Last updated: 2021-07-02
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Knit directory: spanish_data/
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| Rmd | 0ea8ee1 | Bernard | 2021-06-10 | tidied ml codes |
# Helper
library (tidyverse)
# ML
library (mlr3)
library (mlr3learners)
library (mlr3tuning)
library (mlr3viz)
library (mlr3fselect)
library (mlr3pipelines)
library (mlr3hyperband)
set.seed(7832)
lgr::get_logger("mlr3")$set_threshold("warn")
lgr::get_logger("bbotk")$set_threshold("warn")dat <- readRDS("output/df.RDS")
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<- TaskClassif$new (id = "armpain", backend = comb, target = "outcome")
task$nrow
task$feature_names
task$set_col_roles("ID", roles = "name")
# # Set test task
# task_tr <- TaskClassif$new (id = "neckpain", backend = test, target = "imp_np")
# task_tr$set_col_roles("ID", roles = "name")
# Set pre proc sets
poe <- po("encode", method = "one-hot")
poe$train(list(task))[[1]]$data()
poscale <- po("scale", param_vals = list (center = TRUE, scale = TRUE))
poscale$train(list(task))[[1]]$data()evals <- trm("none")
measure <- msr("classif.auc")
measures <- list (msr("classif.auc"),
msr("classif.acc"),
msr("classif.tpr"),
msr("classif.fpr"),
msr("classif.fnr"),
msr("classif.tnr"))
# Set resample
cv_inner <- rsmp("cv", folds = 5)
cv_outer <- rsmp("cv", folds = 3)# Set learner with type proability
lrn_logreg <- lrn("classif.log_reg", id = "log", predict_type = "prob")
# Graph with factor encoding and scaling
grln_logreg <- poscale %>>%
#poe %>>%
lrn_logreg
plot (grln_logreg)
grln_logreg_lnr <- GraphLearner$new(grln_logreg)
# Set autotuner
at_grln_logreg <- AutoFSelector$new(
learner = grln_logreg_lnr,
resampling = cv_inner,
measure = measure,
terminator = trm("combo"),
fselect = fs("sequential", strategy = "sbs"),
store_models = TRUE)
# Runs the outer loop sequentially and the inner loop in parallel
future::plan(list("sequential", "multisession"))
# Nested resampling for internal validation
rr_logreg <- resample(task,
at_grln_logreg,
cv_outer,
store_models = TRUE)
future:::ClusterRegistry("stop")
future::plan("multisession")
# Train learner
at_grln_logreg$train (task, row_ids = train_id)
as.data.table (at_grln_logreg$archive)
at_grln_logreg$fselect_result
future:::ClusterRegistry("stop")
# Predict learner
# prediction = at_grln_logreg$predict(task, row_ids = test_id)
# autoplot(prediction, type = "roc")
# prediction$score (measures)# Set learner with type proability
lrn_kknn <- lrn("classif.kknn", id = "kknn", predict_type = "prob")
# Graph with factor encoding and scaling
grln_kknn <- poscale %>>%
poe %>>%
lrn_kknn
plot (grln_kknn)
grln_kknn_lnr <- GraphLearner$new(grln_kknn)
# Tuning
grln_kknn_lnr$param_set$values$kknn.k <- to_tune(1, 10)
#grln_kknn_lnr$param_set$values$threshold.thresholds <- to_tune(p_dbl (0,1))
# Set autotuner
at_grln_kknn <- AutoTuner$new(
learner = grln_kknn_lnr,
resampling = cv_inner,
measure = measure,
terminator = evals,
tuner = tnr("grid_search", resolution = 10),
store_models = TRUE
)
# Runs the outer loop sequentially and the inner loop in parallel
future::plan(list("sequential", "multisession"))
# Nested resampling for internal validation
rr_kknn <- resample(task,
at_grln_kknn,
cv_outer,
store_models = TRUE)
future:::ClusterRegistry("stop")
future::plan("multisession")
# Train learner
at_grln_kknn$train (task, row_ids = train_id)
at_grln_kknn$archive
at_grln_kknn$tuning_result
future:::ClusterRegistry("stop")
#grln_kknn_lnr$param_set$values <- at_grln_kknn$tuning_instance$result_learner_param_vals
