/
task_parameterestimation.R
889 lines (753 loc) · 31.9 KB
/
task_parameterestimation.R
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
#' Run parameter estimation
#'
#' \code{runParameterEstimation} runs parameter estimation and returns the results in a list.
#'
#' The \href{https://jpahle.github.io/CoRC/articles/task_management.html}{online article on managing tasks} provides some further context.
#'
#' @param randomize_start_values flag
#' @param create_parameter_sets flag
#' @param calculate_statistics flag
#' @param update_model flag
#' @param executable flag
#' @param parameters corc_opt_parm or list of corc_opt_parm objects
#'
#' See also \code{\link{defineParameterEstimationParameter}}.
#' @param experiments copasi_exp or list of copasi_exp objects
#'
#' See also \code{\link{defineExperiments}}.
#' @eval rox_method_param("Parameter Estimation", "_p_CFitTask")
#' @param model A model object.
#' @return A list of results.
#' @family parameter estimation
#' @export
runParameterEstimation <- function(randomize_start_values = NULL, create_parameter_sets = NULL, calculate_statistics = NULL, update_model = NULL, executable = NULL, parameters = NULL, experiments = NULL, method = NULL, model = getCurrentModel()) {
c_datamodel <- assert_datamodel(model)
# does assertions
settings <- pe_assemble_settings(
randomize_start_values = randomize_start_values,
create_parameter_sets = create_parameter_sets,
calculate_statistics = calculate_statistics,
update_model = update_model,
executable = executable
)
c_task <- as(c_datamodel$getTask("Parameter Estimation"), "_p_CFitTask")
# does assertions
method_settings <- pe_assemble_method(method, c_task)
c_problem <- as(c_task$getProblem(), "_p_CFitProblem")
# does assertions
parameter_list <- pe_assemble_parameters(parameters, c_problem)
experiment_list <- pe_assemble_experiments(experiments, c_problem, temp_filenames = TRUE)
# try to avoid doing changes for performance reasons
do_settings <- !is_empty(settings)
do_method <- !is_empty(method_settings)
do_parameters <- !is_empty(parameter_list)
do_experiments <- !is_empty(experiment_list)
c_model <- c_datamodel$getModel()
tryCatch({
# save all previous settings
if (do_settings)
pre_settings <- pe_get_settings(c_task)
if (do_method) {
# keep track of the originally set method
pre_method <- c_task$getMethod()$getSubType()
# change the method first, then save the settings for the new method
if (!is.null(method_settings$method))
c_task$setMethodType(method_settings$method)
c_method <- as(c_task$getMethod(), "_p_COptMethod")
pre_method_settings <- get_method_settings(c_method, with_name = TRUE)
} else {
c_method <- as(c_task$getMethod(), "_p_COptMethod")
}
# apply settings
if (do_settings)
pe_set_settings(settings, c_task)
if (do_method)
set_method_settings(method_settings, c_method)
if (do_parameters)
addParameterEstimationParameter(parameter_list, model = c_datamodel)
if (do_experiments)
addExperiments(experiment_list, model = c_datamodel)
compile_and_check(c_model)
# initialize task
assert_that(
grab_msg(c_task$initializeRaw(OUTPUTFLAG)),
msg = "Initializing the task failed."
)
# save current settings
full_settings <- pe_get_settings(c_task)
full_settings$method <- get_method_settings(c_method, with_name = TRUE)
# run task
process_task(c_task)
# get results
ret <- pe_get_results(c_task, full_settings)
},
finally = {
# revert all settings
if (do_settings)
pe_set_settings(pre_settings, c_task)
if (do_method) {
set_method_settings(pre_method_settings, c_method)
c_task$setMethodType(pre_method)
}
if (do_parameters)
clearParameterEstimationParameters()
if (do_experiments) {
clearExperiments()
# delete all experiment files
# pe_assemble_experiments makes sure they are all tempfiles
try(
experiment_list %>%
map_chr(attr_getter("filename")) %>%
keep(file.exists) %>%
file.remove(),
silent = TRUE
)
}
})
ret
}
#' Set parameter estimation settings
#'
#' \code{setParameterEstimationSettings} sets parameter estimation task settings including parameters, experiments and method options.
