diff --git a/examples/benchmarks/liander2024_optimization_example.py b/examples/benchmarks/liander2024_optimization_example.py new file mode 100644 index 000000000..fd113b128 --- /dev/null +++ b/examples/benchmarks/liander2024_optimization_example.py @@ -0,0 +1,121 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Backtesting pipeline for evaluating energy forecasting models. + +Simulates realistic forecasting scenarios by replaying historical data with +proper temporal constraints. Executes prediction and retraining schedules +that mirror operational deployment conditions, ensuring evaluation results +accurately reflect real-world model performance. +""" + +import logging +import sys +from datetime import timedelta +from pathlib import Path + +from openstef_beam.backtesting.backtest_forecaster.mixins import BacktestForecasterConfig +from openstef_beam.backtesting.backtest_pipeline import BacktestConfig +from openstef_beam.benchmarking.benchmarks.liander2024 import Liander2024TargetProvider +from openstef_beam.evaluation.metric_providers import RCRPSSampleWeightedProvider +from openstef_beam.parameter_tuning.models import ( + FloatDistribution, + IntDistribution, + LGBMParameterSpace, + OptimizationMetric, +) +from openstef_beam.parameter_tuning.optimizer.benchmark_optimizer import BenchmarkOptimizer +from openstef_beam.parameter_tuning.optimizer.optimizer import ( + OptimizerConfig, +) +from openstef_core.types import LeadTime, Quantile +from openstef_models.presets import ForecastingWorkflowConfig + +logger = logging.getLogger(__name__) +handler = logging.StreamHandler(stream=sys.stdout) +logger.addHandler(handler) + + +single_target = False + +horizons = [LeadTime.from_string("PT12H")] +quantiles = [Quantile(0.1), Quantile(0.3), Quantile(0.5), Quantile(0.7), Quantile(0.9)] +forecaster_name = "lgbm" # Choose model type: "lgbm", "xgboost", "gblinear", "lgbmlinear", "hybrid", "flatliner" + + +# Base Forecasting Workflow Config +base_config = ForecastingWorkflowConfig( + model_id="common_model_", + run_name=None, + model=forecaster_name, + horizons=horizons, + quantiles=quantiles, + model_reuse_enable=True, + mlflow_storage=None, + radiation_column="shortwave_radiation", + rolling_aggregate_features=["mean", "median", "max", "min"], + wind_speed_column="wind_speed_80m", + pressure_column="surface_pressure", + temperature_column="temperature_2m", + relative_humidity_column="relative_humidity_2m", + energy_price_column="EPEX_NL", +) + + +# Define hyperparameter search space +params = LGBMParameterSpace( + learning_rate=FloatDistribution(low=0.01, high=0.5, log=True), + num_leaves=IntDistribution(low=5, high=120), + max_depth=IntDistribution(low=1, high=3), + reg_lambda=0.2, +) + +# Set optimization goal +optimization_metric = OptimizationMetric( + metric=RCRPSSampleWeightedProvider(lower_quantile=0.01, upper_quantile=0.99), + direction_minimize=True, +) + +# Load target provider with historical data +target_provider = Liander2024TargetProvider( + data_dir=Path("../data/liander2024-energy-forecasting-benchmark"), +) + +# Create the backtest configuration +backtest_config = BacktestConfig( + prediction_sample_interval=timedelta(minutes=15), + predict_interval=timedelta(hours=6), + train_interval=timedelta(days=7), +) + +# Configure the backtest forecaster +backtest_forecaster_config = BacktestForecasterConfig( + requires_training=True, + predict_length=timedelta(hours=13), + predict_min_length=timedelta(hours=11), + predict_context_length=timedelta(days=14), # Context needed for lag features + predict_context_min_coverage=0.5, + training_context_length=timedelta(days=90), # Three months of training data + training_context_min_coverage=0.5, + predict_sample_interval=timedelta(minutes=15), +) + + +# Create the optimizer configuration +optimizer_config = OptimizerConfig( + base_config=base_config, + parameter_space=params, + backtest_config=backtest_config, + backtest_forecaster_config=backtest_forecaster_config, + optimization_metric=optimization_metric, + n_jobs=4, + n_trials=20, +) + +optimizer = BenchmarkOptimizer(config=optimizer_config) + +best_hyperparams = optimizer.optimize(experiment_name="Liander2024 Benchmark", target_provider=target_provider) + +msg = f"{forecaster_name} - Best hyperparameters found: {best_hyperparams}" +logger.info(msg) diff --git a/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py b/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py index b18773206..00c07bdb7 100644 --- a/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py +++ b/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py @@ -40,6 +40,7 @@ BENCHMARK_RESULTS_PATH_GBLINEAR = OUTPUT_PATH / "GBLinear" N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark + # Model configuration FORECAST_HORIZONS = [LeadTime.from_string("P3D")] # Forecast horizon(s) PREDICTION_QUANTILES = [ diff --git a/packages/openstef-beam/src/openstef_beam/benchmarking/baselines/openstef4.py b/packages/openstef-beam/src/openstef_beam/benchmarking/baselines/openstef4.py index d4e7cc355..ed3a04c51 100644 --- a/packages/openstef-beam/src/openstef_beam/benchmarking/baselines/openstef4.py +++ b/packages/openstef-beam/src/openstef_beam/benchmarking/baselines/openstef4.py @@ -14,15 +14,26 @@ from pydantic import Field, PrivateAttr from pydantic_extra_types.coordinate import Coordinate -from openstef_beam.backtesting.backtest_forecaster.mixins import BacktestForecasterConfig, BacktestForecasterMixin -from openstef_beam.backtesting.restricted_horizon_timeseries import RestrictedHorizonVersionedTimeSeries -from openstef_beam.benchmarking.benchmark_pipeline import BenchmarkContext, BenchmarkTarget, ForecasterFactory +from openstef_beam.backtesting.backtest_forecaster.mixins import ( + BacktestForecasterConfig, + BacktestForecasterMixin, +) +from openstef_beam.backtesting.restricted_horizon_timeseries import ( + RestrictedHorizonVersionedTimeSeries, +) +from openstef_beam.benchmarking.benchmark_pipeline import ( + BenchmarkContext, + BenchmarkTarget, + ForecasterFactory, +) from openstef_core.base_model import BaseConfig, BaseModel from openstef_core.datasets import TimeSeriesDataset from openstef_core.exceptions import FlatlinerDetectedError, NotFittedError from openstef_core.types import Q from openstef_models.presets import ForecastingWorkflowConfig -from openstef_models.workflows.custom_forecasting_workflow import CustomForecastingWorkflow +from openstef_models.workflows.custom_forecasting_workflow import ( + CustomForecastingWorkflow, +) class WorkflowCreationContext(BaseConfig): @@ -54,6 +65,10 @@ class OpenSTEF4BacktestForecaster(BaseModel, BacktestForecasterMixin): default=False, description="When True, saves intermediate input data for debugging", ) + contributions: bool = Field( + default=False, + description="When True, saves base Forecaster prediction contributions for ensemble models in cache_dir", + ) _workflow: CustomForecastingWorkflow | None = PrivateAttr(default=None) _is_flatliner_detected: bool = PrivateAttr(default=False) @@ -62,7 +77,7 @@ class OpenSTEF4BacktestForecaster(BaseModel, BacktestForecasterMixin): @override def model_post_init(self, context: Any) -> None: - if self.debug: + if self.debug or self.contributions: self.cache_dir.mkdir(parents=True, exist_ok=True) @property @@ -82,7 +97,9 @@ def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: # Extract the dataset for training training_data = data.get_window( - start=data.horizon - self.config.training_context_length, end=data.horizon, available_before=data.horizon + start=data.horizon - self.config.training_context_length, + end=data.horizon, + available_before=data.horizon, ) if self.debug: @@ -225,4 +242,8 @@ def create_openstef4_preset_backtest_forecaster( ) -__all__ = ["OpenSTEF4BacktestForecaster", "WorkflowCreationContext", "create_openstef4_preset_backtest_forecaster"] +__all__ = [ + "OpenSTEF4BacktestForecaster", + "WorkflowCreationContext", + "create_openstef4_preset_backtest_forecaster", +] diff --git a/packages/openstef-beam/src/openstef_beam/evaluation/evaluation_pipeline.py b/packages/openstef-beam/src/openstef_beam/evaluation/evaluation_pipeline.py index d7bbe7963..ee008f110 100644 --- a/packages/openstef-beam/src/openstef_beam/evaluation/evaluation_pipeline.py +++ b/packages/openstef-beam/src/openstef_beam/evaluation/evaluation_pipeline.py @@ -235,7 +235,7 @@ def _iterate_subsets( for lead_time in self.config.lead_times: predictions_filtered = predictions.filter_by_lead_time(lead_time=lead_time).select_version() if evaluation_mask is not None: - predictions_filtered = predictions_filtered.filter_index(evaluation_mask) + predictions_filtered = predictions_filtered.filter_index(evaluation_mask) # type: ignore # Remove target column from predictions to avoid duplication if target_column in predictions_filtered.data.columns: diff --git a/packages/openstef-beam/src/openstef_beam/parameter_tuning/__init__.py b/packages/openstef-beam/src/openstef_beam/parameter_tuning/__init__.py new file mode 100644 index 000000000..a84a19bde --- /dev/null +++ b/packages/openstef-beam/src/openstef_beam/parameter_tuning/__init__.py @@ -0,0 +1,5 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Parameter tuning module for OpenSTEF Beam.""" diff --git a/packages/openstef-beam/src/openstef_beam/parameter_tuning/models.py b/packages/openstef-beam/src/openstef_beam/parameter_tuning/models.py new file mode 100644 index 000000000..032902a4f --- /dev/null +++ b/packages/openstef-beam/src/openstef_beam/parameter_tuning/models.py @@ -0,0 +1,315 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Module defining parameter tuning models for OpenSTEF Beam. + +SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +""" + +from typing import Any, Literal, Self + +from optuna.trial import Trial +from pydantic import Field, model_validator + +from openstef_beam.evaluation.metric_providers import MetricProvider +from openstef_core.base_model import BaseConfig, BaseModel +from openstef_models.models.forecasting.forecaster import HyperParams +from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams +from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearHyperParams +from openstef_models.models.forecasting.xgboost_forecaster import XGBoostHyperParams + + +class OptimizationMetric(BaseModel): + """Defines the metric used for optimization during parameter tuning.""" + + metric: MetricProvider = Field(description="Name of the optimization metric.") + direction_minimize: bool = Field(description="Indicates if the metric should be minimized.") + + @property + def name(self) -> str: + """Get the name of the optimization metric. + + Returns: + The name of the metric. + """ + name = self.metric.__class__.__name__ + name_map = { + "RCRPSProvider": "rCRPS", + "RCRPSSampleWeightedProvider": "rCRPS_sample_weighted", + "RMAEProvider": "rMAE", + } + + return name_map[name] + + @model_validator(mode="after") + def validate_metric(self) -> Self: + """Validate that the metric name is present in the name_map. + + Returns: + Self: The validated OptimizationMetric instance. + + Raises: + KeyError: If the metric name is not recognized. + """ + try: + _ = self.name + except KeyError as e: + msg = f"Please add the metric name to the name_map: {self.metric.