[mlpack-svn] r15665 - mlpack/conf/jenkins-conf/benchmark/methods/shogun
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Mon Aug 26 07:32:12 EDT 2013
Author: marcus
Date: Mon Aug 26 07:32:12 2013
New Revision: 15665
Log:
Adjust exception handling for the shogun functions.
Modified:
mlpack/conf/jenkins-conf/benchmark/methods/shogun/allknn.py
mlpack/conf/jenkins-conf/benchmark/methods/shogun/gmm.py
mlpack/conf/jenkins-conf/benchmark/methods/shogun/kernel_pca.py
mlpack/conf/jenkins-conf/benchmark/methods/shogun/kmeans.py
mlpack/conf/jenkins-conf/benchmark/methods/shogun/lars.py
mlpack/conf/jenkins-conf/benchmark/methods/shogun/linear_regression.py
mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py
mlpack/conf/jenkins-conf/benchmark/methods/shogun/pca.py
Modified: mlpack/conf/jenkins-conf/benchmark/methods/shogun/allknn.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/shogun/allknn.py (original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/allknn.py Mon Aug 26 07:32:12 2013
@@ -54,19 +54,19 @@
# 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=',')
- queryFeat = RealFeatures(queryFeat.T)
- else:
- referenceData = np.genfromtxt(self.dataset, delimiter=',')
-
- # Labels are the last row of the dataset.
- labels = MulticlassLabels(referenceData[:, (referenceData.shape[1] - 1)])
- referenceData = referenceData[:,:-1]
-
try:
+ 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=',')
+ queryFeat = RealFeatures(queryFeat.T)
+ else:
+ referenceData = np.genfromtxt(self.dataset, delimiter=',')
+
+ # Labels are the last row of the dataset.
+ labels = MulticlassLabels(referenceData[:, (referenceData.shape[1] - 1)])
+ referenceData = referenceData[:,:-1]
+
with totalTimer:
# Get all the parameters.
k = re.search("-k (\d+)", options)
Modified: mlpack/conf/jenkins-conf/benchmark/methods/shogun/gmm.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/shogun/gmm.py (original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/gmm.py Mon Aug 26 07:32:12 2013
@@ -51,18 +51,18 @@
totalTimer = Timer()
# Load input dataset.
- dataPoints = np.genfromtxt(self.dataset, delimiter=',')
- dataFeat = RealFeatures(dataPoints.T)
-
- # Get all the parameters.
- g = re.search("-g (\d+)", options)
- n = re.search("-n (\d+)", options)
- s = re.search("-n (\d+)", options)
-
- g = 1 if not g else int(g.group(1))
- n = 250 if not n else int(n.group(1))
-
try:
+ dataPoints = np.genfromtxt(self.dataset, delimiter=',')
+ dataFeat = RealFeatures(dataPoints.T)
+
+ # Get all the parameters.
+ g = re.search("-g (\d+)", options)
+ n = re.search("-n (\d+)", options)
+ s = re.search("-n (\d+)", options)
+
+ g = 1 if not g else int(g.group(1))
+ n = 250 if not n else int(n.group(1))
+
# Create the Gaussian Mixture Model.
model = Clustering.GMM(g)
model.set_features(dataFeat)
Modified: mlpack/conf/jenkins-conf/benchmark/methods/shogun/kernel_pca.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/shogun/kernel_pca.py (original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/kernel_pca.py Mon Aug 26 07:32:12 2013
@@ -51,12 +51,12 @@
def RunKPCAShogun(q):
totalTimer = Timer()
- # Load input dataset.
- Log.Info("Loading dataset", self.verbose)
- data = np.genfromtxt(self.dataset, delimiter=',')
- dataFeat = RealFeatures(data.T)
-
try:
+ # Load input dataset.
