diff --git a/.github/workflows/merge.yml b/.github/workflows/merge.yml index 37f301fb5e..d778e68f4c 100644 --- a/.github/workflows/merge.yml +++ b/.github/workflows/merge.yml @@ -96,7 +96,7 @@ 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-SKLearnModel-examples.ipynb, 21-TSMixer-examples.ipynb, 22-anomaly-detection-examples.ipynb, 23-Conformal-Prediction-examples.ipynb, 24-SKLearnClassifierModel-examples.ipynb, 25-FoundationModel-examples.ipynb, 26-NeuralForecast-examples.ipynb, 27-Torch-and-Foundation-Model-Fine-Tuning-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-SKLearnModel-examples.ipynb, 21-TSMixer-examples.ipynb, 22-anomaly-detection-examples.ipynb, 23-Conformal-Prediction-examples.ipynb, 24-SKLearnClassifierModel-examples.ipynb, 25-FoundationModel-examples.ipynb, 26-NeuralForecast-examples.ipynb, 27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb, 28-Explainability-examples.ipynb] steps: - name: "Clone repository" uses: actions/checkout@v6 diff --git a/CHANGELOG.md b/CHANGELOG.md index 13e35c7ee3..7c0e79ec85 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -15,12 +15,24 @@ but cannot always guarantee backwards compatibility. Changes that may **break co **Improved** +- Improvements to `ShapExplainer` : [#3049](https://github.com/unit8co/darts/pull/3049) by [Zhihao Dai](https://github.com/daidahao). + - 🚀🚀 `ShapExplainer` can now also explain any `TorchForecastingModel` including regular torch models (`TiDEModel`, ...) as well as foundation models (`Chronos2`, ...). It supports global and local explanations and can output SHAP values for further analysis. + - Added method `explain_single()` to explain a single model forecast in detail, in addition to the existing batched method `explain()`. This is useful for local explanations of individual predictions with reduced computational cost. + - Method `summary_plot()` can now also be computed on any optional foreground series using parameters `foreground_series`, `foreground_past_covariates`, `foreground_future_covariates`. + - 🔴 Renamed method `force_plot_from_ts()` to `force_plot()` to simplify. + - Added a new [Explainability of Forecasting Models Notebook](https://unit8co.github.io/darts/examples/28-Explainability-examples.html) for detailed usage of `ShapExplainer`. + **Fixed** +- Fixed several bugs in `ShapExplainer` including mismatched SHAP method enum values, feature naming conventions, inconsistent instance count in `explain()`. [#3049](https://github.com/unit8co/darts/pull/3049) by [Zhihao Dai](https://github.com/daidahao). +- Fixed a bug in explainability utils where stationarity tests were not properly conducted due to usage of `all()`. [#3049](https://github.com/unit8co/darts/pull/3049) by [Zhihao Dai](https://github.com/daidahao). + **Dependencies** ### For developers of the library: +- Added `ShapSingleExplainabilityResult` class as the return type of `explain_single()` method in `ShapExplainer` and `TorchExplainer` and to store the SHAP results of a single instance explanation. This is in contrast to the existing `ShapExplainabilityResult` which stores results for batched explanations. [#3049](https://github.com/unit8co/darts/pull/3049) by [Zhihao Dai](https://github.com/daidahao). + ## [0.44.1](https://github.com/unit8co/darts/tree/0.44.1) (2026-05-05) ### For users of the library: diff --git a/darts/explainability/__init__.py b/darts/explainability/__init__.py index 2a475b2e12..fc2d1ae570 100644 --- a/darts/explainability/__init__.py +++ b/darts/explainability/__init__.py @@ -4,6 +4,17 @@ Tools for explaining and interpreting forecasting model predictions, including SHAP-based explainers and model-specific explainability methods. + +`SHAP `__-Based Explainers +------------------------------------------------------------- +- :class:`~darts.explainability.shap_explainer.ShapExplainer`: SHAP-based explainer for Darts' SKLearn + and Torch Models. + +Model-Specific Explainers +------------------------- +- :class:`~darts.explainability.tft_explainer.TFTExplainer`: Explainer for + :class:`TFTModel `. + """ from typing import TYPE_CHECKING @@ -15,18 +26,21 @@ ShapExplainabilityResult as ShapExplainabilityResult, ) from darts.explainability.explainability_result import ( - TFTExplainabilityResult as TFTExplainabilityResult, + ShapSingleExplainabilityResult as ShapSingleExplainabilityResult, ) from darts.explainability.explainability_result import ( - _ExplainabilityResult as _ExplainabilityResult, + TFTExplainabilityResult as TFTExplainabilityResult, ) from darts.explainability.shap_explainer import ShapExplainer as ShapExplainer from darts.explainability.tft_explainer import TFTExplainer as TFTExplainer _LAZY_IMPORTS: dict[str, tuple[str, str | None]] = { "ShapExplainabilityResult": ("darts.explainability.explainability_result", None), + "ShapSingleExplainabilityResult": ( + "darts.explainability.explainability_result", + None, + ), "TFTExplainabilityResult": ("darts.explainability.explainability_result", None), - "_ExplainabilityResult": ("darts.explainability.explainability_result", None), "ShapExplainer": ("darts.explainability.shap_explainer", None), "TFTExplainer": ("darts.explainability.tft_explainer", "(Py)Torch"), } diff --git a/darts/explainability/explainability.py b/darts/explainability/explainability.py index 107cd408fc..91e17c01df 100644 --- a/darts/explainability/explainability.py +++ b/darts/explainability/explainability.py @@ -66,6 +66,11 @@ def __init__( test_stationarity Whether to raise a warning if not all `background_series` are stationary. """ + if not isinstance(model, ForecastingModel): + raise_log( + ValueError("`model` must be a Darts `ForecastingModel` object."), + logger, + ) if not model._fit_called: raise_log( ValueError( @@ -75,7 +80,7 @@ def __init__( ) self.model = model # default forecasting horizon - self.n: int | None = getattr(self.model, "output_chunk_length", None) + self.n: int = self.model.output_chunk_length or 1 # check background input validity and process it ( @@ -83,10 +88,12 @@ def __init__( self.background_past_covariates, self.background_future_covariates, self.target_components, + self.target_components_likelihood, self.static_covariates_components, self.past_covariates_components, self.future_covariates_components, ) = process_input( + n=self.n, model=model, input_type="background", series=background_series, @@ -148,6 +155,7 @@ def _process_foreground( foreground_future_covariates: TimeSeriesLike | None = None, ): return process_input( + n=self.n, model=self.model, input_type="foreground", series=foreground_series, @@ -171,6 +179,6 @@ def _process_horizons_and_targets( horizons=horizons, fallback_horizon=self.n, target_components=target_components, - fallback_target_components=self.target_components, + fallback_target_components=self.target_components_likelihood, check_component_names=self.check_component_names, ) diff --git a/darts/explainability/explainability_result.py b/darts/explainability/explainability_result.py index 5f1bfce0df..167c44b504 100644 --- a/darts/explainability/explainability_result.py +++ b/darts/explainability/explainability_result.py @@ -6,12 +6,16 @@ `. - :class:`ShapExplainabilityResult ` for :class:`ShapExplainer - ` + `. Contains general forecasting model explainability result + based on SHAP values. +- :class:`ShapSingleExplainabilityResult ` for :class:`ShapExplainer + `. Contains the explainability result for a single model forecast. - :class:`TFTExplainabilityResult ` for :class:`TFTExplainer - ` -- :class:`ComponentBasedExplainabilityResult ` for component based explainability - results -- :class:`HorizonBasedExplainabilityResult ` for horizon based explainability results + `. +- :class:`ComponentBasedExplainabilityResult ` for generic component-based + explainability result. +- :class:`HorizonBasedExplainabilityResult ` for generic horizon-based + explainability results. """ from abc import ABC, abstractmethod @@ -21,7 +25,7 @@ import shap from darts import TimeSeries -from darts.logging import get_logger, raise_if, raise_if_not, raise_log +from darts.logging import get_logger, raise_log logger = get_logger(__name__) @@ -35,18 +39,24 @@ class _ExplainabilityResult(ABC): @abstractmethod def get_explanation(self, *args, **kwargs): """Returns one or multiple explanations based on some input parameters.""" - pass class ComponentBasedExplainabilityResult(_ExplainabilityResult): - """Explainability result for general component objects. - The explained components can describe anything. + """ + Stores the explainability results of a :class:`_ForecastingModelExplainer + ` with convenient access to component-based + results. + + Parameters + ---------- + explained_components + The component-based explainability results. Examples -------- >>> explainer = SomeComponentBasedExplainer(model) - >>> explain_results = explainer.explain() - >>> output = explain_results.get_explanation(component="some_component") + >>> result = explainer.explain() + >>> explanation = result.get_explanation(component="some_component") """ def __init__( @@ -65,23 +75,24 @@ def __init__( else: comps_available = explained_components.keys() self.explained_components = explained_components - self.available_components = comps_available + self.available_components = list(comps_available) - def get_explanation(self, component) -> Any | list[Any]: + def get_explanation(self, component: str | None = None) -> Any | list[Any]: """ Returns one or several explanations for a given component. Parameters ---------- component - The component for which to return the explanation. + Optionally, the target series component for which to return the explanation. Must be supplied for + multivariate forecasting models. """ return self._query_explainability_result(self.explained_components, component) def _query_explainability_result( self, attr: dict[str, Any] | list[dict[str, Any]], - component: str, + component: str | None, ) -> Any: """ Helper that extracts and returns the explainability result attribute for a given component. @@ -91,7 +102,8 @@ def _query_explainability_result( attr An explainability result attribute from which to extract the component. component - The component for which to return the content of the attribute. + Optionally, the target series component for which to return the explanation. Must be supplied for + multivariate forecasting models. """ component = self._validate_input_for_querying_explainability_result(component) if isinstance(attr, list): @@ -99,92 +111,109 @@ def _query_explainability_result( else: return attr[component] - def _validate_input_for_querying_explainability_result(self, component) -> str: + def _validate_input_for_querying_explainability_result( + self, component: str | None + ) -> str: """ Helper that validates the input parameters of a method that queries the `ComponentBasedExplainabilityResult`. Parameters ---------- component - The component for which to return the explanation. Does not - need to be specified for univariate series. + Optionally, the target series component for which to return the explanation. Must be supplied for + multivariate forecasting models. """ # validate component argument - raise_if( - component is None and len(self.explained_components) > 1, - f"The component parameter is required when the `{self.__class__.__name__}` has more than one component.", - logger, - ) + if component is None and len(self.available_components) > 1: + raise_log( + ValueError( + "The component parameter is required when the model has more than one component." + ), + logger, + ) - if component is None: - component = self.available_components[0] + component_out = self.available_components[0] if component is None else component - raise_if_not( - component in self.available_components, - f"Component {component} is not available. Available components are: {self.available_components}", - logger, - ) - return component + if component_out not in self.available_components: + raise_log( + ValueError( + f'Component "{component_out}" is not available. ' + f"Available components are: {self.available_components}" + ), + logger, + ) + return component_out class HorizonBasedExplainabilityResult(_ExplainabilityResult): """ Stores the explainability results of a :class:`_ForecastingModelExplainer - ` with convenient access to the horizon - based results. + ` with convenient access to horizon-based + results. - The result is a multivariate `TimeSeries` instance containing the 'explanation' for the (horizon, target_component) - forecast at any timestamp forecastable corresponding to the foreground `TimeSeries` input. + The result is a multivariate ``TimeSeries`` instance containing the "explanation" for the + ``(horizon, target_component)`` forecast at any timestamp forecastable in the foreground series. - The component name convention of this multivariate `TimeSeries` is: - ``"{name}_{type_of_cov}_lag_{idx}"``, where: + The components of the ``TimeSeries`` correspond to the input features used by the model to produce the + forecasts. They are named according to the convention: ``"{name}_{type_of_cov}_lag{idx}"``, where: - - ``{name}`` is the component name from the original foreground series (target, past, or future). + - ``{name}`` is the component name from the original foreground series (target, past covariates, or future + covariates). - ``{type_of_cov}`` is the covariates type. It can take 3 different values: - ``"target"``, ``"past_cov"`` or ``"future_cov"``. - - ``{idx}`` is the lag index. + ``"target"``, ``"pastcov"``, ``"futcov"``. + - ``{idx}`` is the lag index, where ``0`` represents the position of the first predicted step. + + Static covariates are named according to the convention: ``"{name}_statcov_target_{comp}"``, where: + + - ``{name}`` is the variable name of the static covariate. + - ``{comp}`` is the component name of the target series if static covariates are component-specific, or + ``"global_components"`` if they are global. Examples -------- - - Say we have a model with 2 target components named ``"T_0"`` and ``"T_1"``, - 3 past covariates with default component names ``"0"``, ``"1"``, and ``"2"``, - and one future covariate with default component name ``"0"``. - Also, ``horizons = [1, 2]``. - The model is a `SKLearnModel`, with ``lags = 3``, ``lags_past_covariates=[-1, -3]``, - ``lags_future_covariates = [0]``. - - We provide `foreground_series`, `foreground_past_covariates`, `foreground_future_covariates` each of length 5. - - >>> explainer = SomeHorizonBasedExplainer(model) - >>> explain_results = explainer.explain( - >>> foreground_series=foreground_series, - >>> foreground_past_covariates=foreground_past_covariates, - >>> foreground_future_covariates=foreground_future_covariates, - >>> horizons=[1, 2], - >>> target_names=["T_0", "T_1"] - >>> ) - >>> output = explain_results.get_explanation(horizon=1, target="T_1") - - Then the method returns a multivariate TimeSeries containing the *explanations* of - the corresponding `_ForecastingModelExplainer`, with the following component names: - - - T_0_target_lag-1 - - T_0_target_lag-2 - - T_0_target_lag-3 - - T_1_target_lag-1 - - T_1_target_lag-2 - - T_1_target_lag-3 - - 0_past_cov_lag-1 - - 0_past_cov_lag-3 - - 1_past_cov_lag-1 - - 1_past_cov_lag-3 - - 2_past_cov_lag-1 - - 2_past_cov_lag-3 - - 0_fut_cov_lag_0 - - This series has length 3, as the model can explain 5-3+1 forecasts - (timestamp indexes 4, 5, and 6) + Say we have a ``SKLearnModel`` instance with: + + - 1 target component named ``"Y"``, + - 1 future covariate named ``"month"``, + - ``lags = 2``, and ``lags_future_covariates = [-1, 0]``. + + Let's explain the background series that the model was trained on: + + >>> from darts.datasets import AusBeerDataset + >>> from darts.explainability import ShapExplainer + >>> from darts.models import LinearRegressionModel + >>> from darts.utils.timeseries_generation import datetime_attribute_timeseries as dta + >>> + >>> # load a target series and create future covariates holding the calendar month values + >>> series = AusBeerDataset().load() + >>> fc = dta(series, attribute="month", add_length=12) + >>> + >>> # create and fit a model + >>> model = LinearRegressionModel(lags=2, lags_future_covariates=[-1, 0]) + >>> model.fit(series, future_covariates=fc) + >>> + >>> # create an explainer; requires background series if the model was trained on multiple series + >>> explainer = ShapExplainer(model) + >>> # explain the background series (or foreground if passed to `explain()`) + >>> result = explainer.explain() + >>> result.get_explanation(horizon=1) + Y_target_lag-2 Y_target_lag-1 month_futcov_lag-1 month_futcov_lag0 + 1956-07-01 -56.332566 -106.927156 -24.253184 33.064478 + 1956-10-01 -88.545937 -99.569541 13.642416 84.727725 + 1957-01-01 -82.194005 -57.000488 51.538016 -70.262016 + 1957-04-01 -45.443539 -81.175506 -62.148784 -18.598769 + 1957-07-01 -66.314174 -99.043998 -24.253184 33.064478 + ... ... ... ... ... + 2007-10-01 -11.415330 -11.803715 13.642416 84.727725 + 2008-01-01 -6.424526 29.714250 51.538016 -70.262016 + 2008-04-01 29.418521 1.860425 -62.148784 -18.598769 + 2008-07-01 5.371920 -13.905891 -24.253184 33.064478 + 2008-10-01 -8.239364 -3.395013 13.642416 84.727725 + + shape: (210, 4, 1), freq: QS-OCT, size: 6.56 KB + + The explanation has length 210, containing the feature SHAP values for all possible forecast start points + over the background series. """ def __init__( @@ -194,16 +223,20 @@ def __init__( ): self.explained_forecasts = explained_forecasts if isinstance(self.explained_forecasts, list): - raise_if_not( - isinstance(self.explained_forecasts[0], dict), - "The explained_forecasts list must consist of dicts.", - logger, - ) - raise_if_not( - all(isinstance(key, int) for key in self.explained_forecasts[0].keys()), - "The explained_forecasts dict list must have all integer keys.", - logger, - ) + if not isinstance(self.explained_forecasts[0], dict): + raise_log( + ValueError("The `explained_forecasts` list must consist of dicts."), + logger, + ) + if not all( + isinstance(key, int) for key in self.explained_forecasts[0].keys() + ): + raise_log( + ValueError( + "The `explained_forecasts` dict list must have all integer keys." + ), + logger, + ) self.available_horizons = list(self.explained_forecasts[0].keys()) h_0 = self.available_horizons[0] self.available_components = list(self.explained_forecasts[0][h_0].keys()) @@ -215,14 +248,14 @@ def __init__( else: raise_log( ValueError( - "The explained_forecasts dictionary must have all integer keys." + "The `explained_forecasts` dictionary must have all integer keys." ), logger, ) else: raise_log( ValueError( - "The explained_forecasts must be a dictionary or a list of dictionaries." + "The `explained_forecasts` must be a dictionary or a list of dictionaries." ), logger, ) @@ -231,7 +264,7 @@ def get_explanation( self, horizon: int, component: str | None = None ) -> TimeSeries | list[TimeSeries]: """ - Returns one or several `TimeSeries` representing the explanations + Returns one or several ``TimeSeries`` representing the explanations for a given horizon and component. Parameters @@ -239,8 +272,8 @@ def get_explanation( horizon The horizon for which to return the explanation. component - The component for which to return the explanation. Does not - need to be specified for univariate series. + Optionally, the target series component for which to return the explanation. Must be supplied for + multivariate forecasting models. """ return self._query_explainability_result( self.explained_forecasts, horizon, component @@ -263,8 +296,8 @@ def _query_explainability_result( horizon The horizon for which to return the content of the attribute. component - The component for which to return the content of the attribute. Does not - need to be specified for univariate series. + Optionally, the target series component for which to return the explanation. Must be supplied for + multivariate forecasting models. """ component = self._validate_input_for_querying_explainability_result( horizon, component @@ -293,56 +326,81 @@ def _validate_input_for_querying_explainability_result( horizon The horizon for which to return the explanation. component - The component for which to return the explanation. Does not - need to be specified for univariate series. + Optionally, the target series component for which to return the explanation. Must be supplied for + multivariate forecasting models. """ # validate component argument - raise_if( - component is None and len(self.available_components) > 1, - "The component parameter is required when the model has more than one component.", - logger, - ) + if component is None and len(self.available_components) > 1: + raise_log( + ValueError( + "The component parameter is required when the model has more than one component." + ), + logger, + ) - if component is None: - component = self.available_components[0] + component_out = self.available_components[0] if component is None else component - raise_if_not( - component in self.available_components, - f"Component {component} is not available. Available components are: {self.available_components}", - logger, - ) + if component_out not in self.available_components: + raise_log( + ValueError( + f'Component "{component_out}" is not available. ' + f"Available components are: {self.available_components}" + ), + logger, + ) - raise_if_not( - horizon in self.available_horizons, - f"Horizon {horizon} is not available. Available horizons are: {self.available_horizons}", - logger, - ) - return component + if horizon not in self.available_horizons: + raise_log( + ValueError( + f"Horizon {horizon} is not available. Available horizons are: {self.available_horizons}" + ), + logger, + ) + return component_out class ShapExplainabilityResult(HorizonBasedExplainabilityResult): """ Stores the explainability results of a :class:`ShapExplainer ` - with convenient access to the results. It extends the :class:`HorizonBasedExplainabilityResult - ` and carries additional information specific to the Shap explainers. - In particular, in addition to the `explained_forecasts` (which in the case of the `ShapExplainer` are the - shap values), it also provides access to the corresponding `feature_values` and the underlying `shap.Explanation` - object. - - - :func:`get_explanation() `: explained forecast for a given horizon - (and target component) - - :func:`get_feature_values() `: feature values for a given horizon - (and target component). - - :func:`get_shap_explanation_object() `: `shap.Explanation` - object for a given horizon (and target component). + with convenient access to the results. + + It extends the :class:`HorizonBasedExplainabilityResult + ` and carries additional information specific to the SHAP explainers. + + - :func:`get_explanation() `: SHAP values for a given horizon and component in + multivariate ``TimeSeries`` format. + - :func:`get_feature_values() `: input feature values for a given horizon and component in + multivariate ``TimeSeries`` format. + - :func:`get_shap_explanation_object() `: ``shap.Explanation`` object for a given + horizon and component. Examples -------- - >>> explainer = ShapExplainer(model) # requires `background` if model was trained on multiple series - >>> explain_results = explainer.explain() - >>> exlained_fc = explain_results.get_explanation(horizon=1) - >>> feature_values = explain_results.get_feature_values(horizon=1) - >>> shap_objects = explain_results.get_shap_explanation_objects(horizon=1) + >>> from darts.datasets import AusBeerDataset + >>> from darts.explainability import ShapExplainer + >>> from darts.models import LinearRegressionModel + >>> from darts.utils.timeseries_generation import datetime_attribute_timeseries as dta + >>> + >>> # load a target series and create future covariates holding the calendar month values + >>> series = AusBeerDataset().load() + >>> fc = dta(series, attribute="month", add_length=12) + >>> + >>> # create and fit a model + >>> model = LinearRegressionModel(lags=2, lags_future_covariates=[-1, 0]) + >>> model.fit(series, future_covariates=fc) + >>> + >>> # create an explainer; requires background series if the model was trained on multiple series + >>> explainer = ShapExplainer(model) + >>> # explain the background series (or foreground if passed to `explain()`) + >>> result = explainer.explain() + >>> + >>> # get explanations for a specific horizon (and optional component for multivariate models) + >>> # the feature SHAP values for all possible forecast start points + >>> explanation = result.get_explanation(horizon=1) + >>> # the feature values used as model inputs for all possible forecast start points + >>> feature_values = result.get_feature_values(horizon=1) + >>> # the raw shap objects for further processing + >>> shap_object = result.get_shap_explanation_object(horizon=1) """ def __init__( @@ -362,7 +420,7 @@ def get_feature_values( self, horizon: int, component: str | None = None ) -> TimeSeries | list[TimeSeries]: """ - Returns one or several `TimeSeries` representing the feature values + Returns one or several ``TimeSeries`` representing the feature values for a given horizon and component. Parameters @@ -370,8 +428,8 @@ def get_feature_values( horizon The horizon for which to return the feature values. component - The component for which to return the feature values. Does not - need to be specified for univariate series. + Optionally, the target series component for which to return the feature values. Must be supplied for + multivariate forecasting models. """ return self._query_explainability_result( self.feature_values, horizon, component @@ -381,21 +439,123 @@ def get_shap_explanation_object( self, horizon: int, component: str | None = None ) -> shap.Explanation | list[shap.Explanation]: """ - Returns the underlying `shap.Explanation` object for a given horizon and component. + Returns the underlying ``shap.Explanation`` object for a given horizon and component. Parameters ---------- horizon - The horizon for which to return the `shap.Explanation` object. + The horizon for which to return the ``shap.Explanation`` object. component - The component for which to return the `shap.Explanation` object. Does not - need to be specified for univariate series. + Optionally, the target series component for which to return the ``shap.Explanation object``. Must be + supplied for multivariate forecasting models. """ return self._query_explainability_result( self.shap_explanation_object, horizon, component ) +class ShapSingleExplainabilityResult(ComponentBasedExplainabilityResult): + """ + Stores the explainability results of a :class:`ShapExplainer ` + for a single model forecast with convenient access to the results. + + It extends the :class:`ComponentBasedExplainabilityResult ` and + carries additional information specific to the SHAP explainers. + + - :func:`get_explanation() `: SHAP values for a given component in multivariate ``TimeSeries`` + format. + - :func:`get_feature_values() `: input feature values for a given component in + single-timestamp multivariate ``TimeSeries`` format. + - :func:`get_shap_explanation_object() `: ``shap.Explanation`` object for a given + component. + + Examples + -------- + >>> from darts.datasets import AusBeerDataset + >>> from darts.explainability import ShapExplainer + >>> from darts.models import LinearRegressionModel + >>> from darts.utils.timeseries_generation import datetime_attribute_timeseries as dta + >>> + >>> # load a target series and create future covariates holding the calendar month values + >>> series = AusBeerDataset().load() + >>> fc = dta(series, attribute="month", add_length=12) + >>> + >>> # create and fit a model + >>> model = LinearRegressionModel(lags=2, lags_future_covariates=[-1, 0]) + >>> model.fit(series, future_covariates=fc) + >>> + >>> # create an explainer; requires background series if the model was trained on multiple series + >>> explainer = ShapExplainer(model) + >>> # explain the background forecast (or foreground forecast if passed to `explain_single()`) + >>> result = explainer.explain_single() + >>> # get explanations for that forecast (and optional component for multivariate models) + >>> # the feature SHAP values for that forecast + >>> explanation = result.get_explanation() + >>> # the feature values used as model inputs for that forecast + >>> feature_values = result.get_feature_values() + >>> # the raw shap objects for further processing + >>> shap_object = result.get_shap_explanation_object() + """ + + def __init__( + self, + explained_components: dict[str, TimeSeries], + feature_values: dict[str, TimeSeries], + shap_explanation_object: dict[str, shap.Explanation], + ): + super().__init__(explained_components) + self.feature_values = feature_values + self.shap_explanation_object = shap_explanation_object + + def get_explanation(self, component: str | None = None) -> TimeSeries: + """ + Returns the ``TimeSeries`` representing the explanation for a given component. + + The components of the ``TimeSeries`` correspond to the input features used by the model to produce the + forecasts. The time index contains the forecasted timestamps in the future. Therefore, the values of + ``TimeSeries`` are the SHAP values of the features for the forecast at each forecasted timestamp. + + Parameters + ---------- + component + Optionally, the target series component for which to return the explanation. Must be supplied for + multivariate forecasting models. + """ + return self._query_explainability_result(self.explained_components, component) + + def get_feature_values(self, component: str | None = None) -> TimeSeries: + """ + Returns the ``TimeSeries`` representing the feature values for a given component. + + The components of the ``TimeSeries`` correspond to the input features used by the model to produce the + forecasts. The time index contains only one timestamp, which is the first forecasted timestamp in the future. + The values of the ``TimeSeries`` are the feature values used by the model to produce the forecast starting + at that timestamp. + + Parameters + ---------- + component + The component for which to return the feature values. Must be supplied for multivariate forecasting models. + """ + return self._query_explainability_result(self.feature_values, component) + + def get_shap_explanation_object( + self, component: str | None = None + ) -> shap.Explanation: + """ + Returns the underlying ``shap.Explanation`` object for a given component. + + Parameters + ---------- + component + The component for which to return the ``shap.Explanation`` object. Must be supplied for multivariate + forecasting models. + """ + return self._query_explainability_result( + self.shap_explanation_object, component + ) + + class TFTExplainabilityResult(ComponentBasedExplainabilityResult): """ Stores the explainability results of a :class:`TFTExplainer ` @@ -414,13 +574,34 @@ class TFTExplainabilityResult(ComponentBasedExplainabilityResult): Examples -------- - >>> explainer = TFTExplainer(model) # requires `background` if model was trained on multiple series - >>> explain_results = explainer.explain() - >>> attention = explain_results.get_attention() - >>> importances = explain_results.get_feature_importances() - >>> encoder_importance = explain_results.get_encoder_importance() - >>> decoder_importance = explain_results.get_decoder_importance() - >>> static_covariates_importance = explain_results.get_static_covariates_importance() + >>> from darts.datasets import AusBeerDataset + >>> from darts.explainability import TFTExplainer + >>> from darts.models import TFTModel + >>> from darts.utils.timeseries_generation import datetime_attribute_timeseries as dta + >>> + >>> # load a target series and create future covariates holding the calendar month values + >>> series = AusBeerDataset().load().astype("float32") + >>> fc = dta(series, attribute="month", add_length=12, dtype=series.dtype) + >>> + >>> # create and fit a model + >>> model = TFTModel( + >>> input_chunk_length=12, + >>> output_chunk_length=12, + >>> use_reversible_instance_norm=True + >>> ) + >>> model.fit(series, future_covariates=fc) + >>> + >>> # create an explainer + >>> explainer = TFTExplainer(model) + >>> # explain a single forecast: + >>> # - by default, if foreground is not provided, it is the forecast of the background + >>> # - otherwise, it is the forecast of the foreground + >>> result = explainer.explain() + >>> attention = result.get_attention() + >>> feature_importances = result.get_feature_importances() + >>> encoder_importance = result.get_encoder_importance() + >>> decoder_importance = result.get_decoder_importance() + >>> static_cov_importance = result.get_static_covariates_importance() """ def __init__( diff --git a/darts/explainability/shap/__init__.py b/darts/explainability/shap/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/darts/explainability/shap/base_explainer.py b/darts/explainability/shap/base_explainer.py new file mode 100644 index 0000000000..32ff71d4d4 --- /dev/null +++ b/darts/explainability/shap/base_explainer.py @@ -0,0 +1,397 @@ +from abc import ABC, abstractmethod +from collections.abc import Sequence +from enum import Enum +from typing import Any + +import numpy as np +import pandas as pd +import shap + +from darts import TimeSeries +from darts.logging import get_logger, raise_log +from darts.models.forecasting.forecasting_model import ForecastingModel +from darts.typing import TimeSeriesLike + +logger = get_logger(__name__) + +MIN_BACKGROUND_SAMPLE = 10 +MAX_BACKGROUND_SAMPLE = 1000 + + +class SHAPMethod(Enum): + TREE = 0 + DEEP = 2 + KERNEL = 3 + SAMPLING = 4 + PARTITION = 5 + LINEAR = 6 + PERMUTATION = 7 + ADDITIVE = 8 + + +class DartsShapExplanation(shap.Explanation): + def __init__(self, *args, time_index: pd.Index, **kwargs): + super().__init__(*args, **kwargs) + self.time_index = time_index + + +class BaseShapExplainer(ABC): + def __init__( + self, + model: ForecastingModel, + n: int, + target_components: Sequence[str], + past_covariates_components: Sequence[str] | None, + future_covariates_components: Sequence[str] | None, + static_covariates_components: Sequence[str] | None, + background_series: Sequence[TimeSeries], + background_past_covariates: Sequence[TimeSeries] | None, + background_future_covariates: Sequence[TimeSeries] | None, + background_num_samples: int | None, + shap_method: str | None, + batch_size: int | None = None, + **kwargs, + ): + """ + Helper Class to wrap the different cases encountered with SHAP different explainers, multivariates, + horizon etc. + Aim to provide SHAP values for any type of SKLearnModel. Manage the MultioutputRegressor cases. + For darts SKLearnModel only. + """ + self._validate_model(model) + + if isinstance(shap_method, str): + shap_method_upper = shap_method.upper() + if shap_method_upper in {sm.name for sm in self._supported_shap_methods}: + self.shap_method = SHAPMethod[shap_method_upper] + else: + raise_log( + ValueError( + f"Invalid `shap_method='{shap_method}'`. Expected one of the following:" + f" {[e.name.lower() for e in self._supported_shap_methods]}." + ) + ) + else: + self.shap_method = self._get_default_shap_method(model) + + if ( + background_num_samples is not None + and background_num_samples > MAX_BACKGROUND_SAMPLE + ): + raise_log( + ValueError( + f"`background_num_samples` must be less than or equal to " + f"MAX_BACKGROUND_SAMPLE={MAX_BACKGROUND_SAMPLE}. Got {background_num_samples}." + ), + logger, + ) + + self.model = model + + likelihood = model.likelihood + if likelihood is not None: + target_components_likelihood = likelihood.component_names( + components=target_components + ) + logger.warning( + f"The explained model is probabilistic and the SHAP explanations will be computed for the likelihood " + f"parameters of the target components, which includes the following components: " + f"{target_components_likelihood}. Adjust the `target_components` argument accordingly." + ) + else: + target_components_likelihood = target_components + self.target_components_likelihood = target_components_likelihood + self.target_components = target_components + self.past_covariates_components = past_covariates_components + self.future_covariates_components = future_covariates_components + self.static_covariates_components = static_covariates_components + + self.n_targets = len(target_components) + self.n_targets_likelihood = len(target_components_likelihood) + self.n_past_covs = ( + len(past_covariates_components) + if past_covariates_components is not None + else 0 + ) + self.n_future_covs = ( + len(future_covariates_components) + if future_covariates_components is not None + else 0 + ) + self.n_static_covs = ( + len(static_covariates_components) + if static_covariates_components is not None + else 0 + ) + self.n_variables = self.n_targets + self.n_past_covs + self.n_future_covs + + self.n = n + self.output_chunk_length = model.output_chunk_length or 1 + self.output_chunk_shift = model.output_chunk_shift + self.batch_size: int = batch_size or getattr(model, "batch_size", 0) + self.n_output_features = self.output_chunk_length * self.n_targets_likelihood + self.single_output = self.n == 1 and self.n_targets_likelihood == 1 + + self.background_series = background_series + self.background_past_covariates = background_past_covariates + self.background_future_covariates = background_future_covariates + self.feature_names = self._build_feature_names() + + self.background_arr, _ = self.create_shap_array( + series=self.background_series, + past_covariates=self.background_past_covariates, + future_covariates=self.background_future_covariates, + n_samples=background_num_samples, + input_type="background", + ) + + self.explainer = self._build_explainer( + model=self.model, + background_arr=self.background_arr, + shap_method=self.shap_method, + **kwargs, + ) + + def shap_explanations( + self, + foreground_arr: np.ndarray, + foreground_times: pd.Index, + horizons: Sequence[int], + target_components: Sequence[str], + **kwargs, + ) -> dict[int, dict[str, DartsShapExplanation]]: + """ + Computes SHAP explanations for the given foreground data, horizons, and target components. + It returns a nested dictionary of SHAP Explanation objects for each horizon and target component: + + - first dimension: each step in the forecast horizon. + - second dimension: each component of the target time series. + + Parameters + ---------- + foreground_arr + A numpy array of shape `(num_samples, num_features)` containing the input features for SHAP explanations. + horizons + A sequence of integers representing which points/steps in the future to explain, starting from the first + prediction step at 1. Each horizon must be no greater than ``output_chunk_length`` of the explained + forecasting model. + target_components + A sequence of strings with the target components to explain. Each component must be among the target + components of the explained forecasting model. + **kwargs + Additional keyword arguments to be passed to the SHAP explainer when calling it for explanations. + This can include parameters for sampling or approximation methods used by some SHAP explainers. + + Returns + ------- + dict[int, dict[str, DartsShapExplanation]] + A nested dictionary ``{horizon : {target_component : DartsShapExplanation}}`` containing the SHAP + Explanation objects for each horizon and target component, where the SHAP values are extracted and + reshaped for easier accessibility. + """ + # create a nested dictionary {horizon : {target_component : shap.Explanation}} for better accessibility of + # the explanations; note: this is only invoked by ``SKLearnModel`` + explanations = {} + if isinstance(self.explainer, dict): + for h in horizons: + tmp_n = {} + for t_idx, t in enumerate(self.target_components_likelihood): + if t not in target_components: + continue + explanation = self.explainer[h - 1][t_idx](foreground_arr, **kwargs) + explanation = DartsShapExplanation( + values=explanation.values, + data=explanation.data, + base_values=explanation.base_values.ravel(), + feature_names=self.feature_names, + time_index=foreground_times, + ) + tmp_n[t] = explanation + explanations[h] = tmp_n + return explanations + + # the native multioutput forces us to recompute all horizons and targets; can be either + # ``TorchForecastingModel`` or ``SKLearnModel`` with multioutput + explanation: shap.Explanation = self.explainer(foreground_arr, **kwargs) + + # bring arrays into expected shapes: + # `values`: (E, F, C * OCL) + # `base_values`: (E, C * OCL) + # E: number of forecast examples + # F: number of model input features + # C: number of target components (including likelihood parameters) + # OCL: output chunk length + values = explanation.values + base_values = explanation.base_values + + if self.single_output: + if values.shape == foreground_arr.shape: + values = values[:, :, np.newaxis] + if base_values.shape == foreground_arr.shape[:1]: + base_values = base_values[:, np.newaxis] + if base_values.shape == (self.n_output_features,): + # some SHAP explainers (e.g. shap.SamplingExplainer) return 1D base values + base_values = np.repeat( + base_values[np.newaxis, :], repeats=values.shape[0], axis=0 + ) + + assert values.shape == foreground_arr.shape + (self.n_output_features,) + assert base_values.shape == foreground_arr.shape[:1] + (self.n_output_features,) + + for h in horizons: + tmp_n = {} + for t_idx, t in enumerate(self.target_components_likelihood): + if t not in target_components: + continue + + tmp_t = DartsShapExplanation( + values=values[:, :, self.n_targets_likelihood * (h - 1) + t_idx], + data=explanation.data, + base_values=base_values[ + :, self.n_targets_likelihood * (h - 1) + t_idx + ].ravel(), + feature_names=self.feature_names, + time_index=foreground_times, + ) + tmp_n[t] = tmp_t + explanations[h] = tmp_n + + return explanations + + def shap_explanations_single( + self, + foreground_arr: np.ndarray, + foreground_times: pd.Index, + target_components: Sequence[str], + **kwargs, + ) -> dict[str, DartsShapExplanation]: + """ + Similar to :func:`shap_explanations()`, but computes SHAP explanations for only one forecasted timestamp, which + corresponds to the last forecastable timestamp in the foreground series. The output is a dictionary of SHAP + Explanation objects for each target component, where the SHAP values are extracted from the raw Explanation + object returned by the SHAP explainer and reshaped into the expected format for easier accessibility. + + Parameters + ---------- + foreground_arr + A numpy array of shape `(1, num_features)` containing the input features for SHAP explanations for the + single forecasted timestamp. Must have only one sample corresponding to the single forecasted timestamp. + target_components + A sequence of strings with the target components to explain. Each component must be among the target + components of the explained forecasting model. + **kwargs + Additional keyword arguments to be passed to the SHAP explainer when calling it for explanations. + This can include parameters for sampling or approximation methods used by some SHAP explainers. + + Returns + ------- + dict[str, DartsShapExplanation] + A dictionary ``{target_component : DartsShapExplanation}`` containing the SHAP Explanation objects for each + target component, where the SHAP values are extracted and reshaped for easier accessibility. + """ + horizons = list(range(1, self.n + 1)) + explanations = self.shap_explanations( + foreground_arr, + foreground_times, + horizons, + target_components, + **kwargs, + ) + + result = {} + for t in target_components: + if t not in explanations[horizons[0]]: + continue + horizon_expls = [explanations[h][t] for h in horizons] + result[t] = DartsShapExplanation( + values=np.concatenate( + [np.atleast_2d(e.values) for e in horizon_expls], axis=0 + ), + data=np.concatenate( + [np.atleast_2d(e.data) for e in horizon_expls], axis=0 + ), + base_values=np.concatenate([ + np.atleast_1d(e.base_values) for e in horizon_expls + ]), + feature_names=horizon_expls[0].feature_names, + time_index=horizon_expls[0].time_index, + ) + return result + + @abstractmethod + def _build_feature_names(self) -> list[str]: + """ + Builds the feature names for the SHAP explanations based on the input features used by the + forecasting model. See above for the naming convention. + """ + + @staticmethod + @abstractmethod + def _build_explainer( + model: ForecastingModel, + background_arr: np.ndarray, + shap_method: SHAPMethod, + **kwargs, + ) -> Any: + """ + Builds the SHAP explainer based on the specified SHAP method. + + Parameters + ---------- + func + The function wrapper that takes a numpy array of input features and outputs model predictions, to be passed + to the SHAP explainer. + background_arr + The background dataset in the form of a numpy array, to be passed to the SHAP explainer. + shap_method + The SHAP method to use for explanations. Must be one of the methods available in the SHAP library, + specified in the enum ``SHAPMethod``. + **kwargs + Additional keyword arguments to be passed to the SHAP explainer constructor. + """ + + @abstractmethod + def create_shap_array( + self, + series: TimeSeriesLike, + past_covariates: TimeSeriesLike | None, + future_covariates: TimeSeriesLike | None, + n_samples: int | None = None, + input_type: str = "background", + ) -> tuple[np.ndarray, pd.Index]: + """ + Creates the SHAP array for the given input series and covariates, by following the logic of the model's + prediction / inference dataset and prediction step. It returns the SHAP array, the schemas of the + samples, and the prediction times corresponding to each sample in the SHAP array. + + Parameters + ---------- + series + A sequence of target series to be explained. Can be a single TimeSeries or a sequence of TimeSeries. + past_covariates + Optionally, a sequence of past covariate series if required by the forecasting model. Can be a single + TimeSeries or a sequence of TimeSeries. Must be provided if the model uses past covariates. + future_covariates + Optionally, a sequence of future covariate series if required by the forecasting model. Can be a single + TimeSeries or a sequence of TimeSeries. Must be provided if the model uses future covariates. + n_samples + Optionally, an integer for sampling the dataset for the sake of performance. If ``train=True``, + the samples will be randomly drawn from the dataset. If ``train=False``, the last ``n_samples`` samples + will be taken from the dataset. Default: ``None``, which means that all samples in the dataset will be used. + input_type + A string indicating whether the SHAP array is being created for the background or foreground data. This + affects how the dataset is sampled and how the bounds are created. Default: ``background``. + """ + + @property + @abstractmethod + def _supported_shap_methods(self) -> set[SHAPMethod]: + """Specifies the supported SHAP methods.""" + + @abstractmethod + def _get_default_shap_method(self, model) -> SHAPMethod: + """Return the default SHAP method.""" + + @abstractmethod + def _validate_model(self, model: ForecastingModel) -> None: + """Validates the model.""" diff --git a/darts/explainability/shap/sklearn_explainer.py b/darts/explainability/shap/sklearn_explainer.py new file mode 100644 index 0000000000..20ecadbb85 --- /dev/null +++ b/darts/explainability/shap/sklearn_explainer.py @@ -0,0 +1,223 @@ +import numpy as np +import pandas as pd +import shap +from sklearn.multioutput import MultiOutputRegressor + +from darts.explainability.shap.base_explainer import BaseShapExplainer, SHAPMethod +from darts.logging import get_logger, raise_log +from darts.models.forecasting.sklearn_model import SKLearnModel +from darts.typing import TimeSeriesLike +from darts.utils.data.tabularization import create_lagged_prediction_data +from darts.utils.multioutput import MultiOutputMixin + +logger = get_logger(__name__) + +MIN_BACKGROUND_SAMPLE = 10 +MAX_BACKGROUND_SAMPLE = 1000 + + +class SKLearnShapExplainer(BaseShapExplainer): + model: SKLearnModel + default_sklearn_shap_explainers: dict[str, SHAPMethod] = { + # Gradient boosting models + "LGBMRegressor": SHAPMethod.TREE, + "CatBoostRegressor": SHAPMethod.TREE, + "XGBRegressor": SHAPMethod.TREE, + "GradientBoostingRegressor": SHAPMethod.TREE, + "HistGradientBoostingRegressor": SHAPMethod.TREE, + # Tree models + "DecisionTreeRegressor": SHAPMethod.TREE, + "ExtraTreeRegressor": SHAPMethod.TREE, + "ExtraTreesRegressor": SHAPMethod.TREE, + "RandomForestRegressor": SHAPMethod.TREE, + # Ensemble model + "AdaBoostRegressor": SHAPMethod.PERMUTATION, + "BaggingRegressor": SHAPMethod.PERMUTATION, + "RidgeCV": SHAPMethod.PERMUTATION, + "Ridge": SHAPMethod.PERMUTATION, + # Linear models + "LinearRegression": SHAPMethod.LINEAR, + "ARDRegression": SHAPMethod.LINEAR, + "MultiTaskElasticNet": SHAPMethod.LINEAR, + "MultiTaskElasticNetCV": SHAPMethod.LINEAR, + "MultiTaskLasso": SHAPMethod.LINEAR, + "MultiTaskLassoCV": SHAPMethod.LINEAR, + "PassiveAggressiveRegressor": SHAPMethod.LINEAR, + "PoissonRegressor": SHAPMethod.LINEAR, + "QuantileRegressor": SHAPMethod.LINEAR, + "RANSACRegressor": SHAPMethod.LINEAR, + "GammaRegressor": SHAPMethod.LINEAR, + "HuberRegressor": SHAPMethod.LINEAR, + "BayesianRidge": SHAPMethod.LINEAR, + "SGDRegressor": SHAPMethod.LINEAR, + "TheilSenRegressor": SHAPMethod.LINEAR, + "TweedieRegressor": SHAPMethod.LINEAR, + # Gaussian process + "GaussianProcessRegressor": SHAPMethod.PERMUTATION, + # neighbors + "KNeighborsRegressor": SHAPMethod.PERMUTATION, + "RadiusNeighborsRegressor": SHAPMethod.PERMUTATION, + # Neural network + "MLPRegressor": SHAPMethod.PERMUTATION, + } + + def _build_explainer( + self, + model: SKLearnModel, + background_arr: np.ndarray, + shap_method: SHAPMethod, + **kwargs, + ) -> shap.Explainer | dict[int, dict[int, shap.Explainer]]: + if not isinstance(self.model.model, MultiOutputRegressor): + return self._build_explainer_sklearn( + model_sklearn=model.model, + background_arr=background_arr, + shap_method=shap_method, + **kwargs, + ) + + explainers = {} + for i in range(self.n): + explainers[i] = {} + for j in range(self.n_targets_likelihood): + explainers[i][j] = self._build_explainer_sklearn( + model_sklearn=model.get_estimator(horizon=i, target_dim=j), + background_arr=background_arr, + shap_method=shap_method, + **kwargs, + ) + return explainers + + def _build_explainer_sklearn( + self, + model_sklearn, + background_arr: np.ndarray, + shap_method: SHAPMethod, + **kwargs, + ) -> shap.Explainer: + # we define properly the explainer given a shap method + if shap_method == SHAPMethod.TREE: + if kwargs.get("feature_perturbation") == "interventional": + explainer = shap.TreeExplainer(model_sklearn, background_arr, **kwargs) + else: + explainer = shap.TreeExplainer(model_sklearn, **kwargs) + elif shap_method == SHAPMethod.PERMUTATION: + explainer = shap.PermutationExplainer( + model_sklearn.predict, background_arr, **kwargs + ) + elif shap_method == SHAPMethod.PARTITION: + explainer = shap.PartitionExplainer( + model_sklearn.predict, background_arr, **kwargs + ) + elif shap_method == SHAPMethod.KERNEL: + explainer = shap.KernelExplainer( + model_sklearn.predict, background_arr, keep_index=True, **kwargs + ) + elif shap_method == SHAPMethod.LINEAR: + explainer = shap.LinearExplainer(model_sklearn, background_arr, **kwargs) + elif shap_method == SHAPMethod.ADDITIVE: + explainer = shap.AdditiveExplainer(model_sklearn, background_arr, **kwargs) + else: + raise_log(ValueError(f"Unknown SHAP method {shap_method}"), logger=logger) + + logger.info("The SHAP method used is of type: " + str(type(explainer))) + return explainer + + def create_shap_array( + self, + series: TimeSeriesLike, + past_covariates: TimeSeriesLike | None, + future_covariates: TimeSeriesLike | None, + n_samples: int | None = None, + input_type: str = "background", + ) -> tuple[np.ndarray, pd.Index]: + lags_list = self.model._get_lags("target") + lags_past_covariates_list = self.model._get_lags("past") + lags_future_covariates_list = self.model._get_lags("future") + + X, indexes = create_lagged_prediction_data( + target_series=series if lags_list else None, + past_covariates=past_covariates if lags_past_covariates_list else None, + future_covariates=( + future_covariates if lags_future_covariates_list else None + ), + lags=lags_list, + lags_past_covariates=lags_past_covariates_list if past_covariates else None, + lags_future_covariates=( + lags_future_covariates_list if future_covariates else None + ), + uses_static_covariates=self.model.uses_static_covariates, + last_static_covariates_shape=self.model._static_covariates_shape, + ) + # Remove sample axis: + X = X[:, :, 0] + + if input_type == "background" and len(X) <= MIN_BACKGROUND_SAMPLE: + raise_log( + ValueError( + "The number of samples in the background dataset is too small to compute SHAP values." + ) + ) + + index_complete = indexes[0] + for index_i in indexes[1:]: + index_complete = index_complete.append(index_i) + + if n_samples: + X = shap.utils.sample(X, n_samples) + + return X, index_complete + + def _build_feature_names(self) -> list[str]: + return self.model.lagged_feature_names + + @property + def _supported_shap_methods(self) -> set[SHAPMethod]: + return { + SHAPMethod.TREE, + SHAPMethod.DEEP, + SHAPMethod.KERNEL, + SHAPMethod.SAMPLING, + SHAPMethod.PARTITION, + SHAPMethod.LINEAR, + SHAPMethod.PERMUTATION, + SHAPMethod.ADDITIVE, + } + + def _get_default_shap_method(self, model: SKLearnModel) -> SHAPMethod: + if isinstance(model.model, MultiOutputMixin): + sklearn_model = model.get_estimator(horizon=0, target_dim=0) + else: + sklearn_model = model.model + + model_name = type(sklearn_model).__name__ + if model_name in self.default_sklearn_shap_explainers: + shap_method = self.default_sklearn_shap_explainers[model_name] + else: + shap_method = SHAPMethod.KERNEL + return shap_method + + def _validate_model(self, model: SKLearnModel) -> None: + if not isinstance(model, SKLearnModel): + raise_log( + ValueError( + f"Invalid `model` type: `{type(model)}`. Only models of type " + f"`SKLearnModel` are supported." + ), + logger, + ) + + if not model.multi_models: + raise_log( + ValueError( + "Invalid `multi_models` value `False`. Currently, " + "ShapExplainer only supports SKLearnModels " + "with `multi_models=True`." + ), + logger, + ) + + if model.supports_probabilistic_prediction: + logger.warning( + "The model is probabilistic, but num_samples=1 will be used for explainability." + ) diff --git a/darts/explainability/shap/torch_explainer.py b/darts/explainability/shap/torch_explainer.py new file mode 100644 index 0000000000..8ff1890cf5 --- /dev/null +++ b/darts/explainability/shap/torch_explainer.py @@ -0,0 +1,351 @@ +from collections.abc import Sequence + +import numpy as np +import pandas as pd +import shap +import torch + +from darts import TimeSeries +from darts.explainability.shap.base_explainer import BaseShapExplainer, SHAPMethod +from darts.logging import get_logger, raise_log +from darts.models.forecasting.pl_forecasting_module import PLForecastingModule +from darts.models.forecasting.rnn_model import CustomRNNModule +from darts.models.forecasting.torch_forecasting_model import TorchForecastingModel +from darts.typing import TimeSeriesLike +from darts.utils.data.torch_datasets.utils import TorchInferenceDatasetOutput +from darts.utils.historical_forecasts.optimized_historical_forecasts_torch import ( + _create_dataset_bounds, +) +from darts.utils.ts_utils import series2seq + +logger = get_logger(__name__) + +MIN_BACKGROUND_SAMPLE = 10 +MAX_BACKGROUND_SAMPLE = 1000 + +INPUT_PAST_INDICES = [0, 1, 3] +INPUT_FUTURE_INDICES = [4] +INPUT_STATIC_INDICES = [5] + + +class TorchShapExplainer(BaseShapExplainer): + model: TorchForecastingModel + + def create_shap_array( + self, + series: TimeSeriesLike, + past_covariates: TimeSeriesLike | None, + future_covariates: TimeSeriesLike | None, + n_samples: int | None = None, + input_type: str = "background", + ) -> tuple[np.ndarray, pd.Index]: + # convert to sequence of TimeSeries if not already + series_: Sequence[TimeSeries] = series2seq(series) + past_covariates_: Sequence[TimeSeries] | None = series2seq(past_covariates) + future_covariates_: Sequence[TimeSeries] | None = series2seq(future_covariates) + + bounds, _ = _create_dataset_bounds( + model=self.model, + series=series_, + past_covariates=past_covariates_, + future_covariates=future_covariates_, + start=None, + forecast_horizon=self.n, + stride=1, + overlap_end=True, + show_warnings=False, + ) + + # create inference dataset + dataset = self.model._build_inference_dataset( + n=self.n, + series=series_, + past_covariates=past_covariates_, + future_covariates=future_covariates_, + stride=1, + bounds=bounds, + ) + + # sample from dataset if required + n_samples = n_samples or len(dataset) + if input_type == "background": + if len(dataset) < MIN_BACKGROUND_SAMPLE: + raise_log( + ValueError( + f"Background series must contain at least {MIN_BACKGROUND_SAMPLE} samples to create a " + f"valid background. Got background dataset length={len(dataset)}." + ), + logger, + ) + if n_samples > MAX_BACKGROUND_SAMPLE: + logger.warning( + f"Background series contains more than MAX_BACKGROUND_SAMPLE={MAX_BACKGROUND_SAMPLE} samples. " + f"Sampling {MAX_BACKGROUND_SAMPLE} samples to create the background for SHAP explanations." + ) + n_samples = MAX_BACKGROUND_SAMPLE + + # follow the logic of `TorchForecastingModel.predict_from_dataset()` + # to collect samples and collate them into a sample tuple + # collect batch of samples from the end of the dataset + batch: list[TorchInferenceDatasetOutput] = [] + if n_samples < len(dataset): + # randomly sample from the dataset if in training mode + indices = np.random.choice(len(dataset), size=n_samples, replace=False) + else: + indices = range(len(dataset)) + + for i in indices: + batch.append(dataset[i]) + + # follow the logic of `PLForecastingModule.predict_step()` + # to convert to 1D tensor + # - past_target + # - past_covariates + # - future_past_covariates + # - historic_future_covariates + # - future_covariates + # - static_covariates + input_past = self._batch_collate_np(batch, INPUT_PAST_INDICES) + input_future = self._batch_collate_np(batch, INPUT_FUTURE_INDICES) + input_static = self._batch_collate_np(batch, INPUT_STATIC_INDICES) + prediction_times = pd.Index([c[-1] for c in batch]) + + shap_array = np.concatenate( + [ + array.reshape(array.shape[0], -1) + for array in [input_past, input_future, input_static] + if array is not None + ], + axis=-1, + ) + + return shap_array, prediction_times + + @staticmethod + def _batch_collate_np(batch: list[tuple], indices: list[int]) -> np.ndarray | None: + """ + Collates a batch of samples from the inference dataset into a numpy array for SHAP explanations, + based on the specified indices for past covariates, future covariates, and static covariates. + It handles the case where some samples in the batch may have None values for certain inputs, + by skipping those samples when collating. + """ + data = [] + for index in indices: + if batch[0][index] is None: + continue + data.append(np.stack([sample[index] for sample in batch])) + + if len(data) == 0: + return None + else: + data = np.concatenate(data, axis=2) + return data + + def _build_feature_names(self) -> list[str]: + feature_names = [] + input_chunk_length = self.model.input_chunk_length + for i in range(input_chunk_length): + lag = input_chunk_length - i + for t in self.target_components: + feature_names.append(f"{t}_target_lag-{lag}") + if self.past_covariates_components is not None: + for c in self.past_covariates_components: + feature_names.append(f"{c}_pastcov_lag-{lag}") + if self.future_covariates_components is not None: + for c in self.future_covariates_components: + feature_names.append(f"{c}_futcov_lag-{lag}") + + for i in range(self.output_chunk_length): + lag = i + self.output_chunk_shift + if self.future_covariates_components is not None: + for c in self.future_covariates_components: + feature_names.append(f"{c}_futcov_lag{lag}") + + if self.model.uses_static_covariates: + static_covs = self.background_series[0].static_covariates + if static_covs is not None: + # static covariate names + names = static_covs.columns.tolist() + # target components that the static covariates reference to + comps = static_covs.index.tolist() + feature_names += [ + f"{name}_statcov_target_{comp}" for name in names for comp in comps + ] + + return feature_names + + def _build_explainer( + self, + model: TorchForecastingModel, + background_arr: np.ndarray, + shap_method: SHAPMethod, + **kwargs, + ) -> shap.Explainer: + """ + Builds the SHAP explainer based on the specified SHAP method. + + Parameters + ---------- + func + The function wrapper that takes a numpy array of input features and outputs model predictions, to be passed + to the SHAP explainer. + background_arr + The background dataset in the form of a numpy array, to be passed to the SHAP explainer. + shap_method + The SHAP method to use for explanations. Must be one of the methods available in the SHAP library, + specified in the enum ``SHAPMethod``. + **kwargs + Additional keyword arguments to be passed to the SHAP explainer constructor. + """ + # we define properly the explainer given a shap method + # Note: DeepExplainer has some compatibility issues with torch models + if shap_method == SHAPMethod.KERNEL: + explainer_cls = shap.KernelExplainer + elif shap_method == SHAPMethod.SAMPLING: + explainer_cls = shap.SamplingExplainer + elif shap_method == SHAPMethod.PARTITION: + explainer_cls = shap.PartitionExplainer + elif shap_method == SHAPMethod.PERMUTATION: + explainer_cls = shap.PermutationExplainer + else: + raise_log(ValueError(f"Unknown SHAP method {shap_method}")) + + return explainer_cls(self._func_wrapper, background_arr, **kwargs) + + @torch.inference_mode() + def _func_wrapper(self, x_np: np.ndarray) -> np.ndarray: + """ + Wrapper function to adapt the SHAP explainer to the torch forecasting model. It takes as input a numpy array + of shape `(num_samples, num_features)` and outputs a numpy array of shape + `(num_samples, output_chunk_length * n_targets_likelihood)`. + + Internally, it does the following steps: + 1. Reshape the input numpy array into the format expected by the torch forecasting model, separating past + covariates, future covariates, and static covariates based on the slices defined + in :func:`_setup_func_wrapper()`. + 2. If the model is an RNN, handle the special case where future covariates are concatenated to + past covariates with a shift in time dimension. + 3. Pass the reshaped inputs to the model in batches and collect the outputs. + 4. Concatenate the outputs and reshape them into the expected output format for SHAP, which is a 2D array where + each column corresponds to a target component at a specific horizon. + + Parameters + ---------- + x_np + A numpy array of shape `(num_samples, num_features)` containing the input features for SHAP explanations. + + Returns + ------- + np.ndarray + A numpy array of shape `(num_samples, output_chunk_length * n_targets_likelihood)` containing the model + predictions for each target component at each horizon, to be used by the SHAP explainer. + """ + pl_module: PLForecastingModule = self.model.model + input_chunk_length = self.model.input_chunk_length + past_slice = slice(0, input_chunk_length * self.n_variables) + future_slice = slice( + past_slice.stop, + past_slice.stop + self.output_chunk_length * self.n_future_covs, + ) + static_slice = slice(future_slice.stop, None) + + x = torch.from_numpy(x_np).float() + num_samples = x.shape[0] + + x_past = x[:, past_slice] + x_past = x_past.reshape(num_samples, input_chunk_length, self.n_variables) + + if self.n_future_covs > 0: + x_future = x[:, future_slice] + x_future = x_future.reshape( + num_samples, self.output_chunk_length, self.n_future_covs + ) + else: + x_future = None + + if self.n_static_covs > 0: + x_static = x[:, static_slice] + x_static = x_static.reshape(num_samples, -1, self.n_static_covs) + else: + x_static = None + + if isinstance(pl_module, CustomRNNModule): + # handle the special case of RNN where future covariates are concatenated to + # past covariates with a shift in time dimension + if x_future is not None: + x_future = torch.cat( + [ + x_past[:, 1:, -self.n_future_covs :], + x_future, # output chunk length is always 1 for RNN + ], + dim=1, + ) + x_past = torch.cat( + [ + x_past[:, :, : self.n_targets], + x_future, + ], + dim=2, + ) + x_future = None + + # set model to eval mode to deactivate dropout layers + pl_module.eval() + + outputs: list[torch.Tensor] = [] + for i in range(0, num_samples, self.batch_size): + s = slice(i, i + self.batch_size) + batch_x_past = x_past[s].to(pl_module.device) + batch_x_future = ( + x_future[s].to(pl_module.device) if x_future is not None else None + ) + batch_x_static = ( + x_static[s].to(pl_module.device) if x_static is not None else None + ) + + batch_output: torch.Tensor = pl_module(( + batch_x_past, + batch_x_future, + batch_x_static, + )) + + if isinstance(pl_module, CustomRNNModule): + # Note: RNN outputs predictions and hidden states + batch_output = batch_output[0] + # RNN also outputs predictions for all time steps, + # but we only need the last one for SHAP explanations + batch_output = batch_output[:, -1:, :, :] + else: + # Note: TCN has a different `first_prediction_index` than 0 + batch_output = batch_output[:, pl_module.first_prediction_index :, :] + + outputs.append(batch_output) + + # `output`: (batch, output_chunk_length, n_targets, likelihood_parameters) + output = torch.cat(outputs, dim=0) + # flatten the output to shape (batch, output_chunk_length * n_targets_likelihood) + output = output.flatten(start_dim=1) + + return output.cpu().numpy() + + @property + def _supported_shap_methods(self) -> set[SHAPMethod]: + return { + SHAPMethod.KERNEL, + SHAPMethod.SAMPLING, + SHAPMethod.PARTITION, + SHAPMethod.PERMUTATION, + } + + def _get_default_shap_method(self, model) -> SHAPMethod: + return SHAPMethod.PERMUTATION + + def _validate_model(self, model: TorchForecastingModel) -> None: + if not isinstance(model, TorchForecastingModel): + raise_log( + ValueError( + f"Invalid `model` type: `{type(model)}`. Only models of type " + f"`TorchForecastingModel` are supported." + ), + logger, + ) diff --git a/darts/explainability/shap_explainer.py b/darts/explainability/shap_explainer.py index f392fff07c..404e390a91 100644 --- a/darts/explainability/shap_explainer.py +++ b/darts/explainability/shap_explainer.py @@ -1,155 +1,158 @@ """ -Shap Explainer for SKLearnModels --------------------------------- +SHAP Explainer for SKLearn and Torch Models +------------------------------------------- -A `shap explainer `__ specifically for time series -forecasting models. +A `SHAP `__ explainer for Darts' ``SKLearnModel`` and ``TorchForecastingModel`` +instances. -This class is (currently) limited to Darts' `SKLearnModel` instances of forecasting models. It uses shap values to -provide "explanations" of each input features. The input features are the different past lags (of the target and/or -past covariates), as well as potential future lags of future covariates used as inputs by the forecasting model to -produce its forecasts. Furthermore, in the case of multivariate series, the features contain each dimension of -each of the (lagged) series. +For detailed examples and tutorials, see: + +* `Explainability of Forecasting Models + `__. + +:class:`ShapExplainer` computes SHAP values, which measure each input feature's contribution to a prediction +relative to a baseline (average prediction). + +Depending on the model and training data, features can include: + +- lags of the target series (input chunk for torch models), +- lags of past covariates (input chunk for torch models), +- lags of future covariates (input and output chunk for torch models), +- static covariates (global or component-specific). + +.. note:: + All input features except static covariates are named according to the convention + ``"{name}_{type_of_cov}_lag{idx}"``, where: + + - ``{name}`` is the component name from the original foreground series (target, past covariates, or future + covariates). + - ``{type_of_cov}`` is the covariates type. It can take 3 different values: + ``"target"``, ``"pastcov"``, ``"futcov"``. + - ``{idx}`` is the lag index, where ``0`` represents the position of the first predicted step. + + Static covariates are named according to the convention: ``"{name}_statcov_target_{comp}"``, where: + + - ``{name}`` is the variable name of the static covariate. + - ``{comp}`` is the component name of the target series if static covariates are component-specific, or + ``"global_components"`` if they are global. + +.. note:: + SHAP uses a feature-independence assumption. Indirect effects between features are not captured. + +:class:`ShapExplainer` provides the following methods for explaining multiple forecasts in batches: + +- :func:`explain() ` computes SHAP values per forecast horizon and target component. +- :func:`summary_plot() ` shows SHAP value distributions by feature. +- :func:`force_plot() ` shows additive SHAP contributions for one target component and + horizon. + +:class:`ShapExplainer` also provides :func:`explain_single() ` for explaining +a single forecast (equivalent to calling ``model.predict(n=output_chunk_length)``). .. note:: - This explainer is subject to the usual features independence assumption used to compute shap values. - This means that it does not capture potential indirect influence that some lags - may have on the target by influencing other lags. - -- :func:`explain() ` generates the explanations for a given foreground series (or - background series, if foreground is not provided). -- :func:`summary_plot() ` displays a shap plot summary for each horizon and each - component dimension of the target series. -- :func:`force_plot_from_ts() ` displays a shap force_plot for one target - and one horizon, for a given target series. It displays shap values of each lag/covariate with an additive force - layout. + All above methods can use optional foreground data to explain forecasts, with background data as reference. + If foreground data is not provided, background data is used for both. """ +from __future__ import annotations + from collections.abc import Sequence -from enum import Enum -from typing import NewType +from typing import TYPE_CHECKING, Any import matplotlib.pyplot as plt -import pandas as pd import shap -from sklearn.multioutput import MultiOutputRegressor from darts import TimeSeries from darts.explainability.explainability import _ForecastingModelExplainer -from darts.explainability.explainability_result import ShapExplainabilityResult -from darts.logging import get_logger, raise_if, raise_log +from darts.explainability.explainability_result import ( + ShapExplainabilityResult, + ShapSingleExplainabilityResult, +) +from darts.logging import get_logger, raise_log from darts.models.forecasting.sklearn_model import SKLearnModel from darts.typing import TimeSeriesLike -from darts.utils.data.tabularization import create_lagged_prediction_data - -logger = get_logger(__name__) - -MIN_BACKGROUND_SAMPLE = 10 - - -class _ShapMethod(Enum): - TREE = 0 - GRADIENT = 1 - DEEP = 2 - KERNEL = 3 - SAMPLING = 4 - PARTITION = 5 - LINEAR = 6 - PERMUTATION = 7 - ADDITIVE = 8 +from darts.utils.utils import generate_index +if TYPE_CHECKING: + from darts.models.forecasting.torch_forecasting_model import TorchForecastingModel -ShapMethod = NewType("ShapMethod", _ShapMethod) +logger = get_logger(__name__) class ShapExplainer(_ForecastingModelExplainer): - model: SKLearnModel - def __init__( self, - model: SKLearnModel, + model: SKLearnModel | TorchForecastingModel, background_series: TimeSeriesLike | None = None, background_past_covariates: TimeSeriesLike | None = None, background_future_covariates: TimeSeriesLike | None = None, background_num_samples: int | None = None, shap_method: str | None = None, + batch_size: int | None = None, + test_stationarity: bool = False, **kwargs, ): - """ShapExplainer + """SHAP Explainer for SKLearn and Torch Models. - **Definitions** + **Definitions**: - - A background series is a `TimeSeries` used to train the shap explainer. - - A foreground series is a `TimeSeries` that can be explained by a shap explainer after it has been fitted. + - A background series is a ``TimeSeries`` used to train the SHAP explainer. + - A foreground series is a ``TimeSeries`` that can be explained by a SHAP explainer after it has been fitted. - Currently, `ShapExplainer` only works with `SKLearnModel` forecasting models. - The number of explained horizons (t+1, t+2, ...) can be at most equal to `output_chunk_length` of `model`. + The number of explained horizons `(t+1, t+2, ...)` cannot be greater than ``output_chunk_length`` of ``model``. Parameters ---------- model - A `SKLearnModel` to be explained. It must be fitted first. + The ``SKLearnModel`` or ``TorchForecastingModel`` to be explained. It must be fitted first. background_series - One or several series to *train* the `ShapExplainer` along with any foreground series. - Consider using a reduced well-chosen background to reduce computation time. - Optional if `model` was fit on a single target series. By default, it is the `series` used at fitting time. - Mandatory if `model` was fit on multiple (list of) target series. + One or several series to *train* the ``ShapExplainer`` as reference for explanations. Consider using a + reduced well-chosen background to reduce computation time. Optional if ``model`` was fit on a single target + series. By default, it is the ``series`` used at fitting time. Mandatory if ``model`` was fit on multiple + (list of) target series. background_past_covariates A past covariates series or list of series that the model needs once fitted. background_future_covariates A future covariates series or list of series that the model needs once fitted. background_num_samples - Optionally, whether to sample a subset of the original background. Randomly picks - `background_num_samples` training samples of the constructed training dataset - (using ``shap.utils.sample()``). - Generally used for faster computation, especially when `shap_method` is + Optionally, whether to sample a subset of the original background. Randomly picks samples of the + constructed training dataset. Generally used for faster computation, especially when ``shap_method`` is ``"kernel"`` or ``"permutation"``. shap_method - Optionally, the shap method to apply. By default, an attempt is made - to select the most appropriate method based on a pre-defined set of known models. - internal mapping. Supported values : ``"permutation", "partition", "tree", "kernel", "sampling", "linear", - "deep", "gradient", "additive"``. + Optionally, the SHAP method to apply. By default, an attempt is made to select the most appropriate method + based on a pre-defined set of known models internal mapping. Supported values for ``SKLearnModel``: + ``["tree", "kernel", "partition", "linear", "permutation", "additive"]``. Supported values + ``TorchForecastingModel``: ``["kernel", "partition", "sampling", "permutation"]``. + batch_size + Optionally, the batch size to use when ``model`` is a ``TorchForecastingModel``. Increasing the batch size + can significantly reduce computation time. + test_stationarity + Whether to perform stationarity checks and raise a warning if not all `background_series` are stationary. **kwargs - Optionally, additional keyword arguments passed to `shap_method`. + Optionally, additional keyword arguments passed to ``shap_method``. + Examples -------- + + For ``SKLearnModel``: >>> from darts.datasets import AirPassengersDataset - >>> from darts.explainability.shap_explainer import ShapExplainer + >>> from darts.explainability import ShapExplainer >>> from darts.models import LinearRegressionModel - >>> series = AirPassengersDataset().load() - >>> model = LinearRegressionModel(lags=12) - >>> model.fit(series[:-36]) - >>> shap_explain = ShapExplainer(model) - >>> results = shap_explain.explain() - >>> shap_explain.summary_plot() - >>> shap_explain.force_plot_from_ts() + >>> series = AirPassengersDataset().load().astype("float32")[:-36] + >>> model = LinearRegressionModel(lags=12, output_chunk_length=1).fit(series) + >>> explainer = ShapExplainer(model) + >>> result = explainer.explain() + >>> explainer.summary_plot() + >>> explainer.force_plot() + + For ``TorchForecastingModel`` (extending the previous example): + >>> from darts.models import TiDEModel + >>> model = TiDEModel(input_chunk_length=12, output_chunk_length=1).fit(series) + >>> explainer = ShapExplainer(model, batch_size=2048) + >>> result = explainer.explain() + >>> explainer.summary_plot() + >>> explainer.force_plot() """ - - # TODO - # - Optional De-trend if the timeseries is not stationary. - # There would be - # 1) a stationarity test and - # 2) a de-trend methodology for the target. It can be for - # example target - moving_average(input_chunk_length). - - if not issubclass(type(model), SKLearnModel): - raise_log( - ValueError( - "Invalid `model` type. Currently, only models of type `SKLearnModel` are supported." - ), - logger, - ) - - if not model.multi_models: - raise_log( - ValueError( - "Invalid `multi_models` value `False`. Currently, " - "ShapExplainer only supports SKLearnModels " - "with `multi_models=True`." - ), - logger, - ) - super().