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tests/testthat/_snaps/examples.md
In mildsvm: Multiple-Instance Learning with Support Vector Machines
build_fm(), kfm_exact(), kfm_nystrom() examples work
Code
df <- data.frame(X1 = c(2, 3, 4, 5, 6, 7, 8), X2 = c(1, 1.2, 1.3, 1.4, 1.1, 7,
1), X3 = rnorm(7))
fit1 <- kfm_nystrom(df, m = 7, r = 6, kernel = "radial", sigma = 0.05)
fm <- build_fm(fit1, df)
fit2 <- kfm_exact(kernel = "polynomial", degree = 2, const = 1)
fm <- build_fm(fit2, df)
cv_misvm() examples work
Code
set.seed(8)
mil_data <- generate_mild_df(nbag = 20, positive_prob = 0.15, dist = rep(
"mvnormal", 3), mean = list(rep(1, 10), rep(2, 10)), sd_of_mean = rep(0.1, 3))
df <- build_instance_feature(mil_data, seq(0.05, 0.95, length.out = 10))
cost_seq <- 2^seq(-5, 7, length.out = 3)
mdl1 <- cv_misvm(x = df[, 4:123], y = df$bag_label, bags = df$bag_name,
cost_seq = cost_seq, n_fold = 3, method = "heuristic")
mdl2 <- cv_misvm(mi(bag_label, bag_name) ~ X1_mean + X2_mean + X3_mean, data = df,
cost_seq = cost_seq, n_fold = 3)
if (require(gurobi)) {
mdl3 <- cv_misvm(x = df[, 4:123], y = df$bag_label, bags = df$bag_name,
cost_seq = cost_seq, n_fold = 3, method = "mip")
}
Message <packageStartupMessage>
Loading required package: gurobi
Loading required package: slam
Code
predict(mdl1, new_data = df, type = "raw", layer = "bag")
Output
# A tibble: 80 x 1
.pred
<dbl>
1 -1.00
2 -1.00
3 -1.00
4 -1.00
5 1.04
6 1.04
7 1.04
8 1.04
9 -1.13
10 -1.13
# ... with 70 more rows
Code
df %>% dplyr::bind_cols(predict(mdl2, df, type = "class")) %>% dplyr::bind_cols(
predict(mdl2, df, type = "raw")) %>% dplyr::distinct(bag_name, bag_label,
.pred_class, .pred)
Output
bag_label bag_name .pred_class .pred
1 0 bag1 0 -0.5932349
2 1 bag2 1 0.7493612
3 0 bag3 0 -0.9387030
4 1 bag4 1 1.2126533
5 0 bag5 0 -0.8094506
6 0 bag6 0 -0.8083522
7 1 bag7 1 0.6587946
8 0 bag8 0 -0.9032079
9 1 bag9 1 0.5855234
10 1 bag10 1 1.2019300
11 1 bag11 1 1.2689043
12 0 bag12 0 -0.8143970
13 1 bag13 1 0.8591738
14 1 bag14 1 1.0000000
15 1 bag15 1 1.1078369
16 1 bag16 1 1.2117319
17 0 bag17 0 -0.6022075
18 1 bag18 1 0.9355648
19 0 bag19 0 -0.7314129
20 1 bag20 1 1.0393764
generate_mild_df() examples work
Code
set.seed(8)
mild_data <- generate_mild_df(nbag = 7, ninst = 3, nsample = 20, ncov = 2,
nimp_pos = 1, dist = rep("mvnormal", 3), mean = list(rep(5, 1), rep(15, 2), 0))
dplyr::distinct(mild_data, bag_label, bag_name, instance_name)
Output
# An MILD data frame: 21 x 3 with 7 bags, 21 instances
# and instance labels: 0, 0, 0, 0, 0, ...
bag_label bag_name instance_name
<dbl> <chr> <chr>
1 0 bag1 bag1inst1
2 0 bag1 bag1inst2
3 0 bag1 bag1inst3
4 0 bag2 bag2inst1
5 0 bag2 bag2inst2
6 0 bag2 bag2inst3
7 1 bag3 bag3inst1
8 1 bag3 bag3inst2
9 1 bag3 bag3inst3
10 0 bag4 bag4inst1
# ... with 11 more rows
Code
split(mild_data[, 4:5], mild_data$instance_name) %>% sapply(colMeans) %>% round(
2) %>% t()
Warning <warning>
Dropping 'mild_df' class as required column was removed.
Output
X1 X2
bag1inst1 14.95 14.63
bag1inst2 15.53 15.42
bag1inst3 14.27 16.08
bag2inst1 15.29 15.01
bag2inst2 15.78 14.87
bag2inst3 14.53 15.71
bag3inst1 14.79 14.83
bag3inst2 5.36 -0.69
bag3inst3 15.13 15.78
bag4inst1 15.32 14.46
bag4inst2 15.31 15.53
bag4inst3 15.93 14.55
bag5inst1 15.25 14.25
bag5inst2 14.98 16.03
bag5inst3 15.48 14.95
bag6inst1 4.83 0.26
bag6inst2 16.23 16.06
bag6inst3 15.31 14.56
bag7inst1 14.74 14.90
bag7inst2 14.61 15.03
bag7inst3 15.17 15.20
kme() examples work
Code
x = data.frame(instance_name = c("inst_1", "inst_2", "inst_1"), X1 = c(-0.4,
0.5, 2))
kme(x)
Output
[,1] [,2]
[1,] 0.8748808 0.9269533
[2,] 0.9269533 1.0000000
Code
mild_df1 <- generate_mild_df(nbag = 10, positive_degree = 3)
kme(mild_df1)
Output
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.4169122 0.3523994 0.2999620 0.3919451 0.2671537 0.3808291 0.3178319
[2,] 0.3523994 0.4001522 0.3488563 0.3862459 0.3258604 0.3828253 0.3553224
[3,] 0.2999620 0.3488563 0.3830298 0.3729092 0.3410245 0.3829949 0.3563669
[4,] 0.3919451 0.3862459 0.3729092 0.6736729 0.3829977 0.4622801 0.3786055
[5,] 0.2671537 0.3258604 0.3410245 0.3829977 0.3866819 0.3438624 0.3125532
[6,] 0.3808291 0.3828253 0.3829949 0.4622801 0.3438624 0.5928032 0.3992530
[7,] 0.3178319 0.3553224 0.3563669 0.3786055 0.3125532 0.3992530 0.4072973
[8,] 0.2886348 0.3157459 0.2987742 0.3711378 0.2682204 0.3401411 0.2749737
[9,] 0.3872634 0.4011600 0.3740848 0.4325844 0.3109014 0.4722626 0.3594980
[10,] 0.3086363 0.3301951 0.2900993 0.3880930 0.2750935 0.3674942 0.3147702
[11,] 0.3300471 0.3715490 0.3490256 0.3765055 0.3468922 0.3911776 0.3134912
[12,] 0.3496296 0.3733415 0.3661626 0.5378319 0.3513647 0.4593275 0.3566796
[13,] 0.2643695 0.3083248 0.3592159 0.3650698 0.3075189 0.3443388 0.3521353
[14,] 0.3510899 0.3496693 0.3579273 0.4438735 0.3136538 0.4244626 0.3410867
[15,] 0.3306274 0.3505787 0.3460770 0.4034540 0.3067107 0.4154028 0.3552658
[16,] 0.3547158 0.3407101 0.3166309 0.3790609 0.2549436 0.3913973 0.3093184
[17,] 0.3413221 0.3310640 0.3056185 0.4769570 0.2984654 0.3205515 0.3010842
[18,] 0.3385712 0.3715150 0.3362378 0.3772033 0.3236153 0.3631170 0.3673017
[19,] 0.3274643 0.3157874 0.3209078 0.3923291 0.2950952 0.3912085 0.3039875
[20,] 0.3024896 0.3319636 0.3544274 0.3987057 0.3375483 0.4542176 0.3624589
[21,] 0.3037886 0.3305133 0.3058440 0.4023323 0.2933122 0.3999505 0.3038551
[22,] 0.4046782 0.4063992 0.3687206 0.4748479 0.3561605 0.5333569 0.3857608
[23,] 0.2921442 0.3529623 0.2947198 0.3825395 0.2826722 0.3598854 0.2964741
[24,] 0.3670381 0.3722187 0.3556825 0.4504677 0.3279440 0.4540149 0.3728457
[25,] 0.3293533 0.3842095 0.3365811 0.3505797 0.3083936 0.3654656 0.3492568
[26,] 0.4514919 0.4317108 0.3776675 0.5362912 0.3806510 0.4668916 0.3974679
[27,] 0.3072870 0.3522197 0.3407901 0.4046125 0.3505049 0.3589900 0.3183809
[28,] 0.3351851 0.3453512 0.3313021 0.4664949 0.3088645 0.4280774 0.3465275
[29,] 0.4277095 0.3869842 0.3158980 0.4345843 0.2831570 0.3771593 0.3238047
[30,] 0.3918940 0.4233512 0.3803228 0.5079662 0.3517907 0.4494309 0.3917467
[31,] 0.3209992 0.3356011 0.3067535 0.3937972 0.2606738 0.3971114 0.3341408
[32,] 0.3304344 0.3615479 0.3098770 0.3813640 0.3323581 0.3808892 0.3229543
[33,] 0.3633943 0.3933628 0.4126571 0.4078071 0.3401322 0.4569400 0.3942821
[34,] 0.3187196 0.3354348 0.3444837 0.4187835 0.3351518 0.3974189 0.3215929
[35,] 0.3067201 0.3295164 0.3086434 0.4358080 0.2989232 0.3744900 0.3193445
[36,] 0.3709173 0.3094088 0.2352705 0.3380354 0.2257843 0.3056635 0.2402585
[37,] 0.3747756 0.3482068 0.3025652 0.4241205 0.2659261 0.4055969 0.3187351
[38,] 0.3645088 0.3434428 0.3443278 0.3946202 0.2756704 0.4240096 0.3299400
[39,] 0.3080024 0.3640326 0.3355290 0.3848462 0.2924023 0.3766813 0.3508378
[40,] 0.4362794 0.4125146 0.3767652 0.4305746 0.3363838 0.4993309 0.3431162
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 0.2886348 0.3872634 0.3086363 0.3300471 0.3496296 0.2643695 0.3510899
[2,] 0.3157459 0.4011600 0.3301951 0.3715490 0.3733415 0.3083248 0.3496693
[3,] 0.2987742 0.3740848 0.2900993 0.3490256 0.3661626 0.3592159 0.3579273
[4,] 0.3711378 0.4325844 0.3880930 0.3765055 0.5378319 0.3650698 0.4438735
[5,] 0.2682204 0.3109014 0.2750935 0.3468922 0.3513647 0.3075189 0.3136538
[6,] 0.3401411 0.4722626 0.3674942 0.3911776 0.4593275 0.3443388 0.4244626
[7,] 0.2749737 0.3594980 0.3147702 0.3134912 0.3566796 0.3521353 0.3410867
[8,] 0.3827260 0.3907545 0.2988163 0.3308321 0.3751270 0.2927895 0.3424322
[9,] 0.3907545 0.5245163 0.3503800 0.3956145 0.4558087 0.3169918 0.4143504
[10,] 0.2988163 0.3503800 0.3778166 0.3133673 0.3854424 0.2845932 0.3253879
[11,] 0.3308321 0.3956145 0.3133673 0.4192976 0.3827056 0.3105342 0.3439707
[12,] 0.3751270 0.4558087 0.3854424 0.3827056 0.5381580 0.3410197 0.4040592
[13,] 0.2927895 0.3169918 0.2845932 0.3105342 0.3410197 0.4152752 0.3384331
[14,] 0.3424322 0.4143504 0.3253879 0.3439707 0.4040592 0.3384331 0.4227283
[15,] 0.3529021 0.4052576 0.3318198 0.3378625 0.3899277 0.3435705 0.3792346
[16,] 0.3247552 0.4200867 0.3237023 0.3466853 0.3903709 0.3004923 0.3533612
[17,] 0.3023912 0.3428421 0.3114730 0.3110601 0.3819310 0.3000919 0.3629485
[18,] 0.2923464 0.3394161 0.3164056 0.3531750 0.3375635 0.3346717 0.3247464
[19,] 0.2713716 0.3836950 0.2482132 0.3223733 0.3761191 0.2686236 0.3305258
[20,] 0.2857859 0.3744232 0.3174561 0.3395988 0.3984483 0.3315578 0.3550107
[21,] 0.2627253 0.3836853 0.2795323 0.3301624 0.3831656 0.2488448 0.3148869
[22,] 0.3406292 0.4621339 0.3923090 0.3955028 0.4495619 0.3141189 0.4283485
[23,] 0.3157345 0.3856858 0.3494546 0.3538506 0.3917857 0.2697028 0.3082309
[24,] 0.3275562 0.4251279 0.3382506 0.3577489 0.4262159 0.3256938 0.3830319
[25,] 0.3231252 0.3904057 0.3485463 0.3771474 0.3788117 0.3159366 0.3161134
[26,] 0.3373112 0.4363885 0.4001063 0.4178917 0.4635879 0.3431673 0.4212106
[27,] 0.3388433 0.3562988 0.3225072 0.3660954 0.3718644 0.3297816 0.3608778
[28,] 0.2935088 0.3908359 0.3247119 0.3233924 0.4104011 0.3010204 0.3750590
[29,] 0.3255437 0.4056847 0.3709777 0.3798311 0.3901575 0.3028651 0.3793250
[30,] 0.3773517 0.4505271 0.4349212 0.4046103 0.4724630 0.3729573 0.4305713
[31,] 0.3151219 0.4048709 0.3019086 0.2964317 0.3670353 0.2776221 0.3516817
[32,] 0.3101098 0.3605160 0.3237806 0.3617166 0.3625330 0.2743012 0.3224096
[33,] 0.3271257 0.4471554 0.3090527 0.3839613 0.3851377 0.3774804 0.4184469
[34,] 0.3434242 0.3851384 0.2983263 0.3592944 0.3967367 0.3314950 0.3647405
[35,] 0.3355117 0.3918822 0.3211793 0.3102746 0.4325159 0.2788364 0.3399177
[36,] 0.2370985 0.3357440 0.2703925 0.2874996 0.2988993 0.1794404 0.2905320
[37,] 0.3102639 0.4125572 0.3560512 0.3232770 0.4074992 0.2703437 0.3675738
[38,] 0.3191465 0.4189078 0.3061028 0.3536871 0.3836219 0.3285568 0.3830229
[39,] 0.3091127 0.3884269 0.3297345 0.3310730 0.3640645 0.3208774 0.3476835
[40,] 0.3742521 0.5217239 0.3386285 0.4284365 0.4327775 0.2919186 0.4346810
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] 0.3306274 0.3547158 0.3413221 0.3385712 0.3274643 0.3024896 0.3037886
[2,] 0.3505787 0.3407101 0.3310640 0.3715150 0.3157874 0.3319636 0.3305133
[3,] 0.3460770 0.3166309 0.3056185 0.3362378 0.3209078 0.3544274 0.3058440
[4,] 0.4034540 0.3790609 0.4769570 0.3772033 0.3923291 0.3987057 0.4023323
[5,] 0.3067107 0.2549436 0.2984654 0.3236153 0.2950952 0.3375483 0.2933122
[6,] 0.4154028 0.3913973 0.3205515 0.3631170 0.3912085 0.4542176 0.3999505
[7,] 0.3552658 0.3093184 0.3010842 0.3673017 0.3039875 0.3624589 0.3038551
[8,] 0.3529021 0.3247552 0.3023912 0.2923464 0.2713716 0.2857859 0.2627253
[9,] 0.4052576 0.4200867 0.3428421 0.3394161 0.3836950 0.3744232 0.3836853
[10,] 0.3318198 0.3237023 0.3114730 0.3164056 0.2482132 0.3174561 0.2795323
[11,] 0.3378625 0.3466853 0.3110601 0.3531750 0.3223733 0.3395988 0.3301624
[12,] 0.3899277 0.3903709 0.3819310 0.3375635 0.3761191 0.3984483 0.3831656
[13,] 0.3435705 0.3004923 0.3000919 0.3346717 0.2686236 0.3315578 0.2488448
[14,] 0.3792346 0.3533612 0.3629485 0.3247464 0.3305258 0.3550107 0.3148869
[15,] 0.4029821 0.3329319 0.3200534 0.3483374 0.3092085 0.3560920 0.2891205
[16,] 0.3329319 0.4072145 0.3136260 0.3063168 0.3153526 0.3135427 0.3179032
[17,] 0.3200534 0.3136260 0.4178473 0.3216383 0.2921914 0.2881410 0.2887689
[18,] 0.3483374 0.3063168 0.3216383 0.4130369 0.2840406 0.3254596 0.2799843
[19,] 0.3092085 0.3153526 0.2921914 0.2840406 0.3959299 0.3322660 0.3393178
[20,] 0.3560920 0.3135427 0.2881410 0.3254596 0.3322660 0.4044005 0.3257537
[21,] 0.2891205 0.3179032 0.2887689 0.2799843 0.3393178 0.3257537 0.4047794
[22,] 0.4091661 0.3738722 0.3595939 0.3721547 0.3633162 0.4213864 0.3880263
[23,] 0.3054019 0.3466469 0.3022233 0.3044542 0.2604175 0.2995181 0.3488779
[24,] 0.3812947 0.3548821 0.3396105 0.3582028 0.3601843 0.3795341 0.3540605
[25,] 0.3478419 0.3491553 0.2969050 0.3747642 0.2905926 0.3273992 0.3005663
[26,] 0.3904225 0.3992663 0.4359671 0.4253176 0.3837960 0.3945064 0.3896817
[27,] 0.3585833 0.3014212 0.3401771 0.3509750 0.2805411 0.3273872 0.2782515
[28,] 0.3428719 0.3314276 0.3467582 0.3160977 0.3306523 0.3545179 0.3646012
[29,] 0.3394214 0.4170320 0.3993973 0.3675633 0.3038418 0.3064804 0.3261697
[30,] 0.4099238 0.4139901 0.4213449 0.3990849 0.3207753 0.3835597 0.3628194
[31,] 0.3567115 0.3172887 0.3049912 0.3059889 0.3020181 0.3197710 0.3215577
[32,] 0.3483399 0.2912989 0.3062592 0.3634603 0.2945235 0.3313570 0.2940884
[33,] 0.3816499 0.3762535 0.3381053 0.3651828 0.3660556 0.3826280 0.3621237
[34,] 0.3678903 0.3200297 0.3223298 0.3341906 0.3314275 0.3497056 0.2945803
[35,] 0.3583229 0.3023115 0.3210037 0.3041208 0.3219870 0.3326105 0.3030481