__class__.__name__}" + raise KeyError(msg) from e + + return self + + +class Distribution(BaseModel): + """Base class for different types of parameter distributions.""" + + +class CategoricalDistribution(Distribution): + """Categorical distribution for hyperparameter tuning.""" + + type: Literal["categorical"] = Field(default="categorical", description="Type of distribution (categorical).") + choices: list[Any] = Field(description="List of categorical values.") + + +class FloatDistribution(Distribution): + """Float distribution for hyperparameter tuning.""" + + type: Literal["float"] = Field(default="float", description="Type of distribution (float).") + low: float = Field(description="Lower bound of the distribution.") + high: float = Field(description="Upper bound of the distribution.") + log: bool = Field(default=False, description="Whether to sample on a logarithmic scale.") + step: float | None = Field(default=None, description="Step size for the distribution.") + + +class IntDistribution(Distribution): + """Integer distribution for hyperparameter tuning.""" + + type: Literal["int"] = Field(default="int", description="Type of distribution (int).") + low: int = Field(description="Lower bound of the distribution.") + high: int = Field(description="Upper bound of the distribution.") + log: bool = Field(default=False, description="Whether to sample on a logarithmic scale.") + step: int = Field(default=1, description="Step size for the distribution.") + + +DistributionOrParameter = Distribution | int | float | str | bool +FloatOrFloatDistribution = FloatDistribution | float +IntOrIntDistribution = IntDistribution | int +BoolOrCategoricalDistribution = CategoricalDistribution | bool +StrOrCategoricalDistribution = CategoricalDistribution | str + + +class ParameterSpace(BaseConfig): + """Defines a hyperparameter search space for tuning forecasting models.""" + + def items(self) -> list[tuple[str, DistributionOrParameter]]: + """Get an iterator over the parameter space items. + + Returns: + An iterator over (parameter name, Distribution) tuples. + """ + return list(self.__dict__.items()) + + @model_validator(mode="before") + @staticmethod + def check_distribution(value: dict[str, Any]) -> dict[str, DistributionOrParameter]: + """Validate that the value is an instance of Distribution. + + Args: + value: The value to validate. + + Returns: + The validated values. + + Raises: + TypeError: If the value is not an instance of Distribution. + """ + if not all(isinstance(v, DistributionOrParameter) for v in value.values()): + raise TypeError("All values in ParameterSpace must be instances of Distribution.") + + return value + + @model_validator(mode="after") + def check_parameters(self) -> Self: + """Validate that the value is an instance of Distribution. + + Returns: + Self: The validated ParameterSpace instance. + + Raises: + ValueError: If a parameter is not valid for the associated forecasting model. + """ + object_cls = self.__class__ + # Only run validate for subclasses + if object_cls == ParameterSpace: + return self + + default_params: HyperParams = object_cls.default_hyperparams() + hp_class = default_params.__class__ + + allowed_params: list[str] = list(hp_class.model_fields.keys()) + + for param, _val in iter(self): + if param not in allowed_params: + msg = ( + f"Parameter '{param}' is not valid for '{default_params.__name__}'. " + f"Allowed parameters are: {allowed_params}." + ) + raise ValueError(msg) + + return self + + @classmethod + def default_hyperparams(cls) -> HyperParams: + """Get default hyperparameters for the associated forecaster. + + Returns: + HyperParams instance. + """ + raise NotImplementedError("Subclasses must implement the default_hyperparams property.") + + @classmethod + def get_preset(cls, model: str) -> "ParameterSpace": + """Get a preset parameter space for a given model. + + Args: + model: The name of the model to get the preset for. + + Returns: + A ParameterSpace instance with preset hyperparameter distributions. + + Raises: + ValueError: If the model name is not recognized. + """ + if model not in {"lgbm", "xgboost", "gblinear", "lgbmlinear"}: + message = """ + Parameter space model must be one of 'lgbm', 'xgboost', 'gblinear', or 'lgbmlinear'.""" + raise ValueError(message) + + if model == "lgbm": + return LGBMParameterSpace() + if model == "xgboost": + return XGBoostParameterSpace() + if model == "gblinear": + return GBLinearParameterSpace() + if model == "lgbmlinear": + return LGBMLinearParameterSpace() + raise ValueError("Unreachable code reached.") + + def make_optuna(self, trial: Trial) -> dict[str, Any]: + """Convert the parameter space to an Optuna-compatible search space. + + Args: + trial: An Optuna trial object. + + Returns: + A dictionary representing the Optuna search space. + + Raises: + TypeError: If an unsupported distribution type is encountered. + """ + optuna_space: dict[str, str | float | int | None] = {} + for param_name, distribution_or_value in self.items(): + if isinstance(distribution_or_value, FloatDistribution): + optuna_space[param_name] = trial.suggest_float( + param_name, + distribution_or_value.low, + distribution_or_value.high, + log=distribution_or_value.log, + step=distribution_or_value.step, + ) + elif isinstance(distribution_or_value, IntDistribution): + optuna_space[param_name] = trial.suggest_int( + param_name, + distribution_or_value.low, + distribution_or_value.high, + log=distribution_or_value.log, + step=distribution_or_value.step, + ) + elif isinstance(distribution_or_value, CategoricalDistribution): + optuna_space[param_name] = trial.suggest_categorical(param_name, distribution_or_value.choices) + elif isinstance(distribution_or_value, (float, int, str)): + optuna_space[param_name] = distribution_or_value + else: + message = f"Unsupported type: {type(distribution_or_value)}" + raise TypeError(message) + return optuna_space + + +class LGBMParameterSpace(ParameterSpace): + """Preset parameter space for LGBM model.""" + + learning_rate: FloatOrFloatDistribution = Field(default=FloatDistribution(low=1e-3, high=0.3, log=True)) + num_leaves: IntOrIntDistribution = Field(default=IntDistribution(low=20, high=150)) + max_depth: IntOrIntDistribution = Field(default=IntDistribution(low=3, high=15)) + reg_lambda: FloatOrFloatDistribution = Field(default=FloatDistribution(low=1e-3, high=10.0, log=True)) + + @classmethod + def default_hyperparams(cls) -> HyperParams: + """Get default hyperparameters for the associated forecaster. + + Returns: + LGBMHyperParams instance. + """ + return LGBMHyperParams() + + +class LGBMLinearParameterSpace(ParameterSpace): + """Preset parameter space for LGBM Linear model.""" + + n_estimators: IntOrIntDistribution = Field(default=IntDistribution(low=3, high=500)) + learning_rate: FloatOrFloatDistribution = Field(default=FloatDistribution(low=1e-3, high=0.3, log=True)) + num_leaves: IntOrIntDistribution = Field(default=IntDistribution(low=3, high=150)) + max_depth: IntOrIntDistribution = Field(default=IntDistribution(low=1, high=5)) + reg_lambda: FloatOrFloatDistribution = Field(default=FloatDistribution(low=1e-3, high=10.0, log=True)) + colsample_bytree: FloatOrFloatDistribution = Field(default=FloatDistribution(low=0.5, high=1.0)) + max_bin: IntOrIntDistribution = Field(default=IntDistribution(low=10, high=256)) + min_data_in_leaf: IntOrIntDistribution = Field(default=IntDistribution(low=1, high=50)) + min_data_in_bin: IntOrIntDistribution = Field(default=IntDistribution(low=1, high=50)) + min_child_weight: FloatOrFloatDistribution = Field(default=FloatDistribution(low=1e-3, high=100.0, log=True)) + + @classmethod + def default_hyperparams(cls) -> HyperParams: + """Get default hyperparameters for the associated forecaster. + + Returns: + LGBMLinearHyperParams instance. + """ + return LGBMLinearHyperParams() + + +class XGBoostParameterSpace(ParameterSpace): + """Preset parameter space for XGBoost model.""" + + learning_rate: FloatOrFloatDistribution = Field(default=FloatDistribution(low=1e-3, high=0.3, log=True)) + max_depth: IntOrIntDistribution = Field(default=IntDistribution(low=2, high=15)) + n_estimators: IntOrIntDistribution = Field(default=IntDistribution(low=50, high=500)) + subsample: FloatOrFloatDistribution = Field(default=FloatDistribution(low=0.5, high=1.0)) + reg_lambda: FloatOrFloatDistribution = Field(default=FloatDistribution(low=1e-3, high=10.0, log=True)) + + @classmethod + def default_hyperparams(cls) -> HyperParams: + """Get default hyperparameters for the associated forecaster. + + Returns: + XGBoostHyperParams instance. + """ + return XGBoostHyperParams() + + +class GBLinearParameterSpace(ParameterSpace): + """Preset parameter space for GBLinear model.""" + + learning_rate: FloatOrFloatDistribution = Field(default=FloatDistribution(low=1e-3, high=0.3, log=True)) + n_steps: IntOrIntDistribution = Field(default=IntDistribution(low=50, high=500)) + reg_alpha: FloatOrFloatDistribution = Field(default=FloatDistribution(low=1e-5, high=10.0, log=True)) + + @classmethod + def default_hyperparams(cls) -> HyperParams: + """Get default hyperparameters for the associated forecaster. + + Returns: + GBLinearHyperParams instance. + """ + return GBLinearHyperParams() diff --git a/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/__init__.py b/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/__init__.py new file mode 100644 index 000000000..4d26e7f4b --- /dev/null +++ b/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/__init__.py @@ -0,0 +1,5 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Optimizer module for parameter tuning in OpenSTEF Beam.""" diff --git a/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/benchmark_optimizer.py b/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/benchmark_optimizer.py new file mode 100644 index 000000000..ce0a5da23 --- /dev/null +++ b/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/benchmark_optimizer.py @@ -0,0 +1,135 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Backtesting pipeline for evaluating energy forecasting models. + +Simulates realistic forecasting scenarios by replaying historical data with +proper temporal constraints. Executes prediction and retraining schedules +that mirror operational deployment conditions, ensuring evaluation results +accurately reflect real-world model performance. +""" + +import logging +from collections.abc import Callable +from functools import partial +from typing import Any + +from optuna.trial import Trial + +from openstef_beam.backtesting.backtest_pipeline import BacktestPipeline +from openstef_beam.benchmarking.target_provider import BenchmarkTarget, TargetProvider +from openstef_beam.parameter_tuning.optimizer.optimizer import OptimizerTrialContext, OptunaOptimizer +from openstef_core.datasets.versioned_timeseries_dataset import TimeSeriesDataset +from openstef_core.utils.multiprocessing import run_parallel +from