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MovieLensData.py
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MovieLensData.py
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import numpy as np
#from Utils import save_object, load_object
def movie_id_index_20m():
"""
This function creates a dictionary that assigns each movie id a unique identifier. Since the movie ids in the 100k
data range up to 131262, but only ~1600 movies are used, the dictionary gives you a the index in the user item
matrix for a given movie_id
:return: said dictionary
"""
dict = {}
index = 0
with open("ml-20m/movies.csv", 'r', encoding='UTF-8') as f:
for line in f.readlines()[1:]:
movieId = line[:line.find(",")]
movieId = int(movieId)
if movieId not in dict:
dict[movieId] = index
index += 1
return dict
def load_user_item_matrix_100k(max_user=943, max_item=1682):
"""
this function loads the user x items matrix from the **old** movie lens data set.
Both input parameter represent a threshold for the maximum user id or maximum item id
The highest user id is and the highest movie id is for the original data set, however, the masked data
set contains only 943 users and items
:return: user-item matrix
"""
import os.path
#if os.path.isfile("objs/user-item_Matrix_old_" + str(max_user) + "_" + str(max_item)):
# load_object("objs/user-item_Matrix_old_" + str(max_user) + "_" + str(max_item))
df = np.zeros(shape=(max_user, max_item))
with open("ml-100k/u.data", 'r') as f: #u.data u1.base
for line in f.readlines():
user_id, movie_id, rating, _ = line.split()
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_100k_train(max_user=943, max_item=1682):
"""
this function loads the user x items matrix from the **old** movie lens data set.
Both input parameter represent a threshold for the maximum user id or maximum item id
The highest user id is and the highest movie id is for the original data set, however, the masked data
set contains only 943 users and items
:return: user-item matrix
"""
import os.path
#if os.path.isfile("objs/user-item_Matrix_old_" + str(max_user) + "_" + str(max_item)):
# load_object("objs/user-item_Matrix_old_" + str(max_user) + "_" + str(max_item))
df = np.zeros(shape=(max_user, max_item))
with open("ml-100k/u1.base", 'r') as f: #u.data u1.base
for line in f.readlines():
user_id, movie_id, rating, _ = line.split()
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_100k_Complet(max_user=943, max_item=1682):
df = np.zeros(shape=(max_user, max_item))
with open(
"ml-100k/With_Fancy_KNN/TrainingSet_allUsers_KNN_fancy_imputation_100k_k_30.dat", #TrainingSet_allUsers_KNN_fancy_imputation_100k_k_30
'r') as f: #All_allUsers_KNN_fancy_imputation_100k_k_30
for line in f.readlines():
user_id, movie_id, rating, _ = line.split(
"::") # All_Opposite_Gender_KNN_fancy_imputation_1m_k_30VF || All_allUsers_KNN_fancy_imputation_1m_k_30
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id - 1, movie_id - 1] = rating
return df
def load_user_item_matrix_100k_masked(max_user=943, max_item=1682, file_index=-1):
"""
this function loads the user x items matrix from the **old** movie lens data set.
Both input parameter represent a threshold for the maximum user id or maximum item id
The highest user id is 138493 and the highest movie id is 27278 for the original data set, however, the masked data
set contains only 943 users and 1330 items
:return: user-item matrix
"""
import os.path
#if os.path.isfile("objs/user-item_Matrix_old_" + str(max_user) + "_" + str(max_item)):
# load_object("objs/user-item_Matrix_old_" + str(max_user) + "_" + str(max_item))
df = np.zeros(shape=(max_user, max_item))
masked_files = [
# Add path to your file file obfuscated by BlurMe, we start from #0
"ml-100k/blurMe/blurme_obfuscated_0.01_greedy_avg.dat", #0
]
"""with open(masked_files[file_index], 'r') as f:
#for line in f.readlines()[1:]:
user_id, movie_id, rating = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating #np.random.randint(1, 6)"""
with open(masked_files[file_index], 'r') as f:
for line in f.readlines():
user_id, movie_id, rating, _ = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id - 1, movie_id - 1] = rating
return df
def load_user_item_matrix_1m(max_user=6040, max_item=3952):
#id_index, _ = load_movie_id_index_dict()
df = np.zeros(shape=(max_user, max_item))
with open("data/ml-1m/ratings.dat", 'r') as f:
for line in f.readlines():
user_id, movie_id, rating, _ = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_1m_testSet(max_user=6040, max_item=3952):
df = np.zeros(shape=(max_user, max_item))
with open("ml1m/ml1m_original_test.dat", 'r') as f:
for line in f.readlines():
user_id, movie_id, rating, _ = line.split(",")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_100k_testSet(max_user=943, max_item=1682):