__init__( model=model, background_series=background_series, @@ -158,39 +161,44 @@ def __init__( requires_background=True, requires_covariates_encoding=True, check_component_names=True, - test_stationarity=True, + test_stationarity=test_stationarity, ) - if model.supports_probabilistic_prediction: - logger.warning( - "The model is probabilistic, but num_samples=1 will be used for explainability." + if isinstance(self.model, SKLearnModel): + from darts.explainability.shap.sklearn_explainer import SKLearnShapExplainer + + explainer_cls = SKLearnShapExplainer + else: + # lazily import torch dependencies + from darts.explainability.shap.torch_explainer import TorchShapExplainer + from darts.models.forecasting.torch_forecasting_model import ( + TorchForecastingModel, ) - if shap_method is not None: - shap_method = shap_method.upper() - if shap_method in _ShapMethod.__members__: - self.shap_method = _ShapMethod[shap_method] + if isinstance(self.model, TorchForecastingModel): + explainer_cls = TorchShapExplainer else: raise_log( ValueError( - "Invalid `shap_method`. Please choose one value among the following: ['partition', 'tree', " - "'kernel', 'sampling', 'linear', 'deep', 'gradient', 'additive']." - ) + f"Invalid `model` type: `{type(self.model)}`. Only models of type " + f"`SKLearnModel` or `TorchForecastingModel` are supported." + ), + logger, ) - else: - self.shap_method = None - self.explainers = _RegressionShapExplainers( + self.explainer = explainer_cls( model=self.model, n=self.n, target_components=self.target_components, past_covariates_components=self.past_covariates_components, future_covariates_components=self.future_covariates_components, + static_covariates_components=self.static_covariates_components, background_series=self.background_series, background_past_covariates=self.background_past_covariates, background_future_covariates=self.background_future_covariates, - shap_method=self.shap_method, background_num_samples=background_num_samples, + shap_method=shap_method, + batch_size=batch_size, **kwargs, ) @@ -199,88 +207,118 @@ def explain( foreground_series: TimeSeriesLike | None = None, foreground_past_covariates: TimeSeriesLike | None = None, foreground_future_covariates: TimeSeriesLike | None = None, - horizons: Sequence[int] | None = None, + horizons: int | Sequence[int] | None = None, target_components: Sequence[str] | None = None, + **kwargs, ) -> ShapExplainabilityResult: """ - Explains a foreground time series and returns a :class:`ShapExplainabilityResult - `. - The results can be retrieved with method :func:`get_explanation() - `. - The result is a multivariate `TimeSeries` instance containing the 'explanation' - for the (horizon, target_component) forecast at any timestamp forecastable corresponding to - the foreground `TimeSeries` input. - - The component name convention of this multivariate `TimeSeries` is: - ``"{name}_{type_of_cov}_lag_{idx}"``, where: - - - ``{name}`` is the component name from the original foreground series (target, past, or future). - - ``{type_of_cov}`` is the covariates type. It can take 3 different values: - ``"target"``, ``"past_cov"`` or ``"future_cov"``. - - ``{idx}`` is the lag index. + Explains all possible foreground series forecasts (or background, if foreground is not provided) and returns + a :class:`ShapExplainabilityResult ` of + SHAP values. + + The results can then be retrieved with method :func:`get_explanation() + `, + which returns a multivariate ``TimeSeries`` instance containing the SHAP values for the + ``(horizon, target_component)`` forecasts at all timestamps forecastable in the foreground series. + + The components of the ``TimeSeries`` correspond to the input features used by the model to produce + the forecasts. See above for the naming convention. Parameters ---------- foreground_series - Optionally, one or a sequence of target `TimeSeries` to be explained. Can be multivariate. - If not provided, the background `TimeSeries` will be explained instead. + Optionally, one or a sequence of target ``TimeSeries`` to be explained. Can be multivariate. + Default: ``None``, which means that the background series will be used as foreground. foreground_past_covariates - Optionally, one or a sequence of past covariates `TimeSeries` if required by the forecasting model. + Optionally, one or a sequence of past covariates ``TimeSeries`` if required by the forecasting model. foreground_future_covariates - Optionally, one or a sequence of future covariates `TimeSeries` if required by the forecasting model. + Optionally, one or a sequence of future covariates ``TimeSeries`` if required by the forecasting model. horizons Optionally, an integer or sequence of integers representing the future time steps to be explained. - `1` corresponds to the first timestamp being forecasted. - All values must be `<=output_chunk_length` of the explained forecasting model. + ``1`` corresponds to the first timestamp being forecasted. + All values must be no greater than ``output_chunk_length`` of the explained forecasting model. target_components Optionally, a string or sequence of strings with the target components to explain. + **kwargs + Other keyword arguments to be passed to the SHAP explainer. Returns ------- ShapExplainabilityResult - The forecast explanations + The forecast explanations of the specified horizons and target components. Examples -------- - Say we have a model with 2 target components named ``"T_0"`` and ``"T_1"``, - 3 past covariates with default component names ``"0"``, ``"1"``, and ``"2"``, - and one future covariate with default component name ``"0"``. - Also, ``horizons = [1, 2]``. - The model is a `SKLearnModel`, with ``lags = 3``, ``lags_past_covariates=[-1, -3]``, - ``lags_future_covariates = [0]``. - - We provide `foreground_series`, `foreground_past_covariates`, `foreground_future_covariates` each of length 5. - - >>> explain_results = explainer.explain( - >>> foreground_series=foreground_series, - >>> foreground_past_covariates=foreground_past_covariates, - >>> foreground_future_covariates=foreground_future_covariates, - >>> horizons=[1, 2], - >>> target_names=["T_0", "T_1"]) - >>> output = explain_results.get_explanation(horizon=1, component="T_1") - >>> feature_values = explain_results.get_feature_values(horizon=1, component="T_1") - >>> shap_objects = explain_results.get_shap_explanation_objects(horizon=1, component="T_1") - - Then the method returns a multivariate TimeSeries containing the *explanations* of - the `ShapExplainer`, with the following component names: - - - T_0_target_lag-1 - - T_0_target_lag-2 - - T_0_target_lag-3 - - T_1_target_lag-1 - - T_1_target_lag-2 - - T_1_target_lag-3 - - 0_past_cov_lag-1 - - 0_past_cov_lag-3 - - 1_past_cov_lag-1 - - 1_past_cov_lag-3 - - 2_past_cov_lag-1 - - 2_past_cov_lag-3 - - 0_fut_cov_lag_0 - - This series has length 3, as the model can explain 5-3+1 forecasts - (timestamp indexes 4, 5, and 6) + Say we have a ``SKLearnModel`` instance with: + + - 1 target component named ``"Y"``, + - 1 future covariate named ``"month"``, + - ``lags = 2``, and ``lags_future_covariates = [-1, 0]``. + + Let's explain the background series that the model was trained on: + + >>> from darts.datasets import AusBeerDataset + >>> from darts.explainability import ShapExplainer + >>> from darts.models import LinearRegressionModel + >>> from darts.utils.timeseries_generation import datetime_attribute_timeseries as dta + >>> + >>> # load a target series and create future covariates holding the calendar month values + >>> series = AusBeerDataset().load() + >>> fc = dta(series, attribute="month", add_length=12) + >>> + >>> # create and fit a model + >>> model = LinearRegressionModel(lags=2, lags_future_covariates=[-1, 0]) + >>> model.fit(series, future_covariates=fc) + >>> + >>> # create an explainer; requires background series if the model was trained on multiple series + >>> explainer = ShapExplainer(model) + >>> # explain the background series (or foreground if passed to `explain()`) + >>> result = explainer.explain() + >>> + >>> # get explanations for a specific horizon (and optional `component` for multivariate models) + >>> # the feature SHAP values for all possible forecast start points + >>> result.get_explanation(horizon=1) + Y_target_lag-2 Y_target_lag-1 month_futcov_lag-1 month_futcov_lag0 + 1956-07-01 -56.332566 -106.927156 -24.253184 33.064478 + 1956-10-01 -88.545937 -99.569541 13.642416 84.727725 + 1957-01-01 -82.194005 -57.000488 51.538016 -70.262016 + 1957-04-01 -45.443539 -81.175506 -62.148784 -18.598769 + 1957-07-01 -66.314174 -99.043998 -24.253184 33.064478 + ... ... ... ... ... + 2007-10-01 -11.415330 -11.803715 13.642416 84.727725 + 2008-01-01 -6.424526 29.714250 51.538016 -70.262016 + 2008-04-01 29.418521 1.860425 -62.148784 -18.598769 + 2008-07-01 5.371920 -13.905891 -24.253184 33.064478 + 2008-10-01 -8.239364 -3.395013 13.642416 84.727725 + + shape: (210, 4, 1), freq: QS-OCT, size: 6.56 KB + + The explanation has length 210, containing the feature SHAP values for all possible forecast start points + over the background series. + + Now, let's get the feature values that were used as model input to forecast the series: + + >>> result.get_feature_values(horizon=1) + Y_target_lag-2 Y_target_lag-1 month_futcov_lag-1 month_futcov_lag0 + 1956-07-01 284.0 213.0 3.0 6.0 + 1956-10-01 213.0 227.0 6.0 9.0 + 1957-01-01 227.0 308.0 9.0 0.0 + 1957-04-01 308.0 262.0 0.0 3.0 + 1957-07-01 262.0 228.0 3.0 6.0 + ... ... ... ... ... + 2007-10-01 383.0 394.0 6.0 9.0 + 2008-01-01 394.0 473.0 9.0 0.0 + 2008-04-01 473.0 420.0 0.0 3.0 + 2008-07-01 420.0 390.0 3.0 6.0 + 2008-10-01 390.0 410.0 6.0 9.0 + + shape: (210, 4, 1), freq: QS-OCT, size: 6.56 KB + + And also, we can get the raw `shap.Explanation` object for further processing: + + >>> shap_object = result.get_shap_explanation_object(horizon=1) """ + input_type = "foreground" if foreground_series is not None else "background" super().explain( foreground_series, foreground_past_covariates, foreground_future_covariates ) @@ -292,6 +330,7 @@ def explain( _, _, _, + _, ) = self._process_foreground( foreground_series, foreground_past_covariates, @@ -315,15 +354,20 @@ def explain( if foreground_future_covariates: foreground_future_cov_ts = foreground_future_covariates[idx] - foreground_X = self.explainers._create_regression_model_shap_X( - foreground_ts, - foreground_past_cov_ts, - foreground_future_cov_ts, - train=False, + foreground_arr, foreground_times = self.explainer.create_shap_array( + series=foreground_ts, + past_covariates=foreground_past_cov_ts, + future_covariates=foreground_future_cov_ts, + n_samples=None, + input_type=input_type, ) - shap_ = self.explainers.shap_explanations( - foreground_X, horizons, target_names + shap_ = self.explainer.shap_explanations( + foreground_arr=foreground_arr, + foreground_times=foreground_times, + horizons=horizons, + target_components=target_names, + **kwargs, ) shap_values_dict = {} @@ -361,122 +405,332 @@ def explain( shap_explanation_object_list = shap_explanation_object_list[0] return ShapExplainabilityResult( - shap_values_list, feature_values_list, shap_explanation_object_list + explained_forecasts=shap_values_list, + feature_values=feature_values_list, + shap_explanation_object=shap_explanation_object_list, + ) + + def explain_single( + self, + foreground_series: TimeSeries | None = None, + foreground_past_covariates: TimeSeries | None = None, + foreground_future_covariates: TimeSeries | None = None, + target_components: Sequence[str] | None = None, + **kwargs, + ) -> ShapSingleExplainabilityResult: + """ + Explains the last forecast of a foreground series (or background, if foreground is not provided) and + returns a :class:`ShapSingleExplainabilityResult + ` of SHAP values. + + The results can then be retrieved with method :func:`get_explanation() + `, + which returns a multivariate ``TimeSeries`` instance containing the SHAP values for ``target_component`` + starting from the last forecastable timestamp. + + The components of the ``TimeSeries`` correspond to the input features used by the model to produce + the forecast. See above for the naming convention. + + .. note:: + The forecast explained by this method is equivalent to the one obtained by calling + ``model.predict(n=output_chunk_length, series=series, ...)`` where ``series`` is either + ``foreground_series`` or ``background_series`` depending on what was used when calling ``explain_single()``. + + Parameters + ---------- + foreground_series + Optionally, one or a sequence of target ``TimeSeries`` to be explained. Can be multivariate. + Default: ``None``, which means that the background series will be used as foreground. + foreground_past_covariates + Optionally, one or a sequence of past covariates ``TimeSeries`` if required by the forecasting model. + foreground_future_covariates + Optionally, one or a sequence of future covariates ``TimeSeries`` if required by the forecasting model. + target_components + Optionally, a string or sequence of strings with the target components to explain. + **kwargs + Other keyword arguments to be passed to the SHAP explainer. + + Returns + ------- + ShapSingleExplainabilityResult + The forecast explanations of the specified target components for the single forecasted timestamp. + + Examples + -------- + Say we have a ``SKLearnModel`` instance with: + + - 1 target component named ``"Y"``, + - 1 future covariate named ``"month"``, + - ``lags = 2``, and ``lags_future_covariates = [-1, 0]``. + + Let's explain the background series that the model was trained on: + + >>> from darts.datasets import AusBeerDataset + >>> from darts.explainability import ShapExplainer + >>> from darts.models import LinearRegressionModel + >>> from darts.utils.timeseries_generation import datetime_attribute_timeseries as dta + >>> + >>> # load a target series and create future covariates holding the calendar month values + >>> series = AusBeerDataset().load() + >>> fc = dta(series, attribute="month", add_length=12) + >>> + >>> # create and fit a model + >>> model = LinearRegressionModel(lags=2, lags_future_covariates=[-1, 0]) + >>> model.fit(series, future_covariates=fc) + >>> + >>> # create an explainer; requires background series if the model was trained on multiple series + >>> explainer = ShapExplainer(model) + >>> # explain the background forecast (or foreground forecast if passed to `explain_single()`) + >>> result = explainer.explain_single() + >>> + >>> # get explanations for that forecast (and optional component for multivariate models) + >>> # the feature SHAP values for that forecast + >>> result.get_explanation() + Y_target_lag-2 Y_target_lag-1 month_futcov_lag-1 month_futcov_lag0 + 2008-10-01 -8.239364 -3.395013 13.642416 84.727725 + + shape: (1, 4, 1), freq: QS-OCT, size: 6.56 KB + + The explanation has length 210, containing the feature SHAP values for all possible forecast start points + over the background series. + + Now, let's get the feature values that were used as model input to forecast the series: + + >>> result.get_feature_values() + Y_target_lag-2 Y_target_lag-1 month_futcov_lag-1 month_futcov_lag0 + 2008-10-01 390.0 410.0 6.0 9.0 + + shape: (1, 4, 1), freq: QS-OCT, size: 6.56 KB + + And also, we can get the raw `shap.Explanation` object for further processing: + + >>> shap_object = result.get_shap_explanation_object() + """ + input_type = "foreground" if foreground_series is not None else "background" + ( + foreground_series_, + foreground_past_covariates_, + foreground_future_covariates_, + _, + _, + _, + _, + _, + ) = self._process_foreground( + foreground_series, + foreground_past_covariates, + foreground_future_covariates, + ) + _, target_names = self._process_horizons_and_targets(None, target_components) + + foreground_arr, foreground_times = self.explainer.create_shap_array( + series=foreground_series_, + past_covariates=foreground_past_covariates_, + future_covariates=foreground_future_covariates_, + n_samples=None, + input_type=input_type, + ) + + # explain only the last forecasted timestamp + foreground_arr = foreground_arr[-1:] + foreground_times = foreground_times[-1:] + freq = foreground_series_[0].freq + + shap_ = self.explainer.shap_explanations_single( + foreground_arr=foreground_arr, + foreground_times=foreground_times, + target_components=target_names, + **kwargs, + ) + + shap_values_dict = {} + feature_values_dict = {} + shap_explanation_object_dict = {} + for t in target_names: + shap_values_dict[t] = TimeSeries( + times=generate_index( + start=shap_[t].time_index[0], + freq=freq, + length=shap_[t].values.shape[0], + ), + values=shap_[t].values, + components=shap_[t].feature_names, + ) + feature_values_dict[t] = TimeSeries( + times=generate_index( + start=shap_[t].time_index[0], + freq=freq, + length=1, + ), + values=shap_[t].data[:1], + components=shap_[t].feature_names, + ) + shap_explanation_object_dict[t] = shap_[t] + + return ShapSingleExplainabilityResult( + explained_components=shap_values_dict, + feature_values=feature_values_dict, + shap_explanation_object=shap_explanation_object_dict, ) def summary_plot( self, + foreground_series: TimeSeriesLike | None = None, + foreground_past_covariates: TimeSeriesLike | None = None, + foreground_future_covariates: TimeSeriesLike | None = None, horizons: int | Sequence[int] | None = None, target_components: str | Sequence[str] | None = None, num_samples: int | None = None, plot_type: str | None = "dot", + plot_kwargs: dict[str, Any] | None = None, **kwargs, ) -> dict[int, dict[str, shap.Explanation]]: """ - Display a shap plot summary for each horizon and each component dimension of the target. - This method reuses the initial background data as foreground (potentially sampled) to give a general importance - plot for each feature. - If no target names and/or no horizons are provided, all summary plots are produced. + Display a SHAP "Summary Plot" for each horizon and each component dimension of the target. + + On each summary plot, SHAP values of each input feature are plotted with dots (``plot_type="dot"``, + each dot corresponds to a forecasted timestamp), a bar (``plot_type="bar"``), or a violin + (``plot_type="violin"``). The input features are sorted by importance, defined as the mean absolute SHAP value. Parameters ---------- + foreground_series + Optionally, one or a sequence of target ``TimeSeries`` to be explained. Can be multivariate. + Default: ``None``, which means that the background series will be used as foreground. + foreground_past_covariates + Optionally, one or a sequence of past covariates ``TimeSeries`` if required by the forecasting model. + foreground_future_covariates + Optionally, one or a sequence of future covariates ``TimeSeries`` if required by the forecasting model. horizons Optionally, an integer or sequence of integers representing which points/steps in the future to explain, - starting from the first prediction step at 1. `horizons` must `<=output_chunk_length` of the forecasting - model. + starting from the first prediction step at 1. Each horizon must be no greater than ``output_chunk_length`` + of the explained forecasting model. Default: ``None``, which means that all horizons will be plotted. target_components Optionally, a string or sequence of strings with the target components to explain. + Default: ``None``, which means that all target components will be plotted. num_samples - Optionally, an integer for sampling the foreground series (based on the background), - for the sake of performance. + Optionally, an integer for sampling the foreground series for the sake of performance. plot_type - Optionally, specify which of the shap library plot type to use. Can be one of ``'dot', 'bar', 'violin'``. + Optionally, specify which of the SHAP library plot type to use. Can be one of ``"dot"``, ``"bar"``, + ``"violin"``. + plot_kwargs + Optionally, a dictionary of keyword arguments to be passed to ``shap.summary_plot()``. + **kwargs + Other keyword arguments to be passed to the SHAP explainer. Returns ------- dict[int, dict[str, shap.Explanation]] - A nested dictionary {horizon : {component : shap.Explanation}} containing the raw Explanations for all - the horizons and components. + A nested dictionary ``{horizon : {component : shap.Explanation}}`` containing the raw Explanation objects + for all the horizons and components. """ - + input_type = "foreground" if foreground_series is not None else "background" + ( + foreground_series_, + foreground_past_covariates_, + foreground_future_covariates_, + _, + _, + _, + _, + _, + ) = self._process_foreground( + foreground_series, + foreground_past_covariates, + foreground_future_covariates, + ) horizons, target_components = self._process_horizons_and_targets( horizons, target_components ) - if num_samples: - foreground_X_sampled = shap.utils.sample( - self.explainers.background_X, num_samples - ) - else: - foreground_X_sampled = self.explainers.background_X + foreground_arr, foreground_times = self.explainer.create_shap_array( + series=foreground_series_, + past_covariates=foreground_past_covariates_, + future_covariates=foreground_future_covariates_, + n_samples=num_samples, + input_type=input_type, + ) - shaps_ = self.explainers.shap_explanations( - foreground_X_sampled, horizons, target_components + shaps_ = self.explainer.shap_explanations( + foreground_arr=foreground_arr, + foreground_times=foreground_times, + horizons=horizons, + target_components=target_components, + **kwargs, ) for t in target_components: for h in horizons: plt.title( - "Target: `{}` - Horizon: {}".format( - t, "t+" + str(h + self.model.output_chunk_shift) - ) + f"Target: `{t}` - Horizon: t+{h + self.model.output_chunk_shift}" ) shap.summary_plot( - shaps_[h][t], - foreground_X_sampled, + shap_values=shaps_[h][t], + features=foreground_arr, plot_type=plot_type, - **kwargs, + **(plot_kwargs or {}), ) return shaps_ - def force_plot_from_ts( + def force_plot( self, foreground_series: TimeSeries | None = None, foreground_past_covariates: TimeSeries | None = None, foreground_future_covariates: TimeSeries | None = None, horizon: int | None = 1, target_component: str | None = None, + plot_kwargs: dict[str, Any] | None = None, **kwargs, ): """ - Display a shap force_plot for one target and one horizon, for a given foreground_series. - It displays shap values of each lag/covariate with an additive force layout. + Display a SHAP "Force Plot" for one target and one horizon. - Once the plot is displayed, select "original sample ordering" - to observe the time series chronologically. + It shows SHAP values of all input features with an additive force layout for each forecastable timestamp + in the foreground series. At each timestamp, SHAP values of all features and the base value would sum up + to the model prediction. + + .. note:: + Once the plot is displayed, select **"original sample ordering"** to observe the forecasted timestamps + chronologically. Parameters ---------- foreground_series - Optionally, the target series to explain. Can be multivariate. If `None`, will use the `background_series`. + Optionally, the target series to explain. Can be multivariate. Default: ``None``, which means that the + background series will be used as foreground. foreground_past_covariates - Optionally, a past covariate series if required by the forecasting model. If `None`, will use the - `background_past_covariates`. + Optionally, a past covariate series if required by the forecasting model. foreground_future_covariates - Optionally, a future covariate series if required by the forecasting model. If `None`, will use the - `background_future_covariates`. + Optionally, a future covariate series if required by the forecasting model. horizon Optionally, an integer for the point/step in the future to explain, starting from the first prediction - step at 1. `horizons` must not be larger than `output_chunk_length`. + step at 1. Must not be larger than ``output_chunk_length`` of the model. target_component Optionally, the target component to plot. If the target series is multivariate, the target component must be specified. + plot_kwargs + Optionally, a dictionary of keyword arguments to be passed to ``shap.force_plot()``. **kwargs - Optionally, additional keyword arguments passed to `shap.force_plot()`. + Other keyword arguments to be passed to the SHAP explainer. """ - - raise_if( - target_component is None and len(self.target_components) > 1, - "The component parameter is required when the model has more than one component.", - ) + input_type = "foreground" if foreground_series is not None else "background" + if target_component is None and len(self.target_components_likelihood) > 1: + raise_log( + ValueError( + f"The `target_component` parameter is required when the model has more than one component. " + f"Please select a component from {self.target_components_likelihood}." + ), + logger, + ) if target_component is None: - target_component = self.target_components[0] + target_component = self.target_components_likelihood[0] ( - foreground_series, - foreground_past_covariates, - foreground_future_covariates, + foreground_series_, + foreground_past_covariates_, + foreground_future_covariates_, + _, _, _, _, @@ -492,303 +746,25 @@ def force_plot_from_ts( ) horizon, target_component = horizons[0], target_components[0] - foreground_X = self.explainers._create_regression_model_shap_X( - foreground_series, foreground_past_covariates, foreground_future_covariates + foreground_arr, foreground_times = self.explainer.create_shap_array( + series=foreground_series_, + past_covariates=foreground_past_covariates_, + future_covariates=foreground_future_covariates_, + n_samples=None, + input_type=input_type, ) - shap_ = self.explainers.shap_explanations( - foreground_X, [horizon], [target_component] + shap_ = self.explainer.shap_explanations( + foreground_arr=foreground_arr, + foreground_times=foreground_times, + horizons=[horizon], + target_components=[target_component], + **kwargs, ) return shap.force_plot( base_value=shap_[horizon][target_component], - features=foreground_X, + features=foreground_arr, out_names=target_component, - **kwargs, - ) - - -class _RegressionShapExplainers: - """ - Helper Class to wrap the different cases encountered with shap different explainers, multivariates, - horizon etc. - Aim to provide shap values for any type of SKLearnModel. Manage the MultioutputRegressor cases. - For darts SKLearnModel only. - """ - - default_sklearn_shap_explainers = { - # Gradient boosting models - "LGBMRegressor": _ShapMethod.TREE, - "CatBoostRegressor": _ShapMethod.TREE, - "XGBRegressor": _ShapMethod.TREE, - "GradientBoostingRegressor": _ShapMethod.TREE, - "HistGradientBoostingRegressor": _ShapMethod.TREE, - # Tree models - "DecisionTreeRegressor": _ShapMethod.TREE, - "ExtraTreeRegressor": _ShapMethod.TREE, - "ExtraTreesRegressor": _ShapMethod.TREE, - "RandomForestRegressor": _ShapMethod.TREE, - # Ensemble model - "AdaBoostRegressor": _ShapMethod.PERMUTATION, - "BaggingRegressor": _ShapMethod.PERMUTATION, - "RidgeCV": _ShapMethod.PERMUTATION, - "Ridge": _ShapMethod.PERMUTATION, - # Linear models - "LinearRegression": _ShapMethod.LINEAR, - "ARDRegression": _ShapMethod.LINEAR, - "MultiTaskElasticNet": _ShapMethod.LINEAR, - "MultiTaskElasticNetCV": _ShapMethod.LINEAR, - "MultiTaskLasso": _ShapMethod.LINEAR, - "MultiTaskLassoCV": _ShapMethod.LINEAR, - "PassiveAggressiveRegressor": _ShapMethod.LINEAR, - "PoissonRegressor": _ShapMethod.LINEAR, - "QuantileRegressor": _ShapMethod.LINEAR, - "RANSACRegressor": _ShapMethod.LINEAR, - "GammaRegressor": _ShapMethod.LINEAR, - "HuberRegressor": _ShapMethod.LINEAR, - "BayesianRidge": _ShapMethod.LINEAR, - "SGDRegressor": _ShapMethod.LINEAR, - "TheilSenRegressor": _ShapMethod.LINEAR, - "TweedieRegressor": _ShapMethod.LINEAR, - # Gaussian process - "GaussianProcessRegressor": _ShapMethod.PERMUTATION, - # neighbors - "KNeighborsRegressor": _ShapMethod.PERMUTATION, - "RadiusNeighborsRegressor": _ShapMethod.PERMUTATION, - # Neural network - "MLPRegressor": _ShapMethod.PERMUTATION, - } - - def __init__( - self, - model: SKLearnModel, - n: int, - target_components: Sequence[str], - past_covariates_components: Sequence[str], - future_covariates_components: Sequence[str], - background_series: Sequence[TimeSeries], - background_past_covariates: Sequence[TimeSeries], - background_future_covariates: Sequence[TimeSeries], - shap_method: _ShapMethod, - background_num_samples: int | None = None, - **kwargs, - ): - self.model = model - self.target_dim = self.model.input_dim["target"] - self.is_multioutputregressor = isinstance( - self.model.model, MultiOutputRegressor - ) - - self.target_components = target_components - self.past_covariates_components = past_covariates_components - self.future_covariates_components = future_covariates_components - - self.n = n - self.shap_method = shap_method - self.background_series = background_series - self.background_past_covariates = background_past_covariates - self.background_future_covariates = background_future_covariates - - self.single_output = False - if self.n == 1 and self.target_dim == 1: - self.single_output = True - - self.background_X = self._create_regression_model_shap_X( - self.background_series, - self.background_past_covariates, - self.background_future_covariates, - background_num_samples, - train=True, - ) - - if self.is_multioutputregressor: - self.explainers = {} - for i in range(self.n): - self.explainers[i] = {} - for j in range(self.target_dim): - self.explainers[i][j] = self._build_explainer_sklearn( - self.model.get_estimator(horizon=i, target_dim=j), - self.background_X, - self.shap_method, - **kwargs, - ) - else: - self.explainers = self._build_explainer_sklearn( - self.model.model, self.background_X, self.shap_method, **kwargs - ) - - def shap_explanations( - self, - foreground_X: pd.DataFrame, - horizons: Sequence[int] | None = None, - target_components: Sequence[str] | None = None, - ) -> dict[int, dict[str, shap.Explanation]]: - """ - Return a dictionary of dictionaries of shap.Explanation instances: - - the first dimension corresponds to the n forecasts ahead we want to explain (Horizon). - - the second dimension corresponds to each component of the target time series. - Parameters - ---------- - foreground_X - the Dataframe of lags features specific of darts SKLearnModel. - horizons - Optionally, a list of integers representing which points/steps in the future we want to explain, - starting from the first prediction step at 1. Currently, only forecasting models are supported which - provide an `output_chunk_length` parameter. `horizons` must not be larger than `output_chunk_length`. - target_components - Optionally, a list of strings with the target components we want to explain. - - """ - - # create a unified dictionary between multiOutputRegressor estimators and - # native multiOutput estimators - shap_explanations = {} - if self.is_multioutputregressor: - for h in horizons: - tmp_n = {} - for t_idx, t in enumerate(self.target_components): - if t not in target_components: - continue - explainer = self.explainers[h - 1][t_idx](foreground_X) - explainer.base_values = explainer.base_values.ravel() - explainer.time_index = foreground_X.index - tmp_n[t] = explainer - shap_explanations[h] = tmp_n - else: - # the native multioutput forces us to recompute all horizons and targets - shap_explanation_tmp = self.explainers(foreground_X) - for h in horizons: - tmp_n = {} - for t_idx, t in enumerate(self.target_components): - if t not in target_components: - continue - if not self.single_output: - tmp_t = shap.Explanation( - shap_explanation_tmp.values[ - :, :, self.target_dim * (h - 1) + t_idx - ] - ) - tmp_t.data = shap_explanation_tmp.data - tmp_t.base_values = shap_explanation_tmp.base_values[ - :, self.target_dim * (h - 1) + t_idx - ].ravel() - else: - tmp_t = shap_explanation_tmp - tmp_t.base_values = shap_explanation_tmp.base_values.ravel() - - tmp_t.feature_names = shap_explanation_tmp.feature_names - tmp_t.time_index = foreground_X.index - tmp_n[t] = tmp_t - shap_explanations[h] = tmp_n - - return shap_explanations - - def _build_explainer_sklearn( - self, - model_sklearn, - background_X: pd.DataFrame, - shap_method: ShapMethod | None = None, - **kwargs, - ): - model_name = type(model_sklearn).__name__ - - # no shap methods - we need to take the default one - if shap_method is None: - if model_name in self.default_sklearn_shap_explainers: - shap_method = self.default_sklearn_shap_explainers[model_name] - else: - shap_method = _ShapMethod.KERNEL - - # we define properly the explainer given a shap method - if shap_method == _ShapMethod.TREE: - if kwargs.get("feature_perturbation") == "interventional": - explainer = shap.TreeExplainer(model_sklearn, background_X, **kwargs) - else: - explainer = shap.TreeExplainer(model_sklearn, **kwargs) - elif shap_method == _ShapMethod.PERMUTATION: - explainer = shap.PermutationExplainer( - model_sklearn.predict, background_X, **kwargs - ) - elif shap_method == _ShapMethod.PARTITION: - explainer = shap.PermutationExplainer( - model_sklearn.predict, background_X, **kwargs - ) - elif shap_method == _ShapMethod.KERNEL: - explainer = shap.KernelExplainer( - model_sklearn.predict, background_X, keep_index=True, **kwargs - ) - elif shap_method == _ShapMethod.LINEAR: - explainer = shap.LinearExplainer(model_sklearn, background_X, **kwargs) - elif shap_method == _ShapMethod.DEEP: - explainer = shap.LinearExplainer(model_sklearn, background_X, **kwargs) - elif shap_method == _ShapMethod.ADDITIVE: - explainer = shap.AdditiveExplainer(model_sklearn, background_X, **kwargs) - else: - raise ValueError( - "shap_method must be one of the following: " - + ", ".join([e.value for e in _ShapMethod]) - ) - - logger.info("The shap method used is of type: " + str(type(explainer))) - - return explainer - - def _create_regression_model_shap_X( - self, - target_series: TimeSeriesLike | None, - past_covariates: TimeSeriesLike | None, - future_covariates: TimeSeriesLike | None, - n_samples: int | None = None, - train: bool = False, - ) -> pd.DataFrame: - """ - Creates the shap format input for regression models. - The output is a pandas DataFrame representing all lags of different covariates, and with adequate - column names in order to map feature / shap values. - It uses create_lagged_data also used in SKLearnModel to build the tabular dataset. - - """ - - lags_list = self.model._get_lags("target") - lags_past_covariates_list = self.model._get_lags("past") - lags_future_covariates_list = self.model._get_lags("future") - - X, indexes = create_lagged_prediction_data( - target_series=target_series if lags_list else None, - past_covariates=past_covariates if lags_past_covariates_list else None, - future_covariates=( - future_covariates if lags_future_covariates_list else None - ), - lags=lags_list, - lags_past_covariates=lags_past_covariates_list if past_covariates else None, - lags_future_covariates=( - lags_future_covariates_list if future_covariates else None - ), - uses_static_covariates=self.model.uses_static_covariates, - last_static_covariates_shape=self.model._static_covariates_shape, - ) - # Remove sample axis: - X = X[:, :, 0] - - if train: - X = pd.DataFrame(X) - if len(X) <= MIN_BACKGROUND_SAMPLE: - raise_log( - ValueError( - "The number of samples in the background dataset is too small to compute shap values." - ) - ) - else: - X = pd.DataFrame(X, index=indexes[0]) - - if n_samples: - X = shap.utils.sample(X, n_samples) - - # rename output columns to the matching lagged features names - X = X.rename( - columns={ - name: self.model.lagged_feature_names[idx] - for idx, name in enumerate(X.columns.to_list()) - } + **(plot_kwargs or {}), ) - return X diff --git a/darts/explainability/tft_explainer.py b/darts/explainability/tft_explainer.py index ef43fa0d79..88effa8dfb 100644 --- a/darts/explainability/tft_explainer.py +++ b/darts/explainability/tft_explainer.py @@ -182,6 +182,7 @@ def explain( _, _, _, + _, ) = self._process_foreground( foreground_series, foreground_past_covariates, diff --git a/darts/explainability/utils.py b/darts/explainability/utils.py index d6eb4c0819..6baa819976 100644 --- a/darts/explainability/utils.py +++ b/darts/explainability/utils.py @@ -16,6 +16,7 @@ def process_input( + n: int, model: ForecastingModel, input_type: str, series: TimeSeriesLike | None = None, @@ -28,7 +29,16 @@ def process_input( requires_input: bool = True, requires_covariates_encoding: bool = False, test_stationarity: bool = False, -): +) -> tuple[ + Sequence[TimeSeries], + Sequence[TimeSeries] | None, + Sequence[TimeSeries] | None, + Sequence[str], + Sequence[str], + Sequence[str] | None, + Sequence[str] | None, + Sequence[str] | None, +]: """Helper function to process and check either of the background or foreground series input to `_ForecastingModelExplainer`. @@ -41,6 +51,8 @@ def process_input( Parameters ---------- + n + The forecast horizon (``output_chunk_length``) that the model was trained to predict. model any `ForecastingModel`. input_type @@ -103,7 +115,7 @@ def process_input( else "no `background_series` was provided at `Explainer` creation" ) raise_log( - ValueError(f"`{input_type}_series` must be provided {error_msg}"), + ValueError(f"`{input_type}_series` must be provided when {error_msg}"), logger, ) series = fallback_series @@ -113,7 +125,8 @@ def process_input( # if `requires_covariates_encoding=False`) else: if model.encoders.encoding_available: - past_covariates, future_covariates = model.generate_fit_encodings( + past_covariates, future_covariates = model.generate_fit_predict_encodings( + n=n, series=series, past_covariates=past_covariates, future_covariates=future_covariates, @@ -148,6 +161,14 @@ def process_input( test_stationarity=test_stationarity, ) + likelihood = model.likelihood + if likelihood is not None: + target_components_likelihood = likelihood.component_names( + components=target_components + ) + else: + target_components_likelihood = target_components + # make sure to remove any encodings from covariates if downstream tasks require covariates without encodings if not requires_covariates_encoding and model.encoders.encoding_available: if past_covariates is not None and model.encoders.past_encoders: @@ -177,6 +198,7 @@ def process_input( past_covariates, future_covariates, target_components, + target_components_likelihood, static_covariates_components, past_covariates_components, future_covariates_components, @@ -300,11 +322,7 @@ def _check_valid_input( ): """Checks that the input is valid""" if test_stationarity and series is not None: - if not _test_stationarity(series): - logger.warning( - "At least one component of the target series is not stationary. " - "Beware of wrong interpretation of the chosen explainability." - ) + _test_stationarity(series) if input_type not in ["background", "foreground"]: raise_log( @@ -364,5 +382,13 @@ def _check_valid_input( ) -def _test_stationarity(series: TimeSeriesLike): - return all([(stationarity_tests(bs[c]) for c in bs.components) for bs in series]) +def _test_stationarity(series: Sequence[TimeSeries]) -> bool: + for i, bs in enumerate(series): + for c in bs.components: + if not stationarity_tests(bs[c]): + logger.warning( + f"At least component '{c}' of target series at index {i} is not stationary. " + f"Beware of wrong interpretation of the chosen explainability." + ) + return False + return True diff --git a/darts/models/forecasting/torch_forecasting_model.py b/darts/models/forecasting/torch_forecasting_model.py index 6bf1f6c628..5b2042b1d3 100644 --- a/darts/models/forecasting/torch_forecasting_model.py +++ b/darts/models/forecasting/torch_forecasting_model.py @@ -5,7 +5,6 @@ This file contains several abstract classes: * TorchForecastingModel is the super-class of all torch (deep learning) darts forecasting models. - * PastCovariatesTorchModel(TorchForecastingModel) for torch models consuming only past-observed covariates. * FutureCovariatesTorchModel(TorchForecastingModel) for torch models consuming only future values of future covariates. diff --git a/darts/tests/explainability/test_shap_explainer.py b/darts/tests/explainability/test_shap_explainer.py index 61e698fa4d..c8a8c0128d 100644 --- a/darts/tests/explainability/test_shap_explainer.py +++ b/darts/tests/explainability/test_shap_explainer.py @@ -8,24 +8,45 @@ import sklearn from dateutil.relativedelta import relativedelta from numpy.testing import assert_array_equal +from sklearn.base import BaseEstimator, RegressorMixin from sklearn.preprocessing import MinMaxScaler from darts import TimeSeries from darts.dataprocessing.transformers import Scaler from darts.explainability.explainability_result import ShapExplainabilityResult -from darts.explainability.shap_explainer import MIN_BACKGROUND_SAMPLE, ShapExplainer +from darts.explainability.shap.base_explainer import MIN_BACKGROUND_SAMPLE +from darts.explainability.shap_explainer import ShapExplainer from darts.models import ( CatBoostModel, ExponentialSmoothing, LightGBMModel, LinearRegressionModel, SKLearnModel, + XGBModel, ) from darts.tests.conftest import ( GBM_AVAILABLE, LGBM_AVAILABLE, ) from darts.utils.timeseries_generation import linear_timeseries +from darts.utils.utils import generate_index + +xgb_test_params = { + "n_estimators": 1, + "max_depth": 1, + "max_leaves": 1, +} +lgbm_test_params = { + "n_estimators": 1, + "max_depth": 1, + "num_leaves": 2, + "verbosity": -1, +} +cb_test_params = { + "iterations": 1, + "depth": 1, + "verbose": -1, +} def extract_year(index): @@ -33,6 +54,20 @@ def extract_year(index): return (index.year - 1950) / 50 +class CallableAdditiveRegressor(BaseEstimator, RegressorMixin): + def fit(self, X, y): + self.n_features_in_ = X.shape[1] + self.bias_ = float(np.mean(y)) + return self + + def predict(self, X): + X = np.asarray(X) + return self.bias_ + X.sum(axis=1) + + def __call__(self, X): + return self.predict(X) + + class TestShapExplainer: np.random.seed(42) @@ -136,6 +171,9 @@ class TestShapExplainer: np.concatenate([fut_cov_1.reshape(-1, 1), fut_cov_2.reshape(-1, 1)], axis=1), ) + model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel + model_kwargs = lgbm_test_params if LGBM_AVAILABLE else {} + @pytest.mark.skipif(not GBM_AVAILABLE, reason="requires gradient boosting models") @pytest.mark.parametrize( "model", @@ -148,6 +186,7 @@ class TestShapExplainer: "lags_future_covariates": [0], "output_chunk_length": 4, "add_encoders": add_encoders, + **model_kwargs, }, }, { @@ -157,19 +196,20 @@ class TestShapExplainer: "lags_past_covariates": [-1, -2, -6], "lags_future_covariates": [0], "output_chunk_length": 4, + **cb_test_params, + }, + }, + { + "model_cls": XGBModel, + "config": { + "lags": 4, + "lags_past_covariates": [-1, -2, -3], + "lags_future_covariates": [0], + "output_chunk_length": 4, + "add_encoders": add_encoders, + **xgb_test_params, }, }, - # # TODO: add back test once shap fixes issue https://github.com/shap/shap/issues/4184 - # { - # "model_cls": XGBModel, - # "config": { - # "lags": 4, - # "lags_past_covariates": [-1, -2, -3], - # "lags_future_covariates": [0], - # "output_chunk_length": 4, - # "add_encoders": add_encoders, - # }, - # }, ], ) def test_gbm_creation(self, model): @@ -213,10 +253,10 @@ def test_gbm_creation(self, model): if m._supports_native_multioutput: # since xgboost > 2.1.0, model supports native multi-output regression # CatBoostModel supports multi-output for certain loss functions - assert isinstance(shap_explain.explainers.explainers, shap.explainers.Tree) + assert isinstance(shap_explain.explainer.explainer, shap.explainers.Tree) else: assert isinstance( - shap_explain.explainers.explainers[0][0], shap.explainers.Tree + shap_explain.explainer.explainer[0][0], shap.explainers.Tree ) # Bad choice of shap explainer @@ -227,7 +267,10 @@ def test_creation(self): # Model should be a SKLearnModel m = ExponentialSmoothing() m.fit(self.target_ts["price"]) - with pytest.raises(ValueError): + with pytest.raises( + ValueError, + match="Only models of type `SKLearnModel` or `TorchForecastingModel` are supported.", + ): ShapExplainer(m) # For now, multi_models=False not allowed @@ -254,7 +297,7 @@ def test_creation(self): future_covariates=self.fut_cov_ts, ) shap_explain = ShapExplainer(m) - assert isinstance(shap_explain.explainers.explainers, shap.explainers.Linear) + assert isinstance(shap_explain.explainer.explainer, shap.explainers.Linear) # ExtraTreesRegressor - also not a MultiOutputRegressor m = SKLearnModel( @@ -270,7 +313,7 @@ def test_creation(self): future_covariates=self.fut_cov_ts, ) shap_explain = ShapExplainer(m) - assert isinstance(shap_explain.explainers.explainers, shap.explainers.Tree) + assert isinstance(shap_explain.explainer.explainer, shap.explainers.Tree) # No past or future covariates m = LinearRegressionModel( @@ -282,16 +325,16 @@ def test_creation(self): ) shap_explain = ShapExplainer(m) - assert isinstance(shap_explain.explainers.explainers, shap.explainers.Linear) + assert isinstance(shap_explain.explainer.explainer, shap.explainers.Linear) def test_explain(self): - model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel - m = model_cls( + m = self.model_cls( lags=4, lags_past_covariates=[-1, -2, -3], lags_future_covariates=[0], output_chunk_length=4, add_encoders=self.add_encoders, + **self.model_kwargs, ) m.fit( series=self.target_ts, @@ -387,47 +430,48 @@ def test_explain(self): "power_target_lag-2", "price_target_lag-1", "power_target_lag-1", - "0_past_cov_lag-3", - "1_past_cov_lag-3", - "2_past_cov_lag-3", - "darts_enc_pc_cyc_month_sin_past_cov_lag-3", - "darts_enc_pc_cyc_month_cos_past_cov_lag-3", - "darts_enc_pc_cyc_day_sin_past_cov_lag-3", - "darts_enc_pc_cyc_day_cos_past_cov_lag-3", - "darts_enc_pc_pos_relative_past_cov_lag-3", - "darts_enc_pc_cus_custom_past_cov_lag-3", - "0_past_cov_lag-2", - "1_past_cov_lag-2", - "2_past_cov_lag-2", - "darts_enc_pc_cyc_month_sin_past_cov_lag-2", - "darts_enc_pc_cyc_month_cos_past_cov_lag-2", - "darts_enc_pc_cyc_day_sin_past_cov_lag-2", - "darts_enc_pc_cyc_day_cos_past_cov_lag-2", - "darts_enc_pc_pos_relative_past_cov_lag-2", - "darts_enc_pc_cus_custom_past_cov_lag-2", - "0_past_cov_lag-1", - "1_past_cov_lag-1", - "2_past_cov_lag-1", - "darts_enc_pc_cyc_month_sin_past_cov_lag-1", - "darts_enc_pc_cyc_month_cos_past_cov_lag-1", - "darts_enc_pc_cyc_day_sin_past_cov_lag-1", - "darts_enc_pc_cyc_day_cos_past_cov_lag-1", - "darts_enc_pc_pos_relative_past_cov_lag-1", - "darts_enc_pc_cus_custom_past_cov_lag-1", - "0_fut_cov_lag0", - "1_fut_cov_lag0", - "hour_fut_cov_lag0", - "dayofweek_fut_cov_lag0", - "relative_idx_fut_cov_lag0", + "0_pastcov_lag-3", + "1_pastcov_lag-3", + "2_pastcov_lag-3", + "darts_enc_pc_cyc_month_sin_pastcov_lag-3", + "darts_enc_pc_cyc_month_cos_pastcov_lag-3", + "darts_enc_pc_cyc_day_sin_pastcov_lag-3", + "darts_enc_pc_cyc_day_cos_pastcov_lag-3", + "darts_enc_pc_pos_relative_pastcov_lag-3", + "darts_enc_pc_cus_custom_pastcov_lag-3", + "0_pastcov_lag-2", + "1_pastcov_lag-2", + "2_pastcov_lag-2", + "darts_enc_pc_cyc_month_sin_pastcov_lag-2", + "darts_enc_pc_cyc_month_cos_pastcov_lag-2", + "darts_enc_pc_cyc_day_sin_pastcov_lag-2", + "darts_enc_pc_cyc_day_cos_pastcov_lag-2", + "darts_enc_pc_pos_relative_pastcov_lag-2", + "darts_enc_pc_cus_custom_pastcov_lag-2", + "0_pastcov_lag-1", + "1_pastcov_lag-1", + "2_pastcov_lag-1", + "darts_enc_pc_cyc_month_sin_pastcov_lag-1", + "darts_enc_pc_cyc_month_cos_pastcov_lag-1", + "darts_enc_pc_cyc_day_sin_pastcov_lag-1", + "darts_enc_pc_cyc_day_cos_pastcov_lag-1", + "darts_enc_pc_pos_relative_pastcov_lag-1", + "darts_enc_pc_cus_custom_pastcov_lag-1", + "0_futcov_lag0", + "1_futcov_lag0", + "darts_enc_fc_dta_hour_futcov_lag0", + "darts_enc_fc_dta_dayofweek_futcov_lag0", + "darts_enc_fc_pos_relative_futcov_lag0", ] results = shap_explain.explain() # all the features explained are here, in the right order - assert [ - results.get_explanation(i, "price").components.to_list() == components_list - for i in range(1, 5) - ] + for i in range(1, 5): + assert ( + results.get_explanation(i, "price").components.to_list() + == components_list + ) # No past or future covariates m = LinearRegressionModel( @@ -442,12 +486,12 @@ def test_explain(self): assert isinstance(shap_explain.explain(), ShapExplainabilityResult) def test_explain_with_lags_future_covariates_series_of_same_length_as_target(self): - model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel - model = model_cls( + model = self.model_cls( lags=4, lags_past_covariates=[-1, -2, -3], lags_future_covariates=[2], output_chunk_length=1, + **self.model_kwargs, ) model.fit( @@ -474,16 +518,16 @@ def test_explain_with_lags_future_covariates_series_extending_into_future(self): # Constructing future covariates TimeSeries that extends further into the future than the target series date_start = date(2012, 12, 12) date_end = date(2014, 6, 7) - days = pd.date_range(date_start, date_end, freq="d") + days = pd.date_range(date_start, date_end, freq="D") fut_cov = np.random.normal(0, 1, len(days)).astype("float32") fut_cov_ts = TimeSeries.from_times_and_values(days, fut_cov.reshape(-1, 1)) - model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel - model = model_cls( + model = self.model_cls( lags=4, lags_past_covariates=[-1, -2, -3], lags_future_covariates=[2], output_chunk_length=1, + **self.model_kwargs, ) model.fit( @@ -508,18 +552,18 @@ def test_explain_with_lags_covariates_series_older_timestamps_than_target(self): # Constructing covariates TimeSeries with older timestamps than target date_start = date(2012, 12, 10) date_end = date(2014, 6, 5) - days = pd.date_range(date_start, date_end, freq="d") + days = pd.date_range(date_start, date_end, freq="D") fut_cov = np.random.normal(0, 1, len(days)).astype("float32") fut_cov_ts = TimeSeries.from_times_and_values(days, fut_cov.reshape(-1, 1)) past_cov = np.random.normal(0, 1, len(days)).astype("float32") past_cov_ts = TimeSeries.from_times_and_values(days, past_cov.reshape(-1, 1)) - model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel - model = model_cls( + model = self.model_cls( lags=None, lags_past_covariates=[-1, -2], lags_future_covariates=[-1, -2], output_chunk_length=1, + **self.model_kwargs, ) model.fit( @@ -541,13 +585,13 @@ def test_explain_with_lags_covariates_series_older_timestamps_than_target(self): assert explanation.start_time() == self.target_ts.start_time() def test_plot(self, mpl_safe_plotting): - model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel - m_0 = model_cls( + m_0 = self.model_cls( lags=4, lags_past_covariates=[-1, -2, -3], lags_future_covariates=[0], output_chunk_length=4, add_encoders=self.add_encoders, + **self.model_kwargs, ) m_0.fit( series=self.target_ts, @@ -559,7 +603,7 @@ def test_plot(self, mpl_safe_plotting): # We need at least 5 points for force_plot with pytest.raises(ValueError): - shap_explain.force_plot_from_ts( + shap_explain.force_plot( self.target_ts[100:104], self.past_cov_ts[100:104], self.fut_cov_ts[100:104], @@ -567,7 +611,7 @@ def test_plot(self, mpl_safe_plotting): "power", ) - fplot = shap_explain.force_plot_from_ts( + fplot = shap_explain.force_plot( self.target_ts[100:105], self.past_cov_ts[100:105], self.fut_cov_ts[100:105], @@ -578,7 +622,7 @@ def test_plot(self, mpl_safe_plotting): # no component name -> multivariate error with pytest.raises(ValueError): - shap_explain.force_plot_from_ts( + shap_explain.force_plot( self.target_ts[100:108], self.past_cov_ts[100:108], self.fut_cov_ts[100:108], @@ -587,7 +631,7 @@ def test_plot(self, mpl_safe_plotting): # fake component with pytest.raises(ValueError): - shap_explain.force_plot_from_ts( + shap_explain.force_plot( self.target_ts[100:108], self.past_cov_ts[100:108], self.fut_cov_ts[100:108], @@ -597,7 +641,7 @@ def test_plot(self, mpl_safe_plotting): # horizon 0 with pytest.raises(ValueError): - shap_explain.force_plot_from_ts( + shap_explain.force_plot( self.target_ts[100:108], self.past_cov_ts[100:108], self.fut_cov_ts[100:108], @@ -606,7 +650,7 @@ def test_plot(self, mpl_safe_plotting): ) # Check the dimensions of returned values - dict_shap_values = shap_explain.summary_plot(show=False) + dict_shap_values = shap_explain.summary_plot(plot_kwargs={"show": False}) # One nested dict per horizon assert len(dict_shap_values) == m_0.output_chunk_length # Size of nested dict match number of component @@ -633,7 +677,7 @@ def test_plot(self, mpl_safe_plotting): ) shap_explain = ShapExplainer(m) - fplot = shap_explain.force_plot_from_ts( + fplot = shap_explain.force_plot( foreground_series=self.target_ts[100:105], horizon=1, target_component="power", @@ -641,10 +685,10 @@ def test_plot(self, mpl_safe_plotting): assert isinstance(fplot, shap.plots._force.BaseVisualizer) def test_feature_values_align_with_input(self): - model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel - model = model_cls( + model = self.model_cls( lags=4, output_chunk_length=1, + **self.model_kwargs, ) model.fit( series=self.target_ts, @@ -668,10 +712,10 @@ def test_feature_values_align_with_input(self): ) def test_feature_values_align_with_raw_output_shap(self): - model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel - model = model_cls( + model = self.model_cls( lags=4, output_chunk_length=1, + **self.model_kwargs, ) model.fit( series=self.target_ts, @@ -695,12 +739,12 @@ def test_feature_values_align_with_raw_output_shap(self): ), "The shape of the feature values should be the same as the shap values" def test_shap_explanation_object_validity(self): - model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel - model = model_cls( + model = self.model_cls( lags=4, lags_past_covariates=2, lags_future_covariates=[1], output_chunk_length=1, + **self.model_kwargs, ) model.fit( series=self.target_ts, @@ -720,11 +764,15 @@ def test_shap_explanation_object_validity(self): @pytest.mark.parametrize( "config", [(LinearRegressionModel, {})] - # # TODO: add back test once shap fixes issue https://github.com/shap/shap/issues/4184 - # + ([(XGBModel, {})] if XGB_AVAILABLE else []) + ( - [(LightGBMModel, {"likelihood": "quantile", "quantiles": [0.5]})] - if LGBM_AVAILABLE + [ + (XGBModel, {**xgb_test_params}), + ( + LightGBMModel, + {"likelihood": "quantile", "quantiles": [0.5], **lgbm_test_params}, + ), + ] + if GBM_AVAILABLE else [] ), ) @@ -746,8 +794,17 @@ def test_shap_selected_components(self, config): ) shap_explain = ShapExplainer(model) explanation_results = shap_explain.explain() + + lkl = model.likelihood + if lkl is not None: + target_components_lkl = lkl.component_names( + components=self.target_ts.components + ) + else: + target_components_lkl = self.target_ts.components + # check that explain() with selected components gives identical results - for comp in self.target_ts.components: + for comp in target_components_lkl: explanation_comp = shap_explain.explain(target_components=[comp]) assert explanation_comp.available_components == [comp] assert explanation_comp.available_horizons == [1] @@ -767,13 +824,19 @@ def test_shap_selected_components(self, config): and comp in explanation_comp.shap_explanation_object[1] ) + # with likelihood, users must give the likelihood parameter name + if lkl is not None: + with pytest.raises( + ValueError, + match=r"Provide some valid components from: \['price_q0.500', 'power_q0.500'\]", + ): + _ = shap_explain.explain( + target_components=[self.target_ts.components[0]] + ) + def test_shapley_with_static_cov(self): ts = self.target_ts_with_static_covs - model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel - model = model_cls( - lags=4, - output_chunk_length=1, - ) + model = self.model_cls(lags=4, output_chunk_length=1, **self.model_kwargs) model.fit( series=ts, ) @@ -814,11 +877,7 @@ def test_shapley_with_static_cov(self): ] def test_shapley_multiple_series_with_different_static_covs(self): - model_cls = LightGBMModel if LGBM_AVAILABLE else LinearRegressionModel - model = model_cls( - lags=4, - output_chunk_length=1, - ) + model = self.model_cls(lags=4, output_chunk_length=1, **self.model_kwargs) model.fit( series=self.target_ts_multiple_series_with_different_static_covs, ) @@ -870,10 +929,15 @@ def test_shap_regressor_component_specific_lags(self, mpl_safe_plotting): [np.arange(1, 29), np.arange(3, 31), np.arange(106, 161, 2)], axis=1 ), columns=expected_columns, + index=generate_index( + end=ts.end_time() + ts.freq, + length=28, + freq=ts.freq, + ), ) # check that the appropriate lags are extracted - assert all(shap_explain.explainers.background_X == expected_df) + assert all(shap_explain.explainer.background_arr == expected_df) assert model.lagged_feature_names == list(expected_df.columns) # check that explain() can be called @@ -881,3 +945,311 @@ def test_shap_regressor_component_specific_lags(self, mpl_safe_plotting): for comp in ts.components: comps_out = explanation_results.explained_forecasts[1][comp].columns assert all(comps_out == expected_columns) + + def test_explain_single(self): + model = LinearRegressionModel( + lags=4, + output_chunk_length=3, + ) + model.fit(series=self.target_ts) + + explainer = ShapExplainer(model) + foreground_series = self.target_ts[-10:] + results = explainer.explain_single( + foreground_series=foreground_series, + target_components=["price", "power"], + ) + + components = { + f"{name}_target_lag-{lag + 1}" + for name in foreground_series.columns + for lag in range(abs(min(model.lags["target"]))) + } + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(component=None) + with pytest.raises(ValueError, match='Component "test" is not available'): + results.get_explanation(component="test") + + explanation = results.get_explanation(component="price") + assert isinstance(explanation, TimeSeries) + assert explanation.n_timesteps == model.output_chunk_length + assert set(explanation.components) == components + assert np.isfinite(explanation.values()).all() + + prediction = model.predict( + n=model.output_chunk_length, series=foreground_series + ) + assert isinstance(prediction, TimeSeries) + assert prediction.n_timesteps == explanation.n_timesteps + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(component=None) + with pytest.raises(ValueError, match='Component "test" is not available'): + results.get_feature_values(component="test") + + results = explainer.explain_single( + foreground_series=foreground_series, + target_components=["power"], + ) + + feature_values = results.get_feature_values(component="power") + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == 1 + assert set(feature_values.components) == components + assert np.isfinite(feature_values.values()).all() + + with pytest.raises(ValueError, match='Component "test" is not available'): + results.get_shap_explanation_object(component="test") + + shap_explanation_object = results.get_shap_explanation_object(component="power") + explanation = results.get_explanation(component="power") + assert isinstance(shap_explanation_object, shap.Explanation) + assert_array_equal(shap_explanation_object.values, explanation.values()) + assert_array_equal(shap_explanation_object.data[:1], feature_values.values()) + + shap_values_sum = explanation.values().sum(axis=1) + base_values = prediction["power"].values().ravel() - shap_values_sum + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-5, + atol=1e-8, + ) + + def test_explain_single_univariate_target(self): + model = LinearRegressionModel( + lags=4, + output_chunk_length=3, + ) + series = self.target_ts["price"] + foreground_series = series[-10:] + + model.fit(series=series) + explainer = ShapExplainer(model) + results = explainer.explain_single(foreground_series=foreground_series) + + explanation = results.get_explanation() + feature_values = results.get_feature_values() + shap_explanation_object = results.get_shap_explanation_object() + + expected_components = { + f"price_target_lag-{lag + 1}" + for lag in range(abs(min(model.lags["target"]))) + } + + assert isinstance(explanation, TimeSeries) + assert explanation.n_timesteps == model.output_chunk_length + assert set(explanation.components) == expected_components + assert np.isfinite(explanation.values()).all() + + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == 1 + assert set(feature_values.components) == expected_components + assert np.isfinite(feature_values.values()).all() + + assert isinstance(shap_explanation_object, shap.Explanation) + assert_array_equal(shap_explanation_object.values, explanation.values()) + assert_array_equal( + shap_explanation_object.data, + np.repeat(feature_values.values(), model.output_chunk_length, axis=0), + ) + + prediction = model.predict( + n=model.output_chunk_length, series=foreground_series + ) + shap_values_sum = explanation.values().sum(axis=1) + base_values = prediction.values().ravel() - shap_values_sum + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-5, + atol=1e-8, + ) + + def test_explain_single_univariate_single_output(self): + model = LinearRegressionModel( + lags=4, + output_chunk_length=1, + ) + series = self.target_ts["price"] + foreground_series = series[-10:] + + model.fit(series=series) + explainer = ShapExplainer(model) + results = explainer.explain_single(foreground_series=foreground_series) + + explanation = results.get_explanation() + feature_values = results.get_feature_values() + shap_explanation_object = results.get_shap_explanation_object() + + assert explanation.n_timesteps == 1 + assert feature_values.n_timesteps == 1 + assert isinstance(shap_explanation_object, shap.Explanation) + assert_array_equal(shap_explanation_object.values, explanation.values()) + assert_array_equal(shap_explanation_object.data, feature_values.values()) + + def test_explain_single_multioutput_regressor(self): + series = linear_timeseries(length=40, column_name="price").stack( + linear_timeseries(length=40, start_value=50, column_name="power") + ) + model = SKLearnModel( + lags=2, + output_chunk_length=2, + model=sklearn.svm.LinearSVR(), + ) + model.fit(series=series) + + explainer = ShapExplainer( + model, + background_series=series[-20:], + shap_method="permutation", + ) + results = explainer.explain_single( + foreground_series=series[-10:], + target_components=["price"], + ) + + explanation = results.get_explanation() + feature_values = results.get_feature_values() + shap_explanation_object = results.get_shap_explanation_object() + + assert results.available_components == ["price"] + assert explanation.n_timesteps == model.output_chunk_length + assert feature_values.n_timesteps == 1 + assert isinstance(shap_explanation_object, shap.Explanation) + assert_array_equal(shap_explanation_object.values, explanation.values()) + assert_array_equal(shap_explanation_object.data[:1], feature_values.values()) + assert shap_explanation_object.base_values.shape == (model.output_chunk_length,) + + @pytest.mark.parametrize( + ("model", "shap_method", "kwargs", "expected_explainer"), + [ + ( + LinearRegressionModel(lags=2, output_chunk_length=1), + "linear", + {}, + shap.explainers.Linear, + ), + ( + LinearRegressionModel(lags=2, output_chunk_length=1), + "permutation", + {}, + shap.explainers.Permutation, + ), + ( + LinearRegressionModel(lags=2, output_chunk_length=1), + "partition", + {}, + shap.explainers.Partition, + ), + ( + SKLearnModel( + lags=2, + output_chunk_length=1, + model=sklearn.tree.DecisionTreeRegressor(), + ), + "tree", + {"feature_perturbation": "interventional"}, + shap.explainers.Tree, + ), + ( + SKLearnModel( + lags=2, + output_chunk_length=1, + model=CallableAdditiveRegressor(), + ), + "additive", + {}, + shap.explainers.Additive, + ), + ], + ) + def test_explicit_shap_methods( + self, model, shap_method, kwargs, expected_explainer + ): + series = linear_timeseries(length=40, column_name="price") + model.fit(series=series) + + explainer = ShapExplainer( + model, + background_series=series[-20:], + shap_method=shap_method, + **kwargs, + ) + + assert isinstance(explainer.explainer.explainer, expected_explainer) + + def test_gradient_shap_method_raises_builder_error(self): + series = linear_timeseries(length=40, column_name="price") + model = SKLearnModel( + lags=2, + output_chunk_length=1, + model=sklearn.dummy.DummyRegressor(), + ) + model.fit(series=series) + + with pytest.raises(ValueError, match="Invalid `shap_method='gradient'`"): + ShapExplainer( + model, + background_series=series[-20:], + shap_method="gradient", + ) + + def test_force_plot_defaults_to_single_component(self): + series = self.target_ts["price"] + model = LinearRegressionModel( + lags=4, + output_chunk_length=1, + ) + model.fit(series=series) + + shap_explain = ShapExplainer(model) + fplot = shap_explain.force_plot( + foreground_series=series[100:105], + horizon=1, + ) + + assert isinstance(fplot, shap.plots._force.BaseVisualizer) + + def test_regression_shap_explainer_builder_and_background_sampling(self): + series = self.target_ts["price"] + + linear_model = LinearRegressionModel(lags=1, output_chunk_length=1) + linear_model.fit(series=series) + + too_small_background = linear_timeseries( + start_value=1, + end_value=MIN_BACKGROUND_SAMPLE, + length=MIN_BACKGROUND_SAMPLE, + column_name="price", + ) + with pytest.raises(ValueError, match="background dataset is too small"): + ShapExplainer( + linear_model, + background_series=too_small_background, + shap_method="linear", + ) + + linear_explainer = ShapExplainer( + linear_model, + background_series=series[-20:], + background_num_samples=1, + shap_method="linear", + ) + assert isinstance(linear_explainer.explainer.explainer, shap.explainers.Linear) + assert len(linear_explainer.explainer.background_arr) == 1 + + kernel_model = SKLearnModel( + lags=1, + output_chunk_length=1, + model=sklearn.dummy.DummyRegressor(), + ) + kernel_model.fit(series=series) + kernel_explainer = ShapExplainer( + kernel_model, + background_series=series[-20:], + background_num_samples=1, + ) + assert isinstance(kernel_explainer.explainer.explainer, shap.explainers.Kernel) + assert len(kernel_explainer.explainer.background_arr) == 1 diff --git a/darts/tests/explainability/test_torch_explainer.py b/darts/tests/explainability/test_torch_explainer.py new file mode 100644 index 0000000000..89250e2428 --- /dev/null +++ b/darts/tests/explainability/test_torch_explainer.py @@ -0,0 +1,2363 @@ +import itertools +import os +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +import shap +from sklearn.datasets import make_regression + +from darts.tests.conftest import NF_AVAILABLE, TORCH_AVAILABLE, tfm_kwargs + +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) + +from darts import TimeSeries +from darts.dataprocessing.transformers import Scaler +from darts.explainability import ShapExplainer +from darts.explainability.shap.base_explainer import ( + MAX_BACKGROUND_SAMPLE, + MIN_BACKGROUND_SAMPLE, +) +from darts.models import ( + BlockRNNModel, + DLinearModel, + NaiveSeasonal, + NBEATSModel, + NHiTSModel, + RNNModel, + TiDEModel, + TSMixerModel, +) +from darts.models.forecasting.torch_forecasting_model import TorchForecastingModel +from darts.utils.likelihood_models.torch import ( + CauchyLikelihood, + GaussianLikelihood, + QuantileRegression, + TorchLikelihood, +) + +N_PAST_COVARIATES = 3 +N_FUTURE_COVARIATES = 2 +N_TARGETS = 3 + +chronos2_local_dir = ( + Path(__file__).parent.parent + / "models" + / "forecasting" + / "artefacts" + / "chronos2" + / "tiny_chronos2" +).absolute() + + +def encode_year(idx): + return (idx.year - 1950) / 50 + + +ADD_ENCODERS = { + "cyclic": {"future": ["month"]}, + "custom": {"past": [encode_year]}, + "transformer": Scaler(), + "tz": "CET", +} + +ALL_MODELS = [ + (RNNModel, {"model": "LSTM"}), + (BlockRNNModel, {"add_encoders": ADD_ENCODERS}), + (NBEATSModel, {"num_stacks": 2, "num_layers": 2, "layer_widths": 16}), + (NHiTSModel, {"layer_widths": 16}), + (TiDEModel, {"hidden_size": 32, "add_encoders": ADD_ENCODERS}), + (DLinearModel, {"add_encoders": ADD_ENCODERS}), + ( + TSMixerModel, + {"hidden_size": 8, "ff_size": 8, "num_blocks": 1, "add_encoders": ADD_ENCODERS}, + ), + # (Chronos2Model, {"local_dir": chronos2_local_dir, "batch_size": 4}), +] +SHAP_METHODS = [ + "kernel", + "sampling", + "partition", + "permutation", +] +LIKELIHOODS = [ + (GaussianLikelihood, None), + (CauchyLikelihood, None), + (QuantileRegression, {"quantiles": [0.1, 0.5, 0.9]}), +] + + +if NF_AVAILABLE: + from darts.models import NeuralForecastModel + + ALL_MODELS += [ + ( + NeuralForecastModel, + {"model": "MLPMultivariate", "model_kwargs": {"hidden_size": 16}}, + ), + ( + NeuralForecastModel, + { + "model": "PatchTST", + "model_kwargs": { + "encoder_layers": 1, + "patch_len": 3, + "stride": 3, + "n_heads": 4, + "hidden_size": 16, + "linear_hidden_size": 32, + }, + }, + ), + ] + + +kwargs = { + "n_epochs": 1, + **tfm_kwargs, +} + + +class TestShapExplainer: + # set random seed + rng = np.random.default_rng(42) + + X, Y = make_regression( + n_samples=100, + n_features=N_PAST_COVARIATES + N_FUTURE_COVARIATES, + n_informative=3, + n_targets=N_TARGETS, + noise=1, + random_state=42, + ) + X, Y = X.astype(np.float32), Y.astype(np.float32) + multivariate_series = TimeSeries.from_times_and_values( + times=pd.date_range("20200101", periods=80, freq="D"), + values=Y[:80], + columns=[f"T_{i}" for i in range(N_TARGETS)], + ).with_static_covariates(pd.DataFrame({"S_0": [1] * N_TARGETS})) + univariate_series = multivariate_series.univariate_component(0) + past_covariates = TimeSeries.from_times_and_values( + times=pd.date_range("20200101", periods=80, freq="D"), + values=X[:80, :N_PAST_COVARIATES], + columns=[f"P_{i}" for i in range(N_PAST_COVARIATES)], + ) + future_covariates = TimeSeries.from_times_and_values( + times=pd.date_range("20200101", periods=97, freq="D"), + # shift forward by 3 so that future covariate may influence target at time t + values=X[3:, N_PAST_COVARIATES:], + columns=[f"F_{i}" for i in range(N_FUTURE_COVARIATES)], + ) + multiple_multivariate_series = [ + multivariate_series, + multivariate_series + 1, + ] + + @pytest.mark.parametrize("model_cls, model_kwargs", ALL_MODELS) + def test_creation( + self, + model_cls: type[TorchForecastingModel], + model_kwargs: dict | None, + tmpdir_fn, + ): + model = model_cls( + input_chunk_length=10, + output_chunk_length=5, + **(model_kwargs or {}), + **kwargs, + ) + + # cannot create explainer with unfitted model + with pytest.raises(ValueError, match="must be fitted before instantiating."): + explainer = ShapExplainer(model) + + # fit the model + model.fit(series=self.multivariate_series) + + # create explainer with fitted model + explainer = ShapExplainer(model) + + # check explainer attributes + assert explainer.model == model + assert explainer.n == model.output_chunk_length + + # save and load the model to check explainer works with loaded models + save_path = os.path.join(tmpdir_fn, "model.pt") + model.save(save_path) + loaded_model = model_cls.load(save_path) + loaded_explainer = ShapExplainer(loaded_model) + + assert loaded_explainer.model == loaded_model + assert loaded_explainer.n == loaded_model.output_chunk_length + + @pytest.mark.parametrize("model_cls, model_kwargs", ALL_MODELS) + def test_creation_multiple_series( + self, + model_cls: type[TorchForecastingModel], + model_kwargs: dict | None, + tmpdir_fn, + ): + model = model_cls( + input_chunk_length=10, + output_chunk_length=5, + **(model_kwargs or {}), + **kwargs, + ) + + # cannot create explainer with unfitted model + with pytest.raises(ValueError, match="must be fitted before instantiating."): + explainer = ShapExplainer(model) + + # fit the model + model.fit(series=self.multiple_multivariate_series) + + # create explainer with multiple series but no background raises error + with pytest.raises(ValueError, match="`background_series` must be provided"): + explainer = ShapExplainer(model) + + # create explainer with multiple series and background + explainer = ShapExplainer( + model, background_series=self.multiple_multivariate_series + ) + + # check explainer attributes + assert explainer.model == model + assert explainer.n == model.output_chunk_length + + # save and load the model to check explainer works with loaded models + save_path = os.path.join(tmpdir_fn, "model.pt") + model.save(save_path) + loaded_model = model_cls.load(save_path) + loaded_explainer = ShapExplainer( + model, background_series=self.multiple_multivariate_series + ) + + assert loaded_explainer.n == loaded_model.output_chunk_length + + @pytest.mark.parametrize("model_cls, model_kwargs", ALL_MODELS) + def test_explain( + self, + model_cls: type[TorchForecastingModel], + model_kwargs: dict | None, + tmpdir_fn, + ): + model = model_cls( + input_chunk_length=7, + output_chunk_length=3, # RNN model ignores output_chunk_length which is set to 1 internally + **(model_kwargs or {}), + **kwargs, + ) + + # prepare training data + series = self.multivariate_series + past_covariates = ( + self.past_covariates if model.supports_past_covariates else None + ) + future_covariates = ( + self.future_covariates if model.supports_future_covariates else None + ) + + # prepare background data + background_series = series[-20:] + background_past_covariates = ( + past_covariates[-20:] if past_covariates is not None else None + ) + _, background_future_covariates = ( + future_covariates.split_before(background_series.start_time()) + if future_covariates is not None + else (None, None) + ) + + # prepare foreground data (past/future covariates can be reused) + foreground_series = series[-10:] + + # fit the model + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + # create explainer with fitted model + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + ) + + # explain the foreground + results = explainer.explain( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + ) + + valid_horizon = 1 if isinstance(model, RNNModel) else 2 + components = { + f"{name}_target_lag-{lag + 1}" + for name in foreground_series.columns + for lag in range(model.input_chunk_length) + } + if past_covariates is not None: + components.update({ + f"{name}_pastcov_lag-{lag + 1}" + for name in past_covariates.columns + for lag in range(model.input_chunk_length) + }) + if future_covariates is not None: + components.update({ + f"{name}_futcov_lag{lag}" + for name in future_covariates.columns + for lag in range(-model.input_chunk_length, model.output_chunk_length) + }) + if ( + model.supports_static_covariates + and foreground_series.static_covariates is not None + ): + components.update({ + f"{name}_statcov_target_{target}" + for name in foreground_series.static_covariates.columns + for target in foreground_series.columns + }) + if model_kwargs is not None and "add_encoders" in model_kwargs: + components.update({ + f"{prefix}_lag{lag}" + for lag in range(-model.input_chunk_length, model.output_chunk_length) + for prefix in [ + "darts_enc_fc_cyc_month_cos_futcov", + "darts_enc_fc_cyc_month_sin_futcov", + ] + }) + components.update({ + f"{prefix}_lag-{lag + 1}" + for lag in range(model.input_chunk_length) + for prefix in [ + "darts_enc_pc_cus_custom_pastcov", + ] + }) + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(horizon=4, component=None) + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(horizon=2, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_explanation(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_explanation(horizon=4, component="T_0") + + # check explanation is returned for valid horizon, component, and input features + explanation = results.get_explanation(horizon=valid_horizon, component="T_0") + assert isinstance(explanation, TimeSeries) + assert ( + explanation.n_timesteps + == foreground_series.n_timesteps - model.input_chunk_length + 1 + ) + assert set(explanation.components) == components + + # check explanation values are finite + assert np.isfinite(explanation.values()).all() + # check explanation values are additive, i.e., sum of SHAP values across all features equals the difference + # between the prediction and the base value + # base values should be approximately equal across all time steps since the same background is used for all + # predictions + pred = model.historical_forecasts( + series=foreground_series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=valid_horizon, + last_points_only=True, + overlap_end=True, + retrain=False, + ) + assert isinstance(pred, TimeSeries) + shap_values_sum = explanation.values().sum(axis=1) + base_values = pred["T_0"].values().ravel() - shap_values_sum + # assert all base values are approximately equal + np.testing.assert_allclose(base_values, base_values[0], rtol=1e-3, atol=1e-5) + + # invalid component or horizon raises error for feature values as well + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(horizon=4, component=None) + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(horizon=2, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_feature_values(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_feature_values(horizon=4, component="T_0") + + # check feature values are returned for valid horizon and component + feature_values = results.get_feature_values( + horizon=valid_horizon, + component="T_1", + ) + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == explanation.n_timesteps + assert set(feature_values.components) == components + assert np.isfinite(feature_values.values()).all() + + # invalid component or horizon raises error for shap explanation object as well + with pytest.raises(ValueError, match="component parameter is required"): + results.get_shap_explanation_object(horizon=4, component=None) + with pytest.raises(ValueError, match="component parameter is required"): + results.get_shap_explanation_object(horizon=2, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_shap_explanation_object(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_shap_explanation_object(horizon=4, component="T_0") + + # check shap explanation object is returned for valid horizon and component + shap_explanation_object = results.get_shap_explanation_object( + horizon=valid_horizon, + component="T_1", + ) + explanation = results.get_explanation(horizon=valid_horizon, component="T_1") + assert isinstance(explanation, TimeSeries) + assert isinstance(shap_explanation_object, shap.Explanation) + np.testing.assert_array_equal( + shap_explanation_object.values, + explanation.values(), + ) + np.testing.assert_array_equal( + shap_explanation_object.data, + feature_values.values(), + ) + shap_values_sum = explanation.values().sum(axis=1) + base_values = pred["T_1"].values().ravel() - shap_values_sum + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-3, + atol=1e-5, + ) + + # save and load the model to check explainer works with loaded models + save_path = os.path.join(tmpdir_fn, "model.pt") + model.save(save_path) + loaded_model = model_cls.load(save_path) + loaded_explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + ) + assert loaded_explainer.n == loaded_model.output_chunk_length + + loaded_results = loaded_explainer.explain( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + ) + loaded_explanation = loaded_results.get_explanation( + horizon=valid_horizon, component="T_1" + ) + loaded_feature_values = loaded_results.get_feature_values( + horizon=valid_horizon, + component="T_1", + ) + loaded_shap_explanation_object = loaded_results.get_shap_explanation_object( + horizon=valid_horizon, + component="T_1", + ) + assert isinstance(loaded_explanation, TimeSeries) + assert loaded_explanation.n_timesteps == explanation.n_timesteps + assert set(loaded_explanation.components) == components + assert np.isfinite(loaded_explanation.values()).all() + assert isinstance(loaded_feature_values, TimeSeries) + assert loaded_feature_values.n_timesteps == feature_values.n_timesteps + assert set(loaded_feature_values.components) == components + assert np.isfinite(loaded_feature_values.values()).all() + assert isinstance(loaded_shap_explanation_object, shap.Explanation) + + np.testing.assert_array_equal( + loaded_shap_explanation_object.values, + loaded_explanation.values(), + ) + np.testing.assert_array_equal( + loaded_shap_explanation_object.data, + loaded_feature_values.values(), + ) + + # unfortunately, shap and base values are not exactly the same across + # runs with the same background data, even with fixed random seed, + # due to some non-determinism in the SHAP implementation. + + @pytest.mark.parametrize("uses_past_covariates", [True, False]) + @pytest.mark.parametrize("uses_future_covariates", [True, False]) + @pytest.mark.parametrize("uses_static_covariates", [True, False]) + def test_explain_without_foreground( + self, + uses_past_covariates: bool, + uses_future_covariates: bool, + uses_static_covariates: bool, + ): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=6, + output_chunk_length=4, + use_static_covariates=uses_static_covariates, + **(model_kwargs or {}), + **kwargs, + ) + + # prepare training data + series = self.multivariate_series + past_covariates = self.past_covariates if uses_past_covariates else None + future_covariates = self.future_covariates if uses_future_covariates else None + + # prepare background data + background_series = series[-20:] + background_past_covariates = ( + past_covariates[-20:] if past_covariates is not None else None + ) + _, background_future_covariates = ( + future_covariates.split_before(background_series.start_time()) + if future_covariates is not None + else (None, None) + ) + + # fit the model + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + # create explainer with fitted model + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + ) + + # explain the foreground + results = explainer.explain() + + valid_horizon = 1 if isinstance(model, RNNModel) else 2 + components = { + f"{name}_target_lag-{lag + 1}" + for name in background_series.columns + for lag in range(model.input_chunk_length) + } + if past_covariates is not None: + components.update({ + f"{name}_pastcov_lag-{lag + 1}" + for name in past_covariates.columns + for lag in range(model.input_chunk_length) + }) + if future_covariates is not None: + components.update({ + f"{name}_futcov_lag{lag}" + for name in future_covariates.columns + for lag in range(-model.input_chunk_length, model.output_chunk_length) + }) + if uses_static_covariates and background_series.static_covariates is not None: + components.update({ + f"{name}_statcov_target_{target}" + for name in background_series.static_covariates.columns + for target in background_series.columns + }) + if model_kwargs is not None and "add_encoders" in model_kwargs: + components.update({ + f"{prefix}_lag{lag}" + for lag in range(-model.input_chunk_length, model.output_chunk_length) + for prefix in [ + "darts_enc_fc_cyc_month_cos_futcov", + "darts_enc_fc_cyc_month_sin_futcov", + ] + }) + components.update({ + f"{prefix}_lag-{lag + 1}" + for lag in range(model.input_chunk_length) + for prefix in [ + "darts_enc_pc_cus_custom_pastcov", + ] + }) + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(horizon=4, component=None) + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(horizon=2, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_explanation(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 5 is not available."): + results.get_explanation(horizon=5, component="T_0") + + # check explanation is returned for valid horizon, component, and input features + explanation = results.get_explanation(horizon=valid_horizon, component="T_0") + assert isinstance(explanation, TimeSeries) + + # background series would use `generate_fit_encodings` rather than `generate_fit_predict_encodings` + # thus, the length is shorter by `model.output_chunk_length` compared to using the foreground + assert ( + explanation.n_timesteps + == background_series.n_timesteps + - model.input_chunk_length + # - model.output_chunk_length + + 1 + ) + assert set(explanation.components) == components + + # check explanation values are finite + assert np.isfinite(explanation.values()).all() + # check explanation values are additive, i.e., sum of SHAP values across all features equals the difference + # between the prediction and the base value + # base values should be approximately equal across all time steps since the same background is used for all + # predictions + pred = model.historical_forecasts( + series=background_series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=valid_horizon, + last_points_only=True, + overlap_end=True, + retrain=False, + ) + assert isinstance(pred, TimeSeries) + pred = pred[: len(explanation)] + shap_values_sum = explanation.values().sum(axis=1) + base_values = pred["T_0"].values().ravel() - shap_values_sum + # assert all base values are approximately equal + np.testing.assert_allclose(base_values, base_values[0], rtol=1e-5, atol=1e-8) + + # invalid component or horizon raises error for feature values as well + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(horizon=4, component=None) + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(horizon=2, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_feature_values(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 5 is not available."): + results.get_feature_values(horizon=5, component="T_0") + + # check feature values are returned for valid horizon and component + feature_values = results.get_feature_values( + horizon=valid_horizon, + component="T_1", + ) + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == explanation.n_timesteps + assert set(feature_values.components) == components + assert np.isfinite(feature_values.values()).all() + + # invalid component or horizon raises error for shap explanation object as well + with pytest.raises(ValueError, match="component parameter is required"): + results.get_shap_explanation_object(horizon=4, component=None) + with pytest.raises(ValueError, match="component parameter is required"): + results.get_shap_explanation_object(horizon=2, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_shap_explanation_object(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 5 is not available."): + results.get_shap_explanation_object(horizon=5, component="T_0") + + # check shap explanation object is returned for valid horizon and component + shap_explanation_object = results.get_shap_explanation_object( + horizon=valid_horizon, + component="T_1", + ) + explanation = results.get_explanation(horizon=valid_horizon, component="T_1") + assert isinstance(explanation, TimeSeries) + assert isinstance(shap_explanation_object, shap.Explanation) + np.testing.assert_array_equal( + shap_explanation_object.values, + explanation.values(), + ) + np.testing.assert_array_equal( + shap_explanation_object.data, + feature_values.values(), + ) + shap_values_sum = explanation.values().sum(axis=1) + base_values = pred["T_1"].values().ravel() - shap_values_sum + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-5, + atol=1e-8, + ) + + @pytest.mark.parametrize( + "shap_method,single_output", + itertools.product(SHAP_METHODS, [True, False]), + ) + def test_explain_shap_methods( + self, + shap_method: str, + single_output: bool, + ): + series = self.multivariate_series + if single_output: + ocl = 1 + series = series[series.columns.tolist()[:1]] + valid_horizon = 1 + valid_feature = "T_0" + else: + ocl = 3 + valid_horizon = 3 + valid_feature = "T_1" + + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=8, + output_chunk_length=ocl, + **(model_kwargs or {}), + **kwargs, + ) + + # prepare training data + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + # prepare background data + background_series = series[-20:] + background_past_covariates = ( + past_covariates[-20:] if past_covariates is not None else None + ) + _, background_future_covariates = ( + future_covariates.split_before(background_series.start_time()) + if future_covariates is not None + else (None, None) + ) + + # prepare foreground data (past/future covariates can be reused) + foreground_series = series[-10:] + + # fit the model + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + # create explainer with fitted model + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + shap_method=shap_method, + ) + + # explain the foreground + results = explainer.explain( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + ) + + components = { + f"{name}_target_lag-{lag + 1}" + for name in foreground_series.columns + for lag in range(model.input_chunk_length) + } + if past_covariates is not None: + components.update({ + f"{name}_pastcov_lag-{lag + 1}" + for name in past_covariates.columns + for lag in range(model.input_chunk_length) + }) + if future_covariates is not None: + components.update({ + f"{name}_futcov_lag{lag}" + for name in future_covariates.columns + for lag in range(-model.input_chunk_length, model.output_chunk_length) + }) + if ( + model.supports_static_covariates + and foreground_series.static_covariates is not None + ): + components.update({ + f"{name}_statcov_target_{target}" + for name in foreground_series.static_covariates.columns + for target in foreground_series.columns + }) + if model_kwargs is not None and "add_encoders" in model_kwargs: + components.update({ + f"{prefix}_lag{lag}" + for lag in range(-model.input_chunk_length, model.output_chunk_length) + for prefix in [ + "darts_enc_fc_cyc_month_cos_futcov", + "darts_enc_fc_cyc_month_sin_futcov", + ] + }) + components.update({ + f"{prefix}_lag-{lag + 1}" + for lag in range(model.input_chunk_length) + for prefix in [ + "darts_enc_pc_cus_custom_pastcov", + ] + }) + + if not single_output: + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(horizon=1, component=None) + else: + results.get_explanation(horizon=1, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_explanation(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_explanation(horizon=4, component="T_0") + + # check explanation is returned for valid horizon, component, and input features + explanation = results.get_explanation(horizon=valid_horizon, component="T_0") + assert isinstance(explanation, TimeSeries) + assert ( + explanation.n_timesteps + == foreground_series.n_timesteps - model.input_chunk_length + 1 + ) + assert set(explanation.components) == components + + # check explanation values are finite + assert np.isfinite(explanation.values()).all() + # check explanation values are additive, i.e., sum of SHAP values across all features equals the difference + # between the prediction and the base value + # base values should be approximately equal across all time steps since the same background is used for all + # predictions + pred = model.historical_forecasts( + series=foreground_series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=valid_horizon, + last_points_only=True, + overlap_end=True, + retrain=False, + ) + assert isinstance(pred, TimeSeries) + shap_values_sum = explanation.values().sum(axis=1) + base_values = pred["T_0"].values().ravel() - shap_values_sum + # assert all base values are approximately equal + np.testing.assert_allclose(base_values, base_values[0], rtol=1e-3, atol=1e-5) + + # invalid component or horizon raises error for feature values as well + if not single_output: + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(horizon=1, component=None) + else: + results.get_feature_values(horizon=1, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_feature_values(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_feature_values(horizon=4, component="T_0") + + # check feature values are returned for valid horizon and component + feature_values = results.get_feature_values( + horizon=valid_horizon, + component=valid_feature, + ) + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == explanation.n_timesteps + assert set(feature_values.components) == components + assert np.isfinite(feature_values.values()).all() + + # invalid component or horizon raises error for shap explanation object as well + if not single_output: + with pytest.raises(ValueError, match="component parameter is required"): + results.get_shap_explanation_object(horizon=1, component=None) + else: + results.get_shap_explanation_object(horizon=1, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_shap_explanation_object(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_shap_explanation_object(horizon=4, component="T_0") + + # check shap explanation object is returned for valid horizon and component + shap_explanation_object = results.get_shap_explanation_object( + horizon=valid_horizon, + component=valid_feature, + ) + explanation = results.get_explanation( + horizon=valid_horizon, component=valid_feature + ) + assert isinstance(explanation, TimeSeries) + assert isinstance(shap_explanation_object, shap.Explanation) + np.testing.assert_array_equal( + shap_explanation_object.values, + explanation.values(), + ) + np.testing.assert_array_equal( + shap_explanation_object.data, + feature_values.values(), + ) + shap_values_sum = explanation.values().sum(axis=1) + base_values = pred[valid_feature].values().ravel() - shap_values_sum + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-5, + atol=1e-8, + ) + + @pytest.mark.parametrize("likelihood_cls, likelihood_kwargs", LIKELIHOODS) + def test_explain_probabilistic_model( + self, + likelihood_cls: type[TorchLikelihood], + likelihood_kwargs: dict | None, + ): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=5, + output_chunk_length=2, + likelihood=likelihood_cls(**(likelihood_kwargs or {})), + **(model_kwargs or {}), + **kwargs, + ) + + # prepare training data + series = self.multivariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + # prepare background data + background_series = series[-20:] + background_past_covariates = ( + past_covariates[-20:] if past_covariates is not None else None + ) + _, background_future_covariates = ( + future_covariates.split_before(background_series.start_time()) + if future_covariates is not None + else (None, None) + ) + + # prepare foreground data (past/future covariates can be reused) + foreground_series = series[-10:] + + # fit the model + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + # create explainer with fitted model + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + ) + + # explain the foreground + results = explainer.explain( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + ) + + assert model.likelihood is not None + likelihood_components = model.likelihood.component_names(series) + assert set(explainer.explainer.target_components_likelihood) == set( + likelihood_components + ) + + # probabilistic models should have explanations for all components of the likelihood, + # but not for pre-likelihood components + with pytest.raises(ValueError, match='Component "T_0" is not available'): + results.get_explanation(horizon=1, component="T_0") + + valid_horizon = 1 if isinstance(model, RNNModel) else 2 + components = { + f"{name}_target_lag-{lag + 1}" + for name in foreground_series.columns + for lag in range(model.input_chunk_length) + } + if past_covariates is not None: + components.update({ + f"{name}_pastcov_lag-{lag + 1}" + for name in past_covariates.columns + for lag in range(model.input_chunk_length) + }) + if future_covariates is not None: + components.update({ + f"{name}_futcov_lag{lag}" + for name in future_covariates.columns + for lag in range(-model.input_chunk_length, model.output_chunk_length) + }) + if ( + model.supports_static_covariates + and foreground_series.static_covariates is not None + ): + components.update({ + f"{name}_statcov_target_{target}" + for name in foreground_series.static_covariates.columns + for target in foreground_series.columns + }) + if model_kwargs is not None and "add_encoders" in model_kwargs: + components.update({ + f"{prefix}_lag{lag}" + for lag in range(-model.input_chunk_length, model.output_chunk_length) + for prefix in [ + "darts_enc_fc_cyc_month_cos_futcov", + "darts_enc_fc_cyc_month_sin_futcov", + ] + }) + components.update({ + f"{prefix}_lag-{lag + 1}" + for lag in range(model.input_chunk_length) + for prefix in [ + "darts_enc_pc_cus_custom_pastcov", + ] + }) + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(horizon=4, component=None) + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(horizon=2, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_explanation(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_explanation(horizon=4, component=likelihood_components[0]) + + # check explanation is returned for valid horizon, component, and input features + explanation = results.get_explanation( + horizon=valid_horizon, component=likelihood_components[0] + ) + assert isinstance(explanation, TimeSeries) + assert ( + explanation.n_timesteps + == foreground_series.n_timesteps - model.input_chunk_length + 1 + ) + assert set(explanation.components) == components + + # check explanation values are finite + assert np.isfinite(explanation.values()).all() + # check explanation values are additive, i.e., sum of SHAP values across all features equals the difference + # between the prediction and the base value + # base values should be approximately equal across all time steps since the same background is used for all + # predictions + pred = model.historical_forecasts( + series=foreground_series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=valid_horizon, + last_points_only=True, + overlap_end=True, + retrain=False, + predict_likelihood_parameters=True, # output likelihood parameters directly + ) + assert isinstance(pred, TimeSeries) + shap_values_sum = explanation.values().sum(axis=1) + base_values = pred[likelihood_components[0]].values().ravel() - shap_values_sum + # assert all base values are approximately equal + np.testing.assert_allclose(base_values, base_values[0], rtol=1e-3, atol=1e-5) + + # invalid component or horizon raises error for feature values as well + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(horizon=4, component=None) + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(horizon=2, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_feature_values(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_feature_values(horizon=4, component=likelihood_components[0]) + + # check feature values are returned for valid horizon and component + feature_values = results.get_feature_values( + horizon=valid_horizon, + component=likelihood_components[0], + ) + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == explanation.n_timesteps + assert set(feature_values.components) == components + assert np.isfinite(feature_values.values()).all() + + # invalid component or horizon raises error for shap explanation object as well + with pytest.raises(ValueError, match="component parameter is required"): + results.get_shap_explanation_object(horizon=4, component=None) + with pytest.raises(ValueError, match="component parameter is required"): + results.get_shap_explanation_object(horizon=2, component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_shap_explanation_object(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_shap_explanation_object( + horizon=4, component=likelihood_components[-1] + ) + + # check shap explanation object is returned for valid horizon and component + shap_explanation_object = results.get_shap_explanation_object( + horizon=valid_horizon, + component=likelihood_components[-1], + ) + explanation = results.get_explanation( + horizon=valid_horizon, component=likelihood_components[-1] + ) + assert isinstance(explanation, TimeSeries) + assert isinstance(shap_explanation_object, shap.Explanation) + np.testing.assert_array_equal( + shap_explanation_object.values, + explanation.values(), + ) + np.testing.assert_array_equal( + shap_explanation_object.data, + feature_values.values(), + ) + shap_values_sum = explanation.values().sum(axis=1) + base_values = pred[likelihood_components[-1]].values().ravel() - shap_values_sum + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-3, + atol=1e-5, + ) + + def test_explain_univariate(self): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=7, + output_chunk_length=3, # RNN model ignores output_chunk_length which is set to 1 internally + **(model_kwargs or {}), + **kwargs, + ) + + # prepare training data + series = self.univariate_series + past_covariates = ( + self.past_covariates if model.supports_past_covariates else None + ) + future_covariates = ( + self.future_covariates if model.supports_future_covariates else None + ) + + # prepare background data + background_series = series[-20:] + background_past_covariates = ( + past_covariates[-20:] if past_covariates is not None else None + ) + _, background_future_covariates = ( + future_covariates.split_before(background_series.start_time()) + if future_covariates is not None + else (None, None) + ) + + # prepare foreground data (past/future covariates can be reused) + foreground_series = series[-10:] + + # fit the model + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + # create explainer with fitted model + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + ) + + # explain the foreground + results = explainer.explain( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + ) + + valid_horizon = 1 if isinstance(model, RNNModel) else 2 + components = { + f"{name}_target_lag-{lag + 1}" + for name in foreground_series.columns + for lag in range(model.input_chunk_length) + } + if past_covariates is not None: + components.update({ + f"{name}_pastcov_lag-{lag + 1}" + for name in past_covariates.columns + for lag in range(model.input_chunk_length) + }) + if future_covariates is not None: + components.update({ + f"{name}_futcov_lag{lag}" + for name in future_covariates.columns + for lag in range(-model.input_chunk_length, model.output_chunk_length) + }) + if ( + model.supports_static_covariates + and foreground_series.static_covariates is not None + ): + components.update({ + f"{name}_statcov_target_{target}" + for name in foreground_series.static_covariates.columns + for target in foreground_series.columns + }) + if model_kwargs is not None and "add_encoders" in model_kwargs: + components.update({ + f"{prefix}_lag{lag}" + for lag in range(-model.input_chunk_length, model.output_chunk_length) + for prefix in [ + "darts_enc_fc_cyc_month_cos_futcov", + "darts_enc_fc_cyc_month_sin_futcov", + ] + }) + components.update({ + f"{prefix}_lag-{lag + 1}" + for lag in range(model.input_chunk_length) + for prefix in [ + "darts_enc_pc_cus_custom_pastcov", + ] + }) + + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_explanation(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_explanation(horizon=4, component="T_0") + + # check explanation is returned for valid horizon, component, and input features + explanation = results.get_explanation(horizon=valid_horizon) + assert isinstance(explanation, TimeSeries) + assert ( + explanation.n_timesteps + == foreground_series.n_timesteps - model.input_chunk_length + 1 + ) + assert set(explanation.components) == components + + # check explanation values are finite + assert np.isfinite(explanation.values()).all() + # check explanation values are additive, i.e., sum of SHAP values across all features equals the difference + # between the prediction and the base value + # base values should be approximately equal across all time steps since the same background is used for all + # predictions + pred = model.historical_forecasts( + series=foreground_series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=valid_horizon, + last_points_only=True, + overlap_end=True, + retrain=False, + ) + assert isinstance(pred, TimeSeries) + shap_values_sum = explanation.values().sum(axis=1) + base_values = pred["T_0"].values().ravel() - shap_values_sum + # assert all base values are approximately equal + np.testing.assert_allclose(base_values, base_values[0], rtol=1e-5, atol=1e-8) + + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_feature_values(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_feature_values(horizon=4, component="T_0") + + # check feature values are returned for valid horizon and component + feature_values = results.get_feature_values( + horizon=valid_horizon, + ) + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == explanation.n_timesteps + assert set(feature_values.components) == components + assert np.isfinite(feature_values.values()).all() + + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_shap_explanation_object(horizon=valid_horizon, component="T_11") + with pytest.raises(ValueError, match="Horizon 4 is not available."): + results.get_shap_explanation_object(horizon=4, component="T_0") + + # check shap explanation object is returned for valid horizon and component + shap_explanation_object = results.get_shap_explanation_object( + horizon=valid_horizon, + ) + explanation = results.get_explanation(horizon=valid_horizon) + assert isinstance(explanation, TimeSeries) + assert isinstance(shap_explanation_object, shap.Explanation) + np.testing.assert_array_equal( + shap_explanation_object.values, + explanation.values(), + ) + np.testing.assert_array_equal( + shap_explanation_object.data, + feature_values.values(), + ) + shap_values_sum = explanation.values().sum(axis=1) + base_values = pred.values().ravel() - shap_values_sum + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-5, + atol=1e-8, + ) + + def test_explain_multiple_series(self): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=6, + output_chunk_length=3, + **(model_kwargs or {}), + **kwargs, + ) + + series = self.multiple_multivariate_series + past_covariates = [self.past_covariates, self.past_covariates + 1] + future_covariates = [self.future_covariates, self.future_covariates + 1] + + background_series = [ts[-20:] for ts in series] + background_past_covariates = [ts[-20:] for ts in past_covariates] + background_future_covariates = [ + future_cov.split_before(background.start_time())[1] + for future_cov, background in zip(future_covariates, background_series) + ] + + foreground_series = [ts[-10:] for ts in series] + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + ) + results = explainer.explain( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + ) + + explanation = results.get_explanation(horizon=2, component="T_0") + feature_values = results.get_feature_values(horizon=2, component="T_0") + shap_explanation_object = results.get_shap_explanation_object( + horizon=2, component="T_0" + ) + assert isinstance(explanation, list) + assert isinstance(feature_values, list) + assert isinstance(shap_explanation_object, list) + assert len(explanation) == len(series) + assert len(feature_values) == len(series) + assert len(shap_explanation_object) == len(series) + + components = { + f"{name}_target_lag-{lag + 1}" + for name in foreground_series[0].columns + for lag in range(model.input_chunk_length) + } + if past_covariates is not None: + components.update({ + f"{name}_pastcov_lag-{lag + 1}" + for name in past_covariates[0].columns + for lag in range(model.input_chunk_length) + }) + if future_covariates is not None: + components.update({ + f"{name}_futcov_lag{lag}" + for name in future_covariates[0].columns + for lag in range(-model.input_chunk_length, model.output_chunk_length) + }) + if ( + model.supports_static_covariates + and foreground_series[0].static_covariates is not None + ): + components.update({ + f"{name}_statcov_target_{target}" + for name in foreground_series[0].static_covariates.columns + for target in foreground_series[0].columns + }) + if model_kwargs is not None and "add_encoders" in model_kwargs: + components.update({ + f"{prefix}_lag{lag}" + for lag in range(-model.input_chunk_length, model.output_chunk_length) + for prefix in [ + "darts_enc_fc_cyc_month_cos_futcov", + "darts_enc_fc_cyc_month_sin_futcov", + ] + }) + components.update({ + f"{prefix}_lag-{lag + 1}" + for lag in range(model.input_chunk_length) + for prefix in [ + "darts_enc_pc_cus_custom_pastcov", + ] + }) + + for i in range(len(series)): + assert isinstance(explanation[i], TimeSeries) + assert ( + explanation[i].n_timesteps + == foreground_series[i].n_timesteps - model.input_chunk_length + 1 + ) + assert np.isfinite(explanation[i].values()).all() + assert set(explanation[i].components) == components + + assert isinstance(feature_values[i], TimeSeries) + assert feature_values[i].n_timesteps == explanation[i].n_timesteps + assert np.isfinite(feature_values[i].values()).all() + assert set(feature_values[i].components) == components + + assert isinstance(shap_explanation_object[i], shap.Explanation) + np.testing.assert_array_equal( + shap_explanation_object[i].values, + explanation[i].values(), + ) + np.testing.assert_array_equal( + shap_explanation_object[i].data, + feature_values[i].values(), + ) + + def test_explain_when_future_covariates_too_short( + self, + ): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=3, + output_chunk_length=4, + output_chunk_shift=2, + **(model_kwargs or {}), + **kwargs, + ) + + series = self.multivariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + # prepare background data + background_series = series[-20:] + background_past_covariates = ( + past_covariates[-20:] if past_covariates is not None else None + ) + _, background_future_covariates = future_covariates.split_before( + background_series.start_time() + ) + + # prepare foreground data + foreground_series = series[-15:] + foreground_past_covariates = past_covariates.slice_intersect(foreground_series) + foreground_future_covariates = future_covariates.slice_intersect( + foreground_series + ) + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + ) + results = explainer.explain( + foreground_series=foreground_series, + foreground_past_covariates=foreground_past_covariates, + foreground_future_covariates=foreground_future_covariates, + ) + explanation = results.get_explanation(horizon=1, component="T_0") + + trimmed_foreground_series = foreground_series[ + : -model.output_chunk_length - model.output_chunk_shift + ] + expected_n_timesteps = ( + trimmed_foreground_series.n_timesteps - model.input_chunk_length + 1 + ) + + assert isinstance(explanation, TimeSeries) + assert explanation.n_timesteps == expected_n_timesteps + + def test_explain_single(self): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=6, + output_chunk_length=3, + **(model_kwargs or {}), + **kwargs, + ) + + series = self.multivariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + background_series = series[-20:] + background_past_covariates = ( + past_covariates[-20:] if past_covariates is not None else None + ) + _, background_future_covariates = future_covariates.split_before( + background_series.start_time() + ) + + foreground_series = series[-10:] + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + ) + results = explainer.explain_single( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + ) + + components = { + f"{name}_target_lag-{lag + 1}" + for name in foreground_series.columns + for lag in range(model.input_chunk_length) + } + components.update({ + f"{name}_pastcov_lag-{lag + 1}" + for name in past_covariates.columns + for lag in range(model.input_chunk_length) + }) + components.update({ + f"{name}_futcov_lag{lag}" + for name in future_covariates.columns + for lag in range(-model.input_chunk_length, model.output_chunk_length) + }) + if foreground_series.static_covariates is not None: + components.update({ + f"{name}_statcov_target_{target}" + for name in foreground_series.static_covariates.columns + for target in foreground_series.columns + }) + components.update({ + f"{prefix}_lag{lag}" + for lag in range(-model.input_chunk_length, model.output_chunk_length) + for prefix in [ + "darts_enc_fc_cyc_month_cos_futcov", + "darts_enc_fc_cyc_month_sin_futcov", + ] + }) + components.update({ + f"{prefix}_lag-{lag + 1}" + for lag in range(model.input_chunk_length) + for prefix in ["darts_enc_pc_cus_custom_pastcov"] + }) + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_explanation(component="T_11") + + explanation = results.get_explanation(component="T_0") + assert isinstance(explanation, TimeSeries) + assert explanation.n_timesteps == model.output_chunk_length + assert set(explanation.components) == components + assert np.isfinite(explanation.values()).all() + + prediction = model.predict( + n=model.output_chunk_length, + series=foreground_series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + assert isinstance(prediction, TimeSeries) + assert prediction.n_timesteps == explanation.n_timesteps + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_feature_values(component="T_11") + + feature_values = results.get_feature_values(component="T_1") + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == 1 + assert set(feature_values.components) == components + assert np.isfinite(feature_values.values()).all() + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_shap_explanation_object(component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_shap_explanation_object(component="T_11") + + shap_explanation_object = results.get_shap_explanation_object(component="T_1") + explanation = results.get_explanation(component="T_1") + assert isinstance(shap_explanation_object, shap.Explanation) + np.testing.assert_array_equal( + shap_explanation_object.values, + explanation.values(), + ) + np.testing.assert_array_equal( + shap_explanation_object.data[:1], + feature_values.values(), + ) + shap_values_sum = explanation.values().sum(axis=1) + base_values = prediction["T_1"].values().ravel() - shap_values_sum + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-5, + atol=1e-8, + ) + + def test_explain_single_without_foreground(self): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=4, + output_chunk_length=5, + **(model_kwargs or {}), + **kwargs, + ) + + series = self.multivariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + background_series = series[-20:] + background_past_covariates = ( + past_covariates[-20:] if past_covariates is not None else None + ) + _, background_future_covariates = future_covariates.split_before( + background_series.start_time() + ) + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + ) + results = explainer.explain_single() + + components = { + f"{name}_target_lag-{lag + 1}" + for name in background_series.columns + for lag in range(model.input_chunk_length) + } + components.update({ + f"{name}_pastcov_lag-{lag + 1}" + for name in background_past_covariates.columns + for lag in range(model.input_chunk_length) + }) + components.update({ + f"{name}_futcov_lag{lag}" + for name in background_future_covariates.columns + for lag in range(-model.input_chunk_length, model.output_chunk_length) + }) + if background_series.static_covariates is not None: + components.update({ + f"{name}_statcov_target_{target}" + for name in background_series.static_covariates.columns + for target in background_series.columns + }) + components.update({ + f"{prefix}_lag{lag}" + for lag in range(-model.input_chunk_length, model.output_chunk_length) + for prefix in [ + "darts_enc_fc_cyc_month_cos_futcov", + "darts_enc_fc_cyc_month_sin_futcov", + ] + }) + components.update({ + f"{prefix}_lag-{lag + 1}" + for lag in range(model.input_chunk_length) + for prefix in ["darts_enc_pc_cus_custom_pastcov"] + }) + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_explanation(component="T_11") + + explanation = results.get_explanation(component="T_0") + assert isinstance(explanation, TimeSeries) + assert explanation.n_timesteps == model.output_chunk_length + assert set(explanation.components) == components + assert np.isfinite(explanation.values()).all() + + # for single explanation without foreground, it explains the background forecast + prediction = model.predict( + n=model.output_chunk_length, + series=background_series, + past_covariates=background_past_covariates, + future_covariates=background_future_covariates, + ) + assert isinstance(prediction, TimeSeries) + assert prediction.n_timesteps == explanation.n_timesteps + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_feature_values(component="T_11") + + feature_values = results.get_feature_values(component="T_1") + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == 1 + assert set(feature_values.components) == components + assert np.isfinite(feature_values.values()).all() + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_shap_explanation_object(component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_shap_explanation_object(component="T_11") + + shap_explanation_object = results.get_shap_explanation_object(component="T_1") + explanation = results.get_explanation(component="T_1") + assert isinstance(shap_explanation_object, shap.Explanation) + np.testing.assert_array_equal( + shap_explanation_object.values, + explanation.values(), + ) + np.testing.assert_array_equal( + shap_explanation_object.data[:1], + feature_values.values(), + ) + shap_values_sum = explanation.values().sum(axis=1) + base_values = prediction["T_1"].values().ravel() - shap_values_sum + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-5, + atol=1e-8, + ) + + @pytest.mark.parametrize("shap_method", SHAP_METHODS) + def test_explain_single_shap_methods( + self, + shap_method: str, + ): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=6, + output_chunk_length=3, + **(model_kwargs or {}), + **kwargs, + ) + + series = self.multivariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + background_series = series[-20:] + background_past_covariates = ( + past_covariates[-20:] if past_covariates is not None else None + ) + _, background_future_covariates = future_covariates.split_before( + background_series.start_time() + ) + + foreground_series = series[-10:] + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + shap_method=shap_method, + ) + results = explainer.explain_single( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + target_components=["T_0", "T_1"], + ) + + components = { + f"{name}_target_lag-{lag + 1}" + for name in foreground_series.columns + for lag in range(model.input_chunk_length) + } + components.update({ + f"{name}_pastcov_lag-{lag + 1}" + for name in past_covariates.columns + for lag in range(model.input_chunk_length) + }) + components.update({ + f"{name}_futcov_lag{lag}" + for name in future_covariates.columns + for lag in range(-model.input_chunk_length, model.output_chunk_length) + }) + if foreground_series.static_covariates is not None: + components.update({ + f"{name}_statcov_target_{target}" + for name in foreground_series.static_covariates.columns + for target in foreground_series.columns + }) + components.update({ + f"{prefix}_lag{lag}" + for lag in range(-model.input_chunk_length, model.output_chunk_length) + for prefix in [ + "darts_enc_fc_cyc_month_cos_futcov", + "darts_enc_fc_cyc_month_sin_futcov", + ] + }) + components.update({ + f"{prefix}_lag-{lag + 1}" + for lag in range(model.input_chunk_length) + for prefix in ["darts_enc_pc_cus_custom_pastcov"] + }) + + explanation = results.get_explanation(component="T_0") + assert isinstance(explanation, TimeSeries) + assert explanation.n_timesteps == model.output_chunk_length + assert set(explanation.components) == components + assert np.isfinite(explanation.values()).all() + + prediction = model.predict( + n=model.output_chunk_length, + series=foreground_series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + assert isinstance(prediction, TimeSeries) + assert prediction.n_timesteps == explanation.n_timesteps + + feature_values = results.get_feature_values(component="T_1") + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == 1 + assert set(feature_values.components) == components + assert np.isfinite(feature_values.values()).all() + + shap_explanation_object = results.get_shap_explanation_object(component="T_1") + explanation = results.get_explanation(component="T_1") + assert isinstance(shap_explanation_object, shap.Explanation) + np.testing.assert_array_equal( + shap_explanation_object.values, + explanation.values(), + ) + np.testing.assert_array_equal( + shap_explanation_object.data[:1], + feature_values.values(), + ) + shap_values_sum = explanation.values().sum(axis=1) + base_values = prediction["T_1"].values().ravel() - shap_values_sum + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-5, + atol=1e-8, + ) + + @pytest.mark.parametrize("likelihood_cls, likelihood_kwargs", LIKELIHOODS) + def test_explain_single_probabilistic_model( + self, + likelihood_cls: type[TorchLikelihood], + likelihood_kwargs: dict | None, + ): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=5, + output_chunk_length=2, + likelihood=likelihood_cls(**(likelihood_kwargs or {})), + **(model_kwargs or {}), + **kwargs, + ) + + series = self.multivariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + background_series = series[-20:] + background_past_covariates = ( + past_covariates[-20:] if past_covariates is not None else None + ) + _, background_future_covariates = future_covariates.split_before( + background_series.start_time() + ) + + foreground_series = series[-10:] + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + ) + results = explainer.explain_single( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + ) + + assert model.likelihood is not None + likelihood_components = model.likelihood.component_names(series) + assert set(explainer.explainer.target_components_likelihood) == set( + likelihood_components + ) + + with pytest.raises(ValueError, match='Component "T_0" is not available'): + results.get_explanation(component="T_0") + with pytest.raises(ValueError, match="component parameter is required"): + results.get_explanation(component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_explanation(component="T_11") + + components = { + f"{name}_target_lag-{lag + 1}" + for name in foreground_series.columns + for lag in range(model.input_chunk_length) + } + components.update({ + f"{name}_pastcov_lag-{lag + 1}" + for name in past_covariates.columns + for lag in range(model.input_chunk_length) + }) + components.update({ + f"{name}_futcov_lag{lag}" + for name in future_covariates.columns + for lag in range(-model.input_chunk_length, model.output_chunk_length) + }) + if foreground_series.static_covariates is not None: + components.update({ + f"{name}_statcov_target_{target}" + for name in foreground_series.static_covariates.columns + for target in foreground_series.columns + }) + components.update({ + f"{prefix}_lag{lag}" + for lag in range(-model.input_chunk_length, model.output_chunk_length) + for prefix in [ + "darts_enc_fc_cyc_month_cos_futcov", + "darts_enc_fc_cyc_month_sin_futcov", + ] + }) + components.update({ + f"{prefix}_lag-{lag + 1}" + for lag in range(model.input_chunk_length) + for prefix in ["darts_enc_pc_cus_custom_pastcov"] + }) + + explanation = results.get_explanation(component=likelihood_components[0]) + assert isinstance(explanation, TimeSeries) + assert explanation.n_timesteps == model.output_chunk_length + assert set(explanation.components) == components + assert np.isfinite(explanation.values()).all() + + prediction = model.predict( + n=model.output_chunk_length, + series=foreground_series, + past_covariates=past_covariates, + future_covariates=future_covariates, + predict_likelihood_parameters=True, + ) + assert isinstance(prediction, TimeSeries) + assert prediction.n_timesteps == explanation.n_timesteps + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_feature_values(component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_feature_values(component="T_11") + + feature_values = results.get_feature_values(component=likelihood_components[0]) + assert isinstance(feature_values, TimeSeries) + assert feature_values.n_timesteps == 1 + assert set(feature_values.components) == components + assert np.isfinite(feature_values.values()).all() + + with pytest.raises(ValueError, match="component parameter is required"): + results.get_shap_explanation_object(component=None) + with pytest.raises(ValueError, match='Component "T_11" is not available'): + results.get_shap_explanation_object(component="T_11") + + shap_explanation_object = results.get_shap_explanation_object( + component=likelihood_components[-1] + ) + explanation = results.get_explanation(component=likelihood_components[-1]) + assert isinstance(explanation, TimeSeries) + assert isinstance(shap_explanation_object, shap.Explanation) + np.testing.assert_array_equal( + shap_explanation_object.values, + explanation.values(), + ) + np.testing.assert_array_equal( + shap_explanation_object.data[:1], + feature_values.values(), + ) + shap_values_sum = explanation.values().sum(axis=1) + base_values = ( + prediction[likelihood_components[-1]].values().ravel() - shap_values_sum + ) + # sometimes probabilistic models can have more variability in the base values due to the nature of the + # likelihood outputs, so we use a slightly looser tolerance here + np.testing.assert_allclose( + shap_explanation_object.base_values, + base_values, + rtol=1e-3, + atol=1e-8, + ) + + def test_summary_plot(self): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=6, + output_chunk_length=3, + **(model_kwargs or {}), + **kwargs, + ) + + series = self.multivariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + background_series = series[-20:] + background_past_covariates = past_covariates[-20:] + _, background_future_covariates = future_covariates.split_before( + background_series.start_time() + ) + + foreground_series = series[-10:] + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + background_num_samples=10, + ) + + dict_shap_values = explainer.summary_plot( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + num_samples=2, + plot_kwargs={"show": False}, + ) + assert len(dict_shap_values) == model.output_chunk_length + for horizon in range(1, model.output_chunk_length + 1): + assert len(dict_shap_values[horizon]) == series.width + for component in series.components: + assert isinstance( + dict_shap_values[horizon][component], shap.Explanation + ) + + with pytest.raises(ValueError, match="Invalid `target_components`"): + explainer.summary_plot(horizons=[1], target_components=["test"]) + with pytest.raises(ValueError, match=r"All `horizons` must be `>=1`\."): + explainer.summary_plot(horizons=[0], target_components=["T_0"]) + with pytest.raises( + ValueError, + match=r"At least one of the `horizons` is larger than `output_chunk_length`\.", + ): + explainer.summary_plot( + horizons=[model.output_chunk_length + 1], + target_components=["T_0"], + ) + + def test_force_plot(self): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=6, + output_chunk_length=3, + **(model_kwargs or {}), + **kwargs, + ) + + series = self.multivariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + background_series = series[-20:] + background_past_covariates = past_covariates[-20:] + _, background_future_covariates = future_covariates.split_before( + background_series.start_time() + ) + + foreground_series = series[-10:] + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + background_num_samples=10, + ) + + force_plot = explainer.force_plot( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + horizon=2, + target_component="T_0", + ) + assert isinstance(force_plot, shap.plots._force.BaseVisualizer) + + with pytest.raises( + ValueError, match=r"`target_component` parameter is required" + ): + explainer.force_plot( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + horizon=1, + ) + with pytest.raises(ValueError, match="Invalid `target_components`"): + explainer.force_plot( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + horizon=1, + target_component="test", + ) + with pytest.raises(ValueError, match=r"All `horizons` must be `>=1`\."): + explainer.force_plot( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + horizon=0, + target_component="T_0", + ) + with pytest.raises( + ValueError, + match=r"At least one of the `horizons` is larger than `output_chunk_length`\.", + ): + explainer.force_plot( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + horizon=model.output_chunk_length + 1, + target_component="T_0", + ) + + def test_waterfall_plot(self): + model_kwargs = {"add_encoders": ADD_ENCODERS} + model = DLinearModel( + input_chunk_length=7, + output_chunk_length=4, + **(model_kwargs or {}), + **kwargs, + ) + + series = self.multivariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + background_series = series[-20:] + background_past_covariates = past_covariates[-20:] + _, background_future_covariates = future_covariates.split_before( + background_series.start_time() + ) + + foreground_series = series[-10:] + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + background_num_samples=10, + ) + + results = explainer.explain( + foreground_series=foreground_series, + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + ) + + for horizon in range(1, model.output_chunk_length + 1): + for component in series.components: + shap_explanation_object = results.get_shap_explanation_object( + horizon=horizon, component=component + ) + assert isinstance(shap_explanation_object, shap.Explanation) + + waterfall_plot = shap.plots.waterfall( + shap_explanation_object[0], show=False + ) + assert waterfall_plot is not None + + def test_validation_and_helper_branches(self): + model = DLinearModel( + input_chunk_length=6, + output_chunk_length=3, + add_encoders=ADD_ENCODERS, + **kwargs, + ) + + series = self.univariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + background_series = series[-20:] + background_past_covariates = past_covariates[-20:] + _, background_future_covariates = future_covariates.split_before( + background_series.start_time() + ) + + with pytest.raises(ValueError, match="Invalid `shap_method='invalid'`"): + ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + shap_method="invalid", + ) + + with pytest.raises( + ValueError, + match=( + "`background_num_samples` must be less than or equal to " + f"MAX_BACKGROUND_SAMPLE={MAX_BACKGROUND_SAMPLE}" + ), + ): + ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + background_num_samples=MAX_BACKGROUND_SAMPLE + 1, + ) + + short_background_series = series[ + -( + model.input_chunk_length + # + model.output_chunk_length + + MIN_BACKGROUND_SAMPLE + - 4 + ) : + ] + short_background_past_covariates = past_covariates[ + -len(short_background_series) : + ] + _, short_background_future_covariates = future_covariates.split_before( + short_background_series.start_time() + ) + with pytest.raises( + ValueError, + match=( + "Background series must contain at least " + f"{MIN_BACKGROUND_SAMPLE} samples" + ), + ): + ShapExplainer( + model, + background_series=short_background_series, + background_past_covariates=short_background_past_covariates, + background_future_covariates=short_background_future_covariates, + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + background_num_samples=10, + ) + + assert { + el.name.lower() for el in explainer.explainer._supported_shap_methods + } == set(SHAP_METHODS) + + force_plot = explainer.force_plot( + foreground_series=series[-10:], + foreground_past_covariates=past_covariates, + foreground_future_covariates=future_covariates, + horizon=1, + ) + assert isinstance(force_plot, shap.plots._force.BaseVisualizer) + assert explainer.explainer._batch_collate_np([(None,)], [0]) is None + + def test_helper_sampling_and_single_target_filtering(self): + model = DLinearModel( + input_chunk_length=6, + output_chunk_length=3, + add_encoders=ADD_ENCODERS, + **kwargs, + ) + + series = self.multivariate_series + past_covariates = self.past_covariates + future_covariates = self.future_covariates + + model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + background_series = series[-20:] + background_past_covariates = past_covariates[-20:] + _, background_future_covariates = future_covariates.split_before( + background_series.start_time() + ) + + explainer = ShapExplainer( + model, + background_series=background_series, + background_past_covariates=background_past_covariates, + background_future_covariates=background_future_covariates, + background_num_samples=10, + ) + + long_length = ( + model.input_chunk_length + + model.output_chunk_length + + MAX_BACKGROUND_SAMPLE + + 25 + ) + times = pd.date_range("20210101", periods=long_length, freq="D") + long_background_series = TimeSeries.from_times_and_values( + times=times, + values=np.tile(series.values(copy=False), (16, 1))[:long_length], + columns=series.components, + ).with_static_covariates(series.static_covariates) + long_background_past_covariates = TimeSeries.from_times_and_values( + times=times, + values=np.tile(past_covariates.values(copy=False), (16, 1))[:long_length], + columns=past_covariates.components, + ) + long_background_future_covariates = TimeSeries.from_times_and_values( + times=pd.date_range("20210101", periods=long_length + 20, freq="D"), + values=np.tile(future_covariates.values(copy=False), (16, 1))[ + : long_length + 20 + ], + columns=future_covariates.components, + ) + + sampled_background, _ = explainer.explainer.create_shap_array( + long_background_series, + long_background_past_covariates, + long_background_future_covariates, + input_type="background", + ) + assert sampled_background.shape[0] == MAX_BACKGROUND_SAMPLE + + def test_invalid_model_type_check(self): + with pytest.raises( + ValueError, + match="Only models of type `SKLearnModel` or `TorchForecastingModel` are supported", + ): + ShapExplainer(NaiveSeasonal(K=1).fit(self.univariate_series)) diff --git a/darts/utils/historical_forecasts/optimized_historical_forecasts_torch.py b/darts/utils/historical_forecasts/optimized_historical_forecasts_torch.py index 0bd3e45fea..7b9ec5bd58 100644 --- a/darts/utils/historical_forecasts/optimized_historical_forecasts_torch.py +++ b/darts/utils/historical_forecasts/optimized_historical_forecasts_torch.py @@ -46,57 +46,23 @@ def _optimized_historical_forecasts( The data_transformers are applied in historical_forecasts (input and predictions) """ - predict_kwargs = predict_kwargs or {} - if "verbose" not in predict_kwargs: - predict_kwargs["verbose"] = verbose - bounds = [] - for idx, series_ in enumerate(series): - past_covariates_ = past_covariates[idx] if past_covariates is not None else None - future_covariates_ = ( - future_covariates[idx] if future_covariates is not None else None - ) - # obtain forecastable indexes boundaries, adjust target & covariates boundaries accordingly - ( - hist_fct_start, - hist_fct_end, - _, - _, - _, - _, - _, - _, - ) = _get_historical_forecast_boundaries( - model=model, - series=series_, - series_idx=idx, - past_covariates=past_covariates_, - future_covariates=future_covariates_, - start=start, - start_format=start_format, - forecast_horizon=forecast_horizon, - overlap_end=overlap_end, - stride=stride, - freq=series_.freq, - show_warnings=show_warnings, - ) - # latest possible prediction start is one time step after end of target series - if hist_fct_start > series_.end_time(): - left_bound = len(series_) - else: - left_bound = series_.get_index_at_point(hist_fct_start) - - if hist_fct_end > series_.end_time(): - right_bound = len(series_) - else: - right_bound = series_.get_index_at_point(hist_fct_end) - bounds.append((left_bound, right_bound)) - - bounds, cum_lengths = _process_predict_start_points_bounds( + bounds, cum_lengths = _create_dataset_bounds( + model=model, series=series, - bounds=np.array(bounds), + past_covariates=past_covariates, + future_covariates=future_covariates, + start=start, + start_format=start_format, + forecast_horizon=forecast_horizon, stride=stride, + overlap_end=overlap_end, + show_warnings=show_warnings, ) + predict_kwargs = predict_kwargs or {} + if "verbose" not in predict_kwargs: + predict_kwargs["verbose"] = verbose + # TODO: is there a better way to call the super().predict() from TorchForecastingModel, without having to # import it? (avoid circular imports) tfm_cls = [ @@ -173,3 +139,67 @@ def _optimized_historical_forecasts( preds = model_out[pred_idx_start:pred_idx_end] forecasts_list.append(preds) return forecasts_list + + +def _create_dataset_bounds( + model, + series: Sequence[TimeSeries], + past_covariates: Sequence[TimeSeries] | None = None, + future_covariates: Sequence[TimeSeries] | None = None, + start: pd.Timestamp | float | int | None = None, + start_format: Literal["position", "value"] = "value", + forecast_horizon: int = 1, + stride: int = 1, + overlap_end: bool = False, + show_warnings: bool = True, +): + """ + Creates the bounds for the inference dataset based on the input series and whether it is for training or not. + """ + bounds = [] + for idx, series_ in enumerate(series): + past_covariates_ = past_covariates[idx] if past_covariates is not None else None + future_covariates_ = ( + future_covariates[idx] if future_covariates is not None else None + ) + # obtain forecastable indexes boundaries, adjust target & covariates boundaries accordingly + ( + hist_fct_start, + hist_fct_end, + _, + _, + _, + _, + _, + _, + ) = _get_historical_forecast_boundaries( + model=model, + series=series_, + series_idx=idx, + past_covariates=past_covariates_, + future_covariates=future_covariates_, + start=start, + start_format=start_format, + forecast_horizon=forecast_horizon, + overlap_end=overlap_end, + stride=stride, + freq=series_.freq, + show_warnings=show_warnings, + ) + # latest possible prediction start is one time step after end of target series + if hist_fct_start > series_.end_time(): + left_bound = len(series_) + else: + left_bound = series_.get_index_at_point(hist_fct_start) + + if hist_fct_end > series_.end_time(): + right_bound = len(series_) + else: + right_bound = series_.get_index_at_point(hist_fct_end) + bounds.append((left_bound, right_bound)) + + return _process_predict_start_points_bounds( + series=series, + bounds=np.array(bounds), + stride=stride, + ) diff --git a/docs/source/examples.rst b/docs/source/examples.rst index d507cc79c2..e64a3511d6 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -246,6 +246,17 @@ Ensemble models example notebook: examples/19-EnsembleModel-examples.ipynb +Explainability +============== + +Explainability example notebook showcasing the use of Darts' explainability module +for both PyTorch and SKLearn models: + +.. toctree:: + :maxdepth: 1 + + examples/28-Explainability-examples.ipynb + Kalman Filter Model =================== diff --git a/examples/20-SKLearnModel-examples.ipynb b/examples/20-SKLearnModel-examples.ipynb index 9bb44568f2..b2bd4695fb 100644 --- a/examples/20-SKLearnModel-examples.ipynb +++ b/examples/20-SKLearnModel-examples.ipynb @@ -1,9 +1,8 @@ { "cells": [ { - "cell_type": "markdown", - "id": "45bd6e88-1be9-4de1-9933-143eda71d501", "metadata": {}, + "cell_type": "markdown", "source": [ "# Regression Models\n", "\n", @@ -18,65 +17,14 @@ "- probabilistic forecasting\n", "- explainability\n", "- and more" - ] + ], + "id": "e57c871a5e478556" }, { - "cell_type": "code", - "execution_count": 1, - "id": "3ef9bc25-7b86-4de5-80e9-6eff27025b44", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "# fix python path if working locally\n", "from utils import fix_pythonpath_if_working_locally\n", @@ -87,14 +35,14 @@ "from shap import initjs\n", "\n", "initjs()" - ] + ], + "id": "1947485e41ba686e" }, { - "cell_type": "code", - "execution_count": null, - "id": "9d9d76e9-5753-4762-a1cb-c8c61d0313d2", "metadata": {}, + "cell_type": "code", "outputs": [], + "execution_count": null, "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -119,12 +67,12 @@ " SKLearnModel,\n", " XGBModel,\n", ")" - ] + ], + "id": "f42aa313cdcf47a0" }, { - "cell_type": "markdown", - "id": "eacf6328-6b51-43e9-8b44-214f5df15684", "metadata": {}, + "cell_type": "markdown", "source": [ "## Input Dataset\n", "For this notebook, we use the Electricity Consumption Dataset from households in Zurich, Switzerland.\n", @@ -140,35 +88,14 @@ "- **T [°C]**: Measured temperature\n", "- **StrGlo [W/m2]**: Measured solar irradiation\n", "- **RainDur [min]**: Measured raining duration" - ] + ], + "id": "6734a67a07618cd6" }, { - "cell_type": "code", - "execution_count": 3, - "id": "ea0d05f6-03cc-4422-afed-36acb2b94fa7", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "ts_energy = ElectricityConsumptionZurichDataset().load()\n", "\n", @@ -195,12 +122,12 @@ "\n", "ts_weather.plot()\n", "plt.show()" - ] + ], + "id": "ff9383b533cafabd" }, { - "cell_type": "markdown", - "id": "9913460a-b4ee-4b66-8db4-b0e8ac189f77", "metadata": {}, + "cell_type": "markdown", "source": [ "## Darts Regression Models\n", "\n", @@ -257,35 +184,14 @@ "Let's try to apply this to our electricity dataset. Assume, we want to predict the consumption of the next couple of hours after the end of our training set. \n", "\n", "As input features we give it the consumption at the same hour from one and two days ago -> `lags=[-24, -48]`." - ] + ], + "id": "ad9fd67d92f4d9a3" }, { - "cell_type": "code", - "execution_count": 4, - "id": "6a3f3753-b7db-448c-942a-9db51390b1b9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "model = LinearRegressionModel(lags=[-24, -48])\n", "model.fit(ts_energy_train)\n", @@ -294,12 +200,12 @@ "ts_energy_train[-48:].plot(label=\"training\")\n", "ts_energy_val[:12].plot(label=\"validation\")\n", "pred.plot(label=\"forecast\")" - ] + ], + "id": "2cad420a0279be23" }, { - "cell_type": "markdown", - "id": "73f669b6-f14d-4a56-a9f2-069b4565d9d8", "metadata": {}, + "cell_type": "markdown", "source": [ "### Forecasting with covariates\n", "\n", @@ -308,35 +214,14 @@ "Let's assume that instead of having weather measurements, we actually have weather forecasts. Then we could use them as future_covariates. **We only do this here for demonstration purposes!**\n", "\n", "Below is an example that uses the last 24 hours (`24`) from hour target series (electricity consumption) and the values at the predicted time step (`[0]`) of our weather \"forecasts\"." - ] + ], + "id": "3cf020a77df9bd15" }, { - "cell_type": "code", - "execution_count": 5, - "id": "d46d7bff-4acb-4c98-8a79-42afe18b44d7", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "model = LinearRegressionModel(lags=24, lags_future_covariates=[0])\n", "model.fit(ts_energy_train, future_covariates=ts_weather)\n", @@ -345,12 +230,12 @@ "ts_energy_train[-48:].plot(label=\"training\")\n", "ts_energy_val[:12].plot(label=\"validation\")\n", "pred.plot(label=\"forecast\")" - ] + ], + "id": "5a5234c077610100" }, { - "cell_type": "markdown", - "id": "7c7a6c5e-04c3-4a95-9d4a-03ec3517cd63", "metadata": {}, + "cell_type": "markdown", "source": [ "### Forecasting using only covariates\n", "\n", @@ -362,35 +247,14 @@ "How well can we predict the electricity consumption using only the weather \"forecasts\" as input?\n", "\n", "The lags tuple `(24, 1)` means (number of lags in the past, number of lags in the future)." - ] + ], + "id": "797b9c2d3c4166de" }, { - "cell_type": "code", - "execution_count": 6, - "id": "8aaa2095-0a32-4146-9b1b-82cd9e1624f4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "model = LinearRegressionModel(lags=None, lags_future_covariates=(24, 1))\n", "model.fit(series=ts_energy_train, future_covariates=ts_weather)\n", @@ -399,33 +263,20 @@ "ts_energy_train[-48:].plot(label=\"training\")\n", "ts_energy_val[:12].plot(label=\"validation\")\n", "pred.plot(label=\"forecast\")" - ] + ], + "id": "21a6dddae4d1919c" }, { - "cell_type": "markdown", - "id": "0ba7df08-2b2a-4e10-849c-728e269e4044", "metadata": {}, - "source": [ - "And what if we added some calendar information? We can let the model generate calendar attribute encodings for free with `add_encoders`." - ] + "cell_type": "markdown", + "source": "And what if we added some calendar information? We can let the model generate calendar attribute encodings for free with `add_encoders`.", + "id": "a17b06d666b5e128" }, { - "cell_type": "code", - "execution_count": 7, - "id": "d03e283c-21a6-4330-8f50-34f77351818f", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "model = LinearRegressionModel(\n", " lags=None,\n", @@ -442,12 +293,12 @@ "ts_energy_val[:12].plot(label=\"validation\")\n", "pred.plot(label=\"forecast\")\n", "plt.show()" - ] + ], + "id": "2333f4cdb71fdc62" }, { - "cell_type": "markdown", - "id": "08c62699-baf4-404f-bd60-33d4e072192d", "metadata": {}, + "cell_type": "markdown", "source": [ "### Component-specific lags\n", "\n", @@ -455,35 +306,14 @@ "Just pass a dictionary to the `lags*` parameters with the component name as key and the lags as value.\n", "\n", "In the example below, we set the default lags to `(24,1)` (used for the covariates components `'T [°C]'` and `'StrGlo [W/ms]'`) and give feature `'RainDur [min]'` a dedicated lag `[0]`." - ] + ], + "id": "d978c1c75509f437" }, { - "cell_type": "code", - "execution_count": 8, - "id": "76f60b93-6433-4e20-9ae6-32f511fedd65", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "model = LinearRegressionModel(\n", " lags=None, lags_future_covariates={\"default_lags\": (24, 1), \"RainDur [min]\": [0]}\n", @@ -494,12 +324,12 @@ "ts_energy_train[-48:].plot(label=\"training data\")\n", "ts_energy_val[:12].plot(label=\"validation data\")\n", "pred.plot(label=\"forecast\")" - ] + ], + "id": "2549d89c9a2bb51e" }, { - "cell_type": "markdown", - "id": "af170e70-0c06-45f9-a093-df36dd9373c1", "metadata": {}, + "cell_type": "markdown", "source": [ "### Model's output chunk length\n", "\n", @@ -510,35 +340,14 @@ "- an auto-regressive forecast, consuming its own forecast (and future values of covariates) as input for additional predictions (otherwise)\n", "\n", "For example, if we want our model to forecast the next 24 hours of electricity consumption based on the last day of consumption, setting `output_chunk_length=24` ensures that the model will not consume its forecasts or future values of our covariates to predict the entire day." - ] + ], + "id": "2a4eb0d9af70651a" }, { - "cell_type": "code", - "execution_count": 9, - "id": "b376f792-d374-4c6c-b47a-644621248efd", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "model_auto_regression = LinearRegressionModel(lags=24, output_chunk_length=1)\n", "model_single_shot = LinearRegressionModel(lags=24, output_chunk_length=24)\n", @@ -553,12 +362,12 @@ "ts_energy_val[:24].plot(label=\"validation data\")\n", "pred_auto_regression.plot(label=\"forecast (auto-regression)\")\n", "pred_single_shot.plot(label=\"forecast (single-shot)\")" - ] + ], + "id": "b156a4a39b1a7b79" }, { - "cell_type": "markdown", - "id": "948afd96-fbd7-459d-a9ea-af1025968213", "metadata": {}, + "cell_type": "markdown", "source": [ "### Multi-model forecasting\n", "When `output_chunk_length>1`, the model behavior can be further parametrized by modifying the `multi_models` argument.\n", @@ -571,35 +380,14 @@ "\n", "We are still able to predict all points from 1 until `output_chunk_length` by shifting the lags during tabularization.\n", "This means that a new set of input values is used for each forecasted value. Due to the shifting, the minimum length requirement for the training series will also be increased." - ] + ], + "id": "2cebefcbbd9e5f48" }, { - "cell_type": "code", - "execution_count": 10, - "id": "0d40df60-0978-48d1-b156-6a60655c5cfc", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "multi_models = LinearRegressionModel(lags=24, output_chunk_length=24, multi_models=True)\n", "single_model = LinearRegressionModel(\n", @@ -616,14 +404,12 @@ "ts_energy_val[:24].plot(label=\"validation\")\n", "pred_multi_models.plot(label=\"forecast (multi models)\")\n", "pred_single_model.plot(label=\"forecast (single model)\")" - ] + ], + "id": "fd5faaab9e1249a8" }, { + "metadata": {}, "cell_type": "markdown", - "id": "0b05cba7-0a4a-441b-9ab1-57992b9a52b6", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, "source": [ "### Visualization\n", "To visualize what is happening under the hood, let's simplify the model a bit and show the process for different parameters.\n", @@ -648,12 +434,12 @@ "Apart from the auto-regressive prediction, we can see the lags shift per predicted time step because `multi_models=False`.\n", "\n", "The same process also occurs during tabularization for training the model: each green chunk is paired with an orange value, constituting the training dataset." - ] + ], + "id": "861f14480f35d145" }, { - "cell_type": "markdown", - "id": "fdc2cfab-3b39-41c4-8701-ac6ee67b4b63", "metadata": {}, + "cell_type": "markdown", "source": [ "### Probabilistic forecasting\n", "\n", @@ -670,34 +456,14 @@ "**Tip:** `CatBoostModel` and `XGBModel` additionally support native multi-quantile regression (`likelihood=\"multiquantile\"`), which fits all quantiles in one model and is more efficient.\n", "\n", "\n" - ] + ], + "id": "88cada57abdd549a" }, { - "cell_type": "code", - "execution_count": 11, - "id": "38de016d-4029-4c80-bbf8-2fd73716f8cd", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Value_NE5_q0.05 : 19580.123046875\n", - "Value_NE5_q0.50 : 19623.302734375\n", - "Value_NE5_q0.95 : 20418.7421875\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "model = XGBModel(\n", " lags=24, output_chunk_length=1, likelihood=\"quantile\", quantiles=[0.05, 0.5, 0.95]\n", @@ -715,12 +481,12 @@ "ts_energy_val[:24].plot(label=\"validation\")\n", "pred_samples.plot(label=\"forecasts\")\n", "plt.tight_layout()" - ] + ], + "id": "3346189ae0b178b7" }, { - "cell_type": "markdown", - "id": "f578a80a-9ba1-4067-97ab-477bd447a7c3", "metadata": {}, + "cell_type": "markdown", "source": [ "### MultiOutputRegressor wrapper\n", "\n", @@ -730,152 +496,87 @@ "- multivariates series\n", "\n", "For models that don't support it, Darts wraps sklearn's `MultiOutputRegressor` around them to handle the logic under the hood." - ] + ], + "id": "3746480303dd3dd3" }, { - "cell_type": "markdown", - "id": "af6db120-f776-459b-9751-af02fd579188", "metadata": {}, + "cell_type": "markdown", "source": [ "## Explainability\n", "\n", - "We offer explainability for the regression models with Darts' `ShapExplainer`. The explainer uses [shap](https://github.com/shap/shap), a library based on game theory, to give insights into feature importances over time for our forecast horizon." - ] + "We offer explainability for the regression models with Darts' `ShapExplainer`. The explainer uses [shap](https://github.com/shap/shap), a library based on game theory, to give insights into feature importances over time for our forecast horizon.\n", + "\n", + "You can find more detailed examples in our [notebook on Explainability](https://unit8co.github.io/darts/examples/28-Explainability-examples.html)." + ], + "id": "7ec0adcc1a0736bf" }, { - "cell_type": "code", - "execution_count": 12, - "id": "e6a1dbfe-34db-43d9-aec5-3c711d9f80be", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "model = LinearRegressionModel(lags=24, lags_future_covariates=(24, 24))\n", "model.fit(ts_energy_train, future_covariates=ts_weather)\n", - "shap_explainer = ShapExplainer(model=model)\n", - "shap_values = shap_explainer.summary_plot()" - ] + "explainer = ShapExplainer(model=model)\n", + "shap_values = explainer.summary_plot()" + ], + "id": "532fb31e403d43d2" }, { - "cell_type": "code", - "execution_count": 13, - "id": "f96a3875-fb99-4210-b79d-47e7b553a0f1", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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\n", - " Visualization omitted, Javascript library not loaded!
