[7.7][ML] Add information about samples per node to the tree

docs/CHANGELOG.asciidoc

@@ -39,6 +39,8 @@ progress, memory usage, etc. (See {ml-pull}906[#906].)
 
 * Improve initialization of learn rate for better and more stable results in regression
 and classification. (See {ml-pull}948[#948].)
+* Add number of processed training samples to the definition of decision tree nodes.
+(See {ml-pull}991[#991].)
 * Add new model_size_stats fields to instrument categorization.  (See {ml-pull}948[#948]
 and {pull}51879[#51879], issue: {issue}50794[#50749].)
 

include/api/CBoostedTreeInferenceModelBuilder.h

@@ -38,6 +38,7 @@ class API_EXPORT CBoostedTreeInferenceModelBuilder : public maths::CBoostedTree:
                  bool assignMissingToLeft,
                  double nodeValue,
                  double gain,
+                 std::size_t numberSamples,
                  maths::CBoostedTreeNode::TOptionalNodeIndex leftChild,
                  maths::CBoostedTreeNode::TOptionalNodeIndex rightChild) override;
     void addIdentityEncoding(std::size_t inputColumnIndex) override;

include/api/CInferenceModelDefinition.h

@@ -158,7 +158,8 @@ class API_EXPORT CTree final : public CTrainedModel {
                   double threshold,
                   bool defaultLeft,
                   double leafValue,
-                  size_t splitFeature,
+                  std::size_t splitFeature,
+                  std::size_t numberSamples,
                   const TOptionalNodeIndex& leftChild,
                   const TOptionalNodeIndex& rightChild,
                   const TOptionalDouble& splitGain);
@@ -175,6 +176,7 @@ class API_EXPORT CTree final : public CTrainedModel {
         TOptionalNodeIndex m_LeftChild;
         TOptionalNodeIndex m_RightChild;
         std::size_t m_SplitFeature;
+        std::size_t m_NumberSamples;
         double m_Threshold;
         double m_LeafValue;
         TOptionalDouble m_SplitGain;

include/maths/CBoostedTree.h

@@ -265,6 +265,7 @@ class MATHS_EXPORT CBoostedTreeNode final {
                              bool assignMissingToLeft,
                              double nodeValue,
                              double gain,
+                             std::size_t numberSamples,
                              TOptionalNodeIndex leftChild,
                              TOptionalNodeIndex rightChild) = 0;
     };
@@ -306,6 +307,12 @@ class MATHS_EXPORT CBoostedTreeNode final {
     //! Get the total curvature at the rows below this node.
     double curvature() const { return m_Curvature; }
 
+    //! Set the number of samples to \p value.
+    void numberSamples(std::size_t value);
+
+    //! Get number of samples affected by the node.
+    std::size_t numberSamples() const;
+
     //! Get the index of the left child node.
     TNodeIndex leftChildIndex() const { return m_LeftChild.get(); }
 
@@ -348,6 +355,7 @@ class MATHS_EXPORT CBoostedTreeNode final {
     double m_NodeValue = 0.0;
     double m_Gain = 0.0;
     double m_Curvature = 0.0;
+    std::size_t m_NumberSamples = 0;
 };
 
 //! \brief A boosted regression tree model.

include/maths/CBoostedTreeImpl.h

@@ -252,6 +252,9 @@ class MATHS_EXPORT CBoostedTreeImpl final {
     //! Get the root node of \p tree.
     static const CBoostedTreeNode& root(const TNodeVec& tree);
 
+    //! Get the root node of \p tree.
+    static CBoostedTreeNode& root(TNodeVec& tree);
+
     //! Get the forest's prediction for \p row.
     static double predictRow(const CEncodedDataFrameRowRef& row, const TNodeVecVec& forest);
 
@@ -287,6 +290,9 @@ class MATHS_EXPORT CBoostedTreeImpl final {
     //! Record the training state using the \p recordTrainState callback function
     void recordState(const TTrainingStateCallback& recordTrainState) const;
 
