Python CorssValidation Examples

Example #1

__author__ = 'stanley'

from src.Utility.CrossValidation import CorssValidation
from numpy import *
from sklearn import svm

nbaData = genfromtxt('../../NBA2012-15/Classification/NBA12_14.csv',
                     delimiter=',')
nba15test = genfromtxt('../../NBA2012-15/Classification/NBA15.csv',
                       delimiter=',')
# SVM may perform better with standardize features
# For easy implementation, I convert data to the base 10 logarithm
nba15test_scaled = nba15test
nba15test_scaled[:, 1:] = log10(nba15test_scaled[:, 1:])

label = nbaData[:, 0]
features = log10(nbaData[:, 1:])

classifier = svm.SVC(kernel='rbf', probability=True, C=1)

#CV
validation = CorssValidation()

validation.cv(features, label, classifier, nba15test, 'ROC for RBF SVM C=1')

Example #2

File: LDA.py Project: Sandy4321/NBA_Machine-Learning

from numpy import *


nbaData = genfromtxt('../../NBA2012-15/Classification/NBA12_14.csv', delimiter=',')
nba15test = genfromtxt('../../NBA2012-15/Classification/NBA15.csv', delimiter=',')

label = nbaData[:,0]
features = nbaData[:,1:]


# show 2D result
classifier = LDA(n_components=2)
f_reduced = classifier.fit(features,label).transform(features)

show = ScatterWithHistPlot()
show.plot(f_reduced, label)

#CV
validation = CorssValidation()

validation.cv(features,label,classifier,nba15test,'ROC for LDA')

Example #3

__author__ = 'stanley'

from src.Utility.CrossValidation import CorssValidation
from sklearn import linear_model
from numpy import *


nbaData = genfromtxt('../../NBA2012-15/Classification/NBA12_14.csv', delimiter=',')
nba15test = genfromtxt('../../NBA2012-15/Classification/NBA15.csv', delimiter=',')

label = nbaData[:,0]
features = nbaData[:,1:]

# May perform better with standardize features
# For easy implementation, I convert data to the base 10 logarithm
nba15test_scaled = nba15test
nba15test_scaled[:,1:] = log10(nba15test_scaled[:,1:])

label = nbaData[:,0]
features = log10(nbaData[:,1:])

classifier =linear_model.LogisticRegression(penalty='l2',C=100)

#CV
validation = CorssValidation()

validation.cv(features,label,classifier,nba15test,'ROC for Logistic l2 C=100')

Example #4

File: SVM.py Project: kcavagnolo/ml_fun

__author__ = 'stanley'

from src.Utility.CrossValidation import CorssValidation
from numpy import *
from sklearn import svm

nbaData = genfromtxt('../../NBA2012-15/Classification/NBA12_14.csv', delimiter=',')
nba15test = genfromtxt('../../NBA2012-15/Classification/NBA15.csv', delimiter=',')
# SVM may perform better with standardize features
# For easy implementation, I convert data to the base 10 logarithm
nba15test_scaled = nba15test
nba15test_scaled[:,1:] = log10(nba15test_scaled[:,1:])

label = nbaData[:,0]
features = log10(nbaData[:,1:])



classifier = svm.SVC(kernel='rbf', probability=True, C=1)

#CV
validation = CorssValidation()

validation.cv(features,label,classifier,nba15test, 'ROC for RBF SVM C=1')