[mlpack-svn] r15481 - mlpack/conf/jenkins-conf/benchmark/methods/scikit

[fastlab-svn at coffeetalk-1.cc.gatech.edu]

Author: marcus
Date: Tue Jul 16 12:57:26 2013
New Revision: 15481

Log:
Add scikit gmm benchmark script.

Added:
   mlpack/conf/jenkins-conf/benchmark/methods/scikit/gmm.py

Added: mlpack/conf/jenkins-conf/benchmark/methods/scikit/gmm.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/scikit/gmm.py	Tue Jul 16 12:57:26 2013
@@ -0,0 +1,85 @@
+'''
+  @file gmm.py
+  @author Marcus Edel
+
+  Gaussian Mixture Model with scikit.
+'''
+
+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 sklearn import mixture
+
+'''
+This class implements the Gaussian Mixture Model benchmark.
+'''
+class GMM(object):
+
+  ''' 
+  Create the Gaussian Mixture Model benchmark instance.
+  
+  @param dataset - Input dataset to perform Gaussian Mixture Model 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 scikit libary to implement Gaussian Mixture Model.
+
+  @param options - Extra options for the method.
+  @return - Elapsed time in seconds or -1 if the method was not successful.
+  '''
+  def GMMScikit(self, options):
+    totalTimer = Timer()
+
+    # Load input dataset.
+    dataPoints = np.genfromtxt(self.dataset, delimiter=',')
+
+    # Get all the parameters.
+    g = re.search("-g (\d+)", options)
+    n = re.search("-n (\d+)", options)
+    s = re.search("-n (\d+)", options)
+
+    g = 1 if not g else int(g.group(1))
+    n = 250 if not n else int(n.group(1))
+    s = 0 if not s else int(s.group(1))
+
+    # Create the Gaussian Mixture Model.
+    model = mixture.GMM(n_components=g, covariance_type='full', random_state=s, 
+        n_iter=n)
+    with totalTimer:
+      model.fit(dataPoints) 
+
+    return totalTimer.ElapsedTime()
+
+  '''
+  Perform Gaussian Mixture Model. 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 GMM.", self.verbose)
+
+    return self.GMMScikit(options)