Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 7
The loading of the dataset is just basic Python, so let us jump ahead to the learning.
We have a sparse matrix, where there are entries from 1 to 5 whenever we have
a rating (most of the entries are zero to denote that this user has not rated these
movies). This time, as a regression method, for variety, we are going to be using the
LassoCV class:
from sklearn.linear_model import LassoCV
reg = LassoCV(fit_intercept=True, alphas=[.125,.25,.5,1.,2.,4.])
By passing the constructor an explicit set of alphas, we can constrain the values that
the inner cross-validation will use. You may note that the values are multiples of
two, starting with 1/8 up to 4. We will now write a function which learns a model
for the user i:
# isolate this user
u = reviews[i]
We are only interested in the movies that the user u rated, so we must build up the
index of those. There are a few NumPy tricks in here: u.toarray() to convert from a
sparse matrix to a regular array. Then, we ravel() that array to convert from a row
array (that is, a two-dimensional array with a first dimension of 1) to a simple onedimensional
array. We compare it with zero and ask where this comparison is true.
The result, ps, is an array of indices; those indices correspond to movies that the user
has rated:
u = u.array().ravel()
ps, = np.where(u > 0)
# Build an array with indices [0...N] except i
us = np.delete(np.arange(reviews.shape[0]), i)
x = reviews[us][:,ps].T
Finally, we select only the movies that the user has rated:
y = u[ps]
Cross-validation is set up as before. Because we have many users, we are going to
only use four folds (more would take a long time and we have enough training data
with just 80 percent of the data):
err = 0
kf = KFold(len(y), n_folds=4)
for train,test in kf:
# Now we perform a per-movie normalization
# this is explained below
xc,x1 = movie_norm(x[train])