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random_forest_visitors.hxx |

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3 /* Copyright 2014-2015 by Ullrich Koethe and Philip Schill */
4 /* */
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34 /************************************************************************/
35 #ifndef VIGRA_RF3_VISITORS_HXX
36 #define VIGRA_RF3_VISITORS_HXX
37
38 #include <vector>
39 #include <memory>
40 #include "../multi_array.hxx"
41 #include "../multi_shape.hxx"
42 #include <typeinfo>
43
44
45 namespace vigra
46 {
47 namespace rf3
48 {
49
50 /**
51 * @brief Base class from which all random forest visitors derive.
52 *
53 * @details
54 * Due to the parallel training, we cannot simply use a single visitor for all trees.
55 * Instead, each tree gets a copy of the original visitor.
56 *
57 * The random forest training with visitors looks as follows:
58 * - Do the random forest preprocessing (translate labels to 0, 1, 2, ...).
59 * - Call visit_at_beginning() on the original visitor.
60 * - For each tree:
61 * - - Copy the original visitor and give the copy to the tree.
62 * - - Do the preprocessing (create the bootstrap sample, assign weights to the data points, ...).
63 * - - Call visit_before_tree() on the visitor copy.
64 * - - Do the node splitting until the tree is fully trained.
65 * - - Call visit_after_tree() on the visitor copy.
66 * - Call visit_at_end (which gets a vector with pointers to the visitor copies) on the original visitor.
67 */
68 class RFVisitorBase
69 {
70 public:
71
72 RFVisitorBase()
73 :
74 active_(true)
75 {}
76
77 /**
78 * @brief Do something before training starts.
79 */
80 void visit_before_training()
81 {}
82
83 /**
84 * @brief Do something after all trees have been learned.
85 *
86 * @param v vector with pointers to the visitor copies
87 * @param rf the trained random forest
88 */
89 template <typename VISITORS, typename RF, typename FEATURES, typename LABELS>
90 void visit_after_training(VISITORS &, RF &, const FEATURES &, const LABELS &)
91 {}
92
93 /**
94 * @brief Do something before a tree has been learned.
95 *
96 * @param weights the actual instance weights (after bootstrap sampling and class weights)
97 */
98 template <typename TREE, typename FEATURES, typename LABELS, typename WEIGHTS>
99 void visit_before_tree(TREE &, FEATURES &, LABELS &, WEIGHTS &)
100 {}
101
102 /**
103 * @brief Do something after a tree has been learned.
104 */
105 template <typename RF, typename FEATURES, typename LABELS, typename WEIGHTS>
106 void visit_after_tree(RF &,
107 FEATURES &,
108 LABELS &,
109 WEIGHTS &)
110 {}
111
112 /**
113 * @brief Do something after the split was made.
114 */
115 template <typename TREE,
116 typename FEATURES,
117 typename LABELS,
118 typename WEIGHTS,
119 typename SCORER,
120 typename ITER>
121 void visit_after_split(TREE &,
122 FEATURES &,
123 LABELS &,
124 WEIGHTS &,
125 SCORER &,
126 ITER,
127 ITER,
128 ITER)
129 {}
130
131 /**
132 * @brief Return whether the visitor is active or not.
133 */
134 bool is_active() const
135 {
136 return active_;
137 }
138
139 /**
140 * @brief Activate the visitor.
141 */
142 void activate()
143 {
144 active_ = true;
145 }
146
147 /**
148 * @brief Deactivate the visitor.
149 */
150 void deactivate()
151 {
152 active_ = false;
153 }
154
155 private:
156
157 bool active_;
158
159 };
160
161 /////////////////////////////////////////////
162 // The concrete visitors //
163 /////////////////////////////////////////////
164
165 /**
166 * @brief Compute the out of bag error.
167 *
168 * After training, each data point is put down those trees for which it is OOB.
169 * Using bootstrap sampling, each data point is OOB for approximately 37% of
170 * the trees.
171 */
172 class OOBError : public RFVisitorBase
173 {
174 public:
175
176 /**
177 * Save whether a data point is in-bag (weight > 0) or out-of-bag (weight == 0).
178 */
179 template <typename TREE, typename FEATURES, typename LABELS, typename WEIGHTS>
180 void visit_before_tree(
181 TREE & /*tree*/,
182 FEATURES & /*features*/,
183 LABELS & /*labels*/,
184 WEIGHTS & weights
185 ){
186 double const EPS = 1e-20;
187 bool found = false;
188
189 // Save the in-bags.