#'
#' The \href{https://jpahle.github.io/CoRC/articles/task_management.html}{online article on managing tasks} provides some further context.
#'
#' @param randomize_start_values flag
#' @param create_parameter_sets flag
#' @param calculate_statistics flag
#' @param update_model flag
#' @param executable flag
#' @param parameters corc_opt_parm or list of corc_opt_parm objects
#'
#' See also \code{\link{defineParameterEstimationParameter}}.
#' @param experiments copasi_exp or list of copasi_exp objects
#'
#' See also \code{\link{defineExperiments}}.
#' @eval rox_method_param("Parameter Estimation", "_p_CFitTask")
#' @param model a model object
#' @family parameter estimation
#' @export
setParameterEstimationSettings <- function(randomize_start_values = NULL, create_parameter_sets = NULL, calculate_statistics = NULL, update_model = NULL, executable = NULL, parameters = NULL, experiments = NULL, method = NULL, model = getCurrentModel()) {
c_datamodel <- assert_datamodel(model)
# does assertions
settings <- pe_assemble_settings(
randomize_start_values = randomize_start_values,
create_parameter_sets = create_parameter_sets,
calculate_statistics = calculate_statistics,
update_model = update_model,
executable = executable
)
c_task <- as(c_datamodel$getTask("Parameter Estimation"), "_p_CFitTask")
# does assertions
method_settings <- pe_assemble_method(method, c_task)
c_problem <- as(c_task$getProblem(), "_p_CFitProblem")
# does assertions
parameter_list <- pe_assemble_parameters(parameters, c_problem)
experiment_list <- pe_assemble_experiments(experiments, c_problem)
# experiments and parameters get rolled back when not setting them properly
tryCatch(
addParameterEstimationParameter(parameter_list, model = c_datamodel),
error = function(e) {
clearParameterEstimationParameters(c_datamodel)
base::stop(e)
# stop("Failed when applying parameters.")
}
)
tryCatch(
addExperiments(experiment_list, model = c_datamodel),
error = function(e) {
clearExperiments(c_datamodel)
base::stop(e)
# stop("Failed when applying experiments.")
}
)
# switch to given method
if (!is.null(method_settings$method))
c_task$setMethodType(method_settings$method)
c_method <- as(c_task$getMethod(), "_p_COptMethod")
pe_set_settings(settings, c_task)
set_method_settings(method_settings, c_method)
invisible()
}
#' Set parameter estimation settings
#'
#' \code{getParameterEstimationSettings} gets parameter estimation task settings including method options.
#'
#' The \href{https://jpahle.github.io/CoRC/articles/task_management.html}{online article on managing tasks} provides some further context.
#'
#' @param model a model object
#' @return A list of parameter estimation task settings including method options.
#' @family parameter estimation
#' @export
getParameterEstimationSettings <- function(model = getCurrentModel()) {
c_datamodel <- assert_datamodel(model)
c_task <- as(c_datamodel$getTask("Parameter Estimation"), "_p_CFitTask")
c_method <- as(c_task$getMethod(), "_p_COptMethod")
ret <- pe_get_settings(c_task)
ret$method <- get_method_settings(c_method, with_name = TRUE)
ret
}
#' @rdname runParameterEstimation
#' @export
runPE <- runParameterEstimation
#' @rdname setParameterEstimationSettings
#' @export
setPE<- setParameterEstimationSettings
#' @rdname getParameterEstimationSettings
#' @export
getPE <- getParameterEstimationSettings
#' Define a parameter estimation parameter
#'
#' @param ref value reference
#' @param start_value start value
#' @param lower_bound lower value bound
#' @param upper_bound upper value bound
#' @seealso \code{\link{addParameterEstimationParameter}} \code{\link{clearParameterEstimationParameters}}
#' @return corc_opt_parm object for input into related functions
#' @export
defineParameterEstimationParameter <- corc_opt_parm
#' Add a parameter estimation parameter
#'
#' @param ... objects as returned by \code{\link{defineParameterEstimationParameter}}.
