StochTree 0.1.1
Loading...
Searching...
No Matches
partition_tracker.h
1
25#ifndef STOCHTREE_PARTITION_TRACKER_H_
26#define STOCHTREE_PARTITION_TRACKER_H_
27
28#include <stochtree/data.h>
29#include <stochtree/ensemble.h>
30#include <stochtree/log.h>
31#include <stochtree/tree.h>
32
33#include <cmath>
34#include <numeric>
35#include <random>
36#include <set>
37#include <string>
38#include <vector>
39
40namespace StochTree {
41
43class SampleNodeMapper;
44class SamplePredMapper;
45class UnsortedNodeSampleTracker;
46class SortedNodeSampleTracker;
47class FeaturePresortRootContainer;
48
51 public:
60 ForestTracker(Eigen::MatrixXd& covariates, std::vector<FeatureType>& feature_types, int num_trees, int num_observations);
62 void ReconstituteFromForest(TreeEnsemble& forest, ForestDataset& dataset, ColumnVector& residual, bool is_mean_model);
63 void AssignAllSamplesToRoot();
64 void AssignAllSamplesToRoot(int32_t tree_num);
65 void AssignAllSamplesToConstantPrediction(double value);
66 void AssignAllSamplesToConstantPrediction(int32_t tree_num, double value);
67 void UpdatePredictions(TreeEnsemble* ensemble, ForestDataset& dataset);
68 void UpdateSampleTrackers(TreeEnsemble& forest, ForestDataset& dataset);
69 void UpdateSampleTrackersResidual(TreeEnsemble& forest, ForestDataset& dataset, ColumnVector& residual, bool is_mean_model);
70 void ResetRoot(Eigen::MatrixXd& covariates, std::vector<FeatureType>& feature_types, int32_t tree_num);
71 void AddSplit(Eigen::MatrixXd& covariates, TreeSplit& split, int32_t split_feature, int32_t tree_id, int32_t split_node_id, int32_t left_node_id, int32_t right_node_id, bool keep_sorted = false);
72 void RemoveSplit(Eigen::MatrixXd& covariates, Tree* tree, int32_t tree_id, int32_t split_node_id, int32_t left_node_id, int32_t right_node_id, bool keep_sorted = false);
73 double GetSamplePrediction(data_size_t sample_id);
74 double GetTreeSamplePrediction(data_size_t sample_id, int tree_id);
75 void UpdateVarWeightsFromInternalPredictions(ForestDataset& dataset);
76 void SetSamplePrediction(data_size_t sample_id, double value);
77 void SetTreeSamplePrediction(data_size_t sample_id, int tree_id, double value);
78 void SyncPredictions();
79 data_size_t GetNodeId(int observation_num, int tree_num);
80 data_size_t UnsortedNodeBegin(int tree_id, int node_id);
81 data_size_t UnsortedNodeEnd(int tree_id, int node_id);
82 data_size_t UnsortedNodeSize(int tree_id, int node_id);
83 data_size_t SortedNodeBegin(int node_id, int feature_id);
84 data_size_t SortedNodeEnd(int node_id, int feature_id);
85 data_size_t SortedNodeSize(int node_id, int feature_id);
86 std::vector<data_size_t>::iterator UnsortedNodeBeginIterator(int tree_id, int node_id);
87 std::vector<data_size_t>::iterator UnsortedNodeEndIterator(int tree_id, int node_id);
88 std::vector<data_size_t>::iterator SortedNodeBeginIterator(int node_id, int feature_id);
89 std::vector<data_size_t>::iterator SortedNodeEndIterator(int node_id, int feature_id);
90 SamplePredMapper* GetSamplePredMapper() {return sample_pred_mapper_.get();}
91 SampleNodeMapper* GetSampleNodeMapper() {return sample_node_mapper_.get();}
92 UnsortedNodeSampleTracker* GetUnsortedNodeSampleTracker() {return unsorted_node_sample_tracker_.get();}
93 SortedNodeSampleTracker* GetSortedNodeSampleTracker() {return sorted_node_sample_tracker_.get();}
94 int GetNumObservations() {return num_observations_;}
95 int GetNumTrees() {return num_trees_;}
96 int GetNumFeatures() {return num_features_;}
97 bool Initialized() {return initialized_;}
98
99 private:
101 std::vector<double> sum_predictions_;
103 std::unique_ptr<SamplePredMapper> sample_pred_mapper_;
105 std::unique_ptr<SampleNodeMapper> sample_node_mapper_;
109 std::unique_ptr<UnsortedNodeSampleTracker> unsorted_node_sample_tracker_;
