I'm attempting to incorporate logLoss as the performance measure used when tuning randomForest (other classifiers) by way of caret (instead of the default options of Accuracy or Kappa).
The first R script executes without error using defaults. However, I get:
Error in { : task 1 failed - "unused argument (model = method)"
when using the second script.
The function logLoss(predict(rfModel,test[,-c(1,95)],type="prob"),test[,95]) works by way of leveraging a separately trained randomForest model.
The dataframe has 100+ columns and 10,000+ rows. All elements are numeric outside of the 9-level categorical "target" at col=95. A row id is located in col=1.
Unfortunately, I'm not correctly grasping the guidance provided by http://topepo.github.io/caret/training.html, nor having much luck via google searches.
Your help are greatly appreciated.
Working R script:
fitControl = trainControl(method = "repeatedcv",number = 10,repeats = 10)
rfGrid = expand.grid(mtry=c(1,9))
rfFit = train(target ~ ., data = train[,-1],method = "rf",trControl = fitControl,verbose = FALSE,tuneGrid = rfGrid)
Not working R script:
logLoss = function(data,lev=NULL,method=NULL) {
lLoss = 0
epp = 10^-15
for (i in 1:nrow(data)) {
index = as.numeric(lev[i])
p = max(min(data[i,index],1-epp),epp)
lLoss = lLoss - log(p)
}
lLoss = lLoss/nrow(data)
names(lLoss) = c("logLoss")
lLoss
}
fitControl = trainControl(method = "repeatedcv",number = 10,repeats = 10,summaryFunction = logLoss)
rfGrid = expand.grid(mtry=c(1,9))
rfFit = train(target ~ ., data = trainBal[,-1],method = "rf",trControl = fitControl,verbose = FALSE,tuneGrid = rfGrid)
I think you should set summaryFunction=mnLogLoss in trainControl and metric="logLoss" in train (I found it here). Like this:
# load libraries
library(caret)
# load the dataset
data(iris)
# prepare resampling method
control <- trainControl(method="cv", number=5, classProbs=TRUE, summaryFunction=mnLogLoss)
set.seed(7)
fit <- train(Species~., data=iris, method="rf", metric="logLoss", trControl=control)
# display results
print(fit)
Your argument name is not correct (i.e. "unused argument (model = method)"). The webpage says that the last function argument should be called model and not method.
Related
I am trying to do a k-fold cross validation on a model that predicts the joint distribution of the proportion of tree species basal area from satellite imagery. This requires the use of the DiricihletReg::DirichReg() function, which in turn requires that the response variables be prepared as a matrix using the DirichletReg::DR_data() function. I originally tried to accomplish this in the caret:: package, but I found out that caret:: does not support multivariate responses. I have since tried to implement this in the tidymodels:: suite of packages. Following the documentation on how to register a new model in the parsnip:: (I appreciate Max Kuhn's vegetable humor) package, I created a "DREG" model and a "DR" engine. My registered model works when I simply call it on a single training dataset, but my goal is to do kfolds cross-validation, implementing the vfolds_cv(), a workflow(), and the 'fit_resample()' function. With the code I currently have I get warning message stating:
Warning message:
All models failed. See the `.notes` column.
Those notes state that Error in get(resp_char, environment(oformula)): object 'cbind(PSME, TSHE, ALRU2)' not found This, I believe is due to the use of DR_data() to preprocess the response variables into the format necessary for Dirichlet::DirichReg() to run properly. I think the solution I need to implement involve getting this pre-processing to happen in either the recipe() call or in the set_fit() call when I register this model with parsnip::. I have tried to use the step_mutate() function when specifying the recipe, but that performs a function on each column as opposed to applying the function with the columns as inputs. This leads to the following error in the "notes" from the output of fit_resample():
Must subset columns with a valid subscript vector.
Subscript has the wrong type `quosures`.
It must be numeric or character.
Is there a way to get the recipe to either transform several columns to a DirichletRegData class using the DR_data() function with a step_*() function or using the pre= argument in set_fit() and set_pred()?
