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View Code? Open in Web Editor NEWR package to tune parameters for machine learning(Support Vector Machine, Random Forest, and Xgboost), using bayesian optimization with gaussian process
License: Other
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ck37 avsemenov dlutml mariopm haokuo nanaakwasiabayieboateng skjang54 vishalbelsare chat19 crackend msmith01 algoskynet karthy257 yiminchangchienmlbayesopt's Issues
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recipes friendly
when using recipes package, make it available to use recipe object in MlBayesOpt functions
like
rec <- recipes::recipe(data, y ~ .) %>%
step_****() %>%
step_****()
res <- xgb_cv_opt(recipe = rec)
Random Forest optimization
Hello,
Interesting package, I'd like to give it a try sometime soon. Re: the random forest implementation, I'd like to suggest a few changes:
-
Minobspernode - it would be great to support optimizing over the minimum number of observations per node hyperparameter, because that can be used to reduce overfitting in random forests.
-
Ntree - I don't think there is a point in optimization over ntree. Breiman proved in his 2001 RF article that there is no problem with increasing ntree to an arbitrary number - it just converges to a performance plateau (section ~2.1) and ends up wasting computation. So I don't see any benefit to optimizing the ntree as there is not any harm in a larger number of trees (unlike GBM).
What do you think?
Appreciate it,
Chris
Error in table(testlabel, t.pred$predictions) : all arguments must have the same length
Hi,
I tried to run rf_opt where my train has dim=101673, 10 whilst my test dim=43574, 10.
I face this error:
Error in table(testlabel, t.pred$predictions) :
all arguments must have the same length
Timing stopped at: 4.31 0.22 1.81
I've checked there's no NA values on both dataset.
All my variables are factor.
When i run iris_train and iris_test it run smoothly. when i use iris_train as is and sampled the iris_test to 30 it still work.
when I sample my train and test both to 1000 rf_opt run smoothly.
So I'm kind of confussed here. If it because of different number of rows why on iris dataset with different number of rows it still work.
thx.
caret friendly
so far → dependent on e1071, ranger, xgboost packages
improved → caret package only
make it easy to predict
want to write like
res <- xgb_cv_opt(data, label)
pred <- predict(res, newdata)
Error in GP_deviance(param_init_200d[i, ], X, Y, nug_thres, corr = corr) : Infinite values of the Deviance Function, unable to find optimum parameters
When using the xgb_cv_opt function I get the following error after four rounds.
Error in GP_deviance(param_init_200d[i, ], X, Y, nug_thres, corr = corr) : Infinite values of the Deviance Function, unable to find optimum parameters
I found this page
yanyachen/rBayesianOptimization#36
talking about the same error in the rbayesianoptimization package and tried playing around with the parameter ranges but still cannot get the optimization to run. Reproducible example below.
df_example <- read.csv(textConnection("V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,V21,V22,V23,V24,V25,V26,V27,V28,V29,V30,V31,V32,V33,V34,V35,V36,V37,V38,V39,V40,V41,V42,V43,V44,V45,V46,V47,V48,V49,V50,V51,V52,V53,V54,V55,V56,V57,V58,V59,V60,V61,V62,V63,V64,V65,V66,V67,V68,V69,V70,V71,V72,V73,y
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))
set.seed(71)
res0 <- xgb_cv_opt(data = df_example,
label = y,
objectfun = "binary:logistic",
evalmetric = "auc",
n_folds = 3,
classes = 10,
init_points = 4,
n_iter = 5)
Error installing package
Hello, I'm trying to install your package and I get an error.
This is the output:
devtools::install_github("ymattu/MlBayesOpt")
Downloading GitHub repo ymattu/MlBayesOpt@master
from URL https://api.github.com/repos/ymattu/MlBayesOpt/zipball/master
Installing MlBayesOpt
Installing 1 package: xgboost
Installing package into ‘\\SRVR-FT/Redireccionamiento de carpetas/sdonikian/Documents/R/win-library/3.3’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.3/xgboost_0.6-4.zip'
Content type 'application/zip' length 1693578 bytes (1.6 MB)
downloaded 1.6 MB
package ‘xgboost’ successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\sdonikian\AppData\Local\Temp\RtmpqwvNhH\downloaded_packages
"C:/PROGRA~1/R/R-33~1.1/bin/x64/R" --no-site-file --no-environ --no-save --no-restore --quiet CMD \
INSTALL \
"C:/Users/sdonikian/AppData/Local/Temp/RtmpqwvNhH/devtools35030712993/ymattu-MlBayesOpt-e9054a9" \
--library="\\SRVR-FT/Redireccionamiento de carpetas/sdonikian/Documents/R/win-library/3.3" \
--install-tests
* installing *source* package 'MlBayesOpt' ...
** R
** data
*** moving datasets to lazyload DB
** preparing package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded
*** arch - i386
Warning in library(pkg_name, lib.loc = lib, character.only = TRUE, logical.return = TRUE) :
there is no package called 'MlBayesOpt'
Error: loading failed
Ejecución interrumpida
*** arch - x64
Warning in library(pkg_name, lib.loc = lib, character.only = TRUE, logical.return = TRUE) :
there is no package called 'MlBayesOpt'
Error: loading failed
Ejecución interrumpida
ERROR: loading failed for 'i386', 'x64'
* removing '\\SRVR-FT/Redireccionamiento de carpetas/sdonikian/Documents/R/win-library/3.3/MlBayesOpt'
Error: Command failed (1)
Can you help me with this?
