[mlpack-svn] r15446 - mlpack/conf/jenkins-conf/benchmark/methods/scikit

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

Author: marcus
Date: Wed Jul 10 13:59:05 2013
New Revision: 15446

Log:
Add scikit Kernel PCA benchmark script.

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

Added: mlpack/conf/jenkins-conf/benchmark/methods/scikit/kernel_pca.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/scikit/kernel_pca.py	Wed Jul 10 13:59:05 2013
@@ -0,0 +1,108 @@
+'''
+  @file kernel_pca.py
+  @author Marcus Edel
+
+  Kernel Principal Components 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 KernelPCA
+
+'''
+This class implements the Kernel Principal Components Analysis benchmark.
+'''
+class KPCA(object):
+
+  ''' 
+  Create the Kernel Principal Components Analysis benchmark instance.
+  
+  @param dataset - Input dataset to perform KPCA 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 Kernel Principal Components Analysis.
+
+  @param options - Extra options for the method.
+  @return - Elapsed time in seconds or -1 if the method was not successful.
+  '''
+  def KPCAScikit(self, options):
+    totalTimer = Timer()
+
+    # Load input dataset.
+    Log.Info("Loading dataset", self.verbose)
+    data = np.genfromtxt(self.dataset, delimiter=',')
+
+    with totalTimer:
+      # Get the new dimensionality, if it is necessary.
+      dimension = re.search('-d (\d+)', options)
+      if not dimension:
+        d = data.shape[1]
+      else:
+        d = int(dimension.group(1))      
+        if (d > data.shape[1]):
+          Log.Fatal("New dimensionality (" + str(d) + ") cannot be greater "
+            + "than existing dimensionality (" + str(data.shape[1]) + ")!")
+          return -1
+
+      # Get the kernel type and make sure it is valid.
+      kernel = re.search("-k ([^\s]+)", options)
+      if not kernel:
+        Log.Fatal("Choose kernel type, valid choices are 'linear', 'hyptan' " + 
+              "and 'polynomial'.")
+        return -1
+      elif kernel.group(1) == "linear":
+        model = KernelPCA(n_components=d, kernel="linear")
+      elif kernel.group(1) == "hyptan":
+        model = KernelPCA(n_components=d, kernel="sigmoid")
+      elif kernel.group(1) == "polynomial":
+        degree = re.search('-D (\d+)', options)
+        if not degree:
+          degree = 1
+        else:
+          degree = int(degree.group(1))
+
+        model = KernelPCA(n_components=d, kernel="poly", degree=degree)
+      else:
+        Log.Fatal("Invalid kernel type (" + kernel.group(1) + "); valid " +
+            "choices are 'linear', 'hyptan' and 'polynomial'.")
+        return -1
+        
+      out = model.fit_transform(data)
+
+    return totalTimer.ElapsedTime()
+
+  '''
+  Perform Kernel Principal Components 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 KPCA.", self.verbose)
+
+    return self.KPCAScikit(options)