5-results
Bernard
2021-06-29
Last updated: 2021-07-02
Checks: 7 0
Knit directory: spanish_data/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 3ec8912 | Bernard | 2021-07-02 | updated models and build for publish |
| html | 3ec8912 | Bernard | 2021-07-02 | updated models and build for publish |
Load package
# Helper
library (tidyverse)Warning: package 'tidyverse' was built under R version 4.0.3-- Attaching packages --------------------------------------- tidyverse 1.3.0 --v ggplot2 3.3.3 v purrr 0.3.4
v tibble 3.0.4 v dplyr 1.0.2
v tidyr 1.1.2 v stringr 1.4.0
v readr 1.4.0 v forcats 0.5.0Warning: package 'ggplot2' was built under R version 4.0.3Warning: package 'tibble' was built under R version 4.0.3Warning: package 'tidyr' was built under R version 4.0.3Warning: package 'readr' was built under R version 4.0.3Warning: package 'purrr' was built under R version 4.0.3Warning: package 'dplyr' was built under R version 4.0.3Warning: package 'stringr' was built under R version 4.0.3Warning: package 'forcats' was built under R version 4.0.3-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library (cowplot)Warning: package 'cowplot' was built under R version 4.0.3# ML
library (mlr3)Warning: package 'mlr3' was built under R version 4.0.4library (mlr3learners)Warning: package 'mlr3learners' was built under R version 4.0.4library (mlr3tuning)Warning: package 'mlr3tuning' was built under R version 4.0.4Loading required package: paradoxWarning: package 'paradox' was built under R version 4.0.4library (mlr3viz)Warning: package 'mlr3viz' was built under R version 4.0.4library (mlr3fselect)Warning: package 'mlr3fselect' was built under R version 4.0.4library (mlr3pipelines)Warning: package 'mlr3pipelines' was built under R version 4.0.4library (mlr3hyperband)Warning: package 'mlr3hyperband' was built under R version 4.0.5Create tasks
dat <- readRDS("output/df.RDS")
# Neck pain
train <- dat$df_list$np$train_imp
test <- dat$df_list$np$test_imp
comb <- bind_rows(train, test)
train_id <- 1: nrow (train)
test_id <- (nrow (train) + 1): nrow (comb)
# Set training task
task_np <- TaskClassif$new (id = "neckpain", backend = comb, target = "outcome")
task_np$set_col_roles("ID", roles = "name")
# Arm pain
train <- dat$df_list$ap$train_imp
test <- dat$df_list$ap$test_imp
comb <- bind_rows(train, test)
train_id <- 1: nrow (train)
test_id <- (nrow (train) + 1): nrow (comb)
# Set training task
task_ap <- TaskClassif$new (id = "armpain", backend = comb, target = "outcome")
task_ap$set_col_roles("ID", roles = "name")
# NDI
train <- dat$df_list$dis$train_imp
test <- dat$df_list$dis$test_imp
comb <- bind_rows(train, test)
train_id <- 1: nrow (train)
test_id <- (nrow (train) + 1): nrow (comb)
# Set training task
task_dis <- TaskClassif$new (id = "disability", backend = comb, target = "outcome")
task_dis$set_col_roles("ID", roles = "name")
measures <- list (msr("classif.auc"),
msr("classif.acc"),
msr("classif.tpr"),
msr("classif.fpr"),
msr("classif.fnr"),
msr("classif.tnr"))Neck pain
Internal validation
m_list <- readRDS("output/np_result.RDS")
bmr1 <- as_benchmark_result(m_list$rsmp_list$rr_logreg)
bmr2 <- as_benchmark_result(m_list$rsmp_list$rr_kknn)
bmr3 <- as_benchmark_result(m_list$rsmp_list$rr_xgb)
bmr4 <- as_benchmark_result(m_list$rsmp_list$rr_lasso)
bmr5 <- as_benchmark_result(m_list$rsmp_list$rr_rf)
bmr6 <- as_benchmark_result(m_list$rsmp_list$rr_net)
bmr7 <- as_benchmark_result(m_list$rsmp_list$rr_svm)
bmr1$
combine(bmr2)$
combine (bmr3)$
combine (bmr4)$
combine (bmr5)$
combine (bmr6)$
combine (bmr7)
bmr1<BenchmarkResult> of 21 rows with 7 resampling runs
nr task_id learner_id resampling_id iters warnings errors
1 neckpain scale.log.fselector cv 3 0 0
2 neckpain scale.encode.kknn.tuned cv 3 0 0
3 neckpain scale.encode.xgb.tuned cv 3 0 0
4 neckpain scale.encode.lasso.tuned cv 3 0 0
5 neckpain scale.encode.rf.tuned cv 3 0 0
6 neckpain scale.encode.nnet.tuned cv 3 0 0
7 neckpain scale.encode.svm.tuned cv 3 0 0Plot results