113 std::unique_ptr<FeaturePresortRootContainer> presort_container_;
114 std::unique_ptr<SortedNodeSampleTracker> sorted_node_sample_tracker_;
115 std::vector<FeatureType> feature_types_;
116 int num_trees_;
117 int num_observations_;
118 int num_features_;
119 bool initialized_{false};
120
121 void UpdatePredictionsInternal(TreeEnsemble* ensemble, Eigen::MatrixXd& covariates, Eigen::MatrixXd& basis);
122 void UpdatePredictionsInternal(TreeEnsemble* ensemble, Eigen::MatrixXd& covariates);
123 void UpdateSampleTrackersInternal(TreeEnsemble& forest, Eigen::MatrixXd& covariates, Eigen::MatrixXd& basis);
124 void UpdateSampleTrackersInternal(TreeEnsemble& forest, Eigen::MatrixXd& covariates);
125 void UpdateSampleTrackersResidualInternalBasis(TreeEnsemble& forest, ForestDataset& dataset, ColumnVector& residual, bool is_mean_model);
126 void UpdateSampleTrackersResidualInternalNoBasis(TreeEnsemble& forest, ForestDataset& dataset, ColumnVector& residual, bool is_mean_model);
127};
128
131 public:
132 SamplePredMapper(int num_trees, data_size_t num_observations) {
133 num_trees_ = num_trees;
134 num_observations_ = num_observations;
135 // Initialize the vector of vectors of leaf indices for each tree
136 tree_preds_.resize(num_trees_);
137 for (int j = 0; j < num_trees_; j++) {
138 tree_preds_[j].resize(num_observations_);
139 }
140 }
141
142 inline double GetPred(data_size_t sample_id, int tree_id) {
143 CHECK_LT(sample_id, num_observations_);
144 CHECK_LT(tree_id, num_trees_);
145 return tree_preds_[tree_id][sample_id];
146 }
147
148 inline void SetPred(data_size_t sample_id, int tree_id, double value) {
149 CHECK_LT(sample_id, num_observations_);
150 CHECK_LT(tree_id, num_trees_);
151 tree_preds_[tree_id][sample_id] = value;
152 }
153
154 inline int NumTrees() {return num_trees_;}
155
156 inline int NumObservations() {return num_observations_;}
157
158 inline void AssignAllSamplesToConstantPrediction(int tree_id, double value) {
159 for (data_size_t i = 0; i < num_observations_; i++) {
160 tree_preds_[tree_id][i] = value;
161 }
162 }
163
164 private:
165 std::vector<std::vector<double>> tree_preds_;
166 int num_trees_;
167 data_size_t num_observations_;
168};
169
172 public:
173 SampleNodeMapper(int num_trees, data_size_t num_observations) {
174 num_trees_ = num_trees;
175 num_observations_ = num_observations;
176 // Initialize the vector of vectors of leaf indices for each tree
177 tree_observation_indices_.resize(num_trees_);
178 for (int j = 0; j < num_trees_; j++) {
179 tree_observation_indices_[j].resize(num_observations_);
180 }
181 }
182
184 num_trees_ = other.NumTrees();
185 num_observations_ = other.NumObservations();
186 // Initialize the vector of vectors of leaf indices for each tree
187 tree_observation_indices_.resize(num_trees_);
188 for (int j = 0; j < num_trees_; j++) {
189 tree_observation_indices_[j].resize(num_observations_);
190 for (int i = 0; i < num_observations_; i++) {
191 tree_observation_indices_[j][i] = other.GetNodeId(i, j);
192 }
193 }
194 }
195
196 void AddSplit(Eigen::MatrixXd& covariates, TreeSplit& split, int32_t split_feature, int32_t tree_id, int32_t split_node_id, int32_t left_node_id, int32_t right_node_id) {
197 CHECK_EQ(num_observations_, covariates.rows());
198 // Eigen::MatrixXd X = covariates.GetData();
199 for (int i = 0; i < num_observations_; i++) {
200 if (tree_observation_indices_[tree_id][i] == split_node_id) {
201 auto fvalue = covariates(i, split_feature);
202 if (split.SplitTrue(fvalue)) {
203 tree_observation_indices_[tree_id][i] = left_node_id;
204 } else {
205 tree_observation_indices_[tree_id][i] = right_node_id;
206 }
207 }
208 }
209 }
210
211 inline data_size_t GetNodeId(data_size_t sample_id, int tree_id) {