\n", - " Have you run `initjs()` in this notebook? If this notebook was from another\n", - " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", - " this notebook on github the Javascript has been stripped for security. If you are using\n", - " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", - "
\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "# extracting the end of each series to reduce computation time\n", "foreground_target = ts_energy_train[-24 * 2 :]\n", "foreground_future_cov = ts_weather[foreground_target.start_time() :]\n", "\n", - "shap_explainer.force_plot_from_ts(\n", + "explainer.force_plot(\n", " foreground_series=foreground_target,\n", " foreground_future_covariates=foreground_future_cov,\n", ")\n", "\n", - "# the plot cannot be rendered on GitHub or the Documentation page. Run it locally to see it." - ] + "# Plot cannot be rendered on GitHub. Run it locally or visit the documentation page to see it." + ], + "id": "a567d1540cddc027" }, { - "cell_type": "markdown", - "id": "9b0a0b82-5d79-4633-af38-58eb9b56fcfd", "metadata": {}, + "cell_type": "markdown", "source": [ "## Conclusion\n", "\n", "By tabularizing the data and unifying the API across libraries, Darts closes the gap between traditional regression problems and timeseries forecasting.\n", "\n", "`SKLearnModel` and its sub-classes offer a wide range of functionalities which can be used to build strong models in just a few lines of codes." - ] + ], + "id": "d0fab567a6bacdd3" }, { - "cell_type": "markdown", - "id": "b153cc69-2485-4bbd-85ff-c5cd9e107582", "metadata": {}, - "source": "## Appendix" + "cell_type": "markdown", + "source": "## Appendix", + "id": "6d799f56e5cc00f3" }, { - "cell_type": "markdown", - "id": "4b995311-0bdc-4d05-9573-f82466aba6e0", "metadata": {}, + "cell_type": "markdown", "source": [ "### SKLearn models\n", "`SKLearnModel` wraps the Darts API around any sklearn regression model. With this we can use the model in the same way as any other Darts forecasting models.\n", "\n", "As an example, fitting a Bayesian ridge regression on the example dataset takes only a few lines:" - ] + ], + "id": "76fd1de53bc0de5" }, { + "metadata": {}, "cell_type": "code", + "outputs": [], "execution_count": null, - "id": "b2af4e28-2b29-4563-8d8f-1a57c4a5c157", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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u3Tr27NkDwKWXXkrHjh0v+DMNBgN/+ctf2L59OzfffLP2/k8//UT37t2ZOXMmJ0+evODrCO6DLawE7iFkhg0bRnJyMgDLly9n37592r68vDzGjx+P2Wx2OOezzz5zqY2Cc1AUhdnvVYmUR25WBcxVqQaemlwlZm56UmHdDhEz3oIIGR/mvffe07ZvuummRv3smJgY3nnnHZYvX66tJCkrK+OZZ56hTZs2/Otf/+LUqVONek1BH+wTfW1FEvXEPukX0JJ+FUXhtttuY//+/QD069ePmJgYAL799lst2V3wXNJ/ha171e1+nWB4n6p9D02Em8/0Fy0zwxUPKxw8JmLGGxAh46OUl5fz0UcfAWAymbjmmmuccp0RI0bw559/Mn36dAIDAwE1GXjOnDkkJSXxyCOPcPr0aadcW3A+iqJoHpmoqCjatWuns0Uqt956q0PSr9ls5pVXXuHLL78EVFs//fRTLUG9uLhYOmd7ONW9Mf++yeCwaMFgMPC/GQaG9FBfHzsFf/2XrGTyBkTI+CjfffedJiCuuuoqwsPDnXat0NBQnn/+efbu3cs999xDQEAAoK6Ymj17NklJSVJIz0PJysrSVrz169ev3qvdnEVcXBx//etfATh+/Dj/+te/HIo9vvfeeyQmJjoIePuGl4Ln8f16WL9L3e6RAn+5pOYxgQEGvnjCQFu1IDl/7FFzZgTPRoSMj/Luu+9q240dVjobLVu25L///S+ZmZlMmTJFEzQFBQVMnTqV559/3iV2CI2Hu4WV7LFP+n3++eexWCwAPPjgg/zlL38B4LLLLiMyMhKAb775RvqFeTD23ph/3Wg4q6huGmngs1kGAtWfH174BJavEzHjyYiQ8UFOnDjB4sWLAbVHzbBhw1x6/datW/PWW2+RkZHB7bffrr0/Y8YMFixY4FJbhAvD3RJ97Rk6dCgpKSkO7w0aNIjZs2drrwMDA7Xl2oWFhSxfvtylNgqNwy+bFVZtVbc7toarhpz7+F7tDTw9xS75d47C8dMiZjwVETI+yMcff6w9nU6aNEnrGuxqEhMTmTdvHk888YT23p133qnlMQjuj71Hpl+/fjpaUpPqSb9Nmzbl448/1jyBNiS85PnYe2MevtGA0Xj+EOf918DIM1P22Cm49SkpluepiJDxQfQIK52Lf/3rX0ydOhVQWxxcd911rFy5Ul+jhPNiNpvZtGkTAO3atdNWALkTd955J507dyYqKopPPvlE69Zuz4gRI4iIiADg66+/rrE0W3Bv1m5X+H6Dup3cHK6vo4PZz8/Auw8baBalvl68Fl6TzioeiQgZH2P79u1s3LgRUBs+dunSRWeL1NUEL7zwgiaqzGYz48aNY/369TpbJpyLLVu2aDkl7hZWshEVFcWff/7JsWPHuOyyy2o9JigoiCuuuAJQCzj+8MMPrjRRuACO5ipcP6vKi/LPSQb8/euecB4XrYoZGzPeVLTl24LnIELGx3Bm7ZgLwc/Pj3nz5mktDoqKihg9ejQ7d+7U2TLhbNiHldxVyIAqlKuHk6oj4SXPo7BEYcxDCgeOqq97pMBNI+v/OaP6G5g6Xt02V8ANj0NpuXusvhPqhggZH6KyspIPPlAbjfj7+3P99dfrbJEjAQEBfPLJJwwZombq5ebmMmzYMJ555hn27pXHJHfDPtHX3VYs1ZfLL7+csLAwQG3VcbZmk4J7UGFRuPY/Cn+ohclp1QwWP2sgMKBhAuTpKQZ6nimBtDMLZn0QhbSH8xxEyPgQK1eu1LpRp6Wlaf2Q3AmTycQ333xDr169AMjJyWHmzJmkpKTQu3dvnnrqKenZ5CbYPDJBQUF0795dZ2suDJPJpC3JPnXqFD/++KPOFglnQ1EUpjyvsPSMjo4Mg6XPG2jetOFelKBAAx89WtVc8tNfwhl4L2zY1fDk37JyhVkLFeamSwKxsxEh40O4W5Lv2WjSpAlLly5l6NChDu9v2rSJhx9+mPbt29OrVy9+++03nSwUTp48qXnJevfurVVt9mQkvOQZPLZQYaFaPYLAAPh6joHOSRceCuqYaOCNf1R9zrqd0G+Kwl3PW8nNr78YefNr1dbJzyms3ChixpmIkPERCgsLWbRoEaAmQNqePt2VZs2asXLlSvbu3cszzzxDnz59HPZv3ryZf/zjHzpZJ6xbt07b9vSwko3Ro0cTEhICwJdffqmVKBDch7npCo+/o24bDPDBvw0M6dl4+Sy3jDbww0vQrrm6ck1R4O1voMMkhXnfKlitdRck36+vOvb95SJknIkIGR/hiy++0JriXXfddQQFBelsUd1ITk7mwQcfZP369ezfv5/nn3+e6OhoQPXQyM1GHzwl0bc+hISEMGbMGED1OP3yyy86WyTYs/g3hbtfrBIEL95rYPzQxk/KvbQXfPtEDs/fA2Em9b3cfLjzWYVL7lE4VXB+UVJZqbBme9XrRb8gPZ2ciAgZH8GW5AvuHVY6F0lJSUyfPp0RI0YAauPL3bt362yVb+LOFX0vBPvw0meffaajJYI9FRaF255RqKxUX//jWph6rfNWFgX4w7RrYfeHBq4fXvX+7zvUcNH52H4A8ouqXhcUw5Lfz3q4cIGIkPEBCgsLtafLpKQkj7/x9OzZU9vevHmzbnb4KlarVQstxcXFkZiYqLNFjUdaWhrBwcEALFq0iErbnVPQlaW/q9V3AS7vC8/d45rl0c2bGvi/R/1Y+bKBAP8qW87H6q013/v4B/HIOAsRMj7AypUrteWkaWlpbtOhuKGIkNGXPXv2kJeXB6jeGE+fT/aEhYUxevRoQO2avWbNGp0tEsAxx+S+awz4+bl2zg3tbaB/Z3U74zAcPHZuUbJ6W9V+WweYb36FohIRM85AhIwPsHTpUm171KhROlrSOIiQ0RdvDSvZuPLKK7Xt7777Tj9DBADyChW++VXdjo1UPTJ6MPyiKvH0w8ZzH2vzyISa4JYzP7ml5ZAuutgpiJDxchRFYcmSJYDa6bf6kmZPJD4+nri4OEAVMtLozbXYJ/p6y4ole0aPHq15mUTI6M9nP0H5mfZX1w+DgHq0IGhMhl1Utf3DOZZTHzymcOi4un1xZ7hxZJW9H62Q3ypnIELGy9m9ezdZWVkADB48WKte6unYvDInT57UivwJrsHmkTEYDDWWxXsDsbGxWifvP//8k4MHD+pskW/z/rKqm7+9KHA1/TqpHhZQPTJne4Cyz48Z1E39r3lT9fXSdXC6UMRMYyNCxsuxeWMALfbvDUh4SR9KSkrYulX9pe7SpYvWNdrbsC3DBvHK6MmBHIVVZ4RBx9ZwUQf9bAkMMDDkTAHro6dgx4Haj7PPjxnU3YDRaGDCmX6lFRZY9LNz7fRF6i1knnzySUaOHElqaioTJkxg1apVAKSnp3PDDTcwZMgQxo0bV6MyZp8+fRg0aBCDBw9m8ODBLFiwQNtXVlbGI488wpAhQxgzZoxDTofts9PS0khNTWXWrFnSB6UeeFt+jA0RMvqwceNGbSWPN+bH2LAvGPntt9/qaIlv88Hyqu0bRxp0Tywf3uf8eTI2j4zRiJYgfN1lVed9vFI8Mo2Nf31PmDhxIjNmzCAwMJDt27dz77338s0332A2m/nnP/9Jp06dyMrK4u677yY5OZnevXtr53711Vc0bdq0xme+/fbb5Ofns3jxYvbu3cv9999Pp06dSExMJDMzk5deeonXX3+d1q1bM336dObPn89dd911Yd/cBygpKeHnn1X536pVKzp37qyzRY2HCBl98PZEXxs9e/akefPmZGdns3LlSkpKSrSqv4JrUBTFYbXSxBE6GnOG6nky913jKKxOFyr8uV/d7pkC4SHq/r6dILk57MuGlX/A0VyF+BjvWe2nN/X2yCQlJWl9VQwGA2azmZMnT3L11VfTrVs3/P39adu2Lf369WPHjh11+szFixczefJkwsLC6NGjB0OGDGH5clWKL126lBEjRtC5c2fCwsK44447HMIlwtn56aefKC8vB1RvjN5PM41Ju3btMJnUgLUIGdfhja0JasNgMJCWlgaoHmNpIul61u+EPYfU7dSekBiv/+9Xt2Ro2kTd/mkzWCyO3pXf/lTbGoCaG2PDYDBw3TB122qFz39yuqk+Rb09MgBPP/006enplJeXk5qaSnJyssP+yspKtm/frv0Q2Jg0aRIGg4H+/fszdepUIiMjKSgoIDc3l5SUFO249u3bs327Wt953759DBgwQNvXrl07jhw5QllZmVa4yh6z2YzZbHb8kv7+DW5qZz3Ty93qgT3d7QXfyJEjXfIdXDVeBoOB7t278/vvv5OZmUl+fj7h4eFOvaYz8LT5tX+/+rhpMBho3769y+125XiNHj2aefPmAWp4yVNzzDxtjtl4b1nV9sQRrrP/fOM1tDd89qNarXfdToWLu1SJmVV2ib6XdHX8jGuHwpz31e2PflC456/eEWJy9vzy8zu/v6VBQmbmzJnMmDGDDRs2kJmZWWP/m2++SWxsrIMAmTt3Lt26daOwsJBnnnmGxx9/nBdffJGSkhKMRqODKAkNDaWkpASA0tJSQkNDtX22VTelpaW1CpmFCxcyd+5ch/fGjx/Ptdde25CvqnHo0KELOl8P0tPTAVXIpaSkaKuXXIErxis5OVkLdSxfvtyjV9B4yvw6fPgwAE2bNtV1tZgrxqt9+/YEBgZiNpv55ptvmDFjhkd7NT1ljgGYLfDRipaAkaAAK/2SD5OV5dob/9nGq1dSGJ8RA8CiladJCCvQ9v2wPg5Q70tJUYfIyqq6uUcYoX2LBPYcCWTNn7Bm42FaNPWeytHOml9t2rQ57zENEjIARqOR/v3789FHH5GcnKyJls8//5yVK1eyYMECh3/0vXr1AtTOyw888ABjxoyhoqKCkJAQKisrHTwsxcXFWjzaZDJRXFysfU5RUZH2fm3ceuutTJw40fFLXqBH5tChQ7Rq1apOytBdyMzM1ITLJZdcQrdu3c5zRuPgyvEaNGgQH330EQDHjh3zyFL5njS/KisrOXHiBKDmXOkx3q4er9TUVL7//nuys7MpKiqia9euTr9mY+NJc8zGN7/CqUJ1e9wgP7p1au2ya59vvMaPgIcXqtt/7IsiMTEKUGvdbD2TH5PSAvr0aFXj3Ekj4dEz61x+3dOSGRfVOMTjcIf51WAhY8NqtWpPacuXL9c8IpGRkWc9x/ZlFUUhIiKCmJgYMjMztR+JPXv2aOGq5ORkB69PRkYGLVq0qNUbA2rRt4aKlnPh5+fnMT8CgJZjBKqL3NW2u2K87BPJt2zZ4lF/n+p4wvw6fvy4tmIpISFBV3tdNV5/+ctf+P777wE1l6979+5Ov6az8IQ5ZuPD76s8GTeNcn1LAjj7eKW0hKR4KweOwq9/QpnZQEiwgU0ZCuUVqtdoUPfaQyLXD1d4dIF6zCcr4aGJnvH3qAt6zq96XbWkpIQlS5ZQUlKCxWLhhx9+YOPGjfTq1Yu1a9fy3HPP8fLLL9O8eXOH8/bu3cuePXuorKykoKCAF154gf79+2uCIy0tjXnz5lFcXMy2bdv45ZdftA7Ho0aNYsWKFezatYuioiIWLFjgsbFqV+Kty67t6datm+b1k4Rf52MfSqr+b9xbkXoyrievUNFK+evZkuBc2FYvmSvg123qtn1+zKButQuvlJYG+nRUtzdlwO6D3pEnozf1EjIGg4Gvv/6atLQ0hg0bxsKFC5k9ezYpKSksXLiQgoICbrvtNq1WzJw5cwA4deoUM2fOJDU1lfHjx+Pn58djjz2mfe6UKVMICwtj1KhRzJw5k5kzZ5KUlARASkoKU6dOZdq0aaSlpREXF8dtt93WaAPgjZSVlbFy5UpALeffo0cPnS1yDqGhobRv3x6Abdu2YbFYdLbIu8nJydG2fUXItG3blg4d1Cpsa9as4dSpUzpb5P24S0uCc+FYT0YVI46F8M5+7vXD7GrK/ND4tvki9QotmUwm3nrrrVr3vf3222c9r2/fvixatOis+4ODg5k9e/ZZ948dO5axY8fW3VAfZ9WqVZSWlgLet+y6Oj179mT37t2Ul5eze/duunTpordJXosvemRADS/t3r0bq9XKsmXLuP766/U2yatxl5YE5+Kyqqg2P/wBVquieWaaNoH2NdNjNMZfCtP/q25/v0HhP7e653f0JLwnQCdoeGtbgtqQwniuw1eFjISXXEfWUfdpSXAumkUZ6Ham6sjG3Wp46fSZ5ORB3Tnnw2OrOAPtWqrb63ZCSZmEly4UETJeiC0/xs/Pj+HDh+tsjXMRIeM67IVMQkKCjpa4lkGDBmk9pZYsWaIlPAuNz2/bq7YnXHZuQaA3tjwZRYHZ79mFlc6SH2NPak/1/xUWx+8sNAwRMl5GVlYWO3fuBNTKq9HR0Tpb5FxEyLgOX8yRAQgICODyyy8H1Hy/tWvX6myR95J1tGq7c5L7ihhwzJNZvr7q/XPlx9hI7Vl17s+bxSNzoYiQ8TJ8YbWSPfHx8cTFxQGqkFEU+VFwFjaPjJ+fH82aNdPZGtdi30RSwkvOI+tY1b/f1nE6GlIHhvQAf6Pje6Yg6NXu/Oem2q2/+Hlzo5rlk4iQ8TJ8KT/Ghs0rc/LkSV2rzXo7trGNj4/HaDSe52jvYvTo0VqYQ4SM8zh4rGo70c2FTHiIgX6dHN/r3xkCA87vSWoVZyD5jFPz951QVi4PYBeCCBkvwmw288MP6nq+pk2bOhSM82YkvOR8LBYLx46pdxlfyo+x0axZM/r2VQuabN261aPK/XsSttBSYADEeUBUfFi1yryD6lFA3ZYnU25WxYzQcETIeBG7d+/WWjgMHz7cY6p4XigiZJzP8ePHtaZwvpQfY4+sXnIuiqKQdcYj06oZulTzrS/DL3K0cVD3utuc2qPq2J82NZpJPolv3Ol8hAMHDmjbHTt21M8QFyNCxvn4aqKvPfah2l9//VVHS7yT/CIoVHsFu31YycbFXSDkTLccPz8YUI8yVjaPDEjC74UiQsaLsBcytsrIvkC7du20JqIiZJyDr9aQsad79+74+6s1RLdu3Xqeo4X6kmWXH+Puib42AgMM3H7GUTf+UogIrbtHJinBoH3P37ZDuVnETEMRIeNF7N+/X9v2JSFjNBq1Zn6ZmZkUFhbqbJH3IUIGgoKCNE/nzp07MZvNOlvkXXhSoq89r9xn4MCnBj76T/1DYZf2VP9fZob1uxrXLl9ChIwX4aseGXAML8nTcuPjq8XwqmMTzBUVFVq9JqFxsK8hkxjv/vkxNgwGA4nxhgYV73OsJ9OIRvkYImS8CJuQMRqNtGjRQl9jXIzkyTgXyZFRsW/AumXLFh0t8T48qYZMYyF5Mo1DvZpGCu6NTci0atVKi+X7CiJknIuEllTshYx4/hoXV4aWji8/waEPDxMUG0Ro2xBCkkMITQ4lJNHk0rticnNoEQtHTsCvf0KFRXHLbt/ujm/d7byY/Px8Tp8+DUCbNm10tsb1dOvWDYPBgKIoImScgE3IGI1GYmNjdbZGP8Qj4zzsk31bOmmKKYrCvtcOsHvWntoP8ANTKxPBA4Jo/XJrp8csDAYDqT0U/m8FlJTBhl0woKtzr+mNSGjJS8jKytK2fS0/BiA0NJT27dsDsG3bNiwWi84WeRf2VX19pT5RbcTFxWlCbsuWLdISoxGxeWTioyE4qPG9ElazlW1Tt59dxABYoTSrlNMf57H/9QONbkNtXNpL8mQuFN/9RfIyfHXFkj228FJ5eTk7duzQ1xgvwmKxcPz4ccC3w0qgPkHbvDInTpzg6NGj5zlDqAvlZoWcXHU7Mb7xP78ir4L14zdy+IMj2nvtHmrLgKX96f7frqRMTybhqngiekTAGV2R8cxeCrYVNL4x1XDIk9kiwrghiJDxEnx5xZKNPn36aNvr168/x5FCfTh27JjmefB1IQOSJ+MMDh2v2m7sRN/ifcWsGfk7uatPAeAX5EfPud1p92AKUX0jaXldC9o/3I5ec3swaOUA2vw9CQClQmHzXduoLKtsXIOq0a6l6oUCWL0VLBYRM/VFhIyXIEIG+vXrp22LkGk8JNHXEcmTaXwaO9HXUmShKKOYnG+Osmbk7xRnFgMQ2DSQ/l/3pflVZy8h0O6htgS1DwKgaFcRe+ZkXrhB58BgMGhemaJS2JTh1Mt5JZLs6yWIkIHevXvj5+eH1Wpl3bp1epvjNYiQcUSETOOT5SBk6pcfYz5tJuOZvRTtLqIsp5zynDIsRTW9KGEdQunzUW9CEkPO+Xl+gX40fzyeAzcdRDEr7H/jAM1GxhIz0HldLFN7GvhkpeqJ+Wkz9O107uMFR8Qj4yXYhIy/v7/P1ZCxERYWRufOnQE14be0tFRni7wDKYbnSMeOHQkICAAktNRYZB1teA2ZnQ/vJmvuQXJ/OUVxRnGtIqbppTEMWNr/vCLGRnBKEO3/laK+UGDrvduoKHDeAgKpJ3NhiJDxEmxCpnXr1hiNRn2N0ZG+ffsCaoKqLMNuHKQYniOBgYF06qQ+Mu/atYvy8nKdLfJ8DtrlyNQn2bfkYCnZX1TNTz+THyHJIUQPjKL5NQkk/z2JHm93o8/HvQmICKiXTUl3JRI9MAqA0kNl7Hi4fpWczafN7PjXLva+vA/Fem5x0ikRYiPV7VVbobJSxEx9ECHjBeTl5ZGXlwf4bljJhuTJND4SWqqJLbxksVhkhVwjYN+eoD4emf3/PYBy5qaf8kAyIw8N59L1g7n4m370fLs7HR/rQItrmuMXUP9bncHPQI//dsM/TH0wPPJRNke/PXaes1TMp82su2oDB97KYvcTGex9ad+5r2WXJ1NQDFucm5bjdYiQ8QIkP6YKm0cGkDyZRkKETE0kT6ZxsSX7hodAZFjdzik/Uc6hDw4DYAwxkjQlsUH9js6FqZWJzk9XJaz8+Y/tlB45d8i6Iq+CdVdvpGBrVfPaPU9nkvvrqXOel9rDrp6MTKl6IULGCxAhU0W3bt0IClJXHIhHpnGwCRl/f39iYmJ0tsY9sDWPBMmTuVCsVkULLSXGUWcxcuB/B7GWWQFodVNLAqMDnWJfi+uaEzemGQDm3ApWD1lD9qKcWo+tyK9g3dUbKNii1p/xCzzzXayw+c6tlB8/exhS8mQajggZL0CETBWBgYFaYbw9e/ZoITeh4dhyZBISEny6qq894pFpPI6dAnOFul3XsFJFgYWs+QcBMAQYaHNPknOMQxVWXV/sgqlVsHrtPAub79zKptu3YD5ltrOpgnXXbCR/sypiAmMDGbhyADGpqvgvP1bO5ru2aaGw6nRpA9ER6vbPm9W+S0LdkF8lL0CEjCP2eTIbNmzQ0RLPp6KiQqr61kKzZs2Ij1ezUqVVwYXRkETfQ+8ewpKvriJqMb45phbBTrCsiqCmqihJ+GuVgTlfHWXVoF85/v0JKgosrB+/kfw/8oEz9Wq+6kt4p3B6vtWNoDjVW5T7c+5Z82X8/AyMOFPTM68IVv7h1K/kVYiQ8QLs2xP4YsPI6kieTONhX4JfhIwjNq9Mbm6uQx6RUD/sE33rUkOmsqyS/W8eUF8YIPlMJV5nExgdSK95Peg5rzsBUeoKqPJjZjZc9werBv5K3oYzIiYmgH5f9iG8o5rsE9QsiJ5vd9futnueydSqDFfn2qFV3//TlSKO64oIGS/A5pEJCAiQOh/IyqXGRBJ9z47kyTQO9lV96xJaOvJJNuXH1JBO3JhmhLWvY3ZwI9H8rwkMXj2Q2BFNtffKsssACIgOoN+XfYnoHO5wTszgGNo9dKYujRU2T95Sa77M6IshzKRuL/oFzBUiZuqCCBkPR1EUqSFTjXbt2hERoQabxSNzYUgxvLMjeTKNQ9axqpv1+doTKJUK+16r8kC3vU8fD3RwfBB9PupN15c6YwxVf3MDogLov6gPEV3Caz0nZVqyXb6MudZ8GVOQgSsGqtt5RbBCIuN1QoSMh5OXl0dBgZpcJvkxKn5+flp4KTs7myNHjpznDOFsSDG8syNCpnFwCC2dJ0fmaPoxSvary59jBkcTeVGk8ww7DwaDgdY3tWLImoF0ea4Tg34aQES3iLMfbzTUyJc5MO9gjeMmXFYVXvpEwkt1QoSMhyOJvrVjnycj4aWGI6Gls9OhQwcCA9WbkgiZhmNL9vU3VnWBrg1FUdj7clWibNupyU62rG6YWppIvK01ppam8x4b1CyInv+rEsCH3jlUI1F8ZD+ICFW3v1oNZeUiZs6HCBkPR4RM7UieTOMgQubsBAQEaL29du/eTVlZmc4WeSY2j0yrZmA0nj3Z9+SPuRRsU4vMRfSIICbVeU0cnUnMoGiiBqitD4r2FFO4vdBhf1CggSsHqdsFxbBcfr7OiwgZD8d+xZIImSpk5VLjIDky58YWXrJarWzfvl1nazyPgmKFvCJ1+3yJvvtescuNmdqm0av4upLmV1XF0LI/r1lcT8JL9UOEjIdj75GRpddVtGjRQrvxrl+/HqvVqrNFnoktRyYgIECq+taC5MlcGPYrls6V6GvONWsl/kOSQ4gfU88W2W5Gwrh4DP6qWMn+8miNppLD+0DUmZzhb36FUgkvnRMRMh6OhJZqx2AwaF6Z/Px8MjOlC1tDsHlkmjdv7tFPwM5ChMyFkWUvZM6R6Ju7+hScuZfHjW6G4RwhKE8gMCaQppeqDwZlh8s4vS7PcX+Agb8OVreLSmHJWhcb6GGIkPFwpIbM2bHPk5HwUv0pLy/n5MmTgOTHnA2pJXNhONaQOUd+zC+52nbMEM/MjalO86urfq9rCy9dK+GlOiNCxoOxryGTmJgofXCqISuXLgyp6nt+mjZtqo2NtCqoP1lH61ZDJvcXNaxkCDAQfXGUs81yCXGjm+EXrP5mH/3mKNYKx/D3Zb0hpom6/e1vUFwqc+tsyJ3Pgzl9+jSFhWrGu4SVatKnTx9tWzwy9UcSfeuGLbx0+vRpDh8+rLM1nkVdQkulh0sp2VcCQGSfSPzD/F1gmfPxD/cnblRVV+2TP+c67A/wN3D1EHW7pAy++83VFnoOImQ8GFmxdG6io6NJSVHLgm/atImKigqdLfIspBhe3ZA8mYZjH1pq1az2Y07+XNWXqKmXhJVs2IeXcr44WmO/fXjp0x/FI3M2RMh4MLJi6fzY8mTKy8vZtm2bztZ4FlJDpm5InkzDsXlkmkWp5flrI9chP8a7Vs41HdYU/yaqh+nod8eoLKl02J/aQx0bUD0yhSUiZmqj3kLmySefZOTIkaSmpjJhwgRWrVql7XvnnXcYPnw4l112Ga+88opDvHj79u1cf/31DBw4kMmTJzs87ZWVlfHII48wZMgQxowZw9KlSx2umZ6eTlpaGqmpqcyaNUuerM8gK5bOj+TJNBwRMnVDPDINo8KikK3mkp+1hoyiKOSuUoWMMdRI5EVNXGSdazAG+RE/Vv3ylcWVHP/+hMN+f38DV6eq22Vm+HaNqy30DOotZCZOnEh6ejo///wzjz76KI888ggFBQWsXr2azz//nHfeeYdPP/2U1atX88033wBgNpt58MEHue6661i5ciVdu3bl0Ucf1T7z7bffJj8/n8WLFzNnzhyefvppsrKyAMjMzOSll17i+eef57vvviM7O5v58+c30tf3bETInB9ZudRwJEembrRv356goCBAhEx9OHwcbM+6Z0v0LdpdrHW6jr4kCr8A7wsiNL/q3KuXpDje+al31pT9DdNgMGA2mzl58iSLFy/mmmuuoWXLlgBMmjSJJUuWMG7cODZu3IjJZGLcuHEA3HnnnQwfPpycnBwSEhJYvHgxL7zwAmFhYfTo0YMhQ4awfPly7rzzTpYuXcqIESO0UuB33HEHs2fP5q677qrVPrPZjNlsdvyS/v5aT5T6Yiuk5o4F1exzZFq3bu0WNrrbeHXv3h2j0UhlZaVbFsZzt/Gyx17IxMfHu4WN7jhefn5+dOnShT/++IOMjAxKS0s1YeMOuOOYAey3u2e3jqvdvhM/ndS2owdHu+Q7uHq8oi6JJKhZIOXHzRxfcYLy0+UENAnQ9l/SBRJiICcXlvwOpwusNAlziWl1wtnjVZfVuA1K/3766adJT0+nvLyc1NRUkpOT2b9/P2lpadox7du357///S8A+/bt05IuAUwmEy1btmTfvn2EhoaSm5vrsL99+/Zaue99+/YxYMAAbV+7du04cuQIZWVlBAcH17Bt4cKFzJ071+G98ePHc+211zbkq2ocOnTogs53BhkZGQAEBgZSXl6uebHcAXcarw4dOrBjxw62b9/Ozp07CQkJ0dukGrjTeNmwzafAwEAKCgq0FXLugLuNV+vWrfnjjz+wWq389NNPdOzYUW+TauBuY7ZpRyjQFICwgFNkZdWcX4eXVXWuN6e49jfOleMVOiyE8o/MKGaFHe/uJHKcYwjt8t5RvPt9BOYK+GjpCUb3LXGZbXXFWeNVl/zPBgmZmTNnMmPGDDZs2KBVTC0pKSEsrEomhoaGUlKiDnZpaSmhoaEOnxEaGkppaSklJSUYjUYHUXKuc23XKC0trVXI3HrrrUycONHxS16gR+bQoUO0atXKreq0KIrCkSPqP/LExES3SfZ1x/G65JJL2LFjB1arlZMnTzJ48GC9TdJwx/GyYSuG16JFC7cJXbrrePXt25evvvoKgLy8PBITE/U1yA53HbPin6u2e3aMJjHRcUWS1WIlY5Pa7TogJoD2w9ph8HN+RV89xqvJLZGs/UgNfZt/qSDxPsf5M2YQvPu9un2yJBY3ml5uMb8avCDfaDTSv39/PvroI5KTkwkJCaGoqEjbX1xcrD35mkwmiouLHc4vLi7GZDIREhJCZWWlg4flXOfarmEy1d4yPTAwsMGi5Vz4+fm51Y/AyZMntXFJSkpyK9vAvcarf//+zJs3D4ANGzaQmpqqs0U1cafxAjUB/9Qpddlr8+bN3co2cL/x6tKli7a9a9cut7LNhruN2aHjVaGINgkG/KqJlPwtBVgKLQA0HRKD0d/oUvtcOV5RF0US0sZEyf5Scledwny8guD4qvBk5yQFW4+G3QfrFm5xNXrOrwu+qtVq5fDhw7Rp08ahn82ePXtITk4GIDk52WFfaWkphw8fJjk5mYiICGJiYup8bkZGBi1atKjVG+NLyNLrunPRRRdp25s3b9bPEA/CvqqvJPqeH1sOH8COHTt0tMRzyLIrm1LbqqVcL2xLcDYMBkNV0q8Vcr52rCmT0gJsGmGn+2QQuA31EjIlJSUsWbKEkpISLBYLP/zwAxs3bqRXr16kpaXxxRdfcOTIEU6ePMmHH37I6NGjAfVGUlpaSnp6Omazmfnz59O5c2ftBzItLY158+ZRXFzMtm3b+OWXXxgxYgQAo0aNYsWKFezatYuioiIWLFigfa4vIyuW6k7nzp3x91edj7KqpG7I0uv60aZNGy3BV4RM3bDVkAk1QXREzf22tgSgemS8HcfieI6rl4ICDSSf2b37EFitsnrJnnoJGYPBwNdff01aWhrDhg1j4cKFzJ49m5SUFAYNGsRVV13FTTfdxPjx4xk4cCBXXHEFoIZ7nn32WT788EOGDh3Kli1bePzxx7XPnTJlCmFhYYwaNYqZM2cyc+ZM7eackpLC1KlTmTZtGmlpacTFxXHbbbc13gh4KCJk6k5QUJCWfLlz507Ky8t1tsj9ESFTP/z9/enQoQOgepSl1tW5URRFq+rbuhk1OqtXllZyen0eAKbWJkKS3C9Bv7EJ6xBGeNdwAPI25lN6pMxhf6czeTElZXD4RPWzfZt65ciYTCbeeuuts+6/9dZbufXWW2vd16VLFz7++ONa9wUHBzN79uyzfu7YsWMZO3ZsfUz1ekTI1I8ePXrw559/YrFY2LlzJz179tTbJLdGhEz96dy5M1u3bsVisZCZmUmnTp30NsltOZGnFniD2nssnf49D2u5mkPTNNW7w0r2xP+lGYV/qqu3ji87TuJtrbV9HRMh/UxBvF1ZZy8i6Iu4X8aQUCekz1L9kOqr9UOK4dUfyZOpO/b5MbUVw7NvoOhtbQnOha2JJMCxJccd9nVsXeW1kjwZR0TIeCg2j0xQUBBxcSLNz4cImfohDSPrjwiZumPfLLJ1XM0l1Q6JvoN9xyMT3jWc4JbqQpbcVaeoKLBo+zpWOWfYdVByZOwRIeOBKIqiCRl3XHrtjoiQqR8SWqo/ImTqTpadkKnukanIqyB/SwEA4Z3DCIp1nyrJzsZgMGheGaVC4eSPVZWNO9rVjtl10NWWuTdyB/RATp48qRUMlLBS3YiLiyM+Xg3Gb9682aGhqVATm5AxmUw0aeJdjfqcRUpKirY6ToTMuTl4rOrfX/UcmdzVp2wlU4hJ9Z2wko240bWHl6IjDFon7F0SWnJAhIwHIom+DcPmlTl16pRWFVmoHZuQad68eY0VJULtBAQE0L59ewB2796NxWI5zxm+y5EqRwMtYx332YeVfGHZdXWiL4nCP1wVxCe+P4G1oqpwoC28dPQU5BXKw5gNETIeiAiZhiHhpbpRWlpKXl4eIIm+9cUWXiovL3dIyBccOVVQtd20msPv5M9q/RiDv4HoAVEutMo98Av0I3aE2oOqIs/C6d/ztH2OeTIuNsyNESHjgYiQaRgiZOpGbm7VE3GzZs3OcaRQHcmTqRu5Z4RMYIBaEM9GWXYZxZlq65XI3k00z4SvcbbVS50Sq7yjImSqECHjgdia+QGyYqkeiJCpG6dPn9a2o6J874n4QhAhUzdsHpnocMdieHkb87Xt6EG+s1qpOrHDm2LwV8fl2JLjWk6fQ8JvloSWbIiQ8UDy86v+sUsiZt3p0KGDVkZehMzZsYWVACIjI3WzwxMRIVM3Tqk132q0Jig5WKpth3UIdaFF7kVAkwCiL1EfIkqzSinapTZLtg8tSS2ZKkTIeCAiZBqGv7+/1qV4z549NTqyCyoiZBpO+/bttXIIImRqp9ysUHxGr1QXMqVZJdp2SKL3tyU4F46rl9SeBK3jwHRmNbqElqoQIeOBFBRUZcqJkKkfttYEiqLw559/6muMmyKhpYYTFBRESkoKoPb1slqt5znD97BP9I0Od9xn75EJaW3Cl6ltGbafn4EOZ7wye7PBXCHhJRAh45HYe2QiImppGyucFcmTOT/ikbkwbOGl0tJSsrLE/18dW1gJIKbac1hplipk/IL9CGwW6EKr3A9TK5PWRDL/j3zKctQmkrbwUmUl7JUqEoAIGY/EJmSCg4MJDPTtf+z1RYTM+REhc2FInsy5OZtHRlEUSg6pQsbUyiT1i4C4UVVFdo4vV8NL0nOpJiJkPBCbkJGwUv3p3r27ti1CpnYktHRhiJA5Nw5CJqLqpmw+YcZaqobiQhJ9O6xko7bwktSSqYkIGQ/EliMjQqb+REVF0bq1+kuwdetWyWGoBfHIXBgiZM5NroOQqdouyarKjzG1EiEDENEjguAENbs395dTWIosdEqq2i/NI1VEyHgYVqtVhMwFYgsvFRYWOhQXFFREyFwYHTp00MIiImRqYu+RibETMqWH7BJ9xSMDqDV2mp0pjmctt3Lyx1zatQRb1E16LqmIkPEwioqKtOJIkujbMCRP5txIaOnCCAkJoU2bNoAqZKRBqSOn7HoEndUjI0JGI250VZ7MsaXHMQUZSDrTaHPXQWR+IULG45AaMheOvZDZvHmzfoa4KTaPTGBgIMHBwfoa46HYwktFRUUcPnxYZ2vci7Ml+5bK0utaiR4UgzHUCKgJv1aLVcuTKSyB7JPnONlHECHjYYiQuXBstWRAPDK1YRMykZGRsnKkgUiezNmpU46MCBkNY5AfscPONJE8VUHe+nw62bcqkIRfETKehhTDu3CSk5MJCwsDRMjUhi20JGGlhiNC5uycL0fGP8xIQFSAi61yb5pdXhVeyl2VS0f75pGSJyNCxtMQj8yF4+fnR7du3QC1k7j9mPo6lZWVmliWRN+GI0Lm7NgK4gX4V3W+VioVTciYEqWGTHVi7Bpo5v56qtoSbMmRESHjYUhV38bBPk9m69atOlriXth7/ETINJyOHTtq2yJkHNE6X0dUdb4uO1qGcqbcviy9romplUlLgM5bn0/7+EptnxTFEyHjcYhHpnGQlUu1IyuWGofw8HCtXpGsXHJEEzIOib5l2rYsva6dmIGqV8ZabsU/s0Br7yA5MiJkPI7GzpFJSkri5ZdfrvPxP/30EwaDwaHWiCciQqZ2pIZM42ELL+Xl5ZGTk6OzNe6BuUKh6ExOr32fpRK7rtcmH+96fTaiB1Y9WJyyCy8dOQGFJb4tlEXIeBiN7ZFZv349kydPrvPxl1xyCTk5OR7vDerWrZvm1pYl2FWIR6bxkDyZmsjS64bjkCez2jFPZrePe2VEyHgYdcmRURQFi8VSp8+LjY0lJKTuT0CBgYHEx8d7fDJeWFgYKSkpAPz55591Hi9vRzwyjYcImZrYd76Wpdf1w9TSREjSmTyZDfl0Sqhqr+LreTIiZDyMxYsXa9sXX3wxBoOBd955B4PBwLJly+jTpw9BQUGsWrWKvXv3Mm7cOOLi4ggLC6Nv376sWLHC4fOqh5YMBgPz5s3jr3/9KyEhIbRr145vvvlG2189tPTOO+8QGRnJsmXL6NSpExEREdxyyy0OrnSLxcJ9991HZGQkMTExPPTQQ9x8881ceeWVThmjumILL5WVlZGRkaGrLe6CCJnGQ4RMTc7qkTkkHpm6EG2XJ9OxtOqh1tdXLomQ8TA6dOigbW/evJmcnBxatWoFwIMPPshTTz3Fzp076d69O0VFRaSlpbFixQo2bdrEyJEjGTt2LAcPntsPOWvWLK699lq2bt1KWloaEydO5NSpU2c9vqSkhOeff57333+fn376iezsbGbMmKHtf+aZZ/jwww9ZuHAhv/76KwUFBXz11VcXNhCNgOTJ1ERCS41Hp06dtG0RMiq5dpUOYppUeXVtHpmA6AD8w/1dbZbHYEv4BWh6sOo32ddryciMqUafPn04evSow3uVlZUYjUanXjc+Pp4NGzac97jS0qonl3bt2hESEsKuXbsAePzxxxkxYoS2PyYmxuFmPXv2bL788ku++eYb/va3v531GrfccgvXX389AHPmzOG1115j3bp1jBo1qtbjKyoqeOutt2jbti1Wq5Ubb7yRN954Q9v/2muv8c9//pO//vWvALz++usOniW9qC5krrvuOh2tcQ/EI9N4REZG0rx5c7Kzs9m+fTuKonh8SPZCqS20ZK2wUpatrlqSpdfnxj7h17r1NIEBYK6QlUsiZKpx9OhRjhw5orcZZ8WWI2MwGDCZHP/R9+nTx+F1cXExs2bN4ttvvyU7OxuLxUJpael5PTLdu3fXtkNDQwkPD+f48eNnPT4kJIS2bdtqr5s1a6Ydn5+fz7Fjx+jXr5+232g0ctFFF2G1Wmt8lisRj0xNRMg0Lp07dyY7O5tTp05x4sQJmjVrprdJulJbaKnsSBmc+SmQpdfnxpYnU3KglPyN+XS+rJLNB41kHAaLRcHf3zeFsgiZasTHx9d4z1UembpgEzKBgYE1nu5CQ0MdXs+YMYNly5bx/PPPk5KSgslk4pprrsFsNp/zGgEBjuXBDQbDOUVHbcdXr5tR3VZ3qKvRqlUrmjRpQn5+vubV8nUktNS4dO7cWctL27lzpwiZWjpf2yf6ipA5P9EDoyk5cARruZXBQflsJpoKC+zLgfat9LZOH0TIVKN6eMdqtZKVlUViYiJ+fvqnFNmETFBQ0HmPXbVqFbfccosW0ikqKuLAgQPONK8GTZo0IS4ujnXr1jF48GBAFYabNm1yaN6oBwaDgZYtW5Kfn09OTo64/hGPTGNjWxkHsH//flJTU3W0Rn8ccmTOCBn7pdcSWjo/MQOjOfyhGjXoWnQaUPNmdmX5rpDR/84s1BlFUbSCeBaLhQMHDnDy5MmzektSUlJYtGgRmzdvZsuWLdxwww26hHP+/ve/89RTT/H111+ze/du7r//fk6fPu0WoiEhIQFQVy5JzyVHIePptYLcgcTEqjbFWVk+npFJ7TkyJfZCRjwy58U+Tyb+SJUH1ZfzZETIeBBlZWVUVFQAap5J586diY2NPWvOy0svvURUVBSXXHIJY8eOZeTIkfTu3duVJgPw0EMPcf3113PTTTcxYMAAwsLCGDlyJMHBwS63pTo2IQNI9VWqQkthYWE1QoZC/UlKStK2Xe0NdUcccmRq8cjI0uvzY19Pxn9vPgFWte/Sziz9w/V6IaElD8LeY3DppZc61He55ZZbahyflJTEypUrHd679957HV5X/3GtLXfF/in90ksvdTjmlltuqXHtyy+/nMrKqqZm/v7+vPbaa7z22muAGq7r1KkT1157bY1ruZrqQsZ+yawvYvtbS1ipcRCPjCM2IeNvhLAzmsWhGJ6EluqELU8Gs1pPZltoNJnuu0bF6YhHxoPw1IaRWVlZzJ07lz179rBt2zbuvvtu9u/fzw033KC3aeKRqYYImcalSZMm2liKRwZyzwiZmCZVCwBKD6p9loLiAjGanLuowluwrydzcaXqRd2XrZc1+iNCxoPwVCHj5+fHO++8Q9++fRk4cCDbtm1jxYoVbuH9ECFTRXl5uVanSFYsNR42r8yhQ4ccPJW+SPXO15WllZQfU1dRmlpLs8i6Yp8n07NMFTLZJ6G03DfDSxJa8iAau/O1q2jVqhW//vqr3mbUigiZKmTFknNISkpiy5YtWCwWcnJyaNmypd4m6YJ952stP+aQLL1uCPb1ZFqdyiOwaSVmPyMHcqBTkt7WuR7xyHgQnuqRcWdEyFRhL2TEI9N42OfJ+HJ46bT9iqUzHpkSWXrdYKLPdMM2Vipa36V9PvoTVi8hYzabmTVrFmlpaaSmpjJ58mQyMzMBtZT94MGDtf/69+/PtGnTtHP79OnDoEGDtP0LFizQ9pWVlfHII48wZMgQxowZw9KlSx2um56erl1z1qxZ2sodX6Muna+F+mEvZLKzfTjIjGMxPPHINB72K5d8OeE3127FUsyZ5zCHFUvikakX9nky3YrVf7v7ffQnrF6hpcrKSlq0aMHChQtp2rQpH330EdOnT+frr7/m4Ycf5uGHH9aOnThxYo3iT1999RVNmzat8blvv/02+fn5LF68mL1793L//ffTqVMnEhMTyczM5KWXXuL111+ndevWTJ8+nfnz53PXXXc18Ct7LuKRaXzCwsIICwujqKhIPDISWnIK4pFRqa09gUMxPFl6XS/s82S6FZ8C2rIvRwH0r8/laurlkTGZTNxxxx3ExcVhNBqZMGEC2dnZDj+AoFaw3L9/P8OHD6/T5y5evJjJkycTFhZGjx49GDJkCMuXLwdg6dKljBgxgs6dOxMWFsYdd9zBkiVL6mO21+CpOTLujs0r4+tCRtoTOAfxyKg41pBRb7YOS69FyNQLUwsTIW3UMetYmk+gtdJnVy5dULLv1q1biY6OrvH0tmTJEgYNGkRYWJjD+5MmTcJgMNC/f3+mTp1KZGQkBQUF5ObmOpTybt++Pdu3bwdg3759DBgwQNvXrl07jhw5QllZWa0F1cxmc41eQv7+/gQGBjboO9oq4erd4BAcn5jDw8PdwqbquNN41ZWEhAQyMjIoLCyksLCwRs8qZ+JO43Xq1CltOyIiwi1sqo47jVddadWqqm78/v37XW67u4zZSbvC2ZHhClarQkmWuvQaPwhqHqi7jeA+41UXoi+JomR/KQGKmiezLzva6+ZXXVoDNVjIFBUVMWfOHO65554a+5YtW8bUqVMd3ps7dy7dunWjsLCQZ555hscff5wXX3yRkpISjEajgygJDQ2lpESd4KWlpQ43Fps4Ki0trVXILFy4kLlz5zq8N378+Asuvnbo0KELOr8xsO/KXVRU5NZPd+4wXnXFPt9ow4YNDk/QrsIdxss+7FFRUSHzq5FQFIXQ0FCKi4vZu3evbuOq95jtzQrH1hdIKT9BVlYJxQeKAfBv5s+hbPf6m+o9XnXB2qFKPHQrPs2XRyI5cOAQenR/cdZ4tWnT5rzHNEjIlJeXM336dAYNGsS4ceMc9m3ZsoWCggIGDhzo8H6vXr0A1WX9wAMPMGbMGCoqKggJCaGystLBw1JcXExIiFpTwGQyUVxcrH1OUVGR9n5t3HrrrUycONHxS16gR+bQoUO0atVK96aR9jUoOnXqROvWrRv0OcnJydx///3cf//9gNru4IsvvuDKK6+s9fgDBw7Qtm1bNm7ceN5Gj+car/p8jitp27attu3n5+eQ0+Bs3Gl+2dO+fXuXjkNdcdfxOh9JSUls376d7Oxsl9vuLmOm2N1t2ifH0iLGws78PQCEJ4e7zXxzl/GqC6VjS8l+9CgAHUryKSn3I6RJIs1cGBl2h/Gqt5CxWCw8/PDDxMbG1vC6gJrTMmzYsHMKB9uXVRSFiIgIYmJiyMzMpGvXrgDs2bOH5ORkQL3p2lZGAWRkZNCiRYuz9ukJDAxssGg5F35+frpPavscmaioqAuyx2AwaOfn5OSc8/Ns79dnDG6//Xby8/P56quvtPcSExPJycmhadOmuo+lPc2bN9e2jx07pott7jC/7JPJo6OjdbfnXLjDeNUHm5ApLy/n5MmTxMfHu9wGvcfsdGGV96BpEwNlh8q11yGtTW7399R7vOpCSKsQ/CP8sRRYaGlWH/gPHDUQH+N6l4ye41Xvqz755JOUl5fz2GOP1ehebLFY+P777xk1apTD+3v37mXPnj1UVlZSUFDACy+8QP/+/TXBkZaWxrx58yguLmbbtm388ssvjBgxAoBRo0axYsUKdu3aRVFREQsWLGD06NEN/b4ejU3IGAwGwsPDG+1z4+PjCQoKarTPOxtGo5H4+Hj8/d2rDqPUklGRVUvOQ1Yu1WwYKUuvLxyDwUBoipp6EVdRRpCPJvzWS8jk5OSQnp7Opk2bGDp0qFYTZtOmTQCsXbuWoKCgGh2WT506xcyZM0lNTWX8+PH4+fnx2GOPafunTJlCWFgYo0aNYubMmcycOVPLU0hJSWHq1KlMmzaNtLQ04uLiuO222y7sW3so9k/M1bniiiu4+eab2bt3L+PGjSMuLo6wsDD69u3LihUrzvm5BoPBwXOybt06evXqRXBwMH369NH+vjYqKyu5/fbbadOmDSaTiQ4dOvDKK69o+19++WXee+89vv76awwGAwaDgZ9++okDBw5gMBjYvHmzduzPP/9Mv379CAoKIiEhgZkzZ2KxWLT9l156Kffddx8PPvgg0dHRxMfHO8ydxkCEjIqsWnIesnLJsY5MdES1YniyYqnBhLWryiFtbi7xSSFTr0fjhIQENmzYcNb9gwYN4rvvvqvxft++fVm0aNFZzwsODmb27Nln3T927FjGjh1bH1O9EpuQURSFH3/8kWHDhgHqDWjZsmWkp6dTVFREWloas2fPJjg4mHfffZexY8eye/fuOuXUFBcX85e//IXLLruMDz74gP3792u5NDasVistW7bk008/pWnTpqxZs4bJkyeTkJDANddcw5133klOTg6FhYUsXLgQUEMV1QvOHTlyhLS0NG655Rbee+89du3axZ133klwcLCDWHn33Xf5xz/+we+//85vv/3GLbfcwsCBAzWv3YUiQkbF5pHx8/OrseJQuDDEI+PY+To8BA7bViyhhpaEhhFqJ2RalhezLyccX6sl414+fjegz51Wjp6ye0OBysoWGI2AwXnL2uKjYcPcczvIbEImPDyc//u//9OEzGeffUZ0dDTDhg3DaDTSo0cP7ZzZs2fz5Zdf8s033/C3v/3tvHZ8+OGHVFZWsmDBAkJCQujSpQuHDx/m7rvv1o4JCAhg1qxZ2us2bdqwZs0aPv30U6655hpCQ0MxmUyYzeZz5gK88cYbtGrVitdffx2DwUDHjh3Jzs7moYce4tFHH9Xird27d+c///kPoC6/f/311/nhhx9EyDQyNiHTpEkTt88N8DTEIwOnzrQoiI5QvcAONWQktNRgwlKqCRnxyAhHT8GRE9Xf1X+YKioqtM7ECQkJfPHFF7zxxhsEBQXx4Ycfct1112E0GikuLmbWrFl8++23ZGdnY7FYKC0t5eDBg3W6zs6dO+nRo4e2agxwqONj46233mLevHlkZWVRWlqK2Wyu90qknTt3MmDAAIdcq4EDB1JUVMThw4c1D1L37t0dzktISOD48eP1uta5iIqKIigoiPLycp8WMrbQkoSVGh/xyNTsfG1rGGkIMBAcX/viDeH8OHhkzCWsFiEjxEdXe0OBykoLRqO/U711Na5bDfsVS0lJSeTk5PDdd9/Rt29fVq1axYsvvgjAjBkzWLZsGc8//zwpKSmYTCauueaaGkUCz4ainL8N/Keffsq0adN44YUXGDBgAOHh4Tz33HP8/vvvdbqG/bWqJ4zbrm//fkBAgMMxBoOhUYsvGQwG4uPjycrK8lkhoyiK5pGRRN/Gp1mzZgQHB1NWVuaTHhlzhULhmUhSdIQ630rPeGRMrUwYjL4VCmlMQtqEYDAaUCoVWpYXc/gElJsVggJ9Z0xFyFSjenjHarWSlXWExMREXd3t9om+UVFRXHXVVXz44YdkZmbSvn17LrroIgBWrVrFLbfcwl//+ldArbtTnyfAzp078/7771NaWqrV6lm7dq3DMatWreKSSy5xKIa4d+9eh2MCAwMd6t6c7VpffPGFg6BZs2YN4eHhtGjRos42NwYJCQlkZWWRm5uL2Wx2yhJ+d6a4uFj7e4lHpvExGAwkJiaye/duDhw4UKuI92bsO1/HREDF6QosRep8k67XF4YxyA9ToomSfSW0LC8Gq0LWMQPtW53/XG9BAuEeQvWGkRMnTuS7775jwYIFTJo0SduXkpLCokWL2Lx5M1u2bOGGG26ol/fihhtuwM/Pj9tvv50dO3awePFinn/+eYdjUlJS2LBhA8uWLWPPnj088sgjrF+/3uGYpKQktm7dyu7duzl58mStHcvvueceDh06xN///nd27drF119/zX/+8x/+8Y9/uFw02ufJHD161KXXdgek87XzseXJlJSUkJubq68xLkaWXjsX28qlYMVKjKXc5/JkRMh4CNUbRl522WVER0eze/dubrjhBm3fSy+9RFRUFJdccgljx45l5MiRNZbDn4uwsDDS09PZsWMHvXr14l//+hfPPPOMwzF33XUXV111FRMmTKB///7k5ubWaFVxxx130KFDB/r06UNsbCy//vprjWu1aNGCxYsXs27dOnr06MFdd93F7bffzr///e8629tY+HrCr9SQcT6+nCdzys4jI0uvG58aK5d8TMhIaMlDqO6RMRqNNZYzg/rUt3LlSof37r33XofX1X9Eq+fFXHzxxQ61XqofExQUxMKFC7Wl1TaeeuopzfsTGxurdTA/17VSU1NZt25djeNs/PTTTzXes69501iIkMnTtiW05BzshUxWVhZ9+vTR0RrXkmtXAis63KDlx4AsvW4Maq5cisGXlmCLR8ZDqC5khMbFvk2BLwoZCS05H/sl2L7skYlpguPSaxEyF0z1lUu+5pERIeMh2AsZ+27NQuMgHpk8bVuEjHOo7pHxJRxyZMKhLKdMe21qKUuvLxT76r6tyovZ52M/YSJkPATxyDgXETJ52raElpyDT3tkCqpCytERUH78TDkIAwTG+tYKQWcQGBNIQJRapqLFmRyZupTS8BZEyHgI1ZN9hcbF14WMhJacT0JCglYTyec8MtWSfc0n1M7XgTGB+PnLbagxsHllYi3lWAotDnlJ3o7MIA9BPDLOJTY2FqPRCPimkJHQkvPx8/PTqlX7mpBxTPZVNI+MeGMaD/s8mRY+licjQsZDkBwZ5+Ln50dcXBzgm0JGOl+7BlueTH5+voN49HbsPTJNDBas5erqxqBmQTpZ5H2E2q1cauFjeTIiZDwE8cg4H1t46dixY+etSuxtiEfGNfhq80hbsq/RCAGFVe1SgsQj02g4JPyafauWjAgZD0FyZJyPTchYrVZOnKjROdSrESHjGny1KJ59w8iKk3ZCppkImcbCsSheCfuyJdlXcDNsHhmTyVSjiaLQOPhywq8ttBQUFKT12BIaH1/1yOSeETIxEVB+wl7ISGipsQhJNGHwV4vgtfCx6r4iZDwEm5Bp0qQJiqIwefJkoqOjMRgMNarwCg3DXsjUVjXZm5HO167BFz0yFRbHztflx8q1fZLs23j4BfgR0iYEUJN994tHRnA3bEImIiKCpUuX8s477/Dtt9+Sk5ND165ddbauYSQlJfHyyy/rbYaGL3tkbEJGEn2diy96ZE5XW3rt4JGJE49MY2LLkwlSrJQfKcNc4RtiRoSMB2C1WiksVH8NmjRpwt69e0lISOCSSy4hPj4ef//6tcxSFAWLxeIMUz0aXxUylZWVWg6WeGScS4sWLbRl/r7ikale1bf8eJVHRpJ9Gxf7lUvNS4s5eExHY1yICBkPoKioSKvSePjwYf7+979z8OBBDAYDSUlJlJeXc99999GsWTOCg4MZNGgQ69ev187/6aefMBgMLFu2jD59+hAUFMSqVatQFIVnn32W5ORkTCYTPXr04PPPP3e49vbt2xkzZgwRERGEh4czePBg9u7dC8D69esZMWIETZs2pUmTJqSmpvLHH384nP/YY4/RunVrgoKCaN68Offddx8Al156KVlZWUybNg2DwYDBoH+DM18VMvYr4kTIOBd/f39atmwJ+I5HJtdOyMQ0AbO9RyZWPDKNSZhDzyXfyZMRIeMB2N9o+vbty+OPP07Lli3Jyclh/fr1PPjgg3zxxRe8++67/PHHH6SkpDBy5EhOnTrl8DkPPvggTz31FDt37qR79+78+9//ZuHChbz55pts376dadOmMWnSJH7++WcAjhw5wpAhQwgODmblypVs3LiR2267TfPmFBYWcvPNN7Nq1SrWrl1Lu3bt+Mtf/kJRUREAn3/+OS+99BJvv/02GRkZfPXVV3Tr1g2ARYsW0bJlSx5//HFycnLcQjj4qpCR9gSuxZYnk5ubq/1b8WYcPTKGKo+MHwQ2FY9MY+JQFK+8xGdqydQvJuEDrL7sN8x2rk8F1fW+z5jl1Kbogc2CGLRyQK377IVMTEwM4eHhGI1G4uPjKS4u5s033+Sdd95h9OjRAMydO5fvv/+e+fPnM2PGDO3cxx9/nBEjRgBQXFzMiy++yMqVKxkwQL1ucnIyq1ev5u233yY1NZX//ve/NGnShI8//lhbKdW+fXvt8y677DIHO99++22ioqL4/fff6dKlCwcPHiQ+Pp7hw4cTEBBA69at6devHwDR0dEYjUbCw8OJj4+/0OFrFGwF8cC3hIy0J3AtSUlJ/PLLL4DqlenSpYvOFjkXByFj12cpMCYQg1F/T6w3UaN5ZLYCTr1zuQciZKphPl5OWU55jfct6JdTcq5ieHv37qWiooKBAwdq7wUEBNCvXz927tzpcGyfPn207R07dlBWVqYJGxtms5levXoBsHnzZgYPHnzW5d7Hjx/n0UcfZeXKlVoRuZKSEm3Fz/jx43n55ZdJTk5m1KhRpKWlMXbs2Hrn9LiKwMBAmjZtysmTJ31KyEgNGddSfeWS1wsZ+2TfcEXrsyQ1ZBqfgMgAjNGBVJ4y09JczAYfCS255x1FRwKr1TWweWSMRqPTPTJn41zF8Gy5M9VzTBRFqfFeaGiVWrda1RLh3333HS1atHA4LihIteV89URuueUWTpw4wcsvv0xiYiJBQUEMGDCAiooKAFq1asXu3bv5/vvvWbFiBffccw/PPfccP//8s9vWwklISODkyZMcPXq01jH0RiS05Fp8beVSbr5d52s/Cxaz+lryY5xDeIdQ8n4zE20xcySrAvD+cRYhU43q4R2r1UpWVhaJiYn4+emTUnQuj0xKSgqBgYGsXr2aG264AYCKigo2bNjA1KlTz/qZnTt3JigoiIMHD5KamlrrMd27d+fdd9+loqKiVuGxatUq3njjDdLS0gA4dOgQJ0+edDjGZDJxxRVXcMUVV3DvvffSsWNHtm3bRu/evQkMDHS7VgAJCQls27YNs9nMqVOniImJ0dskpyOhJdfia7VkHPosVZjJPbMtNWScQ3i7UPJ+U/9Nl+8vQVECvf6BTISMB1C9YaRtKTaoXpa7776bGTNmEB0dTevWrXn22WcpKSnh9ttvP+tnhoeH88ADDzBt2jSsViuDBg2ioKCANWvWEBYWxs0338zf/vY3XnvtNa677jr++c9/0qRJE9auXUu/fv3o0KEDKSkpvP/++/Tp04eCggJmzJjh4MV55513qKyspH///oSEhPD+++9jMpm0H3JbrsB1111HUFAQTZs2dcLo1Y/qCb++IGQktORafM0jY58jE1pWrgkZqSHjHOwTfqPyizldGEm0l/cZllVLHsD5GkY+/fTTXH311dx444307t2bzMxMli1bdt4wwRNPPMGjjz7KU089RadOnRg5ciTp6em0adMGUBOLV65cSVFREampqVx00UXMnTtX884sWLCA06dP06tXL2688UZtCbiNyMhI5s6dy8CBA+nevTs//PAD6enpmjh4/PHHOXDgAG3btiU2NvaCx6kx8MWVSxJaci2tWrXSnpB9wiNjJ2SCS6RhpLMJq9FzSUdjXIR4ZDyA6kLmmmuucQgbBQcH8+qrr/Lqq6/Wev6ll16q5dLYYzAYuO+++7TaLrXRvXt3li1bVuu+Xr16OdSrAbjqqqu0p8wrr7ySK6+88qyfffHFF7Nly5az7tcDXxQyElpyLYGBgTRv3pwjR474hEcm167ztbHArhie9FlyCvZF8Vqe6bnUp6OOBrkA8ch4ANL52nX4opCR0JLrsYVXjx07Rmlpqc7WOBf7ztfm41UeGcmRcQ4hrU0oZ5pH+kpRPBEyHsD5QktC4+GLQsbeIyOhJddgnydz8OBB/QxxAbZk3xp9lsQj4xQMRgP+rVWvTHNzCfuPuNeCCmcgQsYDqJ7sKzgPXxQy9h4ZEcquwVdWLlVYFAqK1e3o8OpCRjwyzqJJe7ULdoCikLunTGdrnI8IGQ9APDKuw5eFTFhYmNsWK/Q2fGXlkn3n65gmUH7Mrj1BtAgZZxHVuSpPpvxAsY6WuAYRMh6ALUfG39//vEXqhAsjJCRE83r5ipCxhZYkrOQ67D0y3ixkqne+tjWMDIqV9gTOJKxdmLYddKwYc0XNxR7ehAgZD8DmkWnSpInXFzZyB2xeGV8RMjaPjCT6ug5biQOAjIwMHS1xLo5CRqH8THuCQKnq61TsVy41Ly1h2z4djXEBImQ8AJuQkfwY19C8eXNAbaxpX3zQGykrK6OsTI2hi0fGdSQnJ2utQLZv366zNc7DvqpvrH8lSoWtPYGElZyJwxJsczHrd+lojAsQIePmKIri4JERnI8v5cnI0mt98Pf3p1OnTgDs2bOH8vKajWq9gdyq9D6aKlJDxlUERPhjaKqOccvyYtbtlNCSoCOlpaVYLGrnbREyrkGEjOAKunbtCoDFYmH37t06W+Mc7D0yURZZseRKIjuqXpnIygp2bfLulUsiZNwcKYbnenxVyEhoybXYhAzAn3/+qaMlzuNUQZUnINzO6xQoHhmnE903Uts27s6nsMR7vTIiZNwcWXrtenxJyEh7Av3wDSFTtR1SKn2WXElU/0htu1NxHn/s0c8WZ1MvIWM2m5k1axZpaWmkpqYyefJkMjMzAUhPT6d///4MHjxY++/o0aPaudu3b+f6669n4MCBTJ482eEGUVZWxiOPPMKQIUMYM2YMS5cudbhuenq6ds1Zs2ZRUVFxId/Zo5BieK7Hl4SMhJb0wxeETK6dkAkslqq+riTKziPTuSSPdTv1s8XZ1EvIVFZW0qJFCxYuXMjKlSsZMmQI06dP1/b369ePVatWaf/Fx8cDqgB68MEHue6661i5ciVdu3bl0Ucf1c57++23yc/PZ/HixcyZM4enn35aq62QmZnJSy+9xPPPP893331HdnY28+fPb4zv7hGIR8b1+KqQkdCSa2ndujVhYWq9D28VMvYeGX+HhpHikXE2AZEBBLRV51fbskL+2OK9DoB6CRmTycQdd9xBXFwcRqORCRMmkJ2d7fBjWBsbN27EZDIxbtw4goKCuPPOO9mxY4d2k1i8eDGTJ08mLCyMHj16MGTIEJYvXw7A0qVLGTFiBJ07dyYsLIw77riDJUuWNOzbeiCSI+N6fEnISGhJPwwGg+aV2b9/P0VFRTpb1PjYkn2NRlBO2zeMFI+MK4gbGAmAEYXcDQXnPtiDuaB65Fu3biU6Olr7AdyyZQvDhg0jOjqaCRMmcM011wCwb98+UlJStPNMJhMtW7Zk3759hIaGkpub67C/ffv2Wm2Fffv2MWDAAG1fu3btOHLkCGVlZQQHB9ewyWw2YzabHd7z9/cnMLBhTwBWq9Xh/67G/kYTHh6umx11Re/xagzCwsIwmUyUlpaSk5Pj1O+i93jZz6+IiAi3/7vpPV6NTZcuXVi7di2gemX69evX6NfQc8xsHpmoMCg/rnpkDEYD/pFGt/0betMci+rfhMPvHQag6eHT5JyMJi66ca/h7PHy8zu/v6XBQqaoqIg5c+Zwzz33ANC7d28+/vhj4uPj2bFjBw888AAxMTEMHTqU0tJSQkNDHc4PDQ2ltLSUkpISjEajgygJDQ2lpKQEoMa5NldsaWlprUJm4cKFzJ071+G98ePHc+211zb0qwJw6NChCzq/odg3lDObzR5Tzlyv8WosYmNjOXjwIIcPH3bJmOs1XocPH9a2S0tLZX65GFvxRYCff/6ZuLg4p11LjzE7kdcK8CPcVEFJhvqbbozy4+Ah9+/47Q1zzNyyKpzUqSSf71YdZ1jPUqdcy1njZV8F+2w0SMiUl5czffp0Bg0axLhx4wBo0aKFtr9r165cd911/PjjjwwdOhSTyURxsWPjquLiYkwmEyEhIVRWVjp4WIqLiwkJUbt3Vj/X5n49W8+hW2+9lYkTJzp+yQv0yBw6dIhWrVrVSRk2NkajUdtu27atQ48Wd0Tv8WosWrVqxcGDBykoKKBZs2ZO63Gl93jZJ8537tyZ1q1bu9yG+qD3eDU2gwcP1raPHj3qlH/feo1ZhQWKztwz46L8qTytPrGHJIS49e+YN80xpbVCRtQR/E6b6VSax5YTvUhMbNw2N+4wXvUWMhaLhYcffpjY2FimTp161uPsewIlJyfz5Zdfaq9LS0s5fPgwycnJREREEBMTQ2ZmphYv3rNnD8nJydq5tpVRoPYladGiRa3eGIDAwMAGi5Zz4efnp8sfyT5HJioqymP+Yek1Xo2FfZ7M8ePH6/RUcCHoNV72yeTR0dEe8zfz9Pllo3v37tr29u3bnfqdXD1mpwsVQK1d0tJUgWI5056gWZBH/O28ZY5F9ouiYNkxQqyVHFhbhN9k5+Ra6jle9b7qk08+SXl5OY899piDWFmzZo0Wb9+1axeffPKJ9rRx0UUXUVpaSnp6Omazmfnz59O5c2ftZpGWlsa8efMoLi5m27Zt/PLLL4wYMQKAUaNGsWLFCnbt2kVRURELFixg9OjRF/zFPQVJ9tUHe8/E1q1bdbTEudgS9f38/AgPD9fXGB8kLi6O2NhYALZt26azNY3Libyq7Rb+9om+smLJlbQYEqltm7fmoSjeVxivXkImJyeH9PR0Nm3axNChQ7V6MZs2beL333/n2muvZfDgwTz88MPcdNNNmhgJDAzk2Wef5cMPP2To0KFs2bKFxx9/XPvcKVOmEBYWxqhRo5g5cyYzZ84kKSkJgJSUFKZOncq0adNIS0sjLi6O2267rfFGwM2R5df6cOmll2rb33//vX6GOBnbw0dkZKR0VtcJmyf66NGjnDx5UmdrGg97IRNvkBoyehHVv6qsQuKpPPZl62iMk6hXaCkhIYENGzbUuq9Xr15MmzbtrOd26dKFjz/+uNZ9wcHBzJ49+6znjh07lrFjx9bHVK9BCuLpw6WXXoq/vz8Wi4Vly5bpbY7TsHlkpIaMfnTt2pUff/wRUMNLqampOlvUONgLmRir9FnSi4hu4VQGGjGaK7XCeG1bnP88T8LzA4Bejk3IGAwGcf27kPDwcC655BJALcq4b98+nS1qfBRF0YSM1JDRD2+t8GsvZJpYxCOjF37+fhg7qd78ZhVlbF1borNFjY8IGTfHliMTHh7uFYlnnsTIkSO1bW8MLxUVFVFZWQmIkNETrxUy+VW5GGFldg0jJUfG5TQfHKlt567N080OZyF3RjfH5pGR/BjXc/nll2vbtkrT3oS0J3APunTpom17lZDJq9oOkj5LutLi0qp/30GZ+Vgs3pXwK0LGzbEJGcmPcT29evUiJiYGgB9++AGLxaKzRY2LtCdwD5o0aUKrVq0AVch4y6oSeyHjXyh9lvQk8qJIrGdy+dsX5rH9gK7mNDoiZNyYiooKSkvVilLikXE9RqOR4cOHA6qgXLdunc4WNS7S+dp9sIWX8vLyyM72jmUlJ6vWKWA402fJ4G8gIDJAJ4t8l4AIf8wt1BzLpLJC1v/hXQ9lImTcGFl6rT/2eTLeFl6S0JL74I15MjaPjCkIKk6qQiYoNhCDnyzz14PwPpEAGIH9P+bpaUqjI0LGjZFiePpjq4UE3idkJLTkPnizkGnWRMF8RsgESn6MbqQMj9S2yzbn6WaHMxAh48aIR0Z/WrZsSefOnQH4/fffHbwYno4IGffBXsh4Q4Vfq1Uh98xzWOuQCpTKM+0JZMWSbsQPrvK6Ns3Oo7jUO3KxQISMWyPF8NwD2+olq9XKDz/8oLM1jYd9FVlbmXxBHzp16qSVV/AGj8zpQjizsp9WgbJiyR0wtTRREq72KGxfks8fO606W9R4iJBxY8Qj4x546zJseyHTtGlTHS0RTCYTKSkpAOzYsUOr7+Op2K9Yau4nNWTchs6RAJislWxbUaivLY2ICBk3RnJk3IPU1FSto/qyZcu8Z3nsiRPatggZ/bGFl0pLS9m/f7/O1lwY9kImFjuPTJx4ZPQkYVCktn18TZ5udjQ2ImTcGPHIuAchISFaJ/esrCwyMzN1tqhxEI+Me+FNCb/2Qiaq0k7IiEdGVzqlRWrb/nvydLOjsREh48ZIjoz7YB9e8pYmkjYhExoaislk0tkawVuFTBOzCBl3IapbBOX+RgASc09z4rR35MmIkHFjxCPjPnhjnoxNyIg3xj3wJiFjXwwvpNS+qq+ElvTEYDRQlBgJQIzFzPrvvaOBpAgZN0aEjPvQvXt34uLiAPjxxx8x2z1leiJWq5Xc3FxAhIy7kJKSouViebqQOZFXlUcWWFT1byVQ2hPojq0wHkDF1N/JeH4vlkLPrvQrQsaNkWRf98HPz08rjldUVMTatWt1tujCyMvL01bGyNJr9yAgIICOHTsCsHv3bo8Wy/ahJb/8M+0JAqQ9gTsw4K54SvzU8FJAuYWMpzL5qc8q9r91gMoyz1wtJ0LGjRGPjHvhTXkykujrntjCSxaLhT179uhsTcOxFzLKaTW0FBQbhMEg7Qn0Jq57GF9PvIQlUS2oRP17mE+a2fmv3fzcbzWHPjisFTD0FETIuDGS7Ote2BpIgufnyYiQcU+8pcLviTM/XUFGhYrcM+0JJNHXbRgz1sTrzTtzV8oADneO194vO1LGtvu3s2HSJipLPMc7I0LGjbEJGZPJRECAuGT1JiEhge7duwOwceNGBzHgaUgNGffEWxJ+bR6ZxJAKOLMwRmrIuA9/GQDBgZAdFMq/o7oxYOUAYi+vCjGfWH6CdddupKKgQkcr644IGTfm1KlTAERHR+tsiWDD1g1bURSPblcg7Qnck27dumnbnipkFEXRhExSkN2KJfHIuA1hIQbGDFC3T+TBJks4fT/qTd/PL8I/TM2fOf3baX6/Yj3lJ8rP/kFugggZN0VRFFke64bY58l4i5CR+eU+tG7dmrCwMED1+pWVlelsUf0pLAHzmQf5FgHSZ8lduXZoVb7Spz+qOTGxQ5vS/+u+BMaoEYCCbYWsHbOO0kOluthYV0TIuCmFhYVUVKi/BnKjcR8GDBiAv78/AKtXr9bZmoYjQsY98fPzo1+/fgAcOXKEO++80+NaYtgn+sYZpBieuzJmAJjOaMsvfgaLRZ1nTXo24eJv+xHcXG0wWby3hN