/usr/include/vigra/random_forest/rf_visitors.hxx is in libvigraimpex-dev 1.10.0+dfsg-3ubuntu2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 | /************************************************************************/
/* */
/* Copyright 2008-2009 by Ullrich Koethe and Rahul Nair */
/* */
/* This file is part of the VIGRA computer vision library. */
/* The VIGRA Website is */
/* http://hci.iwr.uni-heidelberg.de/vigra/ */
/* Please direct questions, bug reports, and contributions to */
/* ullrich.koethe@iwr.uni-heidelberg.de or */
/* vigra@informatik.uni-hamburg.de */
/* */
/* Permission is hereby granted, free of charge, to any person */
/* obtaining a copy of this software and associated documentation */
/* files (the "Software"), to deal in the Software without */
/* restriction, including without limitation the rights to use, */
/* copy, modify, merge, publish, distribute, sublicense, and/or */
/* sell copies of the Software, and to permit persons to whom the */
/* Software is furnished to do so, subject to the following */
/* conditions: */
/* */
/* The above copyright notice and this permission notice shall be */
/* included in all copies or substantial portions of the */
/* Software. */
/* */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND */
/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES */
/* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND */
/* NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT */
/* HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, */
/* WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING */
/* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR */
/* OTHER DEALINGS IN THE SOFTWARE. */
/* */
/************************************************************************/
#ifndef RF_VISITORS_HXX
#define RF_VISITORS_HXX
#ifdef HasHDF5
# include "vigra/hdf5impex.hxx"
#else
# include "vigra/impex.hxx"
# include "vigra/multi_array.hxx"
# include "vigra/multi_impex.hxx"
# include "vigra/inspectimage.hxx"
#endif // HasHDF5
#include <vigra/windows.h>
#include <iostream>
#include <iomanip>
#include <vigra/multi_pointoperators.hxx>
#include <vigra/timing.hxx>
namespace vigra
{
namespace rf
{
/** \addtogroup MachineLearning Machine Learning
**/
//@{
/**
This namespace contains all classes and methods related to extracting information during
learning of the random forest. All Visitors share the same interface defined in
visitors::VisitorBase. The member methods are invoked at certain points of the main code in
the order they were supplied.
For the Random Forest the Visitor concept is implemented as a statically linked list
(Using templates). Each Visitor object is encapsulated in a detail::VisitorNode object. The
VisitorNode object calls the Next Visitor after one of its visit() methods have terminated.
To simplify usage create_visitor() factory methods are supplied.
Use the create_visitor() method to supply visitor objects to the RandomForest::learn() method.
It is possible to supply more than one visitor. They will then be invoked in serial order.
The calculated information are stored as public data members of the class. - see documentation
of the individual visitors
While creating a new visitor the new class should therefore publicly inherit from this class
(i.e.: see visitors::OOB_Error).
\code
typedef xxx feature_t \\ replace xxx with whichever type
typedef yyy label_t \\ meme chose.
MultiArrayView<2, feature_t> f = get_some_features();
MultiArrayView<2, label_t> l = get_some_labels();
RandomForest<> rf()
//calculate OOB Error
visitors::OOB_Error oob_v;
//calculate Variable Importance
visitors::VariableImportanceVisitor varimp_v;
double oob_error = rf.learn(f, l, visitors::create_visitor(oob_v, varimp_v);
//the data can be found in the attributes of oob_v and varimp_v now
\endcode
*/
namespace visitors
{
/** Base Class from which all Visitors derive. Can be used as a template to create new
* Visitors.
*/
class VisitorBase
{
public:
bool active_;
bool is_active()
{
return active_;
}
bool has_value()
{
return false;
}
VisitorBase()
: active_(true)
{}
void deactivate()
{
active_ = false;
}
void activate()
{
active_ = true;
}
/** do something after the the Split has decided how to process the Region
* (Stack entry)
*
* \param tree reference to the tree that is currently being learned
* \param split reference to the split object
* \param parent current stack entry which was used to decide the split
* \param leftChild left stack entry that will be pushed
* \param rightChild
* right stack entry that will be pushed.
* \param features features matrix
* \param labels label matrix
* \sa RF_Traits::StackEntry_t
*/
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
void visit_after_split( Tree & tree,
Split & split,
Region & parent,
Region & leftChild,
Region & rightChild,
Feature_t & features,
Label_t & labels)
{}
/** do something after each tree has been learned
*
* \param rf reference to the random forest object that called this
* visitor
* \param pr reference to the preprocessor that processed the input
* \param sm reference to the sampler object
* \param st reference to the first stack entry
* \param index index of current tree
*/
template<class RF, class PR, class SM, class ST>
void visit_after_tree(RF& rf, PR & pr, SM & sm, ST & st, int index)
{}
/** do something after all trees have been learned
*
* \param rf reference to the random forest object that called this
* visitor
* \param pr reference to the preprocessor that processed the input
*/
template<class RF, class PR>
void visit_at_end(RF const & rf, PR const & pr)
{}
/** do something before learning starts
*
* \param rf reference to the random forest object that called this
* visitor
* \param pr reference to the Processor class used.
*/
template<class RF, class PR>
void visit_at_beginning(RF const & rf, PR const & pr)
{}
/** do some thing while traversing tree after it has been learned
* (external nodes)
*
* \param tr reference to the tree object that called this visitor
* \param index index in the topology_ array we currently are at
* \param node_t type of node we have (will be e_.... - )
* \param features feature matrix
* \sa NodeTags;
*
* you can create the node by using a switch on node_tag and using the
* corresponding Node objects. Or - if you do not care about the type
* use the NodeBase class.
*/
template<class TR, class IntT, class TopT,class Feat>
void visit_external_node(TR & tr, IntT index, TopT node_t,Feat & features)
{}
/** do something when visiting a internal node after it has been learned
*
* \sa visit_external_node
*/
template<class TR, class IntT, class TopT,class Feat>
void visit_internal_node(TR & tr, IntT index, TopT node_t,Feat & features)
{}
/** return a double value. The value of the first
* visitor encountered that has a return value is returned with the
* RandomForest::learn() method - or -1.0 if no return value visitor
* existed. This functionality basically only exists so that the
* OOB - visitor can return the oob error rate like in the old version
* of the random forest.
*/
double return_val()
{
return -1.0;
}
};
/** Last Visitor that should be called to stop the recursion.
*/
class StopVisiting: public VisitorBase
{
public:
bool has_value()
{
return true;
}
double return_val()
{
return -1.0;
}
};
namespace detail
{
/** Container elements of the statically linked Visitor list.
