Nearest Neighbor Model

include/albatross/NearestNeighbor

@@ -0,0 +1,20 @@
+/*
+ * Copyright (C) 2019 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.
+ */
+
+#ifndef ALBATROSS_NEAREST_NEIGHBOR_MODEL_H
+#define ALBATROSS_NEAREST_NEIGHBOR_MODEL_H
+
+#include "Core"
+
+#include <albatross/src/models/nearest_neighbor.hpp>
+
+#endif

include/albatross/serialize/NearestNeighbor

@@ -0,0 +1,20 @@
+/*
+ * Copyright (C) 2019 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.
+ */
+
+#ifndef ALBATROSS_SERIALIZE_NEAREST_NEIGHBOR_H
+#define ALBATROSS_SERIALIZE_NEAREST_NEIGHBOR_H
+
+#include "Core"
+
+#include "../src/cereal/nearest_neighbor.hpp"
+
+#endif

include/albatross/src/cereal/nearest_neighbor.hpp

@@ -0,0 +1,49 @@
+/*
+ * Copyright (C) 2019 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.
+ */
+
+#ifndef ALBATROSS_CEREAL_NEAREST_NEIGHBOR_HPP_
+#define ALBATROSS_CEREAL_NEAREST_NEIGHBOR_HPP_
+
+namespace albatross {
+
+template <typename DistanceMetric> class NearestNeighborModel;
+
+template <typename FeatureType> struct NearestNeighborFit;
+
+} // namespace albatross
+
+namespace cereal {
+
+template <typename Archive, typename FeatureType>
+inline void
+save(Archive &archive,
+     const albatross::Fit<albatross::NearestNeighborFit<FeatureType>> &fit,
+     const std::uint32_t) {
+  archive(cereal::make_nvp("training_features", fit.training_data.features));
+  archive(cereal::make_nvp("training_targets", fit.training_data.targets));
+}
+
+template <typename Archive, typename FeatureType>
+inline void
+load(Archive &archive,
+     albatross::Fit<albatross::NearestNeighborFit<FeatureType>> &fit,
+     const std::uint32_t) {
+  std::vector<FeatureType> features;
+  archive(cereal::make_nvp("training_features", features));
+  albatross::MarginalDistribution targets;
+  archive(cereal::make_nvp("training_targets", targets));
+  fit.training_data = RegressionDataset<FeatureType>(features, targets);
+}
+
+} // namespace cereal
+
+#endif /* ALBATROSS_CEREAL_NEAREST_NEIGHBOR_HPP_ */

include/albatross/src/evaluation/cross_validation_utils.hpp

@@ -93,15 +93,25 @@ inline MarginalDistribution concatenate_marginal_predictions(
   Eigen::VectorXd variance(n);
   Eigen::Index number_filled = 0;
   // Put all the predicted means back in order.
+  bool has_covariance = false;
   for (const auto &pair : indexer) {
     assert(preds.at(pair.first).size() == pair.second.size());
     set_subset(preds.at(pair.first).mean, pair.second, &mean);
-    set_subset(preds.at(pair.first).covariance.diagonal(), pair.second,
-               &variance);
+    if (preds.at(pair.first).has_covariance()) {
+      has_covariance = true;
+      set_subset(preds.at(pair.first).covariance.diagonal(), pair.second,
+                 &variance);
+    } else {
+      assert(!has_covariance);
+    }
     number_filled += static_cast<Eigen::Index>(pair.second.size());
   }
   assert(number_filled == n);
-  return MarginalDistribution(mean, variance.asDiagonal());
+  if (has_covariance) {
+    return MarginalDistribution(mean, variance.asDiagonal());
+  } else {
+    return MarginalDistribution(mean);
+  }
 }
 
