[mlpack-svn] r15479 - mlpack/conf/jenkins-conf/benchmark/methods/mlpy
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Tue Jul 16 12:52:33 EDT 2013
Author: marcus
Date: Tue Jul 16 12:52:33 2013
New Revision: 15479
Log:
Add mlpy linear regression benchmark script.
Added:
mlpack/conf/jenkins-conf/benchmark/methods/mlpy/linear_regression.py
Added: mlpack/conf/jenkins-conf/benchmark/methods/mlpy/linear_regression.py
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/mlpy/linear_regression.py Tue Jul 16 12:52:33 2013
@@ -0,0 +1,85 @@
+'''
+ @file linear_regression.py
+ @author Marcus Edel
+
+ Linear Regression 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 Linear Regression benchmark.
+'''
+class LinearRegression(object):
+
+ '''
+ Create the Linear Regression benchmark instance.
+
+ @param dataset - Input dataset to perform Linear 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 mlpy libary to implement Linear Regression.
+
+ @param options - Extra options for the method.
+ @return - Elapsed time in seconds or -1 if the method was not successful.
+ '''
+ def LinearRegressionMlpy(self, options):
+ totalTimer = Timer()
+
+ # Load input dataset.
+ # If the dataset contains two files then the second file is the responses
+ # file. In this case we add this to the command line.
+ Log.Info("Loading dataset", self.verbose)
+ if len(self.dataset) == 2:
+ X = np.genfromtxt(self.dataset[0], delimiter=',')
+ y = np.genfromtxt(self.dataset[1], delimiter=',')
+ else:
+ X = np.genfromtxt(self.dataset, delimiter=',')
+ y = X[:, (X.shape[1] - 1)]
+ X = X[:,:-1]
+
+ with totalTimer:
+ # Perform linear regression.
+ model = mlpy.OLS()
+ model.learn(X, y)
+ b = model.beta()
+
+ return totalTimer.ElapsedTime()
+
+ '''
+ Perform Linear 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 Linear Regression.", self.verbose)
+
+ return self.LinearRegressionMlpy(options)
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