Building Machine Learning Systems with Python - Richert, Coelho

coefficients = []
# We are now going to run a leave-1-out cross-validation loop
for u in xrange(reviews.shape[0]): # for all user ids
es0 = np.delete(es,u,1) # all but user u
r0 = np.delete(reviews, u, 0)
P0,P1 = np.where(r0 > 0) # we only care about actual
predictions
X = es[:,P0,P1]
y = r0[r0 > 0]
reg.fit(X.T,y)
coefficients.append(reg.coef_)
prediction = reg.predict(es[:,u,reviews[u] > 0].T)
# measure error as before
The result is an RMSE of almost exactly 1. We can also analyze the coefficients
variable to find out how well our predictors fare:
print coefficients.mean(0) # the mean value across all users
Chapter 8
The values of the array are [ 0.25164062, 0.01258986, 0.60827019]. The
estimate of the most similar movie has the highest weight (it was the best individual
prediction, so it is not surprising), and we can drop the correlation-based method
from the learning process as it has little influence on the final result.
What this setting does is it makes it easy to add a few extra ideas; for example, if the
single most similar movie is a good predictor, how about we use the five most similar
movies in the learning process as well? We can adapt the earlier code to generate the
k-th most similar movie and then use the stacked learner to learn the weights:
es = [
usermodel.estimate_all()
similar_movie.estimate_all(k=1),
similar_movie.estimate_all(k=2),
similar_movie.estimate_all(k=3),
similar_movie.estimate_all(k=4),
similar_movie.estimate_all(k=5),
]
# the rest of the code remains as before!
We gained a lot of freedom in generating new machine learning systems. In this
case, the final result is not better, but it was easy to test this new idea.
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