#' Alternatively, the same parameters as used by \code{\link{defineParameterEstimationParameter}}.
#' @param model a model object
#' @family parameter estimation
#' @seealso \code{\link{defineParameterEstimationParameter}} \code{\link{clearParameterEstimationParameters}}
#' @export
addParameterEstimationParameter <- function(..., model = getCurrentModel()) {
c_datamodel <- assert_datamodel(model)
# flatten all args into a single list
# this list can be used to check if the user gave only corc_opt_parm
arglist_compact <- rlang::squash(unname(list(...)))
# if not all are corc_opt_parm, try handing the args to define... so we get corc_opt_parm
if (!every(arglist_compact, is.corc_opt_parm))
arglist_compact <- list(defineParameterEstimationParameter(...))
walk(arglist_compact, validate_corc_opt_parm)
cl_obj <-
map_chr(arglist_compact, "ref") %>%
map(xn_to_object, c_datamodel = c_datamodel)
# Test if all refs are valid
# This can probably be a more elaborate and safe test (by using dn_to_object(accepted_types))
invalid_refs <- map_lgl(cl_obj, is.null)
assert_that(
!any(invalid_refs),
msg = paste0("Given reference(s) ", paste0(which(invalid_refs), collapse = ", "), " are invalid for this model.")
)
c_task <- as(c_datamodel$getTask("Parameter Estimation"), "_p_CFitTask")
c_problem <- as(c_task$getProblem(), "_p_CFitProblem")
walk2(
arglist_compact, cl_obj,
~ {
c_fititem <- c_problem$addFitItem(.y$getCN())
c_fititem$setStartValue(.x$start)
c_fititem$setLowerBound(CCommonName(tolower(as.character(.x$lower))))
c_fititem$setUpperBound(CCommonName(tolower(as.character(.x$upper))))
}
)
invisible()
}
#' Clear all parameter estimation parameters
#'
#' @param model a model object
#' @seealso \code{\link{addParameterEstimationParameter}} \code{\link{defineParameterEstimationParameter}}
#' @family parameter estimation
#' @export
clearParameterEstimationParameters <- function(model = getCurrentModel()) {
c_datamodel <- assert_datamodel(model)
c_task <- as(c_datamodel$getTask("Parameter Estimation"), "_p_CFitTask")
c_problem <- as(c_task$getProblem(), "_p_CFitProblem")
walk(
seq_len_0(c_problem$getOptItemSize()),
~ c_problem$removeOptItem(0L)
)
invisible()
}
new_copasi_exp <- function(x, experiment_type, experiments, types, mappings, weights, weight_method, normalize_weights_per_experiment, filename) {
assert_that(
is.string(experiment_type),
is.character(types), has_names(types),
is.character(mappings), has_names(mappings),
is.numeric(weights), noPureNA(weights), has_names(weights),
is.string(weight_method),
is.flag(normalize_weights_per_experiment),
is.string(filename)
)
x <- tibble::as_tibble(x)
experiments <- tibble::as_tibble(experiments)
structure(
x,
class = c("copasi_exp", class(x)),
experiment_type = experiment_type,
experiments = experiments,
types = types,
mappings = mappings,
weights = weights,
weight_method = weight_method,
normalize_weights_per_experiment = normalize_weights_per_experiment,
filename = filename
)
}
#' @export
is.copasi_exp <- function(x) {
inherits(x, "copasi_exp")
}
#' @method format copasi_exp
#' @export
format.copasi_exp <- function (x, ...) {
c(
NextMethod(),
"# Experiment Type:",
format(attr(x, "experiment_type")),
"# Experiments:",
format(attr(x, "experiments")),
"# Types:",
format(attr(x, "types")) %>% paste0(names(.), ": ", .),
"# Mappings:",
format(attr(x, "mappings")) %>% paste0(names(.), ": ", .),
"# Weights:",
format(attr(x, "weights")) %>% paste0(names(.), ": ", .),
"# Weight Method:",
format(attr(x, "weight_method")),
"# Normalize Weights per Experiment:",
format(attr(x, "normalize_weights_per_experiment")),
"# Filename:",
format(attr(x, "filename"))
)
}
exp_weight_methods <- tolower(names(.__E___CExperiment__WeightMethod))
exp_allowed_types <- c("time", "independent", "dependent", "ignore")
#' @export