Below is my reproducible example:
##Loading Necessary Packages##
library(tidymodels)
library(DirichletReg)
##Creating Fake Data##
set.seed(88)#For reproducibility
#Response variables#
PSME_BA<-rnorm(100,50, 15)
TSHE_BA<-rnorm(100,40,12)
ALRU2_BA<-rnorm(100,20,0.5)
Total_BA<-PSME_BA+TSHE_BA+ALRU2_BA
#Predictor variables#
B1<-runif(100, 0, 2000)
B2<-runif(100, 0, 1800)
B3<-runif(100, 0, 3000)
#Dataset for modeling#
DF<-data.frame(PSME=PSME_BA/Total_BA, TSHE=TSHE_BA/Total_BA, ALRU2=ALRU2_BA/Total_BA,
B1=B1, B2=B2, B3=B3)
##Modeling the data using Dirichlet regression with repeated k-folds cross validation##
#Registering the model to parsnip::#
set_new_model("DREG")
set_model_mode(model="DREG", mode="regression")
set_model_engine("DREG", mode="regression", eng="DR")
set_dependency("DREG", eng="DR", pkg="DirichletReg")
set_model_arg(
model = "DREG",
eng = "DR",
parsnip = "param",
original = "model",
func = list(pkg = "DirichletReg", fun = "DirichReg"),
has_submodel = FALSE
)
DREG <-
function(mode = "regression", param = NULL) {
# Check for correct mode
if (mode != "regression") {
rlang::abort("`mode` should be 'regression'")
}
# Capture the arguments in quosures
args <- list(sub_classes = rlang::enquo(param))
# Save some empty slots for future parts of the specification
new_model_spec(
"DREG",
args=args,
eng_args = NULL,
mode = mode,
method = NULL,
engine = NULL
)
}
set_fit(
model = "DREG",
eng = "DR",
mode = "regression",
value = list(
interface = "formula",
protect = NULL,
func = c(pkg = "DirichletReg", fun = "DirichReg"),
defaults = list()
)
)
set_encoding(
model = "DREG",
eng = "DR",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "DREG",
eng = "DR",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict.DirichletRegModel"),
args =
list(
object = expr(object$fit),
newdata = expr(new_data),
type = "response"
)
)
)
##Running the Model##
DF$Y<-DR_data(DF[,c(1:3)]) #Preparing the response variables
dreg_spec<-DREG(param="alternative") %>%
set_engine("DR")
dreg_mod<-dreg_spec %>%
fit(Y~B1+B2+B3, data = DF)#Model works when simply run on single dataset
##Attempting Crossvalidation##
#First attempt - simply call Y as the response variable in the recipe#
kfolds<-vfold_cv(DF, v=10, repeats = 2)
rcp<-recipe(Y~B1+B2+B3, data=DF)
dreg_fit<- workflow() %>%
add_model(dreg_spec) %>%
add_recipe(rcp)
dreg_rsmpl<-dreg_fit %>%
fit_resamples(kfolds)#Throws warning about all models failing
#second attempt - use step_mutate_at()#
rcp<-recipe(~B1+B2+B3, data=DF) %>%
step_mutate_at(fn=DR_data, var=vars(PSME, TSHE, ALRU2))
dreg_fit<- workflow() %>%
add_model(dreg_spec) %>%
add_recipe(rcp)
dreg_rsmpl<-dreg_fit %>%
fit_resamples(kfolds)#Throws warning about all models failing
This works, but I'm not sure if it's what you were expecting.
First--getting the data setup for CV and DR_data()
I don't know of any package that has built what would essentially be a translation for CV and DirichletReg. Therefore, that part is manually done. You might be surprised to find it's not all that complicated.
Using the data you created and the modeling objects you created for tidymodels (those prefixed with set_), I created the CV structure that you were trying to use.
df1 <- data.frame(PSME = PSME_BA/Total_BA, TSHE = TSHE_BA/Total_BA,
ALRU2=ALRU2_BA/Total_BA, B1, B2, B3)
set.seed(88)
kDf2 <- kDf1 <- vfold_cv(df1, v=10, repeats = 2)
For each of the 20 subset data frames identified in kDf2, I used DR_data to set the data up for the models.
# convert to DR_data (each folds and repeats)
df2 <- map(1:20,
.f = function(x){
in_ids = kDf1$splits[[x]]$in_id
dd <- kDf1$splits[[x]]$data[in_ids, ] # filter rows BEFORE DR_data
dd$Y <- DR_data(dd[, 1:3])
kDf1$splits[[x]]$data <<- dd
})
Because I'm not all that familiar with tidymodels, next conducted the modeling using DirichReg. I then did it again with tidymodels and compared them. (The output is identical.)