Regards
ensemble models
Error with max_depth in xgb_cv_opt
Hello, I'm getting an error with xgb_cv_opt:
Error in xgb.iter.update(fd$bst, fd$dtrain, iteration - 1, obj) : Invalid Parameter format for max_depth expect int but value='8.26801588479429' Timing stopped at: 0.9 0.08 0.982
I'm currently experimenting with your package, and this is what the call currently looks like:
opt_cv <- xgb_cv_opt(data = train_matrix,
label = y_train,
objectfun = "binary:logistic",
evalmetric = "logloss",
#eta_range = c(0.1, 1),
max_depth_range = c(5, 9),
#nrounds_range = c(100, 400),
n_folds = 5,
init_points = 6,
n_iter = 1)
Obviously, max_depth can't be a non-integer, so what's going on?
XGBoost 0.81.0.1 breaks MIBayesOpt's tests
Hello @ymattu ,
This is Tong maintaining XGBoost R-package. Recently we are planning to submit version 0.81.0.1 to CRAN.
However in the process CRAN alerts that our update breaks your test. The error message is attached. Would appreciate if you could help to check and update. Thanks!
Package: MlBayesOpt
Check: tests
New result: ERROR
Running ‘testthat.R’ [48s/48s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(MlBayesOpt)
>
> test_check("MlBayesOpt")
elapsed = 0.02 Round = 1 mtry_opt = 3.6634 min_node_size = 7.0000 Value = 0.1800
elapsed = 0.02 Round = 2 mtry_opt = 5.4408 min_node_size = 4.0000 Value = 0.1400
elapsed = 0.01 Round = 3 mtry_opt = 3.6190 min_node_size = 7.0000 Value = 0.1800
elapsed = 0.01 Round = 4 mtry_opt = 2.6933 min_node_size = 3.0000 Value = 0.2000
elapsed = 0.01 Round = 5 mtry_opt = 3.5290 min_node_size = 3.0000 Value = 0.1600
elapsed = 0.01 Round = 6 mtry_opt = 8.5781 min_node_size = 5.0000 Value = 0.1500
elapsed = 0.01 Round = 7 mtry_opt = 6.2937 min_node_size = 5.0000 Value = 0.1600
elapsed = 0.01 Round = 8 mtry_opt = 8.1154 min_node_size = 4.0000 Value = 0.1400
elapsed = 0.01 Round = 9 mtry_opt = 3.7041 min_node_size = 4.0000 Value = 0.1700
elapsed = 0.01 Round = 10 mtry_opt = 4.4780 min_node_size = 9.0000 Value = 0.1800
elapsed = 0.01 Round = 11 mtry_opt = 1.9407 min_node_size = 1.0000 Value = 0.1600
elapsed = 0.01 Round = 12 mtry_opt = 7.0937 min_node_size = 6.0000 Value = 0.1300
elapsed = 0.01 Round = 13 mtry_opt = 2.1344 min_node_size = 8.0000 Value = 0.1500
elapsed = 0.01 Round = 14 mtry_opt = 7.1353 min_node_size = 2.0000 Value = 0.1400
elapsed = 0.01 Round = 15 mtry_opt = 7.7371 min_node_size = 8.0000 Value = 0.1400
elapsed = 0.01 Round = 16 mtry_opt = 7.2140 min_node_size = 9.0000 Value = 0.1700
elapsed = 0.01 Round = 17 mtry_opt = 2.0706 min_node_size = 5.0000 Value = 0.1700
elapsed = 0.01 Round = 18 mtry_opt = 7.4475 min_node_size = 3.0000 Value = 0.1400
elapsed = 0.01 Round = 19 mtry_opt = 8.1743 min_node_size = 5.0000 Value = 0.1700
elapsed = 0.01 Round = 20 mtry_opt = 8.4158 min_node_size = 1.0000 Value = 0.1500
elapsed = 0.01 Round = 21 mtry_opt = 2.5509 min_node_size = 3.0000 Value = 0.1700Best Parameters Found: Round = 4 mtry_opt = 2.6933 min_node_size = 3.0000 Value = 0.2000 List of 4 $ Best_Par : Named num [1:2] 2.69 3 ..- attr(*, "names")= chr [1:2] "mtry_opt" "min_node_size" $ Best_Value: num 0.2 $ History :Classes 'data.table' and 'data.frame': 21 obs. of 4 variables: ..$ Round : int [1:21] 1 2 3 4 5 6 7 8 9 10 ... ..$ mtry_opt : num [1:21] 3.66 5.44 3.62 2.69 3.53 ... ..$ min_node_size: num [1:21] 7 4 7 3 3 5 5 4 4 9 ... ..$ Value : num [1:21] 0.18 0.14 0.18 0.2 0.16 0.15 0.16 0.14 0.17 0.18 ... ..- attr(*, ".internal.selfref")=<externalptr> $ Pred :Classes 'data.table' and 'data.frame': 1 obs. of 21 variables: ..$ V1 : num 0.18 ..$ V2 : num 0.14 ..$ V3 : num 0.18 ..$ V4 : num 0.2 ..$ V5 : num 0.16 ..$ V6 : num 0.15 ..$ V7 : num 0.16 ..$ V8 : num 0.14 ..$ V9 : num 0.17 ..$ V10: num 0.18 ..$ V11: num 0.16 ..$ V12: num 0.13 ..$ V13: num 0.15 ..$ V14: num 0.14 ..