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mohamad maheri 2 years ago
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MeLU.py View File

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import torch
import numpy as np
from copy import deepcopy

from torch.autograd import Variable
from torch.nn import functional as F
from collections import OrderedDict

from embeddings import item, user


class user_preference_estimator(torch.nn.Module):
def __init__(self, config):
super(user_preference_estimator, self).__init__()
self.embedding_dim = config['embedding_dim']
self.fc1_in_dim = config['embedding_dim'] * 8
self.fc2_in_dim = config['first_fc_hidden_dim']
self.fc2_out_dim = config['second_fc_hidden_dim']
self.use_cuda = config['use_cuda']

self.item_emb = item(config)
self.user_emb = user(config)
self.fc1 = torch.nn.Linear(self.fc1_in_dim, self.fc2_in_dim)
self.fc2 = torch.nn.Linear(self.fc2_in_dim, self.fc2_out_dim)
self.linear_out = torch.nn.Linear(self.fc2_out_dim, 1)

def forward(self, x, training = True):
rate_idx = Variable(x[:, 0], requires_grad=False)
genre_idx = Variable(x[:, 1:26], requires_grad=False)
director_idx = Variable(x[:, 26:2212], requires_grad=False)
actor_idx = Variable(x[:, 2212:10242], requires_grad=False)
gender_idx = Variable(x[:, 10242], requires_grad=False)
age_idx = Variable(x[:, 10243], requires_grad=False)
occupation_idx = Variable(x[:, 10244], requires_grad=False)
area_idx = Variable(x[:, 10245], requires_grad=False)

item_emb = self.item_emb(rate_idx, genre_idx, director_idx, actor_idx)
user_emb = self.user_emb(gender_idx, age_idx, occupation_idx, area_idx)
x = torch.cat((item_emb, user_emb), 1)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
x = F.relu(x)
return self.linear_out(x)


class MeLU(torch.nn.Module):
def __init__(self, config):
super(MeLU, self).__init__()
self.use_cuda = config['use_cuda']
self.model = user_preference_estimator(config)
self.local_lr = config['local_lr']
self.store_parameters()
self.meta_optim = torch.optim.Adam(self.model.parameters(), lr=config['lr'])
self.local_update_target_weight_name = ['fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'linear_out.weight', 'linear_out.bias']

def store_parameters(self):
self.keep_weight = deepcopy(self.model.state_dict())
self.weight_name = list(self.keep_weight.keys())
self.weight_len = len(self.keep_weight)
self.fast_weights = OrderedDict()

def forward(self, support_set_x, support_set_y, query_set_x, num_local_update):
for idx in range(num_local_update):
if idx > 0:
self.model.load_state_dict(self.fast_weights)
weight_for_local_update = list(self.model.state_dict().values())
support_set_y_pred = self.model(support_set_x)
loss = F.mse_loss(support_set_y_pred, support_set_y.view(-1, 1))
self.model.zero_grad()
grad = torch.autograd.grad(loss, self.model.parameters(), create_graph=True)
# local update
for i in range(self.weight_len):
if self.weight_name[i] in self.local_update_target_weight_name:
self.fast_weights[self.weight_name[i]] = weight_for_local_update[i] - self.local_lr * grad[i]
else:
self.fast_weights[self.weight_name[i]] = weight_for_local_update[i]
self.model.load_state_dict(self.fast_weights)
query_set_y_pred = self.model(query_set_x)
self.model.load_state_dict(self.keep_weight)
return query_set_y_pred

def global_update(self, support_set_xs, support_set_ys, query_set_xs, query_set_ys, num_local_update):
batch_sz = len(support_set_xs)
losses_q = []
if self.use_cuda:
for i in range(batch_sz):
support_set_xs[i] = support_set_xs[i].cuda()
support_set_ys[i] = support_set_ys[i].cuda()
query_set_xs[i] = query_set_xs[i].cuda()
query_set_ys[i] = query_set_ys[i].cuda()
for i in range(batch_sz):
query_set_y_pred = self.forward(support_set_xs[i], support_set_ys[i], query_set_xs[i], num_local_update)
loss_q = F.mse_loss(query_set_y_pred, query_set_ys[i].view(-1, 1))
losses_q.append(loss_q)
losses_q = torch.stack(losses_q).mean(0)
self.meta_optim.zero_grad()
losses_q.backward()
self.meta_optim.step()
self.store_parameters()
return

