def top_recommendations_lda():
params = get_best_params("lda")
info = getMeta()
phi = params["phi"]
kappa = params["kappa"]
reviews = get_test_reviews()
rating_values = np.asarray([0,1.0,2.0,3.0,4.0,5.0])
precision = 0.0
num_users = 0
for user in xrange(info["users"]):
movie_ratings = []
for movie in xrange(info["movies"]):
topic = np.argmax(phi[movie,:])
rating = np.dot(kappa[:,user,topic]/np.sum(kappa[:,user,topic]), rating_values)
movie_ratings.append((movie, rating))
movie_ratings = sorted(movie_ratings, key=lambda x: x[1])
top_movies_for_user = set(movie for movie, rating in movie_ratings[-1000:])
user_precision = 0.0
movies = reviews[user, :].nonzero()[0]
for movie in movies:
if movie in top_movies_for_user:
user_precision += 1
if len(movies) > 0:
num_users += 1
precision += (user_precision / len(movies))
return precision / num_users
def top_recommendations_poisson():
params = get_best_params("poisson")
info = getMeta()
beta = params["beta"]
theta = params["theta"]
reviews = get_test_reviews()
precision = 0.0
num_users = 0
for user in xrange(info["users"]):
movie_ratings = []
for movie in xrange(info["movies"]):
rating = np.dot(theta[user, :], beta[movie, :])
movie_ratings.append((movie, rating))
movie_ratings = sorted(movie_ratings, key=lambda x: x[1])
top_movies_for_user = set(movie for movie, rating in movie_ratings[-1000:])
user_precision = 0.0
movies = reviews[user, :].nonzero()[0]
for movie in movies:
if movie in top_movies_for_user:
user_precision += 1
if len(movies) > 0:
num_users += 1
precision += (user_precision / len(movies))
return precision / num_users
def top_recommendations_poisson():
params = get_best_params("poisson")
info = getMeta()
beta = params["beta"]
theta = params["theta"]
reviews = get_test_reviews()
precision = 0.0
num_users = 0
for user in xrange(info["users"]):
movie_ratings = []
for movie in xrange(info["movies"]):
rating = np.dot(theta[user, :], beta[movie, :])
movie_ratings.append((movie, rating))
movie_ratings = sorted(movie_ratings, key=lambda x: x[1])
top_movies_for_user = set(movie
for movie, rating in movie_ratings[-1000:])
user_precision = 0.0
movies = reviews[user, :].nonzero()[0]
for movie in movies:
if movie in top_movies_for_user:
user_precision += 1
if len(movies) > 0:
num_users += 1
precision += (user_precision / len(movies))
return precision / num_users
def top_recommendations_lda():
params = get_best_params("lda")
info = getMeta()
phi = params["phi"]
kappa = params["kappa"]
reviews = get_test_reviews()
rating_values = np.asarray([0, 1.0, 2.0, 3.0, 4.0, 5.0])
precision = 0.0
num_users = 0
for user in xrange(info["users"]):
movie_ratings = []
for movie in xrange(info["movies"]):
topic = np.argmax(phi[movie, :])
rating = np.dot(
kappa[:, user, topic] / np.sum(kappa[:, user, topic]),
rating_values)
movie_ratings.append((movie, rating))
movie_ratings = sorted(movie_ratings, key=lambda x: x[1])
top_movies_for_user = set(movie
for movie, rating in movie_ratings[-1000:])
user_precision = 0.0
movies = reviews[user, :].nonzero()[0]
for movie in movies:
if movie in top_movies_for_user:
user_precision += 1
if len(movies) > 0:
num_users += 1
precision += (user_precision / len(movies))
return precision / num_users
def test_poisson():
params = get_best_params("poisson")
beta = params["beta"]
theta = params["theta"]
reviews = get_test_reviews()
rmse = 0.0
rmses = []
count = 0
for user, movie in izip(*reviews.nonzero()):
true_rating = reviews[user, movie]
mean_rating = np.dot(theta[user, :], beta[movie, :])
mean_rating = max(1, min(5, mean_rating + 1))
rmse += (true_rating - mean_rating) ** 2
rmses.append((true_rating - mean_rating) ** 2)
count += 1
return math.sqrt(rmse / count)
def test_lda():
params = get_best_params("lda")
info = getMeta()
phi = params["phi"]
kappa = params["kappa"]
reviews = get_test_reviews()
rmse = 0.0
count = 0
rating_values = np.asarray([0,1.0,2.0,3.0,4.0,5.0])
for user, movie in izip(*reviews.nonzero()):
topic = np.argmax(phi[movie,:])
estimated_rating = np.dot(kappa[:,user,topic]/np.sum(kappa[:,user,topic]), rating_values)
true_rating = reviews[user, movie]
rmse += (true_rating - estimated_rating) ** 2
count += 1
return math.sqrt(rmse / count)
def test_poisson():
params = get_best_params("poisson")
beta = params["beta"]
theta = params["theta"]
reviews = get_test_reviews()
rmse = 0.0
rmses = []
count = 0
for user, movie in izip(*reviews.nonzero()):
true_rating = reviews[user, movie]
mean_rating = np.dot(theta[user, :], beta[movie, :])
mean_rating = max(1, min(5, mean_rating + 1))
rmse += (true_rating - mean_rating)**2
rmses.append((true_rating - mean_rating)**2)
count += 1
return math.sqrt(rmse / count)
def test_lda():
params = get_best_params("lda")
info = getMeta()
phi = params["phi"]
kappa = params["kappa"]
reviews = get_test_reviews()
rmse = 0.0
count = 0
rating_values = np.asarray([0, 1.0, 2.0, 3.0, 4.0, 5.0])
for user, movie in izip(*reviews.nonzero()):
topic = np.argmax(phi[movie, :])
estimated_rating = np.dot(
kappa[:, user, topic] / np.sum(kappa[:, user, topic]),
rating_values)
true_rating = reviews[user, movie]
rmse += (true_rating - estimated_rating)**2
count += 1
return math.sqrt(rmse / count)
