, 06-Transformer-examples.ipynb, 07-NBEATS-examples.ipynb, 08-DeepAR-examples.ipynb, 09-DeepTCN-examples.ipynb, 10-Kalman-filter-examples.ipynb, 11-GP-filter-examples.ipynb, 12-Dynamic-Time-Warping-example.ipynb, 13-TFT-examples.ipynb, 15-static-covariates.ipynb, 16-hierarchical-reconciliation.ipynb, 18-TiDE-examples.ipynb, 19-EnsembleModel-examples.ipynb, 20-RegressionModel-examples.ipynb, 21-TSMixer-examples.ipynb, 22-anomaly-detection-examples.ipynb, 23-Conformal-Prediction-examples.ipynb] steps: - name: "Clone repository" uses: actions/checkout@v4 diff --git a/CHANGELOG.md b/CHANGELOG.md index ad43f4371f..92d7fb06e2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,8 +11,26 @@ but cannot always guarantee backwards compatibility. Changes that may **break co **Improved** -- Improvements to `ForecastingModel`: Improved `start` handling for historical forecasts, backtest, residuals, and gridsearch. If `start` is not within the trainable / forecastable points, uses the closest valid start point that is a round multiple of `stride` ahead of start. Raises a ValueError, if no valid start point exists. This guarantees that all historical forecasts are `n * stride` points away from start, and will simplify many downstream tasks. [#2560](https://github.com/unit8co/darts/issues/2560) by [Dennis Bader](https://github.com/dennisbader). -- Added `data_transformers` argument to `historical_forecasts`, `backtest`, `residuals`, and `gridsearch` that allow to automatically apply `DataTransformer` and/or `Pipeline` to the input series without data-leakage (fit on historic window of input series, transform the input series, and inverse transform the forecasts). [#2529](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou) and [Jan Fidor](https://github.com/JanFidor) +- 🚀🚀 Introducing Conformal Prediction to Darts: Add calibrated prediction intervals to any pre-trained global forecasting model with our first two conformal prediction models : [#2552](https://github.com/unit8co/darts/pull/2552) by [Dennis Bader](https://github.com/dennisbader). + - `ConformalNaiveModel`: It uses past point forecast errors to produce calibrated forecast intervals with a specified coverage probability. + - `ConformalQRModel`: It combines quantile regression (or any probabilistic model) with conformal prediction techniques. It adjusts quantile estimates to generate calibrated prediction intervals with a specified coverage probability. + - Both models offer the following support: + - use any pre-trained global forecasting model as the base forecaster + - uni and multivariate forecasts + - single and multiple series forecasts + - single and multi-horizon forecasts + - generate a single or multiple calibrated prediction intervals + - direct quantile value predictions (interval bounds) or sampled predictions from these quantile values + - covariates based on the underlying forecasting model + - Check out this [example notebook](https://unit8co.github.io/darts/examples/23-Conformal-Prediction-examples.html) for more information! +- Improvements to `ForecastingModel.historical_forecasts()`, `backtest()`, `residuals()`, and `gridsearch()`: + - 🚀🚀 Added support for data transformers and pipelines. Use argument `data_transformers` to automatically apply any `DataTransformer` and/or `Pipeline` to the input series without data-leakage (fit on historic window of input series, transform the input series, and inverse transform the forecasts). [#2529](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou) and [Jan Fidor](https://github.com/JanFidor) + - Improved `start` handling. If `start` is not within the trainable / forecastable points, uses the closest valid start point that is a round multiple of `stride` ahead of start. Raises a ValueError, if no valid start point exists. This guarantees that all historical forecasts are `n * stride` points away from start, and will simplify many downstream tasks. [#2560](https://github.com/unit8co/darts/issues/2560) by [Dennis Bader](https://github.com/dennisbader). + - Added support for `overlap_end=True` to `residuals()`. This computes historical forecasts and residuals that can extend further than the end of the target series. Guarantees that all returned residual values have the same length per forecast (the last residuals will contain missing values, if the forecasts extended further into the future than the end of the target series). [#2552](https://github.com/unit8co/darts/pull/2552) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to `metrics`: Added three new quantile interval metrics (plus their aggregated versions) : [#2552](https://github.com/unit8co/darts/pull/2552) by [Dennis Bader](https://github.com/dennisbader). + - Interval Winkler Score `iws()`, and Mean Interval Winkler Scores `miws()` (time-aggregated) ([source](https://otexts.com/fpp3/distaccuracy.html)) + - Interval Coverage `ic()` (binary if observation is within the quantile interval), and Mean Interval Coverage `mic()` (time-aggregated) + - Interval Non-Conformity Score for Quantile Regression `incs_qr()`, and Mean ... `mincs_qr()` (time-aggregated) ([source](https://arxiv.org/pdf/1905.03222)) - Added `series_idx` argument to `DataTransformer` that allows users to use only a subset of the transformers when `global_fit=False` and severals series are used. [#2529](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou) - Updated the Documentation URL of `Statsforecast` models. [#2610](https://github.com/unit8co/darts/pull/2610) by [He Weilin](https://github.com/cnhwl). diff --git a/README.md b/README.md index c87c8c3e35..ee5260bfc0 100644 --- a/README.md +++ b/README.md @@ -174,6 +174,9 @@ series.plot() flavours of probabilistic forecasting (such as estimating parametric distributions or quantiles). Some anomaly detection scorers are also able to exploit these predictive distributions. +* **Conformal Prediction Support:** Our conformal prediction models allow to generate probabilistic forecasts with + calibrated quantile intervals for any pre-trained global forecasting model. + * **Past and Future Covariates support:** Many models in Darts support past-observed and/or future-known covariate (external data) time series as inputs for producing forecasts. @@ -221,51 +224,54 @@ on bringing more models and features. | Model | Sources | Target Series Support:

Univariate/
Multivariate | Covariates Support:

Past-observed/
Future-known/
Static | Probabilistic Forecasting:

Sampled/
Distribution Parameters | Training & Forecasting on Multiple Series | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------------------|--------------------------------------------------------------------------|-------------------------------------------| | **Baseline Models**
([LocalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms)) | | | | | | -| [NaiveMean](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | -| [NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | -| [NaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | -| [NaiveMovingAverage](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [NaiveMean](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [NaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [NaiveMovingAverage](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | | **Statistical / Classic Models**
([LocalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms)) | | | | | | -| [ARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA) | | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | -| [VARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA) | | 🔴 ✅ | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | -| [AutoARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.auto_arima.html#darts.models.forecasting.auto_arima.AutoARIMA) | | ✅ 🔴 | 🔴 ✅ 🔴 | 🔴 🔴 | 🔴 | -| [StatsForecastAutoArima](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA) (faster AutoARIMA) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | -| [ExponentialSmoothing](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing) | | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 | -| [StatsforecastAutoETS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | -| [StatsforecastAutoCES](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | -| [BATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.BATS) and [TBATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.TBATS) | [TBATS paper](https://robjhyndman.com/papers/ComplexSeasonality.pdf) | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 | -| [Theta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta) and [FourTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta) | [Theta](https://robjhyndman.com/papers/Theta.pdf) & [4 Theta](https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R) | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | -| [StatsForecastAutoTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 | -| [Prophet](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet) | [Prophet repo](https://github.com/facebook/prophet) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | -| [FFT](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT) (Fast Fourier Transform) | | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | -| [KalmanForecaster](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster) using the Kalman filter and N4SID for system identification | [N4SID paper](https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf) | ✅ ✅ | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | -| [Croston](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.croston.html#darts.models.forecasting.croston.Croston) method | | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [ARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA) | | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [VARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA) | | 🔴 ✅ | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [AutoARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.auto_arima.html#darts.models.forecasting.auto_arima.AutoARIMA) | | ✅ 🔴 | 🔴 ✅ 🔴 | 🔴 🔴 | 🔴 | +| [StatsForecastAutoArima](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA) (faster AutoARIMA) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [ExponentialSmoothing](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing) | | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 | +| [StatsforecastAutoETS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [StatsforecastAutoCES](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [BATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.BATS) and [TBATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.TBATS) | [TBATS paper](https://robjhyndman.com/papers/ComplexSeasonality.pdf) | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 | +| [Theta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta) and [FourTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta) | [Theta](https://robjhyndman.com/papers/Theta.pdf) & [4 Theta](https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R) | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [StatsForecastAutoTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 | +| [Prophet](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet) | [Prophet repo](https://github.com/facebook/prophet) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [FFT](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT) (Fast Fourier Transform) | | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [KalmanForecaster](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster) using the Kalman filter and N4SID for system identification | [N4SID paper](https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf) | ✅ ✅ | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [Croston](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.croston.html#darts.models.forecasting.croston.Croston) method | | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | | **Global Baseline Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | | -| [GlobalNaiveAggregate](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveAggregate) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ | -| [GlobalNaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveDrift) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ | -| [GlobalNaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveSeasonal) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ | +| [GlobalNaiveAggregate](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveAggregate) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ | +| [GlobalNaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveDrift) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ | +| [GlobalNaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveSeasonal) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ | | **Regression Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | | -| [RegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel): generic wrapper around any sklearn regression model | | ✅ ✅ | ✅ ✅ ✅ | 🔴 🔴 | ✅ | -| [LinearRegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | -| [RandomForest](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest) | | ✅ ✅ | ✅ ✅ ✅ | 🔴 🔴 | ✅ | -| [LightGBMModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | -| [XGBModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | -| [CatBoostModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [RegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel): generic wrapper around any sklearn regression model | | ✅ ✅ | ✅ ✅ ✅ | 🔴 🔴 | ✅ | +| [LinearRegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [RandomForest](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest) | | ✅ ✅ | ✅ ✅ ✅ | 🔴 🔴 | ✅ | +| [LightGBMModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [XGBModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [CatBoostModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | | **PyTorch (Lightning)-based Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | | -| [RNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel) (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | [DeepAR paper](https://arxiv.org/abs/1704.04110) | ✅ ✅ | 🔴 ✅ 🔴 | ✅ ✅ | ✅ | -| [BlockRNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel) (incl. LSTM and GRU) | | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | -| [NBEATSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel) | [N-BEATS paper](https://arxiv.org/abs/1905.10437) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | -| [NHiTSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel) | [N-HiTS paper](https://arxiv.org/abs/2201.12886) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | -| [TCNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel) | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | -| [TransformerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel) | | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | -| [TFTModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel) (Temporal Fusion Transformer) | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | -| [DLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel) | [DLinear paper](https://arxiv.org/pdf/2205.13504.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | -| [NLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel) | [NLinear paper](https://arxiv.org/pdf/2205.13504.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | -| [TiDEModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel) | [TiDE paper](https://arxiv.org/pdf/2304.08424.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | -| [TSMixerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tsmixer_model.html#darts.models.forecasting.tsmixer_model.TSMixerModel) | [TSMixer paper](https://arxiv.org/pdf/2303.06053.pdf), [PyTorch Implementation](https://github.com/ditschuk/pytorch-tsmixer) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [RNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel) (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | [DeepAR paper](https://arxiv.org/abs/1704.04110) | ✅ ✅ | 🔴 ✅ 🔴 | ✅ ✅ | ✅ | +| [BlockRNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel) (incl. LSTM and GRU) | | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | +| [NBEATSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel) | [N-BEATS paper](https://arxiv.org/abs/1905.10437) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | +| [NHiTSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel) | [N-HiTS paper](https://arxiv.org/abs/2201.12886) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | +| [TCNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel) | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | +| [TransformerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel) | | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | +| [TFTModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel) (Temporal Fusion Transformer) | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [DLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel) | [DLinear paper](https://arxiv.org/pdf/2205.13504.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [NLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel) | [NLinear paper](https://arxiv.org/pdf/2205.13504.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [TiDEModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel) | [TiDE paper](https://arxiv.org/pdf/2304.08424.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [TSMixerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tsmixer_model.html#darts.models.forecasting.tsmixer_model.TSMixerModel) | [TSMixer paper](https://arxiv.org/pdf/2303.06053.pdf), [PyTorch Implementation](https://github.com/ditschuk/pytorch-tsmixer) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | | **Ensemble Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)): Model support is dependent on ensembled forecasting models and the ensemble model itself | | | | | | -| [NaiveEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | -| [RegressionEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [NaiveEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [RegressionEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| **Conformal Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)): Model support is dependent on the forecasting model used | | | | | | +| [ConformalNaiveModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalNaiveModel) | [Conformalized Prediction](https://arxiv.org/pdf/1905.03222) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [ConformalQRModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalQRModel) | [Conformalized Quantile Regression](https://arxiv.org/pdf/1905.03222) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | ## Community & Contact Anyone is welcome to join our [Gitter room](https://gitter.im/u8darts/darts) to ask questions, make proposals, diff --git a/darts/ad/anomaly_model/forecasting_am.py b/darts/ad/anomaly_model/forecasting_am.py index e90d088c7f..88ce67b9ce 100644 --- a/darts/ad/anomaly_model/forecasting_am.py +++ b/darts/ad/anomaly_model/forecasting_am.py @@ -120,10 +120,9 @@ def fit( If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise an error if the value is not in `series`' index. Default: `'value'` num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for - deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose - Whether to print progress. + Whether to print the progress. show_warnings Whether to show warnings related to historical forecasts optimization, or parameters `start` and `train_length`. @@ -201,10 +200,9 @@ def score( If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise an error if the value is not in `series`' index. Default: `'value'` num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for - deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose - Whether to print progress. + Whether to print the progress. show_warnings Whether to show warnings related to historical forecasts optimization, or parameters `start` and `train_length`. @@ -289,10 +287,9 @@ def predict_series( If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise an error if the value is not in `series`' index. Default: `'value'` num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for - deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose - Whether to print progress. + Whether to print the progress. show_warnings Whether to show warnings related to historical forecasts optimization, or parameters `start` and `train_length`. @@ -385,10 +382,9 @@ def eval_metric( If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise an error if the value is not in `series`' index. Default: `'value'` num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for - deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose - Whether to print progress. + Whether to print the progress. show_warnings Whether to show warnings related to historical forecasts optimization, or parameters `start` and `train_length`. @@ -491,10 +487,9 @@ def show_anomalies( If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise an error if the value is not in `series`' index. Default: `'value'` num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for - deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose - Whether to print progress. + Whether to print the progress. show_warnings Whether to show warnings related to historical forecasts optimization, or parameters `start` and `train_length`. diff --git a/darts/metrics/__init__.py b/darts/metrics/__init__.py index 72bc38c89a..d8b15c3c2f 100644 --- a/darts/metrics/__init__.py +++ b/darts/metrics/__init__.py @@ -6,52 +6,67 @@ and quantile forecasts. For probabilistic and quantile forecasts, use parameter `q` to define the quantile(s) to compute the deterministic metrics on: - - Aggregated over time: - Absolute metrics: - - :func:`MERR `: Mean Error - - :func:`MAE `: Mean Absolute Error - - :func:`MSE `: Mean Squared Error - - :func:`RMSE `: Root Mean Squared Error - - :func:`RMSLE `: Root Mean Squared Log Error - - Relative metrics: - - :func:`MASE `: Mean Absolute Scaled Error - - :func:`MSSE `: Mean Squared Scaled Error - - :func:`RMSSE `: Root Mean Squared Scaled Error - - :func:`MAPE `: Mean Absolute Percentage Error - - :func:`sMAPE `: symmetric Mean Absolute Percentage Error - - :func:`OPE `: Overall Percentage Error - - :func:`MARRE `: Mean Absolute Ranged Relative Error - - Other metrics: - - :func:`R2 `: Coefficient of Determination - - :func:`CV `: Coefficient of Variation - - - Per time step: - Absolute metrics: - - :func:`ERR `: Error - - :func:`AE `: Absolute Error - - :func:`SE `: Squared Error - - :func:`SLE `: Squared Log Error - - Relative metrics: - - :func:`ASE `: Absolute Scaled Error - - :func:`SSE `: Squared Scaled Error - - :func:`APE `: Absolute Percentage Error - - :func:`sAPE `: symmetric Absolute Percentage Error - - :func:`ARRE `: Absolute Ranged Relative Error - -For probabilistic forecasts (storchastic predictions with `num_samples >> 1`): - - Aggregated over time: +- Aggregated over time: + Absolute metrics: + - :func:`MERR `: Mean Error + - :func:`MAE `: Mean Absolute Error + - :func:`MSE `: Mean Squared Error + - :func:`RMSE `: Root Mean Squared Error + - :func:`RMSLE `: Root Mean Squared Log Error + + Relative metrics: + - :func:`MASE `: Mean Absolute Scaled Error + - :func:`MSSE `: Mean Squared Scaled Error + - :func:`RMSSE `: Root Mean Squared Scaled Error + - :func:`MAPE `: Mean Absolute Percentage Error + - :func:`sMAPE `: symmetric Mean Absolute Percentage Error + - :func:`OPE `: Overall Percentage Error + - :func:`MARRE `: Mean Absolute Ranged Relative Error + + Other metrics: + - :func:`R2 `: Coefficient of Determination + - :func:`CV `: Coefficient of Variation + +- Per time step: + Absolute metrics: + - :func:`ERR `: Error + - :func:`AE `: Absolute Error + - :func:`SE `: Squared Error + - :func:`SLE `: Squared Log Error + + Relative metrics: + - :func:`ASE `: Absolute Scaled Error + - :func:`SSE `: Squared Scaled Error + - :func:`APE `: Absolute Percentage Error + - :func:`sAPE `: symmetric Absolute Percentage Error + - :func:`ARRE `: Absolute Ranged Relative Error + +For probabilistic forecasts (storchastic predictions with `num_samples >> 1`) and quantile forecasts: + +- Aggregated over time: + Quantile metrics: - :func:`MQL `: Mean Quantile Loss - :func:`QR `: Quantile Risk + + Quantile interval metrics: - :func:`MIW `: Mean Interval Width - - Per time step: + - :func:`MWS `: Mean Interval Winkler Score + - :func:`MIC `: Mean Interval Coverage + - :func:`MINCS_QR `: Mean Interval Non-Conformity Score for Quantile Regression + +- Per time step: + Quantile metrics: - :func:`QL `: Quantile Loss + + Quantile interval metrics: - :func:`IW `: Interval Width + - :func:`WS `: Interval Winkler Score + - :func:`IC `: Interval Coverage + - :func:`INCS_QR `: Interval Non-Conformity Score for Quantile Regression For Dynamic Time Warping (DTW) (aggregated over time): - - :func:`DTW `: Dynamic Time Warping Metric + +- :func:`DTW `: Dynamic Time Warping Metric """ from darts.metrics.metrics import ( @@ -62,13 +77,19 @@ coefficient_of_variation, dtw_metric, err, + ic, + incs_qr, iw, + iws, mae, mape, marre, mase, merr, + mic, + mincs_qr, miw, + miws, mql, mse, msse, @@ -86,6 +107,44 @@ sse, ) +ALL_METRICS = { + ae, + ape, + arre, + ase, + coefficient_of_variation, + dtw_metric, + err, + iw, + iws, + mae, + mape, + marre, + mase, + merr, + miw, + miws, + mql, + mse, + msse, + ope, + ql, + qr, + r2_score, + rmse, + rmsle, + rmsse, + sape, + se, + sle, + smape, + sse, + ic, + mic, + incs_qr, + mincs_qr, +} + TIME_DEPENDENT_METRICS = { ae, ape, @@ -98,8 +157,23 @@ sle, sse, iw, + iws, + ic, + incs_qr, } +Q_INTERVAL_METRICS = { + iw, + iws, + miw, + miws, + ic, + mic, + incs_qr, +} + +NON_Q_METRICS = {dtw_metric} + __all__ = [ "ae", "ape", @@ -130,4 +204,10 @@ "sse", "iw", "miw", + "iws", + "miws", + "ic", + "mic", + "incs_qr", + "mincs_qr", ] diff --git a/darts/metrics/metrics.py b/darts/metrics/metrics.py index eb99ef6ab0..911c3f4f7e 100644 --- a/darts/metrics/metrics.py +++ b/darts/metrics/metrics.py @@ -216,6 +216,7 @@ def wrapper_multi_ts_support(*args, **kwargs): iterable=zip(*input_series), verbose=verbose, total=len(actual_series), + desc=f"metric `{func.__name__}()`", ) # `vals` is a list of series metrics of length `len(actual_series)`. Each metric has shape @@ -657,7 +658,7 @@ def err( """Error (ERR). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column and time step :math:`t` as: + component/column, (optional) quantile, and time step :math:`t` as: .. math:: y_t - \\hat{y}_t @@ -702,23 +703,25 @@ def err( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -748,7 +751,7 @@ def merr( """Mean Error (MERR). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column as: + component/column and (optional) quantile as: .. math:: \\frac{1}{T}\\sum_{t=1}^T{(y_t - \\hat{y}_t)} @@ -788,19 +791,22 @@ def merr( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ return np.nanmean( @@ -831,7 +837,7 @@ def ae( """Absolute Error (AE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column and time step :math:`t` as: + component/column, (optional) quantile, and time step :math:`t` as: .. math:: |y_t - \\hat{y}_t| @@ -876,23 +882,25 @@ def ae( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -922,7 +930,7 @@ def mae( """Mean Absolute Error (MAE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column as: + component/column and (optional) quantile as: .. math:: \\frac{1}{T}\\sum_{t=1}^T{|y_t - \\hat{y}_t|} @@ -962,19 +970,22 @@ def mae( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ return np.nanmean( @@ -1009,7 +1020,7 @@ def ase( It is the Absolute Error (AE) scaled by the Mean AE (MAE) of the naive m-seasonal forecast. For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column and time step :math:`t` as: + component/column, (optional) quantile, and time step :math:`t` as: .. math:: \\frac{AE(y_{t_p+1:t_p+T}, \\hat{y}_{t_p+1:t_p+T})}{E_m}, @@ -1073,23 +1084,25 @@ def ase( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. References @@ -1126,7 +1139,7 @@ def mase( It is the Mean Absolute Error (MAE) scaled by the MAE of the naive m-seasonal forecast. For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column as: + component/column and (optional) quantile as: .. math:: \\frac{MAE(y_{t_p+1:t_p+T}, \\hat{y}_{t_p+1:t_p+T})}{E_m}, @@ -1185,19 +1198,22 @@ def mase( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. References @@ -1234,7 +1250,7 @@ def se( """Squared Error (SE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column and time step :math:`t` as: + component/column, (optional) quantile, and time step :math:`t` as: .. math:: (y_t - \\hat{y}_t)^2. @@ -1279,23 +1295,25 @@ def se( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -1325,7 +1343,7 @@ def mse( """Mean Squared Error (MSE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column as: + component/column and (optional) quantile as: .. math:: \\frac{1}{T}\\sum_{t=1}^T{(y_t - \\hat{y}_t)^2}. @@ -1365,19 +1383,22 @@ def mse( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ return np.nanmean( @@ -1412,7 +1433,7 @@ def sse( It is the Squared Error (SE) scaled by the Mean SE (MSE) of the naive m-seasonal forecast. For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column and time step :math:`t` as: + component/column, (optional) quantile, and time step :math:`t` as: .. math:: \\frac{SE(y_{t_p+1:t_p+T}, \\hat{y}_{t_p+1:t_p+T})}{E_m}, @@ -1476,23 +1497,25 @@ def sse( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. References @@ -1529,7 +1552,7 @@ def msse( It is the Mean Squared Error (MSE) scaled by the MSE of the naive m-seasonal forecast. For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column as: + component/column and (optional) quantile as: .. math:: \\frac{MSE(y_{t_p+1:t_p+T}, \\hat{y}_{t_p+1:t_p+T})}{E_m}, @@ -1588,19 +1611,22 @@ def msse( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. References @@ -1636,7 +1662,7 @@ def rmse( """Root Mean Squared Error (RMSE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column as: + component/column and (optional) quantile as: .. math:: \\sqrt{\\frac{1}{T}\\sum_{t=1}^T{(y_t - \\hat{y}_t)^2}} @@ -1676,19 +1702,22 @@ def rmse( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ return np.sqrt( @@ -1721,7 +1750,7 @@ def rmsse( It is the Root Mean Squared Error (RMSE) scaled by the RMSE of the naive m-seasonal forecast. For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column as: + component/column and (optional) quantile as: .. math:: \\frac{RMSE(y_{t_p+1:t_p+T}, \\hat{y}_{t_p+1:t_p+T})}{E_m}, @@ -1780,19 +1809,22 @@ def rmsse( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. References @@ -1826,7 +1858,7 @@ def sle( """Squared Log Error (SLE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column and time step :math:`t` as: + component/column, (optional) quantile, and time step :math:`t` as: .. math:: \\left(\\log{(y_t + 1)} - \\log{(\\hat{y} + 1)}\\right)^2 @@ -1873,23 +1905,25 @@ def sle( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -1920,7 +1954,7 @@ def rmsle( """Root Mean Squared Log Error (RMSLE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column as: + component/column and (optional) quantile as: .. math:: \\sqrt{\\frac{1}{T}\\sum_{t=1}^T{\\left(\\log{(y_t + 1)} - \\log{(\\hat{y}_t + 1)}\\right)^2}} @@ -1962,19 +1996,22 @@ def rmsle( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ return np.sqrt( @@ -2060,23 +2097,25 @@ def ape( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -2113,7 +2152,7 @@ def mape( """Mean Absolute Percentage Error (MAPE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a - percentage value per component/column with: + percentage value per component/column and (optional) quantile with: .. math:: 100 \\cdot \\frac{1}{T} \\sum_{t=1}^{T}{\\left| \\frac{y_t - \\hat{y}_t}{y_t} \\right|} @@ -2161,19 +2200,22 @@ def mape( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -2205,7 +2247,7 @@ def sape( """symmetric Absolute Percentage Error (sAPE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a - percentage value per component/column and time step :math:`t` with: + percentage value per component/column, (optional) quantile and time step :math:`t` with: .. math:: 200 \\cdot \\frac{\\left| y_t - \\hat{y}_t \\right|}{\\left| y_t \\right| + \\left| \\hat{y}_t \\right|} @@ -2259,23 +2301,25 @@ def sape( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -2312,7 +2356,7 @@ def smape( """symmetric Mean Absolute Percentage Error (sMAPE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a - percentage value per component/column with: + percentage value per component/column and (optional) quantile with: .. math:: 200 \\cdot \\frac{1}{T} @@ -2363,19 +2407,22 @@ def smape( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -2406,7 +2453,7 @@ def ope( """Overall Percentage Error (OPE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a - percentage value per component/column with: + percentage value per component/column and (optional) quantile with: .. math:: 100 \\cdot \\left| \\frac{\\sum_{t=1}^{T}{y_t} - \\sum_{t=1}^{T}{\\hat{y}_t}}{\\sum_{t=1}^{T}{y_t}} \\right|. @@ -2452,19 +2499,22 @@ def ope( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -2506,7 +2556,7 @@ def arre( """Absolute Ranged Relative Error (ARRE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a - percentage value per component/column and time step :math:`t` with: + percentage value per component/column, (optional) quantile and time step :math:`t` with: .. math:: 100 \\cdot \\left| \\frac{y_t - \\hat{y}_t} {\\max_t{y_t} - \\min_t{y_t}} \\right| @@ -2556,23 +2606,25 @@ def arre( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -2612,7 +2664,7 @@ def marre( """Mean Absolute Ranged Relative Error (MARRE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a - percentage value per component/column with: + percentage value per component/column and (optional) quantile with: .. math:: 100 \\cdot \\frac{1}{T} \\sum_{t=1}^{T} {\\left| \\frac{y_t - \\hat{y}_t} {\\max_t{y_t} - \\min_t{y_t}} \\right|} @@ -2658,17 +2710,17 @@ def marre( float A single metric score for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - single multivariate series and at least `component_reduction=None`. + - a single multivariate series and at least `component_reduction=None`. - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ return np.nanmean( @@ -2698,7 +2750,7 @@ def r2_score( """Coefficient of Determination :math:`R^2` (see [1]_ for more details). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column as: + component/column and (optional) quantile as: .. math:: 1 - \\frac{\\sum_{t=1}^T{(y_t - \\hat{y}_t)^2}}{\\sum_{t=1}^T{(y_t - \\bar{y})^2}}, @@ -2742,19 +2794,22 @@ def r2_score( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. References @@ -2790,7 +2845,7 @@ def coefficient_of_variation( """Coefficient of Variation (percentage). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per - component/column as a percentage value with: + component/column and (optional) quantile as a percentage value with: .. math:: 100 \\cdot \\text{RMSE}(y_t, \\hat{y}_t) / \\bar{y}, @@ -2833,19 +2888,22 @@ def coefficient_of_variation( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -2921,19 +2979,22 @@ def dtw_metric( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ @@ -2967,7 +3028,7 @@ def qr( sample values summed up along the time axis (QL computes the quantile and loss per time step). For the true series :math:`y` and predicted stochastic/probabilistic series (containing N samples) :math:`\\hat{y}` - of of shape :math:`T \\times N`, it is computed per column/component as: + of of shape :math:`T \\times N`, it is computed per column/component and quantile as: .. math:: 2 \\frac{QL(Z, \\hat{Z}_q)}{Z}, @@ -3007,19 +3068,22 @@ def qr( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ if not pred_series.is_stochastic: @@ -3075,7 +3139,7 @@ def ql( QL computes the quantile of all sample values and the loss per time step. For the true series :math:`y` and predicted stochastic/probabilistic series (containing N samples) :math:`\\hat{y}` - of of shape :math:`T \\times N`, it is computed per column/component and time step :math:`t` as: + of of shape :math:`T \\times N`, it is computed per column/component, quantile and time step :math:`t` as: .. math:: 2 \\max((q - 1) (y_t - \\hat{y}_{t,q}), q (y_t - \\hat{y}_{t,q})), @@ -3120,23 +3184,25 @@ def ql( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ y_true, y_pred = _get_values_or_raise( @@ -3175,7 +3241,7 @@ def mql( time axis. For the true series :math:`y` and predicted stochastic/probabilistic series (containing N samples) :math:`\\hat{y}` - of of shape :math:`T \\times N`, it is computed per column/component as: + of of shape :math:`T \\times N`, it is computed per column/component and quantile as: .. math:: 2 \\frac{1}{T}\\sum_{t=1}^T{\\max((q - 1) (y_t - \\hat{y}_{t,q}), q (y_t - \\hat{y}_{t,q}))}, @@ -3215,19 +3281,22 @@ def mql( Returns ------- float - A single metric score for: + A single metric score (when `len(q) <= 1`) for: - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ return np.nanmean( @@ -3257,18 +3326,19 @@ def iw( n_jobs: int = 1, verbose: bool = False, ) -> METRIC_OUTPUT_TYPE: - """Interval Width (IL). + """Interval Width (IW). - IL gives the width of predicted quantile intervals. + IL gives the width / length of predicted quantile intervals. For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, - it is computed per component/column, quantile interval, and time step + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: - .. math:: \\hat{y}_{t,qh} - \\hat{y}_{t,ql} + .. math:: U_t - L_t, - where :math:`\\hat{y}_{t,qh}` are the upper bound quantile values (of all predicted quantiles or samples) at time - :math:`t`, and :math:`\\hat{y}_{t,ql}` are the lower bound quantile values. + where :math:`U_t` are the predicted upper bound quantile values :math:`\\hat{y}_{q_h,t}` (of all predicted + quantiles or samples) at time :math:`t`, and :math:`L_t` are the predicted lower bound quantile values + :math:`\\hat{y}_{q_l,t}`. Parameters ---------- @@ -3280,7 +3350,7 @@ def iw( For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). q_interval - The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence tuples + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples (multiple intervals) with elements (low quantile, high quantile). q Quantiles `q` not supported by this metric; use `q_interval` instead. @@ -3310,23 +3380,25 @@ def iw( Returns ------- float - A single metric score for: + A single metric score for (with `len(q_interval) <= 1`): - - single univariate series. - - single multivariate series with `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and `time_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n time steps, n components) without time - and component reductions. For: + A numpy array of metric scores. The array has shape (n time steps, n components * n q intervals) without time + and component reductions, and shape (n time steps, n q intervals) without time but component reduction and + `len(q_interval) > 1`. For: - - single multivariate series and at least `component_reduction=None`. - - single uni/multivariate series and at least `time_reduction=None`. + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. - a sequence of uni/multivariate series including `series_reduction` and at least one of `component_reduction=None` or `time_reduction=None`. - List[float] + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ y_true, y_pred = _get_values_or_raise( @@ -3355,18 +3427,19 @@ def miw( n_jobs: int = 1, verbose: bool = False, ) -> METRIC_OUTPUT_TYPE: - """Mean Interval Width (IL). + """Mean Interval Width (MIW). - IL gives the width of predicted quantile intervals aggregated over time. + MIW gives the time-aggregated width / length of predicted quantile intervals. For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, - it is computed per component/column, quantile interval, and time step + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: - .. math:: \\frac{1}{T}\\sum_{t=1}^T{\\hat{y}_{t,qh} - \\hat{y}_{t,ql}} + .. math:: \\frac{1}{T}\\sum_{t=1}^T{U_t - L_t}, - where :math:`\\hat{y}_{t,qh}` are the upper bound quantile values (of all predicted quantiles or samples) at time - :math:`t`, and :math:`\\hat{y}_{t,ql}` are the lower bound quantile values. + where :math:`U_t` are the predicted upper bound quantile values :math:`\\hat{y}_{q_h,t}` (of all predicted + quantiles or samples) at time :math:`t`, and :math:`L_t` are the predicted lower bound quantile values + :math:`\\hat{y}_{q_l,t}`. Parameters ---------- @@ -3378,7 +3451,7 @@ def miw( For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). q_interval - The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence tuples + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples (multiple intervals) with elements (low quantile, high quantile). q Quantiles `q` not supported by this metric; use `q_interval` instead. @@ -3403,19 +3476,22 @@ def miw( Returns ------- float - A single metric score for: + A single metric score for (with `len(q_interval) <= 1`): - - single univariate series. - - single multivariate series with `component_reduction`. - - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. np.ndarray - A numpy array of metric scores. The array has shape (n components,) without component reduction. For: - - - single multivariate series and at least `component_reduction=None`. - - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. - List[float] + A numpy array of metric scores. The array has shape (n components * n q intervals,) without component reduction, + and shape (n q intervals,) with component reduction and `len(q_interval) > 1`. + For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] Same as for type `float` but for a sequence of series. - List[np.ndarray] + list[np.ndarray] Same as for type `np.ndarray` but for a sequence of series. """ return np.nanmean( @@ -3428,3 +3504,633 @@ def miw( ), axis=TIME_AX, ) + + +@interval_support +@multi_ts_support +@multivariate_support +def iws( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Interval Winkler Score (IWS) [1]_. + + IWS gives the length / width of the quantile intervals plus a penalty if the observation is outside the interval. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: + \\begin{equation} + \\begin{cases} + (U_t - L_t) + \\frac{1}{q_l} (L_t - y_t) & \\text{if } y_t < L_t \\\\ + (U_t - L_t) & \\text{if } L_t \\leq y_t \\leq U_t \\\\ + (U_t - L_t) + \\frac{1}{1 - q_h} (y_t - U_t) & \\text{if } y_t > U_t + \\end{cases} + \\end{equation} + + where :math:`U_t` are the predicted upper bound quantile values :math:`\\hat{y}_{q_h,t}` (of all predicted + quantiles or samples) at time :math:`t`, and :math:`L_t` are the predicted lower bound quantile values + :math:`\\hat{y}_{q_l,t}`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n q intervals) without time + and component reductions, and shape (n time steps, n q intervals) without time but component reduction and + `len(q_interval) > 1`. For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://otexts.com/fpp3/distaccuracy.html + """ + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + y_pred_lo, y_pred_hi = _get_quantile_intervals(y_pred, q=q, q_interval=q_interval) + interval_width = y_pred_hi - y_pred_lo + + # `c_alpha = 2 / alpha` corresponds to: + # - `1 / (1 - q_hi)` for the high quantile + # - `1 / q_lo` for the low quantile + c_alpha_hi = 1 / (1 - q_interval[:, 1]) + c_alpha_lo = 1 / q_interval[:, 0] + + score = np.where( + y_true < y_pred_lo, + interval_width + c_alpha_lo * (y_pred_lo - y_true), + np.where( + y_true > y_pred_hi, + interval_width + c_alpha_hi * (y_true - y_pred_hi), + interval_width, + ), + ) + return score + + +@interval_support +@multi_ts_support +@multivariate_support +def miws( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Interval Winkler Score (IWS) [1]_. + + MIWS gives the time-aggregated length / width of the quantile intervals plus a penalty if the observation is + outside the interval. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: \\frac{1}{T}\\sum_{t=1}^T{W_t(y_t, \\hat{y}_{t}, q_h, q_l)}, + + where :math:`W` is the Winkler Score :func:`~darts.metrics.metrics.iws`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n q intervals,) without component reduction, + and shape (n q intervals,) with component reduction and `len(q_interval) > 1`. + For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://otexts.com/fpp3/distaccuracy.html + """ + return np.nanmean( + _get_wrapped_metric(iws, n_wrappers=3)( + actual_series, + pred_series, + intersect, + q=q, + q_interval=q_interval, + ), + axis=TIME_AX, + ) + + +@interval_support +@multi_ts_support +@multivariate_support +def ic( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Interval Coverage (IC). + + IC gives a binary outcome with `1` if the observation is within the interval, and `0` otherwise. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: + \\begin{equation} + \\begin{cases} + 1 & \\text{if } L_t < y_t < U_t \\\\ + 0 & \\text{otherwise} + \\end{cases} + \\end{equation} + + where :math:`U_t` are the predicted upper bound quantile values :math:`\\hat{y}_{q_h,t}` (of all predicted + quantiles or samples) at time :math:`t`, and :math:`L_t` are the predicted lower bound quantile values + :math:`\\hat{y}_{q_l,t}`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n q intervals) without time + and component reductions, and shape (n time steps, n q intervals) without time but component reduction and + `len(q_interval) > 1`. For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + y_pred_lo, y_pred_hi = _get_quantile_intervals(y_pred, q=q, q_interval=q_interval) + return np.where((y_pred_lo <= y_true) & (y_true <= y_pred_hi), 1.0, 0.0) + + +@interval_support +@multi_ts_support +@multivariate_support +def mic( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Interval Coverage (MIC). + + MIC gives the time-aggregated Interval Coverage :func:`~darts.metrics.metrics.ic` - the ratio of observations + being within the interval. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: \\frac{1}{T}\\sum_{t=1}^T{C(y_t, \\hat{y}_{t}, q_h, q_l)}, + + where :math:`C` is the Interval Coverage :func:`~darts.metrics.metrics.ic`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n q intervals,) without component reduction, + and shape (n q intervals,) with component reduction and `len(q_interval) > 1`. + For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + return np.nanmean( + _get_wrapped_metric(ic, n_wrappers=3)( + actual_series, + pred_series, + intersect, + q=q, + q_interval=q_interval, + ), + axis=TIME_AX, + ) + + +@interval_support +@multi_ts_support +@multivariate_support +def incs_qr( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + symmetric: bool = True, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Interval Non-Conformity Score for Quantile Regression (INCS_QR). + + INCS_QR gives the absolute error to the closest predicted quantile interval bound when the observation is outside + the interval. Otherwise, it gives the negative absolute error to the closer bound. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: \\max(L_t - y_t, y_t - U_t) + + where :math:`U_t` are the predicted upper bound quantile values :math:`\\hat{y}_{q_h,t}` (of all predicted + quantiles or samples) at time :math:`t`, and :math:`L_t` are the predicted lower bound quantile values + :math:`\\hat{y}_{q_l,t}`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + symmetric + Whether to return symmetric non-conformity scores. If `False`, returns asymmetric scores (individual scores + for lower- and upper quantile interval bounds; returned in the component axis). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n q intervals) without time + and component reductions, and shape (n time steps, n q intervals) without time but component reduction and + `len(q_interval) > 1`. For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + y_pred_lo, y_pred_hi = _get_quantile_intervals(y_pred, q=q, q_interval=q_interval) + if symmetric: + return np.maximum(y_pred_lo - y_true, y_true - y_pred_hi) + else: + return np.concatenate([y_pred_lo - y_true, y_true - y_pred_hi], axis=SMPL_AX) + + +@interval_support +@multi_ts_support +@multivariate_support +def mincs_qr( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + symmetric: bool = True, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Interval Non-Conformity Score for Quantile Regression (MINCS_QR). + + MINCS_QR gives the time-aggregated INCS_QR :func:`~darts.metrics.metrics.incs_qr`. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: \\frac{1}{T}\\sum_{t=1}^T{INCS_QR(y_t, \\hat{y}_{t}, q_h, q_l)}, + + where :math:`INCS_QR` is the Interval Non-Conformity Score for Quantile Regression + :func:`~darts.metrics.metrics.incs_qr`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + symmetric + Whether to return symmetric non-conformity scores. If `False`, returns asymmetric scores (individual scores + for lower- and upper quantile interval bounds; returned in the component axis). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n q intervals,) without component reduction, + and shape (n q intervals,) with component reduction and `len(q_interval) > 1`. + For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + return np.nanmean( + _get_wrapped_metric(incs_qr, n_wrappers=3)( + actual_series, + pred_series, + intersect, + q=q, + q_interval=q_interval, + symmetric=symmetric, + ), + axis=TIME_AX, + ) diff --git a/darts/models/__init__.py b/darts/models/__init__.py index 17640b195d..1ea802be3a 100644 --- a/darts/models/__init__.py +++ b/darts/models/__init__.py @@ -20,10 +20,16 @@ from darts.models.forecasting.auto_arima import AutoARIMA from darts.models.forecasting.baselines import ( NaiveDrift, + NaiveEnsembleModel, NaiveMean, NaiveMovingAverage, NaiveSeasonal, ) +from darts.models.forecasting.conformal_models import ( + ConformalNaiveModel, + ConformalQRModel, +) +from darts.models.forecasting.ensemble_model import EnsembleModel from darts.models.forecasting.exponential_smoothing import ExponentialSmoothing from darts.models.forecasting.fft import FFT from darts.models.forecasting.kalman_forecaster import KalmanForecaster @@ -108,15 +114,10 @@ except ImportError: XGBModel = NotImportedModule(module_name="XGBoost") +# Filtering from darts.models.filtering.gaussian_process_filter import GaussianProcessFilter from darts.models.filtering.kalman_filter import KalmanFilter - -# Filtering from darts.models.filtering.moving_average_filter import MovingAverageFilter -from darts.models.forecasting.baselines import NaiveEnsembleModel - -# Ensembling -from darts.models.forecasting.ensemble_model import EnsembleModel __all__ = [ "LightGBMModel", @@ -140,7 +141,7 @@ "VARIMA", "BlockRNNModel", "DLinearModel", - "GlobalNaiveDrift", + "GlobalNaiveAggregate", "GlobalNaiveDrift", "GlobalNaiveSeasonal", "NBEATSModel", @@ -165,4 +166,6 @@ "MovingAverageFilter", "NaiveEnsembleModel", "EnsembleModel", + "ConformalNaiveModel", + "ConformalQRModel", ] diff --git a/darts/models/forecasting/__init__.py b/darts/models/forecasting/__init__.py index 37a50aa4bc..b3559f9b62 100644 --- a/darts/models/forecasting/__init__.py +++ b/darts/models/forecasting/__init__.py @@ -50,4 +50,7 @@ Ensemble Models (`GlobalForecastingModel `_) - :class:`~darts.models.forecasting.baselines.NaiveEnsembleModel` - :class:`~darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel` +Conformal Models (`GlobalForecastingModel `_) + - :class:`~darts.models.forecasting.conformal_models.ConformalNaiveModel` + - :class:`~darts.models.forecasting.conformal_models.ConformalQRModel` """ diff --git a/darts/models/forecasting/conformal_models.py b/darts/models/forecasting/conformal_models.py new file mode 100644 index 0000000000..a6bc1ba409 --- /dev/null +++ b/darts/models/forecasting/conformal_models.py @@ -0,0 +1,1862 @@ +""" +Conformal Models +--------------- + +A collection of conformal prediction models for pre-trained global forecasting models. +""" + +import copy +import math +import os +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import Any, BinaryIO, Callable, Optional, Union + +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal + +import numpy as np +import pandas as pd + +from darts import TimeSeries, metrics +from darts.dataprocessing.pipeline import Pipeline +from darts.dataprocessing.transformers import BaseDataTransformer +from darts.logging import get_logger, raise_log +from darts.metrics.metrics import METRIC_TYPE +from darts.models.forecasting.forecasting_model import GlobalForecastingModel +from darts.models.utils import TORCH_AVAILABLE +from darts.utils import _build_tqdm_iterator, _with_sanity_checks +from darts.utils.historical_forecasts.utils import ( + _adjust_historical_forecasts_time_index, +) +from darts.utils.timeseries_generation import _build_forecast_series +from darts.utils.ts_utils import ( + SeriesType, + get_series_seq_type, + series2seq, +) +from darts.utils.utils import ( + _check_quantiles, + generate_index, + likelihood_component_names, + n_steps_between, + quantile_names, + random_method, + sample_from_quantiles, +) + +if TORCH_AVAILABLE: + from darts.models.forecasting.torch_forecasting_model import TorchForecastingModel +else: + TorchForecastingModel = None + +logger = get_logger(__name__) + + +class ConformalModel(GlobalForecastingModel, ABC): + @random_method + def __init__( + self, + model: GlobalForecastingModel, + quantiles: list[float], + symmetric: bool = True, + cal_length: Optional[int] = None, + cal_stride: int = 1, + cal_num_samples: int = 500, + random_state: Optional[int] = None, + ): + """Base Conformal Prediction Model. + + Base class for any conformal prediction model. A conformal model calibrates the predictions from any + pre-trained global forecasting model. It does not have to be trained, and can generate calibrated forecasts + directly using the underlying trained forecasting model. Since it is a probabilistic model, you can generate + forecasts in two ways (when calling `predict()`, `historical_forecasts()`, ...): + + - Predict the calibrated quantile intervals directly: Pass parameters `predict_likelihood_parameters=True`, and + `num_samples=1` to the forecast method. + - Predict stochastic samples from the calibrated quantile intervals: Pass parameters + `predict_likelihood_parameters=False`, and `num_samples>>1` to the forecast method. + + Conformal models can be applied to any of Darts' global forecasting model, as long as the model has been + fitted before. In general the workflow of the models to produce one calibrated forecast/prediction is as + follows: + + - Extract a calibration set: The calibration set for each conformal forecast is automatically extracted from + the most recent past of your input series relative to the forecast start point. The number of calibration + examples (forecast errors / non-conformity scores) to consider can be defined at model creation with + parameter `cal_length`. Note that when using `cal_stride>1`, a longer history is required since the + calibration examples are generated with stridden historical forecasts. + - Generate historical forecasts on the calibration set (using the forecasting model) with a stride `cal_stride`. + - Compute the errors/non-conformity scores (specific to each conformal model) on these historical forecasts + - Compute the quantile values from the errors / non-conformity scores (using our desired quantiles set at model + creation with parameter `quantiles`). + - Compute the conformal prediction: Using these quantile values, add calibrated intervals to (or adjust the + existing intervals of) the forecasting model's predictions. + + Some notes: + + - When computing `historical_forecasts()`, `backtest()`, `residuals()`, ... the above is applied for each + forecast (the forecasting model's historical forecasts are only generated once for efficiency). + - For multi-horizon forecasts, the above is applied for each step in the horizon separately. + + Parameters + ---------- + model + A pre-trained global forecasting model. See the list of models + `here `_. + quantiles + A list of quantiles centered around the median `q=0.5` to use. For example quantiles + [0.1, 0.2, 0.5, 0.8 0.9] correspond to two intervals with (0.9 - 0.1) = 80%, and (0.8 - 0.2) 60% coverage + around the median (model forecast). + symmetric + Whether to use symmetric non-conformity scores. If `False`, uses asymmetric scores (individual scores + for lower- and upper quantile interval bounds). + cal_length + The number of past forecast errors / non-conformity scores to use as calibration for each conformal + forecast (and each step in the horizon). If `None`, considers all scores. + cal_stride + The stride to apply when computing the historical forecasts and non-conformity scores on the calibration + set. The actual conformal forecasts can have a different stride given with parameter `stride` in downstream + tasks (e.g. historical forecasts, backtest, ...) + cal_num_samples + The number of samples to generate for each calibration forecast (if `model` is a probabilistic forecasting + model). The non-conformity scores are computed on the quantile values of these forecasts (using quantiles + `quantiles`). Uses `1` for deterministic models. The actual conformal forecasts can have a different number + of samples given with parameter `num_samples` in downstream tasks (e.g. predict, historical forecasts, ...). + random_state + Control the randomness of probabilistic conformal forecasts (sample generation) across different runs. + """ + if not isinstance(model, GlobalForecastingModel) or not model._fit_called: + raise_log( + ValueError("`model` must be a pre-trained `GlobalForecastingModel`."), + logger=logger, + ) + _check_quantiles(quantiles) + + if cal_length is not None and cal_length < 1: + raise_log( + ValueError("`cal_length` must be `>=1` or `None`."), logger=logger + ) + if cal_stride < 1: + raise_log(ValueError("`cal_stride` must be `>=1`."), logger=logger) + if cal_num_samples < 1: + raise_log(ValueError("`cal_num_samples` must be `>=1`."), logger=logger) + + super().__init__(add_encoders=None) + + # quantiles and interval setup + self.quantiles = np.array(quantiles) + self.idx_median = quantiles.index(0.5) + self.q_interval = [ + (q_l, q_h) + for q_l, q_h in zip( + quantiles[: self.idx_median], quantiles[self.idx_median + 1 :][::-1] + ) + ] + self.interval_range = np.array([ + q_high - q_low for q_low, q_high in self.q_interval + ]) + + if symmetric: + # symmetric considers both tails together + self.interval_range_sym = copy.deepcopy(self.interval_range) + else: + # asymmetric considers tails separately + self.interval_range_sym = 1 - (1 - self.interval_range) / 2 + self.symmetric = symmetric + + # model setup + self.model = model + self.cal_length = cal_length + self.cal_stride = cal_stride + self.cal_num_samples = ( + cal_num_samples if model.supports_probabilistic_prediction else 1 + ) + self._likelihood = "quantile" + self._fit_called = True + + def fit( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + **kwargs, + ) -> "ConformalModel": + """Fit/train the underlying forecasting model on (potentially multiple) series. + + Optionally, one or multiple past and/or future covariates series can be provided as well, depending on the + forecasting model used. The number of covariates series must match the number of target series. + + Notes + ----- + Conformal Models do not require calling `fit()`, since they use pre-trained global forecasting models. + You can call `predict()` directly. Also, make sure that the input series used in `predict()` corresponds to + a calibration set, and not the same as used during training with `fit()`. + + Parameters + ---------- + series + One or several target time series. The model will be trained to forecast these time series. + The series may or may not be multivariate, but if multiple series are provided + they must have the same number of components. + past_covariates + One or several past-observed covariate time series. These time series will not be forecast, but can + be used by some models as an input. The covariate(s) may or may not be multivariate, but if multiple + covariates are provided they must have the same number of components. If `past_covariates` is provided, + it must contain the same number of series as `series`. + future_covariates + One or several future-known covariate time series. These time series will not be forecast, but can + be used by some models as an input. The covariate(s) may or may not be multivariate, but if multiple + covariates are provided they must have the same number of components. If `future_covariates` is provided, + it must contain the same number of series as `series`. + **kwargs + Optional keyword arguments that will passed to the underlying forecasting model's `fit()` method. + + Returns + ------- + self + Fitted model. + """ + # does not have to be trained, but we allow it for unified API + self.model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + **kwargs, + ) + return self + + def predict( + self, + n: int, + series: Union[TimeSeries, Sequence[TimeSeries]] = None, + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + num_samples: int = 1, + verbose: bool = False, + predict_likelihood_parameters: bool = False, + show_warnings: bool = True, + **kwargs, + ) -> Union[TimeSeries, Sequence[TimeSeries]]: + """Forecasts calibrated quantile intervals (or samples from calibrated intervals) for `n` time steps after the + end of the `series`. + + It is important that the input series for prediction correspond to a calibration set - a set different to the + series that the underlying forecasting `model` was trained on. + + Since it is a probabilistic model, you can generate forecasts in two ways: + + - Predict the calibrated quantile intervals directly: Pass parameters `predict_likelihood_parameters=True`, and + `num_samples=1` to the forecast method. + - Predict stochastic samples from the calibrated quantile intervals: Pass parameters + `predict_likelihood_parameters=False`, and `num_samples>>1` to the forecast method. + + Under the hood, the simplified workflow to produce one calibrated forecast/prediction for every step in the + horizon `n` is as follows (note: `cal_length` and `cal_stride` can be set at model creation): + + - Extract a calibration set: The calibration set for each conformal forecast is automatically extracted from + the most recent past of your input series relative to the forecast start point. The number of calibration + examples (forecast errors / non-conformity scores) to consider can be defined at model creation + with parameter `cal_length`. Note that when using `cal_stride>1`, a longer history is required since + the calibration examples are generated with stridden historical forecasts. + - Generate historical forecasts on the calibration set (using the forecasting model) with a stride `cal_stride`. + - Compute the errors/non-conformity scores (specific to each conformal model) on these historical forecasts + - Compute the quantile values from the errors / non-conformity scores (using our desired quantiles set at model + creation with parameter `quantiles`). + - Compute the conformal prediction: Using these quantile values, add calibrated intervals to (or adjust the + existing intervals of) the forecasting model's predictions. + + Parameters + ---------- + n + Forecast horizon - the number of time steps after the end of the series for which to produce predictions. + series + A series or sequence of series, representing the history of the target series whose future is to be + predicted. Will use the past of this series for calibration. The series should not have any overlap with + the series used to train the forecasting model. + past_covariates + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + future_covariates + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + num_samples + Number of times a prediction is sampled from the calibrated quantile predictions using linear + interpolation in-between the quantiles. For larger values, the sample distribution approximates the + calibrated quantile predictions. + verbose + Whether to print the progress. + predict_likelihood_parameters + If set to `True`, generates the quantile predictions directly. Only supported with `num_samples = 1`. + show_warnings + Whether to show warnings related auto-regression and past covariates usage. + **kwargs + Optional keyword arguments that will passed to the underlying forecasting model's `predict()` and + `historical_forecasts()` methods. + + Returns + ------- + Union[TimeSeries, Sequence[TimeSeries]] + If `series` is not specified, this function returns a single time series containing the `n` + next points after then end of the training series. + If `series` is given and is a simple ``TimeSeries``, this function returns the `n` next points + after the end of `series`. + If `series` is given and is a sequence of several time series, this function returns + a sequence where each element contains the corresponding `n` points forecasts. + """ + if series is None: + # then there must be a single TS, and that was saved in super().fit as self.training_series + if self.model.training_series is None: + raise_log( + ValueError( + "Input `series` must be provided. This is the result either from fitting on multiple series, " + "or from not having fit the model yet." + ), + logger, + ) + series = self.model.training_series + + called_with_single_series = get_series_seq_type(series) == SeriesType.SINGLE + + # guarantee that all inputs are either list of TimeSeries or None + series = series2seq(series) + if past_covariates is None and self.model.past_covariate_series is not None: + past_covariates = [self.model.past_covariate_series] * len(series) + if future_covariates is None and self.model.future_covariate_series is not None: + future_covariates = [self.model.future_covariate_series] * len(series) + past_covariates = series2seq(past_covariates) + future_covariates = series2seq(future_covariates) + + super().predict( + n, + series, + past_covariates, + future_covariates, + num_samples, + verbose, + predict_likelihood_parameters, + show_warnings, + ) + + # call predict to verify that all series have required input times + _ = self.model.predict( + n=n, + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + num_samples=self.cal_num_samples, + verbose=verbose, + predict_likelihood_parameters=False, + show_warnings=show_warnings, + **kwargs, + ) + + # generate only the required forecasts for calibration (including the last forecast which is the output of + # `predict()`) + cal_start, cal_start_format = _get_calibration_hfc_start( + series=series, + horizon=n, + output_chunk_shift=self.output_chunk_shift, + cal_length=self.cal_length, + cal_stride=self.cal_stride, + start="end", + start_format="position", + ) + + cal_hfcs = self.model.historical_forecasts( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=n, + num_samples=self.cal_num_samples, + start=cal_start, + start_format=cal_start_format, + stride=self.cal_stride, + retrain=False, + overlap_end=True, + last_points_only=False, + verbose=verbose, + show_warnings=False, + predict_likelihood_parameters=False, + predict_kwargs=kwargs, + ) + cal_preds = self._calibrate_forecasts( + series=series, + forecasts=cal_hfcs, + num_samples=num_samples, + start="end", # uses last hist fc (output of `predict()`) + start_format="position", + forecast_horizon=n, + stride=self.cal_stride, + overlap_end=True, + last_points_only=False, + verbose=verbose, + show_warnings=show_warnings, + predict_likelihood_parameters=predict_likelihood_parameters, + ) + # convert historical forecasts output to simple forecast / prediction + if called_with_single_series: + return cal_preds[0][0] + else: + return [cp[0] for cp in cal_preds] + + @_with_sanity_checks("_historical_forecasts_sanity_checks") + def historical_forecasts( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + forecast_horizon: int = 1, + num_samples: int = 1, + train_length: Optional[int] = None, + start: Optional[Union[pd.Timestamp, int]] = None, + start_format: Literal["position", "value"] = "value", + stride: int = 1, + retrain: Union[bool, int, Callable[..., bool]] = True, + overlap_end: bool = False, + last_points_only: bool = True, + verbose: bool = False, + show_warnings: bool = True, + predict_likelihood_parameters: bool = False, + enable_optimization: bool = True, + data_transformers: Optional[ + dict[str, Union[BaseDataTransformer, Pipeline]] + ] = None, + fit_kwargs: Optional[dict[str, Any]] = None, + predict_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: + """Generates calibrated historical forecasts by simulating predictions at various points in time throughout the + history of the provided (potentially multiple) `series`. This process involves retrospectively applying the + model to different time steps, as if the forecasts were made in real-time at those specific moments. This + allows for an evaluation of the model's performance over the entire duration of the series, providing insights + into its predictive accuracy and robustness across different historical periods. + + Currently, conformal models only support the pre-trained historical forecasts mode (`retrain=False`). + Parameters `retrain` and `train_length` are ignored. + + **Pre-trained Mode:** First, all historical forecasts are generated using the underlying pre-trained global + forecasting model (see :meth:`ForecastingModel.historical_forecasts() + ` for more info). Then it + repeatedly builds a calibration set by either expanding from the beginning of the historical forecasts or by + using a fixed-length moving window with length `cal_length` (the start point can also be configured with + `start` and `start_format`). + The next forecast of length `forecast_horizon` is then calibrated on this calibration set. Subsequently, the + end of the calibration set is moved forward by `stride` time steps, and the process is repeated. + + By default, with `last_points_only=True`, this method returns a single time series (or a sequence of time + series when `series` is also a sequence of series) composed of the last point from each calibrated historical + forecast. This time series will thus have a frequency of `series.freq * stride`. + If `last_points_only=False`, it will instead return a list (or a sequence of lists) with all calibrated + historical forecasts of length `forecast_horizon` and frequency `series.freq`. + + Parameters + ---------- + series + A (sequence of) target time series used to successively compute the historical forecasts. Will use the past + of this series for calibration. The series should not have any overlap with the series used to train the + forecasting model. + past_covariates + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + future_covariates + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + forecast_horizon + The forecast horizon for the predictions. + num_samples + Number of times a prediction is sampled from the calibrated quantile predictions using linear + interpolation in-between the quantiles. For larger values, the sample distribution approximates the + calibrated quantile predictions. + train_length + Currently ignored by conformal models. + start + Optionally, the first point in time at which a prediction is computed. This parameter supports: + ``int``, ``pandas.Timestamp``, and ``None``. + If an ``int``, it is either the index position of the first prediction point for `series` with a + `pd.DatetimeIndex`, or the index value for `series` with a `pd.RangeIndex`. The latter can be changed to + the index position with `start_format="position"`. + If a ``pandas.Timestamp``, it is the time stamp of the first prediction point. + If ``None``, the first prediction point will automatically be set to: + + - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first + predictable point is earlier than the first trainable point. + - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. + - the first trainable point (given `train_length`) otherwise + + Note: If the model uses a shifted output (`output_chunk_shift > 0`), then the first predicted point is also + shifted by `output_chunk_shift` points into the future. + Note: Raises a ValueError if `start` yields a time outside the time index of `series`. + Note: If `start` is outside the possible historical forecasting times, will ignore the parameter + (default behavior with ``None``) and start at the first trainable/predictable point. + start_format + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. + stride + The number of time steps between two consecutive predictions. Must be a round-multiple of `cal_stride` + (set at model creation) and `>=cal_stride`. + retrain + Currently ignored by conformal models. + overlap_end + Whether the returned forecasts can go beyond the series' end or not. + last_points_only + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. + Otherwise, returns a list of historical ``TimeSeries`` forecasts. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + predict_likelihood_parameters + If set to `True`, generates the quantile predictions directly. Only supported with `num_samples = 1`. + enable_optimization + Whether to use the optimized version of `historical_forecasts` when supported and available. + Default: ``True``. + data_transformers + Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series + (possibles keys; "series", "past_covariates", "future_covariates"). If provided, all input series must be + in the un-transformed space. For fittable transformer / pipeline: + + - if `retrain=True`, the data transformer re-fit on the training data at each historical forecast step + (currently ignored by conformal models). + - if `retrain=False`, the data transformer transforms the series once before all the forecasts. + + The fitted transformer is used to transform the input during both training and prediction. + If the transformation is invertible, the forecasts will be inverse-transformed. + fit_kwargs + Currently ignored by conformal models. + predict_kwargs + Optionally, some additional arguments passed to the model `predict()` method. + sample_weight + Currently ignored by conformal models. + + Returns + ------- + TimeSeries + A single historical forecast for a single `series` and `last_points_only=True`: it contains only the + predictions at step `forecast_horizon` from all historical forecasts. + list[TimeSeries] + A list of historical forecasts for: + + - a sequence (list) of `series` and `last_points_only=True`: for each series, it contains only the + predictions at step `forecast_horizon` from all historical forecasts. + - a single `series` and `last_points_only=False`: for each historical forecast, it contains the entire + horizon `forecast_horizon`. + list[list[TimeSeries]] + A list of lists of historical forecasts for a sequence of `series` and `last_points_only=False`. For each + series, and historical forecast, it contains the entire horizon `forecast_horizon`. The outer list + is over the series provided in the input sequence, and the inner lists contain the historical forecasts for + each series. + """ + called_with_single_series = get_series_seq_type(series) == SeriesType.SINGLE + series = series2seq(series) + past_covariates = series2seq(past_covariates) + future_covariates = series2seq(future_covariates) + + # generate only the required forecasts (if `start` is given, we have to start earlier to satisfy the + # calibration set requirements) + cal_start, cal_start_format = _get_calibration_hfc_start( + series=series, + horizon=forecast_horizon, + output_chunk_shift=self.output_chunk_shift, + cal_length=self.cal_length, + cal_stride=self.cal_stride, + start=start, + start_format=start_format, + ) + hfcs = self.model.historical_forecasts( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=forecast_horizon, + num_samples=self.cal_num_samples, + start=cal_start, + start_format=cal_start_format, + stride=self.cal_stride, + retrain=False, + overlap_end=overlap_end, + last_points_only=last_points_only, + verbose=verbose, + show_warnings=False, + predict_likelihood_parameters=False, + enable_optimization=enable_optimization, + data_transformers=data_transformers, + fit_kwargs=fit_kwargs, + predict_kwargs=predict_kwargs, + ) + calibrated_forecasts = self._calibrate_forecasts( + series=series, + forecasts=hfcs, + num_samples=num_samples, + start=start, + start_format=start_format, + forecast_horizon=forecast_horizon, + stride=stride, + overlap_end=overlap_end, + last_points_only=last_points_only, + verbose=verbose, + show_warnings=show_warnings, + predict_likelihood_parameters=predict_likelihood_parameters, + ) + return ( + calibrated_forecasts[0] + if called_with_single_series + else calibrated_forecasts + ) + + def backtest( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + historical_forecasts: Optional[ + Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] + ] = None, + forecast_horizon: int = 1, + num_samples: int = 1, + train_length: Optional[int] = None, + start: Optional[Union[pd.Timestamp, int]] = None, + start_format: Literal["position", "value"] = "value", + stride: int = 1, + retrain: Union[bool, int, Callable[..., bool]] = True, + overlap_end: bool = False, + last_points_only: bool = False, + metric: Union[METRIC_TYPE, list[METRIC_TYPE]] = metrics.mape, + reduction: Union[Callable[..., float], None] = np.mean, + verbose: bool = False, + show_warnings: bool = True, + predict_likelihood_parameters: bool = False, + enable_optimization: bool = True, + data_transformers: Optional[ + dict[str, Union[BaseDataTransformer, Pipeline]] + ] = None, + metric_kwargs: Optional[Union[dict[str, Any], list[dict[str, Any]]]] = None, + fit_kwargs: Optional[dict[str, Any]] = None, + predict_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + ) -> Union[float, np.ndarray, list[float], list[np.ndarray]]: + """Compute error values that the model produced for historical forecasts on (potentially multiple) `series`. + + If `historical_forecasts` are provided, the metric(s) (given by the `metric` function) is evaluated directly on + all forecasts and actual values. The same `series` and `last_points_only` value must be passed that were used + to generate the historical forecasts. Finally, the method returns an optional `reduction` (the mean by default) + of all these metric scores. + + If `historical_forecasts` is ``None``, it first generates the historical forecasts with the parameters given + below (see :meth:`ConformalModel.historical_forecasts() + ` for more info) and then + evaluates as described above. + + The metric(s) can be further customized `metric_kwargs` (e.g. control the aggregation over components, time + steps, multiple series, other required arguments such as `q` for quantile metrics, ...). + + Notes + ----- + Darts has several metrics to evaluate probabilistic forecasts. For conformal models, we recommend using + quantile interval metrics (see `here `_). + You can specify which intervals to evaluate by setting `metric_kwargs={'q_interval': my_intervals}`. To check + all intervals used by your conformal model `my_model`, you can set ``{'q_interval': my_model.q_interval}``. + + Parameters + ---------- + series + A (sequence of) target time series used to successively compute the historical forecasts. Will use the past + of this series for calibration. The series should not have any overlap with the series used to train the + forecasting model. + past_covariates + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + future_covariates + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + historical_forecasts + Optionally, the (or a sequence of / a sequence of sequences of) historical forecasts time series to be + evaluated. Corresponds to the output of :meth:`historical_forecasts() + `. The same `series` and + `last_points_only` values must be passed that were used to generate the historical forecasts. If provided, + will skip historical forecasting and ignore all parameters except `series`, `last_points_only`, `metric`, + and `reduction`. + forecast_horizon + The forecast horizon for the predictions. + num_samples + Number of times a prediction is sampled from the calibrated quantile predictions using linear + interpolation in-between the quantiles. For larger values, the sample distribution approximates the + calibrated quantile predictions. + train_length + Currently ignored by conformal models. + start + Optionally, the first point in time at which a prediction is computed. This parameter supports: + ``int``, ``pandas.Timestamp``, and ``None``. + If an ``int``, it is either the index position of the first prediction point for `series` with a + `pd.DatetimeIndex`, or the index value for `series` with a `pd.RangeIndex`. The latter can be changed to + the index position with `start_format="position"`. + If a ``pandas.Timestamp``, it is the time stamp of the first prediction point. + If ``None``, the first prediction point will automatically be set to: + + - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first + predictable point is earlier than the first trainable point. + - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. + - the first trainable point (given `train_length`) otherwise + + Note: If the model uses a shifted output (`output_chunk_shift > 0`), then the first predicted point is also + shifted by `output_chunk_shift` points into the future. + Note: Raises a ValueError if `start` yields a time outside the time index of `series`. + Note: If `start` is outside the possible historical forecasting times, will ignore the parameter + (default behavior with ``None``) and start at the first trainable/predictable point. + start_format + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. + stride + The number of time steps between two consecutive predictions. + retrain + Currently ignored by conformal models. + overlap_end + Whether the returned forecasts can go beyond the series' end or not. + last_points_only + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. + Otherwise, returns a list of historical ``TimeSeries`` forecasts. + metric + A metric function or a list of metric functions. Each metric must either be a Darts metric (see `here + `_), or a custom metric that has an + identical signature as Darts' metrics, uses decorators :func:`~darts.metrics.metrics.multi_ts_support` and + :func:`~darts.metrics.metrics.multi_ts_support`, and returns the metric score. + reduction + A function used to combine the individual error scores obtained when `last_points_only` is set to `False`. + When providing several metric functions, the function will receive the argument `axis = 1` to obtain single + value for each metric function. + If explicitly set to `None`, the method will return a list of the individual error scores instead. + Set to ``np.mean`` by default. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + predict_likelihood_parameters + If set to `True`, generates the quantile predictions directly. Only supported with `num_samples = 1`. + enable_optimization + Whether to use the optimized version of `historical_forecasts` when supported and available. + Default: ``True``. + data_transformers + Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series + (possibles keys; "series", "past_covariates", "future_covariates"). If provided, all input series must be + in the un-transformed space. For fittable transformer / pipeline: + + - if `retrain=True`, the data transformer re-fit on the training data at each historical forecast step + (currently ignored by conformal models). + - if `retrain=False`, the data transformer transforms the series once before all the forecasts. + + The fitted transformer is used to transform the input during both training and prediction. + If the transformation is invertible, the forecasts will be inverse-transformed. + Only effective when `historical_forecasts=None`. + metric_kwargs + Additional arguments passed to `metric()`, such as `'n_jobs'` for parallelization, `'component_reduction'` + for reducing the component wise metrics, seasonality `'m'` for scaled metrics, etc. Will pass arguments to + each metric separately and only if they are present in the corresponding metric signature. Parameter + `'insample'` for scaled metrics (e.g. mase`, `rmsse`, ...) is ignored, as it is handled internally. + fit_kwargs + Currently ignored by conformal models. + predict_kwargs + Optionally, some additional arguments passed to the model `predict()` method. + sample_weight + Currently ignored by conformal models. + + Returns + ------- + float + A single backtest score for single uni/multivariate series, a single `metric` function and: + + - `historical_forecasts` generated with `last_points_only=True` + - `historical_forecasts` generated with `last_points_only=False` and using a backtest `reduction` + np.ndarray + An numpy array of backtest scores. For single series and one of: + + - a single `metric` function, `historical_forecasts` generated with `last_points_only=False` + and backtest `reduction=None`. The output has shape (n forecasts, *). + - multiple `metric` functions and `historical_forecasts` generated with `last_points_only=False`. + The output has shape (*, n metrics) when using a backtest `reduction`, and (n forecasts, *, n metrics) + when `reduction=None` + - multiple uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None` for "per time step metrics" + list[float] + Same as for type `float` but for a sequence of series. The returned metric list has length + `len(series)` with the `float` metric for each input `series`. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. The returned metric list has length + `len(series)` with the `np.ndarray` metrics for each input `series`. + """ + return super().backtest( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + historical_forecasts=historical_forecasts, + forecast_horizon=forecast_horizon, + num_samples=num_samples, + train_length=train_length, + start=start, + start_format=start_format, + stride=stride, + retrain=retrain, + overlap_end=overlap_end, + last_points_only=last_points_only, + metric=metric, + reduction=reduction, + verbose=verbose, + show_warnings=show_warnings, + predict_likelihood_parameters=predict_likelihood_parameters, + enable_optimization=enable_optimization, + data_transformers=data_transformers, + metric_kwargs=metric_kwargs, + fit_kwargs=fit_kwargs, + predict_kwargs=predict_kwargs, + sample_weight=sample_weight, + ) + + def residuals( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + historical_forecasts: Optional[ + Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] + ] = None, + forecast_horizon: int = 1, + num_samples: int = 1, + train_length: Optional[int] = None, + start: Optional[Union[pd.Timestamp, int]] = None, + start_format: Literal["position", "value"] = "value", + stride: int = 1, + retrain: Union[bool, int, Callable[..., bool]] = True, + overlap_end: bool = False, + last_points_only: bool = True, + metric: METRIC_TYPE = metrics.err, + verbose: bool = False, + show_warnings: bool = True, + predict_likelihood_parameters: bool = False, + enable_optimization: bool = True, + data_transformers: Optional[ + dict[str, Union[BaseDataTransformer, Pipeline]] + ] = None, + metric_kwargs: Optional[dict[str, Any]] = None, + fit_kwargs: Optional[dict[str, Any]] = None, + predict_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + values_only: bool = False, + ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: + """Compute the residuals that the model produced for historical forecasts on (potentially multiple) `series`. + + This function computes the difference (or one of Darts' "per time step" metrics) between the actual + observations from `series` and the fitted values obtained by training the model on `series` (or using a + pre-trained model with `retrain=False`). Not all models support fitted values, so we use historical forecasts + as an approximation for them. + + In sequence this method performs: + + - use pre-computed `historical_forecasts` or compute historical forecasts for each series (see + :meth:`~darts.models.forecasting.conformal_models.ConformalModel.historical_forecasts` for more details). + How the historical forecasts are generated can be configured with parameters `num_samples`, `train_length`, + `start`, `start_format`, `forecast_horizon`, `stride`, `retrain`, `last_points_only`, `fit_kwargs`, and + `predict_kwargs`. + - compute a backtest using a "per time step" `metric` between the historical forecasts and `series` per + component/column and time step (see + :meth:`~darts.models.forecasting.conformal_models.ConformalModel.backtest` for more details). By default, + uses the residuals :func:`~darts.metrics.metrics.err` (error) as a `metric`. + - create and return `TimeSeries` (or simply a np.ndarray with `values_only=True`) with the time index from + historical forecasts, and values from the metrics per component and time step. + + This method works for single or multiple univariate or multivariate series. + It uses the median prediction (when dealing with stochastic forecasts). + + Notes + ----- + Darts has several metrics to evaluate probabilistic forecasts. For conformal models, we recommend using + "per time step" quantile interval metrics (see `here + `_). You can specify which intervals to + evaluate by setting `metric_kwargs={'q_interval': my_intervals}`. To check all intervals used by your conformal + model `my_model`, you can set ``{'q_interval': my_model.q_interval}``. + + Parameters + ---------- + series + A (sequence of) target time series used to successively compute the historical forecasts. Will use the past + of this series for calibration. The series should not have any overlap with the series used to train the + forecasting model. + past_covariates + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + future_covariates + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + historical_forecasts + Optionally, the (or a sequence of / a sequence of sequences of) historical forecasts time series to be + evaluated. Corresponds to the output of :meth:`historical_forecasts() + `. The same `series` and + `last_points_only` values must be passed that were used to generate the historical forecasts. If provided, + will skip historical forecasting and ignore all parameters except `series`, `last_points_only`, `metric`, + and `reduction`. + forecast_horizon + The forecast horizon for the predictions. + num_samples + Number of times a prediction is sampled from the calibrated quantile predictions using linear + interpolation in-between the quantiles. For larger values, the sample distribution approximates the + calibrated quantile predictions. + train_length + Currently ignored by conformal models. + start + Optionally, the first point in time at which a prediction is computed. This parameter supports: + ``int``, ``pandas.Timestamp``, and ``None``. + If an ``int``, it is either the index position of the first prediction point for `series` with a + `pd.DatetimeIndex`, or the index value for `series` with a `pd.RangeIndex`. The latter can be changed to + the index position with `start_format="position"`. + If a ``pandas.Timestamp``, it is the time stamp of the first prediction point. + If ``None``, the first prediction point will automatically be set to: + + - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first + predictable point is earlier than the first trainable point. + - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. + - the first trainable point (given `train_length`) otherwise + + Note: If the model uses a shifted output (`output_chunk_shift > 0`), then the first predicted point is also + shifted by `output_chunk_shift` points into the future. + Note: Raises a ValueError if `start` yields a time outside the time index of `series`. + Note: If `start` is outside the possible historical forecasting times, will ignore the parameter + (default behavior with ``None``) and start at the first trainable/predictable point. + start_format + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. + stride + The number of time steps between two consecutive predictions. + retrain + Currently ignored by conformal models. + overlap_end + Whether the returned forecasts can go beyond the series' end or not. + last_points_only + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. + Otherwise, returns a list of historical ``TimeSeries`` forecasts. + metric + Either one of Darts' "per time step" metrics (see `here + `_), or a custom metric that has an + identical signature as Darts' "per time step" metrics, uses decorators + :func:`~darts.metrics.metrics.multi_ts_support` and :func:`~darts.metrics.metrics.multi_ts_support`, + and returns one value per time step. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + predict_likelihood_parameters + If set to `True`, generates the quantile predictions directly. Only supported with `num_samples = 1`. + enable_optimization + Whether to use the optimized version of `historical_forecasts` when supported and available. + Default: ``True``. + data_transformers + Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series + (possibles keys; "series", "past_covariates", "future_covariates"). If provided, all input series must be + in the un-transformed space. For fittable transformer / pipeline: + + - if `retrain=True`, the data transformer re-fit on the training data at each historical forecast step + (currently ignored by conformal models). + - if `retrain=False`, the data transformer transforms the series once before all the forecasts. + + The fitted transformer is used to transform the input during both training and prediction. + If the transformation is invertible, the forecasts will be inverse-transformed. + Only effective when `historical_forecasts=None`. + metric_kwargs + Additional arguments passed to `metric()`, such as `'n_jobs'` for parallelization, `'m'` for scaled + metrics, etc. Will pass arguments only if they are present in the corresponding metric signature. Ignores + reduction arguments `"series_reduction", "component_reduction", "time_reduction"`, and parameter + `'insample'` for scaled metrics (e.g. mase`, `rmsse`, ...), as they are handled internally. + fit_kwargs + Currently ignored by conformal models. + predict_kwargs + Optionally, some additional arguments passed to the model `predict()` method. + sample_weight + Currently ignored by conformal models. + values_only + Whether to return the residuals as `np.ndarray`. If `False`, returns residuals as `TimeSeries`. + + Returns + ------- + TimeSeries + Residual `TimeSeries` for a single `series` and `historical_forecasts` generated with + `last_points_only=True`. + list[TimeSeries] + A list of residual `TimeSeries` for a sequence (list) of `series` with `last_points_only=True`. + The residual list has length `len(series)`. + list[list[TimeSeries]] + A list of lists of residual `TimeSeries` for a sequence of `series` with `last_points_only=False`. + The outer residual list has length `len(series)`. The inner lists consist of the residuals from + all possible series-specific historical forecasts. + """ + return super().residuals( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + historical_forecasts=historical_forecasts, + forecast_horizon=forecast_horizon, + num_samples=num_samples, + train_length=train_length, + start=start, + start_format=start_format, + stride=stride, + retrain=retrain, + overlap_end=overlap_end, + last_points_only=last_points_only, + metric=metric, + verbose=verbose, + show_warnings=show_warnings, + predict_likelihood_parameters=predict_likelihood_parameters, + enable_optimization=enable_optimization, + data_transformers=data_transformers, + metric_kwargs=metric_kwargs, + fit_kwargs=fit_kwargs, + predict_kwargs=predict_kwargs, + sample_weight=sample_weight, + values_only=values_only, + ) + + @random_method + def _calibrate_forecasts( + self, + series: Sequence[TimeSeries], + forecasts: Union[Sequence[Sequence[TimeSeries]], Sequence[TimeSeries]], + num_samples: int = 1, + start: Optional[Union[pd.Timestamp, int, str]] = None, + start_format: Literal["position", "value"] = "value", + forecast_horizon: int = 1, + stride: int = 1, + overlap_end: bool = False, + last_points_only: bool = True, + verbose: bool = False, + show_warnings: bool = True, + predict_likelihood_parameters: bool = False, + ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: + """Generate calibrated historical forecasts. + + In general the workflow of the models to produce one calibrated forecast/prediction per step in the horizon + is as follows: + + - Generate historical forecasts for `series` with stride `cal_stride` (using the forecasting model) + - Extract a calibration set: The forecasts from the most recent past to use as calibration for one conformal + prediction. The number of examples to use can be defined at model creation with parameter `cal_length`. It + automatically extracts the calibration set from the most recent past of your input series (`series`, + `past_covariates`, ...). + - Compute the errors/non-conformity scores (specific to each conformal model) on these historical forecasts + - Compute the quantile values from the errors / non-conformity scores (using our desired quantiles set at model + creation with parameter `quantiles`). + - Compute the conformal prediction: Using these quantile values, add calibrated intervals to (or adjust the + existing intervals of) the forecasting model's predictions. + """ + cal_stride = self.cal_stride + cal_length = self.cal_length + metric, metric_kwargs = self._residuals_metric + residuals = self.model.residuals( + series=series, + historical_forecasts=forecasts, + overlap_end=overlap_end, + last_points_only=last_points_only, + verbose=verbose, + show_warnings=show_warnings, + values_only=True, + metric=metric, + metric_kwargs=metric_kwargs, + ) + + outer_iterator = enumerate(zip(series, forecasts, residuals)) + if len(series) > 1: + # Use tqdm on the outer loop only if there's more than one series to iterate over + # (otherwise use tqdm on the inner loop). + outer_iterator = _build_tqdm_iterator( + outer_iterator, + verbose, + total=len(series), + desc="conformal forecasts", + ) + + cp_hfcs = [] + for series_idx, (series_, s_hfcs, res) in outer_iterator: + cp_preds = [] + + # no historical forecasts were generated + if not s_hfcs: + cp_hfcs.append(cp_preds) + continue + + last_hfc = s_hfcs if last_points_only else s_hfcs[-1] + + # compute the minimum required number of useful calibration residuals + # at least one or `cal_length` examples + min_n_cal = cal_length or 1 + # `last_points_only=False` requires additional examples to use most recent information + # from all steps in the horizon + if not last_points_only: + min_n_cal += math.ceil(forecast_horizon / cal_stride) - 1 + + # determine first forecast index for conformal prediction + # we need at least one residual per point in the horizon prior to the first conformal forecast + horizon_ocs = forecast_horizon + self.output_chunk_shift + first_idx_train = math.ceil(horizon_ocs / cal_stride) + + # plus some additional examples based on `cal_length` + if cal_length is not None: + first_idx_train += cal_length - 1 + + # check if later we need to drop some residuals without useful information (unknown residuals) + if overlap_end: + delta_end = n_steps_between( + end=last_hfc.end_time(), + start=series_.end_time(), + freq=series_.freq, + ) + else: + delta_end = 0 + + # ignore residuals without useful information + if last_points_only and delta_end > 0: + # useful residual information only up until the forecast *ending* at the last time step in `series` + ignore_n_residuals = delta_end + elif not last_points_only and delta_end >= forecast_horizon: + # useful residual information only up until the forecast *starting* at the last time step in `series` + ignore_n_residuals = delta_end - forecast_horizon + 1 + else: + # ignore at least one forecast residuals from the end, since we can only use prior residuals + ignore_n_residuals = self.output_chunk_shift + 1 + # with last points only, ignore the last `horizon` residuals to avoid look-ahead bias + if last_points_only: + ignore_n_residuals += forecast_horizon - 1 + + # get the last index respecting `cal_stride` + last_res_idx = -math.ceil(ignore_n_residuals / cal_stride) + # get only useful residuals + res = res[:last_res_idx] + + if first_idx_train >= len(s_hfcs) or len(res) < min_n_cal: + raise_log( + ValueError( + "Could not build the minimum required calibration input with the provided " + f"`series` and `*_covariates` at series index: {series_idx}. " + f"Expected to generate at least `{min_n_cal}` calibration forecasts with known residuals " + f"before the first conformal forecast, but could only generate `{len(res)}`." + ), + logger=logger, + ) + + # adjust first index based on `start` + first_idx_start = 0 + if start == "end": + # called from `predict()`; start at the last forecast + first_idx_start = len(s_hfcs) - 1 + elif start is not None: + # called from `historical_forecasts()`: use user-defined start + # the conformal forecastable index ranges from the start of the first valid historical + # forecast until the start of the last historical forecast + historical_forecasts_time_index = ( + s_hfcs[first_idx_train].start_time(), + s_hfcs[-1].start_time(), + ) + # adjust forecast start points in case of output shift or `last_points_only=True` + adjust_idx = ( + self.output_chunk_shift + + int(last_points_only) * (forecast_horizon - 1) + ) * series_.freq + historical_forecasts_time_index = ( + historical_forecasts_time_index[0] - adjust_idx, + historical_forecasts_time_index[1] - adjust_idx, + ) + + # adjust forecastable times based on user start, assuming hfcs were generated with `stride=1` + first_start_time, _ = _adjust_historical_forecasts_time_index( + series=series_, + series_idx=series_idx, + start=start, + start_format=start_format, + stride=stride, + historical_forecasts_time_index=historical_forecasts_time_index, + show_warnings=show_warnings, + ) + # find position relative to start + first_idx_start = n_steps_between( + first_start_time + adjust_idx, + s_hfcs[0].start_time(), + freq=series_.freq, + ) + # adjust by stride + first_idx_start = math.ceil(first_idx_start / cal_stride) + + # get final first index + first_fc_idx = max([first_idx_train, first_idx_start]) + # bring `res` from shape (forecasting steps, n components, n past residuals) into + # shape (forecasting steps, n components, n past residuals) + if last_points_only: + # -> (1, n components, n samples * n past residuals) + res = res.transpose(2, 1, 0) + else: + # rearrange the residuals to avoid look-ahead bias and to have the same number of examples per + # point in the horizon. We want the most recent residuals in the past for each step in the horizon. + res = np.array(res) + + # go through each step in the horizon, use all useful information from the end (most recent values), + # and skip information at beginning (most distant past); + # -> (forecast horizon, n components, n past residuals) + res_ = [] + for idx_horizon in range(forecast_horizon): + n = idx_horizon + 1 + # ignore residuals at beginning + idx_fc_start = math.floor((forecast_horizon - n) / cal_stride) + # keep as many residuals as possible from end + idx_fc_end = -( + math.ceil(forecast_horizon / cal_stride) - (idx_fc_start + 1) + ) + res_.append(res[idx_fc_start : idx_fc_end or None, idx_horizon]) + res = np.concatenate(res_, axis=2).T + + # get the last conformal forecast index (exclusive) based on the residual examples + last_fc_idx = res.shape[2] + math.ceil(horizon_ocs / cal_stride) + + # forecasts are stridden, so stride must be relative + rel_stride = math.ceil(stride / cal_stride) + + def conformal_predict(idx_, pred_vals_): + # get the last residual index for calibration, `cal_end` is exclusive + # to avoid look-ahead bias, use only residuals from before the conformal forecast start point; + # for `last_points_only=True`, the last residual historically available at the forecasting + # point is `horizon_ocs - 1` steps before. The same applies to `last_points_only=False` thanks to + # the residual rearrangement + cal_end = ( + first_fc_idx + + idx_ * rel_stride + - (math.ceil(horizon_ocs / cal_stride) - 1) + ) + # optionally, use only `cal_length` residuals + cal_start = cal_end - cal_length if cal_length is not None else None + + # calibrate and apply interval to the forecasts + q_hat_ = self._calibrate_interval(res[:, :, cal_start:cal_end]) + vals = self._apply_interval(pred_vals_, q_hat_) + + # optionally, generate samples from the intervals + if not predict_likelihood_parameters: + vals = sample_from_quantiles( + vals, self.quantiles, num_samples=num_samples + ) + return vals + + # historical conformal prediction + # for each forecast, compute calibrated quantile intervals based on past residuals + if last_points_only: + inner_iterator = enumerate( + s_hfcs.all_values(copy=False)[first_fc_idx:last_fc_idx:rel_stride] + ) + else: + inner_iterator = enumerate(s_hfcs[first_fc_idx:last_fc_idx:rel_stride]) + + comp_names_out = ( + self._cp_component_names(series_) + if predict_likelihood_parameters + else None + ) + if len(series) == 1: + # only use progress bar if there's no outer loop + inner_iterator = _build_tqdm_iterator( + inner_iterator, + verbose, + total=(last_fc_idx - 1 - first_fc_idx) // rel_stride + 1, + desc="conformal forecasts", + ) + + if last_points_only: + for idx, pred_vals in inner_iterator: + pred_vals = np.expand_dims(pred_vals, 0) + cp_pred = conformal_predict(idx, pred_vals) + cp_preds.append(cp_pred) + cp_preds = _build_forecast_series( + points_preds=np.concatenate(cp_preds, axis=0), + input_series=series_, + custom_columns=comp_names_out, + time_index=generate_index( + start=s_hfcs._time_index[first_fc_idx], + length=len(cp_preds), + freq=series_.freq * stride, + name=series_._time_index.name, + ), + with_static_covs=not predict_likelihood_parameters, + with_hierarchy=False, + ) + else: + for idx, pred in inner_iterator: + pred_vals = pred.all_values(copy=False) + cp_pred = conformal_predict(idx, pred_vals) + cp_pred = _build_forecast_series( + points_preds=cp_pred, + input_series=series_, + custom_columns=comp_names_out, + time_index=pred._time_index, + with_static_covs=not predict_likelihood_parameters, + with_hierarchy=False, + ) + cp_preds.append(cp_pred) + cp_hfcs.append(cp_preds) + return cp_hfcs + + def save( + self, path: Optional[Union[str, os.PathLike, BinaryIO]] = None, **pkl_kwargs + ) -> None: + """ + Saves the conformal model under a given path or file handle. + + Additionally, two files are stored if `self.model` is a `TorchForecastingModel`. + + Example for saving and loading a :class:`ConformalNaiveModel`: + + .. highlight:: python + .. code-block:: python + + from darts.datasets import AirPassengersDataset + from darts.models import ConformalNaiveModel, LinearRegressionModel + + series = AirPassengersDataset().load() + forecasting_model = LinearRegressionModel(lags=4).fit(series) + + model = ConformalNaiveModel( + model=forecasting_model, + quantiles=[0.1, 0.5, 0.9], + ) + + model.save("my_model.pkl") + model_loaded = ConformalNaiveModel.load("my_model.pkl") + .. + + Parameters + ---------- + path + Path or file handle under which to save the ensemble model at its current state. If no path is specified, + the ensemble model is automatically saved under ``"{ConformalNaiveModel}_{YYYY-mm-dd_HH_MM_SS}.pkl"``. + If the forecasting model is a `TorchForecastingModel`, two files (model object and checkpoint) are saved + under ``"{path}.{ModelClass}.pt"`` and ``"{path}.{ModelClass}.ckpt"``. + pkl_kwargs + Keyword arguments passed to `pickle.dump()` + """ + + if path is None: + # default path + path = self._default_save_path() + ".pkl" + + super().save(path, **pkl_kwargs) + + if TORCH_AVAILABLE and issubclass(type(self.model), TorchForecastingModel): + path_tfm = f"{path}.{type(self.model).__name__}.pt" + self.model.save(path=path_tfm) + + @staticmethod + def load(path: Union[str, os.PathLike, BinaryIO]) -> "ConformalModel": + model: ConformalModel = GlobalForecastingModel.load(path) + + if TORCH_AVAILABLE and issubclass(type(model.model), TorchForecastingModel): + path_tfm = f"{path}.{type(model.model).__name__}.pt" + model.model = TorchForecastingModel.load(path_tfm) + return model + + @abstractmethod + def _calibrate_interval( + self, residuals: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + """Computes the lower and upper calibrated forecast intervals based on residuals. + + Parameters + ---------- + residuals + The residuals are expected to have shape (horizon, n components, n historical forecasts * n samples) + """ + + @abstractmethod + def _apply_interval(self, pred: np.ndarray, q_hat: tuple[np.ndarray, np.ndarray]): + """Applies the calibrated interval to the predicted quantiles. Returns an array with `len(quantiles)` + conformalized quantile predictions (lower quantiles, model forecast, upper quantiles) per component. + + E.g. output is `(target1_q1, target1_pred, target1_q2, target2_q1, ...)` + """ + + @property + @abstractmethod + def _residuals_metric(self) -> tuple[METRIC_TYPE, Optional[dict]]: + """Gives the "per time step" metric and optional metric kwargs used to compute residuals / + non-conformity scores.""" + + def _cp_component_names(self, input_series) -> list[str]: + """Gives the component names for generated forecasts.""" + return likelihood_component_names( + input_series.components, quantile_names(self.quantiles) + ) + + def _historical_forecasts_sanity_checks(self, *args: Any, **kwargs: Any) -> None: + super()._historical_forecasts_sanity_checks(*args, **kwargs, is_conformal=True) + + @property + def output_chunk_length(self) -> Optional[int]: + # conformal models can predict any horizon if the calibration set is large enough + return None + + @property + def output_chunk_shift(self) -> int: + return self.model.output_chunk_shift + + @property + def _model_encoder_settings(self): + raise NotImplementedError(f"not supported by `{self.__class__.__name__}`.") + + @property + def extreme_lags( + self, + ) -> tuple[ + Optional[int], + Optional[int], + Optional[int], + Optional[int], + Optional[int], + Optional[int], + int, + Optional[int], + ]: + raise NotImplementedError(f"not supported by `{self.__class__.__name__}`.") + + @property + def min_train_series_length(self) -> int: + raise NotImplementedError(f"not supported by `{self.__class__.__name__}`.") + + @property + def min_train_samples(self) -> int: + raise NotImplementedError(f"not supported by `{self.__class__.__name__}`.") + + @property + def supports_multivariate(self) -> bool: + return self.model.supports_multivariate + + @property + def supports_past_covariates(self) -> bool: + return self.model.supports_past_covariates + + @property + def supports_future_covariates(self) -> bool: + return self.model.supports_future_covariates + + @property + def supports_static_covariates(self) -> bool: + return self.model.supports_static_covariates + + @property + def supports_sample_weight(self) -> bool: + return self.model.supports_sample_weight + + @property + def supports_likelihood_parameter_prediction(self) -> bool: + return True + + @property + def supports_probabilistic_prediction(self) -> bool: + return True + + @property + def uses_past_covariates(self) -> bool: + return self.model.uses_past_covariates + + @property + def uses_future_covariates(self) -> bool: + return self.model.uses_future_covariates + + @property + def uses_static_covariates(self) -> bool: + return self.model.uses_static_covariates + + @property + def considers_static_covariates(self) -> bool: + return self.model.considers_static_covariates + + @property + def likelihood(self) -> str: + return self._likelihood + + +class ConformalNaiveModel(ConformalModel): + def __init__( + self, + model: GlobalForecastingModel, + quantiles: list[float], + symmetric: bool = True, + cal_length: Optional[int] = None, + cal_stride: int = 1, + cal_num_samples: int = 500, + random_state: Optional[int] = None, + ): + """Naive Conformal Prediction Model. + + A probabilistic model that adds calibrated intervals around the median forecast from a pre-trained + global forecasting model. It does not have to be trained and can generated calibrated forecasts + directly using the underlying trained forecasting model. It supports two symmetry modes: + + - `symmetric=True`: + - The lower and upper interval bounds are calibrated with the same magnitude. + - Non-conformity scores: uses metric `ae()` (see absolute error :func:`~darts.metrics.metrics.ae`) to + compute the non-conformity scores on the calibration set. + - `symmetric=False` + - The lower and upper interval bounds are calibrated separately. + - Non-conformity scores: uses metric `err()` (see error :func:`~darts.metrics.metrics.err`) to compute the + non-conformity scores on the calibration set for the upper bounds, an `-err()` for the lower bounds. + + Since it is a probabilistic model, you can generate forecasts in two ways (when calling `predict()`, + `historical_forecasts()`, ...): + + - Predict the calibrated quantile intervals directly: Pass parameters `predict_likelihood_parameters=True`, and + `num_samples=1` to the forecast method. + - Predict stochastic samples from the calibrated quantile intervals: Pass parameters + `predict_likelihood_parameters=False`, and `num_samples>>1` to the forecast method. + + Conformal models can be applied to any of Darts' global forecasting model, as long as the model has been + fitted before. In general the workflow of the models to produce one calibrated forecast/prediction is as + follows: + + - Extract a calibration set: The calibration set for each conformal forecast is automatically extracted from + the most recent past of your input series relative to the forecast start point. The number of calibration + examples (forecast errors / non-conformity scores) to consider can be defined at model creation + with parameter `cal_length`. Note that when using `cal_stride>1`, a longer history is required since + the calibration examples are generated with stridden historical forecasts. + - Generate historical forecasts on the calibration set (using the forecasting model) with a stride `cal_stride`. + - Compute the errors/non-conformity scores (as defined above) on these historical forecasts + - Compute the quantile values from the errors / non-conformity scores (using our desired quantiles set at model + creation with parameter `quantiles`). + - Compute the conformal prediction: Using these quantile values, add calibrated intervals to the forecasting + model's predictions. + + Some notes: + + - When computing `historical_forecasts()`, `backtest()`, `residuals()`, ... the above is applied for each + forecast (the forecasting model's historical forecasts are only generated once for efficiency). + - For multi-horizon forecasts, the above is applied for each step in the horizon separately. + + Parameters + ---------- + model + A pre-trained global forecasting model. See the list of models + `here `_. + quantiles + A list of quantiles centered around the median `q=0.5` to use. For example quantiles + [0.1, 0.2, 0.5, 0.8 0.9] correspond to two intervals with (0.9 - 0.1) = 80%, and (0.8 - 0.2) 60% coverage + around the median (model forecast). + symmetric + Whether to use symmetric non-conformity scores. If `True`, uses metric `ae()` (see + :func:`~darts.metrics.metrics.ae`) to compute the non-conformity scores. If `False`, uses metric `-err()` + (see :func:`~darts.metrics.metrics.err`) for the lower, and `err()` for the upper quantile interval bound. + cal_length + The number of past forecast errors / non-conformity scores to use as calibration for each conformal + forecast (and each step in the horizon). If `None`, considers all scores. + cal_stride + The stride to apply when computing the historical forecasts and non-conformity scores on the calibration + set. The actual conformal forecasts can have a different stride given with parameter `stride` in downstream + tasks (e.g. historical forecasts, backtest, ...) + cal_num_samples + The number of samples to generate for each calibration forecast (if `model` is a probabilistic forecasting + model). The non-conformity scores are computed on the quantile values of these forecasts (using quantiles + `quantiles`). Uses `1` for deterministic models. The actual conformal forecasts can have a different number + of samples given with parameter `num_samples` in downstream tasks (e.g. predict, historical forecasts, ...). + random_state + Control the randomness of probabilistic conformal forecasts (sample generation) across different runs. + """ + super().__init__( + model=model, + quantiles=quantiles, + symmetric=symmetric, + cal_length=cal_length, + cal_num_samples=cal_num_samples, + random_state=random_state, + cal_stride=cal_stride, + ) + + def _calibrate_interval( + self, residuals: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + def q_hat_from_residuals(residuals_): + # compute quantiles of shape (forecast horizon, n components, n quantile intervals) + return np.quantile( + residuals_, + q=self.interval_range_sym, + method="higher", + axis=2, + ).transpose((1, 2, 0)) + + # residuals shape (horizon, n components, n past forecasts) + if self.symmetric: + # symmetric (from metric `ae()`) + q_hat = q_hat_from_residuals(residuals) + return -q_hat, q_hat[:, :, ::-1] + else: + # asymmetric (from metric `err()`) + q_hat = q_hat_from_residuals( + np.concatenate([-residuals, residuals], axis=1) + ) + n_comps = residuals.shape[1] + return -q_hat[:, :n_comps, :], q_hat[:, n_comps:, ::-1] + + def _apply_interval(self, pred: np.ndarray, q_hat: tuple[np.ndarray, np.ndarray]): + # convert stochastic predictions to median + if pred.shape[2] != 1: + pred = np.expand_dims(np.quantile(pred, 0.5, axis=2), -1) + # shape (forecast horizon, n components, n quantiles) + pred = np.concatenate([pred + q_hat[0], pred, pred + q_hat[1]], axis=2) + # -> (forecast horizon, n components * n quantiles) + return pred.reshape(len(pred), -1) + + @property + def _residuals_metric(self) -> tuple[METRIC_TYPE, Optional[dict]]: + return (metrics.ae if self.symmetric else metrics.err), None + + +class ConformalQRModel(ConformalModel): + def __init__( + self, + model: GlobalForecastingModel, + quantiles: list[float], + symmetric: bool = True, + cal_length: Optional[int] = None, + cal_stride: int = 1, + cal_num_samples: int = 500, + random_state: Optional[int] = None, + ): + """Conformalized Quantile Regression Model. + + A probabilistic model that calibrates the quantile predictions from a pre-trained probabilistic global + forecasting model. It does not have to be trained and can generated calibrated forecasts + directly using the underlying trained forecasting model. It supports two symmetry modes: + + - `symmetric=True`: + - The lower and upper quantile predictions are calibrated with the same magnitude. + - Non-conformity scores: uses metric `incs_qr(symmetric=True)` (see Non-Conformity Score for Quantile + Regression :func:`~darts.metrics.metrics.incs_qr`) to compute the non-conformity scores on the calibration + set. + - `symmetric=False` + - The lower and upper quantile predictions are calibrated separately. + - Non-conformity scores: uses metric `incs_qr(symmetric=False)` (see Non-Conformity Score for Quantile + Regression :func:`~darts.metrics.metrics.incs_qr`) to compute the non-conformity scores for the upper and + lower bound separately. + + Since it is a probabilistic model, you can generate forecasts in two ways (when calling `predict()`, + `historical_forecasts()`, ...): + + - Predict the calibrated quantile intervals directly: Pass parameters `predict_likelihood_parameters=True`, and + `num_samples=1` to the forecast method. + - Predict stochastic samples from the calibrated quantile intervals: Pass parameters + `predict_likelihood_parameters=False`, and `num_samples>>1` to the forecast method. + + Conformal models can be applied to any of Darts' global forecasting model, as long as the model has been + fitted before. In general the workflow of the models to produce one calibrated forecast/prediction is as + follows: + + - Extract a calibration set: The calibration set for each conformal forecast is automatically extracted from + the most recent past of your input series relative to the forecast start point. The number of calibration + examples (forecast errors / non-conformity scores) to consider can be defined at model creation with + parameter `cal_length`. Note that when using `cal_stride>1`, a longer history is required since the + calibration examples are generated with stridden historical forecasts. + - Generate historical forecasts (quantile predictions) on the calibration set (using the forecasting model) + with a stride `cal_stride`. + - Compute the errors/non-conformity scores (as defined above) on these historical quantile predictions + - Compute the quantile values from the errors / non-conformity scores (using our desired quantiles set at model + creation with parameter `quantiles`). + - Compute the conformal prediction: Using these quantile values, calibrate the predicted quantiles from the + forecasting model's predictions. + + Some notes: + + - When computing `historical_forecasts()`, `backtest()`, `residuals()`, ... the above is applied for each + forecast (the forecasting model's historical forecasts are only generated once for efficiency). + - For multi-horizon forecasts, the above is applied for each step in the horizon separately. + + Parameters + ---------- + model + A pre-trained global forecasting model. See the list of models + `here `_. + quantiles + A list of quantiles centered around the median `q=0.5` to use. For example quantiles + [0.1, 0.2, 0.5, 0.8 0.9] correspond to two intervals with (0.9 - 0.1) = 80%, and (0.8 - 0.2) 60% coverage + around the median (model forecast). + symmetric + Whether to use symmetric non-conformity scores. If `True`, uses symmetric metric + `incs_qr(..., symmetric=True)` (see :func:`~darts.metrics.metrics.incs_qr`) to compute the non-conformity + scores. If `False`, uses asymmetric metric `incs_qr(..., symmetric=False)` with individual scores for the + lower- and upper quantile interval bounds. + cal_length + The number of past forecast errors / non-conformity scores to use as calibration for each conformal + forecast (and each step in the horizon). If `None`, considers all scores. + cal_stride + The stride to apply when computing the historical forecasts and non-conformity scores on the calibration + set. The actual conformal forecasts can have a different stride given with parameter `stride` in downstream + tasks (e.g. historical forecasts, backtest, ...) + cal_num_samples + The number of samples to generate for each calibration forecast (if `model` is a probabilistic forecasting + model). The non-conformity scores are computed on the quantile values of these forecasts (using quantiles + `quantiles`). Uses `1` for deterministic models. The actual conformal forecasts can have a different number + of samples given with parameter `num_samples` in downstream tasks (e.g. predict, historical forecasts, ...). + random_state + Control the randomness of probabilistic conformal forecasts (sample generation) across different runs. + """ + if not model.supports_probabilistic_prediction: + raise_log( + ValueError( + "`model` must support probabilistic forecasting. Consider using a `likelihood` at " + "forecasting model creation, or use another conformal model." + ), + logger=logger, + ) + super().__init__( + model=model, + quantiles=quantiles, + symmetric=symmetric, + cal_length=cal_length, + cal_num_samples=cal_num_samples, + random_state=random_state, + cal_stride=cal_stride, + ) + + def _calibrate_interval( + self, residuals: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + n_comps = residuals.shape[1] // ( + len(self.interval_range) * (1 + int(not self.symmetric)) + ) + n_intervals = len(self.interval_range) + + def q_hat_from_residuals(residuals_): + # TODO: is there a more efficient way? + # compute quantiles with shape (horizon, n components, n quantile intervals) + # over all past residuals + q_hat_tmp = np.quantile( + residuals_, q=self.interval_range_sym, method="higher", axis=2 + ).transpose((1, 2, 0)) + q_hat_ = np.empty((len(residuals_), n_comps, n_intervals)) + for i in range(n_intervals): + for c in range(n_comps): + q_hat_[:, c, i] = q_hat_tmp[:, i + c * n_intervals, i] + return q_hat_ + + if self.symmetric: + # symmetric has one nc-score per interval (from metric `incs_qr(symmetric=True)`) + # residuals shape (horizon, n components * n intervals, n past forecasts) + q_hat = q_hat_from_residuals(residuals) + return -q_hat, q_hat[:, :, ::-1] + else: + # asymmetric has two nc-score per interval (for lower and upper quantiles, from metric + # `incs_qr(symmetric=False)`) + # lower and upper residuals are concatenated along axis=1; + # residuals shape (horizon, n components * n intervals * 2, n past forecasts) + half_idx = residuals.shape[1] // 2 + q_hat_lo = q_hat_from_residuals(residuals[:, :half_idx]) + q_hat_hi = q_hat_from_residuals(residuals[:, half_idx:]) + return -q_hat_lo, q_hat_hi[:, :, ::-1] + + def _apply_interval(self, pred: np.ndarray, q_hat: tuple[np.ndarray, np.ndarray]): + # get quantile predictions with shape (n times, n components, n quantiles) + pred = np.quantile(pred, self.quantiles, axis=2).transpose((1, 2, 0)) + # shape (forecast horizon, n components, n quantiles) + pred = np.concatenate( + [ + pred[:, :, : self.idx_median] + q_hat[0], # lower quantiles + pred[:, :, self.idx_median : self.idx_median + 1], # model forecast + pred[:, :, self.idx_median + 1 :] + q_hat[1], # upper quantiles + ], + axis=2, + ) + # -> (forecast horizon, n components * n quantiles) + return pred.reshape(len(pred), -1) + + @property + def _residuals_metric(self) -> tuple[METRIC_TYPE, Optional[dict]]: + return metrics.incs_qr, { + "q_interval": self.q_interval, + "symmetric": self.symmetric, + } + + +def _get_calibration_hfc_start( + series: Sequence[TimeSeries], + horizon: int, + output_chunk_shift: int, + cal_length: Optional[int], + cal_stride: int, + start: Optional[Union[pd.Timestamp, int, Literal["end"]]], + start_format: Literal["position", "value"], +) -> tuple[Optional[Union[int, pd.Timestamp]], Literal["position", "value"]]: + """Find the calibration start point (CSP) (for historical forecasts on calibration set). + + - If `start=None`, the CSP is also `None` (all possible hfcs). + - If `start="end"` (when calling `predict()`), returns the CSP as a positional index relative to the end of the + series (<0). + - Otherwise (when calling `historical_forecasts()`), the CSP is the start value (`start_format="value"`) or start + position (`start_format="position"`) adjusted by the positions computed for the case above. + + If this function is called from `historical_forecasts`, the sanity checks guarantee the following: + + - `start` cannot be a `float` + - when `start_format='value'`, all `series` have the same frequency + """ + if start is None: + return start, start_format + + cal_start_format: Literal["position", "value"] + horizon_ocs = horizon + output_chunk_shift + if cal_length is not None: + # we only need `cal_length` forecasts with stride `cal_stride` before the `predict()` start point; + # the last valid calibration forecast must start at least `horizon_ocs` before `predict()` start + add_steps = math.ceil(horizon_ocs / cal_stride) - 1 + start_idx_rel = -cal_stride * (cal_length + add_steps) + cal_start_format = "position" + elif cal_stride > 1: + # we need all forecasts with stride `cal_stride` before the `predict()` start point + max_len_series = max(len(series_) for series_ in series) + start_idx_rel = -cal_stride * math.ceil(max_len_series / cal_stride) + cal_start_format = "position" + else: + # we need all possible forecasts with `cal_stride=1` + start_idx_rel, cal_start_format = None, "value" + + if start == "end": + # `predict()` is relative to the end + return start_idx_rel, cal_start_format + + # `historical_forecasts()` is relative to `start` + start_is_position = isinstance(start, (int, np.int64)) and ( + start_format == "position" or series[0]._has_datetime_index + ) + cal_start_format = start_format + if start_idx_rel is None: + cal_start = start_idx_rel + elif start_is_position: + cal_start = start + start_idx_rel + # if start switches sign, it would be relative to the end; + # correct it to be positive (relative to beginning) + if cal_start < 0 <= start: + cal_start += math.ceil(abs(cal_start) / cal_stride) * cal_stride + else: + cal_start = start + start_idx_rel * series[0].freq + return cal_start, cal_start_format diff --git a/darts/models/forecasting/ensemble_model.py b/darts/models/forecasting/ensemble_model.py index c585efd6c3..72187f8334 100644 --- a/darts/models/forecasting/ensemble_model.py +++ b/darts/models/forecasting/ensemble_model.py @@ -239,9 +239,10 @@ def _stack_ts_multiseq(self, predictions_list): # stacks multiple sequences of timeseries elementwise return [self._stack_ts_seq(ts_list) for ts_list in zip(*predictions_list)] + @property def _model_encoder_settings(self): raise NotImplementedError( - "Encoders are not supported by EnsembleModels. Instead add encoder to the underlying `forecasting_models`." + "Encoders are not supported by EnsembleModels. Instead add encoders to the underlying `forecasting_models`." ) def _make_multiple_predictions( @@ -436,15 +437,6 @@ def save( @staticmethod def load(path: Union[str, os.PathLike, BinaryIO]) -> "EnsembleModel": - """ - Loads the ensemble model from a given path or file handle. - - Parameters - ---------- - path - Path or file handle from which to load the ensemble model. - """ - model: EnsembleModel = GlobalForecastingModel.load(path) for i, m in enumerate(model.forecasting_models): diff --git a/darts/models/forecasting/forecasting_model.py b/darts/models/forecasting/forecasting_model.py index d28b179701..459f705575 100644 --- a/darts/models/forecasting/forecasting_model.py +++ b/darts/models/forecasting/forecasting_model.py @@ -42,10 +42,12 @@ _apply_data_transformers, _apply_inverse_data_transformers, _convert_data_transformers, + _extend_series_for_overlap_end, _get_historical_forecast_predict_index, _get_historical_forecast_train_index, _historical_forecasts_general_checks, _historical_forecasts_sanitize_kwargs, + _process_historical_forecast_for_backtest, _reconciliate_historical_time_indices, ) from darts.utils.timeseries_generation import ( @@ -332,8 +334,7 @@ def predict( n Forecast horizon - the number of time steps after the end of the series for which to produce predictions. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose Optionally, set the prediction verbosity. Not effective for all models. show_warnings @@ -353,8 +354,12 @@ def predict( ), logger, ) - - if self.output_chunk_shift and n > self.output_chunk_length: + is_autoregression = ( + False + if self.output_chunk_length is None + else (n > self.output_chunk_length) + ) + if self.output_chunk_shift and is_autoregression: raise_log( ValueError( "Cannot perform auto-regression `(n > output_chunk_length)` with a model that uses a " @@ -607,7 +612,10 @@ def _historical_forecasts_sanity_checks(self, *args: Any, **kwargs: Any) -> None """ # parse args and kwargs series = args[0] - _historical_forecasts_general_checks(self, series, kwargs) + is_conformal = kwargs.get("is_conformal", False) + _historical_forecasts_general_checks( + self, series, kwargs, is_conformal=is_conformal + ) def _get_last_prediction_time( self, @@ -638,11 +646,11 @@ def historical_forecasts( series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + forecast_horizon: int = 1, num_samples: int = 1, train_length: Optional[int] = None, start: Optional[Union[pd.Timestamp, float, int]] = None, start_format: Literal["position", "value"] = "value", - forecast_horizon: int = 1, stride: int = 1, retrain: Union[bool, int, Callable[..., bool]] = True, overlap_end: bool = False, @@ -658,42 +666,61 @@ def historical_forecasts( predict_kwargs: Optional[dict[str, Any]] = None, sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: - """Compute the historical forecasts that would have been obtained by this model on - (potentially multiple) `series`. - - This method repeatedly builds a training set: either expanding from the beginning of `series` or moving with - a fixed length `train_length`. It trains the model on the training set, emits a forecast of length equal to - forecast_horizon, and then moves the end of the training set forward by `stride` time steps. - - By default, this method will return one (or a sequence of) single time series made up of - the last point of each historical forecast. - This time series will thus have a frequency of ``series.freq * stride``. - If `last_points_only` is set to `False`, it will instead return one (or a sequence of) list of the - historical forecasts series. - - By default, this method always re-trains the models on the entire available history, corresponding to an - expanding window strategy. If `retrain` is set to `False`, the model must have been fit before. This is not - supported by all models. + """Generates historical forecasts by simulating predictions at various points in time throughout the history of + the provided (potentially multiple) `series`. This process involves retrospectively applying the model to + different time steps, as if the forecasts were made in real-time at those specific moments. This allows for an + evaluation of the model's performance over the entire duration of the series, providing insights into its + predictive accuracy and robustness across different historical periods. + + There are two main modes for this method: + + - Re-training Mode (Default, `retrain=True`): The model is re-trained at each step of the simulation, and + generates a forecast using the updated model. In case of multiple series, the model is re-trained on each + series independently (global training is not yet supported). + - Pre-trained Mode (`retrain=False`): The forecasts are generated at each step of the simulation without + re-training. It is only supported for pre-trained global forecasting models. This mode is significantly + faster as it skips the re-training step. + + By choosing the appropriate mode, you can balance between computational efficiency and the need for up-to-date + model training. + + **Re-training Mode:** This mode repeatedly builds a training set by either expanding from the beginning of + the `series` or by using a fixed-length `train_length` (the start point can also be configured with `start` + and `start_format`). The model is then trained on this training set, and a forecast of length `forecast_horizon` + is generated. Subsequently, the end of the training set is moved forward by `stride` time steps, and the process + is repeated. + + **Pre-trained Mode:** This mode is only supported for pre-trained global forecasting models. It uses the same + simulation steps as in the *Re-training Mode* (ignoring `train_length`), but generates the forecasts directly + without re-training. + + By default, with `last_points_only=True`, this method returns a single time series (or a sequence of time + series) composed of the last point from each historical forecast. This time series will thus have a frequency of + `series.freq * stride`. + If `last_points_only=False`, it will instead return a list (or a sequence of lists) of the full historical + forecast series each with frequency `series.freq`. Parameters ---------- series - The (or a sequence of) target time series used to successively train and compute the historical forecasts. + A (sequence of) target time series used to successively train (if `retrain` is not ``False``) and compute + the historical forecasts. past_covariates - Optionally, one (or a sequence of) past-observed covariate series. This applies only if the model - supports past covariates. + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + This applies only if the model supports past covariates. future_covariates - Optionally, one (or a sequence of) of future-known covariate series. This applies only if the model - supports future covariates. + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + This applies only if the model supports future covariates. + forecast_horizon + The forecast horizon for the predictions. num_samples - Number of times a prediction is sampled from a probabilistic model. Use values `>1` only for probabilistic + Number of times a prediction is sampled from a probabilistic model. Use values ``>1`` only for probabilistic models. train_length - Number of time steps in our training set (size of backtesting window to train on). Only effective when - `retrain` is not ``False``. Default is set to `train_length=None` where it takes all available time steps - up until prediction time, otherwise the moving window strategy is used. If larger than the number of time - steps available, all steps up until prediction time are used, as in default case. Needs to be at least - `min_train_series_length`. + Optionally, use a fixed length / number of time steps for every constructed training set (rolling window + mode). Only effective when `retrain` is not ``False``. The default is ``None``, where it uses all time + steps up until the prediction time (expanding window mode). If larger than the number of available time + steps, uses the expanding mode. Needs to be at least `min_train_series_length`. start Optionally, the first point in time at which a prediction is computed. This parameter supports: ``float``, ``int``, ``pandas.Timestamp``, and ``None``. @@ -707,7 +734,7 @@ def historical_forecasts( - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first predictable point is earlier than the first trainable point. - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), - or `retrain` is a Callable and the first trainable point is earlier than the first predictable point. + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. - the first trainable point (given `train_length`) otherwise Note: If `start` is not within the trainable / forecastable points, uses the closest valid start point that @@ -717,21 +744,18 @@ def historical_forecasts( Note: If `start` is outside the possible historical forecasting times, will ignore the parameter (default behavior with ``None``) and start at the first trainable/predictable point. start_format - Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a - `pd.RangeIndex`. - If set to 'position', `start` corresponds to the index position of the first predicted point and can range - from `(-len(series), len(series) - 1)`. - If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise - an error if the value is not in `series`' index. Default: ``'value'`` - forecast_horizon - The forecast horizon for the predictions. + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. stride The number of time steps between two consecutive predictions. retrain Whether and/or on which condition to retrain the model before predicting. - This parameter supports 3 different datatypes: ``bool``, (positive) ``int``, and - ``Callable`` (returning a ``bool``). - In the case of ``bool``: retrain the model at each step (`True`), or never retrains the model (`False`). + This parameter supports 3 different types: ``bool``, (positive) ``int``, and ``Callable`` (returning a + ``bool``). + In the case of ``bool``: retrain the model at each step (`True`), or never retrain the model (`False`). In the case of ``int``: the model is retrained every `retrain` iterations. In the case of ``Callable``: the model is retrained whenever callable returns `True`. The callable must have the following positional arguments: @@ -740,31 +764,31 @@ def historical_forecasts( - `pred_time` (pd.Timestamp or int): timestamp of forecast time (end of the training series) - `train_series` (TimeSeries): train series up to `pred_time` - `past_covariates` (TimeSeries): past_covariates series up to `pred_time` - - `future_covariates` (TimeSeries): future_covariates series up - to `min(pred_time + series.freq * forecast_horizon, series.end_time())` + - `future_covariates` (TimeSeries): future_covariates series up to `min(pred_time + series.freq * + forecast_horizon, series.end_time())` Note: if any optional `*_covariates` are not passed to `historical_forecast`, ``None`` will be passed to the corresponding retrain function argument. - Note: some models do require being retrained every time and do not support anything other - than `retrain=True`. + Note: some models require being retrained every time and do not support anything other than + `retrain=True`. Note: also controls the retraining of the `data_transformers`. overlap_end Whether the returned forecasts can go beyond the series' end or not. last_points_only - Whether to retain only the last point of each historical forecast. - If set to `True`, the method returns a single ``TimeSeries`` containing the successive point forecasts. + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. Otherwise, returns a list of historical ``TimeSeries`` forecasts. verbose - Whether to print progress. + Whether to print the progress. show_warnings Whether to show warnings related to historical forecasts optimization, or parameters `start` and `train_length`. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. - Default: ``False`` + Default: ``False``. enable_optimization - Whether to use the optimized version of historical_forecasts when supported and available. + Whether to use the optimized version of `historical_forecasts` when supported and available. Default: ``True``. data_transformers Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series @@ -777,9 +801,9 @@ def historical_forecasts( The fitted transformer is used to transform the input during both training and prediction. If the transformation is invertible, the forecasts will be inverse-transformed. fit_kwargs - Additional arguments passed to the model `fit()` method. + Optionally, some additional arguments passed to the model `fit()` method. predict_kwargs - Additional arguments passed to the model `predict()` method. + Optionally, some additional arguments passed to the model `predict()` method. sample_weight Optionally, some sample weights to apply to the target `series` labels for training. Only effective when `retrain` is not ``False``. They are applied per observation, per label (each step in @@ -978,7 +1002,9 @@ def retrain_func( # (otherwise use tqdm on the inner loop). outer_iterator = series else: - outer_iterator = _build_tqdm_iterator(series, verbose) + outer_iterator = _build_tqdm_iterator( + series, verbose, total=len(series), desc="historical forecasts" + ) # deactivate the warning after displaying it once if show_warnings is True show_predict_warnings = show_warnings @@ -1074,7 +1100,10 @@ def retrain_func( if len(series) == 1: # Only use tqdm if there's no outer loop iterator = _build_tqdm_iterator( - historical_forecasts_time_index[::stride], verbose + historical_forecasts_time_index[::stride], + verbose, + total=(len(historical_forecasts_time_index) - 1) // stride + 1, + desc="historical forecasts", ) else: iterator = historical_forecasts_time_index[::stride] @@ -1246,11 +1275,11 @@ def backtest( historical_forecasts: Optional[ Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] ] = None, + forecast_horizon: int = 1, num_samples: int = 1, train_length: Optional[int] = None, start: Optional[Union[pd.Timestamp, float, int]] = None, start_format: Literal["position", "value"] = "value", - forecast_horizon: int = 1, stride: int = 1, retrain: Union[bool, int, Callable[..., bool]] = True, overlap_end: bool = False, @@ -1269,51 +1298,49 @@ def backtest( predict_kwargs: Optional[dict[str, Any]] = None, sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ) -> Union[float, np.ndarray, list[float], list[np.ndarray]]: - """Compute error values that the model would have produced when - used on (potentially multiple) `series`. + """Compute error values that the model produced for historical forecasts on (potentially multiple) `series`. - If `historical_forecasts` are provided, the metric (given by the `metric` function) is evaluated directly on - the forecast and the actual values. The same `series` must be passed that was used to generate the historical - forecasts. Otherwise, it repeatedly builds a training set: either expanding from the - beginning of `series` or moving with a fixed length `train_length`. It trains the current model on the - training set, emits a forecast of length equal to `forecast_horizon`, and then moves the end of the training - set forward by `stride` time steps. The metric is then evaluated on the forecast and the actual values. - Finally, the method returns a `reduction` (the mean by default) of all these metric scores. + If `historical_forecasts` are provided, the metric(s) (given by the `metric` function) is evaluated directly on + all forecasts and actual values. The same `series` and `last_points_only` value must be passed that were used + to generate the historical forecasts. Finally, the method returns an optional `reduction` (the mean by default) + of all these metric scores. - By default, this method uses each historical forecast (whole) to compute error scores. - If `last_points_only` is set to `True`, it will use only the last point of each historical - forecast. In this case, no reduction is used. + If `historical_forecasts` is ``None``, it first generates the historical forecasts with the parameters given + below (see :meth:`ForecastingModel.historical_forecasts() + ` for more info) and then + evaluates as described above. - By default, this method always re-trains the models on the entire available history, corresponding to an - expanding window strategy. If `retrain` is set to `False` (useful for models for which training might be - time-consuming, such as deep learning models), the trained model will be used directly to emit the forecasts. + The metric(s) can be further customized `metric_kwargs` (e.g. control the aggregation over components, time + steps, multiple series, other required arguments such as `q` for quantile metrics, ...). Parameters ---------- series - The (or a sequence of) target time series used to successively train and evaluate the historical forecasts. + A (sequence of) target time series used to successively train (if `retrain` is not ``False``) and compute + the historical forecasts. past_covariates - Optionally, one (or a sequence of) past-observed covariate series. This applies only if the model - supports past covariates. + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + This applies only if the model supports past covariates. future_covariates - Optionally, one (or a sequence of) future-known covariate series. This applies only if the model - supports future covariates. + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + This applies only if the model supports future covariates. historical_forecasts Optionally, the (or a sequence of / a sequence of sequences of) historical forecasts time series to be evaluated. Corresponds to the output of :meth:`historical_forecasts() `. The same `series` and - `last_points_only` values must be passed that were used to generate the historical forecasts. - If provided, will skip historical forecasting and ignore all parameters except `series`, - `last_points_only`, `metric`, and `reduction`. + `last_points_only` values must be passed that were used to generate the historical forecasts. If provided, + will skip historical forecasting and ignore all parameters except `series`, `last_points_only`, `metric`, + and `reduction`. + forecast_horizon + The forecast horizon for the predictions. num_samples - Number of times a prediction is sampled from a probabilistic model. Use values `>1` only for probabilistic + Number of times a prediction is sampled from a probabilistic model. Use values ``>1`` only for probabilistic models. train_length - Number of time steps in our training set (size of backtesting window to train on). Only effective when - `retrain` is not ``False``. Default is set to `train_length=None` where it takes all available time steps - up until prediction time, otherwise the moving window strategy is used. If larger than the number of time - steps available, all steps up until prediction time are used, as in default case. Needs to be at least - `min_train_series_length`. + Optionally, use a fixed length / number of time steps for every constructed training set (rolling window + mode). Only effective when `retrain` is not ``False``. The default is ``None``, where it uses all time + steps up until the prediction time (expanding window mode). If larger than the number of available time + steps, uses the expanding mode. Needs to be at least `min_train_series_length`. start Optionally, the first point in time at which a prediction is computed. This parameter supports: ``float``, ``int``, ``pandas.Timestamp``, and ``None``. @@ -1327,7 +1354,7 @@ def backtest( - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first predictable point is earlier than the first trainable point. - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), - or `retrain` is a Callable and the first trainable point is earlier than the first predictable point. + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. - the first trainable point (given `train_length`) otherwise Note: If `start` is not within the trainable / forecastable points, uses the closest valid start point that @@ -1337,41 +1364,40 @@ def backtest( Note: If `start` is outside the possible historical forecasting times, will ignore the parameter (default behavior with ``None``) and start at the first trainable/predictable point. start_format - Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a - `pd.RangeIndex`. - If set to 'position', `start` corresponds to the index position of the first predicted point and can range - from `(-len(series), len(series) - 1)`. - If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise - an error if the value is not in `series`' index. Default: ``'value'`` - forecast_horizon - The forecast horizon for the point predictions. + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. stride The number of time steps between two consecutive predictions. retrain Whether and/or on which condition to retrain the model before predicting. - This parameter supports 3 different datatypes: ``bool``, (positive) ``int``, and - ``Callable`` (returning a ``bool``). - In the case of ``bool``: retrain the model at each step (`True`), or never retrains the model (`False`). + This parameter supports 3 different types: ``bool``, (positive) ``int``, and ``Callable`` (returning a + ``bool``). + In the case of ``bool``: retrain the model at each step (`True`), or never retrain the model (`False`). In the case of ``int``: the model is retrained every `retrain` iterations. In the case of ``Callable``: the model is retrained whenever callable returns `True`. The callable must have the following positional arguments: - - `counter` (int): current `retrain` iteration - - `pred_time` (pd.Timestamp or int): timestamp of forecast time (end of the training series) - - `train_series` (TimeSeries): train series up to `pred_time` - - `past_covariates` (TimeSeries): past_covariates series up to `pred_time` - - `future_covariates` (TimeSeries): future_covariates series up - to `min(pred_time + series.freq * forecast_horizon, series.end_time())` + - `counter` (int): current `retrain` iteration + - `pred_time` (pd.Timestamp or int): timestamp of forecast time (end of the training series) + - `train_series` (TimeSeries): train series up to `pred_time` + - `past_covariates` (TimeSeries): past_covariates series up to `pred_time` + - `future_covariates` (TimeSeries): future_covariates series up to `min(pred_time + series.freq * + forecast_horizon, series.end_time())` Note: if any optional `*_covariates` are not passed to `historical_forecast`, ``None`` will be passed to the corresponding retrain function argument. - Note: some models do require being retrained every time and do not support anything other - than `retrain=True`. + Note: some models require being retrained every time and do not support anything other than + `retrain=True`. Note: also controls the retraining of the `data_transformers`. overlap_end Whether the returned forecasts can go beyond the series' end or not. last_points_only - Whether to use the whole historical forecasts or only the last point of each forecast to compute the error. + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. + Otherwise, returns a list of historical ``TimeSeries`` forecasts. metric A metric function or a list of metric functions. Each metric must either be a Darts metric (see `here `_), or a custom metric that has an @@ -1384,15 +1410,16 @@ def backtest( If explicitly set to `None`, the method will return a list of the individual error scores instead. Set to ``np.mean`` by default. verbose - Whether to print progress. + Whether to print the progress. show_warnings - Whether to show warnings related to parameters `start`, and `train_length`. + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only - supported for probabilistic models with `likelihood="quantile"`, `num_samples = 1` and - `n<=output_chunk_length`. Default: ``False``. + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only + supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. + Default: ``False``. enable_optimization - Whether to use the optimized version of historical_forecasts when supported and available. + Whether to use the optimized version of `historical_forecasts` when supported and available. Default: ``True``. data_transformers Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series @@ -1411,9 +1438,9 @@ def backtest( each metric separately and only if they are present in the corresponding metric signature. Parameter `'insample'` for scaled metrics (e.g. mase`, `rmsse`, ...) is ignored, as it is handled internally. fit_kwargs - Additional arguments passed to the model `fit()` method. + Optionally, some additional arguments passed to the model `fit()` method. predict_kwargs - Additional arguments passed to the model `predict()` method. + Optionally, some additional arguments passed to the model `predict()` method. sample_weight Optionally, some sample weights to apply to the target `series` labels for training. Only effective when `retrain` is not ``False``. They are applied per observation, per label (each step in @@ -1494,58 +1521,13 @@ def backtest( # remember input series type series_seq_type = get_series_seq_type(series) - series = series2seq(series) - - # check that `historical_forecasts` have correct type - expected_seq_type = None - forecast_seq_type = get_series_seq_type(historical_forecasts) - if last_points_only and not series_seq_type == forecast_seq_type: - # lpo=True -> fc sequence type must be the same - expected_seq_type = series_seq_type - elif not last_points_only and forecast_seq_type != series_seq_type + 1: - # lpo=False -> fc sequence type must be one order higher - expected_seq_type = series_seq_type + 1 - - if expected_seq_type is not None: - raise_log( - ValueError( - f"Expected `historical_forecasts` of type {expected_seq_type} " - f"with `last_points_only={last_points_only}` and `series` of type " - f"{series_seq_type}. However, received `historical_forecasts` of type " - f"{forecast_seq_type}. Make sure to pass the same `last_points_only` " - f"value that was used to generate the historical forecasts." - ), - logger=logger, - ) - - # we must wrap each fc in a list if `last_points_only=True` - nested = last_points_only and forecast_seq_type == SeriesType.SEQ - historical_forecasts = series2seq( - historical_forecasts, seq_type_out=SeriesType.SEQ_SEQ, nested=nested + # validate historical forecasts and convert to multiple series with multiple forecasts case + series, historical_forecasts = _process_historical_forecast_for_backtest( + series=series, + historical_forecasts=historical_forecasts, + last_points_only=last_points_only, ) - # check that the number of series-specific forecasts corresponds to the - # number of series in `series` - if len(series) != len(historical_forecasts): - error_msg = ( - f"Mismatch between the number of series-specific `historical_forecasts` " - f"(n={len(historical_forecasts)}) and the number of `TimeSeries` in `series` " - f"(n={len(series)}). For `last_points_only={last_points_only}`, expected " - ) - expected_seq_type = ( - series_seq_type if last_points_only else series_seq_type + 1 - ) - if expected_seq_type == SeriesType.SINGLE: - error_msg += ( - f"a single `historical_forecasts` of type {expected_seq_type}." - ) - else: - error_msg += f"`historical_forecasts` of type {expected_seq_type} with length n={len(series)}." - raise_log( - ValueError(error_msg), - logger=logger, - ) - # we have multiple forecasts per series: rearrange forecasts to call each metric only once; # flatten historical forecasts, get matching target series index, remember cumulative target lengths # for later reshaping back to original @@ -1754,7 +1736,7 @@ def gridsearch( A reduction function (mapping array to float) describing how to aggregate the errors obtained on the different validation series when backtesting. By default it'll compute the mean of errors. verbose - Whether to print progress. + Whether to print the progress. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when there are two or more parameters combinations to evaluate. Each job will instantiate, train, and evaluate a different instance of the model. @@ -1861,7 +1843,10 @@ def gridsearch( # iterate through all combinations of the provided parameters and choose the best one iterator = _build_tqdm_iterator( - zip(params_cross_product), verbose, total=len(params_cross_product) + zip(params_cross_product), + verbose, + total=len(params_cross_product), + desc="gridsearch", ) def _evaluate_combination(param_combination) -> float: @@ -1994,13 +1979,14 @@ def residuals( historical_forecasts: Optional[ Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] ] = None, + forecast_horizon: int = 1, num_samples: int = 1, train_length: Optional[int] = None, start: Optional[Union[pd.Timestamp, float, int]] = None, start_format: Literal["position", "value"] = "value", - forecast_horizon: int = 1, stride: int = 1, retrain: Union[bool, int, Callable[..., bool]] = True, + overlap_end: bool = False, last_points_only: bool = True, metric: METRIC_TYPE = metrics.err, verbose: bool = False, @@ -2013,10 +1999,10 @@ def residuals( metric_kwargs: Optional[dict[str, Any]] = None, fit_kwargs: Optional[dict[str, Any]] = None, predict_kwargs: Optional[dict[str, Any]] = None, - values_only: bool = False, sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + values_only: bool = False, ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: - """Compute the residuals produced by this model on a (or sequence of) `TimeSeries`. + """Compute the residuals that the model produced for historical forecasts on (potentially multiple) `series`. This function computes the difference (or one of Darts' "per time step" metrics) between the actual observations from `series` and the fitted values obtained by training the model on `series` (or using a @@ -2025,7 +2011,7 @@ def residuals( In sequence this method performs: - - compute historical forecasts for each series or use pre-computed `historical_forecasts` (see + - use pre-computed `historical_forecasts` or compute historical forecasts for each series (see :meth:`~darts.models.forecasting.forecasting_model.ForecastingModel.historical_forecasts` for more details). How the historical forecasts are generated can be configured with parameters `num_samples`, `train_length`, `start`, `start_format`, `forecast_horizon`, `stride`, `retrain`, `last_points_only`, `fit_kwargs`, and @@ -2033,7 +2019,7 @@ def residuals( - compute a backtest using a "per time step" `metric` between the historical forecasts and `series` per component/column and time step (see :meth:`~darts.models.forecasting.forecasting_model.ForecastingModel.backtest` for more details). By default, - uses the residuals :func:`~darts.metrics.metrics.err` as a `metric`. + uses the residuals :func:`~darts.metrics.metrics.err` (error) as a `metric`. - create and return `TimeSeries` (or simply a np.ndarray with `values_only=True`) with the time index from historical forecasts, and values from the metrics per component and time step. @@ -2043,13 +2029,14 @@ def residuals( Parameters ---------- series - The univariate TimeSeries instance which the residuals will be computed for. + A (sequence of) target time series used to successively train (if `retrain` is not ``False``) and compute + the historical forecasts. past_covariates - One or several past-observed covariate time series. + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + This applies only if the model supports past covariates. future_covariates - One or several future-known covariate time series. - forecast_horizon - The forecasting horizon used to predict each fitted value. + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + This applies only if the model supports future covariates. historical_forecasts Optionally, the (or a sequence of / a sequence of sequences of) historical forecasts time series to be evaluated. Corresponds to the output of :meth:`historical_forecasts() @@ -2057,15 +2044,16 @@ def residuals( `last_points_only` values must be passed that were used to generate the historical forecasts. If provided, will skip historical forecasting and ignore all parameters except `series`, `last_points_only`, `metric`, and `reduction`. + forecast_horizon + The forecast horizon for the predictions. num_samples - Number of times a prediction is sampled from a probabilistic model. Use values `>1` only for probabilistic + Number of times a prediction is sampled from a probabilistic model. Use values ``>1`` only for probabilistic models. train_length - Number of time steps in our training set (size of backtesting window to train on). Only effective when - `retrain` is not ``False``. Default is set to `train_length=None` where it takes all available time steps - up until prediction time, otherwise the moving window strategy is used. If larger than the number of time - steps available, all steps up until prediction time are used, as in default case. Needs to be at least - `min_train_series_length`. + Optionally, use a fixed length / number of time steps for every constructed training set (rolling window + mode). Only effective when `retrain` is not ``False``. The default is ``None``, where it uses all time + steps up until the prediction time (expanding window mode). If larger than the number of available time + steps, uses the expanding mode. Needs to be at least `min_train_series_length`. start Optionally, the first point in time at which a prediction is computed. This parameter supports: ``float``, ``int``, ``pandas.Timestamp``, and ``None``. @@ -2079,7 +2067,7 @@ def residuals( - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first predictable point is earlier than the first trainable point. - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), - or `retrain` is a Callable and the first trainable point is earlier than the first predictable point. + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. - the first trainable point (given `train_length`) otherwise Note: If `start` is not within the trainable / forecastable points, uses the closest valid start point that @@ -2089,39 +2077,40 @@ def residuals( Note: If `start` is outside the possible historical forecasting times, will ignore the parameter (default behavior with ``None``) and start at the first trainable/predictable point. start_format - Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a - `pd.RangeIndex`. - If set to 'position', `start` corresponds to the index position of the first predicted point and can range - from `(-len(series), len(series) - 1)`. - If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise - an error if the value is not in `series`' index. Default: ``'value'`` - forecast_horizon - The forecast horizon for the point predictions. + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. stride The number of time steps between two consecutive predictions. retrain Whether and/or on which condition to retrain the model before predicting. - This parameter supports 3 different datatypes: ``bool``, (positive) ``int``, and - ``Callable`` (returning a ``bool``). - In the case of ``bool``: retrain the model at each step (`True`), or never retrains the model (`False`). + This parameter supports 3 different types: ``bool``, (positive) ``int``, and ``Callable`` (returning a + ``bool``). + In the case of ``bool``: retrain the model at each step (`True`), or never retrain the model (`False`). In the case of ``int``: the model is retrained every `retrain` iterations. In the case of ``Callable``: the model is retrained whenever callable returns `True`. The callable must have the following positional arguments: - - `counter` (int): current `retrain` iteration - - `pred_time` (pd.Timestamp or int): timestamp of forecast time (end of the training series) - - `train_series` (TimeSeries): train series up to `pred_time` - - `past_covariates` (TimeSeries): past_covariates series up to `pred_time` - - `future_covariates` (TimeSeries): future_covariates series up - to `min(pred_time + series.freq * forecast_horizon, series.end_time())` + - `counter` (int): current `retrain` iteration + - `pred_time` (pd.Timestamp or int): timestamp of forecast time (end of the training series) + - `train_series` (TimeSeries): train series up to `pred_time` + - `past_covariates` (TimeSeries): past_covariates series up to `pred_time` + - `future_covariates` (TimeSeries): future_covariates series up to `min(pred_time + series.freq * + forecast_horizon, series.end_time())` Note: if any optional `*_covariates` are not passed to `historical_forecast`, ``None`` will be passed to the corresponding retrain function argument. - Note: some models do require being retrained every time and do not support anything other - than `retrain=True`. + Note: some models require being retrained every time and do not support anything other than + `retrain=True`. Note: also controls the retraining of the `data_transformers`. + overlap_end + Whether the returned forecasts can go beyond the series' end or not. last_points_only - Whether to use the whole historical forecasts or only the last point of each forecast to compute the error. + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. + Otherwise, returns a list of historical ``TimeSeries`` forecasts. metric Either one of Darts' "per time step" metrics (see `here `_), or a custom metric that has an @@ -2129,15 +2118,16 @@ def residuals( :func:`~darts.metrics.metrics.multi_ts_support` and :func:`~darts.metrics.metrics.multi_ts_support`, and returns one value per time step. verbose - Whether to print progress. + Whether to print the progress. show_warnings - Whether to show warnings related to parameters `start`, and `train_length`. + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only - supported for probabilistic models with `likelihood="quantile"`, `num_samples = 1` and - `n<=output_chunk_length`. Default: ``False``. + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only + supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. + Default: ``False``. enable_optimization - Whether to use the optimized version of historical_forecasts when supported and available. + Whether to use the optimized version of `historical_forecasts` when supported and available. Default: ``True``. data_transformers Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series @@ -2156,11 +2146,9 @@ def residuals( reduction arguments `"series_reduction", "component_reduction", "time_reduction"`, and parameter `'insample'` for scaled metrics (e.g. mase`, `rmsse`, ...), as they are handled internally. fit_kwargs - Additional arguments passed to the model `fit()` method. + Optionally, some additional arguments passed to the model `fit()` method. predict_kwargs - Additional arguments passed to the model `predict()` method. - values_only - Whether to return the residuals as `np.ndarray`. If `False`, returns residuals as `TimeSeries`. + Optionally, some additional arguments passed to the model `predict()` method. sample_weight Optionally, some sample weights to apply to the target `series` labels for training. Only effective when `retrain` is not ``False``. They are applied per observation, per label (each step in @@ -2171,6 +2159,8 @@ def residuals( If a string, then the weights are generated using built-in weighting functions. The available options are `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are computed per time `series`. + values_only + Whether to return the residuals as `np.ndarray`. If `False`, returns residuals as `TimeSeries`. Returns ------- @@ -2210,35 +2200,35 @@ def residuals( data_transformers=data_transformers, fit_kwargs=fit_kwargs, predict_kwargs=predict_kwargs, - overlap_end=False, + overlap_end=overlap_end, sample_weight=sample_weight, ) - residuals = self.backtest( + # remember input series type + series_seq_type = get_series_seq_type(series) + # validate historical forecasts and convert to multiple series with multiple forecasts case + series, historical_forecasts = _process_historical_forecast_for_backtest( series=series, historical_forecasts=historical_forecasts, last_points_only=last_points_only, + ) + + # optionally, add nans to end of series to get residuals of same shape for each forecast + if overlap_end: + series = _extend_series_for_overlap_end( + series=series, historical_forecasts=historical_forecasts + ) + + residuals = self.backtest( + series=series, + historical_forecasts=historical_forecasts, + last_points_only=False, metric=metric, reduction=None, data_transformers=data_transformers, metric_kwargs=metric_kwargs, ) - # remember input series type - series_seq_type = get_series_seq_type(series) - - # convert forecasts and residuals to list of lists of series/arrays - forecast_seq_type = get_series_seq_type(historical_forecasts) - historical_forecasts = series2seq( - historical_forecasts, - seq_type_out=SeriesType.SEQ_SEQ, - nested=last_points_only and forecast_seq_type == SeriesType.SEQ, - ) - if series_seq_type == SeriesType.SINGLE: - residuals = [residuals] - if last_points_only: - residuals = [[res] for res in residuals] - # sanity check residual output q, q_interval = metric_kwargs.get("q"), metric_kwargs.get("q_interval") try: @@ -2801,6 +2791,7 @@ def _optimized_historical_forecasts( show_warnings: bool = True, predict_likelihood_parameters: bool = False, data_transformers: Optional[dict[str, BaseDataTransformer]] = None, + **kwargs, ) -> Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]]: logger.warning( "`optimized historical forecasts is not available for this model, use `historical_forecasts` instead." @@ -3005,12 +2996,11 @@ def predict( One future-known covariate time series for every input time series in `series`. They must match the past covariates that have been used with the :func:`fit()` function for training in terms of dimension. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose - Optionally, whether to print progress. + Whether to print the progress. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. Default: ``False`` show_warnings @@ -3217,8 +3207,7 @@ def predict( the covariate time series that has been used with the :func:`fit()` method for training, and it must contain at least the next `n` time steps/indices after the end of the training target series. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose Optionally, set the prediction verbosity. Not effective for all models. show_warnings @@ -3392,8 +3381,7 @@ def predict( training target series. If `series` is set, it must contain at least the time steps/indices corresponding to the new target series (historic future covariates), plus the next `n` time steps/indices after the end. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose Optionally, set the prediction verbosity. Not effective for all models. show_warnings diff --git a/darts/models/forecasting/regression_model.py b/darts/models/forecasting/regression_model.py index 5302dc0ab1..b5c76a0f0e 100644 --- a/darts/models/forecasting/regression_model.py +++ b/darts/models/forecasting/regression_model.py @@ -988,9 +988,9 @@ def predict( Number of times a prediction is sampled from a probabilistic model. Should be set to 1 for deterministic models. verbose - Optionally, whether to print progress. + Whether to print the progress. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. Default: ``False`` **kwargs : dict, optional diff --git a/darts/models/forecasting/torch_forecasting_model.py b/darts/models/forecasting/torch_forecasting_model.py index f73052dc5c..89ca19401f 100644 --- a/darts/models/forecasting/torch_forecasting_model.py +++ b/darts/models/forecasting/torch_forecasting_model.py @@ -14,9 +14,6 @@ as well as past and future values of some future covariates. * SplitCovariatesTorchModel(TorchForecastingModel) for torch models consuming past-observed as well as future values of some future covariates. - - * TorchParametricProbabilisticForecastingModel(TorchForecastingModel) is the super-class of all probabilistic torch - forecasting models. """ import copy @@ -706,7 +703,7 @@ def fit( Optionally, a custom PyTorch-Lightning Trainer object to perform training. Using a custom ``trainer`` will override Darts' default trainer. verbose - Optionally, whether to print the progress. Ignored if there is a `ProgressBar` callback in + Whether to print the progress. Ignored if there is a `ProgressBar` callback in `pl_trainer_kwargs`. epochs If specified, will train the model for ``epochs`` (additional) epochs, irrespective of what ``n_epochs`` @@ -932,7 +929,7 @@ def fit_from_dataset( Optionally, a custom PyTorch-Lightning Trainer object to perform prediction. Using a custom `trainer` will override Darts' default trainer. verbose - Optionally, whether to print the progress. Ignored if there is a `ProgressBar` callback in + Whether to print the progress. Ignored if there is a `ProgressBar` callback in `pl_trainer_kwargs`. epochs If specified, will train the model for ``epochs`` (additional) epochs, irrespective of what ``n_epochs`` @@ -1237,7 +1234,7 @@ def lr_find( Optionally, a custom PyTorch-Lightning Trainer object to perform training. Using a custom ``trainer`` will override Darts' default trainer. verbose - Optionally, whether to print the progress. Ignored if there is a `ProgressBar` callback in + Whether to print the progress. Ignored if there is a `ProgressBar` callback in `pl_trainer_kwargs`. epochs If specified, will train the model for ``epochs`` (additional) epochs, irrespective of what ``n_epochs`` @@ -1366,7 +1363,7 @@ def predict( batch_size Size of batches during prediction. Defaults to the models' training ``batch_size`` value. verbose - Optionally, whether to print the progress. Ignored if there is a `ProgressBar` callback in + Whether to print the progress. Ignored if there is a `ProgressBar` callback in `pl_trainer_kwargs`. n_jobs The number of jobs to run in parallel. ``-1`` means using all processors. Defaults to ``1``. @@ -1376,8 +1373,7 @@ def predict( (and optionally future covariates) back into the model. If this parameter is not provided, it will be set ``output_chunk_length`` by default. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. dataloader_kwargs Optionally, a dictionary of keyword arguments used to create the PyTorch `DataLoader` instance for the inference/prediction dataset. For more information on `DataLoader`, check out `this link @@ -1388,7 +1384,7 @@ def predict( Optionally, enable monte carlo dropout for predictions using neural network based models. This allows bayesian approximation by specifying an implicit prior over learned models. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. Default: ``False``. show_warnings @@ -1514,7 +1510,7 @@ def predict_from_dataset( batch_size Size of batches during prediction. Defaults to the models ``batch_size`` value. verbose - Optionally, whether to print the progress. Ignored if there is a `ProgressBar` callback in + Whether to print the progress. Ignored if there is a `ProgressBar` callback in `pl_trainer_kwargs`. n_jobs The number of jobs to run in parallel. ``-1`` means using all processors. Defaults to ``1``. @@ -1524,8 +1520,7 @@ def predict_from_dataset( (and optionally future covariates) back into the model. If this parameter is not provided, it will be set ``output_chunk_length`` by default. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. dataloader_kwargs Optionally, a dictionary of keyword arguments used to create the PyTorch `DataLoader` instance for the inference/prediction dataset. For more information on `DataLoader`, check out `this link @@ -1536,7 +1531,7 @@ def predict_from_dataset( Optionally, enable monte carlo dropout for predictions using neural network based models. This allows bayesian approximation by specifying an implicit prior over learned models. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. Default: ``False`` diff --git a/darts/tests/conftest.py b/darts/tests/conftest.py index b0b97a0131..90bf29e20b 100644 --- a/darts/tests/conftest.py +++ b/darts/tests/conftest.py @@ -1,4 +1,5 @@ import logging +import os import shutil import tempfile @@ -40,15 +41,31 @@ def tear_down_tests(): @pytest.fixture(scope="module") def tmpdir_module(): - """Sets up a temporary directory that will be deleted after the test module (script) finished.""" + """Sets up and moves into a temporary directory that will be deleted after the test module (script) finished.""" temp_work_dir = tempfile.mkdtemp(prefix="darts") + # remember origin + cwd = os.getcwd() + # move to temp dir + os.chdir(temp_work_dir) + # go into test with temp dir as input yield temp_work_dir + # move back to origin shutil.rmtree(temp_work_dir) + # remove temp dir + os.chdir(cwd) @pytest.fixture(scope="function") def tmpdir_fn(): - """Sets up a temporary directory that will be deleted after the test function finished.""" + """Sets up and moves into a temporary directory that will be deleted after the test function finished.""" temp_work_dir = tempfile.mkdtemp(prefix="darts") + # remember origin + cwd = os.getcwd() + # move to temp dir + os.chdir(temp_work_dir) + # go into test with temp dir as input yield temp_work_dir + # move back to origin + os.chdir(cwd) + # remove temp dir shutil.rmtree(temp_work_dir) diff --git a/darts/tests/metrics/test_metrics.py b/darts/tests/metrics/test_metrics.py index f3e2b88229..ba58b6fe3f 100644 --- a/darts/tests/metrics/test_metrics.py +++ b/darts/tests/metrics/test_metrics.py @@ -79,6 +79,53 @@ def metric_iw(y_true, y_pred, q_interval=None, **kwargs): return res.reshape(len(y_pred), -1) +def metric_iws(y_true, y_pred, q_interval=None, **kwargs): + # this tests assumes `y_pred` are stochastic values + if isinstance(q_interval, tuple): + q_interval = [q_interval] + q_interval = np.array(q_interval) + q_lo = q_interval[:, 0] + q_hi = q_interval[:, 1] + y_pred_lo = np.quantile(y_pred, q_lo, axis=2).transpose(1, 2, 0) + y_pred_hi = np.quantile(y_pred, q_hi, axis=2).transpose(1, 2, 0) + interval_width = y_pred_hi - y_pred_lo + res = np.where( + y_true < y_pred_lo, + interval_width + 1 / q_lo * (y_pred_lo - y_true), + interval_width, + ) + res = np.where( + y_true > y_pred_hi, interval_width + 1 / (1 - q_hi) * (y_true - y_pred_hi), res + ) + return res.reshape(len(y_pred), -1) + + +def metric_ic(y_true, y_pred, q_interval=None, **kwargs): + # this tests assumes `y_pred` are stochastic values + if isinstance(q_interval, tuple): + q_interval = [q_interval] + q_interval = np.array(q_interval) + q_lo = q_interval[:, 0] + q_hi = q_interval[:, 1] + y_pred_lo = np.quantile(y_pred, q_lo, axis=2).transpose(1, 2, 0) + y_pred_hi = np.quantile(y_pred, q_hi, axis=2).transpose(1, 2, 0) + res = np.where((y_pred_lo <= y_true) & (y_true <= y_pred_hi), 1, 0) + return res.reshape(len(y_pred), -1) + + +def metric_incs_qr(y_true, y_pred, q_interval=None, **kwargs): + # this tests assumes `y_pred` are stochastic values + if isinstance(q_interval, tuple): + q_interval = [q_interval] + q_interval = np.array(q_interval) + q_lo = q_interval[:, 0] + q_hi = q_interval[:, 1] + y_pred_lo = np.quantile(y_pred, q_lo, axis=2).transpose(1, 2, 0) + y_pred_hi = np.quantile(y_pred, q_hi, axis=2).transpose(1, 2, 0) + res = np.maximum(y_pred_lo - y_true, y_true - y_pred_hi) + return res.reshape(len(y_pred), -1) + + class TestMetrics: np.random.seed(42) pd_train = pd.Series( @@ -1853,6 +1900,9 @@ def test_wrong_error_scale(self): [ # only time dependent quantile interval metrics (metrics.iw, metric_iw), + (metrics.iws, metric_iws), + (metrics.ic, metric_ic), + (metrics.incs_qr, metric_incs_qr), ], ) def test_metric_quantile_interval_accuracy(self, config): @@ -1899,6 +1949,12 @@ def check_ref(**test_kwargs): # time dependent but with time reduction metrics.iw, metrics.miw, + metrics.iws, + metrics.miws, + metrics.ic, + metrics.mic, + metrics.incs_qr, + metrics.mincs_qr, ], [True, False], # univariate series [True, False], # single series diff --git a/darts/tests/models/forecasting/test_conformal_model.py b/darts/tests/models/forecasting/test_conformal_model.py new file mode 100644 index 0000000000..2797d35231 --- /dev/null +++ b/darts/tests/models/forecasting/test_conformal_model.py @@ -0,0 +1,1660 @@ +import copy +import itertools +import math +import os + +import numpy as np +import pandas as pd +import pytest + +from darts import TimeSeries, concatenate +from darts.datasets import AirPassengersDataset +from darts.metrics import ae, err, ic, incs_qr, mic +from darts.models import ( + ConformalNaiveModel, + ConformalQRModel, + LinearRegressionModel, + NaiveSeasonal, + NLinearModel, +) +from darts.models.forecasting.conformal_models import _get_calibration_hfc_start +from darts.models.forecasting.forecasting_model import ForecastingModel +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs +from darts.utils import n_steps_between +from darts.utils import timeseries_generation as tg +from darts.utils.timeseries_generation import linear_timeseries +from darts.utils.utils import ( + likelihood_component_names, + quantile_interval_names, + quantile_names, +) + +IN_LEN = 3 +OUT_LEN = 3 +regr_kwargs = {"lags": IN_LEN, "output_chunk_length": OUT_LEN} +tfm_kwargs = copy.deepcopy(tfm_kwargs) +tfm_kwargs["pl_trainer_kwargs"]["fast_dev_run"] = True +torch_kwargs = dict( + {"input_chunk_length": IN_LEN, "output_chunk_length": OUT_LEN, "random_state": 0}, + **tfm_kwargs, +) +pred_lklp = {"num_samples": 1, "predict_likelihood_parameters": True} +q = [0.1, 0.5, 0.9] + + +def train_model( + *args, model_type="regression", model_params=None, quantiles=None, **kwargs +): + model_params = model_params or {} + if model_type == "regression": + return LinearRegressionModel( + **regr_kwargs, + **model_params, + random_state=42, + ).fit(*args, **kwargs) + elif model_type in ["regression_prob", "regression_qr"]: + return LinearRegressionModel( + likelihood="quantile", + quantiles=quantiles, + **regr_kwargs, + **model_params, + random_state=42, + ).fit(*args, **kwargs) + else: + return NLinearModel(**torch_kwargs, **model_params).fit(*args, **kwargs) + + +# pre-trained global model for conformal models +models_cls_kwargs_errs = [ + ( + ConformalNaiveModel, + {"quantiles": q}, + "regression", + ), +] + +if TORCH_AVAILABLE: + models_cls_kwargs_errs.append(( + ConformalNaiveModel, + {"quantiles": q}, + "torch", + )) + + +class TestConformalModel: + """ + Tests all general model behavior for Naive Conformal Model with symmetric non-conformity score. + Additionally, checks correctness of predictions for: + - ConformalNaiveModel with symmetric & asymmetric non-conformity scores + - ConformalQRModel with symmetric & asymmetric non-conformity scores + """ + + np.random.seed(42) + + # forecasting horizon used in runnability tests + horizon = OUT_LEN + 1 + + # some arbitrary static covariates + static_covariates = pd.DataFrame([[0.0, 1.0]], columns=["st1", "st2"]) + + # real timeseries for functionality tests + ts_length = 13 + horizon + ts_passengers = ( + AirPassengersDataset() + .load()[:ts_length] + .with_static_covariates(static_covariates) + ) + ts_pass_train, ts_pass_val = ( + ts_passengers[:-horizon], + ts_passengers[-horizon:], + ) + + # an additional noisy series + ts_pass_train_1 = ts_pass_train + 0.01 * tg.gaussian_timeseries( + length=len(ts_pass_train), + freq=ts_pass_train.freq_str, + start=ts_pass_train.start_time(), + ) + + # an additional time series serving as covariates + year_series = tg.datetime_attribute_timeseries(ts_passengers, attribute="year") + month_series = tg.datetime_attribute_timeseries(ts_passengers, attribute="month") + time_covariates = year_series.stack(month_series) + time_covariates_train = time_covariates[:-horizon] + + # various ts with different static covariates representations + ts_w_static_cov = tg.linear_timeseries(length=ts_length).with_static_covariates( + pd.Series([1, 2]) + ) + ts_shared_static_cov = ts_w_static_cov.stack(tg.sine_timeseries(length=ts_length)) + ts_comps_static_cov = ts_shared_static_cov.with_static_covariates( + pd.DataFrame([[0, 1], [2, 3]], columns=["st1", "st2"]) + ) + + def test_model_construction_naive(self): + local_model = NaiveSeasonal(K=5) + global_model = LinearRegressionModel(**regr_kwargs) + series = self.ts_pass_train + + model_err_msg = "`model` must be a pre-trained `GlobalForecastingModel`." + # un-trained local model + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=local_model, quantiles=q) + assert str(exc.value) == model_err_msg + + # pre-trained local model + local_model.fit(series) + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=local_model, quantiles=q) + assert str(exc.value) == model_err_msg + + # un-trained global model + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=q) + assert str(exc.value) == model_err_msg + + # pre-trained local model should work + global_model.fit(series) + model = ConformalNaiveModel(model=global_model, quantiles=q) + assert model.likelihood == "quantile" + + # non-centered quantiles + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=[0.2, 0.5, 0.6]) + assert str(exc.value) == ( + "quantiles lower than `q=0.5` need to share same difference to `0.5` as quantiles higher than `q=0.5`" + ) + + # quantiles missing median + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=[0.1, 0.9]) + assert str(exc.value) == "median quantile `q=0.5` must be in `quantiles`" + + # too low and high quantiles + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=[-0.1, 0.5, 1.1]) + assert str(exc.value) == "All provided quantiles must be between 0 and 1." + + # `cal_length` must be `>=1` or `None` + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=q, cal_length=0) + assert str(exc.value) == "`cal_length` must be `>=1` or `None`." + + # `cal_stride` must be `>=1` + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=q, cal_stride=0) + assert str(exc.value) == "`cal_stride` must be `>=1`." + + # `num_samples` must be `>=1` + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=q, cal_num_samples=0) + assert str(exc.value) == "`cal_num_samples` must be `>=1`." + + def test_model_hfc_stride_checks(self): + series = self.ts_pass_train + model = LinearRegressionModel(**regr_kwargs).fit(series) + cp_model = ConformalNaiveModel(model=model, quantiles=q, cal_stride=2) + + expected_error_start = ( + "The provided `stride` parameter must be a round-multiple of " + "`cal_stride=2` and `>=cal_stride`." + ) + # `stride` must be >= `cal_stride` + with pytest.raises(ValueError) as exc: + cp_model.historical_forecasts(series=series, stride=1) + assert str(exc.value).startswith(expected_error_start) + + # `stride` must be a round multiple of `cal_stride` + with pytest.raises(ValueError) as exc: + cp_model.historical_forecasts(series=series, stride=3) + assert str(exc.value).startswith(expected_error_start) + + # valid stride + _ = cp_model.historical_forecasts(series=series, stride=4) + + def test_model_construction_cqr(self): + model_det = train_model(self.ts_pass_train, model_type="regression") + model_prob_q = train_model( + self.ts_pass_train, model_type="regression_prob", quantiles=q + ) + model_prob_poisson = train_model( + self.ts_pass_train, + model_type="regression", + model_params={"likelihood": "poisson"}, + ) + + # deterministic global model + with pytest.raises(ValueError) as exc: + ConformalQRModel(model=model_det, quantiles=q) + assert str(exc.value).startswith( + "`model` must support probabilistic forecasting." + ) + # probabilistic model works + _ = ConformalQRModel(model=model_prob_q, quantiles=q) + # works also with different likelihood + _ = ConformalQRModel(model=model_prob_poisson, quantiles=q) + + def test_unsupported_properties(self): + """Tests only here for coverage, maybe at some point we support these properties.""" + model = ConformalNaiveModel(train_model(self.ts_pass_train), quantiles=q) + unsupported_properties = [ + "_model_encoder_settings", + "extreme_lags", + "min_train_series_length", + "min_train_samples", + ] + for prop in unsupported_properties: + with pytest.raises(NotImplementedError): + getattr(model, prop) + + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_save_model_parameters(self, config): + # model creation parameters were saved before. check if re-created model has same params as original + model_cls, kwargs, model_type = config + model = model_cls( + model=train_model( + self.ts_pass_train, model_type=model_type, quantiles=kwargs["quantiles"] + ), + **kwargs, + ) + model_fresh = model.untrained_model() + assert model._model_params.keys() == model_fresh._model_params.keys() + for param, val in model._model_params.items(): + if isinstance(val, ForecastingModel): + # Conformal Models require a forecasting model as input, which has no equality + continue + assert val == model_fresh._model_params[param] + + @pytest.mark.parametrize( + "config", itertools.product(models_cls_kwargs_errs, [{}, pred_lklp]) + ) + def test_save_load_model(self, tmpdir_fn, config): + # check if save and load methods work and if loaded model creates same forecasts as original model + (model_cls, kwargs, model_type), pred_kwargs = config + model = model_cls( + train_model( + self.ts_pass_train, model_type=model_type, quantiles=kwargs["quantiles"] + ), + **kwargs, + ) + + # check if save and load methods work and + # if loaded conformal model creates same forecasts as original ensemble models + expected_suffixes = [ + ".pkl", + ".pkl.NLinearModel.pt", + ".pkl.NLinearModel.pt.ckpt", + ] + + # test save + model.save() + model.save(os.path.join(tmpdir_fn, f"{model_cls.__name__}.pkl")) + + model_prediction = model.predict(5, **pred_kwargs) + + assert os.path.exists(tmpdir_fn) + files = os.listdir(tmpdir_fn) + if model_type == "torch": + # 1 from conformal model, 2 from torch, * 2 as `save()` was called twice + assert len(files) == 6 + for f in files: + assert f.startswith(model_cls.__name__) + suffix_counts = { + suffix: sum(1 for p in os.listdir(tmpdir_fn) if p.endswith(suffix)) + for suffix in expected_suffixes + } + assert all(count == 2 for count in suffix_counts.values()) + else: + assert len(files) == 2 + for f in files: + assert f.startswith(model_cls.