[mlpack-svn] r15410 - mlpack/conf/jenkins-conf/benchmark/methods/shogun
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Thu Jul 4 09:05:36 EDT 2013
Author: marcus
Date: Thu Jul 4 09:05:36 2013
New Revision: 15410
Log:
Add shogun nbc benchmark script.
Added:
mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py
Added: mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py Thu Jul 4 09:05:36 2013
@@ -0,0 +1,91 @@
+'''
+ @file nbc.py
+ @author Marcus Edel
+
+ Naive Bayes Classifier with shogun.
+'''
+
+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 timer import *
+
+import numpy as np
+from shogun.Features import RealFeatures, Labels
+from shogun.Classifier import GaussianNaiveBayes
+
+'''
+This class implements the Naive Bayes Classifier benchmark.
+'''
+class NBC(object):
+
+ '''
+ Create the Naive Bayes Classifier benchmark instance.
+
+ @param dataset - Input dataset to perform NBC on.
+ @param verbose - Display informational messages.
+ '''
+ def __init__(self, dataset, verbose=True):
+ self.verbose = verbose
+ self.dataset = dataset
+
+ '''
+ Destructor to clean up at the end.
+ '''
+ def __del__(self):
+ pass
+
+ '''
+ Use the shogun libary to implement Naive Bayes Classifier.
+
+ @param options - Extra options for the method.
+ @return - Elapsed time in seconds or -1 if the method was not successful.
+ '''
+ def NBCShogun(self, options):
+ totalTimer = Timer()
+
+ Log.Info("Loading dataset", self.verbose)
+ # Load train and test dataset.
+ trainData = np.genfromtxt(self.dataset[0], delimiter=',')
+ testData = np.genfromtxt(self.dataset[1], delimiter=',')
+
+ # Labels are the last row of the training set.
+ labels = Labels(trainData[:,4])
+
+ with totalTimer:
+ # Transform into features.
+ trainFeat = RealFeatures(trainData[:,:-1].T)
+ testFeat = RealFeatures(testData.T)
+
+ # Create and train the classifier.
+ nbc = GaussianNaiveBayes(trainFeat, labels)
+ nbc.train()
+ # Run Naive Bayes Classifier on the test dataset.
+ nbc.apply(testFeat).get_labels()
+
+ return totalTimer.ElapsedTime()
+
+ '''
+ Perform Naive Bayes Classifier. 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 NBC.", self.verbose)
+
+ if len(self.dataset) < 2:
+ Log.Fatal("The method need two datasets.")
+ return -1
+
+ return self.NBCShogun(options)
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