(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]   2   4   8  15  18  24  25  26  28  32  48  50  63  64  70  85  89  97
#>  [19] 102 104 118 120 129 131 135 138 150 151 153 158 161 166 172 192 200 206
#>  [37] 207 214 215 216 217 221 224 227 234 238 246 251 255 261 265 266 268 271
#>  [55] 272 278 279 280 281 287 288 306 313 344 346 348 354 358 362 370 373 377
#>  [73] 388 402 406 411 412 422 423 426 428 429 430 433 435 438 451 455 460 461
#>  [91] 468 470 476 480 488 499 512 518 525 529 532 541 543 555 558 559 561 562
#> [109] 563 582 586 589 592 598 607 611 617 623 633 651 656 683 693 707 712 726
#> [127] 727 730 737 738 746   7  12  13  14  17  21  35  44  46  54  65  66  75
#> [145]  78  79  86  88  91  94 105 109 113 123 128 130 132 137 140 142 143 145
#> [163] 147 162 163 171 181 182 188 191 195 196 202 204 209 211 213 223 225 229
#> [181] 240 242 253 259 262 270 277 282 285 305 308 318 320 322 327 331 336 337
#> [199] 338 359 365 366 368 371 378 381 384 395 409 416 418 431 446 448 452 458
#> [217] 465 467 472 475 477 478 489 493 494 496 498 505 511 527 533 535 538 542
#> [235] 547 554 565 566 567 570 580 581 583 591 599 602 618 619 621 631 636 638
#> [253] 646 658 663 667 668 670 671 680 681 684 691 701 703 705 714 720 723 734
#> [271] 740 747 750   1   6  11  16  23  33  34  36  47  51  52  56  59  60  76
#> [289]  84  87  93  95  96  99 100 107 111 112 117 121 139 144 146 148 149 152
#> [307] 156 157 164 165 167 168 174 175 178 184 193 198 210 212 232 239 241 248
#> [325] 249 250 252 256 257 258 264 267 269 273 274 283 286 294 295 297 300 302
#> [343] 307 310 312 314 316 319 321 324 329 330 332 342 347 351 361 363 364 369
#> [361] 374 375 376 379 382 387 391 393 397 399 407 408 410 417 419 420 421 424
#> [379] 425 432 439 440 445 447 459 466 469 471 473 481 482 485 491 497 501 504
#> [397] 507 514 515 516 517 534 540 545 548 553 560 569 571 572 577 588 590 604
#> [415] 609 624 626 629 630 632 635 637 640 645 647 650 660 665 666 669 674 676
#> [433] 677 678 686 689 695 698 699 700 702 704 716 721 731 742 745 748 749 751
#> [451]   3   5  10  19  29  37  39  42  45  49  55  57  58  61  62  68  77  90
#> [469] 103 106 110 115 122 125 126 127 133 136 155 169 170 183 190 201 203 218
#> [487] 219 220 222 230 231 236 243 245 260 263 289 290 303 309 317 325 345 349
#> [505] 350 356 357 372 383 385 394 400 403 404 405 413 414 427 442 444 449 453
#> [523] 463 464 474 483 486 487 490 492 502 503 509 510 523 528 531 537 539 544
#> [541] 546 550 552 557 564 573 575 578 585 594 596 613 620 627 628 639 641 642
#> [559] 643 644 649 652 653 654 655 657 661 662 664 673 682 688 690 692 696 706
#> [577] 708 710 711 715 718 724 728 729 732 733 735 736 739 741 743 744
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] 508 513 519 520 521 522 524 526 530 536 549 551 556 568 574 576 579 584
#> [127] 587 593 595 597 600 601 603 605 606 608 610 612 614 615 616 622 625 634
#> [145] 648 659 672 675 679 685 687 694 697 709 713 717 719 722 725
# 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:    736     5
#> 748:    739     5
#> 749:    741     5
#> 750:    743     5
#> 751:    744     5