# Fit the training data to the Survived labels and create the decision trees
forest = forest.fit(train_x,train_y)
#find training and cv error
trainpred = forest.predict(train_x).astype(int)
cvpred = forest.predict(cv_x).astype(int)
terr = 1-np.sum(trainpred == train_y)/trainpred.shape[0]
cverr = 1-np.sum(cvpred == cv_y)/cvpred.shape[0]
# Take the same decision trees and run it on the test data
output = forest.predict(test_x).astype(int)
return terr,cverr,output
#load data and seperate into train and cv
data_x, data_y, test_x, headings, submission = LoadData.loadcleandata()
fraction = 0.66
###MAKE PREDICTIONS FOR SUBMISSION###############
##nummodels = 100
##predictions = np.zeros((test_x.shape[0],nummodels))
##for i in range(nummodels):
## rseed = np.random.randint(1)
## train_x,cv_x,train_y,cv_y = sklearn.cross_validation.train_test_split(data_x,data_y,train_size=int(fraction*data_x.shape[0]),random_state=rseed)
## #select important features using randomized logreg
#### rlrtrain_x,rlrcv_x,rlrtest_x = randomlr(train_x,train_y,cv_x,test_x,regp=1,alpha=0.5)
#### terr,cverr,testpred = forestit(rlrtrain_x,train_y,rlrcv_x,cv_y,rlrtest_x,n_est=50)
## #train and predict
## terr,cverr,testpred = forestit(train_x,train_y,cv_x,cv_y,test_x,n_est=100)
## predictions[:,i] = testpred