212 CHECK_LT(sample_id, num_observations_);
213 CHECK_LT(tree_id, num_trees_);
214 return tree_observation_indices_[tree_id][sample_id];
215 }
216
217 inline void SetNodeId(data_size_t sample_id, int tree_id, int node_id) {
218 CHECK_LT(sample_id, num_observations_);
219 CHECK_LT(tree_id, num_trees_);
220 tree_observation_indices_[tree_id][sample_id] = node_id;
221 }
222
223 inline int NumTrees() {return num_trees_;}
224
225 inline int NumObservations() {return num_observations_;}
226
227 inline void AssignAllSamplesToRoot(int tree_id) {
228 for (data_size_t i = 0; i < num_observations_; i++) {
229 tree_observation_indices_[tree_id][i] = 0;
230 }
231 }
232
233 private:
234 std::vector<std::vector<int>> tree_observation_indices_;
235 int num_trees_;
236 data_size_t num_observations_;
237};
238
241 public:
242 FeatureUnsortedPartition(data_size_t n);
243
246
248 void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int left_node_id, int right_node_id, int feature_split, TreeSplit& split);
249
251 void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int left_node_id, int right_node_id, int feature_split, double split_value);
252
254 void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int left_node_id, int right_node_id, int feature_split, std::vector<std::uint32_t> const& category_list);
255
257 void PruneNodeToLeaf(int node_id);
258
260 bool IsLeaf(int node_id);
261
263 bool IsValidNode(int node_id);
264
266 bool LeftNodeIsLeaf(int node_id);
267
269 bool RightNodeIsLeaf(int node_id);
270
272 data_size_t NodeBegin(int node_id);
273
275 data_size_t NodeEnd(int node_id);
276
278 data_size_t NodeSize(int node_id);
279
281 int Parent(int node_id);
282
284 int LeftNode(int node_id);
285
287 int RightNode(int node_id);
288
290 std::vector<data_size_t> indices_;
291
293 std::vector<data_size_t> NodeIndices(int node_id);
294
296 void UpdateObservationMapping(int node_id, int tree_id, SampleNodeMapper* sample_node_mapper);
297
298 private:
299 // Vectors tracking indices in each node
300 std::vector<data_size_t> node_begin_;
301 std::vector<data_size_t> node_length_;
302 std::vector<int32_t> parent_nodes_;
303 std::vector<int32_t> left_nodes_;
304 std::vector<int32_t> right_nodes_;
305 int num_nodes_, num_deleted_nodes_;
306 std::vector<int> deleted_nodes_;
307
308 // Private helper functions
309 void ExpandNodeTrackingVectors(int node_id, int left_node_id, int right_node_id, data_size_t node_start_idx, data_size_t num_left, data_size_t num_right);
310 void ConvertLeafParentToLeaf(int node_id);
311};
312
315 public:
316 UnsortedNodeSampleTracker(data_size_t n, int num_trees) {
317 feature_partitions_.resize(num_trees);
318 num_trees_ = num_trees;
319 for (int i = 0; i < num_trees; i++) {
320 feature_partitions_[i].reset(new FeatureUnsortedPartition(n));
321 }
322 }
323
326
328 void PartitionTreeNode(Eigen::MatrixXd& covariates, int tree_id, int node_id, int left_node_id, int right_node_id, int feature_split, TreeSplit& split) {
329 return feature_partitions_[tree_id]->PartitionNode(covariates, node_id, left_node_id, right_node_id, feature_split, split);
330 }
331
333 void PartitionTreeNode(Eigen::MatrixXd& covariates, int tree_id, int node_id, int left_node_id, int right_node_id, int feature_split, double split_value) {
334 return feature_partitions_[tree_id]->PartitionNode(covariates, node_id, left_node_id, right_node_id, feature_split, split_value);
335 }
336
338 void PartitionTreeNode(Eigen::MatrixXd& covariates, int tree_id, int node_id, int left_node_id, int right_node_id, int feature_split, std::vector<std::uint32_t> const& category_list) {
339 return feature_partitions_[tree_id]->PartitionNode(covariates, node_id, left_node_id, right_node_id, feature_split, category_list);
340 }
341