[36,] 0.2544942 0.3001889 0.3109006 0.2631206 0.2796083 0.2363907 0.2927193
[37,] 0.3423002 0.3730642 0.3483797 0.3060860 0.3134191 0.3240729 0.3292184
[38,] 0.3477659 0.3961409 0.3230067 0.3213942 0.3424865 0.3367813 0.3246403
[39,] 0.3423561 0.3343108 0.3173556 0.3336023 0.2765495 0.3222561 0.3275091
[40,] 0.3966873 0.4149241 0.3552640 0.3474666 0.4134704 0.3821023 0.3961611
[,22] [,23] [,24] [,25] [,26] [,27] [,28]
[1,] 0.4046782 0.2921442 0.3670381 0.3293533 0.4514919 0.3072870 0.3351851
[2,] 0.4063992 0.3529623 0.3722187 0.3842095 0.4317108 0.3522197 0.3453512
[3,] 0.3687206 0.2947198 0.3556825 0.3365811 0.3776675 0.3407901 0.3313021
[4,] 0.4748479 0.3825395 0.4504677 0.3505797 0.5362912 0.4046125 0.4664949
[5,] 0.3561605 0.2826722 0.3279440 0.3083936 0.3806510 0.3505049 0.3088645
[6,] 0.5333569 0.3598854 0.4540149 0.3654656 0.4668916 0.3589900 0.4280774
[7,] 0.3857608 0.2964741 0.3728457 0.3492568 0.3974679 0.3183809 0.3465275
[8,] 0.3406292 0.3157345 0.3275562 0.3231252 0.3373112 0.3388433 0.2935088
[9,] 0.4621339 0.3856858 0.4251279 0.3904057 0.4363885 0.3562988 0.3908359
[10,] 0.3923090 0.3494546 0.3382506 0.3485463 0.4001063 0.3225072 0.3247119
[11,] 0.3955028 0.3538506 0.3577489 0.3771474 0.4178917 0.3660954 0.3233924
[12,] 0.4495619 0.3917857 0.4262159 0.3788117 0.4635879 0.3718644 0.4104011
[13,] 0.3141189 0.2697028 0.3256938 0.3159366 0.3431673 0.3297816 0.3010204
[14,] 0.4283485 0.3082309 0.3830319 0.3161134 0.4212106 0.3608778 0.3750590
[15,] 0.4091661 0.3054019 0.3812947 0.3478419 0.3904225 0.3585833 0.3428719
[16,] 0.3738722 0.3466469 0.3548821 0.3491553 0.3992663 0.3014212 0.3314276
[17,] 0.3595939 0.3022233 0.3396105 0.2969050 0.4359671 0.3401771 0.3467582
[18,] 0.3721547 0.3044542 0.3582028 0.3747642 0.4253176 0.3509750 0.3160977
[19,] 0.3633162 0.2604175 0.3601843 0.2905926 0.3837960 0.2805411 0.3306523
[20,] 0.4213864 0.2995181 0.3795341 0.3273992 0.3945064 0.3273872 0.3545179
[21,] 0.3880263 0.3488779 0.3540605 0.3005663 0.3896817 0.2782515 0.3646012
[22,] 0.5630475 0.3753065 0.4449969 0.3733252 0.5032117 0.3874373 0.4322735
[23,] 0.3753065 0.4471016 0.3311823 0.3651890 0.3853135 0.3199225 0.3380487
[24,] 0.4449969 0.3311823 0.4151443 0.3564034 0.4466317 0.3462490 0.3836512
[25,] 0.3733252 0.3651890 0.3564034 0.4298102 0.4069444 0.3413267 0.3100067
[26,] 0.5032117 0.3853135 0.4466317 0.4069444 0.5818781 0.4032938 0.4299712
[27,] 0.3874373 0.3199225 0.3462490 0.3413267 0.4032938 0.3995114 0.3234780
[28,] 0.4322735 0.3380487 0.3836512 0.3100067 0.4299712 0.3234780 0.4043163
[29,] 0.4160148 0.3796291 0.3704163 0.3798308 0.5021081 0.3503216 0.3583275
[30,] 0.4824457 0.4395676 0.4209159 0.4195498 0.5101399 0.4143516 0.4201927
[31,] 0.3968881 0.3119012 0.3654678 0.3098126 0.3696027 0.3010211 0.3550438
[32,] 0.4147757 0.3143400 0.3609095 0.3576175 0.4206899 0.3590449 0.3179471
[33,] 0.4358190 0.3327537 0.4046812 0.3552375 0.4318491 0.3590810 0.3871017
[34,] 0.3851446 0.2917805 0.3678323 0.3276997 0.3922857 0.3619419 0.3271772
[35,] 0.3826448 0.3047897 0.3715418 0.3309257 0.3824156 0.3248125 0.3375729
[36,] 0.3627135 0.2818273 0.3044356 0.2729893 0.4067361 0.2578816 0.2968476
[37,] 0.4269345 0.3464054 0.3738811 0.3354771 0.4384104 0.3105591 0.3670421
[38,] 0.3923521 0.3149255 0.3712310 0.3314089 0.4082615 0.3153282 0.3489113
[39,] 0.3864728 0.3746239 0.3510428 0.3499524 0.3858336 0.3302087 0.3543242
[40,] 0.5149685 0.3622524 0.4349479 0.3730711 0.4803461 0.3754398 0.3987842
[,29] [,30] [,31] [,32] [,33] [,34] [,35]
[1,] 0.4277095 0.3918940 0.3209992 0.3304344 0.3633943 0.3187196 0.3067201
[2,] 0.3869842 0.4233512 0.3356011 0.3615479 0.3933628 0.3354348 0.3295164
[3,] 0.3158980 0.3803228 0.3067535 0.3098770 0.4126571 0.3444837 0.3086434
[4,] 0.4345843 0.5079662 0.3937972 0.3813640 0.4078071 0.4187835 0.4358080
[5,] 0.2831570 0.3517907 0.2606738 0.3323581 0.3401322 0.3351518 0.2989232
[6,] 0.3771593 0.4494309 0.3971114 0.3808892 0.4569400 0.3974189 0.3744900
[7,] 0.3238047 0.3917467 0.3341408 0.3229543 0.3942821 0.3215929 0.3193445
[8,] 0.3255437 0.3773517 0.3151219 0.3101098 0.3271257 0.3434242 0.3355117
[9,] 0.4056847 0.4505271 0.4048709 0.3605160 0.4471554 0.3851384 0.3918822
[10,] 0.3709777 0.4349212 0.3019086 0.3237806 0.3090527 0.2983263 0.3211793
[11,] 0.3798311 0.4046103 0.2964317 0.3617166 0.3839613 0.3592944 0.3102746
[12,] 0.3901575 0.4724630 0.3670353 0.3625330 0.3851377 0.3967367 0.4325159
[13,] 0.3028651 0.3729573 0.2776221 0.2743012 0.3774804 0.3314950 0.2788364
[14,] 0.3793250 0.4305713 0.3516817 0.3224096 0.4184469 0.3647405 0.3399177
[15,] 0.3394214 0.4099238 0.3567115 0.3483399 0.3816499 0.3678903 0.3583229
[16,] 0.4170320 0.4139901 0.3172887 0.2912989 0.3762535 0.3200297 0.3023115
[17,] 0.3993973 0.4213449 0.3049912 0.3062592 0.3381053 0.3223298 0.3210037
[18,] 0.3675633 0.3990849 0.3059889 0.3634603 0.3651828 0.3341906 0.3041208
[19,] 0.3038418 0.3207753 0.3020181 0.2945235 0.3660556 0.3314275 0.3219870
[20,] 0.3064804 0.3835597 0.3197710 0.3313570 0.3826280 0.3497056 0.3326105
[21,] 0.3261697 0.3628194 0.3215577 0.2940884 0.3621237 0.2945803 0.3030481
[22,] 0.4160148 0.4824457 0.3968881 0.4147757 0.4358190 0.3851446 0.3826448
[23,] 0.3796291 0.4395676 0.3119012 0.3143400 0.3327537 0.2917805 0.3047897
[24,] 0.3704163 0.4209159 0.3654678 0.3609095 0.4046812 0.3678323 0.3715418
[25,] 0.3798308 0.4195498 0.3098126 0.3576175 0.3552375 0.3276997 0.3309257
[26,] 0.5021081 0.5101399 0.3696027 0.4206899 0.4318491 0.3922857 0.3824156
[27,] 0.3503216 0.4143516 0.3010211 0.3590449 0.3590810 0.3619419 0.3248125
[28,] 0.3583275 0.4201927 0.3550438 0.3179471 0.3871017 0.3271772 0.3375729
[29,] 0.5562114 0.4896433 0.3201169 0.3412564 0.3858215 0.3268655 0.2998863
[30,] 0.4896433 0.5604001 0.3818702 0.3887106 0.4317382 0.3790934 0.3781906
[31,] 0.3201169 0.3818702 0.3813176 0.3107423 0.3666519 0.3145105 0.3413322
[32,] 0.3412564 0.3887106 0.3107423 0.4028639 0.3279293 0.3447714 0.3375131
[33,] 0.3858215 0.4317382 0.3666519 0.3279293 0.5115058 0.3683147 0.3183061
[34,] 0.3268655 0.3790934 0.3145105 0.3447714 0.3683147 0.3886675 0.3474166
[35,] 0.2998863 0.3781906 0.3413322 0.3375131 0.3183061 0.3474166 0.4157505
[36,] 0.3992034 0.3401047 0.2711894 0.2902113 0.2902643 0.2515062 0.2591211
[37,] 0.4197093 0.4377885 0.3444593 0.3202151 0.3562870 0.3131996 0.3377408
[38,] 0.4048258 0.4059210 0.3258159 0.2927319 0.4211501 0.3429983 0.2983918
[39,] 0.3608347 0.4316374 0.3462349 0.3132448 0.3930252 0.3068435 0.3068626
[40,] 0.4386672 0.4426953 0.3904327 0.3934714 0.4677351 0.4012890 0.3686771
[,36] [,37] [,38] [,39] [,40]
[1,] 0.3709173 0.3747756 0.3645088 0.3080024 0.4362794
[2,] 0.3094088 0.3482068 0.3434428 0.3640326 0.4125146
[3,] 0.2352705 0.3025652 0.3443278 0.3355290 0.3767652
[4,] 0.3380354 0.4241205 0.3946202 0.3848462 0.4305746
[5,] 0.2257843 0.2659261 0.2756704 0.2924023 0.3363838
[6,] 0.3056635 0.4055969 0.4240096 0.3766813 0.4993309
[7,] 0.2402585 0.3187351 0.3299400 0.3508378 0.3431162
[8,] 0.2370985 0.3102639 0.3191465 0.3091127 0.3742521
[9,] 0.3357440 0.4125572 0.4189078 0.3884269 0.5217239
[10,] 0.2703925 0.3560512 0.3061028 0.3297345 0.3386285
[11,] 0.2874996 0.3232770 0.3536871 0.3310730 0.4284365
[12,] 0.2988993 0.4074992 0.3836219 0.3640645 0.4327775
[13,] 0.1794404 0.2703437 0.3285568 0.3208774 0.2919186
[14,] 0.2905320 0.3675738 0.3830229 0.3476835 0.4346810
[15,] 0.2544942 0.3423002 0.3477659 0.3423561 0.3966873
[16,] 0.3001889 0.3730642 0.3961409 0.3343108 0.4149241
[17,] 0.3109006 0.3483797 0.3230067 0.3173556 0.3552640
[18,] 0.2631206 0.3060860 0.3213942 0.3336023 0.3474666
[19,] 0.2796083 0.3134191 0.3424865 0.2765495 0.4134704
[20,] 0.2363907 0.3240729 0.3367813 0.3222561 0.3821023
[21,] 0.2927193 0.3292184 0.3246403 0.3275091 0.3961611
[22,] 0.3627135 0.4269345 0.3923521 0.3864728 0.5149685
[23,] 0.2818273 0.3464054 0.3149255 0.3746239 0.3622524
[24,] 0.3044356 0.3738811 0.3712310 0.3510428 0.4349479
[25,] 0.2729893 0.3354771 0.3314089 0.3499524 0.3730711
[26,] 0.4067361 0.4384104 0.4082615 0.3858336 0.4803461
[27,] 0.2578816 0.3105591 0.3153282 0.3302087 0.3754398
[28,] 0.2968476 0.3670421 0.3489113 0.3543242 0.3987842
[29,] 0.3992034 0.4197093 0.4048258 0.3608347 0.4386672
[30,] 0.3401047 0.4377885 0.4059210 0.4316374 0.4426953
[31,] 0.2711894 0.3444593 0.3258159 0.3462349 0.3904327
[32,] 0.2902113 0.3202151 0.2927319 0.3132448 0.3934714
[33,] 0.2902643 0.3562870 0.4211501 0.3930252 0.4677351
[34,] 0.2515062 0.3131996 0.3429983 0.3068435 0.4012890
[35,] 0.2591211 0.3377408 0.2983918 0.3068626 0.3686771
[36,] 0.4220373 0.3496548 0.2905701 0.2660560 0.4066057
[37,] 0.3496548 0.4139086 0.3615792 0.3398438 0.4231177
[38,] 0.2905701 0.3615792 0.4243788 0.3349206 0.4348109
[39,] 0.2660560 0.3398438 0.3349206 0.3967751 0.3664759
[40,] 0.4066057 0.4231177 0.4348109 0.3664759 0.6118455
mi_df() examples work
Code
mi_df(bag_label = factor(c(1, 1, 0)), bag_name = c(rep("bag_1", 2), "bag_2"),
X1 = c(-0.4, 0.5, 2), instance_label = c(0, 1, 0))
Output
# An MI data frame: 3 x 3 with 2 bags
# and instance labels: 0, 1, 0
bag_label bag_name X1
<fct> <chr> <dbl>
1 1 bag_1 -0.4
2 1 bag_1 0.5
3 0 bag_2 2
mi() examples work
Code
mil_data <- generate_mild_df(positive_degree = 3, nbag = 10)
with(mil_data, head(mi(bag_label, bag_name)))
Output
bag_label bag_name
[1,] "1" "bag1"
[2,] "1" "bag1"
[3,] "1" "bag1"
[4,] "1" "bag1"
[5,] "1" "bag1"
[6,] "1" "bag1"
Code
df <- get_all_vars(mi(bag_label, bag_name) ~ X1 + X2, data = mil_data)
head(df)
Output
bag_label bag_name X1 X2
1 1 bag1 -0.4464099 1.1019817
2 1 bag1 1.2311306 -0.8395224
3 1 bag1 0.9309844 2.2884239
4 1 bag1 2.0311929 2.1704369
5 1 bag1 0.5209816 -1.1255467
6 1 bag1 -0.4920105 2.4251912
mild_df() examples work
Code
mild_df(bag_label = factor(c(1, 1, 0)), bag_name = c(rep("bag_1", 2), "bag_2"),
instance_name = c("bag_1_inst_1", "bag_1_inst_2", "bag_2_inst_1"), X1 = c(-0.4,
0.5, 2), instance_label = c(0, 1, 0))
Output
# An MILD data frame: 3 x 4 with 2 bags, 3 instances
# and instance labels: 0, 1, 0
bag_label bag_name instance_name X1
<fct> <chr> <chr> <dbl>
1 1 bag_1 bag_1_inst_1 -0.4
2 1 bag_1 bag_1_inst_2 0.5
3 0 bag_2 bag_2_inst_1 2
mild() examples work
Code
mil_data <- generate_mild_df(positive_degree = 3, nbag = 10)
with(mil_data, head(mild(bag_label, bag_name, instance_name)))
Output
bag_label bag_name instance_name
[1,] "1" "bag1" "bag1inst1"
[2,] "1" "bag1" "bag1inst1"
[3,] "1" "bag1" "bag1inst1"
[4,] "1" "bag1" "bag1inst1"
[5,] "1" "bag1" "bag1inst1"
[6,] "1" "bag1" "bag1inst1"
Code
df <- get_all_vars(mild(bag_label, bag_name) ~ X1 + X2, data = mil_data)
head(df)
Output
bag_label bag_name X1 X2
1 1 bag1 0.36529504 -0.3713889
2 1 bag1 0.07733708 -0.5885037
3 1 bag1 -1.59611098 0.3456669
4 1 bag1 -0.16911543 1.4814259
5 1 bag1 -0.85251086 -2.6362243
6 1 bag1 1.40033082 -1.1679564
mior() examples work
Code
if (require(gurobi)) {
set.seed(8)
n <- 15
X <- rbind(mvtnorm::rmvnorm(n / 3, mean = c(4, -2, 0)), mvtnorm::rmvnorm(n /
3, mean = c(0, 0, 0)), mvtnorm::rmvnorm(n / 3, mean = c(-2, 1, 0)))
score <- X %*% c(2, -1, 0)
y <- as.numeric(cut(score, c(-Inf, quantile(score, probs = 1:2 / 3), Inf)))
bags <- seq_along(y)
X <- rbind(X, mvtnorm::rmvnorm(n, mean = c(6, -3, 0)), mvtnorm::rmvnorm(n,
mean = c(-6, 3, 0)))
y <- c(y, rep(-1, 2 * n))
bags <- rep(bags, 3)
repr <- c(rep(1, n), rep(0, 2 * n))
y_bag <- classify_bags(y, bags, condense = FALSE)
mdl1 <- mior(X, y_bag, bags)
predict(mdl1, X, new_bags = bags)
df1 <- dplyr::bind_cols(y = y_bag, bags = bags, as.data.frame(X))
df1 %>% dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "class")) %>%
dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "raw")) %>%
dplyr::distinct(y, bags, .pred_class, .pred)
}
Message <message>
[Step 1] The optimization solution suggests that two intercepts are equal: b[1] == b[2].
[Step 1] The optimization solution suggests that two intercepts are equal: b[2] == b[3].
Warning <warning>
[Step 1] There were NA values in `b`. Replacing with 0.
Message <message>
[Step 2] The optimization solution suggests that two intercepts are equal: b[0] == b[1].
[Step 2] The optimization solution suggests that two intercepts are equal: b[1] == b[2].
[Step 2] The optimization solution suggests that two intercepts are equal: b[2] == b[3].
[Step 2] The optimization solution suggests that endpoints are equal: b[0] == b[K].
[Step 3] The optimization solution suggests that two intercepts are equal: b[1] == b[2].
[Step 3] The optimization solution suggests that two intercepts are equal: b[2] == b[3].
Warning <warning>
[Step 3] There were NA values in `b`. Replacing with 0.
