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| /* | ||
| * Copyright (C) 2018 Swift Navigation Inc. | ||
| * Contact: Swift Navigation <[email protected]> | ||
| * | ||
| * This source is subject to the license found in the file 'LICENSE' which must | ||
| * be distributed together with this source. All other rights reserved. | ||
| * | ||
| * THIS CODE AND INFORMATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND, | ||
| * EITHER EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE IMPLIED | ||
| * WARRANTIES OF MERCHANTABILITY AND/OR FITNESS FOR A PARTICULAR PURPOSE. | ||
| */ | ||
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| #ifndef ALBATROSS_MODELS_RANSAC_GP_H | ||
| #define ALBATROSS_MODELS_RANSAC_GP_H | ||
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| #include "crossvalidation.h" | ||
| #include "gp.h" | ||
| #include "outlier.h" | ||
| #include <random> | ||
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| namespace albatross { | ||
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| template <typename FeatureType, typename CovarianceFunction> | ||
| class RansacGaussianProcessRegression | ||
| : public GaussianProcessRegression<FeatureType, CovarianceFunction> { | ||
| public: | ||
| using BaseModel = GaussianProcessRegression<FeatureType, CovarianceFunction>; | ||
| using FitType = GaussianProcessFit<FeatureType>; | ||
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| RansacGaussianProcessRegression(CovarianceFunction &covariance_function) | ||
| : BaseModel(covariance_function){}; | ||
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| template <typename Archive> void save(Archive &archive) const { | ||
| archive(cereal::base_class<BaseModel>(this)); | ||
| } | ||
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| template <typename Archive> void load(Archive &archive) { | ||
| archive(cereal::base_class<BaseModel>(this)); | ||
| } | ||
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| protected: | ||
| /* | ||
| * Cross validation specializations | ||
| * | ||
| * Note the naming here uses a trailing underscore. This is to avoid | ||
| * name hiding when implementing one of these methods in a derived | ||
| * class: | ||
| * | ||
| * https://stackoverflow.com/questions/1628768/why-does-an-overridden-function-in-the-derived-class-hide-other-overloads-of-the | ||
| */ | ||
| virtual std::vector<JointDistribution> cross_validated_predictions_( | ||
| const RegressionDataset<FeatureType> &dataset, | ||
| const FoldIndexer &fold_indexer, | ||
| const detail::PredictTypeIdentity<JointDistribution> &identity) override { | ||
| const auto folds = folds_from_fold_indexer(dataset, fold_indexer); | ||
| return this->template cross_validated_predictions<JointDistribution>(folds); | ||
| } | ||
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| virtual std::vector<MarginalDistribution> cross_validated_predictions_( | ||
| const RegressionDataset<FeatureType> &dataset, | ||
| const FoldIndexer &fold_indexer, | ||
| const detail::PredictTypeIdentity<MarginalDistribution> &identity) | ||
| override { | ||
| const auto folds = folds_from_fold_indexer(dataset, fold_indexer); | ||
| return this->template cross_validated_predictions<MarginalDistribution>( | ||
| folds); | ||
| } | ||
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| virtual std::vector<Eigen::VectorXd> cross_validated_predictions_( | ||
| const RegressionDataset<FeatureType> &dataset, | ||
| const FoldIndexer &fold_indexer, | ||
| const detail::PredictTypeIdentity<PredictMeanOnly> &identity) override { | ||
| const auto folds = folds_from_fold_indexer(dataset, fold_indexer); | ||
| return this->template cross_validated_predictions<PredictMeanOnly>(folds); | ||
| } | ||
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| FitType | ||
| serializable_fit_(const std::vector<FeatureType> &features, | ||
| const MarginalDistribution &targets) const override { | ||
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| Eigen::MatrixXd cov = | ||
| symmetric_covariance(this->covariance_function_, features); | ||
| if (targets.has_covariance()) { | ||
| cov += targets.covariance; | ||
| } | ||
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| double threshold = 1.; | ||
| std::size_t min_inliers = 3; | ||
| std::size_t min_features = 3; | ||
| std::size_t max_iterations = 20; | ||
| EvaluationMetric<JointDistribution> metric = | ||
| albatross::evaluation_metrics::negative_log_likelihood; | ||
|
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| struct FitAndIndices { | ||
| FitType model_fit; | ||
| Indexer fit_indices; | ||
| }; | ||
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| std::function<FitAndIndices(const Indexer &)> fitter = | ||
| [&](const Indexer &indexer) { | ||
| const auto train_features = subset(indexer, features); | ||
| const auto train_targets = subset(indexer, targets); | ||
| auto train_cov = symmetric_subset(indexer, cov); | ||
| const FitAndIndices fit_and_indices = { | ||
| fit_from_covariance(train_features, train_cov, train_targets), | ||
| indexer}; | ||
| return fit_and_indices; | ||
| }; | ||
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| std::function<double(const Indexer &, const FitAndIndices &)> | ||
| outlier_metric = [&](const Indexer &test_indices, | ||
| const FitAndIndices &fit_and_indices) { | ||
| const auto cross_cov = | ||
| subset(fit_and_indices.fit_indices, test_indices, cov); | ||
| const auto pred_cov = symmetric_subset(test_indices, cov); | ||
| const auto pred = predict_from_covariance_and_fit( | ||
| cross_cov, pred_cov, fit_and_indices.model_fit); | ||
| const auto target = subset(test_indices, targets); | ||
| double metric_value = metric(pred, target); | ||
| return metric_value; | ||
| }; | ||
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| std::function<double(const Indexer &)> model_metric = [&]( | ||
| const Indexer &inliers) { | ||
| RegressionDataset<FeatureType> inlier_dataset(subset(inliers, features), | ||
| subset(inliers, targets)); | ||
| const FoldIndexer inlier_loo = leave_one_out_indexer(inlier_dataset); | ||
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| BaseModel model; | ||
| model.set_params(this->get_params()); | ||
| double mean_metric = cross_validated_scores<double>( | ||
| metric, inlier_dataset, inlier_loo, &model) | ||
| .mean(); | ||
| return mean_metric; | ||
| }; | ||
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| const RegressionDataset<FeatureType> dataset(features, targets); | ||
| const auto loo_indexer = leave_one_out_indexer(dataset); | ||
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| auto inliers = ransac<FitAndIndices>( | ||
| fitter, outlier_metric, model_metric, map_values(loo_indexer), | ||
| threshold, min_features, min_inliers, max_iterations); | ||
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| const auto inlier_features = subset(inliers, features); | ||
| const auto inlier_targets = subset(inliers, targets); | ||
| const auto inlier_cov = symmetric_subset(inliers, cov); | ||
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| return fit_from_covariance(inlier_features, inlier_cov, inlier_targets); | ||
| } | ||
| }; | ||
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| template <typename FeatureType, typename CovFunc> | ||
| std::unique_ptr<RansacGaussianProcessRegression<FeatureType, CovFunc>> | ||
| ransac_gp_pointer_from_covariance(CovFunc covariance_function) { | ||
| return std::make_unique< | ||
| RansacGaussianProcessRegression<FeatureType, CovFunc>>( | ||
| covariance_function); | ||
| } | ||
|
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| } // namespace albatross | ||
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| #endif |
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