[mlpack-svn] r15480 - in mlpack/conf/jenkins-conf/benchmark/methods/weka: . src
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Tue Jul 16 12:54:02 EDT 2013
Author: marcus
Date: Tue Jul 16 12:54:02 2013
New Revision: 15480
Log:
Add weka linear regression method and benchmarl script.
Added:
mlpack/conf/jenkins-conf/benchmark/methods/weka/linear_regression.py
mlpack/conf/jenkins-conf/benchmark/methods/weka/src/LinearRegression.java
Added: mlpack/conf/jenkins-conf/benchmark/methods/weka/linear_regression.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/weka/linear_regression.py Tue Jul 16 12:54:02 2013
@@ -0,0 +1,124 @@
+'''
+ @file linear_regression.py
+ @author Marcus Edel
+
+ Class to benchmark the weka Linear Regression method.
+'''
+
+import os
+import sys
+import inspect
+
+# Import the util path, this method even works if the path contains symlinks to
+# modules.
+cmd_subfolder = os.path.realpath(os.path.abspath(os.path.join(
+ os.path.split(inspect.getfile(inspect.currentframe()))[0], "../../util")))
+if cmd_subfolder not in sys.path:
+ sys.path.insert(0, cmd_subfolder)
+
+from log import *
+from profiler import *
+
+import shlex
+import subprocess
+import re
+import collections
+
+'''
+This class implements the Linear Regression benchmark.
+'''
+class LinearRegression(object):
+
+ '''
+ Create the Linear Regression benchmark instance.
+
+ @param dataset - Input dataset to perform Linear Regression on.
+ @param path - Path to the mlpack executable.
+ @param verbose - Display informational messages.
+ '''
+ def __init__(self, dataset, path=os.environ["WEKA_CLASSPATH"], verbose=True):
+ self.verbose = verbose
+ self.dataset = dataset
+ self.path = path
+
+ '''
+ Destructor to clean up at the end.
+ '''
+ def __del__(self):
+ pass
+
+ '''
+ Linear Regression. If the method has been successfully completed return
+ the elapsed time in seconds.
+
+ @param options - Extra options for the method.
+ @return - Elapsed time in seconds or -1 if the method was not successful.
+ '''
+ def RunMethod(self, options):
+ Log.Info("Perform Linear Regression.", self.verbose)
+
+ # Load input dataset.
+ # If the dataset contains two files then the second file is the responses
+ # file. In this case we add this to the command line.
+ if len(self.dataset) == 2:
+ cmd = shlex.split("java -classpath " + self.path + ":methods/weka" +
+ " LinearRegression -i " + self.dataset[0] + " -r " + self.dataset[1]
+ + " " + options)
+ else:
+ cmd = shlex.split("java -classpath " + self.path + ":methods/weka" +
+ " LinearRegression -i " + self.dataset + " " + options)
+
+ # Run command with the nessecary arguments and return its output as a byte
+ # string. We have untrusted input so we disables all shell based features.
+ try:
+ s = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=False)
+ except Exception:
+ Log.Fatal("Could not execute command: " + str(cmd))
+ return -1
+
+ # Return the elapsed time.
+ timer = self.parseTimer(s)
+ if not timer:
+ Log.Fatal("Can't parse the timer")
+ return -1
+ else:
+ time = self.GetTime(timer)
+ Log.Info(("total time: %fs" % time), self.verbose)
+
+ return time
+
+ '''
+ Parse the timer data form a given string.
+
+ @param data - String to parse timer data from.
+ @return - Namedtuple that contains the timer data.
+ '''
+ def parseTimer(self, data):
+ # Compile the regular expression pattern into a regular expression object to
+ # parse the timer data.
+ pattern = re.compile(r"""
+ .*?total_time: (?P<total_time>.*?)s.*?
+ """, re.VERBOSE|re.MULTILINE|re.DOTALL)
+
+ match = pattern.match(data)
+ if not match:
+ Log.Fatal("Can't parse the data: wrong format")
+ return -1
+ else:
+ # Create a namedtuple and return the timer data.
+ timer = collections.namedtuple("timer", ["total_time"])
+
+ if match.group("total_time").count(".") == 1:
+ return timer(float(match.group("total_time")))
+ else:
+ return timer(float(match.group("total_time").replace(",", ".")))
+
+ '''
+ Return the elapsed time in seconds.
+
+ @param timer - Namedtuple that contains the timer data.
+ @return Elapsed time in seconds.
+ '''
+ def GetTime(self, timer):
+ time = timer.total_time
+ return time
Added: mlpack/conf/jenkins-conf/benchmark/methods/weka/src/LinearRegression.java
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/weka/src/LinearRegression.java Tue Jul 16 12:54:02 2013
@@ -0,0 +1,66 @@
+/**
+ * @file LinearRegression.java
+ * @author Marcus Edel
+ *
+ * Linear Regression with weka.
+ */
+
+import java.io.IOException;
+import weka.core.*;
+import weka.core.converters.ConverterUtils.DataSource;
+
+/**
+ * This class use the weka libary to implement Linear Regression.
+ */
+public class LinearRegression {
+ private static final String USAGE = String
+ .format("Linear Regression.\n\n"
+ + "Required options:\n"
+ + "-i [string] File containing X (regressors).\n\n"
+ + "Options:\n\n"
+ + "(-r) [string] Optional file containing y (responses).\n"
+ + " If not given, the responses are assumed\n"
+ + " to be the last row of the input file.");
+
+ public static void main(String args[]) {
+ Timers timer = new Timers();
+ try {
+ // Get the data set path.
+ String regressorsFile = Utils.getOption('i', args);
+ if (regressorsFile.length() == 0)
+ throw new IllegalArgumentException("Required option: File containing" +
+ " the regressors.");
+
+ // Load input dataset.
+ DataSource source = new DataSource(regressorsFile);
+ Instances data = source.getDataSet();
+
+ // Are the responses in a separate file?
+ String input_responsesFile = Utils.getOption('r', args);
+ if (regressorsFile.length() != 0)
+ {
+ // Merge the two datasets.
+ source = new DataSource(input_responsesFile);
+ Instances responses = source.getDataSet();
+ data = Instances.mergeInstances(data ,responses);
+ }
+
+ // The class is in the last row.
+ data.setClassIndex((data.numAttributes() - 1));
+
+ // Perform Linear Regression.
+ timer.StartTimer("total_time");
+ weka.classifiers.functions.LinearRegression model = new weka.classifiers.functions.LinearRegression();
+ model.buildClassifier(data);
+ double[] b = model.coefficients();
+
+ timer.StopTimer("total_time");
+ timer.PrintTimer("total_time");
+
+ } catch (IOException e) {
+ System.err.println(USAGE);
+ } catch (Exception e) {
+ e.printStackTrace();
+ }
+ }
+}
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