+    //! Populate numberSamples field in the m_BestForest
+    void computeNumberSamples(const core::CDataFrame& frame);
+
 private:
     mutable CPRNG::CXorOShiro128Plus m_Rng;
     std::size_t m_NumberThreads;

include/maths/CTreeShapFeatureImportance.h

@@ -40,18 +40,9 @@ class MATHS_EXPORT CTreeShapFeatureImportance {
     //! by \p offset.
     void shap(core::CDataFrame& frame, const CDataFrameCategoryEncoder& encoder, std::size_t offset);
 
-    //! Compute number of training samples from \p frame that pass every node in the \p tree.
-    static TDoubleVec samplesPerNode(const TTree& tree,
-                                     const core::CDataFrame& frame,
-                                     const CDataFrameCategoryEncoder& encoder,
-                                     std::size_t numThreads);
-
     //! Recursively computes inner node values as weighted average of the children (leaf) values
     //! \returns The maximum depth the the tree.
-    static std::size_t updateNodeValues(TTree& tree,
-                                        std::size_t nodeIndex,
-                                        const TDoubleVec& samplesPerNode,
-                                        std::size_t depth);
+    static size_t updateNodeValues(TTree& tree, std::size_t nodeIndex, std::size_t depth);
 
     //! Get the reference to the trees.
     TTreeVec& trees() { return m_Trees; }
@@ -126,7 +117,6 @@ class MATHS_EXPORT CTreeShapFeatureImportance {
     //! Recursively traverses all pathes in the \p tree and updated SHAP values once it hits a leaf.
     //! Ref. Algorithm 2 in the paper by Lundberg et al.
     void shapRecursive(const TTree& tree,
-                       const TDoubleVec& samplesPerNode,
                        const CDataFrameCategoryEncoder& encoder,
                        const CEncodedDataFrameRowRef& encodedRow,
                        SPath& splitPath,
@@ -146,7 +136,6 @@ class MATHS_EXPORT CTreeShapFeatureImportance {
 private:
     TTreeVec m_Trees;
     std::size_t m_NumberThreads;
-    TDoubleVecVec m_SamplesPerNode;
 };
 }
 }

lib/api/CBoostedTreeInferenceModelBuilder.cc

@@ -89,6 +89,7 @@ void CBoostedTreeInferenceModelBuilder::addNode(std::size_t splitFeature,
                                                 bool assignMissingToLeft,
                                                 double nodeValue,
                                                 double gain,
+                                                std::size_t numberSamples,
                                                 maths::CBoostedTreeNode::TOptionalNodeIndex leftChild,
                                                 maths::CBoostedTreeNode::TOptionalNodeIndex rightChild) {
     auto ensemble{static_cast<CEnsemble*>(m_Definition.trainedModel().get())};
@@ -97,8 +98,9 @@ void CBoostedTreeInferenceModelBuilder::addNode(std::size_t splitFeature,
     if (tree == nullptr) {
         HANDLE_FATAL(<< "Internal error. Tree points to a nullptr.")
     }
-    tree->treeStructure().emplace_back(tree->size(), splitValue, assignMissingToLeft, nodeValue,
-                                       splitFeature, leftChild, rightChild, gain);
+    tree->treeStructure().emplace_back(tree->size(), splitValue, assignMissingToLeft,
+                                       nodeValue, splitFeature, numberSamples,
+                                       leftChild, rightChild, gain);
 }
 
 CBoostedTreeInferenceModelBuilder::CBoostedTreeInferenceModelBuilder(TStrVec fieldNames,

lib/api/CInferenceModelDefinition.cc

@@ -34,6 +34,7 @@ const std::string JSON_LEFT_CHILD_TAG{"left_child"};
 const std::string JSON_LOGISTIC_REGRESSION_TAG{"logistic_regression"};
 const std::string JSON_LT{"lt"};
 const std::string JSON_NODE_INDEX_TAG{"node_index"};
+const std::string JSON_NUMBER_SAMPLES_TAG{"number_samples"};
 const std::string JSON_ONE_HOT_ENCODING_TAG{"one_hot_encoding"};
 const std::string JSON_PREPROCESSORS_TAG{"preprocessors"};
 const std::string JSON_RIGHT_CHILD_TAG{"right_child"};
@@ -79,6 +80,9 @@ void addJsonArray(const std::string& tag,
 void CTree::CTreeNode::addToDocument(rapidjson::Value& parentObject,
                                      TRapidJsonWriter& writer) const {
     writer.addMember(JSON_NODE_INDEX_TAG, rapidjson::Value(m_NodeIndex).Move(), parentObject);
+    writer.addMember(
+        JSON_NUMBER_SAMPLES_TAG,
+        rapidjson::Value(static_cast<std::uint64_t>(m_NumberSamples)).Move(), parentObject);
 