__name__) and f.endswith(".pkl") + + # test load + pkl_files = [] + for filename in os.listdir(tmpdir_fn): + if filename.endswith(".pkl"): + pkl_files.append(os.path.join(tmpdir_fn, filename)) + for p in pkl_files: + loaded_model = model_cls.load(p) + assert model_prediction == loaded_model.predict(5, **pred_kwargs) + + def test_fit(self): + model = ConformalNaiveModel(train_model(self.ts_pass_train), quantiles=q) + assert model.model._fit_called + + # check kwargs will be passed to `model.model.fit()` + assert model.supports_sample_weight + model.model._fit_called = False + model.fit(self.ts_pass_train, sample_weight="linear") + assert model.model._fit_called + + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_single_ts(self, config): + model_cls, kwargs, model_type = config + model = model_cls( + train_model( + self.ts_pass_train, model_type=model_type, quantiles=kwargs["quantiles"] + ), + **kwargs, + ) + pred = model.predict(n=self.horizon, **pred_lklp) + assert pred.n_components == self.ts_pass_train.n_components * len( + kwargs["quantiles"] + ) + assert not np.isnan(pred.all_values()).any().any() + + pred_fc = model.model.predict(n=self.horizon) + assert pred_fc.time_index.equals(pred.time_index) + # the center forecasts must be equal to the forecasting model forecast + fc_columns = likelihood_component_names( + self.ts_pass_val.columns, quantile_names([0.5]) + ) + np.testing.assert_array_almost_equal( + pred[fc_columns].all_values(), pred_fc.all_values() + ) + assert pred.static_covariates is None + + # using a different `n`, gives different results, since we can generate more residuals for the horizon + pred1 = model.predict(n=self.horizon - 1, **pred_lklp) + assert not pred1 == pred[: len(pred1)] + + # wrong dimension + with pytest.raises(ValueError): + model.predict( + n=self.horizon, + series=self.ts_pass_train.stack(self.ts_pass_train), + **pred_lklp, + ) + + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_multi_ts(self, config): + model_cls, kwargs, model_type = config + model = model_cls( + train_model( + [self.ts_pass_train, self.ts_pass_train_1], + model_type=model_type, + quantiles=kwargs["quantiles"], + ), + **kwargs, + ) + with pytest.raises(ValueError): + # when model is fit from >1 series, one must provide a series in argument + model.predict(n=1) + + pred = model.predict(n=self.horizon, series=self.ts_pass_train, **pred_lklp) + assert pred.n_components == self.ts_pass_train.n_components * len( + kwargs["quantiles"] + ) + assert not np.isnan(pred.all_values()).any().any() + + # the center forecasts must be equal to the forecasting model forecast + fc_columns = likelihood_component_names( + self.ts_pass_val.columns, quantile_names([0.5]) + ) + pred_fc = model.model.predict(n=self.horizon, series=self.ts_pass_train) + assert pred_fc.time_index.equals(pred.time_index) + np.testing.assert_array_almost_equal( + pred[fc_columns].all_values(), pred_fc.all_values() + ) + + # check prediction for several time series + pred_list = model.predict( + n=self.horizon, + series=[self.ts_pass_train, self.ts_pass_train_1], + **pred_lklp, + ) + pred_fc_list = model.model.predict( + n=self.horizon, + series=[self.ts_pass_train, self.ts_pass_train_1], + ) + assert len(pred_list) == 2, ( + f"Model {model_cls} did not return a list of prediction" + ) + for pred, pred_fc in zip(pred_list, pred_fc_list): + assert pred.n_components == self.ts_pass_train.n_components * len( + kwargs["quantiles"] + ) + assert pred_fc.time_index.equals(pred.time_index) + assert not np.isnan(pred.all_values()).any().any() + np.testing.assert_array_almost_equal( + pred_fc.all_values(), + pred[fc_columns].all_values(), + ) + + # wrong dimension + with pytest.raises(ValueError): + model.predict( + n=self.horizon, + series=[ + self.ts_pass_train, + self.ts_pass_train.stack(self.ts_pass_train), + ], + **pred_lklp, + ) + + @pytest.mark.parametrize( + "config", + itertools.product( + [(ConformalNaiveModel, {"quantiles": [0.1, 0.5, 0.9]}, "regression")], + [ + {"lags_past_covariates": IN_LEN}, + {"lags_future_covariates": (IN_LEN, OUT_LEN)}, + {}, + ], + ), + ) + def test_covariates(self, config): + (model_cls, kwargs, model_type), covs_kwargs = config + model_fc = LinearRegressionModel(**regr_kwargs, **covs_kwargs) + # Here we rely on the fact that all non-Dual models currently are Past models + if model_fc.supports_future_covariates: + cov_name = "future_covariates" + is_past = False + elif model_fc.supports_past_covariates: + cov_name = "past_covariates" + is_past = True + else: + cov_name = None + is_past = None + + covariates = [self.time_covariates_train, self.time_covariates_train] + if cov_name is not None: + cov_kwargs = {cov_name: covariates} + cov_kwargs_train = {cov_name: self.time_covariates_train} + cov_kwargs_notrain = {cov_name: self.time_covariates} + else: + cov_kwargs = {} + cov_kwargs_train = {} + cov_kwargs_notrain = {} + + model_fc.fit(series=[self.ts_pass_train, self.ts_pass_train_1], **cov_kwargs) + + model = model_cls(model=model_fc, **kwargs) + if cov_name == "future_covariates": + assert model.supports_future_covariates + assert not model.supports_past_covariates + assert model.uses_future_covariates + assert not model.uses_past_covariates + elif cov_name == "past_covariates": + assert not model.supports_future_covariates + assert model.supports_past_covariates + assert not model.uses_future_covariates + assert model.uses_past_covariates + else: + assert not model.supports_future_covariates + assert not model.supports_past_covariates + assert not model.uses_future_covariates + assert not model.uses_past_covariates + + with pytest.raises(ValueError): + # when model is fit from >1 series, one must provide a series in argument + model.predict(n=1) + + if cov_name is not None: + with pytest.raises(ValueError): + # when model is fit using multiple covariates, covariates are required at prediction time + model.predict(n=1, series=self.ts_pass_train) + + with pytest.raises(ValueError): + # when model is fit using covariates, n cannot be greater than output_chunk_length... + # (for short covariates) + # past covariates model can predict up until output_chunk_length + # with train future covariates we cannot predict at all after end of series + model.predict( + n=OUT_LEN + 1 if is_past else 1, + series=self.ts_pass_train, + **cov_kwargs_train, + ) + else: + # model does not support covariates + with pytest.raises(ValueError): + model.predict( + n=1, + series=self.ts_pass_train, + past_covariates=self.time_covariates, + ) + with pytest.raises(ValueError): + model.predict( + n=1, + series=self.ts_pass_train, + future_covariates=self.time_covariates, + ) + + # ... unless future covariates are provided + _ = model.predict( + n=self.horizon, series=self.ts_pass_train, **cov_kwargs_notrain + ) + + pred = model.predict( + n=self.horizon, series=self.ts_pass_train, **cov_kwargs_notrain, **pred_lklp + ) + pred_fc = model_fc.predict( + n=self.horizon, + series=self.ts_pass_train, + **cov_kwargs_notrain, + ) + fc_columns = likelihood_component_names( + self.ts_pass_val.columns, quantile_names([0.5]) + ) + np.testing.assert_array_almost_equal( + pred[fc_columns].all_values(), + pred_fc.all_values(), + ) + + if cov_name is None: + return + + # when model is fit using 1 training and 1 covariate series, time series args are optional + model_fc = LinearRegressionModel(**regr_kwargs, **covs_kwargs) + model_fc.fit(series=self.ts_pass_train, **cov_kwargs_train) + model = model_cls(model_fc, **kwargs) + + if is_past: + # can only predict up until ocl + with pytest.raises(ValueError): + _ = model.predict(n=OUT_LEN + 1, **pred_lklp) + # wrong covariates dimension + with pytest.raises(ValueError): + covs = cov_kwargs_train[cov_name] + covs = {cov_name: covs.stack(covs)} + _ = model.predict(n=OUT_LEN, **covs, **pred_lklp) + # with past covariates from train we can predict up until output_chunk_length + pred1 = model.predict(n=OUT_LEN, **pred_lklp) + pred2 = model.predict(n=OUT_LEN, series=self.ts_pass_train, **pred_lklp) + pred3 = model.predict(n=OUT_LEN, **cov_kwargs_train, **pred_lklp) + pred4 = model.predict( + n=OUT_LEN, **cov_kwargs_train, series=self.ts_pass_train, **pred_lklp + ) + else: + # with future covariates we need additional time steps to predict + with pytest.raises(ValueError): + _ = model.predict(n=1, **pred_lklp) + with pytest.raises(ValueError): + _ = model.predict(n=1, series=self.ts_pass_train, **pred_lklp) + with pytest.raises(ValueError): + _ = model.predict(n=1, **cov_kwargs_train, **pred_lklp) + with pytest.raises(ValueError): + _ = model.predict( + n=1, **cov_kwargs_train, series=self.ts_pass_train, **pred_lklp + ) + # wrong covariates dimension + with pytest.raises(ValueError): + covs = cov_kwargs_notrain[cov_name] + covs = {cov_name: covs.stack(covs)} + _ = model.predict(n=OUT_LEN, **covs, **pred_lklp) + pred1 = model.predict(n=OUT_LEN, **cov_kwargs_notrain, **pred_lklp) + pred2 = model.predict( + n=OUT_LEN, series=self.ts_pass_train, **cov_kwargs_notrain, **pred_lklp + ) + pred3 = model.predict(n=OUT_LEN, **cov_kwargs_notrain, **pred_lklp) + pred4 = model.predict( + n=OUT_LEN, **cov_kwargs_notrain, series=self.ts_pass_train, **pred_lklp + ) + + assert pred1 == pred2 + assert pred1 == pred3 + assert pred1 == pred4 + + @pytest.mark.parametrize( + "config,ts", + itertools.product( + models_cls_kwargs_errs, + [ts_w_static_cov, ts_shared_static_cov, ts_comps_static_cov], + ), + ) + def test_use_static_covariates(self, config, ts): + """ + Check that both static covariates representations are supported (component-specific and shared) + for both uni- and multivariate series when fitting the model. + Also check that the static covariates are present in the forecasted series + """ + model_cls, kwargs, model_type = config + model = model_cls( + train_model(ts, model_type=model_type, quantiles=kwargs["quantiles"]), + **kwargs, + ) + assert model.considers_static_covariates + assert model.supports_static_covariates + assert model.uses_static_covariates + pred = model.predict(OUT_LEN) + assert pred.static_covariates.equals(ts.static_covariates) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], # univariate series + [True, False], # single series + [True, False], # use covariates + [True, False], # datetime index + [1, 3, 5], # different horizons + ), + ) + def test_predict(self, config): + (is_univar, is_single, use_covs, is_datetime, horizon) = config + series = self.ts_pass_train + if not is_univar: + series = series.stack(series) + if not is_datetime: + series = TimeSeries.from_values(series.all_values(), columns=series.columns) + if use_covs: + pc, fc = series, series + fc = fc.append_values(fc.values()[: max(horizon, OUT_LEN)]) + if horizon > OUT_LEN: + pc = pc.append_values(pc.values()[: horizon - OUT_LEN]) + model_kwargs = { + "lags_past_covariates": IN_LEN, + "lags_future_covariates": (IN_LEN, OUT_LEN), + } + else: + pc, fc = None, None + model_kwargs = {} + if not is_single: + series = [ + series, + series.with_columns_renamed( + col_names=series.columns.tolist(), + col_names_new=(series.columns + "_s2").tolist(), + ), + ] + if use_covs: + pc = [pc] * 2 + fc = [fc] * 2 + + # testing lags_past_covariates None but past_covariates during prediction + model_instance = LinearRegressionModel( + lags=IN_LEN, output_chunk_length=OUT_LEN, **model_kwargs + ) + model_instance.fit(series=series, past_covariates=pc, future_covariates=fc) + model = ConformalNaiveModel(model_instance, quantiles=q) + + preds = model.predict( + n=horizon, + series=series, + past_covariates=pc, + future_covariates=fc, + **pred_lklp, + ) + + if is_single: + series = [series] + preds = [preds] + + for s_, preds_ in zip(series, preds): + cols_expected = likelihood_component_names(s_.columns, quantile_names(q)) + assert preds_.columns.tolist() == cols_expected + assert len(preds_) == horizon + assert preds_.start_time() == s_.end_time() + s_.freq + assert preds_.freq == s_.freq + + def test_output_chunk_shift(self): + model_params = {"output_chunk_shift": 1} + model = ConformalNaiveModel( + train_model(self.ts_pass_train, model_params=model_params, quantiles=q), + quantiles=q, + ) + pred = model.predict(n=1, **pred_lklp) + pred_fc = model.model.predict(n=1) + + assert pred_fc.time_index.equals(pred.time_index) + # the center forecasts must be equal to the forecasting model forecast + fc_columns = likelihood_component_names( + self.ts_pass_train.columns, quantile_names([0.5]) + ) + + np.testing.assert_array_almost_equal( + pred[fc_columns].all_values(), pred_fc.all_values() + ) + + @pytest.mark.parametrize( + "config", + itertools.product( + [1, 3, 5], # horizon + [True, False], # univariate series + [True, False], # single series + [q, [0.2, 0.3, 0.5, 0.7, 0.8]], + [ + (ConformalNaiveModel, "regression"), + (ConformalNaiveModel, "regression_prob"), + (ConformalQRModel, "regression_qr"), + ], # model type + [True, False], # symmetric non-conformity score + [None, 1], # train length + ), + ) + def test_conformal_model_predict_accuracy(self, config): + """Verifies that naive conformal model computes the correct intervals for: + - different horizons (smaller, equal, larger than ocl) + - uni/multivariate series + - single/multi series + - single/multi quantile intervals + - deterministic/probabilistic forecasting model + - naive conformal and conformalized quantile regression + - symmetric/asymmetric non-conformity scores + + The naive approach computes it as follows: + + - pred_upper = pred + q_interval(absolute error, past) + - pred_middle = pred + - pred_lower = pred - q_interval(absolute error, past) + + Where q_interval(absolute error) is the `q_hi - q_hi` quantile value of all historic absolute errors + between `pred`, and the target series. + """ + ( + n, + is_univar, + is_single, + quantiles, + (model_cls, model_type), + symmetric, + cal_length, + ) = config + idx_med = quantiles.index(0.5) + q_intervals = [ + (q_hi, q_lo) + for q_hi, q_lo in zip(quantiles[:idx_med], quantiles[idx_med + 1 :][::-1]) + ] + series = self.helper_prepare_series(is_univar, is_single) + pred_kwargs = ( + {"num_samples": 1000} + if model_type in ["regression_prob", "regression_qr"] + else {} + ) + + model_fc = train_model(series, model_type=model_type, quantiles=q) + model = model_cls( + model=model_fc, + quantiles=quantiles, + symmetric=symmetric, + cal_length=cal_length, + ) + pred_fc_list = model.model.predict(n, series=series, **pred_kwargs) + pred_cal_list = model.predict(n, series=series, **pred_lklp) + + if issubclass(model_cls, ConformalNaiveModel): + metric = ae if symmetric else err + metric_kwargs = {} + else: + metric = incs_qr + metric_kwargs = {"q_interval": q_intervals, "symmetric": symmetric} + # compute the expected intervals + residuals_list = model.model.residuals( + series, + retrain=False, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + stride=1, + values_only=True, + metric=metric, + metric_kwargs=metric_kwargs, + **pred_kwargs, + ) + if is_single: + pred_fc_list = [pred_fc_list] + pred_cal_list = [pred_cal_list] + residuals_list = [residuals_list] + + for pred_fc, pred_cal, residuals in zip( + pred_fc_list, pred_cal_list, residuals_list + ): + residuals = np.concatenate(residuals[:-1], axis=2) + + pred_vals = pred_fc.all_values() + pred_vals_expected = self.helper_compute_pred_cal( + residuals, + pred_vals, + n, + quantiles, + model_type, + symmetric, + cal_length=cal_length, + ) + self.helper_compare_preds(pred_cal, pred_vals_expected, model_type) + + @pytest.mark.parametrize( + "config", + itertools.product( + [1, 3, 5], # horizon + [True, False], # univariate series + [True, False], # single series, + [0, 1], # output chunk shift + [None, 1], # train length + [False, True], # use covariates + [q, [0.2, 0.3, 0.5, 0.7, 0.8]], # quantiles + ), + ) + def test_naive_conformal_model_historical_forecasts(self, config): + """Checks correctness of naive conformal model historical forecasts for: + - different horizons (smaller, equal and larger the OCL) + - uni and multivariate series + - single and multiple series + - with and without output shift + - with and without training length + - with and without covariates + """ + n, is_univar, is_single, ocs, cal_length, use_covs, quantiles = config + if ocs and n > OUT_LEN: + # auto-regression not allowed with ocs + return + + series = self.helper_prepare_series(is_univar, is_single) + model_params = {"output_chunk_shift": ocs} + + # for covariates, we check that shorter & longer covariates in the calibration set give expected results + covs_kwargs = {} + if use_covs: + model_params["lags_past_covariates"] = regr_kwargs["lags"] + past_covs = series + if n > OUT_LEN: + append_vals = [[[1.0]] * (1 if is_univar else 2)] * (n - OUT_LEN) + if is_single: + past_covs = past_covs.append_values(append_vals) + else: + past_covs = [pc.append_values(append_vals) for pc in past_covs] + covs_kwargs["past_covariates"] = past_covs + + # forecasts from forecasting model + model_fc = train_model(series, model_params=model_params, **covs_kwargs) + hfc_fc_list = model_fc.historical_forecasts( + series, + retrain=False, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + stride=1, + **covs_kwargs, + ) + # residuals to compute the conformal intervals + residuals_list = model_fc.residuals( + series, + historical_forecasts=hfc_fc_list, + overlap_end=True, + last_points_only=False, + values_only=True, + metric=ae, # absolute error + **covs_kwargs, + ) + + # conformal forecasts + model = ConformalNaiveModel( + model=model_fc, quantiles=quantiles, cal_length=cal_length + ) + hfc_conf_list = model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + stride=1, + **covs_kwargs, + **pred_lklp, + ) + + if is_single: + hfc_conf_list = [hfc_conf_list] + residuals_list = [residuals_list] + hfc_fc_list = [hfc_fc_list] + + # validate computed conformal intervals; conformal models start later since they need past residuals as input + first_fc_idx = len(hfc_fc_list[0]) - len(hfc_conf_list[0]) + for hfc_fc, hfc_conf, hfc_residuals in zip( + hfc_fc_list, hfc_conf_list, residuals_list + ): + for idx, (pred_fc, pred_cal) in enumerate( + zip(hfc_fc[first_fc_idx:], hfc_conf) + ): + # need to ignore additional `ocs` (output shift) residuals + residuals = np.concatenate( + hfc_residuals[: first_fc_idx - ocs + idx], axis=2 + ) + + pred_vals = pred_fc.all_values() + pred_vals_expected = self.helper_compute_pred_cal( + residuals, + pred_vals, + n, + quantiles, + cal_length=cal_length, + model_type="regression", + symmetric=True, + ) + np.testing.assert_array_almost_equal( + pred_cal.all_values(), pred_vals_expected + ) + + # checking that last points only is equal to the last forecasted point + hfc_lpo_list = model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=True, + stride=1, + **covs_kwargs, + **pred_lklp, + ) + if is_single: + hfc_lpo_list = [hfc_lpo_list] + + for hfc_lpo, hfc_conf in zip(hfc_lpo_list, hfc_conf_list): + hfc_conf_lpo = concatenate([hfc[-1:] for hfc in hfc_conf], axis=0) + assert hfc_lpo == hfc_conf_lpo + + @pytest.mark.parametrize( + "config", + itertools.product( + [1, 3, 5], # horizon + [0, 1], # output chunk shift + [None, 1], # cal length, + [1, 2], # cal stride + [False, True], # use start + ), + ) + def test_stridden_conformal_model(self, config): + """Checks correctness of naive conformal model historical forecasts for: + - different horizons (smaller, equal and larger the OCL) + - uni and multivariate series + - single and multiple series + - with and without output shift + - with and without training length + - with and without covariates + """ + is_univar, is_single = True, False + n, ocs, cal_length, cal_stride, use_start = config + if ocs and n > OUT_LEN: + # auto-regression not allowed with ocs + return + + series = self.helper_prepare_series(is_univar, is_single) + # shift second series ahead to cover the non overlapping multi series case + series = [series[0], series[1].shift(120)] + model_params = {"output_chunk_shift": ocs} + + # forecasts from forecasting model + model_fc = train_model(series, model_params=model_params) + hfc_fc_list = model_fc.historical_forecasts( + series, + retrain=False, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + stride=cal_stride, + ) + # residuals to compute the conformal intervals + residuals_list = model_fc.residuals( + series, + historical_forecasts=hfc_fc_list, + overlap_end=True, + last_points_only=False, + values_only=True, + metric=ae, # absolute error + ) + + # conformal forecasts + model = ConformalNaiveModel( + model=model_fc, + quantiles=q, + cal_length=cal_length, + cal_stride=cal_stride, + ) + # the expected positional index of the first conformal forecast + # index = (skip n + ocs points (relative to cal_stride) to avoid look-ahead bias) + (number of cal examples) + first_fc_idx = math.ceil((n + ocs) / cal_stride) + ( + cal_length - 1 if cal_length else 0 + ) + first_start = n_steps_between( + hfc_fc_list[0][first_fc_idx].start_time() - ocs * series[0].freq, + series[0].start_time(), + freq=series[0].freq, + ) + + hfc_conf_list = model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + start=first_start if use_start else None, + start_format="position" if use_start else "value", + stride=cal_stride, + **pred_lklp, + ) + + # also, skip some residuals from output chunk shift + ignore_ocs = math.ceil(ocs / cal_stride) if ocs >= cal_stride else 0 + for hfc_fc, hfc_conf, hfc_residuals in zip( + hfc_fc_list, hfc_conf_list, residuals_list + ): + for idx, (pred_fc, pred_cal) in enumerate( + zip(hfc_fc[first_fc_idx:], hfc_conf) + ): + residuals = np.concatenate( + hfc_residuals[: first_fc_idx - ignore_ocs + idx], axis=2 + ) + pred_vals = pred_fc.all_values() + pred_vals_expected = self.helper_compute_pred_cal( + residuals, + pred_vals, + n, + q, + cal_length=cal_length, + model_type="regression", + symmetric=True, + cal_stride=cal_stride, + ) + assert pred_fc.time_index.equals(pred_cal.time_index) + np.testing.assert_array_almost_equal( + pred_cal.all_values(), pred_vals_expected + ) + + # check that with a round-multiple of `cal_stride` we get identical forecasts + assert model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + start=first_start if use_start else None, + start_format="position" if use_start else "value", + stride=2 * cal_stride, + **pred_lklp, + ) == [hfc[::2] for hfc in hfc_conf_list] + + # checking that last points only is equal to the last forecasted point + hfc_lpo_list = model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=True, + stride=cal_stride, + **pred_lklp, + ) + for hfc_lpo, hfc_conf in zip(hfc_lpo_list, hfc_conf_list): + hfc_conf_lpo = concatenate( + [hfc[-1::cal_stride] for hfc in hfc_conf], axis=0 + ) + assert hfc_lpo == hfc_conf_lpo + + # checking that predict gives the same results as last historical forecast + preds = model.predict( + series=series, + n=n, + **pred_lklp, + ) + hfcs_conf_end = model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + start=-cal_stride, + start_format="position", + stride=cal_stride, + **pred_lklp, + ) + hfcs_conf_end = [hfc[-1] for hfc in hfcs_conf_end] + for pred, last_hfc in zip(preds, hfcs_conf_end): + assert pred == last_hfc + + def test_probabilistic_historical_forecast(self): + """Checks correctness of naive conformal historical forecast from probabilistic fc model compared to + deterministic one, + """ + series = self.helper_prepare_series(False, False) + # forecasts from forecasting model + model_det = ConformalNaiveModel( + train_model(series, model_type="regression", quantiles=q), + quantiles=q, + ) + model_prob = ConformalNaiveModel( + train_model(series, model_type="regression_prob", quantiles=q), + quantiles=q, + ) + hfcs_det = model_det.historical_forecasts( + series, + forecast_horizon=2, + last_points_only=True, + stride=1, + **pred_lklp, + ) + hfcs_prob = model_prob.historical_forecasts( + series, + forecast_horizon=2, + last_points_only=True, + stride=1, + **pred_lklp, + ) + assert isinstance(hfcs_det, list) and len(hfcs_det) == 2 + assert isinstance(hfcs_prob, list) and len(hfcs_prob) == 2 + for hfc_det, hfc_prob in zip(hfcs_det, hfcs_prob): + assert hfc_det.columns.equals(hfc_prob.columns) + assert hfc_det.time_index.equals(hfc_prob.time_index) + self.helper_compare_preds( + hfc_prob, hfc_det.all_values(), model_type="regression_prob" + ) + + def helper_prepare_series(self, is_univar, is_single): + series = self.ts_pass_train + if not is_univar: + series = series.stack(series + 3.0) + if not is_single: + series = [series, series + 5] + return series + + @staticmethod + def helper_compare_preds(cp_pred, pred_expected, model_type, tol_rel=0.1): + if isinstance(cp_pred, TimeSeries): + cp_pred = cp_pred.all_values(copy=False) + if model_type == "regression": + # deterministic fc model should give almost identical results + np.testing.assert_array_almost_equal(cp_pred, pred_expected) + else: + # probabilistic fc models have some randomness + diffs_rel = np.abs((cp_pred - pred_expected) / pred_expected) + assert (diffs_rel < tol_rel).all().all() + + @staticmethod + def helper_compute_pred_cal( + residuals, + pred_vals, + horizon, + quantiles, + model_type, + symmetric, + cal_length=None, + cal_stride=1, + ): + """Generates expected prediction results for naive conformal model from: + + - residuals and predictions from deterministic/probabilistic model + - any forecast horizon + - any quantile intervals + - symmetric/ asymmetric non-conformity scores + - any train length + """ + cal_length = cal_length or 0 + n_comps = pred_vals.shape[1] + half_idx = len(quantiles) // 2 + + # get alphas from quantiles (alpha = q_hi - q_lo) per interval + alphas = np.array(quantiles[half_idx + 1 :][::-1]) - np.array( + quantiles[:half_idx] + ) + if not symmetric: + # asymmetric non-conformity scores look only on one tail -> alpha/2 + alphas = 1 - (1 - alphas) / 2 + if model_type == "regression_prob": + # naive conformal model converts probabilistic forecasts to median (deterministic) + pred_vals = np.expand_dims(np.quantile(pred_vals, 0.5, axis=2), -1) + elif model_type == "regression_qr": + # conformalized quantile regression consumes quantile forecasts + pred_vals = np.quantile(pred_vals, quantiles, axis=2).transpose(1, 2, 0) + + is_naive = model_type in ["regression", "regression_prob"] + pred_expected = [] + for alpha_idx, alpha in enumerate(alphas): + q_hats = [] + # compute the quantile `alpha` of all past residuals (absolute "per time step" errors between historical + # forecasts and the target series) + for idx_horizon in range(horizon): + n = idx_horizon + 1 + # ignore residuals at beginning + idx_fc_start = math.floor((horizon - n) / cal_stride) + # keep as many residuals as possible from end + idx_fc_end = -(math.ceil(horizon / cal_stride) - (idx_fc_start + 1)) + res_n = residuals[idx_horizon, :, idx_fc_start : idx_fc_end or None] + if cal_length is not None: + res_n = res_n[:, -cal_length:] + if is_naive and symmetric: + # identical correction for upper and lower bounds + # metric is `ae()` + q_hat_n = np.quantile(res_n, q=alpha, method="higher", axis=1) + q_hats.append((-q_hat_n, q_hat_n)) + elif is_naive: + # correction separately for upper and lower bounds + # metric is `err()` + q_hat_hi = np.quantile(res_n, q=alpha, method="higher", axis=1) + q_hat_lo = np.quantile(-res_n, q=alpha, method="higher", axis=1) + q_hats.append((-q_hat_lo, q_hat_hi)) + elif symmetric: # CQR symmetric + # identical correction for upper and lower bounds + # metric is `incs_qr(symmetric=True)` + q_hat_n = np.quantile(res_n, q=alpha, method="higher", axis=1) + q_hats.append((-q_hat_n, q_hat_n)) + else: # CQR asymmetric + # correction separately for upper and lower bounds + # metric is `incs_qr(symmetric=False)` + half_idx = len(res_n) // 2 + + # residuals have shape (n components * n intervals * 2) + # the factor 2 comes from the metric being computed for lower, and upper bounds separately + # (comp_1_qlow_1, comp_1_qlow_2, ... comp_n_qlow_m, comp_1_qhigh_1, ...) + q_hat_lo = np.quantile( + res_n[:half_idx], q=alpha, method="higher", axis=1 + ) + q_hat_hi = np.quantile( + res_n[half_idx:], q=alpha, method="higher", axis=1 + ) + q_hats.append(( + -q_hat_lo[alpha_idx :: len(alphas)], + q_hat_hi[alpha_idx :: len(alphas)], + )) + # bring to shape (horizon, n components, 2) + q_hats = np.array(q_hats).transpose((0, 2, 1)) + # the prediction interval is given by pred +/- q_hat + pred_vals_expected = [] + for col_idx in range(n_comps): + q_col = q_hats[:, col_idx] + pred_col = pred_vals[:, col_idx] + if is_naive: + # conformal model corrects deterministic predictions + idx_q_lo = slice(0, None) + idx_q_med = slice(0, None) + idx_q_hi = slice(0, None) + else: + # conformal model corrects quantile predictions + idx_q_lo = slice(alpha_idx, alpha_idx + 1) + idx_q_med = slice(len(alphas), len(alphas) + 1) + idx_q_hi = slice( + pred_col.shape[1] - (alpha_idx + 1), + pred_col.shape[1] - alpha_idx, + ) + # correct lower and upper bounds + pred_col_expected = np.concatenate( + [ + pred_col[:, idx_q_lo] + q_col[:, :1], # lower quantile + pred_col[:, idx_q_med], # median forecast + pred_col[:, idx_q_hi] + q_col[:, 1:], + ], # upper quantile + axis=1, + ) + pred_col_expected = np.expand_dims(pred_col_expected, 1) + pred_vals_expected.append(pred_col_expected) + pred_vals_expected = np.concatenate(pred_vals_expected, axis=1) + pred_expected.append(pred_vals_expected) + + # reorder to have columns going from lowest quantiles to highest per component + pred_expected_reshaped = [] + for comp_idx in range(n_comps): + for q_idx in [0, 1, 2]: + for pred_idx in range(len(pred_expected)): + # upper quantiles will have reversed order + if q_idx == 2: + pred_idx = len(pred_expected) - 1 - pred_idx + pred_ = pred_expected[pred_idx][:, comp_idx, q_idx] + pred_ = pred_.reshape(-1, 1, 1) + + # q_hat_idx = q_idx + comp_idx * 3 + alpha_idx * 3 * n_comps + pred_expected_reshaped.append(pred_) + # only add median quantile once + if q_idx == 1: + break + return np.concatenate(pred_expected_reshaped, axis=1) + + @pytest.mark.parametrize( + "config", + itertools.product( + [1, 3, 5], # horizon + [0, 1], # output chunk shift + [False, True], # use covariates + ), + ) + def test_too_short_input_predict(self, config): + """Checks conformal model predict with minimum required input and too short input.""" + n, ocs, use_covs = config + if ocs and n > OUT_LEN: + return + icl = IN_LEN + min_len = icl + ocs + n + series = tg.linear_timeseries(length=min_len) + series_train = [tg.linear_timeseries(length=IN_LEN + OUT_LEN + ocs)] * 2 + + model_params = {"output_chunk_shift": ocs} + covs_kwargs = {} + covs_kwargs_train = {} + covs_kwargs_too_short = {} + if use_covs: + model_params["lags_past_covariates"] = regr_kwargs["lags"] + covs_kwargs_train["past_covariates"] = series_train + # use shorter covariates, to test whether residuals are still properly extracted + past_covs = series + # for auto-regression, we require longer past covariates + if n > OUT_LEN: + past_covs = past_covs.append_values([1.0] * (n - OUT_LEN)) + covs_kwargs["past_covariates"] = past_covs + covs_kwargs_too_short["past_covariates"] = past_covs[:-1] + + model = ConformalNaiveModel( + train_model( + series=series_train, + model_params=model_params, + **covs_kwargs_train, + ), + quantiles=q, + ) + + # prediction works with long enough input + preds1 = model.predict(n=n, series=series, **covs_kwargs) + assert not np.isnan(preds1.all_values()).any().any() + + # series too short: without covariates, make `series` shorter. Otherwise, use the shorter covariates + series_ = series[:-1] if not use_covs else series + with pytest.raises(ValueError) as exc: + _ = model.predict(n=n, series=series_, **covs_kwargs_too_short) + if not use_covs: + assert str(exc.value).startswith( + "Could not build the minimum required calibration input with the provided `series`" + ) + else: + # if `past_covariates` are too short, then it raises error from the forecasting_model.predict() + assert str(exc.value).startswith( + "The `past_covariates` at list/sequence index 0 are not long enough." + ) + + @pytest.mark.parametrize( + "config", + itertools.product( + [False, True], # last points only + [False, True], # overlap end + [None, 2], # train length + [0, 1], # output chunk shift + [1, 3, 5], # horizon + [True, False], # use covs + ), + ) + def test_too_short_input_hfc(self, config): + """Checks conformal model historical forecasts with minimum required input and too short input.""" + ( + last_points_only, + overlap_end, + cal_length, + ocs, + n, + use_covs, + ) = config + if ocs and n > OUT_LEN: + return + + icl = IN_LEN + ocl = OUT_LEN + horizon_ocs = n + ocs + add_cal_length = cal_length - 1 if cal_length is not None else 0 + # min length to generate 1 conformal forecast + min_len_val_series = ( + icl + horizon_ocs * (1 + int(not overlap_end)) + add_cal_length + ) + + series_train = [tg.linear_timeseries(length=icl + ocl + ocs)] * 2 + series = tg.linear_timeseries(length=min_len_val_series) + + model_params = {"output_chunk_shift": ocs} + covs_kwargs_train = {} + covs_kwargs = {} + covs_kwargs_short = {} + if use_covs: + model_params["lags_past_covariates"] = regr_kwargs["lags"] + covs_kwargs_train["past_covariates"] = series_train + + # `- horizon_ocs` to generate forecasts extending up until end of target series + if not overlap_end: + past_covs = series[:-horizon_ocs] + else: + past_covs = series + + # for auto-regression, we require longer past covariates + if n > OUT_LEN: + past_covs = past_covs.append_values([1.0] * (n - OUT_LEN)) + + # covariates lengths to generate exactly one forecast + covs_kwargs["past_covariates"] = past_covs + + # use too short covariates to check that errors are raised + covs_kwargs_short["past_covariates"] = covs_kwargs["past_covariates"][:-1] + + model = ConformalNaiveModel( + train_model( + series=series_train, + model_params=model_params, + **covs_kwargs_train, + ), + quantiles=q, + cal_length=cal_length, + ) + + hfc_kwargs = { + "last_points_only": last_points_only, + "overlap_end": overlap_end, + "forecast_horizon": n, + } + # prediction works with long enough input + hfcs = model.historical_forecasts( + series=series, + **covs_kwargs, + **hfc_kwargs, + ) + if last_points_only: + hfcs = [hfcs] + + assert len(hfcs) == 1 + for hfc in hfcs: + assert not np.isnan(hfc.all_values()).any().any() + + # input too short: without covariates, make `series` shorter. Otherwise, use the shorter covariates + series_ = series[:-1] if not use_covs else series + with pytest.raises(ValueError) as exc: + _ = model.historical_forecasts( + series=series_, + **covs_kwargs_short, + **hfc_kwargs, + ) + assert str(exc.value).startswith( + "Could not build the minimum required calibration input with the provided `series` and `*_covariates`" + ) + + @pytest.mark.parametrize("quantiles", [[0.1, 0.5, 0.9], [0.1, 0.3, 0.5, 0.7, 0.9]]) + def test_backtest_and_residuals(self, quantiles): + """Residuals and backtest are already tested for quantile, and interval metrics based on stochastic or quantile + forecasts. So, a simple check that they give expected results should be enough. + """ + n_q = len(quantiles) + half_idx = n_q // 2 + q_interval = [ + (q_lo, q_hi) + for q_lo, q_hi in zip(quantiles[:half_idx], quantiles[half_idx + 1 :][::-1]) + ] + lpo = False + + # series long enough for 2 hfcs + series = self.helper_prepare_series(True, True).append_values([0.1]) + # conformal model + model = ConformalNaiveModel(model=train_model(series), quantiles=quantiles) + + hfc = model.historical_forecasts( + series=series, forecast_horizon=5, last_points_only=lpo, **pred_lklp + ) + bt = model.backtest( + series=series, + historical_forecasts=hfc, + last_points_only=lpo, + metric=mic, + metric_kwargs={"q_interval": model.q_interval}, + ) + # default backtest is equal to backtest with metric kwargs + np.testing.assert_array_almost_equal( + bt, + model.backtest( + series=series, + historical_forecasts=hfc, + last_points_only=lpo, + metric=mic, + metric_kwargs={"q_interval": q_interval}, + ), + ) + np.testing.assert_array_almost_equal( + mic( + [series] * len(hfc), + hfc, + q_interval=q_interval, + series_reduction=np.mean, + ), + bt, + ) + + residuals = model.residuals( + series=series, + historical_forecasts=hfc, + last_points_only=lpo, + metric=ic, + metric_kwargs={"q_interval": q_interval}, + ) + # default residuals is equal to residuals with metric kwargs + assert residuals == model.residuals( + series=series, + historical_forecasts=hfc, + last_points_only=lpo, + metric=ic, + metric_kwargs={"q_interval": q_interval}, + ) + expected_vals = ic([series] * len(hfc), hfc, q_interval=q_interval) + expected_residuals = [] + for vals, hfc_ in zip(expected_vals, hfc): + expected_residuals.append( + TimeSeries.from_times_and_values( + times=hfc_.time_index, + values=vals, + columns=likelihood_component_names( + series.components, quantile_interval_names(q_interval) + ), + ) + ) + assert residuals == expected_residuals + + def test_predict_probabilistic_equals_quantile(self): + """Tests that sampled quantiles predictions have approx. the same quantiles as direct quantile predictions.""" + quantiles = [0.1, 0.3, 0.5, 0.7, 0.9] + + # multiple multivariate series + series = self.helper_prepare_series(False, False) + + # conformal model + model = ConformalNaiveModel(model=train_model(series), quantiles=quantiles) + # direct quantile predictions + pred_quantiles = model.predict(n=3, series=series, **pred_lklp) + # sampled predictions + pred_samples = model.predict(n=3, series=series, num_samples=500) + for pred_q, pred_s in zip(pred_quantiles, pred_samples): + assert pred_q.n_samples == 1 + assert pred_q.n_components == series[0].n_components * len(quantiles) + assert pred_s.n_samples == 500 + assert pred_s.n_components == series[0].n_components + + vals_q = pred_q.all_values() + vals_s = pred_s.all_values() + vals_s_q = np.quantile(vals_s, quantiles, axis=2).transpose((1, 2, 0)) + vals_s_q = vals_s_q.reshape(vals_q.shape) + self.helper_compare_preds( + vals_s_q, + vals_q, + model_type="regression_prob", + ) + + @pytest.mark.parametrize( + "config", + [ + # (cal_length, cal_stride, (start_expected, start_format_expected)) + (None, 1, (None, "value")), + (None, 2, (-4, "position")), + (None, 3, (-6, "position")), + (None, 4, (-4, "position")), + (1, 1, (-3, "position")), + (1, 2, (-4, "position")), + (1, 3, (-3, "position")), + (1, 4, (-4, "position")), + ], + ) + def test_calibration_hfc_start_predict(self, config): + """Test calibration historical forecast start point when calling `predict()` ("end" position).""" + cal_length, cal_stride, start_expected = config + series = linear_timeseries(length=4) + horizon = 2 + output_chunk_shift = 1 + assert ( + _get_calibration_hfc_start( + series=[series], + horizon=horizon, + output_chunk_shift=output_chunk_shift, + cal_length=cal_length, + cal_stride=cal_stride, + start="end", + start_format="position", + ) + == start_expected + ) + + @pytest.mark.parametrize( + "config", + [ + # (cal_length, cal_stride, start, start_expected) + (None, 1, None, None), + (None, 1, 1, None), + (1, 1, -1, -4), + (1, 1, 0, 0), + (1, 2, 0, 0), + (1, 3, 0, 0), + (1, 1, 1, 0), + (1, 2, 1, 1), + (1, 3, 1, 1), + (1, 1, -1, -4), + (1, 2, -1, -5), + (1, 3, -1, -4), + ], + ) + def test_calibration_hfc_start_position_hist_fc(self, config): + """Test calibration historical forecast start point when calling `historical_forecasts()` + with start format "position".""" + cal_length, cal_stride, start, start_expected = config + series = linear_timeseries(length=4) + horizon = 2 + output_chunk_shift = 1 + assert _get_calibration_hfc_start( + series=[series], + horizon=horizon, + output_chunk_shift=output_chunk_shift, + cal_length=cal_length, + cal_stride=cal_stride, + start=start, + start_format="position", + ) == (start_expected, "position") + + @pytest.mark.parametrize( + "config", + [ + # (cal_length, cal_stride, start, start_expected) + (None, 1, None, None), + (None, 1, "2020-01-11", None), + (1, 1, "2020-01-09", "2020-01-06"), # start before series start + (1, 1, "2020-01-10", "2020-01-07"), + (1, 2, "2020-01-10", "2020-01-06"), + (1, 3, "2020-01-10", "2020-01-07"), + (2, 1, "2020-01-09", "2020-01-05"), + (2, 1, "2020-01-10", "2020-01-06"), + (2, 2, "2020-01-10", "2020-01-04"), + (2, 3, "2020-01-10", "2020-01-04"), + ], + ) + def test_calibration_hfc_start_value_hist_fc(self, config): + """Test calibration historical forecast start point when calling `historical_forecasts()` + with start format "value".""" + cal_length, cal_stride, start, start_expected = config + if start is not None: + start = pd.Timestamp(start) + if start_expected is not None: + start_expected = pd.Timestamp(start_expected) + series = linear_timeseries(length=4, start=pd.Timestamp("2020-01-10"), freq="d") + horizon = 2 + output_chunk_shift = 1 + assert _get_calibration_hfc_start( + series=[series], + horizon=horizon, + output_chunk_shift=output_chunk_shift, + cal_length=cal_length, + cal_stride=cal_stride, + start=start, + start_format="value", + ) == (start_expected, "value") diff --git a/darts/tests/models/forecasting/test_ensemble_models.py b/darts/tests/models/forecasting/test_ensemble_models.py index 7fa663ddce..92fb932a5e 100644 --- a/darts/tests/models/forecasting/test_ensemble_models.py +++ b/darts/tests/models/forecasting/test_ensemble_models.py @@ -766,14 +766,10 @@ def get_global_ensemble_model(output_chunk_length=5): ) @pytest.mark.parametrize("model_cls", [NaiveEnsembleModel, RegressionEnsembleModel]) - def test_save_load_ensemble_models(self, tmpdir_module, model_cls): + def test_save_load_ensemble_models(self, tmpdir_fn, model_cls): # check if save and load methods work and # if loaded ensemble model creates same forecasts as original ensemble models - cwd = os.getcwd() - os.chdir(tmpdir_module) - os.mkdir(model_cls.__name__) - full_model_path_str = os.path.join(tmpdir_module, model_cls.__name__) - os.chdir(full_model_path_str) + full_model_path_str = os.getcwd() kwargs = {} expected_suffixes = [".pkl", ".pkl.RNNModel_2.pt", ".pkl.RNNModel_2.pt.ckpt"] @@ -827,5 +823,3 @@ def test_save_load_ensemble_models(self, tmpdir_module, model_cls): for p in pkl_files: loaded_model = model_cls.load(p) assert model_prediction == loaded_model.predict(5) - - os.chdir(cwd) diff --git a/darts/tests/models/forecasting/test_global_forecasting_models.py b/darts/tests/models/forecasting/test_global_forecasting_models.py index f8eea72615..22343b3cf7 100644 --- a/darts/tests/models/forecasting/test_global_forecasting_models.py +++ b/darts/tests/models/forecasting/test_global_forecasting_models.py @@ -276,12 +276,10 @@ def test_save_model_parameters(self, config): ), ], ) - def test_save_load_model(self, tmpdir_module, model): + def test_save_load_model(self, tmpdir_fn, model): # check if save and load methods work and if loaded model creates same forecasts as original model - cwd = os.getcwd() - os.chdir(tmpdir_module) model_path_str = type(model).__name__ - full_model_path_str = os.path.join(tmpdir_module, model_path_str) + full_model_path_str = os.path.join(tmpdir_fn, model_path_str) model.fit(self.ts_pass_train) model_prediction = model.predict(self.forecasting_horizon) @@ -293,9 +291,7 @@ def test_save_load_model(self, tmpdir_module, model): assert os.path.exists(full_model_path_str) assert ( len([ - p - for p in os.listdir(tmpdir_module) - if p.startswith(type(model).__name__) + p for p in os.listdir(tmpdir_fn) if p.startswith(type(model).__name__) ]) == 4 ) @@ -305,8 +301,6 @@ def test_save_load_model(self, tmpdir_module, model): assert model_prediction == loaded_model.predict(self.forecasting_horizon) - os.chdir(cwd) - @pytest.mark.parametrize("config", models_cls_kwargs_errs) def test_single_ts(self, config): model_cls, kwargs, err = config diff --git a/darts/tests/models/forecasting/test_local_forecasting_models.py b/darts/tests/models/forecasting/test_local_forecasting_models.py index b9d0bf5084..e1e7361a60 100644 --- a/darts/tests/models/forecasting/test_local_forecasting_models.py +++ b/darts/tests/models/forecasting/test_local_forecasting_models.py @@ -142,8 +142,6 @@ def test_save_model_parameters(self): @pytest.mark.parametrize("model", [ARIMA(1, 1, 1), LinearRegressionModel(lags=12)]) def test_save_load_model(self, tmpdir_module, model): # check if save and load methods work and if loaded model creates same forecasts as original model - cwd = os.getcwd() - os.chdir(tmpdir_module) model_path_str = type(model).