DirichReg Models and summaries of the fits
set.seed(88)
# perform crossfold validation on Dirichlet Model
df2.fit <- map(1:20,
.f = function(x){
Rpt = kDf1$splits[[x]]$id$id
Fld = kDf1$splits[[x]]$id$id2
daf = kDf1$splits[[x]]$data
fit = DirichReg(Y ~ B1 + B2, daf)
list(Rept = Rpt, Fold = Fld, fit = fit)
})
# summary of each fitted model
fit.a <- map(1:20,
.f = function(x){
summary(df2.fit[[x]]$fit)
})
tidymodels and summaries of the fits (the code looks the same, but there are a few differences--the output is the same, though)
# I'm not sure what 'alternative' is supposed to do here?
dreg_spec <- DREG(param="alternative") %>% # this is not model = alternative
set_engine("DR")
set.seed(88)
dfa.fit <- map(1:20,
.f = function(x){
Rpt = kDf1$splits[[x]]$id$id
Fld = kDf1$splits[[x]]$id$id2
daf = kDf1$splits[[x]]$data
fit = dreg_spec %>%
fit(Y ~ B1 + B2, data = daf)
list(Rept = Rpt, Fold = Fld, fit = fit)
})
afit.a <- map(1:20,
.f = function(x){
summary(dfa.fit[[x]]$fit$fit) # extra nest for parsnip
})
If you wanted to see the first model?
fit.a[[1]]
afit.a[[1]]
If you wanted the model with the lowest AIC?
# comare AIC, BIC, and liklihood?
# what do you percieve best fit with?
fmin = min(unlist(map(1:20, ~fit.a[[.x]]$aic))) # dir
# find min AIC model number
paste0((map(1:20, ~ifelse(fit.a[[.x]]$aic == fmin, .x, ""))), collapse = "")
fit.a[[19]]
afit.a[[19]]
I am trying to tune an xgboost model with a multiclass dependent variable in R. I am using MLR to do this, however I run into an error where xgboost doesn't have predict within its namespace - which I assume MLR wants to use. I have had a look online and see that other people have encountered similar issues. However, I can't entirely understand the answers that have been provided (e.g. https://github.com/mlr-org/mlr/issues/935), when I try to implement them the issue persists. My code is as follows:
# Tune parameters
#create tasks
train$result <- as.factor(train$result) # Needs to be a factor variable for makeClass to work
test$result <- as.factor(test$result)
traintask <- makeClassifTask(data = train,target = "result")
testtask <- makeClassifTask(data = test,target = "result")
lrn <- makeLearner("classif.xgboost",predict.type = "response")
# Set learner value and number of rounds etc.
lrn$par.vals <- list(
objective = "multi:softprob", # return class with maximum probability,
num_class = 3, # There are three outcome categories
eval_metric="merror",
nrounds=100L,
eta=0.1
)
# Set parameters to be tuned
params <- makeParamSet(
makeDiscreteParam("booster",values = c("gbtree","gblinear")),
makeIntegerParam("max_depth",lower = 3L,upper = 10L),
makeNumericParam("min_child_weight",lower = 1L,upper = 10L),
makeNumericParam("subsample",lower = 0.5,upper = 1),
makeNumericParam("colsample_bytree",lower = 0.5,upper = 1)
)
# Set resampling strategy
rdesc <- makeResampleDesc("CV",stratify = T,iters=5L)
# search strategy
ctrl <- makeTuneControlRandom(maxit = 10L)
#parallelStartSocket(cpus = detectCores()) # Enable parallel processing
mytune <- tuneParams(learner = lrn
,task = traintask
,resampling = rdesc
,measures = acc
,par.set = params
,control = ctrl
,show.info = T)
The specific error I get is:
Error: 'predict' is not an exported object from 'namespace:xgboost'
My package versions are:
packageVersion("xgboost")
[1] ‘0.6.4’
packageVersion("mlr")
[1] ‘2.8’
Would anyone know what I should do here?
Thanks in advance.
I am new to R programming language and I need to run "xgboost" for some experiments. The problem is that I need to cross-validate the model and get the accuracy and I found two ways that give me different results:
With "caret" using:
library(mlbench)
library(caret)
library(caretEnsemble)
dtrain <- read.csv("student-mat.csv", header=TRUE, sep=";")
formula <- G3~.