     if (m_LeftChild) {
         // internal node
@@ -118,11 +122,13 @@ CTree::CTreeNode::CTreeNode(TNodeIndex nodeIndex,
                             bool defaultLeft,
                             double leafValue,
                             std::size_t splitFeature,
+                            std::size_t numberSamples,
                             const TOptionalNodeIndex& leftChild,
                             const TOptionalNodeIndex& rightChild,
                             const TOptionalDouble& splitGain)
-    : m_DefaultLeft(defaultLeft), m_NodeIndex(nodeIndex), m_LeftChild(leftChild),
-      m_RightChild(rightChild), m_SplitFeature(splitFeature),
+    : m_DefaultLeft(defaultLeft), m_NodeIndex(nodeIndex),
+      m_LeftChild(leftChild), m_RightChild(rightChild),
+      m_SplitFeature(splitFeature), m_NumberSamples(numberSamples),
       m_Threshold(threshold), m_LeafValue(leafValue), m_SplitGain(splitGain) {
 }
 

lib/api/unittest/CDataFrameAnalyzerFeatureImportanceTest.cc

@@ -299,8 +299,9 @@ BOOST_FIXTURE_TEST_CASE(testRegressionFeatureImportanceNoImportance, SFixture) {
             // c1 explains 95% of the prediction value, i.e. the difference from the prediction is less than 2%.
             BOOST_REQUIRE_CLOSE(c1, prediction, 5.0);
             for (const auto& feature : {"c2", "c3", "c4"}) {
-                BOOST_REQUIRE_SMALL(readShapValue(result, feature), 2.0);
-                cNoImportanceMean.add(std::fabs(readShapValue(result, feature)));
+                double c = readShapValue(result, feature);
+                BOOST_REQUIRE_SMALL(c, 2.0);
+                cNoImportanceMean.add(std::fabs(c));
             }
         }
     }

lib/api/unittest/testfiles/inference_json_schema/model_definition.schema.json

@@ -66,6 +66,10 @@
         },
         "right_child": {
           "type": "integer"
+        },
+        "number_samples": {
+          "description": "Number of training samples that were affected by the node.",
+          "type": "integer"
         }
       },
       "required": [
@@ -75,7 +79,8 @@
         "decision_type",
         "default_left",
         "left_child",
-        "right_child"
+        "right_child",
+        "number_samples"
       ],
       "additionalProperties": false
     },
@@ -88,11 +93,16 @@
         },
         "leaf_value": {
           "type": "number"
+        },
+        "number_samples": {
+          "description": "Number of training samples that were affected by the node.",
+          "type": "integer"
         }
       },
       "required": [
         "node_index",
-        "leaf_value"
+        "leaf_value",
+        "number_samples"
       ],
       "additionalProperties": false
     },
@@ -234,10 +244,14 @@
                       "items": {
                         "type": "number"
                       }
+                    },
+                    "num_classes": {
+                      "type": "integer"
                     }
                   },
                   "required": [
-                    "weights"
+                    "weights",
+                    "num_classes"
                   ]
                 }
               },

lib/maths/CBoostedTree.cc

@@ -27,6 +27,7 @@ const std::string SPLIT_FEATURE_TAG{"split_feature"};
 const std::string ASSIGN_MISSING_TO_LEFT_TAG{"assign_missing_to_left "};
 const std::string NODE_VALUE_TAG{"node_value"};
 const std::string SPLIT_VALUE_TAG{"split_value"};
+const std::string NUMBER_SAMPLES_TAG{"number_samples"};
 
 double LOG_EPSILON{std::log(100.0 * std::numeric_limits<double>::epsilon())};
 