def get_weight_avg_norm(self, support_set_x, support_set_y, num_local_update):
tmp = 0.
if self.cuda():
support_set_x = support_set_x.cuda()
support_set_y = support_set_y.cuda()
for idx in range(num_local_update):
if idx > 0:
self.model.load_state_dict(self.fast_weights)
weight_for_local_update = list(self.model.state_dict().values())
support_set_y_pred = self.model(support_set_x)
loss = F.mse_loss(support_set_y_pred, support_set_y.view(-1, 1))
# unit loss
loss /= torch.norm(loss).tolist()
self.model.zero_grad()
grad = torch.autograd.grad(loss, self.model.parameters(), create_graph=True)
for i in range(self.weight_len):
# For averaging Forbenius norm.
tmp += torch.norm(grad[i])
if self.weight_name[i] in self.local_update_target_weight_name:
self.fast_weights[self.weight_name[i]] = weight_for_local_update[i] - self.local_lr * grad[i]
else:
self.fast_weights[self.weight_name[i]] = weight_for_local_update[i]
return tmp / num_local_update

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README.md View File

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# MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation

PyTorch implementation of the paper: "MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation", KDD, 2019.

## Abstract
This paper proposes a recommender system to alleviate the coldstart problem that can estimate user preferences based on only a small number of items. To identify a user’s preference in the cold state, existing recommender systems, such as Netflix, initially provide items to a user; we call those items evidence candidates. Recommendations are then made based on the items selected by the user. Previous recommendation studies have two limitations: (1) the users who consumed a few items have poor recommendations and (2) inadequate evidence candidates are used to identify user preferences. We propose a meta-learning-based recommender system called MeLU to overcome these two limitations. From metalearning, which can rapidly adopt new task with a few examples, MeLU can estimate new user’s preferences with a few consumed items. In addition, we provide an evidence candidate selection strategy that determines distinguishing items for customized preference estimation. We validate MeLU with two benchmark datasets, and the proposed model reduces at least 5.92% mean absolute error than two comparative models on the datasets. We also conduct a user study experiment to verify the evidence selection strategy.

## Usage
### Requirements
- python 3.6+
- pytorch 1.1+
- tqdm 4.32+
- pandas 0.24+

### Preparing dataset
It needs about 22GB of hard disk space.
```python
import os
from data_generation import generate
master_path= "./ml"
if not os.path.exists("{}/".format(master_path)):
os.mkdir("{}/".format(master_path))
generate(master_path)
```

### Training a model
Our model needs support and query sets. The support set is for local update, and the query set is for global update.
```python
import torch
import pickle
from MeLU import MeLU
from options import config
from model_training import training
melu = MeLU(config)
model_filename = "{}/models.pkl".format(master_path)
if not os.path.exists(model_filename):
# Load training dataset.
training_set_size = int(len(os.listdir("{}/warm_state".format(master_path))) / 4)
supp_xs_s = []
supp_ys_s = []
query_xs_s = []
query_ys_s = []
for idx in range(training_set_size):
supp_xs_s.append(pickle.load(open("{}/warm_state/supp_x_{}.pkl".format(master_path, idx), "rb")))
supp_ys_s.append(pickle.load(open("{}/warm_state/supp_y_{}.pkl".format(master_path, idx), "rb")))
query_xs_s.append(pickle.load(open("{}/warm_state/query_x_{}.pkl".format(master_path, idx), "rb")))
query_ys_s.append(pickle.load(open("{}/warm_state/query_y_{}.pkl".format(master_path, idx), "rb")))
total_dataset = list(zip(supp_xs_s, supp_ys_s, query_xs_s, query_ys_s))
del(supp_xs_s, supp_ys_s, query_xs_s, query_ys_s)
training(melu, total_dataset, batch_size=config['batch_size'], num_epoch=config['num_epoch'], model_save=True, model_filename=model_filename)
else:
trained_state_dict = torch.load(model_filename)
melu.load_state_dict(trained_state_dict)
```

### Extracting evidence candidates
We extract evidence candidate list based on the MeLU.
```python
from evidence_candidate import selection
evidence_candidate_list = selection(melu, master_path, config['num_candidate'])
for movie, score in evidence_candidate_list:
print(movie, score)
```
Note that, you may have a different evidence candidate list from the paper. That's because we do not open the random seeds of data generation and model training.