dtrain$G3<-as.factor(dtrain$G3)
control <- trainControl(method="cv", number=10)
seed <- 10
metric <- "Accuracy"
fit.xgb <- train(formula, data=dtrain, method="xgbTree", metric=metric, trControl=control, nthread =4)
fit.xgb
fit.xgbl <- train(formula, data=dtrain, method="xgbLinear", metric=metric, trControl=control, nthread =4)
fit.xgbl
Using the "xgboost" package and the following code:
library(xgboost)
printArray <- function(label, array){
cat(paste(label, paste(array, collapse = ", "), sep = ": \n"), "\n\n")
setwd("D:\\datasets")
dtrain <- read.csv("moodle7original(AtributosyNotaNumericos).csv", header=TRUE, sep=",")
label <- as.numeric(dtrain[[33]])
data <- as.matrix(sapply(dtrain, as.numeric))
croosvalid <-
xgb.cv(
data = data,
nfold = 10,
nround = 10,
label = label,
prediction = TRUE,
objective = "multi:softmax",
num_class = 33
)
print(croosvalid)
printArray("Actual classes", label[label != croosvalid\$pred])
printArray("Predicted classes", croosvalid\$pred[label != croosvalid\$pred])
correctlyClassified <- length(label[label == croosvalid\$pred])
incorrectlyClassified <- length(label[label != croosvalid\$pred])
accurancy <- correctlyClassified * 100 / (correctlyClassified + incorrectlyClassified)
print(paste("Accurancy: ", accurancy))
But the results differ very much on the same dataset. I usually get 99% accuracy on student performance dataset with the second snip of code and ~63% with the first one...
I set the same seed on both of them.
Am I wrong with the second? Please tell me why if so!
Two things are different among codes, the first one is the most grave:
When you call label <- as.numeric(dtrain[[11]]) and data <- as.matrix(sapply(dtrain, as.numeric)), the 11th column in data is actually label. Of course you'll get a high accuracy, the label itself is in the data! That's grave leakage, you should instead use data <- as.matrix(sapply(dtrain[,-11L], as.numeric))
A minor difference is that you are using objective = "multi:softmax" in the second code, caret implements objective = "multi:softprob" for multiclass classification. I dunno how much different that might do, but it's different among codes. Check it.
I cannot thrive to train a model with the caret package without errors.
Here is a minimum reproductible data set: Dataset
And here is the code I'm using...
data <- read.csv('file.csv', header = TRUE, sep = ',')
library(caret)
data <- data[complete.cases(data), ]
data$donation <- as.factor(data$donation)
set.seed(1337)
ctrl <- trainControl(
method="repeatedcv",
number="10",
repeats=10)
data <- data[,-2]
nnet.fit <- train(donation ~ ., data = data,
method = "nnet",
trControl = ctrl,
verbose = FALSE,
tuneLength = 5)
...that leads to this error:
Error in numInClass[i]%/%k : non-numeric argument to binary operator
I have checked this answer but didn't understand how to solve my problem with it.
Any clue ?
Just seen this while trying to find a solution on the same. It's a very simple answer bro:
ctrl <- trainControl(
method="repeatedcv",
number="10",# Here the 10 should not be wrapped in quotation marks as its numeric
repeats=10)
I tried creating a function for Ensemble of Ensemble modelling:
library(foreach)
library(randomForest)
set.seed(10)
Y<-round(runif(1000))
x1<-c(1:1000)*runif(1000,min=0,max=2)
x2<-c(1:1000)*runif(1000,min=0,max=2)
x3<-c(1:1000)*runif(1000,min=0,max=2)
all_data<-data.frame(Y,x1,x2,x3)
bagging = function(dataFile, length_divisor = 4, iterations = 100)
{
fit = list()
predictions = foreach(m = 1 : iterations, .combine = cbind) %do%
{
dataFile$Y = as.factor(dataFile$Y)
rf_fit = randomForest(Y ~ ., data = dataFile, ntree = 100)
fit[[m]] = rf_fit
rf_fit$votes[,2]
}
rowMeans(predictions)
return(list(formula = as.formula("Y ~ ."), trees = fit, ntree = 100, class = dataFile$Y, votes = predictions))
}
final_model = bagging(all_data)
predict(final_model, TestData) # It says predict doesn't support final_model object
# Error in UseMethod("predict") : no applicable method for 'predict' applied to an object of class "list"
It says -
Error in UseMethod("predict") : no applicable method for 'predict' applied to an object of class "list".
I need the above function bagging to return an aggregated model object so that I can predict on new data set.
Your bagging function just returns an arbitrary list. Predict looks to the class of the first parameter to know "the right thing" to do. I assume you want to predict from the randomForest objects stored inside the list? You can loop over your list with Map(). For example
Map(function(x) predict(x, TestData), final_model$trees)
(untested since you didn't seem to provide TestData)