*
* use the create_visitor() factory functions to create visitors up to size 10;
*
*/
template <class Visitor, class Next = StopVisiting>
class VisitorNode
{
public:
StopVisiting stop_;
Next next_;
Visitor & visitor_;
VisitorNode(Visitor & visitor, Next & next)
:
next_(next), visitor_(visitor)
{}
VisitorNode(Visitor & visitor)
:
next_(stop_), visitor_(visitor)
{}
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
void visit_after_split( Tree & tree,
Split & split,
Region & parent,
Region & leftChild,
Region & rightChild,
Feature_t & features,
Label_t & labels)
{
if(visitor_.is_active())
visitor_.visit_after_split(tree, split,
parent, leftChild, rightChild,
features, labels);
next_.visit_after_split(tree, split, parent, leftChild, rightChild,
features, labels);
}
template<class RF, class PR, class SM, class ST>
void visit_after_tree(RF& rf, PR & pr, SM & sm, ST & st, int index)
{
if(visitor_.is_active())
visitor_.visit_after_tree(rf, pr, sm, st, index);
next_.visit_after_tree(rf, pr, sm, st, index);
}
template<class RF, class PR>
void visit_at_beginning(RF & rf, PR & pr)
{
if(visitor_.is_active())
visitor_.visit_at_beginning(rf, pr);
next_.visit_at_beginning(rf, pr);
}
template<class RF, class PR>
void visit_at_end(RF & rf, PR & pr)
{
if(visitor_.is_active())
visitor_.visit_at_end(rf, pr);
next_.visit_at_end(rf, pr);
}
template<class TR, class IntT, class TopT,class Feat>
void visit_external_node(TR & tr, IntT & index, TopT & node_t,Feat & features)
{
if(visitor_.is_active())
visitor_.visit_external_node(tr, index, node_t,features);
next_.visit_external_node(tr, index, node_t,features);
}
template<class TR, class IntT, class TopT,class Feat>
void visit_internal_node(TR & tr, IntT & index, TopT & node_t,Feat & features)
{
if(visitor_.is_active())
visitor_.visit_internal_node(tr, index, node_t,features);
next_.visit_internal_node(tr, index, node_t,features);
}
double return_val()
{
if(visitor_.is_active() && visitor_.has_value())
return visitor_.return_val();
return next_.return_val();
}
};
} //namespace detail
//////////////////////////////////////////////////////////////////////////////
// Visitor Factory function up to 10 visitors //
//////////////////////////////////////////////////////////////////////////////
/** factory method to to be used with RandomForest::learn()
*/
template<class A>
detail::VisitorNode<A>
create_visitor(A & a)
{
typedef detail::VisitorNode<A> _0_t;
_0_t _0(a);
return _0;
}
/** factory method to to be used with RandomForest::learn()
*/
template<class A, class B>
detail::VisitorNode<A, detail::VisitorNode<B> >
create_visitor(A & a, B & b)
{
typedef detail::VisitorNode<B> _1_t;
_1_t _1(b);
typedef detail::VisitorNode<A, _1_t> _0_t;
_0_t _0(a, _1);
return _0;
}
/** factory method to to be used with RandomForest::learn()
*/
template<class A, class B, class C>
detail::VisitorNode<A, detail::VisitorNode<B, detail::VisitorNode<C> > >
create_visitor(A & a, B & b, C & c)
{
typedef detail::VisitorNode<C> _2_t;
_2_t _2(c);
typedef detail::VisitorNode<B, _2_t> _1_t;
_1_t _1(b, _2);
typedef detail::VisitorNode<A, _1_t> _0_t;
_0_t _0(a, _1);
return _0;
}
/** factory method to to be used with RandomForest::learn()
*/
template<class A, class B, class C, class D>
detail::VisitorNode<A, detail::VisitorNode<B, detail::VisitorNode<C,
detail::VisitorNode<D> > > >
create_visitor(A & a, B & b, C & c, D & d)
{
typedef detail::VisitorNode<D> _3_t;
_3_t _3(d);
typedef detail::VisitorNode<C, _3_t> _2_t;
_2_t _2(c, _3);
typedef detail::VisitorNode<B, _2_t> _1_t;
_1_t _1(b, _2);
typedef detail::VisitorNode<A, _1_t> _0_t;
_0_t _0(a, _1);
return _0;
}
/** factory method to to be used with RandomForest::learn()
*/
template<class A, class B, class C, class D, class E>
detail::VisitorNode<A, detail::VisitorNode<B, detail::VisitorNode<C,
detail::VisitorNode<D, detail::VisitorNode<E> > > > >
create_visitor(A & a, B & b, C & c,
D & d, E & e)
{
typedef detail::VisitorNode<E> _4_t;
_4_t _4(e);
typedef detail::VisitorNode<D, _4_t> _3_t;
_3_t _3(d, _4);
typedef detail::VisitorNode<C, _3_t> _2_t;
_2_t _2(c, _3);
typedef detail::VisitorNode<B, _2_t> _1_t;
_1_t _1(b, _2);
typedef detail::VisitorNode<A, _1_t> _0_t;
_0_t _0(a, _1);
return _0;
}
/** factory method to to be used with RandomForest::learn()
*/
template<class A, class B, class C, class D, class E,
class F>
detail::VisitorNode<A, detail::VisitorNode<B, detail::VisitorNode<C,
detail::VisitorNode<D, detail::VisitorNode<E, detail::VisitorNode<F> > > > > >
create_visitor(A & a, B & b, C & c,
D & d, E & e, F & f)
{
typedef detail::VisitorNode<F> _5_t;
_5_t _5(f);
typedef detail::VisitorNode<E, _5_t> _4_t;
_4_t _4(e, _5);
typedef detail::VisitorNode<D, _4_t> _3_t;
_3_t _3(d, _4);
typedef detail::VisitorNode<C, _3_t> _2_t;
_2_t _2(c, _3);
typedef detail::VisitorNode<B, _2_t> _1_t;
_1_t _1(b, _2);
typedef detail::VisitorNode<A, _1_t> _0_t;
_0_t _0(a, _1);
return _0;
}
/** factory method to to be used with RandomForest::learn()
*/
template<class A, class B, class C, class D, class E,
class F, class G>
detail::VisitorNode<A, detail::VisitorNode<B, detail::VisitorNode<C,
detail::VisitorNode<D, detail::VisitorNode<E, detail::VisitorNode<F,
detail::VisitorNode<G> > > > > > >
create_visitor(A & a, B & b, C & c,
D & d, E & e, F & f, G & g)
{
typedef detail::VisitorNode<G> _6_t;
_6_t _6(g);
typedef detail::VisitorNode<F, _6_t> _5_t;
_5_t _5(f, _6);
typedef detail::VisitorNode<E, _5_t> _4_t;
_4_t _4(e, _5);
typedef detail::VisitorNode<D, _4_t> _3_t;
_3_t _3(d, _4);
typedef detail::VisitorNode<C, _3_t> _2_t;
_2_t _2(c, _3);
typedef detail::VisitorNode<B, _2_t> _1_t;
_1_t _1(b, _2);
typedef detail::VisitorNode<A, _1_t> _0_t;
_0_t _0(a, _1);
return _0;
}
/** factory method to to be used with RandomForest::learn()
*/
template<class A, class B, class C, class D, class E,
class F, class G, class H>