copasi_exp <- function(experiment_type = c("time_course", "steady_state"), data = NULL, types = NULL, mappings = NULL, weights = NULL, weight_method = NULL, normalize_weights_per_experiment = FALSE, filename = NULL) {
types <- to_param_vector(types, "character")
if (!is.null(mappings))
mappings <- to_param_vector(mappings, "character")
if (!is.null(weights))
weights <- to_param_vector(weights, "numeric")
# experiment_type
experiment_type <- rlang::arg_match(experiment_type)
experiment_type <- c("time_course" = "timeCourse", "steady_state" = "steadyState")[experiment_type]
# data
if (is.data.frame(data))
data <- list(data)
assert_that(every(data, is.data.frame))
data <- map(data, tibble::as_tibble)
# get experiment names from names of data or use list position ("Experiment_x")
experiment_names <- names(data) %||% rep_along(data, NA_character_)
missing_names <- which(is.na(experiment_names))
experiment_names[missing_names] <- paste0("Experiment_", missing_names)
# gather info on where the individual experiments start because they all get merged
experiment_lengths <- map_int(data, nrow)
experiment_lastrows <- cumsum(experiment_lengths)
data <- vctrs::vec_rbind(!!!data, .name_repair = "check_unique")
data_cols <- names(data)
# types
assert_that(
has_names(types) && !anyDuplicated(names(types)) && all(names(types) %in% data_cols) ||
length(types) == length(data_cols)
)
types <- tolower(types)
types <- args_match(types, exp_allowed_types)
if (has_names(types)) {
# make a full vector with default values and fill with specified ones
types <- replace(rep_along(data_cols, "ignore"), match(names(types), data_cols), types)
}
names(types) <- data_cols
if (experiment_type == "timeCourse")
assert_that(sum(types == "time") == 1L, msg = 'Time course experiements need exactly one "time" mapping.')
else if (experiment_type == "steadyState")
assert_that(!any(types == "time"), msg = 'Steady state experiements cannot have a "time" mapping.')
# mappings
mappings <- mappings %||% data_cols
if (!has_names(mappings))
names(mappings) <- data_cols
assert_that(!anyDuplicated(names(mappings)) && all(names(mappings) %in% data_cols))
# all mappings to time and ignore are forced to be blank
mappings[names(types)[types %in% c("time", "ignore")]] <- ""
# weights
weights <- weights %||% rep_along(data_cols, NaN)
assert_that(noPureNA(weights))
if (!has_names(weights))
names(weights) <- data_cols
assert_that(!anyDuplicated(names(weights)) && all(names(weights) %in% data_cols))
# all weights for type time, ignore and independent are forced to be NA
weights[names(types)[types %in% c("time", "ignore", "independent")]] <- NaN
if (is.null(weight_method)) {
weight_method <- toupper(exp_weight_methods[1])
} else {
weight_method <- tolower(weight_method)
weight_method <- rlang::arg_match(weight_method, exp_weight_methods)
weight_method <- toupper(weight_method)
}
assert_that(is.flag(normalize_weights_per_experiment), noNA(normalize_weights_per_experiment))
# filename
assert_that(is.null(filename) || is.string(filename) && noNA(filename))
if (is.null(filename))
# if no filename given, just use random one.
filename <- stringr::str_sub(tempfile("CoRC_exp_", ""), 2L)
if (!has_extension(filename, "txt"))
filename <- paste0(filename, ".txt")
new_copasi_exp(
data,
experiment_type = experiment_type,
experiments = tibble::tibble(
name = experiment_names,
first_row = c(1L, head(experiment_lastrows + 1L, -1L)),
last_row = experiment_lastrows,
.rows = length(experiment_names)
),
types = types,
mappings = mappings,
weights = weights,
weight_method = weight_method,
normalize_weights_per_experiment = normalize_weights_per_experiment,
filename = filename
)
}
#' Define a parameter estimation experiment
#'
#' \code{defineExperiments} defines a set of experiments given as tidy data frame to the given model.