343 void ResetTreeToRoot(int tree_id, data_size_t n) {
344 feature_partitions_[tree_id].reset(new FeatureUnsortedPartition(n));;
345 }
346
348 void PruneTreeNodeToLeaf(int tree_id, int node_id) {
349 return feature_partitions_[tree_id]->PruneNodeToLeaf(node_id);
350 }
351
353 bool IsLeaf(int tree_id, int node_id) {
354 return feature_partitions_[tree_id]->IsLeaf(node_id);
355 }
356
358 bool IsValidNode(int tree_id, int node_id) {
359 return feature_partitions_[tree_id]->IsValidNode(node_id);
360 }
361
363 bool LeftNodeIsLeaf(int tree_id, int node_id) {
364 return feature_partitions_[tree_id]->LeftNodeIsLeaf(node_id);
365 }
366
368 bool RightNodeIsLeaf(int tree_id, int node_id) {
369 return feature_partitions_[tree_id]->RightNodeIsLeaf(node_id);
370 }
371
373 data_size_t NodeBegin(int tree_id, int node_id) {
374 return feature_partitions_[tree_id]->NodeBegin(node_id);
375 }
376
378 data_size_t NodeEnd(int tree_id, int node_id) {
379 return feature_partitions_[tree_id]->NodeEnd(node_id);
380 }
381
382 std::vector<data_size_t>::iterator NodeBeginIterator(int tree_id, int node_id) {
383 data_size_t node_begin = feature_partitions_[tree_id]->NodeBegin(node_id);
384 auto begin_iter = feature_partitions_[tree_id]->indices_.begin();
385 return begin_iter + node_begin;
386 }
387
388 std::vector<data_size_t>::iterator NodeEndIterator(int tree_id, int node_id) {
389 int node_end = feature_partitions_[tree_id]->NodeEnd(node_id);
390 auto begin_iter = feature_partitions_[tree_id]->indices_.begin();
391 return begin_iter + node_end;
392 }
393
395 data_size_t NodeSize(int tree_id, int node_id) {
396 return feature_partitions_[tree_id]->NodeSize(node_id);
397 }
398
400 int Parent(int tree_id, int node_id) {
401 return feature_partitions_[tree_id]->Parent(node_id);
402 }
403
405 int LeftNode(int tree_id, int node_id) {
406 return feature_partitions_[tree_id]->LeftNode(node_id);
407 }
408
410 int RightNode(int tree_id, int node_id) {
411 return feature_partitions_[tree_id]->RightNode(node_id);
412 }
413
415 std::vector<data_size_t> TreeNodeIndices(int tree_id, int node_id) {
416 return feature_partitions_[tree_id]->NodeIndices(node_id);
417 }
418
420 void UpdateObservationMapping(int node_id, int tree_id, SampleNodeMapper* sample_node_mapper) {
421 feature_partitions_[tree_id]->UpdateObservationMapping(node_id, tree_id, sample_node_mapper);
422 }
423
425 void UpdateObservationMapping(Tree* tree, int tree_id, SampleNodeMapper* sample_node_mapper) {
426 std::vector<int> leaves = tree->GetLeaves();
427 int leaf;
428 for (int i = 0; i < leaves.size(); i++) {
429 leaf = leaves[i];
430 UpdateObservationMapping(leaf, tree_id, sample_node_mapper);
431 }
432 }
433
435 int NumTrees() { return num_trees_; }
436
438 FeatureUnsortedPartition* GetFeaturePartition(int i) { return feature_partitions_[i].get(); }
439
440 private:
441 // Vectors of feature partitions
442 std::vector<std::unique_ptr<FeatureUnsortedPartition>> feature_partitions_;
443 int num_trees_;
444};
445
448 public:
449 NodeOffsetSize(data_size_t node_offset, data_size_t node_size) : node_begin_{node_offset}, node_size_{node_size}, presorted_{false} {
450 node_end_ = node_begin_ + node_size_;
451 }
452
453 ~NodeOffsetSize() {}
454
455 void SetSorted() {presorted_ = true;}
456
457 bool IsSorted() {return presorted_;}
458
459 data_size_t Begin() {return node_begin_;}
460
461 data_size_t End() {return node_end_;}
462
463 data_size_t Size() {return node_size_;}
464
465 private:
466 data_size_t node_begin_;
467 data_size_t node_size_;
468 data_size_t node_end_;
469 bool presorted_;
470};
471
474
486 public:
487 FeaturePresortRoot(Eigen::MatrixXd& covariates, int32_t feature_index, FeatureType feature_type) {
488 feature_index_ = feature_index;
489 ArgsortRoot(covariates);