detail::VisitorNode<A, detail::VisitorNode<B, detail::VisitorNode<C,
detail::VisitorNode<D, detail::VisitorNode<E, detail::VisitorNode<F,
detail::VisitorNode<G, detail::VisitorNode<H> > > > > > > >
create_visitor(A & a, B & b, C & c,
D & d, E & e, F & f,
G & g, H & h)
{
typedef detail::VisitorNode<H> _7_t;
_7_t _7(h);
typedef detail::VisitorNode<G, _7_t> _6_t;
_6_t _6(g, _7);
typedef detail::VisitorNode<F, _6_t> _5_t;
_5_t _5(f, _6);
typedef detail::VisitorNode<E, _5_t> _4_t;
_4_t _4(e, _5);
typedef detail::VisitorNode<D, _4_t> _3_t;
_3_t _3(d, _4);
typedef detail::VisitorNode<C, _3_t> _2_t;
_2_t _2(c, _3);
typedef detail::VisitorNode<B, _2_t> _1_t;
_1_t _1(b, _2);
typedef detail::VisitorNode<A, _1_t> _0_t;
_0_t _0(a, _1);
return _0;
}
/** factory method to to be used with RandomForest::learn()
*/
template<class A, class B, class C, class D, class E,
class F, class G, class H, class I>
detail::VisitorNode<A, detail::VisitorNode<B, detail::VisitorNode<C,
detail::VisitorNode<D, detail::VisitorNode<E, detail::VisitorNode<F,
detail::VisitorNode<G, detail::VisitorNode<H, detail::VisitorNode<I> > > > > > > > >
create_visitor(A & a, B & b, C & c,
D & d, E & e, F & f,
G & g, H & h, I & i)
{
typedef detail::VisitorNode<I> _8_t;
_8_t _8(i);
typedef detail::VisitorNode<H, _8_t> _7_t;
_7_t _7(h, _8);
typedef detail::VisitorNode<G, _7_t> _6_t;
_6_t _6(g, _7);
typedef detail::VisitorNode<F, _6_t> _5_t;
_5_t _5(f, _6);
typedef detail::VisitorNode<E, _5_t> _4_t;
_4_t _4(e, _5);
typedef detail::VisitorNode<D, _4_t> _3_t;
_3_t _3(d, _4);
typedef detail::VisitorNode<C, _3_t> _2_t;
_2_t _2(c, _3);
typedef detail::VisitorNode<B, _2_t> _1_t;
_1_t _1(b, _2);
typedef detail::VisitorNode<A, _1_t> _0_t;
_0_t _0(a, _1);
return _0;
}
/** factory method to to be used with RandomForest::learn()
*/
template<class A, class B, class C, class D, class E,
class F, class G, class H, class I, class J>
detail::VisitorNode<A, detail::VisitorNode<B, detail::VisitorNode<C,
detail::VisitorNode<D, detail::VisitorNode<E, detail::VisitorNode<F,
detail::VisitorNode<G, detail::VisitorNode<H, detail::VisitorNode<I,
detail::VisitorNode<J> > > > > > > > > >
create_visitor(A & a, B & b, C & c,
D & d, E & e, F & f,
G & g, H & h, I & i,
J & j)
{
typedef detail::VisitorNode<J> _9_t;
_9_t _9(j);
typedef detail::VisitorNode<I, _9_t> _8_t;
_8_t _8(i, _9);
typedef detail::VisitorNode<H, _8_t> _7_t;
_7_t _7(h, _8);
typedef detail::VisitorNode<G, _7_t> _6_t;
_6_t _6(g, _7);
typedef detail::VisitorNode<F, _6_t> _5_t;
_5_t _5(f, _6);
typedef detail::VisitorNode<E, _5_t> _4_t;
_4_t _4(e, _5);
typedef detail::VisitorNode<D, _4_t> _3_t;
_3_t _3(d, _4);
typedef detail::VisitorNode<C, _3_t> _2_t;
_2_t _2(c, _3);
typedef detail::VisitorNode<B, _2_t> _1_t;
_1_t _1(b, _2);
typedef detail::VisitorNode<A, _1_t> _0_t;
_0_t _0(a, _1);
return _0;
}
//////////////////////////////////////////////////////////////////////////////
// Visitors of communal interest. //
//////////////////////////////////////////////////////////////////////////////
/** Visitor to gain information, later needed for online learning.
*/
class OnlineLearnVisitor: public VisitorBase
{
public:
//Set if we adjust thresholds
bool adjust_thresholds;
//Current tree id
int tree_id;
//Last node id for finding parent
int last_node_id;
//Need to now the label for interior node visiting
vigra::Int32 current_label;
//marginal distribution for interior nodes
//
OnlineLearnVisitor():
adjust_thresholds(false), tree_id(0), last_node_id(0), current_label(0)
{}
struct MarginalDistribution
{
ArrayVector<Int32> leftCounts;
Int32 leftTotalCounts;
ArrayVector<Int32> rightCounts;
Int32 rightTotalCounts;
double gap_left;
double gap_right;
};
typedef ArrayVector<vigra::Int32> IndexList;
//All information for one tree
struct TreeOnlineInformation
{
std::vector<MarginalDistribution> mag_distributions;
std::vector<IndexList> index_lists;
//map for linear index of mag_distributions
std::map<int,int> interior_to_index;
//map for linear index of index_lists
std::map<int,int> exterior_to_index;
};
//All trees
std::vector<TreeOnlineInformation> trees_online_information;
/** Initialize, set the number of trees
*/
template<class RF,class PR>
void visit_at_beginning(RF & rf,const PR & pr)
{
tree_id=0;
trees_online_information.resize(rf.options_.tree_count_);
}
/** Reset a tree
*/
void reset_tree(int tree_id)
{
trees_online_information[tree_id].mag_distributions.clear();
trees_online_information[tree_id].index_lists.clear();
trees_online_information[tree_id].interior_to_index.clear();
trees_online_information[tree_id].exterior_to_index.clear();
}
/** simply increase the tree count
*/
template<class RF, class PR, class SM, class ST>
void visit_after_tree(RF& rf, PR & pr, SM & sm, ST & st, int index)
{
tree_id++;
}
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
void visit_after_split( Tree & tree,
Split & split,
Region & parent,
Region & leftChild,
Region & rightChild,
Feature_t & features,
Label_t & labels)
{
int linear_index;
int addr=tree.topology_.size();
if(split.createNode().typeID() == i_ThresholdNode)
{
if(adjust_thresholds)
{
//Store marginal distribution
linear_index=trees_online_information[tree_id].mag_distributions.size();
trees_online_information[tree_id].interior_to_index[addr]=linear_index;
trees_online_information[tree_id].mag_distributions.push_back(MarginalDistribution());
trees_online_information[tree_id].mag_distributions.back().leftCounts=leftChild.classCounts_;
trees_online_information[tree_id].mag_distributions.back().rightCounts=rightChild.classCounts_;
trees_online_information[tree_id].mag_distributions.back().leftTotalCounts=leftChild.size_;
trees_online_information[tree_id].mag_distributions.back().rightTotalCounts=rightChild.size_;
//Store the gap
double gap_left,gap_right;
int i;
gap_left=features(leftChild[0],split.bestSplitColumn());
for(i=1;i<leftChild.size();++i)
