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Last update:

[1] “Wed Aug 5 18:19:40 2020”

General glmnet notes

Inspect fitted models during CV

Inspect Ridge regression on VI task in detail because the error is enourmus.

First extract the models.

models <- benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["models"]]

Then look at the fold performances

benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["measures.test"]][["rmse"]]
[1]           28.04421           21.44949           21.93398
[4] 199291640366.92307

We see a high error on Fold 4 (= Laukiz 2). The others are also quite high but not “out of bounds”.

Let’s look at the full lambda sequence

purrr::map_int(models, ~ length(.x[["learner.model"]][["lambda"]]))
[1]   5 100 100 100

Interestingly, the lambda length of model 1 is not 100 (default) but only 5.

Train/predict via {glmnet} manually

To inspect further, let’s refit a {glmnet} model directly on the training data of Fold 4 and inspect what glmnet::cv.glmnet estimates for the lambda sequence:

train_inds_fold4 <- benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["pred"]][["instance"]][["train.inds"]][[4]]

obs_train_f4 <- as.matrix(task_new_buffer2[[2]]$env$data[train_inds_fold4, getTaskFeatureNames(task_new_buffer2[[2]])])
target_f4 <- getTaskTargets(task_new_buffer2[[2]])[train_inds_fold4]

Fit cv.glmnet

set.seed(1)
modf4 <- glmnet::cv.glmnet(obs_train_f4, target_f4, alpha = 0)

modf4$lambda.1se
[1] 17.09715

Predict on Laukiz 2 now.

pred_inds_fold4 <- benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["pred"]][["instance"]][["test.inds"]][[4]]

obs_pred_f4 <- as.matrix(task_new_buffer2[[2]]$env$data[pred_inds_fold4, getTaskFeatureNames(task_new_buffer2[[2]])])

pred <- predict(modf4, newx = obs_pred_f4, s = modf4$lambda.1se)

Calculate the error

truth <- task_new_buffer2[[2]]$env$data[pred_inds_fold4, "defoliation"]

mlr:::measureRMSE(truth, pred)
[1] 97073324139

Ok, RMSE of 97073324139. This is most likely because of a few. observations which were predicted completely out of bounds.

qplot(pred, geom = "histogram")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

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pred[which(pred > 100), , drop = FALSE]
                1
737 2061522943649

Ok, its one observation (row id = 737).

Let’s have a look at the predictor values for this observation.

summary(obs_train_f4[737, ])
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
 -465.332     0.495     1.248   244.077    13.569 15673.108 

Ok, how does this compare to summaries of other observations? NB: Obs. 500 - 510 were chosen randomly. The purpose here is to see if something within the predictors for specific observations looks abnormal.

lapply(seq(500:510), function(x) summary(obs_train_f4[x, ]))
[[1]]
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-241.698    0.543    1.342  136.208    9.122 8310.363 

[[2]]
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-162.222    0.557    1.092   88.503    7.497 5026.049 

[[3]]
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
 -358.944     0.452     1.286   198.458    10.548 12438.312 

[[4]]
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
 -88.5556    0.5348    1.0245   54.8659    7.3913 2870.3848 

[[5]]
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-104.903    0.545    1.063   61.960    8.315 3202.895 

[[6]]
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
 -311.475     0.527     1.277   172.420     9.705 10713.079 

[[7]]
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
 -440.426     0.488     1.384   226.684    11.486 14710.321 

[[8]]
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-135.505    0.601    1.104   76.380    8.666 4416.709 

[[9]]
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
 -408.230     0.563     1.243   217.687     9.761 13471.893 

[[10]]
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-228.969    0.570    1.067   81.748    8.795 4882.058 

[[11]]
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-180.651    0.539    1.101   91.665    7.273 5362.145 

We have some higher values for obs 737 but nothing which stands out.

