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load_data_ver2.py
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load_data_ver2.py
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from Merlot_Model_FM import loadInteractions, loadUserFeatures, loadItemFeatures, loadTagItems, loadUserTags, \
loadUserFeatures_CO
import pickle
import scipy.sparse
import csv
from sklearn.preprocessing import normalize
def save_data(path, data):
print('Saving data...')
with open(path, 'wb') as fle:
pickle.dump(data, fle, protocol=pickle.HIGHEST_PROTOCOL)
def load_data(path):
print('Loading data...')
return pickle.load(open(path, 'rb'))
def load_interactions_without_activities(i_train, n_users, n_items):
print('Loading Interaction without activities...')
row = n_users
column = n_items
A = scipy.sparse.lil_matrix((row, column), dtype=int)
users, items, labels = list(), list(), list()
with open(i_train, encoding='utf-8') as f:
reader = csv.reader(f, delimiter=',')
next(reader, None)
for row in reader:
src = int(row[0])
des = int(row[1])
# make sure it is symmetry
A[src, des] = 1
users.append(src)
items.append(des)
labels.append(1)
return normalize(A), (users, items, labels)
def load_interactions_with_activities_for_ACT(i_train, n_users, n_items, n_activities):
print('Loading Interaction with activities for ACT model...')
row = n_users
column = n_items
A = scipy.sparse.lil_matrix((row, column), dtype=int)
print('pass sparse matrix')
users, items, labels = list(), list(), list()
with open(i_train, encoding='utf-8') as f:
reader = csv.reader(f, delimiter=',')
next(reader, None)
for row in reader:
src = int(row[0])
des = int(row[1])
if n_activities == 0:
act = int(row[2])
elif n_activities == 1:
act = int(row[3])
else:
print('Wrong activities index')
exit()
if act == 1:
A[src, des] = 1
users.append(src)
items.append(des)
labels.append(1)
return normalize(A), (users, items, labels)
def load_item_features_without_activities(i_file, i_neighbors, n_items, n_tags, n_activities):
print('Loading item features without considering the activities...')
row = n_items * n_activities
columns = n_tags
A = scipy.sparse.lil_matrix((row, columns), dtype=int)
with open(i_file, encoding='utf-8') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
rid = int(row[0])
tag = int(row[1])
A[rid, tag] = 1
return normalize(A)
if __name__ == '__main__':
# # load interactions and user observed items
# i_train = '../data/full_data_v7/so/train.csv'
# n_users, n_items, n_activities = 23612, 1020809, 2
# interactions, user_observed_items = loadInteractions(i_train, n_users, n_items, n_activities)
# interactions_user_observed_items = (interactions, user_observed_items)
# print(interactions.shape, len(user_observed_items))
# path_saving = './data_ver1/'
# save_data(path=path_saving + 'interactions_user_observed_items.pickle', data=interactions_user_observed_items)
# # # load interactions and user observed items
# i_train = '../data/full_data_v7/gh/train.csv'
# n_users, n_items, n_activities = 33453, 461931, 2
# interactions, user_observed_items = loadInteractions(i_train, n_users, n_items, n_activities)
# interactions_user_observed_items = (interactions, user_observed_items)
# print(interactions.shape, len(user_observed_items))
# path_saving = './data_ver1/gh'
# save_data(path=path_saving + 'interactions_user_observed_items.pickle', data=interactions_user_observed_items)
# #####################################################################################################
# # load interactions and user observed items
# i_train = '../data/full_data_v7/so/train.csv'
# n_users, n_items, n_activities = 23612, 1020809, 2
# interactions, user_observed_items = loadInteractions(i_train, n_users, n_items, n_activities)
# print(interactions.shape, len(user_observed_items))
#
# # load user features
# i_user_graph = '../data/full_data_v7/so/so_user_user_graph.csv'
# u_features = loadUserFeatures(i_user_graph, n_users, n_items, n_activities, user_observed_items)
# print(u_features.shape)
#
# # load item features
# i_item_graph = '../data/full_data_v7/so/so_item_graph.csv'
# i_item_nearest_neighbours = '../data/full_data_v7/so/so_item_neighbors.csv'
# n_tags = 30354
# i_features = loadItemFeatures(i_item_graph, i_item_nearest_neighbours, n_items, n_tags, n_activities)
# print(i_features.shape)
#
# # load tag items
# i_tag_items = '../data/full_data_v7/so/so_tag_items.csv'
# tag_items = loadTagItems(i_tag_items)
# print(len(tag_items))
#
# # load user tags
# i_user_tags = '../data/full_data_v7/so/so_user_tags.csv'
# user_tags = loadUserTags(i_user_tags)
# print(len(user_tags))
# # saving data
# #####################################################################################################