Output
y bags .pred_class .pred
1 3 1 2 -1.27106961
2 3 2 2 -1.46009539
3 3 3 1 0.55859958
4 3 4 2 -2.14449120
5 3 5 2 -2.39500843
6 1 6 2 -1.35396484
7 2 7 2 -1.52776943
8 2 8 1 -0.27197967
9 2 9 1 -0.03922016
10 2 10 1 1.13574924
11 1 11 1 1.81787187
12 1 12 1 0.86516230
13 2 13 1 0.33191616
14 1 14 2 -1.86835119
15 1 15 1 0.03157952
mismm() example works
Code
set.seed(8)
mil_data <- generate_mild_df(nbag = 15, nsample = 20, positive_prob = 0.15,
sd_of_mean = rep(0.1, 3))
mdl1 <- mismm(mil_data)
mdl2 <- mismm(mild(bag_label, bag_name, instance_name) ~ X1 + X2 + X3, data = mil_data)
if (require(gurobi)) {
mdl3 <- mismm(mil_data, method = "mip", control = list(nystrom_args = list(m = 10,
r = 10)))
predict(mdl3, mil_data)
}
Output
# A tibble: 1,200 x 1
.pred_class
<fct>
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
# ... with 1,190 more rows
Code
predict(mdl1, new_data = mil_data, type = "raw", layer = "bag")
Output
# A tibble: 1,200 x 1
.pred
<dbl>
1 -0.289
2 -0.289
3 -0.289
4 -0.289
5 -0.289
6 -0.289
7 -0.289
8 -0.289
9 -0.289
10 -0.289
# ... with 1,190 more rows
Code
mil_data %>% dplyr::bind_cols(predict(mdl2, mil_data, type = "class")) %>%
dplyr::bind_cols(predict(mdl2, mil_data, type = "raw")) %>% dplyr::distinct(
bag_name, bag_label, .pred_class, .pred)
Output
# A tibble: 15 x 4
bag_label bag_name .pred_class .pred
<dbl> <chr> <fct> <dbl>
1 0 bag1 0 -0.120
2 1 bag2 1 0.211
3 0 bag3 0 -0.0939
4 1 bag4 1 0.0945
5 0 bag5 0 -0.0922
6 0 bag6 0 -0.134
7 1 bag7 1 0.155
8 0 bag8 0 -0.0408
9 1 bag9 1 0.169
10 1 bag10 1 0.250
11 1 bag11 1 0.137
12 0 bag12 0 -0.0584
13 1 bag13 1 0.173
14 1 bag14 1 0.143
15 1 bag15 1 0.329
predict.mismm() examples work
Code
mil_data <- generate_mild_df(nbag = 15, nsample = 20, positive_prob = 0.15,
sd_of_mean = rep(0.1, 3))
mdl1 <- mismm(mil_data, control = list(sigma = 1 / 5))
mil_data %>% dplyr::bind_cols(predict(mdl1, mil_data, type = "class")) %>%
dplyr::bind_cols(predict(mdl1, mil_data, type = "raw")) %>% dplyr::distinct(
bag_name, bag_label, .pred_class, .pred)
Output
# A tibble: 15 x 4
bag_label bag_name .pred_class .pred
<dbl> <chr> <fct> <dbl>
1 0 bag1 0 -0.377
2 1 bag2 1 0.283
3 0 bag3 0 -0.332
4 1 bag4 1 0.132
5 0 bag5 0 -0.335
6 0 bag6 0 -0.248
7 1 bag7 1 0.261
8 0 bag8 0 -0.0604
9 1 bag9 1 0.379
10 1 bag10 1 0.392
11 1 bag11 1 0.301
12 0 bag12 0 -0.282
13 1 bag13 1 0.326
14 1 bag14 1 0.223
15 1 bag15 1 0.459
Code
mil_data %>% dplyr::bind_cols(predict(mdl1, mil_data, type = "class", layer = "instance")) %>%
dplyr::bind_cols(predict(mdl1, mil_data, type = "raw", layer = "instance")) %>%
dplyr::distinct(bag_name, instance_name, bag_label, .pred_class, .pred)
Output
# An MILD data frame: 60 x 5 with 15 bags, 60 instances
# and instance labels: 0, 0, 0, 0, 0, ...
bag_label bag_name instance_name .pred_class .pred
<dbl> <chr> <chr> <fct> <dbl>
1 0 bag1 bag1inst1 0 -0.380
2 0 bag1 bag1inst2 0 -0.421
3 0 bag1 bag1inst3 0 -0.377
4 0 bag1 bag1inst4 0 -0.428
5 1 bag2 bag2inst1 0 -0.321
6 1 bag2 bag2inst2 0 -0.363
7 1 bag2 bag2inst3 0 -0.251
8 1 bag2 bag2inst4 1 0.283
9 0 bag3 bag3inst1 0 -0.332
10 0 bag3 bag3inst2 0 -0.395
# ... with 50 more rows
misvm_orova() examples work
Code
data("ordmvnorm")
x <- ordmvnorm[, 3:7]
Warning <warning>
Dropping 'mi_df' class as required column was removed.
Code
y <- ordmvnorm$bag_label
bags <- ordmvnorm$bag_name
mdl1 <- misvm_orova(x, y, bags)
predict(mdl1, x, new_bags = bags)
Output
# A tibble: 1,000 x 1
.pred_class
<fct>
1 2
2 2
3 2
4 2
5 2
6 3
7 3
8 3
9 3
10 3
# ... with 990 more rows
Code
df1 <- dplyr::bind_cols(y = y, bags = bags, as.data.frame(x))
df1 %>% dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "class")) %>%
dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "raw")) %>%
dplyr::select(-starts_with("V")) %>% dplyr::distinct()
Output
y bags .pred_class .pred_1 .pred_2 .pred_3 .pred_4
1 2 1 2 0.072054072 1.24157688 -0.005075918 -0.783087461
2 4 2 3 -1.251419361 -0.44724656 2.357316056 1.435526426
3 1 3 1 2.266372449 2.26051385 -1.635840749 -2.449979864
4 3 4 2 2.036194818 2.48865381 1.218059767 -0.176115919
5 1 5 1 0.999497111 0.43651819 -0.486774613 -2.238297731
6 5 6 3 1.920010812 1.38391110 4.473871500 3.981368930
7 4 7 3 0.730430029 1.07444170 1.818187547 1.000264053
8 4 8 3 0.036505464 1.31947988 3.995412773 3.083747508
9 3 9 3 0.209234901 -1.10561202 1.961314768 0.552380675
10 4 10 3 -0.504233266 0.76282372 3.153695469 2.711460653
11 5 11 3 0.054740339 -0.68454697 3.078920102 2.023132149
12 3 12 3 -0.164563516 -1.97443357 0.926269029 0.107442544
13 5 13 3 -0.569257195 -1.50596301 4.681906898 4.271581859
14 3 14 1 1.797814850 0.68511482 1.334480188 -0.018156454
15 2 15 2 0.354159148 1.30396993 0.299802741 -0.792499384
16 3 16 2 0.900791960 1.64894819 0.733013977 -0.243052192
17 2 17 1 2.751060969 2.09543404 0.767178044 -0.206214135
18 2 18 2 0.012863158 2.62296119 -0.093230935 -1.409281102
19 2 19 1 1.205695843 1.01526852 -1.054750654 -2.326673454
20 1 20 2 0.001415356 1.07104086 -0.896129480 -2.233466605
21 3 21 3 -0.219947873 0.79395459 2.593885919 1.339418082
22 2 22 1 0.954808999 -0.13140503 0.154058385 -1.048479675
23 3 23 2 -0.951905691 1.20592625 0.833612057 -0.742110448
24 4 24 3 -0.273808301 0.37504428 2.761489395 1.500488098
25 2 25 2 -0.151453073 1.78821302 0.097761509 -0.625524717
26 4 26 3 0.548553011 0.91816008 2.692972337 1.529978372
27 4 27 3 -0.838129504 0.87575492 2.942167421 2.106161023
28 2 28 2 -0.459396273 2.71463070 0.030473723 -0.949329104
29 5 29 3 0.168078546 -0.42048836 3.765049712 3.077313282
30 3 30 3 0.204011620 0.89524867 1.470316006 1.152517057
31 2 31 3 -0.353540848 0.06905779 0.518345886 -1.117263057
32 5 32 3 0.347529726 1.96050469 4.092332114 2.777554297
33 2 33 1 1.551470304 -0.17652552 0.458605980 -0.467230099
34 5 34 3 0.812941100 1.27701647 3.693666669 3.265801954
35 4 35 1 2.870066694 1.07442101 1.734199670 0.962302122
36 3 36 3 -0.551027795 0.06258126 1.308833458 0.575725816
37 4 37 3 0.989409528 -0.63987667 3.171262186 2.424230459
38 2 38 1 2.284649128 0.31490852 -0.048948873 -1.266380708
39 4 39 3 1.067812621 -0.09306674 2.750077773 1.984667458
40 2 40 3 0.520817262 -1.62116210 0.957123790 -0.829010258
41 1 41 1 1.274290440 0.83835316 -1.056238423 -2.538301933
42 1 42 2 1.984378137 2.66491196 -0.485998231 -2.147887984
43 2 43 2 -0.145363418 0.80692325 -0.709848943 -1.783606824
44 2 44 2 0.401963950 1.08925411 0.430524106 -1.210678768
45 2 45 1 2.090161856 1.42710530 0.305093008 -0.860630133
46 3 46 3 -0.179769147 0.67295977 0.923519375 0.166548835
47 3 47 1 3.023586198 -0.04441672 0.870074332 0.082737064
48 4 48 3 0.594910297 -0.40790117 3.108895289 2.441552201
49 2 49 2 0.517934018 0.99982514 0.971428071 -0.477676824
50 4 50 3 0.841524707 0.12691384 2.288197352 1.508173550
51 3 51 1 2.179260305 -0.85912454 0.816258705 -0.086590741
52 5 52 3 1.123590058 1.32385089 4.606127446 3.815318462
53 5 53 3 1.523522406 -0.05627962 2.949281404 2.487092570
54 2 54 2 0.256062043 0.41876378 0.199033146 -0.590743872
55 4 55 2 -0.307830686 1.91476237 1.854087045 1.132002454
56 3 56 3 -1.072896286 -0.08795983 1.536126899 0.926894203
57 5 57 3 2.095149008 -0.07867520 4.525285138 3.668547097
58 1 58 1 1.509681540 0.36224184 -1.179052225 -2.919152012
59 2 59 1 1.500651712 1.23943073 -0.487119290 -1.529960657
60 4 60 3 0.153476330 1.12512087 2.965594188 1.719696833
61 2 61 2 0.107433901 0.99998094 0.251861562 -0.668879441
62 2 62 2 1.021472500 1.10098424 -0.483214988 -1.764205357
63 2 63 2 -0.577488392 0.96101424 -0.147362894 -1.373661079
64 5 64 3 -1.470440642 0.34004044 3.749092557 3.320600526
65 2 65 2 -0.115208922 1.35927809 -0.037468827 -2.140192951
66 4 66 3 0.492635619 1.70587041 3.828368807 3.067970029
67 2 67 2 1.314915397 1.52793709 -0.531016585 -2.183768654
68 4 68 3 0.997063986 1.44167808 2.692431224 1.871823863
69 1 69 2 0.179755225 1.01227117 -1.192076661 -1.692757366
70 2 70 2 0.950844268 1.21782268 0.712069112 -0.805553592
71 4 71 3 0.570080774 0.96193964 2.624338049 2.296915214
72 4 72 3 -1.453889746 0.58589034 2.658095509 1.339789694
73 1 73 2 1.445004530 1.45944954 -1.468585910 -2.382518721
74 5 74 3 0.331242540 1.17657845 5.302493853 4.687040860
75 5 75 3 2.064429323 2.96099424 4.444482071 3.548902076
76 4 76 3 -0.028601890 0.19153408 2.991329788 2.446507117
77 1 77 2 -0.189502244 0.29768322 -1.143863726 -2.113323367
78 3 78 3 0.813181217 -1.00040792 2.280448356 2.116439098
79 2 79 2 -0.712710553 0.55617328 0.517086303 -0.093885228
80 1 80 1 1.630919431 0.73688837 -0.341944665 -1.446730604
81 3 81 3 0.902920480 -0.86940201 2.091036922 0.798079169
82 3 82 1 1.636798724 0.36720409 0.526272127 -0.661387375
83 2 83 1 1.531500293 1.51018878 -0.503977890 -1.535820327
84 5 84 3 0.464437512 1.83063333 4.740010492 3.734087786
85 2 85 1 2.447572647 1.09642106 0.481866056 -0.500450818
86 2 86 1 2.693553783 0.99995510 -0.292739964 -1.303295992
87 3 87 3 0.882733260 0.02994256 2.026985038 0.973188925
88 3 88 3 0.769492513 0.60412049 1.149114506 0.006639411
89 3 89 3 1.163435069 1.04646194 1.424447656 0.273940686
90 2 90 2 0.594042888 1.61961486 -0.251107974 -1.180828674
91 3 91 3 -0.383868294 0.22873649 1.000184845 -0.190455535
92 4 92 3 1.445654335 0.92329461 2.390422626 1.353768980
93 3 93 3 0.895493731 1.56358634 2.242955217 1.189125683
94 2 94 3 0.907150996 0.92386627 1.188409683 -0.471069578
95 3 95 2 0.762152263 1.03743805 0.791851575 -0.165396439
96 3 96 1 0.715915316 -0.39463666 0.255683255 -0.150974367
97 2 97 3 -0.155145008 -0.57753861 0.473907217 -0.555776688
98 2 98 2 0.135714595 0.41352574 -0.208214572 -0.552287748
99 2 99 2 -0.141625157 2.23280495 -0.074145655 -0.817376099
100 2 100 2 -0.019076930 1.04704126 0.434395332 -0.999866642
101 2 101 1 1.277564724 0.63581165 0.362761950 -1.742433608
102 2 102 3 -0.006713045 -0.03519697 0.371099531 -1.000979568
103 2 103 1 1.150995278 0.93893790 -0.453058573 -1.565305108
104 5 104 3 -0.084590673 0.32435353 4.722037336 4.096075587
105 1 105 1 1.049951247 0.51900629 -0.407220119 -1.882503879
106 3 106 3 -0.380301725 0.22443279 1.623335874 0.343203060
107 3 107 2 0.375821293 2.64082208 0.999198140 -0.301840905
108 1 108 1 1.111465715 0.66199956 -1.247840729 -2.510232570
109 3 109 3 0.666430137 0.99386114 2.098551048 1.569297967
110 5 110 3 0.908593319 1.15870068 3.672511343 3.623525435
111 1 111 2 0.200115809 0.27917410 -0.454184020 -2.031033198
112 3 112 1 0.917679336 0.28386471 0.656506910 -0.746684752
113 2 113 1 1.798310728 1.31431887 -0.836225608 -2.183067661
114 1 114 2 0.999975552 1.06284430 -1.139154293 -2.323531747
115 5 115 3 -1.292889704 2.43593054 3.738333559 2.593191003
116 2 116 2 0.195980600 1.12561416 -0.147867431 -0.403210177
117 4 117 3 0.653941590 1.06963348 1.839933954 1.000262988
118 2 118 2 0.573141965 0.73200876 0.536599552 -0.584070336
119 1 119 1 1.209429578 0.35359752 -0.202944858 -1.454486222
120 5 120 3 1.272566079 1.55025619 4.051944143 3.215528155
121 1 121 1 0.308112225 0.18483777 -0.868885078 -1.793322748
122 2 122 2 -1.538849904 0.84322486 -0.120521607 -1.199301261
123 3 123 2 -0.448733944 2.76807920 1.993302722 0.786583597
124 3 124 3 1.581181008 1.76559761 1.819032454 0.840982124
125 2 125 1 0.119633865 0.10412126 -0.099776725 -1.407163380
126 2 126 2 0.393379327 0.86242251 0.069857642 -1.779543657
127 5 127 3 0.590272911 1.32850469 4.183207523 3.832928424
128 3 128 3 0.472820279 0.82437661 1.717874666 1.333198988
129 1 129 2 0.943847045 2.73668518 -1.419371165 -3.087451597
130 3 130 2 0.810135639 1.85824791 0.961275675 -0.917301006
131 4 131 3 0.358246998 -0.44392872 2.906291423 2.561670550
132 2 132 2 0.774586840 1.66293656 0.855561712 -0.917567867
133 3 133 3 -0.550711238 -0.56926697 0.694228446 -0.678812703
134 5 134 3 -0.012112268 1.22860183 4.465948576 3.602452262
135 4 135 4 -0.291949799 1.11774318 2.222342191 2.343471199
136 5 136 3 0.942324879 2.65928102 3.974654849 2.632401842
137 2 137 1 1.778003397 1.11342719 0.160190292 -1.000126956
138 5 138 3 0.855095736 0.55893253 4.161465376 3.197801129
139 5 139 3 0.636543509 1.53739284 3.875415445 3.548614790
140 3 140 3 0.100977767 1.02563567 1.517060405 0.519591116
141 2 141 1 1.363349313 1.09390713 -0.265016510 -1.889752422
142 3 142 3 0.517022989 0.47420072 1.822826898 0.149811006
143 3 143 3 0.420427055 0.62233735 0.844955376 -0.436951189
144 5 144 3 -1.144003804 0.96698257 3.728894705 3.555935707
145 4 145 3 1.202471103 0.65641832 2.188028475 1.084486715
146 1 146 2 1.396454476 1.48357989 -1.094755032 -2.171843353
147 5 147 3 0.777818253 1.31950973 3.630842333 3.439383932
148 3 148 3 -2.086082825 -0.18524611 1.090262754 -0.149644034
149 3 149 3 1.012761279 1.79634256 2.103497227 0.745128077
150 1 150 2 0.885941333 1.48454696 -0.061515541 -0.783359064
151 4 151 3 0.013111638 -0.22119890 1.932968374 1.012871724
152 2 152 2 -0.530687880 -0.06287797 -0.651997451 -1.669859980
153 4 153 3 0.869793218 0.77671031 1.728178617 0.914770455
154 2 154 2 0.190652685 1.71395014 -0.229030835 -1.543881873
155 1 155 2 -0.178580776 0.92305141 -1.036292130 -2.165478548
156 1 156 2 1.000201483 2.92361363 -1.325700627 -2.729749197
157 2 157 2 -0.015094567 1.91893656 0.031534694 -0.946125280
158 4 158 3 -0.812626914 0.95648766 2.806138958 1.876790794
159 2 159 3 -1.147613773 0.70381535 1.078614431 0.001691334
160 3 160 3 -0.005963495 -0.42144411 1.346029283 0.866144024
161 4 161 3 -0.487768292 0.16039940 1.889244350 0.838848852
162 4 162 3 -0.325277453 0.44750972 3.522302671 2.334300848
163 2 163 1 0.810485670 0.21194222 -0.236538595 -0.989193244
164 4 164 3 -0.586188903 -1.40828162 3.153760500 2.445556093
165 2 165 1 1.707600456 1.64506602 -0.354574953 -1.106089709
166 1 166 1 0.516966404 0.43037407 -0.843319109 -2.105307436
167 1 167 1 1.240764247 0.96035169 -1.403132441 -2.723452555
168 2 168 2 1.150271113 3.59719968 0.710025608 -0.772309968
169 3 169 3 1.154990522 1.21336040 1.537909335 0.282153028
170 3 170 2 -0.530602126 1.44399045 0.164801983 -1.276018499
171 4 171 3 -0.157253345 -1.14002239 2.117835761 1.000416289
172 1 172 1 1.374174118 0.40075009 -0.778209698 -2.335232604
173 2 173 1 2.333911387 0.39848361 0.235552515 -0.829819104
174 3 174 3 -0.332185510 -1.33640388 1.172623486 0.066823119
175 2 175 3 -0.327081449 0.10316262 1.302235951 0.484237839
176 2 176 2 1.015949323 1.80174907 0.017039788 -0.945153995
177 4 177 3 0.403523641 -0.63949231 1.274572759 0.172783384