if(features(leftChild[i],split.bestSplitColumn())>gap_left)
gap_left=features(leftChild[i],split.bestSplitColumn());
gap_right=features(rightChild[0],split.bestSplitColumn());
for(i=1;i<rightChild.size();++i)
if(features(rightChild[i],split.bestSplitColumn())<gap_right)
gap_right=features(rightChild[i],split.bestSplitColumn());
trees_online_information[tree_id].mag_distributions.back().gap_left=gap_left;
trees_online_information[tree_id].mag_distributions.back().gap_right=gap_right;
}
}
else
{
//Store index list
linear_index=trees_online_information[tree_id].index_lists.size();
trees_online_information[tree_id].exterior_to_index[addr]=linear_index;
trees_online_information[tree_id].index_lists.push_back(IndexList());
trees_online_information[tree_id].index_lists.back().resize(parent.size_,0);
std::copy(parent.begin_,parent.end_,trees_online_information[tree_id].index_lists.back().begin());
}
}
void add_to_index_list(int tree,int node,int index)
{
if(!this->active_)
return;
TreeOnlineInformation &ti=trees_online_information[tree];
ti.index_lists[ti.exterior_to_index[node]].push_back(index);
}
void move_exterior_node(int src_tree,int src_index,int dst_tree,int dst_index)
{
if(!this->active_)
return;
trees_online_information[dst_tree].exterior_to_index[dst_index]=trees_online_information[src_tree].exterior_to_index[src_index];
trees_online_information[src_tree].exterior_to_index.erase(src_index);
}
/** do something when visiting a internal node during getToLeaf
*
* remember as last node id, for finding the parent of the last external node
* also: adjust class counts and borders
*/
template<class TR, class IntT, class TopT,class Feat>
void visit_internal_node(TR & tr, IntT index, TopT node_t,Feat & features)
{
last_node_id=index;
if(adjust_thresholds)
{
vigra_assert(node_t==i_ThresholdNode,"We can only visit threshold nodes");
//Check if we are in the gap
double value=features(0, Node<i_ThresholdNode>(tr.topology_,tr.parameters_,index).column());
TreeOnlineInformation &ti=trees_online_information[tree_id];
MarginalDistribution &m=ti.mag_distributions[ti.interior_to_index[index]];
if(value>m.gap_left && value<m.gap_right)
{
//Check which site we want to go
if(m.leftCounts[current_label]/double(m.leftTotalCounts)>m.rightCounts[current_label]/double(m.rightTotalCounts))
{
//We want to go left
m.gap_left=value;
}
else
{
//We want to go right
m.gap_right=value;
}
Node<i_ThresholdNode>(tr.topology_,tr.parameters_,index).threshold()=(m.gap_right+m.gap_left)/2.0;
}
//Adjust class counts
if(value>Node<i_ThresholdNode>(tr.topology_,tr.parameters_,index).threshold())
{
++m.rightTotalCounts;
++m.rightCounts[current_label];
}
else
{
++m.leftTotalCounts;
++m.rightCounts[current_label];
}
}
}
/** do something when visiting a extern node during getToLeaf
*
* Store the new index!
*/
};
//////////////////////////////////////////////////////////////////////////////
// Out of Bag Error estimates //
//////////////////////////////////////////////////////////////////////////////
/** Visitor that calculates the oob error of each individual randomized
* decision tree.
*
* After training a tree, all those samples that are OOB for this particular tree
* are put down the tree and the error estimated.
* the per tree oob error is the average of the individual error estimates.
* (oobError = average error of one randomized tree)
* Note: This is Not the OOB - Error estimate suggested by Breiman (See OOB_Error
* visitor)
*/
class OOB_PerTreeError:public VisitorBase
{
public:
/** Average error of one randomized decision tree
*/
double oobError;
int totalOobCount;
ArrayVector<int> oobCount,oobErrorCount;
OOB_PerTreeError()
: oobError(0.0),
totalOobCount(0)
{}
bool has_value()
{
return true;
}
/** does the basic calculation per tree*/
template<class RF, class PR, class SM, class ST>
void visit_after_tree( RF& rf, PR & pr, SM & sm, ST & st, int index)
{
//do the first time called.
if(int(oobCount.size()) != rf.ext_param_.row_count_)
{
oobCount.resize(rf.ext_param_.row_count_, 0);
oobErrorCount.resize(rf.ext_param_.row_count_, 0);
}
// go through the samples
for(int l = 0; l < rf.ext_param_.row_count_; ++l)
{
// if the lth sample is oob...
if(!sm.is_used()[l])
{
++oobCount[l];
if( rf.tree(index)
.predictLabel(rowVector(pr.features(), l))
!= pr.response()(l,0))
{
++oobErrorCount[l];
}
}
}
}
/** Does the normalisation
*/
template<class RF, class PR>
void visit_at_end(RF & rf, PR & pr)
{
// do some normalisation
for(int l=0; l < (int)rf.ext_param_.row_count_; ++l)
{
if(oobCount[l])
{
oobError += double(oobErrorCount[l]) / oobCount[l];
++totalOobCount;
}
}
oobError/=totalOobCount;
}
};
/** Visitor that calculates the oob error of the ensemble
* This rate should be used to estimate the crossvalidation
* error rate.
* Here each sample is put down those trees, for which this sample
* is OOB i.e. if sample #1 is OOB for trees 1, 3 and 5 we calculate
* the output using the ensemble consisting only of trees 1 3 and 5.
*
* Using normal bagged sampling each sample is OOB for approx. 33% of trees
* The error rate obtained as such therefore corresponds to crossvalidation
* rate obtained using a ensemble containing 33% of the trees.
*/
class OOB_Error : public VisitorBase
{
typedef MultiArrayShape<2>::type Shp;
int class_count;
bool is_weighted;
MultiArray<2,double> tmp_prob;
public:
MultiArray<2, double> prob_oob;
/** Ensemble oob error rate
*/
double oob_breiman;
MultiArray<2, double> oobCount;
ArrayVector< int> indices;
OOB_Error() : VisitorBase(), oob_breiman(0.0) {}
#ifdef HasHDF5
void save(std::string filen, std::string pathn)
{
if(*(pathn.end()-1) != '/')
pathn += "/";
const char* filename = filen.c_str();
MultiArray<2, double> temp(Shp(1,1), 0.0);
temp[0] = oob_breiman;
writeHDF5(filename, (pathn + "breiman_error").c_str(), temp);
}
#endif
// negative value if sample was ib, number indicates how often.