#'
#' CoRC uses it's own methodology for defining experimental data for use with a COPASI model.
#' To this end it is required that experimental data be imported to R by the user and transformed to tidy data.
#' For help on data import and tidying see: \code{vignette("tidy-data", "tidyr")}.
#' This function adds required metadata to experimental data for use with CoRC.
#'
#' @param experiment_type string
#' @param data list of tidy data frames
#' @eval paste0("@param types data column types as character vector
#'
#' Allowed types of columns are: ", rox_print_v(exp_allowed_types), ".
#'
#' Type 'time' is only allowed for time course experiments.
#'
#' Can be a named vector to only specify for a subset of data columns.")
#' @param mappings data column mappings as character vector
#'
#' Expects value references.
#'
#' If no mappings are given, column names can serve as mappings.
#'
#' Can be a named vector to only specify for a subset of data columns.
#' @param weights data column weights as numeric vector
#'
#' `NaN` corresponds to automatic weight.
#'
#' Can be a named vector to only specify for a subset of data columns.
#' @eval paste0("@param weight_method string
#'
#' Allowed methods: ", rox_print_v(exp_weight_methods), ".")
#' @param normalize_weights_per_experiment flag
#' @param filename optional string
#'
#' When adding the experiments to a COPASI model, this filename will be used.
#' In use cases, where experiments are only used temporarily, the filename is ignored.
#' @return copasi_exp object for input into related functions
#' @seealso \code{\link{addExperiments}} \code{\link{clearExperiments}}
#' @family parameter estimation
#' @export
defineExperiments <- copasi_exp
#' Add a parameter estimation experiment
#'
#' @param ... objects as returned by \code{\link{defineExperiments}}.
#' Alternatively, the same parameters as used by \code{\link{defineExperiments}}.
#' @param model a model object
#' @seealso \code{\link{defineExperiments}} \code{\link{clearExperiments}}
#' @family parameter estimation
#' @export
addExperiments <- function(..., model = getCurrentModel()) {
c_datamodel <- assert_datamodel(model)
# flatten all args into a single list
# this list can be used to check if the user gave only copasi_exp
arglist_compact <- rlang::squash(unname(list(...)))
# if not all are copasi_exp, try handing the args to define... so we get copasi_exp
if (!every(arglist_compact, is.copasi_exp))
arglist_compact <- list(defineExperiments(...))
c_task <- as(c_datamodel$getTask("Parameter Estimation"), "_p_CFitTask")
c_problem <- as(c_task$getProblem(), "_p_CFitProblem")
c_experiment_set <- c_problem$getExperimentSet()
for (args in arglist_compact) {
experiment_type <- attr(args, "experiment_type")
experiments <- attr(args, "experiments")
types <- attr(args, "types")
mappings <- attr(args, "mappings")
weights <- attr(args, "weights")
weight_method <- attr(args, "weight_method")
normalize_weights_per_experiment <- attr(args, "normalize_weights_per_experiment")
filename <- attr(args, "filename")
# Create experiment file
assert_that(
# If the user has set a manual filename, try to be safe and not overwrite anything
!file.exists(filename) || grepl("CoRC_exp_[0-9a-f]+\\.txt$", filename),
msg = paste0('Experiment file "', filename, '" already exists.')