openstef_models.models.forecasting.forecaster import HyperParams +from openstef_models.presets.forecasting_workflow import ( + ForecastingWorkflowConfig, +) + +logger = logging.getLogger(__name__) + + +class BenchmarkOptimizer(OptunaOptimizer): + """Optuna optimizer for optimizing multiple targets via the Benchmark Interface.""" + + def objective( + self, + trial: Trial, + target_provider: TargetProvider[BenchmarkTarget, Any], + score_for_target: Callable[[HyperParams, BenchmarkTarget], float], + n_jobs: int = 1, + ) -> float: + """Objective function for optimizing over multiple targets. + + Args: + trial: The Optuna trial object. + target_provider: The target provider to get targets, predictors and ground truth from. + score_for_target: Function to score a single target with given hyperparameters. + n_jobs: Number of parallel jobs to run. + + Returns: + The average metric score across all targets. + """ + # Draw hyperparameters from the parameter space + hyperparams = self.parameter_space.default_hyperparams().model_copy( + update=self.parameter_space.make_optuna(trial) + ) + + metrics: list[float] = [] + + if n_jobs > 1: + score_with_params = partial(score_for_target, hyperparams) + + metrics = run_parallel( + score_with_params, + items=target_provider.get_targets()[:4], + n_processes=n_jobs, + ) + else: + for target in target_provider.get_targets(): + score = score_for_target(hyperparams, target) + metrics.append(score) + + return sum(metrics) / len(metrics) + + @staticmethod + def _run_trial_for_target( + hyperparams: HyperParams, + target: BenchmarkTarget, + target_provider: TargetProvider[BenchmarkTarget, Any], + base_config: ForecastingWorkflowConfig, + backtest_maker: Callable[[OptimizerTrialContext], BacktestPipeline], + scoring_maker: Callable[[TimeSeriesDataset, TimeSeriesDataset], float], + ) -> float: + predictors = target_provider.get_predictors_for_target(target) + ground_truth = target_provider.get_measurements_for_target(target) + + trial_context = OptimizerTrialContext( + base_config=base_config, + hyperparams=hyperparams, + target=target, + ) + + backtest = backtest_maker(trial_context) + + predictions: TimeSeriesDataset = backtest.run( + predictors=predictors, + ground_truth=ground_truth, + start=target.benchmark_start, + end=target.benchmark_end, + ) + return scoring_maker(predictions, ground_truth.select_version()) + + def optimize(self, experiment_name: str, target_provider: TargetProvider[BenchmarkTarget, Any]) -> HyperParams: + """Optimize hyperparameters using Optuna over multiple targets. + + In this case we run the optimizer with a single job, parralielization happens at the target level. + + Args: + experiment_name: Name of the optimization experiment. + target_provider: The target provider to get targets, predictors and ground truth from. + + Returns: + The best hyperparameters found during optimization. + """ + target_backtest_factory = partial( + self._run_trial_for_target, + target_provider=target_provider, + base_config=self.base_config, + backtest_maker=self._make_backtest, + scoring_maker=self._score_predictions, + ) + + objective = partial( + self.objective, + target_provider=target_provider, + score_for_target=target_backtest_factory, + n_jobs=self.n_jobs, + ) + + return self._run_optimization_sequential( + objective=objective, + experiment_name=experiment_name, + n_trials=self.n_trials, + ) diff --git a/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/dataset_optimizer.py b/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/dataset_optimizer.py new file mode 100644 index 000000000..0406f9d3c --- /dev/null +++ b/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/dataset_optimizer.py @@ -0,0 +1,119 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Backtesting pipeline for evaluating energy forecasting models. + +Simulates realistic forecasting scenarios by replaying historical data with +proper temporal constraints. Executes prediction and retraining schedules +that mirror operational deployment conditions, ensuring evaluation results +accurately reflect real-world model performance. +""" + +import logging +from collections.abc import Callable +from functools import partial + +from optuna.trial import Trial + +from openstef_beam.backtesting.backtest_pipeline import BacktestPipeline +from openstef_beam.benchmarking.target_provider import BenchmarkTarget +from openstef_beam.parameter_tuning.optimizer.optimizer import OptimizerTrialContext, OptunaOptimizer +from openstef_core.datasets.versioned_timeseries_dataset import TimeSeriesDataset, VersionedTimeSeriesDataset +from openstef_models.models.forecasting.forecaster import HyperParams +from openstef_models.presets.forecasting_workflow import ( + Latitude, + Longitude, +) + +logger = logging.getLogger(__name__) + + +class DatasetOptimizer(OptunaOptimizer): + """Optuna optimizer aimed at optimizing individual datasets.""" + + def optimize( + self, + predictors: VersionedTimeSeriesDataset, + ground_truth: VersionedTimeSeriesDataset, + experiment_name: str, + ) -> HyperParams: + """Optimize hyperparameters using Optuna. + + Args: + predictors: The predictors dataset. + ground_truth: The ground truth dataset. + experiment_name: Name of the optimization experiment. + + Returns: + The best hyperparameters found during optimization. + """ + objective = partial( + self.objective, + backtest_maker=self._make_backtest, + scoring_function=self._score_predictions, + predictors=predictors, + ground_truth=ground_truth, + ) + + if self.n_jobs > 1: + return self._run_optimization_parallel( + experiment_name=experiment_name, objective=objective, n_trials=self.n_trials, n_jobs=self.n_jobs + ) + + return self._run_optimization_sequential( + objective=objective, + experiment_name=experiment_name, + n_trials=self.n_trials, + ) + + def objective( + self, + trial: Trial, + backtest_maker: Callable[[OptimizerTrialContext], BacktestPipeline], + scoring_function: Callable[[TimeSeriesDataset, TimeSeriesDataset], float], + predictors: VersionedTimeSeriesDataset, + ground_truth: VersionedTimeSeriesDataset, + ) -> float: + """Objective function for optimizing over a single dataset. + + Args: + trial: The Optuna trial object. + backtest_maker: Function to create a backtest pipeline for given hyperparameters. + scoring_function: Function to score predictions against ground truth. + predictors: The predictors dataset. + ground_truth: The ground truth dataset. + + Returns: + The score achieved with the given hyperparameters. + """ + # Convert to Optuna + params = self.parameter_space.make_optuna(trial) + + # Generate HyperParams + hyperparams = self.parameter_space.default_hyperparams().model_copy(update=params) + + trial_context = OptimizerTrialContext( + base_config=self.base_config, + hyperparams=hyperparams, + target=BenchmarkTarget( + name="single_dataset", + description="Dataset provided directly for optimization", + latitude=Latitude(0.0), + longitude=Longitude(0.0), + benchmark_start=ground_truth.index[0], + benchmark_end=ground_truth.index[-1], + train_start=ground_truth.index[0], + ), + ) + + backtest = backtest_maker(trial_context) + + predictions: TimeSeriesDataset = backtest.run( + predictors=predictors, + ground_truth=ground_truth, + start=ground_truth.index[0], + end=ground_truth.index[-1], + ) + + return scoring_function(predictions, ground_truth.select_version()) diff --git a/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/optimizer.py b/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/optimizer.py new file mode 100644 index 000000000..8d85d21e4 --- /dev/null +++ b/packages/openstef-beam/src/openstef_beam/parameter_tuning/optimizer/optimizer.py @@ -0,0 +1,320 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Backtesting pipeline for evaluating energy forecasting models. + +Simulates realistic forecasting scenarios by replaying historical data with +proper temporal constraints. Executes prediction and retraining schedules +that mirror operational deployment conditions, ensuring evaluation results +accurately reflect real-world model performance. +""" + +import logging +from abc import ABC, abstractmethod +from collections.abc import Callable +from functools import partial +from pathlib import Path +from typing import Any, Literal + +import optuna +import pandas as pd +from optuna.storages import JournalStorage +from optuna.storages.journal import JournalFileBackend +from optuna.study import Study +from optuna.trial import FrozenTrial +from pydantic import Field + +from openstef_beam.backtesting.backtest_forecaster.mixins import ( + BacktestForecasterConfig, +) +from openstef_beam.backtesting.backtest_pipeline import BacktestConfig, BacktestPipeline +from openstef_beam.benchmarking.baselines.openstef4 import ( + OpenSTEF4BacktestForecaster, + WorkflowCreationContext, +) +from openstef_beam.benchmarking.benchmarks.liander2024 import Liander2024TargetProvider +from openstef_beam.benchmarking.target_provider import BenchmarkTarget +from openstef_beam.parameter_tuning.models import ( + OptimizationMetric, + ParameterSpace, +) +from openstef_core.base_model import BaseConfig +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.datasets.versioned_timeseries_dataset import TimeSeriesDataset +from openstef_core.utils.multiprocessing import run_parallel +from openstef_models.models.forecasting.forecaster import HyperParams +from openstef_models.presets import create_forecasting_workflow +from openstef_models.presets.forecasting_workflow import ( + Coordinate, + ForecastingWorkflowConfig, + LocationConfig, +) +from openstef_models.workflows.custom_forecasting_workflow import ( + CustomForecastingWorkflow, +) + +logger = logging.getLogger(__name__) + + +class OptimizerConfig(BaseConfig): + """Configuration for the optimizer.""" + + parameter_space: ParameterSpace = Field(description="Parameter search space for optimization.") + + optimization_metric: OptimizationMetric = Field(description="Metric used for optimization during parameter tuning.") + + n_jobs: int = Field(default=1, description="Number of parallel jobs to run during optimization.") + n_trials: int = Field(default=100, description="Number of trials to run during optimization.") + timeout: int = Field(default=3600, description="Timeout in seconds for the optimization process.") + verbosity: Literal["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"] = Field( + default="INFO", description="Verbosity level for the optimizer." + ) + + backtest_config: BacktestConfig = Field(description="Backtesting configuration for the optimizer.") + + backtest_forecaster_config: BacktestForecasterConfig = Field( + description="Backtest forecaster configuration for the optimizer." + ) + + base_config: ForecastingWorkflowConfig = Field( + description="Base configuration for creating forecasting workflows during backtesting." + ) + + cache_dir: Path = Field( + default=Path("./optimizer_results/cache"), + description="Directory to use for caching model artifacts during backtesting", + ) + + +class OptimizerRunContext(BaseConfig): + """Context information for workflow