[mlpack-svn] r15445 - mlpack/conf/jenkins-conf/benchmark/methods/mlpy

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

Author: marcus
Date: Wed Jul 10 13:58:01 2013
New Revision: 15445

Log:
Add mlpy Kernel PCA benchmark script.

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

Added: mlpack/conf/jenkins-conf/benchmark/methods/mlpy/kernel_pca.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/mlpy/kernel_pca.py	Wed Jul 10 13:58:01 2013
@@ -0,0 +1,155 @@
+'''
+  @file kernel_pca.py
+  @author Marcus Edel
+
+  Kernel Principal Components Analysis with mlpy.
+'''
+
+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
+import mlpy
+
+'''
+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 mlpy 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 KPCAMlpy(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 'polynomial', " + 
+                "'gaussian', 'linear' and 'hyptan'.")
+          return -1
+      elif kernel.group(1) == "polynomial":
+        degree = re.search('-D (\d+)', options)
+        if not degree:
+          degree = 1
+        else:
+          degree = int(degree.group(1))
+        
+        kernel = mlpy.kernel_polynomial(data, data, d=degree)
+      elif kernel.group(1) == "gaussian":
+        kernel = mlpy.kernel_gaussian(data, data, sigma=2) 
+      elif kernel.group(1) == "linear":
+        kernel = mlpy.kernel_linear(data, data)
+      elif kernel.group(1) == "hyptan":
+        kernel = mlpy.kernel_sigmoid(data, data)
+      else:
+        Log.Fatal("Invalid kernel type (" + kernel.group(1) + "); valid " +
+                "choices are 'polynomial', 'gaussian', 'linear' and 'hyptan'.")
+        return -1
+
+      # Perform Kernel Principal Components Analysis.
+      model = mlpy.KPCA()
+      model.learn(kernel)
+      out = model.transform(kernel, k=d)
+
+      print out
+
+
+
+    return 0
+
+    # # Load input dataset.
+    # # If the dataset contains two files then the second file is the query file 
+    # # In this case we add this to the command line.
+    # Log.Info("Loading dataset", self.verbose)
+    # if len(self.dataset) == 2:
+    #   referenceData = np.genfromtxt(self.dataset[0], delimiter=',')
+    #   queryData = np.genfromtxt(self.dataset[1], delimiter=',')
+    # else:
+    #   referenceData = np.genfromtxt(self.dataset, delimiter=',')
+
+    # # Labels are the last row of the dataset.
+    # labels = referenceData[:, (referenceData.shape[1] - 1)]
+    # referenceData = referenceData[:,:-1]
+
+    # with totalTimer:
+    #   # Get all the parameters.
+    #   k = re.search("-k (\d+)", options)
+    #   if not k:
+    #     Log.Fatal("Required option: Number of furthest neighbors to find.")
+    #     return -1
+    #   else:
+    #     k = int(k.group(1))
+    #     if (k < 1 or k > referenceData.shape[0]):
+    #       Log.Fatal("Invalid k: " + k.group(1) + "; must be greater than 0 and "
+    #         + "less ")
+    #       return -1
+
+    #   # Perform All K-Nearest-Neighbors.
+    #   model = mlpy.KNN(k)
+    #   model.learn(referenceData, labels)
+
+    #   if len(self.dataset) == 2:
+    #     out = model.pred(queryData)
+    #   else:
+    #     out = model.pred(referenceData)
+
+    # 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.KPCAMlpy(options)