490 }
491
493
494 void ArgsortRoot(Eigen::MatrixXd& covariates) {
495 data_size_t num_obs = covariates.rows();
496
497 // Make a vector of indices from 0 to num_obs - 1
498 if (feature_sort_indices_.size() != num_obs){
499 feature_sort_indices_.resize(num_obs, 0);
500 }
501 std::iota(feature_sort_indices_.begin(), feature_sort_indices_.end(), 0);
502
503 // Define a custom comparator to be used with stable_sort:
504 // For every two indices l and r store as elements of `data_sort_indices_`,
505 // compare them for sorting purposes by indexing the covariate's raw data with both l and r
506 auto comp_op = [&](size_t const &l, size_t const &r) { return std::less<double>{}(covariates(l, feature_index_), covariates(r, feature_index_)); };
507 std::stable_sort(feature_sort_indices_.begin(), feature_sort_indices_.end(), comp_op);
508 }
509
510 private:
511 std::vector<data_size_t> feature_sort_indices_;
512 int32_t feature_index_;
513};
514
517 public:
518 FeaturePresortRootContainer(Eigen::MatrixXd& covariates, std::vector<FeatureType>& feature_types) {
519 num_features_ = covariates.cols();
520 feature_presort_.resize(num_features_);
521 for (int i = 0; i < num_features_; i++) {
522 feature_presort_[i].reset(new FeaturePresortRoot(covariates, i, feature_types[i]));
523 }
524 }
525
527
528 FeaturePresortRoot* GetFeaturePresort(int feature_num) {return feature_presort_[feature_num].get(); }
529
530 private:
531 std::vector<std::unique_ptr<FeaturePresortRoot>> feature_presort_;
532 int num_features_;
533};
534
546 public:
547 FeaturePresortPartition(FeaturePresortRoot* feature_presort_root, Eigen::MatrixXd& covariates, int32_t feature_index, FeatureType feature_type) {
548 // Unpack all feature details
549 feature_index_ = feature_index;
550 feature_type_ = feature_type;
551 num_obs_ = covariates.rows();
552 feature_sort_indices_ = feature_presort_root->feature_sort_indices_;
553
554 // Initialize new tree to root
555 data_size_t node_offset = 0;
556 node_offset_sizes_.emplace_back(node_offset, num_obs_);
557 }
558
560
562 void SplitFeature(Eigen::MatrixXd& covariates, int32_t node_id, int32_t feature_index, TreeSplit& split);
563
565 void SplitFeatureNumeric(Eigen::MatrixXd& covariates, int32_t node_id, int32_t feature_index, double split_value);
566
568 void SplitFeatureCategorical(Eigen::MatrixXd& covariates, int32_t node_id, int32_t feature_index, std::vector<std::uint32_t> const& category_list);
569
571 data_size_t NodeBegin(int32_t node_id) {return node_offset_sizes_[node_id].Begin();}
572
574 data_size_t NodeEnd(int32_t node_id) {return node_offset_sizes_[node_id].End();}
575
577 data_size_t NodeSize(int32_t node_id) {return node_offset_sizes_[node_id].Size();}
578
580 std::vector<data_size_t> NodeIndices(int node_id);
581
583 data_size_t SortIndex(data_size_t j) {return feature_sort_indices_[j];}
584
586 FeatureType GetFeatureType() {return feature_type_;}
587
589 void UpdateObservationMapping(int node_id, int tree_id, SampleNodeMapper* sample_node_mapper);
590
592 std::vector<data_size_t> feature_sort_indices_;
593 private:
595 void AddLeftRightNodes(data_size_t left_node_begin, data_size_t left_node_size, data_size_t right_node_begin, data_size_t right_node_size);
596
598 std::vector<NodeOffsetSize> node_offset_sizes_;
599 int32_t feature_index_;
600 FeatureType feature_type_;
601 data_size_t num_obs_;
602};
603
606 public:
607 SortedNodeSampleTracker(FeaturePresortRootContainer* feature_presort_root_container, Eigen::MatrixXd& covariates, std::vector<FeatureType>& feature_types) {
608 num_features_ = covariates.cols();
609 feature_partitions_.resize(num_features_);
610 FeaturePresortRoot* feature_presort_root;
611 for (int i = 0; i < num_features_; i++) {
