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prog_bpr_gru_spatial.py
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prog_bpr_gru_spatial.py
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#!/usr/bin/env python36
# -*- coding: utf-8 -*-
"""
Created on 13/03/2018 3:30 PM
@author: Tangrizzly
"""
from __future__ import print_function
from collections import OrderedDict
import time
import datetime
import cPickle
import numpy as np
import os
import random
from public.BPR import OboBpr
from public.GRU import OboGru
from public.CA_RNN import OboCARNN
from public.GRU_Spatial import OboSpatialGru
from public.Global_Best import GlobalBest
from public.Load_Data_by_length import load_data, fun_data_buys_masks
from public.Load_Data_by_length import fun_random_neg_masks_tra, fun_random_neg_masks_tes, fun_compute_dist_neg
from public.Load_Data_by_length import fun_compute_distance, fun_acquire_prob
from public.Valuate import fun_predict_auc_recall_map_ndcg, fun_save_best_and_losses
__docformat__ = 'restructedtext en'
WHOLE = './poidata/'
PATH_f = os.path.join(WHOLE, 'Foursquare/sequence')
PATH_g = os.path.join(WHOLE, 'Gowalla/sequence')
PATH = PATH_f
def exe_time(func):
def new_func(*args, **args2):
name = func.__name__
start = datetime.datetime.now()
print("-- {%s} start: @ %ss" % (name, start))
back = func(*args, **args2)
end = datetime.datetime.now()
print("-- {%s} start: @ %ss" % (name, start))
print("-- {%s} end: @ %ss" % (name, end))
total = (end - start).total_seconds()
print("-- {%s} total: @ %.3fs = %.3fh" % (name, total, total / 3600.0))
return back
return new_func
class Params(object):
def __init__(self, p=None):
if not p:
t = 't'
assert 't' == t or 'v' == t or 's' == t
p = OrderedDict(
[
('dataset', 'Foursquare.txt'),
# ('dataset', 'Gowalla.txt'),
('mode', 'test' if 't' == t else 'valid' if 'v' == t else 's'),
('load_epoch', 0),
('save_per_epoch', 100),
('split', -2 if 'v' == t else -1),
('at_nums', [5, 10, 15, 20]),
('epochs', 101),
('latent_size', 20),
('alpha', 0.01),
('lambda', 0.001),
('loss_weight', [0.5, 0.5]),
('dd', 200),
('UD', 40),
('mini_batch', 0),
('gru', 0),
('batch_size_train', 1),
('batch_size_test', 32),
])
for i in p.items():
print(i)
dist_num = int(p['UD'] * 1000 / p['dd']) # 1520 = 38*1000/25。idx=[0, 1519+1]
[(user_num, item_num), pois_cordis, (tra_buys, tes_buys), (tra_dist, tes_dist)] = \
load_data(os.path.join(PATH, p['dataset']), p['mode'], p['split'], p['dd'], dist_num)
tra_buys_masks, tra_dist_masks, tra_masks = fun_data_buys_masks(tra_buys, tra_dist, [item_num], [dist_num])
tes_buys_masks, tes_dist_masks, tes_masks = fun_data_buys_masks(tes_buys, tes_dist, [item_num], [dist_num])
tra_buys_neg_masks = fun_random_neg_masks_tra(item_num, tra_buys_masks)
tes_buys_neg_masks = fun_random_neg_masks_tes(item_num, tra_buys_masks, tes_buys_masks)
tra_dist_neg_masks = fun_compute_dist_neg(tra_buys_masks, tra_masks, tra_buys_neg_masks, pois_cordis, p['dd'], dist_num)
usrs_last_poi_to_all_intervals = fun_compute_distance(tra_buys_masks, tra_masks, pois_cordis, p['dd'], dist_num)
self.p = p
self.user_num, self.item_num, self.dist_num = user_num, item_num, dist_num
self.pois_cordis = pois_cordis
self.tra_buys_masks, self.tra_masks, self.tra_buys_neg_masks = tra_buys_masks, tra_masks, tra_buys_neg_masks
self.tes_buys_masks, self.tes_masks, self.tes_buys_neg_masks = tes_buys_masks, tes_masks, tes_buys_neg_masks
self.tra_dist_masks = tra_dist_masks
self.tes_dist_masks = tes_dist_masks
self.tra_dist_neg_masks = tra_dist_neg_masks
self.ulptai = usrs_last_poi_to_all_intervals
def build_model_one_by_one(self, flag=0):
"""
建立模型对象
:param flag: 参数变量、数据
:return:
"""
print('Building the model one_by_one ...')