@@ -370,6 +371,7 @@ void CBoostedTreeNode::acceptPersistInserter(core::CStatePersistInserter& insert
     core::CPersistUtils::persist(ASSIGN_MISSING_TO_LEFT_TAG, m_AssignMissingToLeft, inserter);
     core::CPersistUtils::persist(NODE_VALUE_TAG, m_NodeValue, inserter);
     core::CPersistUtils::persist(SPLIT_VALUE_TAG, m_SplitValue, inserter);
+    core::CPersistUtils::persist(NUMBER_SAMPLES_TAG, m_NumberSamples, inserter);
 }
 
 bool CBoostedTreeNode::acceptRestoreTraverser(core::CStateRestoreTraverser& traverser) {
@@ -388,6 +390,8 @@ bool CBoostedTreeNode::acceptRestoreTraverser(core::CStateRestoreTraverser& trav
                 core::CPersistUtils::restore(NODE_VALUE_TAG, m_NodeValue, traverser))
         RESTORE(SPLIT_VALUE_TAG,
                 core::CPersistUtils::restore(SPLIT_VALUE_TAG, m_SplitValue, traverser))
+        RESTORE(NUMBER_SAMPLES_TAG,
+                core::CPersistUtils::restore(NUMBER_SAMPLES_TAG, m_NumberSamples, traverser))
     } while (traverser.next());
     return true;
 }
@@ -412,8 +416,16 @@ std::ostringstream& CBoostedTreeNode::doPrint(std::string pad,
 }
 
 void CBoostedTreeNode::accept(CVisitor& visitor) const {
-    visitor.addNode(m_SplitFeature, m_SplitValue, m_AssignMissingToLeft,
-                    m_NodeValue, m_Gain, m_LeftChild, m_RightChild);
+    visitor.addNode(m_SplitFeature, m_SplitValue, m_AssignMissingToLeft, m_NodeValue,
+                    m_Gain, m_NumberSamples, m_LeftChild, m_RightChild);
+}
+
+void CBoostedTreeNode::numberSamples(std::size_t numberSamples) {
+    m_NumberSamples = numberSamples;
+}
+
+std::size_t CBoostedTreeNode::numberSamples() const {
+    return m_NumberSamples;
 }
 
 CBoostedTree::CBoostedTree(core::CDataFrame& frame,

lib/maths/CBoostedTreeImpl.cc

@@ -217,6 +217,7 @@ void CBoostedTreeImpl::train(core::CDataFrame& frame,
         this->restoreBestHyperparameters();
         std::tie(m_BestForest, std::ignore) = this->trainForest(
             frame, allTrainingRowsMask, allTrainingRowsMask, m_TrainingProgress);
+
         m_Instrumentation->nextStep(static_cast<std::uint32_t>(m_CurrentRound));
         this->recordState(recordTrainStateCallback);
 
@@ -233,12 +234,56 @@ void CBoostedTreeImpl::train(core::CDataFrame& frame,
 
     this->computeProbabilityAtWhichToAssignClassOne(frame);
 
+    // populate numberSamples field in the final forest
+    this->computeNumberSamples(frame);
+
     // Force progress to one because we can have early exit from loop skip altogether.
     m_Instrumentation->updateProgress(1.0);
     m_Instrumentation->updateMemoryUsage(
         static_cast<std::int64_t>(this->memoryUsage()) - lastMemoryUsage);
 }
 
