def main():
print '[INFO, time: %s] Getting Data....' % (time.strftime('%H:%M:%S'))
preprocesser = Preprocess()
data= preprocesser.read()
print '[INFO, time: %s] Fitting %s ...' % (time.strftime('%H:%M:%S'), 'Auto Encoder')
ae = AutoEncoder(
layers=[
Layer("Sigmoid",units=100)
],
learning_rate=0.01,
n_iter=40,
verbose=True,
)
ae.fit(data[:,:-1])
print '[INFO, time: %s] Transforming Data with %s ...' % (time.strftime('%H:%M:%S'), 'Auto Encoder')
splitRatio = 0.67
train, test = splitDataset(data, splitRatio)
train = np.asarray(train)
test = np.asarray(test)
trainX = train[:,:-1]
trainy = train[:,-1]
testX = test[:,:-1]
testy = test[:,-1]
transformed_trainX = ae.transform(trainX)
transformed_testX = ae.transform(testX)
print '[INFO, time: %s] Fitting %s ...' % (time.strftime('%H:%M:%S'), 'SVM - rbf kernel (i.e. gaussian) with default paramenters')
clf = SVC()
clf.fit(transformed_trainX, trainy)
print '[INFO, time: %s] Making Predictions...' % (time.strftime('%H:%M:%S'))
prediction = clf.predict(transformed_testX)
print '[RESULT, time: %s] accuracy = %f' % (time.strftime('%H:%M:%S'),accuracy_score(testy, prediction))
def main():
print '[INFO, time: %s] Getting Data....' % (time.strftime('%H:%M:%S'))
preprocesser = Preprocess()
data= preprocesser.read()
splitRatio = 0.67
train, test = splitDataset(data, splitRatio)
train = np.asarray(train)
test = np.asarray(test)
trainX = train[:,:-1]
trainy = train[:,-1]
testX = test[:,:-1]
testy = test[:,-1]
print '[INFO, time: %s] Fitting %s ...' % (time.strftime('%H:%M:%S'), 'Gradient Boosting Classifier with 300 estimators')
clf = GradientBoostingClassifier(n_estimators=300)
clf.fit(trainX, trainy)
print '[INFO, time: %s] Making Predictions...' % (time.strftime('%H:%M:%S'))
prediction = clf.predict(testX)
print '[RESULT, time: %s] accuracy = %f' % (time.strftime('%H:%M:%S'),accuracy_score(testy, prediction))
def main():
print '[INFO, time: %s] Getting Data....' % (time.strftime('%H:%M:%S'))
preprocesser = Preprocess()
data= preprocesser.read()
splitRatio = 0.67
train, test = splitDataset(data, splitRatio)
train = np.asarray(train)
test = np.asarray(test)
trainX = train[:,:-1]
trainy = train[:,-1]
testX = test[:,:-1]
testy = test[:,-1]
print '[INFO, time: %s] Fitting %s ...' % (time.strftime('%H:%M:%S'), 'SVM - rbf kernel (i.e. gaussian) with default paramenters')
clf = SVC()
clf.fit(trainX, trainy)
print '[INFO, time: %s] Making Predictions...' % (time.strftime('%H:%M:%S'))
prediction = clf.predict(testX)
print '[RESULT, time: %s] accuracy = %f' % (time.strftime('%H:%M:%S'),accuracy_score(testy, prediction))
