Spatial Cross validation following the "k-means" approach after Brenning 2012.

References

Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. doi: 10.1109/igarss.2012.6352393 .

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVCoords

Active bindings

iters

integer(1)
Returns the number of resampling iterations, depending on the values stored in the param_set.

Methods

Public methods

Inherited methods

Method new()

Create an "coordinate-based" repeated resampling instance.

Usage

ResamplingRepeatedSpCVCoords$new(id = "repeated_spcv_coords")

Arguments

id

character(1)
Identifier for the resampling strategy.


Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVCoords$folds(iters)

Arguments

iters

integer()
Iteration number.


Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVCoords$repeats(iters)

Arguments

iters

integer()
Iteration number.


Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVCoords$instantiate(task)

Arguments

task

Task
A task to instantiate.


Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVCoords$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3) task = tsk("diplodia") # Instantiate Resampling rrcv = rsmp("repeated_spcv_coords", folds = 3, repeats = 5) rrcv$instantiate(task) # Individual sets: rrcv$iters
#> [1] 15
rrcv$folds(1:6)
#> [1] 1 2 3 4 5 1
rrcv$repeats(1:6)
#> [1] 1 1 1 2 2 2
# Individual sets: rrcv$train_set(1)
#> [1] 21 26 27 28 29 30 31 34 35 36 37 38 39 40 44 45 47 48 #> [19] 49 50 51 52 53 59 60 61 62 63 64 65 66 71 72 74 75 76 #> [37] 77 78 79 80 81 82 83 84 85 86 87 88 90 100 101 102 103 107 #> [55] 109 110 111 112 116 117 118 119 120 121 122 125 126 127 149 150 160 161 #> [73] 162 163 165 166 167 168 169 170 171 172 173 174 175 176 177 178 191 192 #> [91] 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 #> [109] 212 214 215 216 217 218 219 220 221 226 227 254 255 256 257 258 259 260 #> [127] 261 262 263 264 265 266 267 268 269 270 274 275 276 277 278 279 280 281 #> [145] 282 283 284 285 286 287 293 297 298 299 300 301 324 325 344 348 349 350 #> [163] 351 352 353 354 355 358 359 360 361 362 363 364 365 366 367 368 369 370 #> [181] 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 #> [199] 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 #> [217] 407 408 409 410 411 412 413 414 415 416 417 418 424 425 441 442 443 444 #> [235] 445 451 452 453 454 460 461 462 463 464 465 467 468 469 470 471 472 473 #> [253] 474 478 480 485 486 487 491 493 496 497 509 510 511 512 513 589 598 605 #> [271] 606 607 614 615 616 619 620 654 655 656 675 682 683 684 688 692 753 754 #> [289] 755 756 757 764 765 768 771 772 778 785 789 802 820 841 875 908 909 910 #> [307] 911 912 913 914 98 99 123 164 211 213 252 253 345 346 347 438 439 440 #> [325] 448 449 450 455 456 490 492 495 503 504 505 506 514 515 516 517 518 519 #> [343] 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 #> [361] 538 539 540 541 542 543 544 545 552 553 554 555 556 557 558 559 560 561 #> [379] 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 #> [397] 580 582 597 604 617 618 622 626 627 628 629 630 631 632 633 634 635 636 #> [415] 637 638 639 645 646 647 648 649 650 697 701 703 719 720 721 722 723 724 #> [433] 725 726 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 #> [451] 748 749 767 770 779 782 783 784 786 790 791 792 794 795 796 797 801 803 #> [469] 804 805 806 807 813 814 815 816 822 823 824 825 826 832 834 837 838 839 #> [487] 840 842 844 846 847 848 851 852 853 854 855 858 859 860 861 862 863 865 #> [505] 869 872 873 879 881 884 885 886 887 890 892 893 894 895 897 898 899 900 #> [523] 903 904 906 907 917 918 919 920
rrcv$test_set(1)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 #> [19] 19 20 22 23 24 25 32 33 41 42 43 46 54 55 56 57 58 67 #> [37] 68 69 70 73 89 91 92 93 94 95 96 97 104 105 106 108 113 114 #> [55] 115 124 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 #> [73] 144 145 146 147 148 151 152 153 154 155 156 157 158 159 179 180 181 182 #> [91] 183 184 185 186 187 188 189 190 222 223 224 225 228 229 230 231 232 233 #> [109] 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 #> [127] 271 272 273 288 289 290 291 292 294 295 296 302 303 304 305 306 307 308 #> [145] 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 326 327 328 #> [163] 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 356 357 419 #> [181] 420 421 422 423 426 427 428 429 430 431 432 433 434 435 436 437 446 447 #> [199] 457 458 459 466 475 476 477 479 481 482 483 484 488 489 494 498 499 500 #> [217] 501 502 507 508 546 547 548 549 550 551 581 583 584 585 586 587 588 590 #> [235] 591 592 593 594 595 596 599 600 601 602 603 608 609 610 611 612 613 621 #> [253] 623 624 625 640 641 642 643 644 651 652 653 657 658 659 660 661 662 663 #> [271] 664 665 666 667 668 669 670 671 672 673 674 676 677 678 679 680 681 685 #> [289] 686 687 689 690 691 693 694 695 696 698 699 700 702 704 705 706 707 708 #> [307] 709 710 711 712 713 714 715 716 717 718 727 728 729 730 731 750 751 752 #> [325] 758 759 760 761 762 763 766 769 773 774 775 776 777 780 781 787 788 793 #> [343] 798 799 800 808 809 810 811 812 817 818 819 821 827 828 829 830 831 833 #> [361] 835 836 843 845 849 850 856 857 864 866 867 868 870 871 874 876 877 878 #> [379] 880 882 883 888 889 891 896 901 902 905 915 916 921 922
intersect(rrcv$train_set(1), rrcv$test_set(1))
#> integer(0)
# Internal storage: rrcv$instance # table
#> row_id rep fold #> 1: 1 1 1 #> 2: 2 1 1 #> 3: 3 1 1 #> 4: 4 1 1 #> 5: 5 1 1 #> --- #> 4606: 918 5 3 #> 4607: 919 5 3 #> 4608: 920 5 3 #> 4609: 921 5 1 #> 4610: 922 5 1