I'm trying to compile a model in Keras (in R) using multiple metrics. The reason for this is to decide which metric works best in evaluating the models created. The code which works for a single metric being:
model %>% compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = metric_mean_absolute_error())
I've seen in python code like
metrics = [metric_mean_absolute_error(), accuracy()]
work well, but in R the brackets give an error. I've tried searching for a way to do this in R, but haven't found luck anywhere. The error is below:
Error in source(file = file, local = envir, echo = echo, encoding = encoding) :
tune.R:30:12: unexpected '['
29: loss = 'mean_squared_error',
30: metrics = [
In R you can create a list with the list() function.
model %>%
compile(
optimizer = 'Adam',
loss = 'mean_squared_error',
metrics = list(metric_mean_absolute_error(),
'accuracy')
)
Related
I get an error when i try to run this code. i followed a guide on youtube for building a neural network. Everything works except when i try to run this code to fit the model.
history <- modnn %>% fit(
train_X_matrix, train_Y, epochs = 50, batch_size = 600,
validation_data = list(validation_X_matrix,validation_Y))
```
the error i get when i try to run the code above, all the names you see are the names of the columns. So the features of the model:
[error in visual studio](https://i.stack.imgur.com/ZaPXw.png)
some extra info about the variables i use. Here i created a matrix of the input variables. They did this in the guide. I tried train_x_data as input then it gave the same error but immediately so not after 1 epoch
```
# dependent and independent variables in 1 dataframe
train_X_data <- data.frame(train_X,train_y)
validation_X_data <- data.frame(validation_X,validation_y)
train_X_matrix <- model.matrix(average_daily_rate ~. -1 , data = train_X_data)
train_Y <- train_X_data$average_daily_rate
validation_X_matrix <- model.matrix(average_daily_rate ~. -1, data = validation_X_data)
validation_Y <- validation_X_data$average_daily_rate
```
The model i use, it is just a simple single layer model for testing.
# 1) single layer model structure
# step 1 make architecture powerful enough
modnn <- keras_model_sequential() %>%
layer_dense(units = 500, activation = "relu",
input_shape = ncol(train_X)) %>%
layer_dense(units = 1)
summary(modnn)
modnn %>% compile(loss = "mse",
optimizer = optimizer_rmsprop(),
metrics = list("mean_absolute_error"))
The error occurs after running the first epoch. I tought it was because the model could not read the names of the columns, but i tried a lot of things and nothing seemed to work.
Does anyone have an idea on how to fix this?
I’m trying to save a R object, which is a linear regression model based on ridge regression using R package glmnet. I'm using saveRDS to save it and it runs without error.
saveRDS(ridge, file = 'rnaClassifer_ridgeReg.RDdata')
HOWEVER, I cannot load the object back to R studio via readRDS, and it keeps giving errors and crashes the R session.
readRDS('rnaClassifer_ridgeReg.RDdata')
Note here this is a R object with size of 161MB after saving as rnaClassifer_ridgeReg.RDdata (which can be downloaded from here). My local laptop has 8 cores / 32 GB, which I would think is enough?
Here I'm also attaching the dataset (here) used to build the regression model, along with the code. Feel free to run the commands below to generate the R object ridge, and see if you can save it and load it successfully back to R.
library (caret)
library (glmnet)
data.lm.train = read.table('data.txt.gz', header = T, sep = '\t', quote = '', check.names = F)
lambda <- 10^seq(-3, 3, length = 100)
### ridge regression
set.seed(666)
ridge <- train(
dnaScore ~., data = data.lm.train, method = "glmnet",
trControl = trainControl("cv", number = 10),
tuneGrid = expand.grid(alpha = 0, lambda = lambda)
)
Any help would be highly appreciated!
I have a large list of task/learner/resampling combinations. I execute the resampling via
design = data.table(
task = list_of_tasks,
learner = list_of_learners,
resampling = list_of_resamplings
)
bmr = benchmark(design)
tab = bmr$aggregate(c(msr("classif.acc")))
The last command fails and I get the following error message:
Error in assert_classif(truth, response = response) : Assertion on 'response' failed: Contains missing values (element 1).
How can I check what went wrong? The learners have worked for slightly different tasks and they are all combination of "standard" learners (svm, naive bayes) with a preceding po("scale"). There are not missing data in the predictors of the targets of the tasks.
At least one learner predicted NAs. Search for NAs in the predictions to identify the failing learner.
library(mlr3)
library(mlr3misc)
# experiment
learner_rpart = lrn("classif.rpart")
learner_debug = lrn("classif.debug", predict_missing = 0.5)
task = tsk("pima")
resampling = rsmp("cv", folds = 3)
design = benchmark_grid(task, list(learner_rpart, learner_debug), resampling)
bmr = benchmark(design)
# search for predictions with NAs
tab = as.data.table(bmr)
tab[map_lgl(tab$prediction, function(pred) any(is.na(pred$response)))]
You should post a new question with a reprex including the failing learner, task and resampling.
I'm exploring the XGBoost in R.
After training the model, I wanted to see the feature-importance data.
xgb.importance(model = bst)
The above call shows the following error. What might be wrong?
Error in xgb.model.dt.tree(feature_names = feature_names, text = model_text_dump, : feature_names has less elements than there are features used in the model
PN - I checked the following section of the xgboost lib code, but still couldn't figure out the actual issue.
# assign feature_names when available
if (!is.null(feature_names)) {
if (length(feature_names) <= max(as.numeric(td$Feature), na.rm = TRUE))
stop("feature_names has less elements than there are features used in the model")
td[isLeaf == FALSE, Feature := feature_names[as.numeric(Feature) + 1] ]
}
Ref - https://github.com/dmlc/xgboost/blob/master/R-package/R/xgb.model.dt.tree.R
I see that the nfeatures variable of the trained model is same as the number of features passed to this model.
Dose your model have feature_names function?
Perhaps try xgb.importance(feature_names=colnames(bst$feature_names), model = bst). Works for me.
I'm getting an error stating that the 'weightedMean' argument is not found for the 'numFun' parameter in the kNN imputation function within the VIM R package. I'm attempting to impute data in a fairly large dataset, and I want to use kNN with 5 neighbors using weighted means.
Here is my code:
df.imputed <- kNN(df, variable = c(...), dist_var = c(...), numFun = weightedMean, k = 5, weightDist = TRUE, trace = TRUE, imp_var = TRUE)
And the exact error is:
Error in args(numFun) : object 'weightedMean' not found
Based on the documentation (https://cran.r-project.org/web/packages/VIM/VIM.pdf page 29) it seems like this should work.
Try put this way numFun = weighted.mean,
at least worked for me