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interaction_sqlova.py
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# Adapted from SQLova script for interaction.
# @author: Ziyu Yao
# Oct 7th, 2020
#
import os, sys, argparse, re, json, pickle, math
from copy import deepcopy
from matplotlib.pylab import *
import torch.nn as nn
import torch
import torch.nn.functional as F
import random as python_random
# import torchvision.datasets as dsets
import numpy as np
import time, datetime, pytimeparse
import SQLova_model.bert.tokenization as tokenization
from SQLova_model.bert.modeling import BertConfig, BertModel
from SQLova_model.sqlova.utils.utils_wikisql import *
from SQLova_model.sqlova.model.nl2sql.wikisql_models import *
from SQLova_model.sqlnet.dbengine import DBEngine
from SQLova_model.agent import Agent
from SQLova_model.world_model import WorldModel
from SQLova_model.error_detector import *
from MISP_SQL.question_gen import QuestionGenerator
from SQLova_model.environment import UserSim, RealUser, ErrorEvaluator, GoldUserSim
from user_study_utils import *
np.set_printoptions(precision=3)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
EARLY_STOP_EPOCH_STAGE1=10
EARLY_STOP_EPOCH_STAGE2=5
EARLY_THRESHOLD=30000
def construct_hyper_param(parser):
parser.add_argument("--bS", default=1, type=int, help="Batch size")
parser.add_argument("--model_type", default='Seq2SQL_v1', type=str,
help="Type of model.")
parser.add_argument('--seed', type=int, default=0, help='Random seed.')
parser.add_argument('--model_dir', type=str, required=True, help='Which folder to save the model checkpoints.')
# 1.2 BERT Parameters
parser.add_argument("--vocab_file",
default='vocab.txt', type=str,
help="The vocabulary file that the BERT model was trained on.")
parser.add_argument("--max_seq_length",
default=222, type=int, # Set based on maximum length of input tokens.
help="The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded.")
parser.add_argument("--num_target_layers",
default=2, type=int,
help="The Number of final layers of BERT to be used in downstream task.")
parser.add_argument('--lr_bert', default=1e-5, type=float, help='BERT model learning rate.')
parser.add_argument('--no_pretraining', action='store_true', help='Use BERT pretrained model')
parser.add_argument("--bert_type_abb", default='uS', type=str,
help="Type of BERT model to load. e.g.) uS, uL, cS, cL, and mcS")
# 1.3 Seq-to-SQL module parameters
parser.add_argument('--lS', default=2, type=int, help="The number of LSTM layers.")
parser.add_argument('--dr', default=0.3, type=float, help="Dropout rate.")
parser.add_argument('--lr', default=1e-3, type=float, help="Learning rate.")
parser.add_argument("--hS", default=100, type=int, help="The dimension of hidden vector in the seq-to-SQL module.")
# 1.4 Execution-guided decoding beam-size. It is used only in test.py
# parser.add_argument('--EG',
# default=False,
# action='store_true',
# help="If present, Execution guided decoding is used in test.")
# parser.add_argument('--beam_size', # used for non-interactive decoding only
# type=int,
# default=4,
# help="The size of beam for smart decoding")
# Job setting
parser.add_argument('--job', default='test_w_interaction', choices=['test_w_interaction', 'online_learning'],
help='Set the job. For parser pretraining, see other scripts.')
# Data setting
parser.add_argument('--data', default='dev', choices=['dev', 'test', 'user_study', 'online'],
help='which dataset to test.')
parser.add_argument('--data_seed', type=int, default=0, choices=[0, 10, 100],
help='Seed for simulated online data order.')
# Model (initialization/testing) setting
parser.add_argument('--setting', default='full_train',
choices=['full_train', 'online_pretrain_1p', 'online_pretrain_5p', 'online_pretrain_10p'],
help='Model setting; checkpoints will be loaded accordingly.')