$ V15: num 0.14 ..$ V16: num 0.17 ..$ V17: num 0.17 ..$ V18: num 0.14 ..$ V19: num 0.17 ..$ V20: num 0.15 ..$ V21: num 0.17 ..- attr(*, ".internal.selfref")=<externalptr> elapsed = 0.01 Round = 1 gamma_opt = 3.3299 cost_opt = 61.5259 Value = 0.1900 elapsed = 0.01 Round = 2 gamma_opt = 5.5515 cost_opt = 28.7558 Value = 0.2100 elapsed = 0.01 Round = 3 gamma_opt = 3.2744 cost_opt = 70.8278 Value = 0.1700 elapsed = 0.01 Round = 4 gamma_opt = 2.1175 cost_opt = 21.9740 Value = 0.1600 elapsed = 0.01 Round = 5 gamma_opt = 3.1619 cost_opt = 19.3146 Value = 0.1600 elapsed = 0.01 Round = 6 gamma_opt = 9.4727 cost_opt = 46.3378 Value = 0.1600 elapsed = 0.01 Round = 7 gamma_opt = 6.6175 cost_opt = 41.6790 Value = 0.1400 elapsed = 0.01 Round = 8 gamma_opt = 8.8943 cost_opt = 33.0888 Value = 0.1300 elapsed = 0.01 Round = 9 gamma_opt = 3.3808 cost_opt = 29.9110 Value = 0.0800 elapsed = 0.01 Round = 10 gamma_opt = 4.3481 cost_opt = 88.7062 Value = 0.1500 elapsed = 0.01 Round = 11 gamma_opt = 1.1767 cost_opt = 5.2563 Value = 0.1300 elapsed = 0.01 Round = 12 gamma_opt = 7.6174 cost_opt = 60.4227 Value = 0.1500 elapsed = 0.01 Round = 13 gamma_opt = 1.4188 cost_opt = 79.6450 Value = 0.1700 elapsed = 0.01 Round = 14 gamma_opt = 7.6693 cost_opt = 6.2103 Value = 0.0900 elapsed = 0.01 Round = 15 gamma_opt = 8.4215 cost_opt = 78.2717 Value = 0.1300 elapsed = 0.01 Round = 16 gamma_opt = 7.7677 cost_opt = 83.7658 Value = 0.1800 elapsed = 0.01 Round = 17 gamma_opt = 1.3391 cost_opt = 45.6691 Value = 0.1100 elapsed = 0.01 Round = 18 gamma_opt = 8.0596 cost_opt = 22.1903 Value = 0.1500 elapsed = 0.01 Round = 19 gamma_opt = 8.9679 cost_opt = 46.9767 Value = 0.2000 elapsed = 0.01 Round = 20 gamma_opt = 9.2699 cost_opt = 3.9481 Value = 0.1100 elapsed = 0.01 Round = 21 gamma_opt = 9.0152 cost_opt = 39.2284 Value = 0.2000 Best Parameters Found: Round = 2 gamma_opt = 5.5515 cost_opt = 28.7558 Value = 0.2100 List of 4 $ Best_Par : Named num [1:2] 5.55 28.76 ..- attr(*, "names")= chr [1:2] "gamma_opt" "cost_opt" $ Best_Value: num 0.21 $ History :Classes 'data.table' and 'data.frame': 21 obs. of 4 variables: ..$ Round : int [1:21] 1 2 3 4 5 6 7 8 9 10 ... ..$ gamma_opt: num [1:21] 3.33 5.55 3.27 2.12 3.16 ... ..$ cost_opt : num [1:21] 61.5 28.8 70.8 22 19.3 ... ..$ Value : num [1:21] 0.19 0.21 0.17 0.16 0.16 0.16 0.14 0.13 0.08 0.15 ... ..- attr(*, ".internal.selfref")=<externalptr> $ Pred :Classes 'data.table' and 'data.frame': 100 obs. of 21 variables: ..$ V1 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..$ V2 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ... ..$ V3 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..$ V4 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ... ..$ V5 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ... ..$ V6 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..$ V7 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..$ V8 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..$ V9 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ... ..$ V10: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..$ V11: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ... ..$ V12: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..$ V13: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ... ..$ V14: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ... ..$ V15: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..$ V16: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..$ V17: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ... ..$ V18: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ... ..$ V19: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..$ V20: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ... ..$ V21: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ... ..