cross_validation.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn import decomposition
from sklearn import cross_validation

## read data
features = np.loadtxt('features_ALL.txt', delimiter=',')
ratings = np.loadtxt('labels.txt', delimiter=',')
predictions = np.zeros(ratings.size);

for i in range(0, 500):
	features_train = np.delete(features, i, 0)
	features_test = features[i, :]
	ratings_train = np.delete(ratings, i, 0)
	ratings_test = ratings[i]
	pca = decomposition.PCA(n_components=20)
	pca.fit(features_train)
	features_train = pca.transform(features_train)
	features_test = pca.transform(features_test)
	regr = linear_model.LinearRegression()
	regr.fit(features_train, ratings_train)
	predictions[i] = regr.predict(features_test)
	print i

np.savetxt('cross_valid_predictions.txt', predictions, delimiter=',', fmt = '%.04f')

corr = np.corrcoef(predictions, ratings)[0, 1]
print corr