p = self.p
size = p['latent_size']
if 0 == flag:
model = OboBpr(
train=[self.tra_buys_masks, self.tra_masks, self.tra_buys_neg_masks],
test= [self.tes_buys_masks, self.tes_masks, self.tes_buys_neg_masks],
alpha_lambda=[p['alpha'], p['lambda']],
n_user=self.user_num,
n_item=self.item_num,
n_in=size,
n_hidden=size)
elif 1 == flag:
model = OboGru(
train=[self.tra_buys_masks, self.tra_masks, self.tra_buys_neg_masks],
test= [self.tes_buys_masks, self.tes_masks, self.tes_buys_neg_masks],
alpha_lambda=[p['alpha'], p['lambda']],
n_user=self.user_num,
n_item=self.item_num,
n_in=size,
n_hidden=size)
elif 2 == flag:
model = OboSpatialGru(
train=[self.tra_buys_masks, self.tra_masks, self.tra_buys_neg_masks],
test= [self.tes_buys_masks, self.tes_masks, self.tes_buys_neg_masks],
dist= [self.tra_dist_masks, self.tes_dist_masks, self.tra_dist_neg_masks],
alpha_lambda=[p['alpha'], p['lambda']],
n_user=self.user_num,
n_item=self.item_num,
n_dists=[self.dist_num, 1.0*p['dd']/1000],
n_in=size, # 总的input是2 * size。
n_hidden=size)
else:
model = OboCARNN(
train=[self.tra_buys_masks, self.tra_masks, self.tra_buys_neg_masks],
test= [self.tes_buys_masks, self.tes_masks, self.tes_buys_neg_masks],
dist= [self.tra_dist_masks, self.tes_dist_masks, self.tra_dist_neg_masks],
alpha_lambda=[p['alpha'], p['lambda']],
n_user=self.user_num,
n_item=self.item_num,
n_dists=[self.dist_num, 1.0*p['dd']/1000],
n_in=size,
n_hidden=size,
ulptai=self.ulptai)
model_name = model.__class__.__name__
print('\t the current Class name is: {val}'.format(val=model_name))
return model, model_name
def compute_start_end(self, flag):
"""
获取mini-batch的各个start_end(np.array类型，一组连续的数值)
:param flag: 'train', 'test'
:return: 各个start_end组成的list
"""
assert flag in ['train', 'test', 'test_auc']
if 'train' == flag:
size = self.p['batch_size_train']
elif 'test' == flag:
size = self.p['batch_size_test'] # test: top-k and acquire user vector
else:
size = self.p['batch_size_test'] * 10 # test: auc
user_num = self.user_num
rest = (user_num % size) > 0
n_batches = np.minimum(user_num // size + rest, user_num)
batch_idxs = np.arange(n_batches, dtype=np.int32)
starts_ends = []
for bidx in batch_idxs:
start = bidx * size
end = np.minimum(start + size, user_num)
start_end = np.arange(start, end, dtype=np.int32)
starts_ends.append(start_end)
return batch_idxs, starts_ends
def train_valid_or_test(pas):
"""
主程序
:return:
"""
# 建立参数、数据、模型、模型最佳值
p = pas.p
model, model_name = pas.build_model_one_by_one(flag=p['gru'])
best = GlobalBest(at_nums=p['at_nums']) # 存放最优数据
_, starts_ends_tes = pas.compute_start_end(flag='test')
_, starts_ends_auc = pas.compute_start_end(flag='test_auc')
# 直接取出来部分变量，后边就不用加'pas.'了。
user_num, item_num, dist_num = pas.user_num, pas.item_num, pas.dist_num
tra_buys_masks, tra_masks, tra_buys_neg_masks = pas.tra_buys_masks, pas.tra_masks, pas.tra_buys_neg_masks
tes_buys_masks, tes_masks, tes_buys_neg_masks = pas.tes_buys_masks, pas.tes_masks, pas.tes_buys_neg_masks
dd = p['dd']
pois_cordis = pas.pois_cordis
ulptai = pas.ulptai
del pas
ini_epoch = 0
if 2 == p['gru']:
ini_epoch = p['load_epoch']
if p['load_epoch'] != 0:
print('Loading model ...')