execution within backtesting.""" + + base_config: ForecastingWorkflowConfig = Field( + description="Base configuration for creating forecasting workflows during backtesting." + ) + + parameter_space: ParameterSpace = Field(description="ParameterSpace for the optimization run") + + target_provider: Liander2024TargetProvider = Field(description="Target provider") + + +class OptimizerTrialContext(BaseConfig): + """Context information for workflow execution within backtesting.""" + + base_config: ForecastingWorkflowConfig = Field( + description="Base configuration for creating forecasting workflows during backtesting." + ) + + hyperparams: HyperParams = Field(description="Hyperparameters for the current optimization trial.") + + target: BenchmarkTarget = Field(description="Benchmark target for the current optimization trial.") + + +class OptunaOptimizer(ABC): + """Optimizer using Optuna for hyperparameter tuning.""" + + def __init__(self, config: OptimizerConfig): + """Initialize the Optuna optimizer. + + Args: + config: Configuration for the optimizer. + """ + self.base_config = config.base_config + self.backtest_config = config.backtest_config + self.backtest_forecaster_config = config.backtest_forecaster_config + + self.n_trials = config.n_trials + self.n_jobs = config.n_jobs + + self.cache_dir = config.cache_dir + + self.horizons = config.base_config.horizons + self.quantiles = config.base_config.quantiles + + logger.setLevel(config.verbosity) + + # Parameter space + self.parameter_space: ParameterSpace = config.parameter_space + + self.direction = "minimize" if config.optimization_metric.direction_minimize else "maximize" + self.metric = config.optimization_metric.metric + self.metric_name: str = config.optimization_metric.name + + @abstractmethod + def optimize(self, *args: Any, **kwargs: Any) -> HyperParams: + """Optimize hyperparameters using Optuna. + + Args: + *args: Positional arguments. + **kwargs: Keyword arguments. + + Returns: + The best hyperparameters found during optimization. + """ + msg = "Subclasses must implement the optimize method." + raise NotImplementedError(msg) + + @abstractmethod + def objective(self, *args: Any, **kwargs: Any) -> float: + """Objective function for optimizing hyperparameters. + + Args: + *args: Positional arguments. + **kwargs: Keyword arguments. + + Returns: + The metric value as a float. + """ + msg = "Subclasses must implement the objective method." + raise NotImplementedError(msg) + + def _score_predictions( + self, predictions: ForecastDataset | TimeSeriesDataset, ground_truth: TimeSeriesDataset + ) -> float: + """Score the predictions using the a pre-set optimization metric. + + Args: + predictions: The predictions made by the forecasting model. + ground_truth: The ground truth dataset. + + Returns: + The metric value as a float. + """ + target = ForecastInputDataset.from_timeseries( + dataset=ground_truth, + target_column="load", + ) + target = target.pipe_pandas(pd.DataFrame.dropna) # type: ignore + + predictions_selected = predictions.select_version() + + # Remove target column from predictions to avoid duplication + if "load" in predictions_selected.data.columns: + predictions_selected = predictions_selected.pipe_pandas(lambda df: df.drop(columns=["load"])) + + final_set = ForecastDataset( + data=target.data.join(predictions_selected.data, how="inner"), + sample_interval=predictions.sample_interval, + target_column="load", + ).filter_quantiles(quantiles=self.quantiles) + + metric_value = self.metric(final_set) + + if "global" in metric_value: + metric_value = metric_value["global"] + + return float(metric_value[self.metric_name]) # type: ignore + + @staticmethod + def logger_callback(study: Study, trial: FrozenTrial) -> None: + """Log the progress of the optimization study. To be used as an Optuna callback.""" + logger.info("Current value: %s, Current params: %s", trial.value, trial.params) + logger.info( + "Best value: %s, Best params: %s", + study.best_value, + study.best_trial.params, + ) + + @staticmethod + def _run_optimization_job( + _: int, config: dict[str, Any], objective: Callable[..., float], logger_callback: Callable[..., None] + ) -> None: + study = optuna.create_study( + study_name=config["experiment_name"], + storage=JournalStorage(JournalFileBackend(file_path=config["log_path"])), + load_if_exists=True, + direction=config["direction"], + ) + study.optimize( + objective, + timeout=3600, + n_jobs=1, + n_trials=config["trials_per_job"], + callbacks=[logger_callback], + ) + + def _run_optimization_parallel( + self, experiment_name: str, objective: Callable[..., float], n_trials: int, n_jobs: int + ) -> HyperParams: + path = f"./optimization_results/{experiment_name}_journal.log" + + trials_per_job = int(n_trials / n_jobs) + 1 + + run_parallel( + partial( + self._run_optimization_job, + config={ + "experiment_name": experiment_name, + "direction": self.direction, + "trials_per_job": trials_per_job, + "log_path": path, + }, + objective=objective, + logger_callback=self.logger_callback, + ), + items=iter(range(trials_per_job)), + n_processes=n_jobs, + ) + study = optuna.load_study( + study_name=experiment_name, + storage=JournalStorage(JournalFileBackend(file_path=path)), + ) + + return self.parameter_space.default_hyperparams().model_copy(update=study.best_trial.params) + + def _run_optimization_sequential( + self, experiment_name: str, objective: Callable[..., float], n_trials: int + ) -> HyperParams: + path = f"./optimization_results/{experiment_name}_journal.log" + + study = optuna.create_study( + study_name=experiment_name, + direction=self.direction, + storage=JournalStorage(JournalFileBackend(file_path=path)), + ) + study.optimize(objective, timeout=3600, n_trials=n_trials, callbacks=[self.logger_callback]) + + return self.parameter_space.default_hyperparams().model_copy(update=study.best_trial.params) + + def _make_backtest(self, context: OptimizerTrialContext) -> BacktestPipeline: + forecaster = self._make_backtest_forecaster(context=context) + + return BacktestPipeline( + forecaster=forecaster, + config=self.backtest_config, + ) + + def _make_backtest_forecaster(self, context: OptimizerTrialContext) -> OpenSTEF4BacktestForecaster: + + prefix = "test" + target = context.target + base_config = context.base_config + hyperparams = context.hyperparams + + def _create_workflow(context: WorkflowCreationContext) -> CustomForecastingWorkflow: + # Create a new workflow instance with fresh model. + return create_forecasting_workflow( + config=base_config.model_copy( + update={ + "model_id": f"{prefix}_{target.name}", + "run_name": context.step_name, + "location": LocationConfig( + name=target.name, + description=target.description, + coordinate=Coordinate( + latitude=target.latitude, + longitude=target.longitude, + ), + ), + f"{base_config.model}_hyperparams": hyperparams, + } + ) + ) + + return OpenSTEF4BacktestForecaster( + config=self.backtest_forecaster_config, + workflow_factory=_create_workflow, + cache_dir=self.cache_dir, + debug=False, + ) diff --git a/packages/openstef-beam/tests/unit/benchmarking/test_benchmark_pipeline.py b/packages/openstef-beam/tests/unit/benchmarking/test_benchmark_pipeline.py index fb73e9f0c..a6f56f07a 100644 --- a/packages/openstef-beam/tests/unit/benchmarking/test_benchmark_pipeline.py +++ b/packages/openstef-beam/tests/unit/benchmarking/test_benchmark_pipeline.py @@ -23,6 +23,7 @@ from openstef_beam.evaluation import EvaluationConfig, EvaluationReport, EvaluationSubsetReport, SubsetMetric from openstef_core.datasets import ForecastDataset, TimeSeriesDataset, VersionedTimeSeriesDataset from openstef_core.types import AvailableAt +from openstef_models.presets.forecasting_workflow import Latitude, Longitude from tests.utils.mocks import MockForecaster, MockMetricsProvider, MockTargetProvider if TYPE_CHECKING: @@ -37,8 +38,8 @@ def test_targets() -> list[BenchmarkTarget]: BenchmarkTarget( name="location1", description="Test location 1", - latitude=52.0, - longitude=5.0, + latitude=Latitude(52.0), + longitude=Longitude(5.0), limit=100.0, benchmark_start=datetime.fromisoformat("2023-01-15"), benchmark_end=datetime.fromisoformat("2023-02-15"), @@ -47,8 +48,8 @@ def test_targets() -> list[BenchmarkTarget]: BenchmarkTarget( name="location2", description="Test location 2", - latitude=53.0, - longitude=6.0, + latitude=Latitude(53.0), + longitude=Longitude(6.0), limit=200.0, benchmark_start=datetime.fromisoformat("2023-01-15"), benchmark_end=datetime.fromisoformat("2023-02-15"), diff --git a/packages/openstef-beam/tests/unit/parameter_tuning/__init__.py b/packages/openstef-beam/tests/unit/parameter_tuning/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-beam/tests/unit/parameter_tuning/optimizer/__init__.py b/packages/openstef-beam/tests/unit/parameter_tuning/optimizer/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-beam/tests/unit/parameter_tuning/optimizer/test_benchmark_optimizer.py b/packages/openstef-beam/tests/unit/parameter_tuning/optimizer/test_benchmark_optimizer.py new file mode 100644 index 000000000..14ee8c069 --- /dev/null +++ b/packages/openstef-beam/tests/unit/parameter_tuning/optimizer/test_benchmark_optimizer.py @@ -0,0 +1,305 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from datetime import datetime, timedelta +from typing import Any, override + +import pandas as pd +import pytest +from pydantic import ConfigDict, Field + +from openstef_beam.backtesting import BacktestConfig +from openstef_beam.backtesting.backtest_forecaster import BacktestForecasterConfig +from openstef_beam.benchmarking import BenchmarkTarget +from openstef_beam.benchmarking.target_provider import TargetProvider +from openstef_beam.evaluation.metric_providers import ( + MetricProvider, + RCRPSProvider, + RCRPSSampleWeightedProvider, + RMAEProvider, +) +from openstef_beam.evaluation.models.subset import QuantileMetricsDict +from openstef_beam.parameter_tuning.models import ( + CategoricalDistribution, + FloatDistribution, + IntDistribution, + OptimizationMetric, + ParameterSpace, +) +from openstef_beam.parameter_tuning.optimizer.benchmark_optimizer import BenchmarkOptimizer +from openstef_beam.parameter_tuning.optimizer.optimizer import OptimizerConfig +from openstef_core.datasets import ForecastDataset, VersionedTimeSeriesDataset +from openstef_core.mixins.predictor import HyperParams +from openstef_core.types import LeadTime, Quantile +from openstef_models.presets import ForecastingWorkflowConfig +from openstef_models.presets.forecasting_workflow import Latitude, Longitude + + +class MockHyperParams(HyperParams): + learning_rate: float = Field(default=0.1) + n_estimators: int = Field(default=100) + booster: str = Field(default="gbtree") + + +class MockParameterSpace(ParameterSpace): + learning_rate: FloatDistribution = Field(default=FloatDistribution(low=0.01, high=0.2)) + n_estimators: IntDistribution = Field(default=IntDistribution(low=50, high=200)) + booster: CategoricalDistribution = Field(default=CategoricalDistribution(choices=["gbtree", "dart"])) + + @classmethod + def default_hyperparams(cls) -> MockHyperParams: + return MockHyperParams() + + +# Importing from utils.mocks raised, Error. Hence duplicate code +class MockMetricsProvider(MetricProvider): + """Mock implementation of a MetricProvider for testing purposes.""" + + mocked_result: QuantileMetricsDict + + def __call__(self, subset: ForecastDataset) -> QuantileMetricsDict: + return self.mocked_result + + +class MockTargetProvider(TargetProvider[BenchmarkTarget, None]): + """Test implementation of TargetProvider for testing.""" + + model_config = ConfigDict( + arbitrary_types_allowed=True, + strict=False, + ) + + targets: list[BenchmarkTarget] + measurements: VersionedTimeSeriesDataset + predictors: VersionedTimeSeriesDataset + metrics: list[MetricProvider] + + @override + def get_targets(self, filter_args: Any = None) -> list[BenchmarkTarget]: + targets = self.targets + if filter_args is not None: + targets = [t for t in targets if t.name == filter_args.name] + return targets + + @override + def get_measurements_for_target(self, target: BenchmarkTarget) -> VersionedTimeSeriesDataset: + return self.measurements + + @override + def get_predictors_for_target(self, target: BenchmarkTarget) -> VersionedTimeSeriesDataset: + return self.predictors + + @override + def get_metrics_for_target(self, target: BenchmarkTarget) -> list[MetricProvider]: + return self.metrics + + @override + def get_evaluation_mask_for_target(self, target: BenchmarkTarget) -> pd.DatetimeIndex | None: + return None + + +# Test fixtures +@pytest.fixture +def targets() -> list[BenchmarkTarget]: + """Create test targets with realistic date ranges.""" + return [ + BenchmarkTarget( + name="location1", + description="Test location 1", + latitude=Latitude(52.0), + longitude=Longitude(5.0), + limit=100.0, + benchmark_start=datetime.fromisoformat("2023-01-15"), + benchmark_end=datetime.fromisoformat("2023-02-15"), + train_start=datetime.fromisoformat("2023-01-01"), + ), + BenchmarkTarget( + name="location2", + description="Test location 2", + latitude=Latitude(53.0), + longitude=Longitude(6.0), + limit=200.0, + benchmark_start=datetime.fromisoformat("2023-01-15"), + benchmark_end=datetime.fromisoformat("2023-02-15"), + train_start=datetime.fromisoformat("2023-01-01"), + ), + ] + + +@pytest.fixture +def datasets(targets: list[BenchmarkTarget]) -> tuple[VersionedTimeSeriesDataset, VersionedTimeSeriesDataset]: + """Create test datasets with timestamps aligned to target benchmark periods.""" + # Create datasets covering the entire benchmark period plus some training data + start_date = min(t.train_start for t in targets) + end_date = max(t.benchmark_end for t in targets) + # Use a 1-hour interval for consistency with evaluation requirements + sample_interval = timedelta(hours=1) + timestamps = pd.date_range(start_date, end_date, freq="1h") + + # Create measurements dataset with value column and necessary metadata + measurements = VersionedTimeSeriesDataset.from_dataframe( + data=pd.DataFrame( + { + "value": range(len(timestamps)), + "available_at": timestamps, # Make available immediately for simplicity + }, + index=timestamps, + ), + sample_interval=sample_interval, + ) + + # Create predictors dataset with the same sample interval + predictors = VersionedTimeSeriesDataset.from_dataframe( + data=pd.DataFrame( + { + "feature1": range(len(timestamps)), + "feature2": range(len(timestamps), 2 * len(timestamps)), + "available_at": timestamps, # Make available immediately for simplicity + }, + index=timestamps, + ), + sample_interval=sample_interval, + ) + + return measurements, predictors + + +@pytest.fixture +def quantiles() -> list[Quantile]: + """Provide common quantiles for testing.""" + return [Quantile(0.1), Quantile(0.5), Quantile(0.9)] + + +@pytest.fixture +def horizons() -> list[LeadTime]: + """Provide common lead times for testing.""" + return [LeadTime.from_string("PT12H")] + + +@pytest.fixture +def forecaster_config() -> BacktestForecasterConfig: + """Create a realistic forecaster config with all required fields.""" + return BacktestForecasterConfig( + requires_training=True, + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=1), + predict_context_length=timedelta(hours=48), + predict_context_min_coverage=0.8, + training_context_length=timedelta(days=14), + training_context_min_coverage=0.8, + predict_sample_interval=timedelta(hours=1), + # The quantiles property is provided by the base class, no need to specify here + ) + + +@pytest.fixture +def backtest_config() -> BacktestConfig: + """Create a realistic backtest config.""" + return BacktestConfig( + prediction_sample_interval=timedelta(hours=1), + predict_interval=timedelta(hours=6), + train_interval=timedelta(days=7), + ) + + +@pytest.fixture(params=["wrcrps", "rmae", "rcrps"]) +def optimization_metric(request: pytest.FixtureRequest) -> OptimizationMetric: + """Create different optimization metrics for testing.""" + metric = request.param + if metric == "wrcrps": + provider = RCRPSSampleWeightedProvider(lower_quantile=0.01, upper_quantile=0.99) + elif metric == "rmae": + provider = RMAEProvider() + else: + provider = RCRPSProvider(lower_quantile=0.01, upper_quantile=0.99) + return OptimizationMetric(metric=provider, direction_minimize=True) + + +@pytest.fixture +def workflow_config(horizons: list[LeadTime], quantiles: list[Quantile]) -> ForecastingWorkflowConfig: + + return ForecastingWorkflowConfig( + model_id="common_model_", + run_name=None, + model="flatliner", + horizons=horizons, + quantiles=quantiles, + model_reuse_enable=True, + mlflow_storage=None, + radiation_column="shortwave_radiation", + rolling_aggregate_features=["mean", "median", "max", "min"], + wind_speed_column="wind_speed_80m", + pressure_column="surface_pressure", + temperature_column="temperature_2m", + relative_humidity_column="relative_humidity_2m", + energy_price_column="EPEX_NL", + ) + + +@pytest.fixture(params=[1, 4]) +def n_jobs(request: pytest.FixtureRequest) -> int: + """Parameterize tests with different numbers of jobs.""" + return request.param + + +@pytest.fixture +def optimizer_config( + n_jobs: int, + backtest_config: BacktestConfig, + forecaster_config: BacktestForecasterConfig, + workflow_config: ForecastingWorkflowConfig, +) -> OptimizerConfig: + + return OptimizerConfig( + n_trials=5, + n_jobs=n_jobs, + parameter_space=MockParameterSpace(), + optimization_metric=OptimizationMetric( + metric=RCRPSSampleWeightedProvider(lower_quantile=0.01, upper_quantile=0.99), + direction_minimize=True, + ), + backtest_config=backtest_config, + backtest_forecaster_config=forecaster_config, + base_config=workflow_config, + ) + + +@pytest.fixture +def optimizer(optimizer_config: OptimizerConfig) -> BenchmarkOptimizer: + """Create a BenchmarkOptimizer with a simple config.""" + return BenchmarkOptimizer(config=optimizer_config) + + +@pytest.fixture +def target_provider( + targets: list[BenchmarkTarget], datasets: tuple[VersionedTimeSeriesDataset, VersionedTimeSeriesDataset] +) -> MockTargetProvider: + measurements, predictors = datasets + # Set up mock metrics + mock_metrics: list[MetricProvider] = [ + MockMetricsProvider( + mocked_result={ + "global": { + "rmae": 0.5, + } + } + ) + ] + + # Set up target provider with test data + return MockTargetProvider( + targets=targets, + measurements=measurements, + predictors=predictors, + metrics=mock_metrics, + ) + + +def test_benchmark_optimizer_end_to_end( + target_provider: MockTargetProvider, + optimizer: BenchmarkOptimizer, +): + """End-to-end test of the BenchmarkOptimizer with mock data and callbacks.""" + + assert True # Placeholder assertion to indicate test ran successfully diff --git a/packages/openstef-beam/tests/unit/parameter_tuning/test_models.py b/packages/openstef-beam/tests/unit/parameter_tuning/test_models.py new file mode 100644 index 000000000..78e6a911d --- /dev/null +++ b/packages/openstef-beam/tests/unit/parameter_tuning/test_models.py @@ -0,0 +1,117 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +import pytest +from pydantic import Field + +from openstef_beam.evaluation.metric_providers import MetricProvider, RCRPSSampleWeightedProvider +from openstef_beam.parameter_tuning.models import ( + CategoricalDistribution, + FloatDistribution, + GBLinearParameterSpace, + IntDistribution, + LGBMLinearParameterSpace, + LGBMParameterSpace, + OptimizationMetric, + ParameterSpace, + XGBoostParameterSpace, +) +from openstef_core.mixins.predictor import HyperParams + + +def test_optimization_metric_init(): + """Test that OptimizationMetric can be initialized with valid metric provider and direction.""" + + metric_provider = RCRPSSampleWeightedProvider(lower_quantile=0.01, upper_quantile=0.99) + optimization_metric = OptimizationMetric( + metric=metric_provider, + direction_minimize=True, + ) + + assert optimization_metric.metric == metric_provider + assert optimization_metric.direction_minimize is True + + +def test_optization_metric_unknown(): + """Test that OptimizationMetric raises ValueError for unknown metric provider.""" + + class UnknownMetricProvider(MetricProvider): + pass + + unknown_provider = UnknownMetricProvider() + + with pytest.raises(KeyError): + OptimizationMetric( + metric=unknown_provider, + direction_minimize=True, + ) + + +def test_parameter_space_init(): + """Test that ParameterSpace can be initialized with valid Distribution instances.""" + + class MockHyperParams(HyperParams): + learning_rate: float = Field(default=0.1) + n_estimators: int = Field(default=100) + booster: str = Field(default="gbtree") + + class MockParameterSpace(ParameterSpace): + learning_rate: FloatDistribution = Field(...) + n_estimators: IntDistribution = Field(...) + booster: CategoricalDistribution = Field(...) + + @classmethod + def default_hyperparams(cls) -> MockHyperParams: + return MockHyperParams() + + param_space = MockParameterSpace( + learning_rate=FloatDistribution(low=0.01, high=0.1), + n_estimators=IntDistribution(low=50, high=200), + booster=CategoricalDistribution(choices=["gbtree", "dart"]), + ) + + assert isinstance(param_space.learning_rate, FloatDistribution) + assert isinstance(param_space.n_estimators, IntDistribution) + assert isinstance(param_space.booster, CategoricalDistribution) + + +def test_lgbm_parameter_space_preset(): + """Test that LGBMParameterSpace preset returns correct distributions.""" + + lgbm_params = LGBMParameterSpace() + + assert isinstance(lgbm_params.learning_rate, FloatDistribution) + assert isinstance(lgbm_params.num_leaves, IntDistribution) + assert isinstance(lgbm_params.max_depth, IntDistribution) + assert isinstance(lgbm_params.reg_lambda, FloatDistribution) + + +def test_lgbm_linear_parameter_space_preset(): + """Test that LGBMLinearParameterSpace preset returns correct distributions.""" + + lgbm_linear_params = LGBMLinearParameterSpace() + + assert isinstance(lgbm_linear_params.learning_rate, FloatDistribution) + assert isinstance(lgbm_linear_params.num_leaves, IntDistribution) + assert isinstance(lgbm_linear_params.max_depth, IntDistribution) + assert isinstance(lgbm_linear_params.reg_lambda, FloatDistribution) + + +def test_gblinear_parameter_space_preset(): + """Test