// value >=0 if sample was oob, 0 means fail 1, correct
template<class RF, class PR>
void visit_at_beginning(RF & rf, PR & pr)
{
class_count = rf.class_count();
tmp_prob.reshape(Shp(1, class_count), 0);
prob_oob.reshape(Shp(rf.ext_param().row_count_,class_count), 0);
is_weighted = rf.options().predict_weighted_;
indices.resize(rf.ext_param().row_count_);
if(int(oobCount.size()) != rf.ext_param_.row_count_)
{
oobCount.reshape(Shp(rf.ext_param_.row_count_, 1), 0);
}
for(int ii = 0; ii < rf.ext_param().row_count_; ++ii)
{
indices[ii] = ii;
}
}
template<class RF, class PR, class SM, class ST>
void visit_after_tree(RF& rf, PR & pr, SM & sm, ST & st, int index)
{
// go through the samples
int total_oob =0;
// FIXME: magic number 10000: invoke special treatment when when msample << sample_count
// (i.e. the OOB sample ist very large)
// 40000: use at most 40000 OOB samples per class for OOB error estimate
if(rf.ext_param_.actual_msample_ < pr.features().shape(0) - 10000)
{
ArrayVector<int> oob_indices;
ArrayVector<int> cts(class_count, 0);
std::random_shuffle(indices.begin(), indices.end());
for(int ii = 0; ii < rf.ext_param_.row_count_; ++ii)
{
if(!sm.is_used()[indices[ii]] && cts[pr.response()(indices[ii], 0)] < 40000)
{
oob_indices.push_back(indices[ii]);
++cts[pr.response()(indices[ii], 0)];
}
}
for(unsigned int ll = 0; ll < oob_indices.size(); ++ll)
{
// update number of trees in which current sample is oob
++oobCount[oob_indices[ll]];
// update number of oob samples in this tree.
++total_oob;
// get the predicted votes ---> tmp_prob;
int pos = rf.tree(index).getToLeaf(rowVector(pr.features(),oob_indices[ll]));
Node<e_ConstProbNode> node ( rf.tree(index).topology_,
rf.tree(index).parameters_,
pos);
tmp_prob.init(0);
for(int ii = 0; ii < class_count; ++ii)
{
tmp_prob[ii] = node.prob_begin()[ii];
}
if(is_weighted)
{
for(int ii = 0; ii < class_count; ++ii)
tmp_prob[ii] = tmp_prob[ii] * (*(node.prob_begin()-1));
}
rowVector(prob_oob, oob_indices[ll]) += tmp_prob;
}
}else
{
for(int ll = 0; ll < rf.ext_param_.row_count_; ++ll)
{
// if the lth sample is oob...
if(!sm.is_used()[ll])
{
// update number of trees in which current sample is oob
++oobCount[ll];
// update number of oob samples in this tree.
++total_oob;
// get the predicted votes ---> tmp_prob;
int pos = rf.tree(index).getToLeaf(rowVector(pr.features(),ll));
Node<e_ConstProbNode> node ( rf.tree(index).topology_,
rf.tree(index).parameters_,
pos);
tmp_prob.init(0);
for(int ii = 0; ii < class_count; ++ii)
{
tmp_prob[ii] = node.prob_begin()[ii];
}
if(is_weighted)
{
for(int ii = 0; ii < class_count; ++ii)
tmp_prob[ii] = tmp_prob[ii] * (*(node.prob_begin()-1));
}
rowVector(prob_oob, ll) += tmp_prob;
}
}
}
// go through the ib samples;
}
/** Normalise variable importance after the number of trees is known.
*/
template<class RF, class PR>
void visit_at_end(RF & rf, PR & pr)
{
// ullis original metric and breiman style stuff
int totalOobCount =0;
int breimanstyle = 0;
for(int ll=0; ll < (int)rf.ext_param_.row_count_; ++ll)
{
if(oobCount[ll])
{
if(argMax(rowVector(prob_oob, ll)) != pr.response()(ll, 0))
++breimanstyle;
++totalOobCount;
}
}
oob_breiman = double(breimanstyle)/totalOobCount;
}
};
/** Visitor that calculates different OOB error statistics
*/
class CompleteOOBInfo : public VisitorBase
{
typedef MultiArrayShape<2>::type Shp;
int class_count;
bool is_weighted;
MultiArray<2,double> tmp_prob;
public:
/** OOB Error rate of each individual tree
*/
MultiArray<2, double> oob_per_tree;
/** Mean of oob_per_tree
*/
double oob_mean;
/**Standard deviation of oob_per_tree
*/
double oob_std;
MultiArray<2, double> prob_oob;
/** Ensemble OOB error
*
* \sa OOB_Error
*/
double oob_breiman;
MultiArray<2, double> oobCount;
MultiArray<2, double> oobErrorCount;
/** Per Tree OOB error calculated as in OOB_PerTreeError
* (Ulli's version)
*/
double oob_per_tree2;
/**Column containing the development of the Ensemble
* error rate with increasing number of trees
*/
MultiArray<2, double> breiman_per_tree;
/** 4 dimensional array containing the development of confusion matrices
* with number of trees - can be used to estimate ROC curves etc.
*
* oobroc_per_tree(ii,jj,kk,ll)
* corresponds true label = ii
* predicted label = jj
* confusion matrix after ll trees
*
* explanation of third index:
*
* Two class case:
* kk = 0 - (treeCount-1)
* Threshold is on Probability for class 0 is kk/(treeCount-1);
* More classes:
* kk = 0. Threshold on probability set by argMax of the probability array.
*/
MultiArray<4, double> oobroc_per_tree;
CompleteOOBInfo() : VisitorBase(), oob_mean(0), oob_std(0), oob_per_tree2(0) {}
#ifdef HasHDF5
/** save to HDF5 file
*/
void save(std::string filen, std::string pathn)
{
if(*(pathn.end()-1) != '/')
pathn += "/";
const char* filename = filen.c_str();
MultiArray<2, double> temp(Shp(1,1), 0.0);
writeHDF5(filename, (pathn + "oob_per_tree").c_str(), oob_per_tree);
writeHDF5(filename, (pathn + "oobroc_per_tree").c_str(), oobroc_per_tree);
writeHDF5(filename, (pathn + "breiman_per_tree").c_str(), breiman_per_tree);
temp[0] = oob_mean;
writeHDF5(filename, (pathn + "per_tree_error").c_str(), temp);
temp[0] = oob_std;
writeHDF5(filename, (pathn + "per_tree_error_std").c_str(), temp);
temp[0] = oob_breiman;
writeHDF5(filename, (pathn + "breiman_error").c_str(), temp);
temp[0] = oob_per_tree2;
writeHDF5(filename, (pathn + "ulli_error").c_str(), temp);
}
#endif
// negative value if sample was ib, number indicates how often.