## Citation
If you use this code, please cite the paper.
```
@inproceedings{lee2019melu,
title={MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation},
author={Lee, Hoyeop and Im, Jinbae and Jang, Seongwon and Cho, Hyunsouk and Chung, Sehee},
booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={1073--1082},
year={2019},
organization={ACM}
}
```

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data_generation.py View File

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import re
import os
import json
import torch
import numpy as np
import random
import pickle
from tqdm import tqdm

from options import states
from dataset import movielens_1m


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_


def generate(master_path):
dataset_path = "movielens/ml-1m"
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))

if not os.path.exists("{}/warm_state/".format(master_path)):
for state in states:
os.mkdir("{}/{}/".format(master_path, state))
if not os.path.exists("{}/log/".format(master_path)):
os.mkdir("{}/log/".format(master_path))

dataset = movielens_1m()

# hashmap for item information
if not os.path.exists("{}/m_movie_dict.pkl".format(master_path)):
movie_dict = {}
for idx, row in dataset.item_data.iterrows():
m_info = item_converting(row, rate_list, genre_list, director_list, actor_list)
movie_dict[row['movie_id']] = m_info
pickle.dump(movie_dict, open("{}/m_movie_dict.pkl".format(master_path), "wb"))
else:
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)):
user_dict = {}
for idx, row in dataset.user_data.iterrows():
u_info = user_converting(row, gender_list, age_list, occupation_list, zipcode_list)
user_dict[row['user_id']] = u_info
pickle.dump(user_dict, open("{}/m_user_dict.pkl".format(master_path), "wb"))
else:
user_dict = pickle.load(open("{}/m_user_dict.pkl".format(master_path), "rb"))

for state in states:
idx = 0
if not os.path.exists("{}/{}/{}".format(master_path, "log", state)):
os.mkdir("{}/{}/{}".format(master_path, "log", state))
with open("{}/{}.json".format(dataset_path, state), encoding="utf-8") as f:
dataset = json.loads(f.read())
with open("{}/{}_y.json".format(dataset_path, state), encoding="utf-8") as f:
dataset_y = json.loads(f.read())
for _, user_id in tqdm(enumerate(dataset.keys())):
u_id = int(user_id)
seen_movie_len = len(dataset[str(u_id)])
indices = list(range(seen_movie_len))

if seen_movie_len < 13 or seen_movie_len > 100:
continue

random.shuffle(indices)
tmp_x = np.array(dataset[str(u_id)])
tmp_y = np.array(dataset_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((movie_dict[m_id], 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((movie_dict[m_id], 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:]])

pickle.dump(support_x_app, open("{}/{}/supp_x_{}.pkl".format(master_path, state, idx), "wb"))
pickle.dump(support_y_app, open("{}/{}/supp_y_{}.pkl".format(master_path, state, idx), "wb"))
pickle.dump(query_x_app, open("{}/{}/query_x_{}.pkl".format(master_path, state, idx), "wb"))
pickle.dump(query_y_app, open("{}/{}/query_y_{}.pkl".format(master_path, state, idx), "wb"))
with open("{}/log/{}/supp_x_{}_u_m_ids.txt".format(master_path, state, idx), "w") as f:
for m_id in tmp_x[indices[:-10]]:
f.write("{}\t{}\n".format(u_id, m_id))
with open("{}/log/{}/query_x_{}_u_m_ids.txt".format(master_path, state, idx), "w") as f:
for m_id in tmp_x[indices[-10:]]:
f.write("{}\t{}\n".format(u_id, m_id))
idx += 1

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dataset.py View File

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import datetime
import pandas as pd


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

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embeddings.py View File

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import torch
import torch.nn as nn
import torch.nn.functional as F


class item(torch.nn.Module):
def __init__(self, config):
super(item, self).__init__()
self.num_rate = config['num_rate']
self.num_genre = config['num_genre']
self.num_director = config['num_director']
self.num_actor = config['num_actor']
self.embedding_dim = config['embedding_dim']

self.embedding_rate = torch.nn.Embedding(
num_embeddings=self.num_rate,
embedding_dim=self.embedding_dim
)
self.embedding_genre = torch.nn.Linear(
in_features=self.num_genre,
out_features=self.embedding_dim,
bias=False
)
self.embedding_director = torch.nn.Linear(
in_features=self.num_director,
out_features=self.embedding_dim,
bias=False
)
self.embedding_actor = torch.nn.Linear(
in_features=self.num_actor,
out_features=self.embedding_dim,
bias=False
)

def forward(self, rate_idx, genre_idx, director_idx, actors_idx, vars=None):
rate_emb = self.embedding_rate(rate_idx)
genre_emb = self.embedding_genre(genre_idx.float()) / torch.sum(genre_idx.float(), 1).view(-1, 1)
director_emb = self.embedding_director(director_idx.float()) / torch.sum(director_idx.float(), 1).view(-1, 1)
actors_emb = self.embedding_actor(actors_idx.float()) / torch.sum(actors_idx.float(), 1).view(-1, 1)
return torch.cat((rate_emb, genre_emb, director_emb, actors_emb), 1)


