Here is a start. I was wondering how long it would take to at least
substitute 800 different patterns into 4M vectors. Here is my test.
It took longer (99 sec) to create the test data than to do the
substitutes (52 secs). Now some variations on this can provide the
other information that you are probably after in less than a day ( I
would guess less than an hour)
> n <- 1000
> x <- paste0("$"
+ , sample(LETTERS, n, TRUE)
+ , sample(LETTERS, n, TRUE)
+ , sample(LETTERS, n, TRUE)
+ , sample(LETTERS, n, TRUE)
+ )
> x <- x[!duplicated(x)][1:800]
>
> n <- 4000000
> system.time({
+ output <- replicate(n, paste(sample(x,2), collapse = ' '))
+ })
user system elapsed
99.85 0.22 100.37
>
> system.time({
+ pattern <- paste0("\\", x, collapse = "|")
+ z <- gsub(pattern, "[ticker]", output, perl = TRUE)
+ })
user system elapsed
52.05 0.00 52.21
>
>
> str(output)
chr [1:4000000] "$JHVN $VKOL" "$GTEU $CEGL" "$LOEY $ETQK" "$AFDO
$SDLH" "$MOIN $WEVR" ...
> str(z)
chr [1:4000000] "[ticker] [ticker]" "[ticker] [ticker]" "[ticker] [ticker]" ...
> str(pattern)
chr
"\\$MATF|\\$GFGC|\\$SRYC|\\$HLWS|\\$GHFB|\\$BGVU|\\$GFDW|\\$PSFN|\\$ONDY|\\$SXUH|\\$EBDJ|\\$YNQY|\\$NDBT|\\$TOQK|\\$IUBN|\\$VSMT"|
__truncated__
>
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
On Wed, Nov 6, 2013 at 8:11 AM, Simon Pickert <simon.pick...@t-online.de> wrote:
> Dear experts,
> I’ve been on this for weeks now, and couldn’t find a solution..Sorry for the
> long description. I figured I post many details, so you get the problem
> entirely, although it’s not hard to grasp.
>
> **Situation:**
> Data frame consisting of 4 million entries (total size: 250 MB). Two columns:
> `ID` and `TEXT`. Text strings are each up to 200 characters.
>
>
> **Task:**
> Preprocessing the text strings
>
> Example Data:
>
>
> +——————+—————————————————+
> | ID | Text
> |
> +——+—————————————————————+
> | 123 | $AAPL is up +5% |
> | 456 | $MSFT , $EBAY doing great. www.url.com |
> ..
> +——+—————————————————————+
>
> Should become
>
> +——————+——————————————————————————————————-——+
> | ID | Text clean
> | First Ticker | All Ticker | Ticker Count
> +——+————————————————————+——————+———— +———————-—+
> | 123 | [ticker] is up [positive_percentage] |
> $aapl | $aapl | 1
> | 456 | [ticker] [ticker] doing great [url] [pos_emotion] |
> $msft | $msft,$ebay | 2
> ..
> +——+————————————————————+——————-+——————+——————+
>
>
>
> **Problem:**
> It takes too long. On my 8GB RAM Dual-Core machine: Cancelled after 1 day. On
> a 70GB 8-Core Amazon EC2 instance: Cancelled after 1 day.
>
>
> **Details:**
> I am basically
>
> - Counting how often certain words appear in one string
> - Write this number into a new column (COUNT)
> - Replace this (counted) word
> - Replace other words (which I don't need to count before)
> - Replace some regular expressions
>
> The vectors which are used as patterns look like this:
>
> "\\bWORD1\\b|\\bWORD2\\b|\\bWORD3\\b|\\bWORD4\\b..."