178 2 178 1 1.088188898 0.51355476 -0.078971488 -0.744362702
179 2 179 2 -0.164286920 1.15585033 -0.356885685 -1.492212171
180 3 180 1 1.518219908 -0.07355124 1.000179024 -0.598403065
181 3 181 1 2.102301483 1.52374512 0.422707945 -0.485696539
182 2 182 1 0.579630818 0.47244020 -0.683296348 -1.185304536
183 3 183 2 1.902651105 2.28923504 2.161821025 1.637501923
184 1 184 1 1.540113414 1.46968383 -0.429542642 -1.565683732
185 2 185 1 1.909916536 -0.52104883 0.749757617 -0.642477039
186 4 186 3 0.141069414 1.23662770 2.006894338 0.999448779
187 2 187 2 0.542736128 1.86111012 0.294251330 -0.339388904
188 3 188 1 1.384725699 -1.51205961 -0.039519134 -1.270476702
189 3 189 1 1.864789106 1.47779347 1.182041810 0.389903457
190 5 190 3 -0.574479151 0.11035482 4.014472592 3.270696550
191 4 191 3 0.068318643 1.33079012 2.639410743 1.645692123
192 2 192 2 0.326891249 1.53619415 0.133795222 -1.210934106
193 2 193 1 2.007857820 0.70729058 -0.565017074 -1.370434743
194 2 194 2 1.067989221 3.13948837 0.476273288 -0.756853035
195 5 195 3 1.210197177 -0.75076373 4.808854585 4.473698884
196 3 196 3 0.362578606 1.26753424 1.347520007 -0.131381656
197 2 197 3 -0.640280205 0.29798615 1.585048445 0.502836911
198 3 198 3 0.461652471 -2.05551325 1.000172781 0.125844624
199 1 199 1 1.000201245 -0.28377772 -0.529427122 -1.847119454
200 1 200 1 1.815506761 0.29396669 -0.726221317 -1.510439568
.pred_5
1 -2.58002883
2 -0.07684673
3 -4.35822799
4 -1.95356763
5 -3.99207671
6 1.93351275
7 -0.66240349
8 1.18028676
9 -1.16592851
10 0.77978484
11 1.00035160
12 -1.50176706
13 2.41059782
14 -1.53078230
15 -2.92647034
16 -1.84243289
17 -1.57981776
18 -3.17228600
19 -3.74323245
20 -4.02264278
21 -0.19119280
22 -2.75209990
23 -2.69603183
24 -0.24203523
25 -2.69850191
26 -0.19472477
27 0.53465529
28 -2.63182959
29 1.00007002
30 -0.92024133
31 -2.47197917
32 1.49964739
33 -2.12023746
34 1.68804362
35 -0.58162609
36 -0.81888860
37 0.78236444
38 -3.10579054
39 0.47156372
40 -2.18379805
41 -4.40018543
42 -3.90076015
43 -3.27682785
44 -2.74666887
45 -2.68843170
46 -1.81494788
47 -1.47035622
48 0.46722703
49 -1.99203120
50 -0.12349569
51 -2.10959676
52 2.09263402
53 0.99979963
54 -2.73581967
55 -1.01611189
56 -0.83358768
57 1.92926482
58 -4.54639348
59 -3.41947711
60 0.19544692
61 -2.46984187
62 -3.65850087
63 -3.19360474
64 1.82827101
65 -3.47411810
66 1.21509269
67 -4.21862304
68 0.38832820
69 -3.81505895
70 -2.07675393
71 0.53085258
72 0.42778565
73 -4.48460656
74 2.92744210
75 2.14952714
76 0.54757355
77 -4.11282145
78 -0.05549175
79 -2.04012566
80 -3.52356153
81 -0.74699403
82 -2.63327923
83 -3.76415925
84 2.40164351
85 -2.37326774
86 -3.29600258
87 -0.64494508
88 -1.55588564
89 -1.23462421
90 -3.14457588
91 -1.55665731
92 -0.46741107
93 -0.36071430
94 -2.13941056
95 -1.88584105
96 -2.31279814
97 -2.72506481
98 -2.55486628
99 -2.62755342
100 -2.93103959
101 -3.13908949
102 -2.36598414
103 -3.13311286
104 2.47607255
105 -3.87597827
106 -1.13580430
107 -1.80780393
108 -4.02513628
109 -0.53160920
110 1.69841162
111 -3.88190764
112 -2.56385998
113 -3.86837437
114 -4.14417630
115 0.99942799
116 -2.65863372
117 -0.76754823
118 -2.27942602
119 -3.27317032
120 1.72560788
121 -4.02924537
122 -2.98682699
123 -0.63185022
124 -1.05365132
125 -3.25180121
126 -3.09785064
127 1.89772202
128 -1.03007030
129 -4.76932157
130 -1.83026680
131 0.70763604
132 -2.51050644
133 -2.30067396
134 1.96546545
135 0.10061140
136 1.41747201
137 -3.10551589
138 1.72763109
139 1.97737571
140 -1.46789447
141 -3.59107922
142 -1.20986782
143 -1.97876091
144 1.71976446
145 0.03445753
146 -3.79717233
147 1.31584588
148 -1.83044632
149 -0.99217495
150 -3.09378730
151 -0.50430058
152 -3.69861394
153 -1.01143389
154 -3.10652562
155 -3.98430332
156 -4.34843602
157 -2.82600550
158 0.29058746
159 -1.71037418
160 -1.14003962
161 -0.86219140
162 0.86711891
163 -2.71227855
164 1.13242136
165 -3.25077099
166 -4.21184001
167 -4.32908290
168 -2.53025092
169 -1.20668692
170 -2.53536903
171 -0.89845528
172 -4.16588482
173 -2.50545962
174 -1.58712329
175 -1.15430985
176 -2.73924017
177 -1.37722823
178 -2.54843043
179 -3.27672842
180 -2.12158393
181 -2.24123529
182 -3.47317552
183 -0.10937610
184 -3.30503450
185 -2.34538027
186 -0.92687356
187 -2.09575427
188 -2.95018409
189 -1.63822035
190 1.89281930
191 -0.10896206
192 -2.93719691
193 -3.31089481
194 -2.75203056
195 2.99187180
196 -1.53222689
197 -1.06215402
198 -1.69979180
199 -3.50721222
200 -3.37582254
misvm() examples work
Code
set.seed(8)
mil_data <- generate_mild_df(nbag = 20, positive_prob = 0.15, sd_of_mean = rep(
0.1, 3))
df <- build_instance_feature(mil_data, seq(0.05, 0.95, length.out = 10))
mdl1 <- misvm(x = df[, 4:123], y = df$bag_label, bags = df$bag_name, method = "heuristic")
mdl2 <- misvm(mi(bag_label, bag_name) ~ X1_mean + X2_mean + X3_mean, data = df)
if (require(gurobi)) {
mdl3 <- misvm(x = df[, 4:123], y = df$bag_label, bags = df$bag_name, method = "mip")
}
predict(mdl1, new_data = df, type = "raw", layer = "bag")
Output
# A tibble: 80 x 1
.pred
<dbl>
1 -1.04
2 -1.04
3 -1.04
4 -1.04
5 1.00
6 1.00
7 1.00
8 1.00
9 -1.00
10 -1.00
# ... with 70 more rows
Code
df %>% dplyr::bind_cols(predict(mdl2, df, type = "class")) %>% dplyr::bind_cols(
predict(mdl2, df, type = "raw")) %>% dplyr::distinct(bag_name, bag_label,
.pred_class, .pred)
Output
bag_label bag_name .pred_class .pred
1 0 bag1 0 -0.11805071
2 1 bag2 1 1.01732791
3 0 bag3 0 -0.24540426
4 1 bag4 1 1.00046917
5 0 bag5 1 0.15460188
6 0 bag6 1 0.87469487
7 1 bag7 1 0.16754553
8 0 bag8 1 1.00811386
9 1 bag9 1 0.99998275
10 1 bag10 1 2.67168111
11 1 bag11 1 0.29471379
12 0 bag12 1 1.52487131
13 1 bag13 1 2.15326561
14 1 bag14 1 0.99956477
15 1 bag15 0 -0.38940230
16 1 bag16 1 0.67654218
17 0 bag17 1 0.39241276
18 1 bag18 0 -0.11878006
19 0 bag19 1 0.06554383
20 1 bag20 1 0.85951804
omisvm() examples work
Code
if (require(gurobi)) {
data("ordmvnorm")
x <- ordmvnorm[, 3:7]
y <- ordmvnorm$bag_label
bags <- ordmvnorm$bag_name
mdl1 <- omisvm(x, y, bags, weights = NULL)
predict(mdl1, x, new_bags = bags)
df1 <- dplyr::bind_cols(y = y, bags = bags, as.data.frame(x))
df1 %>% dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "class")) %>%
dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "raw")) %>%
dplyr::distinct(y, bags, .pred_class, .pred)
}
Warning <warning>
Dropping 'mi_df' class as required column was removed.
Output
y bags .pred_class .pred
1 2 1 2 2.12567387
2 4 2 4 4.65794584
3 1 3 1 -0.83805734
4 3 4 3 2.63966213
5 1 5 1 -0.25380927
6 5 6 5 7.77042870
7 4 7 3 3.72950569
8 4 8 5 6.80992822
9 3 9 3 3.70282546
10 4 10 4 5.94699034
11 5 11 4 5.94752631
12 3 12 3 3.19506580
13 5 13 5 8.35594177
14 3 14 3 3.15834447
15 2 15 2 1.33977357
16 3 16 3 2.47991026
17 2 17 3 2.70733748
18 2 18 2 0.88989980
19 2 19 2 0.55035855
20 1 20 1 -0.48857803
21 3 21 4 4.62498956
22 2 22 2 1.34357488
23 3 23 2 2.07230182
24 4 24 4 5.11838378
25 2 25 2 1.62530071
26 4 26 4 5.04146527
27 4 27 4 6.12983599
28 2 28 2 1.38152030
29 5 29 5 6.60015162
30 3 30 3 3.26062479
31 2 31 2 1.81985451
32 5 32 5 7.11161632
33 2 33 2 2.21294409
34 5 34 5 7.29111446
35 4 35 4 4.46349207
36 3 36 3 3.98620574
37 4 37 4 6.06686466
38 2 38 2 1.56219942
39 4 39 4 5.30727742
40 2 40 2 2.27510342
41 1 41 1 -0.81342852
42 1 42 1 0.02151881
43 2 43 2 0.52168663
44 2 44 2 1.44739630
45 2 45 2 1.59950373
46 3 46 3 2.59372869
47 3 47 3 2.74693863
48 4 48 4 6.14110841
49 2 49 2 2.32840644
50 4 50 4 5.02891382
51 3 51 3 2.44752466
52 5 52 5 7.74094074
53 5 53 5 6.42830548
54 2 54 2 1.31239708
55 4 55 3 3.99384803
56 3 56 3 3.79146236
57 5 57 5 8.07575177
58 1 58 1 -0.54392096
59 2 59 2 0.48349162
60 4 60 4 5.71926852
61 2 61 2 1.34308764
62 2 62 2 0.51765081
63 2 63 2 0.95240309
64 5 64 5 7.39404182
65 2 65 2 0.79525838
66 4 66 5 6.70635073
67 2 67 1 0.21166463
68 4 68 4 5.30727742
69 1 69 1 -0.17442229
70 2 70 3 2.54644169
71 4 71 4 5.61509347
72 4 72 4 5.51145838
73 1 73 1 -0.41594083
74 5 74 5 9.28598108
75 5 75 5 7.96521824
76 4 76 4 5.62411564
77 1 77 1 -0.05464613
78 3 78 4 4.42463829
79 2 79 2 1.94267090
80 1 80 2 0.78140124
81 3 81 3 3.95317076
82 3 82 2 1.57994465
83 2 83 2 0.59626748
84 5 84 5 8.85967969
85 2 85 2 1.96307114
86 2 86 2 0.53479058
87 3 87 4 4.53134302
88 3 88 3 3.36033245
89 3 89 3 3.60200338
90 2 90 2 1.06057133
91 3 91 3 2.98476241
92 4 92 3 4.15237641
93 3 93 4 4.60544506
94 2 94 3 2.42829931
95 3 95 3 2.51276246
96 3 96 2 2.09600292
97 2 97 2 1.47889650
98 2 98 2 1.37766926
99 2 99 2 1.15593677
100 2 100 2 1.27380967
101 2 101 2 1.19192283
102 2 102 2 2.26486948
103 2 103 2 0.96767384
104 5 104 5 8.52363527
105 1 105 2 0.36592471
106 3 106 3 3.24715264
107 3 107 3 2.85117594
108 1 108 1 -0.66022643
109 3 109 4 4.67142427
110 5 110 5 7.27179022
111 1 111 1 -0.18336695
112 3 112 2 2.21778165
113 2 113 1 0.02990462
114 1 114 1 -0.42337571
115 5 115 5 6.48828236
116 2 116 2 1.27682708
117 4 117 3 4.25799913
118 2 118 2 2.10080070
119 1 119 2 0.93064817
120 5 120 5 7.52021032
121 1 121 1 -0.29092487
122 2 122 2 0.88786343
123 3 123 3 3.98453366
124 3 124 3 3.65453342
125 2 125 2 0.90799958
126 2 126 2 1.26472093
127 5 127 5 7.55024109
128 3 128 3 4.01900088
129 1 129 1 -1.05125879
130 3 130 3 2.62876392
131 4 131 4 6.07376153
132 2 132 2 1.90236816
133 3 133 2 2.25972489
134 5 134 5 8.11634426
135 4 135 4 5.00440266
136 5 136 5 6.81334557
137 2 137 2 1.01226568
138 5 138 5 7.64971991
139 5 139 5 7.45437571
140 3 140 3 3.24438924
141 2 141 1 0.18105533
142 3 142 3 3.94018318
143 3 143 3 2.36976230
144 5 144 5 7.27290297
145 4 145 4 4.89267331
146 1 146 1 -0.15294844
147 5 147 5 7.39521045
148 3 148 3 3.09050461
149 3 149 3 3.76402819
150 1 150 2 1.17147884
151 4 151 3 4.25669454
152 2 152 2 0.37705873
153 4 153 3 3.50975303
154 2 154 2 1.31948372
155 1 155 1 -0.28261632
156 1 156 1 -0.93086447
157 2 157 2 1.29143142
158 4 158 4 5.41757327
159 2 159 3 2.81103227
160 3 160 3 3.63819060
161 4 161 3 4.16650748
162 4 162 5 6.54752782
163 2 163 2 1.20572586
164 4 164 4 6.13185612
165 2 165 2 0.56934837
166 1 166 1 -0.42607856
167 1 167 1 -0.78088361
168 2 168 2 1.62783817
169 3 169 3 3.76726554
170 3 170 2 1.76488593
171 4 171 4 4.41643401
172 1 172 1 -0.46522749
173 2 173 2 1.48310131
174 3 174 3 2.76200714
175 2 175 3 2.96845063
176 2 176 2 1.57232998
177 4 177 3 3.37146742
178 2 178 2 1.27492919
179 2 179 2 0.56000795
180 3 180 3 2.50825115
181 3 181 2 2.04157031
182 2 182 2 0.69159336
183 3 183 4 4.68423662
184 1 184 2 0.64191641
185 2 185 2 1.95328294
186 4 186 3 4.06195481
187 2 187 2 2.27932700
188 3 188 2 1.08385635
189 3 189 3 2.90625148
190 5 190 5 7.49152508
191 4 191 4 5.01998550
192 2 192 2 1.51988937
193 2 193 2 0.86665906
194 2 194 2 1.34357488
195 5 195 5 8.45275065
196 3 196 3 3.40346084
197 2 197 3 3.41311656
198 3 198 3 2.35031158
199 1 199 2 0.98324723
200 1 200 2 0.55294597
smm() examples work
Code
set.seed(8)
n_instances <- 10
n_samples <- 20
y <- rep(c(1, -1), each = n_samples * n_instances / 2)
instances <- as.character(rep(1:n_instances, each = n_samples))
x <- data.frame(x1 = rnorm(length(y), mean = 1 * (y == 1)), x2 = rnorm(length(y),
mean = 2 * (y == 1)), x3 = rnorm(length(y), mean = 3 * (y == 1)))
df <- data.frame(instance_name = instances, y = y, x)
mdl <- smm(x, y, instances)
mdl2 <- smm(y ~ ., data = df)
df %>% dplyr::bind_cols(predict(mdl, type = "raw", new_data = x, new_instances = instances)) %>%
dplyr::bind_cols(predict(mdl, type = "class", new_data = x, new_instances = instances)) %>%
dplyr::distinct(instance_name, y, .pred, .pred_class)
Output
instance_name y .pred .pred_class
1 1 1 1.0000000 1
2 2 1 0.9216801 1
3 3 1 1.0847376 1
4 4 1 0.9357988 1
5 5 1 0.9247263 1
6 6 -1 -0.9880539 -1
7 7 -1 -0.7498163 -1
8 8 -1 -0.9375437 -1
9 9 -1 -1.0000000 -1
10 10 -1 -1.0317597 -1
summarize_samples() examples work
Code
fns <- list(mean = mean, sd = sd)
suppressMessages({
summarize_samples(mtcars, group_cols = c("cyl", "gear"), .fns = fns) %>%
print()
summarize_samples(mtcars, group_cols = c("cyl", "gear"), .fns = fns, cor = TRUE) %>%
print()
})
Output
# A tibble: 8 x 20
cyl gear mpg_mean disp_mean hp_mean drat_mean wt_mean qsec_mean vs_mean
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4 3 21.5 120. 97 3.7 2.46 20.0 1
2 4 4 26.9 103. 76 4.11 2.38 19.6 1
3 4 5 28.2 108. 102 4.1 1.83 16.8 0.5
4 6 3 19.8 242. 108. 2.92 3.34 19.8 1
5 6 4 19.8 164. 116. 3.91 3.09 17.7 0.5
6 6 5 19.7 145 175 3.62 2.77 15.5 0
7 8 3 15.0 358. 194. 3.12 4.10 17.1 0
8 8 5 15.4 326 300. 3.88 3.37 14.6 0
# ... with 11 more variables: am_mean <dbl>, carb_mean <dbl>, mpg_sd <dbl>,
# disp_sd <dbl>, hp_sd <dbl>, drat_sd <dbl>, wt_sd <dbl>, qsec_sd <dbl>,
# vs_sd <dbl>, am_sd <dbl>, carb_sd <dbl>
# A tibble: 8 x 56
cyl gear mpg_mean disp_mean hp_mean drat_mean wt_mean qsec_mean vs_mean
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4 3 21.5 120. 97 3.7 2.46 20.0 1
2 4 4 26.9 103. 76 4.11 2.38 19.6 1
3 4 5 28.2 108. 102 4.1 1.83 16.8 0.5
4 6 3 19.8 242. 108. 2.92 3.34 19.8 1
5 6 4 19.8 164. 116. 3.91 3.09 17.7 0.5
6 6 5 19.7 145 175 3.62 2.77 15.5 0
7 8 3 15.0 358. 194. 3.12 4.10 17.1 0
8 8 5 15.4 326 300. 3.88 3.37 14.6 0
# ... with 47 more variables: am_mean <dbl>, carb_mean <dbl>, mpg_sd <dbl>,
# disp_sd <dbl>, hp_sd <dbl>, drat_sd <dbl>, wt_sd <dbl>, qsec_sd <dbl>,
# vs_sd <dbl>, am_sd <dbl>, carb_sd <dbl>, cov_var_1 <dbl>, cov_var_2 <dbl>,
# cov_var_3 <dbl>, cov_var_4 <dbl>, cov_var_5 <dbl>, cov_var_6 <dbl>,
# cov_var_7 <dbl>, cov_var_8 <dbl>, cov_var_9 <dbl>, cov_var_10 <dbl>,
# cov_var_11 <dbl>, cov_var_12 <dbl>, cov_var_13 <dbl>, cov_var_14 <dbl>,
# cov_var_15 <dbl>, cov_var_16 <dbl>, cov_var_17 <dbl>, cov_var_18 <dbl>, ...
svor_exc() examples work
Code
data("ordmvnorm")
x <- ordmvnorm[, 3:7]
Warning <warning>
Dropping 'mi_df' class as required column was removed.
Code
y <- attr(ordmvnorm, "instance_label")
mdl1 <- svor_exc(x, y)
Message <message>
The SMO algorithm reached the maximum of 500 steps.
Code
predict(mdl1, x)
Output
# A tibble: 1,000 x 1
.pred_class
<fct>
1 1
2 1
3 2
4 1
5 1
6 1
7 1
8 4
9 2
10 2
# ... with 990 more rows
Code
predict(mdl1, x, type = "raw")
Output
# A tibble: 1,000 x 1
.pred
<dbl>
1 -0.464
2 -1.08
3 0.500
4 -0.253
5 0.0886
6 -0.0116
7 -0.146
8 2.15
9 0.266
10 0.216
# ... with 990 more rows
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mildsvm documentation built on July 14, 2022, 9:08 a.m.