/S1lG0p0gvU8+LCBk3RW407kloaCi9e/cGYOfOnR6bJyM5Mu7Lc889p1Va/uCDD3jqqad0tqh+OPRZUuwaRkoNGbci1FQVXjqZDz9vqdoX1j6MAUv6Edo2BICy7DLW/mUdeZvya/kk/REh46aIkHFfbH2XAH799VcdLWk4kiPjvvTu3ZsPPvhAe/2vf/2Lzz//XEeL6od9Vd9Ii4SW3BmH8NJKR8+fqaWJi7/rR0T3cADMuRVsnLiJylL3W80kQsZNESHjvgwaNEjb9tTwkm1+GQwGoqKidLZGqM5VV13FnDlztNc33XQT69ev19GiumPvkQkrk9CSO5N2MYSoESS++KUqvGQjKDaI/l/3JfIitY5Z+bFyTq/Lc7GV50eEjJsiQsZ9GThwoLa9atUqHS1pOLbQUlRUlJbzI7gXM2fO5KabbgKgtLSUK664gkOHDuls1fmxFzKmUjshI8uv3Q778FJuPvy4qeYxAREBJN7ZWnudu/qUi6yrOyJk3BQRMu5LbGysVkp+48aNlJR4XuM1WRHn/hgMBv73v/9pHsCjR49yxRVXUFTkvkmX4NhnKaBQzZHxCzTgHyGC2R2xDy999lPtieUxg6pKgJwSISPUFVtDP5CbjTtiy5OxWCysW7dOZ2vqh9ls1vp4SX6MexMUFMSXX35JmzZtANi8eTOTJk3CarXqbNnZsffIkGer6ivtCdwV+/DSol+gwlJTzAQnBBOaEgpA3h/5WIrcq8mkCBk3RTwy7o19noynhZdEJHsWTZs25dtvv9XalHz99df85z//0dmqs2MTMn6KFesZISP5Me5LSLCBv9iHl/6o/biYwapXRrEonP79tIusqxsiZNwUETLujScn/MrSa8+jc+fOfPrpp/j5qT/Zs2fPZtGiRTpbVTs2IdPUUq61JwhuEaybPcL5qa04XnVsQgYgd5V7hZdEyLgp9kImJiZGR0uE2mjTpg3NmzcHYM2aNVgs7uVqPRey9NozGTlyJM8884z2+uabb2b79u06WlQ7NiHTNrCqhoxJhIxbM/piCFVLF/HlqtrDS9EDRcgI9cR2swkNDdWKYwnug8Fg0LwyRUVFbN26VWeL6o54+zyX6dOnc/311wPqvLvyyis5fdq93Py2ztdJxqqy9sEt5TfMnQkJNjD2EnX7VAGsrCW8FNQ0kPDOavuM/K0FVOS7T0NJETJuiqwqcX/sC+N5Up6MCBnPxWAwMG/ePHr27AlAZmYmEydOpLLSPYqUlZYrFJ/RLy2o6hNlaikeGXdn/KVV4aWJTyg88F8ruw86ema01UtWOLXGfQS0CBk3xGq1agmZcqNxXzw1T0ZyZDybkJAQvvzySy3kvGTJEh599FGdrVI5mVe1HVdZFVqy9e0R3JfRF0OMWveO3Hx44RPoOEkh9e9WPliuUFquEG23DNud6smIkHFD8vPztScsudG4L926ddNWkqxevdpjmvtJjoznk5SUxKefforRaARgzpw5btHGwH7pdXRZVWhJPDLujynIwMqXDVw3DAIDqt7/ZQvcOFuhxVUKnx6PgjOOGxEywjkR179nYDQaueQSNbB89OhR9u7dq7NFdUPml3dw2WWX8fzzz2uvb7nlFt2Tf0/Y9VkKL1E9MgZ/g/RZ8hC6tzXw0X/8OPKFgRf/ZqBjVUFfThfC1Hf8Cems9l4q/LMQc675LJ/kWkTIuCFyo/Ec7PNkPCW8JPPLe7j//vuZNGkSAMXFxQ79mfTA3iMTnK/myAQ3D8ZglGJ4nkTTSAPTrjWw430Dq143MKi7+r6iQElHu/DSr+7hlREh44bIjcZz8MTCeLYcmYCAAMLDw3W2RrgQbG0MgoPV0I3eVaZtQibIWomxWF3VIjVkPBeDwcCg7gamXFElRPfHuN8ybBEybogIGc+hb9++BASoAWVP88jExsZK2XgvwGQy0a1bN0BdxVRYWKibLbY+S7EVdiuWRMh4PD1TqrbXEKl52Nyl71K9hIzZbGbWrFmkpaWRmprK5MmTyczMBCA9PZ0bbriBIUOGMG7cuBqJZ3369GHQoEEMHjyYwYMHs2DBAm1fWVkZjzzyCEOGDGHMmDEsXbrU4dz09HTtmrNmzaKiwn3WrzsDETKeg8lkom/fvgDs2bOHY8eO6WzRuVEURZb2eyG9evXStrds2aKbHTaPjIOQkURfj6djawg602Viw0F/mvRSFzkU7Smm/Fj5Oc50DfUSMpWVlbRo0YKFCxeycuVKhgwZwvTp0wFV5Pzzn/9k5cqVvPjii/zvf//jjz8cq+p89dVXrFq1ilWrVnHbbbdp77/99tvk5+ezePFi5syZw9NPP01WVhagPmG89NJLPP/883z33XdkZ2czf/78C/3ebo0IGc/CPrz066+/6mjJ+SkuLqa8XP3hkbnlPdgLmU2bNulmR21CRkJLno+/v4Fuyep2xmEI7+9eeTL1EjImk4k77riDuLg4jEYjEyZMIDs7m7y8PK6++mq6deuGv78/bdu2pV+/fuzYsaNOn7t48WImT55MWFgYPXr0YMiQISxfvhyApUuXMmLECDp37kxYWBh33HEHS5Ysqf839SBEyHgWnpTwKzVkvBN3ETInz6xacvTISFVfb8AWXlIUONWmSsicWq1/YTz/Czl569atREdHExkZ6fB+ZWUl27dvJy0tzeH9SZMmYTAY6N+/P1OnTiUyMpKCggJyc3NJSakKwrVv315bRrhv3z4GDBig7WvXrh1HjhyhrKxMS3Czx2w2YzY7Lgnz9/cnMLBh3VetVqvD/12B/c0mOjrapde+UPQYL725+OKLte1Vq1bV67u7eryOHz+ubTdt2tTj/k6+OL/qQpcuXfDz88NqtbJp0yaH8XHlmNk8Ms2VKiETlBDoUX8vmWO1Y58nsz24CW0CDCgVCqdWnyL8/lCnjZetUeq5aLCQKSoqYs6cOdxzzz019r355pvExsY6CJC5c+fSrVs3CgsLeeaZZ3j88cd58cUXKSkpwWg0OoiS0NBQSkpKACgtLSU0NFTbFxYWpr1fm5BZuHAhc+fOdXhv/PjxXHvttQ39qgAcOnTogs6vD9nZ2dp2UVGRFmbzJFw5Xu5A+/bt2bNnD5s2bWLHjh0Oc7YuuGq87L2k/v7+Hjm3wPfmV11o27YtGRkZbN++nYyMjBoPb64Ys2OnWgJG4itLtPdOKCc4laV/+KG+yBxzJC4sCIgHYNWOEjp1DaZ0Uykl+0upOFrBIZwzXm3atDnvMQ0SMuXl5UyfPp1BgwYxbtw4h32ff/45K1euZMGCBQ4rImyuz6ioKB544AHGjBlDRUUFISEhVFZWOnhYiouLCQkJAdRwVnFxsfY5RUVF2vu1ceuttzJx4kTHL3mBHplDhw7RqlWrOinDxsC26iAiIsLBU+UJ6DFe7sDQoUPZs2cPlZWVZGdnM3z48Dqd5+rxsv83mZKSQmJiotOv2Zj46vyqC3369CEjI4OKigqKioq031xXjVmFBfLP/FTHVqhecWOokTZd2njU6jiZY7UTEwsGgxpayjwaTvPhCezdtA+A4g2ldLs7WbfxqreQsVgsPPzww8TGxjJ16lSHfcuXL9c8ItXDTfbYvqyiKERERBATE0NmZiZdu3YF1NUfyclqZlFycrK2MgogIyODFi1a1OqNAQgMDGywaDkXfn5+Lvsj2a8q8dR/SK4cL3dg8ODBvP322wCsWbOGyy+/vF7nu2q8Tp2qejJu1qyZx/6NfG1+1YXevXvz0UcfAWrY/6KLLnLY7+wxO12oAAooCk1K1dCSqWWw1kbB05A55khEGLRraWXPIdi2H6JujAZUIVOyoUTX8ar3VZ988knKy8t57LHHHFT22rVree6553j55Zdp3ry5wzl79+7VnlYLCgp44YUX6N+/vyY40tLSmDdvHsXFxWzbto1ffvmFESNGADBq1ChWrFjBrl27KCoqYsGCBYwePfpCvrNbY7FYOH1aTZ6SZEzPwVM6YUsiufeid8KvLT8morIC/0o1X0ISfb0LW55MuRmOx0XiF6xKiOINJbr2mquXRyYnJ4f09HSCgoIYOnSo9v6rr77KwoULKSgocFhWPXr0aB5++GFOnTrFU089xfHjxwkNDaVfv3489thj2nFTpkxh9uzZjBo1ioiICGbOnElSUhKgur+nTp3KtGnTKC4u5rLLLnO4hrdx+vRpbULIjcZzaN26NYmJiWRlZfHTTz+xd+9e2rZtq7dZNRAh4724i5CRpdfeS892Bj79Ub0/bc7yo12/SHJ/OYXlqIXSrFLCksN0sateQiYhIYENGzbUus/mVq+Nvn37smjRorPuDw4OZvbs2WfdP3bsWMaOHVt3Qz0YudF4LnfeeSf//ve/sVqtPPfcc7z11lt6m1QDWX7tvURHR9O6dWsOHjzI5s2bsVqtLnX111oMT4SMV9GrXdX25gyFiwdFk/uLGq7OXXVKNyEjAUA3Q4SM53LvvfdqvYsWLlzosPrMXZD55d3YvDJFRUUu78YuHhnvx34J9uZMiBlcVU8mb31+LWe4BhEybobcaDyXyMhIrRyB2WzmpZde0tmimtjmV3h4OEFBQTpbIzQ2eoaXTuTX0mdJ2hN4FfExBuLOaJdNGRDRM4IOj7cn6YPWdH2ps252iZBxM0TIeDZTp07VBMKbb77psErIHZA+S95Nz549te3Nmze79Non89T/NxWPjFdjCy+dKoDs0360uTsRU8dgrZGkHoiQcTNEyHg28fHx3H777YBaD+n111/X2aIqKisryc3NBWRueSv6emTU/zezFzLNRch4G9XDS+6ACBk3w3ajAbnZeCozZszQame88sorWhFHvbFfERcbG6uzNYIzaNWqFdHRqu/f5UImT/2/zSMT2CwQY7Bn1pARzk7PlCrPy6YMHQ2xQ4SMmyEeGc8nKSmJG264AVAL0P3vf//T2SIVmVvej8Fg0Lwyx44dIycnx2XXPpEHRsVKtEXtrm4Sb4xX0qt91fbmDP1qx9gjQsbNkJuNd/DQQw9p2y+88ALl5eU6WqMic8s30Cu8dCIPoivKsflggiXR1ytp2xxCz9Q5FI+MUCu2m43BYCAqKkpna4SG0qVLF6688kpAbQL63nvv6WsQUkPGV9BDyFitCrkFUkPGFzAaDXRXOwhx4CjkFeprD4iQcTtsQiYqKgp//wY3JxfcgH/+85/a9jPPPIPFYtHRGkePjOTIeC/2K5dcJWROF0JlZbUaMtKewGuxL4y3xbXlimpFhIybIctjvYd+/foxbNgwQO039vnnn+tqj4SWfIMOHTpgMqkiwlVLsKWqr2/Rs11Vwu9mNwgviZBxIyoqKsjPV9cwyo3GO7D3yjz11FO6NlaT0JJvYDQa6d69O6AKaNtvijMRIeNbOLQqcIMl2CJk3AhZeu19XHbZZfTr1w+ArVu38ttvv+lmi4SWfAf7PJktW7Y4/Xonz2ilWIsUw/MFurSBMxUm2CJCRrBHXP/eh8FgYMqUKdrrlStX6maLzC/fwV7IuCK8VN0jYwgwEBQnLTC8FVOQgY6t1e3tB6C8QldzRMi4E3Kj8U6GDh2qbf/000+62WGbX35+fkRGRupmh+B8XL1yqbqQCW4ejMFPv5L1gvOxhZcslZCZHaCrLSJk3AgRMt5JUlISrVurjy+//vqrbjVlbDky0dHRWuVhwTvp2rWr9jd2jUdGIbjSQnilujJP8mO8H/sKvzuyAnW0RISMWyFCxjsxGAyaV6asrIx169bpYodtfkl+jPdjMpno2LEjADt27HC6eD6RJ/kxvkZPu4TfHQdFyAhnECHjvVx66aXath7hpbKyMq3nk8wt38AWXrJYLGRkOHeN7Ik8aFpRJZbEI+P92DePFI+MoCFCxnvRW8jIijjfwz5PZvv27U691ol8iK0o1V5LMTzvJ6aJgVbN1O2dBwOxWvWzRYSMGyFCxntJSkoiMTERgDVr1rg8T0ZqyPge9kJmx44dTr3WiTyIFY+Mz2HzyhSV+bHfdf1JayBCxo0QIePd2OfJ/P777y69ttSQ8T3sWxU4U8goinJGyNi3JxAh4ws4dMLWsZ6MCBk3wnazMRqNNGnSRGdrhMZGz/CSiGTfIyoqiqSkJAB27txJZWWlU65TWALmimpVfUXI+AT2K5f0LIwnQsaNsN1sYmJi8POTP423kZqaqm2LkBFcgc0rU1JSQmamc+40WlXfM0LGP8xIQIS+dUUE19C7PQzsCjcOK2BQd/3skPbKboQ0jPRukpKSSEpK4sCBA6xZs4aysjICA12T7S85Mr7J5MmTGT16NHFxcbRp08Yp1ziRBygKTW3F8CTR12dIjDfwy+sKWVmnSUyM0M0Oeex3E0pLSykuLgbkRuPN2PJkysvLXZonIzkyvsno0aO544476N69u9NE8/HTEFFZQZCiLluRRF/B1YiQcRNkeaxvoFeejISWBGexcLFCM0n0FXREhIybIDca38A+T+bHH3902XVlfgnO4OfNCl+uQgsrAZiai5ARXIsIGTdBbjS+QWJioparsHbtWsrKys5zRuNgy5EJDg4mNDTUJdcUvBurVWH6fxVAll4L+iJCxk0QIeM72OfJrF271iXXtE8kNxikK7Fw4Xz4PWzcrW53CbVfei3JvoJrESHjJoiQ8R3s82R+/vlnp19PURRZESc0KiVlCv/8n6K9Tm0pDSMF/RAh4yaIkPEdXF1PpqCggIqKCkDmltA4vPAJHDmzov8vl0BEsZ2QkRwZwcWIkHETZNWS79C6dWuSk5MB1+TJyNJroTHJOanwzP+p3hijEZ6720DZEXUOBzYLxBgktxXBtciMcxPEI+Nb2MJLZrOZTZs2OfVaMreExuSR+QrFZxpd33UFtG+uUHZUbRgpNWQEPRAh4ybIzca3sCX8Ak5P+JW5JTQWWzIVFixWt5uEwX9uNVB+tBzOpMtIfoygByJk3ATbzSYwMJCwsDCdrRGcjX2ejLOFjLQnEGrj/xZXcnB7aZ2PVxR1ubVyRrT8+0YDsZEGSg/LiiVBX0TIuAmyPNa3aNWqFW3btgVgy5YtlJSUOO1akiMjVOeP38vImbKeJSM38tZHFVityjmPVxSFed/CDxvV120S4O9Xq9un1p7WjpOu14IeiJBxA2R5rG9inyfjTK+MhJaE6qy7YxudSvJpVVrM4X/+ySV3W9mcUbuY+WO3wqX3KUx+rmr/M3cZCAo0YCm2cODNA+qbfhA7QoSy4HpEyLgBJSUl2soVudH4DvZ5Mj/88IPTriNCRqjOmPc6YQ7yB+CSwuO0+Xk/fSYrTH/dSlGJKliO5irc/rSVPpMVftlSde4Nw+GaS9XtgwsPYc5Vl/Y3vyqBsBSpGi24HhEyboDcaHyTYcOG4een/hP84IMPsFgsTrmO5MgI1WnVK4xL3u0OZ6LYN57YS5/Tx3nxU+h8kypo2t2gJvbacmJSWsDXcwx88IgBg8FAZUkl+14/oO40QNtpybp8F0EQIeMGiJDxTeLj4xkzZgwAhw8fZvHixY1+jYqKCrZsUR+nDQYDMTExjX4NwTNpNiKWDv9up71+4MiftCov4tBxePFTKDqTB9wkDF6418D29wxcMcig5fAdfO8Q5hNmABLGxRPeURYpCPpQLyFjNpuZNWsWaWlppKamMnnyZDIzM7X977zzDsOHD+eyyy7jlVdeQVGqYqrbt2/n+uuvZ+DAgUyePJmcnBxtX1lZGY888ghDhgxhzJgxLF261OG66enp2jVnzZqlVSn1FkTI+C5TpkzRtt96661G//z33nuPrKwsAEaMGEFgYGCjX0PwXJLvb0PClfEAhFgreer4FkIr1d9XPz+450rI+D8D/5hgIDCgahFCZWkl+17dr71OeUC8MYJ+1EvIVFZW0qJFCxYuXMjKlSsZMmQI06dPB2D16tV8/vnnvPPOO3z66aesXr2ab775BlAF0IMPPsh1113HypUr6dq1K48++qj2uW+//Tb5+fksXryYOXPm8PTTT2s/vpmZmbz00ks8//zzfPfdd2RnZzN//vzG+v5ugQgZ3+Xyyy+nRYsWACxdupQDBw402mebzWaeeOIJ7fVjjz3WaJ8teAcGg4Fur3YhvGs4AFEFJXwU8iczJihsWWDgv//wIzay5irKQ+8fpvyY6o2JHxtHeKdwl9otCPbUS8iYTCbuuOMO4uLiMBqNTJgwgezsbPLy8li8eDHXXHMNLVu2pGnTpkyaNIklS5YAsHHjRkwmE+PGjSMoKIg777yTHTt2aF6ZxYsXM3nyZMLCwujRowdDhgxh+fLlgPrjPmLECDp37kxYWBh33HGH9rnegr2QEde/b2E0GrnuuusAdfXa3LlzG+2z33nnHe2BYNSoUQwYMKDRPlvwHvxD/bno/V4ERAcAYPzjJLef2kvX5NrLQFSWVbL3FfHGCO6D/4WcvHXrVqKjo4mMjGT//v2kpaVp+9q3b89///tfAPbt20dKSoq2z2Qy0bJlS/bt20doaCi5ubkO+9u3b8/27du1c+1/gNu1a8eRI0coKysjOLhmzQKz2YzZbHb8kv7+DXapW61Wh/87A/tkzOjoaKdey9m4Yry8CavVyrXXXssrr7yCxWJh/vz5PPLIIxccAiovL+fJJ5/UXv/nP//xir+JzK/6U5cxC24ZRM953dkw/g+USoW9L+/Hv4k/SXcnYjA6CppDHxxWq/kCzdJiCesc5lV/D5lj9cPZ42VbEHEuGixkioqKmDNnDvfccw+gLiG2r0gbGhqqFfkqLS0lNNRxWV5oaCilpaWUlJRgNBodRMm5zrVdo7S0tFYhs3DhwhpPtePHj+faa69t6FcF4NChQxd0/rnYv7/q6aaiokJ7ivZknDle3kZsbCyXX345ixcv5tixY8yfP9/hoaAhfPDBBxw8eBBQl3nHxcV5xbyyIfOr/px3zFpDs6lNOfaC+mC1e1YGWZ8dJOHhOII7qr+1VrOVvS8e0E4JucHkVfPKHplj9cNZ49WmTZvzHtMgIVNeXs706dMZNGgQ48aNAyAkJISioiLtmOLiYkJCQgDVA1NcXOzwGcXFxZhMJkJCQqisrHTwsJzrXNs1TKbaS2HfeuutTJw40fFLXqBH5tChQ7Rq1apOyrAhlJeXa9vdunWjdevWTrmOK3DFeHkTtvGaOnWqtmpp0aJF3H333Q3+zLKyMv73v/9pr59++mkSExMv2FZ3QOZX/anPmLV+qDV7zJnsf+0AAGU7yjlw8yESp7Qm5cG25HyRg+WYWiYg9vKmdBzZwdnmuxyZY/XDHcar3kLGYrHw8MMPExsby9SpU7X327RpQ2ZmJoMGDQJgz549JCersdPk5GS+/PJL7djS0lIOHz5McnIyERERxMTEkJmZSdeuXWs9135lVEZGBi1atKjVGwNqryJnrMzw8/Nz2h8pNzdX227WrJlX/ONx5nh5I5dddhnt2rUjIyODlStXkpmZSfv27Rv0WQsWLODw4cMAjB07ln79+jWmqW6BzK/6U9cx6/RYB+JGN+PPadsp2l2MUqlw4I0sjqUfw1pRtRK13YwUr/4byByrH3qOV72v+uSTT1JeXs5jjz3m0BMoLS2NL774giNHjnDy5Ek+/PBDRo8eDcBFF11EaWkp6enpmM1m5s+fT+fOnUlISNDOnTdvHsXFxWzbto1ffvmFESNGAGqS4ooVK9i1axdFRUUsWLBA+1xvwZbsa/NQCb6HwWBwWIpt71GpD6WlpTz11FPa61mzZl2wbYLvEd0/ikE/XUL7h1PwC1JvE6WHyrTcmNjhTYns3URPEwVBo15CJicnh/T0dDZt2sTQoUMZPHgwgwcPZtOmTQwaNIirrrqKm266ifHjxzNw4ECuuOIKQPWSPPvss3z44YcMHTqULVu28Pjjj2ufO2XKFMLCwhg1ahQzZ85k5syZJCUlAZCSksLUqVOZNm0aaWlpxMXFcdtttzXeCLgB0mdJALj55psJCgoC1FwvW9uK+vC///2P7OxsAK688kp69erVqDYKvoNfoB8p09syeNUlxAyOdtiX8kBbnawShJoYFPuqdUINrFYrWVlZJCYmOsVtpigKgYGBWCwWevXqxR9//NHo13Alzh4vb6P6eN1444188MEHALz//vtMmjSpzp9VWlpKcnIyR48eBWDz5s306NHDKXbrhcyv+tMYY6YoCkc+yebggkPEXh5LOy8WMjLH6oc7jNcFLb8WLpyCggKtx454ZIQpU6ZoQuatt95yEDJWq5XNmzezfPlyCgsLiYiIICIigvDwcCIiIli9erUmYq6++mqvEzGCfhgMBlpe14KW17XQ2xRBqIEIGZ2Rqr6CPQMHDqRLly5s376dX3/9ld9++42cnBy+++47Fi9erAmV8/Gf//zHyZYKgiC4ByJkdGb16tXatq1UveC7GAwG7rrrLv7+978DcMkll9T7MyZMmEC3bt0a2zRBEAS3RISMztj3jbrqqqt0tERwF2688UYeeughrSikDZPJxLBhwxgzZgwpKSkUFhZSUFDg8F9ISIhDWQRBEARvR4SMjuzZs4dVq1YB0KlTJy6++GKdLRLcgSZNmvD444/z4IMP0rp1a8aMGcOYMWO49NJLz1oIUhAEwVcRIaMjCxcu1LZvu+02h7o8gm8zffp0pk2bhsFgkHkhCIJwDkTI6ITFYuHdd98F1BYKN910k84WCe6GLP0UBEE4P/JLqRNLly4lJycHUMvIN2vWTGeLBEEQBMHzECGjE/ZJvt5WqVgQBEEQXIUIGR04duwY3377LQAJCQmMGjVKZ4sEQRAEwTMRIaMD77//vlbN95ZbbsHfX1KVBEEQBKEhiJBxMYqiOISVbr31Vh2tEQRBEATPRoSMi/ntt9/YtWsXAEOGDKFdu3Y6WyQIgiAInosIGRdj7425/fbbdbREEARBEDwfETIupKioiE8++QSA8PBwrr76ap0tEgRBEATPRoSMC/n0008pLi4G4Prrryc0NFRniwRBEATBsxEh40KkdowgCIIgNC6y7reR+emnn/jkk08oKyvTeuQYDAYqKytZs2YNAF26dKFfv356mikIgiAIXoEImUbiyJEjTJ8+XcuBORe33367NAIUBEEQhEZAQksXSEVFBS+88AIdO3ask4hp1aoVN998swssEwRBEATvRzwyF8DPP//Mvffey/bt27X3mjZtylNPPcWgQYNQFMXhP4B27doRFBSkl8mCIAiC4FWIkGkAR48eZcaMGXzwwQfaewaDgSlTpvDkk08SHR2to3WCIAiC4DuIkGkAr7zyioOI6dOnD2+88QZ9+/bV0SpBEARB8D0kR6YBPPzwwzRv3pyoqCjeeust1q5dKyJGEARBEHRAPDINIDw8nEWLFpGcnExsbKze5giCIAiCzyJCpoH0799fbxMEQRAEweeR0JIgCIIgCB6LCBlBEARBEDwWETKCIAiCIHgsImQEQRAEQfBYRMgIgiAIguCxiJARBEEQBMFjESEjCIIgCILHIkJGEARBEASPRYSMIAiCIAgeiwgZQRAEQRA8FhEygiAIgiB4LCJkBEEQBEHwWETICIIgCILgsRgURVH0NkIQBEEQBKEhiEdGEARBEASPRYSMIAiCIAgeiwgZQRAEQRA8FhEygiAIgiB4LCJkBEEQBEHwWETICIIgCILgsYiQEQRBEATBYxEhIwiCIAiCxyJCRhAEQRAEj0WEjCAIgiAIHosIGUEQBEEQPBZ/vQ3Qk127dlFeXk5iYiKRkZFYrVb8/ETbnY39+/djMBiIj48nODhYxqsOyByrHzLH6ofMr/oh86t+eMr88kkhYzab+c9//sPvv/9Ox44dOXXqFI888ghdunTR2zS3JC8vj8cff5yMjAyaN29OREQE06dPJz4+HkVRMBgMepvodsgcqx8yx+qHzK/6IfOrfnja/HI/aeUC9u/fT35+Pt9++y0vv/wyAwYMYM6cOWRmZuptmttx6tQpnnjiCcLDw0lPT2fatGmEhoYyf/58APkBOAsyx+qOzLH6I/Or7sj8qj+eNr98Ushs2rSJvLw8QkJCCAwM5P777yc2NpYlS5Zw+vRpvc1zK/z8/BgyZAh33303AO3bt6dnz574+/ujKIrO1rkvMsfqjsyx+iPzq+7I/Ko/nja/vF7IbN++nWeffZZFixZx5MgRAHr27AnAgQMHtOMmTZrEhg0btGN8lQMHDvDnn39qryMiIhg+fLjmgvXz8+PkyZOcOHFCnmTOIHOsfsgcqx8yv+qHzK/64Q3zy6uFzLJly7jnnnsICgoiPT2d559/nnXr1pGYmEi7du1Yvny5dmyfPn0IDw9n2bJlAD6n1C0WC6+88go33HADr7zyCsXFxdq+0NBQoGpMDh06xCWXXKLtt1qtrjXWjZA5VndkjtUfmV91R+ZX/fGW+eXVQua3337jzjvv5P777+epp56iZ8+ePPvss5hMJnr37k1GRgbr1q3Tjh8+fDi7du3yyeSvjIwM8vPzueeee4iPj+ezzz4DHOPHtol77NgxWrdurb3vqz8CIHOsPsgcqz8yv+qOzK/64y3zy2uFjMViwd/fH4vFAkB8fDzjx48nJCSEt99+m7/85S+0aNGCuXPnkpubC8CePXu4+OKL3eoP5CoSEhK46aabuPLKK7noootYt24dBw4cwGAwaP/IjUYj+fn5lJaW0qtXL1avXs0ll1zC//3f/+lsvT7IHKsfMsfqh8yv+iHzq3540/zyGiFT3c3l7++PwWAgPz+foqIiAEJCQrjrrrv45ptvKCwsZPLkyYSGhjJjxgxuuOEGfv75Z/r166eH+S6n+nhFRkaSlJREWFgYPXv2pEWLFnz66acADnUD1q9fz+nTp7n11luZM2cOjz76KDfddJNLbdcLmWP1Q+bYhSHzq37I/KofXjW/FA+moqJCOXr0aI33KysrFUVRlA0bNihXX3218ueffypWq1VRFEU5efKk8sADDyhLlixRFEVRzGazsnfvXmXZsmWuM1wnzjZetbFy5Url7rvvVn777TeH95csWaIMHDhQmTdvnjNMdDsqKiqU/fv313hf5ljtnG28akPmmKLNGdv/bcj8qp2zjVdtyPzynfnlsR6ZTz/9lLS0NB599FFefvllMjIyABwqD1500UV06NCBTz/9VMu0joyMJCcnh4iICEB1NSYnJ3P55Zfr80VcxLnGyx7lzFN0586d6dSpk5bYtWLFCk6ePEn//v1Zvnw5t99+u2u/gA589tlnjBw5kkcffZRZs2axYcMGQObY2TjXeNkjc0zliy++4G9/+xv79u1zCH8AMr9q4VzjZY/MLxVfml8eKWTWr1/PN998wxtvvMH06dMpKiritdde4/jx4/j5+WG1WrU/2t///ndOnz7N3Llz2b59O5s2bcJqtdK0aVMAtyy33NjUZbxs2GKfcXFxjBo1ih07dtCvXz/ee+89rFYrUVFRhISE6PVVXMaBAwf47rvvePXVV3niiSdo0qQJL730EkeOHMHPz4/KykqZY3bUZbxs+PocM5vNLFiwgPnz52O1WrWk1OrzROaXSl3Hy4bML9+bXwZFcaM1VOfB9iT8+eef8/vvv/Pcc88BsHv3bp588km6devGjBkzahyfkZFBeno6O3fu5ODBg9x7771cccUVen0Nl1Hf8bKnsLCQ2267jfz8fKZOnUpaWporTdedP/74gyeeeIKPPvqI4OBgioqKePbZZ8nNzeW///2vdpyvzzEbdR0ve3x1jpWVlbFu3ToCAwOpqKjg448/5oYbbmDgwIE1etnI/KrfeNkj88uH5peuga06smLFCmX//v1KRUWFoiiKMn/+fOXf//63UlRUpCiKopSVlSnjx49Xxo0bp2zbtk1RlNpjqHXND/F0GmO8du3apbz55puuNVxHPvvsM+W3335TCgsLFUVRlDVr1igPPfSQQ75Hdna2Mnz4cGXNmjWKolTFme3xlTnWGOPlS3Os+ngVFBQoiqIoeXl5yhtvvKHMmDFDG59z5X/46vxqyHj50vz64osvlE2bNiklJSWKoijauPnK/HJrj8ySJUt4+eWXadGiBZWVlTRr1oznnnuOY8eOMW3aNAYOHMiYMWP4888/+fXXX4mPjyc0NJQ77rjD4XMUN1vz7iwaa7x8iW+//ZbXXnuN9u3bYzAYKCwsZOHChZw6dYr77ruPG2+8kREjRmhPMW+99Rb79u3j2WefdfgcX5ljjTVevkL18SoqKuJ///sf/v5V/Xo3bdrE+++/z8CBA7n66quprKzEaDQ6fI6vzq+GjpevsHXrVh566CESEhJQFAWTycS0adNo166ddowvzC+3DX6tW7eO//u//2PWrFksWLCAp59+ml9//ZUNGzYQFxfHnXfeSW5uLrNmzeLDDz9k4sSJAFrs016fefIfqK405nj5Cr///juLFy/mscce47XXXuPf//43hw4dYteuXURHRzN48GAWLVpEdna2dk5sbCxGo9Ghaij4xhxrzPHyBWobr4MHD7J//34ArX5Hhw4d6NOnDz/++CMlJSUYjUYKCgqAqn+Xvjq/GjpevsKWLVsYNGgQCxYs4NVXXyUyMpLPPvvMobmjL8wv//Mfog/t27dnzJgxXHzxxVRUVJCQkMCIESPYtWsXffr0YejQoQwdOpTDhw/TsmVLQFXzNjz9D1NfZLzqz0UXXURSUhJxcXHk5eXx2GOPYTQaOXjwIB07dmTKlClMmTKFb775hpEjR9K2bVsqKyvx9/fXSp77EjJe9eNs45WVlUW7du00L0NISAgDBgwgKyuLV199ldzcXOLj4/nHP/7hU/8uZbzqR2VlJVu2bKF3794AhIeHc8MNN/DFF1+wYsUKUlJSAN8YL7f1yERGRnLdddcBEBAQAMC+ffto1aoVgLYKIiEhgeLiYl566SXWrl3LgAED9DFYZ2S86o+/vz9xcXHk5+fz5JNP0rRpU5544gmysrJ4++23KSoqYurUqRw/fpyZM2cyZ84cXn31VS6++GLA957+ZLzqx9nGa//+/cybN49Dhw5px7Zq1Yr9+/fzxRdfEBISwv333+81N5m6IuNVd6xWK0ajkTZt2vD9999r73ft2pUuXbpw+PBhdu7cqb3v9eOlR2JOfbBarUpFRYVSVFSk3HrrrcqOHTsckpWOHj2q/Pvf/1ZuueWWOhfi8mZkvBqGLZlQURRl//79yt/+9jetIFRhYaHy/fffK2+99Zayb98+vUx0K2S86kf18fr73/+ujVdRUZHyj3/8Qxk/frxy6NAhvUx0K2S86sbhw4eVK6+8Ulm1apXDe3fffbeyfv165f/bu/uYKuv/j+PPEyg0UGEU96ao0wRFmKw2503JnLmBY8NZLSWkGxJ0o3Lzj3Rqm4rOOZeBNR3mnDFv5hJkVkK4LCvvCKWmjkYC2eHWmziinnM4vz/4nusnqd+6vgrHA6/HP7BzOFzv68Xdm8/nc30ul6v757G/5+Wxxb4XLlygtLSUUaNGMWXKFCIiIgCMPWDuXtwF3aMLK1asMO6JYbVaGTp0KE8++SRtbW3GNe/9lfIyz2xmTqcTu92Ov78/ixYtIj4+nnfffdcTpXuE8jLnYfOaNGkSeXl5OJ1O2tvbefrppz1xGn1GeZnT0NDAtWvXmDhx4j2XTbvvk9TV1UVRURGVlZXs2bPHeD4jI4OZM2eSmZk5IPLyyNRSeXk52dnZDBo0iLKyMjZu3Mjx48eB7rUavr6+3Lx5k1OnTnHnzh2ge+V1UlISAMuXL+fVV1/FarVisVj6/R9l5WWemczsdjvQvYOlv78/ra2tBAYGDqhpN+VlzqPIyz3l5uPj06//yIDyMsPhcLB161ZefvlltmzZwl9//cUTTzzRY2rW19eXzs5Ozp49y9y5c3G5XKxfv57m5mZsNhuBgYFMnjwZ6P95gYcamRMnTrBo0SLy8vJYt24dkydPZvPmzXR1dWGxWNi9ezczZsygurra6ELdG/bMnDkTX19fo7MfCJSXeWYy8/Hx4datWxw7doyCggLmz59PVFSU0QgOBMrLHOVljvL692pra7l69So5OTlER0cbO/Pebc+ePUyfPp2qqipCQ0NZu3YtV65cYcWKFaSkpBASEsL48eM9UL1n9OlVS11dXbhcLgYNGmQsPg0PDyc9PZ3Kykq2bNlCbm4u9fX17Nq1i9jYWOO1nZ2dREZGsmbNGmM1dn+nvMz7XzPz9/fHarVy6dIlCgoKBswvAeVljvIyR3mZFxERQUZGBk899RSVlZUcOXKEF154wfhHtL6+noaGhh55xcTEsGnTJurr6xk8eDAjRozw5Cn0vd5cgNPQ0OAqLCx0Xbx4scfj+fn5rs2bN/dY0PXjjz+65syZ42ppaTEeczqdLofD4XK5XMautP2Z8jLvYTNzOBxGZu63/ZnyMkd5maO8zHlQXm6XL192rV271pWfn3/f551O5313GB9oem1qqby8nNzcXHbt2sXJkyfp6Ogwnps9ezbff/89v//+uzHvN3bsWCZNmsRPP/0E/P89INw7EPb3fSiUl3mPIjMfHx8js/6+O6jyMkd5maO8zHlQXq671sI888wzTJkyhfr6er777rser3f/zveGmzr2tl6bWrLZbCxZsgSbzUZ5eTlxcXEkJiYCkJCQQGxsLPv27SMoKIjhw4cTFBTEH3/8Ydw6vF9d4/4vKC/zlJk5yssc5WWO8jLnQXm5c3D957YBsbGxxMbGUl5eztSpU6moqODZZ58lKirKw2fw+Oi1Vi45OZmpU6eSlpbGkCFD+Pbbb2ltbTWeX7p0KdevX2f79u2cP3+e6upqHA6HcUXNQPumVl7mKTNzlJc5yssc5WXOP+XlziM0NJTZs2dz8eJFnnvuOYqKivr9aJVZfbKPzNmzZ9m2bRuvvfYa06dPx2KxYLFYqK2tpaysjJqaGurr61m8eDFpaWm9Xc5jT3mZp8zMUV7mKC9zlJc5f8/r7umijo4OsrKyaG9vJy8vj5SUFA9W+njqsw3xNm3aREdHB2+88Yaxbb5bc3MzoaGhfVGG11Be5ikzc5SXOcrLHOVlzt/zck8t1dbWcvToURYvXuzpEh9bvd7IuBckNTY2sn79elJTU/nzzz9xOp0sWLAAf3//3jy811Fe5ikzc5SXOcrLHOVlzoPycjgcZGZmGvfOkwfr9eXO7iGy6OhoQkNDWblyJcXFxUyYMEHf0PehvMxTZuYoL3OUlznKy5wH5TVx4kQ1Mf9WX1zjbbfbXQUFBa7nn3/etX///r44pFdTXuYpM3OUlznKyxzlZY7yejh9tkbm5MmTTJo0CT8/v744nNdTXuYpM3OUlznKyxzlZY7y+t957O7XIiIiIg9LWwKKiIiI11IjIyIiIl5LjYyIiIh4LTUyIiIi4rXUyIiIiIjXUiMjIiIiXkuNjIiIiHgtNTIiYtrp06dJSkoiKSmJK1eueLocERnAfD1dgIg8Xtw3rftvpk2bxoQJEwAYPHhwX5T1j06fPs0777wDQElJCZGRkR6uSET6ghoZEelh3LhxhISEANDc3ExzczMAY8eONZqWGTNmkJaW5qkSRUQMukWBiDzQp59+yvbt24Geoxz3G/1YvXo1hw8fJiIiguzsbLZt20ZHRwdz584lNzeXgoICSkpKGDJkCJmZmcybN884TktLC4WFhfzwww9cu3aNsLAwUlNTyczMxNe3+/+t8+fPU1hYyKVLl7h58ybBwcGMGzeO999/n7KyMqPOu6WkpLB69Wp2797NkSNHsFqt2Gw2hg4dSkJCAkuWLGHEiBEAlJaWsmbNGgDy8/MpKiri8uXLTJ48mTVr1nDs2DF27NjBrVu3mDVrFsuWLTNqS0pKAiAvL49ff/2V48eP4+/vT3p6OtnZ2Vgslt748ogIWiMjIo9Ya2sr+fn5DBo0CJvNRnFxMQsXLqSkpITAwECsVisbN26krq4OgGvXrpGZmUlpaSmdnZ3ExMRgtVr55JNPWLt2LQBdXV3k5eVx6tQpfH19iYmJwW63c/z4caxWK2FhYcTExBg1jB07lgkTJhAdHQ3AmTNnaGhoICQkhJEjR3Ljxg0qKyvJycnh9u3b95zDqlWruHPnDnfu3OHEiRO8/fbbbNiwAT8/P65fv86BAwc4dOjQPa8rLCykqqqKIUOGcPXqVXbs2MHevXt7I2YR+Q81MiLySNntdj7++GMOHjxIWFgYAA0NDRQXF3PgwAH8/Pzo6urizJkzAOzbt4+mpiZCQkL44osvKC4uZsOGDQAcPnyYhoYGbty4wfXr1wHYuXMnn3/+OUePHmXv3r2MGjWKtLQ0li9fbtSwadMmPvvsM958800Ali5dSmVlJfv372fv3r189NFHADQ1NVFdXX3POWRlZXHgwAFeeuklAOrq6li1ahUHDx4kISEB6B6V+ru4uDhKS0spKSkhMTHRqFdEeo/WyIjII+WetgEIDw+nqamJ0aNHG9NSwcHBWK1W2tvbAfjll18AaGtrY9asWT0+l8vloqamhjlz5hAfH8+5c+eYN28ew4cPZ/To0UydOtVoNv4bq9XKunXrqK2t5ebNm9w9o97S0nLPx0+fPh2AiIgI47Fp06YBEBUVxc8//2zUf7fk5GRjuik5OZmqqira2tq4evUqwcHB/1iniJinRkZEHqmAgADjfR8fn3sec68XcTcT7rcBAQE9pofc/P39ge5pmy+//JLq6mrq6uqoqKjg66+/prW1lYyMjAfW09jYyLJly7Db7QQEBDB+/HgcDgeXLl0CuqetHnQO7voBAgMD71u/iHiWGhkR8ai4uDhOnDiBj48P69atM0ZubDYblZWVvPjii7hcLs6dO0dqaqpxtdSHH35ISUkJVVVVZGRkGA0PQGdnp/H+xYsXsdvtAGzdupX4+Hi++uorPvjgg0d+LhUVFcYi5m+++QaAkJAQjcaI9CI1MiLiUfPnz+fQoUM0NzeTnp5OTEwMNpuNpqYmHA4HKSkpOJ1OcnJyCAgIICwsDIvFYiwWHjNmDADR0dH4+vricDjIyckhIiKCBQsWMGbMGHx8fHA6nSxdupTw8HDa2tp65VwuXLhAamoqFovFuGz99ddf75VjiUg3LfYVEY8KDg5m586dpKamMmzYMH777Tdu375NYmIi7733HtA9xZOenk5kZCTNzc00NjYSERHBwoULeeuttwAICgpi2bJlhIWF0d7eTk1NDW1tbYwcOZKVK1cSFRWFw+EgKCjIuBrqUcvJySEpKYmOjg6GDRtGVlYWr7zySq8cS0S6aR8ZEZGH5N5HZtWqVaSmpnq4GpGBRSMyIiIi4rXUyIiIiIjX0tSSiIiIeC2NyIiIiIjXUiMjIiIiXkuNjIiIiHgtNTIiIiLitdTIiIiIiNdSIyMiIiJeS42MiIiIeC01MiIiIuK11MiIiIiI1/o/0WSCNZc1wDwAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "model = SKLearnModel(\n", " lags=24,\n", @@ -891,37 +592,20 @@ "ts_energy_train[-48:].plot(label=\"training\")\n", "ts_energy_val[:24].plot(label=\"validation\")\n", "pred.plot(label=\"forecast\")" - ] + ], + "id": "ed7b5b06093b4ef4" }, { - "cell_type": "markdown", - "id": "9ea22e77-ad17-4fe4-a757-18447fa2ef50", "metadata": {}, - "source": [ - "The underlying model methods remain accessible and by combining the `BasesianRidge.coef_` attribute with the `RegressionModel.lagged_feature_names` attribute, the coefficients of the regression model can easily be interpreted:" - ] + "cell_type": "markdown", + "source": "The underlying model methods remain accessible and by combining the `BasesianRidge.coef_` attribute with the `RegressionModel.lagged_feature_names` attribute, the coefficients of the regression model can easily be interpreted:", + "id": "92398cf4eba3e67f" }, { - "cell_type": "code", - "execution_count": 15, - "id": "a5ff0e6d-3b37-4c17-82c3-96faa8f3ca8d", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Value_NE5_target_lag-24': -0.3195560563281926,\n", - " 'Value_NE5_target_lag-23': 0.37621876784175745,\n", - " 'Value_NE5_target_lag-22': 0.005325414282057739,\n", - " 'Value_NE5_target_lag-21': -0.11885377506043901,\n", - " 'Value_NE5_target_lag-20': 0.12892167797527437}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "# extract the coefficients of the first timestamp estimator\n", "coef_values = model.model.estimators_[0].coef_\n", @@ -931,53 +615,30 @@ "coefficients = {name: val for name, val in zip(coef_names, coef_values)}\n", "# see the coefficient of the target value at last timestep before the forecasted period\n", "{c_name: c_val for idx, (c_name, c_val) in enumerate(coefficients.items()) if idx < 5}" - ] + ], + "id": "12daaac7a57f2289" }, { - "cell_type": "markdown", - "id": "4989e3b1-e875-4b11-8de7-554e5ebbad90", "metadata": {}, - "source": [ - "One of the limitation of the `SKLearnModel` class is that it does not provide probabilistic forecasting out of the box but it is possible to implement it by creating a new class inheriting from both `SKLearnModel` and `_LikelihoodMixin` and implementing the missing methods (the `LinearRegressionModel` class can be used as a template)." - ] + "cell_type": "markdown", + "source": "One of the limitation of the `SKLearnModel` class is that it does not provide probabilistic forecasting out of the box but it is possible to implement it by creating a new class inheriting from both `SKLearnModel` and `_LikelihoodMixin` and implementing the missing methods (the `LinearRegressionModel` class can be used as a template).", + "id": "1bae5c92696acee9" }, { - "cell_type": "markdown", - "id": "c0e9aee2-7692-4407-a56c-e8ee2d37cb8d", "metadata": {}, + "cell_type": "markdown", "source": [ "## Custom models\n", "\n", "You can even implement your own model as long as it works with tabular data and provides the `fit()` and `predict()` methods:" - ] + ], + "id": "855169a23ed7a7d7" }, { + "metadata": {}, "cell_type": "code", + "outputs": [], "execution_count": null, - "id": "d860f4cf-d9f4-4b17-99ef-848dc5325a13", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "class CustomRegressionModel:\n", " def __init__(self, weights: np.ndarray):\n", @@ -1012,54 +673,24 @@ "ts_energy_train[-48:].plot(label=\"training\")\n", "ts_energy_val[:24].plot(label=\"validation\")\n", "pred.plot(label=\"forecast\")" - ] + ], + "id": "ab7cf0298ab6b19d" }, { - "cell_type": "markdown", - "id": "e8a2e524-a5c6-48bf-b804-cd5506e1bf15", "metadata": {}, + "cell_type": "markdown", "source": [ "## Example of the boosted tree models\n", "\n", "Make sure to have the dependencies for LightGBM, CatBoost, and XGBoost installed. You can check out our installation guide [here](https://github.com/unit8co/darts/blob/master/INSTALL.md)" - ] + ], + "id": "4716b99dd4e42599" }, { - "cell_type": "code", - "execution_count": 17, - "id": "8d3f6fe5-d7ba-44e0-a0e1-6f4cfb2e75ea", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LightGBMModel MAPE: 2.2682070257112743\n", - "XGBoostModel MAPE: 3.5364068635935895\n", - "CatboostModel MAPE: 2.373275454432286\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "lgbm_model = LightGBMModel(lags=24, output_chunk_length=24, verbose=0)\n", "xgboost_model = XGBModel(lags=24, output_chunk_length=24)\n", @@ -1082,7 +713,8 @@ "pred_lgbm.plot(label=\"lgbm\")\n", "pred_xgboost.plot(label=\"xgboost\")\n", "pred_catboost.plot(label=\"catboost\")" - ] + ], + "id": "aa071d8a1fbcc0" } ], "metadata": { diff --git a/examples/28-Explainability-examples.ipynb b/examples/28-Explainability-examples.ipynb new file mode 100644 index 0000000000..7d4c5f9e2b --- /dev/null +++ b/examples/28-Explainability-examples.ipynb @@ -0,0 +1,8797 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1abd8e5c", + "metadata": {}, + "source": [ + "# SHAP Explainability of Forecasting Models\n", + "\n", + "This notebook demonstrates how to use Darts' `SHAPExplainer` to explain the predictions of forecasting models using SHAP. `ShapExplainer` can explain any of Darts':\n", + "\n", + "- `SKLearnModel`: For example `LinearRegressionModel`, `CatBoostModel`, etc.\n", + "- `TorchForecastingModel`: Including regular torch models such as `DLinearModel`, `TiDEModel`, etc., as well as foundation models such as `Chronos2Model`, `TimesFM2p5Model`, etc.\n", + "\n", + "The explainers are based on [**SHAP** (SHapley Additive exPlanations)](https://github.com/slundberg/shap)[1] , a game-theoretic framework for attributing a model’s prediction to its input features. They compute SHAP values for each feature (lagged targets & covariates) at each forecast horizon, quantifying each feature's contribution to the prediction.\n", + "\n", + "A **SHAP value** measures how much a feature contributes to moving a prediction away from a baseline prediction (average prediction). The sign and magnitude of the SHAP value indicate the direction and strength of the feature's influence on the forecast:\n", + "\n", + "- Positive SHAP value: the feature pushes the forecast **up**.\n", + "- Larger absolute value: stronger impact on the prediction.\n", + "\n", + "In forecasting, this helps answer questions like:\n", + "\n", + "- Which lagged values were most influential?\n", + "- How did covariates affect the forecast?\n", + "- Why did the model predict a spike or drop at a specific horizon?\n", + "\n", + "Throughout this notebook, we will use `ElectricityConsumptionZurichDataset` as an example dataset. Explaining *complex models* over *a large dataset* can be **computationally intensive**.\n", + "\n", + "For demonstration purposes, we will use a subset of the data and simple models (`LinearRegressionModel` and `DLinearModel`). Same principles and methodology apply to larger datasets and complex models.\n", + "\n", + "[1] Lundberg, Scott M & Lee, Su-In, \"A Unified Approach to Interpreting Model Predictions\", 2017. http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ecc7d596", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f0ada0a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import logging\n", + "import warnings\n", + "\n", + "import numpy as np\n", + "import plotly\n", + "import shap\n", + "import torch\n", + "from statsmodels.tools.sm_exceptions import InterpolationWarning\n", + "\n", + "from darts import concatenate, set_option\n", + "from darts.datasets import ElectricityConsumptionZurichDataset\n", + "from darts.explainability import ShapExplainer\n", + "from darts.metrics import mae, mic, miw\n", + "from darts.models import DLinearModel, LinearRegressionModel\n", + "from darts.utils.likelihood_models import QuantileRegression\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "warnings.filterwarnings(\"ignore\", category=InterpolationWarning)\n", + "logging.disable(logging.CRITICAL)\n", + "plotly.offline.init_notebook_mode()\n", + "shap.initjs()\n", + "\n", + "set_option(\"plotting.use_darts_style\", True)" + ] + }, + { + "cell_type": "markdown", + "id": "452df02f", + "metadata": {}, + "source": [ + "## 1. Data Preparation\n", + "The [Electricity Consumption Zurich Dataset](https://unit8co.github.io/darts/generated_api/darts.datasets.datasets.html#darts.datasets.datasets.ElectricityConsumptionZurichDataset) contains consumption data recorded at 15-minute intervals in Zurich, Switzerland between January 2015 and August 2022. It includes:\n", + "\n", + "- `\"Value_NE5\"`: consumption of households & SMEs.\n", + "- `\"Value_NE7\"`: consumption of business & services.\n", + "- Weather measurements such as temperature (`\"T [°C]\"`) and humidity (`\"Hr [%Hr]\"`).\n", + "\n", + "For computational efficiency, we will focus on forecasting *hourly* households & SMEs consumption (`\"Value_NE5\"`) using temperature (`\"T [°C]\"`) as a future covariate. We use only the last four weeks of data (three weeks for training, one week for testing). \n", + "\n", + "
\n", + "\n", + "**Warning**: We assume temperature is known in the future for simplicity. In practice, you would supply weather forecasts as future covariates to get realistic results.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c4af5888", + "metadata": {}, + "outputs": [], + "source": [ + "data = ElectricityConsumptionZurichDataset().load().astype(\"float32\")\n", + "# resample to hourly data and use only the last four weeks of data\n", + "data = data.resample(\"h\", method=\"sum\")[-28 * 24 - 1 : -1]\n", + "# target: hourly consumption of households & SMEs\n", + "series = data[\"Value_NE5\"].with_columns_renamed(\"Value_NE5\", \"consumption\")\n", + "# future covariate: temperature\n", + "future_covariates = data[\"T [°C]\"].with_columns_renamed(\"T [°C]\", \"temperature\") / 4.0\n", + "# add \"hour of day\" and \"day of week\" as future covariates\n", + "future_covariates = (\n", + " future_covariates.add_datetime_attribute(\"hour\")\n", + " .add_datetime_attribute(\"dayofweek\")\n", + " .astype(\"float32\")\n", + ")\n", + "# split target into train & test\n", + "train, test = series[: -24 * 7], series[-24 * 7 :]" + ] + }, + { + "cell_type": "markdown", + "id": "5587bd31", + "metadata": {}, + "source": [ + "Let's quickly visualize the target series of hourly consumption to see what we are working with:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ddc3b510", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "train: %{y:.3g}", + "legendgroup": "train", + "line": { + "color": "#000000" + }, + "mode": "lines", + "name": "train", + "type": "scatter", + "x": [ + "2022-08-03T00:00:00.000000", + "2022-08-03T01:00:00.000000", + "2022-08-03T02:00:00.000000", + "2022-08-03T03:00:00.000000", + "2022-08-03T04:00:00.000000", + "2022-08-03T05:00:00.000000", + "2022-08-03T06:00:00.000000", + "2022-08-03T07:00:00.000000", + "2022-08-03T08:00:00.000000", + "2022-08-03T09:00:00.000000", + "2022-08-03T10:00:00.000000", + "2022-08-03T11:00:00.000000", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = train.plotly(label=\"train\")\n", + "test.plotly(label=\"test\", fig=fig)\n", + "fig.update_layout(yaxis_title=\"Consumption (MWh)\", autosize=True)" + ] + }, + { + "cell_type": "markdown", + "id": "f175d5c8", + "metadata": {}, + "source": [ + "## 2. Scikit-learn Model Set-up\n", + "\n", + "We will first train a scikit-learn model (`LinearRegressionModel`) before using `ShapExplainer` to explain its predictions, both globally (which features are most important overall) and locally (why a specific prediction was made).\n", + "\n", + "### 2.1 Model Training\n", + "\n", + "We will fit a scikit-learn linear regression model to forecast the next 24 hours of consumption, using:\n", + "- past 24 hours of consumption (`lags=24`).\n", + "- future 24 hours of temperature and datetime features (`lags_future_covariates=(0, 24)`).\n", + "\n", + "
\n", + "\n", + "**Info**: While we do not apply data scaling here for simplicity, you may want to scale your data before training non-linear models for better accuracy. Note that SHAP values would be computed in the scaled feature space.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e5de8eb4", + "metadata": {}, + "outputs": [], + "source": [ + "# fit a linear regression model\n", + "sklearn_model = LinearRegressionModel(\n", + " lags=24,\n", + " lags_future_covariates=(0, 24),\n", + " output_chunk_length=24,\n", + " random_state=42,\n", + ").fit(train, future_covariates=future_covariates)" + ] + }, + { + "cell_type": "markdown", + "id": "81dcfffe", + "metadata": {}, + "source": [ + "### 2.2 Model Evaluation\n", + "Let's evaluate the model's performance on the holdout test set using a rolling historical forecast strategy, i.e., forecasting 24 hours ahead at a time, then rolling forward by 24 hours and repeating until the end of the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f307e008", + "metadata": {}, + "outputs": [], + "source": [ + "historical_forecasts_kwargs = dict(\n", + " series=series,\n", + " future_covariates=future_covariates,\n", + " start=test.start_time(),\n", + " forecast_horizon=24,\n", + " stride=24,\n", + " retrain=False,\n", + " last_points_only=False,\n", + ")\n", + "# a list of seven non-overlapping `TimeSeries` is returned, each containing the 24-hour forecast\n", + "sklearn_preds = sklearn_model.historical_forecasts(**historical_forecasts_kwargs)\n", + "# concatenate the list into a single `TimeSeries` containing all forecasts\n", + "sklearn_pred = concatenate(sklearn_preds)" + ] + }, + { + "cell_type": "markdown", + "id": "5a1a857c", + "metadata": {}, + "source": [ + "We will compute the mean absolute error (MAE) of the forecasts and visualize the predictions against the actual values." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0c67b606", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "actual: %{y:.3g}", + "legendgroup": "actual", + "line": { + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# compute mean absolute error\n", + "error = mae(test, sklearn_pred)\n", + "# plot predictions against actuals\n", + "fig = test.plotly(label=\"actual\")\n", + "sklearn_pred.plotly(label=f\"LR (MAE: {error:.2f})\", fig=fig)\n", + "fig.update_layout(yaxis_title=\"Consumption (kWh)\", autosize=True)" + ] + }, + { + "cell_type": "markdown", + "id": "3bcce340", + "metadata": {}, + "source": [ + "## 3. Global Explainability\n", + "Now let's use `ShapExplainer` to get a global view of feature importance, which tells us which features were most influential in the model's predictions across the test set.\n", + "\n", + "To explain the model's predictions, we need to prepare two sets of data:\n", + "\n", + "- **Background data**: a representative set of data used to compute the baseline prediction.\n", + "- **Foreground data**: the specific input instances for which we want to explain the predictions.\n", + "\n", + "
\n", + "\n", + "**Info**:\n", + "Background data does not have to be the same as training data, but it should be representative of the data distribution from which the foreground data is drawn.