__name__ model_path_pathlike = pathlib.Path(model_path_str + "_pathlike") model_path_binary = model_path_str + "_binary" @@ -186,8 +184,6 @@ def test_save_load_model(self, tmpdir_module, model): for loaded_model in loaded_models: assert model_prediction == loaded_model.predict(self.forecasting_horizon) - os.chdir(cwd) - def test_save_load_model_invalid_path(self): # check if save and load methods raise an error when given an invalid path model = ARIMA(1, 1, 1) diff --git a/darts/tests/models/forecasting/test_probabilistic_models.py b/darts/tests/models/forecasting/test_probabilistic_models.py index 141fd43dcd..fd63793463 100644 --- a/darts/tests/models/forecasting/test_probabilistic_models.py +++ b/darts/tests/models/forecasting/test_probabilistic_models.py @@ -12,6 +12,7 @@ BATS, TBATS, CatBoostModel, + ConformalNaiveModel, ExponentialSmoothing, LightGBMModel, LinearRegressionModel, @@ -61,13 +62,16 @@ lgbm_available = not isinstance(LightGBMModel, NotImportedModule) cb_available = not isinstance(CatBoostModel, NotImportedModule) +# conformal models require a fitted base model +# in tests below, the model is re-trained for new input series. +# using a fake trained model should allow the same API with conformal models +conformal_forecaster = LinearRegressionModel(lags=10, output_chunk_length=5) +conformal_forecaster._fit_called = True + # model_cls, model_kwargs, err_univariate, err_multivariate models_cls_kwargs_errs = [ (ExponentialSmoothing, {}, 0.3, None), (ARIMA, {"p": 1, "d": 0, "q": 1, "random_state": 42}, 0.03, None), -] - -models_cls_kwargs_errs += [ ( BATS, { @@ -92,6 +96,17 @@ 0.04, 0.04, ), + ( + ConformalNaiveModel, + { + "model": conformal_forecaster, + "cal_length": 1, + "random_state": 42, + "quantiles": [0.1, 0.5, 0.9], + }, + 0.04, + 0.04, + ), ] xgb_test_params = { @@ -137,7 +152,7 @@ **tfm_kwargs, }, 0.06, - 0.05, + 0.06, ), ( BlockRNNModel, @@ -285,7 +300,7 @@ def test_probabilistic_forecast_accuracy_multivariate(self, config): def helper_test_probabilistic_forecast_accuracy(self, model, err, ts, noisy_ts): model.fit(noisy_ts[:100]) - pred = model.predict(n=100, num_samples=100) + pred = model.predict(n=50, num_samples=100) # test accuracy of the median prediction compared to the noiseless ts mae_err_median = mae(ts[100:], pred) diff --git a/darts/tests/models/forecasting/test_regression_models.py b/darts/tests/models/forecasting/test_regression_models.py index 220c8d2c24..9aed20f431 100644 --- a/darts/tests/models/forecasting/test_regression_models.py +++ b/darts/tests/models/forecasting/test_regression_models.py @@ -1005,33 +1005,31 @@ def test_models_runnability(self, config): model, mode = config train_y, test_y = self.sine_univariate1.split_before(0.7) # testing past covariates + model_instance = model(lags=4, lags_past_covariates=None, multi_models=mode) with pytest.raises(ValueError): # testing lags_past_covariates None but past_covariates during training - model_instance = model(lags=4, lags_past_covariates=None, multi_models=mode) model_instance.fit( series=self.sine_univariate1, past_covariates=self.sine_multivariate1, ) + model_instance = model(lags=4, lags_past_covariates=3, multi_models=mode) with pytest.raises(ValueError): # testing lags_past_covariates but no past_covariates during fit - model_instance = model(lags=4, lags_past_covariates=3, multi_models=mode) model_instance.fit(series=self.sine_univariate1) # testing future_covariates + model_instance = model(lags=4, lags_future_covariates=None, multi_models=mode) with pytest.raises(ValueError): # testing lags_future_covariates None but future_covariates during training - model_instance = model( - lags=4, lags_future_covariates=None, multi_models=mode - ) model_instance.fit( series=self.sine_univariate1, future_covariates=self.sine_multivariate1, ) + model_instance = model(lags=4, lags_future_covariates=(0, 3), multi_models=mode) with pytest.raises(ValueError): # testing lags_covariate but no covariate during fit - model_instance = model(lags=4, lags_future_covariates=3, multi_models=mode) model_instance.fit(series=self.sine_univariate1) # testing input_dim diff --git a/darts/tests/utils/historical_forecasts/test_historical_forecasts.py b/darts/tests/utils/historical_forecasts/test_historical_forecasts.py index 66dc2f8542..1f739f9599 100644 --- a/darts/tests/utils/historical_forecasts/test_historical_forecasts.py +++ b/darts/tests/utils/historical_forecasts/test_historical_forecasts.py @@ -1,5 +1,6 @@ import itertools import logging +import math from copy import deepcopy from itertools import product from typing import Optional @@ -22,6 +23,7 @@ ARIMA, AutoARIMA, CatBoostModel, + ConformalNaiveModel, LightGBMModel, LinearRegressionModel, NaiveDrift, @@ -35,6 +37,7 @@ from darts.utils import n_steps_between from darts.utils import timeseries_generation as tg from darts.utils.ts_utils import SeriesType, get_series_seq_type +from darts.utils.utils import likelihood_component_names, quantile_names if TORCH_AVAILABLE: import torch @@ -1600,13 +1603,13 @@ def f_encoder(idx): assert ohfc[0].start_time() == first_ts_expected # check hist fc end assert ohfc[-1].end_time() == last_ts_expected - for hfc, ohfc in zip(hfc, ohfc): - assert hfc.columns.equals(series.columns) - assert ohfc.columns.equals(series.columns) - assert len(ohfc) == n_pred_points_expected - assert (hfc.time_index == ohfc.time_index).all() + for hfc_, ohfc_ in zip(hfc, ohfc): + assert hfc_.columns.equals(series.columns) + assert ohfc_.columns.equals(series.columns) + assert len(ohfc_) == n_pred_points_expected + assert (hfc_.time_index == ohfc_.time_index).all() np.testing.assert_array_almost_equal( - hfc.all_values(), ohfc.all_values() + hfc_.all_values(), ohfc_.all_values() ) def test_hist_fc_end_exact_with_covs(self): @@ -3287,3 +3290,453 @@ def test_historical_forecast_additional_sanity_checks(self): assert str(err.value).startswith( "Since `start_format='position'`, `start` must be an integer, received" ) + + @pytest.mark.parametrize( + "config", + itertools.product( + [False, True], # use covariates + [True, False], # last points only + [True, False], # overlap end + [1, 3], # stride + [ + 3, # horizon < ocl + 5, # horizon == ocl + 7, # horizon > ocl -> autoregression + ], + [False, True], # use integer indexed series + [False, True], # use multi-series + [0, 1], # output chunk shift + ), + ) + def test_conformal_historical_forecasts(self, config): + """Tests historical forecasts output naive conformal model with last points only, covariates, stride, + different horizons and overlap end. + Tests that the returned dimensions, lengths and start / end times are correct. + """ + ( + use_covs, + last_points_only, + overlap_end, + stride, + horizon, + use_int_idx, + use_multi_series, + ocs, + ) = config + q = [0.1, 0.5, 0.9] + pred_lklp = {"num_samples": 1, "predict_likelihood_parameters": True} + # compute minimum series length to generate n forecasts + icl = 3 + ocl = 5 + horizon_ocs = horizon + ocs + min_len_val_series = icl + horizon_ocs + int(not overlap_end) * horizon_ocs + n_forecasts = 3 + # get train and val series of that length + series = self.ts_pass_val[: min_len_val_series + n_forecasts - 1] + if use_int_idx: + series = TimeSeries.from_values( + values=series.all_values(), + columns=series.columns, + ) + # check that too short input raises error + series_too_short = series[:-n_forecasts] + + # optionally, generate covariates + if use_covs: + pc = tg.gaussian_timeseries( + start=series.start_time(), + end=series.end_time() + max(0, horizon - ocl) * series.freq, + freq=series.freq, + ) + fc = tg.gaussian_timeseries( + start=series.start_time(), + end=series.end_time() + (max(ocl, horizon) + ocs) * series.freq, + freq=series.freq, + ) + else: + pc, fc = None, None + + # first train the ForecastingModel + model_kwargs = ( + {} + if not use_covs + else {"lags_past_covariates": icl, "lags_future_covariates": (icl, ocl)} + ) + forecasting_model = LinearRegressionModel( + lags=icl, output_chunk_length=ocl, output_chunk_shift=ocs, **model_kwargs + ) + forecasting_model.fit(series, past_covariates=pc, future_covariates=fc) + + # add an offset and rename columns in second series to make sure that conformal hist fc works as expected + if use_multi_series: + series = [ + series, + (series + 10).shift(1).with_columns_renamed(series.columns, "test_col"), + ] + pc = [pc, pc.shift(1)] if pc is not None else None + fc = [fc, fc.shift(1)] if fc is not None else None + + # conformal model + model = ConformalNaiveModel(forecasting_model, quantiles=q) + + hfc_kwargs = dict( + { + "retrain": False, + "last_points_only": last_points_only, + "overlap_end": overlap_end, + "stride": stride, + "forecast_horizon": horizon, + }, + **pred_lklp, + ) + # cannot perform auto regression with output chunk shift + if ocs and horizon > ocl: + with pytest.raises(ValueError) as exc: + _ = model.historical_forecasts( + series=series, + past_covariates=pc, + future_covariates=fc, + **hfc_kwargs, + ) + assert str(exc.value).startswith("Cannot perform auto-regression") + return + + # compute conformal historical forecasts + hist_fct = model.historical_forecasts( + series=series, past_covariates=pc, future_covariates=fc, **hfc_kwargs + ) + # raises error with too short target series + with pytest.raises(ValueError) as exc: + _ = model.historical_forecasts( + series=series_too_short, + past_covariates=pc, + future_covariates=fc, + **hfc_kwargs, + ) + assert str(exc.value).startswith( + "Could not build the minimum required calibration input with the provided `series`" + ) + + if not isinstance(series, list): + series = [series] + hist_fct = [hist_fct] + + for ( + series_, + hfc, + ) in zip(series, hist_fct): + if not isinstance(hfc, list): + hfc = [hfc] + + n_preds_with_overlap = ( + len(series_) + - icl # input for first prediction + - horizon_ocs # skip first forecasts to avoid look-ahead bias + + 1 # minimum one forecast + ) + if not last_points_only: + # last points only = False gives a list of forecasts per input series + # where each forecast contains the predictions over the entire horizon + n_pred_series_expected = n_preds_with_overlap + n_pred_points_expected = horizon + first_ts_expected = series_.time_index[icl] + series_.freq * ( + horizon_ocs + ocs + ) + last_ts_expected = series_.end_time() + series_.freq * horizon_ocs + # no overlapping means less predictions + if not overlap_end: + n_pred_series_expected -= horizon_ocs + else: + # last points only = True gives one contiguous time series per input series + # with only predictions from the last point in the horizon + n_pred_series_expected = 1 + n_pred_points_expected = n_preds_with_overlap + first_ts_expected = series_.time_index[icl] + series_.freq * ( + horizon_ocs + ocs + horizon - 1 + ) + last_ts_expected = series_.end_time() + series_.freq * horizon_ocs + # no overlapping means less predictions + if not overlap_end: + n_pred_points_expected -= horizon_ocs + + # no overlapping means less predictions + if not overlap_end: + last_ts_expected -= series_.freq * horizon_ocs + + # adapt based on stride + if stride > 1: + if not last_points_only: + n_pred_series_expected = n_pred_series_expected // stride + int( + n_pred_series_expected % stride + ) + else: + n_pred_points_expected = n_pred_points_expected // stride + int( + n_pred_points_expected % stride + ) + first_ts_expected = hfc[0].start_time() + last_ts_expected = hfc[-1].end_time() + + cols_excpected = likelihood_component_names( + series_.columns, quantile_names(q) + ) + # check length match between optimized and default hist fc + assert len(hfc) == n_pred_series_expected + # check hist fc start + assert hfc[0].start_time() == first_ts_expected + # check hist fc end + assert hfc[-1].end_time() == last_ts_expected + for hfc_ in hfc: + assert hfc_.columns.tolist() == cols_excpected + assert len(hfc_) == n_pred_points_expected + + @pytest.mark.parametrize( + "config", + itertools.product( + [False, True], # last points only + [None, 1, 2], # cal length + [False, True], # use start + ["value", "position"], # start format + [False, True], # use integer indexed series + [False, True], # use multi-series + [0, 1], # output chunk shift + ), + ) + def test_conformal_historical_start_cal_length(self, config): + """Tests naive conformal model historical forecasts without `cal_stride`.""" + ( + last_points_only, + cal_length, + use_start, + start_format, + use_int_idx, + use_multi_series, + ocs, + ) = config + q = [0.1, 0.5, 0.9] + pred_lklp = {"num_samples": 1, "predict_likelihood_parameters": True} + # compute minimum series length to generate n forecasts + icl = 3 + ocl = 5 + horizon = 5 + horizon_ocs = horizon + ocs + add_cal_length = cal_length - 1 if cal_length is not None else 0 + add_start = 2 * int(use_start) + min_len_val_series = icl + 2 * horizon_ocs + add_cal_length + add_start + n_forecasts = 3 + # get train and val series of that length + series = self.ts_pass_val[: min_len_val_series + n_forecasts - 1] + + if use_int_idx: + series = TimeSeries.from_values( + values=series.all_values(), + columns=series.columns, + ) + + # first train the ForecastingModel + forecasting_model = LinearRegressionModel( + lags=icl, + output_chunk_length=ocl, + output_chunk_shift=ocs, + ) + forecasting_model.fit(series) + + # optionally compute the start as a positional index + start_position = icl + horizon_ocs + add_cal_length + add_start + start = None + if use_start: + if start_format == "value": + start = series.time_index[start_position] + else: + start = start_position + + # add an offset and rename columns in second series to make sure that conformal hist fc works as expected + if use_multi_series: + series = [ + series, + (series + 10).shift(1).with_columns_renamed(series.columns, "test_col"), + ] + + # compute conformal historical forecasts (skips some of the first forecasts to get minimum required cal set) + model = ConformalNaiveModel( + forecasting_model, quantiles=q, cal_length=cal_length + ) + hist_fct = model.historical_forecasts( + series=series, + retrain=False, + start=start, + start_format=start_format, + last_points_only=last_points_only, + forecast_horizon=horizon, + overlap_end=False, + **pred_lklp, + ) + + if not isinstance(series, list): + series = [series] + hist_fct = [hist_fct] + + for idx, ( + series_, + hfc, + ) in enumerate(zip(series, hist_fct)): + if not isinstance(hfc, list): + hfc = [hfc] + + # multi series: second series is shifted by one time step (+/- idx); + # start_format = "value" requires a shift + add_start_series_2 = idx * int(use_start) * int(start_format == "value") + + n_preds_without_overlap = ( + len(series_) + - icl # input for first prediction + - horizon_ocs # skip first forecasts to avoid look-ahead bias + - horizon_ocs # cannot compute with `overlap_end=False` + + 1 # minimum one forecast + - add_cal_length # skip based on train length + - add_start # skip based on start + + add_start_series_2 # skip based on start if second series + ) + if not last_points_only: + n_pred_series_expected = n_preds_without_overlap + n_pred_points_expected = horizon + # seconds series is shifted by one time step (- idx) + first_ts_expected = series_.time_index[ + start_position - add_start_series_2 + ocs + ] + last_ts_expected = series_.end_time() + else: + n_pred_series_expected = 1 + n_pred_points_expected = n_preds_without_overlap + # seconds series is shifted by one time step (- idx) + first_ts_expected = ( + series_.time_index[start_position - add_start_series_2] + + (horizon_ocs - 1) * series_.freq + ) + last_ts_expected = series_.end_time() + + cols_excpected = likelihood_component_names( + series_.columns, quantile_names(q) + ) + # check historical forecasts dimensions + assert len(hfc) == n_pred_series_expected + # check hist fc start + assert hfc[0].start_time() == first_ts_expected + # check hist fc end + assert hfc[-1].end_time() == last_ts_expected + for hfc_ in hfc: + assert hfc_.columns.tolist() == cols_excpected + assert len(hfc_) == n_pred_points_expected + + @pytest.mark.parametrize( + "config", + itertools.product( + [False, True], # last points only + [None, 2], # cal length + ["value", "position"], # start format + [2, 4], # stride + [1, 2], # cal stride + [0, 1], # output chunk shift + ), + ) + def test_conformal_historical_forecast_start_stride(self, caplog, config): + """Tests naive conformal model with `start` being the first forecastable index is identical to a start + before forecastable index (including stride, cal stride). + """ + ( + last_points_only, + cal_length, + start_format, + stride, + cal_stride, + ocs, + ) = config + q = [0.1, 0.5, 0.9] + pred_lklp = {"num_samples": 1, "predict_likelihood_parameters": True} + # compute minimum series length to generate n forecasts + icl = 3 + ocl = 5 + horizon = 2 + + # the position of the first conformal forecast start point without look-ahead bias; assuming min cal_length=1 + horizon_ocs = math.ceil((horizon + ocs) / cal_stride) * cal_stride + # adjust by the number of calibration examples + add_cal_length = cal_stride * (cal_length - 1) if cal_length is not None else 0 + # the minimum series length is the sum of the above, plus the length of one forecast (horizon + ocs) + min_len_val_series = icl + horizon_ocs + add_cal_length + horizon + ocs + n_forecasts = 3 + # to get `n_forecasts` with `stride`, we need more points + n_forecasts_stride = stride * n_forecasts - int(1 % stride > 0) + # get train and val series of that length + series = tg.linear_timeseries( + length=min_len_val_series + n_forecasts_stride - 1 + ) + + # first train the ForecastingModel + forecasting_model = LinearRegressionModel( + lags=icl, + output_chunk_length=ocl, + output_chunk_shift=ocs, + ) + forecasting_model.fit(series) + + # optionally compute the start as a positional index + start_position = icl + horizon_ocs + add_cal_length + if start_format == "value": + start = series.time_index[start_position] + start_too_early = series.time_index[start_position - 1] + start_too_early_stride = series.time_index[start_position - stride] + else: + start = start_position + start_too_early = start_position - 1 + start_too_early_stride = start_position - stride + start_first_fc = series.time_index[start_position] + series.freq * ( + horizon + ocs - 1 if last_points_only else ocs + ) + too_early_warn_exp = "is before the first predictable/trainable historical" + + hfc_params = { + "series": series, + "retrain": False, + "start_format": start_format, + "stride": stride, + "last_points_only": last_points_only, + "forecast_horizon": horizon, + } + # compute regular historical forecasts + hist_fct_all = forecasting_model.historical_forecasts(start=start, **hfc_params) + assert len(hist_fct_all) == n_forecasts + assert hist_fct_all[0].start_time() == start_first_fc + assert ( + hist_fct_all[1].start_time() - stride * series.freq + == hist_fct_all[0].start_time() + ) + + # compute conformal historical forecasts (starting at first possible conformal forecast) + model = ConformalNaiveModel( + forecasting_model, quantiles=q, cal_length=cal_length, cal_stride=cal_stride + ) + with caplog.at_level(logging.WARNING): + hist_fct = model.historical_forecasts( + start=start, **hfc_params, **pred_lklp + ) + assert too_early_warn_exp not in caplog.text + caplog.clear() + assert len(hist_fct) == len(hist_fct_all) + assert hist_fct_all[0].start_time() == hist_fct[0].start_time() + assert ( + hist_fct[1].start_time() - stride * series.freq == hist_fct[0].start_time() + ) + + # start one earlier gives warning + with caplog.at_level(logging.WARNING): + _ = model.historical_forecasts( + start=start_too_early, **hfc_params, **pred_lklp + ) + assert too_early_warn_exp in caplog.text + caplog.clear() + + # starting stride before first valid start, gives identical results + hist_fct_too_early = model.historical_forecasts( + start=start_too_early_stride, **hfc_params, **pred_lklp + ) + assert hist_fct_too_early == hist_fct diff --git a/darts/tests/utils/historical_forecasts/test_utils.py b/darts/tests/utils/historical_forecasts/test_utils.py index fdb14ed1a5..7554d807e7 100644 --- a/darts/tests/utils/historical_forecasts/test_utils.py +++ b/darts/tests/utils/historical_forecasts/test_utils.py @@ -102,10 +102,12 @@ def test_historical_forecasts_check_start(self): (True, 0.9, "position"), (True, 0, "position"), (True, 0, "value"), + (True, -1, "position"), (False, pd.Timestamp("2000-01-01"), "value"), (False, 0.9, "value"), (False, 0.9, "position"), (False, 0, "position"), + (False, -1, "position"), ], ) def test_historical_forecasts_check_start_invalid(self, config): diff --git a/darts/tests/utils/test_utils.py b/darts/tests/utils/test_utils.py index d629851cea..003d2253aa 100644 --- a/darts/tests/utils/test_utils.py +++ b/darts/tests/utils/test_utils.py @@ -1,3 +1,5 @@ +import itertools + import numpy as np import pandas as pd import pytest @@ -15,6 +17,7 @@ n_steps_between, quantile_interval_names, quantile_names, + sample_from_quantiles, ) @@ -631,3 +634,94 @@ def test_quantile_interval_names(self, config): q, names_expected = config names = quantile_interval_names(q, "a") assert names == names_expected + + @pytest.mark.parametrize("ndim", [2, 3]) + def test_generate_samples_shape(self, ndim): + """Checks that the output shape of generated samples from quantiles and quantile predictions + is as expected.""" + n_time_steps = 10 + n_columns = 5 + n_quantiles = 20 + num_samples = 50 + + q = np.linspace(0, 1, n_quantiles) + q_pred = np.random.rand(n_time_steps, n_columns, n_quantiles) + if ndim == 2: + q_pred = q_pred.reshape((n_time_steps, n_columns * n_quantiles)) + y_pred = sample_from_quantiles(q_pred, q, num_samples) + assert y_pred.shape == (n_time_steps, n_columns, num_samples) + + @pytest.mark.parametrize( + "config", + itertools.product( + [1, 2], # n times + [2, 3], # ndim + [1, 2], # n components + ), + ) + def test_generate_samples_output(self, config): + """Tests sample generation from quantiles and quantile predictions for: + + - single/multiple time steps + - from 2 or 3 dimensions + - uni/multivariate + """ + np.random.seed(42) + n_times, ndim, n_comps = config + num_samples = 100000 + + q = np.array([0.2, 0.5, 0.75]) + q_pred = np.array([[[1.0, 2.0, 3.0]]]) + if n_times == 2: + q_pred = np.concatenate([q_pred, np.array([[[5.0, 7.0, 9.0]]])], axis=0) + if n_comps == 2: + q_pred = np.concatenate([q_pred, q_pred + 1.0], axis=1) + if ndim == 2: + q_pred = q_pred.reshape((len(q_pred), -1)) + y_pred = sample_from_quantiles(q_pred, q, num_samples) + + q_pred = q_pred.reshape((q_pred.shape[0], n_comps, len(q))) + for i in range(n_comps): + # edges must be identical to min/max predicted quantiles + assert y_pred[:, i].min() == q_pred[:, i].min() + assert y_pred[:, i].max() == q_pred[:, i].max() + + # check that sampled quantiles values equal to the predicted quantiles + assert np.quantile(y_pred[:, i], q[0], axis=1) == pytest.approx( + q_pred[:, i, 0], abs=0.02 + ) + assert np.quantile(y_pred[:, i], q[1], axis=1) == pytest.approx( + q_pred[:, i, 1], abs=0.02 + ) + assert np.quantile(y_pred[:, i], q[2], axis=1) == pytest.approx( + q_pred[:, i, 2], abs=0.02 + ) + + # for each component and quantile, check that the expected ratio of sampled values is approximately + # equal to the quantile + assert (y_pred[:, i] == q_pred[:, i, 0:1]).mean(axis=1) == pytest.approx( + 0.2, abs=0.02 + ) + assert ( + (q_pred[:, i, 0:1] < y_pred[:, i]) & (y_pred[:, i] <= q_pred[:, i, 1:2]) + ).mean(axis=1) == pytest.approx(0.3, abs=0.02) + assert ( + (q_pred[:, i, 1:2] < y_pred[:, i]) & (y_pred[:, i] < q_pred[:, i, 2:3]) + ).mean(axis=1) == pytest.approx(0.25, abs=0.02) + assert (y_pred[:, i] == q_pred[:, i, 2:3]).mean(axis=1) == pytest.approx( + 0.25, abs=0.02 + ) + + # between the quantiles, the values must be linearly interpolated + # check that number of unique values is approximately equal to the difference between two adjacent quantiles + mask1 = (q_pred[:, i, 0:1] < y_pred[:, i]) & ( + y_pred[:, i] < q_pred[:, i, 1:2] + ) + share_unique1 = len(np.unique(y_pred[:, i][mask1])) / num_samples + assert share_unique1 == pytest.approx(n_times * (q[1] - q[0]), abs=0.05) + + mask2 = (q_pred[:, i, 1:2] < y_pred[:, i]) & ( + y_pred[:, i] < q_pred[:, i, 2:3] + ) + share_unique2 = len(np.unique(y_pred[:, i][mask2])) / num_samples + assert share_unique2 == pytest.approx(n_times * (q[2] - q[1]), abs=0.05) diff --git a/darts/utils/historical_forecasts/optimized_historical_forecasts_regression.py b/darts/utils/historical_forecasts/optimized_historical_forecasts_regression.py index a9a3218856..934294d926 100644 --- a/darts/utils/historical_forecasts/optimized_historical_forecasts_regression.py +++ b/darts/utils/historical_forecasts/optimized_historical_forecasts_regression.py @@ -41,7 +41,9 @@ def _optimized_historical_forecasts_last_points_only( The data_transformers are applied in historical_forecasts (input and predictions) """ forecasts_list = [] - iterator = _build_tqdm_iterator(series, verbose) + iterator = _build_tqdm_iterator( + series, verbose, total=len(series), desc="historical forecasts" + ) for idx, series_ in enumerate(iterator): past_covariates_ = past_covariates[idx] if past_covariates is not None else None future_covariates_ = ( @@ -204,7 +206,9 @@ def _optimized_historical_forecasts_all_points( Rely on _check_optimizable_historical_forecasts() to check that the assumptions are verified. """ forecasts_list = [] - iterator = _build_tqdm_iterator(series, verbose) + iterator = _build_tqdm_iterator( + series, verbose, total=len(series), desc="historical forecasts" + ) for idx, series_ in enumerate(iterator): past_covariates_ = past_covariates[idx] if past_covariates is not None else None future_covariates_ = ( diff --git a/darts/utils/historical_forecasts/utils.py b/darts/utils/historical_forecasts/utils.py index 86a4ef64b9..c8502cd7c4 100644 --- a/darts/utils/historical_forecasts/utils.py +++ b/darts/utils/historical_forecasts/utils.py @@ -14,9 +14,8 @@ ) from darts.logging import get_logger, raise_log from darts.timeseries import TimeSeries -from darts.utils import n_steps_between from darts.utils.ts_utils import SeriesType, get_series_seq_type, series2seq -from darts.utils.utils import generate_index +from darts.utils.utils import generate_index, n_steps_between logger = get_logger(__name__) @@ -28,7 +27,9 @@ ] -def _historical_forecasts_general_checks(model, series, kwargs): +def _historical_forecasts_general_checks( + model, series, kwargs, is_conformal: bool = False +): """ Performs checks common to ForecastingModel and RegressionModel backtest() methods @@ -38,9 +39,6 @@ def _historical_forecasts_general_checks(model, series, kwargs): The forecasting model. series Either series when called from ForecastingModel, or target_series if called from RegressionModel - signature_params - A dictionary of the signature parameters of the calling method, to get the default values - Typically would be signature(self.backtest).parameters kwargs Params specified by the caller of backtest(), they take precedence over the arguments' default values """ @@ -61,6 +59,18 @@ def _historical_forecasts_general_checks(model, series, kwargs): logger, ) + # check stride for ConformalModel + if is_conformal and ( + n.stride < model.cal_stride or n.stride % model.cal_stride > 0 + ): + raise_log( + ValueError( + f"The provided `stride` parameter must be a round-multiple of `cal_stride={model.cal_stride}` " + f"and `>=cal_stride`. Received `stride={n.stride}`" + ), + logger, + ) + series = series2seq(series) if n.start is not None: @@ -86,13 +96,23 @@ def _historical_forecasts_general_checks(model, series, kwargs): ), logger, ) - if isinstance(n.start, float) and not 0.0 <= n.start <= 1.0: - raise_log( - ValueError("if `start` is a float, must be between 0.0 and 1.0."), - logger, - ) + if isinstance(n.start, float): + if is_conformal: + raise_log( + ValueError( + "`start` of type float is not supported for `ConformalModel`." + ), + logger, + ) + if not 0.0 <= n.start <= 1.0: + raise_log( + ValueError("if `start` is a float, must be between 0.0 and 1.0."), + logger, + ) + series_freq = None for idx, series_ in enumerate(series): + start_is_value = False # check specifically for int and Timestamp as error by `get_timestamp_at_point` is too generic if isinstance(n.start, pd.Timestamp): if not series_._has_datetime_index: @@ -110,6 +130,7 @@ def _historical_forecasts_general_checks(model, series, kwargs): ), logger, ) + start_is_value = True elif isinstance(n.start, (int, np.int64)): if n.start_format == "position" or series_.has_datetime_index: if n.start >= len(series_): @@ -120,13 +141,32 @@ def _historical_forecasts_general_checks(model, series, kwargs): ), logger, ) - elif n.start > series_.time_index[-1]: # format "value" and range index + else: + if ( + n.start > series_.time_index[-1] + ): # format "value" and range index + raise_log( + ValueError( + f"`start` time `{n.start}` is larger than the last index `{series_.time_index[-1]}` " + f"for series at index: {idx}." + ), + logger, + ) + start_is_value = True + + # `ConformalModel` with `start_format='value'` requires all series to have the same frequency + if is_conformal and start_is_value: + if series_freq is None: + series_freq = series_.freq + + if series_freq != series_.freq: raise_log( ValueError( - f"`start` time `{n.start}` is larger than the last index `{series_.time_index[-1]}` " - f"for series at index: {idx}." + f"Found mismatching `series` time index frequencies `{series_freq}` and `{series_.freq}`. " + f"`start_format='value'` with `ConformalModel` is only supported if all series in " + f"`series` have the same frequency." ), - logger, + logger=logger, ) # find valid start position relative to the series start time, otherwise raise an error @@ -498,8 +538,12 @@ def _check_start( if isinstance(start, float): # fraction of series start = series.get_index_at_point(start) - else: + elif start >= 0: + # start >= 0 is relative to the start start = series.start_time() + start * series.freq + else: + # start < 0 is relative to the end + start = series.end_time() + (start + 1) * series.freq else: start_format_msg = "time " ref_msg = "" if not is_historical_forecast else "historical forecastable " @@ -570,7 +614,7 @@ def _get_historical_forecastable_time_index( Returns ------- - Union[pd.DatetimeIndex, pd.RangeIndex, Tuple[int, int], Tuple[pd.Timestamp, pd.Timestamp], None] + Union[pd.DatetimeIndex, pd.RangeIndex, tuple[int, int], tuple[pd.Timestamp, pd.Timestamp], None] The longest time_index that can be used for historical forecasting, either as a range or a tuple. Examples @@ -1230,3 +1274,93 @@ def _apply_inverse_data_transformers( return forecasts[0] if called_with_single_series else forecasts else: return forecasts + + +def _process_historical_forecast_for_backtest( + series: Union[TimeSeries, Sequence[TimeSeries]], + historical_forecasts: Union[ + TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]] + ], + last_points_only: bool, +): + """Checks that the `historical_forecasts` have the correct format based on the input `series` and + `last_points_only`. If all checks have passed, it converts `series` and `historical_forecasts` format into a + multiple series case with `last_points_only=False`. + """ + # remember input series type + series_seq_type = get_series_seq_type(series) + series = series2seq(series) + + # check that `historical_forecasts` have correct type + expected_seq_type = None + forecast_seq_type = get_series_seq_type(historical_forecasts) + if last_points_only and not series_seq_type == forecast_seq_type: + # lpo=True -> fc sequence type must be the same + expected_seq_type = series_seq_type + elif not last_points_only and forecast_seq_type != series_seq_type + 1: + # lpo=False -> fc sequence type must be one order higher + expected_seq_type = series_seq_type + 1 + + if expected_seq_type is not None: + raise_log( + ValueError( + f"Expected `historical_forecasts` of type {expected_seq_type} " + f"with `last_points_only={last_points_only}` and `series` of type " + f"{series_seq_type}. However, received `historical_forecasts` of type " + f"{forecast_seq_type}. Make sure to pass the same `last_points_only` " + f"value that was used to generate the historical forecasts." + ), + logger=logger, + ) + + # we must wrap each fc in a list if `last_points_only=True` + nested = last_points_only and forecast_seq_type == SeriesType.SEQ + historical_forecasts = series2seq( + historical_forecasts, seq_type_out=SeriesType.SEQ_SEQ, nested=nested + ) + + # check that the number of series-specific forecasts corresponds to the + # number of series in `series` + if len(series) != len(historical_forecasts): + error_msg = ( + f"Mismatch between the number of series-specific `historical_forecasts` " + f"(n={len(historical_forecasts)}) and the number of `TimeSeries` in `series` " + f"(n={len(series)}). For `last_points_only={last_points_only}`, expected " + ) + expected_seq_type = series_seq_type if last_points_only else series_seq_type + 1 + if expected_seq_type == SeriesType.SINGLE: + error_msg += f"a single `historical_forecasts` of type {expected_seq_type}." + else: + error_msg += f"`historical_forecasts` of type {expected_seq_type} with length n={len(series)}." + raise_log( + ValueError(error_msg), + logger=logger, + ) + return series, historical_forecasts + + +def _extend_series_for_overlap_end( + series: Sequence[TimeSeries], + historical_forecasts: Sequence[Sequence[TimeSeries]], +): + """Extends each target `series` to the end of the last historical forecast for that series. + Fills the values all missing dates with `np.nan`. + + Assumes the input meets the multiple `series` case with `last_points_only=False` (e.g. the output of + `darts.utils.historical_forecasts.utils_process_historical_forecast_for_backtest()`). + """ + series_extended = [] + append_vals = [np.nan] * series[0].n_components + for series_, hfcs_ in zip(series, historical_forecasts): + # find number of missing target time steps based on the last forecast + missing_steps = n_steps_between( + hfcs_[-1].end_time(), series[0].end_time(), freq=series[0].freq + ) + # extend the target if it is too short + if missing_steps > 0: + series_extended.append( + series_.append_values(np.array([append_vals] * missing_steps)) + ) + else: + series_extended.append(series_) + return series_extended diff --git a/darts/utils/timeseries_generation.py b/darts/utils/timeseries_generation.py index bb9a6d8a1e..1094303736 100644 --- a/darts/utils/timeseries_generation.py +++ b/darts/utils/timeseries_generation.py @@ -746,6 +746,7 @@ def _build_forecast_series( with_static_covs: bool = True, with_hierarchy: bool = True, pred_start: Optional[Union[pd.Timestamp, int]] = None, + time_index: Union[pd.DatetimeIndex, pd.RangeIndex] = None, ) -> TimeSeries: """ Builds a forecast time series starting after the end of an input time series, with the @@ -764,24 +765,26 @@ def _build_forecast_series( with_hierarchy If set to `False`, do not copy the input_series `hierarchy` attribute pred_start - Optionally, give a custom prediction start point. + Optionally, give a custom prediction start point. Only effective if `time_index` is `None`. + time_index + Optionally, the index to use for the forecast time series. Returns ------- TimeSeries New TimeSeries instance starting after the input series """ - time_index_length = ( - len(points_preds) - if isinstance(points_preds, np.ndarray) - else len(points_preds[0]) - ) - - time_index = _generate_new_dates( - time_index_length, - input_series=input_series, - start=pred_start, - ) + if time_index is None: + time_index_length = ( + len(points_preds) + if isinstance(points_preds, np.ndarray) + else len(points_preds[0]) + ) + time_index = _generate_new_dates( + time_index_length, + input_series=input_series, + start=pred_start, + ) values = ( points_preds if isinstance(points_preds, np.ndarray) diff --git a/darts/utils/torch.py b/darts/utils/torch.py index 710e0809b8..81edf78d01 100644 --- a/darts/utils/torch.py +++ b/darts/utils/torch.py @@ -4,24 +4,21 @@ """ from functools import wraps -from inspect import signature -from typing import Any, Callable, TypeVar +from typing import Callable, TypeVar +import numpy as np import torch.nn as nn import torch.nn.functional as F -from numpy.random import randint from sklearn.utils import check_random_state from torch import Tensor from torch.random import fork_rng, manual_seed -from darts.logging import get_logger, raise_if_not +from darts.logging import get_logger, raise_log +from darts.utils.utils import MAX_NUMPY_SEED_VALUE, MAX_TORCH_SEED_VALUE, _is_method T = TypeVar("T") logger = get_logger(__name__) -MAX_TORCH_SEED_VALUE = (1 << 31) - 1 # to accommodate 32-bit architectures -MAX_NUMPY_SEED_VALUE = (1 << 31) - 1 - class MonteCarloDropout(nn.Dropout): """ @@ -53,26 +50,6 @@ def mc_dropout_enabled(self) -> bool: return self._mc_dropout_enabled or self.training -def _is_method(func: Callable[..., Any]) -> bool: - """Check if the specified function is a method. - - Parameters - ---------- - func - the function to inspect. - - Returns - ------- - bool - true if `func` is a method, false otherwise. - """ - spec = signature(func) - if len(spec.parameters) > 0: - if list(spec.parameters.keys())[0] == "self": - return True - return False - - def random_method(decorated: Callable[..., T]) -> Callable[..., T]: """Decorator usable on any method within a class that will provide an isolated torch random context. @@ -82,22 +59,22 @@ def random_method(decorated: Callable[..., T]) -> Callable[..., T]: ---------- decorated A method to be run in an isolated torch random context. - """ # check that @random_method has been applied to a method. - raise_if_not( - _is_method(decorated), "@random_method can only be used on methods.", logger - ) + if not _is_method(decorated): + raise_log(ValueError("@random_method can only be used on methods."), logger) @wraps(decorated) def decorator(self, *args, **kwargs) -> T: if "random_state" in kwargs.keys(): + # get random state for first time from model constructor self._random_instance = check_random_state(kwargs["random_state"]) elif not hasattr(self, "_random_instance"): + # get random state for first time from other method self._random_instance = check_random_state( - randint(0, high=MAX_NUMPY_SEED_VALUE) + np.random.randint(0, high=MAX_NUMPY_SEED_VALUE) ) - + # handle the randomness with fork_rng(): manual_seed(self._random_instance.randint(0, high=MAX_TORCH_SEED_VALUE)) return decorated(self, *args, **kwargs) diff --git a/darts/utils/utils.py b/darts/utils/utils.py index f05699d44c..1ce2955a6a 100644 --- a/darts/utils/utils.py +++ b/darts/utils/utils.py @@ -7,12 +7,13 @@ from enum import Enum from functools import wraps from inspect import Parameter, getcallargs, signature -from typing import Callable, Optional, TypeVar, Union +from typing import Any, Callable, Optional, TypeVar, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from pandas._libs.tslibs.offsets import BusinessMixin +from sklearn.utils import check_random_state from tqdm import tqdm from tqdm.notebook import tqdm as tqdm_notebook @@ -25,6 +26,9 @@ logger = get_logger(__name__) +MAX_TORCH_SEED_VALUE = (1 << 31) - 1 # to accommodate 32-bit architectures +MAX_NUMPY_SEED_VALUE = (1 << 31) - 1 + # Enums class SeasonalityMode(Enum): @@ -265,6 +269,23 @@ def _parallel_apply( return returned_data +def _is_method(func: Callable[..., Any]) -> bool: + """Check if the specified function is a method. + + Parameters + ---------- + func + the function to inspect. + + Returns + ------- + bool + true if `func` is a method, false otherwise. + """ + spec = signature(func) + return len(spec.parameters) > 0 and list(spec.parameters.keys())[0] == "self" + + def _check_quantiles(quantiles): raise_if_not( all([0 < q < 1 for q in quantiles]), @@ -587,3 +608,114 @@ def expand_arr(arr: np.ndarray, ndim: int): if len(shape) != ndim: arr = arr.reshape(shape + tuple(1 for _ in range(ndim - len(shape)))) return arr + + +def sample_from_quantiles( + vals: np.ndarray, + quantiles: np.ndarray, + num_samples: int, +): + """Generates `num_samples` samples from quantile predictions using linear interpolation. The generated samples + should have quantile values close to the quantile predictions. For the lowest and highest quantiles, the lowest + and highest quantile predictions are repeated. + + Parameters + ---------- + vals + A numpy array of quantile predictions/values. Either an array with two dimensions + (n times, n components * n quantiles), or with three dimensions (n times, n components, n quantiles). + In the two-dimensional case, the order is first by ascending column, then by ascending quantile value + `(comp_0_q_0, comp_0_q_1, ... comp_n_q_m)` + quantiles + A numpy array of quantiles. + num_samples + The number of samples to generate. + """ + if not 2 <= vals.ndim <= 3: + raise_log( + ValueError( + "`vals` must have either two dimensions with `(n times, n components * n quantiles)` or three " + "dimensions with shape `(n times, n components, n quantiles)`" + ) + ) + n_time_steps = len(vals) + n_quantiles = len(quantiles) + if vals.ndim == 2: + if vals.shape[1] % n_quantiles > 0: + raise_log( + ValueError( + "`vals` with two dimension must have shape `(n times, n components * n quantiles)`." + ) + ) + vals = vals.reshape((n_time_steps, -1, n_quantiles)) + elif vals.ndim == 3 and vals.shape[2] != n_quantiles: + raise_log( + ValueError( + "`vals` with three dimension must have shape `(n times, n components, n quantiles)`." + ) + ) + n_columns = vals.shape[1] + + # Generate uniform random samples + random_samples = np.random.uniform(0, 1, (n_time_steps, n_columns, num_samples)) + # Find the indices of the quantiles just below and above the random samples + lower_indices = np.searchsorted(quantiles, random_samples, side="right") - 1 + upper_indices = lower_indices + 1 + + # Handle edge cases + lower_indices = np.clip(lower_indices, 0, n_quantiles - 1) + upper_indices = np.clip(upper_indices, 0, n_quantiles - 1) + + # Gather the corresponding quantile values and vals values + q_lower = quantiles[lower_indices] + q_upper = quantiles[upper_indices] + z_lower = np.take_along_axis(vals, lower_indices, axis=2) + z_upper = np.take_along_axis(vals, upper_indices, axis=2) + + y = z_lower + # Linear interpolation + mask = q_lower != q_upper + y[mask] = z_lower[mask] + (z_upper[mask] - z_lower[mask]) * ( + random_samples[mask] - q_lower[mask] + ) / (q_upper[mask] - q_lower[mask]) + return y + + +def random_method(decorated: Callable[..., T]) -> Callable[..., T]: + """Decorator usable on any method within a class that will provide a random context. + + The decorator will store a `_random_instance` property on the object in order to persist successive calls to the + RNG. + + This is the equivalent to `darts.utils.torch.random_method` but for non-torch models. + + Parameters + ---------- + decorated + A method to be run in an isolated torch random context. + """ + # check that @random_method has been applied to a method. + if not _is_method(decorated): + raise_log(ValueError("@random_method can only be used on methods."), logger) + + @wraps(decorated) + def decorator(self, *args, **kwargs): + if "random_state" in kwargs.keys(): + # get random state for first time from model constructor + self._random_instance = check_random_state( + kwargs["random_state"] + ).get_state() + elif not hasattr(self, "_random_instance"): + # get random state for first time from other method + self._random_instance = check_random_state( + np.random.randint(0, high=MAX_NUMPY_SEED_VALUE) + ).get_state() + + # handle the randomness + np.random.set_state(self._random_instance) + result = decorated(self, *args, **kwargs) + # update the random state after the function call + self._random_instance = np.random.get_state() + return result + + return decorator diff --git a/docs/source/conf.py b/docs/source/conf.py index 21a00c2efe..eb798536ba 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -49,10 +49,10 @@ "inherited-members": None, "show-inheritance": None, "ignore-module-all": True, - "exclude-members": "ForecastingModel,LocalForecastingModel,FutureCovariatesLocalForecastingModel," + "exclude-members": "LocalForecastingModel,FutureCovariatesLocalForecastingModel," + "TransferableFutureCovariatesLocalForecastingModel,GlobalForecastingModel,TorchForecastingModel," + "PastCovariatesTorchModel,FutureCovariatesTorchModel,DualCovariatesTorchModel,MixedCovariatesTorchModel," - + "SplitCovariatesTorchModel,TorchParametricProbabilisticForecastingModel," + + "SplitCovariatesTorchModel," + "min_train_series_length," + "untrained_model,first_prediction_index,future_covariate_series,past_covariate_series," + "initialize_encoders,register_datapipe_as_function,register_function,functions," diff --git a/docs/source/examples.rst b/docs/source/examples.rst index fe68dd1e1a..4efe4c1b53 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -86,6 +86,15 @@ Regression models example notebook: examples/20-RegressionModel-examples.ipynb +Conformal Prediction +================= + +Conformal prediction example notebook: + +.. toctree:: + :maxdepth: 1 + + examples/23-Conformal-Prediction-examples.ipynb Fast Fourier Transform ====================== diff --git a/docs/userguide/covariates.md b/docs/userguide/covariates.md index 97f82c6d92..8df7dc94eb 100644 --- a/docs/userguide/covariates.md +++ b/docs/userguide/covariates.md @@ -154,6 +154,7 @@ GFMs are models that can be trained on multiple target (and covariate) time seri | [TiDEModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel) | ✅ | ✅ | ✅ | | [TSMixerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tsmixer_model.html#darts.models.forecasting.tsmixer_model.TSMixerModel) | ✅ | ✅ | ✅ | | Ensemble Models (f) | ✅ | ✅ | ✅ | +| Conformal Prediction Models (g) | ✅ | ✅ | ✅ | **Table 1: Darts' forecasting models and their covariate support** @@ -170,6 +171,8 @@ GFMs are models that can be trained on multiple target (and covariate) time seri (f) Ensemble Model including [RegressionEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel), and [NaiveEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel). The covariate support is given by the covariate support of the ensembled forecasting models. +(g) Conformal Prediction Model including [ConformalNaiveModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalNaiveModel), and [ConformalQRModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalQRModel). The covariate support is given by the covariate support of the underlying forecasting model. + ---- ## Quick guide on how to use past and/or future covariates with Darts' forecasting models diff --git a/examples/23-Conformal-Prediction-examples.ipynb b/examples/23-Conformal-Prediction-examples.ipynb new file mode 100644 index 0000000000..09277e879c --- /dev/null +++ b/examples/23-Conformal-Prediction-examples.ipynb @@ -0,0 +1,1577 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "45bd6e88-1be9-4de1-9933-143eda71d501", + "metadata": {}, + "source": [ + "# Conformal Prediction Models\n", + "\n", + "The following is a demonstration of the conformal prediction models in Darts.\n", + "\n", + "TLDR;\n", + "\n", + "- Conformal prediction in Darts constructs valid prediction intervals without distributional assumptions.\n", + "- We use Split Conformal Prediction (SCP) due to its simplicity and efficiency.\n", + "- You can apply conformal prediction to any pre-trained global forecasting model.\n", + "- To improve your experience, our conformal models automatically extract the relevant calibration data from your input series required to generate the interval.\n", + "- We offer useful features to configure the extraction and make your conformal models more adaptive and efficient (`cal_length`, `cal_stride`).\n", + "- Conformal prediction supports all use cases (uni- and multivariate, single and multiple series, and single and multi-horizon forecasts, providing direct quantile value predictions or sampled predictions).\n", + "- We'll demonstrate how to use and evaluate conformal prediction on four examples using real-world data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3ef9bc25-7b86-4de5-80e9-6eff27025b44", + "metadata": {}, + "outputs": [], + "source": [ + "# fix python path if working locally\n", + "from utils import fix_pythonpath_if_working_locally\n", + "\n", + "fix_pythonpath_if_working_locally()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9d9d76e9-5753-4762-a1cb-c8c61d0313d2", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from darts import concatenate, metrics\n", + "from darts.datasets import ElectricityConsumptionZurichDataset\n", + "from darts.models import ConformalNaiveModel, ConformalQRModel, LinearRegressionModel" + ] + }, + { + "cell_type": "markdown", + "id": "6ec264e9-af99-4d88-9fcc-1e71db03b294", + "metadata": {}, + "source": [ + "## Conformal Prediction for Time Series Forecasting\n", + "\n", + "*Conformal prediction is a technique for constructing prediction intervals that try to achieve valid coverage in finite samples, without making distributional assumptions.* [(source)](https://arxiv.org/pdf/1905.03222)\n", + "\n", + "In other words: If we want a prediction interval that includes 80% of all actual values over some period of time, then a conformal model attempts to generate such intervals that actually have 80% of points inside.\n", + "\n", + "There are different techniques to perform conformal prediction. In Darts, we currently use **Split Conformal Prediction [(SCP, Lei\n", + "et al., 2018)](https://www.stat.cmu.edu/~ryantibs/papers/conformal.pdf)** (with some nice adaptions) due to its simplicity and efficiency. \n", + "\n", + "### Split Conformal Prediction\n", + "SCP adds calibrated prediction intervals with a specified confidence level to a base model's forecasts. It involves splitting the data into a training (+ optional validation) set and a calibration (+ test) set. The model is trained on the training set, and the calibration set is used to compute the prediction intervals to ensure they contain the true values with the desired probability.\n", + "\n", + "#### Advantages\n", + "\n", + "- **Valid Coverage**: Provides valid prediction intervals that are guaranteed to contain the true value with a specified confidence level on finite samples.\n", + "- **Model-agnostic**: Can be applied to any predictive model:\n", + " - Either adds calibrated prediction intervals to point forecasting models\n", + " - Or calibrates the predicted intervals in case of probabilistic forecasting models\n", + "- **Distribution-free**: No distributional assumptions about the data except that the errors on the calibration set are exchangeable (e.g. we don't need to assume that our data is normally distributed and then fit a model with a `GaussianLikelihood`).\n", + "- **Efficient**: Split Conformal Prediction is efficient since it does not require model re-training.\n", + "- **Interpretable**: The method is interpretable due to its simplicity.\n", + "- **Useful Applications**: It's used to provide more reliable and informative predictions to help decision-making in several industries. See this [article on conformal prediction](https://medium.com/@data-overload/conformal-prediction-a-critic-to-predictive-models-27501dcc76d4)\n", + "\n", + "#### Disadvantages\n", + "\n", + "- **Requires a Calibration Set**: Conformal prediction requires another data / hold-out set that is used solely to compute the calibrated prediction intervals. This can be inefficient for small datasets.\n", + "- **Exchangeability of Calibration Data** (a): The accuracy of the prediction intervals depends on the representativeness of the calibration data (or rather the forecast errors produced on the calibration set). The coverage is not guaranteed anymore if there is a **distribution shift** in forecast errors (e.g. series with a trend but forecasting model is not able to predict the trend).\n", + "- **Conservativeness** (a): May produce wider intervals than necessary, leading to conservative predictions.\n", + "\n", + "(a) Darts conformal models have some parameters to control the extraction of the calibration set for more adaptiveness (see more infos [here](#Darts-features-to-make-your-Conformal-Models-more-adaptive))." + ] + }, + { + "cell_type": "markdown", + "id": "d5dc6eb5-2eeb-4495-9074-1a44ac9154ab", + "metadata": {}, + "source": [ + "## Darts Conformal Models\n", + "\n", + "Darts' conformal models add calibrated prediction intervals to the forecasts of any **pre-trained global forecasting model**. \n", + "There is no need to train the conformal models themselves (e.g. no `fit()` required) and you can directly call `predict()` or `historical_forecasts()`. Behind the hood, Darts will automatically extract the calibration set from the past of your input series and use it to generate the calibrated prediction intervals (see [here](#Workflow-behind-the-hood) for more detail).\n", + "\n", + "> **Important**: The `series` passed to the forecast methods **should not have any overlap** with the series used to **train** the forecasting model, since this will lead to overly optimistic prediction intervals.\n", + "\n", + "### Model support\n", + "\n", + "All conformal models in Darts support:\n", + "\n", + "- any **pre-trained global forecasting model** as the base forecaster (you can find a list [here](https://unit8co.github.io/darts/#forecasting-models))\n", + "- **uni-** and **multivariate** forecasts (single / multi-columns)\n", + "- **single** and **multiple series** forecasts\n", + "- **single** and **multi-horizon** forecasts\n", + "- generate a **single** or **multiple calibrated prediction intervals**\n", + "- **direct quantile value** predictions (interval bounds) or **sampled predictions** from these quantile values\n", + "- **any covariates** based on the underlying forecasting model\n", + "\n", + "### Direct Interval Predictions or Sampled Predictions\n", + "Conformal models are probabilistic, so you can forecast in two ways (when calling `predict()`, `historical_forecasts()`, ...):\n", + "\n", + "- Forecast the calibrated quantile interval bounds directly (example [here](https://unit8co.github.io/darts/quickstart/00-quickstart.html#Direct-Parameter-Predicitons)).\n", + " - `predict(..., predict_likelihood_parameters=True)`\n", + "- Generate stochastic forecasts by sampling from these calibrated quantile intervals (examples [here](https://unit8co.github.io/darts/quickstart/00-quickstart.html#Probabilistic-Sample-Predictions)):\n", + " - `predict(..., num_samples=1000)`\n", + "\n", + "### Workflow behind the hood\n", + "\n", + "> Note: `cal_length` and `cal_stride` will be further explained [below](#Darts-features-to-make-your-Conformal-Models-more-adaptive).\n", + "\n", + "In general, the workflow of the models to produce one calibrated forecast/prediction is as follows (using `predict()`):\n", + "\n", + "- **Extract a calibration set**: The calibration set for each conformal forecast is automatically extracted from\n", + " the most recent past of your input series relative to the forecast start point. The number of calibration examples\n", + " (forecast errors / non-conformity scores) to consider can be defined at model creation\n", + " with parameter `cal_length`. Note that when using `cal_stride>1`, a longer history is required since\n", + " the calibration examples are generated with stridden historical forecasts.\n", + "- Generate **historical forecasts** on the calibration set (using the forecasting model) with a stride `cal_stride`.