class user(torch.nn.Module):
def __init__(self, config):
super(user, self).__init__()
self.num_gender = config['num_gender']
self.num_age = config['num_age']
self.num_occupation = config['num_occupation']
self.num_zipcode = config['num_zipcode']
self.embedding_dim = config['embedding_dim']

self.embedding_gender = torch.nn.Embedding(
num_embeddings=self.num_gender,
embedding_dim=self.embedding_dim
)

self.embedding_age = torch.nn.Embedding(
num_embeddings=self.num_age,
embedding_dim=self.embedding_dim
)

self.embedding_occupation = torch.nn.Embedding(
num_embeddings=self.num_occupation,
embedding_dim=self.embedding_dim
)

self.embedding_area = torch.nn.Embedding(
num_embeddings=self.num_zipcode,
embedding_dim=self.embedding_dim
)

def forward(self, gender_idx, age_idx, occupation_idx, area_idx):
gender_emb = self.embedding_gender(gender_idx)
age_emb = self.embedding_age(age_idx)
occupation_emb = self.embedding_occupation(occupation_idx)
area_emb = self.embedding_area(area_idx)
return torch.cat((gender_emb, age_emb, occupation_emb, area_emb), 1)

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evidence_candidate.py View File

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import os
import torch
import pickle

from MeLU import MeLU
from options import config


def selection(melu, master_path, topk):
if not os.path.exists("{}/scores/".format(master_path)):
os.mkdir("{}/scores/".format(master_path))
if config['use_cuda']:
melu.cuda()
melu.eval()

target_state = 'warm_state'
dataset_size = int(len(os.listdir("{}/{}".format(master_path, target_state))) / 4)
grad_norms = {}
for j in list(range(dataset_size)):
support_xs = pickle.load(open("{}/{}/supp_x_{}.pkl".format(master_path, target_state, j), "rb"))
support_ys = pickle.load(open("{}/{}/supp_y_{}.pkl".format(master_path, target_state, j), "rb"))
item_ids = []
with open("{}/log/{}/supp_x_{}_u_m_ids.txt".format(master_path, target_state, j), "r") as f:
for line in f.readlines():
item_id = line.strip().split()[1]
item_ids.append(item_id)
for support_x, support_y, item_id in zip(support_xs, support_ys, item_ids):
support_x = support_x.view(1, -1)
support_y = support_y.view(1, -1)
norm = melu.get_weight_avg_norm(support_x, support_y, config['inner'])
try:
grad_norms[item_id]['discriminative_value'] += norm.item()
grad_norms[item_id]['popularity_value'] += 1
except:
grad_norms[item_id] = {
'discriminative_value': norm.item(),
'popularity_value': 1
}

d_value_max = 0
p_value_max = 0
for item_id in grad_norms.keys():
grad_norms[item_id]['discriminative_value'] /= grad_norms[item_id]['popularity_value']
if grad_norms[item_id]['discriminative_value'] > d_value_max:
d_value_max = grad_norms[item_id]['discriminative_value']
if grad_norms[item_id]['popularity_value'] > p_value_max:
p_value_max = grad_norms[item_id]['popularity_value']
for item_id in grad_norms.keys():
grad_norms[item_id]['discriminative_value'] /= float(d_value_max)
grad_norms[item_id]['popularity_value'] /= float(p_value_max)
grad_norms[item_id]['final_score'] = grad_norms[item_id]['discriminative_value'] * grad_norms[item_id]['popularity_value']

movie_info = {}
with open("./movielens/ml-1m/movies_extrainfos.dat", encoding="utf-8") as f:
for line in f.readlines():
tmp = line.strip().split("::")
movie_info[tmp[0]] = "{} ({})".format(tmp[1], tmp[2])

evidence_candidates = []
for item_id, value in list(sorted(grad_norms.items(), key=lambda x: x[1]['final_score'], reverse=True))[:topk]:
evidence_candidates.append((movie_info[item_id], value['final_score']))
return evidence_candidates

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main.py View File

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import os
import torch
import pickle

from MeLU import MeLU
from options import config
from model_training import training
from data_generation import generate
from evidence_candidate import selection


if __name__ == "__main__":
# master_path= "./ml"
master_path = "/media/external_3TB/3TB/rafie/maheri/melr"
# master_path = "/media/external_10TB/10TB/pourmand/ml"
if not os.path.exists("{}/".format(master_path)):
print("inajm")
os.mkdir("{}/".format(master_path))
# preparing dataset. It needs about 22GB of your hard disk space.
generate(master_path)