# path_saving = './data_ver1/'
# save_data(path=path_saving + 'interactions.pickle', data=interactions)
# save_data(path=path_saving + 'user_observed_items.pickle', data=user_observed_items)
# save_data(path=path_saving + 'u_features.pickle', data=u_features)
# save_data(path=path_saving + 'i_features.pickle', data=i_features)
# save_data(path=path_saving + 'tag_items.pickle', data=tag_items)
# save_data(path=path_saving + 'user_tags.pickle', data=user_tags)
# exit()
# # load user features Co-participant
# i_user_graph = '../data/full_data_v7/so/so_user_user_graph.csv'
# n_users = 23612
# u_features_CO = loadUserFeatures_CO(i_user_graph, n_users)
# print(u_features_CO.shape)
# path_saving = './data_ver1/'
# save_data(path=path_saving + 'u_features_CO.pickle', data=u_features_CO)
# exit()
# # # #####################################################################################################
# # # #####################################################################################################
# # # load user and items without considering any activities
# # i_train = '../data/full_data_v7/so/train.csv'
# n_users, n_items = 23612, 1020809
# # interactions_non_activities = load_interactions_without_activities(i_train=i_train, n_users=n_users,
# # n_items=n_items)
#
# # load item features for keen model (don't care about the activities)
# i_item_graph = '../data/full_data_v7/so/so_item_graph.csv'
# i_item_nearest_neighbours = '../data/full_data_v7/so/so_item_neighbors.csv'
# n_tags, n_activities = 30354, 1
# i_features_non_activities = load_item_features_without_activities(i_item_graph, i_item_nearest_neighbours, n_items,
# n_tags, n_activities)
# print(i_features_non_activities.shape)
# # # #####################################################################################################
# path_saving = './data_ver1/so/'
# # save_data(path=path_saving + 'interactions_non_activities.pickle', data=interactions_non_activities)
# save_data(path=path_saving + 'i_features_non_activities.pickle', data=i_features_non_activities)
# #####################################################################################################
# # load user and items without considering any activities -- SMALL TRAIN DATA
# i_train = '../data/full_data_v7/so/train_small.csv'
# n_users, n_items = 23612, 1020809
# interactions_non_activities = load_interactions_without_activities(i_train=i_train, n_users=n_users,
# n_items=n_items)
# print(interactions_non_activities.shape)
# path_saving = './data_test/'
# save_data(path=path_saving + 'interactions_non_activities.pickle', data=interactions_non_activities)
# exit()
# # # #####################################################################################################
# # # #####################################################################################################
# # # load user and items considering any activities for ACT model
# i_train = '../data/full_data_v7/so/train.csv'
# n_users, n_items = 23612, 1020809
# interactions_with_first_activities_for_ACT = load_interactions_with_activities_for_ACT(i_train, n_users, n_items,
# n_activities=0)
# path_saving = './data_ver1/'
# save_data(path=path_saving + 'interactions_with_first_activities_for_ACT.pickle',
# data=interactions_with_first_activities_for_ACT)
#
# interactions_with_second_activities_for_ACT = load_interactions_with_activities_for_ACT(i_train, n_users, n_items,
# n_activities=0)
# path_saving = './data_ver1/'
# save_data(path=path_saving + 'interactions_with_second_activities_for_ACT.pickle',
# data=interactions_with_second_activities_for_ACT)
# # # #####################################################################################################
# # # #####################################################################################################
# TESTING PHASE
# i_train = '../data/full_data_v7/so/test.csv'
# n_users, n_items = 23612, 1020809
# interactions_non_activities_testing = load_interactions_without_activities(i_train=i_train, n_users=n_users,
# n_items=n_items)
# # return two elements, iteractions and (users, items, labels)
# print(len(interactions_non_activities_testing))
# path_saving = './data_ver1/'
# save_data(path=path_saving + 'interactions_non_activities_testing.pickle', data=interactions_non_activities_testing)
# i_train = '../data/full_data_v7/so/test.csv'
# n_users, n_items = 23612, 1020809
# interactions_with_first_activities_for_ACT = load_interactions_with_activities_for_ACT(i_train, n_users, n_items,
# n_activities=0)
# path_saving = './data_ver1/'
# save_data(path=path_saving + 'interactions_with_first_activities_for_ACT_testing.pickle',