190 is_in_bag_.resize(weights.size(), true);
191 for (size_t i = 0; i < weights.size(); ++i)
192 {
193 if (std::abs(weights[i]) < EPS)
194 {
195 is_in_bag_[i] = false;
196 found = true;
197 }
198 }
199
200 if (!found)
201 throw std::runtime_error("OOBError::visit_before_tree(): The tree has no out-of-bags.");
202 }
203
204 /**
205 * Compute the out-of-bag error.
206 */
207 template <typename VISITORS, typename RF, typename FEATURES, typename LABELS>
208 void visit_after_training(
209 VISITORS & visitors,
210 RF & rf,
211 const FEATURES & features,
212 const LABELS & labels
213 ){
214 // Check the input sizes.
215 vigra_precondition(rf.num_trees() > 0, "OOBError::visit_after_training(): Number of trees must be greater than zero after training.");
216 vigra_precondition(visitors.size() == rf.num_trees(), "OOBError::visit_after_training(): Number of visitors must be equal to number of trees.");
217 size_t const num_instances = features.shape()[0];
218 auto const num_features = features.shape()[1];
219 for (auto vptr : visitors)
220 vigra_precondition(vptr->is_in_bag_.size() == num_instances, "OOBError::visit_after_training(): Some visitors have the wrong number of data points.");
221
222 // Get a prediction for each data point using only the trees where it is out of bag.
223 typedef typename std::remove_const<LABELS>::type Labels;
224 Labels pred(Shape1(1));
225 oob_err_ = 0.0;
226 for (size_t i = 0; i < (size_t)num_instances; ++i)
227 {
228 // Get the indices of the trees where the data points is out of bag.
229 std::vector<size_t> tree_indices;
230 for (size_t k = 0; k < visitors.size(); ++k)
231 if (!visitors[k]->is_in_bag_[i])
232 tree_indices.push_back(k);
233
234 // Get the prediction using the above trees.
235 auto const sub_features = features.subarray(Shape2(i, 0), Shape2(i+1, num_features));
236 rf.predict(sub_features, pred, 1, tree_indices);
237 if (pred(0) != labels(i))
238 oob_err_ += 1.0;
239 }
240 oob_err_ /= num_instances;
241 }
242
243 /**
244 * the out-of-bag error
245 */
246 double oob_err_;
247
248 private:
249 std::vector<bool> is_in_bag_; // whether a data point is in-bag or out-of-bag
250 };
251
252
253
254 /**
255 * @brief Compute the variable importance.
256 */
257 class VariableImportance : public RFVisitorBase
258 {
259 public:
260
261 VariableImportance(size_t repetition_count = 10)
262 :
263 repetition_count_(repetition_count)
264 {}
265
266 /**
267 * Resize the variable importance array and store in-bag / out-of-bag information.
268 */
269 template <typename TREE, typename FEATURES, typename LABELS, typename WEIGHTS>
270 void visit_before_tree(
271 TREE & tree,
272 FEATURES & features,
273 LABELS & /*labels*/,
274 WEIGHTS & weights
275 ){
276 // Resize the variable importance array.
277 // The shape differs from the shape of the actual output, since the single trees
278 // only store the impurity decrease without the permutation importances.
279 auto const num_features = features.shape()[1];
280 variable_importance_.reshape(Shape2(num_features, tree.num_classes()+2), 0.0);
281
282 // Save the in-bags.
283 double const EPS = 1e-20;
284 bool found = false;
285 is_in_bag_.resize(weights.size(), true);
286 for (size_t i = 0; i < weights.size(); ++i)
287 {
288 if (std::abs(weights[i]) < EPS)
289 {
290 is_in_bag_[i] = false;
291 found = true;
292 }
293 }
294 if (!found)
295 throw std::runtime_error("VariableImportance::visit_before_tree(): The tree has no out-of-bags.");
296 }
297
298 /**
299 * Calculate the impurity decrease based variable importance after every split.
300 */
301 template <typename TREE,
302 typename FEATURES,
303 typename LABELS,
304 typename WEIGHTS,
305 typename SCORER,
306 typename ITER>
307 void visit_after_split(TREE & tree,
308 FEATURES & /*features*/,
309 LABELS & labels,
310 WEIGHTS & weights,
311 SCORER & scorer,
312 ITER begin,
313 ITER /*split*/,
314 ITER end)
315 {
316 // Update the impurity decrease.
