R: Error in is_symbol(x) : object '.' not found (keras) - r

I am using the R programming language. I am trying to follow the R tutorial over here on neural networks (lstm) and time series: https://blogs.rstudio.com/ai/posts/2018-06-25-sunspots-lstm/
I decided to create my own time series data ("y.mon") for this tutorial (the same format and the same variable names) :
library(tidyverse)
library(glue)
library(forcats)
library(timetk)
library(tidyquant)
library(tibbletime)
library(cowplot)
library(recipes)
library(rsample)
library(yardstick)
library(keras)
library(tfruns)
library(dplyr)
library(lubridate)
library(tibbletime)
library(timetk)
index = seq(as.Date("1749/1/1"), as.Date("2016/1/1"),by="day")
index <- format(as.Date(index), "%Y/%m/%d")
value <- rnorm(97520,27,2.1)
final_data <- data.frame(index, value)
y.mon<-aggregate(value~format(as.Date(index),
format="%Y/%m"),data=final_data, FUN=sum)
y.mon$index = y.mon$`format(as.Date(index), format = "%Y/%m")`
y.mon$`format(as.Date(index), format = "%Y/%m")` = NULL
y.mon %>%
mutate(index = paste0(index, '/01')) %>%
tk_tbl() %>%
mutate(index = as_date(index)) %>%
as_tbl_time(index = index) -> y.mon
From here on, I follow the instructions in the tutorial (replacing the "sun_spots data" with "y.mon". Everything works fine until this point (I posted a question yesterday that got closed for being too detailed https://stackoverflow.com/questions/65527230/r-error-in-is-symbolx-object-not-found-keras - the code can be followed from the rstudio tutorial) :
#ERROR
coln <- colnames(compare_train)[4:ncol(compare_train)]
cols <- map(coln, quo(sym(.)))
rsme_train <-
map_dbl(cols, function(col)
rmse(
compare_train,
truth = value,
estimate = !!col,
na.rm = TRUE
)) %>% mean()
rsme_train
Error in is_symbol(x) : object '.' not found
I found another stackoverflow post which deals with a similar problem:Getting error message while calculating rmse in a time series analysis
According to this stackoverflow post, this first error can be resolved like this:
coln <- colnames(compare_train)[4:ncol(compare_train)]
rsme_train <-
map_df(coln, function(col)
rmse(
compare_train,
truth = value,
estimate = !!col,
na.rm = TRUE
)) %>%
pull(.estimate) %>%
mean()
rsme_train
However, the following section of the tutorial has a similar section in which the same error persists even after applying the corrections:
compare_test %>% write_csv(str_replace(model_path, ".hdf5", ".test.csv"))
compare_test[FLAGS$n_timesteps:(FLAGS$n_timesteps + 10), c(2, 4:8)] %>% print()
cols <- map(coln, quo(sym(.)))
rsme_test <-
map_dbl(cols, function(col)
rmse(
compare_test,
truth = value,
estimate = !!col,
na.rm = TRUE
)) %>% mean()
rsme_test
#errors:
Error in stri_replace_first_regex(string, pattern, fix_replacement(replacement), :
object 'model_path' not found
Error in is_symbol(x) : object '.' not found
These errors are preventing me from finishing the rest of the tutorial.
Can someone please show me how to fix these?
Thanks

Try using coln in map_dbl :
rsme_test <- map_dbl(coln, function(col)
rmse(
compare_test,
truth = value,
estimate = !!col,
na.rm = TRUE
)) %>% mean()