- attr(*, ".internal.selfref")=<externalptr> elapsed = 0.01 Round = 1 gamma_opt = 3.3299 cost_opt = 61.5259 Value = 0.1900 elapsed = 0.01 Round = 2 gamma_opt = 5.5515 cost_opt = 28.7558 Value = 0.2300 elapsed = 0.01 Round = 3 gamma_opt = 3.2744 cost_opt = 70.8278 Value = 0.1900 elapsed = 0.01 Round = 4 gamma_opt = 2.1175 cost_opt = 21.9740 Value = 0.1900 elapsed = 0.01 Round = 5 gamma_opt = 3.1619 cost_opt = 19.3146 Value = 0.1900 elapsed = 0.01 Round = 6 gamma_opt = 9.4727 cost_opt = 46.3378 Value = 0.2200 elapsed = 0.01 Round = 7 gamma_opt = 6.6175 cost_opt = 41.6790 Value = 0.2200 elapsed = 0.01 Round = 8 gamma_opt = 8.8943 cost_opt = 33.0888 Value = 0.2200 elapsed = 0.01 Round = 9 gamma_opt = 3.3808 cost_opt = 29.9110 Value = 0.1900 elapsed = 0.01 Round = 10 gamma_opt = 4.3481 cost_opt = 88.7062 Value = 0.2300 elapsed = 0.01 Round = 11 gamma_opt = 1.1767 cost_opt = 5.2563 Value = 0.2000 elapsed = 0.01 Round = 12 gamma_opt = 7.6174 cost_opt = 60.4227 Value = 0.2200 elapsed = 0.01 Round = 13 gamma_opt = 1.4188 cost_opt = 79.6450 Value = 0.1800 elapsed = 0.01 Round = 14 gamma_opt = 7.6693 cost_opt = 6.2103 Value = 0.2200 elapsed = 0.01 Round = 15 gamma_opt = 8.4215 cost_opt = 78.2717 Value = 0.2300 elapsed = 0.01 Round = 16 gamma_opt = 7.7677 cost_opt = 83.7658 Value = 0.2200 elapsed = 0.01 Round = 17 gamma_opt = 1.3391 cost_opt = 45.6691 Value = 0.1800 elapsed = 0.01 Round = 18 gamma_opt = 8.0596 cost_opt = 22.1903 Value = 0.2200 elapsed = 0.01 Round = 19 gamma_opt = 8.9679 cost_opt = 46.9767 Value = 0.2200 elapsed = 0.01 Round = 20 gamma_opt = 9.2699 cost_opt = 3.9481 Value = 0.1800 elapsed = 0.01 Round = 21 gamma_opt = 9.6352 cost_opt = 14.7148 Value = 0.2200 Best Parameters Found: Round = 2 gamma_opt = 5.5515 cost_opt = 28.7558 Value = 0.2300 List of 4 $ Best_Par : Named num [1:2] 5.55 28.76 ..- attr(*, "names")= chr [1:2] "gamma_opt" "cost_opt" $ Best_Value: num 0.23 $ History :Classes 'data.table' and 'data.frame': 21 obs. of 4 variables: ..$ Round : int [1:21] 1 2 3 4 5 6 7 8 9 10 ... ..$ gamma_opt: num [1:21] 3.33 5.55 3.27 2.12 3.16 ... ..$ cost_opt : num [1:21] 61.5 28.8 70.8 22 19.3 ... ..$ Value : num [1:21] 0.19 0.23 0.19 0.19 0.19 0.22 0.22 0.22 0.19 0.23 ... ..- attr(*, ".internal.selfref")=<externalptr> $ Pred :Classes 'data.table' and 'data.frame': 1 obs. of 21 variables: ..$ V1 : num 0.19 ..$ V2 : num 0.23 ..$ V3 : num 0.19 ..$ V4 : num 0.19 ..$ V5 : num 0.19 ..$ V6 : num 0.22 ..$ V7 : num 0.22 ..$ V8 : num 0.22 ..$ V9 : num 0.19 ..$ V10: num 0.23 ..$ V11: num 0.2 ..$ V12: num 0.22 ..$ V13: num 0.18 ..$ V14: num 0.22 ..$ V15: num 0.23 ..$ V16: num 0.22 ..$ V17: num 0.18 ..$ V18: num 0.22 ..$ V19: num 0.22 ..$ V20: num 0.18 ..$ V21: num 0.22 ..- attr(*, ".internal.selfref")=<externalptr> elapsed = 0.02 Round = 1 eta_opt = 0.2854 max_depth_opt = 5.0000 nrounds_opt = 112.9858 subsample_opt = 0.4052 bytree_opt = 0.5438 Value = -0.3026 elapsed = 0.01 Round = 2 eta_opt = 0.2589 max_depth_opt = 5.0000 nrounds_opt = 147.5089 subsample_opt = 0.8555 bytree_opt = 0.4354 Value = -0.1330 elapsed = 0.01 Round = 3 eta_opt = 0.7183 max_depth_opt = 5.0000 nrounds_opt = 109.4287 subsample_opt = 0.4120 bytree_opt = 0.7854 Value = -0.0753 elapsed = 0.01 Round = 4 eta_opt = 0.4457 max_depth_opt = 4.0000 nrounds_opt = 92.0318 subsample_opt = 0.4004 bytree_opt = 0.9258 Value = -0.0841 elapsed = 0.01 Round = 5 eta_opt = 0.7929 max_depth_opt = 6.0000 nrounds_opt = 76.3611 subsample_opt = 0.5287 bytree_opt = 0.8673 Value = -0.0526 elapsed = 0.01 Round = 6 eta_opt = 0.5479 max_depth_opt = 5.0000 nrounds_opt = 78.9520 subsample_opt = 0.9030 bytree_opt = 0.8784 Value = -0.0263 elapsed = 0.01 Round = 7 eta_opt = 0.7459 max_depth_opt = 6.0000 nrounds_opt = 98.4645 subsample_opt = 0.8779 bytree_opt = 0.6732 Value = -0.0263 elapsed = 0.01 Round = 8 eta_opt = 0.9927 