317 typename SCORER::Functor functor;
318 auto const region_impurity = functor.region_score(labels, weights, begin, end);
319 auto const split_impurity = scorer.best_score_;
320 variable_importance_(scorer.best_dim_, tree.num_classes()+1) += region_impurity - split_impurity;
321 }
322
323 /**
324 * Compute the permutation importance.
325 */
326 template <typename RF, typename FEATURES, typename LABELS, typename WEIGHTS>
327 void visit_after_tree(RF & rf,
328 const FEATURES & features,
329 const LABELS & labels,
330 WEIGHTS & /*weights*/)
331 {
332 // Non-const types of features and labels.
333 typedef typename std::remove_const<FEATURES>::type Features;
334 typedef typename std::remove_const<LABELS>::type Labels;
335
336 typedef typename Features::value_type FeatureType;
337
338 auto const num_features = features.shape()[1];
339
340 // For the permutation importance, the features must be permuted (obviously).
341 // This cannot be done on the original feature matrix, since it would interfere
342 // with other threads in concurrent training. Therefore, we have to make a copy.
343 Features feats;
344 Labels labs;
345 copy_out_of_bags(features, labels, feats, labs);
346 auto const num_oobs = feats.shape()[0];
347
348 // Compute (standard and class-wise) out-of-bag success rate with the original sample.
349 MultiArray<1, double> oob_right(Shape1(rf.num_classes()+1), 0.0);
350 vigra::MultiArray<1,int> pred( (Shape1(num_oobs)) );
351 rf.predict(feats, pred, 1);
352 for (size_t i = 0; i < (size_t)labs.size(); ++i)
353 {
354 if (labs(i) == pred(i))
355 {
356 oob_right(labs(i)) += 1.0; // per class
357 oob_right(rf.num_classes()) += 1.0; // total
358 }
359 }
360
361 // Get out-of-bag success rate after permuting the j'th dimension.
362 UniformIntRandomFunctor<MersenneTwister> randint;
363 for (size_t j = 0; j < (size_t)num_features; ++j)
364 {
365 MultiArray<1, FeatureType> backup(( Shape1(num_oobs) ));
366 backup = feats.template bind<1>(j);
367 MultiArray<2, double> perm_oob_right(Shape2(1, rf.num_classes()+1), 0.0);
368
369 for (size_t k = 0; k < repetition_count_; ++k)
370 {
371 // Permute the current dimension.
372 for (int ii = num_oobs-1; ii >= 1; --ii)
373 std::swap(feats(ii, j), feats(randint(ii+1), j));
374
375 // Get the out-of-bag success rate after permuting.
376 rf.predict(feats, pred, 1);
377 for (size_t i = 0; i < (size_t)labs.size(); ++i)
378 {
379 if (labs(i) == pred(i))
380 {
381 perm_oob_right(0, labs(i)) += 1.0; // per class
382 perm_oob_right(0, rf.num_classes()) += 1.0; // total
383 }
384 }
385 }
386
387 // Normalize and add to the importance matrix.
388 perm_oob_right /= repetition_count_;
389 perm_oob_right.bind<0>(0) -= oob_right;
390 perm_oob_right *= -1;
391 perm_oob_right /= num_oobs;
392 variable_importance_.subarray(Shape2(j, 0), Shape2(j+1, rf.num_classes()+1)) += perm_oob_right;
393
394 // Copy back the permuted dimension.
395 feats.template bind<1>(j) = backup;
396 }
397 }
398
399 /**
400 * Accumulate the variable importances from the single trees.
401 */
402 template <typename VISITORS, typename RF, typename FEATURES, typename LABELS>
403 void visit_after_training(
404 VISITORS & visitors,
405 RF & rf,
406 const FEATURES & features,
407 const LABELS & /*labels*/
408 ){
409 vigra_precondition(rf.num_trees() > 0, "VariableImportance::visit_after_training(): Number of trees must be greater than zero after training.");
410 vigra_precondition(visitors.size() == rf.num_trees(), "VariableImportance::visit_after_training(): Number of visitors must be equal to number of trees.");
411
412 // Sum the variable importances from the single trees.
413 auto const num_features = features.shape()[1];
414 variable_importance_.reshape(Shape2(num_features, rf.num_classes()+2), 0.0);
415 for (auto vptr : visitors)
416 {
417 vigra_precondition(vptr->variable_importance_.shape() == variable_importance_.shape(),
418 "VariableImportance::visit_after_training(): Shape mismatch.");
419 variable_importance_ += vptr->variable_importance_;
420 }
421
422 // Normalize the variable importance.
423 variable_importance_ /= rf.num_trees();
424 }
425
426 /**
427 * This Array has the same entries as the R - random forest variable
428 * importance.