)
assert_that(file.create(filename))
# for write out, we need to convert numeric to character, to conserve NaN
args_out <- args
for (i in seq_len(length(args_out)))
if (is.numeric(args_out[[i]]))
args_out[[i]] <- as.character(args_out[[i]])
readr::write_tsv(args_out, filename, na = "NA")
# make sure the file gets deleted on error
tryCatch({
# Construct individual experiments
cl_experiments <- pmap(experiments, function(name, first_row, last_row, ...) {
exp <- avert_gc(CExperiment(c_experiment_set, name))
exp$setFirstRow(first_row + 1L)
exp$setLastRow(last_row + 1L)
exp
})
col_count <- ncol(args)
col_names <- colnames(args)
# Set all experiment's settings
walk_swig(cl_experiments, "setHeaderRow", 1L)
walk_swig(cl_experiments, "setFileName", normalizePathC(filename))
walk_swig(cl_experiments, "setExperimentType", experiment_type)
walk_swig(cl_experiments, "setNumColumns", col_count)
walk_swig(cl_experiments, "setWeightMethod", weight_method)
walk_swig(cl_experiments, "setNormalizeWeightsPerExperiment", normalize_weights_per_experiment)
# Get the object maps and assign roles and mappings
cl_object_maps <- map_swig(cl_experiments, "getObjectMap")
walk_swig(cl_object_maps, "setNumCols", col_count)
# Transfer values from named vectors to a full representation of all data columns
types_ordered <- rep("ignore", col_count)
types_ordered[match(names(types), col_names)] <- types
mappings_ordered <- rep("", col_count)
mappings_ordered[match(names(mappings), col_names)] <-
map(mappings, xn_to_object, c_datamodel = c_datamodel) %>%
map_chr(get_cn) %>%
replace_na("")
weights_ordered <- rep(NaN, col_count)
weights_ordered[match(names(weights), col_names)] <- weights
# We have a list of object_maps and all need the same types and mappings
types_ordered %>% iwalk(~ walk_swig(cl_object_maps, "setRole", .y - 1L, .x))
mappings_ordered %>% iwalk(~ walk_swig(cl_object_maps, "setObjectCN", .y - 1L, .x))
weights_ordered %>% iwalk(~ walk_swig(cl_object_maps, "setScale", .y - 1L, .x))
# Add all experiments to COPASI
cl_experiments %>% walk(~ c_experiment_set$addExperiment(.x))
# possibly compile
# c_experiment_set$compile(problem$getMathContainer())
},
error = function(e) {
file.remove(filename)
base::stop(e)
})
}
invisible()
}
#' Clear all parameter estimation experiments
#'
#' @param model a model object
#' @seealso \code{\link{addExperiments}}
#' @family parameter estimation
#' @export
clearExperiments <- function(model = getCurrentModel()) {
c_datamodel <- assert_datamodel(model)
c_task <- as(c_datamodel$getTask("Parameter Estimation"), "_p_CFitTask")
c_problem <- as(c_task$getProblem(), "_p_CFitProblem")
c_experiment_set <- c_problem$getExperimentSet()
replicate(
n = c_experiment_set$getExperimentCount(),
expr = c_experiment_set$removeExperiment(0L)
)
invisible()
}
#' Clear all parameter estimation validation data.
#'
#' @param model a model object
#' @family parameter estimation
#' @export
clearValidations <- function(model = getCurrentModel()) {
c_datamodel <- assert_datamodel(model)
c_task <- as(c_datamodel$getTask("Parameter Estimation"), "_p_CFitTask")
c_problem <- as(c_task$getProblem(), "_p_CFitProblem")
c_crossvalidation_set <- c_problem$getCrossValidationSet()
replicate(
n = c_crossvalidation_set$getExperimentCount(),
expr = c_crossvalidation_set$removeExperiment(0L)
)
invisible()
}
pe_assemble_parameters <- function(parameters, c_problem) {
assert_that(is.null(parameters) || is.list(parameters) && every(parameters, is.corc_opt_parm) || is.corc_opt_parm(parameters))
if (is.corc_opt_parm(parameters))
parameters <- list(parameters)
if (is_empty(parameters))
return(list())
assert_that(
c_problem$getOptItemSize() == 0L,
msg = "This function can not set parameters if there are already parameters set in COPASI. Consider using `ParameterEstimationParameters()`."
