[mlpack-svn] r15437 - mlpack/conf/jenkins-conf/benchmark/methods/mlpy

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

Author: marcus
Date: Tue Jul  9 15:15:04 2013
New Revision: 15437

Log:
Add mlpy All K-Nearest-Neighbors benchmark script.

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

Added: mlpack/conf/jenkins-conf/benchmark/methods/mlpy/allknn.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/mlpy/allknn.py	Tue Jul  9 15:15:04 2013
@@ -0,0 +1,103 @@
+'''
+  @file allknn.py
+  @author Marcus Edel
+
+  All K-Nearest-Neighbors 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 All K-Nearest-Neighbors benchmark.
+'''
+class ALLKNN(object):
+
+  ''' 
+  Create the All K-Nearest-Neighbors benchmark instance.
+  
+  @param dataset - Input dataset to perform All K-Nearest-Neighbors 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 All K-Nearest-Neighbors.
+
+  @param options - Extra options for the method.
+  @return - Elapsed time in seconds or -1 if the method was not successful.
+  '''
+  def AllKnnMlpy(self, options):
+    totalTimer = Timer()
+
+    # 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 K-Means Clustering. 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 ALLKNN.", self.verbose)
+
+    return self.AllKnnMlpy(options)