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Copy pathsimilar_movie.py
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55 lines (49 loc) · 1.47 KB
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# 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
import numpy as np
from load_ml100k import load
from all_correlations import all_correlations
def nn_movie(ureviews, reviews, uid, mid, k=1):
X = ureviews
y = ureviews[mid].copy()
y -= y.mean()
y /= (y.std() + 1e-5)
corrs = np.dot(X, y)
likes = corrs.argsort()
likes = likes[::-1]
c = 0
pred = 3.
for ell in likes:
if ell == mid:
continue
if reviews[uid, ell] > 0:
pred = reviews[uid, ell]
if c == k:
return pred
c += 1
return pred
def all_estimates(reviews, k=1):
reviews = reviews.astype(float)
k -= 1
nusers, nmovies = reviews.shape
estimates = np.zeros_like(reviews)
for u in range(nusers):
ureviews = np.delete(reviews, u, 0)
ureviews -= ureviews.mean(0)
ureviews /= (ureviews.std(0) + 1e-4)
ureviews = ureviews.T.copy()
for m in np.where(reviews[u] > 0)[0]:
estimates[u, m] = nn_movie(ureviews, reviews, u, m, k)
return estimates
if __name__ == '__main__':
reviews = load().toarray()
estimates = all_estimates(reviews)
error = (estimates - reviews)
error **= 2
error = error[reviews > 0]
print(np.sqrt(error).mean())