m_path = './model/' + p['dataset'] + '/' + model_name + '_size' + \
str(p['latent_size']) + '_UD' + str(p['UD']) + '_dd' + str(p['dd']) + '_epoch' + str(
p['load_epoch'])
with open(m_path, 'rb') as f:
loaded_objects = cPickle.load(f)
model.load_params(loaded_objects)
ini_epoch = p['load_epoch'] + 1
# 主循环
losses = []
times0, times1, times2, times3 = [], [], [], []
for epoch in np.arange(ini_epoch, p['epochs']):
print("Epoch {val} ==================================".format(val=epoch))
if epoch > 0:
tra_buys_neg_masks = fun_random_neg_masks_tra(item_num, tra_buys_masks)
tes_buys_neg_masks = fun_random_neg_masks_tes(item_num, tra_buys_masks, tes_buys_masks)
if p['gru'] in [0, 1]:
model.update_neg_masks(tra_buys_neg_masks, tes_buys_neg_masks)
else:
tra_dist_neg_masks = fun_compute_dist_neg(tra_buys_masks, tra_masks, tra_buys_neg_masks, pois_cordis, dd, dist_num)
model.s_update_neg_masks(tra_buys_neg_masks, tes_buys_neg_masks, tra_dist_neg_masks)
# ----------------------------------------------------------------------------------------------------------
print("\tTraining ...")
t0 = time.time()
loss = 0.
ls = [0, 0]
total_ls = []
random.seed(str(123 + epoch))
user_idxs_tra = np.arange(user_num, dtype=np.int32)
random.shuffle(user_idxs_tra)
if 0 == p['gru']:
for uidx in user_idxs_tra:
tra = tra_buys_masks[uidx]
neg = tra_buys_neg_masks[uidx]
for i in np.arange(sum(tra_masks[uidx])):
loss += model.train(uidx, [tra[i], neg[i]])
elif 1 == p['gru'] or 3 == p['gru']:
for uidx in user_idxs_tra:
loss += model.train(uidx)
else:
for uidx in user_idxs_tra:
los, a, b, ls = model.train(uidx)
loss += los
a_b = [a, b]
a_b.extend(ls)
total_ls.append(a_b)
rnn_l2_sqr = model.l2.eval()
def cut2(x):
return '%0.2f' % x
print('\t\tsum_loss = {val} = {v1} + {v2}'.format(val=loss + rnn_l2_sqr, v1=loss, v2=rnn_l2_sqr))
losses.append('{v1}'.format(v1=int(loss + rnn_l2_sqr)))
# ls = model.loss_weight
print('\t\tloss_weight = {v1}, {v2}'.format(v1=ls[0], v2=ls[1]))
t1 = time.time()
times0.append(t1 - t0)
# ----------------------------------------------------------------------------------------------------------
print("\tPredicting ...")