+void CBoostedTreeImpl::computeNumberSamples(const core::CDataFrame& frame) {
+    for (auto& tree : m_BestForest) {
+        if (tree.size() == 1) {
+            root(tree).numberSamples(frame.numberRows());
+        } else {
+            auto result = frame.readRows(
+                m_NumberThreads,
+                core::bindRetrievableState(
+                    [&](TSizeVec& samplesPerNode, const TRowItr& beginRows, const TRowItr& endRows) {
+                        for (auto row = beginRows; row != endRows; ++row) {
+                            auto encodedRow{m_Encoder->encode(*row)};
+                            const CBoostedTreeNode* node{&root(tree)};
+                            samplesPerNode[0] += 1;
+                            std::size_t nextIndex;
+                            while (node->isLeaf() == false) {
+                                if (node->assignToLeft(encodedRow)) {
+                                    nextIndex = node->leftChildIndex();
+                                } else {
+                                    nextIndex = node->rightChildIndex();
+                                }
+                                samplesPerNode[nextIndex] += 1;
+                                node = &(tree[nextIndex]);
+                            }
+                        }
+                    },
+                    TSizeVec(tree.size())));
+            auto& state = result.first;
+            TSizeVec totalSamplesPerNode{std::move(state[0].s_FunctionState)};
+            for (std::size_t i = 1; i < state.size(); ++i) {
+                for (std::size_t nodeIndex = 0;
+                     nodeIndex < totalSamplesPerNode.size(); ++nodeIndex) {
+                    totalSamplesPerNode[nodeIndex] += state[i].s_FunctionState[nodeIndex];
+                }
+            }
+            for (std::size_t i = 0; i < tree.size(); ++i) {
+                tree[i].numberSamples(totalSamplesPerNode[i]);
+            }
+        }
+    }
+}
+
 void CBoostedTreeImpl::recordState(const TTrainingStateCallback& recordTrainState) const {
     recordTrainState([this](core::CStatePersistInserter& inserter) {
         this->acceptPersistInserter(inserter);
@@ -987,6 +1032,10 @@ const CBoostedTreeNode& CBoostedTreeImpl::root(const TNodeVec& tree) {
     return tree[0];
 }
 
+CBoostedTreeNode& CBoostedTreeImpl::root(TNodeVec& tree) {
+    return tree[0];
+}
+
 double CBoostedTreeImpl::predictRow(const CEncodedDataFrameRowRef& row,
                                     const TNodeVecVec& forest) {
     double result{0.0};
@@ -1138,9 +1187,8 @@ std::size_t CBoostedTreeImpl::maximumTreeSize(std::size_t numberRows) const {
 }
 
 namespace {
-const std::string VERSION_7_5_TAG{"7.5"};
-const std::string VERSION_7_6_TAG{"7.6"};
-const TStrVec SUPPORTED_VERSIONS{VERSION_7_5_TAG, VERSION_7_6_TAG};
+const std::string VERSION_7_7_TAG{"7.7"};
+const TStrVec SUPPORTED_VERSIONS{VERSION_7_7_TAG};
 
 const std::string BAYESIAN_OPTIMIZATION_TAG{"bayesian_optimization"};
 const std::string BEST_FOREST_TAG{"best_forest"};
@@ -1204,7 +1252,7 @@ CBoostedTreeImpl::TStrVec CBoostedTreeImpl::bestHyperparameterNames() {
 }
 
 void CBoostedTreeImpl::acceptPersistInserter(core::CStatePersistInserter& inserter) const {
-    core::CPersistUtils::persist(VERSION_7_6_TAG, "", inserter);
+    core::CPersistUtils::persist(VERSION_7_7_TAG, "", inserter);
     core::CPersistUtils::persist(BAYESIAN_OPTIMIZATION_TAG, *m_BayesianOptimization, inserter);
     core::CPersistUtils::persist(BEST_FOREST_TEST_LOSS_TAG, m_BestForestTestLoss, inserter);
     core::CPersistUtils::persist(CURRENT_ROUND_TAG, m_CurrentRound, inserter);
@@ -1256,15 +1304,7 @@ void CBoostedTreeImpl::acceptPersistInserter(core::CStatePersistInserter& insert
 }
 
 bool CBoostedTreeImpl::acceptRestoreTraverser(core::CStateRestoreTraverser& traverser) {
-    if (traverser.name() == VERSION_7_5_TAG) {
-        // Force downsample factor to 1.0.
-        m_DownsampleFactorOverride = 1.0;
-        m_DownsampleFactor = 1.0;
-        m_BestHyperparameters.downsampleFactor(1.0);
-        // We can't stop cross-validation early because we haven't gathered the
-        // per fold test losses.
-        m_StopCrossValidationEarly = false;
-    } else if (traverser.name() != VERSION_7_6_TAG) {
+    if (traverser.name() != VERSION_7_7_TAG) {
         LOG_ERROR(<< "Input error: unsupported state serialization version. "
                   << "Currently supported versions: "
                   << core::CContainerPrinter::print(SUPPORTED_VERSIONS) << ".");