[mlpack-svn] r15471 - mlpack/conf/jenkins-conf/benchmark/methods/shogun

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

Author: marcus
Date: Mon Jul 15 11:59:47 2013
New Revision: 15471

Log:
Add shogun LARS benchmark script.

Added:
   mlpack/conf/jenkins-conf/benchmark/methods/shogun/lars.py

Added: mlpack/conf/jenkins-conf/benchmark/methods/shogun/lars.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/lars.py	Mon Jul 15 11:59:47 2013
@@ -0,0 +1,94 @@
+'''
+  @file lars.py
+  @author Marcus Edel
+
+  Least Angle Regression 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 RegressionLabels, RealFeatures
+from shogun.Regression import LeastAngleRegression
+
+'''
+This class implements the Least Angle Regression benchmark.
+'''
+class LARS(object):
+
+  ''' 
+  Create the All Least Angle Regression benchmark instance.
+  
+  @param dataset - Input dataset to perform Least Angle Regression 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 Least Angle Regression.
+
+  @param options - Extra options for the method.
+  @return - Elapsed time in seconds or -1 if the method was not successful.
+  '''
+  def LARSShogun(self, options):
+    totalTimer = Timer()
+
+    # Load input dataset.
+    Log.Info("Loading dataset", self.verbose)
+    inputData = np.genfromtxt(self.dataset[0], delimiter=',')
+    responsesData = np.genfromtxt(self.dataset[1], delimiter=',')
+    inputFeat = RealFeatures(inputData.T)
+    responsesFeat = RegressionLabels(responsesData)
+
+    # Get all the parameters.
+    lambda1 = re.search("-l (\d+)", options)
+    if not lambda1:
+        lambda1 = 0.0
+      else:
+        lambda1 = int(lambda1.group(1))    
+
+    with totalTimer:
+      # Perform LARS.
+      model = LeastAngleRegression(False)
+      model.set_max_l1_norm(lambda1)
+      model.set_labels(responsesFeat)
+      model.train(inputFeat)
+      model.get_w(model.get_path_size() - 1)
+
+    return totalTimer.ElapsedTime()
+
+  '''
+  Perform Least Angle 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 LARS.", self.verbose)
+
+    if len(self.dataset) < 2:
+      Log.Fatal("The method need two datasets.")
+      return -1
+
+    return self.LARSShogun(options)