# Predict learner
# prediction = at_grln_kknn$predict(task, row_ids = test_id)
# prediction$score (measures)
# autoplot(prediction, type = "roc")lrn_xgb <- lrn("classif.xgboost", id = "xgb", predict_type = "prob", eta = 0.01)
grln_xgb <- poscale %>>%
poe %>>%
lrn_xgb
plot (grln_xgb)
grln_xgb_lnr <- GraphLearner$new(grln_xgb)
grln_xgb_lnr$param_set
ps_xgb = ParamSet$new(
params = list(
ParamDbl$new("xgb.eta", lower = 0.001, upper = 0.2),
ParamDbl$new("xgb.max_depth", lower = 1, upper = 20),
ParamDbl$new("xgb.nrounds", lower = 100, upper = 5000, tags = "budget"),
ParamDbl$new("xgb.colsample_bytree", lower = 0.5, upper = 1),
ParamDbl$new("xgb.colsample_bylevel", lower = 0.5, upper = 1),
ParamDbl$new("xgb.subsample", lower = 0.5, upper = 1),
ParamDbl$new("xgb.gamma", lower = -7, upper = 6),
ParamDbl$new("xgb.lambda", lower = -10, upper = 10),
ParamDbl$new("xgb.alpha", lower = -10, upper = 10)
))
ps_xgb$trafo = function(x, param_set) {
idx_gamma = grep("gamma", names(x))
x[[idx_gamma]] = 2^(x[[idx_gamma]])
idx_lambda = grep("lambda", names(x))
x[[idx_lambda]] = as.integer (2^(x[[idx_lambda]]))
idx_alpha = grep("alpha", names(x))
x[[idx_alpha]] = as.integer (2^(x[[idx_alpha]]))
idx_nrounds = grep("nrounds", names(x))
x[[idx_nrounds]] = as.integer (x[[idx_nrounds]])
idx_depth = grep("depth", names(x))
x[[idx_depth]] = as.integer (x[[idx_depth]])
x
}
bind_rows(generate_design_grid(ps_xgb, 3)$transpose())
at_grln_xgb <- AutoTuner$new (
learner = grln_xgb_lnr,
resampling = cv_inner,
measure = measure,
search_space = ps_xgb,
terminator = evals,
tuner = tnr("hyperband", eta = 5),
store_models = TRUE
)
# Runs the outer loop sequentially and the inner loop in parallel
future::plan(list("sequential", "multisession"))
# Nested resampling for internal validation
rr_xgb <- resample(task,
at_grln_xgb,
cv_outer,
store_models = TRUE)
future:::ClusterRegistry("stop")
future::plan("multisession")
# test learner
at_grln_xgb$train (task, row_ids = train_id)
at_grln_xgb$archive
at_grln_xgb$tuning_result
future:::ClusterRegistry("stop")
# prediction = at_grln_xgb$predict(task, row_ids = test_id)
# prediction$score (measures)
# autoplot(prediction, type = "roc")lrn_lasso <- lrn("classif.glmnet", id = "lasso", predict_type = "prob")
grln_lasso <- poscale %>>%
poe %>>%
lrn_lasso
plot (grln_lasso)
grln_lasso_lnr <- GraphLearner$new(grln_lasso)
grln_lasso_lnr$param_set
grln_lasso_lnr$param_set$values$lasso.s <- to_tune(0, 1)
at_grln_lasso <- AutoTuner$new (
learner = grln_lasso_lnr,
resampling = cv_inner,
measure = measure,
terminator = evals,
tuner = tnr("grid_search", resolution = 100),
store_models = TRUE
)
# Runs the outer loop sequentially and the inner loop in parallel
future::plan(list("sequential", "multisession"))
# Nested resampling for internal validation
rr_lasso <- resample(task,
at_grln_lasso,
cv_outer,
store_models = TRUE)
future:::ClusterRegistry("stop")
future::plan("multisession")
# test learner
at_grln_lasso$train (task, row_ids = train_id)
at_grln_lasso$archive
at_grln_lasso$tuning_result
future:::ClusterRegistry("stop")
# prediction = at_grln_lasso$predict(task, row_ids = test_id)
# prediction$score (measures)
# autoplot(prediction, type = "roc")lrn_rf <- lrn("classif.ranger", id = "rf", predict_type = "prob")
grln_rf <- poscale %>>%
poe %>>%
lrn_rf
plot (grln_rf)
grln_rf_lnr <- GraphLearner$new(grln_rf)
grln_rf_lnr$param_set
ps_rf <- ParamSet$new(list (
ParamInt$new ("rf.mtry", lower = 5, upper = 15, tags = "budget"),
ParamDbl$new ("rf.sample.fraction", lower = 0.5, upper = 1),
ParamInt$new ("rf.min.node.size", lower = 1, upper = 20)
))