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build_fm(), kfm_exact(), kfm_nystrom() examples work
Code
df <- data.frame(X1 = c(2, 3, 4, 5, 6, 7, 8), X2 = c(1, 1.2, 1.3, 1.4, 1.1, 7,
1), X3 = rnorm(7))
fit1 <- kfm_nystrom(df, m = 7, r = 6, kernel = "radial", sigma = 0.05)
fm <- build_fm(fit1, df)
fit2 <- kfm_exact(kernel = "polynomial", degree = 2, const = 1)
fm <- build_fm(fit2, df)
cv_misvm() examples work
Code
set.seed(8)
mil_data <- generate_mild_df(nbag = 20, positive_prob = 0.15, dist = rep(
"mvnormal", 3), mean = list(rep(1, 10), rep(2, 10)), sd_of_mean = rep(0.1, 3))
df <- build_instance_feature(mil_data, seq(0.05, 0.95, length.out = 10))
cost_seq <- 2^seq(-5, 7, length.out = 3)
mdl1 <- cv_misvm(x = df[, 4:123], y = df$bag_label, bags = df$bag_name,
cost_seq = cost_seq, n_fold = 3, method = "heuristic")
mdl2 <- cv_misvm(mi(bag_label, bag_name) ~ X1_mean + X2_mean + X3_mean, data = df,
cost_seq = cost_seq, n_fold = 3)
if (require(gurobi)) {
mdl3 <- cv_misvm(x = df[, 4:123], y = df$bag_label, bags = df$bag_name,
cost_seq = cost_seq, n_fold = 3, method = "mip")
}
Message <packageStartupMessage>
Loading required package: gurobi
Loading required package: slam
Code
predict(mdl1, new_data = df, type = "raw", layer = "bag")
Output
# A tibble: 80 x 1
.pred
<dbl>
1 -1.00
2 -1.00
3 -1.00
4 -1.00
5 1.04
6 1.04
7 1.04
8 1.04
9 -1.13
10 -1.13
# ... with 70 more rows
Code
df %>% dplyr::bind_cols(predict(mdl2, df, type = "class")) %>% dplyr::bind_cols(
predict(mdl2, df, type = "raw")) %>% dplyr::distinct(bag_name, bag_label,
.pred_class, .pred)
Output
bag_label bag_name .pred_class .pred
1 0 bag1 0 -0.5932349
2 1 bag2 1 0.7493612
3 0 bag3 0 -0.9387030
4 1 bag4 1 1.2126533
5 0 bag5 0 -0.8094506
6 0 bag6 0 -0.8083522
7 1 bag7 1 0.6587946
8 0 bag8 0 -0.9032079
9 1 bag9 1 0.5855234
10 1 bag10 1 1.2019300
11 1 bag11 1 1.2689043
12 0 bag12 0 -0.8143970
13 1 bag13 1 0.8591738
14 1 bag14 1 1.0000000
15 1 bag15 1 1.1078369
16 1 bag16 1 1.2117319
17 0 bag17 0 -0.6022075
18 1 bag18 1 0.9355648
19 0 bag19 0 -0.7314129
20 1 bag20 1 1.0393764
generate_mild_df() examples work
Code
set.seed(8)
mild_data <- generate_mild_df(nbag = 7, ninst = 3, nsample = 20, ncov = 2,
nimp_pos = 1, dist = rep("mvnormal", 3), mean = list(rep(5, 1), rep(15, 2), 0))
dplyr::distinct(mild_data, bag_label, bag_name, instance_name)
Output
# An MILD data frame: 21 x 3 with 7 bags, 21 instances
# and instance labels: 0, 0, 0, 0, 0, ...
bag_label bag_name instance_name
<dbl> <chr> <chr>
1 0 bag1 bag1inst1
2 0 bag1 bag1inst2
3 0 bag1 bag1inst3
4 0 bag2 bag2inst1
5 0 bag2 bag2inst2
6 0 bag2 bag2inst3
7 1 bag3 bag3inst1
8 1 bag3 bag3inst2
9 1 bag3 bag3inst3
10 0 bag4 bag4inst1
# ... with 11 more rows
Code
split(mild_data[, 4:5], mild_data$instance_name) %>% sapply(colMeans) %>% round(
2) %>% t()
Warning <warning>
Dropping 'mild_df' class as required column was removed.
Output
X1 X2
bag1inst1 14.95 14.63
bag1inst2 15.53 15.42
bag1inst3 14.27 16.08
bag2inst1 15.29 15.01
bag2inst2 15.78 14.87
bag2inst3 14.53 15.71
bag3inst1 14.79 14.83
bag3inst2 5.36 -0.69
bag3inst3 15.13 15.78
bag4inst1 15.32 14.46
bag4inst2 15.31 15.53
bag4inst3 15.93 14.55
bag5inst1 15.25 14.25
bag5inst2 14.98 16.03
bag5inst3 15.48 14.95
bag6inst1 4.83 0.26
bag6inst2 16.23 16.06
bag6inst3 15.31 14.56
bag7inst1 14.74 14.90
bag7inst2 14.61 15.03
bag7inst3 15.17 15.20
kme() examples work
Code
x = data.frame(instance_name = c("inst_1", "inst_2", "inst_1"), X1 = c(-0.4,
0.5, 2))
kme(x)
Output
[,1] [,2]
[1,] 0.8748808 0.9269533
[2,] 0.9269533 1.0000000
Code
mild_df1 <- generate_mild_df(nbag = 10, positive_degree = 3)
kme(mild_df1)
Output
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.4169122 0.3523994 0.2999620 0.3919451 0.2671537 0.3808291 0.3178319
[2,] 0.3523994 0.4001522 0.3488563 0.3862459 0.3258604 0.3828253 0.3553224
[3,] 0.2999620 0.3488563 0.3830298 0.3729092 0.3410245 0.3829949 0.3563669
[4,] 0.3919451 0.3862459 0.3729092 0.6736729 0.3829977 0.4622801 0.3786055
[5,] 0.2671537 0.3258604 0.3410245 0.3829977 0.3866819 0.3438624 0.3125532
[6,] 0.3808291 0.3828253 0.3829949 0.4622801 0.3438624 0.5928032 0.3992530
[7,] 0.3178319 0.3553224 0.3563669 0.3786055 0.3125532 0.3992530 0.4072973
[8,] 0.2886348 0.3157459 0.2987742 0.3711378 0.2682204 0.3401411 0.2749737
[9,] 0.3872634 0.4011600 0.3740848 0.4325844 0.3109014 0.4722626 0.3594980
[10,] 0.3086363 0.3301951 0.2900993 0.3880930 0.2750935 0.3674942 0.3147702
[11,] 0.3300471 0.3715490 0.3490256 0.3765055 0.3468922 0.3911776 0.3134912
[12,] 0.3496296 0.3733415 0.3661626 0.5378319 0.3513647 0.4593275 0.3566796
[13,] 0.2643695 0.3083248 0.3592159 0.3650698 0.3075189 0.3443388 0.3521353
[14,] 0.3510899 0.3496693 0.3579273 0.4438735 0.3136538 0.4244626 0.3410867
[15,] 0.3306274 0.3505787 0.3460770 0.4034540 0.3067107 0.4154028 0.3552658
[16,] 0.3547158 0.3407101 0.3166309 0.3790609 0.2549436 0.3913973 0.3093184
[17,] 0.3413221 0.3310640 0.3056185 0.4769570 0.2984654 0.3205515 0.3010842
[18,] 0.3385712 0.3715150 0.3362378 0.3772033 0.3236153 0.3631170 0.3673017
[19,] 0.3274643 0.3157874 0.3209078 0.3923291 0.2950952 0.3912085 0.3039875
[20,] 0.3024896 0.3319636 0.3544274 0.3987057 0.3375483 0.4542176 0.3624589
[21,] 0.3037886 0.3305133 0.3058440 0.4023323 0.2933122 0.3999505 0.3038551
[22,] 0.4046782 0.4063992 0.3687206 0.4748479 0.3561605 0.5333569 0.3857608
[23,] 0.2921442 0.3529623 0.2947198 0.3825395 0.2826722 0.3598854 0.2964741
[24,] 0.3670381 0.3722187 0.3556825 0.4504677 0.3279440 0.4540149 0.3728457
[25,] 0.3293533 0.3842095 0.3365811 0.3505797 0.3083936 0.3654656 0.3492568
[26,] 0.4514919 0.4317108 0.3776675 0.5362912 0.3806510 0.4668916 0.3974679
[27,] 0.3072870 0.3522197 0.3407901 0.4046125 0.3505049 0.3589900 0.3183809
[28,] 0.3351851 0.3453512 0.3313021 0.4664949 0.3088645 0.4280774 0.3465275
[29,] 0.4277095 0.3869842 0.3158980 0.4345843 0.2831570 0.3771593 0.3238047
[30,] 0.3918940 0.4233512 0.3803228 0.5079662 0.3517907 0.4494309 0.3917467
[31,] 0.3209992 0.3356011 0.3067535 0.3937972 0.2606738 0.3971114 0.3341408
[32,] 0.3304344 0.3615479 0.3098770 0.3813640 0.3323581 0.3808892 0.3229543
[33,] 0.3633943 0.3933628 0.4126571 0.4078071 0.3401322 0.4569400 0.3942821
[34,] 0.3187196 0.3354348 0.3444837 0.4187835 0.3351518 0.3974189 0.3215929
[35,] 0.3067201 0.3295164 0.3086434 0.4358080 0.2989232 0.3744900 0.3193445
[36,] 0.3709173 0.3094088 0.2352705 0.3380354 0.2257843 0.3056635 0.2402585
[37,] 0.3747756 0.3482068 0.3025652 0.4241205 0.2659261 0.4055969 0.3187351
[38,] 0.3645088 0.3434428 0.3443278 0.3946202 0.2756704 0.4240096 0.3299400
[39,] 0.3080024 0.3640326 0.3355290 0.3848462 0.2924023 0.3766813 0.3508378
[40,] 0.4362794 0.4125146 0.3767652 0.4305746 0.3363838 0.4993309 0.3431162
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 0.2886348 0.3872634 0.3086363 0.3300471 0.3496296 0.2643695 0.3510899
[2,] 0.3157459 0.4011600 0.3301951 0.3715490 0.3733415 0.3083248 0.3496693
[3,] 0.2987742 0.3740848 0.2900993 0.3490256 0.3661626 0.3592159 0.3579273
[4,] 0.3711378 0.4325844 0.3880930 0.3765055 0.5378319 0.3650698 0.4438735
[5,] 0.2682204 0.3109014 0.2750935 0.3468922 0.3513647 0.3075189 0.3136538
[6,] 0.3401411 0.4722626 0.3674942 0.3911776 0.4593275 0.3443388 0.4244626
[7,] 0.2749737 0.3594980 0.3147702 0.3134912 0.3566796 0.3521353 0.3410867
[8,] 0.3827260 0.3907545 0.2988163 0.3308321 0.3751270 0.2927895 0.3424322
[9,] 0.3907545 0.5245163 0.3503800 0.3956145 0.4558087 0.3169918 0.4143504
[10,] 0.2988163 0.3503800 0.3778166 0.3133673 0.3854424 0.2845932 0.3253879
[11,] 0.3308321 0.3956145 0.3133673 0.4192976 0.3827056 0.3105342 0.3439707
[12,] 0.3751270 0.4558087 0.3854424 0.3827056 0.5381580 0.3410197 0.4040592
[13,] 0.2927895 0.3169918 0.2845932 0.3105342 0.3410197 0.4152752 0.3384331
[14,] 0.3424322 0.4143504 0.3253879 0.3439707 0.4040592 0.3384331 0.4227283
[15,] 0.3529021 0.4052576 0.3318198 0.3378625 0.3899277 0.3435705 0.3792346
[16,] 0.3247552 0.4200867 0.3237023 0.3466853 0.3903709 0.3004923 0.3533612
[17,] 0.3023912 0.3428421 0.3114730 0.3110601 0.3819310 0.3000919 0.3629485
[18,] 0.2923464 0.3394161 0.3164056 0.3531750 0.3375635 0.3346717 0.3247464
[19,] 0.2713716 0.3836950 0.2482132 0.3223733 0.3761191 0.2686236 0.3305258
[20,] 0.2857859 0.3744232 0.3174561 0.3395988 0.3984483 0.3315578 0.3550107
[21,] 0.2627253 0.3836853 0.2795323 0.3301624 0.3831656 0.2488448 0.3148869
[22,] 0.3406292 0.4621339 0.3923090 0.3955028 0.4495619 0.3141189 0.4283485
[23,] 0.3157345 0.3856858 0.3494546 0.3538506 0.3917857 0.2697028 0.3082309
[24,] 0.3275562 0.4251279 0.3382506 0.3577489 0.4262159 0.3256938 0.3830319
[25,] 0.3231252 0.3904057 0.3485463 0.3771474 0.3788117 0.3159366 0.3161134
[26,] 0.3373112 0.4363885 0.4001063 0.4178917 0.4635879 0.3431673 0.4212106
[27,] 0.3388433 0.3562988 0.3225072 0.3660954 0.3718644 0.3297816 0.3608778
[28,] 0.2935088 0.3908359 0.3247119 0.3233924 0.4104011 0.3010204 0.3750590
[29,] 0.3255437 0.4056847 0.3709777 0.3798311 0.3901575 0.3028651 0.3793250
[30,] 0.3773517 0.4505271 0.4349212 0.4046103 0.4724630 0.3729573 0.4305713
[31,] 0.3151219 0.4048709 0.3019086 0.2964317 0.3670353 0.2776221 0.3516817
[32,] 0.3101098 0.3605160 0.3237806 0.3617166 0.3625330 0.2743012 0.3224096
[33,] 0.3271257 0.4471554 0.3090527 0.3839613 0.3851377 0.3774804 0.4184469
[34,] 0.3434242 0.3851384 0.2983263 0.3592944 0.3967367 0.3314950 0.3647405
[35,] 0.3355117 0.3918822 0.3211793 0.3102746 0.4325159 0.2788364 0.3399177
[36,] 0.2370985 0.3357440 0.2703925 0.2874996 0.2988993 0.1794404 0.2905320
[37,] 0.3102639 0.4125572 0.3560512 0.3232770 0.4074992 0.2703437 0.3675738
[38,] 0.3191465 0.4189078 0.3061028 0.3536871 0.3836219 0.3285568 0.3830229
[39,] 0.3091127 0.3884269 0.3297345 0.3310730 0.3640645 0.3208774 0.3476835
[40,] 0.3742521 0.5217239 0.3386285 0.4284365 0.4327775 0.2919186 0.4346810
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] 0.3306274 0.3547158 0.3413221 0.3385712 0.3274643 0.3024896 0.3037886
[2,] 0.3505787 0.3407101 0.3310640 0.3715150 0.3157874 0.3319636 0.3305133
[3,] 0.3460770 0.3166309 0.3056185 0.3362378 0.3209078 0.3544274 0.3058440
[4,] 0.4034540 0.3790609 0.4769570 0.3772033 0.3923291 0.3987057 0.4023323
[5,] 0.3067107 0.2549436 0.2984654 0.3236153 0.2950952 0.3375483 0.2933122
[6,] 0.4154028 0.3913973 0.3205515 0.3631170 0.3912085 0.4542176 0.3999505
[7,] 0.3552658 0.3093184 0.3010842 0.3673017 0.3039875 0.3624589 0.3038551
[8,] 0.3529021 0.3247552 0.3023912 0.2923464 0.2713716 0.2857859 0.2627253
[9,] 0.4052576 0.4200867 0.3428421 0.3394161 0.3836950 0.3744232 0.3836853
[10,] 0.3318198 0.3237023 0.3114730 0.3164056 0.2482132 0.3174561 0.2795323
[11,] 0.3378625 0.3466853 0.3110601 0.3531750 0.3223733 0.3395988 0.3301624
[12,] 0.3899277 0.3903709 0.3819310 0.3375635 0.3761191 0.3984483 0.3831656
[13,] 0.3435705 0.3004923 0.3000919 0.3346717 0.2686236 0.3315578 0.2488448
[14,] 0.3792346 0.3533612 0.3629485 0.3247464 0.3305258 0.3550107 0.3148869
[15,] 0.4029821 0.3329319 0.3200534 0.3483374 0.3092085 0.3560920 0.2891205
[16,] 0.3329319 0.4072145 0.3136260 0.3063168 0.3153526 0.3135427 0.3179032
[17,] 0.3200534 0.3136260 0.4178473 0.3216383 0.2921914 0.2881410 0.2887689
[18,] 0.3483374 0.3063168 0.3216383 0.4130369 0.2840406 0.3254596 0.2799843
[19,] 0.3092085 0.3153526 0.2921914 0.2840406 0.3959299 0.3322660 0.3393178
[20,] 0.3560920 0.3135427 0.2881410 0.3254596 0.3322660 0.4044005 0.3257537
[21,] 0.2891205 0.3179032 0.2887689 0.2799843 0.3393178 0.3257537 0.4047794
[22,] 0.4091661 0.3738722 0.3595939 0.3721547 0.3633162 0.4213864 0.3880263
[23,] 0.3054019 0.3466469 0.3022233 0.3044542 0.2604175 0.2995181 0.3488779
[24,] 0.3812947 0.3548821 0.3396105 0.3582028 0.3601843 0.3795341 0.3540605
[25,] 0.3478419 0.3491553 0.2969050 0.3747642 0.2905926 0.3273992 0.3005663
[26,] 0.3904225 0.3992663 0.4359671 0.4253176 0.3837960 0.3945064 0.3896817
[27,] 0.3585833 0.3014212 0.3401771 0.3509750 0.2805411 0.3273872 0.2782515
[28,] 0.3428719 0.3314276 0.3467582 0.3160977 0.3306523 0.3545179 0.3646012
[29,] 0.3394214 0.4170320 0.3993973 0.3675633 0.3038418 0.3064804 0.3261697
[30,] 0.4099238 0.4139901 0.4213449 0.3990849 0.3207753 0.3835597 0.3628194
[31,] 0.3567115 0.3172887 0.3049912 0.3059889 0.3020181 0.3197710 0.3215577
[32,] 0.3483399 0.2912989 0.3062592 0.3634603 0.2945235 0.3313570 0.2940884
[33,] 0.3816499 0.3762535 0.3381053 0.3651828 0.3660556 0.3826280 0.3621237
[34,] 0.3678903 0.3200297 0.3223298 0.3341906 0.3314275 0.3497056 0.2945803
[35,] 0.3583229 0.3023115 0.3210037 0.3041208 0.3219870 0.3326105 0.3030481