max_depth_opt = 4.0000 nrounds_opt = 116.6771 subsample_opt = 0.4510 bytree_opt = 0.6461 Value = -0.0351 elapsed = 0.01 Round = 9 eta_opt = 0.4420 max_depth_opt = 5.0000 nrounds_opt = 129.5805 subsample_opt = 0.7996 bytree_opt = 0.8865 Value = -0.0175 elapsed = 0.01 Round = 10 eta_opt = 0.7997 max_depth_opt = 5.0000 nrounds_opt = 106.6147 subsample_opt = 0.9646 bytree_opt = 0.7630 Value = -0.0263 elapsed = 0.01 Round = 11 eta_opt = 0.9412 max_depth_opt = 6.0000 nrounds_opt = 152.1588 subsample_opt = 0.4912 bytree_opt = 0.7928 Value = -0.0577 elapsed = 0.01 Round = 12 eta_opt = 0.2909 max_depth_opt = 5.0000 nrounds_opt = 96.4243 subsample_opt = 0.7413 bytree_opt = 0.6119 Value = -0.0943 elapsed = 0.01 Round = 13 eta_opt = 0.6865 max_depth_opt = 6.0000 nrounds_opt = 111.3159 subsample_opt = 0.4600 bytree_opt = 0.5622 Value = -0.1579 elapsed = 0.01 Round = 14 eta_opt = 0.2130 max_depth_opt = 5.0000 nrounds_opt = 99.9155 subsample_opt = 0.3928 bytree_opt = 0.9956 Value = -0.1491 elapsed = 0.01 Round = 15 eta_opt = 0.3405 max_depth_opt = 5.0000 nrounds_opt = 128.5783 subsample_opt = 0.7814 bytree_opt = 0.7801 Value = -0.0351 elapsed = 0.01 Round = 16 eta_opt = 0.4475 max_depth_opt = 6.0000 nrounds_opt = 93.2215 subsample_opt = 0.2824 bytree_opt = 0.5279 Value = -0.3428 elapsed = 0.01 Round = 17 eta_opt = 0.1121 max_depth_opt = 4.0000 nrounds_opt = 113.0691 subsample_opt = 0.7400 bytree_opt = 0.4776 Value = -0.1367 elapsed = 0.01 Round = 18 eta_opt = 0.4441 max_depth_opt = 5.0000 nrounds_opt = 138.9680 subsample_opt = 0.2095 bytree_opt = 0.6869 Value = -0.5022 elapsed = 0.01 Round = 19 eta_opt = 0.8827 max_depth_opt = 5.0000 nrounds_opt = 77.5822 subsample_opt = 0.3209 bytree_opt = 0.9544 Value = -0.1053 elapsed = 0.01 Round = 20 eta_opt = 0.4063 max_depth_opt = 5.0000 nrounds_opt = 148.7789 subsample_opt = 0.2290 bytree_opt = 0.7593 Value = -0.3567 elapsed = 0.01 Round = 21 eta_opt = 1.0000 max_depth_opt = 4.0000 nrounds_opt = 106.3408 subsample_opt = 0.6443 bytree_opt = 0.6353 Value = -0.0577 Best Parameters Found: Round = 9 eta_opt = 0.4420 max_depth_opt = 5.0000 nrounds_opt = 129.5805 subsample_opt = 0.7996 bytree_opt = 0.8865 Value = -0.0175 List of 4 $ Best_Par : Named num [1:5] 0.442 5 129.58 0.8 0.887 ..- attr(*, "names")= chr [1:5] "eta_opt" "max_depth_opt" "nrounds_opt" "subsample_opt" ... $ Best_Value: num -0.0175 $ History :Classes 'data.table' and 'data.frame': 21 obs. of 7 variables: ..$ Round : int [1:21] 1 2 3 4 5 6 7 8 9 10 ... ..$ eta_opt : num [1:21] 0.285 0.259 0.718 0.446 0.793 ... ..$ max_depth_opt: num [1:21] 5 5 5 4 6 5 6 4 5 5 ... ..$ nrounds_opt : num [1:21] 113 147.5 109.4 92 76.4 ... ..$ subsample_opt: num [1:21] 0.405 0.855 0.412 0.4 0.529 ... ..$ bytree_opt : num [1:21] 0.544 0.435 0.785 0.926 0.867 ... ..$ Value : num [1:21] -0.3026 -0.133 -0.0753 -0.0841 -0.0526 ... ..- attr(*, ".internal.selfref")=<externalptr> $ Pred :Classes 'data.table' and 'data.frame': 100 obs. of 210 variables: ..$ V1 : num [1:100] 0 0 0 9 0 9 5 5 0 6 ... ..$ V2 : num [1:100] 0 0 0 9 0 9 5 5 0 6 ... ..$ V3 : num [1:100] 0 0 0 9 0 9 5 5 0 6 ... ..$ V4 : num [1:100] 0 0 0 9 0 9 5 5 0 6 ... ..$ V5 : num [1:100] 0 0 0 9 0 9 5 5 0 6 ... ..$ V6 : num [1:100] 0 0 0 9 0 9 5 5 0 6 ... ..$ V7 : num [1:100] 0 0 0 9 0 9 5 5 0 6 ... ..$ V8 : num [1:100] 0 0 0 9 0 9 5 5 0 6 ... ..$ V9 : num [1:100] 0 0 0 9 0 9 5 5 0 6 ... ..$ V10 : num [1:100] 0 0 0 9 0 9 5 5 0 6 ... ..$ V1.1 : num [1:100] 5 5 5 8 9 8 3 0 5 8 ... ..$ V2.1 : num [1:100] 5 5 5 8 9 8 3 0 5 8 ... ..$ V3.1 : num [1:100] 5 5 5 8 9 8 3 0 5 8 ... ..$ V4.1 : num [1:100] 5 5 5 8 9 8 3 0 5 8 ... ..$ V5.1 : num [1:100] 5 5 5 8 9 8 3 0 5 8 ... ..$ V6.1 : num [1:100] 5 5 5 8 9 8 3 0 5 8 ... ..$ V7.1 : num [1:100] 5 5 5 8 9 8 3 0 5 8 ... ..$ V8.1 : num [1:100] 5 5 5 8 9 8 3 0 5 8 ... ..