(sperrorest) Coordinate-based k-means clustering

Splits data by clustering in the coordinate space. See the upstream implementation at sperrorest::partition_kmeans() and Brenning (2012) for further information.

Details

Universal partitioning method that splits the data in the coordinate space. Useful for spatially homogeneous datasets that cannot be split well with rectangular approaches like ResamplingSpCVBlock.

Parameters

• folds (integer(1))
  Number of folds.

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 -> ResamplingSpCVCoords

Active bindings

iters

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

Methods

Public methods

• ResamplingSpCVCoords$new()

• ResamplingSpCVCoords$instantiate()

• ResamplingSpCVCoords$clone()

Inherited methods

• mlr3::Resampling$format()
• mlr3::Resampling$help()
• mlr3::Resampling$print()
• mlr3::Resampling$test_set()
• mlr3::Resampling$train_set()

---

Method new()

Create an "coordinate-based" repeated resampling instance.

For a list of available arguments, please see sperrorest::partition_cv.

Usage

ResamplingSpCVCoords$new(id = "spcv_coords")

Arguments

id

character(1)
Identifier for the resampling strategy.

---

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVCoords$instantiate(task)

Arguments

task

mlr3::Task
A task to instantiate.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVCoords$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
task = tsk("ecuador")

# Instantiate Resampling
rcv = rsmp("spcv_coords", folds = 5)
rcv$instantiate(task)

# Individual sets:
rcv$train_set(1)
#>   [1]   1   6  11  16  23  33  34  36  47  51  52  56  59  60  76  84  87  95
#>  [19]  96  99 100 107 111 112 117 121 139 144 146 148 149 152 156 157 164 165
#>  [37] 167 168 174 175 178 184 193 198 210 212 232 239 241 248 249 252 256 257
#>  [55] 258 264 267 269 273 274 283 286 294 295 297 300 302 307 310 312 314 316
#>  [73] 319 321 324 329 330 332 342 347 351 361 363 364 369 374 375 376 379 382
#>  [91] 387 391 393 397 399 407 408 410 417 420 421 424 425 432 439 440 445 447
#> [109] 459 466 469 471 473 481 482 485 491 497 507 514 515 516 517 534 540 545
#> [127] 553 560 571 572 577 584 588 590 604 609 624 626 629 632 635 637 640 645
#> [145] 647 650 660 665 666 674 676 677 678 686 687 695 698 699 700 702 704 716
#> [163] 721 731 742 745 748 749   2   3   4   5   8  15  18  19  24  25  26  28
#> [181]  32  48  50  58  63  64  70  85  89 102 104 118 120 125 126 127 129 131
#> [199] 133 135 138 150 151 153 158 161 166 169 170 172 192 203 206 207 214 216
#> [217] 217 218 220 221 224 227 231 234 236 238 245 251 255 260 261 265 266 268
#> [235] 271 272 278 280 281 287 290 303 309 344 346 354 357 358 362 370 373 377
#> [253] 388 394 402 406 411 412 422 423 426 428 429 433 435 438 442 451 455 460
#> [271] 461 468 470 476 480 488 499 509 518 523 525 529 531 541 543 555 558 561
#> [289] 562 582 585 589 592 598 612 623 633 644 653 656 661 683 693 706 707 708
#> [307] 712 718 727 730 737 738 746   7  12  13  14  17  21  35  44  46  54  65
#> [325]  66  75  78  79  86  88  91  94  97 105 109 113 123 128 130 132 137 140
#> [343] 142 143 145 147 162 163 171 181 182 188 191 195 196 200 202 204 209 211
#> [361] 213 215 223 225 229 240 242 246 253 259 262 270 277 279 282 285 288 305
#> [379] 306 308 313 318 320 322 327 331 336 337 338 348 359 365 366 368 371 378
#> [397] 381 384 395 409 416 418 430 431 446 448 452 458 465 467 472 475 477 478
#> [415] 489 493 494 496 498 505 511 512 527 532 533 535 538 542 547 554 559 563
#> [433] 565 566 567 570 580 581 583 586 591 599 602 607 611 617 618 619 621 631
#> [451] 636 638 646 651 658 663 667 668 671 680 681 684 691 701 703 705 714 720
#> [469] 723 726 734 740 747 750  10  29  37  39  42  45  49  55  57  61  62  68
#> [487]  77  90  93 103 106 110 115 122 136 155 183 190 201 219 222 230 243 250
#> [505] 263 289 317 325 345 349 350 356 372 383 385 400 403 404 405 413 414 419
#> [523] 427 444 449 453 463 464 474 483 486 487 490 492 501 502 503 504 510 528
#> [541] 537 539 544 546 548 550 552 557 564 569 573 575 578 594 596 613 620 627
#> [559] 628 630 639 641 642 643 649 652 654 655 657 662 664 669 673 682 688 689
#> [577] 690 692 696 710 711 715 724 728 729 732 733 735 736 739 741 743 744 751
rcv$test_set(1)
#>   [1]   9  20  22  27  30  31  38  40  41  43  53  67  69  71  72  73  74  80
#>  [19]  81  82  83  92  98 101 108 114 116 119 124 134 141 154 159 160 173 176
#>  [37] 177 179 180 185 186 187 189 194 197 199 205 208 226 228 233 235 237 244
#>  [55] 247 254 275 276 284 291 292 293 296 298 299 301 304 311 315 323 326 328
#>  [73] 333 334 335 339 340 341 343 352 353 355 360 367 380 386 389 390 392 396
#>  [91] 398 401 415 434 436 437 441 443 450 454 456 457 462 479 484 495 500 506
#> [109] 508 513 519 520 521 522 524 526 530 536 549 551 556 568 574 576 579 587
#> [127] 593 595 597 600 601 603 605 606 608 610 614 615 616 622 625 634 648 659
#> [145] 670 672 675 679 685 694 697 709 713 717 719 722 725
# check that no obs are in both sets
intersect(rcv$train_set(1), rcv$test_set(1)) # good!
#> integer(0)

# Internal storage:
rcv$instance # table
#> Key: <fold>
#>      row_id  fold
#>       <int> <int>
#>   1:      9     1
#>   2:     20     1
#>   3:     22     1
#>   4:     27     1
#>   5:     30     1
#>  ---             
#> 747:    739     5
#> 748:    741     5
#> 749:    743     5
#> 750:    744     5
#> 751:    751     5