Related

Object not found in function environment for nested objects

I have a code snippet which I am trying to convert into a function. This function is supposed to look for potential spelling errors in a manual-entry field. The snippet works and you can try it out like this, using the starwars data from the tidyverse package:
require(tidyverse)
require(rlang) # loaded for {{ to force function arguments as well as the with_env() function
require(RecordLinkage) # loaded for the jarowinkler() function
starwars_cleaning <- starwars %>%
add_count(name, name = "Freq_name") %>% # this keeps track of which spelling is more frequent
distinct(name, .keep_all = T) %>% # this prevents duplicated comparisons and self-comparisons
nest_by(homeworld, .key = ".Nest") %>%
mutate(Mapped = list(imap_dfr(.x = .Nest$name,
.f = ~jarowinkler(str1 = .x,
str2 = .Nest$name[-.y]) %>%
list() %>%
tibble(Score_n = ., Match_n = list(.Nest$name[-.y]),
Freq_n = list(.Nest$Freq_name[-.y]))
)))
The function should accept the variable(s) to nest on (ellipses) and the variable to look for potential misspelled matches in as arguments. Right now, it looks like this:
string_matching <- function(.df, .string_col, ...){
.df$.tmp_string <- .df %>% select({{.string_col}})
.df <- .df %>%
add_count(.tmp_string, name = "Freq_name") %>%
distinct(.tmp_string, .keep_all = T) %>%
nest_by(..., .key = ".Nest") %>%
mutate(Mapped_n = list(with_env(env = current_env(), # same error with or without specifying the execution environment for imap
expr = imap_dfr(.x = .Nest$.tmp_string,
.f = ~jarowinkler(str1 = .x,
str2 = .Nest$.tmp_string[-.y]) %>%
list() %>%
tibble(Score_n = ., Match_n = list(.Nest$.tmp_string[-.y]),
Freq_n = list(.Nest$Freq_name[-.y]))
)
))
)
return(.df)
}
starwars %>%
string_matching(name, homeworld)
On the starwars data, it isn't very useful, clearly. And I cut down some of the features of this code to get a MWE--but that's the idea. When I wrap the code up like this in a function, it returns invalid argument to unary operator (apparently caused by the [-.y]). I tried the force() command after reading this post since this problem apparently comes up a lot. Because of the current error and that post, I thought the problem might have to do with the function environment causing imap_dfr() to lose track of the data somehow. I tried to wrap the call to map in with_env() and instruct it to use the function environment rather than its own. I also tried to break up the function by assigning an intermediate object to the global environment so that it could be found in the mapping step of the function:
assign(x = "TEMP", value = .df$.Nest, envir = global_env())
That landed me with the same 'unary operator` error. I'm not sure what to try next. I seem to be going in circles. Any insights into what is causing this problem and how to fix it would be greatly appreciated.
I don't think the post you pointed to is really related here. I don't think your problem is related to execution environment. The problem really is how you've handled passing the variable to your function. When you create your tmp_string, you are calling select() which is returning a tibble rather than the vector of column values. Instead, use pull() to extract those values.
string_matching <- function(.df, .string_col, ...){
.df$.tmp_string <- .df %>% pull({{.string_col}})
.df <- .df %>%
add_count(.tmp_string, name = "Freq_name") %>%
distinct(.tmp_string, .keep_all = T) %>%
nest_by(..., .key = ".Nest") %>%
mutate(Mapped_n = list(with_env(env = current_env(), # same error with or without specifying the execution environment for imap
expr = imap_dfr(.x = .Nest$.tmp_string,
.f = ~jarowinkler(str1 = .x,
str2 = .Nest$.tmp_string[-.y]) %>%
list() %>%
tibble(Score_n = ., Match_n = list(.Nest$.tmp_string[-.y]),
Freq_n = list(.Nest$Freq_name[-.y]))
)
))
)
return(.df)
}
Or you could write your code to avoid the need for that temp column completely
string_matching <- function(.df, .string_col, ...){
col <- rlang::ensym(.string_col)
.df <- .df %>%
add_count(!!col, name = "Freq_name") %>%
distinct(!!col, .keep_all = T) %>%
nest_by(..., .key = ".Nest") %>%
mutate(Mapped_n = list(imap_dfr(.x = .Nest %>% pull(!!col),
.f = ~jarowinkler(str1 = .x,
str2 = (.Nest %>% pull(col))[-.y]) %>%
list() %>%
tibble(Score_n = ., Match_n = list((.Nest %>% pull(col))[-.y]),
Freq_n = list(.Nest$Freq_name[-.y]))
))
)
return(.df)
}