\n", + "Both background and foreground data are optional when initializing the explainer, but providing them allows for more targeted and meaningful explanations:\n", + "
    \n", + "
  • When background data is not provided, training data will be used as the background data by default.
    • \n", + "
  • When foreground data is not provided, background data will be used as the foreground data by default.
    • \n", + "
  • For global explanations, foreground data is often not needed, as we want to understand overall feature importance across the dataset.
    • \n", + "
\n", + "\n", + "
\n", + "\n", + "### 3.1 Explainer Initialization\n", + "\n", + "Let's initialize a `ShapExplainer` with our trained model and background data, and then generate a summary plot of global feature importance:\n", + "\n", + "
\n", + "\n", + "Background Sampling\n", + "\n", + "By default, `ShapExplainer` uses all forecastable time steps in the background series to compute the baseline prediction. To speed up computation, you can specify the number of randomly drawn samples from the background when initializing the explainer using the `background_num_samples` parameter.\n", + "
\n", + "\n", + "
\n", + "\n", + "SHAP Method\n", + "\n", + "By default, `ShapExplainer` chooses the SHAP method based on the model type (e.g., `\"linear\"` for linear models). You can also specify the SHAP method to use via the `shap_method` parameter when initializing the explainer. Supported values are: `\"tree\"`, `\"kernel\"`, `\"partition\"`, `\"linear\"`, `\"permutation\"`, and `\"additive\"`.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f41622a9", + "metadata": {}, + "outputs": [], + "source": [ + "shap_explainer = ShapExplainer(\n", + " model=sklearn_model,\n", + " background_series=test,\n", + " background_future_covariates=future_covariates,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3fec0d26", + "metadata": {}, + "source": [ + "### 3.2 Summary Scatter Plot\n", + "\n", + "We will inspect the SHAP values at two forecast horizons: 12 hours and 24 hours ahead. The **summary scatter plot** (also known as *beeswarm plot*) will show the distribution of SHAP values for each feature, indicating their overall impact on the model's predictions at those horizons.\n", + "\n", + "
\n", + "\n", + "**Info**:\n", + "Horizons are indexed starting from 1, i.e., horizon 1 corresponds to the first forecasted time step after the end of the input series, horizon 2 corresponds to the second forecasted time step, and so on.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "400ed598", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = shap_explainer.summary_plot(horizons=[12, 24], max_display=10)" + ] + }, + { + "cell_type": "markdown", + "id": "bc38317a", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**Info**:\n", + "Each scatter point on the plot represents a forecast instance at the specified horizon. The point color indicates the feature value (**red** for high, **blue** for low) and the x-axis position indicates the SHAP value (impact on the prediction).\n", + "\n", + "Features on the y-axis are ordered by average impact (mean absolute SHAP value), with the most influential features at the top.\n", + "\n", + "
\n", + "\n", + "For 12-hour horizon, we see that the most important features are different lags of the target, i.e., `consumption_target_lag-*`. For 24-hour horizon, the most important features additionally include temporal features, particularly the hour of the day, i.e., `hour_futcov_lag*`.\n", + "\n", + "
\n", + "\n", + "**Info**:\n", + "Input features include all lagged values of the target and covariates used by the model for forecasting. They are named according to the following convention: `\"{name}_{type}_lag{idx}\"`, where:\n", + "\n", + "- ``{name}`` is the component name from the original foreground series (target, past, or future).\n", + "- ``{type}`` is the covariates type. It can take 3 different values:\n", + " ``\"target\"``, ``\"pastcov\"``, ``\"futcov\"``.\n", + "- ``{idx}`` is the lag index.\n", + "\n", + "If the model uses static covariates, they are included as well and named according to the convention: `\"{name}_statcov_target_{comp}\"`, where:\n", + "\n", + "- ``{name}`` is the variable name of the static covariate.\n", + "- ``{comp}`` is the component name of the target series if static covariates are component-specific, or `\"global_components\"` if they are global.\n", + "\n", + "
\n", + "\n", + "### 3.3 Summary Bar Plot\n", + "\n", + "To get a clearer view of the overall feature importance, we can also plot the average impact (mean absolute SHAP value) of each feature using a **bar plot**.\n", + "\n", + "For instance, for the 12-hour horizon:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1625a1ff", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "shap_dict = shap_explainer.summary_plot(\n", + " horizons=[12],\n", + " plot_type=\"bar\",\n", + " max_display=10,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2b8c8070", + "metadata": {}, + "source": [ + "### 3.4 Scatter Dependence Plot\n", + "To understand how the value of a specific feature affects the model's predictions, we can use a **scatter dependence plot**. This plot shows the correlation between the feature values and their SHAP values.\n", + "\n", + "The `.summary_plot()` method returns a nested dictionary of `shap.Explanation` objects for each horizon and target. Within each `shap.Explanation` object, the SHAP values and corresponding feature values can be accessed for any feature of interest.\n", + "\n", + "For example, for the `consumption_target_lag-24` feature at the 12-hour horizon:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d1901a94", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# access the `shap.Explanation` object for the 12-hour horizon and\n", + "# the `consumption` target component\n", + "shap_object = shap_dict[12][\"consumption\"]\n", + "# create a scatter dependence plot for the `consumption_target_lag-24` feature\n", + "shap.plots.scatter(shap_object[:, \"consumption_target_lag-24\"])" + ] + }, + { + "cell_type": "markdown", + "id": "51db7c18", + "metadata": {}, + "source": [ + "Because `LinearRegressionModel` is a linear model, we can see a clear linear relationship between the feature values and their SHAP values. In the case of `consumption_target_lag-24`, higher feature values lead to higher SHAP values." + ] + }, + { + "cell_type": "markdown", + "id": "fa5cbe3e", + "metadata": {}, + "source": [ + "## 4. Local Explainability\n", + "Now let's look at local explanations, which tell us why the model made a specific prediction for given input instances.\n", + "\n", + "
\n", + "\n", + "**Info**:\n", + "
    \n", + "
  • For local explanations, it is recommended to provide specific foreground data to the explainer that you want to explain.
    • \n", + "
  • When foreground data is not provided, background data will be used as the foreground data by default.
    • \n", + "
\n", + "
\n", + "\n", + "### 4.1 Batched Explanations\n", + "We will call explainer's `.explain()` method to compute SHAP values for all forecastable instances in the foreground data. The SHAP values for each horizon and target are stored and returned in a `ShapExplainabilityResult` object.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c25c241c", + "metadata": {}, + "outputs": [], + "source": [ + "result = shap_explainer.explain(\n", + " foreground_series=test,\n", + " foreground_future_covariates=future_covariates,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cd586da6", + "metadata": {}, + "source": [ + "The `ShapExplainabilityResult` object provides `.get_explanation()` method to retrieve the SHAP explanation for any specific horizon and target as a `TimeSeries`, where:\n", + "\n", + "- Components correspond to the input features.\n", + "- Time index corresponds to the **start time** of forecast instances in the foreground data.\n", + "\n", + "For the 12-hour horizon, the SHAP values are:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "24ea7bc8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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2022-08-30 00:00:00-10160.5000001530.862305311.685089211.255249-380.730743...487.851776428.385498341.000000147.774750385.206299

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" + ], + "text/plain": [ + " consumption_target_lag-24 consumption_target_lag-23 consumption_target_lag-22 consumption_target_lag-21 consumption_target_lag-20 ... hour_futcov_lag22 dayofweek_futcov_lag22 temperature_futcov_lag23 hour_futcov_lag23 dayofweek_futcov_lag23\n", + "2022-08-25 00:00:00 -8576.616211 1479.537109 284.641479 202.597275 -172.985184 ... 487.851776 40.706322 -16.966320 147.774750 33.419735\n", + "2022-08-25 01:00:00 -10263.247070 1408.389771 219.286346 14.395938 988.871216 ... 533.360352 40.706322 62.218163 -137.361099 -142.473541\n", + "2022-08-25 02:00:00 -9762.847656 1079.852051 8.703232 -123.068840 1642.009033 ... -513.336548 -153.133270 107.700172 -124.963890 -142.473541\n", + "2022-08-25 03:00:00 -7452.150879 21.258911 -145.109497 -200.344696 1906.641602 ... -467.827972 -153.133270 166.924896 -112.566673 -142.473541\n", + "2022-08-25 04:00:00 -6.771461 -751.951782 -231.575348 -231.654648 2297.426514 ... -422.319427 -153.133270 213.061462 -100.169464 -142.473541\n", + "... ... ... ... ... ... ... ... ... ... ... ...\n", + "2022-08-29 20:00:00 -7009.775391 1101.682373 219.693695 254.758255 -2342.299072 ... 305.817535 428.385498 120.134026 98.185913 385.206299\n", + "2022-08-29 21:00:00 -7605.689453 1081.899902 277.650543 271.057831 -2441.373779 ... 351.326111 428.385498 222.550552 110.583122 385.206299\n", + "2022-08-29 22:00:00 -7466.553711 1373.246704 295.888550 282.779846 -2461.621338 ... 396.834656 428.385498 256.253021 122.980331 385.206299\n", + "2022-08-29 23:00:00 -9515.676758 1464.928467 309.004578 285.175415 -1836.845825 ... 442.343231 428.385498 284.393005 135.377548 385.206299\n", + "2022-08-30 00:00:00 -10160.500000 1530.862305 311.685089 211.255249 -380.730743 ... 487.851776 428.385498 341.000000 147.774750 385.206299\n", + "\n", + "shape: (121, 96, 1), freq: h, size: 45.38 KB" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shap_values = result.get_explanation(horizon=12)\n", + "shap_values" + ] + }, + { + "cell_type": "markdown", + "id": "0f43a534", + "metadata": {}, + "source": [ + "There are 96 input features in total, including lagged targets (24), temperature (24), and datetime attributes (48). There are 121 (=24*7-24-24+1) forecast instances in the foreground data. The first forecast instance starts at 2022-08-25 00:00:00.\n" + ] + }, + { + "cell_type": "markdown", + "id": "19f2bc12", + "metadata": {}, + "source": [ + "### 4.2 Waterfall Plot\n", + "A **waterfall plot** shows SHAP values for one forecast instance.\n", + "It starts at the model’s baseline prediction, then shows how each feature pushes the prediction up or down to reach the final value.\n", + "\n", + "`ShapExplainabilityResult` has a `.get_shap_explanation_object()` method that returns a `shap.Explanation` for a specific horizon and target.\n", + "That object includes all forecast instances, but `shap.plots.waterfall()` displays only one, so we must select the specific instance by indexing into it.\n", + "\n", + "For the first forecast instance at the 12-hour horizon, the waterfall plot is:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f7199b68", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# access the `shap.Explanation` object for the 12-hour horizon and target component\n", + "shap_object = result.get_shap_explanation_object(horizon=12)\n", + "# create a waterfall plot for the first forecast instance\n", + "shap.plots.waterfall(shap_object[0])" + ] + }, + { + "cell_type": "markdown", + "id": "99ff50a8", + "metadata": {}, + "source": [ + "Once again, we are seeing lags of the target as the most impactful features for 12-hour horizon, with `consumption_target_lag-13` pushing the prediction up and `consumption_target_lag-24` pushing it down.\n", + "\n", + "Because the first forecast instance starts at 2022-08-25 00:00:00, the final prediction value here corresponds to to the prediction at 2022-08-25 11:00:00 (remember that horizons are indexed starting from 1).\n", + "\n", + "We may confirm this by comparing the model's historical forecast at that time step (within small tolerance due to SHAP's approximation):" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f5d09879", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
consumption
Timestamp
2022-08-25 11:00:00123945.664062

shape: (1, 1, 1), freq: h, size: 4.00 B

" + ], + "text/plain": [ + " consumption\n", + "Timestamp \n", + "2022-08-25 11:00:00 123945.664062\n", + "\n", + "shape: (1, 1, 1), freq: h, size: 4.00 B" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sklearn_pred[35]" + ] + }, + { + "cell_type": "markdown", + "id": "0755818a", + "metadata": {}, + "source": [ + "### 4.3 Bar Plot\n", + "We can also visualize ONLY the SHAP values for that instance, without the baseline and prediction values, using a **local bar plot**:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5691bf36", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "shap.plots.bar(shap_object[0])" + ] + }, + { + "cell_type": "markdown", + "id": "9bbc2a51", + "metadata": {}, + "source": [ + "### 4.4 Force Plot\n", + "Another common way to visualize local explanations is the **force plot**, which provides a more compact view of feature contributions from *ALL* forecast instances in the foreground.\n", + "It shows the SHAP values for all features and forecast instances in the foreground.\n", + "\n", + "The explainer's `.force_plot()` method accepts the foreground data, along with the horizon and target to visualize as arguments, and returns a SHAP force plot.\n", + "\n", + "Let's visualize the force plot for the 12-hour horizon:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8639b8ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + " Visualization omitted, Javascript library not loaded!
\n", + " Have you run `initjs()` in this notebook? If this notebook was from another\n", + " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", + " this notebook on github the Javascript has been stripped for security. If you are using\n", + " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shap_explainer.force_plot(\n", + " foreground_series=test,\n", + " foreground_future_covariates=future_covariates,\n", + " horizon=12,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "62580b25", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**Info**:\n", + "The force plot uses the same color scheme as the bar plot above (**red** for positive SHAP values, **blue** for negative SHAP values).\n", + "\n", + "Forecast instances are laid out along the x-axis, ordered by instance similarity. Because the forecast instances are orginally ordered by time, it is recommended to change the ordering (top menu) to **\"original sampling ordering\"** for better interpretability.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c4d38295", + "metadata": {}, + "source": [ + "### 4.5 Individual Explanation\n", + "\n", + "Computing SHAP values for all forecast instances in the foreground can be computationally intensive.\n", + "Alternatively, we can compute SHAP values for a single forecast instance by calling the explainer's `.explain_single()` method.\n", + "\n", + "This method accepts the optional foreground data and target components as arguments, and returns a `ShapSingleExplainabilityResult` object containing the SHAP explanation for that specific instance at all horizons.\n", + "\n", + "Let say we want to explain the model's prediction for the last day (2022-08-30 00:00:00 to 2022-08-30 23:00:00) in the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f04a4044", + "metadata": {}, + "outputs": [], + "source": [ + "result_single = shap_explainer.explain_single(\n", + " # last historical target known to the model before forecasting the last day\n", + " foreground_series=test[:-24],\n", + " # future covariates must extend for at least 24 hours for forecasting the last day\n", + " foreground_future_covariates=future_covariates,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fd28f54b", + "metadata": {}, + "source": [ + "Like `ShapExplainabilityResult`, the `ShapSingleExplainabilityResult` object has a `.get_explanation()` method that returns the SHAP explanation as a `TimeSeries`, where:\n", + "\n", + "- Components correspond to the input features.\n", + "- Time index corresponds to the **forecasted time steps** for the specific instance.\n", + "\n", + "
\n", + "\n", + "**Info**:\n", + "Unlike the previous case, however, the `horizon` argument is not needed here since the `TimeSeries` index corresponds to the entire forecast horizon.\n", + "\n", + "Instead of being the start time of forecast instances, the index here corresponds to the forecasted time steps for the specific instance.\n", + "\n", + "
\n", + "\n", + "For the last day forecast, the SHAP values are:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bd4eeae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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consumption_target_lag-24consumption_target_lag-23consumption_target_lag-22consumption_target_lag-21consumption_target_lag-20...hour_futcov_lag22dayofweek_futcov_lag22temperature_futcov_lag23hour_futcov_lag23dayofweek_futcov_lag23
2022-08-30 00:00:00312.100800-3887.8747562191.1911622437.993164-573.889526...-840.56018122.066118-7.806287-577.70233215.583846
2022-08-30 01:00:006083.263184-12577.513672863.2415164393.475098-167.985840...-1878.00280829.848068-2.478446-1829.97851620.395519
2022-08-30 02:00:002297.485840-1769.521729-9078.1025394080.288330117.446838...-2579.93042041.094463-3.679584-3208.68945331.747749
2022-08-30 03:00:00-2776.5446781297.26025419.175505-2914.070312-95.215630...-2540.77270561.072117-4.035876-3958.31518654.731003
2022-08-30 04:00:00-4874.411133-519.8378301779.6645513971.779053-1620.843872...-1799.03308185.08304613.849454-3720.54614382.232506
....................................
2022-08-30 19:00:005613.7163091068.211182-412.795441-2285.319336351.740936...1587.914795391.677856-198.3974912261.726074344.760345
2022-08-30 20:00:006595.375488130.6092221397.953979-2774.16650446.217293...1219.252075362.630707-295.8388372038.275146322.846222
2022-08-30 21:00:007919.741211-1110.8918461436.128174-1592.343140-107.391541...972.831604349.208954-351.3149721741.608887313.429382
2022-08-30 22:00:008792.078125-950.937927497.203583-1605.584839160.129883...986.942932344.090851-370.6959841554.852173311.060394
2022-08-30 23:00:006810.1181642722.783447-499.861603-1496.33020076.960831...782.761719339.983826-369.1135561384.544067307.074463

shape: (24, 96, 1), freq: h, size: 9.00 KB

" + ], + "text/plain": [ + " consumption_target_lag-24 consumption_target_lag-23 consumption_target_lag-22 consumption_target_lag-21 consumption_target_lag-20 ... hour_futcov_lag22 dayofweek_futcov_lag22 temperature_futcov_lag23 hour_futcov_lag23 dayofweek_futcov_lag23\n", + "2022-08-30 00:00:00 312.100800 -3887.874756 2191.191162 2437.993164 -573.889526 ... -840.560181 22.066118 -7.806287 -577.702332 15.583846\n", + "2022-08-30 01:00:00 6083.263184 -12577.513672 863.241516 4393.475098 -167.985840 ... -1878.002808 29.848068 -2.478446 -1829.978516 20.395519\n", + "2022-08-30 02:00:00 2297.485840 -1769.521729 -9078.102539 4080.288330 117.446838 ... -2579.930420 41.094463 -3.679584 -3208.689453 31.747749\n", + "2022-08-30 03:00:00 -2776.544678 1297.260254 19.175505 -2914.070312 -95.215630 ... -2540.772705 61.072117 -4.035876 -3958.315186 54.731003\n", + "2022-08-30 04:00:00 -4874.411133 -519.837830 1779.664551 3971.779053 -1620.843872 ... -1799.033081 85.083046 13.849454 -3720.546143 82.232506\n", + "... ... ... ... ... ... ... ... ... ... ... ...\n", + "2022-08-30 19:00:00 5613.716309 1068.211182 -412.795441 -2285.319336 351.740936 ... 1587.914795 391.677856 -198.397491 2261.726074 344.760345\n", + "2022-08-30 20:00:00 6595.375488 130.609222 1397.953979 -2774.166504 46.217293 ... 1219.252075 362.630707 -295.838837 2038.275146 322.846222\n", + "2022-08-30 21:00:00 7919.741211 -1110.891846 1436.128174 -1592.343140 -107.391541 ... 972.831604 349.208954 -351.314972 1741.608887 313.429382\n", + "2022-08-30 22:00:00 8792.078125 -950.937927 497.203583 -1605.584839 160.129883 ... 986.942932 344.090851 -370.695984 1554.852173 311.060394\n", + "2022-08-30 23:00:00 6810.118164 2722.783447 -499.861603 -1496.330200 76.960831 ... 782.761719 339.983826 -369.113556 1384.544067 307.074463\n", + "\n", + "shape: (24, 96, 1), freq: h, size: 9.00 KB" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shap_values = result_single.get_explanation()\n", + "shap_values" + ] + }, + { + "cell_type": "markdown", + "id": "32505218", + "metadata": {}, + "source": [ + "Because the forecast horizon is 24 hours, the SHAP values are provided for each of the 24 forecasted time steps from 2022-08-30 00:00:00 to 2022-08-30 23:00:00.\n", + "\n", + "### 4.6 Waterfall Plot\n", + "\n", + "Like `ShapExplainabilityResult`, `ShapSingleExplainabilityResult` also has a `.get_shap_explanation_object()` method that returns a `shap.Explanation` object for the specific instance.\n", + "\n", + "We can visualize the SHAP values for the forecast at the 12-hour horizon using a **waterfall plot**:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b9b6614a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# access the `shap.Explanation` object for the (optional) target component\n", + "shap_object = result_single.get_shap_explanation_object()\n", + "# create a waterfall plot for the forecast instance at the 12-hour horizon\n", + "# note that `shap_object` is indexed starting from 0, so the 12-hour horizon\n", + "# corresponds to index 11\n", + "shap.plots.waterfall(shap_object[11])" + ] + }, + { + "cell_type": "markdown", + "id": "b89fd43c", + "metadata": {}, + "source": [ + "A **local bar plot** can be created similarly to before, using `shap.plots.bar()` on the `shap.Explanation` object for that horizon.\n", + "\n", + "Again, we can confirm the model's historical forecast at that time step (2022-08-30 11:00:00) matches the prediction value in the waterfall plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4eec5c69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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consumption
Timestamp
2022-08-30 11:00:00136841.0

shape: (1, 1, 1), freq: h, size: 4.00 B

" + ], + "text/plain": [ + " consumption\n", + "Timestamp \n", + "2022-08-30 11:00:00 136841.0\n", + "\n", + "shape: (1, 1, 1), freq: h, size: 4.00 B" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sklearn_pred[-13]" + ] + }, + { + "cell_type": "markdown", + "id": "d7bc4eb2", + "metadata": {}, + "source": [ + "### 4.7 Heatmap\n", + "Finally, we can also visualize the SHAP values for top features and horizons for that specific instance using a **heatmap**.\n", + "\n", + "Using the `shap.Explanation` object for the instance, we can create a heatmap of SHAP values across the entire forecast horizon:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "8a8f5864", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# heatmap orders horizons by similarity by default\n", + "# we override this ordering to show the horizons in their natural order with `instance_order``\n", + "ax = shap.plots.heatmap(\n", + " shap_object, instance_order=np.arange(shap_object.shape[0]), show=False\n", + ")\n", + "ax.set_xlabel(\"Horizon (hours)\");" + ] + }, + { + "cell_type": "markdown", + "id": "3c378d36", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**Info**:\n", + "The top panel shows the forecasted values across the horizon, and the bottom panel shows the SHAP values for each feature and horizon.\n", + "\n", + "Each row on the heatmap corresponds to a feature, and each column corresponds to a horizon in the forecast instance. \n", + "\n", + "On the bottom panel, each cell is colored according to the SHAP value, with the same color scheme as before (**red** for positive SHAP values, **blue** for negative SHAP values).\n", + "\n", + "
\n", + "\n", + "We can see that different lags of the target have varying influence across the forecast horizon. Many of them, like `lag-10` and `lag-16` have a strong influence on the prediction at the `lag` + 24 horizon. That corresponds to the same time step of the next day, indicating the model is learning daily seasonality patterns in the data." + ] + }, + { + "cell_type": "markdown", + "id": "9419b06c", + "metadata": {}, + "source": [ + "## 5. PyTorch Model Set-up\n", + "Now let's explain a PyTorch forecasting model. Before that, we will quickly train a `DLinearModel` on the same dataset and evaluate its performance.\n", + "\n", + "As per its name, `DLinearModel` is a simple linear model that applies a separate linear layer to trend and seasonality components extracted from the input series. It supports past, future, and static covariates as well as probabilistic forecasting.\n", + "\n", + "### 5.1 Model Training\n", + "We will fit a `DLinearModel` to forecast the next 24 hours of consumption, using:\n", + "- past 24 hours of consumption (`input_chunk_length=24`).\n", + "- past 24 hours of temperature and datetime features as future covariates (`input_chunk_length=24`).\n", + "- future 24 hours of temperature and datetime features as future covariates (`output_chunk_length=24`).\n", + "\n", + "Notice the difference to `LinearRegressionModel` where we only used future values of future covariates." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "76a97f34", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7fa523d2bcea44448bb400928a19f11b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? 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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# a list of seven non-overlapping `TimeSeries` is returned, each containing the 24-hour forecast\n", + "torch_preds = torch_model.historical_forecasts(**historical_forecasts_kwargs)\n", + "# concatenate the list into a single `TimeSeries` containing all forecasts\n", + "torch_pred = concatenate(torch_preds)\n", + "# compute mean absolute error\n", + "error = mae(test, torch_pred)\n", + "# plot predictions against actuals\n", + "fig = test.plotly(label=\"actual\")\n", + "torch_pred.plotly(label=f\"DL (MAE: {error:.2f})\", fig=fig)\n", + "fig.update_layout(yaxis_title=\"Consumption (kWh)\", autosize=True)" + ] + }, + { + "cell_type": "markdown", + "id": "6b124ef5", + "metadata": {}, + "source": [ + "## 6. Torch Explainability\n", + "\n", + "`ShapExplainer` can be used identically for torch models as for the sklearn-like regression models shown earlier.\n", + "\n", + "There are only two differences:\n", + "\n", + "- **SHAP Method choice**:\n", + " - For `SKLearnModel`: chooses the SHAP method based on the underlying regressor model type.\n", + " - For `TorchForecastingModel`: uses a [**Permutation explainer**](https://shap.readthedocs.io/en/latest/generated/shap.PermutationExplainer.html) by default.\n", + "- **Likelihood Parameter Explainability**:\n", + " - For `SKLearnModel`: can only explain the median (quantile) or mean (poisson) predictions.\n", + " - For `TorchForecastingModel`: can explain all likelihood parameters of probabilistic forecasts.\n", + "\n", + "### 6.1 Explainer Initialization\n", + "\n", + "Let's create a `ShapExplainer` with our trained model and background data:\n", + "\n", + "
\n", + "\n", + "SHAP Method\n", + "\n", + "By default, `ShapExplainer` uses Permutation explainer for explaining PyTorch models, which is applicable to any model.\n", + "You can also specify the SHAP method to use via the `shap_method` parameter when initializing the explainer. Supported values are: `\"kernel\"`, `\"sampling\"`, `\"partition\"`, and `\"permutation\"`.\n", + "\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "Batch Size\n", + "\n", + "By default, `ShapExplainer` uses the same batch size for SHAP value computation as the one for model training.\n", + "You can specify a different batch size via the `batch_size` parameter when initializing the explainer.\n", + "\n", + "Unlike model training, SHAP value computation is done under *PyTorch inference mode* and may consume less memory.\n", + "A larger batch size could better utilize accelerator memory and speed up computation.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6c84e87c", + "metadata": {}, + "outputs": [], + "source": [ + "torch_explainer = ShapExplainer(\n", + " model=torch_model,\n", + " background_series=test,\n", + " background_future_covariates=future_covariates,\n", + " batch_size=4096,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2228ae94", + "metadata": {}, + "source": [ + "### 6.2 Summary Scatter Plot\n", + "We will inspect the SHAP values globally at the 12-hour horizon using a summary scatter plot, just like we did for `ShapExplainer`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ffdc4238", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = torch_explainer.summary_plot(horizons=[12], max_display=10)" + ] + }, + { + "cell_type": "markdown", + "id": "665c4778", + "metadata": {}, + "source": [ + "Once again, we see that the most important features are different lags of the target, i.e., `consumption_target_lag-*`.\n", + "\n", + "### 6.3 Batched Explanations\n", + "Now let's compute SHAP values for all forecast instances in the foreground data by calling the explainer's `.explain()` method, and retrieve the SHAP explanation for the 12-hour horizon:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "041bdf54", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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consumption_target_lag-24temperature_futcov_lag-24hour_futcov_lag-24dayofweek_futcov_lag-24consumption_target_lag-23...hour_futcov_lag22dayofweek_futcov_lag22temperature_futcov_lag23hour_futcov_lag23dayofweek_futcov_lag23
2022-08-25 00:00:00191.304375-3.589570-5.494023-2.0692973729.447969...0.00.00.00.00.0
2022-08-25 01:00:00228.924023-4.063008-4.977891-2.0655863550.107383...0.00.00.00.00.0
2022-08-25 02:00:00217.761484-4.663711-4.461211-2.0687112721.967773...0.00.00.00.00.0

shape: (3, 168, 1), freq: h, size: 3.94 KB

" + ], + "text/plain": [ + " consumption_target_lag-24 temperature_futcov_lag-24 hour_futcov_lag-24 dayofweek_futcov_lag-24 consumption_target_lag-23 ... hour_futcov_lag22 dayofweek_futcov_lag22 temperature_futcov_lag23 hour_futcov_lag23 dayofweek_futcov_lag23\n", + "2022-08-25 00:00:00 191.304375 -3.589570 -5.494023 -2.069297 3729.447969 ... 0.0 0.0 0.0 0.0 0.0\n", + "2022-08-25 01:00:00 228.924023 -4.063008 -4.977891 -2.065586 3550.107383 ... 0.0 0.0 0.0 0.0 0.0\n", + "2022-08-25 02:00:00 217.761484 -4.663711 -4.461211 -2.068711 2721.967773 ... 0.0 0.0 0.0 0.0 0.0\n", + "\n", + "shape: (3, 168, 1), freq: h, size: 3.94 KB" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use `.explain()` method to compute SHAP values for all forecast instances in the foreground\n", + "# and return them in a `ShapExplainabilityResult` object\n", + "result = torch_explainer.explain(\n", + " # explaining all forecast instances can be intensive, so here we explain only the first\n", + " # three (26-24+1=3) instances.\n", + " foreground_series=test[:26],\n", + " foreground_future_covariates=future_covariates,\n", + ")\n", + "# Use `.get_explanation()` method to retrieve the SHAP values for the 12-hour horizon\n", + "result.get_explanation(horizon=12)" + ] + }, + { + "cell_type": "markdown", + "id": "ce29a38e", + "metadata": {}, + "source": [ + "### 6.4 Waterfall Plot\n", + "We can also visualize the SHAP values for a specific forecast instance at the 12-hour horizon using a waterfall plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "8b99e48e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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consumption
Timestamp
2022-08-25 11:00:00130946.0625

shape: (1, 1, 1), freq: h, size: 4.00 B

" + ], + "text/plain": [ + " consumption\n", + "Timestamp \n", + "2022-08-25 11:00:00 130946.0625\n", + "\n", + "shape: (1, 1, 1), freq: h, size: 4.00 B" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# access the `shap.Explanation` object for the 12-hour horizon and target component\n", + "shap_object = result.get_shap_explanation_object(horizon=12)\n", + "# create a waterfall plot for the first forecast instance\n", + "shap.plots.waterfall(shap_object[0])\n", + "# confirm the predicted value at the 12-hour horizon for the first forecast instance\n", + "# (2022-08-25 00:00:00) matches the value in `torch_pred` at the same horizon and instance index\n", + "torch_pred[35]" + ] + }, + { + "cell_type": "markdown", + "id": "8a9db1ae", + "metadata": {}, + "source": [ + "### 6.5 Individual Explanation\n", + "Finally, we can compute SHAP values for a single forecast instance using the explainer's `.explain_single()` method, and visualize the SHAP values for that instance at the 12-hour horizon using a