+ Log.Info("Loading dataset", self.verbose)
+ data = np.genfromtxt(self.dataset, delimiter=',')
+ dataFeat = RealFeatures(data.T)
+
with totalTimer:
# Get the new dimensionality, if it is necessary.
dimension = re.search('-d (\d+)', options)
Modified: mlpack/conf/jenkins-conf/benchmark/methods/shogun/kmeans.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/shogun/kmeans.py (original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/kmeans.py Mon Aug 26 07:32:12 2013
@@ -109,13 +109,13 @@
if seed:
Math_init_random(seed.group(1))
+ try:
+ data = np.genfromtxt(self.dataset, delimiter=',')
- data = np.genfromtxt(self.dataset, delimiter=',')
-
- dataFeat = RealFeatures(data.T)
- distance = EuclideanDistance(dataFeat, dataFeat)
+ dataFeat = RealFeatures(data.T)
+ distance = EuclideanDistance(dataFeat, dataFeat)
- try:
+
# Create the K-Means object and perform K-Means clustering.
with totalTimer:
model = Clustering.KMeans(int(clusters.group(1)), distance)
Modified: mlpack/conf/jenkins-conf/benchmark/methods/shogun/lars.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/shogun/lars.py (original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/lars.py Mon Aug 26 07:32:12 2013
@@ -51,17 +51,17 @@
totalTimer = Timer()
# Load input dataset.
- Log.Info("Loading dataset", self.verbose)
- inputData = np.genfromtxt(self.dataset[0], delimiter=',')
- responsesData = np.genfromtxt(self.dataset[1], delimiter=',')
- inputFeat = RealFeatures(inputData.T)
- responsesFeat = RegressionLabels(responsesData)
-
- # Get all the parameters.
- lambda1 = re.search("-l (\d+)", options)
- lambda1 = 0.0 if not lambda1 else int(lambda1.group(1))
-
try:
+ Log.Info("Loading dataset", self.verbose)
+ inputData = np.genfromtxt(self.dataset[0], delimiter=',')
+ responsesData = np.genfromtxt(self.dataset[1], delimiter=',')
+ inputFeat = RealFeatures(inputData.T)
+ responsesFeat = RegressionLabels(responsesData)
+
+ # Get all the parameters.
+ lambda1 = re.search("-l (\d+)", options)
+ lambda1 = 0.0 if not lambda1 else int(lambda1.group(1))
+
with totalTimer:
# Perform LARS.
model = LeastAngleRegression(False)
Modified: mlpack/conf/jenkins-conf/benchmark/methods/shogun/linear_regression.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/shogun/linear_regression.py (original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/linear_regression.py Mon Aug 26 07:32:12 2013
@@ -53,16 +53,16 @@
# 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]
-
try:
+ 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 = LeastSquaresRegression(RealFeatures(X.T), RegressionLabels(y))
Modified: mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py (original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py Mon Aug 26 07:32:12 2013
@@ -51,14 +51,14 @@
totalTimer = Timer()
Log.Info("Loading dataset", self.verbose)
- # Load train and test dataset.
- trainData = np.genfromtxt(self.dataset[0], delimiter=',')
- testData = np.genfromtxt(self.dataset[1], delimiter=',')
+ try:
+ # Load train and test dataset.
+ trainData = np.genfromtxt(self.dataset[0], delimiter=',')
+ testData = np.genfromtxt(self.dataset[1], delimiter=',')
- # Labels are the last row of the training set.
- labels = MulticlassLabels(trainData[:, (trainData.shape[1] - 1)])
+ # Labels are the last row of the training set.
+ labels = MulticlassLabels(trainData[:, (trainData.shape[1] - 1)])
- try:
with totalTimer:
# Transform into features.
trainFeat = RealFeatures(trainData[:,:-1].T)
Modified: mlpack/conf/jenkins-conf/benchmark/methods/shogun/pca.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/shogun/pca.py (original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/pca.py Mon Aug 26 07:32:12 2013
@@ -56,9 +56,9 @@
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
- feat = RealFeatures(self.data.T)
-
try:
+ feat = RealFeatures(self.data.T)
+
with totalTimer:
# Find out what dimension we want.
match = re.search('-d (\d+)', options)
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