# for interaction
parser.add_argument('--num_options', type=str, default='3',
help='[INTERACTION] Number of options (inf or an int number).')
parser.add_argument('--user', type=str, default='sim', choices=['sim', 'gold_sim', 'real'],
help='[INTERACTION] The user setting.')
parser.add_argument('--err_detector', type=str, default='any',
help='[INTERACTION] The error detector: '
'(1) prob=x for using policy probability threshold;'
'(2) stddev=x for using Bayesian dropout threshold (need to set --dropout and --passes);'
'(3) any for querying about every policy action;'
'(4) perfect for using a simulated perfect detector.')
parser.add_argument('--friendly_agent', type=int, default=0, choices=[0, 1],
help='[INTERACTION] If 1, the agent will not trigger further interactions '
'if any wrong decision is not resolved during parsing.')
parser.add_argument('--output_path', type=str, default='temp', help='[INTERACTION] Where to save outputs.')
parser.add_argument('--dropout', type=float, default=0.0,
help='[INTERACTION] Dropout rate for Bayesian dropout-based uncertainty analysis.')
parser.add_argument('--passes', type=int, default=1,
help='[INTERACTION] Number of decoding passes for Bayesian dropout-based uncertainty analysis.')
parser.add_argument('--ask_structure', type=int, default=0, choices=[0, 1],
help='[INTERACTION] Set to True to allow questions about query structure (WHERE clause).')
# for online learning
parser.add_argument('--update_iter', default=1000, type=int,
help="[LEARNING] Number of iterations per update.")
parser.add_argument('--supervision', default='misp_neil',
choices=['full_expert', 'misp_neil', 'misp_neil_pos', 'misp_neil_perfect',
'bin_feedback', 'bin_feedback_expert',
'self_train', 'self_train_0.5'],
help='[LEARNING] Online learning supervision based on different algorithms.')
parser.add_argument('--start_iter', default=0, type=int, help='[LEARNING] Iteration to start.')
parser.add_argument('--end_iter', default=-1, type=int, help='[LEARNING] Iteration to end.')
parser.add_argument('--auto_iter', default=0, type=int, choices=[0, 1],
help='[LEARNING] If 1, unless args.start_iter > 0 is specified, the system will automatically '
'search for `start_iter` given the aggregated training data. '
'Only applies to args.supervision = misp_neil/bin_feedback(_expert).')
args = parser.parse_args()
map_bert_type_abb = {'uS': 'uncased_L-12_H-768_A-12',
'uL': 'uncased_L-24_H-1024_A-16',
'cS': 'cased_L-12_H-768_A-12',
'cL': 'cased_L-24_H-1024_A-16',
'mcS': 'multi_cased_L-12_H-768_A-12'}
args.bert_type = map_bert_type_abb[args.bert_type_abb]
print(f"BERT-type: {args.bert_type}")
# Decide whether to use lower_case.
if args.bert_type_abb == 'cS' or args.bert_type_abb == 'cL' or args.bert_type_abb == 'mcS':
args.do_lower_case = False
else:
args.do_lower_case = True
# Seeds for random number generation
if args.data == "online":
print("## online data seed: %d" % args.data_seed)
print("## random seed: %d" % args.seed)
python_random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
# args.toy_model = not torch.cuda.is_available()
args.toy_model = False
args.toy_size = 12
print("Testing data: {}".format(args.data))
return args
def get_bert(BERT_PT_PATH, bert_type, do_lower_case, no_pretraining):
bert_config_file = os.path.join(BERT_PT_PATH, f'bert_config_{bert_type}.json')
vocab_file = os.path.join(BERT_PT_PATH, f'vocab_{bert_type}.txt')
init_checkpoint = os.path.join(BERT_PT_PATH, f'pytorch_model_{bert_type}.bin')
bert_config = BertConfig.from_json_file(bert_config_file)
tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_file, do_lower_case=do_lower_case)
bert_config.print_status()
model_bert = BertModel(bert_config)
if no_pretraining:
pass
else:
model_bert.load_state_dict(torch.load(init_checkpoint, map_location='cpu'))
print("Load pre-trained parameters.")