heatmap:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ee724ddf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NW1xr3LixHXDAAfbwww/b7NmzNYhCiFJFwlgIIURUeIilvul2223n3CSpQTx9+nRn6aFsixBCxGpNpmzbd999Z/Pnz3f1zcluffXVV7uFt7333tvlLKDcmxCiKAUni3EYSBgLIYQoEENMmSViiD/55BOXdZZMtPfdd581b95coyWESBjKuWE1/vzzz51L9auvvmrZ2dnuu6dVq1YuVOOmm25yWe7JfC+EEGEjYSyEECIP4gH32GMPu/XWW+3KK6+0YcOGOTfqunXrapSEEKFAAj/ij6mRjEgm232HDh2cizXZ7nfYYQe75pprbMSIEZabm6tZEIKs1NE2kRAaQSGEELZ06VI799xzrXv37k4E//LLL3bnnXe6jLNCCFFS8J1z7LHH2ksvveTyG5DxnrJPr7zyinXp0sWaNWtmF110kX355Ze2efNmTYwQImlIGAshRAaDiyIPoMQMk0X2ySefdFZi3BiFEKK0k3eR8f7xxx+3uXPn2vDhw51lmZrJhxxyiCsD1adPH/vggw9cPWUhMgbFGIeChLEQQmQoJLg56KCD3IMlD5mTJk1yZVay5I4lhEgxqI+OxZhcByQBxKvlsssuc9mujzvuOFcG6vjjj3cLfcuWLSvt7goh0hAJYyGEyMDkWsQQ77rrri7z9JAhQ1ziG5LhCCFEqkPNdC8XwoQJE9yiHhn0yXTNQh+W5B49eqgMlCi7yGIcChLGQgiRQVAmZffdd3eZX0lmw0Ml1mIhhEhXyGJ97bXX2o8//mjz5s2zRx55xLlhe2WgOnXq5GopT5w4URmuRdlAybdCQcJYCCEygCVLltjZZ59t+++/v9WvX9+VQLnjjjusatWqpd01IYRIGk2aNLG+ffu6OGQyXL/22msuq/Xdd9/tcifsvPPOTkQrw7UQIhIJYyGEKOPJtV588UWXXIsENU8//bQrydSuXbvS7poQQoReBurUU0+1N9980y0Ofvrpp3bAAQe470TilZs2bWoXXnihq6VMiIkQaYNcqUNBwlgIIcookydPtgMPPNDOOussO+yww1wc3nnnnafkWkKIjKNy5cp2+OGH21NPPeXcrclwfeaZZ9rQoUPdfpJ3nXLKKfbGG2/YqlWrSru7QohSQMJYiBTk559/tvPPP9+JGmKjnnjiCfvzzz9t7733tpEjR8bV5rfffmudO3e2WbNmJb2/IrXYsGGD3XzzzbbbbrvZ7NmzXR1QaoAquZYQQvyb4free++1qVOn2m+//ebcq/kdCzPhJj179nQiesGCBRoykXooxjgUym3Fz04IkTKQJZhVa5KJHHPMMValShUncP7v//7PNm/e7P5Rx8sZZ5zh4q94GAiL3NxcmzlzprVs2VKWyVLgm2++cfF1f/31l1133XX2n//8J+44Ys1l2UFzWXbQXIYLi8eEnbARdrJlyxYXl9yxY0e3UM3P9u3bW82aNRM+l+ZSxE33G6Pv/+42DWoCVEjkzUKI5PPhhx9aTk6OSxTSuHFjt2/8+PE2atQoGzhwYEJtI7ixJE6bNs0lIxFlB+LnyDJN7Fy3bt3s/ffft7Zt25Z2t4QQIq0gi/Xll1/utqVLl7r4Y/7/Ui8Zsbx+/Xp3nCeWPcGcLLEsROAYY5F0JIyFSDF+/fVX94/ZE8Xw9ttvW+3ata1r164JtY1r9l133WXvvvuucxsT6Q9OPy+88IITxfz+7LPPupjiLNyshBBCxE29evWcpxUbsGhNrgZE8ujRo4sUy2wdOnSQWBYijZAwFiJFePLJJ13GYA9WoAEhS+1ZRHGFChUKxJIef/zxVq5cOWchrFSpUt5r1Kn9+OOP3c9DDz3U7atWrZpb1SbZSCzCGHevWI+N5T0iPqhBfNlllzl3Px7ccJFv2LBh0sZfc1l20FyWHTSXpQcLjnjisJG4K1Isjx07NqpYRiB7QtkvljWXyZmTjEQW41CQMBYiRdh3331dLOhDDz3khCyJQQDL8bp166KW1yH++IILLrDbb7/dWZVPP/10t/+RRx5xLtnEmHqi2GPXXXe1H3/80cUyb7vttoH6xj/7ZcuWud+33357mzt3rittQX8RYsQUA1k9sVri1ksSE9oncQkCnr4S30zsq7cSzz806kwCMcn8zrUi8Js3b27Tp093r9WtW9cqVqxoCxcudH9nZ2e7/qxdu9bt570kJwMs65zLS5hCOytXrrTVq1e7hCv0n2PpJ6U8qlev7jKUAqU71qxZ4zKS0jfczekDMWbbbLONe5jh2oFr4cFnxYoV7u+ddtrJXRsPSTVq1LA6deq4xFdA0qtNmzbZ8uXL3d+0y2vsY7GCRC9eUjTGk/Phwgfbbbed65833rQ1ceJEl0yLOeb6WDxhwYN+M+70iwysXI9/vLn+RYsWub/xSmCevPFmTHGxB/rOPm+8mT/6ztiwOEOfOI833vRr/vz57u9mzZq5fvjHm3Z5AGT8GBv/eDOHzA+LOzvuuGO+8WZ+5syZk/c5oB/eeHMs9x1x98wh94h/vNnvv2dpxxtv7lP/PUvf/OPNtXj3LOflswLME/3037OMpzfeXLv/nmWs/OPNObx7lr/94837/fcs1+kfb/89yzX4x5ux9t+z/vFmHP33LA/t/vH237PMpX+8uef89yz3qDfe3E/+e5Y2Yv2OAH1HxP8d4X0Go31H+O9Z8I83n+tkf0fwWiZ/R/DZ5bp23313O+ecc9zfnIv2sSwTDsXitVcSimOpqYzAZo4Ya4Sz//9aaX1HMN/p9B3BuAmRLJR8S4gU4uuvv3aW3AceeCDPbfqjjz6yW2+91e677z7r3r17gffwgEAWTf45IZRYqR40aJCrzUhm60g+++wzu+mmm1wMc48ePUKxGPNAwINSxq7khgQPC6+99pq7R3jIufHGG+3KK6/M5ymQTDSXZQfNZdlBc5meRFqW2caNG+eEKCAmSbpJzfk2bdrk/UScIpRFdDL2OePgW6Lv/3JASfekTCGLsRApBP80gX+IHt5qLKvA0WDFHXfafv362dVXX+1Wp08++eSootjfjtduWP94eE/G/sMKAcqJXHrppc5tulevXm6hhMWHkkBzWXbQXJYdNJfpBQuYVJhgO/vss90+rKsjRoxwC51TpkyxP/74w20k/PL+R2MRRhwjkv2CmZ9YcYUQyUPCWIgUE8a4Bnnub4A7ExRVWY0sxKw0U//4kEMOcYmYCkMV2tILXNrIJP7www+7h6Mvv/zSDjrooNLulhBCiARhYRuXacIyjjrqqHz/p3Fp94QyG88HL730Up4rM7A46glm/4Y7sijjKMY4FCSMhUghJk+e7ASuH2KLPIFUGF988UVePBfuWJ6YjgYr0/52RWrCg9Grr77qFjmII7vzzjtDdZsWQgiRGvA/nDhotv333z/fa8QLI5L9ohkLM7lFCK0CFtixKBPLi/CO3GRpFiI6EsZCpAgk9CG5R6Qw9uoNe0lDIhk5cqQNGDDA/fPE5YqY5NNOO80l44iG147qGKcuJGrBbXrYsGF20kknObdpkq0IIYTIbEiWteeee7rND4nDSMDltzCTpIqqFiS68kSztzAeTTB7GwmusGaLFEYW41CQMBYiheOLAaGMFZgY02jlevr37+8yYZKZGtcrEnixcoyYigbvYTU5aEZqUXLgFcAiB/NHlmu5TQshhAgC3kReKaloib/I9E2W6MiNvBX89HulschONmvPzTuaeEagC1HWkDAWIsWEcaTFmFXbAw880L799lu3Iuy50lKy4oorrnD/oAYOHJhX4uiYY46xd99912W73GOPPfK1RfbLX375xY4++ugSvDIRxG365Zdfdtmm5TYthBAimSB0PUFbGAhjPMoihTNllBDPkVZnrMqUkWRDjHu/U9JJlABKbhoKEsZCpFB8MSuw0VxmTzjhBPv444+day0llqjpRyZqjqfusT9e6LzzzrNPPvnE7X/uuefytYM1mVqAxx9/fIlckyieX3/91blNDx8+XG7TQgghSgUqVrBRXzkaWJ2pjYxYpk42i/kTJ0508c0kh/TKOlJ72i+UPeEswSzSAdUxFiJNQAivX7/ennnmmbjbOP30090/rXvvvdfCgn+OrDDjfqVyTYWzYsWKPLdpvAR4sAhaV7qk0FyWHTSXZQfNZdmhrMwlC+6Um0Io+7dp06blCWZcs6MJ5sJKUYpiOOrO6Ps//o+GLgFkMRYiTSAjMUm1SLbVuXPnmN+PKzb/pP7v//4vlP6J2NymiQ1fu3at3XXXXc4lXtmmhRBCpCNVqlTJq9EcKZjxhvOLZTzaHnzwwbzSkQjmSLG86667KnO2yAcLLCQmxcuOpHIYFwBPBJLNkmuHraiqLEGQMBYiTSCL9KhRo+J+P1mrEdWi9OAL/ZJLLrEffvjBTj75ZBcbrmzTQgghyqpg9gSLH7zfIgUz4WIPPPCAE8yvvPKK83ATRZDGHgaxQFLZ119/3QYPHuwqtxQFiWUPP/xw93zVsGFDiwcJYyGECBlWNm+66SZ79NFHndv00KFDXUI1IYQQItOoWrWqSw4amSAUwUzsclFJwkTmcM8999iHH35omzdvzvMwKIolS5Y4j7w33njDjjvuOLvmmmtiPqeEsRBChADZpUlKQobwTz/91H2p33333Xb55ZfLbVoIIYSIIpjbt2+vcQlCBtQxfvvtt91PQs322msv23vvvV1JUzztatas6Z6rVq9e7bKp44GAV+VPP/3kKri89dZbEsZCCFGaUO4CdzDEMG4/xFexIn7dddfZ2Wef7WKphBBCCCFE0eA5cOqppzr36GrVqkU9pnLlyla/fn23oHLKKae4sqQYI7Aax4MsxkIIkQC47uDqgxj+6quvnMsPq5q33nqrK7O1/fbba3yFEEIIkTwywGL87rvvxvweBHSvXr3cFg8SxkIIESPUcnz//ffdl/Z3333n3Hm6devmkmlRI1oJtYQQQgghksfYsWPdT3K1VK9e3cJAwlgIIQJArcn33nvPieERI0ZY+fLlXQKtxx57zI455hhr1KiRxlEIIYQQ4ZMhWan9XHjhha7e91NPPVUg0/m4cePsggsucOWaEqngImEshEgqZARctmyZi60lPoS4Wko2pCNTpkxxQphtzJgxLpblkEMOseeff96OOuooq1u3bml3UQghhBAiI9haSHZq6hwHyVxdHBLGQoikcu+997qahH5IjJCdne1cjKNtiOew3GJigS/VCRMm5IlhfidehcQPpP0/4ogjbJtttintbgohhBAik8mAGGNYsGCBzZs3z/yQgXrLli35RDFVQABvvkSQMBZCJJXx48fb77//7txd+DKbM2dOvg03ZH5GFmqvU6dOocLZ20jP7ycnJ8c2btzosj97m//vwn6P9tratWvtm2++salTp7rzYBEmgdahhx5aaDZEIYQQQggRDh999JE988wz+QwY5HOJBm7UTZo0Seh8EsZCiKSD9bdly5bWtm3bQo9Zv369zZ07t4BwZiPBAl+GCxcuzPcerLUVK1bME7X+FcOgsJqIazcbrtH+30mg9cADD1iPHj3c30IIIYQQKUeGWIwh0kW6KJdpqoEkgoSxEKJUqFq1qu24445uKwwEMBmg/aIZK7EnZiPFbXF/83uFCvraE0IIIUQakyHJt3beeWc78sgj3e+ffPKJswrvs88++XK8sA9Pv06dOlnXrl0TOp+eEIUQKQtCdtttt3WbEEIIIYTIHPbff3+3ecIYzjnnnAJZqZOFhLEQQgghhBBCpAsZ5ErtQYgdNGjQwMJCwlgIIYQQQgghRMpCaJ3/Z2F06NAh7nNIGAshhBBCCCFEupAhMcZ+LrzwQhdPXBS8PmrUKIsXCWMhhBBCCCGEEClNURmpk4GEsRBCCCGEEEKkCxkYY3zkP9mp/axYscJ+/fVXW716tWVnZyeclEvCWAghhBBCCCFEyjJgwICo+9euXWuXXnqpTZo0yf7zn/8kdI7Mc1AXQgghhBBCiHS2GEfbMpDq1avbEUccYTk5OfbYY48l1JYsxkIIIYQQQggh0i7meOnSpfb111+7vydPnpxQexLGQgghhBBCCJEulM88p9+99tqr2IzUderUSegcEsZCCCGEEEIIIdI6I/UZZ5yR0DkkjIUQQgghhBAiXcjAeOLGjRsXqGPM38QYk5H6uOOOs86dOyd0DgljIYQQQgghhBApy8cffxz6OSSMhRBCCCGEECJdyECLsZ8tW7bY4sWL3e8NGjSw8uXLWzKQMBZCFMm6detcbbjWrVtbtWrVNFpCCCGEKHX0fJJ5zJ492x555BH74YcfbNOmTW5fpUqVrEuXLnbxxRdby5YtE2pfwlgIUSSI4o4dO9o+d4+yWtt3KHa0qmTl2qD2Zid8kGMbcrPK7KJntYrhdbxx9dCatmoxfOtXLJdr52ab3TR8i20OkPQiJ9dCo0KICTjrVw1vLuuHuJZUt0rwY7Ms13Ypb/bp9FwLMk01K1loVK0Q3niH+X1So2J4bcfU761brbKZTVm21axc8Z/LDVssNCqF+LlsWiO8tmtXCfFG2ZQT/Njc3H/fk1X8YK4rlxyrWDSGzS3+XoqXwdPDaXvRlIn22kV72ZgxY6xDh+KfT8oUAe6Xsvg8etFFF9natWvzJeLauHGjffPNNzZy5Eh78sknnSEnXjJvVIUQQgghhBBCpA2DBg2yNWvW5InimjVrug3YhwcBxySChLEQQgghhBBCpAu4mUTbyjATJ050Wajbtm1r7733ng0dOtRt/N6uXbu8YxJBwlgIIYQQQgghRMpSq1Yt9/Pcc8915Zk8+P2cc85xv9euXTuhc0gYCyGEEEIIIUS6kIEW46OOOsq5TC9YsKDAa94+ahkngpJvCSGEEEIIIYRIWUiwhus0WamXL1+ez336tddes5122sl23313Gzt2bIH3BUXCWAghhBBCCJGWUDkjCP5MxmlPBmalvvTSS12MMfP47LPP5nuNfX/++addcskl+fZz/KhRowKfQ8JYCCGEEEIIIURK4y1uRFvkSMbCh4SxEEIIIYQQQqQLZTyeOBpHHHGEswCHiYSxEEIIIYQQQoiU5eabbw79HBLGQgghhBBCCJEuZGCM8S233OIsxpRmat68eb7XVqxYYcOHD3e/H3nkkXGfQ8JYCCGEEEIIIUTK8sknnzhhfMwxxxQQxjNmzHDCOSsrS8JYCCGEEEIIITKCDIwxLop169YlJQGXLMZCCCGEEEIIIVKKb7/91r777rt8+55//nmrU6dO3t+I4XHjxrnfq1WrltD5JIzTnNGjR1vfvn1twIABdtRRR5V2d4SZXXDBBTZ//nz7+OOPNR5CCCGEECK5ZIjFeMqUKXku1J4IHjFiRNRjOWbnnXdO6HyZF7mdhkyePNmefPJJmzdvnqUDCMLXXnvN0hXGmhWqdId4iwceeMAtnOy///7WqVMnd21CCCGEECLNk29F28ooW7dudcKXjd+jbXXr1rV+/foldB5ZjNNkteTpp5+2jh07WtOmTfO91qFDB/vhhx+sQoUKKSWMsZiedtpplo4w1mS0Q0ymM7/99pu9+uqrLkFBmzZt7Oeffy7tLgkhhBBCCBEIvGHRPwjfiy66yAnja665xnbccce8Y0i4tc0229h2221n5cuXt0RIHTUl4oKboXLlyhkzehs2bHCLAKm0EJCq7Lfffvb111+7L4vff//devfuXdpdEkIIIYQQiZIhrtRNmjRxG7Rv394JYzwgd9hhh1DOF7q62Lx5s3OrHTJkiM2cOdMJmhYtWjiL3Mknn5x3HG7Cjz/+uI0aNcpWr15tDRs2tEMOOcTOPfdcq1KlSt5xuIJi0XvnnXfs008/ddvy5ctt2223tUsuucS6du2a7/z4pb/11ls2a9Ysy8nJsXr16tmuu+5qV199dV7gNqsRDPpTTz1VbPwu1lDSgT/22GP266+/2ocffujOz8oFKxi0PWbMGPc6LtDVq1e3Xr162XnnnZevbe+cV111lXN3nThxolWsWNG6detmV1xxhXMH8F8v0BcPxo9C14XFGK9fv96effZZ+/LLL23RokVWs2ZN23vvvd1qi3eDRV4jqzGvvPKKzZ49240T/e7Tp09M800fsBYDN67HE0884f6eMGGCm7vx48fbwoUL3coOY3fmmWfaAQcckK8tro/54xoeeughZxlnrBlzLOdTp051Y8c8sDjA3ONCcdBBB+WNj58vvvjC3nzzTfe+LVu25J2X47178Oijj867b9j845QIsVw3cA898sgjzlugRo0advDBB9txxx3nPjPnn3++XXjhhcWes1atWgn1WQghhBBCiFQgUqelnTBGFF966aXuIb9z587Ws2dPq1Spkv3555/2zTff5AljhBQCbM2aNXbiiSc64cx7yDqG6EFkRloIET3sO+OMM9x5Xn/9dSdM33vvvTx3Y0Qzx7HCgPhDPCFKEFjLli3Ll9EsVhAtiKtTTjnFCW4EJdeKaL7tttuciOF6EXWIQvp0+OGH52sDwYpQPfDAA61Hjx42adIk++ijj+yPP/6wl156yS0I8NqSJUvs/ffft7PPPtu5CUBk/S4/9Ie+MHa0yxixMPDuu++6hQfabtSoUb738BpjgjDEwvj555/bww8/7I477LDDAo8LCw6MDYW2Ef0eXr+J3SX2FTGKQF+5cqUToP3797fbb7896rlY8ECos0iC4CfjHNfDYgNinjlo0KCBm9fLLrssar+4h5577jnbd9993b2ApZ178Prrr7drr73WTjrpJHc/3HrrrXbTTTe5e4Y5TBaxXDeZ9Zg/FjP4XDAf3EfMZ0mwadMmt0WmwK+ctdWqZOUW+/7K/xzj/Syri56VQ+x4pRDHpGIMbVcsl5vvZ3H8kxsjFCqE2nZ4jSfm1FU0sUSTZVluvp/FUS6xihfFtB3eeIf6dRLimMTE1tz8P4s9Psy+hNh0wMuLh9zcEO+U3OAdz/3nWO9nsceH+F2VlWCZm6KoFNIXSoUwv6hSnXR9eEoANFZxYFHmOT4lhTGWYgQugg5x48f/JfDoo486SyDWP8/ii7XywQcftJdfftkJiGOPPTbf+2vXrm33339/XpYyrJGICIQxosITI1hssUT7hbXf8hoviOIXXnjBWXk94YcovO6665ygb9u2rdtPEWqsl2+//XYBYTxnzhwnHv2xuNtvv727rjfeeMPOOuss22mnnWy33XZzwhiLr98KWxhYtRFRWCOxPnvw/iuvvNIJV8S7nwULFjiLJtZJf7+xsMYijInLZd43btxY4HoBcevNjwfCljHAwh3tXLhLRPb3rrvusrVr19ozzzxje+yxh9vHQssNN9zgFhb8sOCAKI68Dzkvc8b9d8QRR7h7hT7zgWrWrFnU/sdLLNc9aNAgd1+z31sA4fNAtuuSgPvX81LwC+OrWi2yXXaZG7id/9v9b88Bkf70bp4eif9E8bQtH/AzHKIosX/X3dKLjZZSVF4zJ9hxlp6sWBNi25ZazF5Y+v8vdwyz7W3DaXfCmoX2UjhNixTEn526qARdKSuMBw8e7KxekW7EgMXOE8jff/+9tWrVqoAbNMKQ5EEI3EhhjKjwD067du3yLIkeiDxiUocPH27du3cvcjBjBcu2J4oBCyPssssueaIYOIa+RbP2eW7WfvgbVwGsmVx/PPBexhch6IfxJY054824e3PguUB7ohiwVuMWjutvMqlatWre78wNG+y5557Oao3XgL8fgMU7clEC6zDj6olij9NPP91ZV/1g/WbuEb9YsiPjcKmPRqIqvBrCIuh1L1261MUD4zrt9wpgYefUU09N+nxEg/uGcfRbsHHxHzS5odXe3KzY92MpRhTf8GsT25ibVWYXPavGYnqNkUaJleErkqoxfOtjKUYUvzSnqW3eWvxc5oQopiqEmGyzXtXw5rLevx/9pFPn3yijYsFSjCj+fUszyw1ga97m339vSadqiOb/ML9Pqoc4JjH1e2uuE8UbazQ3K1f8XG7YYqFRKcTPZZPq4bVdq0qIN8qmnMCH8iyGKM5u1CTfM1lhrCsXng/Kj/PCs75+NSOcthfMW2wZSxnOQF0UiN9oJEvjhSqMEakI3qKSQ2EpxiKFpTRajGT9+vVt7tyCK9zRXIk5HhdV/wP+2LFjnYs1r5HBuUuXLk50IEoTAYuiHxYAIDJrtPeav1/+NvziGnA1Z3+0aw4KsbK4Fnt9irS+EreKQPTimKNdT7TxTAa4a2PBR4zyeyTRhHHLli0L3DO4VEfuB2LNI/nrr7/cB4nFjMJAkIZJ0Ov2SnJFu7Zo+3Cz98P9lGhsMfcgm4dXLH1jbjnbEEDoeiCKN5RhYZwVoivephC9wyrE0TaiOJAwDtOtMsS2c0J07Q1Rk8Rl1EUUBxHGIQ6JbQ3RHTTMfluqtY0oDiCMU67fQZsO8bk/K8x/PHEIFkRxEGGcFeKg5IbolrwppC/wML+7RepBaGok6BnqGuMti/cuIYqJkLapfQv7AvGvJBCrjAvzTz/95ErVIJKJ5/QSWnniurBVBiyTsZ4/0TThpUVJ9Ju5wZ0YoYrFH8s6YpCx5IbGwyBanI0/+Vq8MMck8Cps3sLKbpfIdQch0vWcxZ+SSE4ghBBCCCFKiXS1KiQAZZuiQT4ljLBeklu8MVNSGGPhIuEQiXz8Fig/JDzCejt9+vQCr61atcpZxHD/jRfOiwux56aNWzVxtrhoEw8MWFY5VySJWG2DQPskDvNbjRkr9vstn7G6B2D9/fHHH112bxI3+WGcGW9itMOisP6SDRprdbSsyh988EHg9rlncE0my3kk3G+RZGdnu9Wkxo0b5yUBK0liuW4vY3i0a4u2j/hoP9G8BIQQQgghhCirtGjRwhmiyMlEXp94CdVBHWsWgpMkQoVZdrGaEb9IaSPEix+SW2FJI6FTPETGk0Lr1q3dT7+LMIOJoCJLtF+gYm0OE5JHRZ6Dv9nvv2YvPjWaeI8G72XcGD8/xOUyzsTVBnHZiRdcb+lrZByAd87I/WQpJ448Fus22aUpcUX8qx8WPCLxkmghIqN5AUS6UdP/ZLqQx3LdhA5gUcblmuRs/kzjZF6PhIRq/q1NmzZJ67cQQgghhEhBeLaMtpVhPvmnlKp/o5oPSWNJxuuFWyZCqBZjkgUNGzbMCWMSCvHgjqkbqyXWL0roAJmCKSNELDBxoFj4cHsmiRKuoWRHjgfaxWJKYizKDmFBxXUVi6Y/4zCleqhxe/HFF9sJJ5zgrLifffZZUlx4iwJXbly6p02b5gQN2ZSZYKzFuNx6kGQKcUVmZQQnQhmrMIm+okEiLW6WF1980cWsMobUJsbFgLJHkRnCkw39Yt7vuecel1GbvuPWgLWWWHLKRZF8Co8C4tDJJE5N38hs0kVBmauRI0fa5Zdf7uaPutd4A3iLIZGJ2cjojIsxWaApmUQMNt4InJMFA9ry9x/3exYWsDLT1qGHHhr3eMR63WQSZ45Y8eLzgNs1nwXEceS1FQVxy2Q398ci//LLL3lfHiSkI+u5EEIIIYQQqV6uqahnYF7zDKApKYxxEaY0EDV+hwwZ4oQwrs1YaBFvfvdRRAhB1WQQRsAiZEmehTiIrGEcFEQFggIBggWQpEQkA6Nurb/sEZmNqXeM8KREFCILgYzlDgEWFpyHskOUqWJ8GC+s7Lh6+7MYI85IPY7Q5XgEEosFhQljxotxZ0GC6ydLNQsE+OAj/mkvTMhojDv40KFDXcZlrNfMLWPO+HK9CHcSaBHby9jjahyLMGbxAKFLe1hSWXDBXR73eEpNRSZ8QxgznwhFjufcJB/j/CzI+KG28d133+1WoLDeQyLCGAt3LNdNDAU1pLFw0wfmjoRx3BtkKi8qmZ0fFlEiExWMHj3abcBnTMJYCCGEECLNyMAY46KyUgP6xguTjZdyW4s6gwgNFgZYEFCipOSCyKR+M8mu4i13laqw0MAH/o477khIqMcK3huI9X3uHmW1tu9Q7PFVsnJtUPu5dtUvzcp0VupqIZZrahximZJqMZZrOjd7jj07u3mZLtdUP8RyTfVDLL1VN8ZyTbuUn20TtmQHykpdM3pakIwu11Qjlco1rZ5lG7dpUabLNTXNX6AiqdROoXJNM+fPtZZNmpV6uaZhc8OTA4Onh9P2oilj7bWL9gp8fJmSPHe9F33/9cdbWeWpKMllsRLjWYm3MWVX4zWmpn1WaiFwS/a7u/OFh7sy4LafrnAdxLj7LcN4CRA/jfW5sKx8QgghhBAiA0hXq0IC4P0ZNhLGInC8KkK0KJJRQzcWiBcmdpk4XdyTiWsmhha342QnocIVn9jzokCkR9ZgjgdEMR4FuE4Tj8y5cYknu3WfPn1cgi4hhBBCCCEyjQ0bNrjcVOTrAUJ0MYglIzeUhLEIxMCBA118bFGUdA1dkkchhkmURrbppk2bWt++fUNxoaZgOC7FRUHcN3HDiYIbSJcuXVxmai9pFgIZN+pevXol3L4QQgghhEhjyngG6sLguf+2224rUHmIMrTkY/LK88aLhHEpQXbsdKJ3797Ws2fPIo8p6Rq6ZG9mKwn69etXbLksMl0nA9ylBwwYkJS2hBBCCCGESHcmTZrkEihjDIuMF6dME6+RSDmRzNQSxiIQlBtiy1RUH1gIIYQQIvUYM2aM81rMKDIwxvj555/PK13KcznlWGHixIku+S6vUeWICj7xImEshBBCCCGEECJlGTdunMtCTTlerMN+7rnnHnv77bddrqFEyEwHdSGEEEIIIYRIV4txtK0Ms+qfkMZu3boVeM2LLS4u7LE4JIyFEEIIIYQQQqQsXi4jEnBFMnz48HzHxItcqYUQQgghhBAiXcjArNR77LGHff311/bOO+/YhAkTbJdddskXY4ybdaKx5hLGQgghhBBCCCFSlrPPPtu+//57l5WaDNVsHmSprlixovXp0yehc0gYCyEC8chBFaxDh+K/MnJzc23mTLN3j61gWQFWNKevyJ9yP5m0CLGCWIXpC8Jr/K9F4bX959+1sYOQW76czczezm6dPsyytgSYp3UbLTRaNwuv7Tq1wms7N7ymbe7a4N0oZzYzu5odMW+yZQX5yOWG97kMNQ6uQvnw2m4Q4hdKpeCPY7lbt9pMM9t59RLLKhdgLNdsSKxvRXYmNz0tYktXh9d2LOPNJbaua/bNb4G+K6qFeH8fGuJcHlqzWijtjq00y16zDKWMxxNHgzJMZJy+/fbbC9QxrlWrlt14440JlWoCCWMhhBBCCCGEEClN9+7dbe+997aRI0faTKwwZtayZUvr3LmzValSJeH2JYyFEEIIIYQQIl3IwBhjDwTw/vvvb2GQuaMqhBBCCCGEEELIYiyEEEIIIYQQaUQGxhiXBLIYCyGEEEIIIYTIaBRjLIQQQgghhEhLOnbsGHU/JXzKLLIYh4KEsRBCCCGEEEKkCxmcfCtMNKpCCCGEEEIIITIaWYyFEEIIIYQQIl3IAFfqW265Jeb3lCtXzm666aa4zylhLIQQQgghhBAiZfjkk0+c0I0VCWMhhBBCCCGEyAQywGIcTwK1eIS0H1mMhRBCCCGEEEKkDE888USJn1PCOMUYPXq09e3b1wYMGGBHHXVUaXdHmNkFF1xg8+fPt48//ljjIYQQQgghSpcMyErdsZAyXGEiYVwKTJ482b799lsnfJs2bWqpDoJw9erVdtppp1k68uSTT1qrVq1s//33t3Tmu+++c/fN+PHjbeHChVajRg3bfvvt7YwzzrB9990337HLli2zhx9+2P744w9btGiRbdiwwRo2bGgdOnSws88+27Kzs0vtOoQQQgghhIgHnml//PFH++uvv2z9+vXOoLh48WL3WqNGjRJyp5YwLgWmTJliTz/9tFsJiRTGCJcffvjBKlSokFLCGItpugpjxvrII49Me2F85513WvXq1a179+7WsmVLW7lypZubyy+/3C666CI799xz845dtWqVzZw50zp37myNGze2KlWq2KxZs+yjjz6yoUOH2vPPP+9EtRBCCCGESDMyJMY4kmHDhtmtt97qnoE9EMZnnXWWMwoNGjTIunbtavGSOupLOLKysqxy5coZterDIkAqLQSkKrfffrvtueee+fadfPLJbsEC8d+rVy+rWbOm27/tttvac889V6CNHj16WJ8+feytt96y66+/vsT6LoQQQgghRLxMmDDBrr32WtuyZUteUi6sw+XLl7cDDjjA3nnnHWf8SUQYx+ygvnnzZnvxxRfdw3iXLl2c9erMM8+0N998M99x8+bNsxtvvNEOOeQQ22effeyYY46xRx991AmhSDfXTp062YwZM9zrhx9+uDv+1FNPteHDh0dN3d27d29n/ePCafd///ufLV++PO8YXJSJC40Wv8u5/LGi/M6+n376Kc+yyHUhHn777Td3zJgxY5w1jvMdeuih9swzzxRo2zvnpEmT3MpFt27d7MADD3Sxwqxg+K/Xq8vFcZyb7eabby60j4CrwCOPPOKul/GhH6Qjx5Jb2DViHTzppJPc8VwX8xYrXNfYsWPdeby+snEe7yal78cff7wbt/3228/OOecc++abbwq0xXG8l7liDLg3GCdcfWHq1Kl2ySWXuHFGwDF2K1asyDc+fr744gs3L5zTm7Ovvvoq3z3Ie737xt//RInlur17CBdmjmXuBg4caNOmTXN94Z4IQqQoBizBjGFOTo6zEBdHkyZN8izKQgghhBAiTS3G0bYyzDPPPOOed6tWrVrAC5SQSZg4cWJC56gQqyi+9NJL3UM+Lpo9e/a0SpUq2Z9//ukEAdYrQEQhUtasWWMnnniitWjRwr0H981ff/3VHnvssQIWQkQG+4iX5Dyvv/66XXPNNfbee+/luRt/+umn7rj27ds7UYlllVhLXI8Rn3Xq1Il7IBCdrECccsopbtBfeeUVd60IuNtuu82OO+44d71ffvmly5JGnxDxfhB4uLQiiBF2iGTEKXGeL730khMxvLZkyRJ7//33nVDabrvt3HubN29eaN/oD31h7GiXMcIt9t1337VRo0a5tvGp98NrjMnRRx9t22yzjX3++ecu5pTjDjvssMDjcvXVV7uxQaBeddVVefu9fhPzyqLGQQcd5EQXrg2I0P79+zsLZ7RzIX7r1avnRC2Cv1q1au56zjvvPLcCxBw0aNDAzetll10WtV/cQ1hEia3lXsDSzj2IFZTVJBYEuB9wt2ABgXuGOUwWsVz3uHHj3PxhzeVzwXxwHzGfycBbWKhbt27Ue4fPIT9nz55tTz31lNuPQC+MTZs2uc1j3bp17mdubq7bisM7JsixjhhT8cdC0C7E1baF128L839b+eCN5/5zrPezWCqEmAwk1P/3Ic5lmMQwJrnl8v9MZtsxk65th3mfxPA96H33uJ9bS/v+TtO2w7xPYvgazM3K/zOZbacUIY134O8zUSYYP368sxBfccUVLhyQZ/FIw4/3TFwiwvi1117Ls3whbvz4H4Kx/GIVfOCBB/LM2bh5Pvjgg/byyy87AXHsscfme3/t2rXt/vvvzwuYxpKGiEAYIyqAASDG8vHHH88nrBFGiYIofuGFF6xixYp5wg9ReN111zlB37ZtW7cfiy3W17fffruAMJ4zZ44Tj/5YXCaO63rjjTec//tOO+1ku+22mxPGe++9dyDrJdZfRBSWeW4GD95/5ZVXOuGKePezYMEC51JAgiZ/v7HsxyKMWZFh3jdu3FjgegFx682PB8KWMXj22WejnmuHHXYo0N+77rrL1q5d61aD9thjD7ePhZYbbrjBLSz4YcEBURx5H3Je5oz774gjjnD3Cn1GGDdr1ixq/+Mllusm3oH7mv3eAgifh2heDfHEq3/99ddO+HONkZCcoF+/fnl/syDBPcP4FAb3O94TkcKYBa8gVmkPhHgQwvy/NndFiI2XD7HtHWqmVNuzu24bSldEgmxTLea3zG4e+3uEx78LhqnQ9mxbH+zA6pamhLiyWT3Mz0Ecn8udCy5si+KZv7RS5g5TBmSljsTzOo6WuNh7VsUQVGLCePDgwc7qhWUvEix2nkD+/vvvnUk70scbYfjqq686gRspjBEV/ixi7dq1y7MkeiDyGBRcrHHhTrSIsx8s254oBoQG7LLLLnmiGDiGvkWz9iHEEDx++BsrHdZMrj8eeC/jixD0w/juvPPObrwZd28OPBdoTxQD1updd93VrbYkE9wZPJgb76bF7RerNdZKfz8Ai3fkogTWYcbVE8Uep59+urOu+sH6zdwj7rBk+8GlmezNuMHj1RAWQa976dKl9vvvv9vBBx+czyuAhR3CBRKZDxafsFAzt4QTRIM5Z6GAhY3p06c793MyjPPFUVhcN/cZ4+63eOOuzWocSb+Kg3sRUUzma/89WRgzVoZnFWi+TWhNW4UZC8NrfMbf2RVDYc7SwIdiKUYUZw+fYVlbAszT+hCFw05/rwaHQsMQFyLCZMXfDwJBLSuI4uw56ywryEcuN0RrXZjufhVCXLGqH+IXSsXgj2NYihHF2VbVsoIsLa7NH8KWNm45YT74L1sTXtsxjDeWYkRx9pRllpVbyvd3mHMZxyJeEJYuCXOxSqQaJJPFCDlkyJB8OhJv0w8//ND9nmi1n5iEMSIVwVtUcige1lHt0TLe1qpVy+rXr29z584t8Fo0V2KO92cd44GdeFdcrHmNDM64hCI6EKWJEGlt85IYRRtgXvP3y9+GX1wDrubsj3bNQSFWFtdir0+R1leshghEvyttNOth5HgmA9y1seAjRv2x1B7RhHGkuOKewaU6mugiiVQkpGfnQ8BiRmEgSMMk6HUzdxDt2qLtw83eD/cT8xYJ84i1nOPxzChMsOKJgWeBt2jAYgKLUPT5v//9b9T3cM+yebBABYjcIELXI/Dx5bam5XNVoAfSVPRODCJwI0AUBxLGOblp6u2cpv54cYwJojiQME5TD9m0vU9iWejf+u93UFag96Wr73qafsfG8TWIKA4kjEP8ig217ZDGO9B3WVmljMcTRwODIKG2hNaOHDkyb/8JJ5zgDDIYzTDkJELKpAIu7AHayzoGxCrjwkyirJ9//tmJZOI5SV6E66cnrguzJGOZjPX8ZDpLR0qi38wN7sQIVcQWlnXEIGOJ+zceBtHiTLFwJgpz/NBDDxU6bywYpNp1ByHS9ZzFHy8u2C+KL774YhfjfN9990VNylUYLLDstddeLvYda7NfAAshhBBCCJGKkOSWrNPEEWMA8/QeVmRo2LChS9BcYsIYqxQP4yTmKeyBmoRHWG9x24yETLhYuHD/jRfOy4qB56aNWzUxk7hoEw8MWFajZd1NxGobBNoncZjfasxYsd9v+YzVBRzrL7GiuMCSuMkP48x4YxkMi8L6SxZprNXnn3++XXjhhfle++CDDwK3zz2Da3K0+FXut0hw0R0xYoRzqfCSgJUksVy3lwwg2rVF24fbs59ILwFPFCPK7733XpdxPFZwq2aRiJhuCWMhhBBCiDQjA2OMa9eu7fL1kJcIHeAv2YQHMQl4o3lZxkJWrNYsBCedisTrHFYzzNiTJ092nfZDcissaZEptoMSGU8KrVu3dj/9LsJYlhFU/sxkCFSszWGC0Ig8B3+z33/NXnxq0JI5vJdxY/z8EJfLOOMiG4uLa6zgSktf/dZ78M4ZuZ8s5f5McUGs22SXJsU68ax+WPCIxEuihYiM5gUQ6UZN/5PpQh7LdRM6gEUZl2tvRQuI8cUdJBLcnv1bmzZt8l5jDnCfZjHknnvuKTKzdGGu5LwXbwu8KxLJ4i6EEEIIIURJglGMEELKs6KL2PidfbyWKDFZjEkWNGzYMCeMSSjEgzvxxjxsY/2ihA7w8E4ZIWKBiQPFwofbM0mUcA0lO3I80C4WUxJjUXYICyquq6wU+DMOU6qHJENY1vA7x4r72WefJcWFtygQG7h0U58WQUM2ZVxWsRbjcutBkinEFZmVETsIZazCJPqKBom0yORNHWJiVhlDfOnJOk2W4cgM4cmGfjHviDEyatN33Hex1hJLTrkokk/hUUAcOpnEd9xxxwLZpIuCMlfEC1x++eVu/nCHwBvAWwyJTMxGRmdcjMkCTckkXITxRuCcLBj4Yw/oP+73fHj40NAWtYTjJdbrJpM4c0Qmaz4PuF3zWfAy5wX1IKANMnLTd+4b7mk/zI0XTsC18hlEPBMnj4jnvuQ9nNfzrhBCCCGEEGlGBsYYR3pUogeSTUzCGBdhSgNR45eMYAhhXDGx0CLe/O6jPJhT75cMwghYhCzJsxAHhWXDLQ5EBYICAYIFEHM5ycCoW+sve0RmY+odIzwpEYXIQiBjuUOAhQXnwbzPqgXjw3hhZcfV25/FGHFGCSGELscjVFgsKEwYM16MOwsSXD9ZqlkgoKYx4j8ZKyRFQYZi3MHx6yfjMtZr5pYxZ3y5XoQ7CbSI7WXscTWORRizeIDQpT0sqSy44C6PgKPUVGTCN4Qx80kZLI7n3CQf4/wsyPjBteLuu+92ZYiw3kMiwhgLdyzX3bFjR1dDGgs3fWDuSBjHvUGm8qKS2fnx2uXeYotkwIABecKYsaPGN6toJNpizrg/WUQgK3iYMdhCCCGEEEIkwi233BLzezA2obHipdzWSH9QERcsDLAgEJkoSSQGYpD6zSS7irfcVarCQgPC/4477khIqIcN3h6Ie2qY461QHIhwPEiwpAdx8Z++IryvoBYhVuGpMH1BeI3/lViB+iKZlT/zeXHlmmZ2385afvdXsKzU6zZaaLQumGk/aTRKLCap1Fj+90Jf0HJNM7OrWcvZKtcUNw1C/EKpFEO5pq1bbaats5ZWLVhW6jUq11SApastNNbEVq5pZuu61nJSGS/XVDOcck1jJ0+0jucWXqGkTEucD0ZF33/s35VIygJ77rlnTHmZmG+Ox0s0XjIvclukLF4tYP8NjrsyeCWH0hGug4RXfvASIH4a6zOiUwghhBBCCJH/GTrIlixSplyTKFmosxspRCMprIZuWBAvzOoQcbq4JxPX/Msvvzi3Y38SqmSAKz6x50VBTHpkDeZ4IPEbHgW4TmNF5dy4xJPduk+fPi5BlxBCCCGEEIHIgKzUTzzxRL6/SbhLGCN5ltAMxBhjIf7tt99caCX5hihFmggSxhnKwIEDXXxsUUSroRsm3bt3d2KYBFHc/CSN6tu3bygu1HxwcBEuCuK+iRtOFGLESYJFZmoShAECGTfqXr16Jdy+EEIIIYQQZYmOER6VJDimAgzP8P7nZ6ohIYopY0rYX+fOneM+p4RxkiA7djpBAeyePXsWeUxkDd2wIXszW0nQr1+/Ystl8SFLBrhLkxhLCCGEEEKIhMnArNQffPCB+0lC50hIMItLNUa/RKr1SBhnKJQbYstUku2aLYQQQgghhAgHr4QrlmMqIlHRBmbMmOEq90BxRq/ikDAWQgghhBBCpCVBq2aUKTLQYtyuXTsbN26cTZ482U466aS8UrjkJQLijQsrfRuUsh+5LYQQQgghhBAibbn66qutevXqeZmo161b5zbv72rVqtlVV12V0DkkjIUQQgghhBAinbJSR9vKMK1atbKXX37ZVatBBHtgOWYfr3FMIsiVWgghhBBCCCFEStO8eXO78847nYV42bJlbl/dunWdG3UykDAWQgghhBBCiHQhA2OM/VC2aebMmXklUHfaaaektCthLIQIxsbNZhs2FX9cbu7fPzk2gFvP9sPGhzcDP00Nr+01fyd7CIVVIbbdNjv4sRX+mb+V68xy/pnXougWYrb3hrXCa3tJYlksi6RC+fDa3qFgyYpC2brVLGeN2XYNyVBS/PFN61pYrNoc3gPdms2hNW11q4TXdpVNAb5b/d+xi9eZNagZzHWy5t8JakJh3cbw2l62Jry2GbuwaNU0ts/lhpVme2wX7HNZ81/30WSzrmKl0NoO8u8jHtaUX2sZSxl3my6MP/74w26++Wb766+/8u2n0g77W7dubYmQmaMqhBBCCCGEECItmD17tvXt29eJYi/hlrdNmzbNvTZnzpyEziFhLIQQQgghhBDp5EodbSvDPPfcc3lZqOvVq2f77ruvdenSxerXr+9e5zWvnnG8yJVaCCGEEEIIIUTK8tNPP7kkWz169LDbbrvNKlT4W8bm5OTYjTfeaF999ZU7JhFkMRZCCCGEEEKIdCEDLcbL/slCfdRRR+WJYuB39vmPiRcJYyGEEEIIIYQQKUv16tXdz99++63Aa94+75h4kSu1EEIIIYQQQqQLGZiVepdddrEffvjBxRqTgIu/YeLEifb11187N2tvX7xIGAshhBBCCCHSko4dOxbYR4ImUbY4/fTTbcSIEW5uEcJsHuzLysqyM844I6FzZN5ygxBCCCGEEEKkKxkYY7znnnta//79XUxxZLkm9vFap06dEjqHLMZCCCGEEEIIIVKaXr162X777WdDhw61WbNmuX0tWrRwmaobNWqUcPsSxkIIIYQQQgiRLpRx63BRIIBPO+00CwMJYyGEEEIIIYQQKc/atWtt/vz5tnr16qix5B06dIi7bQljIYQQQgghhEgXMjAr9erVq+3ee++1L774wnJzc6MeQ2bqUaNGxX0OCWMhhBBCCCGEECnLnXfeaV999VWo55AwTjFGjx5tffv2tQEDBthRRx1V2t0RZnbBBRc4l42PP/5Y4yGEEEIIIUqXDIwxHjFihLMIV6pUyWWorl27dtLPIWFcCkyePNm+/fZbJ3ybNm1qqQ6CEPeFsALdw+bJJ5+0Vq1a2f7772/pzHfffefum/Hjx9vChQutRo0atv3227uabfvuu2+R78Xl5Nxzz7XffvvNunbtag888ECJ9VsIIYQQQohEqFixoq1fv95uuukmO+SQQywMMs9BPQWYMmWKPf300zZv3ryoAeM//PCDHX744ZZKwvj111+3dIWxRlCWBReSX3/91aWpv+aaa+zUU0+1RYsW2eWXX27PPvtske99++23bdq0aSXWVyGEEEIIEWKMcbStDHPYYYe5ZFtLliwJ7RyyGKcYWVlZVrlyZcsUNmzY4Ipys4miuf32253riJ+TTz7ZWfIR/9R2q1mzZoH3YV1+7LHHnEu4LMVCCCGEECLduPzyy90z7cMPP+yMjG3atLHq1asXOO7II4+M+xwxq5HNmzfba6+9ZkOGDLGZM2c6QUNhZTrBQ7oH1tDHH3/cZQbDDbdhw4bO7I07Z5UqVfK5ufJQ/84779inn37qtuXLl9u2225rl1xyiXP79PPJJ5/YW2+95Yo65+TkWL169WzXXXe1q6++2urUqeOOwUW5SZMm9tRTTxUbv4s19JZbbnHCAWvchx9+6M6/4447OqscbY8ZM8a9jgs0E4AAOe+88/K17Z3zqquucuJj4sSJzuTfrVs3u+KKK6xu3br5rhfoiwfjd/PNNxcaY4zrAFbBL7/80lkJEUB77723XXTRRe680a6RVZVXXnnFZs+e7caJfvfp0yem+aYPxNdCp06d8vY/8cQT7u8JEya4ufPce8uXL+/G7swzz7QDDjggX1tcH/PHNTz00EPOMs5YM+a4lE+dOtWNHfPA4gBz369fPzvooIPyxscPWenefPNN974tW7bknZfjvXvw6KOPzrtv2PzjlAixXDdwDz3yyCPug4wL9MEHH2zHHXec+8ycf/75duGFFxZ7zkhRDHyWuMdeffVV93nkfo3k7rvvtmbNmjkLs4SxEEIIIUSak4ExxsuXL7e5c+c6/ffZZ5+5LRJikEtMGCOKL730UveQ37lzZ+vZs6cLgP7zzz/tm2++yRPGCCkE2Jo1a+zEE090wpn3PP/88070IDIjLYSIHvYRL8l5cN1FmL733nt5cbiIZo5r3769E3+IJ0QJAmvZsmV5wjgeEC2Iq1NOOcUNOIKSa0U033bbbU7EcL2IOkQhfYp0d0awIlQPPPBA69Gjh02aNMk++ugj++OPP+yll15yIobXcAF4//337eyzz7btttvOvbd58+aF9o3+0BfGjnYZIxYG3n33XbfwQNsUu/bDa4wJwnCbbbaxzz//3K2wcByuCEFhwYGxWbFihRP9Hl6/cVGeMWOGE6MI9JUrVzoB2r9/f2fhjHYuFjwQ6iySIPirVavmrofFBsQ8c9CgQQM3r5dddlnUfnEPPffccy62lnsBSzv34PXXX2/XXnutnXTSSe5+uPXWW10sAvcMc5gsYrnucePGufljMYPPBfPBfcR8JgPuO/AWX/yQvW/YsGFurBDvQdi0aZPbPNatW5cXp1xYenw/3jFBjg39y71isGuOi0phth2iB0WF4K5Wuf8c6/0sljD/T0epVZg00vX5IoYxyf3nWO9n8W8I+PmNg9zc8AZ8a3jdDnNIgn9fxvMdG+aghPm5tDRtO20/l2G2HU67W8P8UIqUDCnEGIb4jVa/OBnE9PSFpRiBi6BD3BT2gXr00Uedqsc65Vl8sVY++OCD9vLLLzsBceyxx+Z7P5nF7r//fnexgDUSEYEwRlR4YgSLLZZov7D2W17jBVH8wgsvOCuvJ/wQhdddd50T9G3btnX7jznmGLcSQcxmpDCeM2eOE4/+JFUkR+K63njjDTvrrLNsp512st12280JYyy+fitsYWDVRkRhjcT67MH7r7zySidcEe9+FixY4CyaWCf9/cbCGoswJmEV875x48aocc+IW29+PBC2jAEW7mjn2mGHHQr096677nIFu5955hnbY4893D4WWm644Qa3sOCHBQeEXuR9yHmZM+6/I444wt0r9BlhjMU0mXHbsVz3oEGD3H3Nfm8BhM8Drs2JggX666+/dsKfa/TDwtTAgQPt+OOPj2pJLgzud8+rwS+M5y9bYjMX/u09EITZixcGO7BtQUGfNMJsO4OYfXib0u4Cd2J4TdcNcZEjTHLWxPyW2VvWBjww9rbLOgG/0UqM2Uv/XhQts2wTZuNbwmt6w8qY3zJ746rQ2i7LrFq6wDKWDLQYjx071j1PYzzr3r271apVK083loowHjx4sLN6RboRAxY7TyB///33LgtwpBs0whCXTwRupDBGVPgvrl27dnmWRA9EHjGpw4cPdwOSzMHAsu2JYkBowC677JInioFj6Fs0a5/nZu2Hv3HpxprJ9ccD72V8EYJ+GN+dd97ZjTfj7s2B5wLtiWLAWo04wvU3mVStWjXvd+aGzXP7xWqNOPP3A7B4Ry5KYB1mXD1R7HH66ac766ofrN/MPeIXS7YfElORvZnsy3g1hEXQ6166dKn9/vvvznXa7xXAwg6uzYnMB4tPWKiZ2//9738FXmchihW1SAFfHNxnjLvf4o27dpO69a1lo3/d9guDexFRnN2gUb57slC+mWihMSbEhGNr14fX9qq/76dQaJ1/AaUosBQjirM/+8OycgKszO/bykKjfsH4+aSxbHV4bQf01IiLevm/W4sCixSiOLt8dcsK8r+zSfweWMWxanN4D3RrN4fWtNX5Nwos6VTxeekE+o5dusiy6zUM9h27MXjbMbMuxLaXh7g4UyHEz2WdGD+XG1dZduWawT6X2/z77JFs1lWsFFrbQf59xEPN+eElYRKpB0ZUPIXxDuW5NAxiEsaIVARvUcmheFjHwoSlNBKUff369Z1/eCTRXIk5HhdV/wM7qwW4WPMaGZy7dOniREe04OtYiLS2eUmMopVT4jV/v/xt+MU14GrO/mjXHBRiZVkdiZZYCesrVkMEot+VNvJ6oo1nMsBdGws+YpTfI4kmjFu2bFngnsGlOnI/EGseyV9//eUEH4sZhYEgDZOg1+1lHo92bdH2RWba435i3iJhHrGWczyeGZFt/fLLL/bBBx84V3Jct2OBe5bNgwUq4AEs0EPYPwQ+PjdEl7bNIVoFNoXZdo6l0hMKojiQMA7T8zHJq8Il1m9LrTHh4TvQA3iI2U2zQrR0lAsxKWuYCV9j+W71vyfQ+8IclDA/l5ambaft5zLEtkNqt1wZz8JcJBl47b1797Z77rnHGZVSQhiHSWEfSL8PObHKuDD/9NNP9vPPPzuRTDynl9DKE9eFWZKxTMZ6/qBxmalGSfTbs0YiVLH4Y1lHDDKWuH/jYRAtZsWffC1emGMSeBU2bywYpNp1ByHS9ZzFn8gkcojiiy++2MU433fffVGTcvHFgds+Hg8kX/ODdZt9COYwiqMLIYQQQgiRTDCkofVefPFFZwDC0zTS+AYktS0RYYxViodxEvP4LUp+SHiE9Xb69OkFXlu1apWzcOH+Gy+cFxdiz00bt2ribHHRJh4YsKxyrkgSsdoGgfZJHOa3GjNW7PdbPmN1Acf6++OPP7rs3pHWP8aZ8Q5T4BTWXwLgsVZHy6qMtTIo3DO4JpNVORLut0iys7NtxIgR1rhx47wkYCVJLNftZQyPdm3R9hEf7SfSS8ATxYjye++91/bZZ5+ofSQBHlbraAnHyMjNftz8vc+MEEIIIYRIEzIwxvjpp5/O0yRYjQsLR0xEGGfFas1CcJJEqDDLLlYzzNuUNkK8+CG5FZY0EjrFQ2Q8KbRu3dr99LsIY1lGUHnZej2BirU5TEgeFXkO/ma//5q9+NRo4j0avJdxY/z8EJfLOBNXG6YLDK609DUyA5x3zsj9ZCknjjwW6zbZpSlxRTyrHxY8IvGSaCEio3kBRLpR0/9kupDHct2EDmBRxuWa5Gz+TONkXo+EhGr+jRptHswB7tMshmARJoygMMimTkKzyA1ok98j4/yFEEIIIYRIVXj2LmpLlJgsxiQLovQLwpiEQjy4E2/MgzrWL0roAA/vlBEiFpg4UCx8uD2TRAnX0HjrS9EuFlMSY1F2CAsqrqusHvgzDlOqhxq3WNZOOOEEZ8Wl1lUyXHiLAvM+qxnTpk1z4oNsypRrwlqMy60Hpn/EFZmVETsIZazCuL1Gg0RaZPLGdYCYVcYQV1iyTlP2KDJDeLKhX8w7YoyM2vQd912stcSSUy4K91w8CohDJ5M4NX0js0kXBWWuRo4c6Yp3M3/UvcYbwFsMiUzMRkZnXIzJAk3JJGKw8UbgnCwY0Ja//7jfs7CAlZm2Dj300LjHI9brJpM4c0Qmaz4PuH3wWUAcR15bUdAGGbnpO/dNZP025sYLJyA5XWFwz3i1noUQQgghRJqRgRbjAQMGhH6OmIQxLsKUBqLG75AhQ5wQxrUZCy3ize8+igih3i8ZhBGwCFmSZyEOImsYBwVRgaBAgGABJCkRycCoW+sve0RmY+odIzzJzIvIQiBjuUOAhQXnwRJHMiTGh/HCyo6rtz+LMeKMEkIIXY5HILFYUJgwZrwYdxYkuH6yVLNAQE1jxD/thQkZinEHHzp0qMu4jPWauWXMGV+uF+FOAi1iexl7XI1jEcYsHiB0aQ9LKgsuuMvj6kupqciEbwhj5pMyWBzPuUk+xvlZkPFD9rq7777blSHCeg+JCGMs3LFcd8eOHV0NaSzc9IG5I2Ec9waZyotKZufHa5d7iy3aF0ZR9bCFEEIIIYRIR46M07AaC+W2hlUhOcNgYYAFgchESSIxEIPUbybZVbzlrlIVFhoQ/nfccUdCQj1s8PZA3I8ZMdI6/FPGrChYOKHeMaWdArn4f5ncEmL5+GlqeG2vCbNcU4htt82OqVzTzKPbWcuPJgbLSt0txHrHDQtmZ08aSwLWEE21sjD1t4mpLMzMnDXWskKNYNlvm9ZNy3JNa0Is11Q3hco1zVy8wFo2aBzsO3ZDmOWaNobX9rI0LddUN7ZyTTM3rLSWVWoF+1zW/LtKRBikY7mmcb+Mte77/GsY81Pm5c2kuQmXZBQFybxc3yJl8WoB+7/UcFcG3PbTFa5j48b8Dw94CRA/jfUZ0SmEEEIIIYQoPVKmXJMoWchYHClEIymshm5YEC9M7DJxurgnE9dMOnbcjv1JqJIBrvjEnhcFMenR0sDHConf8CjAdZp4ZM6NSzzZrfv06eMSdAkhhBBCCBGIDIwxLgkkjDOUgQMHuvjYoohWQzdMSBiFGCapFNmmmzZtan379g3Fhbp///7ORbi4WAbihhOFGHEySJOZmgRhgEDGjZqSSUIIIYQQQgQmxGo0mYyEcZIgO3Y60bt3b+vZs2eRx0TW0A0bsjezlQT9+vUrtlwWma6TAe7SJZFJTwghhBBCCBEfEsYZCuWG2DKVZLtmCyGEEEIIUSLIlToUJIyFEEIIIYQQacmYMWNc+J/IDNauXWvz58935YCjZR9P5F6QMBZCCCGEEEKIdCEDLcarV6+2e++917744gtXti4a5cqVs1GjRsV9DgljIYQQQgghhBApy5133mlfffVVqOeQMBZCCCGEEEKIdCEDs1KPGDHCWYSp9tKpUydXUpbfk4mEsRBCCCGEEEKIlIUqL3D11VfbCSecEMo5JIyFEMHI3fr3FuS4vJ8Bjj+qU3gzsOcO4bW9Ym14bTesFVrTG2rWCHysi+GZO8s2XNTTsgKsTlepEGLM04Ll4bW9dE14bX8/Mby2Z/1dFz0QlSuY/fdws1veMtuYU/zxrZtZWNTcf5fw2m4TXr/XVAr+2YmVJVYp8LFb7e/YuqVWycpZ8Z/L+nUrW2hUCd7vmGlcJ7Sm120Nz9pWIYam3XfsnJW2qX6tQN+xsbQdK9W2RI/ZTAqrw/l/WWNtiP+HU50MjDHu0qWLDRkyxGrUCO+7OPPs8EIIIYQQQggh0oYrr7zSmjZtag8++KB9//33tmZN8he1ZTEWQgghhBBCiHQhAy3GPXv2dD8p0XTNNddEPUZZqYUQQgghhBBClFm2bt3qhC9btPrFyUAWYyGEEEIIIYRIFzIwK3Xjxo2dKA4TCWMhhBBCCCGEECnLxx9/HPo5JIyFEEIIIYQQaUnHjh3zfg/LxTblyMAY45JAwlgIIYQQQgghREqTk5Njr732mivbNGvWLLevRYsWdthhh9mpp55qFSokJm0ljIUQQgghhBAiXchAi3FOTo5dcskl9ssvv+TzDpg6darbfvjhB3vkkUcSEseZF7kthBBCCCGEECJteOWVV2zs2LFOEPtd5r2/eQ1rciJIGAshhBBCCCFEOmWljraVYb744gv3s0mTJnb//fe7v7/88ksbNGiQNW3a1InjwYMHJ3QOuVILIYQQQgghhEhZiCmmXNNll11mXbt2zdvfrVs327Bhg/3nP//JizuOFwljIYQQQgghhEgXMjDGuFwRNYw91+pE6xxLGAshhBBCCCGESFmys7Ndkq2HHnrIqlWrZu3atXP7J06c6JJuIYo5JhEkjFOM0aNHW9++fW3AgAF21FFHlXZ3hJldcMEFNn/+/BIpLC6EEEIIIUSRlPF44mgccsghThgvXLjQ+vXrV8BijDCmbFMiZN6opgCTJ0+2J5980ubNm2fpAIIw0SxvpQlj/e2331q6M2PGDHvggQfcwsn+++9vnTp1ctcWBGIvjjnmGPeeu+++O/S+CiGEEEIIkSzOOOMMa9++fV4Wav8Ge+yxh5122mkJnUPCuBSYMmWKPf3001GFcYcOHVwdrsMPP9xSSRi//vrrlq4w1mVBGP/222/26quvupWyNm3axPTeJ554wpYvXx5a34QQQgghRAnGGEfbyjAVKlSwRx991NUy3mmnnaxSpUpu4/dLL73UvZZIDWN3jqT1ViSFrKwsq1y5csaMJpZMbuJEb+RMYL/99rOvv/7attlmG/v999+td+/egd43adIkt7BBFj8szkIIIYQQQqQbFStWtLPOOsttYRCzxXjz5s324osvOlN1ly5drHv37nbmmWfam2++me84rKE33nij8wffZ599nBsnSh4h5AdXUNw7cRPldSylHH/qqafa8OHDC5z/k08+cYIAV1JSddPu//73v3zWMGJziQuNFr/LufyxovzOvp9++slZFo888kh3XX369HEWOhgzZoyde+657nyHHnqoPfPMMwXa9s6JCMHVldThBx54oIsVXrZsWb7rveWWW9zvHMe52W6++eZC+wjr1693geVcL+NDP2666SYX+1rYNX700Ud20kknueO5LuYtVrguCmZzHq+vbJwHJkyY4Pp+/PHHu3FDvJ1zzjn2zTffFGiL43gvc8UYcG8wTosWLXKvEzfAKhDj3KNHDzd2K1asyDc+fqhfxrxwTm/Ovvrqq3z3IO/17ht//xMlluv27qGzzz7bHcvcDRw40KZNmxaTO3StWrWcKI6FLVu22O233+7uAe5HIYQQQgiR5mSgxbgkqBCrKMZUzUN+586drWfPns6E/eeffzpBcPLJJ7vjEFGIlDVr1tiJJ55oLVq0cO95/vnn7ddff7XHHnusgIUQkcE+/Mc5Dxaua665xt577z1XtBk+/fRTdxz+5YhKLKu4leJ6jPisU6dO3AOB6EREnHLKKZaTk2OvvPKKu1YE3G233WbHHXecu14KSeOWSp8i3Z0ReBdddJETIAg7RDLi9I8//rCXXnrJqlSp4l5bsmSJvf/++04obbfddu69zZs3L7Rv9Ie+MHa0yxhRp+vdd9+1UaNGubYbNWqU7z28xpgcffTRTkx9/vnn9vDDD7vjYglMv/rqq93YIFCvuuqqvP1ev3FRZlHjoIMOcgW3V65c6URo//79nSCLdi7Eb7169ZyoRfCTWY7rOe+881ycAHPQoEEDN69YOaPBPfTcc8/Zvvvu6+4FLO3cg9dff71de+21bkGA++HWW291CwjcM8xhsojluseNG+fmr2bNmu5zwXxwHzGfYUNsOP285557Ar9n06ZNbvNYt26d+5m7Nddyc3OLfb93TJBj/znQQuOfuJOQGg+x6fDGJPC8xDGXubnl0nMuwwwqqlg+vLYrB/8XnlupQr6fxVIhxEEJ89ktRT47YXZ76z/98H6W6ucyzO/vENvODfHrJJZex/wdG2efAnYm7T6XuWH+HxYZSYVYH7I9yxfixo//Q43lF6sgbpteAeZevXrZgw8+aC+//LITEMcee2y+99euXdvuv//+vPpTWNIQEQhjRIUnRqpXr26PP/54PmGNMEoURPELL7zgTPSe8EMUXnfddU7Qt23b1u3HYov19e233y4gjOfMmePEoz/we/vtt3fX9cYbbzizP37wu+22mxPGe++9dyDrJdZfRBSW+SuuuCJvP++/8sornXBFvPtZsGCBvfPOO1ajRo18/cayH4swxjLPvG/cuDFq3DPi1psfD4QtY/Dss89GPdcOO+xQoL933XWXrV271lnjCZ4HFlpuuOEGt7DghwUHRHHkfch5mTPuvyOOOMLdK/QZYdysWbOkxm3Hct2DBg1y9zX7vQUQPg/RvBqSydy5c501mgUHFnKCJnvjfsd7IlIYz1++1GYuXhD4/LOX/u0JUGYJM+Jh1bKUanvx/DlWptmxVoht722pxOz+h1iZJsU+O2Gydmmwz+Wa0HsiEmXhvDL+HRsS89etsowlA7NSp5wwHjx4sLN68aAdCRY7TyB///331qpVqzxR7IEwJHkQAjdSGCMq/EWZqU3lWRI9EHm4YuNijQt3okWc/WDZ9kQxYGGEXXbZJU8UA8fQt2jWPoQYgscPfz/11FPOmhmvPzzvZXwRgn4Y35133tmNN+PuzYHnAu2JYsBaveuuu9r48eMtmVStWjXvd+bGc5Xfc889ndUarwF/PwCLd+SiBNZhxtUTxR6nn366s676wfrN3CN+sWT7waX5u+++c27weDWERdDrXrp0qYsHPvjgg/N5BbCwQ7hAsufDz//93/+5BYHI8S4O7jPG3W/xxuW9SZ161rJB42Lfz72IKM6u1zDfPVko1UJUmAvz3x9JZeXa8NpuUDO0pjdsk//zWNxcIoobNGkeaC6rVCiXnnM54d//M0nnh0nhtT1naeBDsRQjirPv/cKyNuUU/4admlhodPv3f2rSaRVev9dUD/7ZiZWNAabEA0sxorh6veZWLsDnsl61ED+X6zaG13ZQ74Y4WLc1PFERi7MF37GI4kZNg33HhunIkbUlN+3+Xy6tFt7/SpGZxPStg0hF8BaVHApLMRYmLKXRYiTr16/vLFmRRHMl5nhcVP0P7MS74mLNa2RwJmYT0YEoTQQEhB8WAMBz4458zd8vfxt+cQ24mrM/2jUHBUsfrsVenyKtr2S5RiDWrVu30OuJNp7JAHdtLPiIUX8stUc0YdyyZcsC9wwu1ZH7Ydttty2w76+//nIu1yxmFAaCNEyCXrdnpY12bdH24Wbvh/uJeYuVzz77zLnZY/mNNbGZl+XPgwUqyCqXFUzo/gPHBjo+zFXPJC6eRWk8xKbDG5NY5tD/niDvywozvinMuQzTP3HzFksJNfUPiOKsIO/LCdOt0jLmsxNmtxHF5Ur7cxnm93eIbWeFKIzj6Xbw79j4+hSoD2n4ucwKNS4jxVE8cSikTCrgwr4QvNpUQKwyLswkyvr555+dSCaeE3dRBIAnrguzJGOZjPX85cuHGB8WIiXRb+YGd2KEKhZ/LOuIQcYS9288DKLFzWC9ThTm+KGHHip03lgwSLXrDkKk6zmLP3gcxALxwbjvs2hELPfs2bPdfi/JGaKdfYQvxJrMSwghhBBClDIZKIwXLPg7nA8ja1jVbGJqFQsXiXx48PZblPyQ8Ajr7fTp0wu8tmrVKmcRw/03XjgvLsSemzZu1cTZ4qJNPDBgWeVckSRitQ0C7ZM4zG81ZqzY77d8xuoCjvX3xx9/tNWrVxcQMowz443ICYvC+ksWaazV559/vl144YX5Xvvggw8Ct889g2vyzJkzC7zG/RZJdna2jRgxwho3bpyXBKwkieW6ScwF0a4t2j7io/1E8xIoDuLBscLz2YiW2R1XdDbi1YlbF0IIIYQQIpUhTBQjFAaj3XffPd9r5CMiAS98+OGHcZ8jK1ZrFoKTJEKFWXbpMPGIkydPduLFD8mtsKSR0CkeIuNJoXXr1u6n30UYyzKCyrOQeQIVa3OYkDwq8hz8zX7/NXvxqdHEezR4L+PG+PkhLpdxJq42TBcvXGnpq996D945I/eTpZw48lis22SXnjhxootn9cOCRyReEi1EZDQvgEg3avqfTBfyWK6bVS0syrhck5zNn2mczOuRkFDNv7Vp0ybm/nF/kcwscvO+MBhr/ua+EUIIIYQQaQbPotG2Ms7WQipUYBQifDGyjG2oFmOSBQ0bNswJYxIK8eBOvDFWS6xflNABMgUT30gsMHGgWPhweyaJEq6hZEeOB9rFYkpiLMoOYUHFdRWLpj/jMKV6qHF78cUX2wknnOCsuMRcJsOFtyhw5calm/q0CBpWLyjXhLUYl1sPkkwhrsisjOBEyGAVJtFXYSskZPKmDjGTzhjiCkvWaVxlIzOEJxv6xbxT8oeM2vSdJFNYa4klp1wUyafwKCAOnUziO+64Y4Fs0kVBmauRI0fa5Zdf7uavYcOGztrpLYZEJmYjozMrRmSBpmQSMdh4I3BOFgxoy99/3O9ZWMDKTFvUEo6XWK8byyxzRCZrPg+4XfNZQBxHXltR4AJNdnN/LPIvv/ySV1ebhHRkPce9hDGJxIt35l6L9roQQgghhBCpAs++bH7I7eO5VQPGQ579IdHEzDEJY1yEKQ1Ejd8hQ4Y4IYxrMxZaxJvffRQRQr1fXDYRsAhZkmchDuL1C0dUICgQIFgASUpEMjDq1vrLHpHZmHrHCE9KRCGyEMhY7hBgYcF5sMRRporxYbywsuPq7c9ijDijhBBCl+MRSCwWFCaMGS/GnQUJrp8s1SwQUNMY8U97YUKGYtzBhw4d6jIucwMyt4w548v1ItxJoEVsL2OPq3EswpjFA4Qu7WFJZcEFd3nc4yk1FZnwDWHMfCIUOZ5zk3yM87Mg4wdL6d133+3KEGG9h0SEMRbuWK67Y8eOroY0Fm76wNyRMI57g0zlRSWz88MiCuPuZ/To0W4DPmMIYyGEEEIIUYbJkBjj1157Lc8A5FmMvdDZaPAsnAjlthZmkxYxwcIACwKxJkoSRYPIJA6WZFfxlrtKVVho4MN9xx13JCTUwwZvD8T9mOE/Wod/ypgVBQsn1DumtFOpl2tasDy8tleEWK6pYXh1dTfUjK1c08K5s6xRsxalX64pzLn8tWC8f9L4fmJ4bc/Kn8W+KHIrV7CZ/z3cWt7xWbCs1K0LVjZIGvtHXwROCm3C6/eaiAoLyWRDjOWa1iyeZTUatAiUlbq+yjWldLmm+XNmWZPmLcp2uaaQ/l+O/XWcdTyoe759kjVli6eeesp54wYFrZCIJ23KZKUWArdkv7s7X264KwNu++kK10GMu98yjJcA8dNYnxGdQgghhBBCiH+hbC4hpJ6hBldpvCP9pWBZVMIbE0/W448/3hJBwjhDwV8fIVoU8dbQjRfihYldJk4X92Timomhxe04niRURYErPrHnRYFIj6zBHA+IYjwKcJ0mHplz4xJPdus+ffq4BF1CCCGEEEKIfyHU1MtNhUYAQmgjs1InCwnjDGXgwIEuPrYo4qmhmwgkj0IMkyiNbNOsEvXt2zcUF+r+/fu7laei4INI3HCiECNOTWEyU3tJsxDIuFH36tUr4faFEEIIIUTmkLs180KPn/gnzw4GtLCQME4SZMdOJ3r37m09e/Ys8ph4augmAtmb2UqCfv36FVsui0zXyQB36QEDBiSlLSGEEEIIITKNmjVrujK1GJkic/MMHjzYhSmSlDmRRLQSxhkK5YbYMpVku2YLIYQQQoiSZ8yYMXlxqJlCJlqMH3nkEfvxxx/t2GOPLSCMf/31V1c5Bw/N+++/P+5zlP1K0EIIIYQQQggh0pZJkya5n/vss0+B10jSS7Lb33//PaFzyGIshBBCCCGEEGluMS7LrPonBDJaaTOyVfuPiRdZjIUQQgghhBBCpCy1a9d2Pz/99NMCr3n7Eq2mI4uxEEIIIYQQQqQJmWgx3n333W3o0KH27bff2nnnnWddu3Z1+3/44QcXY4zVONEyThLGQgghhBBCCCFSltNPP92++eYbF0s8fvx4t3mwDxfrM844I6FzSBgLIQKxsWJF21CpUrHH5ebmup8cGy0OJJIVa8Nb9syt8bfbTRjUqBNe21VC/GaOKX6m3L9ZLks902Xt6qE1vemgxFaYi2LRvuG1XaNi8GO38rlcOMtWPtTXygX4XNbO3WSh8fvs8Np+dmhoTdf4c354bbduHvjY3ApZtubItlbv+cGWlfP3922RdAuxCkPb7NCaXrY5vGi/nBCtbfWrBj/Wm70KWcRNFn981tbwOr4hxOjKFVW3CaXdJVXC+7+Q6gT45Jc5dt11V/vvf/9rd999t23alP9/VKVKlax///7umESQMBZCCCGEEEIIkdIcffTRLiv1V199ZbNmzXL7WrRoYQcddJA1aNAg4fYljIUQQgghhBAiTcjEGGMPBPCpp55qYSBhLIQQQgghhBAi5Vm8eLGrabx69eq88D0/Rx55ZNxtSxgLIYQQQgghRJqQiRbjLVu22J133mmffPKJS7YVDTJTSxgLIYQQQgghhCiTvP766/bRRx+Feo7wUtAJIYQQQgghRIh07NjRWQrZMsliHG0rywwePNjN8S677OL+5vcjjjjCJeOC3XbbzdU3TgQJYyGEEEIIIYQQKYuXhbpPnz55+4477jh76KGH7JRTTrEJEybYTjvtlNA5JIyFEEIIIYQQIk3IRItxTk6O+1mrVi0rX768+33dunXuZ5cuXVwirqeeeiqhc0gYCyGEEEIIIYRIWWrWrOl+bty40WrXru1+f+edd2z69On26aefur9nz56d0DmUlVoIIYQQQggh0oSybh2ORtOmTW3ZsmWuTBNxxt99950NGzbMbV7McYsWLSwRZDEWQgghhBBCCJGy7LrrrlaxYkUXa3zmmWdahQoVXNkmb4O+ffsmdA5ZjIUQQgghhBAiTchEi3G/fv3c5vH000/bu+++a0uWLLHGjRvbsccea+3atUvoHBLGKcbo0aPdaseAAQPsqKOOKu3uCDO74IILbP78+fbxxx9rPIQQQgghhChlcKf2SjclCwnjUmDy5Mn27bffOuGLv3yqgyDEn/+0006zdOTJJ5+0Vq1a2f7772/pzIwZM+yDDz6wSZMmuW3NmjV2/vnn24UXXhj1eLLzUQz9vffec8K+Tp06dtBBB7mFl6pVq5Z4/4UQQgghROJkosXYY/jw4U5HzZ071/3drFkz94zftWtXSxQJ41JgypQpzvxPQfJIYdyhQwf74YcfnN98KgljhFW6CmPG+sgjj0x7Yfzbb7/Zq6++as2bN7c2bdrYzz//XOTxgwYNsjfeeMMOOOAAO+OMM+yvv/5yf7Mw89hjj1lWllIMCCGEEEKkG5kojDds2GDXXXed/fjjj/n2jxkzxj766CPr3Lmz3XPPPValSpW4z5E66ks4ECuVK1fOqJucRYBUWghIVfbbbz/7+uuvbZtttrHff//devfuXeix06ZNszfffNOJ4nvvvTdvPwsxAwcOtC+++MIOO+ywEuq5EEIIIYQQ8fPggw/aiBEjCn195MiR7hjEc7zEbDLavHmzvfjii856SDHl7t27u8xgPIT7mTdvnt144412yCGH2D777GPHHHOMPfroo04IRbq5durUybmJ8vrhhx/ujj/11FOdqTySTz75xAkCz2ROu//73/9s+fLlecfgokxcaLT4Xc7ljxXld/b99NNPeZZFrqtPnz7OQuetRJx77rnufIceeqg988wzBdr2zomLK66q3bp1swMPPNDFCpNa3H+9t9xyi/ud4zg3280331xoH2H9+vX2yCOPuOtlfOjHTTfd5Cy5hV0jqycnnXSSO57rYt5ihesaO3asO4/XVzbOAxMmTHB9P/744924Id7OOecc++abbwq0xXG8l7liDLg3GKdFixa516dOnWqXXHKJG+cePXq4sVuxYkW+8fGDuGNeOKc3Z1999VW+e5D3eveNv/+JEst1e/fQ2Wef7Y5l7hCniFf6wj0RBAqaI4qDMGTIEJehL9LKf9xxx7mVtM8++yxQO0IIIYQQIvUsxtG2ssyXX37pfvIcyzP3fffd57wjeb4mRJDnXu+YeKkQqyi+9NJL3UM+5uqePXtapUqV7M8//3SC4OSTT3bHIaIQKcRAnnjiia6mFO95/vnn7ddff3VunJEWQkQG+3D55DzERl5zzTUuPtJzN6Z4M8e1b9/eiUosqwsXLnSux4hPYijjBdG5ZcsWO+WUUywnJ8deeeUVd60IuNtuu80JCq6XAX/iiSdcnxDxfhB4F110kRPECDtEMuL0jz/+sJdeeslNJK+RPe399993E7nddtu59+IeWxj0h74wdrTLGJGqnExso0aNcm03atQo33t4jTE5+uijnZj6/PPP7eGHH3bHxWIpvPrqq93YIFCvuuqqvP1ev/HxZ1GD2NUmTZrYypUrnQjt37+/3X777VHPhfitV6+eE7UI/mrVqrnrOe+889xNzRw0aNDAzetll10WtV/cQ88995ztu+++7l7A0s49eP3119u1117rFgS4H2699Va3gMA9wxwmi1iue9y4cW7+KEzO54L54D5iPsMCizJjEpmdj8/Mzjvv7F4vjE2bNrnNY926de5n7tZcF7dcHN4xQY6FrcEOi4t/sveHQsDLS7m2YyHWucwtVy7MzoTYdHhth3p/x9D21n+u0ftZmmNiId4mViHEEJFK5VOi37n/HOv9LNXxDvOzUy63zP9viPU7NsyO5+aWS7vvwaDfZ6JssGnTJler+PLLL7devXrl7cfIhm7AjRoNWWLC+LXXXsuzfCFu/Pg/1Fh+sQo+8MADeYHQXADm7ZdfftkJCFJq+6ldu7bdf//97oIBSxoiAmGMqPDESPXq1e3xxx/PJ6wTrVkFiOIXXnjB1cfyhB+iEHM8gr5t27ZuPxZbrK9vv/12AWE8Z84cJx79Vrrtt9/eXRexnWeddZbttNNOtttuuzlhvPfeeweyXmL9RURhmb/iiivy9vP+K6+80glXxLufBQsW2DvvvGM1atTI128s+7EIYyzzzPvGjRsLXC8gbr358UDYMgbPPvts1HPtsMMOBfp711132dq1a501fo899nD7WGi54YYb3MKCHxYcEMWR9yHnZc64/4444gh3r9BnhDGB+dH6Hy+xXDerWdzX7PcWQPg8RPNqSBaLFy92nykWriJp2LChjR8/3n15ePe7H+53vCcihfGyRQts4dxZwfswf46VZf5dOij7LJxXtucyXVkRx3tWLp4TWtuBqRuiwDx21/DatjDbjp3Zh7Uu7S6YLV5Q2j1IOf7+jxkbc+foOzYeNq3I3PuvrFuHo0EeJlypI42B4O1L1Cs0JmE8ePBgZ/XCsheJl8gHgfz999+7LMCR2cEQhiQPQuBGCmNEhSeKAUuXZ0n0QOThio2LNS7c/uMTBcu2XyRgYQTSgHuiGDiGvkWz9iHE/CsYwN9PPfWUs2Zy/fHAexlfhKAfxhfrH+PNuPuTKeEC7YliwFpNYWwEUTLxZzdmbjxX+T333NNZrfEa8PcDsHhHLkpgHWZcPVHscfrppxdwi8D6zdwjfrFk+8Gl+bvvvnNu8Hg1hEXQ6166dKmzzh588MH5vAJY2CFcINnz4e9TNNELnlgu7BjuM8bdb/FmNa5uw8bWqFmLYs/NvYgobtCkeaAEXys3Wlr+46gefXiTQpUUCblnLhHFjZoGm8tK5UM0TW0IbyliUyGflWSwOJ6n5IDUqBibZQVRXKtBcysXYC5r5Ya49DP570yiofB94d4wCTM9xIfwnZsFPhRLMaI4e/Aky8oJYDHbt5WFRqvg/Y6V5eUKLuwmi5wQ/zfUqxrbdyyiuFnzYN+xWSFajDeEaDEO6/98pdpLwmlYpCQYvzCWYUTDyIbRyzNMYnxCHOO5mQgxPX4hUhG8RSWHwlKMhQlLabQYyfr16+el1/YTzZWY43FR9T+wE++KizWvsXJAzCaiA1GaCN7gerAAANHKKfGav1/+NiKFBiKE/dGuOSjEyuIi4PXJDzcGWa4RiHXr1i30eqKNZzLAXRsLPmLUH0vtEU0Yt2zZssA9g0t15H7YdtttC+wjuzIu1yxmFAaCNEyCXjdzB9GuLdo+3Oz9cD8xb7HCQog/7t6P5yZdWNY+7lm/pZkFKsgqlxVTJmuODXJ8uRA9H8uF+PATZlLvVEsYHnQus7LKpeWghJmhPdT7O462EcVBhHFW7ClIghOmpSOIUIyXTVtSqt+I4kDCOMzxDvOzE+KHJ9X+NwT+jg1RGGeF6HMf1lQG+S4rq+QWei+EGTtRuuCpSXgp4pjwSE8XrVq1yv3k72glTD/88MPA50gRu0ThDyYIIA9ilXFhJlEWpWoQycRzkrwI109PXBdmScYyGev5y5cP0eUrREqi38wNNylCFYs/lnXEIGOJ+zceBtHiZhJJo+7BHD/00EOFzhsLBql23UGIdD1n8QePg1hhIYX+IYIj3amJhcfNujCLshBCCCGEEKkExiae/9l4FvcEMb+zj7+9ff79sRCTMMbCRcKhaA/bHiQ8wno7ffr0Aq/RWSxiuP/GC+fFhdhz08atmjhbXLS99NysGPgHxiMRq20QaD8ybpOxYr/f8hnrJGH9pWbX6tWrC2QlZpwZb4ROWBTWX7JIY60+//zzC6zQfPDBB4Hb557BNXnmzJkFXuN+iyQ7O9vFGDRu3DgvCVhJEst1k5gLol1btH3ER/uJ5iUQBMQ6aesnTpyYFxYAxIrTdwS3EEIIIYRIPzI17dhWn8G0sN8TIStWaxaCEz/uSLwOYTUjHnHy5MkFak2R3ApLGgmd4iEynhRat/47+YTfRRjLMoLKKwPkCVSszWFC8qjIc/A3+/3X7MWnRhPv0eC9jBvj54e4XMaZuNowXQFxpaWvkTedd87I/WQpJ448Fus22aURccSz+mHBIxIviRYiMpoXQKQbNf1Ppgt5LNdN6AAiFZdrYiA8cAUh83okJFTzb23atImrj5TCYkGDxGl+SPpGbLFqGAshhBBCiHTh559/jnnDyzg0izHJgoYNG+aEMQmFeHAn3hirJdYvSugAmYIpI0QsMHGgWPhweyaJEpYqsiPHA+1iMcUCRoA1FlRcVxEA/ozDlOqhxu3FF19sJ5xwgrPiUrc1GS68RYErNy7d1KdF0OADT7kmrMW43HqQZApxRWZlBCdCGaswib6iQSItMnlThxg3AsZw9uzZLus0ZY8iM4QnG/rFvJMGnYza9J0kU1hriSWnXBRiC48C4tDJJL7jjjsWyCZdFJS5wsJJCnbmj8zJeAN4iyGRidnI6IyLMVmgKZmE6zDeCJyTBQPa8vefDwYLC1iZaYtawvES63WTSZw5IpM1nwfcrvksII4jr60oiFsmu7k/FvmXX37Jq6tNQjqyngP9IPHbW2+95RIREIuPazXv5/6RMBZCCCGESE8yMSt1SRCTMMZFmNJA1PgdMmSIE8K4NmOhRbz53UcRIdT7JYMwAhYhS/IsxEFkDeOgICoQFAgQLIAkJSIZGHVr/em5yWxMvWOEJyWiEFkIZCx3CLCw4DyUHaJMFePDeCFAcPX2ZzFGnFFCCKHL8QgkFgsKE8aMF+POggTXT5ZqFgioaYz4p70wIUMx7uBDhw51GZexXjO3jDnjy/Ui3EmgRWwvY4+7bizCmMUDhC7tYUllwQV3edzjKTUVmfANYcx8IvQ4nnOTfIzzsyDjh9rGd999tytDhPUeEhHGWLhjue6OHTu6GtJYuOkDc0fCOO4NMpUXlczOD4sojLuf0aNHuw34jHnC2MveR/I4Pi8sMuBuTwksr+6zEEIIIYQQ6cTatWtt/vz5Tl9Gc6FOJFyw3NZkOWVnOCwMsCAQT6IkUTiITOo3k+wq3nJXqQoLDQj/O+64IyGhHjZ4eyDuR4wabe0DfNm4Ej9zZ7nSTkEE+Io0LdcUS6mcdC7XNH/OLGvSPNhcqlxTQRalULmmFQtnWe1GLQJlcq0dZrmm32eH1/Y3E8Jr+8/54bXdumBljqLKNc08sq21/OT3YFmpu8UXkhOIttmhNb0sTcs11Y+xXNPsWbMsu0WLMl2uKaz/8+PHjbVDu+avW5spsmbK8ujXuXOdspuVevXq1Xbvvfc6r+DCktzihYnXcrykyOOXEH/X1fW7u/Plhrsy4LafrnAdxLj7LcN4CRA/jfUZ0SmEEEIIIYSIzp133mlfffWVhYmEcYZCvCpCtCjiraEbL8QLE7tMfCzuycQ1E0OL23G8SagKA1d8Ys+LApEeWYM5HhDFeBTgOk08MufGJZ7s1n369HEJuoQQQgghhAhCJsYYjxgxwlmECeNFL4RRkUfCOEMZOHCgi48tinhr6MYLyaMQwyRKI9s08bHEw4bhQk1CKlyEi4K4b+KGE4UYcZJfkZnaS5qFQMaNmgRZQgghhBBCiKINdhjOyNNE9ZUwkDBOEmTHTid69+5tPXv2LPKYeGvoxgvZm9lKgn79+hVbLotM18kAd+kBAwYkpS0hhBBCCJHZZKLF+LDDDrM333wzz8gUBhLGGQrlhtgylWS7ZgshhBBCCCHCgZKuCxcudJVeqALDs3z16tULHBdvWWCQMBZCCCGEEEKkJWPGjEmoRE86kokW4+XLl7vysSSwJeySLRJikCWMhRBCCCGEEEKU2azUU6dOdeI3rLJcshgLIYQQQgghRJqQiRbjsWPHOlFMDiAS9lI5h7+TiYSxEEIIIYQQQoiUhfJMxBhff/311q1bt1DOkRVKq0IIIYQQQgghQrEYR9vKMr1793Yu1OPHjw/tHLIYCyECUXnrFquSu6XY43Jzc91Pjs2y4r+lG1YrH9oMbCq+u3ETZCziZvm68NquXTCDY2Hkbv17LittzbWsAP9wl60Pb621RqWKobVdafnq0Npu/uuM0Nq2738PfGhuxfK24vROVmvQe5a1OcC9O2ORhUbrZuG13bVteG2f2yO0ptfUqBH4WPcdO3+WrTn/MMvKKv4ztyHHQqN+teS6Mfqpu2FTaG1b1UqhNb1qY3B18s+/S1uzySzAVJJayMKiUnj/iq1x9XD6Pa9qeOMhUjP5VvPmze3FF1+0X375xdq1a2c1onx3nn/++XGfQ8JYCCGEEEIIIdKEsm4djsbTTz+dF1OM1bgwy7GEsRBCCCGEEEKIMsvWYrJRJ5qMSxZjIYQQQgghhEgTMtFiPGDAgNDPIWEshBBCCCGEECJlOfLII0M/h4SxEEIIIYQQQqQJmWgxLgkkjIUQQgghhBBpSceOHQPHoIr04eijj3Yxw3fffbe1bt3ajjnmmEDv+/DDD+M+p4SxEEIIIYQQQqQJmWAxnj9/vhPGGzdudH/PmzevyORaLIoo+ZYQQgghhBBCZAj/lMAu82yN8AAI2yNAFmMhhBBCCCGEECnDzz//XOTfYSBhLIQQQgghhBBpQia4UpcGWaVyViGEEEIIIYQQIgoLFiyweFm4cGFc75MwFkIIIYQQQog0shhH28oSxx57rN1www02cuRI27JlS7HH5+bm2k8//WT/+c9/3HvjQa7UQgghhBBCCCFSBsTw0KFD3bbNNtu4slyUbcrOznZ/w6pVq2z27Nk2adIkGzt2rK1evTqh7NQSxinG6NGjrW/fvjZgwAA76qijSrs7wswuuOAClzL+448/1ngIIYQQQohSpaxZh6Pxf//3f/bUU0/ZX3/95QTwt99+67bC8DJWb7/99nb++edbPEgYlwKTJ092E4vwbdq0qaU6CEJWYE477TRLR5588klr1aqV7b///pbOzJgxwz744AO3Ksa2Zs0a98G/8MILox77zDPPuOMWL15sOTk51rhxY+vSpYv17t3b6tevXyrXIIQQQgghRHEcdNBB1qNHD/vhhx/so48+cj83bdoU9dhKlSpZ165dnbbiWVcW4zRiypQp9vTTTzuXgEhh3KFDBzfxFSpUSClhjMU0XYUxY33kkUemvTD+7bff7NVXX7XmzZtbmzZtikxbv2jRIluyZIkdcMAB1rBhQytfvrz9+eef9v7779sXX3xhr732mtWtW7dE+y+EEEIIIRInEyzGgMBF8LJh5Pnjjz+cBXnFihXu9dq1a9t2223nnouToZ1SR30JR1ZWllWuXDljRmPDhg3uRk6lhYBUZb/99rOvv/7axVX8/vvvzvJbGHvttZfbImHh5frrr3eLHX369Am5x0IIIYQQQiQOWmHXXXd1W1jErEY2b97srE1DhgyxmTNnuk62aNHCWeROPvnkvOPmzZtnjz/+uI0aNcq54WK1OuSQQ+zcc8+1KlWq5HNzxaL3zjvv2Keffuq25cuX27bbbmuXXHKJWyHw88knn9hbb71ls2bNcisH9erVcwN09dVXW506ddwxmNGbNGni/NKLi99FINxyyy322GOP2a+//moffvihO/+OO+5o11xzjWt7zJgx7nVcoKtXr269evWy8847L1/b3jmvuuoqe+CBB2zixIlWsWJF69atm11xxRV51jnveoG+eDB+N998c6ExxuvXr7dnn33WvvzyS2cNrFmzpu2999520UUXufNGu0Z87V955RUXlM440e9YxRB9wFoMnTp1ytv/xBNPuL8nTJjg5m78+PEuNTqWScbuzDPPdNZKP1wf88c1PPTQQ84yzlgz5ljOp06d6saOeWBxgLnv16+fc6XwxscPls8333zTvY8Afe+8HO/dg0cffXTefcPmH6dEiOW6gXvokUcecd4CNWrUsIMPPtiOO+4495kpzB06klq1almi4E4NfCaFEEIIIUT6kSkW45KmQqyi+NJLL3UP+Z07d7aePXs6n25cNL/55ps8YYyQQoARA3niiSc64cx7nn/+eSd6EJmRFkJED/vOOOMMd57XX3/dCdP33nsvz90Y0cxx7du3d+IP8YQoQWAtW7YsTxjHA6IFcXXKKac4wY2g5FoRzbfddpsTMVwvog5RSJ8OP/zwfG0gWBGqBx54oPOJJ74Tn3jM/i+99JJbEOA1XFxxaT377LOd+R9wjy0M+kNfGDvaZYxYGHj33XfdwgNtN2rUKN97eI0xQRhiYfz888/t4YcfdscddthhgceFBQfGBpcFRL+H129ipYlnRYwi0FeuXOkEaP/+/e3222+Pei4WPBDqLJIg+KtVq+auh8UGxDxz0KBBAzevl112WdR+cQ8999xztu+++7p7AUs79yDW0GuvvdZOOukkdz/ceuutdtNNN7l7hjlMFrFc97hx49z8sZjB54L54D5iPsNm48aNboz5iesJCxJA/EVhEL/hj+FYt26d+5mbu9Wlwi8O75ggx/59XHyZA4O1HVrTga8vLraG2vEYDo1tLlOk27G3HWbHw7u9zSqWD3xoboWsfD+LpXKIXjxB+xAPYY53iPdJbtp+LkMc8HC/wENsOrhi2fpPP/gZ4tUGItypLJd+/4dFRhLTfz4sxQhcBB3iprCb89FHH3WWQKx/nsUXa+WDDz5oL7/8shMQkfWl8BG///7784KlsUYiIhDGiApPjGCxxRLtF9Z+y2u8IIpfeOEFZ+X1hB+i8LrrrnOCvm3btm7/Mccc46yXb7/9dgFhPGfOHCce/bG4ZEbjut544w0766yzbKeddrLddtvNCWMsvn4rbGFg1UZEYY3E+uzB+6+88konXBHvkUWxsWhinfT3GwtrLMKYuFzmHWEVeb2AuPXmxwNhyxhg4Y52rh122KFAf++66y5bu3atSxi1xx57uH0stFC/jIUFPyw4IIoj70POy5xx/x1xxBHuXqHPCONmzZpF7X+8xHLdgwYNcvc1+70FED4PZLsOG5J13XvvvXl/s6DD2LNQUBjc755Xg18Yz1+yyGbOnxv43LMX/u1pIFKMNctjfsvs+fOstFllacp224TYdvH/PyKZfXKHULqSEaxallJtL184x0qbNaXdgTLCikWlP5fpiOfRmInIYpwCwnjw4MHO6hXpRgxY7DyB/P3337sswJFu0AhDkgchcCOFMaLCn0GsXbt2eZZED0QeManDhw+37t27x51xLBpYtj1RDJ5w2GWXXfJEMXAMfYtm7fPcrP3wNy7dWDO5/njgvYwvQtAP47vzzju78WbcvTnwXKA9UQxYq3ELx/U3mVStWjXvd+aGDfbcc09ntcZrwN8PwOIduSiBdZhx9USxx+mnn+6sq36wfjP3iF8v+N4fh/vdd9+5RFV4NYRF0OteunSpiwfGddrvFcDCzqmnnpr0+Yi2sEFYAlZjQgEYm8gxi4T7jHH3W7wJCWhSv6G1bNKs2HNyLyKKsxs1yXdPFnp8DFavWNlUfD34uKmSG2Ljq/9ejAiFWtUDH+rmcv48y27SNNBcLt8cniWweqXQmrZKK0IMLfjt3/9hSWfE5MCHYilGFGe/OdaycgJYWWYtttDY6d/wn6SzT+vw2m4TXhWJNdXz/58s7nOJKK7TqHmgz+XGHAuNetVCNDNuiJ59NilUCe8LZdXG2CzGiOLaDZtbuQBzGSaVwvtXbFUqhHOf8IwlRKkJY0Qqgreo5FBYirEwYSmNFiNJmZi5cwtanaK5EnM8Lqr+B3aKN+NizWskEsIlFNGBKE0ELIp+WACAaOWUeM3fL38bfnENuJqzP9o1B4VYWVyLvT5FWl+JW0Xs+LMMR15PtPFMBrhrY8FHcPF7JNGEccuWLQvcMwi3yP2AqIsEl2BcrlnMKK0vy6DXzdxBtGuLtg83ez/cT4nEFuM677nZI5Jx5SdpF0I+cqHFf8+yebBABVlZ5QI9hHlwbKDjQ3wYyAoxBifLQmy8XIgPSHGMd9C5DPPBLsxnxqwwxzvMOLDNsS/OIIqzgrwvTDUVRJin4niHeJ/E8t0a++cyzk4F6kOIwjjUD32Ycxn8Jsz1fXfGcw+kz3CHc5+U9piVJrIYh0PKpAIu7Ob2ijUDscq4MP/000+uVA0imXhOL6GVJ64LsyRjmYz1/CRVSkdKot/MDe7ECFUs/ljWEYOMJe7feBhEi//wJ1+LF+aYeNnC5o0Fg1S77iBEup6z+BOZRC4RcOVncQs3+8KEsRBCCCGEEJlGTMIYCxcJh0jM47co+SHhEdbb6dOnF3ht1apVziKG+2+8eAWcPTdt3KqJs8VFm3hgwLLKuSJJxGobBNoncZjfasxYsd9v+YzVBRzr748//ugyCZO4yQ/jzHgTox0WhfWXbNBYq6NlVSa2NSjcM7gmk+U8Eu63SLKzs23EiBEuw7KXBKwkieW6vYzh0a4t2j7io/1E8xJIFOLFk+05IIQQQgghSoZMthhv2LDB6SIMVHickmtq8eK/w3/wkkwk1DYrVmsWgpMkQoVZdrGaEY9IPCPixQ/JrbCk4dIZD9FiI1u3/jueyP+gj2UZQUWWaL9AxdocJiSPijwHf7Pff81efGo08R4N3su4MX5+iMtlnImrDdOdBFda+uq33oN3zsj9ZCknjjwW6zbZpSlxRTyrHxY8IvGSaCEio3kBRLpR0/9kCsFYrpvQASzKuFyTnM2faZzM65GQUM2/UbA8HiJdsv1lqqZNmxZqDTghhBBCCCGSzbBhw1weJYyhVAl68cUX3X7yOFGJB21UYhZjkgXRIYQxCYV4cCfeGKsl1i9K6ACZgikjRCwwcaBY+HB7JokSrqFkR44H2sViSmIsVgSwoOK6ysqAP+MwpXqocXvxxRfbCSec4Ky4n332WVJceIsCV25cuhEeCBqyKVOuCWsxLrceJJlCXJFZGcGJUMYqTKKvaHADkMmbySdmlTGkNjHusJQ9iswQnmzoF/N+zz33uIza9J0kU1hriSWnXBSrN3gUEIdOJnFq+kZmky4KylyNHDnSLr/8cjd/1L3GG8BbDIlMzEZGZ1yMyQJNySRisBGDnJMPBW35+4/7PQsLWJlp69BDD417PGK9bjKJM0dksubzgNs1nwXEceS1FQVxy2Q39wvfX375xWXyBhLS4SrtZfnmGOaJa2ZhiH7xuWChAC8LIYQQQgiRfmSixXjChAmuJCtGMc84xTM0BrYDDjjA6aKhQ4cWSP4cmjDGRZjSQNT4HTJkiBPCuDZjoUW8+d1HESEoeTIII2ARssQ0Ig4iaxgHBVGBoECAYAEkKRHxkgySv+wRmY2pd4zwpEQUIguBjOUOARYWnAdBQpkqxofxwsqOCPFnMUaoUEIIocvxCCQWCwoTxowX486CBNdPlmoWCKhpjPinvTAhQzHu4NxsZFzGes3cMuaML9eLcMedgdhexh5X41iEMYsHCF3aw5LKggs3NitClJqKTPiGMGY+EYocz7lJPsb5WZDxQ23ju+++25UhwnoPiQhjPoCxXHfHjh1dDWks3PSBuSNhHPcGK1xFJbPzwyIK4x5pAWYDPmOeMOb6qPvNghDJzfji4D45/vjjXfKtsO8ZIYQQQgghkgWGIDQTBp699torn5cmehDwPk2Eclsj/UFFXLAwwIJAMhMlCXMik/rNJLuKt9xVqsJCA8L/jjvuSEiohw3eHoj7MSNHWYf2xddAZeGEeseUdlK5pjhZFWK5ptqxlWuaOXeOtWwWrCzMshDLNdUIs1zT8hDLNf1aME9C0vj+98CHUhZt5umdrOWro4NlpZ7xbyhS0mldfNm3uOn6b3nFpNOuYPWMZLEmonpDcZ/LpfNnWb0mLQJ9LjeEmGC8vso1JVSuyZXeWjDL6jQONpdhko7lmrznEz+ZIms++jP6dR69Y4iZ4ksZKqvgPYnBC89Ncv1g+MErFE9ltAJ5l2IJ54wkc/Oci5TDqwXs/3LDXRlw209XuA4SXvlhxYv4aazPkV/qQgghhBBCiII6IVopXUoFgxemmPblmkTJwopLpBCNJNEaurFCvDAxscTp4p5MXDMxtLgdx5uEqjBwxSf2vCiISY+swRwPxPfiUYDrNPHInBuXeLJb9+nTxyXoEkIIIYQQIgiZGGPcuHFjl8iWcNVjjz02nwHqww8/LFQ0x4KEcYYycOBAFx9bFMmuoVscJI9CDBMXS2A9Nzcp2MNwoe7fv79zwSkK4r6JG04UYsS7dOniMlN7SbMQyLhR9+rVK+H2hRBCCCGEKMt07drV5RUih44/yS55pEhKjFs1lZESQcI4SZAdO50gAVPPnj2LPCaMGrpFQfZmtpKgX79+xZbLItN1MsBdesCAAUlpSwghhBBCZDaZaDE+55xzXH4eyvFSmtWr6uKVQyUJMvomESSMMxSC1tkylWS7ZgshhBBCiJJnzJgxzstRlG1q167tKvRQ0WfEiBH5SjbhmUlSrkRDQCWMhRBCCCGEECJNyLXMpHHjxq5cKl6fuE9DdnZ20rxcJYyFEEIIIYQQQqQFCOF27dolvV0JYyGEEEIIIYRIEzIxxviWW24p9hjcqm+66aa4zyFhLIQQQgghhBAiZaGajpdwKxrEHEsYCyGEEEIIIUSGkIkWY/ASbkVSlGCOBVmMhRDBKJ9lViEreEYIjs0q/vis+ctDm4Eqi1aG1rbV3ya8tpvXt5Qg959/NBXKB5rLustCHO+p88Nre/gf4bU9b1l4be/YJPix3me33jZmOQHSthy3t4XGjo1Da3pd5Sqhtb0hJ7SmrW6l4A91ubnlbKmZ1ahUzrKyin9fjU3rLTQ2VQyx6fDazs0JT1XUqBRDP3LNlv/zngBfsZa1JcSUS6vWhdd2kO+ceFi22jKVTBTGTzzxRIF9K1ascBmqKZu73XbbWf/+/RM6h4SxEEIIIYQQQoiUpWPHjlH39+jRwypXrmzvvPOOjR8/3vbcc8+4zxFgfUoIIYQQQgghRCqQu3Vr1C1TadGihXOzfv/99xNqRxZjIYQQQgghhBApnXwrktzcXFu6dKm9+uqr7u/lyxMLz5MwFkIIIYQQQog0IVPLNZUrIskWr7Vu3Tqhc0gYCyGEEEIIIYRIy6zU0LhxY7vuuusSal/CWAghhBBCCJH2SZmKEk5liUy0GJ9//vlRrcQ1atSw7Oxs69y5s1WokJi0lTAWQgghhBBCCJGyXHDBBaGfQ8JYCCGEEEIIIdKETLQYlwQSxkIIIYQQQgghUoZjjjkmrvd9+OGHcZ9TwlgIIYQQQggh0oRMsBjPmzevyCzUkRBfHsvx0ZAwFkIIIYQQQgiRUpR0MjUJYyGEEEIIIYRIEzLBYvzzzz+X+DmzSvyMokhGjx5tnTp1so8//lgjlUJZ8I466qjS7oYQQgghhBAiJGQxLgUmT55s3377rRNbTZs2tVQHkb569Wo77bTTLB158sknrVWrVrb//vtbOjNjxgz74IMPbNKkSW5bs2aNq+l24YUXRj2eBZZoVK1a1YYNGxZyb4UQQgghRBhkgsW4MBYvXuyeg9Emubm5BV4/8sgjLV4kjEuBKVOm2NNPP+0KkkcK4w4dOtgPP/yQcIHqZAvj+fPnp60wZqz5kKS7MP7tt9/s1VdftebNm1ubNm0CuZi0b9/ejjvuuHz7UuneEkIIIYQQoji2bNlid955p33yySeFxh6TfEvCuAyRlZVllStXtkxhw4YNTqhJrBXPfvvtZ19//bVts8029vvvv1vv3r2LfU+zZs3s8MMPT8pcCSGEEEKI0icTLcavv/66ffTRR6GeI2bT0ebNm+21116zIUOG2MyZM52gadGihVPnJ598cr4U248//riNGjXKmbobNmxohxxyiJ177rlWpUqVfG6uWPTeeecd+/TTT922fPly23bbbe2SSy6xrl275js/qwRvvfWWzZo1y3JycqxevXq266672tVXX2116tRxx+Ci3KRJE3vqqacKxO/27dvXBgwYkBczijX0lltusccee8x+/fVXV/uK8++44452zTXXuLbHjBnjXscFunr16tarVy8777zz8rXtnfOqq66yBx54wCZOnGgVK1a0bt262RVXXGF169bNd71AXzwYv5tvvjlqH2H9+vX27LPP2pdffmmLFi2ymjVr2t57720XXXSRO2+0a2Q15ZVXXrHZs2e7caLfffr0iWm+6QPW4kjX3CeeeML9PWHCBDd348ePt4ULF1r58uXd2J155pl2wAEH5GuL62P+uIaHHnrIWcYZa8Ycy/nUqVPd2DEPLA4w9/369bODDjoob3z8fPHFF/bmm2+697GK5J2X47178Oijj867b9j845QIsVw3cA898sgjzlugRo0advDBBztLLp+Zotyh/dSqVSuuvvKZZatWrVpc7xdCCCGEEKI0GTx4sLMIt2vXzj2H8zvGn2XLltmPP/5ou+22m9NGJSaMebi+9NJL3UN+586drWfPnlapUiX7888/7ZtvvskTxggpBBgxkCeeeKITzrzn+eefd6IHkRlpIUT0sO+MM85w52FVAGH63nvv5bkbI5o5DvdQxB/iCVGCwGJQPGEcD4gWxNUpp5ziBDeCkmtFNN92221OxHC9iDpEIX2KtMQhWBGqBx54oPXo0cP5v7Oy8ccff9hLL73kFgR4bcmSJfb+++/b2Wefbdttt517L+6xhUF/6AtjR7uMEQsD7777rlt4oO1GjRrlew+vMSYIQyyMn3/+uT388MPuuMMOOyzwuLDgwNisWLHCiX4Pr9/EShP7ihhFoK9cudIJ0P79+9vtt98e9VwseCDUWSRB8CPYuB4WGxDzzEGDBg3cvF522WVR+8U99Nxzz9m+++7r7gUs7dyD119/vV177bV20kknufvh1ltvtZtuuimqS3EixHLd48aNc/PHYgafC+aD+4j5DJuhQ4e6uefeZjwQ5BdffLET50IIIYQQIv0oGFlb9pk1a5b7ybM0z9vAs/3uu+9u9913nzOcopFKTBhjKUbgIugQN378wc+PPvqoswRi/fMsvlgrH3zwQXv55ZedgDj22GPzvb927dp2//335xVmxhrJhSOMERWeGMFiiyXaL6z9ltd4QTi88MILzsrrCT9E4XXXXecEfdu2bd3+Y445xlkv33777QLCeM6cOU48+mNxt99+e3ddb7zxhp111lm20047uRUNhDGrGoUlSPKDVRsRhTUS67MH77/yyiudcEW8+1mwYIGzaHoCyOs3FtZYhDFxucz7xo0bo7rkIm69+fFA2DIGWLijnWuHHXYo0N+77rrL1q5da88884ztsccebh8LLTfccINbWPDDggOiOPI+5LzMGfffEUcc4e4V+owwTrZLcSzXPWjQIHdfs99bAOHzQLbrMGFFDeGenZ3tFqlYaOBLY+zYsW78CrMgb9q0yW0e69aty/uMR0tyEIl3TJBjHSVcoy5phNnvoGMXMik1l3//awiHCiEWaKhUPiX6nVs+K9/PUiXE+zvwvRoHW0P8WObmlgvvcxnm90mYc/nP82AobYf59R1Dt1NqLsO8wUP635CJ7sSZTE5OTp4HJZ6afG68Z9QuXbo4rYW3cDTPzVCEMSZsrF6RbsSAxQ7o5Pfff++yAEe6QSMMSR6EwI0UxogKTxR7D/WeJdEDkUdM6vDhw6179+75jk8ULNueKAYsjLDLLrvkiWLgGPoWzdrnuVn74W8mCWsm1x8PvJfxRQj6YXx33nlnN96MuzcHngu03yqItRq3cFx/kwkZjj2YGzbYc889ndUaQRZpnYxczWFRAtHGuHqi2OP000931lU/WECZe8QvluzIONzvvvvOJarCqyEsgl730qVLXTwwllq/VwALO6eeemrS58PPiy++mO9vFkZYmMHajkcG4j4aLAR57v7gfenMX7jQZs6eHfj8s+fOtVInPu/zYOSsCa/tmSG2HQeEY5Q6TULMvXD8bpYpzD54p9LugtnSRaXdg5RjVbp+LkVSmDtnjkYyDuZviOeTUzbIxEWBmjVrOm9YjHUYVPkdIyDesHgVJ+N7MSZhjEhF8BaVHApLMQ/SWEojQeHXr1/f5kZ5YI7mSszxuKh6IAyxduFizWtkcGaFANGBKE0ELIqRgw/Ryinxmr9f/jb84hpwNWd/tGsOCrGyuBZ7fYq0vhK3ikD04pijXU+08UwG3JRY8BGj/B5JNGHcsmXLAvcMLtWR+4FY80j++usv53LNYkZhIEjDJOh1M3cQ7dqi7cPN3g/3U7yxxdEgYReil4WIwoQxnzMWJPyu4MTKN2nUyFpmZxd7DhZpEMXZzZrlW6wplIXJvSfzsTjEf5r1QnRHb1bPUgE3l7NnO6+DQHMZ5nhPWxBe2z9ODq/tBcvDa3v7xoEPxVKMKM7+cqplbQlgGdqnlYXG9g1Da3pd5X9zmCSbjX8bK0KhTtVy4X0u16y30KiU/5knmWwqXz4tRUUsTiLMJaK4WfPmgeYy0Gc3Xlb/vQgeCjnhDPjSKgWfi0XZpWnTpu6Zm9xVGC55Bqf8qFeCFKMZ4buJkDJ1Wwr7QvCn4+ZicWH+6aefXKkaRDLxnF5CK09cF2ZJxjIZ6/kx1acjJdFv5gZ3YoQqFn8s64hBxhL3bzwMorkH+ZOvxQtzTAKvwuaNBYNUu+4gRLqes/gTmUQuEbBUs8gSaWmPXMxh8/Bcrrm+QA9h/xD4+BDd5UIlzH7HMM4lQUrMZZir4zkhPmxuKvz/Tmn0mwfrrDCvt5Tv71i+o2KlXIgfy6yscuF9LsP8PknTuQzz+ySebgedy6wwvwfDvMHLhfOdE8fHpsyQiRZjPF9JhIyhlvBSjDyee7WnDRINr41JGGPhIuEQ8Yf+B2c/JPjBejt9+vQCr61atcpZxHD/jRfOiwux56aNWzVxtrhoEw8MWFY5VySJWG2DQPskDvNbjRkr9vstn7G6gGP9JdsaKyQkbvLDODPeuBSERWH9JRs01upoWZU/+OCDwO1zz+CaTJbzSLjfImGVfMSIEda4ceO8JGAlSSzX7WUMj3Zt0fYRH+0nmpdAIuB+QsI6vlyEEEIIIYRIB/r16+c2D/ISEb64ePFipwkI0yUsMxGyYrVmIThJIlSYZZcVL9wuUfSIFz8kt8KSRkKneIhm5WrdurX76XcRxrKMoCJLtF+gYm0OE5JHRZ6Dv9nvv2YvPjWaeI8G72XcGD8/rJQwzsTVhrmyisWQvkYW0/bOGbmfLOXEkcdi3Sa7NCWucNv1w4JHJF4SLURkNC+ASDdq+p9MF/JYrpvQASzKuHuQnM2DFS7ifCMhoZp/a9OmTVx9LMwiTEZ1xozPqBBCCCGESE+LcbStLLNmTf78K4hgEuxSdee///1vwqI4ZosxyYLw40YYk1CIB3fijbFaYv0iqQ+QKZgyQsQCEweKhQ+3Z5Io4RpKEqB4oF0spiTGItAaCyquq14dKw9K9VDjlrI0J5xwgrPifvbZZ0lx4S0KXLlx6Z42bZoTNGRTplwT1mJcbj2YOMQVmYERnAhlrML4y0eDRFpk8iaZEjGrjCHxRQScU/YoMkN4sqFfzPs999zjMmrTd5JMYa0llpxyUSSfwqMA9wYyiVPTNzKbdFFQ5mrkyJF2+eWXu/mj7jXeAJ7Ai0zMRkZnXIzJAk3mZdyD8UbgnCwY0Ja//7jfs7DAihJtHXrooXGPR6zXTSZx5oiYXj4PuF3zWfDcP4J6EPCFQMY9fyzyL7/84lbMgIR0JNcCPqPUeCPrOZ8VYrgZF+o3Mx7+muNCCCGEEEKkMjy74zFM+Vx+Rpb+TQYxtYiLMKWBqPE7ZMgQJ4RxbcZCi3jzu48iQrBOkUEYAcvDOUl9EAfxXgiiAkGBAMECSFIikoFRt9Zf9ojMxtQ7RnhSIgqRhUDGcocACwvOQ9khylQxPowXVnZcvf1ZjBFnrHAgdDkegcRiQWHCmPFi3BE7XD9ZqlkgoKYx4p/2woRETLiDUxMXlwWs18wtY874cr0Id8QXsb2MPa7GsQhjFg8QurSHJZUFF2563OMpNRWZ8A1hzHwiFDmec5N8jPOzIOOH2sZ33323y7aM9R4SEcZYuGO57o4dO7rVLCzc9IG5I2Ec9waZyotKZueHRRTG3Q9Clw34jHnCmHMSA03/+KywmMHnlPuF+Qx6TiGEEEIIkVqUdetwNPD+RQN5OujAAw90IhmDYbIotzXSH1TEBQsDLAgkM1GSMCcyCbAn2VW85a5SFRYaEP533HFHQkI9bPD2QGiP+fnnQF8+LJxQ1okM1oFc/BcUnggsYRaFmPG6fv54/6TSvL6lAm4uZ850XhGB5jLM8Z46P7y2hwdfxIuZeQWz1ieNHf/OYRCE3ApZNvOwVtZy8ORgybe6xRfGEYgdG6dlVuoNIWalrhtjVuqYPpfKSp3SWalnz5pl2S1alH5W6lVhZqUOp99jx4+zjgfnD8/MFFkz8OfoY3rNnqmVvDOZHH/88fnKMXkelxiGMDaxJZp8t+yOnkg7vFrA/i833JUBt/10hesg6ZUfvASIn8b6jOgUQgghhBAiCJkYY/zee+85L9HzzjvPhTXyfM22YMEC54VLyK+/3Ghal2sSJQvxqpFCNJJk19AtDuKFiV0mThf3ZOKaiaHF7TjeJFSFgXsxsedFQUx6ZA3meF0/8ChgJYsVfs6NSzzZrfv06eMSdAkhhBBCCCEKB43ARlUYQgbxvvzqq69cfifg2ToRJIwzlIEDB7r406JIdg3d4iB5FGKYRGlkTqaQN/XIwnCh7t+/v3MRLgrivokbThRixLt06eIyU3tJsxDIuFH36tUr4faFEEIIIUTmUNatw8WBpXjZsmVuI1EvbtXJcKOXME4SZMdOJ3r37u0C1osi2TV0i4PszWwlAXXQiiuXRabrZIC79IABA5LSlhBCCCGEyGwyURjn5ua6hLNYiSmPunz58rzXEMXE6e+1114JnUPCOEOh3BBbppJs12whhBBCCCFEOBxyyCF5Ri2/dbh169bO2MfriYYnShgLIYQQQggh0pIxY8YktWRPOpCJFuOVK/+tfEG4Jbl7EMSUfE0WEsZCCCGEEEIIIVIWEgIfdNBBTgzvvvvuoZxDwlgIIYQQQggh0oRMtBgPHjzYJbQNE9UxFkIIIYQQQgiRsoQtit05Qj+DEEIIIYQQQoikkIkW45JAFmMhhBBCCCGEEBmNLMZCiEAs2VjOFqwvV+xxW3P/Pmbh+nJWLqv4461WndBmoFq98NoOkwqbw1sKrlYxwJzES90aoTW9bq9WobW9bLfw2q5ZKbSmreaWTcEPzs01W7zA7MzuZlkB1sSnzLPQeP6b0JquNn1BeG3v2CS0tm3vnYIfy0e4bnmzX6abBfmqaJttYbEit3xobW/YHFrT1rh6iN+DfNaC8k/JmSxqsPrKzxTGhhDtWauqhPf9HRbLqqZfn5OFLMbhIIuxEEIIIYQQQoiMRhZjIYQQQgghhEgTYvBLKBPk5OTY9OnTbfPmza5ucfXq1UM5j4SxEEIIIYQQQoiU49NPP7VBgwbZ6tWr3d/ly5e3U045xS6//HIrVy65YRESxkIIIYQQQgiRJmRKjPGYMWPslltucb9v3brVCWGsx6+++qqzGp933nlJPZ9ijIUQQgghhBBCpBSvvPKKE8Rs4P/5+uuv5/2dLCSMhRBCCCGEEGlJx44dnSUx2W61qW4xjraVNSZOnOjmdY899rCPP/7YvvnmGzvxxBPda7hWz549O6nnkzAWQgghhBBCCJFSrFy50v3s06ePNW7c2GrUqGGXXHJJ3uurVq1K6vkUYyyEEEIIIYQQaUJZtA5HIzc311mMt9lmm7x9iGOPLVu2WDKRMBZCCCGEEEIIkZKMGDEiqtt0tP1HHnlk3OeRMBZCCCGEEEKINCFTLMYezz//vPnx4smj7ZcwFkIIIYQQQghR5tia5OzThSGLsRBCCCGEEEKkCZliMW7fvn2JZhuXME4xRo8ebX379rUBAwbYUUcdVdrdEWZ2wQUX2Pz5812aeCGEEEIIIUT4PPXUU1aSSBiXApMnT7Zvv/3WCd+mTZtaqoMgpFbYaaedZunIk08+aa1atbL999/f0pkZM2bYBx98YJMmTXLbmjVr7Pzzz7cLL7ww6vHEXXjHzp0715o0aSJxL4QQQgiR5mSKxTgWeNb94osv7Oyzz7Z4UR3jUmDKlCn29NNP27x58wq81qFDB/vhhx/s8MMPt1QSxq+//rqlK4w1CxHpzm+//WavvvqqLVy40Nq0aVPs8Y8++qjzQGjWrJnVrFmzRPoohBBCCCFESbBkyRJ77bXXXJ3j4447zh5//PGE2pPFOMXIysqyypUrW6awYcMGq1ChgttE0ey333729ddfu1puv//+u/Xu3bvI47EuN2/e3P1+0kkn2fr16zXEQgghhBBpTiZbjFesWGFDhw61IUOG2K+//pqXmIuficYjx6xGNm/e7JQ5nZk5c6YTNC1atHCpsU8++eS847CGotpHjRrl3HAbNmxohxxyiJ177rlWpUqVfG6uWPTeeecd+/TTT922fPly23bbbe2SSy6xrl275jv/J598Ym+99ZbNmjXLcnJyrF69erbrrrva1VdfbXXq1HHH4KKM22ikX3q0+F2sobfccos99thjbnA//PBDd/4dd9zRrrnmGtf2mDFj3Ou4QFevXt169epl5513Xr62vXNeddVV9sADD9jEiROtYsWK1q1bN7viiiusbt26+a4X6IsH43fzzTcXGmOMqHn22Wftyy+/tEWLFjkL4N57720XXXSRO2+0a+QGeeWVV1x9L8aJfrOiEgv0gfha6NSpU97+J554wv09YcIEN3fjx493lszy5cu7sTvzzDPtgAMOyNcW18f8cQ0PPfSQs4wz1ow5LuVTp051Y8c8sDjA3Pfr188OOuigvPHxg7vEm2++6d5HgW/vvBzv3YNHH3103n3D5h+nRIjluoF76JFHHnHeAhQmP/jgg93KFp+Zotyh/dSqVSumPnqiWAghhBBCiHRl7dq19s0337hn/59//tk990dmq+Y5uUuXLiUnjBHFl156qXvI79y5s/Xs2dMqVapkf/75p+usJ4wRUggwYiBPPPFEJ5x5DzGPiB5EZqSFENHDvjPOOMOdB9ddhOl7772XF4eLaOY4MpQh/hBPiBIE1rJly/KEcTwgWhjkU045xQluBCXXimi+7bbbnIjhehF1iEL6FOnujGBFqB544IHWo0cPF9v50Ucf2R9//GEvvfSSWxDgNcz+77//vvOB32677YoVMfSHvjB2tMsYsTDw7rvvuoUH2m7UqFG+9/AaY4IwxML4+eef28MPP+yOO+ywwwKPCwsOjA2rM4h+D6/fuCgT+4oYRaCvXLnSCdD+/fvb7bffHvVcLHgg1FkkQfBXq1bNXQ+LDdzgzEGDBg3cvF522WVR+8U99Nxzz9m+++7r7gUs7dyD119/vV177bXOQsr9cOutt9pNN93k7hnmMFnEct3jxo1z88diBp8L5oP7iPlMRTZt2uQ2j3Xr1rmfW3Nz3VYc3jFBjg2bFOhCXITZ7dzc4Kupuf8MoPczwBvi7Vapro5vzU3PezDwvMQzl2FSIcQorkrl07PfMRg5cv0/g7wvxDnfGuK3VbifyxCz3Ib4uQyz32GOd1hsTcdOJ4lMsRh/+eWXTgyPGDHC6cNopZuwEp9zzjnOyFSiFmMsxQhcBB3ixo//Q01sI5ZArH+exRdr5YMPPmgvv/yyExDHHntsvvfXrl3b7r///rwLwhqJiEAYIyo8MYLFFku0X1j7La/xgih+4YUXnJXXE36Iwuuuu84J+rZt27r9xxxzjLNevv322wWE8Zw5c5x49Cep2n777d11vfHGG3bWWWfZTjvtZLvttpsTxlh8/VbYwsCqjYjCGon12YP3X3nllU64It79LFiwwFk0sU76+42FNRZhTMIq5n3jxo1R454Rt978eCBsGQMs3NHOtcMOOxTo71133eVWg5555hnbY4893D4WWm644Qa3sOCHBQdEceR9yHmZM+6/I444wt0r9BlhTJxtMuO2Y7nuQYMGufua/d4CCJ8Hsl2nItzvnleDXxhvWrHANiyZFbidjcvmWGmzobQ7UEbA60TEx/IUa3v20kXBDozNQSU2DmsVYuNhtp1azK4bcBFg8YKwu5J2zFxiKcXsuXNLuwtpyYblurfLOv/5z3/cM7RfDGdnZztDIRvaCDC4JaOsU0zCePDgwc7qFelGDFjsPIH8/fffuyzAkW7QCEOSByFwI4UxosJ/Qe3atcuzJHog8ohJHT58uHXv3j2pda2wbHuiGLAwwi677JInioFj6Fs0a5/nZu2Hv3HpxprJ9ccD72V8I7OsMb4777yzG2/G3ZsDzwXaE8WAtRq3cFx/k0nVqlXzfmdu2GDPPfd0Vmu8Bvz9ACzekYsSWIcZV08Ue5x++ulutcgP1m/mHvGLJTsyDve7775ziarwagiLoNe9dOlSFw+M67TfK4CFnVNPPTXp85EMuM8Yd7/Fm5CASrUbW5X6LYp9P5ZiRHHlus2tnO+eLA2qpmnoepiGqWoVY7MYI4r5J+T/fimUnL9dm8Jg3dbwBmV5iCso21QKr+2aW/717Ag0l0sXWXa9hsHm8s8QHzh/nBxe2zMWhtf29o3Da7vTDoEPzf1HFGcv2xIsg2qrZhYWK7PCu8E3hPd1Yo2qp47FGFGc3axZoM/lhhAtxquDf52kDFXqpNgKRwmSG2E1LeuUK1fOheSipQhdDIuYHhsRqQjeopJDYSnGwoSlNBJ8v+vXr+/SaUcSzZWY43FR9T+wjx071rlY8xoZnPElR3QgShMBi6IfL4tvtHJKvObvl78Nv7gGXM3ZH+2ag0KsLK7F0TILY30lbhWB6MUxR7ueaOOZDHDXxoKPGOX3SKIJ45YtWxa4Z3CpjtwPxJpH8tdff7mVIxYzCgNBGiZBr9vLPB7t2qLtw83eD/dTrLHFicI9y+bBAhUgcmMRurEeHwalfPqU7HdWVuwPVjywBRJTWeH9o84KURiXC3W8LaXGJPBchklOiO6Pm7akZ79j+ej88xHOCvqRC3G+w/yOL7c1tb4HwyTo5zIrFp/7FPoeDIty6dhpETe4VJPfCksxoYxh5NJJGXtKYV8IftM5scq4MP/0008u8BqRTDynl9DKG6DCLMleoHYs5yepUjpSEv1mbnAnRqhi8ceyjhhkLHH/xsMgWtyMP/lavDDHJPAqbN5YMEi16w5CpOs5iz8lXdxcCCGEEEKkLpkSY9y5c+d8ybZIhMxGrqEwLMcxCWMsXCQcIjGP36Lkh4RHWG+nT59e4LVVq1Y5ixjuv/HCeXEh9ty0casmzhYXbeKBAcsq54okEattEGifwHC/1ZixYr/f8hmrCzjW3x9//NFl9yZxkx/GmfEmRjssCusv2aCxVkfLqkypoKBwz+CazCpQJNxvkeDWSRB+48aN85KAlSSxXLeXMTzatUXbR3y0H9UfFkIIIYQQmSiMH374YecV+9VXXzmLMeF9ntGU5M+eRiFpMl6ahNpGhmXGQlas1iwEJ0mEIvE6idWMeETUPOLFD8mtsKSR0CkeIuNJoXXr1u6n30UYyzKCiizRfoGKtTlMSB4VeQ7+Zr//mr341GjiPRq8l3Fj/PwQl8s4E1cbpmscrrT0NTILnHfOyP3cqMSRx2LdJrs0Ja644f2w4BGJl0QLERnNCyDSjZr+J9OFPJbrJnQAizIu1yRn82caJ/N6JCRU829t2rRJWr+FEEIIIYRIJ2rXru3CJ/GgxDOTRMQ8H/Mc7j2LUxEJzRCk/GnSLMYkCxo2bJgTxiQU4sGdeGOslli/MGsDmYIpI0QsMBeChQ+3Z5Io4RpKduR4oF0spiTGouwQFlQGiNUCf8ZhSvWwqnDxxRfbCSec4Ky4n332WVJceIsCV25cuqdNm+YmjGzKlGvCWozLrQdJphBXZFZGcCKUsQqT6CsaJNIik/eLL77oVkMYQxLikHWaLGyRGcKTDf1i3u+55x6XUZu+k2QKay2x5JSLIvkUHgXEoZNJHPeGyGzSRUGZq5EjR9rll1/u5o+613gDeIshkYnZyOjMB4Qs0MQZEIONNwLnZMGAtvz9x/2ehQWszLR16KGHxj0esV43H2DmiEzWfB5wu+azgDiOvLaiIG6Z7Ob+WORffvnFZfIGVsnIeu5BeTOvBjXjyOfAOxZLNsnLhBBCCCFEehFiNoWUBv1HEl82tNCQIUOc5iO8MRnEJIxxEaY0EOZqOoIQxrUZCy3izYOHbkQI9X7JIIyA5UJInoU4iKxhHBREBYICAYIFkKREJAOjbq2/7BEmdOodIzwpEYXIQiBjuUOAhQXnoewQZaoYH8YLKzuu3v4sxogzSgghdDkegcRiQWHCmPFi3FmQ4PrJUs0CAcHniH/aCxMyFOMOPnToUJdxGes1c8uYM75cL8KdBFrE9jL2uBrHIoxZPEDo0h6WVBZccJfHPZ5SU5EJ3xDGzCdCkeM5N8nHOD8LMn6obXz33Xe7MkRY7yERYYyFO5br7tixo3MFwcJNH5g7EsZxb5Bdr6hkdn5YRGHc/YwePdptwGfML4w//PBDtyDlx3s/iysSxkIIIYQQIh3Jzs52lZLYCHNEe0VWsomVclsj/UFFXLAwwIKAEiUlF0QmNcpIdhVvuatUhYUGhP8dd9yRkFAPG8Q14n7I8NG22x4dApVrot4xpZ1KOyt1tZRJL5i+5ZrwBsIroiyXa1oWYrmmmilUrmnm4gXWskHjYHM55e+M+qEwLPiiacxMD7HM1I5/54wIhb3/XdQsDir2zKxb3lpSrinIE1zbbAuLFWlarqlxCpVrmjl7trUMWBIvzHJNq9KwXNP4X8bawV3/NYxBpsiaswdH/4A8f1h6Jg1OFdL0sVGURXBL9ru78+WGuzLgtp+ucB3EuPstw3gJEAuB9RnRKYQQQgghhPgXPDNjgfDERDwiJYwzFOJVEaJFUdI1dIkXJnaZOF3ck4lrJoYWt+NkJ6HCFZ+Y26JApEfWYI4HRDEeBbhOY3nj3Lh64PbRp08fl6BLCCGEEEKIIGRKVupbbrkl5mo+EsYiZgYOHFjsKkxJ19AleRRimERpZJtu2rSp9e3bNxQX6v79+xeIv42EuG/ihhOFGPEuXbq4zNRe0iwEMm7UvXr1Srh9IYQQQgghyiJbY3CPj1VERyKLcZIgO3Y60bt3b+vZs2eRx5R0DV2yN7OVBP369Su2XBaZrpMB7tIDBgxISltCCCGEECKzyRSLcfv27QuIXQxb7CPhbDI8O/1IGGcolBtiy1RUH1gIIYQQQojU5akonquEXQJViXbfffeknq90U8YKIYQQQgghRJyMGTPGudtmSkZqz2IcbROJIWEshBBCCCGEECKjkSu1EEIIIYQQQqQJsg6Hg4SxEEIIIYQQQoiUK9dUGM8//7zVqVMn3z6Sct10001xn0/CWAghhBBCCCHShEyxGH/yyScFslJ7f48YMSLqeySMhRBCCCGEEEKUKbaqjrEQItWoXdmsftXij8vNNZtrZvWqmmUFSO9XISuxYuxF9iXEDJUbckJr2qpt3RJe46s2BT+WyXTvWRdoMnO3CXCDxAldCIsg93W8VFm1JrzG/+CTFhA+Zk0qm42faRbkYzH8DwuNGYvCa3v7RuG1vdu24bW9bcPgx27NNVuz3KxFfbNyxX8u11WsZKER4ldV46ohmsTWxfA9GCvVKsf+Hr5fA3zHbtoc3phUC9GHNKx/81UrWsaSKRbj888/v0TPJ1dqIYQQQgghhBApxQUXXFCi55MwFkIIIYQQQog0IVMsxiWNhLEQQgghhBBCiLRgzZo19tJLL9mkSZMsNzfX2rZta6eccorVrVs3oXYljIUQQgghhBAiTcgUi/Ezzzzjtlq1atlHH31klStXtg0bNljv3r1tzpw5ecf99NNPLoM1Yrl+/fpxny9AahwhhBBCCCGESD06duzoSvhElvUR6c/EiRNty5Yttt9++zlRDO+8847Nnj07L2O1ty1ZssSee+65hM4nYSyEEEIIIYQQaWQxjraVNWbMmOEWPNq1a5e379tvv837/YADDrD77rvPdtxxRyeOR44cmdD55EothBBCCCGEECKlWL58ufvZtGlT9zMnJ8d+//139zuC+YYbbrA6deo49+r//ve/tnDhwoTOJ2EshBBCCCGEEGlCWbQOR2Pjxo3u57p169zPCRMm2ObNm50o3nnnnZ0oBi/pVoUKiUlbuVILIYQQQgghhEgpGjRokBdXPG3aNHv55ZfzXuvUqVPe78QXQ7169RI6n4SxEEIIIYQQQqQJuYVsZY299trLxQ6TdfrUU0+1YcOG5b120EEH5f0+ZswY97N58+YJnU/CWAghhBBCCCFESnHBBRc4K7A/+zQcdthheQm51q9fb0OHDnXu1QjpRFCMsRBCCCGEEEKkCZkSY9ywYUN79dVX7c0337RJkyZZ9erVnfg95phj8o5hf5cuXdzv+++/f0LnkzBOMUaPHm19+/a1AQMG2FFHHVXa3RH/rFbNnz/fPv74Y42HEEIIIYQQJQQW44svvrjQ19u3b++2ZCBhXApMnjzZ1eBC+Hrpx1MZBOHq1avttNNOs3TkySeftFatWiW8ipQKtdw++OADtzLGtmbNGjv//PPtwgsvLHDszJkz7fPPP3f13ObMmWObNm1ycRc9evRw81i1atVSuQYhhBBCCJEYmWIxLmkUY1wKTJkyxZ5++mmbN29egdc6dOhgP/zwgx1++OGWSsL49ddft3SFsfYXA09XfvvtN+dOQo22Nm3aFHnsRx99ZK+99poTw+edd55dfvnl1rJlS3v88cftnHPOcfXehBBCCCFEegrjaJtIDFmMU4ysrCyrXLmyZQoINGqOJVp3LBPYb7/97Ouvv7ZtttnGFTfv3bt3ocdiGT777LOtRo0aeftOPPFEy87Otueee84+/PBDO/nkk0uo50IIIYQQQpQxizFFlV988UXnjkmgc/fu3e3MM890QdF+sIbeeOONdsghh9g+++zjgqQfffTRApYq3FypQ4WbKK9jKeV4UnIPHz68wPk/+eQTJwhwi+3atatr93//+58tX7487xhclIkLjRa/y7n8saL8zj7SgGNZPPLII9119enTx1novBTg5557rjvfoYceas8880yBtr1z4uJKjHC3bt3swAMPdLHCy5Yty3e9t9xyi/ud4zg3280331xoH72Ma4888oi7XsaHftx0000u9rWwa8RqeNJJJ7njuS7mLVa4rrFjx7rzeH1l4zxeoW36fvzxx7txQ7xhkfzmm28KtMVxvJe5Ygy4NxinRYsWudenTp1ql1xyiRtnhB1jt2LFinzj4+eLL75w88I5vTn76quv8t2DXo0z7ht//xMlluv27iGEKscydwMHDnT12OgL90QQatWq5URxENq2bZtPFHsw5sC5hRBCCCFE+iGLcThUiFUUX3rppe4hv3PnztazZ0+rVKmS/fnnn04QeBYoRBQihRhIrFQtWrRw73n++eft119/tccee6yAhRCRwb4zzjjDnQfX3Wuuucbee++9vDjcTz/91B1HgDWiEssqbqW4HiM+69SpE/dAIDq3bNlip5xyiuXk5Ngrr7zirhUBd9ttt9lxxx3nrvfLL7+0J554wvUp0t0ZgXfRRRc5QYywQyQjTv/44w976aWXrEqVKu41ilC///77Tihtt912xdbdoj/0hbGjXcZo1qxZ9u6779qoUaNc240aNcr3Hl5jTI4++mgnpog3ffjhh91xpDgPytVXX+3GBoF61VVX5e33+o2LMosa1BJr0qSJrVy50onQ/v372+233x71XIhfAukRtQj+atWquevB5Zc07MwBBb2Z18suuyxqv7iHsHzuu+++7l7A0s49eP3119u1117rFgS4H2699Va3gMA9wxwmi1iue9y4cW7+atas6T4XzAf3EfNZ0vB5Ka4AOvHIbB7r1q1zP7fm5lpubvFV8rxjghzrjrNyFha5/9/encBZPe9/HP/MtEiLVmmR0qJSuZEK3WRfSuS62XJVUslSuJHCP1yuIqWuLClFKOoSuWSXaNFiqZAolUhJtGmd+T9eX37jN2fOzPzOzPnNzJnzfj4e85g68zu/89vOzPn8Pp/v5/tHW/9Q1h3ihIFp6WGuPC3m7XDfAzwt6DnPiyJySGJfd5gbHsNbJy0l8/dclQxxpFXpEuGtO8ztDu9XVUwXuPd77ffvwX8nJ977Mj1B3/QxnMtY/16GeUzCvL5DWjefS0QKLTBmzKKX+SK48fO/qcn8khV88MEHXfYPXbt2tdGjR9vkyZNdANGlS5dMz69UqZKNGjXKzUEFMmkEEQTGBBVeMEKbbsZJ+gNrAqP8IiieNGmSlSpVKiPwIygcNGiQC+jJwIGMLdnXadOmZQmMaXJE8OhvUlW/fn23X1OnTrUePXpYo0aN7KijjnKBcdu2bQNlL8n+EkSRmR8wYEDG4zz/+uuvd4Erwbvfhg0bbPr06RlZQ2+7yezHEhiTmee87969O+q4Z4Jb7/x4CGw5BhMmTIj6Wg0aNMiyvcOGDbMdO3a4bHzLli3dY9xoGTx4sLux4McNB4LiyOuQ1+Wccf116tTJXStsM4Fx7dq14zpuO5b9HjlypLuuedy7AcL7IVpVQ5i4xtmGEiVKuKx1drjeqZ6IDIw3/rjB1q9bG/j1flj/XT63WIqKdb9sDrbgn4U7CSVhR9zXjH3YzboaAZ/zt6Ni3x7Ju+2xv3nW7fgltHUXBQH3rlhYt25dYW9CQtqyaYMlK40nLgKB8axZs1zWi8xeJDJ2XoD8/vvvuy7AXlDsITCkeRABbmRgTFDhBcVg0mYvk+ghyKMUmxJrSrj9y+cXmW0vKIbX9rt58+YZQTFYhm2Llu0jECPg8eP/48aNc9lM9j8veC7Hl0DQj+N7xBFHuOPNcffOgVcC7S+lJVvdokUL++yzzyye/N2NOTdeqXzr1q1d1pqqgciSXjLekQEb2WGOqxcUe7p16+ayq35kvzn3BL9ksv0oaZ49e7Yrg6eqISxB93vz5s1uPPDpp5+eqSqAGzsMF4j3+cjJAw884F6Pmwn16tXLdjmuM467P+NNyXv1Q2pY7TqH5fo6XIsExTVrH5rpmsxOydTEzBjv2hfaqq1s+v7wVr7rz2qAINlOguI6lapaakru5zKtQnjdzjf9fn8mFBVDbOtQZtv28Fa+IvNQmpyQKSYorrNht6UGeVvMW2GhWbspvHXXqx7euls3Cm/djWvG9HuNoLhOuUqWGuBz0M6Dgg3ByYu9If6qqlg6xOzorr3hrbts8F8o/L0kKKb/R5C/l1t3h3dMQvxTHNq6K//4UzgrlqQVU2BMkErAm1NzKDLFZJjIlEYbI1mtWjVbv359lp9FKyVmeUpU/R/YGe9KiTU/o4MzYzYJOghK84OMoh83ABBtOiV+5t8u/zr8wTUoNefxaPscFGNlKS32tiky+0qXawLEKlWqZLs/0Y5nPFCuTQafYNQ/ltoTLTCmO3LkNUNJdeTjiBbArV692pVcczMjOwSkYQq6317n8Wj7Fu0xyuz9uJ44b/nFtj7//POunDzyBkskrlm+PNygQkpqaqA/3J7UgMunhvnXOMTAOIZDEfu6Q9zumDb8j0IgguJA5z7EgxIgLi+a5zLMDc/DZUJQHCgw3hdiieKeEKOpMLc7zI6vMV0nv+8jQXGQ6yuW39uxSgn1V1UR+T1YAOsO/vdSgbEfn0uSlTLG4SgyrYCz+4VAAORhrDIlzDTKWrhwoQuSGc9J8yJKP73gOrtMMpnJWF+fstNEVBDbzbmhnJhAlYy/1/CJY0n5NxUG0cbNkL3OL87xmDFjsj1v3DAoavsdRGTpOTd/qDjID94flFBTRTBkyJB8rUtERERExJI9MCbDRcMhGvP4M0p+NDwie7tq1aosP9u6davLiFH+m1e8LiXEXpk2ZdWMs6VEm/HAILPKa0XKT9Y2CNZP4zB/1phjxeP+zGesJeBkf+fNm2fbtm3L0pWY48zxZox2WLLbXrpIk63u3bu39e3bN9PPZsyYEXj9XDOUJq9ZsybLz7jeIlFyNHfuXKtRo0ZGE7CCFMt+05gL0fYt2mOMj/aLViUQC++mEePL6RIfz+EHIiIiIlLwlDEOR2qs2SwCTrJP2WV2yZoxHnHFihUuePGjuRWZNBo65UXkeFI0adLEffeXCJNZJqDypgHyAlSyzWGieVTka/B/Hvfvszc+NVrwHg3P5bhx/PwYl8txZlxtmKVSlNKyrf7sPbzXjHycLuWMI48lu0136eXLl7vxrH7c8IjkNdEiiIxWBRBZRs32x7OEPJb9ZugAGWVKrmnO5u80Tuf1SDRU8381bdo0z9tJQMyX14AszGtERERERCRpMsY0C5ozZ44LjGkoxAd3xhuTtST7xRQ6oLkP0wgxFphxoGT4KHumiRKloWSv8oL1kjGlMRbTDpFBpXSVLJi/4zBT9TDH7dVXX20XXHCBy+K++uqrcSnhzQml3AQizBFLQEM3ZaZrIltMya2HJlMEKXRWJuAkUCYrTKOvaCiBpZM38xAzZpVjSLMGuk4z7U5kh/B4Y7s47/fdd5/rqM2202SKbC1jyZkuiuZTVBQwDp1O4g0bNszSTTonTHM1f/5869+/vzt/1atXd9UA3s2QyMZsdHSmxJgu0EyZxBhsqhF4TW4YsC7/9lN+z40FssysK6euzLmJdb/pJM45opM17wfKrnkvEBxH7ltOGLdMd3P/WOSPP/44Y15tGtLR9RyMJyZbzP62adPGlXf7MR49zOZkIiIiIhIOZYyLQGBMiTBTAzHH7+uvv+4CYUqbydASvPnLRwlCmO+XDsIEsASyNP0hOIicwzgoggoCCgIQMoA0JaIZGPPW+qc9orMx8x0TeDJFFEEWATKZOwKwsPA6TDvENFUcH44XWXZKvf1djAlWyOAR6LI8ARI3C7ILjDleHHduSLD/dKnmBgFzGhP8s74w0aGYcvC3337bdVwme8255ZhzfNlfAncaaDG2l2NPqXEsgTE3Dwh0WR+ZVG64UC5PeTxTTUU2fCMw5nwSKLI8r02wx+tzQ8aPuY2HDx/upiEie4/8BMZkuGPZ71atWrk5pMlwsw2cOxrGcW3QqTynZnZ+3EThuPstWrTIfYH3mBcYc+PKm7aL7YrEzRUFxiIiIiIiv0tJj6wHlTzhxgA3BPLbKEkyI8hk/maaXeV1uquiihsNBP733HNPvgL1sFHtQXC/YOEiF1DnhhsnzHfM1E6arinBp2tKS7M1WzZZ3coHBzqXYU7XtDHE6ZoqhTld09YQp2v6Yn1M0zWtqXmA1f0h4HRNHwS/sRmzb/8c5hR39Q8Jb93HNQ5v3U2zziSR0zRqa7ZvsbrlKwfqSr2zYoWEbDBeKUmma6LiksozTdcUu48/XmJ/bftnYgzJEtYc/0z0N9+8bonZNLio0KBDKTK8uYD9v9woVwZl+4mK/di9e3emx6gSYPw02WeCThERERERKTxFZromKViMV40MRCPFaw7doBgvzNhlxulSnsy4ZsbQUnacnyZU0VCKz9jznDAmPXIO5ryg8RsVBZROc1eY16Yknu7W3bt3dw26RERERESC0BjjcCgwTlIjRoxw42NzEo85dGNB8yiCYRql0W26Vq1adtVVV4VSQn3TTTe5EuGcMO472vjcWDFGvF27dq4ztdc0iwCZMuquXbvme/0iIiIiIpI/CozjhO7YieTyyy+3s88+O8dl8juHbqzo3sxXQbjhhhtynS6LTtfxQLn00KFD47IuEREREUluyhiHQ4FxkmK6Ib6SVbxLs0VERESk4C1evDhQc1CR3CgwFhERERERSRBphb0BxZS6UouIiIiIiEhSU8ZYREREREQkQWiMcTiUMRYREREREZGkpoyxiIiIiIhIglDGOBwKjEUkkG9+MSv9+zTMOUs3q2hmn282s5TcF5+9LrwWEsMWhLfu8qVDW7Vd3yq8Yp5rlswLvnCJFLMOh5vN+Mhsf3qui6deOdbCUsMS1Ij4z8Pu2divc+Bl09PSzDattU1HNbKU1Nyvr41HNLaw1Cof2qqtytKvw1v5k++Gt+6XFwZftmxps1kDzf56q9nOPbkvvnVn/rYtp3W3PDy0ddu1HUNb9dozjw9t3W8vC/53p4SlWftyZk9/nmb7CzkYalIlwB/sPKpTIZz1/rIrnPVK8lJgLCIiIiIikiDS0kO8S5LENMZYREREREREkpoyxiIiIiIiIglCY4zDoYyxiIiIiIiIJDVljEVERERERBKEMsbhUGAsIiIiIiIJqVWrVgX2WulqelWsKTAWERERERFJEMoYh0OBsYiIiIiISIJQYBwONd8SERERERGRpKaMsYiIiIiISIJQxjgcyhiLiIiIiIhIUlPGWEREREREJEEoYxwOZYzzaP369fbPf/7TTjvtNDv22GPtjjvusGS3aNEidyxmzpxpRcWKFSusX79+dvLJJ7tte+yxxwp7k0REREREJBkyxt999509+eSTtmTJEtuwYYOVLl3aqlatas2aNbPOnTu7ACXR3XnnnbZy5Uq74oor3L4deuihOS4/ceJE+/LLL90XQXXNmjWzDSAJsl955ZWoPxs2bJgLxiOP96OPPmofffSRbdu2zQ455BA7++yzrUePHnbAAQdYPH3//fduu0866SRr3LixFWX79u2zm2++2X2/6qqrrEKFCtaoUaNQX5PAm+PC8RERERERiTdljBMkMP7888+tT58+VrJkSevUqZPVr1/fdu/ebevWrbP58+db2bJlEz4w3rNnj3388cd24YUX2j/+8Y9Azxk7dqxVrFjRBU0Er0HcddddWR5r3rx5pv9/++231rNnT9u/f7917drVatWqZUuXLrXx48fbsmXLbMyYMZaSkmLxDIwff/xx9zpFPTDmBgRf119/vV100UUF8pocm3POOUeBsYiIiIhIMgfGBAa7du2yZ5991o444ogsP//pp58s0f3888+Wnp5uBx10UODnzJgxIyOrTED922+/5fqcjh075rrMf/7zH9u+fbsLhP/yl7+4xy644AKrW7euC8Zfe+21QOtJRDt27LBy5cpl+/PNmze779yQKA54X3HDiS8RERERSU7KGCfIGOO1a9e6QCRaUIxq1aplyj5mN+6Tx/gZy/hLjHnsl19+cf8+9dRT7cQTT3Rjfb2A+4UXXrC///3vdsIJJ7gA8b333gu87ax3+PDhLtN93HHHue/8n8f920BG0LsJwPbwxfjanORWah0NwTdBb1paWrbL8LqHHXZYRlDsoWQdQcf7Eqg/9NBDdt5559nxxx9vZ555pv3f//2f/fDDDxnLsC5Kkr1Scm/fqRCI9PLLL7sbAKyL40VpfXYVBgMHDnTnkmX/9re/2YQJE1z5sx+vwT5RNk559CmnnGIdOnTIdn9Y3tsu/7Z61xPHdvr06XbZZZdZu3btrH379ta3b9+o53HatGl2zTXXuPJ0rguOze23357p2vSuZVAG772e91her/UtW7a47T/jjDPcNm7cuNH9nOuCaoAuXbq440Z5/ZAhQ9zx8aNag/VzXNlPSrzJno8ePTrbYyciIiIikmzinnoiAFyzZo298847LngJQ//+/a169eouSKNE+7nnnrObbrrJNVh68cUXXXDHuGYeHzRokAuWa9euneM6CTQYL8z6zj33XGvSpIlr3ETwtHDhQhfYkZ0kwCDoHzlypHs9vnD44YfHfT8JYsiKlipVyo4++mi7+uqrs5RS792718qUKZPlud5jy5cvd0FgTuXUBKHXXnutffrppy5AJVjkBsd///tfW7BggT311FNu3DLbQNk246XPP/98939UqVIl0/p4Hll1jiPjeslak9lmHWeddVbGch988IE7b3Xq1HGvSQaeMnACua+++srdlPDbuXOnC16POuoodyx4jexwLrlZELmtlStXdt8J+l9//XW3vwTcHEe2kwD4vvvuyxR0P/300+64E1By0+ebb75xFQBcF1OnTrVKlSq59VL6znp5LV4zHtgexrD36tXL3bxgKIJ3rTJ+n2PMcAVuDHGtMq588uTJbgw7OIbcpOAmT7du3VzJPdc4257TUAG+/MfdSU/7/Ss33jJBlnW/hNItLGVLpoW37hKJOV1AWomUmJcN/Jzy8e1pUCyUDK/HZXoON02zWzbwc8J7Wwb91ZAnafEbOZRV6RDf9GVLB1407Y9lve+52r/fQlOmVHjrTk0pEu+dWJWI4c1TwtIyfc9NmJd3SnqYxzvRVlz0KWOcIJ+/+ABPMEVWz8tk0nSrVatWcQseWR8Brx+l22TTCIbLly/vHmvdurVdcsklLlgm8MsJgS/BIOtlrK6HIJhAieCQ7sYEZWS9CYwbNmwYSpkygdCll15qTZs2tQMPPNAFiVOmTLErr7zSZfratm2bsSxB0erVq11g5M/Ge5lPgpqtW7fmWE5MJpigmPHSAwYMyHic12F8Lpnkf/3rX+6mB48RbHIcstt3AjaCNO88cKOCrDHnxguMyWSyTgLORx55JKM8mCw/DbJGjRqV0eXa8+uvv7qfExTnhswu64y2re+++64LgsmwcqPDc/HFF7vA/4EHHnCVCN7NBIJfzoMfP2c7XnrpJevevbv7Oa9BYMxNmHhdFw0aNHDHyW/EiBFu7DT75q/MIMBnH7ix4HVJp2KC6gmyzkGxXqohIgPj8rs2WMWdawOvp+JvmbPX2emY+b5KXHU8Pbx1J6o1HWL/Pbzur/WCLfjZv2PfIMm7TcHfj57fNgd7X2Y/SCX/tu4Icd3VQryt1P+UIrXudS/0D2VTksKW2N87QbXPw5vnhHLrrdDtDW/VaVtCWu/WDeGsWJJW3P+CEISQYeNr7ty5LujyynnJpA0dOjRPZcV+BLt+rJfAmKyYF4yBAIssLwFvbgggyPpFZvoInAgSCKYIjAvCddddlyVzTEBJsExXagJ9D5nW2267zZWTk0mnKRZNtwieCAzJBjM2NafAmH1LTU11QaHfX//6Vxd4vf/++66cm2WCIEDznwey1y1atLDPPvss4zFunjAGmIwoGVA/Sn4JjFkmslFb0GZnOXn11VfddcFx9ZfJg3LlcePGuWuGcdrwgmKOAUEix5Tjwj5yrMPE+fUj+09QzzVP1YR/+9lObjTQ5M7DNq5atcq+/vprdyMnCK4DssueTz75xB2X7WVq2K9lD8t9BelpLij+9cBDzVJyv2Y+/C681NTIxeHdTS4fYqLk6pbhZRl7f/p+4GXJFBMU1/ngW0vdH+A8XffnDRX5w91/vpfibVOvs2PKkBEUH1j1UEsJ8Lt80x+FImGoGWLUXfnzVeGt/Nng752Yvbok8KJkigmK6/xtjKXu/LO6J1vbcu9pkmctfv87GYo+Z4S26nWntglt3bNj+JtGppigeO6O2rY/wOjGMLOER1QOL2Nc+8+PhHGVelDi9y3Kq+TNlYcrlFurfAD3MlaMUV28eLHLrNHJmQCOoJny4LyKLIumXBcEhZEozyXTmBvGd5KhjWxsxP/JfDPNUmFiG04//XR3k4FSdS9oI2AmOGK6JsqMwbEluKFUmTG8OTWo8vb94IMPjtpMjIwlGWteI7JkOjvRytYJzP3ngSx3dp23I5tnebhx4Z3r/KCTNyXqjNvNDmXa3jGm7JibI5Slk+n2C9phPK+8bfAw5pjjSPAbOW2Xx38D48Ybb3Q3o8gkc1640UCQS8Y7uxsdDEPgy0P5tkOQGyDQzRBw+X0h1mzuzDxUPa4C3ifKk30hTjEfKMCN8pxAz9ue+f0hnMzwPr4ECXCjPSfQ80Ks2Yzl10isUkMMHGxPiCXJQQLcCATFgQLjHSG+L3eFmWZML1LvnaD25+FvGkFxoMDYwpMexxlMCuw9H+YvE0lKobe3ZawjZbRkcykFpmSXAKNly5Y5jntlLGR2SpQoEdPjZNmKA2/cKEGqP2Ai6CGzTVaQsaEEswSQNI2ivNqfvS0I2Z2HaOeE0u3sGrURrPtFG0udF7w2Qfbdd9+d7TIcQ3CtUoZPlQPfufnC3NBcu5Ri59QYzS+v13rkPnvHrU2bNq6EOzdkxRlj/OGHH7p5xZnrmptUZJwffvjhfN2gEhEREZGCpzHG4SiweV8IDCjzJDD2Out6GUrGwEZiDGVBIptGJpYyWX/WmP9TVptb866CQNMkbwxyJDJ8Rx55ZMb/yRSTXWR8b27Yt3nz5rnsZ2RGljJcMs40mEK85kQmA+6V//rHTBcEmn1xTinvzsiGZmPWrFkucKUDtP8aoBFWLNnieF3rXtacjHfQ40a2njHPfBFY0wiNMfOzZ8/ONussIiIiIpJM4l6DQIln5FQ7YJyrN/aRhlEg4CLIo1TVn9VlyplYplmKB7oQE0jSbdiP//O41306bARckeW6oJT7rbfecg3MchujzfNpIEWwHGRMLllFMp+TJk3K9DhZRjpz+8tuvUAySHl6TphiiNJsXjPaurheCP7CQPUC+0tTsdxKuL3sd2TVwRNPPBE1W8zxibY/8brWOQ+Uz5PJ5nqIxuvWTUAfGbxzY6Nx48ZxOYciIiIiUjgZ42hfUsQyxnRr5gM3wRRjjSkF/fHHH13mjSwdQYm/CRBz3dKVmMZRBKd0V2a6H0pZyXoWFMpS3377bdeBmmCQ4IHvlJ1Stnz55Zfna/3/+9//MuYEphSa6YHGjx+fUSLNcQHHiGNBsEpmk4zqypUrXTksQdGtt96aab1MHUTHYRpl0YyJoIh5dMlC0iG5Xr16gZpl8Rw6czPe+JhjjnHZaTpLE8zRIMtDYE6Qx884t2QvCXDpAB4L9ovtZg5jOk0z7RD7SyDHGGAagt1///1Zmm/FA1lS9vn55593NxwYc0tGnEoGGoQRrHLewXmgsRsl3zRmo/SYpmCUrXtZdD+qIihXJuCvUaOGC0SZ9zie1zrng8qLwYMHu2uWzDfbxfXFzQzGyjPGn0ZhBNG8F7meyTZzfjl3ZLB5XEREREREQgiMafZDiSadbJnLmI7DjHElGCb4JCDx4zGWoVMwTboIvG6//Xb74osvCjQwZhsnTJjgprqhCzOBKEEhQRtNrXJrYJUbAi3GePrRMAsEol5gzGsyfpSpiriZQOaUccI03qKhVmSgS3BGQExmm6CY/WD8KE2tIuc8zg6l42RP2f8333zTBaUEvMzxy5REBHgeguF77rnHBXjcBGFMM9sfa2DsZY0Jxvmi0zKZeQI2MuJ0RaareFhoSEXQTYdvglhuVHDsmb/afyOAsfDcLOEmBueL8cWcHzpX9+7dO8t6b7nlFjd3MFMeeRlvLzCO17XOOSZjTRM7zhfXK5ltrgO2t0uXLhnnig7uBOp8EShzLREQcy1FjuEWERERkaJP2eFwpKQXl85UIhIKbugwD/mzby6ypkcdE2y6pp1rf5/aKUDHyNnrwvsVNGxBiNM1/dm4O+6ubxVep81rlrwb03RNzHtcd/bqYF2prxybv40rjkb0CG3VG/tlvtGc23RNOzettbIHHxaoI+/GEOcarhViP8gqS78Ob+VPBn/vxOzlhTFN17Rm1kCre9aIYF2pt4Y491bL2OdFD+zajqGteu2Zx4e27rfXxjZdU/ty62zOjjqFPl1TkyrhdaWuk/8JRaJa+skSO6dD/CsLc1JUwqaqD0WfdmPztQXWPqpY0tETERERERFJEMoYh0MTgImIiIiIiEhSU8ZYREREREQkQShjHA5ljEVERERERCSpKWMsIiIiIiKSIJQxDocyxiIiIiIikpCYApNu0QXxJcWbMsYiIiIiIiIJQhnjcChjLCIiIiIiIklNGWMREREREZEEoYxxOJQxFhERERERkaSmjLGIiIiIiEiCUMY4HAqMRSSQxlVSrGX1lFyX27Nnv02c8Yb17NnTSpcukevyQdaZVwNaqSgmi6NPDXz89u3ZY29MnPjHuSyd+xN6BV+35F/1GJblfTnjjeDvy+plLTG1aZSY6x7bJ/b35dJRwd6XksVhIR6Tns2D/03bs2efTZzovS/1kTxWP5UP7/NDUafAOBwp6eo9LiI52Llzp3355ZfWpEkTK1s290/L27dvt5NOOsnee+89K1++vI5tAtO5LD50LosPncviQ+eyYD+fFCelHtgX9fG9/4zfDZb58+fbzJkzbdmyZbZ+/Xrr2rWrDRo0KMtyxx57bJbHqlataq+//nrG/z/44AN76qmnbNWqVbZjxw6rXr26dejQwfr06ZPps2LQ19y4caONGjXK5s6d66bRatWqlQ0cONBq166dr33W7SkRyRF/bI455hgdJRERESkykvnzSUFkjOfNm2crV650x3jr1q05LnvRRRfZWWedlfH/UqVKZfo5z2/WrJlbrmLFivbNN9/YuHHj3PexY8fG9Jr79++3/v3722+//Wa33nqrq5xhXf369bOpU6fm6yaJAmMRERERERHJMGDAALvhhhvcvxctWmQ5qVGjhrVo0SLbn3fs2DFLlpmA9p577rFNmzbZwQcfHPg133rrLfv6669typQp1qjR70NcjjzySOvSpYu9+OKL1q1bN8srDcATERERERFJEGnZfMVTamq4YSKZY+zduzem11yxYoUr1faCYlCa3aBBA5szZ06+tkmBsYjEFXcAe/furaYwxYDOZfGhc1l86FwWHzqXUlxMmjTJ2rZt63rMDB482DZs2JBtGfTu3bvd2PDx48fbiSeeaLVq1Yrptfbs2RP1Mybl26tXr7b8UCm1iMQVv6z69u2ro1oM6FwWHzqXxYfOZfGhcyl5lT6w6IRwnTp1svbt21uVKlXcmGEC3l69erlS54MOOijTsp07d3aNs3DCCSe4UupY1alTx63DX4JNIzYaexF050fROaoiIiIiIiISShf0n376KdflateunaV5Vk7uvPPOjH/TNKtly5Z22WWXufG+3bt3z7Ts6NGjXdMsgtgJEya48cQ03ypRIvdpBD00+Xr00Ufd695yyy1uWx988EG33ljWE40CYxERERERkWKMplV33313rstNnz7d6tWrl+fXYexv3bp1Xbl0tJ/hqKOOcg2zLr30Unv33XfttNNOi2ls8r///W+76667XMMtLyAnc51bk7DcKDAWEREREREpxggivUCyKGjUqJGVLFnSvvvuu5ife/zxx9srr7xia9eudUMSyHLT0bp58+b52iY13xIREREREZF8o2v0mjVrXEY4J8uWLbN9+/a5oDYvKJs+/PDD3fO//fZb++ijj+z888+3/FDGWETiZsuWLXbHHXfY4sWLXet8xn60adNGRzgB9enTx/3R8sbrHH300TZmzJjC3iwJgDI4xnYxz+MVV1yRqRnezJkz7ZFHHrEdO3bYKaecYkOGDIlpLJkUjXNJuWC/fv2sTJkyGcvy/uR9KkUPXXTvvfde98GdcZ58mL/xxhtdOanX0ffpp5+2tLQ0O++886x///6WkpJS2JstSe6HH36w5cuXu3/v2rXL1q9f78qx4ZU+T5482WV8W7Vq5Zpv8btq4sSJdsghh2TKTt90003WtGlTlyU+4IAD7KuvvnLP5f90so7lNb3fd8ybXL58ebeuJ554wpVSt27dOl/7rMBYROJm+PDhbm45foktWLDAtex/4YUXMuaqk8Ry2223WceOHQt7MyRG1apVczc2Zs2alelxPrCMHDnSHnroITf+6+abb3bdQwmwJLHOJciSzJgxo1C2S2LDFDVMSUOzIW4av/nmm67pEDeqlixZYtOmTXPBMTc6rrnmGvf+LEolr5KcFi1alKmx1ty5c92X9zNwrb7zzjvumuaGa+XKla1du3Z29dVXW4UKFTKe26xZM3vjjTfsySefdDeAatas6bK7NOny35wN8pqgK/WwYcNs69at7r3Vs2dPu+SSS/K9zynp6enp+V6LiCQ9WuWTgXrppZfcnULwge6cc86xc889N+mPT6Lh3PHBTIFx4qI5CTeqvCwjATFVHbfffnvGhwwqPBinJYl1Ljl3NNFRYJy46Kw7atQolzWrX7++XXnlle5xgmW+xo0bV9ibKJJ0NMZYROKCBghly5bNCIrRsGFD15JfEhPZRUqXuPO7cuXKwt4cySfei15HUO/9uWHDBndTSxLPjz/+aKeffrrLujz++OMuKymJ8/eSTBfzsa5evTrL+5K5YEWk4KmUWkTigvnjypUrl+kx/v/rr7/qCCcgxriRxUhNTbXnnnvO/Z/xjpHnWBL3PcrYLBAYc1NLEgdTqUyZMsUOO+ww13SGfg4HHnigK0uUoo1xk1Rt9OjRw70Hef/535f8m/eqiBQ8ZYxFJC74UMb4Ej/+rw/ciYkpDzh3jHnr3r27+/fSpUsLe7Mkju9RmgBB79HEHHtMcMyNK25g9erVy43zk6KNDrzcxCBT3Lt374z3n/99yb95r4pIwVNgLCJxQeaCO980RPBQDsaHNkl8fACXxMZ7kQZc/vdnjRo1FBgXA3p/Fn00HCJTTLdpxvZ7XafpUB35vmzQoEEhbqlI8tInHRGJC+56d+jQwR577DFXKjZnzhz3x57HJLFs27bN5s+f76YY2bt3rz3zzDNuPBxZZEmMrNTu3bvdB3HGnfJvvtPsh6ziF1984bLF3vQWknjnkuZbjA/3xqvS7fjEE08s7M2VXBqobd682XXSLVnyz5GMNDhk9gamvOHn/L5V00ORwqGu1CISN3S8HTp0qJvHmCZcgwYNsrZt2+oIJ+B5ZEzxmjVr3Ae4I444wq6//npr0qRJYW+aBMDNKZox+fG+7Ny5s+t2+/DDD2eax7h06dI6rgl2LundQADFTSzmDiWQoquxP+CSooO5WXn/MX+rP7vvzT3NvK/ePMbMBqB5jEUKhwJjERERERERSWoqpRYREREREZGkpsBYREREREREkpoCYxEREREREUlqCoxFREREREQkqSkwFhERERERkaSmwFhERERERESSmgJjERERERERSWoKjEVERERERCSpKTAWERFJIn369LFjjz3W7rjjDitK9u7da507d7Y2bdrYunXr3GMzZ85028pXohg6dKjb3hdeeKGwN0VERGKgwFhERKQIBKoEhX6LFi3KCAoJEOOlfv361rx5czv00EOtKHnppZfshx9+sBNPPNHq1Kljieqyyy5z3ydMmGD79u0r7M0REZGASgZdUERERBIXGdlSpUrZLbfcYkXR9OnT3fezzz7bivLxy02jRo2sQYMG9s0339icOXPs5JNPLpDtExGR/FFgLCIikkA2bNhgDz/8sC1YsMB++eUXq1q1qsuy9uvXzypWrOiWoUz6lVdesWOOOcbatWtnzz33nG3atMkWLlzoMtRLliyxc845xy332GOP2eOPPx71tV5++WWrVauW/frrr/boo4/a+++/bz/99JN7nbZt29o111xjNWrUcMt666lZs6YNGDDAHnnkEfvxxx+tadOmduutt1q9evWy3SeCyK+//tpKlCjhtjeaTz/91B544AG3LOsiwG/RokXGzz/55BMbP368LV261Pbs2WO1a9e2c88917p16+bWC7LyZKV79+5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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# use `.explain_single()` method to compute SHAP values for a single forecast instance,\n", + "# and return them in a `SHAPSingleExplanationResult` object\n", + "result_single = torch_explainer.explain_single(\n", + " # last historical target known to the model before forecasting the last day\n", + " foreground_series=test[:-24],\n", + " # future covariates must extend for at least 24 hours for forecasting the last day\n", + " foreground_future_covariates=future_covariates,\n", + ")\n", + "# access the `shap.Explanation` object for the (optional) target component\n", + "shap_object = result_single.get_shap_explanation_object()\n", + "# heatmap orders horizons by similarity by default\n", + "# we override this ordering to show the horizons in their natural order with `instance_order``\n", + "ax = shap.plots.heatmap(\n", + " shap_object, instance_order=np.arange(shap_object.shape[0]), show=False\n", + ")\n", + "ax.set_xlabel(\"Horizon (hours)\");" + ] + }, + { + "cell_type": "markdown", + "id": "d0149957", + "metadata": {}, + "source": [ + "Similar to Section 4.7, many lags of the target, like `lag-14` and `lag-10`, have a strong influence on the prediction at the `lag` + 24 horizon, indicating the model is learning daily seasonality patterns in the data." + ] + }, + { + "cell_type": "markdown", + "id": "bac4ce80", + "metadata": {}, + "source": [ + "## 7. Probabilistic Torch Explainability\n", + "For probabilistic torch models, `ShapExplainer` can also explain the likelihood parameters of probabilistic forecasts, such as quantiles for `QuantileRegression` likelihood and $(\\mu, \\sigma)$ for `GaussianLikelihood`. This could be useful for understanding what features are driving the model's uncertainty estimates, in addition to the deterministic point predictions.\n", + "\n", + "### 7.1 Model Training\n", + "Here, we demonstrate how to explain quantiles of a probabilistic `DLinearModel` with `QuantileRegression` likelihood.\n", + "The model is trained to forecast **three quantiles** (0.1, 0.5, and 0.9) of the target series to provide an estimate of the forecast distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "997e3254", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "999d22d947564bf4bb5ee52b3180460b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot predictions against actuals\n", + "fig = test.plotly(label=\"actual\")\n", + "prob_pred.plotly(fig=fig)\n", + "fig.update_layout(yaxis_title=\"Consumption (kWh)\", autosize=True)" + ] + }, + { + "cell_type": "markdown", + "id": "7cdd0f9a", + "metadata": {}, + "source": [ + "Notice that there are three forecasted target components corresponding to the three quantiles:\n", + "- `consumption_q0.100`.\n", + "- `consumption_q0.500`.\n", + "- `consumption_q0.900`.\n", + "\n", + "We would need them to specify the target components to explain when calling methods like `.get_shap_explanation_object()` on `ShapExplainabilityResult` or `ShapSingleExplainabilityResult` returned by `ShapExplainer` for probabilistic forecasts.\n", + "\n", + "### 7.3 Explainer Initialization\n", + "We can initialize a `ShapExplainer` for the probabilistic model in the same way as before, by providing the model and background data:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "fbb8029c", + "metadata": {}, + "outputs": [], + "source": [ + "prob_explainer = ShapExplainer(\n", + " model=prob_model,\n", + " background_series=test,\n", + " background_future_covariates=future_covariates,\n", + " batch_size=4096,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5778b380", + "metadata": {}, + "source": [ + "### 7.4 Explainability of Quantiles\n", + "To explain the predicted quantiles, we can call the explainer's `.explain()` method for batched explanations or `.explain_single()` method for individual explanation.\n", + "Optionally, we can specify the target components corresponding to the quantiles when calling these methods to get explanations for specific quantiles of interest.\n", + "\n", + "Here, we are interested in explaining the 0.9 quantile of the last day forecast. We first compute SHAP values for the last day forecast via `.explain_single()` method, which returns a `ShapSingleExplainabilityResult` object:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "f7bd7347", + "metadata": {}, + "outputs": [], + "source": [ + "result_single = prob_explainer.explain_single(\n", + " # last historical target known to the model before forecasting the last day\n", + " foreground_series=test,\n", + " # future covariates must extend for at least 24 hours for forecasting the last day\n", + " foreground_future_covariates=future_covariates,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7ee8ad92", + "metadata": {}, + "source": [ + " We then visualize the SHAP values for the 0.9 quantile over the forecast horizon using a heatmap:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "de616699", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# access the `shap.Explanation` object for the 90th percentile target component\n", + "shap_object = result_single.get_shap_explanation_object(component=\"consumption_q0.900\")\n", + "# heatmap orders horizons by similarity by default\n", + "# we override this ordering to show the horizons in their natural order with `instance_order``\n", + "ax = shap.plots.heatmap(\n", + " shap_object, instance_order=np.arange(shap_object.shape[0]), show=False\n", + ")\n", + "ax.set_xlabel(\"Horizon (hours)\");" + ] + }, + { + "cell_type": "markdown", + "id": "6cf21065", + "metadata": {}, + "source": [ + "The heatmap above shows that different lags of the target are driving the predicted upper bound across the forecast horizon. Many of them, like `lag-14` and `lag-13`, have a strong influence on the prediction at the `lag` + 24 horizon, indicating the model is learning daily seasonality patterns in the data for the upper bound as well." + ] + }, + { + "cell_type": "markdown", + "id": "87344149", + "metadata": {}, + "source": [ + "## 8. Conclusion\n", + "\n", + "In this notebook, we demonstrated the capabilities of Darts' explainability module to:\n", + "- Explain both scikit-learn and PyTorch forecasting models using **SHAP values**--features' contributions to predictions.\n", + "- Provide **global explanations** to identify overall important features and correlations between feature values and contributions.\n", + "- Provide **local explanations** to predictions for given input instance(s) over the whole forecast horizon or at specific horizons.\n", + "- Provide explainability for **probabilistic forecasts** of PyTorch models to understand what features are driving the model's uncertainty estimates.\n", + "- **Sample** background and foreground data for efficient SHAP value computation.\n", + "\n", + "Check out the API reference for more details:\n", + "- [Darts Explainability Module Documentation](https://unit8co.github.io/darts/generated_api/darts.explainability.html)\n", + "- [ShapExplainer Documentation](https://unit8co.github.io/darts/generated_api/darts.explainability.shap_explainer.html#darts.explainability.shap_explainer.ShapExplainer)\n", + "\n", + "
\n", + "\n", + "Explainability module is in active development. If you experience issues or have suggestions, please report them via [GitHub issues](https://github.com/unit8co/darts/issues).\n", + "\n", + "
\n", + "\n", + "## References\n", + "\n", + "- Lundberg, Scott M & Lee, Su-In, \"A Unified Approach to Interpreting Model Predictions\", 2017. http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions\n", + "- Christoph Molnar, \"Chapter 18 SHAP\", Interpretable Machine Learning, 2025. https://christophm.github.io/interpretable-ml-book/shap.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8261fc5-4801-4249-9a65-50746f77cfc3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.12.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + 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