ynames <- c("Area under Curve", "Accuracy", "True positive",
"False positive", "False negative", "True negative")
mod_names <- c("Logistic","KKNN","Xgboost", "Lasso", "RF", "Nnet", "SVM")
plot_list <- vector ("list", length (measures))
for (n in seq_along(measures)) {
plot_list[[n]] <- autoplot(bmr1, measure = measures[[n]]) +
theme_cowplot() +
scale_x_discrete(labels = mod_names) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
ylab (ynames[n])
} Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.plot_grid(
plotlist = plot_list,
align="hv"
)
External validation
pred_list <- vector ("list", length (m_list$model_list))
for (n in seq_along(m_list$model_list)) {
m <- m_list$model_list[[n]]
p <- m$predict(task_np, row_ids = test_id)
pred_list[[n]] <- p$score (measures)
} Warning: package 'kknn' was built under R version 4.0.5names(pred_list) <- mod_names
pred <- pred_list %>%
bind_rows(.id = "models")
names (pred)[-1] <- c("Auc", "Acc", "Tpr",
"Fpr", "Fnr", "Tnr")Plot results
pred_plot <- pred %>%
pivot_longer(cols = -models,
names_to = "Measures",
values_to = "Performance") %>%
ggplot() +
geom_bar(aes (x = Measures, y = Performance, fill = models),
position = "dodge", stat = "identity") +
scale_fill_manual(values = c("#030303", "#0000FF", "#EE0000", "#00CD00", "#EE9A00", "#912CEE", "#8B4726")) +
theme_cowplot()
pred_plot
Arm pain
Internal validation
m_list <- readRDS("output/ap_result.RDS")
bmr1 <- as_benchmark_result(m_list$rsmp_list$rr_logreg)
bmr2 <- as_benchmark_result(m_list$rsmp_list$rr_kknn)
bmr3 <- as_benchmark_result(m_list$rsmp_list$rr_xgb)
bmr4 <- as_benchmark_result(m_list$rsmp_list$rr_lasso)
bmr5 <- as_benchmark_result(m_list$rsmp_list$rr_rf)
bmr6 <- as_benchmark_result(m_list$rsmp_list$rr_net)
bmr7 <- as_benchmark_result(m_list$rsmp_list$rr_svm)
bmr1$
combine(bmr2)$
combine (bmr3)$
combine (bmr4)$
combine (bmr5)$
combine (bmr6)$
combine (bmr7)
bmr1<BenchmarkResult> of 21 rows with 7 resampling runs
nr task_id learner_id resampling_id iters warnings errors
1 armpain scale.log.fselector cv 3 0 0
2 armpain scale.encode.kknn.tuned cv 3 0 0
3 armpain scale.encode.xgb.tuned cv 3 0 0
4 armpain scale.encode.lasso.tuned cv 3 0 0
5 armpain scale.encode.rf.tuned cv 3 0 0
6 armpain scale.encode.nnet.tuned cv 3 0 0
7 armpain scale.encode.svm.tuned cv 3 0 0Plot results
ynames <- c("Area under Curve", "Accuracy", "True positive",
"False positive", "False negative", "True negative")
mod_names <- c("Logistic","KKNN","Xgboost", "Lasso", "RF", "Nnet", "SVM")
plot_list <- vector ("list", length (measures))
for (n in seq_along(measures)) {
plot_list[[n]] <- autoplot(bmr1, measure = measures[[n]]) +
theme_cowplot() +
scale_x_discrete(labels = mod_names) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
ylab (ynames[n])
} Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.plot_grid(
plotlist = plot_list,
align="hv"
)
External validation
pred_list <- vector ("list", length (m_list$model_list))
for (n in seq_along(m_list$model_list)) {
m <- m_list$model_list[[n]]
p <- m$predict(task_ap, row_ids = test_id)
pred_list[[n]] <- p$score (measures)
} Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
prediction from a rank-deficient fit may be misleadingWarning: package 'kknn' was built under R version 4.0.5names(pred_list) <- mod_names
pred <- pred_list %>%
bind_rows(.id = "models")
names (pred)[-1] <- c("Auc", "Acc", "Tpr",
"Fpr", "Fnr", "Tnr")Plot results
pred_plot <- pred %>%
pivot_longer(cols = -models,
names_to = "Measures",
values_to = "Performance") %>%
ggplot() +
geom_bar(aes (x = Measures, y = Performance, fill = models),
position = "dodge", stat = "identity") +
scale_fill_manual(values = c("#030303", "#0000FF", "#EE0000", "#00CD00", "#EE9A00", "#912CEE", "#8B4726")) +
theme_cowplot()
pred_plot
Disability
Internal validation
m_list <- readRDS("output/dis_result.RDS")
bmr1 <- as_benchmark_result(m_list$rsmp_list$rr_logreg)
bmr2 <- as_benchmark_result(m_list$rsmp_list$rr_kknn)
bmr3 <- as_benchmark_result(m_list$rsmp_list$rr_xgb)