 template <typename PredictionMetricType, typename FeatureType,

include/albatross/src/models/nearest_neighbor.hpp

@@ -0,0 +1,115 @@
+/*
+ * Copyright (C) 2019 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.
+ */
+
+#ifndef ALBATROSS_SRC_MODELS_NEAREST_NEIGHBOR_MODEL_HPP_
+#define ALBATROSS_SRC_MODELS_NEAREST_NEIGHBOR_MODEL_HPP_
+
+namespace albatross {
+
+template <typename DistanceMetric> class NearestNeighborModel;
+
+template <typename FeatureType> struct NearestNeighborFit;
+
+template <typename FeatureType> struct Fit<NearestNeighborFit<FeatureType>> {
+
+  Fit() : training_data(){};
+
+  Fit(const RegressionDataset<FeatureType> &dataset) : training_data(dataset){};
+
+  bool operator==(const Fit<NearestNeighborFit<FeatureType>> &other) const {
+    return training_data == other.training_data;
+  }
+
+  RegressionDataset<FeatureType> training_data;
+};
+
+template <typename DistanceMetric>
+class NearestNeighborModel
+    : public ModelBase<NearestNeighborModel<DistanceMetric>> {
+
+public:
+  NearestNeighborModel() : distance_metric(){};
+
+  std::string get_name() const { return "nearest_neighbor_model"; };
+
+  template <typename FeatureType>
+  Fit<NearestNeighborFit<FeatureType>>
+  _fit_impl(const std::vector<FeatureType> &features,
+            const MarginalDistribution &targets) const {
+    return Fit<NearestNeighborFit<FeatureType>>(
+        RegressionDataset<FeatureType>(features, targets));
+  }
+
+  template <typename FeatureType>
+  auto fit_from_prediction(const std::vector<FeatureType> &features,
+                           const JointDistribution &prediction) const {
+    const NearestNeighborModel<DistanceMetric> m(*this);
+    MarginalDistribution marginal_pred(
+        prediction.mean, prediction.covariance.diagonal().asDiagonal());
+    Fit<NearestNeighborFit<FeatureType>> fit = {
+        RegressionDataset<FeatureType>(features, marginal_pred)};
+    FitModel<NearestNeighborModel, Fit<NearestNeighborFit<FeatureType>>>
+        fit_model(m, fit);
+    return fit_model;
+  }
+
+  template <typename FeatureType>
+  MarginalDistribution
+  _predict_impl(const std::vector<FeatureType> &features,
+                const Fit<NearestNeighborFit<FeatureType>> &fit,
+                PredictTypeIdentity<MarginalDistribution> &&) const {
+    const Eigen::Index n = static_cast<Eigen::Index>(features.size());
+    Eigen::VectorXd mean = Eigen::VectorXd::Zero(n);
+    mean.fill(NAN);
+    Eigen::VectorXd variance = Eigen::VectorXd::Zero(n);
+    variance.fill(NAN);
+
+    for (std::size_t i = 0; i < features.size(); ++i) {
+      const auto min_index =
+          index_with_min_distance(features[i], fit.training_data.features);
+      mean[i] = fit.training_data.targets.mean[min_index];
+      variance[i] = fit.training_data.targets.get_diagonal(min_index);
+    }
+
+    if (fit.training_data.targets.has_covariance()) {
+      return MarginalDistribution(mean, variance.asDiagonal());
+    } else {
+      return MarginalDistribution(mean);
+    }
+  }
+
+private:
+  template <typename FeatureType>
+  std::size_t
+  index_with_min_distance(const FeatureType &ref,
+                          const std::vector<FeatureType> &features) const {
+    assert(features.size() > 0);
+
+    std::size_t min_index = 0;
+    double min_distance = distance_metric(ref, features[0]);
+
+    for (std::size_t i = 1; i < features.size(); ++i) {
+      const double dist = distance_metric(ref, features[i]);
+      if (dist < min_distance) {
+        min_index = i;
+        min_distance = dist;
+      }
+    }
+    return min_index;
+  }
+
+  DistanceMetric distance_metric;
+};
+
+} // namespace albatross
+
+#endif // ALBATROSS_SRC_MODELS_NEAREST_NEIGHBOR_MODEL_HPP_

include/albatross/src/models/null_model.hpp

@@ -32,19 +32,13 @@ class NullModel : public ModelBase<NullModel> {
 
   std::string get_name() const { return "null_model"; };
 
-  /*
-   * The Gaussian Process Regression model derives its parameters from
-   * the covariance functions.
-   */
   ParameterStore get_params() const override { return params_; }
 
   void unchecked_set_param(const std::string &name,
                            const Parameter &param) override {
     params_[name] = param;
   }
 