[36,] 0.2544942 0.3001889 0.3109006 0.2631206 0.2796083 0.2363907 0.2927193
[37,] 0.3423002 0.3730642 0.3483797 0.3060860 0.3134191 0.3240729 0.3292184
[38,] 0.3477659 0.3961409 0.3230067 0.3213942 0.3424865 0.3367813 0.3246403
[39,] 0.3423561 0.3343108 0.3173556 0.3336023 0.2765495 0.3222561 0.3275091
[40,] 0.3966873 0.4149241 0.3552640 0.3474666 0.4134704 0.3821023 0.3961611
[,22] [,23] [,24] [,25] [,26] [,27] [,28]
[1,] 0.4046782 0.2921442 0.3670381 0.3293533 0.4514919 0.3072870 0.3351851
[2,] 0.4063992 0.3529623 0.3722187 0.3842095 0.4317108 0.3522197 0.3453512
[3,] 0.3687206 0.2947198 0.3556825 0.3365811 0.3776675 0.3407901 0.3313021
[4,] 0.4748479 0.3825395 0.4504677 0.3505797 0.5362912 0.4046125 0.4664949
[5,] 0.3561605 0.2826722 0.3279440 0.3083936 0.3806510 0.3505049 0.3088645
[6,] 0.5333569 0.3598854 0.4540149 0.3654656 0.4668916 0.3589900 0.4280774
[7,] 0.3857608 0.2964741 0.3728457 0.3492568 0.3974679 0.3183809 0.3465275
[8,] 0.3406292 0.3157345 0.3275562 0.3231252 0.3373112 0.3388433 0.2935088
[9,] 0.4621339 0.3856858 0.4251279 0.3904057 0.4363885 0.3562988 0.3908359
[10,] 0.3923090 0.3494546 0.3382506 0.3485463 0.4001063 0.3225072 0.3247119
[11,] 0.3955028 0.3538506 0.3577489 0.3771474 0.4178917 0.3660954 0.3233924
[12,] 0.4495619 0.3917857 0.4262159 0.3788117 0.4635879 0.3718644 0.4104011
[13,] 0.3141189 0.2697028 0.3256938 0.3159366 0.3431673 0.3297816 0.3010204
[14,] 0.4283485 0.3082309 0.3830319 0.3161134 0.4212106 0.3608778 0.3750590
[15,] 0.4091661 0.3054019 0.3812947 0.3478419 0.3904225 0.3585833 0.3428719
[16,] 0.3738722 0.3466469 0.3548821 0.3491553 0.3992663 0.3014212 0.3314276
[17,] 0.3595939 0.3022233 0.3396105 0.2969050 0.4359671 0.3401771 0.3467582
[18,] 0.3721547 0.3044542 0.3582028 0.3747642 0.4253176 0.3509750 0.3160977
[19,] 0.3633162 0.2604175 0.3601843 0.2905926 0.3837960 0.2805411 0.3306523
[20,] 0.4213864 0.2995181 0.3795341 0.3273992 0.3945064 0.3273872 0.3545179
[21,] 0.3880263 0.3488779 0.3540605 0.3005663 0.3896817 0.2782515 0.3646012
[22,] 0.5630475 0.3753065 0.4449969 0.3733252 0.5032117 0.3874373 0.4322735
[23,] 0.3753065 0.4471016 0.3311823 0.3651890 0.3853135 0.3199225 0.3380487
[24,] 0.4449969 0.3311823 0.4151443 0.3564034 0.4466317 0.3462490 0.3836512
[25,] 0.3733252 0.3651890 0.3564034 0.4298102 0.4069444 0.3413267 0.3100067
[26,] 0.5032117 0.3853135 0.4466317 0.4069444 0.5818781 0.4032938 0.4299712
[27,] 0.3874373 0.3199225 0.3462490 0.3413267 0.4032938 0.3995114 0.3234780
[28,] 0.4322735 0.3380487 0.3836512 0.3100067 0.4299712 0.3234780 0.4043163
[29,] 0.4160148 0.3796291 0.3704163 0.3798308 0.5021081 0.3503216 0.3583275
[30,] 0.4824457 0.4395676 0.4209159 0.4195498 0.5101399 0.4143516 0.4201927
[31,] 0.3968881 0.3119012 0.3654678 0.3098126 0.3696027 0.3010211 0.3550438
[32,] 0.4147757 0.3143400 0.3609095 0.3576175 0.4206899 0.3590449 0.3179471
[33,] 0.4358190 0.3327537 0.4046812 0.3552375 0.4318491 0.3590810 0.3871017
[34,] 0.3851446 0.2917805 0.3678323 0.3276997 0.3922857 0.3619419 0.3271772
[35,] 0.3826448 0.3047897 0.3715418 0.3309257 0.3824156 0.3248125 0.3375729
[36,] 0.3627135 0.2818273 0.3044356 0.2729893 0.4067361 0.2578816 0.2968476
[37,] 0.4269345 0.3464054 0.3738811 0.3354771 0.4384104 0.3105591 0.3670421
[38,] 0.3923521 0.3149255 0.3712310 0.3314089 0.4082615 0.3153282 0.3489113
[39,] 0.3864728 0.3746239 0.3510428 0.3499524 0.3858336 0.3302087 0.3543242
[40,] 0.5149685 0.3622524 0.4349479 0.3730711 0.4803461 0.3754398 0.3987842
[,29] [,30] [,31] [,32] [,33] [,34] [,35]
[1,] 0.4277095 0.3918940 0.3209992 0.3304344 0.3633943 0.3187196 0.3067201
[2,] 0.3869842 0.4233512 0.3356011 0.3615479 0.3933628 0.3354348 0.3295164
[3,] 0.3158980 0.3803228 0.3067535 0.3098770 0.4126571 0.3444837 0.3086434
[4,] 0.4345843 0.5079662 0.3937972 0.3813640 0.4078071 0.4187835 0.4358080
[5,] 0.2831570 0.3517907 0.2606738 0.3323581 0.3401322 0.3351518 0.2989232
[6,] 0.3771593 0.4494309 0.3971114 0.3808892 0.4569400 0.3974189 0.3744900
[7,] 0.3238047 0.3917467 0.3341408 0.3229543 0.3942821 0.3215929 0.3193445
[8,] 0.3255437 0.3773517 0.3151219 0.3101098 0.3271257 0.3434242 0.3355117
[9,] 0.4056847 0.4505271 0.4048709 0.3605160 0.4471554 0.3851384 0.3918822
[10,] 0.3709777 0.4349212 0.3019086 0.3237806 0.3090527 0.2983263 0.3211793
[11,] 0.3798311 0.4046103 0.2964317 0.3617166 0.3839613 0.3592944 0.3102746
[12,] 0.3901575 0.4724630 0.3670353 0.3625330 0.3851377 0.3967367 0.4325159
[13,] 0.3028651 0.3729573 0.2776221 0.2743012 0.3774804 0.3314950 0.2788364
[14,] 0.3793250 0.4305713 0.3516817 0.3224096 0.4184469 0.3647405 0.3399177
[15,] 0.3394214 0.4099238 0.3567115 0.3483399 0.3816499 0.3678903 0.3583229
[16,] 0.4170320 0.4139901 0.3172887 0.2912989 0.3762535 0.3200297 0.3023115
[17,] 0.3993973 0.4213449 0.3049912 0.3062592 0.3381053 0.3223298 0.3210037
[18,] 0.3675633 0.3990849 0.3059889 0.3634603 0.3651828 0.3341906 0.3041208
[19,] 0.3038418 0.3207753 0.3020181 0.2945235 0.3660556 0.3314275 0.3219870
[20,] 0.3064804 0.3835597 0.3197710 0.3313570 0.3826280 0.3497056 0.3326105
[21,] 0.3261697 0.3628194 0.3215577 0.2940884 0.3621237 0.2945803 0.3030481
[22,] 0.4160148 0.4824457 0.3968881 0.4147757 0.4358190 0.3851446 0.3826448
[23,] 0.3796291 0.4395676 0.3119012 0.3143400 0.3327537 0.2917805 0.3047897
[24,] 0.3704163 0.4209159 0.3654678 0.3609095 0.4046812 0.3678323 0.3715418
[25,] 0.3798308 0.4195498 0.3098126 0.3576175 0.3552375 0.3276997 0.3309257
[26,] 0.5021081 0.5101399 0.3696027 0.4206899 0.4318491 0.3922857 0.3824156
[27,] 0.3503216 0.4143516 0.3010211 0.3590449 0.3590810 0.3619419 0.3248125
[28,] 0.3583275 0.4201927 0.3550438 0.3179471 0.3871017 0.3271772 0.3375729
[29,] 0.5562114 0.4896433 0.3201169 0.3412564 0.3858215 0.3268655 0.2998863
[30,] 0.4896433 0.5604001 0.3818702 0.3887106 0.4317382 0.3790934 0.3781906
[31,] 0.3201169 0.3818702 0.3813176 0.3107423 0.3666519 0.3145105 0.3413322
[32,] 0.3412564 0.3887106 0.3107423 0.4028639 0.3279293 0.3447714 0.3375131
[33,] 0.3858215 0.4317382 0.3666519 0.3279293 0.5115058 0.3683147 0.3183061
[34,] 0.3268655 0.3790934 0.3145105 0.3447714 0.3683147 0.3886675 0.3474166
[35,] 0.2998863 0.3781906 0.3413322 0.3375131 0.3183061 0.3474166 0.4157505
[36,] 0.3992034 0.3401047 0.2711894 0.2902113 0.2902643 0.2515062 0.2591211
[37,] 0.4197093 0.4377885 0.3444593 0.3202151 0.3562870 0.3131996 0.3377408
[38,] 0.4048258 0.4059210 0.3258159 0.2927319 0.4211501 0.3429983 0.2983918
[39,] 0.3608347 0.4316374 0.3462349 0.3132448 0.3930252 0.3068435 0.3068626
[40,] 0.4386672 0.4426953 0.3904327 0.3934714 0.4677351 0.4012890 0.3686771
[,36] [,37] [,38] [,39] [,40]
[1,] 0.3709173 0.3747756 0.3645088 0.3080024 0.4362794
[2,] 0.3094088 0.3482068 0.3434428 0.3640326 0.4125146
[3,] 0.2352705 0.3025652 0.3443278 0.3355290 0.3767652
[4,] 0.3380354 0.4241205 0.3946202 0.3848462 0.4305746
[5,] 0.2257843 0.2659261 0.2756704 0.2924023 0.3363838
[6,] 0.3056635 0.4055969 0.4240096 0.3766813 0.4993309
[7,] 0.2402585 0.3187351 0.3299400 0.3508378 0.3431162
[8,] 0.2370985 0.3102639 0.3191465 0.3091127 0.3742521
[9,] 0.3357440 0.4125572 0.4189078 0.3884269 0.5217239
[10,] 0.2703925 0.3560512 0.3061028 0.3297345 0.3386285
[11,] 0.2874996 0.3232770 0.3536871 0.3310730 0.4284365
[12,] 0.2988993 0.4074992 0.3836219 0.3640645 0.4327775
[13,] 0.1794404 0.2703437 0.3285568 0.3208774 0.2919186
[14,] 0.2905320 0.3675738 0.3830229 0.3476835 0.4346810
[15,] 0.2544942 0.3423002 0.3477659 0.3423561 0.3966873
[16,] 0.3001889 0.3730642 0.3961409 0.3343108 0.4149241
[17,] 0.3109006 0.3483797 0.3230067 0.3173556 0.3552640
[18,] 0.2631206 0.3060860 0.3213942 0.3336023 0.3474666
[19,] 0.2796083 0.3134191 0.3424865 0.2765495 0.4134704
[20,] 0.2363907 0.3240729 0.3367813 0.3222561 0.3821023
[21,] 0.2927193 0.3292184 0.3246403 0.3275091 0.3961611
[22,] 0.3627135 0.4269345 0.3923521 0.3864728 0.5149685
[23,] 0.2818273 0.3464054 0.3149255 0.3746239 0.3622524
[24,] 0.3044356 0.3738811 0.3712310 0.3510428 0.4349479
[25,] 0.2729893 0.3354771 0.3314089 0.3499524 0.3730711
[26,] 0.4067361 0.4384104 0.4082615 0.3858336 0.4803461
[27,] 0.2578816 0.3105591 0.3153282 0.3302087 0.3754398
[28,] 0.2968476 0.3670421 0.3489113 0.3543242 0.3987842
[29,] 0.3992034 0.4197093 0.4048258 0.3608347 0.4386672
[30,] 0.3401047 0.4377885 0.4059210 0.4316374 0.4426953
[31,] 0.2711894 0.3444593 0.3258159 0.3462349 0.3904327
[32,] 0.2902113 0.3202151 0.2927319 0.3132448 0.3934714
[33,] 0.2902643 0.3562870 0.4211501 0.3930252 0.4677351
[34,] 0.2515062 0.3131996 0.3429983 0.3068435 0.4012890
[35,] 0.2591211 0.3377408 0.2983918 0.3068626 0.3686771
[36,] 0.4220373 0.3496548 0.2905701 0.2660560 0.4066057
[37,] 0.3496548 0.4139086 0.3615792 0.3398438 0.4231177
[38,] 0.2905701 0.3615792 0.4243788 0.3349206 0.4348109
[39,] 0.2660560 0.3398438 0.3349206 0.3967751 0.3664759
[40,] 0.4066057 0.4231177 0.4348109 0.3664759 0.6118455
mi_df() examples work
Code
mi_df(bag_label = factor(c(1, 1, 0)), bag_name = c(rep("bag_1", 2), "bag_2"),
X1 = c(-0.4, 0.5, 2), instance_label = c(0, 1, 0))
Output
# An MI data frame: 3 x 3 with 2 bags
# and instance labels: 0, 1, 0
bag_label bag_name X1
<fct> <chr> <dbl>
1 1 bag_1 -0.4
2 1 bag_1 0.5
3 0 bag_2 2
mi() examples work
Code
mil_data <- generate_mild_df(positive_degree = 3, nbag = 10)
with(mil_data, head(mi(bag_label, bag_name)))
Output
bag_label bag_name
[1,] "1" "bag1"
[2,] "1" "bag1"
[3,] "1" "bag1"
[4,] "1" "bag1"
[5,] "1" "bag1"
[6,] "1" "bag1"
Code
df <- get_all_vars(mi(bag_label, bag_name) ~ X1 + X2, data = mil_data)
head(df)
Output
bag_label bag_name X1 X2
1 1 bag1 -0.4464099 1.1019817
2 1 bag1 1.2311306 -0.8395224
3 1 bag1 0.9309844 2.2884239
4 1 bag1 2.0311929 2.1704369
5 1 bag1 0.5209816 -1.1255467
6 1 bag1 -0.4920105 2.4251912
mild_df() examples work
Code
mild_df(bag_label = factor(c(1, 1, 0)), bag_name = c(rep("bag_1", 2), "bag_2"),
instance_name = c("bag_1_inst_1", "bag_1_inst_2", "bag_2_inst_1"), X1 = c(-0.4,
0.5, 2), instance_label = c(0, 1, 0))
Output
# An MILD data frame: 3 x 4 with 2 bags, 3 instances
# and instance labels: 0, 1, 0
bag_label bag_name instance_name X1
<fct> <chr> <chr> <dbl>
1 1 bag_1 bag_1_inst_1 -0.4
2 1 bag_1 bag_1_inst_2 0.5
3 0 bag_2 bag_2_inst_1 2
mild() examples work
Code
mil_data <- generate_mild_df(positive_degree = 3, nbag = 10)
with(mil_data, head(mild(bag_label, bag_name, instance_name)))
Output
bag_label bag_name instance_name
[1,] "1" "bag1" "bag1inst1"
[2,] "1" "bag1" "bag1inst1"
[3,] "1" "bag1" "bag1inst1"
[4,] "1" "bag1" "bag1inst1"
[5,] "1" "bag1" "bag1inst1"
[6,] "1" "bag1" "bag1inst1"
Code
df <- get_all_vars(mild(bag_label, bag_name) ~ X1 + X2, data = mil_data)
head(df)
Output
bag_label bag_name X1 X2
1 1 bag1 0.36529504 -0.3713889
2 1 bag1 0.07733708 -0.5885037
3 1 bag1 -1.59611098 0.3456669
4 1 bag1 -0.16911543 1.4814259
5 1 bag1 -0.85251086 -2.6362243
6 1 bag1 1.40033082 -1.1679564
mior() examples work
Code
if (require(gurobi)) {
set.seed(8)
n <- 15
X <- rbind(mvtnorm::rmvnorm(n / 3, mean = c(4, -2, 0)), mvtnorm::rmvnorm(n /
3, mean = c(0, 0, 0)), mvtnorm::rmvnorm(n / 3, mean = c(-2, 1, 0)))
score <- X %*% c(2, -1, 0)
y <- as.numeric(cut(score, c(-Inf, quantile(score, probs = 1:2 / 3), Inf)))
bags <- seq_along(y)
X <- rbind(X, mvtnorm::rmvnorm(n, mean = c(6, -3, 0)), mvtnorm::rmvnorm(n,
mean = c(-6, 3, 0)))
y <- c(y, rep(-1, 2 * n))
bags <- rep(bags, 3)
repr <- c(rep(1, n), rep(0, 2 * n))
y_bag <- classify_bags(y, bags, condense = FALSE)
mdl1 <- mior(X, y_bag, bags)
predict(mdl1, X, new_bags = bags)
df1 <- dplyr::bind_cols(y = y_bag, bags = bags, as.data.frame(X))
df1 %>% dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "class")) %>%
dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "raw")) %>%
dplyr::distinct(y, bags, .pred_class, .pred)
}
Message <message>
[Step 1] The optimization solution suggests that two intercepts are equal: b[1] == b[2].
[Step 1] The optimization solution suggests that two intercepts are equal: b[2] == b[3].
Warning <warning>
[Step 1] There were NA values in `b`. Replacing with 0.
Message <message>
[Step 2] The optimization solution suggests that two intercepts are equal: b[0] == b[1].
[Step 2] The optimization solution suggests that two intercepts are equal: b[1] == b[2].
[Step 2] The optimization solution suggests that two intercepts are equal: b[2] == b[3].
[Step 2] The optimization solution suggests that endpoints are equal: b[0] == b[K].
[Step 3] The optimization solution suggests that two intercepts are equal: b[1] == b[2].
[Step 3] The optimization solution suggests that two intercepts are equal: b[2] == b[3].
Warning <warning>
[Step 3] There were NA values in `b`. Replacing with 0.