#id_index, _ = load_movie_id_index_dict()
df = np.zeros(shape=(max_user, max_item))
with open("ml-100k/u1.test", 'r') as f: # u.data u1.base
for line in f.readlines():
user_id, movie_id, rating, _ = line.split()
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
df[user_id - 1, movie_id - 1] = rating
return df
def load_user_item_matrix_1m_trainMasked(max_user=6040, max_item=3952, file_index=-1):
df = np.zeros(shape=(max_user, max_item))
masked_files = [
"ml-1m/BlurMe/TrainingSet_blurMe_ML1M_obfuscated_0.1_greedy_avg_top-1.dat",#0
]
with open(masked_files[file_index], 'r') as f:
for line in f.readlines():
user_id, movie_id, rating, _ = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_100k_trainMasked(max_user=943, max_item=1682, file_index=-1):
df = np.zeros(shape=(max_user, max_item))
masked_files = [
"ml-100k/blurMe/TrainingSet_blurme_obfuscated_0.1_greedy_avg.dat",#0
]
with open(masked_files[file_index], 'r') as f:
for line in f.readlines():
user_id, movie_id, rating, _ = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_1m_all(max_user=6040, max_item=3952):
"""
this function loads the user x items matrix from the movie lens data set.
Both input parameter represent a threshold for the maximum user id or maximum item id
The highest user id is 6040 and the highest movie id is 3952 for the original data set, however, the masked data
set contains only 943 users and 1330 items
:return: user-item matrix
"""
df = np.zeros(shape=(max_user, max_item))
with open("data/ml-1m/ratings.dat", 'r') as f: #
for line in f.readlines():
user_id, movie_id, rating, _ = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_1m_trainingSet(max_user=6040, max_item=3952):
"""
this function loads the user x items matrix from the movie lens data set.
Both input parameter represent a threshold for the maximum user id or maximum item id
The highest user id is 6040 and the highest movie id is 3952 for the original data set, however, the masked data
set contains only 943 users and 1330 items
:return: user-item matrix
"""
df = np.zeros(shape=(max_user, max_item))
with open("ml1m/ml1m_original_training.dat", 'r') as f: #data/ml-1m/trainingSet_ml_1m_1.dat
for line in f.readlines():
user_id, movie_id, rating, _ = line.split(",")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_1m_half_1(max_user=6040, max_item=3952):
"""
this function loads the user x items matrix from the movie lens data set.
Both input parameter represent a threshold for the maximum user id or maximum item id
The highest user id is 6040 and the highest movie id is 3952 for the original data set, however, the masked data
set contains only 943 users and 1330 items
:return: user-item matrix
"""
df = np.zeros(shape=(max_user, max_item))
with open("data/ml-1m/ratings_ml1m_half_1.dat", 'r') as f:
for line in f.readlines():
user_id, movie_id, rating, _ = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_1m_half_2(max_user=6040, max_item=3952):
"""
this function loads the user x items matrix from the movie lens data set.
Both input parameter represent a threshold for the maximum user id or maximum item id
The highest user id is 6040 and the highest movie id is 3952 for the original data set, however, the masked data
set contains only 943 users and 1330 items
:return: user-item matrix
"""
df = np.zeros(shape=(max_user, max_item))
with open("data/ml-1m/ratings_ml1m_half_2.dat", 'r') as f:
for line in f.readlines():
user_id, movie_id, rating, _ = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_1m_Features(max_user=6040, max_item=1582):
"""
this function loads the user x items matrix from the movie lens data set.
Both input parameter represent a threshold for the maximum user id or maximum item id
The highest user id is 6040 and the highest movie id is 3952 for the original data set, however, the masked data
set contains only 943 users and 1330 items
:return: user-item matrix of selected/ specific features/ items
"""
#import os.path
#if os.path.isfile("objs/user-item_Matrix_1m_" + str(max_user) + "_" + str(max_item)):
# load_object("objs/user-item_Matrix_1m_" + str(max_user) + "_" + str(max_item))
#id_index, _ = load_movie_id_index_dict()
df = np.zeros(shape=(max_user, max_item)) #popular_items = 590
with open("data/ml-1m/critical_items.dat", 'r') as f: #critical_items = 1582, highly_rated_items = 500
for line in f.readlines():
user_id, movie_id, rating, _ = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
#save_object(df, "objs/user-item_Matrix_1m_" + str(max_user) + "_" + str(max_item))
return df
def load_user_item_matrix_1m_complet(max_user=6040, max_item=3952):
"""
this function loads the user x items matrix from the movie lens data set.