Let’s look at the model coefficients and Partial Dependence Plots (PDP):

coef(modf4)
90 x 1 sparse Matrix of class "dgCMatrix"
                             1
(Intercept)    531.11225516839
bf2_Boochs       0.01856478558
bf2_Boochs2     -0.10174641139
bf2_CARI        -0.00101151781
bf2_Carter2     -0.54044992476
bf2_Carter3     -4.74060904178
bf2_Carter4      3.57395447927
bf2_Carter5      2.40033358279
bf2_Carter6     -0.01223220666
bf2_CI          17.44558789878
bf2_CI2         -0.09234363302
bf2_ClAInt      -0.00029867499
bf2_CRI1         3.93199597949
bf2_CRI2         3.93621597231
bf2_CRI3        -0.14102965215
bf2_CRI4         0.07745521805
bf2_D1          -0.21172763780
bf2_D2           4.48364959574
bf2_Datt       -29.92657011501
bf2_Datt2       -0.44902414236
bf2_Datt3       -5.82225051092
bf2_Datt4        9.73689861012
bf2_Datt5        0.43034087514
bf2_Datt6        0.52067426584
bf2_DD          -0.00466742370
bf2_DDn          0.00165874779
bf2_DPI         -2.94066199649
bf2_DWSI4       -0.27682042303
bf2_EGFR        -0.01998100611
bf2_EVI         -0.00201410021
bf2_GDVI_2      10.27846237651
bf2_GDVI_3      31.18505681072
bf2_GDVI_4       0.93958347942
bf2_GI          -0.34477144062
bf2_Gitelson     2.64273830373
bf2_Gitelson2   -0.02585043394
bf2_GMI1         0.16818982267
bf2_GMI2        -0.05229428190
bf2_Green.NDVI   1.73562944987
bf2_Maccioni   -16.16650612962
bf2_MCARI        0.00522029772
bf2_MCARI2      -0.00049064534
bf2_mND705      -0.64532841126
bf2_mNDVI        3.53489589242
bf2_MPRI        -0.03442634938
bf2_MSAVI        3.65757418934
bf2_mSR          0.00161463593
bf2_mSR2        -0.08935382573
bf2_mSR705      -0.09691169765
bf2_MTCI        -0.43694064196
bf2_MTVI        -0.00121734402
bf2_NDVI         1.93976587113
bf2_NDVI2       -0.49852301101
bf2_NDVI3        0.71581375642
bf2_NPCI         0.53555171520
bf2_OSAVI        1.71019327640
bf2_PARS         0.05888230273
bf2_PRI         -3.59215290150
bf2_PRI_norm   -95.26848890029
bf2_PRI.CI2     -2.84067572332
bf2_PSRI        -6.87122623622
bf2_PSSR         0.01087461932
bf2_PSND        -6.96686024116
bf2_SPVI.1      -0.00146273294
bf2_PWI         -2.54395514623
bf2_RDVI        -0.03620253189
bf2_REP_Li      -0.66792472297
bf2_SAVI         1.27179842863
bf2_SIPI        -5.23226754782
bf2_SPVI.2      -0.00146824633
bf2_SR          -0.01204737752
bf2_SR1         -0.05555531799
bf2_SR2         -0.01914132415
bf2_SR3          0.16764479685
bf2_SR4          0.99188378152
bf2_SR5         -5.57341447292
bf2_SR6         -0.22843650281
bf2_SR7         -0.09924316268
bf2_SR8         -1.27085004094
bf2_Sum_Dr1     -0.01285185223
bf2_Sum_Dr2     -0.00934767797
bf2_SRPI         0.01663453441
bf2_TCARI       -0.00050333567
bf2_TCARI2       0.00193142448
bf2_TGI         -0.00012288617
bf2_TVI         -0.00002379354
bf2_Vogelmann   -1.45492700073
bf2_Vogelmann2   6.48132112495
bf2_Vogelmann3   5.67673100608
bf2_Vogelmann4   4.98343236576