429 * Matrix is featureCount by (classCount +2)
430 * variable_importance_(ii,jj) is the variable importance measure of
431 * the ii-th variable according to:
432 * jj = 0 - (classCount-1)
433 * classwise permutation importance
434 * jj = rowCount(variable_importance_) -2
435 * permutation importance
436 * jj = rowCount(variable_importance_) -1
437 * gini decrease importance.
438 *
439 * permutation importance:
440 * The difference between the fraction of OOB samples classified correctly
441 * before and after permuting (randomizing) the ii-th column is calculated.
442 * The ii-th column is permuted rep_cnt times.
443 *
444 * class wise permutation importance:
445 * same as permutation importance. We only look at those OOB samples whose
446 * response corresponds to class jj.
447 *
448 * gini decrease importance:
449 * row ii corresponds to the sum of all gini decreases induced by variable ii
450 * in each node of the random forest.
451 */
452 MultiArray<2, double> variable_importance_;
453
454 /**
455 * how often the permutation takes place
456 */
457 size_t repetition_count_;
458
459 private:
460
461 /**
462 * Copy the out-of-bag features and labels.
463 */
464 template <typename F0, typename L0, typename F1, typename L1>
465 void copy_out_of_bags(
466 F0 const & features_in,
467 L0 const & labels_in,
468 F1 & features_out,
469 L1 & labels_out
470 ) const {
471 auto const num_instances = features_in.shape()[0];
472 auto const num_features = features_in.shape()[1];
473
474 // Count the out-of-bags.
475 size_t num_oobs = 0;
476 for (auto x : is_in_bag_)
477 if (!x)
478 ++num_oobs;
479
480 // Copy the out-of-bags.
481 features_out.reshape(Shape2(num_oobs, num_features));
482 labels_out.reshape(Shape1(num_oobs));
483 size_t current = 0;
484 for (size_t i = 0; i < (size_t)num_instances; ++i)
485 {
486 if (!is_in_bag_[i])
487 {
488 auto const src = features_in.template bind<0>(i);
489 auto out = features_out.template bind<0>(current);
490 out = src;
491 labels_out(current) = labels_in(i);
492 ++current;
493 }
494 }
495 }
496
497 std::vector<bool> is_in_bag_; // whether a data point is in-bag or out-of-bag
498 };
499
500
501
502 /////////////////////////////////////////////
503 // The visitor chain //
504 /////////////////////////////////////////////
505
506 /**
507 * @brief The default visitor node (= "do nothing").
508 */
509 class RFStopVisiting : public RFVisitorBase
510 {};
511
512 namespace detail
513 {
514
515 /**
516 * @brief Container elements of the statically linked visitor list. Use the create_visitor() functions to create visitors up to size 10.
517 */
518 template <typename VISITOR, typename NEXT = RFStopVisiting, bool CPY = false>
519 class RFVisitorNode
520 {
521 public:
522
523 typedef VISITOR Visitor;
524 typedef NEXT Next;
525
526 typename std::conditional<CPY, Visitor, Visitor &>::type visitor_;
527 Next next_;
528
529 RFVisitorNode(Visitor & visitor, Next next)
530 :
531 visitor_(visitor),
532 next_(next)
533 {}
534
535 explicit RFVisitorNode(Visitor & visitor)
536 :
537 visitor_(visitor),
538 next_(RFStopVisiting())
539 {}
540
541 explicit RFVisitorNode(RFVisitorNode<Visitor, Next, !CPY> & other)
542 :
543 visitor_(other.visitor_),
544 next_(other.next_)
545 {}
546
547 explicit RFVisitorNode(RFVisitorNode<Visitor, Next, !CPY> const & other)
548 :
549 visitor_(other.visitor_),
550 next_(other.next_)
551 {}
552
553 void visit_before_training()
554 {
555 if (visitor_.is_active())
556 visitor_.visit_before_training();
557 next_.visit_before_training();
558 }
559
560 template <typename VISITORS, typename RF, typename FEATURES, typename LABELS>
561 void visit_after_training(VISITORS & v, RF & rf, const FEATURES & features, const LABELS & labels)
562 {
563 typedef typename VISITORS::value_type VisitorNodeType;
564 typedef typename VisitorNodeType::Visitor VisitorType;
565 typedef typename VisitorNodeType::Next NextType;
566
567 // We want to call the visit_after_training function of the concrete visitor (e. g. OOBError).
568 // Since v is a vector of visitor nodes (and not a vector of concrete visitors), we have to
569 // extract the concrete visitors.