Output
y bags .pred_class .pred
1 3 1 2 -1.27106961
2 3 2 2 -1.46009539
3 3 3 1 0.55859958
4 3 4 2 -2.14449120
5 3 5 2 -2.39500843
6 1 6 2 -1.35396484
7 2 7 2 -1.52776943
8 2 8 1 -0.27197967
9 2 9 1 -0.03922016
10 2 10 1 1.13574924
11 1 11 1 1.81787187
12 1 12 1 0.86516230
13 2 13 1 0.33191616
14 1 14 2 -1.86835119
15 1 15 1 0.03157952
mismm() example works
Code
set.seed(8)
mil_data <- generate_mild_df(nbag = 15, nsample = 20, positive_prob = 0.15,
sd_of_mean = rep(0.1, 3))
mdl1 <- mismm(mil_data)
mdl2 <- mismm(mild(bag_label, bag_name, instance_name) ~ X1 + X2 + X3, data = mil_data)
if (require(gurobi)) {
mdl3 <- mismm(mil_data, method = "mip", control = list(nystrom_args = list(m = 10,
r = 10)))
predict(mdl3, mil_data)
}
Output
# A tibble: 1,200 x 1
.pred_class
<fct>
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
# ... with 1,190 more rows
Code
predict(mdl1, new_data = mil_data, type = "raw", layer = "bag")
Output
# A tibble: 1,200 x 1
.pred
<dbl>
1 -0.289
2 -0.289
3 -0.289
4 -0.289
5 -0.289
6 -0.289
7 -0.289
8 -0.289
9 -0.289
10 -0.289
# ... with 1,190 more rows
Code
mil_data %>% dplyr::bind_cols(predict(mdl2, mil_data, type = "class")) %>%
dplyr::bind_cols(predict(mdl2, mil_data, type = "raw")) %>% dplyr::distinct(
bag_name, bag_label, .pred_class, .pred)
Output
# A tibble: 15 x 4
bag_label bag_name .pred_class .pred
<dbl> <chr> <fct> <dbl>
1 0 bag1 0 -0.120
2 1 bag2 1 0.211
3 0 bag3 0 -0.0939
4 1 bag4 1 0.0945
5 0 bag5 0 -0.0922
6 0 bag6 0 -0.134
7 1 bag7 1 0.155
8 0 bag8 0 -0.0408
9 1 bag9 1 0.169
10 1 bag10 1 0.250
11 1 bag11 1 0.137
12 0 bag12 0 -0.0584
13 1 bag13 1 0.173
14 1 bag14 1 0.143
15 1 bag15 1 0.329
predict.mismm() examples work
Code
mil_data <- generate_mild_df(nbag = 15, nsample = 20, positive_prob = 0.15,
sd_of_mean = rep(0.1, 3))
mdl1 <- mismm(mil_data, control = list(sigma = 1 / 5))
mil_data %>% dplyr::bind_cols(predict(mdl1, mil_data, type = "class")) %>%
dplyr::bind_cols(predict(mdl1, mil_data, type = "raw")) %>% dplyr::distinct(
bag_name, bag_label, .pred_class, .pred)
Output
# A tibble: 15 x 4
bag_label bag_name .pred_class .pred
<dbl> <chr> <fct> <dbl>
1 0 bag1 0 -0.377
2 1 bag2 1 0.283
3 0 bag3 0 -0.332
4 1 bag4 1 0.132
5 0 bag5 0 -0.335
6 0 bag6 0 -0.248
7 1 bag7 1 0.261
8 0 bag8 0 -0.0604
9 1 bag9 1 0.379
10 1 bag10 1 0.392
11 1 bag11 1 0.301
12 0 bag12 0 -0.282
13 1 bag13 1 0.326
14 1 bag14 1 0.223
15 1 bag15 1 0.459
Code
mil_data %>% dplyr::bind_cols(predict(mdl1, mil_data, type = "class", layer = "instance")) %>%
dplyr::bind_cols(predict(mdl1, mil_data, type = "raw", layer = "instance")) %>%
dplyr::distinct(bag_name, instance_name, bag_label, .pred_class, .pred)
Output
# An MILD data frame: 60 x 5 with 15 bags, 60 instances
# and instance labels: 0, 0, 0, 0, 0, ...
bag_label bag_name instance_name .pred_class .pred
<dbl> <chr> <chr> <fct> <dbl>
1 0 bag1 bag1inst1 0 -0.380
2 0 bag1 bag1inst2 0 -0.421
3 0 bag1 bag1inst3 0 -0.377
4 0 bag1 bag1inst4 0 -0.428
5 1 bag2 bag2inst1 0 -0.321
6 1 bag2 bag2inst2 0 -0.363
7 1 bag2 bag2inst3 0 -0.251
8 1 bag2 bag2inst4 1 0.283
9 0 bag3 bag3inst1 0 -0.332
10 0 bag3 bag3inst2 0 -0.395
# ... with 50 more rows
misvm_orova() examples work
Code
data("ordmvnorm")
x <- ordmvnorm[, 3:7]
Warning <warning>
Dropping 'mi_df' class as required column was removed.
Code
y <- ordmvnorm$bag_label
bags <- ordmvnorm$bag_name
mdl1 <- misvm_orova(x, y, bags)
predict(mdl1, x, new_bags = bags)
Output
# A tibble: 1,000 x 1
.pred_class
<fct>
1 2
2 2
3 2
4 2
5 2
6 3
7 3
8 3
9 3
10 3
# ... with 990 more rows
Code
df1 <- dplyr::bind_cols(y = y, bags = bags, as.data.frame(x))
df1 %>% dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "class")) %>%
dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "raw")) %>%
dplyr::select(-starts_with("V")) %>% dplyr::distinct()
Output
y bags .pred_class .pred_1 .pred_2 .pred_3 .pred_4
1 2 1 2 0.072054072 1.24157688 -0.005075918 -0.783087461
2 4 2 3 -1.251419361 -0.44724656 2.357316056 1.435526426
3 1 3 1 2.266372449 2.26051385 -1.635840749 -2.449979864
4 3 4 2 2.036194818 2.48865381 1.218059767 -0.176115919
5 1 5 1 0.999497111 0.43651819 -0.486774613 -2.238297731
6 5 6 3 1.920010812 1.38391110 4.473871500 3.981368930
7 4 7 3 0.730430029 1.07444170 1.818187547 1.000264053
8 4 8 3 0.036505464 1.31947988 3.995412773 3.083747508
9 3 9 3 0.209234901 -1.10561202 1.961314768 0.552380675
10 4 10 3 -0.504233266 0.76282372 3.153695469 2.711460653
11 5 11 3 0.054740339 -0.68454697 3.078920102 2.023132149
12 3 12 3 -0.164563516 -1.97443357 0.926269029 0.107442544
13 5 13 3 -0.569257195 -1.50596301 4.681906898 4.271581859
14 3 14 1 1.797814850 0.68511482 1.334480188 -0.018156454
15 2 15 2 0.354159148 1.30396993 0.299802741 -0.792499384
16 3 16 2 0.900791960 1.64894819 0.733013977 -0.243052192
17 2 17 1 2.751060969 2.09543404 0.767178044 -0.206214135
18 2 18 2 0.012863158 2.62296119 -0.093230935 -1.409281102
19 2 19 1 1.205695843 1.01526852 -1.054750654 -2.326673454
20 1 20 2 0.001415356 1.07104086 -0.896129480 -2.233466605
21 3 21 3 -0.219947873 0.79395459 2.593885919 1.339418082
22 2 22 1 0.954808999 -0.13140503 0.154058385 -1.048479675
23 3 23 2 -0.951905691 1.20592625 0.833612057 -0.742110448
24 4 24 3 -0.273808301 0.37504428 2.761489395 1.500488098
25 2 25 2 -0.151453073 1.78821302 0.097761509 -0.625524717
26 4 26 3 0.548553011 0.91816008 2.692972337 1.529978372
27 4 27 3 -0.838129504 0.87575492 2.942167421 2.106161023
28 2 28 2 -0.459396273 2.71463070 0.030473723 -0.949329104
29 5 29 3 0.168078546 -0.42048836 3.765049712 3.077313282
30 3 30 3 0.204011620 0.89524867 1.470316006 1.152517057
31 2 31 3 -0.353540848 0.06905779 0.518345886 -1.117263057
32 5 32 3 0.347529726 1.96050469 4.092332114 2.777554297
33 2 33 1 1.551470304 -0.17652552 0.458605980 -0.467230099
34 5 34 3 0.812941100 1.27701647 3.693666669 3.265801954
35 4 35 1 2.870066694 1.07442101 1.734199670 0.962302122
36 3 36 3 -0.551027795 0.06258126 1.308833458 0.575725816
37 4 37 3 0.989409528 -0.63987667 3.171262186 2.424230459
38 2 38 1 2.284649128 0.31490852 -0.048948873 -1.266380708
39 4 39 3 1.067812621 -0.09306674 2.750077773 1.984667458
40 2 40 3 0.520817262 -1.62116210 0.957123790 -0.829010258
41 1 41 1 1.274290440 0.83835316 -1.056238423 -2.538301933
42 1 42 2 1.984378137 2.66491196 -0.485998231 -2.147887984
43 2 43 2 -0.145363418 0.80692325 -0.709848943 -1.783606824
44 2 44 2 0.401963950 1.08925411 0.430524106 -1.210678768
45 2 45 1 2.090161856 1.42710530 0.305093008 -0.860630133
46 3 46 3 -0.179769147 0.67295977 0.923519375 0.166548835
47 3 47 1 3.023586198 -0.04441672 0.870074332 0.082737064
48 4 48 3 0.594910297 -0.40790117 3.108895289 2.441552201
49 2 49 2 0.517934018 0.99982514 0.971428071 -0.477676824
50 4 50 3 0.841524707 0.12691384 2.288197352 1.508173550
51 3 51 1 2.179260305 -0.85912454 0.816258705 -0.086590741
52 5 52 3 1.123590058 1.32385089 4.606127446 3.815318462
53 5 53 3 1.523522406 -0.05627962 2.949281404 2.487092570
54 2 54 2 0.256062043 0.41876378 0.199033146 -0.590743872
55 4 55 2 -0.307830686 1.91476237 1.854087045 1.132002454
56 3 56 3 -1.072896286 -0.08795983 1.536126899 0.926894203
57 5 57 3 2.095149008 -0.07867520 4.525285138 3.668547097
58 1 58 1 1.509681540 0.36224184 -1.179052225 -2.919152012
59 2 59 1 1.500651712 1.23943073 -0.487119290 -1.529960657
60 4 60 3 0.153476330 1.12512087 2.965594188 1.719696833
61 2 61 2 0.107433901 0.99998094 0.251861562 -0.668879441
62 2 62 2 1.021472500 1.10098424 -0.483214988 -1.764205357
63 2 63 2 -0.577488392 0.96101424 -0.147362894 -1.373661079
64 5 64 3 -1.470440642 0.34004044 3.749092557 3.320600526
65 2 65 2 -0.115208922 1.35927809 -0.037468827 -2.140192951
66 4 66 3 0.492635619 1.70587041 3.828368807 3.067970029
67 2 67 2 1.314915397 1.52793709 -0.531016585 -2.183768654
68 4 68 3 0.997063986 1.44167808 2.692431224 1.871823863
69 1 69 2 0.179755225 1.01227117 -1.192076661 -1.692757366
70 2 70 2 0.950844268 1.21782268 0.712069112 -0.805553592
71 4 71 3 0.570080774 0.96193964 2.624338049 2.296915214
72 4 72 3 -1.453889746 0.58589034 2.658095509 1.339789694
73 1 73 2 1.445004530 1.45944954 -1.468585910 -2.382518721
74 5 74 3 0.331242540 1.17657845 5.302493853 4.687040860
75 5 75 3 2.064429323 2.96099424 4.444482071 3.548902076
76 4 76 3 -0.028601890 0.19153408 2.991329788 2.446507117
77 1 77 2 -0.189502244 0.29768322 -1.143863726 -2.113323367
78 3 78 3 0.813181217 -1.00040792 2.280448356 2.116439098
79 2 79 2 -0.712710553 0.55617328 0.517086303 -0.093885228
80 1 80 1 1.630919431 0.73688837 -0.341944665 -1.446730604
81 3 81 3 0.902920480 -0.86940201 2.091036922 0.798079169
82 3 82 1 1.636798724 0.36720409 0.526272127 -0.661387375
83 2 83 1 1.531500293 1.51018878 -0.503977890 -1.535820327
84 5 84 3 0.464437512 1.83063333 4.740010492 3.734087786
85 2 85 1 2.447572647 1.09642106 0.481866056 -0.500450818
86 2 86 1 2.693553783 0.99995510 -0.292739964 -1.303295992
87 3 87 3 0.882733260 0.02994256 2.026985038 0.973188925
88 3 88 3 0.769492513 0.60412049 1.149114506 0.006639411
89 3 89 3 1.163435069 1.04646194 1.424447656 0.273940686
90 2 90 2 0.594042888 1.61961486 -0.251107974 -1.180828674
91 3 91 3 -0.383868294 0.22873649 1.000184845 -0.190455535
92 4 92 3 1.445654335 0.92329461 2.390422626 1.353768980
93 3 93 3 0.895493731 1.56358634 2.242955217 1.189125683
94 2 94 3 0.907150996 0.92386627 1.188409683 -0.471069578
95 3 95 2 0.762152263 1.03743805 0.791851575 -0.165396439
96 3 96 1 0.715915316 -0.39463666 0.255683255 -0.150974367
97 2 97 3 -0.155145008 -0.57753861 0.473907217 -0.555776688
98 2 98 2 0.135714595 0.41352574 -0.208214572 -0.552287748
99 2 99 2 -0.141625157 2.23280495 -0.074145655 -0.817376099
100 2 100 2 -0.019076930 1.04704126 0.434395332 -0.999866642
101 2 101 1 1.277564724 0.63581165 0.362761950 -1.742433608
102 2 102 3 -0.006713045 -0.03519697 0.371099531 -1.000979568
103 2 103 1 1.150995278 0.93893790 -0.453058573 -1.565305108
104 5 104 3 -0.084590673 0.32435353 4.722037336 4.096075587
105 1 105 1 1.049951247 0.51900629 -0.407220119 -1.882503879
106 3 106 3 -0.380301725 0.22443279 1.623335874 0.343203060
107 3 107 2 0.375821293 2.64082208 0.999198140 -0.301840905
108 1 108 1 1.111465715 0.66199956 -1.247840729 -2.510232570
109 3 109 3 0.666430137 0.99386114 2.098551048 1.569297967
110 5 110 3 0.908593319 1.15870068 3.672511343 3.623525435
111 1 111 2 0.200115809 0.27917410 -0.454184020 -2.031033198
112 3 112 1 0.917679336 0.28386471 0.656506910 -0.746684752
113 2 113 1 1.798310728 1.31431887 -0.836225608 -2.183067661
114 1 114 2 0.999975552 1.06284430 -1.139154293 -2.323531747
115 5 115 3 -1.292889704 2.43593054 3.738333559 2.593191003
116 2 116 2 0.195980600 1.12561416 -0.147867431 -0.403210177
117 4 117 3 0.653941590 1.06963348 1.839933954 1.000262988
118 2 118 2 0.573141965 0.73200876 0.536599552 -0.584070336
119 1 119 1 1.209429578 0.35359752 -0.202944858 -1.454486222
120 5 120 3 1.272566079 1.55025619 4.051944143 3.215528155
121 1 121 1 0.308112225 0.18483777 -0.868885078 -1.793322748
122 2 122 2 -1.538849904 0.84322486 -0.120521607 -1.199301261
123 3 123 2 -0.448733944 2.76807920 1.993302722 0.786583597
124 3 124 3 1.581181008 1.76559761 1.819032454 0.840982124
125 2 125 1 0.119633865 0.10412126 -0.099776725 -1.407163380
126 2 126 2 0.393379327 0.86242251 0.069857642 -1.779543657
127 5 127 3 0.590272911 1.32850469 4.183207523 3.832928424
128 3 128 3 0.472820279 0.82437661 1.717874666 1.333198988
129 1 129 2 0.943847045 2.73668518 -1.419371165 -3.087451597
130 3 130 2 0.810135639 1.85824791 0.961275675 -0.917301006
131 4 131 3 0.358246998 -0.44392872 2.906291423 2.561670550
132 2 132 2 0.774586840 1.66293656 0.855561712 -0.917567867
133 3 133 3 -0.550711238 -0.56926697 0.694228446 -0.678812703
134 5 134 3 -0.012112268 1.22860183 4.465948576 3.602452262
135 4 135 4 -0.291949799 1.11774318 2.222342191 2.343471199
136 5 136 3 0.942324879 2.65928102 3.974654849 2.632401842
137 2 137 1 1.778003397 1.11342719 0.160190292 -1.000126956
138 5 138 3 0.855095736 0.55893253 4.161465376 3.197801129
139 5 139 3 0.636543509 1.53739284 3.875415445 3.548614790
140 3 140 3 0.100977767 1.02563567 1.517060405 0.519591116
141 2 141 1 1.363349313 1.09390713 -0.265016510 -1.889752422
142 3 142 3 0.517022989 0.47420072 1.822826898 0.149811006
143 3 143 3 0.420427055 0.62233735 0.844955376 -0.436951189
144 5 144 3 -1.144003804 0.96698257 3.728894705 3.555935707
145 4 145 3 1.202471103 0.65641832 2.188028475 1.084486715
146 1 146 2 1.396454476 1.48357989 -1.094755032 -2.171843353
147 5 147 3 0.777818253 1.31950973 3.630842333 3.439383932
148 3 148 3 -2.086082825 -0.18524611 1.090262754 -0.149644034
149 3 149 3 1.012761279 1.79634256 2.103497227 0.745128077
150 1 150 2 0.885941333 1.48454696 -0.061515541 -0.783359064
151 4 151 3 0.013111638 -0.22119890 1.932968374 1.012871724
152 2 152 2 -0.530687880 -0.06287797 -0.651997451 -1.669859980
153 4 153 3 0.869793218 0.77671031 1.728178617 0.914770455
154 2 154 2 0.190652685 1.71395014 -0.229030835 -1.543881873
155 1 155 2 -0.178580776 0.92305141 -1.036292130 -2.165478548
156 1 156 2 1.000201483 2.92361363 -1.325700627 -2.729749197
157 2 157 2 -0.015094567 1.91893656 0.031534694 -0.946125280
158 4 158 3 -0.812626914 0.95648766 2.806138958 1.876790794
159 2 159 3 -1.147613773 0.70381535 1.078614431 0.001691334
160 3 160 3 -0.005963495 -0.42144411 1.346029283 0.866144024
161 4 161 3 -0.487768292 0.16039940 1.889244350 0.838848852
162 4 162 3 -0.325277453 0.44750972 3.522302671 2.334300848
163 2 163 1 0.810485670 0.21194222 -0.236538595 -0.989193244
164 4 164 3 -0.586188903 -1.40828162 3.153760500 2.445556093
165 2 165 1 1.707600456 1.64506602 -0.354574953 -1.106089709
166 1 166 1 0.516966404 0.43037407 -0.843319109 -2.105307436
167 1 167 1 1.240764247 0.96035169 -1.403132441 -2.723452555
168 2 168 2 1.150271113 3.59719968 0.710025608 -0.772309968
169 3 169 3 1.154990522 1.21336040 1.537909335 0.282153028
170 3 170 2 -0.530602126 1.44399045 0.164801983 -1.276018499
171 4 171 3 -0.157253345 -1.14002239 2.117835761 1.000416289
172 1 172 1 1.374174118 0.40075009 -0.778209698 -2.335232604
173 2 173 1 2.333911387 0.39848361 0.235552515 -0.829819104
174 3 174 3 -0.332185510 -1.33640388 1.172623486 0.066823119
175 2 175 3 -0.327081449 0.10316262 1.302235951 0.484237839
176 2 176 2 1.015949323 1.80174907 0.017039788 -0.945153995
177 4 177 3 0.403523641 -0.63949231 1.274572759 0.172783384