// value >=0 if sample was oob, 0 means fail 1, correct
template<class RF, class PR>
void visit_at_beginning(RF & rf, PR & pr)
{
class_count = rf.class_count();
if(class_count == 2)
oobroc_per_tree.reshape(MultiArrayShape<4>::type(2,2,rf.tree_count(), rf.tree_count()));
else
oobroc_per_tree.reshape(MultiArrayShape<4>::type(rf.class_count(),rf.class_count(),1, rf.tree_count()));
tmp_prob.reshape(Shp(1, class_count), 0);
prob_oob.reshape(Shp(rf.ext_param().row_count_,class_count), 0);
is_weighted = rf.options().predict_weighted_;
oob_per_tree.reshape(Shp(1, rf.tree_count()), 0);
breiman_per_tree.reshape(Shp(1, rf.tree_count()), 0);
//do the first time called.
if(int(oobCount.size()) != rf.ext_param_.row_count_)
{
oobCount.reshape(Shp(rf.ext_param_.row_count_, 1), 0);
oobErrorCount.reshape(Shp(rf.ext_param_.row_count_,1), 0);
}
}
template<class RF, class PR, class SM, class ST>
void visit_after_tree(RF& rf, PR & pr, SM & sm, ST & st, int index)
{
// go through the samples
int total_oob =0;
int wrong_oob =0;
for(int ll = 0; ll < rf.ext_param_.row_count_; ++ll)
{
// if the lth sample is oob...
if(!sm.is_used()[ll])
{
// update number of trees in which current sample is oob
++oobCount[ll];
// update number of oob samples in this tree.
++total_oob;
// get the predicted votes ---> tmp_prob;
int pos = rf.tree(index).getToLeaf(rowVector(pr.features(),ll));
Node<e_ConstProbNode> node ( rf.tree(index).topology_,
rf.tree(index).parameters_,
pos);
tmp_prob.init(0);
for(int ii = 0; ii < class_count; ++ii)
{
tmp_prob[ii] = node.prob_begin()[ii];
}
if(is_weighted)
{
for(int ii = 0; ii < class_count; ++ii)
tmp_prob[ii] = tmp_prob[ii] * (*(node.prob_begin()-1));
}
rowVector(prob_oob, ll) += tmp_prob;
int label = argMax(tmp_prob);
if(label != pr.response()(ll, 0))
{
// update number of wrong oob samples in this tree.
++wrong_oob;
// update number of trees in which current sample is wrong oob
++oobErrorCount[ll];
}
}
}
int breimanstyle = 0;
int totalOobCount = 0;
for(int ll=0; ll < (int)rf.ext_param_.row_count_; ++ll)
{
if(oobCount[ll])
{
if(argMax(rowVector(prob_oob, ll)) != pr.response()(ll, 0))
++breimanstyle;
++totalOobCount;
if(oobroc_per_tree.shape(2) == 1)
{
oobroc_per_tree(pr.response()(ll,0), argMax(rowVector(prob_oob, ll)),0 ,index)++;
}
}
}
if(oobroc_per_tree.shape(2) == 1)
oobroc_per_tree.bindOuter(index)/=totalOobCount;
if(oobroc_per_tree.shape(2) > 1)
{
MultiArrayView<3, double> current_roc
= oobroc_per_tree.bindOuter(index);
for(int gg = 0; gg < current_roc.shape(2); ++gg)
{
for(int ll=0; ll < (int)rf.ext_param_.row_count_; ++ll)
{
if(oobCount[ll])
{
int pred = prob_oob(ll, 1) > (double(gg)/double(current_roc.shape(2)))?
1 : 0;
current_roc(pr.response()(ll, 0), pred, gg)+= 1;
}
}
current_roc.bindOuter(gg)/= totalOobCount;
}
}
breiman_per_tree[index] = double(breimanstyle)/double(totalOobCount);
oob_per_tree[index] = double(wrong_oob)/double(total_oob);
// go through the ib samples;
}
/** Normalise variable importance after the number of trees is known.
*/
template<class RF, class PR>
void visit_at_end(RF & rf, PR & pr)
{
// ullis original metric and breiman style stuff
oob_per_tree2 = 0;
int totalOobCount =0;
int breimanstyle = 0;
for(int ll=0; ll < (int)rf.ext_param_.row_count_; ++ll)
{
if(oobCount[ll])
{
if(argMax(rowVector(prob_oob, ll)) != pr.response()(ll, 0))
++breimanstyle;
oob_per_tree2 += double(oobErrorCount[ll]) / oobCount[ll];
++totalOobCount;
}
}
oob_per_tree2 /= totalOobCount;
oob_breiman = double(breimanstyle)/totalOobCount;
// mean error of each tree
MultiArrayView<2, double> mean(Shp(1,1), &oob_mean);
MultiArrayView<2, double> stdDev(Shp(1,1), &oob_std);
rowStatistics(oob_per_tree, mean, stdDev);
}
};
/** calculate variable importance while learning.
*/
class VariableImportanceVisitor : public VisitorBase
{
public:
/** This Array has the same entries as the R - random forest variable
* importance.
* Matrix is featureCount by (classCount +2)
* variable_importance_(ii,jj) is the variable importance measure of
* the ii-th variable according to:
* jj = 0 - (classCount-1)
* classwise permutation importance
* jj = rowCount(variable_importance_) -2
* permutation importance
* jj = rowCount(variable_importance_) -1
* gini decrease importance.
*
* permutation importance:
* The difference between the fraction of OOB samples classified correctly
* before and after permuting (randomizing) the ii-th column is calculated.
* The ii-th column is permuted rep_cnt times.
*
* class wise permutation importance:
* same as permutation importance. We only look at those OOB samples whose
* response corresponds to class jj.
*
* gini decrease importance:
* row ii corresponds to the sum of all gini decreases induced by variable ii
* in each node of the random forest.
*/
MultiArray<2, double> variable_importance_;
int repetition_count_;
bool in_place_;
#ifdef HasHDF5
void save(std::string filename, std::string prefix)
{
prefix = "variable_importance_" + prefix;
writeHDF5(filename.c_str(),
prefix.c_str(),
variable_importance_);
}
#endif
/* Constructor
* \param rep_cnt (defautl: 10) how often should
* the permutation take place. Set to 1 to make calculation faster (but
* possibly more instable)
*/
VariableImportanceVisitor(int rep_cnt = 10)
: repetition_count_(rep_cnt)
{}
/** calculates impurity decrease based variable importance after every
* split.
