[mlpack-svn] r15436 - mlpack/conf/jenkins-conf/benchmark/methods/matlab
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Tue Jul 9 15:13:32 EDT 2013
Author: marcus
Date: Tue Jul 9 15:13:32 2013
New Revision: 15436
Log:
Add matlab All K-Nearest-Neighbors method and benchmarl script.
Added:
mlpack/conf/jenkins-conf/benchmark/methods/matlab/ALLKNN.m
mlpack/conf/jenkins-conf/benchmark/methods/matlab/allknn.py
Added: mlpack/conf/jenkins-conf/benchmark/methods/matlab/ALLKNN.m
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/matlab/ALLKNN.m Tue Jul 9 15:13:32 2013
@@ -0,0 +1,80 @@
+% @file ALLKNN.m
+% @author Marcus Edel
+%
+% All K-Nearest-Neighbors with matlab.
+
+function allknn(cmd)
+% This program will calculate the all k-nearest-neighbors of a set of
+% points using kd-trees. You may specify a separate set of reference points
+% and query points, or just a reference set which will be used as both the
+% reference and query set.
+%
+% Required options:
+% (-k) [int] Number of furthest neighbors to find.
+% (-t) [string] A file containing the training set.
+%
+% Options:
+% (-l) [int] Leaf size for tree building. Default value 20.
+% (-N) If true, O(n^2) naive mode is used for computation.
+% (-q) [string] File containing query points (optional).
+% Default value ''.
+
+% Load input dataset.
+referenceFile = regexp(cmd, '.*?-r ([^\s]+)', 'tokens', 'once');
+referenceData = csvread(referenceFile{:});
+
+% Get all the parameters.
+queryFile = regexp(cmd, '.*?-q ([^\s]+)', 'tokens', 'once');
+k = regexp(cmd,'.* -k (\d+)','tokens','once');
+leafSize = str2double(regexp(cmd,'.* -l (\d+)','tokens','once'));
+
+if ~isempty(queryFile)
+ disp('[INFO ] Load query data.');
+ queryData = csvread(queryFile{:});
+end
+
+if ~isempty(k)
+ k = str2double(k)
+else
+ disp('[Fatal] Required options: Number of furthest neighbors to find.');
+ return;
+end
+
+total_time = tic;
+% Sanity check on k value: must be greater than 0, must be less than the
+% number of reference points.
+if k > size(referenceData, 2)
+ msg = [...
+ '[Fatal] Invalid k: %i; must be greater than 0 and less '...
+ 'than or equal to the number of reference points (%i)'...
+ ];
+ disp(sprintf(msg, k, size(referenceData, 2)))
+ return;
+end
+
+if isempty(leafSize)
+ leafSize = 20;
+end
+
+if strfind(cmd, '-N') > 0
+ if isempty(queryFile)
+ [IDX, D] = knnsearch(referenceData, referenceData, 'K', k, ...
+ 'distance', 'euclidean', 'NSMethod', 'exhaustive');
+ else
+ [IDX, D] = knnsearch(referenceData, queryData, 'K', k, ...
+ 'distance', 'euclidean', 'NSMethod', 'exhaustive');
+ end
+else
+ if isempty(queryFile)
+ [IDX, D] = knnsearch(referenceData, referenceData, 'K', k, ...
+ 'distance', 'euclidean', 'NSMethod', 'kdtree', 'BucketSize', ...
+ leafSize);
+ else
+ [IDX, D] = knnsearch(referenceData, queryData, 'K', k, ...
+ 'distance', 'euclidean', 'NSMethod', 'kdtree', 'BucketSize', ...
+ leafSize);
+ end
+end
+
+disp(sprintf('[INFO ] total_time: %fs', toc(total_time)))
+end
Added: mlpack/conf/jenkins-conf/benchmark/methods/matlab/allknn.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/matlab/allknn.py Tue Jul 9 15:13:32 2013
@@ -0,0 +1,121 @@
+'''
+ @file allknn.py
+ @author Marcus Edel
+
+ Class to benchmark the matlab All K-Nearest-Neighbors 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 All K-Nearest-Neighbors benchmark.
+'''
+class ALLKNN(object):
+
+ '''
+ Create the All K-Nearest-Neighbors benchmark instance.
+
+ @param dataset - Input dataset to perform ALLKNN on.
+ @param path - Path to the mlpack executable.
+ @param verbose - Display informational messages.
+ '''
+ def __init__(self, dataset, path=os.environ["MATLAB_BIN"], verbose = True):
+ self.verbose = verbose
+ self.dataset = dataset
+ self.path = path
+
+ '''
+ Destructor to clean up at the end.
+ '''
+ def __del__(self):
+ pass
+
+ '''
+ All K-Nearest-Neighbors. 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 ALLKNN.", self.verbose)
+
+ # If the dataset contains two files then the second file is the query file.
+ # In this case we add this to the command line.
+ if len(self.dataset) == 2:
+ inputCmd = "-r " + self.dataset[0] + " -q " + self.dataset[1] + " " + options
+ else:
+ inputCmd = "-r " + self.dataset + " " + options
+
+ # Split the command using shell-like syntax.
+ cmd = shlex.split(self.path + "matlab -nodisplay -nosplash -r \"try, " +
+ "ALLKNN('" + inputCmd + "'), catch, exit(1), end, exit(0)\"")
+
+ # 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"])
+
+ return timer(float(match.group("total_time")))
+
+ '''
+ 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
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