[mlpack-svn] r15490 - mlpack/conf/jenkins-conf/benchmark/methods/scikit

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

Author: marcus
Date: Wed Jul 17 13:16:33 2013
New Revision: 15490

Log:
Add scikit ICA benchmark script.

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

Added: mlpack/conf/jenkins-conf/benchmark/methods/scikit/ica.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/scikit/ica.py	Wed Jul 17 13:16:33 2013
@@ -0,0 +1,79 @@
+'''
+  @file ica.py
+  @author Marcus Edel
+
+  Independent component analysis 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.decomposition import FastICA
+
+'''
+This class implements the independent component analysis benchmark.
+'''
+class ICA(object):
+
+  ''' 
+  Create the independent component analysis benchmark instance.
+  
+  @param dataset - Input dataset to perform independent component analysis 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 independent component analysis.
+
+  @param options - Extra options for the method.
+  @return - Elapsed time in seconds or -1 if the method was not successful.
+  '''
+  def ICAScikit(self, options):
+    totalTimer = Timer()
+
+    # Load input dataset.
+    data = np.genfromtxt(self.dataset, delimiter=',')
+
+    s = re.search('-s (\d+)', options)
+    s = 0 if not s else int(s.group(1))
+
+    # Perform ICA.
+    with totalTimer:
+      model = FastICA(random_state=s)
+      ic = model.fit(data).transform(data)
+      mixing = model.get_mixing_matrix()
+
+    return totalTimer.ElapsedTime()
+
+  '''
+  Perform independent component analysis. 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 ICA.", self.verbose)
+
+    return self.ICAScikit(options)