如何在R中读取/写入libsvm数据？
libsvm格式是稀疏数据，如
<class/target>[ <attribute number>:<attribute value>]*
（参见Compressed Row Storage (CRS)）例如，
1 10:3.4 123:0.5 34567:0.231
0.2 22:1 456:03
我相信我自己可以鞭打一些东西，但我宁愿使用现成的东西。但是，R库foreign似乎没有提供必要的功能。
答案 0 :(得分：13)
e1071已下架：install.packages("e1071")
library(e1071)
read.matrix.csr(...)
write.matrix.csr(...)
注意 ：它在R中实现，而不是在C中实现，因此它是 dog-slow < /强> 的
它甚至有一个特殊的小插图Support Vector Machines—the Interface to libsvm in package e1071。
r.vw与vowpal_wabbit 注意 ：它在R中实现，而不是在C中实现，因此它是 dog-slow < /强> 的
答案 1 :(得分：10)
我一直在使用zygmuntz解决方案在一个拥有25k观测值（行）的数据集上运行了近5个小时。它做了3k-ish行。花了这么长时间我在此期间对此进行了编码（基于zygmuntz的代码）：
require(Matrix)
read.libsvm = function( filename ) {
content = readLines( filename )
num_lines = length( content )
tomakemat = cbind(1:num_lines, -1, substr(content,1,1))
# loop over lines
makemat = rbind(tomakemat,
do.call(rbind,
lapply(1:num_lines, function(i){
# split by spaces, remove lines
line = as.vector( strsplit( content[i], ' ' )[[1]])
cbind(i, t(simplify2array(strsplit(line[-1],
':'))))
})))
class(makemat) = "numeric"
#browser()
yx = sparseMatrix(i = makemat[,1],
j = makemat[,2]+2,
x = makemat[,3])
return( yx )
}
这在同一台机器上运行了几分钟（zygmuntz解决方案也可能存在内存问题，不确定）。希望这可以帮助任何有同样问题的人。
请记住，如果你需要在R中做大计算，VECTORIZE！
编辑：修复了我今天早上发现的索引错误。
答案 2 :(得分：4)
我提出了自己的 ad hoc 解决方案，利用了一些data.table实用程序，
它几乎没有在我找到的测试数据集上运行（Boston Housing data）。
将其转换为data.table（与解决方案正交，但在此处添加以便于重现）：
library(data.table)
x = fread("/media/data_drive/housing.data.fw",
sep = "\n", header = FALSE)
#usually fixed-width conversion is harder, but everything here is numeric
columns = c("CRIM", "ZN", "INDUS", "CHAS",
"NOX", "RM", "AGE", "DIS", "RAD",
"TAX", "PTRATIO", "B", "LSTAT", "MEDV")
DT = with(x, fread(paste(gsub("\\s+", "\t", V1), collapse = "\n"),
header = FALSE, sep = "\t",
col.names = columns))
这是：
DT[ , fwrite(as.data.table(paste0(
MEDV, " | ", sapply(transpose(lapply(
names(.SD), function(jj)
paste0(jj, ":", get(jj)))),
paste, collapse = " "))),
"/path/to/output", col.names = FALSE, quote = FALSE),
.SDcols = !"MEDV"]
#what gets sent to as.data.table:
#[1] "24 | CRIM:0.00632 ZN:18 INDUS:2.31 CHAS:0 NOX:0.538 RM:6.575
# AGE:65.2 DIS:4.09 RAD:1 TAX:296 PTRATIO:15.3 B:396.9 LSTAT:4.98 MEDV:24"
#[2] "21.6 | CRIM:0.02731 ZN:0 INDUS:7.07 CHAS:0 NOX:0.469 RM:6.421
# AGE:78.9 DIS:4.9671 RAD:2 TAX:242 PTRATIO:17.8 B:396.9 LSTAT:9.14 MEDV:21.6"
# ...
可能是fwrite比as.data.table理解的更好的方法，但我想不到一个（until setDT works on vectors）。< / p>
我复制了这个以测试它在更大的数据集上的性能（只是炸掉当前的数据集）：
DT2 = rbindlist(replicate(1000, DT, simplify = FALSE))
与此处报道的一些时间相比，此操作相当快（我还没有直接比较）：
system.time(.)