570 // A vector cannot hold references, so we use pointers instead.
571 if (visitor_.is_active())
572 {
573 std::vector<VisitorType*> visitors;
574 for (auto & x : v)
575 visitors.push_back(&x.visitor_);
576 visitor_.visit_after_training(visitors, rf, features, labels);
577 }
578
579 // Remove the concrete visitors that we just visited.
580 std::vector<NextType> nexts;
581 for (auto & x : v)
582 nexts.push_back(x.next_);
583
584 // Call the next visitor node in the chain.
585 next_.visit_after_training(nexts, rf, features, labels);
586 }
587
588 template <typename TREE, typename FEATURES, typename LABELS, typename WEIGHTS>
589 void visit_before_tree(TREE & tree, FEATURES & features, LABELS & labels, WEIGHTS & weights)
590 {
591 if (visitor_.is_active())
592 visitor_.visit_before_tree(tree, features, labels, weights);
593 next_.visit_before_tree(tree, features, labels, weights);
594 }
595
596 template <typename RF, typename FEATURES, typename LABELS, typename WEIGHTS>
597 void visit_after_tree(RF & rf,
598 FEATURES & features,
599 LABELS & labels,
600 WEIGHTS & weights)
601 {
602 if (visitor_.is_active())
603 visitor_.visit_after_tree(rf, features, labels, weights);
604 next_.visit_after_tree(rf, features, labels, weights);
605 }
606
607 template <typename TREE,
608 typename FEATURES,
609 typename LABELS,
610 typename WEIGHTS,
611 typename SCORER,
612 typename ITER>
613 void visit_after_split(TREE & tree,
614 FEATURES & features,
615 LABELS & labels,
616 WEIGHTS & weights,
617 SCORER & scorer,
618 ITER begin,
619 ITER split,
620 ITER end)
621 {
622 if (visitor_.is_active())
623 visitor_.visit_after_split(tree, features, labels, weights, scorer, begin, split, end);
624 next_.visit_after_split(tree, features, labels, weights, scorer, begin, split, end);
625 }
626
627 };
628
629 } // namespace detail
630
631 /**
632 * The VisitorCopy can be used to set the copy argument of the given visitor chain to true.
633 */
634 template <typename VISITOR>
635 struct VisitorCopy
636 {
637 typedef detail::RFVisitorNode<typename VISITOR::Visitor, typename VisitorCopy<typename VISITOR::Next>::type, true> type;
638 };
639
640 template <>
641 struct VisitorCopy<RFStopVisiting>
642 {
643 typedef RFStopVisiting type;
644 };
645
646
647
648 //////////////////////////////////////////////////////////
649 // Visitor factory functions for up to 10 visitors. //
650 // FIXME: This should be a variadic template. //
651 //////////////////////////////////////////////////////////
652
653 template<typename A>
654 detail::RFVisitorNode<A>
655 create_visitor(A & a)
656 {
657 typedef detail::RFVisitorNode<A> _0_t;
658 _0_t _0(a);
659 return _0;
660 }
661
662 template<typename A, typename B>
663 detail::RFVisitorNode<A, detail::RFVisitorNode<B> >
664 create_visitor(A & a, B & b)
665 {
666 typedef detail::RFVisitorNode<B> _1_t;
667 _1_t _1(b);
668 typedef detail::RFVisitorNode<A, _1_t> _0_t;
669 _0_t _0(a, _1);
670 return _0;
671 }
672
673 template<typename A, typename B, typename C>
674 detail::RFVisitorNode<A, detail::RFVisitorNode<B, detail::RFVisitorNode<C> > >
675 create_visitor(A & a, B & b, C & c)
676 {
677 typedef detail::RFVisitorNode<C> _2_t;
678 _2_t _2(c);
679 typedef detail::RFVisitorNode<B, _2_t> _1_t;
680 _1_t _1(b, _2);
681 typedef detail::RFVisitorNode<A, _1_t> _0_t;
682 _0_t _0(a, _1);
683 return _0;
684 }
685
