PythonForDataScience/crossValidate.py at master · datanalyst/PythonForDataScience · GitHub

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from sklearn.ensemble import RandomForestClassifier

from sklearn import cross_validation

import logloss

import numpy as np

def main():

#read in data, parse into training and target sets

dataset = np.genfromtxt(open('Data/train.csv','r'), delimiter=',', dtype='f8')[1:]

target = np.array([x[0] for x in dataset])

train = np.array([x[1:] for x in dataset])

#In this case we'll use a random forest, but this could be any classifier

cfr = RandomForestClassifier(n_estimators=100)

#Simple K-Fold cross validation. 5 folds.

cv = cross_validation.KFold(len(train), k=5, indices=False)

#iterate through the training and test cross validation segments and

#run the classifier on each one, aggregating the results into a list

results = []

for traincv, testcv in cv:

probas = cfr.fit(train[traincv], target[traincv]).predict_proba(train[testcv])

results.append( logloss.llfun(target[testcv], [x[1] for x in probas]) )

#print out the mean of the cross-validated results

print "Results: " + str( np.array(results).mean() )

if __name__=="__main__":

main()