612 feature_presort_root = feature_presort_root_container->GetFeaturePresort(i);
613 feature_partitions_[i].reset(new FeaturePresortPartition(feature_presort_root, covariates, i, feature_types[i]));
614 }
615 }
616
618 void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int feature_split, TreeSplit& split) {
619 for (int i = 0; i < num_features_; i++) {
620 feature_partitions_[i]->SplitFeature(covariates, node_id, feature_split, split);
621 }
622 }
623
625 void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int feature_split, double split_value) {
626 for (int i = 0; i < num_features_; i++) {
627 feature_partitions_[i]->SplitFeatureNumeric(covariates, node_id, feature_split, split_value);
628 }
629 }
630
632 void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int feature_split, std::vector<std::uint32_t> const& category_list) {
633 for (int i = 0; i < num_features_; i++) {
634 feature_partitions_[i]->SplitFeatureCategorical(covariates, node_id, feature_split, category_list);
635 }
636 }
637
639 data_size_t NodeBegin(int node_id, int feature_index) {
640 return feature_partitions_[feature_index]->NodeBegin(node_id);
641 }
642
644 data_size_t NodeEnd(int node_id, int feature_index) {
645 return feature_partitions_[feature_index]->NodeEnd(node_id);
646 }
647
649 data_size_t NodeSize(int node_id, int feature_index) {
650 return feature_partitions_[feature_index]->NodeSize(node_id);
651 }
652
653 std::vector<data_size_t>::iterator NodeBeginIterator(int node_id, int feature_index) {
654 data_size_t node_begin = NodeBegin(node_id, feature_index);
655 auto begin_iter = feature_partitions_[feature_index]->feature_sort_indices_.begin();
656 return begin_iter + node_begin;
657 }
658
659 std::vector<data_size_t>::iterator NodeEndIterator(int node_id, int feature_index) {
660 data_size_t node_end = NodeEnd(node_id, feature_index);
661 auto begin_iter = feature_partitions_[feature_index]->feature_sort_indices_.begin();
662 return begin_iter + node_end;
663 }
664
666 std::vector<data_size_t> NodeIndices(int node_id, int feature_index) {
667 return feature_partitions_[feature_index]->NodeIndices(node_id);
668 }
669
671 data_size_t SortIndex(data_size_t j, int feature_index) {return feature_partitions_[feature_index]->SortIndex(j); }
672
674 void UpdateObservationMapping(int node_id, int tree_id, SampleNodeMapper* sample_node_mapper, int feature_index = 0) {
675 feature_partitions_[feature_index]->UpdateObservationMapping(node_id, tree_id, sample_node_mapper);
676 }
677
678 private:
679 std::vector<std::unique_ptr<FeaturePresortPartition>> feature_partitions_;
680 int num_features_;
681};
682
683} // namespace StochTree
684
685#endif // STOCHTREE_PARTITION_TRACKER_H_
Internal wrapper around Eigen::VectorXd interface for univariate floating point data....
Definition data.h:194
Data structure that tracks pre-sorted feature values through a tree's split lifecycle.
Definition partition_tracker.h:545
void SplitFeature(Eigen::MatrixXd &covariates, int32_t node_id, int32_t feature_index, TreeSplit &split)
Split numeric / ordered categorical feature and update sort indices.
data_size_t NodeEnd(int32_t node_id)
End position of node indexed by node_id.
Definition partition_tracker.h:574
std::vector< data_size_t > NodeIndices(int node_id)
Data indices for a given node.
std::vector< data_size_t > feature_sort_indices_
Feature sort indices.
Definition partition_tracker.h:592
data_size_t NodeSize(int32_t node_id)
Size (in observations) of node indexed by node_id.
Definition partition_tracker.h:577
FeatureType GetFeatureType()
Feature type.
Definition partition_tracker.h:586
data_size_t SortIndex(data_size_t j)
Feature sort index j.
Definition partition_tracker.h:583
data_size_t NodeBegin(int32_t node_id)
Start position of node indexed by node_id.