686 template<typename A, typename B, typename C, typename D>
687 detail::RFVisitorNode<A, detail::RFVisitorNode<B, detail::RFVisitorNode<C,
688 detail::RFVisitorNode<D> > > >
689 create_visitor(A & a, B & b, C & c, D & d)
690 {
691 typedef detail::RFVisitorNode<D> _3_t;
692 _3_t _3(d);
693 typedef detail::RFVisitorNode<C, _3_t> _2_t;
694 _2_t _2(c, _3);
695 typedef detail::RFVisitorNode<B, _2_t> _1_t;
696 _1_t _1(b, _2);
697 typedef detail::RFVisitorNode<A, _1_t> _0_t;
698 _0_t _0(a, _1);
699 return _0;
700 }
701
702 template<typename A, typename B, typename C, typename D, typename E>
703 detail::RFVisitorNode<A, detail::RFVisitorNode<B, detail::RFVisitorNode<C,
704 detail::RFVisitorNode<D, detail::RFVisitorNode<E> > > > >
705 create_visitor(A & a, B & b, C & c, D & d, E & e)
706 {
707 typedef detail::RFVisitorNode<E> _4_t;
708 _4_t _4(e);
709 typedef detail::RFVisitorNode<D, _4_t> _3_t;
710 _3_t _3(d, _4);
711 typedef detail::RFVisitorNode<C, _3_t> _2_t;
712 _2_t _2(c, _3);
713 typedef detail::RFVisitorNode<B, _2_t> _1_t;
714 _1_t _1(b, _2);
715 typedef detail::RFVisitorNode<A, _1_t> _0_t;
716 _0_t _0(a, _1);
717 return _0;
718 }
719
720 template<typename A, typename B, typename C, typename D, typename E,
721 typename F>
722 detail::RFVisitorNode<A, detail::RFVisitorNode<B, detail::RFVisitorNode<C,
723 detail::RFVisitorNode<D, detail::RFVisitorNode<E, detail::RFVisitorNode<F> > > > > >
724 create_visitor(A & a, B & b, C & c, D & d, E & e, F & f)
725 {
726 typedef detail::RFVisitorNode<F> _5_t;
727 _5_t _5(f);
728 typedef detail::RFVisitorNode<E, _5_t> _4_t;
729 _4_t _4(e, _5);
730 typedef detail::RFVisitorNode<D, _4_t> _3_t;
731 _3_t _3(d, _4);
732 typedef detail::RFVisitorNode<C, _3_t> _2_t;
733 _2_t _2(c, _3);
734 typedef detail::RFVisitorNode<B, _2_t> _1_t;
735 _1_t _1(b, _2);
736 typedef detail::RFVisitorNode<A, _1_t> _0_t;
737 _0_t _0(a, _1);
738 return _0;
739 }
740
741 template<typename A, typename B, typename C, typename D, typename E,
742 typename F, typename G>
743 detail::RFVisitorNode<A, detail::RFVisitorNode<B, detail::RFVisitorNode<C,
744 detail::RFVisitorNode<D, detail::RFVisitorNode<E, detail::RFVisitorNode<F,
745 detail::RFVisitorNode<G> > > > > > >
746 create_visitor(A & a, B & b, C & c, D & d, E & e, F & f, G & g)
747 {
748 typedef detail::RFVisitorNode<G> _6_t;
749 _6_t _6(g);
750 typedef detail::RFVisitorNode<F, _6_t> _5_t;
751 _5_t _5(f, _6);
752 typedef detail::RFVisitorNode<E, _5_t> _4_t;
753 _4_t _4(e, _5);
754 typedef detail::RFVisitorNode<D, _4_t> _3_t;
755 _3_t _3(d, _4);
756 typedef detail::RFVisitorNode<C, _3_t> _2_t;
757 _2_t _2(c, _3);
758 typedef detail::RFVisitorNode<B, _2_t> _1_t;
759 _1_t _1(b, _2);
760 typedef detail::RFVisitorNode<A, _1_t> _0_t;
761 _0_t _0(a, _1);
762 return _0;
763 }
764
765 template<typename A, typename B, typename C, typename D, typename E,
766 typename F, typename G, typename H>
767 detail::RFVisitorNode<A, detail::RFVisitorNode<B, detail::RFVisitorNode<C,
768 detail::RFVisitorNode<D, detail::RFVisitorNode<E, detail::RFVisitorNode<F,
769 detail::RFVisitorNode<G, detail::RFVisitorNode<H> > > > > > > >
770 create_visitor(A & a, B & b, C & c, D & d, E & e, F & f, G & g, H & h)
771 {
772 typedef detail::RFVisitorNode<H> _7_t;
773 _7_t _7(h);
774 typedef detail::RFVisitorNode<G, _7_t> _6_t;
775 _6_t _6(g, _7);
776 typedef detail::RFVisitorNode<F, _6_t> _5_t;
