def create_average_ratings_for_movies_with_ratings(global_average_rating,
minimum_number_of_ratings,
movie_ids_to_ratings):
session = Session()
clear_average_rating_table()
average_ratings = []
processed_movie = 0
for movie_id, list_of_ratings in movie_ids_to_ratings.items():
processed_movie += 1
number_of_ratings = len(list_of_ratings)
if number_of_ratings < minimum_number_of_ratings:
average_movie_rating_value = (
sum(list_of_ratings) + global_average_rating *
(minimum_number_of_ratings - number_of_ratings)
) / minimum_number_of_ratings
else:
average_movie_rating_value = sum(
list_of_ratings) / number_of_ratings
average_ratings.append({
'movie_id':
movie_id,
'average_rating':
round(average_movie_rating_value, 1)
})
if processed_movie % 10000 == 0:
print('Average calculation progress: ', processed_movie / 1000,
'k')
session.bulk_insert_mappings(AverageMovieRating, average_ratings)
session.commit()
def calculate_predicted_ratings_based_on_user_similarity(ratings_list, users_similarity_list):
user_ids_to_real_position = dict()
movie_ids_to_real_position = dict()
user_position = 0
movie_position = 0
for user_id, movie_id, rating in ratings_list:
if user_id not in user_ids_to_real_position:
user_ids_to_real_position[user_id] = user_position
user_position += 1
if movie_id not in movie_ids_to_real_position:
movie_ids_to_real_position[movie_id] = movie_position
movie_position += 1
user_size = len(user_ids_to_real_position)
movie_size = len(movie_ids_to_real_position)
user_user_similarity_matrix = np.zeros((user_size, user_size))
for user_id, compare_user_id, similarity in users_similarity_list:
column_number = user_ids_to_real_position[user_id]
row_number = user_ids_to_real_position[compare_user_id]
user_user_similarity_matrix[row_number, column_number] = similarity
items_users_ratings_matrix = np.zeros((movie_size, user_size))
session = Session()
ratings_list = session.execute("SELECT user_id, movie_id, rating FROM ratings")
for user_id, movie_id, rating in ratings_list:
column_number = user_ids_to_real_position[user_id]
row_number = movie_ids_to_real_position[movie_id]
items_users_ratings_matrix[row_number, column_number] = rating
unnormalized_predicted_ratings = np.matmul(items_users_ratings_matrix, user_user_similarity_matrix)
items_users_ratings_matrix_value_as_1 = np.nonzero(items_users_ratings_matrix)
items_users_ratings_matrix[items_users_ratings_matrix_value_as_1] = 1
absolute_sum_of_similarities = np.matmul(items_users_ratings_matrix, user_user_similarity_matrix)
z = np.divide(unnormalized_predicted_ratings, absolute_sum_of_similarities)
clear_rating_predictions_table()
predictions = []
progress = 0
number_to_calculate = len(user_ids_to_real_position)
for user_id, user_real_position in user_ids_to_real_position.items():
for movie_id, movie_real_position in movie_ids_to_real_position.items():
predicted_rating = round(z[movie_real_position, user_real_position], 1)
if predicted_rating == 0 or np.isnan(predicted_rating):
average_movie_rating = session.execute(
"SELECT average_rating FROM average_movie_rating WHERE movie_id = :param_movie_id",
{'param_movie_id': movie_id}).fetchone()
predicted_rating = average_movie_rating[0]
predictions.append(
{'user_id': user_id, 'movie_id': movie_id,
'rating': predicted_rating})
session.bulk_insert_mappings(RatingsPredictions, predictions)
session.commit()
predictions.clear()
progress += 1
print('Progress: ', round(100 * (progress / number_to_calculate), 2), '%')
def save_parameter(description, value):
session = Session()
management = session.query(Management).filter(
Management.description == description).first()
if management is None:
management = Management(description, value)
else:
management.value = value
session.add(management)
# commit the record the database
session.commit()
import sys
import numpy as np
import progressbar