*/
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
void visit_after_split( Tree & tree,
Split & split,
Region & parent,
Region & leftChild,
Region & rightChild,
Feature_t & features,
Label_t & labels)
{
//resize to right size when called the first time
Int32 const class_count = tree.ext_param_.class_count_;
Int32 const column_count = tree.ext_param_.column_count_;
if(variable_importance_.size() == 0)
{
variable_importance_
.reshape(MultiArrayShape<2>::type(column_count,
class_count+2));
}
if(split.createNode().typeID() == i_ThresholdNode)
{
Node<i_ThresholdNode> node(split.createNode());
variable_importance_(node.column(),class_count+1)
+= split.region_gini_ - split.minGini();
}
}
/**compute permutation based var imp.
* (Only an Array of size oob_sample_count x 1 is created.
* - apposed to oob_sample_count x feature_count in the other method.
*
* \sa FieldProxy
*/
template<class RF, class PR, class SM, class ST>
void after_tree_ip_impl(RF& rf, PR & pr, SM & sm, ST & st, int index)
{
typedef MultiArrayShape<2>::type Shp_t;
Int32 column_count = rf.ext_param_.column_count_;
Int32 class_count = rf.ext_param_.class_count_;
/* This solution saves memory uptake but not multithreading
* compatible
*/
// remove the const cast on the features (yep , I know what I am
// doing here.) data is not destroyed.
//typename PR::Feature_t & features
// = const_cast<typename PR::Feature_t &>(pr.features());
typedef typename PR::FeatureWithMemory_t FeatureArray;
typedef typename FeatureArray::value_type FeatureValue;
FeatureArray features = pr.features();
//find the oob indices of current tree.
ArrayVector<Int32> oob_indices;
ArrayVector<Int32>::iterator
iter;
for(int ii = 0; ii < rf.ext_param_.row_count_; ++ii)
if(!sm.is_used()[ii])
oob_indices.push_back(ii);
//create space to back up a column
ArrayVector<FeatureValue> backup_column;
// Random foo
#ifdef CLASSIFIER_TEST
RandomMT19937 random(1);
#else
RandomMT19937 random(RandomSeed);
#endif
UniformIntRandomFunctor<RandomMT19937>
randint(random);
//make some space for the results
MultiArray<2, double>
oob_right(Shp_t(1, class_count + 1));
MultiArray<2, double>
perm_oob_right (Shp_t(1, class_count + 1));
// get the oob success rate with the original samples
for(iter = oob_indices.begin();
iter != oob_indices.end();
++iter)
{
if(rf.tree(index)
.predictLabel(rowVector(features, *iter))
== pr.response()(*iter, 0))
{
//per class
++oob_right[pr.response()(*iter,0)];
//total
++oob_right[class_count];
}
}
//get the oob rate after permuting the ii'th dimension.
for(int ii = 0; ii < column_count; ++ii)
{
perm_oob_right.init(0.0);
//make backup of original column
backup_column.clear();
for(iter = oob_indices.begin();
iter != oob_indices.end();
++iter)
{
backup_column.push_back(features(*iter,ii));
}
//get the oob rate after permuting the ii'th dimension.
for(int rr = 0; rr < repetition_count_; ++rr)
{
//permute dimension.
int n = oob_indices.size();
for(int jj = 1; jj < n; ++jj)
std::swap(features(oob_indices[jj], ii),
features(oob_indices[randint(jj+1)], ii));
//get the oob success rate after permuting
for(iter = oob_indices.begin();
iter != oob_indices.end();
++iter)
{
if(rf.tree(index)
.predictLabel(rowVector(features, *iter))
== pr.response()(*iter, 0))
{
//per class
++perm_oob_right[pr.response()(*iter, 0)];
//total
++perm_oob_right[class_count];
}
}
}
//normalise and add to the variable_importance array.
perm_oob_right /= repetition_count_;
perm_oob_right -=oob_right;
perm_oob_right *= -1;
perm_oob_right /= oob_indices.size();
variable_importance_
.subarray(Shp_t(ii,0),
Shp_t(ii+1,class_count+1)) += perm_oob_right;
//copy back permuted dimension
for(int jj = 0; jj < int(oob_indices.size()); ++jj)
features(oob_indices[jj], ii) = backup_column[jj];
}
}
/** calculate permutation based impurity after every tree has been
* learned default behaviour is that this happens out of place.
* If you have very big data sets and want to avoid copying of data
* set the in_place_ flag to true.
*/
template<class RF, class PR, class SM, class ST>
void visit_after_tree(RF& rf, PR & pr, SM & sm, ST & st, int index)
{
after_tree_ip_impl(rf, pr, sm, st, index);
}
/** Normalise variable importance after the number of trees is known.
*/
template<class RF, class PR>
void visit_at_end(RF & rf, PR & pr)
{
variable_importance_ /= rf.trees_.size();
}
};
/** Verbose output
*/
class RandomForestProgressVisitor : public VisitorBase {
public:
RandomForestProgressVisitor() : VisitorBase() {}
template<class RF, class PR, class SM, class ST>
void visit_after_tree(RF& rf, PR & pr, SM & sm, ST & st, int index){
if(index != rf.options().tree_count_-1) {
std::cout << "\r[" << std::setw(10) << (index+1)/static_cast<double>(rf.options().tree_count_)*100 << "%]"
<< " (" << index+1 << " of " << rf.options().tree_count_ << ") done" << std::flush;
}
else {
std::cout << "\r[" << std::setw(10) << 100.0 << "%]" << std::endl;
}
}
template<class RF, class PR>
void visit_at_end(RF const & rf, PR const & pr) {
std::string a = TOCS;
std::cout << "all " << rf.options().tree_count_ << " trees have been learned in " << a << std::endl;
}
template<class RF, class PR>
void visit_at_beginning(RF const & rf, PR const & pr) {
TIC;
std::cout << "growing random forest, which will have " << rf.options().tree_count_ << " trees" << std::endl;
}
private:
USETICTOC;
};
/** Computes Correlation/Similarity Matrix of features while learning
* random forest.
*/
class CorrelationVisitor : public VisitorBase
{
public:
/** gini_missc(ii, jj) describes how well variable jj can describe a partition
* created on variable ii(when variable ii was chosen)
*/
MultiArray<2, double> gini_missc;
MultiArray<2, int> tmp_labels;
/** additional noise features.
*/
MultiArray<2, double> noise;
MultiArray<2, double> noise_l;
/** how well can a noise column describe a partition created on variable ii.
*/
MultiArray<2, double> corr_noise;
MultiArray<2, double> corr_l;
/** Similarity Matrix
*
* (numberOfFeatures + 1) by (number Of Features + 1) Matrix
* gini_missc
* - row normalized by the number of times the column was chosen
* - mean of corr_noise subtracted
* - and symmetrised.