model_bert.to(device)
return model_bert, tokenizer, bert_config
def get_models(args, BERT_PT_PATH, trained=False, path_model_bert=None, path_model=None):
# some constants
agg_ops = ['', 'MAX', 'MIN', 'COUNT', 'SUM', 'AVG']
cond_ops = ['=', '>', '<', 'OP'] # do not know why 'OP' required. Hence,
print(f"Batch_size = {args.bS}")
# print(f"Batch_size = {args.bS * args.accumulate_gradients}")
print(f"BERT parameters:")
print(f"learning rate: {args.lr_bert}")
# print(f"Fine-tune BERT: {args.fine_tune}")
# Get BERT
model_bert, tokenizer, bert_config = get_bert(BERT_PT_PATH, args.bert_type, args.do_lower_case,
args.no_pretraining)
args.iS = bert_config.hidden_size * args.num_target_layers # Seq-to-SQL input vector dimenstion
# Get Seq-to-SQL
n_cond_ops = len(cond_ops)
n_agg_ops = len(agg_ops)
print(f"Seq-to-SQL: the number of final BERT layers to be used: {args.num_target_layers}")
print(f"Seq-to-SQL: the size of hidden dimension = {args.hS}")
print(f"Seq-to-SQL: LSTM encoding layer size = {args.lS}")
print(f"Seq-to-SQL: dropout rate = {args.dr}")
print(f"Seq-to-SQL: learning rate = {args.lr}")
model = Seq2SQL_v1(args.iS, args.hS, args.lS, args.dr, n_cond_ops, n_agg_ops)
model = model.to(device)
if trained:
assert path_model_bert != None
assert path_model != None
if torch.cuda.is_available():
res = torch.load(path_model_bert)
else:
res = torch.load(path_model_bert, map_location='cpu')
model_bert.load_state_dict(res['model_bert'])
model_bert.to(device)
if torch.cuda.is_available():
res = torch.load(path_model)
else:
res = torch.load(path_model, map_location='cpu')
model.load_state_dict(res['model'])
return model, model_bert, tokenizer, bert_config
def get_opt(model, model_bert, fine_tune):
if fine_tune:
opt = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=0)
opt_bert = torch.optim.Adam(filter(lambda p: p.requires_grad, model_bert.parameters()),
lr=args.lr_bert, weight_decay=0)
else:
opt = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=0)
opt_bert = None
return opt, opt_bert
def report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_q, pr_ans,
cnt_list, current_cnt):
cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x = current_cnt
print(f'cnt = {cnt} / {cnt_tot} ===============================')
print(f'headers: {hds}')
print(f'nlu: {nlu}')
# print(f's_sc: {s_sc[0]}')
# print(f's_sa: {s_sa[0]}')
# print(f's_wn: {s_wn[0]}')
# print(f's_wc: {s_wc[0]}')
# print(f's_wo: {s_wo[0]}')
# print(f's_wv: {s_wv[0][0]}')
print(f'===============================')
print(f'g_sc : {g_sc}')
print(f'pr_sc: {pr_sc}')
print(f'g_sa : {g_sa}')
print(f'pr_sa: {pr_sa}')
print(f'g_wn : {g_wn}')
print(f'pr_wn: {pr_wn}')
print(f'g_wc : {g_wc}')
print(f'pr_wc: {pr_wc}')
print(f'g_wo : {g_wo}')
print(f'pr_wo: {pr_wo}')
print(f'g_wv : {g_wv}')
# print(f'pr_wvi: {pr_wvi}')
print('g_wv_str:', g_wv_str)
print('p_wv_str:', pr_wv_str)