R-markdown error compiling with missing value on certain paragraph

Hye everyone, I have problem with R Markdown,
I tried to compiled below R Code into pdf file but the problem is it has some issue with omitting NA values,
I use tinytex by the way.
R-version: 4.0.0
library(tidyr)
library(dplyr)
dl <- tempfile()
download.file("http://files.grouplens.org/datasets/movielens/ml-10m.zip", dl)
ratings <- read.table(text = gsub("::", "\t", readLines(unzip(dl, "ml-10M100K/ratings.dat"))),
col.names = c("userId", "movieId", "rating", "timestamp"))
movies <- str_split_fixed(readLines(unzip(dl, "ml-10M100K/movies.dat")), "\\::", 3)
colnames(movies) <- c("movieId", "title", "genres")
movies <- as.data.frame(movies) %>% mutate(movieId = as.numeric(levels(movieId))[movieId],
title = as.character(title),
genres = as.character(genres))
movielens <- left_join(ratings, movies, by = "movieId")
edx <- movielens[-test_index,]
edx <- edx %>% mutate(year = as.numeric(str_sub(title,-5,-2)))
split_edx <- edx %>% separate_rows(genres, sep = "\\|")
genres_popularity <- split_edx %>%
na.omit() %>% # omit missing values
select(movieId, year, genres) %>% # select columns we are interested in
mutate(genres = as.factor(genres)) %>% # turn genres in factors
group_by(year, genres) %>% # group data by year and genre
summarise(number = n()) %>% # count
complete(year = full_seq(year, 1), genres, fill = list(number = 0)) # add missing years/genres
I got this error:
Error in if (any(((x - rng[1])%%period > tol) & (period - (x - rng[1])%%period > :
missing value where TRUE/FALSE needed
Calls: ... dots_cols -> eval_tidy -> full_seq -> full_seq.numeric
Execution halted
This happen actually after I installed tinytex and miktex for r markdon latex, but before this it runs perfectly for execution.
Does anybody know why?
When I do rerun your code and I get to the
edx %>% separate_rows(genres, sep = "\\|")
My computer takes forever to progress the data, I will have to try later when I am home on my larger machine, I will try seeing I can help you

extract weights from a RWeka SMOreg model

I am using the awesome RWeka package in order to fit a SMOreg model as implemented in Weka. While everything is working fine, I have some problem extracting the weights from the fitted model.
As all Weka classifier object, my model has a nice print method that shows me all the features and their relative weights. However, I am not able to extract this weights in any way.
You can see for yourself by running the following code:
library(RWeka)
data("mtcars")
SMOreg_classifier <- make_Weka_classifier("weka/classifiers/functions/SMOreg")
model_SMOreg <- SMOreg_classifier(mpg ~ ., data = mtcars)
Now, if you simply call the model
model_SMOreg
you'll see that it prints all the features used in the model with their relative weight. I would like to access those weights as a vector or, even better, as a 2-columns table with one column containing the names of the features and the other containing the weights.
I am working on a Windows 7 x64 system, using RStudio Version 1.0.153, R 3.4.2 Short Summer and RWeka 0.4-35.
Does someone know how to do this ?
I think you cannot get this in numeric format.
attr(model_SMOreg, "meta")$class # "Weka_classifier"
getAnywhere("print.Weka_classifier")
Result:
A single object matching ‘print.Weka_classifier’ was found
It was found in the following places
registered S3 method for print from namespace RWeka
namespace:RWeka
with value
function (x, ...)
{
writeLines(.jcall(x$classifier, "S", "toString"))
invisible(x)
}
<bytecode: 0x8328630>
<environment: namespace:RWeka>
So we see: print.Weka_classifier() makes a .writeLines() call which in turn makes a rJava::.jcall call, which returns a string.
Thus, I think you need to parse the weights yourself, perhaps by calling the capture.output() method.
Based on the suggestion of #knb I have wrote a function to extract the weights from a SMOreg model and return a tibble with one column for the features name and one for the features weight, with the row arranged following the absolute value of the weight.
Note that this function only works for the SMOreg classifier, as the output of other classifiers is slightly different in terms of layout. However, I think the function can be easily adapted for other classifiers.
library(stringr)
library(tidyverse)
extract_weights_from_SMOreg <- function(model) {
oldw <- getOption("warn")
options(warn = -1)
raw_output <- capture.output(model)
trimmed_output <- raw_output[-c(1:3,(length(raw_output) - 4): length(raw_output))]
df <- data_frame(features_name = vector(length = length(trimmed_output) + 1, "character"),
features_weight = vector(length = length(trimmed_output) + 1, "numeric"))
for (line in 1:length(trimmed_output)) {
string_as_vector <- trimmed_output[line] %>%
str_split(string = ., pattern = " ") %>%
unlist(.)
numeric_element <- trimmed_output[line] %>%
str_split(string = ., pattern = " ") %>%
unlist(.) %>%
as.numeric(.)
position_mul <- string_as_vector[is.na(numeric_element)] %>%
str_detect(string = ., pattern = "[*]") %>%
which(.)
numeric_element <- numeric_element %>%
`[`(., c(1:position_mul))
text_element <- string_as_vector[is.na(numeric_element)]
there_is_plus <- string_as_vector[is.na(numeric_element)] %>%
str_detect(string = ., pattern = "[+]") %>%
sum(.)
if (there_is_plus) { sign_is <- "+"} else { sign_is <- "-"}
feature_weight <- numeric_element[!is.na(numeric_element)]
if (sign_is == "-") {df[line, "features_weight"] <- feature_weight * -1} else {df[line, "features_weight"] <- numeric_element[!(is.na(numeric_element))]}
df[line, "features_name"] <- paste(text_element[(position_mul + 1): length(text_element)], collapse = " ")
}
intercept_line <- raw_output[length(raw_output) - 4]
there_is_plus_intercept <- intercept_line %>%
str_detect(string = ., pattern = "[+]") %>%
sum(.)
if (there_is_plus_intercept) { intercept_sign_is <- "+"} else { intercept_sign_is <- "-"}
numeric_intercept <- intercept_line %>%
str_split(string = ., pattern = " ") %>%
unlist(.) %>%
as.numeric(.) %>%
`[`(., length(.))
df[nrow(df), "features_name"] <- "intercept"
if (intercept_sign_is == "-") {df[nrow(df), "features_weight"] <- numeric_intercept * -1} else {df[nrow(df), "features_weight"] <- numeric_intercept}
options(warn = oldw)
df <- df %>%
arrange(desc(abs(features_weight)))
return(df)
}
Here an example for one model
library(RWeka)
data("mtcars")
SMOreg_classifier <- make_Weka_classifier("weka/classifiers/functions/SMOreg")
mpg_model_weights <- extract_weights_from_SMOreg(SMOreg_classifier(data = mtcars, mpg ~ .))
mpg_model_weights