)
walk(parameters, validate_corc_opt_parm)
parameters
}
pe_assemble_experiments <- function(experiments, c_problem, temp_filenames = FALSE) {
assert_that(is.null(experiments) || is.list(experiments) && every(experiments, is.copasi_exp) || is.copasi_exp(experiments))
if (is.copasi_exp(experiments))
experiments <- list(experiments)
if (is_empty(experiments))
return(list())
assert_that(
c_problem$getExperimentSet()$getExperimentCount() == 0L,
msg = "This function can not set experiments if there are already experiments set in COPASI. Consider using `clearExperiments()`."
)
# force temporary experiment file names so they can be deleted safely
if (temp_filenames)
experiments <-
experiments %>%
map(~ {
attr(.x, "filename") <- tempfile("CoRC_exp_", fileext = ".txt")
# attr(.x, "filename") <- stringr::str_sub(tempfile("CoRC_exp_", "", ".txt"), 2L)
.x
})
experiments
}
# The following functions should be the basis for implementation of any task
# They should allow for a common workflow with most tasks
# does assertions
# returns a list of settings
pe_assemble_settings <- function(randomize_start_values, create_parameter_sets, calculate_statistics, update_model, executable) {
assert_that(
is.null(randomize_start_values) || is.flag(randomize_start_values) && noNA(randomize_start_values),
is.null(create_parameter_sets) || is.flag(create_parameter_sets) && noNA(create_parameter_sets),
is.null(calculate_statistics) || is.flag(calculate_statistics) && noNA(calculate_statistics),
is.null(update_model) || is.flag(update_model) && noNA(update_model),
is.null(executable) || is.flag(executable) && noNA(executable)
)
list(
randomize_start_values = randomize_start_values,
create_parameter_sets = create_parameter_sets,
calculate_statistics = calculate_statistics,
update_model = update_model,
executable = executable
) %>%
discard(is.null)
}
# does assertions
# returns a list of method settings
pe_assemble_method <- function(method, c_task) {
if (is.null(method))
return(list())
assert_that(
is.string(method) || is.list(method) && (is_empty(method) || !is.null(names(method))),
msg = "method must be a string (a length one character vector) or a named list."
)
if (is_scalar_character(method))
method <- list(method = method)
if (hasName(method, "method")) {
valid_methods <- names(.__E___CTaskEnum__Method)[c_task$getValidMethods() + 1L]
# hack to get nice error message if method string is not accepted.
method$method <- args_match(method$method, name = "method", valid_methods)
}
method
}
# gets full list of settings
pe_get_settings <- function(c_task) {
c_problem <- as(c_task$getProblem(), "_p_CFitProblem")
list(
randomize_start_values = as.logical(c_problem$getRandomizeStartValues()),
create_parameter_sets = as.logical(c_problem$getCreateParameterSets()),
calculate_statistics = as.logical(c_problem$getCalculateStatistics()),
update_model = as.logical(c_task$isUpdateModel()),
executable = as.logical(c_task$isScheduled())
)
}
# sets all settings given in list
pe_set_settings <- function(data, c_task) {
if (is_empty(data))
return()
c_problem <- as(c_task$getProblem(), "_p_CFitProblem")
if (!is.null(data$randomize_start_values))
c_problem$setRandomizeStartValues(data$randomize_start_values)
if (!is.null(data$create_parameter_sets))
c_problem$setCreateParameterSets(data$create_parameter_sets)
if (!is.null(data$calculate_statistics))
c_problem$setCalculateStatistics(data$calculate_statistics)
if (!is.null(data$update_model))
c_task$setUpdateModel(data$update_model)
if (!is.null(data$executable))
c_task$setScheduled(data$executable)
}