if 0 == p['gru']:
model.update_trained_items()
model.update_trained_users()
elif 1 == p['gru']:
model.update_trained_items()
all_hus = []
for start_end in starts_ends_tes:
sub_all_hus = model.predict(start_end)
all_hus.extend(sub_all_hus)
model.update_trained_users(all_hus)
elif 2 == p['gru']:
model.update_trained_items()
model.update_trained_dists()
all_hus = []
all_sus = []
for start_end in starts_ends_tes:
[sub_all_hus, sub_all_sus] = model.predict(start_end)
all_hus.extend(sub_all_hus)
all_sus.extend(sub_all_sus)
probs = fun_acquire_prob(all_sus, ulptai, dist_num)
model.update_trained_users(all_hus)
model.update_prob(probs)
else:
model.update_trained_items()
model.update_trained_dists()
all_hus = []
for start_end in starts_ends_tes:
sub_all_hus = model.predict(start_end)
all_hus.extend(sub_all_hus)
model.update_trained_users(all_hus)
t2 = time.time()
times1.append(t2 - t1)
fun_predict_auc_recall_map_ndcg(
p, model, best, epoch, starts_ends_auc, starts_ends_tes, tes_buys_masks, tes_masks)
best.fun_print_best(epoch)
t3 = time.time()
times2.append(t3-t2)
print('\tavg. time (train, user, test): %0.0fs,' % np.average(times0),
'%0.0fs,' % np.average(times1), '%0.0fs' % np.average(times2),
'| alpha, lam: {v1}'.format(v1=', '.join([str(lam) for lam in [p['alpha'], p['lambda']]])),
'| model: {v1}'.format(v1=model_name))
# ----------------------------------------------------------------------------------------------------------
if epoch == p['epochs'] - 1:
print("\tBest and losses saving ...")
path = os.path.join(os.path.split(__file__)[0], '..', 'Results_best_and_losses', PATH.split('/')[-2])
fun_save_best_and_losses(path, model_name, epoch, p, best, losses)
if 2 == p['gru']:
size = p['latent_size']
fil_name = 'size' + str(size) + 'UD' + str(p['UD']) + 'dd' + str(p['dd']) + 'loss.txt'
fil = os.path.join(path, fil_name)
np.savetxt(fil, total_ls)
if 2 == p['gru'] and epoch % p['save_per_epoch'] == 0 and epoch != 0:
m_path = './model/' + p['dataset'] + '/' + model_name + '_size' + \
str(p['latent_size']) + '_UD' + str(p['UD']) + '_dd' + str(p['dd']) + '_epoch' + str(epoch)
with open(m_path, 'wb') as file:
save_model = [model.loss_weight.get_value(), model.wd.get_value(), model.lt.get_value(), model.di.get_value(),
model.ui.get_value(), model.wh.get_value(), model.bi.get_value(), model.vs.get_value(),
model.bs.get_value()]
cPickle.dump(save_model, file, protocol=cPickle.HIGHEST_PROTOCOL)
for i in p.items():
print(i)
print('\t the current Class name is: {val}'.format(val=model_name))
def cal_s(pas):
p = pas.p
model, model_name = pas.build_model_one_by_one(flag=p['gru'])
_, starts_ends_tes = pas.compute_start_end(flag='test')
user_num, dist_num = pas.user_num, pas.dist_num
ulptai = pas.ulptai
del pas
m_path = './model/' + p['dataset'] + '/' + model_name + '_size' +\
str(p['latent_size']) + '_UD' + str(p['UD']) + '_dd' + str(p['dd']) + '_epoch' + str(p['load_epoch'])
print('Loading model ...')
with open(m_path, 'rb') as f:
loaded_objects = cPickle.load(f)
model.load_params(loaded_objects)
print("\tPredicting ...")
model.update_trained_items()
model.update_trained_dists()
all_sus = []
for start_end in starts_ends_tes:
[_, sub_all_sus] = model.predict(start_end)
all_sus.extend(sub_all_sus)
np.save('./Lmdd/' + p['dataset'] + '_size' + str(p['latent_size']) + '_UD' + str(p['UD']) + '_dd' + str(p['dd']) + '_epoch' + str(p['load_epoch']) + 'last1',
all_sus)
@exe_time
def main():
pas = Params()
if pas.p['mode'] == 's':
cal_s(pas)
else:
train_valid_or_test(pas)
if '__main__' == __name__:
main()