Both input parameter represent a threshold for the maximum user id or maximum item id
The highest user id is 6040 and the highest movie id is 3952 for the original data set, however, the masked data
set contains only 943 users and 1330 items
:return: user-item matrix
"""
#id_index, _ = load_movie_id_index_dict()
df = np.zeros(shape=(max_user, max_item))
with open("ml1m/TrainingSet_users_KNN_fancy_imputation_ML1M_k_30.dat", 'r') as f:
for line in f.readlines():
user_id, movie_id, rating, _ = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
return df
def load_user_item_matrix_1m_binary():
X = load_user_item_matrix_1m()
ratings = np.argwhere(X>0)
print(ratings)
X = np.zeros(shape=X.shape)
for x, y in ratings:
X[x, y] = 1
return X
def load_user_item_matrix_1m_masked(max_user=6040, max_item=3952, file_index=-1):
files = [
# BlurMe and BlurMore Final
"ml-1m/BlurMe/All_blurMe_ML1M_obfuscated_0.01_greedy_avg_top-1.dat", #0
]
#id_index, _ = load_movie_id_index_dict()
df = np.zeros(shape=(max_user, max_item))
with open(files[file_index], 'r') as f:
for line in f.readlines():
user_id, movie_id, rating, _ = line.split("::")
user_id, movie_id, rating = int(user_id), int(movie_id), float(rating)
if user_id <= max_user and movie_id <= max_item:
df[user_id-1, movie_id-1] = rating
return df
def load_movie_id_index_dict():
id_index = {}
index_id = {}
with open("ml-1m/movies.dat", 'r') as f:
for index, line in enumerate(f.readlines()):
id, name, genres = line.split("::")
id_index[int(id)] = index
index_id[index] = int(id)
return id_index, index_id
def load_user_item_matrix_1m_limited_ratings(limit=20):
import FlixsterDataSub as FDS
user_item = FDS.load_user_item_matrix_FX_TrainingSet() #load_user_item_matrix_1m_trainingSet()
user_item_limited = np.zeros(shape=user_item.shape)
for user_index, user in enumerate(user_item):
# filter rating indices
rating_index = np.argwhere(user > 0).reshape(1, -1)[0]
# shuffle them
np.random.shuffle(rating_index)
for i in rating_index[:limit]:
user_item_limited[user_index, i] = user[i]
#print(np.sum(user_item_limited, axis=1))
return user_item_limited
def load_user_genre_matrix_1m(one_hot=False, top=5):
user_item = load_user_item_matrix_1m()
movie_genre = load_movie_genre_matrix_1m(combine=True)
print(user_item.shape, movie_genre.shape)
user_genre = np.zeros(shape=(user_item.shape[0], movie_genre.shape[1]))
print(user_genre)
for user_index, user in enumerate(user_item):
for movie_index, rating in enumerate(user):
if rating > 0:
user_genre[user_index, :] += movie_genre[movie_index, :]
if one_hot:
u_g_one = np.zeros(shape=user_genre.shape)
for user_index, user in enumerate(user_genre):
top_5_index = user.argsort()[-top:][::-1]
for index in top_5_index:
u_g_one[user_index, index] = 1
user_genre = u_g_one
return user_genre
def load_user_genre_matrix_100k(one_hot=False, top=5):
user_item = load_user_item_matrix_100k()
movie_genre = load_movie_genre_matrix_100k(combine=False)
print(user_item.shape, movie_genre.shape)
user_genre = np.zeros(shape=(user_item.shape[0], movie_genre.shape[1]))
for user_index, user in enumerate(user_item):
for movie_index, rating in enumerate(user):
if rating > 0:
user_genre[user_index, :] += movie_genre[movie_index, :]
if one_hot:
u_g_one = np.zeros(shape=user_genre.shape)
for user_index, user in enumerate(user_genre):
top_5_index = user.argsort()[-top:][::-1]
for index in top_5_index:
u_g_one[user_index, index] = 1
user_genre = u_g_one
return user_genre
def load_user_genre_matrix_100k_obfuscated(one_hot=False, top=5):
user_item = load_user_item_matrix_100k_masked()
movie_genre = load_movie_genre_matrix_100k(combine=False)
print(user_item.shape, movie_genre.shape)
