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execute_few_shot_new_align_newscls.py
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execute_few_shot_new_align_newscls.py
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# -*- coding: utf-8 -*-
import torch
import torch.optim as optim
from process_utils import data_generator, tools
from models import HetGNN
from torch.utils.data import DataLoader, RandomSampler
import random
torch.set_num_threads(2)
from evaluate_utils.my_application import *
from tqdm import tqdm
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
from process_utils.Iterator import MINDIterator
from config import hparams
import pickle
import wandb
from torch.optim.lr_scheduler import ReduceLROnPlateau
import torch.nn as nn
from process_utils.MLMIter import AlignDatset
wandb.init(project="NewsRec", name="newsrec")
class model_class(object):
def __init__(self, args, args_mind):
super(model_class, self).__init__()
self.args = args
self.gpu = args.cuda
input_data = data_generator.input_data(self.args, args_mind)
self.input_data = input_data
if args.db == 'mind':
self.train_behaviors_file = self.args.data_path + "behaviors.tsv"
else:
self.train_behaviors_file = self.args.data_path + "behaviors.tsv"
self.save_root = self.args.data_path
# CTR prediction
self.train_iterator = MINDIterator(
batch_size = self.args.mini_batch_s,
npratio=self.args.npratio,
col_spliter="\t",
)
if args.db == 'mind':
feature_list = [input_data.p_title_embed, input_data.p_abstract_embed,
input_data.p_v_for_embed, input_data.p_category_embed,
input_data.p_body_embed, input_data.p_title_embedmulti]
else:
feature_list = [input_data.p_title_embed, input_data.p_abstract_embed,
input_data.p_v_for_embed, input_data.p_category_embed,
np.zeros((1,1)), input_data.p_title_embedmulti]
if self.args.db == 'mind':
feature_list[5] = torch.from_numpy(np.array(feature_list[5]))
if self.gpu:
feature_list[5] = feature_list[5].cuda()
self.p_title_embedmulti = feature_list[5]
if self.args.db == 'adressa':
# get train behaviors + get train clicks + dict
df = pd.read_csv(self.train_behaviors_file, sep='\t', header=None)
df.columns = ['impre_id', 'user', 'time', 'his', 'impr']
uid2union_id = self.load_obj("uid2union_id")
news2union_id = self.load_obj("news2union_id")
u_n_train = {}
for idx, row in tqdm(df.iterrows()):
user = uid2union_id[row['user']]
impr = [i.split("-")[0] for i in row['impr'].split(" ") if i.split("-")[1] == '1']
impr = [news2union_id[i] for i in impr]
# append
u_n_train[user] = u_n_train.get(user, []) + impr
self.model = HetGNN.RecModel(args, feature_list,
input_data.a_neigh_list_train,
u_n_train)
else:
self.model = HetGNN.RecModel(args, feature_list,
input_data.a_neigh_list_train)
if self.gpu:
self.model.cuda()
# self.model.init_weights()
def save_obj(self, obj, name):
with open(self.save_root + name + '.pkl', 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_obj(self, name):
with open(self.save_root + name + '.pkl', 'rb') as f:
return pickle.load(f)
def convert_data_for_hetGNN(self, batch_data_input):
"""
Data to HetGNN
Args:
batch_data_input:
Returns:
"""
ctr_samples = []
candidate_raw = batch_data_input['candidate_raw']
user_raw = batch_data_input['user_raw']
uid2union_id = self.load_obj("uid2union_id")
news2union_id = self.load_obj("news2union_id")
news2union_id[-1] = -1 # padding
for idx, uid in enumerate(user_raw):
raw_news = candidate_raw[idx]
ctr_samples.append((uid2union_id[uid], [news2union_id[i] for i in raw_news]))
return ctr_samples
def save_train_samples(self, ctr_samples):
"""
Save train samples for train evaluation
Args:
ctr_samples:
Returns: None
"""
all_df = []
labels = []
impr = 0
for sample in ctr_samples:
uid = sample[0]
news = [i for i in sample[1] if i != -1]
all_df.append((uid, impr, news))
labels.append((impr, [1] + [0] * (len(news)-1)))
impr += 1
pd.DataFrame(all_df, columns=['user_id', 'impression_id', 'impressions_candidate'])\
.to_csv(self.args.data_path + "train_samples.txt", index=False)
labels_df = pd.DataFrame(labels, columns=['impression_id', 'impressions_label'])
label_file = open(self.args.data_path + "train_labels.txt", 'w')
k = 0
for idx, row in labels_df.iterrows():
if k == len(labels_df) - 1:
label_file.write(str(row['impression_id']) + " " + str(row['impressions_label']).replace(" ", ""))
else:
label_file.write(
str(row['impression_id']) + " " + str(row['impressions_label']).replace(" ", "") + "\n")
k += 1
label_file.close()
def eval_rec(self):