# gathers all results
pe_get_results <- function(c_task, settings) {
c_problem <- as(c_task$getProblem(), "_p_CFitProblem")
c_method <- as(c_task$getMethod(), "_p_COptMethod")
cl_items <- get_sv(c_problem$getOptItemList()) %>% map(as, Class = "_p_CFitItem")
c_experiment_set <- c_problem$getExperimentSet()
cl_experiments <-
seq_len_0(c_experiment_set$getExperimentCount()) %>%
map(~ c_experiment_set$getExperiment(.x))
cl_dependent_obj <- swigfix_resolve_obj_cvector(c_experiment_set, CExperimentSet_getDependentObjects, "CObjectInterface")
evals <- c_problem$getFunctionEvaluations()
evaltime <- c_problem$getExecutionTime()
main <-
list(
"Objective Value" = c_problem$getSolutionValue(),
"Root Mean Square" = c_problem$getRMS(),
"Standard Deviation" = c_problem$getStdDeviation(),
"Validation Objective Value" = c_problem$getCrossValidationSolutionValue(),
"Validation Root Mean Square" = c_problem$getCrossValidationRMS(),
"Validation Standard Deviation" = c_problem$getCrossValidationSD(),
"Function Evaluations" = evals,
"CPU Time [s]" = evaltime,
"Evaluations/second [1/s]" = evals / evaltime
) %>%
transform_names()
vals <- get_cv(c_problem$getSolutionVariables())
std_dev <- get_cv(c_problem$getVariableStdDeviations())
parameters <-
tibble::tibble(
"Parameter" = get_key(cl_items),
"Lower Bound" = map_swig_dbl(cl_items, "getLowerBoundValue"),
"Start Value" = map_swig_dbl(cl_items, "getLastStartValue"),
"Value" = vals,
"Upper Bound" = map_swig_dbl(cl_items, "getUpperBoundValue"),
"Std. Deviation" = std_dev,
"Coeff. of Variation [%]" = abs(100 * std_dev / vals),
"Gradient" = get_cv(c_problem$getVariableGradients()),
.rows = length(cl_items),
.name_repair = transform_names_worker
)
experiments <-
tibble::tibble(
"Experiment" = map_swig_chr(cl_experiments, "getObjectName"),
"Objective Value" = map_swig_dbl(cl_experiments, "getObjectiveValue"),
"Root Mean Square" = map_swig_dbl(cl_experiments, "getRMS"),
"Error Mean" = map_swig_dbl(cl_experiments, "getErrorMean"),
"Error Mean Std. Deviation" = map_swig_dbl(cl_experiments, "getErrorMeanSD"),
.rows = length(cl_experiments),
.name_repair = transform_names_worker
)
fitted_values <-
tibble::tibble(
"Fitted Value" = get_key(cl_dependent_obj),
"Objective Value" = get_cv(c_experiment_set$getDependentObjectiveValues()),
"Root Mean Square" = get_cv(c_experiment_set$getDependentRMS()),
"Error Mean" = get_cv(c_experiment_set$getDependentErrorMean()),
"Error Mean Std. Deviation" = get_cv(c_experiment_set$getDependentErrorMeanSD()),
.rows = length(cl_dependent_obj),
.name_repair = transform_names_worker
)
correlation <- get_annotated_matrix(c_problem$getCorrelations())
fim <- get_annotated_matrix(c_problem$getFisherInformation())
fim_eigenvalues <- get_annotated_matrix(c_problem$getFisherInformationEigenvalues())
fim_eigenvectors <- get_annotated_matrix(c_problem$getFisherInformationEigenvectors())
fim_scaled <- get_annotated_matrix(c_problem$getScaledFisherInformation())
fim_scaled_eigenvalues <- get_annotated_matrix(c_problem$getScaledFisherInformationEigenvalues())
fim_scaled_eigenvectors <- get_annotated_matrix(c_problem$getScaledFisherInformationEigenvectors())
protocol <- c_method$getMethodLog()$getPlainLog()
list(
settings = settings,
main = main,
parameters = parameters,
experiments = experiments,
fitted_values = fitted_values,
correlation = correlation,
fim = fim,
fim_eigenvalues = fim_eigenvalues,
fim_eigenvectors = fim_eigenvectors,
fim_scaled = fim_scaled,
fim_scaled_eigenvalues = fim_scaled_eigenvalues,
fim_scaled_eigenvectors = fim_scaled_eigenvectors,
protocol = protocol
)
}