that GBLinearParameterSpace preset returns correct distributions.""" + + gblinear_params = GBLinearParameterSpace() + + assert isinstance(gblinear_params.learning_rate, FloatDistribution) + assert isinstance(gblinear_params.reg_alpha, FloatDistribution) + + +def test_xgboost_parameter_space_preset(): + """Test that XGBoostParameterSpace preset returns correct distributions.""" + + xgboost_params = XGBoostParameterSpace() + + assert isinstance(xgboost_params.learning_rate, FloatDistribution) + assert isinstance(xgboost_params.max_depth, IntDistribution) + assert isinstance(xgboost_params.subsample, FloatDistribution) diff --git a/packages/openstef-core/src/openstef_core/datasets/versioned_timeseries_dataset.py b/packages/openstef-core/src/openstef_core/datasets/versioned_timeseries_dataset.py index e430e4a49..930cd254e 100644 --- a/packages/openstef-core/src/openstef_core/datasets/versioned_timeseries_dataset.py +++ b/packages/openstef-core/src/openstef_core/datasets/versioned_timeseries_dataset.py @@ -190,7 +190,7 @@ def read_parquet( parts: list[TimeSeriesDataset] = [ TimeSeriesDataset( - data=df.loc[df.part_id == i, part_info["columns"]], + data=df.loc[df.part_id == i, part_info["columns"]], # type: ignore sample_interval=timedelta_from_isoformat(part_info.get("sample_interval", "PT1H")), ) for i, part_info in enumerate(parts_metadata) diff --git a/packages/openstef-models/pyproject.toml b/packages/openstef-models/pyproject.toml index 1b2f9d54b..0c3459fb0 100644 --- a/packages/openstef-models/pyproject.toml +++ b/packages/openstef-models/pyproject.toml @@ -14,9 +14,7 @@ description = "Core models for OpenSTEF" readme = "README.md" keywords = [ "energy", "forecasting", "machinelearning" ] license = "MPL-2.0" -authors = [ - { name = "Alliander N.V", email = "openstef@lfenergy.org" }, -] +authors = [ { name = "Alliander N.V", email = "openstef@lfenergy.org" } ] requires-python = ">=3.12,<4.0" classifiers = [ "Development Status :: 5 - Production/Stable", diff --git a/packages/openstef-models/src/openstef_models/explainability/mixins.py b/packages/openstef-models/src/openstef_models/explainability/mixins.py index dda56059b..2e1fa81ca 100644 --- a/packages/openstef-models/src/openstef_models/explainability/mixins.py +++ b/packages/openstef-models/src/openstef_models/explainability/mixins.py @@ -13,6 +13,7 @@ import pandas as pd import plotly.graph_objects as go +from openstef_core.datasets.validated_datasets import ForecastInputDataset from openstef_core.types import Q, Quantile from openstef_models.explainability.plotters.feature_importance_plotter import FeatureImportancePlotter @@ -44,6 +45,19 @@ def feature_importances(self) -> pd.DataFrame: """ raise NotImplementedError + @abstractmethod + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> pd.DataFrame: + """Get feature contributions for each prediction. + + Args: + data: Input dataset for which to compute feature contributions. + scale: Whether to scale contributions to sum to the prediction value. + + Returns: + DataFrame with contributions per feature. + """ + raise NotImplementedError + def plot_feature_importances(self, quantile: Quantile = Q(0.5)) -> go.Figure: """Create interactive treemap visualization of feature importances. diff --git a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py index 78f4abb4a..01dde236e 100644 --- a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py +++ b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py @@ -19,7 +19,9 @@ from openstef_beam.evaluation.metric_providers import MetricDirection from openstef_core.base_model import BaseConfig from openstef_core.datasets.timeseries_dataset import TimeSeriesDataset -from openstef_core.datasets.versioned_timeseries_dataset import VersionedTimeSeriesDataset +from openstef_core.datasets.versioned_timeseries_dataset import ( + VersionedTimeSeriesDataset, +) from openstef_core.exceptions import ( MissingColumnsError, ModelNotFoundError, @@ -134,7 +136,11 @@ def on_fit_end( fig.write_html(data_path / "feature_importances.html") # pyright: ignore[reportUnknownMemberType] # Store the trained model - self.storage.save_run_model(model_id=context.workflow.model_id, run_id=run_id, model=context.workflow.model) + self.storage.save_run_model( + model_id=context.workflow.model_id, + run_id=run_id, + model=context.workflow.model, + ) self._logger.info("Stored trained model for run %s", run_id) # Format the metrics for MLflow @@ -151,7 +157,9 @@ def on_fit_end( @override def on_predict_start( - self, context: WorkflowContext[CustomForecastingWorkflow], data: VersionedTimeSeriesDataset | TimeSeriesDataset + self, + context: WorkflowContext[CustomForecastingWorkflow], + data: VersionedTimeSeriesDataset | TimeSeriesDataset, ): if context.workflow.model.is_fitted: return @@ -175,7 +183,11 @@ def on_predict_start( return context.workflow.model = old_model - self._logger.info("Loaded model from MLflow run %s for model %s", run_id, context.workflow.model_id) + self._logger.info( + "Loaded model from MLflow run %s for model %s", + run_id, + context.workflow.model_id, + ) def _run_model_selection(self, workflow: CustomForecastingWorkflow, result: ModelFitResult) -> None: # Find the latest successful run for this model diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/base_case_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/base_case_forecaster.py index 215e85344..4b021d2b3 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/base_case_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/base_case_forecaster.py @@ -189,6 +189,23 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + @override + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = True) -> pd.DataFrame: + """Generate feature contributions. + + Args: + data: The forecast input dataset containing target variable history. + scale: Whether to scale contributions to sum to 1. Defaults to True. + + Returns: + pd.DataFrame containing the prediction contributions. + """ + return pd.DataFrame( + data=1.0, + index=data.index, + columns=["load_" + quantile.format() for quantile in self.config.quantiles], + ) + @property @override def feature_importances(self) -> pd.DataFrame: diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/constant_median_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/constant_median_forecaster.py index 9461cdddb..e516472a2 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/constant_median_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/constant_median_forecaster.py @@ -141,3 +141,20 @@ def feature_importances(self) -> pd.DataFrame: index=["load"], columns=[quantile.format() for quantile in self.config.quantiles], ) + + @override + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = True) -> pd.DataFrame: + """Generate feature contributions. + + Args: + data: The forecast input dataset containing target variable history. + scale: Whether to scale contributions to sum to 1. Defaults to True. + + Returns: + pd.DataFrame containing the prediction contributions. + """ + return pd.DataFrame( + data=1.0, + index=data.index, + columns=["load_" + quantile.format() for quantile in self.config.quantiles], + ) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py index fa7f141d3..e4ab21437 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py @@ -117,3 +117,13 @@ def feature_importances(self) -> pd.DataFrame: index=["load"], columns=[quantile.format() for quantile in self.config.quantiles], ) + + @override + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = True) -> pd.DataFrame: + + forecast_index = data.create_forecast_range(horizon=self.config.max_horizon) + + return pd.DataFrame( + data={quantile.format(): 0.0 for quantile in self.config.quantiles}, + index=forecast_index, + ) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py index 74c904364..f08dc4269 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py @@ -272,7 +272,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None raise InputValidationError("The input data is empty after dropping NaN values.") # Fit the scalers - self._target_scaler.fit(data.target_series.to_frame()) + self._target_scaler.fit(data.target_series.to_frame().to_numpy()) # Prepare training data input_data, target, sample_weight = self._prepare_fit_input(data) @@ -326,6 +326,47 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = True) -> pd.DataFrame: + """Get feature contributions for each prediction. + + Args: + data: Input dataset for which to compute feature contributions. + scale: If True, scale contributions to sum to 1.0 per quantile. + + Returns: + DataFrame with contributions per feature. + """ + # Get input features for prediction + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + xgb_input: xgb.DMatrix = xgb.DMatrix(data=input_data) + + # Generate predictions + booster = self._gblinear_model.get_booster() + predictions_array: np.ndarray = booster.predict(xgb_input, pred_contribs=True, strict_shape=True)[:, :, :-1] + + # Remove last column + contribs = predictions_array / np.sum(predictions_array, axis=-1, keepdims=True) + + # Flatten to 2D array, name columns accordingly + contribs = contribs.reshape(contribs.shape[0], -1) + df = pd.DataFrame( + data=contribs, + index=input_data.index, + columns=[ + f"{feature}_{quantile.format()}" for feature in input_data.columns for quantile in self.config.quantiles + ], + ) + + if scale: + # Scale contributions so that they sum to 1.0 per quantile and are positive + for q in self.config.quantiles: + quantile_cols = [col for col in df.columns if col.endswith(f"_{q.format()}")] + row_sums = df[quantile_cols].abs().sum(axis=1) + df[quantile_cols] = df[quantile_cols].abs().div(row_sums, axis=0) + + # Construct DataFrame with appropriate quantile columns + return df + @property @override def feature_importances(self) -> pd.DataFrame: diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py index 03c667b00..3e51db120 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py @@ -56,13 +56,13 @@ class LGBMHyperParams(HyperParams): "increase training time and risk overfitting.", ) learning_rate: float = Field( - default=0.49, # 0.3 + default=0.3, alias="eta", description="Step size shrinkage used to prevent overfitting. Range: [0,1]. Lower values require " "more boosting rounds.", ) max_depth: int = Field( - default=2, # 8, + default=5, description="Maximum depth of trees. Higher values capture more complex patterns but risk " "overfitting. Range: [1,∞]", ) @@ -93,7 +93,7 @@ class LGBMHyperParams(HyperParams): # Tree Structure Control num_leaves: int = Field( - default=100, # 31 + default=31, description="Maximum number of leaves. 