777 _5_t _5(f, _6);
778 typedef detail::RFVisitorNode<E, _5_t> _4_t;
779 _4_t _4(e, _5);
780 typedef detail::RFVisitorNode<D, _4_t> _3_t;
781 _3_t _3(d, _4);
782 typedef detail::RFVisitorNode<C, _3_t> _2_t;
783 _2_t _2(c, _3);
784 typedef detail::RFVisitorNode<B, _2_t> _1_t;
785 _1_t _1(b, _2);
786 typedef detail::RFVisitorNode<A, _1_t> _0_t;
787 _0_t _0(a, _1);
788 return _0;
789 }
790
791 template<typename A, typename B, typename C, typename D, typename E,
792 typename F, typename G, typename H, typename I>
793 detail::RFVisitorNode<A, detail::RFVisitorNode<B, detail::RFVisitorNode<C,
794 detail::RFVisitorNode<D, detail::RFVisitorNode<E, detail::RFVisitorNode<F,
795 detail::RFVisitorNode<G, detail::RFVisitorNode<H, detail::RFVisitorNode<I> > > > > > > > >
796 create_visitor(A & a, B & b, C & c, D & d, E & e, F & f, G & g, H & h, I & i)
797 {
798 typedef detail::RFVisitorNode<I> _8_t;
799 _8_t _8(i);
800 typedef detail::RFVisitorNode<H, _8_t> _7_t;
801 _7_t _7(h, _8);
802 typedef detail::RFVisitorNode<G, _7_t> _6_t;
803 _6_t _6(g, _7);
804 typedef detail::RFVisitorNode<F, _6_t> _5_t;
805 _5_t _5(f, _6);
806 typedef detail::RFVisitorNode<E, _5_t> _4_t;
807 _4_t _4(e, _5);
808 typedef detail::RFVisitorNode<D, _4_t> _3_t;
809 _3_t _3(d, _4);
810 typedef detail::RFVisitorNode<C, _3_t> _2_t;
811 _2_t _2(c, _3);
812 typedef detail::RFVisitorNode<B, _2_t> _1_t;
813 _1_t _1(b, _2);
814 typedef detail::RFVisitorNode<A, _1_t> _0_t;
815 _0_t _0(a, _1);
816 return _0;
817 }
818
819 template<typename A, typename B, typename C, typename D, typename E,
820 typename F, typename G, typename H, typename I, typename J>
821 detail::RFVisitorNode<A, detail::RFVisitorNode<B, detail::RFVisitorNode<C,
822 detail::RFVisitorNode<D, detail::RFVisitorNode<E, detail::RFVisitorNode<F,
823 detail::RFVisitorNode<G, detail::RFVisitorNode<H, detail::RFVisitorNode<I,
824 detail::RFVisitorNode<J> > > > > > > > > >
825 create_visitor(A & a, B & b, C & c, D & d, E & e, F & f, G & g, H & h, I & i,
826 J & j)
827 {
828 typedef detail::RFVisitorNode<J> _9_t;
829 _9_t _9(j);
830 typedef detail::RFVisitorNode<I, _9_t> _8_t;
831 _8_t _8(i, _9);
832 typedef detail::RFVisitorNode<H, _8_t> _7_t;
833 _7_t _7(h, _8);
834 typedef detail::RFVisitorNode<G, _7_t> _6_t;
835 _6_t _6(g, _7);
836 typedef detail::RFVisitorNode<F, _6_t> _5_t;
837 _5_t _5(f, _6);
838 typedef detail::RFVisitorNode<E, _5_t> _4_t;
839 _4_t _4(e, _5);
840 typedef detail::RFVisitorNode<D, _4_t> _3_t;
841 _3_t _3(d, _4);
842 typedef detail::RFVisitorNode<C, _3_t> _2_t;
843 _2_t _2(c, _3);
844 typedef detail::RFVisitorNode<B, _2_t> _1_t;
845 _1_t _1(b, _2);
846 typedef detail::RFVisitorNode<A, _1_t> _0_t;
847 _0_t _0(a, _1);
848 return _0;
849 }
850
851
852
853 } // namespace rf3
854 } // namespace vigra
855
856 #endif
vigra::rf3::RFVisitorBase::visit_before_tree
void visit_before_tree(TREE &, FEATURES &, LABELS &, WEIGHTS &)
Do something before a tree has been learned.
Definition: random_forest_visitors.hxx:99
vigra::rf3::VariableImportance::visit_before_tree
void visit_before_tree(TREE &tree, FEATURES &features, LABELS &, WEIGHTS &weights)
Definition: random_forest_visitors.hxx:270
vigra::rf3::RFVisitorBase::visit_after_split
void visit_after_split(TREE &, FEATURES &, LABELS &, WEIGHTS &, SCORER &, ITER, ITER, ITER)
Do something after the split was made.