$ V9.1 : num [1:100] 5 5 5 8 9 8 3 0 5 8 ... ..$ V10.1 : num [1:100] 5 5 5 8 9 8 3 0 5 8 ... ..$ V1.2 : num [1:100] 9 7 5 7 9 7 3 0 6 6 ... ..$ V2.2 : num [1:100] 9 7 5 7 9 7 3 0 6 6 ... ..$ V3.2 : num [1:100] 9 7 5 7 9 7 3 0 6 6 ... ..$ V4.2 : num [1:100] 9 7 5 7 9 7 3 0 6 6 ... ..$ V5.2 : num [1:100] 9 7 5 7 9 7 3 0 6 6 ... ..$ V6.2 : num [1:100] 9 7 5 7 9 7 3 0 6 6 ... ..$ V7.2 : num [1:100] 9 7 5 7 9 7 3 0 6 6 ... ..$ V8.2 : num [1:100] 9 7 5 7 9 7 3 0 6 6 ... ..$ V9.2 : num [1:100] 9 7 5 7 9 7 3 0 6 6 ... ..$ V10.2 : num [1:100] 9 7 5 7 9 7 3 0 6 6 ... ..$ V1.3 : num [1:100] 7 7 3 8 7 9 0 9 6 6 ... ..$ V2.3 : num [1:100] 7 7 3 8 7 9 0 9 6 6 ... ..$ V3.3 : num [1:100] 7 7 3 8 7 9 0 9 6 6 ... ..$ V4.3 : num [1:100] 7 7 3 8 7 9 0 9 6 6 ... ..$ V5.3 : num [1:100] 7 7 3 8 7 9 0 9 6 6 ... ..$ V6.3 : num [1:100] 7 7 3 8 7 9 0 9 6 6 ... ..$ V7.3 : num [1:100] 7 7 3 8 7 9 0 9 6 6 ... ..$ V8.3 : num [1:100] 7 7 3 8 7 9 0 9 6 6 ... ..$ V9.3 : num [1:100] 7 7 3 8 7 9 0 9 6 6 ... ..$ V10.3 : num [1:100] 7 7 3 8 7 9 0 9 6 6 ... ..$ V1.4 : num [1:100] 9 6 3 8 9 8 3 0 6 6 ... ..$ V2.4 : num [1:100] 9 6 3 8 9 8 3 0 6 6 ... ..$ V3.4 : num [1:100] 9 6 3 8 9 8 3 0 6 6 ... ..$ V4.4 : num [1:100] 9 6 3 8 9 8 3 0 6 6 ... ..$ V5.4 : num [1:100] 9 6 3 8 9 8 3 0 6 6 ... ..$ V6.4 : num [1:100] 9 6 3 8 9 8 3 0 6 6 ... ..$ V7.4 : num [1:100] 9 6 3 8 9 8 3 0 6 6 ... ..$ V8.4 : num [1:100] 9 6 3 8 9 8 3 0 6 6 ... ..$ V9.4 : num [1:100] 9 6 3 8 9 8 3 0 6 6 ... ..$ V10.4 : num [1:100] 9 6 3 8 9 8 3 0 6 6 ... ..$ V1.5 : num [1:100] 8 7 3 8 7 9 3 0 6 6 ... ..$ V2.5 : num [1:100] 8 7 3 8 7 9 3 0 6 6 ... ..$ V3.5 : num [1:100] 8 7 3 8 7 9 3 0 6 6 ... ..$ V4.5 : num [1:100] 8 7 3 8 7 9 3 0 6 6 ... ..$ V5.5 : num [1:100] 8 7 3 8 7 9 3 0 6 6 ... ..$ V6.5 : num [1:100] 8 7 3 8 7 9 3 0 6 6 ... ..$ V7.5 : num [1:100] 8 7 3 8 7 9 3 0 6 6 ... ..$ V8.5 : num [1:100] 8 7 3 8 7 9 3 0 6 6 ... ..$ V9.5 : num [1:100] 8 7 3 8 7 9 3 0 6 6 ... ..$ V10.5 : num [1:100] 8 7 3 8 7 9 3 0 6 6 ... ..$ V1.6 : num [1:100] 8 7 3 8 9 5 3 0 6 5 ... ..$ V2.6 : num [1:100] 8 7 3 8 9 5 3 0 6 5 ... ..$ V3.6 : num [1:100] 8 7 3 8 9 5 3 0 6 5 ... ..$ V4.6 : num [1:100] 8 7 3 8 9 5 3 0 6 5 ... ..$ V5.6 : num [1:100] 8 7 3 8 9 5 3 0 6 5 ... ..$ V6.6 : num [1:100] 8 7 3 8 9 5 3 0 6 5 ... ..$ V7.6 : num [1:100] 8 7 3 8 9 5 3 0 6 5 ... ..$ V8.6 : num [1:100] 8 7 3 8 9 5 3 0 6 5 ... ..$ V9.6 : num [1:100] 8 7 3 8 9 5 3 0 6 5 ... ..$ V10.6 : num [1:100] 8 7 3 8 9 5 3 0 6 5 ... ..$ V1.7 : num [1:100] 8 6 9 8 0 8 2 0 6 6 ... ..$ V2.7 : num [1:100] 8 6 9 8 0 8 2 0 6 6 ... ..$ V3.7 : num [1:100] 8 6 9 8 0 8 2 0 6 6 ... ..$ V4.7 : num [1:100] 8 6 9 8 0 8 2 0 6 6 ... ..$ V5.7 : num [1:100] 8 6 9 8 0 8 2 0 6 6 ... ..$ V6.7 : num [1:100] 8 6 9 8 0 8 2 0 6 6 ... ..$ V7.7 : num [1:100] 8 6 9 8 0 8 2 0 6 6 ... ..$ V8.7 : num [1:100] 8 6 9 8 0 8 2 0 6 6 ... ..$ V9.7 : num [1:100] 8 6 9 8 0 8 2 0 6 6 ... ..$ V10.7 : num [1:100] 8 6 9 8 0 8 2 0 6 6 ... ..$ V1.8 : num [1:100] 7 7 3 8 9 8 3 0 6 6 ... ..$ V2.8 : num [1:100] 7 7 3 8 9 8 3 0 6 6 ... ..$ V3.8 : num [1:100] 7 7 3 8 9 8 3 0 6 6 ... ..$ V4.8 : num [1:100] 7 7 3 8 9 8 3 0 6 6 ... ..$ V5.8 : num [1:100] 7 7 3 8 9 8 3 0 6 6 ... ..$ V6.8 : num [1:100] 7 7 3 8 9 8 3 0 6 6 ... ..$ V7.8 : num [1:100] 7 7 3 8 9 8 3 0 6 6 ... ..$ V8.8 : num [1:100] 7 7 3 8 9 8 3 0 6 6 ... ..$ V9.8 : num [1:100] 7 7 3 8 9 8 3 0 6 6 ... ..$ V10.8 : num [1:100] 7 7 3 8 9 8 3 0 6 6 ... ..$ V1.9 : num [1:100] 8 7 3 8 9 9 3 0 6 7 ... ..$ V2.9 : num [1:100] 8 7 3 8 9 9 3 0 6 7 ... ..$ V3.9 : num [1:100] 8 7 3 8 9 9 3 0 6 7 ... ..$ V4.9 : num [1:100] 8 7 3 8 9 9 3 0 6 7 ... ..$ V5.9 : num [1:100] 8 7 3 8 9 9 3 0 6 7 ... ..$ V6.9 : num [1:100] 8 7 3 8 9 9 3 0 6 7 ... ..$ V7.9 : num [1:100] 8 7 3 8 9 9 3 0 6 7 ... ..$ V8.9 : num [1:100] 8 7 3 8 9 9 3 0 6 7 ... ..$ V9.9 : num [1:100] 8 7 3 8 9 9 3 0 6 7 ... .. [list output truncated] ..