# This code is supporting material for the book

# Building Machine Learning Systems with Python

# by Willi Richert and Luis Pedro Coelho

# published by PACKT Publishing

#

# It is made available under the MIT License

from __future__ import print_function

from all_correlations import all_correlations

import numpy as np

from scipy import sparse

from load_ml100k import load

reviews = load()

def estimate_user(user, rest):

bu = user > 0

br = rest > 0

ws = all_correlations(bu, br)

selected = ws.argsort()[-100:]

estimates = rest[selected].mean(0)

estimates /= (.1 + br[selected].mean(0))

return estimates

def train_test(user, rest):

estimates = estimate_user(user, rest)

bu = user > 0

br = rest > 0

err = estimates[bu] - user[bu]

null = rest.mean(0)

null /= (.1 + br.mean(0))

nerr = null[bu] - user[bu]

return np.dot(err, err), np.dot(nerr, nerr)

def cross_validate_all():

err = []

for i in xrange(reviews.shape[0]):

err.append(

train_test(reviews[i], np.delete(reviews, i, 0))

)

revs = (reviews > 0).sum(1)

err = np.array(err)

rmse = np.sqrt(err / revs[:, None])

print(np.mean(rmse, 0))

print(np.mean(rmse[revs > 60], 0))

def all_estimates(reviews):

reviews = reviews.toarray()

estimates = np.zeros_like(reviews)

for i in xrange(reviews.shape[0]):

estimates[i] = estimate_user(reviews[i], np.delete(reviews, i, 0))

return estimates