[mlpack-svn] r15438 - mlpack/conf/jenkins-conf/benchmark/methods/scikit
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Tue Jul 9 15:16:21 EDT 2013
Author: marcus
Date: Tue Jul 9 15:16:21 2013
New Revision: 15438
Log:
Add scikit All K-Nearest-Neighbors benchmark script.
Added:
mlpack/conf/jenkins-conf/benchmark/methods/scikit/allknn.py
Added: mlpack/conf/jenkins-conf/benchmark/methods/scikit/allknn.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/scikit/allknn.py Tue Jul 9 15:16:21 2013
@@ -0,0 +1,110 @@
+'''
+ @file allknn.py
+ @author Marcus Edel
+
+ All K-Nearest-Neighbors 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.neighbors import NearestNeighbors
+
+'''
+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 scikit 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 AllKnnScikit(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=',')
+
+ with totalTimer:
+ # Get all the parameters.
+ k = re.search("-k (\d+)", options)
+ leafSize = re.search("-l (\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
+
+ if not leafSize:
+ l = 20
+ elif leafSize.group(1) < 0:
+ Log.Fatal("Invalid leaf size: " + str(leafSize.group(1)) + ". Must be " +
+ "greater than or equal to 0.")
+ return -1
+ else:
+ l = int(leafSize.group(1))
+
+ # Perform All K-Nearest-Neighbors.
+ model = NearestNeighbors(n_neighbors=k, algorithm='kd_tree', leaf_size=l)
+ model.fit(referenceData)
+
+ if len(self.dataset) == 2:
+ out = model.kneighbors(queryData, k, return_distance=True)
+ else:
+ out = model.kneighbors(referenceData, k, return_distance=True)
+
+ return totalTimer.ElapsedTime()
+
+ '''
+ Perform All K-Nearest-Neighbors. 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.AllKnnScikit(options)
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