- attr(*, ".internal.selfref")=<externalptr> elapsed = 0.04 Round = 1 eta_opt = 0.3996 max_depth_opt = 5.0000 nrounds_opt = 103.8797 subsample_opt = 0.6901 bytree_opt = 0.5783 Value = 1.0000 elapsed = 0.05 Round = 2 eta_opt = 0.5996 max_depth_opt = 5.0000 nrounds_opt = 125.7482 subsample_opt = 0.3096 bytree_opt = 0.6693 Value = 1.0000 elapsed = 0.03 Round = 3 eta_opt = 0.3946 max_depth_opt = 5.0000 nrounds_opt = 73.3337 subsample_opt = 0.1606 bytree_opt = 0.8845 Value = 0.1800 elapsed = 0.04 Round = 4 eta_opt = 0.2905 max_depth_opt = 4.0000 nrounds_opt = 129.3648 subsample_opt = 0.1475 bytree_opt = 0.5431 Value = 0.1800 elapsed = 0.04 Round = 5 eta_opt = 0.3845 max_depth_opt = 4.0000 nrounds_opt = 106.4619 subsample_opt = 0.3976 bytree_opt = 0.4083 Value = 1.0000 elapsed = 0.04 Round = 6 eta_opt = 0.9525 max_depth_opt = 5.0000 nrounds_opt = 127.4542 subsample_opt = 0.2646 bytree_opt = 0.4167 Value = 1.0000 elapsed = 0.04 Round = 7 eta_opt = 0.6955 max_depth_opt = 5.0000 nrounds_opt = 119.2315 subsample_opt = 0.5751 bytree_opt = 0.4965 Value = 1.0000 elapsed = 0.03 Round = 8 eta_opt = 0.9005 max_depth_opt = 5.0000 nrounds_opt = 81.0287 subsample_opt = 0.8342 bytree_opt = 0.6838 Value = 1.0000 elapsed = 0.03 Round = 9 eta_opt = 0.4042 max_depth_opt = 5.0000 nrounds_opt = 73.5520 subsample_opt = 0.5461 bytree_opt = 0.6483 Value = 1.0000 elapsed = 0.05 Round = 10 eta_opt = 0.4913 max_depth_opt = 6.0000 nrounds_opt = 144.0938 subsample_opt = 0.1334 bytree_opt = 0.6559 Value = 0.1900 elapsed = 0.03 Round = 11 eta_opt = 0.2058 max_depth_opt = 4.0000 nrounds_opt = 72.1364 subsample_opt = 0.4510 bytree_opt = 0.4659 Value = 1.0000 elapsed = 0.03 Round = 12 eta_opt = 0.7855 max_depth_opt = 5.0000 nrounds_opt = 81.2798 subsample_opt = 0.3255 bytree_opt = 0.7891 Value = 1.0000 elapsed = 0.05 Round = 13 eta_opt = 0.2276 max_depth_opt = 6.0000 nrounds_opt = 124.3278 subsample_opt = 0.9381 bytree_opt = 0.7298 Value = 1.0000 elapsed = 0.04 Round = 14 eta_opt = 0.7902 max_depth_opt = 4.0000 nrounds_opt = 115.3598 subsample_opt = 0.6396 bytree_opt = 0.9333 Value = 1.0000 elapsed = 0.06 Round = 15 eta_opt = 0.8579 max_depth_opt = 6.0000 nrounds_opt = 155.7652 subsample_opt = 0.9330 bytree_opt = 0.6380 Value = 1.0000 elapsed = 0.05 Round = 16 eta_opt = 0.7991 max_depth_opt = 6.0000 nrounds_opt = 159.1933 subsample_opt = 0.9602 bytree_opt = 0.7328 Value = 1.0000 elapsed = 0.04 Round = 17 eta_opt = 0.2204 max_depth_opt = 5.0000 nrounds_opt = 112.8439 subsample_opt = 0.8948 bytree_opt = 0.4939 Value = 1.0000 elapsed = 0.04 Round = 18 eta_opt = 0.8253 max_depth_opt = 4.0000 nrounds_opt = 126.4373 subsample_opt = 0.6642 bytree_opt = 0.4461 Value = 1.0000 elapsed = 0.05 Round = 19 eta_opt = 0.9071 max_depth_opt = 5.0000 nrounds_opt = 129.1942 subsample_opt = 0.6238 bytree_opt = 0.6919 Value = 1.0000 elapsed = 0.03 Round = 20 eta_opt = 0.9343 max_depth_opt = 4.0000 nrounds_opt = 86.8685 subsample_opt = 0.9110 bytree_opt = 0.5663 Value = 1.0000 ── 1. Error: (unknown) (@test-xgb_opt.R#9) ──────────────────────────────────── task 1 failed - "non-finite value supplied by optim" 1: xgb_opt(train_data = tr, train_label = y, test_data = ts, test_label = y, objectfun = "multi:softmax", evalmetric = "merror", classes = 10, init_points = 20, n_iter = 1) at testthat/test-xgb_opt.R:9 2: BayesianOptimization(xgb_holdout, bounds = list(eta_opt = eta_range, max_depth_opt = max_depth_range, nrounds_opt = nrounds_range, subsample_opt = subsample_range, bytree_opt = bytree_range), init_points, init_grid_dt = NULL, n_iter, acq, kappa, eps, optkernel, verbose = TRUE) 3: Utility_Max(DT_bounds, GP, acq = acq, y_max = max(DT_history[, Value]), kappa = kappa, eps = eps) %>% Min_Max_Inverse_Scale_Vec(., lower = DT_bounds[, Lower], upper = DT_bounds[, Upper]) %>% magrittr::set_names(., DT_bounds[, Parameter]) %>% inset(., DT_bounds[Type == "integer", Parameter], round(extract(., DT_bounds[Type == "integer", Parameter]))) 4: eval(lhs, parent, parent) 5: eval(lhs, parent, parent) 6: Utility_Max(DT_bounds, GP, acq = acq, y_max = max(DT_history[, Value]), kappa = kappa, eps = eps) 7: foreach(i = 1:nrow(Mat_tries), .combine = "rbind") %do% { optim_result <- optim(par = Mat_tries[i, ], fn = Utility, GP = GP, acq = acq, y_max = y_max, kappa = kappa, eps = eps, method = "L-BFGS-B", lower = rep(0, length(DT_bounds[, Lower])), upper = rep(1, length(DT_bounds[, Upper])), control = list(maxit = 100, factr = 5e+11)) c(optim_result$par, optim_result$value) } %>% data.table(.) %>% setnames(., old = names(.), new = c(DT_bounds[, Parameter], "Negetive_Utility")) 8: eval(lhs, parent, parent) 9: eval(lhs, parent, parent) 10: foreach(i = 1:nrow(Mat_tries), .combine = "rbind") %do% { optim_result <- optim(par = Mat_tries[i, ], fn = Utility, GP = GP, acq = acq, y_max = y_max, kappa = kappa, eps = eps, method = "L-BFGS-B", lower = rep(0, length(DT_bounds[, Lower])), upper = rep(1, length(DT_bounds[, Upper])), control = list(maxit = 100, factr = 5e+11)) c(optim_result$par, optim_result$value) } 11: e$fun(obj, substitute(ex), parent.frame(), e$data) ══ testthat results ═══════════════════════════════════════════════════════════ OK: 0 SKIPPED: 0 FAILED: 1 1. Error: (unknown) (@test-xgb_opt.R#9) Error: testthat unit tests failed Execution halted
add function argument to allow for nthread > 1
Currently xgboost is only run using a single thread due to nthread = 1 in xgb_cb_opt.R and xgb_opt.R. It would be nice if the user would have the option to override this default setting.
Random Forest example fails
Hello,
I'm trying the random forest example shown on the readme and running into an error - any ideas?
> library(MlBayesOpt)
> set.seed(123)
> mod <- rf_opt(
+ train_data = iris_train,
+ train_label = iris_train$Species,
+ test_data = iris_test,
+ test_label = iris_test$Species,
+ mtry_range = c(1L, 4L)
+ )
Error in eval(f[[2]], envir = data) : object 'trainlabel' not found Timing stopped at: 0.196 0.003 0.2
> traceback()
15: eval(f[[2]], envir = data)
14: eval(f[[2]], envir = data)
13: data.frame(eval(f[[2]], envir = data))
12: parse.formula(formula, data)
11: ranger(trainlabel ~ ., dtrain, num.trees = num_trees_opt, mtry = mtry_opt)
10: (function (num_trees_opt, mtry_opt)
{
model <- ranger(trainlabel ~ ., dtrain, num.trees = num_trees_opt,
mtry = mtry_opt)
t.pred <- predict(model, dat = dtest)
Pred <- sum(diag(table(testlabel, t.pred$predictions)))/nrow(dtest)
list(Score = Pred, Pred = Pred)
})(num_trees_opt = 288, mtry_opt = 4)
9: do.call(what = FUN, args = as.list(This_Par))
8: system.time({
This_Score_Pred <- do.call(what = FUN, args = as.list(This_Par))
})
7: eval(expr, pf)
6: eval(expr, pf)
5: withVisible(eval(expr, pf))
4: evalVis(expr)
3: utils::capture.output({
This_Time <- system.time({
This_Score_Pred <- do.call(what = FUN, args = as.list(This_Par))
})
})
2: BayesianOptimization(rf_holdout, bounds = list(num_trees_opt = num_tree_range,
mtry_opt = mtry_range), init_points, init_grid_dt = NULL,
n_iter, acq, kappa, eps, verbose = TRUE)
1: rf_opt(train_data = iris_train, train_label = iris_train$Species,
test_data = iris_test, test_label = iris_test$Species, mtry_range = c(1L,
4L))Thanks,
Chris
Error: Invalid parameter format for num_class expect int but value = 'NA'
hello,
When I use MIBayesOpt to optimize xgboost model to solve a linear regression problem like predict house price, I choose objectfun = "reg:linear , this is not a classification problem means no classes parameter, but it seems i have to give a num_class?
hope for u reply!
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