# user system elapsed
# 8.392 0.000 8.385
我还使用writeLines代替fwrite进行了测试，但后者更好。
我再次寻找，看到可能需要一段时间来弄清楚发生了什么。也许magrittr - 管道版本会更容易理解：
DT[ ,
#1) prepend each column's values with the column name
lapply(names(.SD), function(jj)
paste0(jj, ":", get(jj))) %>%
#2) transpose this list (using data.table's fast tool)
# (was column-wise, now row-wise)
#3) concatenate columns, separated by " "
transpose %>% sapply(paste, collapse = " ") %>%
#4) prepend each row with the target value
# (with Vowpal Wabbit in mind, separate with a pipe)
paste0(MEDV, " | ", .) %>%
#5) convert this to a data.table to use fwrite
as.data.table %>%
#6) fwrite it; exclude nonsense column name,
# and force quotes off
fwrite("/path/to/data",
col.names = FALSE, quote = FALSE),
.SDcols = !"MEDV"]
阅读这些文件更容易**
#quickly read data; don't split within lines
x = fread("/path/to/data", sep = "\n", header = FALSE)
#tstrsplit is transpose(strsplit(.))
dt1 = x[ , tstrsplit(V1, split = "[| :]+")]
#even columns have variable names
nms = c("target_name",
unlist(dt1[1L, seq(2L, ncol(dt1), by = 2L),
with = FALSE]))
#odd columns have values
DT = dt1[ , seq(1L, ncol(dt1), by = 2L), with = FALSE]
#add meaningful names
setnames(DT, nms)
**这将不使用“参差不齐”/稀疏输入数据。在这种情况下，我认为没有办法扩展它。
答案 3 :(得分：2)
答案 4 :(得分：0)
我选择了两跳解决方案 - 首先将R数据转换为另一种格式，然后转换为LIBSVM：
我的数据集是200K x 500，这只需要3-5分钟。
答案 5 :(得分：0)
这个问题是很久以前问的，并且有几个答案。因为我的数据是长格式的，所以大多数答案都对我不起作用，因此我无法在R中一次性编码。因此，这就是我的看法。我编写了一个函数，对数据进行一次热编码，然后保存它，而不必先将矩阵转换为稀疏矩阵。
RCPP代码：
127.0.0.1 - - [31/May/2020 16:19:13] "GET / HTTP/1.1" 500 -
Traceback (most recent call last):
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\app.py", line 2464, in __call__
return self.wsgi_app(environ, start_response)
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\app.py", line 2450, in wsgi_app
response = self.handle_exception(e)
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\app.py", line 1867, in handle_exception
reraise(exc_type, exc_value, tb)
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\_compat.py", line 39, in reraise
raise value
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\app.py", line 2447, in wsgi_app
response = self.full_dispatch_request()
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\app.py", line 1952, in full_dispatch_request
rv = self.handle_user_exception(e)
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\app.py", line 1821, in handle_user_exception
reraise(exc_type, exc_value, tb)
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\_compat.py", line 39, in reraise
raise value
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\app.py", line 1950, in full_dispatch_request
rv = self.dispatch_request()
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\app.py", line 1936, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File "C:\Users\Egemen\Desktop\Stock\stock\routes.py", line 36, in main
return render_template("main.html")
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\templating.py", line 137, in render_template
return _render(
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\flask\templating.py", line 120, in _render
rv = template.render(context)
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\jinja2\environment.py", line 1090, in render
self.environment.handle_exception()
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\jinja2\environment.py", line 832, in handle_exception
reraise(*rewrite_traceback_stack(source=source))
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\jinja2\_compat.py", line 28, in reraise
raise value.with_traceback(tb)
File "C:\Users\Egemen\Desktop\Stock\stock\templates\main.html", line 1, in top-level template code
{% extends "layout.html" %}
File "C:\Users\Egemen\Desktop\Stock\stock\templates\layout.html", line 29, in top-level template code
{% block form %}