*
*/
MultiArray<2, double> similarity;
/** Distance Matrix 1-similarity
*/
MultiArray<2, double> distance;
ArrayVector<int> tmp_cc;
/** How often was variable ii chosen
*/
ArrayVector<int> numChoices;
typedef BestGiniOfColumn<GiniCriterion> ColumnDecisionFunctor;
BestGiniOfColumn<GiniCriterion> bgfunc;
void save(std::string file, std::string prefix)
{
/*
std::string tmp;
#define VAR_WRITE(NAME) \
tmp = #NAME;\
tmp += "_";\
tmp += prefix;\
vigra::writeToHDF5File(file.c_str(), tmp.c_str(), NAME);
VAR_WRITE(gini_missc);
VAR_WRITE(corr_noise);
VAR_WRITE(distance);
VAR_WRITE(similarity);
vigra::writeToHDF5File(file.c_str(), "nChoices", MultiArrayView<2, int>(MultiArrayShape<2>::type(numChoices.size(),1), numChoices.data()));
#undef VAR_WRITE
*/
}
template<class RF, class PR>
void visit_at_beginning(RF const & rf, PR & pr)
{
typedef MultiArrayShape<2>::type Shp;
int n = rf.ext_param_.column_count_;
gini_missc.reshape(Shp(n +1,n+ 1));
corr_noise.reshape(Shp(n + 1, 10));
corr_l.reshape(Shp(n +1, 10));
noise.reshape(Shp(pr.features().shape(0), 10));
noise_l.reshape(Shp(pr.features().shape(0), 10));
RandomMT19937 random(RandomSeed);
for(int ii = 0; ii < noise.size(); ++ii)
{
noise[ii] = random.uniform53();
noise_l[ii] = random.uniform53() > 0.5;
}
bgfunc = ColumnDecisionFunctor( rf.ext_param_);
tmp_labels.reshape(pr.response().shape());
tmp_cc.resize(2);
numChoices.resize(n+1);
// look at all axes
}
template<class RF, class PR>
void visit_at_end(RF const & rf, PR const & pr)
{
typedef MultiArrayShape<2>::type Shp;
similarity.reshape(gini_missc.shape());
similarity = gini_missc;;
MultiArray<2, double> mean_noise(Shp(corr_noise.shape(0), 1));
rowStatistics(corr_noise, mean_noise);
mean_noise/= MultiArrayView<2, int>(mean_noise.shape(), numChoices.data());
int rC = similarity.shape(0);
for(int jj = 0; jj < rC-1; ++jj)
{
rowVector(similarity, jj) /= numChoices[jj];
rowVector(similarity, jj) -= mean_noise(jj, 0);
}
for(int jj = 0; jj < rC; ++jj)
{
similarity(rC -1, jj) /= numChoices[jj];
}
rowVector(similarity, rC - 1) -= mean_noise(rC-1, 0);
similarity = abs(similarity);
FindMinMax<double> minmax;
inspectMultiArray(srcMultiArrayRange(similarity), minmax);
for(int jj = 0; jj < rC; ++jj)
similarity(jj, jj) = minmax.max;
similarity.subarray(Shp(0,0), Shp(rC-1, rC-1))
+= similarity.subarray(Shp(0,0), Shp(rC-1, rC-1)).transpose();
similarity.subarray(Shp(0,0), Shp(rC-1, rC-1))/= 2;
columnVector(similarity, rC-1) = rowVector(similarity, rC-1).transpose();
for(int jj = 0; jj < rC; ++jj)
similarity(jj, jj) = 0;
FindMinMax<double> minmax2;
inspectMultiArray(srcMultiArrayRange(similarity), minmax2);
for(int jj = 0; jj < rC; ++jj)
similarity(jj, jj) = minmax2.max;
distance.reshape(gini_missc.shape(), minmax2.max);
distance -= similarity;
}
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
void visit_after_split( Tree & tree,
Split & split,
Region & parent,
Region & leftChild,
Region & rightChild,
Feature_t & features,
Label_t & labels)
{
if(split.createNode().typeID() == i_ThresholdNode)
{
double wgini;
tmp_cc.init(0);
for(int ii = 0; ii < parent.size(); ++ii)
{
tmp_labels[parent[ii]]
= (features(parent[ii], split.bestSplitColumn()) < split.bestSplitThreshold());
++tmp_cc[tmp_labels[parent[ii]]];
}
double region_gini = bgfunc.loss_of_region(tmp_labels,
parent.begin(),
parent.end(),
tmp_cc);
int n = split.bestSplitColumn();
++numChoices[n];
++(*(numChoices.end()-1));
//this functor does all the work
for(int k = 0; k < features.shape(1); ++k)
{
bgfunc(columnVector(features, k),
tmp_labels,
parent.begin(), parent.end(),
tmp_cc);
wgini = (region_gini - bgfunc.min_gini_);
gini_missc(n, k)
+= wgini;
}
for(int k = 0; k < 10; ++k)
{
bgfunc(columnVector(noise, k),
tmp_labels,
parent.begin(), parent.end(),
tmp_cc);
wgini = (region_gini - bgfunc.min_gini_);
corr_noise(n, k)
+= wgini;
}
for(int k = 0; k < 10; ++k)
{
bgfunc(columnVector(noise_l, k),
tmp_labels,
parent.begin(), parent.end(),
tmp_cc);
wgini = (region_gini - bgfunc.min_gini_);
corr_l(n, k)
+= wgini;
}
bgfunc(labels, tmp_labels, parent.begin(), parent.end(),tmp_cc);
wgini = (region_gini - bgfunc.min_gini_);
gini_missc(n, columnCount(gini_missc)-1)
+= wgini;
region_gini = split.region_gini_;
#if 1
Node<i_ThresholdNode> node(split.createNode());
gini_missc(rowCount(gini_missc)-1,
node.column())
+=split.region_gini_ - split.minGini();
#endif
for(int k = 0; k < 10; ++k)
{
split.bgfunc(columnVector(noise, k),
labels,
parent.begin(), parent.end(),
parent.classCounts());
corr_noise(rowCount(gini_missc)-1,
k)
+= wgini;
}
#if 0
for(int k = 0; k < tree.ext_param_.actual_mtry_; ++k)
{
wgini = region_gini - split.min_gini_[k];
gini_missc(rowCount(gini_missc)-1,
split.splitColumns[k])
+= wgini;
}
for(int k=tree.ext_param_.actual_mtry_; k<features.shape(1); ++k)
{
split.bgfunc(columnVector(features, split.splitColumns[k]),
labels,
parent.begin(), parent.end(),
parent.classCounts());
wgini = region_gini - split.bgfunc.min_gini_;
gini_missc(rowCount(gini_missc)-1,
split.splitColumns[k]) += wgini;
}
#endif
// remember to partition the data according to the best.
gini_missc(rowCount(gini_missc)-1,
columnCount(gini_missc)-1)
+= region_gini;
SortSamplesByDimensions<Feature_t>
sorter(features, split.bestSplitColumn(), split.bestSplitThreshold());
std::partition(parent.begin(), parent.end(), sorter);
}
}
};
} // namespace visitors
} // namespace rf
} // namespace vigra
//@}
#endif // RF_VISITORS_HXX
|