Feature “bf2_PRI_norm” has a quite high value (-95).

plotres(modf4)

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plot_glmnet(modf4$glmnet.fit)

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plotmo(modf4$glmnet.fit)
 plotmo grid:    bf2_Boochs bf2_Boochs2 bf2_CARI bf2_Carter2 bf2_Carter3
                   1.998693    2.845148 80.11412   0.1234949  0.08682775
 bf2_Carter4 bf2_Carter5 bf2_Carter6   bf2_CI  bf2_CI2 bf2_ClAInt
   0.2861524    1.622418    19.10886 1.087452 5.280886   877.1538
   bf2_CRI1   bf2_CRI2 bf2_CRI3  bf2_CRI4   bf2_D1    bf2_D2  bf2_Datt
 0.04910712 0.06965439 -9.37543 -6.282868 1.514274 0.7339561 0.8177696
 bf2_Datt2 bf2_Datt3  bf2_Datt4 bf2_Datt5 bf2_Datt6   bf2_DD   bf2_DDn
  4.216579 0.7465023 0.01912338 0.7204213 0.3120446 63.41494 -266.1666
   bf2_DPI bf2_DWSI4 bf2_EGFR  bf2_EVI bf2_GDVI_2 bf2_GDVI_3 bf2_GDVI_4
 0.5886079  1.382126 7.359851 4.944853  0.9875098  0.9989018  0.9998978
  bf2_GI bf2_Gitelson bf2_Gitelson2 bf2_GMI1 bf2_GMI2 bf2_Green.NDVI
 1.43903   0.04093419      8.943006 8.631567 5.866379        0.80809
 bf2_Maccioni bf2_MCARI bf2_MCARI2 bf2_mND705 bf2_mNDVI bf2_MPRI bf2_MSAVI
    0.7883106  25.39617   397.1709  0.6687387 0.9220925 9.782586 0.9283319
  bf2_mSR bf2_mSR2 bf2_mSR705 bf2_MTCI bf2_MTVI  bf2_NDVI bf2_NDVI2
 28.73996  3.88811   5.140166 3.635395 247.0532 0.8593618 0.6208416
 bf2_NDVI3  bf2_NPCI bf2_OSAVI bf2_PARS     bf2_PRI bf2_PRI_norm
 -0.137058 0.2540456  1.005096 16.79849 -0.02239909 0.0009611926
 bf2_PRI.CI2   bf2_PSRI bf2_PSSR  bf2_PSND bf2_SPVI.1  bf2_PWI bf2_RDVI
  -0.1033416 0.03712631 13.42027 0.9324896    238.338 1.013997 11.67572
 bf2_REP_Li bf2_SAVI bf2_SIPI bf2_SPVI.2   bf2_SR  bf2_SR1  bf2_SR2
   724.5711  1.29702 1.042882    238.338 14.16137 5.866379 9.883037
  bf2_SR3  bf2_SR4   bf2_SR5  bf2_SR6   bf2_SR7   bf2_SR8 bf2_Sum_Dr1
 8.631567 2.146826 0.4827658 3.288378 0.3703371 0.5433803    38.28711
 bf2_Sum_Dr2  bf2_SRPI bf2_TCARI bf2_TCARI2  bf2_TGI  bf2_TVI
    33.57344 0.6054507  31.51822   3.176928 922.7046 8765.983
 bf2_Vogelmann bf2_Vogelmann2 bf2_Vogelmann3 bf2_Vogelmann4
       1.84361     -0.2100082       1.316905     -0.2384276