"""
eval rec task
Returns:
"""
pass
def load_feature_list(self, input_data):
if self.args.db == 'mind':
feature_list = [input_data.p_title_embed, input_data.p_abstract_embed,
input_data.p_v_for_embed, input_data.p_category_embed,
input_data.p_body_embed, input_data.p_title_embedmulti]
else:
feature_list = [input_data.p_title_embed, input_data.p_abstract_embed,
input_data.p_v_for_embed, input_data.p_category_embed,
np.zeros((1,1)), input_data.p_title_embedmulti]
if self.args.db == 'mind':
feature_list[5] = torch.from_numpy(np.array(feature_list[5]))
if self.gpu:
feature_list[5] = feature_list[5].cuda()
return feature_list
def change_config(self, args, args_mind=None):
self.args = args
input_data = data_generator.input_data(self.args, args_mind)
self.input_data = input_data
self.train_behaviors_file = self.args.data_path + "behaviors.tsv"
self.save_root = self.args.data_path
feature_list = self.load_feature_list(input_data)
self.model.change_config(args, feature_list, input_data.a_neigh_list_train, None)
self.p_title_embedmulti = feature_list[5]
self.train_iterator = MINDIterator(
batch_size = self.args.mini_batch_s,
npratio=self.args.npratio,
col_spliter="\t",
)
def get_bilingual_dict(self):
no2en = pd.read_csv("../../muse/no-en.txt", sep='\t', header=None)
no2endict = {}
for idx, row in no2en.iterrows():
no2endict[row[0]] = no2endict.get(row[0], []) + [row[1]]
return no2endict
def cross(self, x, disable=False):
if not disable and (self.token_rate >= random.random()):
if x in self.no2endict:
return self.no2endict[x][random.randint(0, len(self.no2endict[x]) - 1)]
else:
return x
else:
return x
def cross_str(self, x, disable=False):
raw = x.lower().split(" ")
out = ""
for xx in raw:
out += self.cross(xx, disable)
out += " "
return out
def re_encode_text(self, df):
from bpemb import BPEmb
import numpy as np
bpemb_no = BPEmb(lang="multi", dim=300, vs=320000)
max_title_token = 30
p_title_embed = np.zeros((len(df) + 1, max_title_token), dtype=np.int)
def bpe_encode_text(x, bpemb_en, max_title_size):
if isinstance(x, str):
tokens = bpemb_en.encode_ids(x)
else:
tokens = []
return tokens[:max_title_size] + [0] * (max_title_size - len(tokens))
for idx, row in df.iterrows():
title = row['title']
encode = bpe_encode_text(title, bpemb_no, max_title_token)
embeds = np.asarray(encode, dtype='int32')
p_title_embed[row['nid']] = embeds
return p_title_embed
def add_code_switch(self, sen_rate, token_rate):
"""
all news titles -> replace -> encode -> update
Args:
sen_rate:
token_rate:
Returns:
"""
self.sen_rate = sen_rate
self.token_rate = token_rate
self.no2endict = self.get_bilingual_dict()
ad_news2id = self.load_obj('news2union_id')
ad_newsid2title = pd.read_csv(self.args.data_path + "newsid2title.csv")
docid2title = {}
for idx, row in ad_newsid2title.iterrows():
docid2title[row['doc_id']] = row['title']
nid2title = {}
for docid, nid in ad_news2id.items():
nid2title[nid] = docid2title[docid]
nid2titledf = pd.DataFrame(nid2title.items())
nid2titledf.columns = ['nid', 'title']
nid2titledf['title_cross'] = nid2titledf['title'].apply(
lambda x: self.cross_str(x, not (sen_rate >= random.random())))
p_title_embed = self.re_encode_text(nid2titledf)
p_title_embed = torch.from_numpy(np.array(p_title_embed))
if self.gpu:
p_title_embed = p_title_embed.cuda()
self.model.update_token_cs(p_title_embed)
def add_code_switchbynews(self, news_rate, news2token):
"""
replace news with new token
Args:
sen_rate:
token_rate:
Returns:
"""
# read adressa news id -> same subcategory english news id
adid2mind = self.load_obj('adid2mind')
for i in range(self.args.P_n):
if i in adid2mind:
# judge
if news_rate > random.random():
# search and replace
mind_news_id = random.choice(adid2mind[i])
token = news2token[mind_news_id]
# print(type(news2token))
# print(type(token))
self.p_title_embedmulti[i] = token
self.model.update_token_cs(self.p_title_embedmulti)
if __name__ == '__main__':
best_valid_auc = 0
# lr depends on transfer setting
args_mind = read_args(db='mind', lr=3e-4)
args_ad = read_args(db='adressa', lr=3e-4)
if args_mind.range != 'Model/engTonor':
print("set lr")
args_mind.lr = 1e-4
args_ad.lr = 1e-4
print("few shot method is {}".format(args_mind.few_shot_method))
print("------arguments-------")
for k, v in vars(args_mind).items():
print(k + ': ' + str(v))
for k, v in vars(args_ad).items():
print(k + ': ' + str(v))
# fix random seed
random.seed(args_mind.random_seed)
np.random.seed(args_mind.random_seed)