File "C:\Users\Egemen\Desktop\Stock\stock\templates\main.html", line 27, in block "form"
<option value={{i+1}}>{{months[i]}}</option>
File "C:\Users\Egemen\Desktop\Stock\env\Lib\site-packages\jinja2\environment.py", line 452, in getitem
return obj[argument]
jinja2.exceptions.UndefinedError: 'months' is undefined
127.0.0.1 - - [31/May/2020 16:19:13] "GET /?__debugger__=yes&cmd=resource&f=style.css HTTP/1.1" 200 -
127.0.0.1 - - [31/May/2020 16:19:13] "GET /?__debugger__=yes&cmd=resource&f=jquery.js HTTP/1.1" 200 -
127.0.0.1 - - [31/May/2020 16:19:13] "GET /?__debugger__=yes&cmd=resource&f=debugger.js HTTP/1.1" 200 -
127.0.0.1 - - [31/May/2020 16:19:13] "GET /?__debugger__=yes&cmd=resource&f=console.png HTTP/1.1" 200 -
127.0.0.1 - - [31/May/2020 16:19:13] "GET /?__debugger__=yes&cmd=resource&f=ubuntu.ttf HTTP/1.1" 200 -
R函数充当包装器：
// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadillo.h>
#include <Rcpp.h>
#include <iostream>
#include <fstream>
#include <string>
using namespace Rcpp;
// Reading data frame from R and saving it as an libFM file
// [[Rcpp::export]]
std::string createNumber(int x, double y) {
std::string s1 = std::to_string(x);
std::string s2 = std::to_string(y);
std::string X_elem = s1 + ":" + s2;
return X_elem;
}
// [[Rcpp::export]]
std::string createRowLibFM(arma::rowvec row_to_fm, arma::vec factor_levels, arma::vec position) {
int n = factor_levels.n_elem;
std::string total = std::to_string(row_to_fm[0]);
for (int i = 1; i < n; i++) {
if (factor_levels[i] > 1) {
total = total + " " + createNumber(position[i - 1] + row_to_fm[i], 1);
}
if (factor_levels[i] == 1) {
total = total + " " + createNumber(position[i], row_to_fm[i]);
}
}
return total;
}
// [[Rcpp::export]]
void writeFile(std::string file, arma::mat all_data, arma::vec factor_levels) {
int n = all_data.n_rows;
arma::vec position = arma::cumsum(factor_levels);
std::ofstream temp_file;
temp_file.open (file.c_str());
for (int i = 0; i < n; i++) {
std::string temp_row = createRowLibFM(all_data.row(i), factor_levels, position);
temp_file << temp_row + "\n";
}
temp_file.close();
}
将其与假数据进行比较。
writeFileFM <- function(temp.data, path = 'test.txt') {
### Dealing with y function
if (!(any(colnames(temp.data) %in% 'y'))) {
stop('No y column is given')
} else {
temp.data <- temp.data %>% select(y, everything()) ## y is required to be first column for writeFile
}
### Dealing with factors/strings
temp.classes <- sapply(temp.data, class)
class.num <- rep(0, length(temp.classes))
map.list <- list()
for (i in 2:length(temp.classes)) { ### since y is always the first column
if (any(temp.classes[i] %in% c('factor', 'character'))) {
temp.col <- as.factor(temp.data[ ,i]) ### incase it is character
temp.unique <- levels(temp.col)
factors.new <- seq(0, length(temp.unique) - 1, 1)
levels(temp.col) <- factors.new
temp.data[ ,i] <- temp.col
### Saving changes
class.num[i] <- length(temp.unique)
map.list[[i - 1]] <- data.frame('original.value' = temp.unique,
'transform.value' = factors.new)
} else {
class.num[i] <- 1 ### Numeric values require only 1 column
}
}
### Writing file
print('Writing file to disc')
writeFile(all_data = sapply(temp.data, as.numeric), file = path, factor_levels = class.num)
return(map.list)
}
结果。
### Creating data to save
set.seed(999)
n <- 10000
factor.lvl1 <- 3
factor.lvl2 <- 2
temp.data <- data.frame('x1' = sample(stri_rand_strings(factor.lvl1, 7), n, replace = TRUE),
'x2' = sample(stri_rand_strings(factor.lvl2, 4), n, replace = TRUE),
'x3' = rnorm(n),
'x4' = rnorm(n),
'y' = rnorm(n))
### Comparing to other method
library(data.table)
library(e1071)
microbenchmark::microbenchmark(
temp.data.table <- model.matrix( ~ 0 + x1 + x2 + x3 + x4, data = temp.data,
contrasts = list(x2 = contrasts(temp.data$x2, contrasts = FALSE))),
write.matrix.csr(temp.data.table, 'out.txt'),
writeFileFM(temp.data))
它比e1071选件更快，并且当增加观察次数时该选件失败，但建议的方法仍然适用。