print(f'g_sql_q: {g_sql_q}')
print(f'pr_sql_q: {pr_sql_q}')
print(f'g_ans: {g_ans}')
print(f'pr_ans: {pr_ans}')
print(f'--------------------------------')
print(cnt_list)
print(f'acc_lx = {cnt_lx/cnt:.3f}, acc_x = {cnt_x/cnt:.3f}\n',
f'acc_sc = {cnt_sc/cnt:.3f}, acc_sa = {cnt_sa/cnt:.3f}, acc_wn = {cnt_wn/cnt:.3f}\n',
f'acc_wc = {cnt_wc/cnt:.3f}, acc_wo = {cnt_wo/cnt:.3f}, acc_wv = {cnt_wv/cnt:.3f}')
print(f'===============================')
def print_result(epoch, acc, dname):
ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x = acc
print(f'{dname} results ------------')
print(
f" Epoch: {epoch}, ave loss: {ave_loss}, acc_sc: {acc_sc:.3f}, acc_sa: {acc_sa:.3f}, acc_wn: {acc_wn:.3f}, \
acc_wc: {acc_wc:.3f}, acc_wo: {acc_wo:.3f}, acc_wvi: {acc_wvi:.3f}, acc_wv: {acc_wv:.3f}, acc_lx: {acc_lx:.3f}, acc_x: {acc_x:.3f}"
)
def real_user_interaction(data_loader, data_table, user, agent, tokenizer,
max_seq_length, num_target_layers, path_db, save_path):
dset_name = "test"
if os.path.isfile(save_path):
saved_results = json.load(open(save_path, "r"))
interaction_records = saved_results['interaction_records']
count_exit = saved_results['count_exit']
time_spent = datetime.timedelta(seconds=pytimeparse.parse(saved_results['time_spent']))
st_pos = saved_results['st']
current_cnt = eval(saved_results['current_cnt'])
[cnt_tot, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x] = current_cnt
else:
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
interaction_records = {}
count_exit = 0
time_spent = datetime.timedelta()
st_pos = 0
cnt_tot = 1
cnt = 0
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
for iB, t in enumerate(data_loader):
assert len(t) == 1
if cnt < st_pos:
cnt += 1
continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, data_table, no_hs_t=True, no_sql_t=True)
g_sql_q = generate_sql_q(sql_i, tb)
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi_corenlp = get_g_wvi_corenlp(t)
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(agent.world_model.bert_config, agent.world_model.model_bert, tokenizer,
nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx, g_wvi_corenlp)
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
os.system('clear') # clear screen
print_header(len(data_loader.dataset) - cnt) # interface header
print(bcolors.BOLD + "Suppose you are given a table with the following " +
bcolors.BLUE + "header" + bcolors.ENDC +
bcolors.BOLD + ":" + bcolors.ENDC)
user.show_table(t[0]['table_id']) # print table
print(bcolors.BOLD + "\nAnd you want to answer the following " +
bcolors.PINK + "question" + bcolors.ENDC +
bcolors.BOLD + " based on this table:" + bcolors.ENDC)
print(bcolors.PINK + bcolors.BOLD + t[0]['question'] + bcolors.ENDC + "\n")
print(bcolors.BOLD + "To help you get the answer automatically,"
" the system has the following yes/no questions for you."