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Let’s figure out which are the ten most important features and create PDPs for these:

top_ten_abs <- coef(modf4) %>%
  as.matrix() %>%
  as.data.frame() %>%
  dplyr::rename(coef = `1`) %>%
  dplyr::mutate(feature = rownames(coef(modf4))) %>%
  dplyr::slice(-1) %>%
  dplyr::mutate(coef_abs = abs(coef)) %>%
  dplyr::arrange(desc(coef_abs)) %>%
  dplyr::slice(1:10) %>%
  dplyr::pull(feature)

top_ten_abs
 [1] "bf2_PRI_norm"   "bf2_GDVI_3"     "bf2_Datt"       "bf2_CI"        
 [5] "bf2_Maccioni"   "bf2_GDVI_2"     "bf2_Datt4"      "bf2_PSND"      
 [9] "bf2_PSRI"       "bf2_Vogelmann2"

Partial Dependence Plots

For PDP we use a model trained with {mlr} and check for equality first.

lrn <- makeLearner("regr.cvglmnet", alpha = 0)
task_f4 <- subsetTask(task_new_buffer2[[2]], train_inds_fold4)

set.seed(1)
mod_mlr <- train(lrn, task_f4)

Check lambda sequence and lambda.1se:

mod_mlr$learner.model$lambda
  [1] 4983.9093118 4541.1525452 4137.7290693 3770.1446232 3435.2153661
  [6] 3130.0403011 2851.9761478 2598.6144474 2367.7607022 2157.4153674
 [11] 1965.7565324 1791.1241401 1632.0056082 1487.0227281 1354.9197274
 [16] 1234.5523933 1124.8781613 1024.9470858  933.8936117  850.9290772
 [21]  775.3348833  706.4562693  643.6966414  586.5124060  534.4082604
 [26]  486.9329034  443.6751264  404.2602510  368.3468845  335.6239625
 [31]  305.8080548  278.6409100  253.8872196  231.3325788  210.7816301
 [36]  192.0563710  174.9946123  159.4485733  145.2836015  132.3770068
 [41]  120.6169984  109.9017167  100.1383511   91.2423360   83.1366183
 [46]   75.7509903   69.0214811   62.8898030   57.3028462   52.2122193
 [51]   47.5738296   43.3475017   39.4966291   35.9878575   32.7907954
 [56]   29.8777515   27.2234944   24.8050342   22.6014233   20.5935752
 [61]   18.7640987   17.0971479   15.5782844   14.1943526   12.9333654
 [66]   11.7844009   10.7375072    9.7836166    8.9144671    8.1225304
 [71]    7.4009472    6.7434674    6.1443964    5.5985451    5.1011858
 [76]    4.6480105    4.2350941    3.8588600    3.5160495    3.2036934
 [81]    2.9190861    2.6597625    2.4234765    2.2081816    2.0120128
 [86]    1.8332711    1.6704084    1.5220139    1.3868024    1.2636027
 [91]    1.1513477    1.0490651    0.9558691    0.8709523    0.7935793
 [96]    0.7230799    0.6588435    0.6003136    0.5469834    0.4983909
mod_mlr$learner.model$lambda.1se
[1] 17.09715

Check for equality between {mlr} and {glmnet} directly

all.equal(modf4$lambda.1se, mod_mlr$learner.model$lambda.1se)
[1] TRUE
pdp <- generatePartialDependenceData(mod_mlr, task_f4, features = top_ten_abs)
Loading required package: mmpf
plotPartialDependence(pdp)

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Individual PDP

pdp_ind <- generatePartialDependenceData(mod_mlr, task_f4,
  features = top_ten_abs,
  individual = TRUE
)
plotPartialDependence(pdp_ind)

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Let’s look at the x values for observation 737:

obs_train_f4[737, top_ten_abs]
  bf2_PRI_norm     bf2_GDVI_3       bf2_Datt         bf2_CI   bf2_Maccioni 
  0.0005331477   0.9997067366   0.8283409902   1.1411477583   0.8079021105 
    bf2_GDVI_2      bf2_Datt4       bf2_PSND       bf2_PSRI bf2_Vogelmann2 
  0.9946317460   0.0085985645   0.9510653828   0.0191196652  -0.2698871749 

Looks ok - they are all within a normal range with respectv to the PDP estimates.