Definition: random_forest_visitors.hxx:121
vigra::MultiArrayView::shape
const difference_type & shape() const
Definition: multi_array.hxx:1648
vigra::rf3::RFVisitorBase::visit_after_tree
void visit_after_tree(RF &, FEATURES &, LABELS &, WEIGHTS &)
Do something after a tree has been learned.
Definition: random_forest_visitors.hxx:106
vigra::rf3::RFVisitorBase::deactivate
void deactivate()
Deactivate the visitor.
Definition: random_forest_visitors.hxx:150
vigra::MultiArray< 1, double >
vigra::MultiArray::reshape
void reshape(const difference_type &shape)
Definition: multi_array.hxx:2861
vigra::rf3::RFVisitorBase
Base class from which all random forest visitors derive.
Definition: random_forest_visitors.hxx:68
vigra::rf3::VariableImportance::repetition_count_
size_t repetition_count_
Definition: random_forest_visitors.hxx:457
vigra::rf3::RFStopVisiting
The default visitor node (= "do nothing").
Definition: random_forest_visitors.hxx:509
vigra::UniformIntRandomFunctor
Definition: random.hxx:669
vigra::rf3::OOBError::visit_after_training
void visit_after_training(VISITORS &visitors, RF &rf, const FEATURES &features, const LABELS &labels)
Definition: random_forest_visitors.hxx:208
vigra::rf3::VariableImportance
Compute the variable importance.
Definition: random_forest_visitors.hxx:257
vigra::rf3::OOBError
Compute the out of bag error.
Definition: random_forest_visitors.hxx:172
vigra::rf3::OOBError::oob_err_
double oob_err_
Definition: random_forest_visitors.hxx:246
vigra::rf3::VariableImportance::visit_after_tree
void visit_after_tree(RF &rf, const FEATURES &features, const LABELS &labels, WEIGHTS &)
Definition: random_forest_visitors.hxx:327
vigra::rf3::VisitorCopy
Definition: random_forest_visitors.hxx:635
vigra::rf3::RFVisitorBase::activate
void activate()
Activate the visitor.
Definition: random_forest_visitors.hxx:142
vigra::TinyVector
Class for fixed size vectors.This class contains an array of size SIZE of the specified VALUETYPE...
Definition: accessor.hxx:940
vigra::rf3::VariableImportance::visit_after_split
void visit_after_split(TREE &tree, FEATURES &, LABELS &labels, WEIGHTS &weights, SCORER &scorer, ITER begin, ITER, ITER end)
Definition: random_forest_visitors.hxx:307
vigra::rf3::OOBError::visit_before_tree
void visit_before_tree(TREE &, FEATURES &, LABELS &, WEIGHTS &weights)
Definition: random_forest_visitors.hxx:180
vigra::rf3::detail::RFVisitorNode
Container elements of the statically linked visitor list. Use the create_visitor() functions to creat...
Definition: random_forest_visitors.hxx:519
vigra::rf3::VariableImportance::variable_importance_
MultiArray< 2, double > variable_importance_
Definition: random_forest_visitors.hxx:452
vigra::abs
FFTWComplex< R >::NormType abs(const FFTWComplex< R > &a)
absolute value (= magnitude)
Definition: fftw3.hxx:1002
vigra::rf3::RFVisitorBase::is_active
bool is_active() const
Return whether the visitor is active or not.
Definition: random_forest_visitors.hxx:134
vigra::rf3::RFVisitorBase::visit_before_training
void visit_before_training()
Do something before training starts.
Definition: random_forest_visitors.hxx:80
vigra::MultiArrayView::subarray
MultiArrayView subarray(difference_type p, difference_type q) const
Definition: multi_array.hxx:1528
vigra::rf::visitors::create_visitor
detail::VisitorNode< A > create_visitor(A &a)
Definition: rf_visitors.hxx:344
vigra::rf3::RFVisitorBase::visit_after_training
void visit_after_training(VISITORS &, RF &, const FEATURES &, const LABELS &)
Do something after all trees have been learned.
Definition: random_forest_visitors.hxx:90
vigra::rf3::VariableImportance::visit_after_training
void visit_after_training(VISITORS &visitors, RF &rf, const FEATURES &features, const LABELS &)
Definition: random_forest_visitors.hxx:403

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.11.0 (Fri May 19 2017)