user_genre = np.zeros(shape=(user_item.shape[0], movie_genre.shape[1]))
for user_index, user in enumerate(user_item):
for movie_index, rating in enumerate(user):
if rating > 4:
user_genre[user_index, :] += movie_genre[movie_index, :]
if one_hot:
u_g_one = np.zeros(shape=user_genre.shape)
for user_index, user in enumerate(user_genre):
top_5_index = user.argsort()[-top:][::-1]
for index in top_5_index:
u_g_one[user_index, index] = 1
user_genre = u_g_one
return user_genre
def load_gender_vector_1m(max_user=6040):
"""
this function loads and returns the gender for all users with an id smaller than max_user
:param max_user: the highest user id to be retrieved
:return: the gender vector
"""
gender_vec = []
with open("data/ml-1m/users.dat", 'r') as f:
for line in f.readlines()[:max_user]:
user_id, gender, age, occ, postcode = line.split("::")
if gender == "M":
gender_vec.append(0)
else:
gender_vec.append(1)
return np.asarray(gender_vec)
def load_gender_vector_100k(max_user=943):
"""
this function loads and returns the gender for all users with an id smaller than max_user
:param max_user: the highest user id to be retrieved
:return: the gender vector
"""
gender_vec = []
with open("data/ml-100k/userObs.csv", 'r') as f:
for line in f.readlines()[1:]:
if len(line) < 2:
continue
else:
userid, age, gender, occupation, zipcode = line.split(", ")
if gender == "M":
gender_vec.append(0)
else:
gender_vec.append(1)
return np.asarray(gender_vec)
def load_occupation_vector_1m(max_user=6040):
"""
this function loads and returns the occupation for all users with an id smaller than max_user
:param max_user: the highest user id to be retrieved
:return: the occupation vector
"""
occ_vec = []
with open("ml-1m/users.dat", 'r') as f:
for line in f.readlines()[:max_user]:
user_id, gender, age, occ, postcode = line.split("::")
occ_vec.append(int(occ))
return np.asarray(occ_vec)
def load_occupation_vector_100k(max_user=943):
occ_labels = {}
with open("ml-100k/occupationLabels.csv", 'r') as f:
for line in f.readlines():
occ, label = line.replace("\n", "").split(",")
occ_labels[occ] = int(label)
#print(occ_labels)
occ_vector = []
with open("ml-100k/u.user", 'r') as f:
for line in f.readlines()[1:]:
if len(line) < 2:
continue
else:
userid, age, gender, occupation, zipcode = line.split("|")
occ_vector.append(occ_labels[occupation])
#print(occ_vector)
return np.asarray(occ_vector)
def load_age_vector_1m(border=30):
age_vector = []
with open("data/ml-1m/users.dat", 'r') as f:
for line in f.readlines():
userid, gender, age, occupation, zipcode = line.split("::")
if int(age) < border:
age_vector.append(0)
else:
age_vector.append(1)
return np.asarray(age_vector)
def load_age_vector_100k(border=30):
age_vector = []
with open("data/ml-100k/userObs.csv", 'r') as f:
for line in f.readlines()[1:]:
if len(line) < 2:
continue
else:
userid, age, gender, occupation, zipcode = line.split(", ")
if int(age) < border:
age_vector.append(0)
else:
age_vector.append(1)
return np.asarray(age_vector)
def data_exploration():
import pandas as pd
df = pd.read_csv("ml-100k/userObs.csv")
#import matplotlib
from matplotlib import pyplot as plt
import collections
a = df[' age']
counter = collections.Counter(a)
plt.bar(counter.keys(), counter.values())
plt.show()
def gender_user_dictionary_1m():
gender_dict = {}
with open("data/ml-1m/users.dat", 'r') as f:
for line in f.readlines():
userid, gender, age, occupation, zipcode = line.split("::")
if userid not in gender_dict:
gender_dict[int(userid)-1] = gender
return gender_dict
def load_movie_genre_matrix_1m(combine=False):
"""
This function loads the movie genre matrix for ML 1m. Said matrix is MxG, where M denotes the movie_id and G the
genre id.