"\n(When no question prompts, please " +
bcolors.GREEN + "continue" + bcolors.ENDC +
bcolors.BOLD + " to the next case)\n" + bcolors.ENDC)
start_signal = input(bcolors.BOLD + "Ready? please press '" +
bcolors.GREEN + "Enter" + bcolors.ENDC + bcolors.BOLD + "' to start!" + bcolors.ENDC)
while start_signal != "":
start_signal = input(bcolors.BOLD + "Ready? please press '" +
bcolors.GREEN + "Enter" + bcolors.ENDC + bcolors.BOLD + "' to start!" + bcolors.ENDC)
start_time = datetime.datetime.now()
# init decode
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
input_item = [tb, nlu_t, nlu, hds]
else:
input_item = [wemb_n, l_n, wemb_h, l_hpu, l_hs, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu]
init_hyp = agent.world_model.decode(input_item, dec_beam_size=1, bool_verbal=False)[0]
# interaction
g_sql = sql_i[0]
g_sql["g_wvi"] = g_wvi[0]
hyp, bool_exit = agent.real_user_interactive_parsing_session(
user, input_item, g_sql, init_hyp, bool_verbal=False)
print("\nPredicted SQL: {}\n".format(hyp.sql))
per_time_spent = datetime.datetime.now() - start_time
time_spent += per_time_spent
print("Your time spent: {}".format(per_time_spent))
if bool_exit:
count_exit += 1
# post survey
print("-" * 50)
print("Post-study Survey: ")
bool_unclear = input("Is the " + bcolors.BOLD + bcolors.PINK + "initial question" +
bcolors.ENDC + " clear?\nPlease enter y/n: ")
while bool_unclear not in {'y', 'n'}:
bool_unclear = input("Is the " + bcolors.BOLD + bcolors.PINK + "initial question" +
bcolors.ENDC + " clear?\nPlease enter y/n: ")
print("-" * 50)
pr_sc = [hyp.sql_i['sel']]
pr_sa = [hyp.sql_i['agg']]
pr_wn = [len(hyp.sql_i['conds'])]
pr_wc = [[col for col, _, _ in hyp.sql_i['conds']]]
pr_wo = [[op for _, op, _ in hyp.sql_i['conds']]]
pr_sql_i = [hyp.sql_i]
pr_sql_q = [hyp.sql]
# Follosing variables are just for the consistency with no-EG case.
pr_wvi = None # not used
pr_wv_str = None
pr_wv_str_wp = None
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_wvi1_list, \
cnt_lx1_list, cnt_x1_list, cnt_list1, g_ans, pr_ans = agent.evaluation(
[pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, pr_sql_i], [g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, sql_i], engine, tb)
# save interaction records
record = {'nl': t[0]['question'], 'true_sql': g_sql_q[0], 'true_sql_i': "{}".format(sql_i[0]),
'init_sql': init_hyp.sql, 'init_sql_i': "{}".format(init_hyp.sql_i),
'sql': hyp.sql, 'sql_i': "{}".format(hyp.sql_i),
'dec_seq': "{}".format(hyp.dec_seq), 'tag_seq': "{}".format(hyp.tag_seq),
'logprob': "{}".format(hyp.logprob), #test time without dropout
'lx_correct': int(sum(cnt_lx1_list)), 'x_correct': int(sum(cnt_x1_list)),
'exit': bool_exit, 'q_counter': user.q_counter,
'questioned_indices': user.questioned_pointers,
'questioned_tags': "{}".format(user.questioned_tags),
'per_time_spent': str(per_time_spent), 'bool_unclear':bool_unclear,
'feedback_records': "{}".format(user.feedback_records),
'undo_semantic_units': "{}".format(user.undo_semantic_units),
'idx': iB}
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
record.update({'logprob_list': "{}".format(hyp.logprob_list),
'test_tag_seq': "{}".format(hyp.test_tag_seq)})
# interaction_records.append(record)
interaction_records[cnt] = record
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
current_cnt = [cnt_tot, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
cnt += 1
print("Saving records...")
json.dump({'interaction_records': interaction_records,
'current_cnt': "{}".format(current_cnt),
'st': cnt, 'time_spent': str(time_spent),
'count_exit': count_exit},
open(save_path, "w"), indent=4)
end_signal = input(bcolors.GREEN + bcolors.BOLD +
"Next? Press 'Enter' to continue, Ctrl+C to quit." + bcolors.ENDC)
if end_signal != "":
return
print(bcolors.RED + bcolors.BOLD + "Congratulations! You have completed all your task!" + bcolors.ENDC)
print("Your average time spent: {}".format((time_spent / len(interaction_records))))
print("You exited %d times." % count_exit)
def interaction(data_loader, data_table, user, agent, tokenizer,
max_seq_length, num_target_layers, detail=False, st_pos=0, cnt_tot=1,
path_db=None, dset_name='test', wikisql_sample_ids=None, bool_interaction=True):
ave_loss = 0
cnt = 0
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
cnt_list = []
results = []
interaction_records = []
count_exit = 0
time_spent = 0.