# # training model.
# melu = MeLU(config)
# model_filename = "{}/models.pkl".format(master_path)
# if not os.path.exists(model_filename):
# # Load training dataset.
# training_set_size = int(len(os.listdir("{}/warm_state".format(master_path))) / 4)
# supp_xs_s = []
# supp_ys_s = []
# query_xs_s = []
# query_ys_s = []
# for idx in range(training_set_size):
# supp_xs_s.append(pickle.load(open("{}/warm_state/supp_x_{}.pkl".format(master_path, idx), "rb")))
# supp_ys_s.append(pickle.load(open("{}/warm_state/supp_y_{}.pkl".format(master_path, idx), "rb")))
# query_xs_s.append(pickle.load(open("{}/warm_state/query_x_{}.pkl".format(master_path, idx), "rb")))
# query_ys_s.append(pickle.load(open("{}/warm_state/query_y_{}.pkl".format(master_path, idx), "rb")))
# total_dataset = list(zip(supp_xs_s, supp_ys_s, query_xs_s, query_ys_s))
# del(supp_xs_s, supp_ys_s, query_xs_s, query_ys_s)
# training(melu, total_dataset, batch_size=config['batch_size'], num_epoch=config['num_epoch'], model_save=True, model_filename=model_filename)
# else:
# trained_state_dict = torch.load(model_filename)
# melu.load_state_dict(trained_state_dict)
#
# # selecting evidence candidates.
# evidence_candidate_list = selection(melu, master_path, config['num_candidate'])
# for movie, score in evidence_candidate_list:
# print(movie, score)

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model_training.py View File

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import os
import torch
import pickle
import random

from MeLU import MeLU
from options import config, states


def training(melu, total_dataset, batch_size, num_epoch, model_save=True, model_filename=None):
if config['use_cuda']:
melu.cuda()

training_set_size = len(total_dataset)
melu.train()
for _ in range(num_epoch):
random.shuffle(total_dataset)
num_batch = int(training_set_size / batch_size)
a,b,c,d = zip(*total_dataset)
for i in range(num_batch):
try:
supp_xs = list(a[batch_size*i:batch_size*(i+1)])
supp_ys = list(b[batch_size*i:batch_size*(i+1)])
query_xs = list(c[batch_size*i:batch_size*(i+1)])
query_ys = list(d[batch_size*i:batch_size*(i+1)])
except IndexError:
continue
melu.global_update(supp_xs, supp_ys, query_xs, query_ys, config['inner'])

if model_save:
torch.save(melu.state_dict(), model_filename)

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movielens/ml-1m/item_cold_state.json
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movielens/ml-1m/item_cold_state_y.json
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movielens/ml-1m/m_actor.txt
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movielens/ml-1m/m_age.txt View File

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1
18
25
35
45
50
56

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movielens/ml-1m/m_director.txt
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movielens/ml-1m/m_gender.txt View File

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M
F

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movielens/ml-1m/m_genre.txt View File

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Animation
Family
Romance
nan
Documentary
Action
Music
Horror
Comedy
Adventure
History
War
Short
Film-Noir
Adult
Crime
Drama
Thriller
Mystery
Sport
Musical
Sci-Fi
Biography
Fantasy
Western

+ 21
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movielens/ml-1m/m_occupation.txt View File

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0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

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movielens/ml-1m/m_rate.txt View File

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PG-13
UNRATED
NC-17
PG
G
R

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movielens/ml-1m/m_zipcode.txt
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movielens/ml-1m/movies.dat
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movielens/ml-1m/movies_extrainfos.dat
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movielens/ml-1m/ratings.dat
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movielens/ml-1m/user_and_item_cold_state.json
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movielens/ml-1m/user_cold_state_y.json
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movielens/ml-1m/users.dat
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movielens/ml-1m/warm_state.json
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movielens/ml-1m/warm_state_y.json
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options.py View File

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config = {
# item
'num_rate': 6,
'num_genre': 25,
'num_director': 2186,
'num_actor': 8030,
'embedding_dim': 32,
'first_fc_hidden_dim': 64,
'second_fc_hidden_dim': 64,
# user
'num_gender': 2,
'num_age': 7,
'num_occupation': 21,
'num_zipcode': 3402,
# cuda setting
'use_cuda': True,
# model setting
'inner': 1,
'lr': 5e-5,
'local_lr': 5e-6,
'batch_size': 32,
'num_epoch': 20,
# candidate selection
'num_candidate': 20,
}

states = ["warm_state", "user_cold_state", "item_cold_state", "user_and_item_cold_state"]

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