178 2 178 1 1.088188898 0.51355476 -0.078971488 -0.744362702
179 2 179 2 -0.164286920 1.15585033 -0.356885685 -1.492212171
180 3 180 1 1.518219908 -0.07355124 1.000179024 -0.598403065
181 3 181 1 2.102301483 1.52374512 0.422707945 -0.485696539
182 2 182 1 0.579630818 0.47244020 -0.683296348 -1.185304536
183 3 183 2 1.902651105 2.28923504 2.161821025 1.637501923
184 1 184 1 1.540113414 1.46968383 -0.429542642 -1.565683732
185 2 185 1 1.909916536 -0.52104883 0.749757617 -0.642477039
186 4 186 3 0.141069414 1.23662770 2.006894338 0.999448779
187 2 187 2 0.542736128 1.86111012 0.294251330 -0.339388904
188 3 188 1 1.384725699 -1.51205961 -0.039519134 -1.270476702
189 3 189 1 1.864789106 1.47779347 1.182041810 0.389903457
190 5 190 3 -0.574479151 0.11035482 4.014472592 3.270696550
191 4 191 3 0.068318643 1.33079012 2.639410743 1.645692123
192 2 192 2 0.326891249 1.53619415 0.133795222 -1.210934106
193 2 193 1 2.007857820 0.70729058 -0.565017074 -1.370434743
194 2 194 2 1.067989221 3.13948837 0.476273288 -0.756853035
195 5 195 3 1.210197177 -0.75076373 4.808854585 4.473698884
196 3 196 3 0.362578606 1.26753424 1.347520007 -0.131381656
197 2 197 3 -0.640280205 0.29798615 1.585048445 0.502836911
198 3 198 3 0.461652471 -2.05551325 1.000172781 0.125844624
199 1 199 1 1.000201245 -0.28377772 -0.529427122 -1.847119454
200 1 200 1 1.815506761 0.29396669 -0.726221317 -1.510439568
.pred_5
1 -2.58002883
2 -0.07684673
3 -4.35822799
4 -1.95356763
5 -3.99207671
6 1.93351275
7 -0.66240349
8 1.18028676
9 -1.16592851
10 0.77978484
11 1.00035160
12 -1.50176706
13 2.41059782
14 -1.53078230
15 -2.92647034
16 -1.84243289
17 -1.57981776
18 -3.17228600
19 -3.74323245
20 -4.02264278
21 -0.19119280
22 -2.75209990
23 -2.69603183
24 -0.24203523
25 -2.69850191
26 -0.19472477
27 0.53465529
28 -2.63182959
29 1.00007002
30 -0.92024133
31 -2.47197917
32 1.49964739
33 -2.12023746
34 1.68804362
35 -0.58162609
36 -0.81888860
37 0.78236444
38 -3.10579054
39 0.47156372
40 -2.18379805
41 -4.40018543
42 -3.90076015
43 -3.27682785
44 -2.74666887
45 -2.68843170
46 -1.81494788
47 -1.47035622
48 0.46722703
49 -1.99203120
50 -0.12349569
51 -2.10959676
52 2.09263402
53 0.99979963
54 -2.73581967
55 -1.01611189
56 -0.83358768
57 1.92926482
58 -4.54639348
59 -3.41947711
60 0.19544692
61 -2.46984187
62 -3.65850087
63 -3.19360474
64 1.82827101
65 -3.47411810
66 1.21509269
67 -4.21862304
68 0.38832820
69 -3.81505895
70 -2.07675393
71 0.53085258
72 0.42778565
73 -4.48460656
74 2.92744210
75 2.14952714
76 0.54757355
77 -4.11282145
78 -0.05549175
79 -2.04012566
80 -3.52356153
81 -0.74699403
82 -2.63327923
83 -3.76415925
84 2.40164351
85 -2.37326774
86 -3.29600258
87 -0.64494508
88 -1.55588564
89 -1.23462421
90 -3.14457588
91 -1.55665731
92 -0.46741107
93 -0.36071430
94 -2.13941056
95 -1.88584105
96 -2.31279814
97 -2.72506481
98 -2.55486628
99 -2.62755342
100 -2.93103959
101 -3.13908949
102 -2.36598414
103 -3.13311286
104 2.47607255
105 -3.87597827
106 -1.13580430
107 -1.80780393
108 -4.02513628
109 -0.53160920
110 1.69841162
111 -3.88190764
112 -2.56385998
113 -3.86837437
114 -4.14417630
115 0.99942799
116 -2.65863372
117 -0.76754823
118 -2.27942602
119 -3.27317032
120 1.72560788
121 -4.02924537
122 -2.98682699
123 -0.63185022
124 -1.05365132
125 -3.25180121
126 -3.09785064
127 1.89772202
128 -1.03007030
129 -4.76932157
130 -1.83026680
131 0.70763604
132 -2.51050644
133 -2.30067396
134 1.96546545
135 0.10061140
136 1.41747201
137 -3.10551589
138 1.72763109
139 1.97737571
140 -1.46789447
141 -3.59107922
142 -1.20986782
143 -1.97876091
144 1.71976446
145 0.03445753
146 -3.79717233
147 1.31584588
148 -1.83044632
149 -0.99217495
150 -3.09378730
151 -0.50430058
152 -3.69861394
153 -1.01143389
154 -3.10652562
155 -3.98430332
156 -4.34843602
157 -2.82600550
158 0.29058746
159 -1.71037418
160 -1.14003962
161 -0.86219140
162 0.86711891
163 -2.71227855
164 1.13242136
165 -3.25077099
166 -4.21184001
167 -4.32908290
168 -2.53025092
169 -1.20668692
170 -2.53536903
171 -0.89845528
172 -4.16588482
173 -2.50545962
174 -1.58712329
175 -1.15430985
176 -2.73924017
177 -1.37722823
178 -2.54843043
179 -3.27672842
180 -2.12158393
181 -2.24123529
182 -3.47317552
183 -0.10937610
184 -3.30503450
185 -2.34538027
186 -0.92687356
187 -2.09575427
188 -2.95018409
189 -1.63822035
190 1.89281930
191 -0.10896206
192 -2.93719691
193 -3.31089481
194 -2.75203056
195 2.99187180
196 -1.53222689
197 -1.06215402
198 -1.69979180
199 -3.50721222
200 -3.37582254
misvm() examples work
Code
set.seed(8)
mil_data <- generate_mild_df(nbag = 20, positive_prob = 0.15, sd_of_mean = rep(
0.1, 3))
df <- build_instance_feature(mil_data, seq(0.05, 0.95, length.out = 10))
mdl1 <- misvm(x = df[, 4:123], y = df$bag_label, bags = df$bag_name, method = "heuristic")
mdl2 <- misvm(mi(bag_label, bag_name) ~ X1_mean + X2_mean + X3_mean, data = df)
if (require(gurobi)) {
mdl3 <- misvm(x = df[, 4:123], y = df$bag_label, bags = df$bag_name, method = "mip")
}
predict(mdl1, new_data = df, type = "raw", layer = "bag")
Output
# A tibble: 80 x 1
.pred
<dbl>
1 -1.04
2 -1.04
3 -1.04
4 -1.04
5 1.00
6 1.00
7 1.00
8 1.00
9 -1.00
10 -1.00
# ... with 70 more rows
Code
df %>% dplyr::bind_cols(predict(mdl2, df, type = "class")) %>% dplyr::bind_cols(
predict(mdl2, df, type = "raw")) %>% dplyr::distinct(bag_name, bag_label,
.pred_class, .pred)
Output
bag_label bag_name .pred_class .pred
1 0 bag1 0 -0.11805071
2 1 bag2 1 1.01732791
3 0 bag3 0 -0.24540426
4 1 bag4 1 1.00046917
5 0 bag5 1 0.15460188
6 0 bag6 1 0.87469487
7 1 bag7 1 0.16754553
8 0 bag8 1 1.00811386
9 1 bag9 1 0.99998275
10 1 bag10 1 2.67168111
11 1 bag11 1 0.29471379
12 0 bag12 1 1.52487131
13 1 bag13 1 2.15326561
14 1 bag14 1 0.99956477
15 1 bag15 0 -0.38940230
16 1 bag16 1 0.67654218
17 0 bag17 1 0.39241276
18 1 bag18 0 -0.11878006
19 0 bag19 1 0.06554383
20 1 bag20 1 0.85951804
omisvm() examples work
Code
if (require(gurobi)) {
data("ordmvnorm")
x <- ordmvnorm[, 3:7]
y <- ordmvnorm$bag_label
bags <- ordmvnorm$bag_name
mdl1 <- omisvm(x, y, bags, weights = NULL)
predict(mdl1, x, new_bags = bags)
df1 <- dplyr::bind_cols(y = y, bags = bags, as.data.frame(x))
df1 %>% dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "class")) %>%
dplyr::bind_cols(predict(mdl1, df1, new_bags = bags, type = "raw")) %>%
dplyr::distinct(y, bags, .pred_class, .pred)
}
Warning <warning>
Dropping 'mi_df' class as required column was removed.
Output
y bags .pred_class .pred
1 2 1 2 2.12567387
2 4 2 4 4.65794584
3 1 3 1 -0.83805734
4 3 4 3 2.63966213
5 1 5 1 -0.25380927
6 5 6 5 7.77042870
7 4 7 3 3.72950569
8 4 8 5 6.80992822
9 3 9 3 3.70282546
10 4 10 4 5.94699034
11 5 11 4 5.94752631
12 3 12 3 3.19506580
13 5 13 5 8.35594177
14 3 14 3 3.15834447
15 2 15 2 1.33977357
16 3 16 3 2.47991026
17 2 17 3 2.70733748
18 2 18 2 0.88989980
19 2 19 2 0.55035855
20 1 20 1 -0.48857803
21 3 21 4 4.62498956
22 2 22 2 1.34357488
23 3 23 2 2.07230182
24 4 24 4 5.11838378
25 2 25 2 1.62530071
26 4 26 4 5.04146527
27 4 27 4 6.12983599
28 2 28 2 1.38152030
29 5 29 5 6.60015162
30 3 30 3 3.26062479
31 2 31 2 1.81985451
32 5 32 5 7.11161632
33 2 33 2 2.21294409
34 5 34 5 7.29111446
35 4 35 4 4.46349207
36 3 36 3 3.98620574
37 4 37 4 6.06686466
38 2 38 2 1.56219942
39 4 39 4 5.30727742
40 2 40 2 2.27510342
41 1 41 1 -0.81342852
42 1 42 1 0.02151881
43 2 43 2 0.52168663
44 2 44 2 1.44739630
45 2 45 2 1.59950373
46 3 46 3 2.59372869
47 3 47 3 2.74693863
48 4 48 4 6.14110841
49 2 49 2 2.32840644
50 4 50 4 5.02891382
51 3 51 3 2.44752466
52 5 52 5 7.74094074
53 5 53 5 6.42830548
54 2 54 2 1.31239708
55 4 55 3 3.99384803
56 3 56 3 3.79146236
57 5 57 5 8.07575177
58 1 58 1 -0.54392096
59 2 59 2 0.48349162
60 4 60 4 5.71926852
61 2 61 2 1.34308764
62 2 62 2 0.51765081
63 2 63 2 0.95240309
64 5 64 5 7.39404182
65 2 65 2 0.79525838
66 4 66 5 6.70635073
67 2 67 1 0.21166463
68 4 68 4 5.30727742
69 1 69 1 -0.17442229
70 2 70 3 2.54644169
71 4 71 4 5.61509347
72 4 72 4 5.51145838
73 1 73 1 -0.41594083
74 5 74 5 9.28598108
75 5 75 5 7.96521824
76 4 76 4 5.62411564
77 1 77 1 -0.05464613
78 3 78 4 4.42463829
79 2 79 2 1.94267090
80 1 80 2 0.78140124
81 3 81 3 3.95317076
82 3 82 2 1.57994465
83 2 83 2 0.59626748
84 5 84 5 8.85967969
85 2 85 2 1.96307114
86 2 86 2 0.53479058
87 3 87 4 4.53134302
88 3 88 3 3.36033245
89 3 89 3 3.60200338
90 2 90 2 1.06057133
91 3 91 3 2.98476241
92 4 92 3 4.15237641
93 3 93 4 4.60544506
94 2 94 3 2.42829931
95 3 95 3 2.51276246
96 3 96 2 2.09600292
97 2 97 2 1.47889650
98 2 98 2 1.37766926
99 2 99 2 1.15593677
100 2 100 2 1.27380967
101 2 101 2 1.19192283
102 2 102 2 2.26486948
103 2 103 2 0.96767384
104 5 104 5 8.52363527
105 1 105 2 0.36592471
106 3 106 3 3.24715264
107 3 107 3 2.85117594
108 1 108 1 -0.66022643
109 3 109 4 4.67142427
110 5 110 5 7.27179022
111 1 111 1 -0.18336695
112 3 112 2 2.21778165
113 2 113 1 0.02990462
114 1 114 1 -0.42337571
115 5 115 5 6.48828236
116 2 116 2 1.27682708
117 4 117 3 4.25799913
118 2 118 2 2.10080070
119 1 119 2 0.93064817
120 5 120 5 7.52021032
121 1 121 1 -0.29092487
122 2 122 2 0.88786343
123 3 123 3 3.98453366
124 3 124 3 3.65453342
125 2 125 2 0.90799958
126 2 126 2 1.26472093
127 5 127 5 7.55024109
128 3 128 3 4.01900088
129 1 129 1 -1.05125879
130 3 130 3 2.62876392
131 4 131 4 6.07376153
132 2 132 2 1.90236816
133 3 133 2 2.25972489
134 5 134 5 8.11634426
135 4 135 4 5.00440266
136 5 136 5 6.81334557
137 2 137 2 1.01226568
138 5 138 5 7.64971991
139 5 139 5 7.45437571
140 3 140 3 3.24438924
141 2 141 1 0.18105533
142 3 142 3 3.94018318
143 3 143 3 2.36976230
144 5 144 5 7.27290297
145 4 145 4 4.89267331
146 1 146 1 -0.15294844
147 5 147 5 7.39521045
148 3 148 3 3.09050461
149 3 149 3 3.76402819
150 1 150 2 1.17147884
151 4 151 3 4.25669454
152 2 152 2 0.37705873
153 4 153 3 3.50975303
154 2 154 2 1.31948372
155 1 155 1 -0.28261632
156 1 156 1 -0.93086447
157 2 157 2 1.29143142
158 4 158 4 5.41757327
159 2 159 3 2.81103227
160 3 160 3 3.63819060
161 4 161 3 4.16650748
162 4 162 5 6.54752782
163 2 163 2 1.20572586
164 4 164 4 6.13185612
165 2 165 2 0.56934837
166 1 166 1 -0.42607856
167 1 167 1 -0.78088361
168 2 168 2 1.62783817
169 3 169 3 3.76726554
170 3 170 2 1.76488593
171 4 171 4 4.41643401
172 1 172 1 -0.46522749
173 2 173 2 1.48310131
174 3 174 3 2.76200714
175 2 175 3 2.96845063
176 2 176 2 1.57232998
177 4 177 3 3.37146742
178 2 178 2 1.27492919
179 2 179 2 0.56000795
180 3 180 3 2.50825115
181 3 181 2 2.04157031
182 2 182 2 0.69159336
183 3 183 4 4.68423662
184 1 184 2 0.64191641
185 2 185 2 1.95328294
186 4 186 3 4.06195481
187 2 187 2 2.27932700
188 3 188 2 1.08385635
189 3 189 3 2.90625148
190 5 190 5 7.49152508
191 4 191 4 5.01998550
192 2 192 2 1.51988937
193 2 193 2 0.86665906
194 2 194 2 1.34357488
195 5 195 5 8.45275065
196 3 196 3 3.40346084
197 2 197 3 3.41311656
198 3 198 3 2.35031158
199 1 199 2 0.98324723
200 1 200 2 0.55294597
smm() examples work
Code
set.seed(8)
n_instances <- 10
n_samples <- 20
y <- rep(c(1, -1), each = n_samples * n_instances / 2)
instances <- as.character(rep(1:n_instances, each = n_samples))
x <- data.frame(x1 = rnorm(length(y), mean = 1 * (y == 1)), x2 = rnorm(length(y),
mean = 2 * (y == 1)), x3 = rnorm(length(y), mean = 3 * (y == 1)))
df <- data.frame(instance_name = instances, y = y, x)
mdl <- smm(x, y, instances)
mdl2 <- smm(y ~ ., data = df)
df %>% dplyr::bind_cols(predict(mdl, type = "raw", new_data = x, new_instances = instances)) %>%
dplyr::bind_cols(predict(mdl, type = "class", new_data = x, new_instances = instances)) %>%
dplyr::distinct(instance_name, y, .pred, .pred_class)
Output
instance_name y .pred .pred_class
1 1 1 1.0000000 1
2 2 1 0.9216801 1
3 3 1 1.0847376 1
4 4 1 0.9357988 1
5 5 1 0.9247263 1
6 6 -1 -0.9880539 -1
7 7 -1 -0.7498163 -1
8 8 -1 -0.9375437 -1
9 9 -1 -1.0000000 -1
10 10 -1 -1.0317597 -1
summarize_samples() examples work
Code
fns <- list(mean = mean, sd = sd)
suppressMessages({
summarize_samples(mtcars, group_cols = c("cyl", "gear"), .fns = fns) %>%
print()
summarize_samples(mtcars, group_cols = c("cyl", "gear"), .fns = fns, cor = TRUE) %>%
print()
})
Output
# A tibble: 8 x 20
cyl gear mpg_mean disp_mean hp_mean drat_mean wt_mean qsec_mean vs_mean
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4 3 21.5 120. 97 3.7 2.46 20.0 1
2 4 4 26.9 103. 76 4.11 2.38 19.6 1
3 4 5 28.2 108. 102 4.1 1.83 16.8 0.5
4 6 3 19.8 242. 108. 2.92 3.34 19.8 1
5 6 4 19.8 164. 116. 3.91 3.09 17.7 0.5
6 6 5 19.7 145 175 3.62 2.77 15.5 0
7 8 3 15.0 358. 194. 3.12 4.10 17.1 0
8 8 5 15.4 326 300. 3.88 3.37 14.6 0
# ... with 11 more variables: am_mean <dbl>, carb_mean <dbl>, mpg_sd <dbl>,
# disp_sd <dbl>, hp_sd <dbl>, drat_sd <dbl>, wt_sd <dbl>, qsec_sd <dbl>,
# vs_sd <dbl>, am_sd <dbl>, carb_sd <dbl>
# A tibble: 8 x 56
cyl gear mpg_mean disp_mean hp_mean drat_mean wt_mean qsec_mean vs_mean
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4 3 21.5 120. 97 3.7 2.46 20.0 1
2 4 4 26.9 103. 76 4.11 2.38 19.6 1
3 4 5 28.2 108. 102 4.1 1.83 16.8 0.5
4 6 3 19.8 242. 108. 2.92 3.34 19.8 1
5 6 4 19.8 164. 116. 3.91 3.09 17.7 0.5
6 6 5 19.7 145 175 3.62 2.77 15.5 0
7 8 3 15.0 358. 194. 3.12 4.10 17.1 0
8 8 5 15.4 326 300. 3.88 3.37 14.6 0
# ... with 47 more variables: am_mean <dbl>, carb_mean <dbl>, mpg_sd <dbl>,
# disp_sd <dbl>, hp_sd <dbl>, drat_sd <dbl>, wt_sd <dbl>, qsec_sd <dbl>,
# vs_sd <dbl>, am_sd <dbl>, carb_sd <dbl>, cov_var_1 <dbl>, cov_var_2 <dbl>,
# cov_var_3 <dbl>, cov_var_4 <dbl>, cov_var_5 <dbl>, cov_var_6 <dbl>,
# cov_var_7 <dbl>, cov_var_8 <dbl>, cov_var_9 <dbl>, cov_var_10 <dbl>,
# cov_var_11 <dbl>, cov_var_12 <dbl>, cov_var_13 <dbl>, cov_var_14 <dbl>,
# cov_var_15 <dbl>, cov_var_16 <dbl>, cov_var_17 <dbl>, cov_var_18 <dbl>, ...
svor_exc() examples work
Code
data("ordmvnorm")
x <- ordmvnorm[, 3:7]
Warning <warning>
Dropping 'mi_df' class as required column was removed.
Code
y <- attr(ordmvnorm, "instance_label")
mdl1 <- svor_exc(x, y)
Message <message>
The SMO algorithm reached the maximum of 500 steps.
Code
predict(mdl1, x)
Output
# A tibble: 1,000 x 1
.pred_class
<fct>
1 1
2 1
3 2
4 1
5 1
6 1
7 1
8 4
9 2
10 2
# ... with 990 more rows
Code
predict(mdl1, x, type = "raw")
Output
# A tibble: 1,000 x 1
.pred
<dbl>
1 -0.464
2 -1.08
3 0.500
4 -0.253
5 0.0886
6 -0.0116
7 -0.146
8 2.15
9 0.266
10 0.216
# ... with 990 more rows
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