Definition partition_tracker.h:571
void UpdateObservationMapping(int node_id, int tree_id, SampleNodeMapper *sample_node_mapper)
Update SampleNodeMapper for all the observations in node_id.
void SplitFeatureNumeric(Eigen::MatrixXd &covariates, int32_t node_id, int32_t feature_index, double split_value)
Split numeric / ordered categorical feature and update sort indices.
void SplitFeatureCategorical(Eigen::MatrixXd &covariates, int32_t node_id, int32_t feature_index, std::vector< std::uint32_t > const &category_list)
Split unordered categorical feature and update sort indices.
Container class for FeaturePresortRoot objects stored for every feature in a dataset.
Definition partition_tracker.h:516
Data structure for presorting a feature by its values.
Definition partition_tracker.h:484
Mapping nodes to the indices they contain.
Definition partition_tracker.h:240
void PartitionNode(Eigen::MatrixXd &covariates, int node_id, int left_node_id, int right_node_id, int feature_split, double split_value)
Partition a node based on a new split rule.
void PartitionNode(Eigen::MatrixXd &covariates, int node_id, int left_node_id, int right_node_id, int feature_split, TreeSplit &split)
Partition a node based on a new split rule.
int RightNode(int node_id)
Right child of node_id.
int Parent(int node_id)
Parent node_id.
data_size_t NodeEnd(int node_id)
One past the last index of data points contained in node_id.
std::vector< data_size_t > indices_
Data indices.
Definition partition_tracker.h:290
void PartitionNode(Eigen::MatrixXd &covariates, int node_id, int left_node_id, int right_node_id, int feature_split, std::vector< std::uint32_t > const &category_list)
Partition a node based on a new split rule.
bool RightNodeIsLeaf(int node_id)
Whether node_id's right child is a leaf.
void UpdateObservationMapping(int node_id, int tree_id, SampleNodeMapper *sample_node_mapper)
Update SampleNodeMapper for all the observations in node_id.
data_size_t NodeSize(int node_id)
Number of data points contained in node_id.
std::vector< data_size_t > NodeIndices(int node_id)
Data indices for a given node.
void ReconstituteFromTree(Tree &tree, ForestDataset &dataset)
Reconstitute a tree partition tracker from root based on a tree.
bool IsLeaf(int node_id)
Whether node_id is a leaf.
void PruneNodeToLeaf(int node_id)
Convert a (currently split) node to a leaf.
bool LeftNodeIsLeaf(int node_id)
Whether node_id's left child is a leaf.
int LeftNode(int node_id)
Left child of node_id.
bool IsValidNode(int node_id)
Whether node_id is a valid node.
data_size_t NodeBegin(int node_id)
First index of data points contained in node_id.
API for loading and accessing data used to sample tree ensembles The covariates / bases / weights use...
Definition data.h:272
"Superclass" wrapper around tracking data structures for forest sampling algorithms
Definition partition_tracker.h:50
ForestTracker(Eigen::MatrixXd &covariates, std::vector< FeatureType > &feature_types, int num_trees, int num_observations)
Construct a new ForestTracker object.
Tracking cutpoints available at a given node.
Definition partition_tracker.h:447
Class storing sample-node map for each tree in an ensemble.
Definition partition_tracker.h:171
Class storing sample-prediction map for each tree in an ensemble.
Definition partition_tracker.h:130
Data structure for tracking observations through a tree partition with each feature pre-sorted.
Definition partition_tracker.h:605
data_size_t NodeSize(int node_id, int feature_index)
One past the last index of data points contained in node_id.
Definition partition_tracker.h:649
void UpdateObservationMapping(int node_id, int tree_id, SampleNodeMapper *sample_node_mapper, int feature_index=0)
Update SampleNodeMapper for all the observations in node_id.
Definition partition_tracker.h:674
std::vector< data_size_t > NodeIndices(int node_id, int feature_index)
Data indices for a given node.
Definition partition_tracker.h:666
void PartitionNode(Eigen::MatrixXd &covariates, int node_id, int feature_split, double split_value)
Partition a node based on a new split rule.
Definition partition_tracker.h:625
void PartitionNode(Eigen::MatrixXd &covariates, int node_id, int feature_split, TreeSplit &split)
Partition a node based on a new split rule.