torch.manual_seed(args_mind.random_seed)
torch.cuda.manual_seed_all(args_mind.random_seed)
# model + different lr
model_mind = model_class(args_ad, args_mind)
embed_d = args_ad.embed_d
parameters = list(model_mind.model.parameters())
optimizer_mind = optim.Adam(parameters, lr=args_mind.lr, weight_decay=1e-8)
for iter_i in range(args_ad.train_iter_n):
model_mind.change_config(args_ad, args_mind)
model_mind.model.train()
print('epoch ' + str(iter_i) + ' ...' + "lr is {}".format(optimizer_mind.param_groups[0]['lr']))
if args_mind.few_shot_method in [2]:
# news classification
model_mind.change_config(args_ad, args_mind)
model_mind.model.train()
loss_record = 0
newsids = list(range(args_ad.P_n))
bz = 64
for i in range(args_mind.news_cls_iter):
for i in range(0, len(newsids), bz):
tem_ids = newsids[i: i+bz]
# TODO
loss = args_mind.loss_weight_align * model_mind.model.news_align_loss(tem_ids)
optimizer_mind.zero_grad()
loss.backward()
optimizer_mind.step()
loss_record += 1
if loss_record % 30 == 0:
print("news cls loss: {}".format(loss))
if args_mind.few_shot_method in [1,2]:
# adressa training samples
model_mind.change_config(args_ad, args_mind)
model_mind.model.train()
loss_record = 0
for mind_data_input in model_mind.train_iterator.load_data_from_file(model_mind.train_behaviors_file):
ctr_samples = model_mind.convert_data_for_hetGNN(mind_data_input)
c_out, p_out = model_mind.model(ctr_samples, 0)
# TODO
loss_mind = args_mind.loss_weight * tools.cross_entropy_loss(c_out, p_out, embed_d)
optimizer_mind.zero_grad()
loss_mind.backward()
optimizer_mind.step()
loss_record += 1
if loss_record % 60 == 0:
print("ad loss: {}".format(loss_mind))
wandb.log({"ad_loss": loss_mind})
if args_mind.few_shot.split("_")[-1] != '0shot':
loss_record = 0
# Few-shot setting: mind training samples
model_mind.change_config(args_mind, args_mind)
model_mind.model.train()
for mind_data_input in model_mind.train_iterator.load_data_from_file(model_mind.train_behaviors_file):
ctr_samples = model_mind.convert_data_for_hetGNN(mind_data_input)
c_out, p_out = model_mind.model(ctr_samples, 0)
loss_mind = tools.cross_entropy_loss(c_out, p_out, embed_d)
optimizer_mind.zero_grad()
loss_mind.backward()
optimizer_mind.step()
loss_record += 1
if loss_record % 1 == 0:
print("mind loss: {}".format(loss_mind))
wandb.log({"mind_loss": loss_mind})
# evaluation adressa
# model_mind.model.eval()
# a_embed, p_embed = model_mind.model([], 16)
# r = recommend_evaluator(model_mind.args, iter=iter_i)
# mind_auc = r.a_p_recommendation(a_embed, p_embed, None, model_mind)
############################ ############################
# evaluation
############################ ############################
# if iter_i % 3 == 0:
# print("Start evaluating...")
model_mind.change_config(args_mind, args_mind)
model_mind.model.eval()
a_embed, p_embed = model_mind.model([], 16)
r = recommend_evaluator(model_mind.args, iter=iter_i)
val_auc = r.a_p_recommendation(a_embed, p_embed, None, model_mind, best_valid_auc)
if val_auc >= best_valid_auc:
best_valid_auc = val_auc
if model_mind.args.save_emb:
# save mind embedding
model_mind.save_obj(a_embed, "vis_mind_user_embed2")
model_mind.save_obj(p_embed, 'vis_mind_news_embed2')
# save adressa embedding
model_mind.change_config(args_ad, args_mind)
model_mind.model.eval()
a_embed, p_embed = model_mind.model([], 16, mode='test')
model_mind.save_obj(a_embed, 'vis_ad_user_embed2')
model_mind.save_obj(p_embed, 'vis_ad_news_embed2')
############################ ############################
# evaluation
############################ ############################
# print("Start evaluating...")
# model_mind.model.eval()
# a_embed, p_embed = model_mind.model([], 16, mode='test')
# r = recommend_evaluator(model_mind.args, iter=args_ad.train_iter_n - 1)
# mind_auc = r.a_p_recommendation(a_embed, p_embed, None, model_mind)
# save adressa embedding
# if model_mind.args.save_emb:
# model_mind.save_obj(a_embed, 'vis_ad_user_embed')
# model_mind.save_obj(p_embed, 'vis_ad_news_embed')
#
# model_mind.change_config(args_mind, args_mind)
# triple_index = 16
# model_mind.model.eval()
# a_embed, p_embed = model_mind.model([], triple_index)
#
# # save mind embedding
# if model_mind.args.save_emb:
# model_mind.save_obj(a_embed, "vis_mind_user_embed")
# model_mind.save_obj(p_embed, 'vis_mind_news_embed')
#
# r = recommend_evaluator(model_mind.args, iter=args_ad.train_iter_n-1)
# mind_auc = r.a_p_recommendation(a_embed, p_embed, None, model_mind)