bind_rows(generate_design_grid(ps_rf, 5)$transpose())
at_grln_rf <- AutoTuner$new (
learner = grln_rf_lnr,
resampling = cv_inner,
measure = measure,
terminator = evals,
search_space = ps_rf,
tuner = tnr("hyperband", eta = 5),
store_models = TRUE
)
# Runs the outer loop sequentially and the inner loop in parallel
future::plan(list("sequential", "multisession"))
# Nested resampling for internal validation
rr_rf <- resample(task,
at_grln_rf,
cv_outer,
store_models = TRUE)
future:::ClusterRegistry("stop")
future::plan("multisession")
# test learner
at_grln_rf$train (task, row_ids = train_id)
at_grln_rf$archive
at_grln_rf$tuning_result
future:::ClusterRegistry("stop")
# prediction = at_grln_rf$predict(task, row_ids = test_id)
# prediction$score (measures)
# autoplot(prediction, type = "roc")lrn_net <- lrn("classif.nnet", id = "nnet", predict_type = "prob")
grln_net <- poscale %>>%
poe %>>%
lrn_net
plot (grln_net)
grln_net_lnr <- GraphLearner$new(grln_net)
grln_net_lnr$param_set
ps_net <- ParamSet$new(list (
ParamInt$new ("nnet.size", lower = 1, upper = 10),
ParamDbl$new ("nnet.decay", lower = 0.1, upper = 0.5)
))
bind_rows(generate_design_grid(ps_net, 5)$transpose())
at_grln_net <- AutoTuner$new (
learner = grln_net_lnr,
resampling = cv_inner,
measure = measure,
terminator = evals,
search_space = ps_net,
tuner = tnr("grid_search", resolution = 10),
store_models = TRUE
)
# Runs the outer loop sequentially and the inner loop in parallel
future::plan(list("sequential", "multisession"))
# Nested resampling for internal validation
rr_net <- resample(task,
at_grln_net,
cv_outer,
store_models = TRUE)
future:::ClusterRegistry("stop")
future::plan("multisession")
# test learner
at_grln_net$train (task, row_ids = train_id)
at_grln_net$archive
at_grln_net$tuning_result
future:::ClusterRegistry("stop")
# prediction = at_grln_net$predict(task, row_ids = test_id)
# prediction$score (measures)
# autoplot(prediction, type = "roc")lrn_svm <- lrn("classif.svm", id = "svm", type = "C-classification", kernel = "radial", predict_type = "prob")
grln_svm <- poscale %>>%
poe %>>%
lrn_svm
plot (grln_svm)
grln_svm_lnr <- GraphLearner$new(grln_svm)
grln_svm_lnr$param_set
ps_svm <- ParamSet$new(list (
ParamDbl$new ("svm.cost", lower = 0.1, upper = 10),
ParamDbl$new ("svm.gamma", lower = 0, upper = 5)
))
bind_rows(generate_design_grid(ps_svm, 5)$transpose())
at_grln_svm <- AutoTuner$new (
learner = grln_svm_lnr,
resampling = cv_inner,
measure = measure,
terminator = evals,
search_space = ps_svm,
tuner = tnr("grid_search", resolution = 10),
store_models = TRUE
)
# Runs the outer loop sequentially and the inner loop in parallel
future::plan(list("sequential", "multisession"))
# Nested resampling for internal validation
rr_svm <- resample(task,
at_grln_svm,
cv_outer,
store_models = TRUE)
future:::ClusterRegistry("stop")
future::plan("multisession")
# test learner
at_grln_svm$train (task, row_ids = train_id)
at_grln_svm$archive
at_grln_svm$tuning_result
future:::ClusterRegistry("stop")
# prediction = at_grln_svm$predict(task, row_ids = test_id)
# prediction$score (measures)
# autoplot(prediction, type = "roc")rsmp_list <- list (rr_logreg = rr_logreg,
rr_kknn = rr_kknn,
rr_xgb = rr_xgb,
rr_lasso = rr_lasso,
rr_rf = rr_rf,
rr_net = rr_net,
rr_svm = rr_svm)
model_list <- list (at_grln_logreg = at_grln_logreg,
at_grln_kknn = at_grln_kknn,
at_grln_xgb = at_grln_xgb,
at_grln_lasso = at_grln_lasso,
at_grln_rf = at_grln_rf,
at_grln_net = at_grln_net,
at_grln_svm = at_grln_svm)
saveRDS (list (rsmp_list = rsmp_list,
model_list = model_list),
"output/ap_result.RDS")
sessionInfo()