:return: said matrix
"""
if combine:
# Since Drama co-occurs so frequently with Romance and Comedy, we will consider movies that are romantic
# dramas just as dramas. Same for Drama & Comedy and Romantic & Comedy
genres = ["Action", "Adventure", "Animation", "Children", "Comedy", "Crime", "Documentary", "Drama",
"Fantasy", "Film-Noir", "Horror", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller", "War",
"Western"]
matrix = np.zeros(shape=(3952, len(genres)))
with open("ml-1m/movies.dat", 'r') as f:
for line in f.readlines():
id, name, genre = line.replace("\n", "").split("::")
genre = genre.split("|")
if "Drama" in genre and "Romance" in genre:
genre.remove("Romance")
if "Drama" in genre and "Comedy" in genre:
genre.remove("Comedy")
if "Romance" in genre and "Comedy" in genre:
genre.remove("Romance")
for g in genre:
matrix[int(id) - 1, genres.index(g)] = 1
else:
genres = ["Action", "Adventure", "Animation", "Children", "Comedy", "Crime", "Documentary", "Drama", "Fantasy", "Film-Noir", "Horror", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western"]
matrix = np.zeros(shape=(3952, len(genres)))
with open("data/ml-1m/movies.dat", 'r') as f:
for line in f.readlines():
id, name, genre = line.replace("\n", "").split("::")
genre = genre.split("|")
for g in genre:
matrix[int(id)-1, genres.index(g)] = 1
return matrix
def load_movie_genre_matrix_100k(combine=False):
"""
This function loads the movie genre matrix for ML 100k. Said matrix is MxG, where M denotes the movie_id and G the
genre id.
:return: said matrix
"""
if combine:
# Since Drama co-occurs so frequently with Romance and Comedy, we will consider movies that are romantic
# dramas just as dramas. Same for Drama & Comedy and Romantic & Comedy
print("not implemented yet")
else:
genres = ["unknown", "Action", "Adventure", "Animation", "Children", "Comedy", "Crime", "Documentary", "Drama", "Fantasy", "Film-Noir", "Horror", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western"]
matrix = np.zeros(shape=(3952, len(genres)))
with open("data/ml-100k/u.item", 'r') as f:
for id, line in enumerate(f.readlines()):
genre_vector = line.replace("\n", "")[-37:]
genre_vector = genre_vector.split("|")
genre_vector = np.asarray([int(x) for x in genre_vector])
matrix[int(id)-1, :] += genre_vector
return matrix
def load_movie_id_dictionary_1m():
dict = {}
with open("data/ml-1m/movies.dat", 'r') as f:
for line in f.readlines():
id, name, genres = line.split("::")
dict[int(id)] = name
return dict
def load_movie_id_dictionary_100k():
dict = {}
with open("data/ml-100k/u.item", 'r') as f:
for line in f.readlines():
start = line.find("|")
end = line.find("|", start+1)
id = line[0:start]
name = line[start+1:end]
dict[int(id)] = name
return dict
"""
import pandas as pd
import matplotlib.pyplot as plt
user_df = pd.read_csv ("data/ml-1m/users.dat", sep = "::")
user_df.columns = ["userid", "gender", "age", "ocuupation", "zipcode"]
# count the number of male and female raters
gender_counts = user_df.gender.value_counts()
# plot the counts
plt.figure(figsize=(12, 5))
plt.bar(x= gender_counts.index[0], height=gender_counts.values[0], color="lightskyblue")
plt.bar(x= gender_counts.index[1], height=gender_counts.values[1], color="lightpink")
plt.title("Number of Male and Female users for ML1M Data", fontsize=16, fontweight="bold")
plt.xlabel("Gender", fontsize=19)
plt.ylabel("Counts", fontsize=19)
plt.savefig("images/gender_dist_ml1m.pdf", bbox_inches='tight')
plt.show()
df = pd.read_csv ("data/ml-1m/ratings.dat", sep= "::")
df.columns = ['userid', 'itemid', 'rating', 'timestamp']
print (df.shape)
grouped = df.groupby('userid')
half_df = grouped.apply(lambda x: x.sample(frac=0.5))
half_df.to_csv ("data/ml-1m/ratings_ml1m_half_1.csv", index= False)
print(half_df)
data = pd.concat([df, half_df])
half_df_2 = data.drop_duplicates(keep = False)
print(half_df_2)
half_df_2.to_csv ("data/ml-1m/ratings_ml1m_half_2.csv", index= False)
# half_df = df.sample (frac= 0.5)
# df_new = df.groupby('userid')['userid'].transform('count').unique()
"""