-  // If the implementing class doesn't have a fit method for this
-  // FeatureType but the CovarianceFunction does.
   template <typename FeatureType>
   Fit<NullModel> _fit_impl(const std::vector<FeatureType> &features,
                            const MarginalDistribution &targets) const {
@@ -87,5 +81,4 @@ class NullModel : public ModelBase<NullModel> {
 
 } // namespace albatross
 
-#endif /* THIRD_PARTY_ALBATROSS_INCLUDE_ALBATROSS_SRC_MODELS_NULL_MODEL_HPP_   \
-        */
+#endif // ALBATROSS_SRC_MODELS_NULL_MODEL_HPP_

tests/test_cross_validation.cc

@@ -53,16 +53,22 @@ TYPED_TEST_P(RegressionModelTester, test_logo_predict_variants) {
   auto dataset = this->test_case.get_dataset();
   auto model = this->test_case.get_model();
 
-  // Here we assume that the test case is linear, then split
-  // it using a group function which will not preserve order
-  // and make sure that cross validation properly reassembles
-  // the predictions
-  LeaveOneGroupOut<typename decltype(dataset)::Feature> logo(group_by_interval);
-  const auto prediction = model.cross_validate().predict(dataset, logo);
-
-  EXPECT_TRUE(is_monotonic_increasing(prediction.mean()));
-
-  expect_predict_variants_consistent(prediction);
+  // The nearest neighbor approach is not capable of modelling linear
+  // trends and in turn fails this test.
+  if (!std::is_same<decltype(model),
+                    NearestNeighborModel<EuclideanDistance>>::value) {
+    // Here we assume that the test case is linear, then split
+    // it using a group function which will not preserve order
+    // and make sure that cross validation properly reassembles
+    // the predictions
+    LeaveOneGroupOut<typename decltype(dataset)::Feature> logo(
+        group_by_interval);
+    const auto prediction = model.cross_validate().predict(dataset, logo);
+
+    EXPECT_TRUE(is_monotonic_increasing(prediction.mean()));
+
+    expect_predict_variants_consistent(prediction);
+  }
 }
 
 TYPED_TEST_P(RegressionModelTester, test_loo_predict_variants) {
@@ -110,7 +116,9 @@ TYPED_TEST_P(RegressionModelTester, test_score_variants) {
   // Here we make sure the cross validated mean absolute error is reasonable.
   // Note that because we are running leave one out cross validation, the
   // RMSE for each fold is just the absolute value of the error.
-  if (!std::is_same<decltype(model), NullModel>::value) {
+  if (!std::is_same<decltype(model), NullModel>::value &&
+      !std::is_same<decltype(model),
+                    NearestNeighborModel<EuclideanDistance>>::value) {
     EXPECT_LE(cv_scores.mean(), 0.1);
   }
 }

tests/test_models.h

@@ -12,6 +12,7 @@
 
 #include <albatross/GP>
 #include <albatross/LeastSquares>
+#include <albatross/NearestNeighbor>
 #include <albatross/NullModel>
 #include <albatross/Ransac>
 #include <gtest/gtest.h>
@@ -181,6 +182,17 @@ class MakeNullModel {
   }
 };
 
+class MakeNearestNeighborModel {
+public:
+  NearestNeighborModel<EuclideanDistance> get_model() const {
+    return NearestNeighborModel<EuclideanDistance>();
+  }
+
+  RegressionDataset<double> get_dataset() const {
+    return make_toy_linear_data();
+  }
+};
+
 template <typename ModelTestCase>
 class RegressionModelTester : public ::testing::Test {
 public:
@@ -189,7 +201,8 @@ class RegressionModelTester : public ::testing::Test {
 
 typedef ::testing::Types<MakeLinearRegression, MakeGaussianProcess,
                          MakeAdaptedGaussianProcess, MakeRansacGaussianProcess,
-                         MakeRansacAdaptedGaussianProcess, MakeNullModel>
+                         MakeRansacAdaptedGaussianProcess, MakeNullModel,
+                         MakeNearestNeighborModel>
     ExampleModels;
 
 TYPED_TEST_CASE_P(RegressionModelTester);

tests/test_serialize.cc

@@ -16,6 +16,7 @@
 #include <albatross/serialize/Common>
 #include <albatross/serialize/GP>
 #include <albatross/serialize/LeastSquares>
+#include <albatross/serialize/NearestNeighbor>
 #include <albatross/serialize/Ransac>
 
 #include <gtest/gtest.h>