>
> Thus, those 'replacement vectors' are character vectors of length 1, each
> containing up to 800 words
>
>
>
> **Main code:**
>
> library("parallel")
> library("stringr")
>
> preprocessText<-function(x){
>
> # Replace the 'html-and'
> arguments<-list(pattern="\\&\\;",replacement="and",x=x,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> # Remove some special characters
>
> arguments<-list(pattern="[^-[:alnum:]\\'\\:\\/\\$\\%\\.\\,\\+\\-\\#\\@\\_\\!\\?+[:space:]]",replacement="",x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> # Lowercase
> arguments<-list(string=y,pattern=tolower(rep_ticker))
> first<-do.call(str_match,arguments)
>
> # Identify signal words and count them
> # Need to be done in parts, because otherwise R can't handle this many
> at once
> arguments<-list(string=x, pattern=rep_words_part1)
> t1<-do.call(str_extract_all,arguments)
>
> arguments<-list(string=x, pattern=rep_words_part2)
> t2<-do.call(str_extract_all,arguments)
>
> arguments<-list(string=x, pattern=rep_words_part3)
> t3<-do.call(str_extract_all,arguments)
>
> arguments<-list(string=x, pattern=rep_words_part4)
> t4<-do.call(str_extract_all,arguments)
>
> count=length(t1[[1]])+length(t2[[1]])+length(t3[[1]])+length(t4[[1]])
> signal_words=c(t1[[1]],t2[[1]],t3[[1]],t4[[1]])
>
>
> # Replacements
>
> arguments<-list(pattern=rep_wordsA,replacement="[ticker]",x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_wordB_part1,replacement="[ticker] ",x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_wordB_part2,replacement="[ticker] ",x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_wordB_part3,replacement="[ticker2] ",x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_wordB_part4,replacement=“[ticker2] ",x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_email,replacement=" [email_address] ",x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_url,replacement=" [url] ",x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_wordC,replacement=" [wordC] ",x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> # Some regular expressions
> arguments<-list(pattern="\\+[[:digit:]]*.?[[:digit:]]+%",replacement="
> [positive_percentage] ",x=y, ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern="-[[:digit:]]*.?[[:digit:]]+%",replacement="
> [negative_percentage] ",x=y, ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern="[[:digit:]]*.?[[:digit:]]+%",replacement="
> [percentage] ",x=y, ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern="\\$[[:digit:]]*.?[[:digit:]]+",replacement="
> [dollar_value] ",x=y,ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern="\\+[[:digit:]]*.?[[:digit:]]+",replacement="
> [pos_number] ",x=y, ignore.case=TRUE)# remaining numbers
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern="\\-[[:digit:]]*.?[[:digit:]]+",replacement="
> [neg_number] ",x=y, ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern="[[:digit:]]*.?[[:digit:]]+",replacement="
> [number] ",x=y, ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_question,replacement=" [question] ", x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
>
> # Unify synonyms
> arguments<-list(pattern=rep_syno1,replacement="happy", x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_syno2,replacement="sad", x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_syno3,replacement="people", x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_syno4,replacement="father", x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_syno5,replacement="mother", x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rep_syno6,replacement="money", x=y,
> ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> # Remove words
> # Punctuation (I know there a pre-defined R commands for this, but I
> need to customize this
> arguments<-list(pattern=rem_punct,replacement="", x=y, ignore.case=TRUE)
> y<-do.call(gsub, arguments)
>
> arguments<-list(pattern=rem_linebreak,replacement=" ", x=y,
> ignore.case=TRUE) #Remove line breaks
> y<-do.call(gsub, arguments)
>
> #Append Positive or Negative Emotion
> arguments<-list(x=y)
> y<-do.call(appendEmotion, arguments)
>
>
> # Output
> result<-list(
> textclean=y,
> first_ticker=first,
> all_ticker=signal_words,
> ticker_count=count)
>
> return(result)
> }
>
> resultList<-mclapply(dataframe$text_column,preprocessText)
>
> ** end main code **
>
> (The return would be a list, which I plan to convert to a data.frame. Don’t
> get that far though).
>
>
> Before, I also tried to call each `gsub` seperately, thus performing the
> first `gsub` on every text string, then the second `gsub` and so on.. but I
> guess that this was even less efficient.
>
> The code itself works, but for me it seems that this can be speeded up.
> Unfortunately I'm not familiar with hash tables, which is what I heard could
> be a solution.
>
> Appreciate your ideas and help very much!
>
>
>
>
> *Definition of the one function called inside `preprocessText`*
>
> appendEmotion<-function(x){
>
> if (grepl(app_pos,x)){
> x<-paste(x," [pos_emotion] ")
> }
> if(grepl(app_neg,x)){
> x<-paste(x," [neg_emotion] ")
> }
> #Output
> return(x)
> }
>
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