from dataAccess import Session, Ratings
from dataAccess.entities import RatingsPredictionsBySVD, Sample
session = Session()
session.query(RatingsPredictionsBySVD).delete()
session.commit()
# Remove limit to use all ratings
ratings_list = session.query(Ratings).limit(1000).all()
# ratings_list = session.query(Ratings).all()
samples = session.query(Sample).all()
user_to_index = {}
movie_to_index = {}
for rating in ratings_list:
if rating.user_id not in user_to_index:
user_to_index[rating.user_id] = len(user_to_index)
if rating.movie_id not in movie_to_index:
movie_to_index[rating.movie_id] = len(movie_to_index)
for sample in samples:
if sample.user_id not in user_to_index:
user_to_index[sample.user_id] = len(user_to_index)
if sample.movie_id not in movie_to_index:
def clear_users_similarity_table():
session = Session()
session.query(UsersSimilarity).delete()
session.commit()
def calculate_users_similarity(ratings_list):
user_ids_to_movie_ratings = dict()
processed_ratings = 0
for user_id, movie_id, rating in ratings_list:
processed_ratings += 1
if user_id in user_ids_to_movie_ratings:
user_ids_to_movie_ratings[user_id][movie_id] = rating
else:
user_ids_to_movie_ratings[user_id] = dict([(movie_id, rating)])
if processed_ratings % 100000 == 0:
print('Mapping progress: ', processed_ratings / 1000, 'k')
clear_users_similarity_table()
session = Session()
user_similarities_ready_to_save = []
progress = 0
number_to_calculate = len(user_ids_to_movie_ratings)
similarity_range_factor = get_parameter("similarity_range_factor").value
for compared_user_id, list_of_compared_user_ratings in user_ids_to_movie_ratings.items(
):
inner_progress = 0
for id_of_user_for_comparision, list_of_user_for_comparison_ratings in user_ids_to_movie_ratings.items(
):
inner_progress += 1
if inner_progress % 10000 == 0:
print('Inner progress: ', inner_progress / 1000, 'k')
if compared_user_id != id_of_user_for_comparision & id_of_user_for_comparision > compared_user_id:
movie_ids_to_ratings_of_compared_user = dict()
movie_ids_to_ratings_of_user_for_comparison = dict()
prepare_vectors_for_comparison(
list_of_compared_user_ratings,
list_of_user_for_comparison_ratings,
movie_ids_to_ratings_of_compared_user,
movie_ids_to_ratings_of_user_for_comparison)
normalized_rating_for_compared_user = calculate_normalized_rating_vector(
movie_ids_to_ratings_of_compared_user.values())
normalized_rating_for_user_for_comparision = calculate_normalized_rating_vector(
movie_ids_to_ratings_of_user_for_comparison.values())
users_similarity = round(
1 - cosine(normalized_rating_for_compared_user,
normalized_rating_for_user_for_comparision), 3)
if isnan(users_similarity):
continue
if users_similarity < similarity_range_factor:
continue
user_similarities_ready_to_save.append({
"user_id":
compared_user_id,
'compare_user_id':
id_of_user_for_comparision,
'similarity':
users_similarity
})
user_similarities_ready_to_save.append({
"user_id":
id_of_user_for_comparision,
'compare_user_id':
compared_user_id,
'similarity':
users_similarity
})
session.execute(
"INSERT INTO users_similarity(user_id, compare_user_id, similarity) VALUES (:user_id, :compare_user_id, :similarity)",
{
'user_id': compared_user_id,
'compare_user_id': id_of_user_for_comparision,
'similarity': users_similarity
})
session.execute(
"INSERT INTO users_similarity(user_id, compare_user_id, similarity) VALUES (:user_id, :compare_user_id, :similarity)",
{
'user_id': id_of_user_for_comparision,
'compare_user_id': compared_user_id,
'similarity': users_similarity
})
session.commit()
progress += 1
print('Progress: ', round(100 * (progress / number_to_calculate), 2),
'%')
def clear_rating_predictions_table():
session = Session()
session.query(RatingsPredictions).delete()
session.commit()
def clear_average_rating_table():
session = Session()
session.query(AverageMovieRating).delete()
session.commit()