\n", + "- Compute the **errors/non-conformity scores** (specific to each conformal model) on these historical forecasts\n", + "- Compute the **quantile values** from the errors / non-conformity scores (using our desired quantiles set at model\n", + " creation with parameter `quantiles`).\n", + "- Compute the conformal prediction: Using these quantile values, add **calibrated intervals** to (or adjust the\n", + " existing intervals of) the forecasting model's predictions.\n", + "\n", + "For **multi-horizon forecasts**, the above is applied for each step in the horizon separately.\n", + "\n", + "When computing `historical_forecasts()`, `backtest()`, `residuals()`, ... the above is applied for each forecast (the forecasting model's historical forecasts are only generated once for efficiency).\n", + "\n", + "### Available Conformal Models\n", + "\n", + "At the time of writing (Darts version 0.32.0), we have two conformal models:\n", + "\n", + "#### `ConformalNaiveModel`\n", + "\n", + "Adds calibrated intervals around the median forecast of **any pre-trained global forecasting model**. It supports two symmetry modes:\n", + "\n", + "- `symmetric=True`:\n", + " - The lower and upper interval bounds are calibrated with the same magnitude.\n", + " - Non-conformity scores: uses the [absolute error](https://unit8co.github.io/darts/generated_api/darts.metrics.metrics.html#darts.metrics.metrics.ae) `ae()` to compute the non-conformity scores on the calibration set.\n", + "- `symmetric=False`\n", + " - The lower and upper interval bounds are calibrated separately.\n", + " - Non-conformity scores: uses the [error](https://unit8co.github.io/darts/generated_api/darts.metrics.metrics.html#darts.metrics.metrics.err) `err()` to compute the\n", + " non-conformity scores on the calibration set for the upper bounds, and `-err()` for the lower bounds.\n", + "\n", + "#### `ConformalQRModel` (Conformalized Quantile Regression Model)\n", + "\n", + "Calibrates the quantile predictions of a **pre-trained probabilistic global forecasting model**. It supports two symmetry modes:\n", + "\n", + "- `symmetric=True`:\n", + " - The lower and upper quantile predictions are calibrated with the same magnitude.\n", + " - Non-conformity scores: uses the [Non-Conformity Score for Quantile Regression](https://unit8co.github.io/darts/generated_api/darts.metrics.metrics.html#darts.metrics.metrics.incs_qr) `incs_qr(symmetric=True)` on the calibration set.\n", + "- `symmetric=False`\n", + " - The lower and upper quantile predictions are calibrated separately.\n", + " - Non-conformity scores: uses the [Asymmetric Non-Conformity Score for Quantile Regression](https://unit8co.github.io/darts/generated_api/darts.metrics.metrics.html#darts.metrics.metrics.incs_qr) `incs_qr(symmetric=False)` for the upper and lower bound on the calibration set.\n", + "\n", + "### Darts features to make your Conformal Models more adaptive\n", + "\n", + "As mentioned in [Split Conformal Prediction - Disadvantages](#Disadvantages), the calibration set has a large impact on the effectiveness of our conformal prediction technique.\n", + "\n", + "We implemented some cool features to make our automatic extraction of the calibration set even more powerful for you.\n", + "\n", + "All our conformal models have the following two parameters at model creation:\n", + "\n", + "- `cal_length`: The number of non-conformity scores (NCS) in the most recent past to use as calibration for each conformal forecast (and each step in the horizon).\n", + " - If `None` acts as an expanding window mode\n", + " - If `>=1` uses a moving fixed-length window mode\n", + " - Benefits:\n", + " - Using `cal_length` makes your model react more quickly to distribution shifts in NCS.\n", + " - Using `cal_length` reduces the computational cost to perform the calibration.\n", + " - Caution: Use large enough values to have enough example for calibration.\n", + "- `cal_stride`: (default=1) The stride (number of time steps between two consecutive forecasts) to apply when computing the historical forecasts and non-conformity scores on the calibration set.\n", + " - This is useful if we want to run our models on a scheduled basis (e.g. once every 24 hours) and are only interested in the NCS that were produced on this schedule.\n", + " - Caution: `cal_stride>1` requires a longer `series` history (roughly `cal_length * stride` points)." + ] + }, + { + "cell_type": "markdown", + "id": "eacf6328-6b51-43e9-8b44-214f5df15684", + "metadata": {}, + "source": [ + "## Examples\n", + "\n", + "We will show four examples:\n", + "\n", + "1) How to perform conformal prediction and compare different models based on the quantified uncertainty. For simplicity, we will use a single step horizon `n=1`.\n", + "2) How to perform multistep horizon conformal forecasts\n", + "3) How to perform multistep horizon conformal forecasts on a scheduled basis\n", + "4) An example of conformalized quantile regression.\n", + "\n", + "### Input Dataset\n", + "For both examples, we use the Electricity Consumption Dataset from households in Zurich, Switzerland.\n", + "\n", + "The dataset has a quarter-hourly frequency (15 Min time intervals), but we resample it to hourly frequency to keep things simple.\n", + "\n", + "To keep it simple, we will not use any covariates and only concentrate on the electricity consumption as the target we want to predict. The conformal model's covariate support and API is identical to the base-forecaster.\n", + "\n", + "**Target series** (the series we want to forecast):\n", + "- **Value_NE5**: Electricity consumption by households on grid level 5 (in kWh)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "90b31843-8f60-4dd8-b6e4-87206d67e585", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mkwlhYWEu369fvz46derk8M/dQ//KlSsghKB27doO9d67d69Q72vXrglleIOUc8mXp3Ry+6BBg9CoUSPhml6zZg2MRmOFDxb+4Xv27FlJ5eTk5CA+Pt5BQNaqVQtBQUHIyckRvR4bG+uwj9DQUNE9V9F17Qk1atQQ/R0SEuL29eLiYtHr8fHxDvvkX7M/Rink5OQgKCgIcXFxotc5jkN8fLxH542Hv2+cvccjt13iOA6jR49GcXEx4uPj8eijj0o80nKc3Q8bN27EsGHDUK9ePXzxxRfYs2cP9u/fj7Fjx4p+g2vXrkGv1zv9HRjuYaPSGJL47rvvYDQasXHjRiQkJAiv//33304/78w1iI2NxZ9//unwun3jXbNmTQDAyy+/jAcffNDp/ps3by616g77fuedd/DOO+/gwoUL+OGHHzB9+nRcvXoVW7ZsEcp+//33XY5oshdyUhwSfr9ff/216Pw5IyEhAStXrgQAnDx5El9++SVmz54Ns9mM5cuXuy3jr7/+qrAucqhZsyY4jsPu3budjobhX+MfVp44ATRS3aa4uDivy7JHp9Nh0qRJmDFjBhYvXoylS5eiT58+FV5rnTp1Qo0aNfD999/jjTfeqPAYYmNjsW/fPhBCRJ+9evUqysrKhGtFDhVd14Dt4V9SUuLw3evXr3tUZkU4E2X8a85ES0XExsairKwM165dE4kjQgiys7PRuXNnj+vKd3bcnQe57dLly5cxadIktG/fHseOHcO0adPw3nvvyaqXs2vpiy++QOPGjbFhwwbR+/a/bVxcHCwWC7KzsyV3OBg2mGPEkAR/A9IPR0IIVqxYIXkfPXv2xM2bNwWLl2f9+vWiv5s3b46mTZvi8OHDTh2OTp06CaEFvj7uenquaNiwIZ5++mn07dtXEBS33347qlevjuPHj7ssm+8hy+Gee+5BUFAQTp8+7XK/zmjWrBleeeUVtGnTpkLR06JFC+Tk5CA/P192/VwxYMAAEEKQlZXltM58aOC2225DtWrVsHz5chBCXO7Pm9+L5t5778Wvv/4qO6RaUfnjxo1DSEgIRowYgX///RdPP/10hfsMDg7GSy+9hPT0dLz++utOP3P16lUhRNenTx8UFhbiu+++E33ms88+E973BmfXNQA0atQIR44cEX325MmTHoelK+KXX34RXBbANtx8w4YNaNKkieD2ybke+PPyxRdfiF7/5ptvYDQavTpvZ86cQWxsrFNHm0dOu2SxWPDwww+D4zhs3rwZb7zxBt5//31s3LhRtE9XDpY7OI5DSEiISBRlZ2fj+++/F33u3nvvBQAsW7bM7f48qUNlhzlGDEn07dsXISEhePjhh/Hiiy+iuLgYy5Ytw40bNyTvY9SoUViyZAlGjhyJuXPnIikpCZs3b8bPP/8MwNZj5/noo49w77334p577sHo0aNRr1495Obm4sSJE/jrr7/w1VdfAQBat24NAPj4448RFRWFsLAwNG7c2GmPND8/H7169cIjjzyCFi1aICoqCvv378eWLVuEHmBkZCTef/99jBo1Crm5uRgyZAhq1aqFa9eu4fDhw7h27VqFDY0zGjVqhNdeew3/93//hzNnzqBfv36IiYnBlStX8Oeff8JgMGDOnDk4cuQInn76aQwdOhRNmzZFSEgIduzYgSNHjmD69Oluy7jzzjtBCMG+fftw9913O7x/4cIF7N271+H1uLg4l3PG3H777XjiiScwZswYHDhwAD169IDBYMDly5fx22+/oU2bNnjqqacQGRmJxYsXY9y4cbjrrrswfvx41K5dGxkZGTh8+DA++OADABCE1MKFC3HvvfdCr9ejbdu2ssXma6+9hs2bN6NHjx6YMWMG2rRpg7y8PGzZsgXPP/88WrRo4fR7fPnvvvsuRo0aheDgYDRv3lx4oFWvXh2PPfYYli1bhoSEBAwcOFBSfV544QWcOHECr776Kv7880888sgjaNCgAfLz87Fr1y58/PHHmDNnDm6//XY89thj+PDDDzFq1CicO3cObdq0wW+//Yb58+cjJSUFd911l6xzIeW6BoBHH30UI0eOxMSJEzF48GCcP38eb775pkNoSilq1qyJ3r17Y+bMmTAYDFi6dCnS09NFHSE592/fvn1xzz334KWXXkJBQQFuv/12HDlyBK+++iqSk5M9ClXx7N27Fz179qzQ7ZPaLr366qvYvXs3tm7divj4eEydOhVpaWl4/PHHkZycjMaNGwOwXY8bN27EsmXL0LFjR+h0OpedJJ4BAwZg48aNmDhxIoYMGYLMzEy8/vrrqFOnDk6dOiV8rnv37nj00Ucxd+5cXLlyBQMGDEBoaCgOHTqEiIgIPPPMM0Id1q9fjw0bNiAxMRFhYWGyc6EqHdrlfTP8GWej0lJTU4X5MOrVq0deeOEFsnnzZodRJfw8Rs64cOECefDBB0lkZCSJiooigwcPJps2bSIAhLk1eA4fPkyGDRtGatWqRYKDg0l8fDzp3bu3w+ifd955hzRu3Jjo9Xq38xgVFxeTJ598krRt21aYZ6d58+bk1VdfdZi/Ji0tjfTv35/UqFGDBAcHk3r16pH+/fsLQ40JKR95du3aNYeyXM1j9N1335FevXqR6OhoEhoaShISEsiQIUOEaQCuXLlCRo8eTVq0aEEMBgOJjIwkbdu2JUuWLCFlZWVOj4vHYrGQRo0aCcPjeSoalTZixAjhs65mvl61ahXp2rUrMRgMJDw8nDRp0oQ89thj5MCBA6LPbdq0ifTs2ZMYDAYSERFBWrVqRRYuXCi8X1JSQsaNG0fi4uIIx3FO5zFyBpzMY5SZmUnGjh1L4uPjhTlthg0bRq5cuSI6bvvr4eWXXyZ169YlOp3O6YionTt3EgBkwYIFTuviju+//57079+fxMXFkaCgIBITE0N69epFli9fTkpKSoTP5eTkkCeffJLUqVOHBAUFkYSEBPLyyy+7nMfIHnqEmdTr2mq1kjfffJMkJiaSsLAw0qlTJ7Jjxw6Xo9Loa52Q8nmH6FnVCXF+H/D1Xrp0KWnSpAkJDg4mLVq0IGvXrnU4Flf3r6t5jF566SWSkJAgzF311FNPuZzHyB77YyXENo0EAPLNN984fN4ZFbVLW7duJTqdzuF6zcnJIQ0bNiSdO3cWroXc3FwyZMgQUr16deF+IKTiuccWLFhAGjVqREJDQ0nLli3JihUrnLY5FouFLFmyhLRu3ZqEhISQatWqkW7duonmwTp37hy5++67SVRUFJvH6D84Qtz43gyGD5g/fz5eeeUVXLhwQZPZoisTixcvxrx585CVlYXw8HCtqxOQTJ06FcuWLUNmZqZHuTAMW7hn0qRJglPoz8ycOROfffYZTp8+jaAgFkRhsFAaw8fwDWWLFi1QWlqKHTt24L333sPIkSOZKFIA/mH04YcfYtq0aVpXJ6DYu3cvTp48iaVLl2LChAlMFFUB8vLy8OGHH+L9999nooghwK4Ehk+JiIjAkiVLcO7cOZSUlKBhw4Z46aWX8Morr2hdtUpBWFgYPv/8cxw6dEjrqgQc3bp1Q0REBAYMGIC5c+dqXR2GDzh79ixefvllPPLII1pXheFHsFAag8FgMBgMxn+w4foMBoPBYDAY/8GEEYPBYDAYDMZ/MGHEYDAYDAaD8R9MGDEYDAaDwWD8BxNGfoLVasXZs2clr6Re2ajqxw+wcwCwc1DVjx9g56CqHz+g/TlgwojBYDAYDAbjP5gwYjAYDAaDwfgPJowYDAaDwWAw/oMJIwaDwWAwGIz/YMKIwWAwGAwG4z+YMGIwGAwGg8H4DyaMGAwGg8FgMP6DCSMGg8FgMBiM/2DCiMFgMBgMBuM/mDBiMBgMBoPB+A8mjBgMBoPBYDD+gwkjBoPBYDAYjP9gwojhNXfeeScmT56sdTUYDAaDwfAaJoyqOAMHDsRdd93l9L09e/aA4zj89ddfPq6Ve2bPng2O4/Dkk0+KXv/777/BcRzOnTsHADh37hw4jnP6b+/evQCAnTt3On0/PT3d14fFYDAYDD8gSMvC582bh127dqG4uBjx8fF4+umn0b17d8yfPx+bN28WPmc2m3HbbbdhyZIlAIBOnTohLCwMHMcBAMaMGYOxY8cCAIqLizFv3jykpaUhKioKzzzzDPr16yfsKzU1FcuWLYPRaETv3r0xY8YMBAcH+/Co/YvHH38cDz74IM6fP4+EhATRe6tWrUL79u3RoUMHjWrnmrCwMKxcuRLPP/88mjVr5vaz27dvxy233CJ6LTY2VvT3v//+i+joaOHvuLg45SrLYDAYlZy9e/fi2rVr6N+/P3S6wPZcNK39iBEjkJqairS0NMyaNQszZ85EQUEBZsyYgd27dwv/kpKS0LNnT9F3v/vuO+F9XhQBwEcffYT8/Hxs2rQJ8+fPx4IFC3D+/HkAQEZGBpYsWYK33noLP/30Ey5duoSVK1f69Jj9jQEDBqBWrVpYs2aN6PWioiJs2LAB999/Px5++GHUr18fERERaNOmDdatW+d2nxzH4bvvvhO9Vr16dVEZWVlZGD58OGJiYhAbG4v7778fFy9elFzv5s2bo1evXnjllVcq/GxsbCzi4+NF/+zFcK1atUTv6/V6yXVhMBiMqsyZM2dwxx134L777sOkSZNACNG6Sl6hqWPUqFEjYZvjOJjNZly/fl3Ucz979izOnj3rMtxjz6ZNm7B48WJERkaiXbt26NGjB7Zu3Yrx48djy5Yt6Nu3L1q1agUAGDduHObOnesQkuExm80wm82i14KCghASEiLzSCvGarWK/vcVOp0Ojz76KNasWYNXXnlFcOE2bNgAs9mMsWPHYv369XjhhRcQHR2NTZs24dFHH0WjRo3QtWtXYT+EEFHdrVarw7HwrxUVFaFXr1644447sHPnTgQFBWHu3LkYPXo0jh49irCwMLd15m+6+fPno2vXrti3bx86d+4sOod0+c7qQtcJAJKTk1FcXIxWrVphxowZ6NWrl5zTqAhaXQP+RFU/B1X9+AF2DgLx+Pfs2QOLxQIAWL58OVq1aoVJkyZ5vD+1zoFUJ0tTYQQACxYsQGpqKkpKStCzZ08kJiaK3t+8eTPuuOMOREZGil4fOXIkOI5D165dMXnyZFSvXh0FBQXIyclBUlKS8LlmzZrh2LFjAGyqtlu3bsJ7TZs2RVZWFoqLi50+jFevXo0VK1aIXhs6dCiGDRsm+fjuu+8+XL9+XfLnlaBmzZr44YcfJH/+nnvuwVtvvYUNGzYI52f58uW4++67UVZWhiFDhgifHThwIL799lusWrUK8fHxAGzhy4KCAsGZA4Br166J/rZarcjJycH58+fx5ZdfwmKxiITY7Nmz0b59e3z99dfo3r272/rm5eXBbDYjNjYWKSkpmDJlCtauXYvLly8DsLlRHMchKysLAHD77bc73BCHDx+GXq8HIQTz589H69atYTab8e2336Jv375Yt24dunTpIvkcKklmZqYm5foTVf0cVPXjB9g5CKTjP378uOjvd999FwMGDPB6v0qfg8aNG0v6nObCaPr06XjhhRdw4MABZGRkOLz/888/O4x4WrFiBdq0aYObN29i4cKFeO211/D222+jqKgIer1eJHIMBgOKiooAACaTCQaDQXiPF1smk8mpMBozZgxGjBghek2uY3Tjxg1kZ2dL/rwS6PV6h3whdyQkJOC2227Dpk2b8NBDD+H06dPYv38/tmzZgvr162PhwoX48ssvkZWVhZKSEpSUlKBmzZpCGWFhYYiOjhaVGRcXJ/pbp9MhNjYWCQkJOH/+PM6fP4+2bduK6lFSUoKCgoIK6169enWEhIQgISEBb7/9Nm655Rb8+++/qFOnDgCgXr16SEhIEJyl9evXo2XLlqJ98AI8ISFB5A498MADyMvLwxdffIGhQ4dKPodKYLVakZmZiQYNGgR8jN5Tqvo5qOrHD7BzEIjHX1JSIvo7Oztb1jPIHq3PgebCCLA9yLt27Yp169YhMTFRcC0OHz6MgoIC3H777aLPJycnAwBiYmIwbdo09O/fH6WlpYiIiIDFYhE5QEajEREREQCA8PBwGI1GYT+FhYXC684ICQnxOmzGuypSsFgsiuS2xMfHy76YHn/8cTz99NNYunQpPv30UyQkJKBv375YtGgR3nnnHbzzzjto06YNDAYDJk+ejNLSUlEZHMcJf/Mju+j3+c/rdDoQQtCxY0esXbtWeN9qtSIrKwvt27evsO68y6TT6dC0aVOMHz8eM2bMEPLF+HL4/SQkJFSYoE3TrVs3fPHFF5o1SnTdqypV/RxU9eMH2DkIpOPn3Xoeo9EIo9GIqKgor/ar1TnwC2HEY7VaRQm4W7ZsQZ8+fdyKE/6kEUIQHR2N2NhYZGRkoHXr1gCAkydPCu5AYmKiyJU6deoU6tWrV2FOizccOHBA0uesVqswMkyLC2HYsGF47rnn8L///Q+ffvopxo8fD47jsHv3bgwaNAgjR44U6nnq1CkHB4YmLi5OdKOcOnVKcO0AoEOHDtiwYQNq1aol5JNZrVYEBwejWrVqsus+a9YsNGnSBOvXr5f9XWccOnRIcJ8YDAaD4R57YQQAly5dQvPmzTWojfdoJkeLioqwefNmFBUVoaysDL/88gsOHjwouEFlZWXYtm2baKg9AJw+fRonT56ExWJBQUEBFi9ejK5duwriKSUlBZ988gmMRiOOHj2KXbt2oW/fvgCAfv36Yfv27UhPT0dhYSFWrVqFe++917cH7qdERkZi+PDhmDFjBi5duoTRo0cDAJKSkrBt2zb88ccfOHHiBCZMmFBhaLB379744IMP8Ndff+HAgQN48sknRaPARowYgZo1a2LQoEHYvXs3zp49i7S0NLz22muyRqbx1K5dG88//zzee+89p+/n5OQgOztb9K+4uBgA8M477+C7777DqVOncOzYMbz88sv45ptv8PTTT8uuB4PBYFRFnAkjZ68FCpoJI47j8P333yMlJQV9+vTB6tWrMXfuXCFxeu/evQgNDXWYQyc3NxfTp09Hz549MXToUOh0OsyePVt4f8KECYiMjES/fv0wffp0TJ8+XRj9lpSUhMmTJ2PKlClISUlB7dq1RUP9qzqPP/44bty4gbvuugsNGzYEAMycORMdOnTAPffcgzvvvBPx8fG4//773e5n8eLFaNCgAXr06IFHHnkE06ZNE8KZABAREYFdu3ahYcOGePDBB9GyZUuMGzcOxcXFohGJcnjhhRccEvR57rrrLtSpU0f0j59OwGw2Y9q0aWjbti26d++O3377DT/99BMefPBBj+rBYDAYVY1Lly5Jei1Q4EigTzhQSdA6lKY1Vf34AXYOAHYOqvrxA+wcBNrxFxUViQY18bz11luYOnWqR/vU+hz4/1lnMBgMBoPhl9AhM3qwUSA7RkwYMfyOyMhIl/92796tdfUYDAaD8R+0MOrYsaPT1wMNvxqVxmAAtsVgXVGvXj3fVYTBYDAYbqGdoY4dO+Knn34CwIQRg6Eo9MzlDAaDwfBfaAGUlJSEqKgo3Lx5k4XSGAwGg8FgVD1oYcSP+rV/PdBgwojBYDAYDIZH0AKobt26qFu3LgDg5s2bwuoSgQYTRgwGg8FgMDyCDpnRjhEQuK4RE0YMBoPBYDA8ghc/oaGhqF69uuAYAYE7ZJ8JIwaD4TFlZWVaV4HBYGgIL4zq1KkDjuOYY8RgMKomhBAMHToUUVFRWLdundbVYTAYGlBSUoLc3FwAEJwi2jFiwojBYFQZ0tLS8PXXX6O4uNjjaf8ZDEZgYz8ijf4fYKE0BoNRhfjwww+F7cuXL8NoNGpYG21ZvHgx7r77bvzzzz9aV4XB8CkVCSPmGDEYjCrBpUuX8O2334peO3DggEa10ZbMzExMmzYN27Ztw4gRI7SuDqMKYTabceedd6Jp06Y4c+aMJnVwJoxY8jWDwahybN68GRaLRfTanj17NKqNtvz777/C9pEjRzSsCUMLCCGYM2cOHnnkEZ///rt27UJaWhoyMjKwbNkyn5bNYz+HEQBERUXBYDA4vB9IMGHEYDBkkZOT4/DaypUrA3YyN284e/as6G+z2axRTRha8Oeff2L27NlYt24d2rdvj/Xr1/us7Ly8PGFbK1FuP4cRDy+SmGPEYDCqBAUFBQ6vZWRkoFatWppZ+lpx6tQp0d8ZGRma1IMQokm5VZ0///xT2CaE4JlnnoHVavVJ2XRHRCth5CyURm8XFBQEZP4hE0YMBkMWtDBq0KCBsG0ymfDDDz9oUSUQQnD9+nWfl2svhE6cOOHT8gsKCjBx4kRERUVhypQpPi2bARw6dEj09/Xr1/H333/7pGxacGRnZ+Pq1as+KZfGlTAK9CH7TBgxGAxZ0MLo448/RqNGjYS/nYXZ1IYQgrvvvhu1atXCihUrfFq2lsKotLQUt956K5YtWwaj0Yh33nkH58+f91n5DDgVQb/88otPyrYPXR89etQn5dLwobLg4GDExsYKrwf6yDQmjBgMhixoYdS2bVt8+eWXwt85OTn4888/cePGDVXrcPPmTRw6dAiEEGRlZWH79u0ghOCJJ55QtVwaq9WqqTA6duyYQ3n/+9//fFZ+VcdsNuPYsWMAAJ2u/FG6fft2n5RvH6I6fPiwT8ql4UVPfHy86BwwYcRgMKoUtDCKjo4W9RSXLVuGrl27olOnTqolIlssFnTs2BEdOnTAokWLcOXKFVXKqYjLly/DZDKJXvOlMLpw4YLDa2vXrmX5Rj4iPT1duMaHDRuGevXqAQB2796NkpIS1cu3F0a+zjMihAgOca1atUTvBfqQfSaMGIwAw2g04tNPP0V6erom5fPCiOM4GAwG1KhRw+EzZ86cwb59+1QpPysrS0h6fumll3Dx4kXR+74aGeYs0To9Pd1hKgO1yMzMdHjt2LFjmoRUqiJ0GC05ORl9+vQBYMu1++uvv1Qv3z6U5mthZDKZhETzqKgo0Xu8SAS0CfF5CxNGDEaAMXPmTIwePRq9evVycCx8AS+MoqOjwXEcqlWrBr1e7/A5Zw9uJSgqKhL9/dlnn/mkXHuczXRtMpl8NjKNdoweeOABYfv777/3SflVHVqItG/fHrfccovwt71YVwN7x+jYsWM+XdT55s2bwra9MOrcubMwl9E333yD4uJin9VLCZgwYjACjCVLlgCwjUTZsWOHz8unhRFgc45iYmIcPkdPfqgk9sJo48aNor/PnTunSrk0FosFH3zwgfD3gAEDhG37kUpqQQujSZMmCds//fSTT8qv6tCjIBs2bOjz8JG9Y2Q2m/H+++9j2bJlPhEidPmRkZGi9wwGAx588EEAQH5+PjZt2qR6fZSECSMGI4CZP38+Jk2apJoIcYa9MAIgyjPiUSvfxl4Y2eOLkVlffvmlEMrs3r07JkyYILynhTC67bbb0Lp1awC2uXWuXbvmkzpUZfLz84Xt6OhokTDKyspSvXxn8wM9//zzmDhxIj755BPVy3fnGAHAo48+Kmx//vnnqtdHSZgwYjACjNDQUGH7jz/+wNKlS9GjRw+fhHAsFovQINPCyFmekVo5UBUJI184RvSDZ/bs2UhOThb+9pUw4kOGcXFxCA8PR//+/QHYkmK3bNnikzpUZewHIdB5NVo4RjTz58/3afn2jhEA9O7dG3FxcQBsy5cEEkwYMRgBhrOw1dWrV3HfffepnvhL9xIrcoxOnjypSn38QRjxQ5AjIyPRq1cv1K1bV3gI8NMIqElZWZngSjRs2BAABGEEAGvWrGGj01SGF0Y6nQ4Gg8HnoTR3M0rbjxJTg4ocI71eL+Rd5ebmipYw8XeYMGIwAgxnS3IAttDV8ePHfVZ2RY5RSUmJw1piSuAPwojvLUdFRYHjOHAcJ7hG169fVz2UcunSJWFEEC+MunXrhsTERADAjh07cNddd+Gzzz5jAkkl7AchREVFCc6JLx2jmjVrOrwXFBTks/IB544RADRp0kTYPn36tOp1UgomjBiMAKKsrMytMFB7CL8rYeTMMVKrPv4gjPjeMv1A8GU4jR55xy/LEhQUJAqh7NixA6NGjdIkQb8qwOcY0feBLxdP5R0jZ6KEnlTx+vXr+OyzzxSf76sixwhgwojBYPgAujECgJEjR2L16tXC30wY2RJf1ZzLiBAi9Ja1EkZ04jXvGAG2iQa7du0q+qxW811VdpwNQuDzjG7evCncq7/99hs++OADxRdT5fdnMBjw/PPPi967cuWK4CiOGTMGo0aNwtChQxUtnzlGDAbDL6CFybBhw/D555+jc+fOwmtaCSNnoTRAndXmKxJGVqtV1VF6ZrNZmC+G7ilr7RgBtqkTvv/+e3Tq1El4TYv16yo7paWlwhxizhwjwOYaXb9+HXfffTeeeeYZvPnmm4qVb7VaRY7Riy++iLFjxwrvWywW5OTkgBCCH3/8EYBtRm4lYY4Rg8HwC5wJk6SkJGGCRbWXpJDrGGkhjADni3sqBf1AoHvKSUlJwt9qCyN6LTr7HJPatWvjvffeE/5mwkh56PugWrVqwra9MNq1a5cgoF577TXFyqcndjUYDKhduzZWrlwpEkfZ2dnIzs4WfY93kZSAOUYMBsMvcNZLCw0NFZJu//33X0UbP3vs527h0coxCg4OFrYTEhKEbTWFEf1AoHvKOp0O7dq1A2CbSyk3N1e1OtBhGX6GYRpaqDJhpDyuOgj2wkita8CVKImPjxe2L1++7HD/SelUSEWKY1S9enWhbWDCiMFgqIKrBrlFixYAbA2fmssRSHWMOnbsCMCWC6P0gpp047569WoEBwcjLCwM69evF173lTCy7ynT4TQ168CEkba4ug/ouYxGjhzp4BIpJZRc/f60MMrOzhbWFORxN/eRXKQ4RkC5a3Tx4kWfLK6rBJoKo3nz5uGee+5Bz549MXz4cCEGmpqaiq5du6J79+7CP9oSPHbsGB5++GHcfvvteOKJJ0QZ+MXFxZg5cyZ69OiB/v37O0x0lpqaipSUFPTs2RNz5sxBaWmpbw6WwVCAioQRoG6ekdQco6ZNmwKwJSorPWSfFkadOnVCVlYWMjMzceutt6JOnToAbKJErWHqrkJpgO/yjOgHo7OHUvXq1cFxHAAmjNRASigNcFy3T6lQNy1KaGHEX/+ATRjZO0ZKCiMpjhFQLozUaAvUQlNhNGLECKSmpiItLQ2zZs3CzJkzhQuuS5cu2L17t/CPV8JmsxkvvvgiHnroIezYsQOtW7fGrFmzhH1+9NFHwtos8+fPx4IFC4QlAjIyMrBkyRK89dZb+Omnn3Dp0iWsXLnS9wfOYHiIK2HSsmVLYVvNuWukOkZJSUnCttLhNFoYRUREIC4uTsizad++PQBbz3zHjh24//77sXTpUkXLdxVKo8sHoOoK6xU5Rnq9XpgIlAkj5XEVUu7UqROaNWvm8ntKCSNXwph2jF566SW88cYbou9p6RgBwJkzZxQrX000FUaNGjVCSEgIANtoCrPZLFqYzxkHDx5EeHg4Bg0ahNDQUIwfPx7Hjx8XXKNNmzbhiSeeQGRkJNq1a4cePXpg69atAIAtW7agb9++aNWqFSIjIzFu3Dhs3rxZ3YNkMBTElTDp16+f0DitXbsWq1at8mn5kZGRwszLixcv9qkwoqGFyV133YXvv/8ezz77bIXtihzcOUatW7dGeHg4AGD79u149tln8eSTT4qSZZWgImEElItVJoyUx9V9EBoaiqNHjzoMn+dRagJWV44RLYwq+p63uLsPaAIxAVv96TErYMGCBUhNTUVJSQl69uyJxMREHDt2DIcPH0afPn1Qo0YNDB8+HEOGDAFgU5x0oxseHo769evjzJkzMBgMyMnJEb3frFkzHDt2TPhut27dhPeaNm2KrKwsFBcXIywszKFuZrPZYT6UoKAgQcwpCZ8wq2birD9T1Y8fkHYO6J5qZGSk8NnatWvjo48+wogRIwAAs2bNwsiRI0XJyUrgqnwA+P7773HlyhXEx8fj999/F17PyMiQ/LtKOQe0MAoLCxN91n4OH8A2dPnMmTMuE8TlQj8UDQaDqPygoCB0794dW7duxdWrV/H+++8DsC3yOnLkyAr3LfU+4IURx3EIDQ11+vnY2FicOnUK+fn5MJvNPpkNWQkCoS2gl7ewvw+CgoLQr18/vP322w7fO378eIXHJeX4aVFCX4MVLQVSUFCg2HnlRVZYWBh0Op3L/TZu3FjYltoWqHUN6HTSvCDN75Tp06fjhRdewIEDB4SeZYcOHbB+/XrEx8fj+PHjmDZtGmJjY9GrVy+YTCaHHpLBYIDJZEJRURH0er1I5BgMBqEhtf8ur3JNJpNTYbR69WqsWLFC9NrQoUMxbNgwZQ7eCfYx6apGVT9+wP05oBOri4qKRCvJd+vWDX379sW2bduEMPG9996raN2uXr0qbOfn5ztdyf78+fOihW6PHj0qe8V7d+eAHqp+5coVIZcGAG655RZ06dIFf/75p+g7hw4dEtYy8xZ6csWSkhKHY+vYsaPgUvNs3LgR3bt3l1xGRfcBfw7Cw8NF9aHhnSsAOHz4sNOlI/wZf24L6HNuNpsdrgFXAkXOveDu+Ol9OLsGXXH27FnZ96Ir+GvQYDC43SfdFvzzzz+yylf6GqBFmjs0F0aALR7etWtXrFu3DomJiSJXp3Xr1njooYfw66+/olevXggPD3eYQdRoNCI8PBwRERGwWCwiB8hoNAp2u/13ecVLNyA0Y8aMEXrgPGo6RpmZmWjQoIFkVVuZqOrHD0g7B7QIaNasmWiIOgBMnToV27ZtAwBMmjQJ48ePx1tvveXW6pYDnbvUvHlzl/dCw4YNYTAYYDQacfnyZYd6ukLKOeAnV4yIiECjRo0c3l+3bh2Sk5NFzo7ZbJZch4qgjzkhIcFhv0OHDnXI7ahWrZqk8qXeB7yTHRkZ6XK/9evXF7YjIiIUO361CYS2gK5XkyZNJJ/bK1euoGHDhqL72B4px0935OvXry8q/6677sL27dtdfk+p66C4uBiALZTobp8NGjRAWFgYiouLJbcFWl8DfiGMeKxWq9OhxvRFlJiYiG+//Vb422Qy4eLFi0hMTER0dDRiY2ORkZGB1q1bA7Ct8M3P8ZKYmCjKdzh16hTq1avn1C0CbA2gGiLIHTqdzm8bA19Q1Y8fcH8OaAu9WrVqDp/r27cvkpKShOt8xYoVaNGihcucB7nQozhDQ0PdNvCNGzcW9RDl/K7uzgHvAEdERDj9TGJiIvbt24cZM2YIbUVWVpZi1xXduYqOjnbYLz+XEc3FixcVO366DgaDweXnaIfoxo0bAXdf+XNbQN+H1atXl1xPi8WCsrIykYviCnfHT+es2V+D33zzDbZt24Y+ffrg888/x8svvyxcL0VFRYqdU/4cREVFud2nTqdDYmIijh8/LiRfS62DVteAZlddUVERNm/ejKKiIpSVleGXX37BwYMHkZycjD/++EOw6dLT07FhwwbBhu7YsSNMJhNSU1NhNpuxcuVKtGrVShimmJKSgk8++QRGoxFHjx7Frl270LdvXwC2BNXt27cjPT0dhYWFWLVqleKhBgZDTVwlffLodDrMmzdP9No///yjWPm8MAoKCnIrioBy29psNiu6qCYtjFzRokULkWuj5NxO7kalAbbfYOLEiaLXlA4JuFtAlIfNZaQerobr03z55ZcAbJOQ3nLLLcLrSiRAu0q+BmztwuDBg1G9enU888wzWLt2raJlA7Z2gJ+TSIobzSdgl5SU+GSBXW/RTBjxa/qkpKSgT58+WL16NebOnYukpCTs27cPw4YNQ/fu3TFjxgw89thjgrgJCQnBm2++ibVr16JXr144fPiwaBKtCRMmIDIyEv369cP06dMxffp0wW5PSkrC5MmTMWXKFKSkpKB27dqiKdQZDH+HbpBdzR0ybNgwURxfySGyvDCSktRNx/OVrIMUYQSIJ9tTUhhJGY2zaNEirF+/XgjTZ2ZmKjaFgtVqFRwDVyPSACaM1MTVcH2aIUOG4JdffsFff/2FVq1aCa8rsZhsRfNY0dDvKyWMKuoc2BNoI9M0C6WFh4dj+fLlTt+bMmUKpkyZ4vK7t9xyi2iWW5qwsDDMnTvX5XcHDhyIgQMHyqssg+En8MIoIiLC7Sijhg0bombNmrh+/bpmwogPYQO2pM8ePXooUgepwigyMhLVq1dHXl6eao6Rq4dSREQEhg8fjtWrV+Pnn39GcXExcnJyFEmApkflMWGkDRU5t4Ct89+7d28AyosTd46RPWoLIzmOEWATRj179lSkHmrhnwFcBoPhFL5BdtUY0/COjZJT8XvqGCk1421paako+boi+ATkixcvKubYyOktN2zYUNhWKpwm9aHIhJF68PehXq+XdB3Sv5MSjpG7ubzsUUMYSZ31mifQHCMmjBiMAEKOMOIdG0KIYkN0tRZGch4IQLkwKikpUUwcSJ3YDrCNyOFxNaxeLlImdwSYMFIT+j6sKNcOUF6c0PPrVZTI7Q+OEX0f0Et4+StMGDEYAQIhRHgoyxFGgHI5PoEqjADl8oz4h4Jer6/woUQ/EJRyjJgw0h4+x0iKWwIo7xjRo0MruhfpOmrlGNEh5EC4FpkwYjACBJPJBIvFAkBaY2Sf46MEcoRRZGSk0CBWRmEUFRVVoVugpTCiZ/pWalV3hg1+Dh8p1yCgrmNU0b1Il00LGm+Q6xjR16KSy/OoBRNGDEaAwDfGgOtJSWm0doyActcoKytLkTwnb4SRUsKEf7jIDSGoIYzc1SE8PFz4neglLBjewwsTqfeBlo5RSEiI8BmlHCM5oTy+DrzLzRwjBoOhGHRjJGXiUX8SRoQQRRwbucKIXppBqQaZdowqgp4yQKncCqmOEcdxiImJASBeRoXhPfx9IHUCYPp3UkKc0MJISh14Aa3kPEY8UtuCQFrUmAkjBiNAkCuM6tevLwzpV1oYSV2Q1H72ZW+RK4yqV68ubCvhmhBChIeLFMcoIiJCCLfRdfcGOUO1mTBSHqvVKoS0pYoC+lrxtWNEl6+UMOJHhgLS2wJeGOXm5vr1AsEAE0YMRsAgtzEMCgoShIkSvTRCiGzHiH8wA5VDGBUVFQnD/qUII47jhCWH6GUcvEGqYwSUn/+bN2+KHmYMz/HELVHTMdJCGHlyDvi2yGq1+n1olwkjBiNAkOsYAeXCQImGiO8lA9IbQ1qYVAZhJHfGX6A8H4zOEfMGOcKIPn56tmaG58gNYwHqOkZ6vV5y+YWFhYrM5+WNYwT4fwI2E0YMRoDgjTC6efOmSNh4gie9RNoxUsqx4dFaGFUkSnh4YaSlYwSwcJpS+INjRCd/S5lHiRfxhBBFrkNaGMl1jAD/zzNiwojBCBC8EUaAeBkDT/BWGGnhGEVGRgqrcyshjOiRdVJG4wDwi1AawISRUsgZKs+jlmPkiWOlxJB9ui1gjhGDwdAMT4SJko6J1uUDYnEhZcoCjuOEOighDDw5B2o6RhXlOTFhpDyehNLUyjHyRJgpUb4njlEgTTjKhBGDESB46xhpIYyUfjB7cg74OighzLwVRkrkd3gyKg1gcxkphSfXgFqOkVbCyBPHiIXSGAyG4ngiCqpVqyZsa+0YaSWM6AR0b4WJN8KIHtXnDZ4mXzPHSBk8CaWFhYUJuUCVzTFioTQGg6EZge4YaeXY8OfAarV6/VDwRhgByoTTWI6RtngSSuM4ThAnSjhGcmfepjtISrg13gzXV6oOasKEEYMRIHjr2Hg7XNuT8unVx7V2jABtxCGffA1oK4xYKE0ZPLkGgPLfSgvHSOmlaZhjxGAw/IJAFAU6nU7orWrtGClRB0/cArUcI47jKkxAZ46R8ngqjJR0jOSOSmvYsKGwff78ecXKB1jyNYPB0JBAFEZ0HaqqY6SWMKKXG3EFyzFSHk+uQUBbxyghIUHYvnDhgtfle+IYhYeHC1NsMMeIwWAoQiCKAkA8KkyL5GetzwEtjJSY/ZqfS4kO0bmCOUbK461jZDabvU7ClyuM6tevL2xr5RgBgbOQLBNGDEaAEIij0oByYVJWVuZ1GCEQhZHSOUb8dSDlGoiKilJ0gkuG9zlGgHfhNIvFIizCKucajI+PB6CdYwSUz8CtRDhRTZgwYjAChEAUBYCyrkUgumZKh9J4x0jKzNs6nU7RUCbD81CaUnMZeXof8nlGly9fFh2DJ3gywSNQLg7pxZj9ESaMGIwAwVtRoMWoNPs6aD2XUmUQRnIcI0DZHC+GMo6RN3lGnpbP5xkRQnDx4kWPy7evgxzHiM8xslqtXoszNWHCiMEIEDwRRmFhYcJnmWNUNYURnePFh2AYnuNtjhGgnGMkx7FScmSap44Rvb4hve6hv8GEEYMRIHgiCui1wrwVBZ42hloLEyVHZvlD8rWnwkiJCS4Z3o9KA7R1jADv84y8dYwAJowYDIYCeNogKjWPEHOMtE++tlqtgkCVkmMEsJFpSuNPjpEnOUaAso6Rp8LInxOwmTBiMAIET3uqvDDIz8/3KpTiD8JI62VJtA6leSsMmTDynkDNMaKFkbezX3taB+YYMRgMRfFWGBFCcPPmTY/L99axArxPAPfkHERERECv1ytefqAII+YYKYun9yEtCry5Djy5BgGxQPamHQCUcYyYMGIwGF7jrTACvBMGSvSUvW0M+TpwHCeInYqgl87wNscnEB0jtl6asmjtlniafK3UPEr2dWCOEYPB0AwlHBtvHoz+JIzklA+U5/l4K0y0Tr7m5zACWI6RVmgtCrSeYBJgjhGDwfATAnViOSUTLuWOyOLhxYkWwkjJ5GuWY6Q9SoTStBBGtED3VpSw4foMBsMvUGKYsNbCSCvHSEth5E+hNCaMvEfr+8DT8nU6nVAHJUNp/JIzUmDCiMFgKEqgCiMlQ2n+5BhJrYNawsiTUBrLMfIerR0bT8sHyu9FpUJpwcHB4DhO8veYMGIwGIri6WgUpUJZ/hBK89YxMpvNsFgsXpcvpw5KCiM6x4g5RtoQqKPSAOWEEX8fyMkvAgJHGMk7KoWZN28edu3aheLiYsTHx+Ppp59G9+7dkZqainXr1uHixYuIiYnBo48+iiFDhgjf69SpE8LCwgSlOmbMGIwdOxaALblx3rx5SEtLQ1RUFJ555hn069dP+G5qaiqWLVsGo9GI3r17Y8aMGbIvLgZDC5QYjaJFT1XJxtBTx4jO8ykpKRHVSQ7e5hh5m3zNQmnao/V94Gk7ACjvGDFhpAIjRozACy+8gJCQEBw7dgyTJk3CDz/8ALPZjJdffhktW7bE+fPn8dRTTyExMREdOnQQvvvdd9+hZs2aDvv86KOPkJ+fj02bNuH06dN47rnn0LJlSyQkJCAjIwNLlizBBx98gIYNG2Lq1KlYuXIlnnzySV8eNoPhEYEaStPpdAgLC0NxcbHmOUaArbfuS2GkdY4RPSqRCSPv8Sdh5KljxK9uLycM5qwOcssPFGGkaSitUaNGws3NcRzMZjOuX7+OwYMHo02bNggKCkKTJk3QpUsXHD9+XNI+N23ahCeeeAKRkZFo164devToga1btwIAtmzZgr59+6JVq1aIjIzEuHHjsHnzZtWOj8FQEk8tdK2FEQDFkz49zTECvBMn/jQqTWqOUVBQEKKiogCwHCMlUCKkrZUw4utACPHqWmSOkcosWLAAqampKCkpQc+ePZGYmCh632Kx4NixY0hJSRG9PnLkSHAch65du2Ly5MmoXr06CgoKkJOTg6SkJOFzzZo1w7FjxwAAZ86cQbdu3YT3mjZtiqysLBQXF4saLx6z2Sy6CQDbhSC3UZYCv1RDVV39uqofP1DxOeCvRZ1OB47jJJ8r+touLCz0+BzT94Jer5e1H4PBgNzcXBQVFbn9ntRzEBwcLKt8+hwYjUafnwPeMTOZTF4dP/0wk3MOYmJicPPmTdy4ccPv7zF/bwvoayAoKMij+9DdfVDR8dN5ZnLKB8TC5ObNm06fe1KgHSM55dMdFHf3oVrXgNQRdJKE0YoVK2RXYPz48ZI+N336dLzwwgs4cOAAMjIyHN5ftmwZ4uLiRIJmxYoVaNOmDW7evImFCxfitddew9tvv42ioiLo9XrRj20wGARlajKZRL1nfn4Xk8nk9AJZvXq1w7EPHToUw4YNk3RsnuDtGjaBTlU/fsD1OeDdluDgYFmLQNLT/2dnZ3u8gGRubq6wff36dVn74Xu2hYWFkr7n7BwQQoSeqtVqlVU+nXB9+vRpjzs3BQUFwnZ2drZoPhd3hIaGori4GAUFBR4fPwBkZWUJ20ajUfI54Nu9Gzdu4Ny5cx6HUHyJv7YFtOt29epVyeKCEAKdTger1YobN25U+Nu5Ov7s7GxhW+r1xEP/7idPnvTYtaHFmZzy6VCulDZE6WugcePGkj4nSRh9/PHHsm8kqcIIsPW8unbtinXr1iExMVEQQV9//TV27NiBVatWicpPTk4GYOsFTZs2Df3790dpaSkiIiJgsVhEDpDRaBRUcnh4uMjK5xfyo1UszZgxYzBixAjRa2o6RpmZmWjQoIGseSEqC1X9+AHp5yA0NBQJCQmS90svA6LX62V9l4Z+ADRs2FDWfvg8F5PJ5PZ77s4B3VOPioqSVT6dj1ijRg2PzwEdOkhMTBQlNrsjIiIC+fn5sFgsHh8/IM4Xql27tuTjqFWrFk6cOAGz2YxatWp5nGPlC/y9LaDDV40aNUKDBg0kfzciIgKFhYVur4OKjp8PiwJAfHy8rGs5Li5O2K5evbrH9wHv5ISHh8vaB33dEUI8PgdqIzmUFhERgebNm1f4ufT0dI9jl1arFRcvXgQAbN26VXBs6Jlb7eFPGiEE0dHRiI2NRUZGBlq3bg3Apor58FxiYqLIlTp16hTq1avnUvGHhISoIoLcodPp/LIx8BVV/fgB1+eAHpEl5xzRDWlRUZHH55d2R0JDQ2Xth3csysrKYLFYKsyNcHYOaNcnODjY44nlSkpKfH4O6HmUpHzH1TVAlx8WFia5fFrAFRQUiGZD91f8tS3w9DcAyoWRlPvQ1fHT94Hc+5D+3aVei86gc4zULF+ra0CyMEpMTMRHH31U4efGjBkj5PS4o6ioCGlpaejZsydCQkKQlpaGgwcP4tlnn8XevXuxaNEiLF26FHXr1hV97/Tp07BYLGjSpAmMRiMWL16Mrl27CgImJSUFn3zyCebNm4czZ85g165dWLNmDQCgX79+mDBhAh544AHUr18fq1atwr333iv1FDAYmkLn18jBn5I++TrQzodUPB2VB2ibfE2Xr8U8RoDjkH37dpUhHSXuA61HpQHeDYSgJ3iUg5LLkqiJJGE0YMAA1K9fX9IOb7/9djRq1KjCz3Ech++//x4LFy4EIQQNGjTA3LlzkZSUhEWLFqGgoECYmwgA7r33XsyYMQO5ubl44403cPXqVRgMBnTp0gWzZ88WPjdhwgTMnTsX/fr1Q3R0NKZPny7UJykpCZMnT8aUKVOEeYzoMhgMf8bTEVn+MCrNvg6eCCNvyldKGHk6IokWRt4Mk/ZUHLK5jJSDCSPPJ3hUcuoONZF0VK+++qrkHY4bN07S58LDw7F8+XKn77lzpjp37oyNGze6fD8sLAxz5851+f7AgQMxcOBASXVkMPwJTyc39AdhpIRr5U+OkVyLny/farWitLTU4xC9p+eATkdgQ/a9w5vrkBZGngpkfxBGnjpGgO0cVAphZA+fGJWbmwtCiOg9ehJGBoOhHJ4KI71ej9DQUJSUlPiFMPK0Dko5Rt7MPu3pxHb2s18rIYykzmMEMMdISZS4D7wRyEosCQJ4fh9aLBbhuS/XMQJs54CfusNfkX1U//zzD/7v//4Ply9fdniP4zjs27dPkYoxGAwxnuYYAbbGqKSkxC8sfE/r4M1SCEpNsqjUjL/R0dEele9pjhFdHj19A0M+nrqGgON14IkwUmJJEL58T6CTzz0VRt6U7wtkp3svWLAAly5dAiHE6T8Gg6E8hBCPHSNAmTWS/CmUplWOkafCiBYm9FxIcvE0jKPksiRVHW/uQyXuA61Dad6UDwSGMJIt986dO4egoCA899xzSExMhF6vV6NeDAaDgh6iG4jCSOsGWWlhJPc3oHN8vAllMWGkPZ6KY6ByCCOlHCOz2YyysjKP9qE2smuUmJgIk8mEhx56SI36MBgMJ3iT8Akos1aZPzlGWidfyz1+pZKfPc0xYsJIObwRRkoMVw/kXD/7OphMJtE8a/6C7FDa1KlTkZ2dja+++kqYOZrBYKiLt8KInmCRbtjkoGRuhTflA4GXfE0nP3sjjDzNMWLCSDmqeihNKccI8N9wmqSj6tKli8NrixYtwqJFi0SvseRrBkMdvMmvARwbRHezybvCm56yEg2yN+JQ6+RrNRwjJoy0QetQmtaj0pR0jPxVGEnq9rlKtGbJ1wyGb/BmJAqgbIMYiLkV/hRKYzlGgY0/3QdazGnGHKP/kDPBI4PBUB6lcoyAwBVGLMeI5Rj5AyyUVi6MKqtjJHlJEAaDoR1K5RgB3jfInvQStbbwK4swYjlG2qN1B0FrYUSX761j5M1gEDWRfFT9+/dHhw4d0K5dOyQnJ6NJkyZq1ovBYFAonWPkCYH8QFAi+Zqe8Ver5GsWStOeQL4PgoODERwcjNLSUr9wjLwZCKEmkoXR1atX8fPPP+Pnn38GAERFRaF9+/ZITk5G+/bt0bJlSzanEYOhEoGeY6SEY6V18rU3DyQWSqscEEIEYeBtKE2L6xCw3Yt5eXmaOUZKDYRQE8lH9eCDD+LQoUM4d+4cCCEoKCjA7t27sXv3bgC2m7RNmzZYunSpapVlMKoqSuYYedtT9baXqEUoLTg4GDqdDlarVZMHUnR0NDiOAyFEk+TroKAgBAUFoayszG8fRoGAP4zIUiKs7o0w8jb52n7dQH9E8lG9/PLLAID8/HwcOnRI+Pfvv//CarWiuLgYBw4cUK2iDEZVRskco0ANpXlzDjiOQ3h4OIxGoybCSKfTITo6Gvn5+YrkGHEcJ9uhDwsLQ2FhIRNGXuAPwkgJxwjQbrh+pRJGPNWqVcOdd96Jli1bomXLlvj999+xbds20ZIFDAZDWQI9x0jpRWQ9nXXYG2Hk7W9QvXp1r4URPSKK4zhZ3w0PD2fCyEv8ybkFtFmrjDlGFOfOnRNcor///hvZ2dkAbDHXkJAQtG3bFh06dFCtogxGVSbQHaPQ0FAhlKTFBI9AeZ6Np42xtw+kmJgYnD9/Hnl5eSCEyBY2QPk5kJNfxMMfPxNGnuNPjpFer/foGuKFSWlpKaxWq+xZ7JljRDF06FDhR4iIiMCtt96KDh06oH379mjdurVfLgTHYFQWvE2+9rZBtlqtsFqtADxrDDmOQ0REBIxGo6aOEaBd0iufgG02m1FcXCxKiJaKN3PoMGHkPUoKIy06KIBYmJSUlMi+Dplj5ISYmBgMGjQIHTt2RLt27UQHyWAw1EFrx8jbBwLgfSjLW3HIt1VaCyPANvu1J8KIzzFiwkgb/Gk+MSWEkScC3dvh+oEwQlLWqLS///4bZ8+exZo1a7BmzRrodDo0b95ccI6Sk5MRHR2tZn0ZjCqJ1jlGSgkjwPPG0NtzwJdfUlLidQjBk4ei/ZD9unXryt6HEo5RaWkpLBYLm17FA7y9DyIjI4VtTxdh9+YaALx3bJQcrh/wjhE/Kq2goEDIM/r777+Rnp6OEydOYO3atWwRWQZDJbR2jLztJQLah7LsJ3mkwxq+KF+JuYyUyDECbL8B/ZBmSEOJa5DPtfNUGCntGMnF27agUgkjnujoaPTs2RPNmjVDUlIS9uzZg+3bt7NRaQyGimid9OlvjpE3jgmgjTBSYvZrJRwjgAkjT/H2GtTpdDAYDCgsLAxYYcQcI4pz587hr7/+EkamXb16VXiPnyafwWCog7cJj0qG0jwdaEGHsjwZlaWkY+SJOFM6x8gTlMgxAvw3t8PfUaKDoLUw8nZ5HJZ8TUGPSqOFUK1atYRlQdhwfQZDHWhHVgthpEQozdukT297694uRaC1MLJYLMJ1wISRNighjCIjI3HlypVK4RixUBpsgqhBgwZITk4W/tWrV0+tujEYjP+ghYknSbPeDhP2tpcIOD6Y5QqjQHeMoqKihG1PHop0+UrkGDHk420CPlCegO1t8rU/5Bh50hYEwnUo+ajeeOMNJCcnIzY21uVn8vLyRL0iBoOhDLRj5Ikw0ul0CAsLQ3FxsUc5RmoII7komWOkhTDydqi21sfPUCakzAujkpISlJaWyr6W+HuROUbqIXm86l133YXY2Fi88cYbTt/Pzs7G448/rljFGAxGOUoIE2/WSPIHYaT0qDRfl+9tOJPPLwKUST5nyEeJkDKd9O7Nvejpfai1Y1SphBHPxo0bsXDhQtFr586dw+OPP47MzEzFKsZgMMrx1jECKpcwCkTHyNuRgcwx0h5vQ9qAd3MZWa1WIcdXCWGkxX0QFBQkzCFWaYRRWFgYvvnmGyxatAgA8M8//2DcuHG4evUq6tSpo3gFGQyG9sLI2+RvwPueorcTPGr9QPDWMaKPn+UYaYMS94E3wsgf7kNvO0kcxwl18FdhJPuoPvzwQzz77LP46quvcO3aNezbtw9FRUVo2rQp3nvvPTXqyGBUeZRwbOhVteUOl/c3xygQk6+9TYBXMpTGhJFnKO0Yyb0OlLgPtZ7gka9DUVGR3woj2Y5R27Zt8dFHH6FatWrYuXMnioqK0LlzZ3zyySeoWbOmGnVkMKo8SjpGFotF5D5IwR+EkdahJCWXZfE2lFZZ16jyd7R2jPxBGCk5p5m/CiNJR7VixQqH1zp16oTt27cjIiICrVu3xtq1awEA48ePV7aGDAZDkQbZPpQjJxzjD8Koqidf078Bc4y0QQnHiL4OAlEYKTmnmb9eh5LO7Mcff+zUduc4DiaTCWvWrBFeY8KIwVAepRvkoqIi1KhRw6Py/cEx8uQcaC3MgoODodfrYbFYNJkygQkj7/Enx8jTdsAfHKNKkWMUHx8ve/p+BoOhHEqE0rzJcfEHYUTP3+JJe6T1qDiO42AwGFBQUOC1Y+TJNeBt8jlD+1Fp/uYYVWlhlJqaqkrh8+bNw65du1BcXIz4+Hg8/fTT6N69OwBgzZo1+OKLL2C1WjFo0CA8++yzQmN47NgxzJ07FxcuXMAtt9yCOXPmCCPiiouLMW/ePKSlpSEqKgrPPPMM+vXrJzqWZcuWwWg0onfv3pgxY4bHdiCD4SuUnMcICOykTyUmttPCMQJs4lQJYcQcI21Q4j4I9FFpStwHfB0sFgvKyso8Pha1kJR8XVhYKPkEFhcXS/6xR4wYgdTUVKSlpWHWrFmYOXMmCgoK8Ntvv+Hrr7/GmjVr8OWXX+K3337DDz/8AMBmp7/44ot46KGHsGPHDrRu3RqzZs0S9vnRRx8hPz8fmzZtwvz587FgwQKcP38eAJCRkYElS5bgrbfewk8//YRLly5h5cqVkurKYGiJksnXQGA7Rv6Q4+StOPUklObtQ5EJI+9R4j5kjpH/T/IoSRj16tULEydOlLTDp556Cn369JH02UaNGgmWNMdxMJvNuH79OjZt2oQhQ4agfv36qFmzJkaOHInNmzcDAA4ePIjw8HAMGjQIoaGhGD9+PI4fP47Lly8DADZt2oQnnngCkZGRaNeuHXr06IGtW7cCALZs2YK+ffuiVatWiIyMxLhx44T9Mhj+jNLCSO6DubIJI62STrWcZJMJI+/R2jHyB1GihGPk77OwSz6zZrMZ2dnZkj7Hz8wphQULFiA1NRUlJSXo2bMnEhMTcfbsWaSkpAifadasGT788EMAwJkzZ5CUlCS8Fx4ejvr16+PMmTMwGAzIyckRvd+sWTMcO3ZM+G63bt2E95o2bYqsrCwUFxeLLhb6WOyHNQcFBXm8eKA7rFar6P+qRlU/fsD9OaAbI51O59F5ohujmzdvytqHfeKzJ+XTo+CKioqc7sPdOaCFkVrlu4P+DTw9B3yeV3FxMcrKyoQZgHncHT/9G3hyDXh7/L7Cn9sCWph4eh/SuX7O7kOp14Cn1yD9/DKZTF7dB56eA/paNBqNDgNB1LoG7O83V0gWRidPnsR9993ncYVcMX36dLzwwgs4cOAAMjIyANhuWlpVGwwGoYdrMplEPV/+fZPJhKKiIuj1epHIcfddvgyTyeRUGK1evdphqoKhQ4di2LBh3hyyW6r6sipV/fgB5+egoKBA2L5y5Yro/pAK3TO7cOGCEGKWAt0pKigokPVdnvz8fGH7ypUrbvfh7Bzw9ec4zqPyc3Nzhe2cnBzZ+8jJyfHq+4C4YU5PT3doy3icHT/vigM2p0GL+vsSf2wLrl27Jmzn5uZ6dA7pezk7O9vlPpwd/4ULF4Rtk8nkUfnXr18Xtm/cuCF7H/R9fPXqVY+MAtoBP336tEsBpPQ10LhxY0mfkyyM5LhAckeM6PV6dO3aFevWrUNiYiIiIiJEFqPRaBRUdnh4uIMNbTQaER4ejoiICFgsFpED5O67fBl0T5pmzJgxGDFihOg1NR2jzMxMNGjQQLKqrUxU9eMH3J8DWrg3bNgQCQkJsvffoEED0f7k7KN69erCdq1atTwqn34wBwcHO92HlOsgNDTUo/LpXirHcbL3Qff069Wr51Ed6J5xbGwsateuLXrf3fHT361Zs6bs8mkx7cnx+wp/bguio6OF7Tp16nh0Du2PyX4f7o7/xo0bwnb16tU9Kj8mJkbY9uQ6oJ9/CQkJqF+/vuw6xMbGirblnANfIEkY8YnPamO1WnHx4kU0btwYGRkZuOOOOwDY3KrExEQAQGJiIr799lvhOyaTCRcvXkRiYiKio6MRGxuLjIwMtG7d2ul3eVcKAE6dOoV69eo5dYsA2wWghghyh06n87vGwJdU9eMHnJ8DuocVHBzs0TmKiooStouLi2Xtg+7ReVo+7Y6UlJS43Yezc0CH0rwtX+7xA+LfIDQ01KM60OLEXR2cHb+3vwF9/CaTye/vM39sC5S4D2lxZTQaZV0DtEHhi/vQGUrcB7QZYTabZZ0DXyCpxDp16sj+VxFFRUXYvHkzioqKUFZWhl9++QUHDx5EcnIyUlJS8M033yArKwvXr1/H2rVrce+99wIAOnbsCJPJhNTUVJjNZqxcuRKtWrUSykxJScEnn3wCo9GIo0ePYteuXejbty8AoF+/fti+fTvS09NRWFiIVatWCftlMPwZNo+R9snXSo5KA3z/G4SHhwtuviej4hjKz0CvRfK1t6vbKznBo6d1UBvNJg/gOA7ff/89Fi5cCEIIGjRogLlz5yIpKQlJSUk4deoUHnvsMVitVtx///1CflNISAjefPNNvP7661iwYAFatWqF1157TdjvhAkTMHfuXPTr1w/R0dGYPn06GjVqBABISkrC5MmTMWXKFGEeo7Fjx2px+AyGLNRYEkQO/iCM+AbZ0/L5HrbVavVKmHlTB6XEqSfimOM4REREwGg0ejQqjqHMBI8hISEIDg5GaWmpJvchv7q9p4u4KrkkCMCEkYjw8HAsX77c5ftjxozBmDFjnL53yy23YP369U7fCwsLw9y5c13ud+DAgRg4cKC8yjIYGqP0kiCBKIy8dYw4jhPyDL0VRt4O1we0mTLBYDAwYeQFSnRQAFtI9caNG5osCQJ4t7q9kovIAv4pjPwrgMtgMJyidChNi4eytzNPeyuM6DpoNcGjN46Rkq4hE0aeoZQw4XPNtAilAd4tyeEPbYHaMGHEYAQAlWFJEDpZVG5jSAjxekkQoLynqlUozR8cI4AJI09RSph4KoyUcqy8EUZVIceICSMGIwCoDEuC8KEsQH5jSI/I8uaB4Gn5gDKhNC1zjIDya8CTif0YytyHgFgYyZkKx58cI29GjPm7MPLozP7555/Yv38/cnJyRD8qx3GidcsYDIYyKJ18rYVbAcDjHB8lywe0XysN0Eac2g/ZdzXBJMM5SgsT3gmVKrT9QRjx94E3zm2lE0YrV67ERx995PA6IYQJIwZDJZRwC+jGSIuHMuC5MFG6/OLiYqHN8mUdvBGnSohje8eKCSN5KBXKokWF2WzWTBiVlpbCYrHIalOUzPUDKokw+uabb0AIQVBQEGrUqOGVnchgMKShhIWv0+kQERGBoqIizYSRp8nPSgsjwNYgu5r1vqI6aB1K08KxYiiXfE1PHGw2myULVCVHpfEUFxfLEshK5vrx5fsbsu8uo9GImJgYfPnll6JlAhgMhnrQwsibmWD5tQOrqmNkPxpGjjBSOpSmZfI1wISRJyjlGNkLI6mocR/IFUbezidmX36lGJXWvXt3BAUFiZYXYDAY6sI3iHq9XvZahDR8g1RSUuJR+YAywqikpERW8q9ajpEcAn2CR4AJI29RyzGSitKj0gDP7wOWY0TRokUL/PLLLxg/fjzuvvtuh1W+BwwYoFjlGAyGDb5B9DZ0zS+kqrUw4usg1bFRo3wtXCtvhInWs58zKq9jJAelHaNKIYzeffddcByHf/75B//884/oPY7jmDBiMFRAiYRHwL+EkZxQlhJhLGfly4GvgzfDlFkoLbBRwzGir2055TPHSD08OrOu5l2QMx8Dg8GQTmV0jOQIE63Lp+vgTfks+TqwYY6RMo4Rffxy2yJfIPvI9u/fr0Y9GAyGG/gG2VvHiG+QSktLYbVaJTsfaiU/S0Xr8uk6eNNTVsoxYjlG2qCUY2Q/XN/X5SvhGHlzH/IdNEDe8fsKj4+spKQEZ86cAQAkJiaKDpTBYCgLnXztDfYNEt1ASinf2zoo4RhpNUxYiZ5yaGgoOI4DIYQ5RgEIc4yUmeCRbocqhWMEAKtWrcLq1auFAwoNDcXjjz+O0aNHK1k3BoPxH0qH0gBbg+SJMFKqQZbTIFaWUBrHcQgNDUVxcbHsBwJLvtYeNipNmfvA30NpsjMIf/jhByxbtkyYOZYQguLiYixduhQ//vijGnVkMKo8SoXSPO2pad1T9Sdh5E1PGdA2z8ubUB5DuetQa8fIU+eUECK0RUo5RpUilPbll18CAO68807cc889AICff/4ZO3fuxPr169moNAZDBdQIpWkhjLQuX4lRaf4wMpDlGGmDUovIBuqoNDWEoT86RrKP7OzZs6hbty4WLVokvHbXXXfhvvvuw9mzZxWtHIPBsKFWKE0qlaVBViL52h+EEQulaUNlcYyUuA+9cYx0Oh2CgoJQVlbml8JIdihNr9ejpKREdIL4g2PrpjEY6qD0QxlgjlGghdJYjpH2qLWIrFS0HpWm1HxiQPl9UClCac2aNcORI0fwxBNPoFevXuA4Djt27MCNGzfQtm1bNerIYFR51HCMArWnqtWSIP4USmPCSBvo38CbNQurumME2O4Do9Hol46R7DP76KOPYtq0aaKZrwkh4DgOjz32mOIVZDAYys9jBASWY+QPM1+zUBpDjfswkEalKekY8efAH4WRbMnbs2dPzJkzB7Vr1xZGpcXHx2POnDno0aOHGnVkMKo8Widfa90gV8ZQmsViEZ1XqeUDLPlaK5S6DyuDY8RCaXakpKQgJSUFN27cAADExMQoWikGgyGmsiRf0+UHqjBSMs/LbDb7dCFdvV6P0NBQlJSUMGHkAVo7RloLI9ox0irXzhdIOrPZ2dkIDg5GbGwssrOznb7PEx8fr1ztGAwGAP+ax8ib3AqtJ3j0dFSaxWIR1oJU+jeQKoyUcu0MBgMTRh6ihmNUFYfrA/4dSpN0ZAMHDkSbNm2watUqDBw4EBzHOf0cx3HYt2+fohVkMKo6VqtVeChr7RgFBQW5vP/llq9F0qenyddKJ53yaCEOIyIikJuby4SRB/iTY6T1qDSl7oNKE0rjG2kGg6E+Sk0qBygjjLxBa8dIibXa/MG18+Y64POMmDCSj1KOkRLD9QPdMbLPtfOn6X4kHdny5cuFm2n58uWqVojBYIjxp4eyksIokHKM1Ji/BdBGHNLCiB9RzJCG1o6RPw2CUNo5jYiI8Gp/SiLpzHbs2FHY5jgOBoMBzZs3F33GbDbDarUqWzsGg1GpHCOtRYESjpFWoTQlc4z4/ZWWlooe0gz3VPVRaWoM1wf8TxjJzqKcMGECFi5c6PT1nj17KlIpBoNRTmUSRlo7RsHBwYJDEqihNI7jvEqAZ0P2PUep+yBQhZFaHQR/yzPy/O6yw2QysdwjBkMF1BgJAnjWIGstCrytA8dxgmuk9VIIgGfnwFtxTPfMi4qKvNpXVUOpaTP8SRgFWkjZF0g+sieffFLYPnPmjOhvk8mE06dPIyoqStnaMRgM5hhBWXEYHh6OoqKigAulKfUb0OFEf3sg+TtqOEaeDtf3pi0ICgqCXq+HxWLRzDHydBZ+XyD51z148CA4jgPHcTAajTh48KDDZ7p06aJo5RgMhnK5JYD2D2WtlwQByoVBoIXSlEr89VScMrRPvlbyOgwLC4PRaPQL59TfQmmSj2zAgAEAgB9//BExMTG4/fbbhffCwsKQkJCAQYMGKV9DBqOKo1QvEdBeGPmLYwQEXghB69+Aof1wfSU7SZ4II3/oIPgCyUf26quvAgAOHDiAFi1aCH8zGAx1qUyhNK1zjIByYVBVQ2lMGHlOZXKMvM21Y6E0itTUVADA+fPnkZGRAQBo0qQJGjVqJGs/ZrMZb7zxBvbt2wej0YjmzZvjxRdfRFJSEubPn4/NmzeLPnvbbbdhyZIlAIBOnTohLCxMGF0yZswYjB07FoDtR543bx7S0tIQFRWFZ555Bv369RPVf9myZTAajejduzdmzJjh9Q/MYKiJP/TSlHooBwUFQafTwWq1apbbQD8QpM7j40+/gbfimAkjz6ksw/WB8uvAHxyjgA2l8RQWFuK1117Dzp07Ra/37NkTs2bNkpyAbbFYUK9ePaxevRo1a9bEunXrMHXqVHz//feYMWMGZsyYIXx2xIgRDlMBfPfdd6hZs6bDfj/66CPk5+dj06ZNOH36NJ577jm0bNkSCQkJyMjIwJIlS/DBBx+gYcOGmDp1KlauXClKJGcw/I3K5BhxHIfQ0FCYTCbNQ2mA7RzQQsEVLJTGoJfmqQyOkSfCSI0lQQD/c4xkD9efP38+fv31VxBCRP/S0tLwxhtvSN5PeHg4xo0bh9q1a0Ov12P48OG4dOkS8vLyRJ87e/Yszp49i7vuukvSfjdt2oQnnngCkZGRaNeuHXr06IGtW7cCALZs2YK+ffuiVatWiIyMxLhx40TOFIPhj1QmYQSUN8hahdI8meTRH0JpLPlaW5S8D5UQRt7MZQVo7xhVqlDa7t27wXEcRo0ahXvuuQcA8PPPP2PNmjXYvXu3xxU5cuQIatSogerVq4te37x5M+644w5ERkaKXh85ciQ4jkPXrl0xefJkVK9eHQUFBcjJyUFSUpLwuWbNmuHYsWMAbNMMdOvWTXivadOmyMrKQnFxsdNeo9lsdrhog4KCVJkplp81vKrOHl7Vjx9wfQ7oa1Cv13t1juiHenFxsaR9EUJED2VvfyNeGDgr39U5oHuqfCjOU+h73Wg0olq1ahV+x1e/gbv7gBan3pRPC7OioiK/u+f8tS2wdw29qR8trEpLS31+DQDl94HFYoHZbJYkdJS8D+jnqJz7wBukiknZwigiIgLx8fGYNGmS8FpSUhJ+/fVXFBYWyt0dAFt4bv78+Zg4caLDez///DMmT54sem3FihVo06YNbt68iYULF+K1117D22+/jaKiIuj1elHDZzAYhEnMTCaTaNZXXmyZTCanwmj16tVYsWKF6LWhQ4di2LBhHh2nFDIzM1XbdyBQ1Y8fcDwHFy9eFLaLiopw/vx5j/edm5srbOfl5UnaF91LLCsr86p8oLyn6e5Y7M/BjRs3hO1r1655VQe6sc3IyJA0jwz9GxiNRq/KLygoELazs7Od7svZfcA/lKxWq1fl0+10VlaW17+nWvhbW0DPEm42mxW5D8rKynDz5k3J1wDvcOr1eq/LpydkPnnypOjZ6IorV64I2/n5+V7VgT6frq5Dpa+Bxo0bS/qcbGH0wAMPYMOGDbhx4wZiYmIAADk5Obh+/ToeffRRubtDSUkJpk6dijvuuMNhuP/hw4dRUFAgmhoAAJKTkwEAMTExmDZtGvr374/S0lJEREQIE1bxQsdoNAozvYaHh4t+DL6BoK11mjFjxmDEiBGi19R0jDIzM9GgQQOvLdJApKofP+D6HFy7dk3YjomJQUJCgsdl1KhRQ9jW6/WS9kVb7ZGRkV6VD5QvSVFaWuqwL1fngL5HGzRo4FUdYmNjRdtS9nXixAlhu2bNml6VX79+fWE7IiJCtC939wEv6MLDw70qv27dusK2Er+n0vhrW5Cfny9sK3HegoODUVZWBo7jJF8D/N/BwcFel09HZ2rVquU0Z9ce2l2Nj4/3qg61a9cWtqOioiSfA18gWxhdvnwZZrMZQ4YMQceOHcFxHA4cOABCCC5evIg5c+YAsCVZzpo1y+2+ysrKMGPGDMTFxTm4QoAtJ6hPnz5uhQh/0gghiI6ORmxsLDIyMtC6dWsANiWcmJgIAEhMTBRG0gHAqVOnUK9ePZfJlyEhIT5fYFGn0/lVY+BrqvrxA47ngHY4+FFdnkILDLPZLGlfSpYPiHOMXO3L/hzQ+R0hISFe1YFeEsNdHWjocxAcHKz6b+DsPqDDmb4+fi3wt7ZA6fsgJCQEJpNJ1jVAh9K8Ld+TtoB2j729D+nyy8rKJJ8DXyC7xE2bNqG0tBQFBQXYuXMnfv31VxQUFKCoqAg//fQTfvrpJ/z444/48ccfK9zXvHnzUFJSgtmzZzsMmS0rK8O2bdtEQ+0B4PTp0zh58iQsFgsKCgqwePFidO3aVRAwKSkp+OSTT2A0GnH06FHs2rULffv2BQD069cP27dvR3p6OgoLC7Fq1Srce++9ck8Bg+FTlEz6pPNbpCY8KplwCYiFkdT1FbVOvmaj0hhK3wf8M0uLNQsBz64DfxiE4Atkn93k5GRJ835UxOXLl5GamorQ0FD06tVLeP29995DcnIy9u7di9DQUHTo0EH0vdzcXLzxxhu4evUqDAYDunTpgtmzZwvvT5gwAXPnzkW/fv0QHR2N6dOnC3MsJSUlYfLkyZgyZYowjxE//xGD4a8oOdstP1y+pKREM2FkP38J/bcr1FgSBNBmVJqno3GYMNIWJTsogHfCSInyPbkOlLwPK9WotI8//liRguvUqYMDBw64fP+OO+7ATz/95PB6586dsXHjRpffCwsLw9y5c12+P3DgQAwcOFBeZRkMDVFySRAAmgsj+wZZijDS2jHSeoJHeg4dNsGjNjDHSPv7wFf4TwCXwWA4RemeKt8g+YNjpEUdtO4pe3L8Sq+RxcOEkXSU/A2AcmEkZVSkfR20EkZqTfAY8DNfd+nSxeV7HMdh3759XlWIwWCIUUuY+ItjJLcOSi0JAmg/waPUB4LWwpChvHNb1R2jShVKk5osyWAwlEGt3Iaq6hgFYiiNCSPtUdox4gV2oAojlnxN8eqrr4r+LiwsxK+//oq///4bTz31lGIVYzAYNpRukAPdMaqKo9KUdCuYMPIMtRyjsrIyWK1WWcPlK0PydaUKpQ0YMMDhtaFDh+Lhhx/GyZMnFakUg8EoR43ka4A5RoD2oTTmGAUOauUYATbBIWcQgj90UCqzY6RI8jXHceA4Dr///rsSu2MwGBRqJV/br9HkCqWFmdaOkdbls+TrwEQtxwiQn2vmD8nXLMeI4sknnxT9bbVakZWVhWvXriEuLk6xijEYDBtqhdIAW4PsauZ3HjUdo6oYStPr9dDr9bBYLMwxCiDUdIykCiOtR6VpPQjBV8g+uwcPHgTHcU6TsIcMGaJIpRgMRjlqhdIAbYQRXZ4WOTZah9IA229QVFSkyfHzy0lYrVYmjGSgpmMkZcg+PZeVEveh1h0Efw6lyT6y/v37O8x8XaNGDXTu3Bm33nqrYhVjMBg21AqlAdIaJH9yjPR6vdcz72s9Kg3wThgpMft5WFgYioqKmDCSgVoTPALSHBN/GwSh1QzwvkD22aWX32AwGOqjdfKzPzhGfE+1MvSUAfkJ8EqHcZgwko/SHRRaWMgVRmxUmrrIPrLCwkIUFhaievXqCAsLwy+//IJDhw6hadOmGDRokBp1ZDCqNMwxUjbplBZGWvSUAe1HBvIPRSaMpMMcI+0HIfgK2aPS5s6di/vuuw/nzp3Drl27MH36dHz55ZeYN28ePv30UzXqyAhQvvjiCzRr1gzLli3TuioBjZpJn4HiGKk1GkfLUBpQeYTR33//jfPnzyuyL39F6+RrNVxDHi2WBNHr9cLcTQEvjE6cOIGoqCi0aNECO3bsAMdx6NSpEwghThd9ZVRNioqK8Oijj+LUqVOYNGmS1tUJaNRMvtZaGMntqSrh1gRiKE2tMIoSwuinn35CcnIymjdvjqysLK/3569oPVzfn+5DperA3wf+FkqTLYyuX7+O2rVrAwAyMjLQvHlzLF26FAkJCcjOzla8gozA5KuvvhK2CSGiG4ohD6VDaXIbRDWnC9DCMQoJCRESuLUclQZUDsdo4MCBAGzHsmHDBq/3569o7RipKYwC5T7wFbKFUUhICAoLC1FSUoILFy6gcePGAGwnydvRIozAhxCC3bt3Y/To0aLXb9y4oU2FKgFKN8iRkZHCdmFhYYWf96eeqhLlcxwnuEZah9KsVqukToNaYZTS0lLRvuVCCBFN3eJvPX8l0Xq4vtLXoMFgELaLiookfUdp51Tuuo2+QrYwaty4MbKzs9G3b18UFxejdevWAICrV68KThKj6rJu3Tr06NHD4fXr169rUJvKgdINcnR0tLBdUFAgq/zKIIyAcnEoRRgC6oXSAO3Dmd48lM6ePSv6uzJHDfzJMVKiHZDbQbKvA3OMKMaOHYugoCCYTCbUq1cPKSkp+Oeff1BQUCCIJEbVZcuWLU5fz8nJ8XFNKg9Kh9K0FkYRERHCttFolPQdtYTRzZs3ZZUPKPtAAOQLI62Gajtj165dor8zMzM93pe/o/Rv4M1wfaVGZ/JRHk86CFIWva0If80xkn1277jjDmzatAnZ2dlITExESEgIGjdujG+//RbVqlVTo46MAIJeSHjw4MH45ptvADBh5A1KN4j0faqFMKItfK2EUVRUFADPesqVzTFiwkgaWjtGSpev0+lgMBhQWFgou4OgVOoMfx/427QRHp3d6tWro3r16sLfBoNB1Ngxqg5WqxW7du3CxYsXUadOHaSnpwMAGjZsiL59+wrCiIXSPEdNxyg/P7/Czyv9UKYtfK2FUUlJCUpLSyt0gbQOpfnDUG1n/Pnnn6K/K7MwUjPHSAtxDNjuRX5uQikoOdEqUH4fFhUVoaysTLH9eovsWphMJqxZswb79+936gJ8//33ilSM4f+kpaVh4sSJOH78uMN7zZs3R2xsrPC3Lx2jPXv24MKFCxg6dKgidq/WKP1Q1DqURneitG6QAVs4rUaNGm4/r3QoTa5r5q+O0ZUrVxz+NpvNood+ZUHp+5AOKUtJflZLGAHynVOlyo+JiRG28/PzRc8MLZF9dPPnz8fPP/8MAA4LybJRaVUHQggeeeQRXLp0yen7zZs3R82aNYW/fSWMTp8+jV69eqGkpAR//vknFi9e7JNy1aSyJV/7QyiNdq3kCqPKEM5UQhhZrVbk5uaKXiOE4NKlS2jUqJE31fNL1Ex+1kIcA/JDynwHRYnOAQBR5CkvLy9whdHvv/8OAGjRogUSEhL8xvpi+JaTJ0+6FEWAo2Pkq1Da5s2bBVv67bffxqBBg5yOkgsklA6l+cNDmeM4EEIkPRCsViusVqti5QOOjlFFKB1K80ac+kvydV5envC70GRmZlZ6YaSFc6r0NQCUizOz2SzJ6VO6g2IvjPwF2UcXEhKCunXr4rPPPlOjPowA4bfffhO233jjDZw5cwYrVqwQXmvWrJkmoTT7ZNCpU6di//79PilbLdQMpUnJMaJFgRINMsdxiIyMxM2bNyUJI6WPHxALI7kPJSV6y3KFkT/mGLnq7FTWPCOlOyhazydmXwej0VihMFLaMaJDaf40153sBIwHHngAeXl5LJm2ikMLozvuuAN333236H21c4xmz56NuLg4jBkzBmfOnAFQPrkkzV9//eV3Ix7konRPkRYFct0KpRpEvrcsN4SgVPlyHSOlH0pahzOVEEb0PV2nTh1h+//+7//w/PPPY9WqVZ5X0A/RehCCGh0ET8UZc4zsyMrKQklJCYYMGYLOnTuLTizHcZg1a5aiFWT4J7wACQ0NRefOnR1u7AYNGkCn0wmjHpQURoQQLFy4EMXFxVizZg0+//xz9O7dGyaTyWGCOavVimPHjqFjx46Kle9rlO6pBgcHIyIiAkVFRZIeykouHMnDCyOtesqBHErzF2FEd47bt2+Py5cvAwDOnTuHJUuWALBNCNyrVy8vauo/KC1MvAmlqZVrJ7UOauUY+QuyHaPNmzfDYrHAaDQiLS0NP/30E3766Sf8+OOP+PHHH9WoI8PPuHz5Mk6fPg0A6Ny5M0JDQ1GjRg0MHjwYADBkyBBhNBjvGinpMBYWFooac4vFgm3btolcLDqZ9siRI4qVrQVqNIj8g1mLhzLguWOk9QNBp9MpMtKxMuQY0ff0nXfeKXrI8WzatMmjffsjlTH5Wq5jpPToUPqa8adQmuyjS05OZqPPqjh///23sN2tWzdhe+3atXjxxReRnJwsvBYbG4vz588jNzcXhBBFrh36BmrYsCHy8/MdcmWefvppvPbaawACXxgp7RgBtgdzdna27BwjpXqKfINcUlICi8Xi9rj8wTFSOoRQ2Ryjxo0b4/fff8cvv/wCo9GIl19+GQCwc+dO4TMffPABUlNTsXDhQrRv396jMrWksq1Z6E0d1Mgx8ifHSPbZ/fjjj9WoByOAoMNV9OiT0NBQdOnSRfRZfsi+xWJBfn6+016lXOghwvfccw/ef/99ZGVlYdu2bVi6dCmSk5MxceJEJozcQDtGFQlWNR0jwNZbpoWCL8r3NPlaK2Hkj8nXdHi8Zs2aaNWqFVq1agUA+N///oejR4/iwIEDmD9/Pm699VY888wzAGxzHdGdq0BBacfIm7mslGoH5N4HajpGAS2MeA4ePIgTJ04AAFq1aoUOHTooVimGf0NP6lbRwsH2Q/aVEEa0Y1SjRg2EhoYiMTEREyZMwIQJE4T34uPjkZ2djcOHDyvmVmmBGsKAH7LPD5mne47uylc6xwjQXhjJyTFS6vi1dozoyQWlzmFjD+0Y0XOWAbbQ2tGjRwHYkrFpDh8+7FF5WqO0OA0NDYVer4fFYpElSpQqH9DeMao0obSSkhJMmzYN+/btE73etWtXLF68uFLOeMoQ46kwysnJQVJSktfl0zcQbcXa07ZtW2RnZyMnJwfZ2dmikTOBhJqOEWB7MLsTRmo0yHIST/0px8gfQmlKXANK9NRpYWQ/Md+dd96J999/36P9+itK/wYcx8FgMKCgoEC2MFLqOStHGFksFmFSZzVmvvYnx0h2FuEnn3yCvXv3ghAi+rdv3z6sXLlSjToy/Aw6lBYfH+/2s3FxccK2/fIBnkKH0twJo3bt2gnbBw8eVKRsLVBjmK6cuYx84Ri5Qw1hpnWOEV2+FuvVKTF/TEXCyJ3YDkTUHC4vJZSmZq4fULEwUqN8erLZgBZG27Ztg06nw/PPP4+tW7di69atmDJlCgAIS4UwKjdyHKP69esL2xcvXlSkfPtQmis6d+4sbNsvdhlIqJFbIGf2a7UtfDnCSKt5jJQOpen1ekEcapFjpERPnc8xioqKEi2KC9juy59//hn33nuvx3X0N9SceVquY6SGMKroPlCj/KCgIKEO/hRKky2Mrly5goSEBDz88MOIiYlBTEwMHnnkETRq1EgxR4Dh3/C/c3h4eIW9QjWEkVTHqGvXrsK2feg3kPBFKM0d/uQYqSGMtEi+BrSdMkGJ3A7eMXK1vtVtt92GtWvXOn3PZDJ5VKaWqOEYyZm2ojI6RkB5Gx7QjlFERASuXLmCa9euCa9dvXoVV65cETV2FWE2mzFnzhykpKSgZ8+eeOKJJ5CRkQEASE1NRdeuXdG9e3fhHx2+OXbsGB5++GHcfvvteOKJJ4SJxQDbCIuZM2eiR48e6N+/P7Zs2SIqNzU1VShzzpw5oh+bIQ1eGMXHx1eY0NygQQNhW6mlAqQ6Rg0aNBBCfX/++afTdZ0CAbVDaVo4RnJyjLTuKQOVTxhVq1ZNuHc9eSBZLBahg2KfeE0TExMjSvTm8aeHoFTUdIxKS0thNpvdfrayCiNepPvTNSFbGHXo0AFFRUUYMmQIJk+ejClTpmDo0KEwmUyi+WsqwmKxoF69eli9ejV27NiBHj16YOrUqcL7Xbp0we7du4V//APObDbjxRdfxEMPPYQdO3agdevWotm2P/roI+Tn52PTpk2YP38+FixYgPPnzwMAMjIysGTJErz11lv46aefcOnSJZYXJZPS0lLBQq8ojAaoH0pz5xhxHCdMH5CXl4dTp04pUr6vUaNBDtQcIyUtfH7IuhahNMBxygR3KH0N6HQ6oXxPHCN6AVl3wgiwOcv2+FPYRCpazyNECyctnFO1hVFxcbHfLN8kWxg9+eSTwnICf/zxB37//XcUFRUhPDwcTz31lOT9hIeHY9y4cahduzb0ej2GDx+OS5cuVagaDx48iPDwcAwaNAihoaEYP348jh8/LrhGmzZtwhNPPIHIyEi0a9cOPXr0wNatWwEAW7ZsQd++fdGqVStERkZi3Lhx2Lx5s9xTUKW5evWqsC1FGEVFRQn5LEo5RlJDaUDlCKepEUqr6jlGQPlDQWvHiJ8ywR1q/Ab8veOJSHE3VN+ekSNHOrwWiMJIjfvQ0w6CFqPS1A6lAf7jGsm+w5o0aYJPP/0Ua9aswfHjxwHY5jEaPXq0aLI/uRw5cgQ1atQQ1OPhw4fRp08f1KhRA8OHD8eQIUMAAGfOnBEN+Q4PD0f9+vVx5swZGAwGhyHhzZo1w7Fjx4Tv0jM1N23aFFlZWSguLhZNeMZjNpsd7M2goCBVpiTge1/+Hu6hw5a1atWSVN/69esjPz8fFy9ehMVicRp+k3P8fKOq1+sRGRnp9jt0Avb+/fudNtL+gqtzQPdUOY5T5BqhG8T8/Hy3+6QbRL1er0j5tItQWFjocOx0GSUlJcJ2UFCQYvdIVFQUrl27hps3b1a4T1oYKVk+T15eHiIiIlxeA3Q7pFQd6BCGq/vSFYcOHRK269ev77Y+M2fOxKFDh7Br1y7htZycHJff8de2kP4NdDqdIvWjhVFBQQGsVquka0Cp+5AOc1Z0H6h1H9KdtNzcXNFzRelrQOpyPh51PRo1aoTZs2d78lWnFBYWYv78+Zg4cSIAW7hu/fr1iI+Px/HjxzFt2jTExsaiV69eMJlMDrlMBoMBJpMJRUVF0Ov1IpFjMBhQVFQEAA7f5R8OJpPJqTBavXo1VqxYIXpt6NChGDZsmDIH7gSlXBW1oGeRDgsLE8KU7uB7lCUlJTh06JDLZE1A2vHzrlV0dDQuXLjg9rN0DtJff/0lqb5aY38O6J7cpUuXFOmt8fcEAFy4cMHteaEdlcuXL4u+6yl07zgrK8uhfPocZGVlib6n1G/Ij6S6efNmhfvkxaHValWsfNp1SE9PFwlQ+2uAdkmvX7+uSB14cVpaWor09HSnuUCuoNfFbNGiRYX1WbNmDT799FPMmTMHgC2toaLv+FtbSLsZ165dc/rMkAv94M/IyBD9BvbHT7t0N27cUOQaoMvPzc11u0/6vZKSEtXuA7rTpPQ10LhxY0mfkyyMDh48iB9//BG33XYb+vbtK3pv27Zt+OOPPzBgwADZq5iXlJRg6tSpuOOOOzBo0CAAQL169YT3W7dujYceegi//vorevXqhfDwcAfL0Wg0Ijw8HBEREbBYLCIHyGg0Cheb/Xf5B46zGDgAjBkzBiNGjBC9pqZjlJmZKaxK76/QuRDNmzdHQkJChd9JSkpCWlqa8Lez78g5fv5BXbNmzQrLT0hIQM2aNXH9+nWcPXtWUn21wtU5oEMnTZo0UWQGb9qFys/Pd3te6PITExMVmZ+GbqCCgoKE8p2dA9pqj4uLU+w35EWz2WxG3bp1XQpOq9UqXPcGg0Gx8ukJRyMjI5GQkODyGqAfwg0bNlSkDnQoPDo6WtTuVgQ/L5her8cDDzwg6ZpITEwUtvV6vctj8Ne2kG73GzVqJMqf9BT6GoiKinJ7DdCiqV69eopdhwaDAUajEWaz2e0+7XM7lSq/YcOGwnZoaKjbc+ArJAuj9evXIy0tDaNGjXJ4r1mzZpgxYwYKCwtlCaOysjLMmDEDcXFxmDx5ssvP0Q+CxMREfPvtt8LfJpMJFy9eRGJiIqKjoxEbG4uMjAy0bt0aAHDy5EnhhkxMTBRGvgHAqVOnUK9ePZfKPyQkxOczeSu1erda0KMR4+PjJdWVvvAvXbqETp06ufxsRcfPr7kG2G5OKeW3atUKu3btwuXLl5Gfn19hXpLW2J8D3knQ6/WK5TY0btwYQUFBKCsrw+nTp92eR1pEhYSEKL66fFFRkcM+6XNA53YoVT4gDmUZjUaXIxztk26VKp8OIRQWFor2a38N0HUIDQ1VpA708ebn54tGkLojOzsb6enpAGyhanfLudDQTnF+fn6Fx+BvbaEavwF9DdrfB764BgCbKDcajQ7XoD1q3Yd0e1xQUOD2HPgKySWeOHECsbGxTvOIEhISEBcXJ+QcSWXevHkoKSnB7NmzReLnjz/+ENRpeno6NmzYgO7duwMAOnbsCJPJhNTUVJjNZqxcuRKtWrUSlHdKSgo++eQTGI1GHD16FLt27RIcrn79+mH79u1IT09HYWEhVq1aVakmIPMFciZ35KF7Vt5ao/n5+ULvXarA4Re2BCCs7xdIqJH4S7s0GRkZbkdFVcZRaYD0SR7VKt/TKRPUWKdKTtIr7f7eeeedkr+nxGzbWkLn+CjVYZZzH6gxKg0ovw+0HpUGBGDydW5urtseRVRUlKyH3uXLl5GamorQ0FD06tVLeP29997Dvn378Oqrr6K4uBhxcXF47LHHBHETEhKCN998E6+//joWLFiAVq1aCauoA8CECRMwd+5c9OvXD9HR0Zg+fbog5pKSkoQpBoxGI3r37o2xY8dKrjMDOHfunLBdt25dSd+hrxtvh+xLncOIhhZGx48fx2233eZVHXyNGkPFAdv9cPr0aRQWFuLatWuoVauW2/IB6cmLFRFIwkiNYdqA9sLIU6FCJ17zHVY1y/MX/GkeISUjGVJn3/aFMPKX60LyXW4wGHDx4kUUFBQ4WKf5+fnIzMyUNcFjnTp1cODAAafvJScnC8uMOOOWW27B+vXrnb4XFhaGuXPnuvzuwIEDMXDgQMn1ZIj5559/ANjysqSOQqSFES2sPEHOUH0ee2EUaKjhGAG2fCWejIwMl8KILl+J/CbAP4SR1HWaKqsw8vSBRNdVqmsMMGHkDH+YtoK/DktKSmAymVzm3Fal4fqSu38tW7ZEaWkpXnnlFdHD6caNG5g5cyZKS0vRsmVLVSrJ8A+Kiopw+vRpADZxKtU9aNy4sfBA9XaSRU8cI/q6DMRQmpqOEQ//uzqDFwZKlq/1zNeA9AZZa2EGqBNG8fSBRD/A5XSG/dEZkIMa14E/3Af0Qt90Dqmvyg/oUNr999+PPXv2YO/evRg4cCAaNmwIjuNw/vx5lJaWguM4PPDAA2rWlaExJ06cEHJR+OR2KfAjDc6dO4dTp06BEOKx80DnN0gdFVKnTh1Uq1YN+fn5wpxWgYSvHCNX8A2ikuWHhIQIyd9a9ZSlNshqOUb0xIjuHkiAOmEUTx0c+veSM0IxLCwMYWFhKC4uDkhhxItTvV6vaOIzj1bCiHaKr127Jhos44vy/VEYSf51e/fujQEDBoAQArPZjNOnTyMjIwNmsxmEEAwYMECUK8SofPBhNECeMAJsIxcBmw1f0UPAFVarFZ9//jkAW+N0//33S/oex3FCfTMzM/3m5pOKGo4NIHaM3AkjNcrnOE7yApq+cIzcPajVEkZSe+qAfyVf0w9wOY4R4J8LhkpFDefWH0LK9HVIr2zgq/L9McQqS/a++uqrmDFjBlq2bInQ0FCEhISgZcuW+L//+z/RemWMyok3wqhp06bC9smTJz0qPy0tTUjwv+eee2TlN7Rt21bYpiepDATUcGwA8VxC7kJpapXPh0Kzs7PdjorTuqeqdQhDrToo4Rh5Koz85QEoBzWEkdZrpQHah9IiIyMFB85fBLPslu6BBx5gIbMqihKOEWDLM7rjjjtkl//VV18J24899pis77Zr107YPnLkCHr06CG7fK1QyzEKDw9HvXr1kJWVhTNnzvi8/KZNm+Ls2bPIz8+XPCquMjlGkZGRCA0NRUlJiSxhpFQdvHWMgoODZYf1+HNuNBpRUlIizD4eCKgtjLRYKw3QXhjpdDpUq1YNN27c8Bth5D+zZzH8Hj4/p1q1apKH6vPQjpGnCdiXLl0StuUKG1oYHT582KPytUItxwYoz9O6fv26qOHzRfm0WP73339dfk5rx0gtYcRxnPBQkiqMlBwZ6K1j5MkM6PRIVnrYfyDA/wZKihJ6ZCK95Ie78gH1coy0CKUB/uckMmHEkERZWZkwB1HTpk1lN870Q9DTUBq9RpecdZ0Am8PF1znQQmlqOTaAbfZywLbUi6uHs1rlN2/eXNh2d01oPX+Kmg8EXhhdv35dUjhRyfLDwsKEhzw/m7wUeMdIbhgNEE8I+csvv8j+vpbwoSylr0FeFFTUYdQ6pKvmfUAvaOzuPvAVTBgxJHHjxg3hgnUV8nBHQkKC0Nv21DEymUzCtlxhFBkZKYzCOnr0qGh6e39HTceIF0aAbdJVX5YvVSxrPVxfLccIKH8olZWVua2DGg9loHySS3cTXNrjjWPUu3dvYXvHjh2yv68laohTjuOE++DChQuiNs5V+UrXwZ+EkcViqTCk6AuYMGJIIicnR9imhxlLJSgoSFizzl2irzt4x0iv13t0Y/IJ2CaTyeM6+Bqr1SqsgK2mYwTYkqCdoZZjpHUoLTw8XNifFjlGgPyHktbCiBAiPLg8cYwaN24sLEXz+++/o7i4WPY+tEKt34C+D6RMm8FxnGJrJgLiNey0DqUB/hFOY8KokpOZmYnXX3/d6/ARHf/2RBgB5Q9ho9HotmfkCv47ct0iHnoG7EARRmo+lAFpwkgtx6hhw4ZC8q0WjhHHcZKGj/silAZIE0ZKL2otVxiVlJQIbqsnjhHHcejTp4+wrz/++EP2PrRCrd9AqnOqlmsYFBQkjBDV2jEC/GNkGhNGlRhCCAYPHoxZs2ZJnvPHFbQwonsYcqBnqvakV8A7Rq6mrK8I+9W9AwE1FnCl0dIx0ul0QlJ+RkaG6FhpfNEgV3XHqLi42OX5p/FmqD5Pt27dhO309HSP9qEFagkTuSFlpYUZUJ4ewYSRDUWE0dChQ9GlSxd07dpVid0xFGLnzp3Yv38/AODs2bNeWZRKOEa0MKFDc1Lx1jGSswSDv6DGMG2aioSRxWIRcsvUKJ9PwC4tLXW5jp4vLPyCggKXeWdVQRgB0lwjeq4dTxwjQNwOVLRGnL9ACFF12goeKcJIjQ4Sfx0WFha6dPN9FUrzh7ZZEWFECBH+MfyDvXv34t577xW9dvbsWY/3p4Qwoh0jer09qXjrGNG9EuYY2ahIGKldPp93BkAY9WiPr3qqrq4JfwqlKV0+PVxcijBSwjGiOyiBIozUvA/8SRgBrq9DX92H/pBjpEj3Z9iwYX6h8hg2fvjhBwwaNMjh9bNnz6JDhw4e7VNrx4gQwhwjFRwbevZwZ8JI7fL9qUHOy8tzujCxrxwjKYmvajpGUkSKEo4RLcbUFEYWi0WxJGW1JlcEbOexbt26uHTpkt8II2frpVWlUJpiwojhP7iaH8RVqEIKWjtGpaWlQqiDOUbKER4eLiywq4VjRF9Lria403o0DAullaOEY6S2MLJYLEhJScHevXuxceNG9OnTRxhN56mYU2s5Dp5mzZrh0qVLuH79usvrUE1hJGWSRxZKYwQ0/HpiADB//nxh259CaXIdI2/mMOLxt16JFNR2bACgTp06ACoOpVUFx8jX5Us5fjXzW7zJMfJXYfTrr79i69atKCgowOjRo5GZmYkOHTqgevXq2LBhg0f7VPMaAByXTHKGWsnfgPYdlIAMpXXp0kXSzjiOw759+7yqEMN7Lly4AMA26mfkyJGYMWMGAO+EES1knIUbpECH0uQ6RvSs1546RoEYSlNbmAC2PKP09HQUFhaisLBQ1KtW+4GgdYOstWNUvXp1BAUFoaysTBNh6I1j5K+hNPoZdPHiRXTr1g1ZWVkAgEWLFmH48OGy9+lLYXTy5EmnC2SrOSqNvg+0yLXzN2EkyTGik6sr+sfQHl4Y1a1bF/Xr10dYWBgAZRyjmJgYjx8O3oTSvFkOhIcWRoESSlO7QQbECdhXrlwRvcccI3XPAcdxQofBlYuqZn6LFqG08PBwIfdHDWH022+/if7mRREAHDx4ULTmolT8wTFSM5SmtXNKd5orWjPOF0i6y1999VW168HwkIKCAqSlpaF3794wGAwwmUzCA6Zhw4bgOA6NGjVCeno6zp07B0KIR4tQ8herp2E0wLvkazqU5qljFBQUBIPBAKPRyBwjCvtlQfilUwD1Q3n+5Bhp8UAAbOfgypUrmhy/FsP1OY5DdHQ0bty4obgwunTpkoMwsufHH3/EE088IWu/aopTQNpcRr4SRhXlOKlRh5o1a0Kn08FqtTp0zrRAUks3YMAAtevB8ABCCO677z6kpaWhXbt2WL58OX7//Xfh/QYNGgCwTcOfnp4Ok8mEK1euiB6EUigtLRUeGp5O7gho7xgBtgbAaDQyx4iCbhTtH1RqJ19HR0cjODgYpaWlFS5iq0Yd6GvS1WrvaotT/p4ymUxO55BR8xrQYrg+X67Swujll1/GggULnL4XFxcnXF8//PCDbGGkdvJ148aNodfrYbFYnDpG9HxildEx0uv1qFmzJq5eveoXwkhy8nVaWhr+/vtv4e/CwkLROjfbt2/H+vXrFa0cwz3bt29HWloaAODw4cPo1q0bpk2bJrzPD7ls1KiR8JonI9NoEeONYxQeHi6E9bRIvgbKw2nMMSqHfsDRAhRQ3zHiOE64pipyTHQ6HXQ6ZceLdO3aVXCNNmzY4HStKl8JI8D5feErx8hXw/WBckGmpDD6/PPPXb735ptvom7dugBso3btr/OKULuDEhISgsaNGwOwOUb2aSlql+8PzimfV3XlyhXN03IktzLTpk3De++9J/zdq1cvTJw4Ufh77dq1ePvtt5WtHcMt9IgzZ/DCiJ5Ez5OV7ZUYkcbDPwS0SL4GyntGRqNRdKP7K75wjGihab+ytdqOEVCeZ3Tt2jWnDaKaIYTIyEhMmTIFgK1X7sxx8GUCupbCyNeOEWDr8ChxH5aWlopyiUJDQ0X1e+CBB4TIR3FxMbZv3y57/zxq3Qf8RI9Go9FhyLzajpXWjhFQLozMZrPmjj4brh+gHDt2DDt37hT+djaRGS+M6MVTjx8/LrssJYURH7rIycmR1StQyjFyFzbyR3ztGNkLI19MF8BfU2azWeRI2NdBrfKfffZZ4Rw4e2D60jFy5pqp+VDUIscIkB/Cqwg6obpevXo4duwYfvjhB/Ts2RNr1qxBtWrVMHDgQOEzqampsvavdo4RIM4zsh8oo7YoMRgMwjNEa2EEuJ/s1BcwYRSg8GugAcDChQuRlZWFyZMniz7D5xh5K4xod8ebHCP6+yUlJS7X5HGGUo5RoA3Z90VP1V0ozZeOEeB8ZJqajhFguybq1asHwPk1UVVCaVo4RoAyHRR+JC5gW7uzSZMm6N27N3bu3IlRo0YBAPr06SO0HampqbBarZL3r7ZjAwBJSUnCNj0XHaC+MOM4Tug0+oMw0jrPiAmjAOXo0aPCdvv27VG7dm088MADos/wjlHDhg0Fl+XYsWOyy6IbLrpB8wRPE7DVcIy0tmul4AvHyF0ozZeOEeDcMVFbGAHinBdf53cEaihNKcdIaWHkbDkLwNahuuuuuwDYHrwHDhyQvH9fdFDoPB9396HagzCYMJIpjP79918MGjRIWIeL/vvff/9VpYIM59DCqG3btgCAbt26iT7DixCdTie4RmfOnJHl1ADqCSM5CdjMMWKOkZrCiL8u+KUjaHzpGDnrLKjpFtC/va9mvgbUFUa8U+6M/v37C9u7d++WvH9f34f2bbSvhZG7XD+16hCwobTS0lJcunRJiOeazWbh70BIZK1MHDlyBICtt8lfUMHBwVi8eDHCw8Mxc+ZM0XxFvDAihMgWsXSDSfcwPcHT2a+ZY1T5c4wA7R0jwPG60DrHSM0Hkk6nE+5pOY6RTqcTRph6grvz7Ql06MmVYwQAt956q7D9559/St6/L3KMtBZGvGNltVqdXgtqjg4F/MsxknyXJycnezQxIEN56Lke2rZtK/pdnn/+eTz33HMOydh0ntGxY8fQvn17yeUp6RjRD0Fna3O5IpAdo/z8fGzfvh133323bGFZlUalAf4hjAoKCoScI7p8QP1JLn0dSgNsnZ2bN2/KGq5vMBi8eh5oEUoDgFtuuQXh4eEwmUyiPM2K8EWOEX0f2ju3vijffmSafVuv9n1IL2QbMMLo448/VrMeDBk4C6PROBuhdssttwjbchOwlXSM6DmV5CxRooZjpKYwIoTg6NGjqFGjBgYMGIDDhw+jXbt22LNnjyxhV9nnMQLEwkDrUBrg3jFSow5aJl8DkOUY8Z/xJr8IUE8YBQcHix6w9gQFBaFDhw74/fffcfbsWVy/fl3SSNuqFEoDbG2jvcBU+z70J8eIJV8HILQwatOmjaTvtGzZUth2NeW8K5R0jPhJzAB5wkjpeYwAdUNpn332Gdq1a4cGDRrg8OHDAGyTcNITcEqhKjhGderUEbadrWOlhWNEo7Y4rV69uuC+OBNGarsFvDAqLCx0O4UGIUQQrt5O26GUMDp58iRatmwptIkNGjSoMMxDL4ou1TXyJ2GkViivok6jLx2jgMoxYvgH9AzkUoURHRqQE8IClHWMPBVGSs98DajrGH355ZdOX1+6dCnS09Ml70frHCNflE8ny9oPUwZ87xjZP6jVds30er1ofi97fOUYWa1WdOvWDZcvX3b6uYKCApSUlACA09Xf5aCUMFq+fLnofpJSr86dOwvbnggjX+QYuXNufeUY2aP2fRgcHCzcB8wxYsjm4MGDAGyNtFRhFBYWJjT+ci86JR2jmJgYYR9nzpyR/D2lHCN6SKzc2belQghxaHDpOu/atUvyvnzh2NB106JBjoyMFH4Xe2FECIHFYlG1fEB68rVadeDDaVoKIwDYt28f1q1b5/RzdC/eX4SRfSeDngvIFbRj9Ouvv0oqx9fOrT+E0uzxRQeFXhZES5gwCjCMRqOQI9SmTRtZI0P4i85Tx0in03nl1gC2icT4JUouXLggeui4QynHqKKh4Upw4cIFYd/169fHr7/+KppRed++fZL35YscH47jhHOqhWMElCfMXrx4URBCgG8eCIC2oTSgXBjl5+c7jPBV+xw0adJE9LeraTToh5W7PB4pKCWM6KRrjuPw8MMPV/idxMREQUClpaWJlhJxhS+Sn7XOMapovTRfCqOioiKHtsiXMGEUYPz999/CjK0dO3aU9V3+ort586asRRT5hisqKkqRkYl8OM1iseDixYuSvqOkY8Qnp6sVx6bdotGjR+POO+9EcnKy8FCVI4x84VYA5Y2yFo4RUC6MSktLRSPTfFW+1ORrtYUR4PhQUjuM8/LLL+POO+8U/nb1QKKFkT84RoQQIRyfmJiIq1ev4t57763wexzHYeTIkcI+XDlkNL64DoODg4V92wsjLUal2eMLYfTss8/i448/xrfffqtqR6wiNBNGZrMZc+bMQUpKCnr27IknnnhCWNk6NTUVjzzyCHr06IFBgwbh66+/Fn23U6dOuOOOO9C9e3d0794dq1atEt4rLi7GzJkz0aNHD/Tv3x9btmwRfTc1NVUoc86cOQE3/xIfRgNs50EO8fHxwrYcq5J3jLzNL+LxJM+Ibii8EUY6nU5wjXwhjPh8hvDwcGEE4fHjxyU/DHzhGAHlwkgrx4jOM6JzXPzBMfJFHaQKIzXKj4uLw7vvviv87UoY0feLt46RErl+V69eFYR88+bNZSWEjxgxQtj+4osvKvy8L3KMgHI3XOscoxs3bji87wth9MADD2D8+PG47777EBoaqlo5FaGZMLJYLKhXrx5Wr16NHTt2oEePHpg6dSoAm2h6+eWXsWPHDrz99tv4+OOP8ddff4m+/91332H37t3YvXs3xo4dK7z+0UcfIT8/H5s2bcL8+fOxYMECnD9/HgCQkZGBJUuW4K233sJPP/2ES5cuYeXKlb47aAWgFz+UK4w8HQ7JPyi8zS/i8UQY8Q1FUFCQ1zcm36hfvXpV1kK2UnEmjIDy3AZCiEjgusNXjpGrUJqvHSNAPDJNC8dIi1AaXb79Qrq+HhHlC8coMjJSuOakusb20DmKfHheKklJScK9efjwYafzZ9H46jrkf4fi4mKX5VfWUWn+hGZeVXh4OMaNGyf8PXz4cLz77rvIy8vD4MGDhdebNGmCLl264Pjx4+jQoUOF+920aRMWL16MyMhItGvXDj169MDWrVsxfvx4bNmyBX379hUmOxw3bhzmzp2LJ5980um+zGazyMIEbA2jGhcmHx5ztbBhSUkJ+vfvLyQLBgcHo1WrVrIWQqQbs8uXL0v6rsViERrKqKgoWeW5gp7L6PTp07BarRUeP+8YRUREeF0H3jEym83Iy8sTPZTkQAjBsmXLsGnTJpjNZsyaNQt5eXnYuXMnAFt+Ue3atYX6du7cGcuXLwcA/PHHH+jZs6dof87OAX396XQ6Rc6/M+hQmsViEUKmdIOsZvn169cXti9duiSUw4+CAmz3nlrl0/Py5OXlicrxxTmg3djCwkKX14Ber1elfDpX0Wg0Oi2Dzk2sWbOm1/VISEjAiRMncOHCBdE1V1FbwMNHGABbZ0tufTp27Ch0Yv7991/RckX20NehWr8BIL4P6TJ8cR/QHd/c3FyHMmhhpNbx80i9BuQidcZu7YJ4dhw5cgQ1atQQqVbA9mA+duwYUlJSRK+PHDkSHMeha9eumDx5MqpXr46CggLk5OSIRiY0a9ZMWDj1zJkzovXEmjZtiqysLBQXFztNYl69ejVWrFghem3o0KEYNmyYt4frEmfDlQFg27ZtohEUnTt3lp1ETfd2jx8/Lmn2a7r3HBwcLLhv3kBbpEeOHBHt09Xx8/UICQnxug507/jQoUMiB0sO+/fvxzPPPCP8nZ6eLlpnaOTIkaK60uV8/vnnePjhh53mbNHngE6Ezc3NVeT8O4NvMAghOHnypHA/0OGTGzduqFY+3Qu9dOmScA7oxNjS0lLVyqev86tXr4rKoaeruHz5sipJobQrZTQaRdcAfZ/n5+ercg7oY8zJyXFaxrlz54RtJX6LWrVq4cSJEyguLsbBgwdFAyMA120Bz6FDh4TtyMhI2fWhQ2979uxB3bp1XX6WdpTUvA/pHCNX10BBQYEq5dMC/OLFi6Iy7Duvah2/PRVdA3KR2tb7hTAqLCzE/PnzMXHiRIf3li1bhri4OJGgWbFiBdq0aYObN29i4cKFeO211/D222+jqKgIer1eJHIMBoMQhjGZTKKHIt9LNJlMToXRmDFjRLFoQF3HKDMz0+UEZfSF2KFDB3zzzTey4/z0JI9lZWVISEio8Du0zR0XFyfpOxVRp04dBAUFoaysDOfOnUNCQkKFx8/3VqKioryuA31zBAcHe7y/H3/8UfQ3/RB/8MEHMXfuXJHwSUhIQLdu3bBnzx78+++/uHbtmijU5uwc0CPw6tWrp8j5dwbdW65Zs6aQ80I7GXXr1lWtfPo8Xbp0STgHtFtTrVo11cqnR8KZzWZRObRoS0xM9GrxVFfQOVZGo1F0DdC/QZ06dVQ5B/TxW61Wp2XQIb4OHTp4nQPSvHlzpKWlOZRZUVvAQ0+30aVLF9nnhR62f+PGDbffp+/DBg0aqHYd8saA2WxGnTp1hGcN7ebUrl1btfJjYmJw48YN5OXlicqgHavIyEjVyueReg2ohebCqKSkBFOnTsUdd9yBQYMGid77+uuvsWPHDqxatUrUcCYnJwOw/YjTpk1D//79UVpaioiICFgsFpEDZDQahYs6PDxc1Nvjb3RXybwhISGqJto5w9UCfXSO1caNG0WJ1FKhe0RXrlyRdMHRjWG1atUUuUjDwsKE2WrT09NRWloqPHxcHT8vbsPDw72uAx1SvH79usf7o4cK2/PUU085XZpl3Lhx2LNnDwBg5cqV6Nq1q8Nn6HNAP7BCQkJUayToUJLJZBLKoZ0MNcuvX7++EKa6fPmycA58dfw6nQ6RkZEoLCxEQUGBqBxfnAP7HCP6GqDLDw0NVaV8nU6H0NBQlJSUwGg0Oi2DzzGqVq2aVwMgeOiQemZmpmiBV75O7o6Vzk9s0qSJ7PPSokULYfvUqVNuv++L3wAQu9n0c8xX5deuXRs3btxweD7Q92FwcLDPxIpaC9ZWWK7PS6QoKyvDjBkzEBcXh8mTJ4ve27p1K1avXo0PPvjAIbxGQ4cAoqOjERsbK4o9nzx5UkjMS0xMFL136tQp1KtXz6tVon0BnawbGxvrdpFEd3iSfK3krNc0/AitsrKyCmeCJoSIcoy8Ramp593Zybx4t2fYsGGCCFm9erXDoAJ7fJX06WpZEF8lfwcFBQlLg9BhA18dP1AuTrQYlUY7Au6Sr7UYEcXD3yveJl7z0K6DJ6EZPrQXGxvr0cCQRo0aCZ2XU6dOuf2sL4bLA66T4H11H/C/rdFo1KR8f0FTYTRv3jyUlJRg9uzZIkdo7969WLRoEd555x2HuO/p06dx8uRJWCwWFBQUYPHixejatavQYKSkpOCTTz6B0WjE0aNHsWvXLvTt2xcA0K9fP2zfvh3p6ekoLCzEqlWrJM17oTUXL14UJgzs2LGjx3MJeSKM6IeEGsIIsOUZuYNOBHQnkqWitjBKSEgQDb+miYyMxJQpUwDYRMewYcOwceNGl6PjfDVc3tVyBL6aLgAoFyauyle7QeYfrq7mMeI4TrXeK31vaTUy0NWUDYDNveDbAm+H6vN4I4wIIYKAppc7kkNwcLDQaT516pTbEaq+/g0A18JITXHs6hnBhJGPuHz5MlJTU3Ho0CH06tVLmJPo0KFDWL16NQoKCjB27Fjh9fnz5wOwPSSnT5+Onj17YujQodDpdJg9e7aw3wkTJiAyMhL9+vXD9OnTMX36dMGyTUpKwuTJkzFlyhSkpKSgdu3aoqH+/go9tFvupI40oaGhgrCQmrhNO0ZKDdcH5AkjOmSlRGxbbWFU0ejJV155RfjM6dOnMXjwYLz33ntOP6t1g+wrx4iuQ1FRkfCQ0sIxKiwsFIUO+HOgpjD0d2Gk5HIgPN4Io7y8PMHF8aY+TZs2BWA75oceekiUYE7ja9cO0KaDwISRDc1yjOrUqYMDBw44fe+jjz5y+b3OnTtj48aNLt8PCwvD3LlzXb4/cOBADBw4UHpF/QBaGEmZssAdtWvXRl5eHrKzs0EIqdB9UjuUBkBYGdsVdKPpL8KouLhYaDhuvfVW7N27V3ivot8oJCQEX375JVJSUnDy5EkAtnw6eoQbj68cI1ehNF86RvyDmRCC4uJiGAwGTRwjwHbd850IX8zfYj9cn0YLYWTfNig5uSMPPQhDrjBSak6lZs2aYdOmTQBsCz+XlpY6fb5o3UFhwsi3sCVB/ICsrCxcunTJpZVLx79pQeEJvLAwGo1uk4d5lFxAlqZOnTpCuOnw4cNuP0s3mp7mV9EoIYzoc0cnkQK2xrYimjRpgvT0dOGc/vnnnw7LAADaNMh0T1ULxwgofyhoJYzo694XjhFdtr1j4+v8FovF4rAiAN1BUiKcDdjmA+JH42kljOgEbMC2qKyzdriq5RgB2uX6+QNMGPkBb7zxBho0aIB27drhgQcecJh1lF4iwdN4Og897cHu3bsr/LxajhHHccJEm9nZ2Q69ZBqlQ2kGg0FwSDwVRvYu1pw5cwDYztFdd90laR8cx2Ho0KEAbA0v7TrxaJFj5CqUprZj5My10iKUBvheGPmDY+TKNQSUW6vQHv5+zs/Pd7l4rTPoh7YnI3R5hg0bJhoZmpeX5zQRW4vfgD7nvhJmrpaNYsKI4XNOnDgBwNYg/vDDDw6LGvKNQFRUlNdzqHTv3l3Y/u233yr8vFqOESCeu8XdCtdKh9KActdIztIo7ur04osv4tNPP8WuXbvczqBrDz379a5duxze17pB9mWD6My10soxohOwfRFKMxgMQuhK61Aa4F4YKTEylIeeZNbZ9e8KpRyjmJgY7N27V5R+4WyRZ1/lGPmTY8SEEUNTevXqJZoygF4DCCh3jPjhzN5w6623CkNUtXSMAPEyEFKEEcdxou94Ax+Sy8nJqXCdJHd1AmzCKCwsDI899pik2cRpaGHET3ZHU5UcI61DaVo6RhzHCdM4aJ187awOagkj2l3dtm2b5O8puW4bAJFrVJEwqiqhNCaMGJryyiuv4O+//xb+pmebNhqNgjhRQhgZDAYhOfj48eMYNWqUW1GipmNEixx3C0nyIqRu3bqK3ZR0gnRFcwnZU1paiu3btwt/e+NiNWzYUPi+s4VltQ6jaOUYaZ1jRDtGvhBGdPnuhJGv3Ar7uYzUEkY9e/YUfldPhZE3oTQeegZ6fxFGrpxbNa8BOv+SCSOG5tC5Q7RQofOLlBBGgDic9tlnnwn5Mc5Qq0EEXB8zjclkEuZwUnIaeloYSV3pnueFF14Q8oEaNGggKdnaHXxIsbCw0GHRYq3nMWKOke9WFecdWX/PMVKyHYiMjBTyHjMyMkSzWbuDzjFSwjGqVq2akIj9999/O6xu76scH61D2mFhYcJ9QAsjXx2/v8CEkZ8QEREhXJC0e0ILIyV6RgDw6KOPiv52N/M03UAoPUO4lFCa0onXPPR8UHIco88++wzvvvsuAFsDsX79eq8bCnqUjxazLgPaW/iu6lBVRqUB5cKIHy7Po/U1AKjbQeIn4AWADRs2SPoO/9DW6XSixWC9gR8MUlZW5hBe538DvV7v8QS7UvCH+5AXmswxYvgFvPDJysoSGka6Z6SUY9S+fXvRRU8vxmgPvXig0sKIdoxchdKUHqrP07x5c6GBl+oYnT17Fk888YTw94cffojbbrvN67rExMQI2/bCSAvHiHYsqtKoNK1DabwwIoRo8lDUShgNHjxYEBsLFy502x7x8O1XXFyc0zUJPYHOoaRzK4Hy30DttTNd/Qa+dGx4YXTz5k1hChEmjBiawQsjs9ks9FjUCKUBtlgy79i4a4jUdIzi4+OFRs2VY0QLOG+nKqDR6/VCovTZs2clNcabN28WhOK4ceMwfvx4Reoi1TFS88FMh5GcjcgCKv+oNK1DafYTTPJoMYeOr3KMAKBly5YYNWoUANtw+TfffNPlZ69cuYKHH34YmZmZAJSbhRuQNmWCFveAfX3oBZ/VIC4uTtjmn0NMGDE0gw6V8UJBLWEEQBhWnpub63JySV4I6HQ6xR/Mer1eOCZ3OUY83k5VYA+dZ/T1119X+Hl6fpMRI0YoVg8pjpHaq0y7EkZa5Rj503B9XztGgHNhBlROxwgAXn/9dcE12rlzp8vPLV68GOvXrxf+VlIY0YLD3jHixana16CrPC96bjv6XlUDOjTJzy3FhBFDM+ibnA8tqZFjxMPPPF1SUuJyRW3eMVLaLeLhXaArV644JB4DYmGkdB3uu+8+Yfv5558XludwRUZGhrDNr7GkBLRjZL+AqS97qrx75w+OEf9Q8OXM284cI6vVKixg7EthRD+YfeUauku+pu9DNYRR/fr1XSaf09hPMaJkmyAllKbFPQCUCyODwaB6HWhhxBwjhubQjhDvoKiRY8RDT0ToKpTEC6PQ0FBFy+ahE7CdzUJNh/KUnHEXsCV98osIG41GIanaFbxjFB4eruhvIcUxUvuhzHGc09XlaWGiVC6HK/wx+dqXwqwiYRQUFKRJ4i+gvmMElDs27oSRfcehU6dOipXvbzlG9Hngj1tttwhwLozo31+tZ4E/wYSRH+HOMQoJCZE1o7IUpAgjPpSmlmNECyNaBPKo6RgBEE1V4G4+p7KyMmHizaSkJEXDWv7gGAHljS5t2/ONc1hYmKoPZcD5g1nt39++fP535X8HX4YSXeUYafFQdpdjpHQHhaciYUQIEQ3nHz16NCZNmqR4+c7q4MspG/gy6CVS+HtSqXXq3OFMGNHnXclBMP4KE0Z+hLsco/j4eMUfTHIcI7VDaYDz5TnUdIwA94t30mRmZgqNY1JSkqJ18AfHCChvdPPz84WcM34OKaVWVHeHs1AO/XBQumNgD+2aOXOMtM4x0iqMA/iHY5SdnS20BykpKVi9erWQDqAE7hwjX+UYcRznsFxRaWmp8HtoJYxOnz4tvNakSRPV66A1TBj5EbQw4h2jGzduAICiDQAPvU9XCzjyjpFa9in9sLMXBYDYMVBDGLl7GNDQiddK5hcB7kel8Q9mXzpGpaWlKC4uhtVq9akwcvZb0NelGveAPfbhRF+G8ioKpWmV+AuUCyO9Xq9aPXhhVFpa6jTfkF4qKTExUfHy/SHHCCi/165duwar1SpykX0hjOj7jBdGfH4lx3Fo3Lix6nXQGiaM/Ihq1aoJDfO///6L0tJS4cGo9IgswD8cIzpmbt8Y0eWrVQe9Xi+IPn8URnR+idrYj0zLzc0VEo/pIbxq4SyU42thxJ8DrR0j2jXhr0u1Qlg8UhyjiIgI1UKqdCjL2WAQOpyjxsPZ1fm3WCxCB03tofJAuTAqKyvDjRs3RKFtrR2j+vXrqx7S9geYMPIjOI5DmzZtANhmfKZzbtRoFCsSRlarVei5qeUY0aEsZxa62o4RUP5AcCeM6BFpaobS7HOMtHCM+HrwbhHge8fIXhgFBQUpvlafM/gyTCaTqGPC18EXZQPlwsxisfjMtZOSY6RWGA0Qiw5n96LawsjVcH26s+KL5Gf7hVzpNkGL5OsbN24Iz4eqEEYDmDDyO3hhBIgXM1SjQapIGNF2tlaOkS+Sb6UIo3///VfYVtoxioyMFJJ+/ckxokcJ+kIYhYSECCPf7ENpNWrUUD35G3AUJ1pNMMk/DHNycgTXTsk5e5wh1TFSi4ocI61Cab4WJvbCyNeOEZ0Afv369SqXXwQwYeR30MKIX6gUUKdBqijHSO0wFiAvlKalY3T06FEAtoapbt26ipav0+mEBk/L5Gv6t8jLyxMJI1+E0jiOc/gt+OvSF2E0wHEuI186Rs6EET0gQW1hRN9f/iiMtAql0cLIF64l3Qm5evWqz4URx3GCa2QvjJR2y/0VJoz8DH9yjOh10vwhlOYLx8jZDOC5ublCMnzbtm1VcS5cCSNfJn3aTxvga8cIKL/OjUYjzGazcE34ShjZz35dlYSRTqcTzj99LxJCfC6MnLUFvDCqUaOGKgLFVSjN144Rfa9p4RgB5eG0nJwc5hgxtKd169bCttqOEZ3b4kwYVTXHyGq1isQgz5EjR4Tttm3bqlIH/rcoKCgQiTOtHCMtcowAsUj1deI14OgY0eFkXwojXiD7UhgBcJiuALB1kPhrUkvHiE8EVnqiW57Q0FDhN/anUJqvywfKhVFxcbGo/WPCiKEJ1apVQ6NGjQCIR8So0SCFh4cLYkMrxygiIkLIK3GXYxQUFKTag8ld0ikAHD58WNhWSxjxPUGLxSL0lgkhsFgsALRJvtbCMeJ/i6KiIk2EkX2OET3pp9rhxLCwMOF35l0CXwsj/jqkXQq1lwPhcZd8XVZWJtybaoWzOI5zuiyJ1snXWjpGAPDHH38I20wYMTSDDqfxqNUg8Q8crXKM6En13DlGag4RrWguI7rH1K5dO1XqQLt3/NxVvgzjAO6FkS9yjIDy36K4uFjkWGnhGOXn5/s0v4LjONEkm4DvhRF//Ddv3hREuS8mdwTcO0a0UKFzgZSG3zcLpZULo8zMTABA3bp1feZYaQ0TRn6Is965Wg0Sn2eUm5vrkF/jC2EElDc27nKM1JzDRaow4jgOt9xyiyp1oBs8viH09cKN/iCM6Oucb5AB7RwjNadpcAb/G2gtjIByceCL5UAA98KIdm3UTIDm66ClMKpZs6aQx6hF8jVfB3tatWrlk7L9ASaM/BBnPSK1hVFJSYnIMudf41Fz4UC6l2qPLxwjdzP+WiwW/PPPPwBsw/TVmGgTEDd4/uAY5eXlCY5NVFSU6pML8tDn98KFC8K2VsnXvk48pSeYJIRoFkoDygW6Fo6R/X1Itw2+cIyMRqMwTYKvR6UFBQUJ7bI/OEY8TBgxNMXZja/Wg8ndyDRfOUZ8Y2M2mx2Sn7V2jPLy8oTzoMbcKTx0KM3fHCNfuUWA+MGrhTCyF4e8YxQeHq5a0q+z8i0WC4xGoyCM9Hq96mvF0eUD5YLAH0JpvnKM6LaXbwu0SH7mr/fz588LHSVflt+yZUtJr1VWmDDyQ3zpGLmby8jXjhHgOPMzL4y0yjGi/1ZzOQB7pwIQCyNfLiIL2K4FXij7KvEa0N4xolcO//bbbwXHKDExUZiEU03o6yAvL08QRrVr1/ZJ+YHgGPkilEaXqaUwAoDffvsNgK0N9tVyHN27d3f4rZljxNAULUJpgHaOkSthRAgRxJlWjpGvhJGz3A5f9ZJ5wsPDBQHGhw8B7YTR+fPnhW1fCaOkpCT07NkTAHDy5ElBnPpqYjtamAwfPhyXLl0C4JswGhAYjpEvQmmA8/vQV8KoV69eDq/5KowG2Nr73r17i15jjhFDU6qaMHLmltiX7w/CSK38IsD5A0mLGW/5etBlOxslqRbx8fHCdnp6urDtK2EEAM8//7zDa74apkxfB/Qw6aoujHzlGDmb/drXOUYA8Pjjj+OHH35wGKHmS1JSUkR/+zKkrjVMGPkh/iKMtAil0b0zXwkzfxBGzsShFkmX9j3iqKgoPPfccz4pGwBGjhzp9OHji/wangEDBqBZs2ai13zlGLlyJHz1QPbXUJqWjhF/P4aFhSEkJES1smk4jkP//v2x5P/bu/OoKOv9geNvZB0GUCRjExXXUm8umVIipEAhhnpyyUpNzSVpw4v9NDpdd01v5bF7SzkueK+n3TYxxHLJNK2u5lYdQ5S8gKLiAs6ww/z+gHmaR0Hpd5jnmZ/zeZ3TaXxm+S7MPPOZz3d5Vq5UjvXv31+Tsq3i4+OV22FhYZqWrTcJjByQo8wx0nsozXaVnDNljKxfAnoERvfee6/q3y+99JKmvxRDQ0NZtGiR6lhwcLBmX0hQd2mM9957T3Xs+kDJXhoLjCIiIjQvX+uMke1r65UxutkcIz328Hn88ceZPHkyXl5ezJw5U9Oyw8PDmTt3Lp06dWLt2rWalq03+8/oFH+as2WMmjKUdrtnjBxhKA0gPT2de++9l4yMDMLDw5k9e7Ym5dpKTEzE1dWVLVu2UFFRoWnGyuree+/lwIEDzJw5k44dO/Lggw9qUm5DX/orVqxgxowZmpSvZ2Dk6uqKt7c3paWlDpExun4oTY/AyMXFhQ0bNrB+/Xq7XKPxVpYtW8ayZcs0L1dvEhg5IEcJjPTIGNn+MnSmjJGjDKUZjUbmzJnDnDlzNCmvMTNnzuTZZ5/VtQ4REREcPnxY0zKv/zvHxMTw0ksv6VK+9f2n1SVBoC5jU1pa6hBzjK5du0Ztba0SlOm567MeQZEz020orbKykgULFpCQkEB0dDTTp09X7TK7ceNGYmNjGTJkCKtWrVLtyvzLL7/w+OOPM3DgQKZPn865c+eU+8rLy3n11VeJiopi2LBhZGVlqcrNyMhQylywYIFqSbSjcLZ9jJoylKZVxuj6E7IeGSPrl4AegZHQ1/VfvuHh4bqVr3XGCP4YytJrVZrt56yoqAiz2ax89zjL5TCEjoFRTU0NoaGhpKens2vXLqKiokhJSQHq9m3YvHkzGzdu5KOPPmLfvn1s2bIFqAuo/ud//odx48axa9cuevbsyd/+9jflddPS0iguLiYzM5OlS5fy2muvKct+c3JyWLlyJa+//jpffvklZ8+eZf369do3/hb0yhjptY+RrEqr28DR2kY9M0ZCX9d/+VovKK2VhjJGhYWFyjF7bllh+/o3G0qzZ8YoJCREuX327Fld9jAS+tNtKM1gMDB16lTl34899hirVq3i6tWrZGZmMnr0aNq2bQvUrVTZtm0bI0aM4NChQxgMBkaMGAHAtGnTiI2N5dy5cwQHB5OZmckbb7yBj48PvXr1Iioqiq+++opp06aRlZVFXFycslHV1KlTWbx4Mc8880yDdaysrKSyslJ1zM3NzS4TQa3bz9fW1t4QBLi4uODu7q48pjl5enpiMBgoKyvj8uXLqjJsMzYeHh52KR/UgWBxcbFSju3J0cvLy27l2/a3yWRSlWN7/TaDwWC3OkDdCb+srEzpA9sdb/38/OxatqOw/Rw4o+t/FLVr107TvjAajbi4uGCxWCguLqampoYdO3YAde//u+++2671sQZGFRUVVFZWKuda26E0Hx8fu9XBdnfzgoIC1WfQ19dXk7+Fs38GwH590NRNUh1mjtGxY8do3bo1rVq1Ijc3V7WHQteuXXn77bcBOH36tGrprMFgoG3btpw+fRqj0cilS5dU93ft2pVffvlFee7999+v3NelSxcKCgooLy9vcKgmPT39htn4Y8aMYezYsc3T6AZYL5xpNBqVwMBgMKh2AW5uLVu2pKysjIsXL6o21SsqKlJuX758WXVfc7INPgoLC5VybC8iWl5ebrfybU9+RUVFqnJsh2mvXbtmtzrAH1nBq1evcubMGU3LdjS2f3tncn2mxGAwaP53NxqNmEwmioqK2LlzJwUFBUDdcnF776Xj6uqq3P7tt9+U7JD1XOTm5kZhYaHd5txUVVUpgWFubi6//fabcp+Li4umfwtn/QzYau4+aOrQtEMERiaTiaVLl5KUlATUjS/bpmyNRqMy5lxWVnbDkIbRaKSsrIzS0lJcXV1VQc7Nnmsto6ysrMHAaPLkyTz55JOqY/bMGOXl5REWFkaLFi3w8/NTTpJGo5H27ds3e5lWbdq0obCwkOLiYlU5ttfnCg8Pt1sdbK8TZjablXJs3wPBwcF2K9928zyLxaIqx/YXRqdOnez6dwgICCA3NxeTyURYWJgqW9mzZ09NLguit+s/B87m+o0cIyIiVMM7WmjdujUmkwmz2azaAX348OF2ff+D+uKlLVu2VC7RYh3W9/Pzs/vw4p133sn58+e5dOmS6nshLCzM7u0H+QyA/n2g+5m2oqKClJQUIiMjleExb29vVRbBbDYrv6YNBkOD80AMBgPe3t7U1NSoMkA3e661jMbmr3h4eGi6fwrUfRG3aNECX19fJWPg7e1t1zeHdZ5ReXk55eXlSn/ZzjGyZx1atWqFr68v165do6CgQCnHNjAwGAx2K9/62rW1tZjNZlU5tpNAfX197fp3sM5hsFgslJaWKnM8fHx8NH8f6s36OXA21/9ACwkJ0bwfrO/D4uJivvrqK+V4fHy83etiO8eppKREKc86x8jen0Go20vr/PnznDt3jr179yrHw8PDNf1bOOtnwJZefaBrr1dXV5OamkqbNm1ITk5WjoeHh6tWqGVnZytXNu/YsaPqvrKyMvLz8+nYsSN+fn4EBAQ0+bknT54kNDRUswvz/Rm2cw3sOfEY1Js82q5M02ryNfyxs2peXp6yCkSr5fouLi5KJlGvydegnlRaUlKiBEYy8dq5WN8HPj4+unwpWAOj8vJydu7cCdQFC1pcK8s2Y2Q7lG+dY6TFDuDWDF1NTQ1paWlA3Rf0sGHD7F62cAy6BkZLliyhoqKC+fPnq8aMExIS+OSTTygoKKCoqIh3332XoUOHAnUbr5WVlZGRkUFlZSXr16+ne/fuyqS5hIQE1q1bh9ls5vjx43z77bfExcUBdb94duzYwYkTJzCZTGzYsEF5XUdjGxjZew+Lxpbsa7VcH+pOvNYyravjtFquDzhEYHT9UmnrihgJjJzLv/71L5KSklTZCi3Zvt+qq6sBGDhwoCZ76TS0E391dbVyLrDnUn0r67kI/lghGhkZ6VTXCnN2ug2lnTt3joyMDDw9PVVXEn7rrbeIjIzk5MmTTJw4kdraWkaOHMnw4cOBuuGtFStWsGjRIl577TW6d+/OwoULlefPmDGDxYsXEx8fj5+fH3PnzlXGpDt37kxycjKzZs3CbDYzZMgQpkyZomm7m8o2EKipqbFrWY0FRnpkjADy8/O54447NFuuD44RGNn+Gi4qKlKG8SQwci69evVi+PDhug2jNLQsXavrdDUUGGm1uaNVQ3O6Ro4cafdyhePQLTAKDg7m4MGDjd4/efJkJk+e3OB9PXr04IMPPmjwPi8vLxYvXtzo6yYmJpKYmPjnKqsD29UZWgZGtnsZ2QYmWgZGeXl59O7dW7OhNLh1YOTh4aGajG4Ptl9ItqsQJTASWrrrrrtuOHbfffdpUnZDw/pabe5oZZsxspLAyLk498wuB2a7AsmazrYX23H9ixcvKretgZG7u7sqULMH25NRfn6+qnyw/1Ca9YRr3T/FynZloL3ZBka2y4JlYzmhpTFjxqj+3aJFC/r27atJ2bbnIkfJGA0YMEDzHciFviQwclBaZoysG2mCOlNhHUqzd7YIbswYgXaTr6Hx4UQtAyPbX8OSMRJ66datmypr5Onpafcdr61sM0bWydda7XptdX3G6PHHH7d7mcKxSGDkoLQMjGz3Bfn999+V29aMjRar9myDMz0yRg3NbQDHyBhJYCS0ZrsoRathNGj4c2i7Ok2Lz8L1GSN7bugrHJMERg5Ky8DIuokaqAMjyRhJYCSc05w5c7jzzjtxdXVl/vz5mpXb0Ofw5MmTyjHr1iv2FBAQoJwThw0bprpMiHAOum/wKBqmZWBkMBgICgpSXY4DtM0Y+fr64uPjg8lkajBjpOVeTtZfqpWVlcr8Li0CI9thAttLEUhgJLQWGBjI6dOnuXbtGkFBQZqV6+7ujp+fHyUlJcrn0DYw6tKli93r4OLiQmZmJllZWUyYMMHu5QnHI4GRg7JdAaXF/iEdOnSgsLCQs2fPUlFRgaenp6aBEdSlsLOzs8nPz8disahWiOkRGGm5VB8an2StxcZ6QlzPaDRq8r6/3h133EFJSYkyhKZ1YAR1K5979OihSVnC8chQmoN69dVXlX1MNm3aZPfybK8BZJ34q+VQGqD8Mi0vL6eoqEg5Mbq4uNg9a+IIgVF4eLiqHgDTp0/ngQcesHvZQjgK62fgypUr1NTUkJ2drRy3HWoTwl4kY+SgOnbsyE8//cTVq1eJioqye3nXT8Bu27atpsNIgGosPz8/XwmMWrdubfftAhqa26B1YOTl5cXnn3/Ol19+yb59+xg1ahQvvviiJhlDIRyF9bNosVg4d+6cMrSuVbZICAmMHFivXr00K8s2MDpz5oxq346GNjyzB9vAKC8vTwmMbPc2sRdHyBhBXV8vWbLE6S8eKZyX7Wfxxx9/VG5LYCS0ImdfAdyYMbL+SgP1Unp7sp3keerUKWVjNy2uUdRQYGQymZRjesy1EMIZ2f4QOnDggHJbAiOhFQmMBKCeY6RXYGSbMTpy5IhyW4uMkSMMpQkh1D9SXn/9deW2BEZCKxIYCUAdGJ0+fdrpAiNvb29l9V1DQ2la7fwrhLO7fgGCVdeuXTWuiXBWEhgJoC4wsAZA1iXzVnoMpR07dky5rUVgBH+ckPWcYySEs2to5VlMTAy9e/fWvjLCKUlgJBTdunUD6gKDo0ePKse1Cox8fHwa3MtHj8DIYrGodsCWC7kKoY3o6GglQ/vUU09x7Ngxvv76a1mQIDQjq9KEomvXruzcuROAffv2AXU7cGu5821YWBjFxcWqY1oFRtZfqpWVlZSWllJQUKDcd/31k4QQ9hEUFMQ333zDnXfeqbpUkBBakRBcKKwZI1vBwcF230PIVkPZKa0zRlCXNbINjLTaskAIUfcjRT5zQi8SGAlFQ5MbtRpGu1l5EhgJIYTQigRGQuEIgVFDqXOth9IALl68qARG/v7+eHt7a1IHIYQQ+pI5RkLRoUMH3N3dqaqqUo5pHRjZ7rhtpVVgZLuR5IQJE7hw4QIg2SIhhHAmkjESCldXVzp37qw6pvXkx9jYWNW1wdzc3PDz89Ok7MTERCVrZA2KQAIjIYRwJhIYCZU+ffoot0NCQnj00Uc1LT8wMFB1Nfnq6mrNLqLapUsXjh8/fsM+KhIYCSGE85DASKgsXryYqVOn8uabb5Kdna26hppWRowYoXmZViEhIURERKiOSWAkhBDOQwIjoRIeHs7atWuZNWuWbrs9Dx8+XLlte6kSrfTv31/1bwmMhBDCeUhgJBxOt27dmDVrFm3btuXtt9/WvHwJjIQQwnlJYCQc0ptvvkleXh7Dhg3TvOz77rtP9W8JjIQQwnlIYCTEda7fHkACIyGEcB4SGAnRgJdffhmA3r17q/Y3EkIIcXuTwEiIBixatIgffviBffv2abZdgBBCCP3JztdCNMDV1fWGSdhCCCFuf5IxEkIIIYSoJ4GREEIIIUQ9CYyEEEIIIepJYCSEEEIIUU/XyddpaWns2LGD33//ncWLF/Pwww8DsHTpUrZt26Y8rrKykgceeICVK1cC0K9fP7y8vJTVQpMnT2bKlCkAlJeXs2TJEvbs2YOvry/PP/888fHxymtlZGSwevVqzGYzQ4YMITU1FXd3d62aLIQQQggHpmtgFBYWRkpKCmvWrFEdT01NJTU1Vfn3k08+SXR0tOoxn3/++Q0b8UFdsFVcXExmZianTp3ixRdf5O6776Z9+/bk5OSwcuVK/vnPf9KuXTtSUlJYv349zzzzjH0aKIQQQoj/V3QdSktISCAiIgIPD49GH5Obm0tubi6xsbFNes3MzEymT5+Oj48PvXr1Iioqiq+++gqArKws4uLi6N69Oz4+PkydOlWVmRJCCCGEc3P4fYy2bdtGZGQkPj4+quPjx4/HxcWFAQMGkJycTKtWrSgpKeHSpUt07txZeVzXrl355ZdfADh9+jT333+/cl+XLl0oKCigvLwcLy+vG8qurKyksrJSdczNze2mgdz/VW1trer/zsbZ2w/SByB94OztB+kDZ28/2K8PWrRoWi7I4QOj7du3k5ycrDq2du1a/vKXv3Dt2jWWL1/OwoULefPNNyktLcXV1VUV5BiNRkpLSwEoKyvDaDQq91mDrbKysgYDo/T0dNauXas6NmbMGMaOHdtczbtBXl6e3V77/wNnbz9IH4D0gbO3H6QPnL390Px9EB4e3qTHOXRgdPToUUpKShg4cKDqeJ8+fQDw9/dn9uzZDBs2jKqqKry9vampqVFlgMxmM97e3gAYDAbMZrPyOiaTSTnekMmTJ/Pkk0+qjtkzY5SXl0dYWFiTo9rbibO3H6QPQPrA2dsP0gfO3n7Qvw8cOjDKysoiJibmpoGItdMsFgt+fn4EBASQk5NDz549AcjOzqZjx44AdOzYkZycHOW5J0+eJDQ0tMFsEYCHh4ddgqCbadGihdN+GEDaD9IHIH3g7O0H6QNnbz/o1we69np1dTUVFRVYLBbltnVMsbq6mq+//lq11B7g1KlTZGdnU1NTQ0lJCW+88QYDBgxQApiEhATWrVuH2Wzm+PHjfPvtt8TFxQEQHx/Pjh07OHHiBCaTiQ0bNjB06FBtGy2EEEIIh6Vrxmjx4sVs3boVgMOHDzNv3jzWrFlDv379+P777/H09KRv376q51y+fJlly5Zx4cIFjEYj/fv3Z/78+cr9M2bMYPHixcTHx+Pn58fcuXPp0KEDAJ07dyY5OZlZs2Yp+xhZ9z8SQgghhHCxWCwWvSsh6sZUz5w5Q/v27Z0yfers7QfpA5A+cPb2g/SBs7cf9O8DCYyEEEIIIeo5ZzgqhBBCCNEACYyEEEIIIepJYCSEEEIIUU8CIyGEEEKIehIYCSGEEELUk8BICCGEEKKeBEZCCCGEEPUkMBJCCCGEqCeBkRBCCCFEPQmMhBBCCCHqSWCkocTERI4fP653NRzClStXePHFFxk4cCCPPvooP/74IwDffPMNo0aNIjo6mocffpg333yTmpoanWvb/Bprf0ZGBgMGDGDQoEHKf4WFhTrX1j4a64OlS5eq2j9gwABmzZqlc23to7E+KC8vZ8mSJcTFxfHQQw+xadMmnWtqH2lpaYwZM4b77ruP7du3K8d/+uknpk2bRmRkJM8//7yONbSvxtrvLOdBaLwP9DwXSmAkdLF8+XLatGnDzp07eeGFF5g7dy4lJSV0796ddevWsWfPHj7++GNycnL47LPP9K5us2us/QD9+/dn7969yn9BQUE619Y+GuuD1NRUVfs7d+5MdHS03tW1i8b6YP369Zw9e5bPPvuMf//733z66accOHBA7+o2u7CwMFJSUujRo4fquJeXF6NGjWLSpEn6VEwjjbXfWc6D0HgfgH7nQgmMdHDs2DEmTpxIdHQ0jzzyCB988IFyX1paGn/729+YM2cOUVFRTJo0iXPnzulY2+ZXWlrKnj17eOaZZ/Dy8uLBBx+kU6dOfPvtt9x55534+/urHl9QUKBTTe3jZu13Fk3tg9zcXHJzc4mNjdWppvZzsz44cOAATzzxBD4+PgQFBTF8+HC+/PJLvavc7BISEoiIiMDDw0N1vHv37sTHxxMYGKhTzbTRWPud4Txo1Vgf6EkCIx24ubmRmprK7t27WbFiBatXr+bEiRPK/bt372bcuHHs2rWLdu3asXbtWh1r2/z++9//4uPjwx133KEc69KlC6dPnwbgyJEjREdHM2TIEHJychgxYoReVbWLW7X/6NGjxMTEMGbMGDZv3qxXNe3qVn1gtW3bNiIjI/Hx8dG6inZ3qz6wWCzKcYvFckPfiNvb7X4ebAq9zoVumpUkFN27d1fdHjhwIEePHuWuu+4CICIigj59+gDw0EMPkZaWpks97aWsrAyj0ag6ZjQaMZlMAPTu3Zs9e/ZQUFDA1q1b8fX11aOadnOz9vft25cPPviAoKAgfv31V2bPnk1AQACDBw/Wqbb2cav3gNX27dtJTk7WsGbauVkfRERE8P7779OrVy+uXbvG1q1bdaql0Mvtfh68FT3PhZIx0sGpU6dISkoiNjaW6Ohodu/eTXFxsXK/bQrVy8uL0tJSPappNwaDAbPZrDpmNpsxGAyqY6GhoXTq1Ik33nhDy+rZ3c3aHxoaSkhICC1atKBnz56MGzeO3bt361RT+2nKe+Do0aOUlJQwcOBArauniZv1wdNPP01ISAijR4/mhRdeICYmhjZt2uhUU6Gn2/U8eCt6ngslMNLBihUr6N27N1u3bmXPnj0MHjxYlTa/3bVr1w6TyURRUZFy7OTJk3Ts2PGGx1osFvLz87Wsnt39mfa7uLhoWTXNNKUPsrKyiImJcai5B83pZn1gMBh45ZVX2L59O5s3b8bFxUWVaRbO5XY8D/5ZWp4LJTDSQWlpKT4+Pnh6enL48GG+++47vaukKW9vb6KiokhLS6O8vJw9e/Zw6tQpoqKi2LFjh7IkMy8vj40bN9KvXz+da9y8btb+/fv3c+XKFQBOnDjBhx9+yKBBg3SucfO7WR8AVFdX8/XXXxMfH69zTe3nZn1w/vx5ioqKqKmp4fvvvycjI4MnnnhC7yo3u+rqaioqKrBYLMrt2tpaamtrqaiooLq6WnX7dtNY+53hPGjVWB/oeS50sThTqkJniYmJLFu2jNLSUpYsWcKVK1cYMGAAXl5etG3blhkzZpCWlsalS5dITU0F4ODBgyxbtoxPPvlE59o3rytXrjBv3jwOHTpEYGAgc+bMYcCAAWzYsIHNmzdTUlJCy5YtiY2NJSkpCU9PT72r3Kwaa//KlSvJzMykvLycNm3aMHbsWMaNG6d3de2isT4A2LdvH8uWLSMjI4MWLW7f32+N9cHBgweZN28eV69epUOHDsyePVuZd3g7mT9//g3zp9asWQPAM888ozr+yCOPMH/+fK2qponG2n/s2DGnOA9C432wd+9e3c6FEhhpKCYmhvT0dNq1a6d3VYQQQgjRgNv3p5iDOXjwIADBwcE610QIIYQQjZHl+hpYsmQJ33//Pa+88gru7u56V0cIIYQQjZChNCGEEEKIejKUJoQQQghRTwIjIYQQQoh6EhgJIYQQQtSTwEgIIYQQop4ERkII3R08eJB+/frRr18/zp49q3d1hBD1KisrWbBgAQkJCURHRzN9+nRycnKU+zdu3EhsbCxDhgxh1apVyuWtqqureemllxg6dCj9+vVTXfoG6jZ2vP/++xk0aBCDBg1i7NixN61HWloaS5cubf4GNkCW6wsh7CoxMZFz587d9DGDBg2iZ8+eAA5zbbSDBw8quy9v2bKFkJAQnWskhPZqamoIDQ0lPT2dO+64g/fff5+UlBS++OIL9u3bx+bNm9m4cSNeXl7MnDmTDh06MGLECAD69u3LxIkTmTx5coOvPWPGDCZNmqRha5pGAiMhhF1169aNgIAAAC5cuMCFCxcA6Nq1qxIERUdHM3LkSL2qKIRohMFgYOrUqcq/H3vsMVatWsXVq1fJzMxk9OjRtG3bFoDx48ezbds2RowYgZubG48//rhd6lRbW8ucOXM4cuQINTU13HfffaSmptKyZUvOnj3L6NGjeemll5TLy7zwwgsMGzasya8vgZEQwq5ef/115XZaWhpr165VjluzMNahNPgjO2O9hlJwcDAzZsxg9erVmEwmhg8fzrPPPsvbb7/Nli1b8PX1ZdKkSYwePVop5+LFi7zzzjscOHCAq1evEhgYSGJiIpMmTcLNre60d/z4cd555x2ys7MpLS3F39+fbt26kZKSwpdffqnUE2D48OHAH9fr2rRpE9u2baOwsBCz2Yyfnx+9e/fmueeeo3379gBkZGSwYMECAF577TU2bNjAmTNnuPfee1mwYAHffPMN69ato7y8nLi4OGbPnq3UzdoXycnJ/Prrr+zduxcvLy9GjRrFjBkzNL3SuBC2jh07RuvWrWnVqhW5ubkkJCQo93Xt2pW33367ya+1adMmNm3aRPv27Xnuuefo27dvk587ePBgFi5cSE1NDS+//DLr1q0jJSUFgKqqKs6cOcPWrVs5dOgQc+bMISYmBi8vrya9tswxEkI4tKKiIl577TXc3d0xm828//77TJgwgS1btuDj40NhYSErVqwgNzcXgKtXrzJp0iQyMjIoKysjPDycwsJC1qxZw5IlS4C6X5zJycn85z//wc3NjfDwcKqqqti7dy+FhYUEBgYSHh6u1KFr16707NlT+WV86NAh8vLyCAgIoEOHDpSUlLB7926SkpKoqKi4oQ3z5s2jsrKSyspK9u/fz/Tp01m+fDmenp4UFxezefNmvvjiixue984773D48GF8fX25cuUK69at48MPP7RHNwtxSyaTiaVLl5KUlARAaWkpPj4+yv1Go5HS0tImvda4ceP47LPPyMrKYsyYMcyaNYvCwsImPbdFixYkJCRgMBjw8fHhiSee4MiRI8r9FouFqVOn4u7uTkREBB4eHuTn5ze5nZIxEkI4tKqqKlavXs0999xDYmIi58+fJy8vj08//RR/f39iY2OpqKjg0KFDhIeH89FHH3H+/HkCAgL44IMP8Pf355tvvmH27Nls3bqVKVOm4OvrS3FxMQDp6elK5urUqVP4+/vTr18/2rZtq8wxss1uATz//PO0b99eyfD88MMPPPvss5w/f56jR4/Sv39/VRumTJnC008/zauvvsq2bdvIzc1l0aJFDB06lKlTp3LkyBEOHjzIqFGjVM/r0aMHq1evBmDmzJkcPnyY9PR0za4yLoRVRUUFKSkpREZGKnOIvL29MZlMymPMZjPe3t5Ner277rpLuT106FAyMzP54YcfGDFiBGPHjlXmJX788ccEBQWpnltdXc2qVavYvXs3165dw2Kx0KpVK+V+Dw8PVcDm5eVFWVlZk9sqgZEQwqFZh6kAgoKCOH/+PJ06dVICFX9/fwoLC7l8+TIAv/zyCwCXLl0iLi5O9VoWi4Wff/6ZoUOHcs8993Ds2DFGjx5NWFgYnTp1IjIykvj4+FvWqbCwkKVLl5KTk0NpaSm2V1a6ePHiDY+PiooC1BeRHjRoEAChoaEcOXJEqb+tmJgYJfiKiYnh8OHDXLp0iStXruDv73/LegrRHKqrq0lNTaVNmzYkJycrx8PDw8nJySEyMhKA7OxsOnbs+H8qw3Z4+KOPPrrpY7OyspQfCW3atOHAgQPNumJNAiMhhEMzGo3KbVdX1xuOWU+o1uDE+n+j0agaDrOyzjN45513yMrK4ujRo+Tm5rJz506++uorioqKmDhxYqP1yc/PZ/bs2VRVVWE0Grn77ruprq4mOzsbqBuma6wN1voDyi/a6+svhKNZsmQJFRUVLF++XBXAJCQksHz5cuLi4vD09OTdd9/lySefVO6vrKxU3tdVVVVUVFTg6ekJwM6dO3nggQdwd3dn165dHD16lLlz5zapPmazGQ8PD3x9fbl69SqbNm1qxtZKYCSEuM306NGD/fv34+rqytKlS5XMktlsZvfu3QwePBiLxcKxY8dITExUVsMtXLiQLVu2cPjwYSZOnKiaqGmbhv/tt9+oqqoC4B//+Af33HMP27dv55VXXmn2tuzcuVOZVL5r1y4AAgICJFskNHPu3DkyMjLw9PRk8ODByvG33nqLyMhITp48ycSJE6mtrWXkyJHKQgWAUaNGKUNiiYmJQN1CC4D33nuPhQsX4uLiQvv27fn73/9+yy0xrEHZsGHD+O6774iLiyMwMJCRI0c269w7CYyEELeVsWPH8sUXX3DhwgVGjRpFeHg4ZrOZ8+fPU11dzSOPPEJNTQ1JSUkYjUYCAwNxcXFRJm937twZgLZt2+Lm5kZ1dTVJSUkEBwczfvx4OnfujKurKzU1NTz//PMEBQVx6dIlu7TlxIkTJCYm4uLiomxz8NRTT9mlLCEaEhwcrAQzDZk8eXKj+xRlZGQ0+rz169f/qXqUlZXh5+cH1GVb33rrLdX948ePByAkJIT9+/c3uR4NkVVpQojbir+/P+np6SQmJtKyZUtOnTpFRUUFffr04a9//StQN6Q1atQoQkJCuHDhAvn5+QQHBzNhwgSmTZsGQKtWrZg9ezaBgYFcvnyZn3/+mUuXLtGhQwdeffVVQkNDqa6uplWrVspqt+aWlJREv379MJlMtGzZkilTpsjEa+F0TCYTBw4cUE3YticXiwxsCyGEQ7HuYzRv3jxlCEIIZ3T48GFSUlJ48MEHSU1NVRYj2JMMpQkhhBDCIfXp00eZX6cVGUoTQgghhKgnQ2lCCCGEEPUkYySEEEIIUU8CIyGEEEKIehIYCSGEEELUk8BICCGEEKKeBEZCCCGEEPUkMBJCCCGEqCeBkRBCCCFEPQmMhBBCCCHqSWAkhBBCCFHvfwFFAt3ctAXalwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "series = ElectricityConsumptionZurichDataset().load().astype(np.float32)\n", + "\n", + "# extract target and resample to hourly frequency\n", + "series = series[\"Value_NE5\"].resample(freq=\"h\")\n", + "\n", + "# plot 2 weeks of hourly consumption\n", + "ax = series[: 2 * 7 * 24].plot()\n", + "ax.set_ylabel(\"El. Consuption [kWh]\")\n", + "ax.set_title(\"Target series (Electricity Consumption) extract\");" + ] + }, + { + "cell_type": "markdown", + "id": "ab445a33-9a50-4695-8de4-09bcc007f787", + "metadata": {}, + "source": [ + "Extract a train, calibration and test set. Note that `cal` does not overlap with the training set `train`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "29a5b91e-543f-46e0-8dbd-12da2f09522f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_start = pd.Timestamp(\"2015-01-01\")\n", + "cal_start = pd.Timestamp(\"2016-01-01\")\n", + "test_start = pd.Timestamp(\"2017-01-01\")\n", + "test_end = pd.Timestamp(\"2018-01-01\")\n", + "\n", + "train = series[train_start : cal_start - series.freq]\n", + "cal = series[cal_start : test_start - series.freq]\n", + "test = series[test_start:test_end]\n", + "\n", + "ax = train.plot(label=\"train\")\n", + "cal.plot(label=\"val\")\n", + "test.plot(label=\"test\")\n", + "\n", + "ax.set_ylabel(\"El. Consuption [kWh]\")\n", + "ax.set_title(\"Dataset splits\");" + ] + }, + { + "cell_type": "markdown", + "id": "cd792a32-744a-4815-86d9-d3d7b3677859", + "metadata": {}, + "source": [ + "### Example 1: Compare different models on single step horizon forecasts\n", + "\n", + "Let's see how we can use conformal prediction in Darts. We'll show how to:\n", + "\n", + "- use conformal prediction (predict and historical forecasts)\n", + "- evaluate the prediction intervals (simple prediction and backtest).\n", + "- compare two different base forecasting models using conformal prediction.\n", + "\n", + "To demonstrate the process, we focus first only on one base forecasting model.\n", + "\n", + "#### Train the base forecaster\n", + "\n", + "Let's use a `LinearRegressionModel` as our base forecasting model. We configure it to use the last two hours as lookback to forecast the next hour (single step horizon; multi horizon will be covered in Example 2).\n", + "\n", + "- train it on the `train` set\n", + "- forecast the next hour after the end of the `cal` set" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8a9952be-a6c4-4da1-aabe-70c8f019b222", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "horizon = 1\n", + "\n", + "# train the model\n", + "model = LinearRegressionModel(lags=2, output_chunk_length=horizon)\n", + "model.fit(train)\n", + "\n", + "# forecast\n", + "pred = model.predict(n=horizon, series=cal)\n", + "\n", + "# plot\n", + "ax = series[pred.start_time() - 7 * 24 * series.freq : pred.end_time()].plot(\n", + " label=\"actual\"\n", + ")\n", + "pred.plot(label=\"pred\")\n", + "ax.set_title(\"First 1-step point prediction\");" + ] + }, + { + "cell_type": "markdown", + "id": "f8a80d6b-2818-4079-b39a-1848a2f049c1", + "metadata": {}, + "source": [ + "Great, we have our single step forecast. But without knowing the actual target value at that time, we wouldn't have any estimate of the uncertainty." + ] + }, + { + "cell_type": "markdown", + "id": "8e5bbfe1-2e10-4675-844d-d965c0371ca3", + "metadata": {}, + "source": [ + "#### Apply Conformal Prediction\n", + "\n", + "Now let's apply conformal prediction to quantify the uncertainty. We use the symmetric (default) naive model, including the quantile levels we want to forecast. Also:\n", + "\n", + "- we don't need to train / fit the conformal model\n", + "- we should supply a `series` to `predict()` that does not have an overlap with the series used to train the model. In our case `cal` has no overlap with `train`.\n", + "- the API is identical to Darts' forecasting models.\n", + "\n", + "Let's configure the conformal model:\n", + "- add a 90% quantile interval (quantiles 0.05 - 0.95) (`quantiles`).\n", + "- consider only the last 4 weeks of non-conformity scores to calibrate the prediction intervals (`cal_length`).\n", + "\n", + "> Note: you can add any number of intervals, e.g. `[0.10, 0.20, 0.50, 0.80, 0.90]` would add the 80% (0.10 - 0.90) and 60% (0.20 - 0.80) intervals" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "358f91ad-770d-4389-bf95-53004d8ec93f", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d89437eb2ec14fa997bdc230faa8e1e5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quantiles = [0.05, 0.50, 0.95]\n", + "four_weeks = 4 * 7 * 24\n", + "pred_kwargs = {\"predict_likelihood_parameters\": True, \"verbose\": True}\n", + "\n", + "# create conformal model\n", + "cp_model = ConformalNaiveModel(model=model, quantiles=quantiles, cal_length=four_weeks)\n", + "\n", + "# conformal forecast\n", + "pred = cp_model.predict(n=horizon, series=cal, **pred_kwargs)\n", + "\n", + "# plot\n", + "ax = series[pred.start_time() - 7 * 24 * series.freq : pred.end_time()].plot(\n", + " label=\"actual\"\n", + ")\n", + "pred.plot(label=\"cp\")\n", + "ax.set_title(\"First 1-step conformal prediction\");" + ] + }, + { + "cell_type": "markdown", + "id": "3897a238-4543-4542-895f-e2e62dda32bc", + "metadata": {}, + "source": [ + "Great, we can see the added prediction interval (turquoise, dark blue) around the base model's forecast (purple).\n", + "It's clear that the predicted interval contains the actual value. Let's look at how to evaluate this forecast." + ] + }, + { + "cell_type": "markdown", + "id": "80001270-a5af-4514-83ac-5c392b10bf37", + "metadata": {}, + "source": [ + "#### Evaluate Conformal Prediction\n", + "\n", + "Darts has dedicated metrics for prediction intervals. You can find them on [our metrics page](https://unit8co.github.io/darts/generated_api/darts.metrics.html) under the *Quantile interval metrics*. You can use them as standalone metrics or for backtesting.\n", + "\n", + "- `(m)ic`: (Mean) Interval Coverage\n", + "- `(m)iw`: (Mean) Interval Width\n", + "- `(m)iws`: (Mean) Interval Winkler Score\n", + "- `(m)incs_qr`: (Mean) Interval Non-Conformity Score for Quantile Regression\n", + "\n", + "> Note: for `backtest()` use the (m)ean metrics such as `mic()`, and for `residuals()` the per-time step metrics such as `ic()`.\n", + "\n", + "Let's check the interval coverage (the ratio of actual values being within each interval) and the interval width:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9470a0bc-0ac9-407b-9749-0d6ce19e4d7d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IntervalCoverageWidth
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" + ], + "text/plain": [ + " Interval Coverage Width\n", + "0 0.9 1.0 3321.12" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "q_interval = cp_model.q_interval # [(0.05, 0.95)]\n", + "q_range = cp_model.interval_range # [0.9]\n", + "\n", + "\n", + "def compute_metrics(pred_):\n", + " mic = metrics.mic(series, pred_, q_interval=q_interval)\n", + " miw = metrics.miw(series, pred_, q_interval=q_interval)\n", + " return pd.DataFrame({\"Interval\": q_range, \"Coverage\": mic, \"Width\": miw}).round(2)\n", + "\n", + "\n", + "compute_metrics(pred)" + ] + }, + { + "cell_type": "markdown", + "id": "bb765655-53f4-41a2-83cd-96c87c88fc26", + "metadata": {}, + "source": [ + "Okay, we see an interval width of 3.3 MWh, and a coverage of 100%. We would expect a coverage of 90% (on finite samples). But so far we've only looked at 1 example. How does it perform on the entire test set?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "23567754-d132-47d8-aa1c-33a048ff0d28", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0643a2e4c65b46c4967e73a5286e76cf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_historical_forecasts(hfcs_):\n", + " fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 4.3))\n", + " test[: 2 * 7 * 24].plot(ax=ax1)\n", + " hfcs_[: 2 * 7 * 24].plot(ax=ax1)\n", + " ax1.set_title(\"Predictions on the first two weeks\")\n", + " ax1.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.25), ncol=4, fontsize=9)\n", + "\n", + " test.plot(ax=ax2)\n", + " hfcs_.plot(ax=ax2, lw=0.2)\n", + " ax2.set_title(\"Predictions on the entire test set\")\n", + " ax2.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.25), ncol=4, fontsize=9)\n", + "\n", + "\n", + "plot_historical_forecasts(hfcs)" + ] + }, + { + "cell_type": "markdown", + "id": "10b8f9f4-a1f8-42c5-96dd-294440290fca", + "metadata": {}, + "source": [ + "Nice, we just performed a one-year simulation of applying conformal prediction in under 1 second! The intervals also seem to be well calibrated.\n", + "Let's find out by computing the metrics on all historical forecasts (backtest)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "73bf5226-e09b-447d-991d-f6efd71cbb7d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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00.90.9016092908.944092
\n", + "
" + ], + "text/plain": [ + " Interval Coverage Width\n", + "0 0.9 0.901609 2908.944092" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bt = cp_model.backtest(\n", + " cal_test,\n", + " historical_forecasts=hfcs,\n", + " last_points_only=True,\n", + " metric=[metrics.mic, metrics.miw],\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + ")\n", + "bt = pd.DataFrame({\"Interval\": q_range, \"Coverage\": bt[0], \"Width\": bt[1]})\n", + "bt" + ] + }, + { + "cell_type": "markdown", + "id": "36eb467d-adbd-4538-9b11-a2bd4927bd9b", + "metadata": {}, + "source": [ + "Great! Our interval indeed covers 90% of all actual values. The mean width / uncertainty range is just under 3MWh.\n", + "\n", + "It would also be interesting to see how the coverage and widths behaved over time.\n", + "\n", + "The coverage metric `ic()` gives a binary value for each time step (whether the interval contains the actual). To get the coverage ratios over some period of time, we compute the moving average with a window of 4 weeks." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fc72247b-8e34-4a43-b82d-f9f096c9bd37", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_moving_average_metrics(hfcs_, metric=metrics.ic):\n", + " \"\"\"Computes the moving 4-week average of a specific time-dependent metric.\"\"\"\n", + " # compute metric on each time step\n", + " residuals = cp_model.residuals(\n", + " cal_test,\n", + " historical_forecasts=hfcs_,\n", + " last_points_only=True,\n", + " metric=metric,\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + " )\n", + "\n", + " # let's apply a moving average to the residuals with a winodow of 4 weeks\n", + " windowed_residuals = residuals.window_transform(\n", + " transforms={\"function\": \"mean\", \"mode\": \"rolling\", \"window\": four_weeks}\n", + " )\n", + " return windowed_residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "da696430-0bea-4adf-8bb4-5315e4a18ca1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "covs = compute_moving_average_metrics(hfcs, metrics.ic)\n", + "widths = compute_moving_average_metrics(hfcs, metrics.iw)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 4.3))\n", + "covs.plot(ax=ax1, label=\"coverages\")\n", + "ax1.set_ylabel(\"Ratio covered [-]\")\n", + "ax1.set_title(\"Moving 4-week average of Interval Coverages\")\n", + "\n", + "widths.plot(ax=ax2, label=\"widths\")\n", + "ax2.set_ylabel(\"Width [kWh]\")\n", + "ax2.set_title(\"Moving 4-week average of Interval Widths\");" + ] + }, + { + "cell_type": "markdown", + "id": "62f26595-5286-4c6b-9191-cf6535971e47", + "metadata": {}, + "source": [ + "Also here, the coverage looks stable around 90% over the entire year -> the conformal model is valid.\n", + "\n", + "The interval widths range from 2.5 - 3.5 MWh. The adaptivity/responsiveness of the widths to changes in model performance is mainly controlled by the value of `cal_length`." + ] + }, + { + "cell_type": "markdown", + "id": "c4888c37-8cde-4c70-a807-f0f74e3536e3", + "metadata": {}, + "source": [ + "#### Comparison with another model\n", + "\n", + "Okay now let's compare the uncertainty of our first model with a more powerful regression model.\n", + "\n", + "- Use the last week (7*24) of consumption as lookback window\n", + "- Also use a cyclic encoding of the hour of the day and day of week as a future covariate\n", + "\n", + "The process is exactly the same as for the first model, so we won't go into any detail." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6ca89f61-3da1-4e89-86a0-edee7474ee3f", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6f1d228446304cadacfc27e9ca1be4ef", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IntervalCoverageWidth
00.90.8984131662.243896
\n", + "" + ], + "text/plain": [ + " Interval Coverage Width\n", + "0 0.9 0.898413 1662.243896" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "add_encoders = {\"cyclic\": {\"future\": [\"hour\", \"dayofweek\"]}}\n", + "input_length = 7 * 24\n", + "model2 = LinearRegressionModel(\n", + " lags=input_length,\n", + " lags_future_covariates=(input_length, 1),\n", + " output_chunk_length=1,\n", + " add_encoders=add_encoders,\n", + ")\n", + "model2.fit(train)\n", + "\n", + "cp_model2 = ConformalNaiveModel(\n", + " model=model2, quantiles=quantiles, cal_length=four_weeks\n", + ")\n", + "hfcs2 = cp_model2.historical_forecasts(\n", + " series=cal_test,\n", + " forecast_horizon=horizon,\n", + " start=test.start_time(),\n", + " last_points_only=True,\n", + " stride=horizon,\n", + " **pred_kwargs,\n", + ")\n", + "plot_historical_forecasts(hfcs2)\n", + "\n", + "bt2 = cp_model.backtest(\n", + " cal_test,\n", + " historical_forecasts=hfcs2,\n", + " last_points_only=True,\n", + " metric=[metrics.mic, metrics.miw],\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + ")\n", + "bt2 = pd.DataFrame({\"Interval\": q_range, \"Coverage\": bt2[0], \"Width\": bt2[1]})\n", + "bt2" + ] + }, + { + "cell_type": "markdown", + "id": "027d41cc-7f43-414e-bc7e-9658fadc5851", + "metadata": {}, + "source": [ + "Nice! We achieve again 90% coverage, but our average **interval width decreased from 2.9 MWh to 1.7 MWh!**\n", + "Finally, let's also look at the metrics over time and compare our two models." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "aa8a446d-5d58-4b5a-a7fb-2d2c33069909", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IntervalCoverageWidth
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Model 20.90.8981662.244
\n", + "
" + ], + "text/plain": [ + " Interval Coverage Width\n", + "Model 1 0.9 0.902 2908.944\n", + "Model 2 0.9 0.898 1662.244" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "covs2 = compute_moving_average_metrics(hfcs2, metrics.ic)\n", + "widths2 = compute_moving_average_metrics(hfcs2, metrics.iw)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 4.3))\n", + "covs.plot(ax=ax1, label=\"coverages model 1\")\n", + "covs2.plot(ax=ax1, label=\"coverages model 2\")\n", + "ax1.set_ylabel(\"Ratio covered [-]\")\n", + "ax1.set_title(\"Moving 4-week average of Interval Coverages\")\n", + "\n", + "widths.plot(ax=ax2, label=\"widths model 1\")\n", + "widths2.plot(ax=ax2, label=\"widths model 2\")\n", + "ax2.set_ylabel(\"Width [kWh]\")\n", + "ax2.set_title(\"Moving 4-week average of Interval Widths\")\n", + "\n", + "bts = pd.concat([bt, bt2], axis=0).round(3)\n", + "bts.index = [\"Model 1\", \"Model 2\"]\n", + "bts" + ] + }, + { + "cell_type": "markdown", + "id": "a451393c-35a3-4af9-81e6-48e197e74b9e", + "metadata": {}, + "source": [ + "Stable coverage over time for both models, but consistently lower interval widths for Model 2 -> we can clearly say that Model 2 is the winner (through **lower uncertainty**)." + ] + }, + { + "cell_type": "markdown", + "id": "49feed57-19b9-42d2-bb88-201c56034e96", + "metadata": {}, + "source": [ + "### Example 2: Multi-horizon forecasts\n", + "\n", + "Multi-horizon forecasts are supported out of the box. Simply set `n>1` (or `forecast_horizon`), and the model generates calibrated prediction intervals for each step." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9816887f-095e-44d8-afd2-67ced7698a37", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5581bbe9a69240718e7e746c28ccbb5f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/696 [00:00" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_horizon = 24\n", + "pred = cp_model.predict(n=multi_horizon, series=cal, **pred_kwargs)\n", + "\n", + "# plot\n", + "ax = series[pred.start_time() - 7 * 24 * series.freq : pred.end_time()].plot(\n", + " label=\"actual\"\n", + ")\n", + "pred.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "9970b109-f4c7-4784-999c-a47af6c23d3c", + "metadata": {}, + "source": [ + "Oh, why do we have such large intervals now? It's because we used Model 1 (the worse one) that was trained to predict only the next hour. Then under the hood we perform auto-regression to generate the 24-hour forecasts on the calibration set. Consequently, this results in larger errors / non-conformity scores the further ahead we predict, and ultimately in higher model uncertainty.\n", + "\n", + "We can perform much better if we use a base-forecaster that was trained on predicting the next 24 hours directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "db681fd0-5cca-435a-b4bb-72d1cb97aa7a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6b1cccc2e3bd441382af4022099b735e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_horizon = 24\n", + "\n", + "model = LinearRegressionModel(lags=input_length, output_chunk_length=multi_horizon).fit(\n", + " train\n", + ")\n", + "cp_model = ConformalNaiveModel(model=model, quantiles=quantiles, cal_length=four_weeks)\n", + "\n", + "pred = cp_model.predict(n=multi_horizon, series=cal, **pred_kwargs)\n", + "\n", + "# plot\n", + "ax = series[pred.start_time() - 7 * 24 * series.freq : pred.end_time()].plot(\n", + " label=\"actual\"\n", + ")\n", + "pred.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "3138c737-2b42-48c9-812a-05fd0c42b963", + "metadata": {}, + "source": [ + "### Example 3: Multi-horizon Forecasts on a Scheduled Basis with valid Coverage\n", + "\n", + "But what if we want to apply multi-horizon forecasts on a scheduled basis?\n", + "\n", + "E.g. we want to make a one-day (24 hour) forecast every 24 hours.\n", + "\n", + "By default, the calibration set considers all possible historical forecasts on the calibration set (`cal_stride=1`).\n", + "This would use examples generated outside our 24-hour schedule, and might lead to invalid coverages.\n", + "\n", + "Setting `cal_stride=24` will extract the correct examples." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f616f864-2ab8-4d82-8a0e-90da8d43d640", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "03f79ddd66f84399aaa02edd0f89429a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IntervalCoverageWidth
00.90.9022834772.75975
\n", + "" + ], + "text/plain": [ + " Interval Coverage Width\n", + "0 0.9 0.902283 4772.75975" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# conformal model\n", + "cp_model = ConformalNaiveModel(\n", + " model=model,\n", + " quantiles=quantiles,\n", + " cal_length=100,\n", + " cal_stride=multi_horizon, # stride for calibration set\n", + ")\n", + "\n", + "hfcs = cp_model.historical_forecasts(\n", + " series=cal_test,\n", + " forecast_horizon=multi_horizon,\n", + " start=test.start_time(),\n", + " last_points_only=False, # return each multi-horizon forecast\n", + " stride=multi_horizon, # use the same stride for historical forecasts\n", + " **pred_kwargs,\n", + ")\n", + "\n", + "# concatenate the forecasts into a single TimeSeries\n", + "hfcs_concat = concatenate(hfcs, axis=0)\n", + "plot_historical_forecasts(hfcs_concat)\n", + "\n", + "bt = cp_model.backtest(\n", + " cal_test,\n", + " historical_forecasts=hfcs,\n", + " last_points_only=False,\n", + " metric=[metrics.mic, metrics.miw],\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + ")\n", + "pd.DataFrame({\"Interval\": q_range, \"Coverage\": bt[0], \"Width\": bt[1]})" + ] + }, + { + "cell_type": "markdown", + "id": "bfa1fa34-aa8e-433d-8998-612daceb22b8", + "metadata": {}, + "source": [ + "Great, we also achieve valid coverage when applying our model only once per day.\n", + "\n", + "Since we have multi-horizon forecasts, it's also important to check the coverage and width for each step in the horizon:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "db5a32f3-0a21-4be3-b23b-09647432f921", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_hfc_horizon_metric(metric=metrics.ic):\n", + " # computes the metric per historical forecast, horizon and component with\n", + " # shape `(n forecasts, horizon, n components, 1)`\n", + " residuals = cp_model.residuals(\n", + " cal_test,\n", + " historical_forecasts=hfcs,\n", + " last_points_only=False,\n", + " metric=metric,\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + " values_only=True,\n", + " )\n", + " # create array and drop component and sample axes\n", + " residuals = np.array(residuals)[:, :, 0, 0]\n", + "\n", + " # compute the mean over all forecasts (365 1-day forecasts) for each horizon\n", + " return np.mean(residuals, axis=0)\n", + "\n", + "\n", + "covs_horizon = compute_hfc_horizon_metric(metrics.ic)\n", + "widths_horizon = compute_hfc_horizon_metric(metrics.iw)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "699c9790-2fb2-445e-8983-0a3174ff23c5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 8.6), sharex=True)\n", + "\n", + "horizons = [i + 1 for i in range(24)]\n", + "ax1.plot(horizons, covs_horizon)\n", + "ax2.plot(horizons, widths_horizon)\n", + "\n", + "ax1.set_ylabel(\"coverage ratio [-]\")\n", + "ax1.set_title(\"Interval coverage per step in horizon\")\n", + "\n", + "ax2.set_xlabel(\"horizon\")\n", + "ax2.set_ylabel(\"width [kWh]\")\n", + "ax2.set_title(\"Interval width per step in horizon\");" + ] + }, + { + "cell_type": "markdown", + "id": "785c893b-ae78-48f4-982a-46ed0e5df748", + "metadata": {}, + "source": [ + "The coverages are valid for all steps in the horizon and range between 89% and 92%.\n", + "\n", + "In general, the widths increase with higher horizon. After horizon 16 they drop again, due to the nature of the target series (low Electricity consumption during the night -> lower uncertainty.)" + ] + }, + { + "cell_type": "markdown", + "id": "b6563158-d607-4991-bec9-bbadc2a69326", + "metadata": {}, + "source": [ + "### Example 4: Conformalized Quantile Regression\n", + "\n", + "Finally, let's check out an example of our `ConformalQRModel`. The API is exactly the same. \n", + "\n", + "The only difference is that it requires a **probabilistic** base forecaster.\n", + "\n", + "Let's use a linear model with quantile regression and perform the same single step forecast as in example 1." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "59a7d058-241b-4fe3-87d1-baf77b3638a0", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ec085a67dc854b55a80d5ab3f9256734", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IntervalCoverageWidth
00.90.900241770.154514
\n", + "" + ], + "text/plain": [ + " Interval Coverage Width\n", + "0 0.9 0.90024 1770.154514" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# probabilistic regression model (with quantiles)\n", + "model = LinearRegressionModel(\n", + " lags=input_length,\n", + " output_chunk_length=horizon,\n", + " likelihood=\"quantile\",\n", + " quantiles=quantiles,\n", + ").fit(train)\n", + "\n", + "# conformalized quantile regression model\n", + "cp_model = ConformalQRModel(model=model, quantiles=quantiles, cal_length=four_weeks)\n", + "hfcs = cp_model.historical_forecasts(\n", + " series=cal_test,\n", + " forecast_horizon=horizon,\n", + " start=test.start_time(),\n", + " last_points_only=True,\n", + " stride=horizon,\n", + " **pred_kwargs,\n", + ")\n", + "plot_historical_forecasts(hfcs)\n", + "\n", + "bt = cp_model.backtest(\n", + " cal_test,\n", + " historical_forecasts=hfcs,\n", + " last_points_only=True,\n", + " metric=[metrics.mic, metrics.miw],\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + ")\n", + "pd.DataFrame({\"Interval\": q_range, \"Coverage\": bt[0], \"Width\": bt[1]})" + ] + }, + { + "cell_type": "markdown", + "id": "98998cdf-3c8e-48d6-86e0-b0ad908a988f", + "metadata": {}, + "source": [ + "Same coverage, but slightly larger intervals than in the naive conformal prediction case." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}