0 means no limit. Only relevant when grow_policy='lossguide'.", ) @@ -265,7 +265,9 @@ def is_fitted(self) -> bool: return self._lgbm_model.is_fitted @staticmethod - def _prepare_fit_input(data: ForecastInputDataset) -> tuple[pd.DataFrame, np.ndarray, pd.Series]: + def _prepare_fit_input( + data: ForecastInputDataset, + ) -> tuple[pd.DataFrame, np.ndarray, pd.Series]: input_data: pd.DataFrame = data.input_data() target: np.ndarray = np.asarray(data.target_series.values) sample_weight: pd.Series = data.sample_weight_series @@ -312,6 +314,43 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> pd.DataFrame: + """Get feature contributions for each prediction. + + Args: + data: Input dataset for which to compute feature contributions. + scale: If True, scale contributions to sum to 1.0 per quantile. + + Returns: + DataFrame with contributions per feature. + """ + # Get input features for prediction + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + + contributions: list[pd.DataFrame] = [] + + for i, quantile in enumerate(self.config.quantiles): + # Get model for specific quantile + model: LGBMRegressor = self._lgbm_model.models[i] # type: ignore + + # Generate contributions using LightGBM's built-in method, and remove bias term + contribs_quantile: np.ndarray[float] = model.predict(input_data, pred_contrib=True)[:, :-1] # type: ignore + + if scale: + # Scale contributions so that they sum to 1.0 per quantile + contribs_quantile = np.abs(contribs_quantile) / np.sum(np.abs(contribs_quantile), axis=1, keepdims=True) + + contributions.append( + pd.DataFrame( + data=contribs_quantile, + index=input_data.index, + columns=[f"{feature}_{quantile.format()}" for feature in input_data.columns], + ) + ) + + # Construct DataFrame + return pd.concat(contributions, axis=1) + @property @override def feature_importances(self) -> pd.DataFrame: diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py index eace689fb..5ffc1eee4 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py @@ -267,7 +267,9 @@ def is_fitted(self) -> bool: return self._lgbmlinear_model.is_fitted @staticmethod - def _prepare_fit_input(data: ForecastInputDataset) -> tuple[pd.DataFrame, np.ndarray, pd.Series]: + def _prepare_fit_input( + data: ForecastInputDataset, + ) -> tuple[pd.DataFrame, np.ndarray, pd.Series]: input_data: pd.DataFrame = data.input_data() target: np.ndarray = np.asarray(data.target_series.values) sample_weight: pd.Series = data.sample_weight_series @@ -314,6 +316,19 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + @override + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> pd.DataFrame: + """Get feature contributions for each prediction. + + Args: + data: Input dataset for which to compute feature contributions. + scale: If True, scale contributions to sum to 1.0 per quantile. + + Returns: + DataFrame with contributions per feature. + """ + raise NotImplementedError("predict_contributions is not yet implemented for LGBMLinearForecaster") + @property @override def feature_importances(self) -> pd.DataFrame: @@ -333,4 +348,8 @@ def feature_importances(self) -> pd.DataFrame: return weights_abs / total -__all__ = ["LGBMLinearForecaster", "LGBMLinearForecasterConfig", "LGBMLinearHyperParams"] +__all__ = [ + "LGBMLinearForecaster", + "LGBMLinearForecasterConfig", + "LGBMLinearHyperParams", +] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py index 0e371a339..c5415e2d6 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py @@ -420,6 +420,48 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> pd.DataFrame: + """Get feature contributions for each prediction. + + Args: + data: Input dataset for which to compute feature contributions. + scale: If True, scale contributions to sum to 1.0 per quantile. + + Returns: + DataFrame with contributions per feature. + """ + # Get input features for prediction + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + xgb_input: xgb.DMatrix = xgb.DMatrix(data=input_data) + + # Generate predictions + booster = self._xgboost_model.get_booster() + predictions_array: np.ndarray = booster.predict(xgb_input, pred_contribs=True, strict_shape=True)[:, :, :-1] + + # Remove last column + contribs = predictions_array / np.sum(predictions_array, axis=-1, keepdims=True) + + # Flatten to 2D array, name columns accordingly + contribs = contribs.reshape(contribs.shape[0], -1) + + df = pd.DataFrame( + data=contribs, + index=input_data.index, + columns=[ + f"{feature}_{quantile.format()}" for feature in input_data.columns for quantile in self.config.quantiles + ], + ) + + if scale: + # Scale contributions so that they sum to 1.0 per quantile and are positive + for q in self.config.quantiles: + quantile_cols = [col for col in df.columns if col.endswith(f"_{q.format()}")] + row_sums = df[quantile_cols].abs().sum(axis=1) + df[quantile_cols] = df[quantile_cols].abs().div(row_sums, axis=0) + + # Construct DataFrame with appropriate quantile columns + return df + @property @override def feature_importances(self) -> pd.DataFrame: diff --git a/packages/openstef-models/src/openstef_models/models/forecasting_model.py b/packages/openstef-models/src/openstef_models/models/forecasting_model.py index f2de3c4b3..9acea87fa 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting_model.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting_model.py @@ -381,7 +381,7 @@ def restore_target[T: TimeSeriesDataset]( target_series = original_dataset.select_features([target_column]).select_version().data[target_column] def _transform_restore_target(df: pd.DataFrame) -> pd.DataFrame: - return df.assign(**{str(target_series.name): df.index.map(target_series)}) # pyright: ignore[reportUnknownMemberType] + return df.assign(**{str(target_series.name): df.index.map(target_series)}) # type: ignore return dataset.pipe_pandas(_transform_restore_target) diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 358160fd7..54423d663 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -30,7 +30,6 @@ from openstef_models.models import ForecastingModel from openstef_models.models.forecasting.flatliner_forecaster import FlatlinerForecaster from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster -from openstef_models.models.forecasting.hybrid_forecaster import HybridForecaster from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster @@ -52,8 +51,14 @@ RollingAggregatesAdder, ) from openstef_models.transforms.time_domain.lags_adder import LagsAdder -from openstef_models.transforms.time_domain.rolling_aggregates_adder import AggregationFunction -from openstef_models.transforms.validation import CompletenessChecker, FlatlineChecker, InputConsistencyChecker +from openstef_models.transforms.time_domain.rolling_aggregates_adder import ( + AggregationFunction, +) +from openstef_models.transforms.validation import ( + CompletenessChecker, + FlatlineChecker, + InputConsistencyChecker, +) from openstef_models.transforms.weather_domain import ( AtmosphereDerivedFeaturesAdder, DaylightFeatureAdder, @@ -109,7 +114,7 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob run_name: str | None = Field(default=None, description="Optional name for this workflow run.") # Model configuration - model: Literal["xgboost", "gblinear", "flatliner", "hybrid", "lgbm", "lgbmlinear"] = Field( + model: Literal["xgboost", "gblinear", "flatliner", "residual", "lgbm", "lgbmlinear"] = Field( description="Type of forecasting model to use." ) # TODO(#652): Implement median forecaster quantiles: list[Quantile] = Field( @@ -145,11 +150,6 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob description="Hyperparameters for LightGBM forecaster.", ) - hybrid_hyperparams: HybridForecaster.HyperParams = Field( - default=HybridForecaster.HyperParams(), - description="Hyperparameters for Hybrid forecaster.", - ) - location: LocationConfig = Field( default=LocationConfig(), description="Location information for the forecasting workflow.", @@ -169,13 +169,10 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob default="relative_humidity", description="Name of the relative humidity column in datasets.", ) + selected_features: FeatureSelection = Field( default=FeatureSelection.ALL, - description="Feature selection for which features to include/exclude.", - ) - selected_features: FeatureSelection = Field( - default=FeatureSelection.ALL, - description="Feature selection for which features to include/exclude.", + description="Feature seletion for which features to include/exclude.", ) predict_history: timedelta = Field( @@ -226,7 +223,16 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob ) sample_weight_exponent: float = Field( default_factory=lambda data: 1.0 - if data.get("model") in {"gblinear", "lgbmlinear", "lgbm", "hybrid", "xgboost"} + if data.get("model") + in { + "gblinear", + "lgbmlinear", + "lgbm", + "learned_weights", + "stacking", + "residual", + "xgboost", + } else 0.0, description="Exponent applied to scale the sample weights. " "0=uniform weights, 1=linear scaling, >1=stronger emphasis on high values. " @@ -330,9 +336,13 @@ def create_forecasting_workflow( LagsAdder( history_available=config.predict_history, horizons=config.horizons, - add_trivial_lags=config.model not in {"gblinear", "hybrid"}, # GBLinear uses only 7day lag. + add_trivial_lags=config.model + not in { + "gblinear", + "residual", + }, # GBLinear uses only 7day lag. target_column=config.target_column, - custom_lags=[timedelta(days=7)] if config.model in {"gblinear", "hybrid"} else [], + custom_lags=[timedelta(days=7)] if config.model in {"gblinear", "residual"} else [], ), WindPowerFeatureAdder( windspeed_reference_column=config.wind_speed_column, @@ -471,28 +481,6 @@ def create_forecasting_workflow( add_quantiles_from_std=False, ), ] - elif config.model == "hybrid": - preprocessing = [ - *checks, - *feature_adders, - *feature_standardizers, - Imputer( - selection=Exclude(config.target_column), - imputation_strategy="mean", - fill_future_values=Include(config.energy_price_column), - ), - NaNDropper( - selection=Exclude(config.target_column), - ), - ] - forecaster = HybridForecaster( - config=HybridForecaster.Config( - quantiles=config.quantiles, - horizons=config.horizons, - hyperparams=config.hybrid_hyperparams, - ) - ) - postprocessing = [QuantileSorter()] else: msg = f"Unsupported model type: {config.model}" raise ValueError(msg) diff --git a/packages/openstef-models/src/openstef_models/transforms/general/__init__.py b/packages/openstef-models/src/openstef_models/transforms/general/__init__.py index 32a8b979c..e601043c1 100644 --- a/packages/openstef-models/src/openstef_models/transforms/general/__init__.py +++ b/packages/openstef-models/src/openstef_models/transforms/general/__init__.py @@ -13,6 +13,7 @@ from openstef_models.transforms.general.empty_feature_remover import ( EmptyFeatureRemover, ) +from openstef_models.transforms.general.flagger import Flagger from openstef_models.transforms.general.imputer import Imputer from openstef_models.transforms.general.nan_dropper import NaNDropper from openstef_models.transforms.general.sample_weighter import SampleWeighter @@ -23,6 +24,7 @@ "Clipper", "DimensionalityReducer", "EmptyFeatureRemover", + "Flagger", "Imputer", "NaNDropper", "SampleWeighter", diff --git a/packages/openstef-models/src/openstef_models/transforms/general/flagger.py b/packages/openstef-models/src/openstef_models/transforms/general/flagger.py new file mode 100644 index 000000000..5c3675148 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/transforms/general/flagger.py @@ -0,0 +1,97 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Transform for flagging feature values inside or outside observed training ranges. + +This module provides functionality to clip feature values to their observed +minimum and maximum ranges during training. It is useful to flag data drift and +can be used to inform forecast combiners which models might perform better. +""" + +from typing import override + +import pandas as pd +from pydantic import Field, PrivateAttr + +from openstef_core.base_model import BaseConfig +from openstef_core.datasets import TimeSeriesDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.transforms import TimeSeriesTransform +from openstef_models.utils.feature_selection import FeatureSelection + + +class Flagger(BaseConfig, TimeSeriesTransform): + """Transform that flags specified features to their observed min and max values. + + This transform flags the peaks for the metalearner to know when to expect outliers and + extrapolate from its training set. + + + Example: + >>> import pandas as pd + >>> from datetime import timedelta + >>> from openstef_core.datasets import TimeSeriesDataset + >>> from openstef_models.transforms.general import Flagger + >>> from openstef_models.utils.feature_selection import FeatureSelection + >>> # Create sample training dataset + >>> training_data = pd.DataFrame({ + ... 