algs.NaiveBayes({'usecolumnones': True}),
'Linear Regression':
algs.LinearRegressionClass(),
'Logistic Regression Reg':
algs.LogitReg({
'regularizer': 'l2',
'lamb': 0.001,
'stepsize': 0.001
}),
'Logistic Regression':
algs.LogitReg({
'lamb': 0.001,
'stepsize': 0.001
}),
'kernel Logistic Regression':
algs.KernelLogitReg({'k': 30}),
'Hamming kernel Logistic Regression':
algs.KernelLogitReg({
'kernel': 'hamming',
'k': 20
}),
'Neural Network':
algs.NeuralNet({'epochs': 100}),
'Neural Network2':
algs.NeuralNet2({'epochs': 100})
}
numalgs = len(classalgs)
cls = {
'Logistic RegressionRegularized':
algs.LogitReg({
'Random':
algs.Classifier(),
'Naive Bayes':
algs.NaiveBayes({'usecolumnones': False}),
'Naive Bayes Ones':
algs.NaiveBayes({'usecolumnones': True}),
'Linear Regression':
algs.LinearRegressionClass(),
'Logistic Regression':
algs.LogitReg(),
'Neural Network':
algs.NeuralNet({'epochs': 100}),
'LinearKernelLogitReg':
algs.KernelLogitReg({
'kernel': 'linear',
'regwgt': 0.01,
'regularizer': 'None'
}),
'HammingKernelLogitReg':
algs.KernelLogitReg({
'kernel': 'hamming',
'regwgt': 0.01,
'regularizer': 'None'
})
}
numalgs = len(classalgs)
parameters = (
#{'regwgt': 0.0, 'nh': 4},
{
'regwgt': 0.01,
best_algorithm = classalgs[learnername]
return best_algorithm
if __name__ == '__main__':
trainsize = 5000
testsize = 5000
numruns = 10
classalgs = {
'Random': algs.Classifier(),
'Naive Bayes': algs.NaiveBayes({'usecolumnones': False}),
'Naive Bayes Ones': algs.NaiveBayes({'usecolumnones': True}),
'Linear Regression': algs.LinearRegressionClass(),
'Logistic Regression': algs.LogitReg(),
'Kernel Logistic Regression': algs.KernelLogitReg({'kernel':
'linear'}),
'Neural Network': algs.NeuralNet({'epochs': 100})
}
numalgs = len(classalgs)
parameters = (
{
'regwgt': 0.0,
'nh': 4
},
{
'regwgt': 0.01,
'nh': 8
},
{
'regwgt': 0.05,
def geterror(ytest, predictions):
return (100.0-getaccuracy(ytest, predictions))
if __name__ == '__main__':
trainsize = 5000
testsize = 5000
numruns = 10
classalgs = {'Random': algs.Classifier(),
'Naive Bayes': algs.NaiveBayes({'usecolumnones': False}),
'Naive Bayes Ones': algs.NaiveBayes({'usecolumnones': True}),
'Linear Regression': algs.LinearRegressionClass(),
'Logistic Regression': algs.LogitReg(),
'Linear Logistic Regression': algs.KernelLogitReg({'kernel': 'linear', 'stepsize': 2e-7, 'tolerance': 5e-6}),
# 'NoKernel Logistic Regression': algs.KernelLogitReg(),
'Hamming Logistic Regression': algs.KernelLogitReg({'kernel': 'hamming', 'stepsize': 2e-7, 'tolerance': 5e-6}),
'Neural Network': algs.NeuralNet({'epochs': 100}),
}
numalgs = len(classalgs)
parameters = (
{'regwgt': 0.0, 'nh': 4},
{'regwgt': 0.01, 'nh': 8},
{'regwgt': 0.05, 'nh': 16},
{'regwgt': 0.1, 'nh': 32},
)
numparams = len(parameters)
if __name__ == '__main__':
trainsize = 5000
testsize = 5000
numruns = 1
classalgs = {'Random': algs.Classifier(),
'Linear Regression': algs.LinearRegressionClass(),
'Naive Bayes': algs.NaiveBayes({'usecolumnones': False}),
'Naive Bayes Ones': algs.NaiveBayes({'usecolumnones': True}),
'Linear Regression': algs.LinearRegressionClass(),
'Logistic Regression': algs.LogitReg(),
'Neural Network': algs.NeuralNet({'epochs': 100}),
'Neural Network 2': algs.NeuralNet2({'epochs': 100}),
'KernelLogitReg': algs.KernelLogitReg(),
# 'KernelLogitReg': algs.KernelLogitReg({'kernel': 'hamming'})
##################### for calling cross validation use these instead ####################
# 'Neural Network 4 32': algs.NeuralNet({'nh': 4, 'batch_size': 32}),
# 'Neural Network 16 32': algs.NeuralNet({'nh': 16, 'batch_size': 32}),
# 'Neural Network 4 128': algs.NeuralNet({'nh': 4, 'batch_size': 128}),
# 'Neural Network 16 128': algs.NeuralNet({'nh': 16, 'batch_size': 128}),
# 'Logistic Regression 100 32': algs.LogitReg({'epochs': 100, 'batch_size': 32}),
# 'Logistic Regression 1000 32': algs.LogitReg({'epochs': 1000, 'batch_size': 32}),
# 'Logistic Regression 100 128': algs.LogitReg({'epochs': 100, 'batch_size': 128}),
# 'Logistic Regression 1000 128': algs.LogitReg({'epochs': 1000, 'batch_size': 128})
}
numalgs = len(classalgs)
# 'Naive Bayes': algs.NaiveBayes({'usecolumnones': False}),
# 'Naive Bayes Ones': algs.NaiveBayes({'usecolumnones': True}),
'Linear Regression':
algs.LinearRegressionClass(),
'Logistic Regression':
algs.LogitReg(),
'Neural Network':
algs.NeuralNet({'hiddenLayers': 1}),
'Neural Network':
algs.NeuralNet({
'epochs': 100,
'hiddenLayers': 2
}),
'Kernel Logistic Regression linear':
algs.KernelLogitReg({
'kernel': 'linear',
'regularizer': 'l2'
}),
'Kernel Logistic Regression hamming':
algs.KernelLogitReg({
'kernel': 'hamming',
'regularizer': 'l2'
})
}
k_foldClass = 0
k_foldStratified = 1
numalgs = len(classalgs)
k_fold = 5
parameters = (
{