FakeNewsMitigation/NearalDiffuseModel/run-script.py

import argparse
import math
import time
import numpy
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.optim as optim
import transformer.Constants as Constants
from transformer.Models import Decoder
from transformer.Optim import ScheduledOptim
from DataLoader_Feat import DataLoader
import pickle

CUDA = 1
parser = argparse.ArgumentParser()



def get_performance(crit, pred, gold):
    loss = crit(pred, gold.contiguous().view(-1))
    pred = pred.max(1)[1]
    

    gold = gold.contiguous().view(-1)
    n_correct = pred.data.eq(gold.data)
    n_correct = n_correct.masked_select(gold.ne(Constants.PAD).data).sum()
    return loss, n_correct

def closest_cluster(input_vector):
    min_result = float('-inf')
    min_index = -1
    for cluster_ind, cluster_vec in clusters_vecs.items():
        result = numpy.dot(cluster_vec, input_vector)
#        print("result:{0}".format(result))
        if min_result < result:
            min_result = result
            min_index = cluster_ind
    return min_index


def get_criterion(user_size):
    ''' With PAD token zero weight '''
    weight = torch.ones(user_size)
    weight[Constants.PAD] = 0
    weight[Constants.EOS] = 0
    return nn.CrossEntropyLoss(weight, size_average=False)


parser.add_argument('-epoch', type=int, default=75)

parser.add_argument('-batch_size', type=int, default=4)  # CHANGE 8->32
parser.add_argument('-d_model', type=int, default=1)
parser.add_argument('-d_inner_hid', type=int, default=64)
parser.add_argument('-d_k', type=int, default=64)
parser.add_argument('-d_v', type=int, default=64)
parser.add_argument('-window_size', type=int, default=3)

parser.add_argument('-finit', type=int, default=0)
parser.add_argument('-n_head', type=int, default=8)

parser.add_argument('-n_warmup_steps', type=int, default=1000)
parser.add_argument('-dropout', type=float, default=0.1)
parser.add_argument('-embs_share_weight', action='store_true')

parser.add_argument('-proj_share_weight', action='store_true')
parser.add_argument('-save_model', default='Lastfm_test')

parser.add_argument('-no_cuda', action='store_true')
# torch.cuda.set_device(1)
opt = parser.parse_args()
opt.cuda = not opt.no_cuda

opt.d_word_vec = opt.d_model
# ========= Preparing DataLoader =========#
train_data = DataLoader(use_valid=False, batch_size=opt.batch_size, cuda=opt.cuda)

# ========= Preparing Model =========#

opt.user_size = train_data.user_size

model = Decoder(
    opt.user_size,
    d_k=opt.d_k,
    d_v=opt.d_v,
    d_model=opt.d_model,
    d_word_vec=opt.d_word_vec,
    d_inner_hid=opt.d_inner_hid,
    n_head=opt.n_head,
    kernel_size=opt.window_size,
    dropout=opt.dropout)

optimizer = ScheduledOptim(
    optim.Adam(
        model.parameters(),
        betas=(0.9, 0.98), eps=1e-09),
    opt.d_model, opt.n_warmup_steps)

model.load_state_dict(torch.load(f"pa-our-twitterLastfm_test49.chkpt")["model"])
model.cuda(CUDA)
crit = get_criterion(train_data.user_size)
crit.cuda(CUDA)
model.eval()
total_loss = 0
n_total_words = 0
n_total_correct = 0

idx2vec = open('/media/external_3TB/3TB/ramezani/pmoini/Trial/data3/idx2vec.pickle', 'rb')
idx2vec = pickle.load(idx2vec)

#clusters_vecs = pickle.load(open('../DeepDiffuseCode/user_embedding/dim_160/clusters_vectors.p', 'rb'))
#user_to_cluster = pickle.load(open('../DeepDiffuseCode/user_embedding/dim_160/users_to_cluster.p', 'rb'))
#id_to_user = pickle.load(open('./data/idx2u.pickle', 'rb'))


for batch in tqdm(train_data, mininterval=2, desc='  - (Test)  -  ', leave=False):

    tgt = batch
    gold = tgt[:, 1:]

    h0 = torch.zeros(1, batch.size(0), 8 * opt.d_word_vec)
    if opt.cuda:
        h0 = h0.cuda(CUDA)
    


    pred, *_ = model(tgt, h0)
    loss, n_correct = get_performance(crit, pred, gold)
    # note keeping
    n_words = gold.data.ne(Constants.PAD).sum()
    n_total_words += n_words
    n_total_correct += n_correct
    # total_loss += loss.item()


print(n_total_correct / n_total_words)

#    total_loss = total_loss.data.cpu().numpy()
# n_total_correct = n_total_correct.data.cpu().numpy()
# n_total_words = n_total_words.data.cpu().numpy()