bmr4 <- as_benchmark_result(m_list$rsmp_list$rr_lasso)
bmr5 <- as_benchmark_result(m_list$rsmp_list$rr_rf)
bmr6 <- as_benchmark_result(m_list$rsmp_list$rr_net)
bmr7 <- as_benchmark_result(m_list$rsmp_list$rr_svm)
bmr1$
combine(bmr2)$
combine (bmr3)$
combine (bmr4)$
combine (bmr5)$
combine (bmr6)$
combine (bmr7)
bmr1<BenchmarkResult> of 21 rows with 7 resampling runs
nr task_id learner_id resampling_id iters warnings errors
1 disability scale.log.fselector cv 3 0 0
2 disability scale.encode.kknn.tuned cv 3 0 0
3 disability scale.encode.xgb.tuned cv 3 0 0
4 disability scale.encode.lasso.tuned cv 3 0 0
5 disability scale.encode.rf.tuned cv 3 0 0
6 disability scale.encode.nnet.tuned cv 3 0 0
7 disability scale.encode.svm.tuned cv 3 0 0Plot results
ynames <- c("Area under Curve", "Accuracy", "True positive",
"False positive", "False negative", "True negative")
mod_names <- c("Logistic","KKNN","Xgboost", "Lasso", "RF", "Nnet", "SVM")
plot_list <- vector ("list", length (measures))
for (n in seq_along(measures)) {
plot_list[[n]] <- autoplot(bmr1, measure = measures[[n]]) +
theme_cowplot() +
scale_x_discrete(labels = mod_names) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
ylab (ynames[n])
} Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.plot_grid(
plotlist = plot_list,
align="hv"
)
External validation
pred_list <- vector ("list", length (m_list$model_list))
for (n in seq_along(m_list$model_list)) {
m <- m_list$model_list[[n]]
p <- m$predict(task_dis, row_ids = test_id)
pred_list[[n]] <- p$score (measures)
} Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
prediction from a rank-deficient fit may be misleadingWarning: package 'kknn' was built under R version 4.0.5names(pred_list) <- mod_names
pred <- pred_list %>%
bind_rows(.id = "models")
names (pred)[-1] <- c("Auc", "Acc", "Tpr",
"Fpr", "Fnr", "Tnr")Plot results
pred_plot <- pred %>%
pivot_longer(cols = -models,
names_to = "Measures",
values_to = "Performance") %>%
ggplot() +
geom_bar(aes (x = Measures, y = Performance, fill = models),
position = "dodge", stat = "identity") +
scale_fill_manual(values = c("#030303", "#0000FF", "#EE0000", "#00CD00", "#EE9A00", "#912CEE", "#8B4726")) +
theme_cowplot()
pred_plot
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)
Matrix products: default
locale:
[1] LC_COLLATE=English_United Kingdom.1252
[2] LC_CTYPE=English_United Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] mlr3hyperband_0.1.2 mlr3pipelines_0.3.4 mlr3fselect_0.5.1
[4] mlr3viz_0.5.3 mlr3tuning_0.8.0 paradox_0.7.1
[7] mlr3learners_0.4.5 mlr3_0.11.0 cowplot_1.1.1
[10] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[16] tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0
[19] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.9.2 bbotk_0.3.2
[4] httr_1.4.2 rprojroot_2.0.2 tools_4.0.2
[7] backports_1.2.1 R6_2.5.0 DBI_1.1.0
[10] mlr3measures_0.3.1 colorspace_2.0-0 nnet_7.3-14
[13] withr_2.3.0 mlr3misc_0.8.0 tidyselect_1.1.0
[16] compiler_4.0.2 git2r_0.27.1 glmnet_4.0-2
[19] cli_2.2.0 rvest_0.3.6 lgr_0.4.2
[22] xml2_1.3.2 labeling_0.4.2 scales_1.1.1
[25] checkmate_2.0.0 palmerpenguins_0.1.0 digest_0.6.27
[28] rmarkdown_2.6 pkgconfig_2.0.3 htmltools_0.5.0
[31] parallelly_1.24.0 dbplyr_2.0.0 rlang_0.4.10
[34] readxl_1.3.1 rstudioapi_0.13 shape_1.4.5
[37] generics_0.1.0 farver_2.0.3 jsonlite_1.7.2
[40] magrittr_2.0.1 Matrix_1.2-18 Rcpp_1.0.6
[43] munsell_0.5.0 fansi_0.4.1 lifecycle_0.2.0
[46] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[49] grid_4.0.2 parallel_4.0.2 promises_1.1.1
[52] crayon_1.3.4 lattice_0.20-41 splines_4.0.2
[55] haven_2.3.1 hms_0.5.3 knitr_1.30
[58] ps_1.5.0 pillar_1.4.7 ranger_0.12.1
[61] igraph_1.2.6 uuid_0.1-4 xgboost_1.3.1.1
[64] codetools_0.2-18 reprex_0.3.0 glue_1.4.2
[67] evaluate_0.14 data.table_1.14.0 modelr_0.1.8
[70] foreach_1.5.1 vctrs_0.3.6 httpuv_1.5.4
[73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[76] xfun_0.20 broom_0.7.4.9000 e1071_1.7-4
[79] later_1.1.0.1 class_7.3-17 survival_3.2-7
[82] iterators_1.0.13 kknn_1.3.1 ellipsis_0.3.1