Definition partition_tracker.h:618
data_size_t SortIndex(data_size_t j, int feature_index)
Feature sort index j for feature_index.
Definition partition_tracker.h:671
data_size_t NodeBegin(int node_id, int feature_index)
First index of data points contained in node_id.
Definition partition_tracker.h:639
void PartitionNode(Eigen::MatrixXd &covariates, int node_id, int feature_split, std::vector< std::uint32_t > const &category_list)
Partition a node based on a new split rule.
Definition partition_tracker.h:632
data_size_t NodeEnd(int node_id, int feature_index)
One past the last index of data points contained in node_id.
Definition partition_tracker.h:644
Class storing a "forest," or an ensemble of decision trees.
Definition ensemble.h:37
Representation of arbitrary tree split rules, including numeric split rules (X[,i] <= c) and categori...
Definition tree.h:961
bool SplitTrue(double fvalue)
Whether a given covariate value is True or False on the rule defined by a TreeSplit object.
Definition tree.h:993
Decision tree data structure.
Definition tree.h:69
std::vector< std::int32_t > const & GetLeaves() const
Get indices of all leaf nodes.
Definition tree.h:567
Mapping nodes to the indices they contain.
Definition partition_tracker.h:314
void PartitionTreeNode(Eigen::MatrixXd &covariates, int tree_id, int node_id, int left_node_id, int right_node_id, int feature_split, std::vector< std::uint32_t > const &category_list)
Partition a node based on a new split rule.
Definition partition_tracker.h:338
void UpdateObservationMapping(Tree *tree, int tree_id, SampleNodeMapper *sample_node_mapper)
Update SampleNodeMapper for all the observations in tree.
Definition partition_tracker.h:425
int RightNode(int tree_id, int node_id)
Right child of node_id.
Definition partition_tracker.h:410
bool IsLeaf(int tree_id, int node_id)
Whether node_id is a leaf.
Definition partition_tracker.h:353
bool RightNodeIsLeaf(int tree_id, int node_id)
Whether node_id's right child is a leaf.
Definition partition_tracker.h:368
bool IsValidNode(int tree_id, int node_id)
Whether node_id is a valid node.
Definition partition_tracker.h:358
void UpdateObservationMapping(int node_id, int tree_id, SampleNodeMapper *sample_node_mapper)
Update SampleNodeMapper for all the observations in node_id.
Definition partition_tracker.h:420
data_size_t NodeBegin(int tree_id, int node_id)
First index of data points contained in node_id.
Definition partition_tracker.h:373
int NumTrees()
Number of trees.
Definition partition_tracker.h:435
int LeftNode(int tree_id, int node_id)
Left child of node_id.
Definition partition_tracker.h:405
void ResetTreeToRoot(int tree_id, data_size_t n)
Convert a tree to root.
Definition partition_tracker.h:343
void PartitionTreeNode(Eigen::MatrixXd &covariates, int tree_id, int node_id, int left_node_id, int right_node_id, int feature_split, TreeSplit &split)
Partition a node based on a new split rule.
Definition partition_tracker.h:328
data_size_t NodeSize(int tree_id, int node_id)
One past the last index of data points contained in node_id.
Definition partition_tracker.h:395
bool LeftNodeIsLeaf(int tree_id, int node_id)
Whether node_id's left child is a leaf.
Definition partition_tracker.h:363
int Parent(int tree_id, int node_id)
Parent node_id.
Definition partition_tracker.h:400
void ReconstituteFromForest(TreeEnsemble &forest, ForestDataset &dataset)
Reconstruct the node sample tracker based on the splits in a forest.
data_size_t NodeEnd(int tree_id, int node_id)
One past the last index of data points contained in node_id.
Definition partition_tracker.h:378
void PartitionTreeNode(Eigen::MatrixXd &covariates, int tree_id, int node_id, int left_node_id, int right_node_id, int feature_split, double split_value)
Partition a node based on a new split rule.
Definition partition_tracker.h:333
FeatureUnsortedPartition * GetFeaturePartition(int i)
Number of trees.
Definition partition_tracker.h:438
std::vector< data_size_t > TreeNodeIndices(int tree_id, int node_id)
Data indices for a given node.
Definition partition_tracker.h:415
void PruneTreeNodeToLeaf(int tree_id, int node_id)
Convert a (currently split) node to a leaf.
Definition partition_tracker.h:348
Definition category_tracker.h:40