def train(xTrain, yTrain, metric):
print 'RandomForestClassifier'
global forest
forest = RandomForestClassifier()
forest.fit(xTrain, yTrain)
global trainResults
trainResults = forest.predict(xTrain)
i.setSuccess(trainResults, metric)
def train(xTrain, yTrain, metric):
print 'goosting'
global gboost
gboost = GBC()
gboost.fit(xTrain,yTrain)
global trainResults
trainResults = gboost.predict_proba(xTrain)[:,1]
i.setSuccess(trainResults, metric)
def train(xTrain, yTrain, metric):
print 'adaboost'
global boost
boost = AdaBoostClassifier()
boost.fit(xTrain,yTrain)
global trainResults
trainResults = boost.predict_proba(xTrain)[:,1]
i.setSuccess(trainResults, metric)
def train(xTrain, yTrain, metric):
print 'adaboost'
global boost
boost = AdaBoostClassifier()
boost.fit(xTrain, yTrain)
global trainResults
trainResults = boost.predict_proba(xTrain)[:, 1]
i.setSuccess(trainResults, metric)
def train(xTrain, yTrain, metric):
print 'svm'
global vector
vector = SVC()
vector.fit(xTrain, yTrain)
global trainResults
trainResults = vector.predict(xTrain)
i.setSuccess(trainResults, metric)
def train(xTrain, yTrain, metric):
print 'logistic'
global logis
logis = LogisticRegression()
logis.fit(xTrain, yTrain)
global trainResults
trainResults = logis.predict_proba(xTrain)[:, 1]
i.setSuccess(trainResults, metric)
def train(xTrain, yTrain, metric):
print 'svm'
global vector
vector = SVC()
vector.fit(xTrain, yTrain)
global trainResults
trainResults = vector.predict(xTrain)
i.setSuccess(trainResults, metric)
def train(xTrain, yTrain, metric):
print 'goosting'
global gboost
gboost = GBC()
gboost.fit(xTrain, yTrain)
global trainResults
trainResults = gboost.predict_proba(xTrain)[:, 1]
i.setSuccess(trainResults, metric)
def train(xTrain, yTrain, metric):
print 'logistic'
global logis
logis = LogisticRegression()
logis.fit(xTrain,yTrain)
global trainResults
trainResults = logis.predict_proba(xTrain)[:,1]
i.setSuccess(trainResults, metric)