Train/predict via {glmnet} manually on dataset with filtered feature correlation

To inspect further, let’s refit a {glmnet} model directly on the training data of Fold 4 and inspect what glmnet::cv.glmnet estimates for the lambda sequence:

train_inds_fold4 <- benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["pred"]][["instance"]][["train.inds"]][[4]]

obs_train_f4 <- as.matrix(task_new_buffer2_reduced_cor[[2]]$env$data[train_inds_fold4, getTaskFeatureNames(task_new_buffer2_reduced_cor[[2]])])
target_f4 <- getTaskTargets(task_new_buffer2_reduced_cor[[2]])[train_inds_fold4]

Fit cv.glmnet

set.seed(1)
modf4 <- glmnet::cv.glmnet(obs_train_f4, target_f4, alpha = 0)

modf4$lambda.1se
[1] 17.09715

Predict on Laukiz 2 now.

pred_inds_fold4 <- benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["pred"]][["instance"]][["test.inds"]][[4]]

obs_pred_f4 <- as.matrix(task_new_buffer2_reduced_cor[[2]]$env$data[pred_inds_fold4, getTaskFeatureNames(task_new_buffer2_reduced_cor[[2]])])

pred <- predict(modf4, newx = obs_pred_f4, s = modf4$lambda.1se)

Calculate the error

truth <- task_new_buffer2_reduced_cor[[2]]$env$data[pred_inds_fold4, "defoliation"]

mlr:::measureRMSE(truth, pred)
[1] 97098172699

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRblas.so
LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] mmpf_0.0.5         tidyselect_0.2.5   plotmo_3.5.6      
 [4] TeachingDemos_2.10 plotrix_3.7-7      Formula_1.2-3     
 [7] magrittr_1.5       ggplot2_3.3.0      glmnet_3.0-2      
[10] Matrix_1.2-15      mlr_2.17.0.9001    ParamHelpers_1.12 
[13] drake_7.12.0      

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.3        txtq_0.1.4        lattice_0.20-38  
 [4] prettyunits_1.0.2 assertthat_0.2.1  rprojroot_1.3-2  
 [7] digest_0.6.25     foreach_1.4.4     R6_2.4.1         
[10] backports_1.1.5   evaluate_0.13     pillar_1.4.3     
[13] rlang_0.4.5       progress_1.2.0    data.table_1.12.8
[16] whisker_0.3-2     R.utils_2.8.0     R.oo_1.23.0      
[19] checkmate_2.0.0   rmarkdown_1.13    labeling_0.3     
[22] splines_3.6.1     stringr_1.4.0     igraph_1.2.4.1   
[25] munsell_0.5.0     compiler_3.6.1    httpuv_1.4.5.1   
[28] xfun_0.5          pkgconfig_2.0.3   shape_1.4.4      
[31] BBmisc_1.11       htmltools_0.3.6   tibble_2.1.3     
[34] workflowr_1.6.1   codetools_0.2-16  XML_3.98-1.17    
[37] fansi_0.4.1       crayon_1.3.4      dplyr_0.8.3      
[40] withr_2.1.2       later_1.0.0       R.methodsS3_1.7.1
[43] grid_3.6.1        gtable_0.3.0      lifecycle_0.1.0  
[46] git2r_0.26.1      storr_1.2.1       scales_1.1.0     
[49] cli_2.0.2         stringi_1.3.1     farver_2.0.3     
[52] fs_1.4.1          promises_1.0.1    parallelMap_1.4  
[55] filelock_1.0.2    vctrs_0.2.4       fastmatch_1.1-0  
[58] iterators_1.0.10  tools_3.6.1       glue_1.4.0       
[61] purrr_0.3.4       hms_0.5.3         parallel_3.6.1   
[64] survival_2.43-3   yaml_2.2.0        colorspace_1.4-0 
[67] base64url_1.4     knitr_1.23