# data=interactions_with_first_activities_for_ACT)
#
# interactions_with_second_activities_for_ACT = load_interactions_with_activities_for_ACT(i_train, n_users, n_items,
# n_activities=0)
# path_saving = './data_ver1/'
# save_data(path=path_saving + 'interactions_with_second_activities_for_ACT_testing.pickle',
# data=interactions_with_second_activities_for_ACT)
# # # #####################################################################################################
# # # #####################################################################################################
# GH
# # # #####################################################################################################
# # # #####################################################################################################
# # # load user features Co-participant
# i_user_graph = '../data/full_data_v7/gh/gh_user_user_graph.csv'
# n_users = 33453
# u_features_CO = loadUserFeatures_CO(i_user_graph, n_users)
# print(u_features_CO.shape)
# path_saving = './data_ver1/gh/'
# save_data(path=path_saving + 'u_features_CO.pickle', data=u_features_CO)
# # # #####################################################################################################
# # # #####################################################################################################
# # # # load user and items without considering any activities
# i_train = '../data/full_data_v7/gh/train.csv'
# n_users, n_items = 33453, 461931
# interactions_non_activities = load_interactions_without_activities(i_train=i_train, n_users=n_users,
# n_items=n_items)
# path_saving = './data_ver1/gh/'
# save_data(path=path_saving + 'interactions_non_activities.pickle', data=interactions_non_activities)
# # # #####################################################################################################
# # # #####################################################################################################
# load item features for keen model (don't care about the activities)
# i_item_graph = '../data/full_data_v7/gh/gh_item_graph.csv'
# i_item_nearest_neighbours = '../data/full_data_v7/gh/gh_item_neighbors.csv'
# n_users, n_items = 33453, 461931
# n_tags, n_activities = 12150, 1
# i_features_non_activities = load_item_features_without_activities(i_item_graph, i_item_nearest_neighbours, n_items,
# n_tags, n_activities)
# print(i_features_non_activities.shape)
# path_saving = './data_ver1/gh/'
# save_data(path=path_saving + 'i_features_non_activities.pickle', data=i_features_non_activities)
# # # #####################################################################################################
# # # #####################################################################################################
# # load user and items considering any activities for ACT model
# i_train = '../data/full_data_v7/gh/train.csv'
# n_users, n_items = 33453, 461931
# interactions_with_first_activities_for_ACT = load_interactions_with_activities_for_ACT(i_train, n_users, n_items,
# n_activities=0)
# path_saving = './data_ver1/gh/'
# save_data(path=path_saving + 'interactions_with_first_activities_for_ACT.pickle',
# data=interactions_with_first_activities_for_ACT)
#
# interactions_with_second_activities_for_ACT = load_interactions_with_activities_for_ACT(i_train, n_users, n_items,
# n_activities=0)
# path_saving = './data_ver1/gh/'
# save_data(path=path_saving + 'interactions_with_second_activities_for_ACT.pickle',
# data=interactions_with_second_activities_for_ACT)
# # # #####################################################################################################
# load tag items
# path_saving = './data_ver1/gh/'
# i_tag_items = '../data/full_data_v7/gh/gh_tag_items.csv'
# tag_items = loadTagItems(i_tag_items)
# print(len(tag_items))
# save_data(path=path_saving + 'tag_items.pickle', data=tag_items)
#
# # # load user tags
# path_saving = './data_ver1/gh/'
# i_user_tags = '../data/full_data_v7/gh/gh_user_tags.csv'
# user_tags = loadUserTags(i_user_tags)
# print(len(user_tags))
# save_data(path=path_saving + 'user_tags.pickle', data=user_tags)
# # # #####################################################################################################
i_train = '../data/full_data_v7/gh/train.csv'
n_users, n_items, n_activities = 33453, 461931, 2
interactions, user_observed_items = loadInteractions(i_train, n_users, n_items, n_activities)
interactions_user_observed_items = (interactions, user_observed_items)
print(interactions.shape, len(user_observed_items))
path_saving = './data_ver1/gh/'
save_data(path=path_saving + 'interactions_user_observed_items.pickle', data=interactions_user_observed_items)