'load': [100, 90, 110], + ... 'temperature': [19, 20, 21] + ... }, index=pd.date_range('2025-01-01', periods=3, freq='1h')) + >>> training_dataset = TimeSeriesDataset(training_data, timedelta(hours=1)) + >>> test_data = pd.DataFrame({ + ... 'load': [90, 140, 100], + ... 'temperature': [18, 20, 22] + ... }, index=pd.date_range('2025-01-06', periods=3, + ... freq='1h')) + >>> test_dataset = TimeSeriesDataset(test_data, timedelta(hours=1)) + >>> # Initialize and apply transform + >>> flagger = Flagger(selection=FeatureSelection(include=['load', 'temperature'])) + >>> flagger.fit(training_dataset) + >>> transformed_dataset = flagger.transform(test_dataset) + >>> transformed_dataset.data['load'].tolist() + [0, 0, 1] + >>> transformed_dataset.data['temperature'].tolist() + [0, 1, 0] + + """ + + selection: FeatureSelection = Field(default=FeatureSelection.ALL, description="Features to flag.") + + _feature_mins: pd.Series = PrivateAttr(default_factory=pd.Series) + _feature_maxs: pd.Series = PrivateAttr(default_factory=pd.Series) + _is_fitted: bool = PrivateAttr(default=False) + + @property + @override + def is_fitted(self) -> bool: + return self._is_fitted + + @override + def fit(self, data: TimeSeriesDataset) -> None: + features = self.selection.resolve(data.feature_names) + self._feature_mins = data.data.reindex(features, axis=1).min() + self._feature_maxs = data.data.reindex(features, axis=1).max() + self._is_fitted = True + + @override + def transform(self, data: TimeSeriesDataset) -> TimeSeriesDataset: + if not self._is_fitted: + raise NotFittedError(self.__class__.__name__) + + features = self.selection.resolve(data.feature_names) + transformed_data = data.data.copy(deep=False).loc[:, features] + + # compute min & max of the features + min_aligned = self._feature_mins.reindex(features) + max_aligned = self._feature_maxs.reindex(features) + + outside = (transformed_data[features] <= min_aligned) | (transformed_data[features] >= max_aligned) + transformed_data = (~outside).astype(int) + + return TimeSeriesDataset(data=transformed_data, sample_interval=data.sample_interval) + + @override + def features_added(self) -> list[str]: + return [] diff --git a/packages/openstef-models/src/openstef_models/transforms/time_domain/lags_adder.py b/packages/openstef-models/src/openstef_models/transforms/time_domain/lags_adder.py index 33c881280..a9b2bce38 100644 --- a/packages/openstef-models/src/openstef_models/transforms/time_domain/lags_adder.py +++ b/packages/openstef-models/src/openstef_models/transforms/time_domain/lags_adder.py @@ -201,7 +201,7 @@ def generate_minute_lags(max_horizon: timedelta) -> list[timedelta]: base_lags = pd.Index(hourly_lags).union(pd.Index(subhourly_lags)) # Filter: only lags that are far enough in the past (>= forecast horizon) - valid_lags = cast(pd.TimedeltaIndex, base_lags[base_lags >= max_horizon]) + valid_lags = cast(pd.TimedeltaIndex, base_lags[base_lags >= max_horizon]) # type: ignore # Convert to Python timedelta list (no duplicates possible) return list(valid_lags.to_pytimedelta()) diff --git a/packages/openstef-models/src/openstef_models/utils/data_split.py b/packages/openstef-models/src/openstef_models/utils/data_split.py index f27ca83e5..cef654aad 100644 --- a/packages/openstef-models/src/openstef_models/utils/data_split.py +++ b/packages/openstef-models/src/openstef_models/utils/data_split.py @@ -160,7 +160,7 @@ def stratified_train_test_split[T: TimeSeriesDataset]( _, test_other_days = _sample_dates_for_split(dates=other_days, test_fraction=test_fraction, rng=rng) # Combine all train and test dates - test_dates = cast(pd.DatetimeIndex, test_max_days.union(test_min_days).union(test_other_days)) + test_dates = cast(pd.DatetimeIndex, test_max_days.union(test_min_days).union(test_other_days)) # type: ignore return split_by_dates(dataset=dataset, dates_test=test_dates) diff --git a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py index 763932268..911c1876c 100644 --- a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py +++ b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py @@ -92,7 +92,9 @@ def fit( kwargs[self.learner_eval_sample_weight_param] = eval_sample_weight if "early_stopping_rounds" in self.hyperparams and self.learner_eval_sample_weight_param is not None: - model.set_params(early_stopping_rounds=self.hyperparams["early_stopping_rounds"]) # type: ignore + model.set_params( # type: ignore + early_stopping_rounds=self.hyperparams["early_stopping_rounds"] + ) if feature_name: self.model_feature_names = feature_name diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py index 58ca98159..24d3b8e01 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py @@ -4,6 +4,7 @@ from datetime import timedelta +import numpy as np import pandas as pd import pytest @@ -132,3 +133,32 @@ def test_gblinear_forecaster__feature_importances( col_sums = feature_importances.sum(axis=0) pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=expected_columns), atol=1e-10) assert (feature_importances >= 0).all().all() + + +def test_gblinear_forecaster_predict_contributions( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: GBLinearForecasterConfig, +): + """Test basic fit and predict workflow with output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = GBLinearForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict_contributions(sample_forecast_input_dataset, scale=True) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + input_features = sample_forecast_input_dataset.input_data().columns + expected_columns = [f"{col}_{q.format()}" for col in input_features for q in expected_quantiles] + assert list(result.columns) == expected_columns, f"Expected columns {expected_columns}, got {list(result.columns)}" + + # Contributions should sum to 1.0 per quantile + for q in expected_quantiles: + quantile_cols = [col for col in result.columns if col.endswith(f"_{q.format()}")] + col_sums = result[quantile_cols].sum(axis=1) + pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=result.index, dtype=np.float32), atol=1e-10) diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py index 9e51d1047..91fddda99 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py @@ -22,7 +22,7 @@ def base_config() -> XGBoostForecasterConfig: """Base configuration for XGBoost forecaster tests.""" return XGBoostForecasterConfig( horizons=[LeadTime(timedelta(days=1))], - quantiles=[Q(0.1), Q(0.5), Q(0.9)], + quantiles=[Q(0.1), Q(0.3), Q(0.5), Q(0.7), Q(0.9)], hyperparams=XGBoostHyperParams( n_estimators=10, # Small for fast tests ), @@ -167,3 +167,32 @@ def test_xgboost_forecaster__feature_importances( col_sums = feature_importances.sum(axis=0) pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=expected_columns), atol=1e-10) assert (feature_importances >= 0).all().all() + + +def test_xgboost_forecaster_predict_contributions( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: XGBoostForecasterConfig, +): + """Test basic fit and predict workflow with output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = XGBoostForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict_contributions(sample_forecast_input_dataset, scale=True) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + input_features = sample_forecast_input_dataset.input_data().columns + expected_columns = [f"{col}_{q.format()}" for col in input_features for q in expected_quantiles] + assert list(result.columns) == expected_columns, f"Expected columns {expected_columns}, got {list(result.columns)}" + + # Contributions should sum to 1.0 per quantile + for q in expected_quantiles: + quantile_cols = [col for col in result.columns if col.endswith(f"_{q.format()}")] + col_sums = result[quantile_cols].sum(axis=1) + pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=result.index), atol=1e-10, check_dtype=False) diff --git a/packages/openstef-models/tests/unit/transforms/general/test_flagger.py b/packages/openstef-models/tests/unit/transforms/general/test_flagger.py new file mode 100644 index 000000000..b250099f4 --- /dev/null +++ b/packages/openstef-models/tests/unit/transforms/general/test_flagger.py @@ -0,0 +1,62 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta + +import pandas as pd +import pytest + +from openstef_core.datasets import TimeSeriesDataset +from openstef_models.transforms.general import Flagger +from openstef_models.utils.feature_selection import FeatureSelection + + +@pytest.fixture +def train_dataset() -> TimeSeriesDataset: + """Training dataset with three features A, B, C.""" + return TimeSeriesDataset( + data=pd.DataFrame( + {"A": [1.0, 2.0, 3.0], "B": [1.0, 2.0, 3.0], "C": [1.0, 2.0, 3.0]}, + index=pd.date_range("2025-01-01", periods=3, freq="1h"), + ), + sample_interval=timedelta(hours=1), + ) + + +@pytest.fixture +def test_dataset() -> TimeSeriesDataset: + """Test dataset with values outside training ranges.""" + return TimeSeriesDataset( + data=pd.DataFrame( + {"A": [2, 2], "B": [0.0, 2.0], "C": [1, 4]}, + index=pd.date_range("2025-01-06", periods=2, freq="1h"), + ), + sample_interval=timedelta(hours=1), + ) + + +def test_flagger__fit_transform( + train_dataset: TimeSeriesDataset, + test_dataset: TimeSeriesDataset, +): + """Test fit and transform flags correctly leaves other columns unchanged.""" + # Arrange + flagger = Flagger(selection=FeatureSelection(include={"A", "B", "C"})) + + # Act + flagger.fit(train_dataset) + transformed_dataset = flagger.transform(test_dataset) + + # Assert + # Column C should remain unchanged + expected_df = pd.DataFrame( + { + "A": [1, 1], + "B": [0, 1], + "C": [0, 0], # Unchanged + }, + index=test_dataset.index, + ) + pd.testing.assert_frame_equal(transformed_dataset.data, expected_df) + assert 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