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dataset.py
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dataset.py
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import os
import pickle
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset
import random
import re
import datetime
import pandas as pd
import json
from tqdm import tqdm
class movielens_1m(object):
def __init__(self):
self.user_data, self.item_data, self.score_data = self.load()
def load(self):
path = "movielens/ml-1m"
profile_data_path = "{}/users.dat".format(path)
score_data_path = "{}/ratings.dat".format(path)
item_data_path = "{}/movies_extrainfos.dat".format(path)
profile_data = pd.read_csv(
profile_data_path, names=['user_id', 'gender', 'age', 'occupation_code', 'zip'],
sep="::", engine='python'
)
item_data = pd.read_csv(
item_data_path, names=['movie_id', 'title', 'year', 'rate', 'released', 'genre', 'director', 'writer', 'actors', 'plot', 'poster'],
sep="::", engine='python', encoding="utf-8"
)
score_data = pd.read_csv(
score_data_path, names=['user_id', 'movie_id', 'rating', 'timestamp'],
sep="::", engine='python'
)
score_data['time'] = score_data["timestamp"].map(lambda x: datetime.datetime.fromtimestamp(x))
score_data = score_data.drop(["timestamp"], axis=1)
return profile_data, item_data, score_data
def item_converting(row, rate_list, genre_list, director_list, actor_list):
rate_idx = torch.tensor([[rate_list.index(str(row['rate']))]]).long()
genre_idx = torch.zeros(1, 25).long()
for genre in str(row['genre']).split(", "):
idx = genre_list.index(genre)
genre_idx[0, idx] = 1
director_idx = torch.zeros(1, 2186).long()
for director in str(row['director']).split(", "):
idx = director_list.index(re.sub(r'\([^()]*\)', '', director))
director_idx[0, idx] = 1
actor_idx = torch.zeros(1, 8030).long()
for actor in str(row['actors']).split(", "):
idx = actor_list.index(actor)
actor_idx[0, idx] = 1
return torch.cat((rate_idx, genre_idx, director_idx, actor_idx), 1)
def user_converting(row, gender_list, age_list, occupation_list, zipcode_list):
gender_idx = torch.tensor([[gender_list.index(str(row['gender']))]]).long()
age_idx = torch.tensor([[age_list.index(str(row['age']))]]).long()
occupation_idx = torch.tensor([[occupation_list.index(str(row['occupation_code']))]]).long()
zip_idx = torch.tensor([[zipcode_list.index(str(row['zip'])[:5])]]).long()
return torch.cat((gender_idx, age_idx, occupation_idx, zip_idx), 1)
def load_list(fname):
list_ = []
with open(fname, encoding="utf-8") as f:
for line in f.readlines():
list_.append(line.strip())
return list_
class Metamovie(Dataset):
def __init__(self, args, partition='train', test_way=None, path=None):
super(Metamovie, self).__init__()
#self.dataset_path = args.data_root
self.partition = partition
#self.pretrain = pretrain
self.dataset_path = args.data_root
dataset_path = self.dataset_path
rate_list = load_list("{}/m_rate.txt".format(dataset_path))
genre_list = load_list("{}/m_genre.txt".format(dataset_path))
actor_list = load_list("{}/m_actor.txt".format(dataset_path))
director_list = load_list("{}/m_director.txt".format(dataset_path))
gender_list = load_list("{}/m_gender.txt".format(dataset_path))
age_list = load_list("{}/m_age.txt".format(dataset_path))
occupation_list = load_list("{}/m_occupation.txt".format(dataset_path))
zipcode_list = load_list("{}/m_zipcode.txt".format(dataset_path))
self.dataset = movielens_1m()
master_path = self.dataset_path
if not os.path.exists("{}/m_movie_dict.pkl".format(master_path)):
self.movie_dict = {}
for idx, row in self.dataset.item_data.iterrows():
m_info = item_converting(row, rate_list, genre_list, director_list, actor_list)
self.movie_dict[row['movie_id']] = m_info
pickle.dump(self.movie_dict, open("{}/m_movie_dict.pkl".format(master_path), "wb"))
else:
self.movie_dict = pickle.load(open("{}/m_movie_dict.pkl".format(master_path), "rb"))
# hashmap for user profile
if not os.path.exists("{}/m_user_dict.pkl".format(master_path)):
self.user_dict = {}
for idx, row in self.dataset.user_data.iterrows():
u_info = user_converting(row, gender_list, age_list, occupation_list, zipcode_list)
self.user_dict[row['user_id']] = u_info
pickle.dump(self.user_dict, open("{}/m_user_dict.pkl".format(master_path), "wb"))
else:
self.user_dict = pickle.load(open("{}/m_user_dict.pkl".format(master_path), "rb"))
if partition == 'train' or partition == 'valid':
self.state = 'warm_state'
else:
if test_way is not None:
if test_way == 'old':
self.state = 'warm_state'
elif test_way == 'new_user':
self.state = 'user_cold_state'
elif test_way == 'new_item':
self.state = 'item_cold_state'
else:
self.state = 'user_and_item_cold_state'
print(self.state)
with open("{}/{}.json".format(dataset_path, self.state), encoding="utf-8") as f:
self.dataset_split = json.loads(f.read())
with open("{}/{}_y.json".format(dataset_path, self.state), encoding="utf-8") as f:
self.dataset_split_y = json.loads(f.read())
length = len(self.dataset_split.keys())
self.final_index = []
for _, user_id in tqdm(enumerate(list(self.dataset_split.keys()))):
u_id = int(user_id)
seen_movie_len = len(self.dataset_split[str(u_id)])
if seen_movie_len < 13 or seen_movie_len > 100:
continue
else:
self.final_index.append(user_id)
def __getitem__(self, item):
user_id = self.final_index[item]
u_id = int(user_id)
seen_movie_len = len(self.dataset_split[str(u_id)])
indices = list(range(seen_movie_len))
random.shuffle(indices)
tmp_x = np.array(self.dataset_split[str(u_id)])
tmp_y = np.array(self.dataset_split_y[str(u_id)])
support_x_app = None
for m_id in tmp_x[indices[:-10]]:
m_id = int(m_id)
tmp_x_converted = torch.cat((self.movie_dict[m_id], self.user_dict[u_id]), 1)
try:
support_x_app = torch.cat((support_x_app, tmp_x_converted), 0)
except:
support_x_app = tmp_x_converted
query_x_app = None
for m_id in tmp_x[indices[-10:]]:
m_id = int(m_id)
u_id = int(user_id)
tmp_x_converted = torch.cat((self.movie_dict[m_id], self.user_dict[u_id]), 1)
try:
query_x_app = torch.cat((query_x_app, tmp_x_converted), 0)
except:
query_x_app = tmp_x_converted
support_y_app = torch.FloatTensor(tmp_y[indices[:-10]])
query_y_app = torch.FloatTensor(tmp_y[indices[-10:]])
return support_x_app, support_y_app.view(-1,1), query_x_app, query_y_app.view(-1,1)
def __len__(self):
return len(self.final_index)