Python子进程调用预编译的Java

这可以从Windows命令行运行：

c:\mallet\bin\mallet run

我试过了

subprocess.call(['c:\mallet\bin\mallet', 'run'])

并得到一个错误

WindowsError: [Error 2] The system cannot find the file specified

我已经尝试过

subprocess.call(['c:/mallet/bin/mallet', 'run'])

并得到错误

WindowsError: [Error 193] %1 is not a valid Win32 application

我必须传递给subprocess.call（）什么？

为了完整起见，我想传递的完整命令是：

bin\mallet run cc.mallet.topics.tui.DMRLoader texts.txt features.txt instance.mallet

我模糊的想法是，这是一个我已经以某种方式调用的预编译的Java类，但是我真的不明白我在这里做什么。

这是文件夹中的两个槌文件bin：

mallet.bat

@echo off

rem This batch file serves as a wrapper for several
rem  MALLET command line tools.

if not "%MALLET_HOME%" == "" goto gotMalletHome

echo MALLET requires an environment variable MALLET_HOME.
goto :eof

:gotMalletHome

set MALLET_CLASSPATH=%MALLET_HOME%\class;%MALLET_HOME%\lib\mallet-deps.jar
set MALLET_MEMORY=1G
set MALLET_ENCODING=UTF-8

set CMD=%1
shift

set CLASS=
if "%CMD%"=="import-dir" set CLASS=cc.mallet.classify.tui.Text2Vectors
if "%CMD%"=="import-file" set CLASS=cc.mallet.classify.tui.Csv2Vectors
if "%CMD%"=="import-smvlight" set CLASS=cc.mallet.classify.tui.SvmLight2Vectors
if "%CMD%"=="train-classifier" set CLASS=cc.mallet.classify.tui.Vectors2Classify
if "%CMD%"=="train-topics" set CLASS=cc.mallet.topics.tui.Vectors2Topics
if "%CMD%"=="infer-topics" set CLASS=cc.mallet.topics.tui.InferTopics
if "%CMD%"=="estimate-topics" set CLASS=cc.mallet.topics.tui.EstimateTopics
if "%CMD%"=="hlda" set CLASS=cc.mallet.topics.tui.HierarchicalLDATUI
if "%CMD%"=="prune" set CLASS=cc.mallet.classify.tui.Vectors2Vectors
if "%CMD%"=="split" set CLASS=cc.mallet.classify.tui.Vectors2Vectors
if "%CMD%"=="bulk-load" set CLASS=cc.mallet.util.BulkLoader
if "%CMD%"=="run" set CLASS=%1 & shift

if not "%CLASS%" == "" goto gotClass

echo Mallet 2.0 commands: 
echo   import-dir        load the contents of a directory into mallet instances (one per file)
echo   import-file       load a single file into mallet instances (one per line)
echo   import-svmlight   load a single SVMLight format data file into mallet instances (one per line)
echo   train-classifier  train a classifier from Mallet data files
echo   train-topics      train a topic model from Mallet data files
echo   infer-topics      use a trained topic model to infer topics for new documents
echo   estimate-topics   estimate the probability of new documents given a trained model
echo   hlda              train a topic model using Hierarchical LDA
echo   prune             remove features based on frequency or information gain
echo   split             divide data into testing, training, and validation portions
echo Include --help with any option for more information


goto :eof

:gotClass

set MALLET_ARGS=

:getArg

if "%1"=="" goto run
set MALLET_ARGS=%MALLET_ARGS% %1
shift
goto getArg

:run

java -Xmx%MALLET_MEMORY% -ea -Dfile.encoding=%MALLET_ENCODING% -classpath %MALLET_CLASSPATH% %CLASS% %MALLET_ARGS%

:eof

和 mallet

#!/bin/bash


malletdir=`dirname $0`
malletdir=`dirname $malletdir`

cp=$malletdir/class:$malletdir/lib/mallet-deps.jar:$CLASSPATH
#echo $cp

MEMORY=1g

JAVA_COMMAND="java -Xmx$MEMORY -ea -Djava.awt.headless=true -Dfile.encoding=UTF-8 -server -classpath $cp"

CMD=$1
shift

help()
{
cat <<EOF
Mallet 2.0 commands: 

  import-dir         load the contents of a directory into mallet instances (one per file)
  import-file        load a single file into mallet instances (one per line)
  import-svmlight    load SVMLight format data files into Mallet instances
  train-classifier   train a classifier from Mallet data files
  classify-dir       classify data from a single file with a saved classifier
  classify-file      classify the contents of a directory with a saved classifier
  classify-svmlight  classify data from a single file in SVMLight format
  train-topics       train a topic model from Mallet data files
  infer-topics       use a trained topic model to infer topics for new documents
  evaluate-topics    estimate the probability of new documents under a trained model
  hlda               train a topic model using Hierarchical LDA
  prune              remove features based on frequency or information gain
  split              divide data into testing, training, and validation portions

Include --help with any option for more information
EOF
}

CLASS=

case $CMD in
    import-dir) CLASS=cc.mallet.classify.tui.Text2Vectors;;
    import-file) CLASS=cc.mallet.classify.tui.Csv2Vectors;;
        import-svmlight) CLASS=cc.mallet.classify.tui.SvmLight2Vectors;;
    train-classifier) CLASS=cc.mallet.classify.tui.Vectors2Classify;;
        classify-dir) CLASS=cc.mallet.classify.tui.Text2Classify;;
        classify-file) CLASS=cc.mallet.classify.tui.Csv2Classify;;
        classify-svmlight) CLASS=cc.mallet.classify.tui.SvmLight2Classify;;
    train-topics) CLASS=cc.mallet.topics.tui.Vectors2Topics;;
    infer-topics) CLASS=cc.mallet.topics.tui.InferTopics;;
    evaluate-topics) CLASS=cc.mallet.topics.tui.EvaluateTopics;;
    hlda) CLASS=cc.mallet.topics.tui.HierarchicalLDATUI;;
    prune) CLASS=cc.mallet.classify.tui.Vectors2Vectors;;
    split) CLASS=cc.mallet.classify.tui.Vectors2Vectors;;
    bulk-load) CLASS=cc.mallet.util.BulkLoader;;
    run) CLASS=$1; shift;;
    *) echo "Unrecognized command: $CMD"; help; exit 1;;
esac

$JAVA_COMMAND $CLASS $*