Write a function in R to change a group of datasets layout

I have many datasets in tibble format, with variables as rows. I want to change the layout and wrangle individual dataset. To save myself from repetitive work and risk of making mistakes. I wrote this function in R to do this.
library(tidyverse)
change_data_layout<- function(data_df){
data_df_2 <- data_df %>% mutate(samples = colnames()) %>% t()
colnames(data_df_2) <-data_df_2[1,]
rownames <- rownames(data_df_2) [2:nrow(data_df_2)]
data_df_3 <- data_df_2[1:nrow(data_df_2),] %>% as_tibble() %>% mutate(samples = rownames)
colnames(data_df_3) <- data_df_3 [1,]
data_df_4 <- data_df_3[2:nrow(data_df_3),]
data_final <- data_df_4 %>%
mutate_each(funs(type.convert)) %>% mutate_if(is.factor, as.character)
return(data_final)
}
However, when I run this function as :
dataset1_final <- change_data_layout(dataset1)
I got this error message:
Error: argument "x" is missing, with no default
Called from: mutate_impl(.data, dots)
Any help and suggestions?

purrring with NULL listcolumns in R

library(tidyverse)
data(mtcars)
mtcars <- rownames_to_column(mtcars,var = "car")
mtcars$make <- map_chr(mtcars$car,~strsplit(.x," ")[[1]][1])
mt2 <- mtcars %>% select(1:8,make) %>% nest(-make,.key = "l")
mt4<-mt2[1:5,]
mt4[c(1,5),"l"] <- list(list(NULL))
Now, I´d like to run the following function for each make of car:
fun_mt <- function(df){
a <- df %>%
filter(cyl<8) %>%
arrange(mpg) %>%
slice(1) %>%
select(mpg,disp)
return(a)
}
mt4 %>% mutate(newdf=map(l,~possibly(fun_mt(.x),otherwise = "NA"))) %>% unnest(newdf)
However, the NULL columns refuse to evaluate due to
Error: no applicable method for 'filter_' applied to an object of class "NULL"
I also tried using the safely and possibly approach, but still I get an error msg:
Error: Don't know how to convert NULL into a function
Any good solutions to this?
The problem is that NULL gets passed into the function fun_mt(). You wanted to catch this with possibly(). But possibly() is a function operator, i.e. you pass it a function and it returns a function. So, your call should have been
~ possibly(fun_mt, otherwise = "NA"))(.x)
But this doesn't yet work with unnest(). Instead of a character "NA" (a bad idea anyway, rather use a proper NA) you would have to default to a data frame:
~ possibly(fun_mt, otherwise = data.frame(mpg = NA, disp = NA))(.x)

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