count_failure = 0
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
for iB, t in enumerate(data_loader):
if wikisql_sample_ids is not None and iB not in wikisql_sample_ids:
continue
cnt += len(t)
if cnt < st_pos:
continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, data_table, no_hs_t=True, no_sql_t=True)
g_sql_q = generate_sql_q(sql_i, tb)
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi_corenlp = get_g_wvi_corenlp(t)
start_time = time.time()
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(agent.world_model.bert_config, agent.world_model.model_bert, tokenizer,
nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
try:
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx, g_wvi_corenlp)
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
count_failure += 1
results1 = {}
results1["error"] = "Skip happened"
results1["nlu"] = nlu[0]
results1["table_id"] = tb[0]["id"]
results.append(results1)
print("## Failure %d" % count_failure)
interaction_records.append({'nl': t[0]['question'], 'true_sql': g_sql_q[0],
'true_sql_i': "{}".format(sql_i[0]), "q_counter": 0,
"questioned_indices": []})
continue
print("\n" + "#" * 50)
print("NL input: {}\nTrue SQL: {}".format(t[0]['question'], g_sql_q[0]))
# init decode
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
input_item = [tb, nlu_t, nlu, hds]
else:
input_item = [wemb_n, l_n, wemb_h, l_hpu, l_hs, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu]
hyp = agent.world_model.decode(input_item, dec_beam_size=1, bool_verbal=False)[0]
print("## time spent per decode: {:.3f}".format(time.time() - start_time))
print("-" * 50 + "\nBefore interaction: \ninitial SQL: {}".format(hyp.sql))
Hypothesis.print_hypotheses([hyp])
pr_sc = [hyp.sql_i['sel']]
pr_sa = [hyp.sql_i['agg']]
pr_wn = [len(hyp.sql_i['conds'])]
pr_wc = [[col for col, _, _ in hyp.sql_i['conds']]]
pr_wo = [[op for _, op, _ in hyp.sql_i['conds']]]
pr_sql_i = [hyp.sql_i]
pr_wvi = None # not used
print("initial evaluation: ")
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_wvi1_list, \
cnt_lx1_list, cnt_x1_list, cnt_list1, g_ans, pr_ans = agent.evaluation(
[pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, pr_sql_i],
[g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, sql_i], engine, tb, bool_verbal=True)
if not bool_interaction:
record = {'nl': t[0]['question'], 'true_sql': g_sql_q[0], 'true_sql_i': "{}".format(sql_i[0]),
'sql': "{}".format(hyp.sql), 'sql_i': "{}".format(hyp.sql_i),
'dec_seq': "{}".format(hyp.dec_seq), 'tag_seq': "{}".format(hyp.tag_seq),
'logprob': "{}".format(hyp.logprob),
'lx_correct': int(sum(cnt_lx1_list)), 'x_correct': int(sum(cnt_x1_list)),
"q_counter": 0, "questioned_indices": []}
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
record.update({'logprob_list': "{}".format(hyp.logprob_list),
'test_tag_seq': "{}".format(hyp.test_tag_seq)})
interaction_records.append(record)
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
# report
if detail:
pr_wv_str = None
current_cnt = [cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_i, pr_ans,
cnt_list1, current_cnt)
continue
# interaction
g_sql = sql_i[0]
g_sql["g_wvi"] = g_wvi[0]
hyp, bool_exit = agent.interactive_parsing_session(user, input_item, g_sql, hyp, bool_verbal=False)
print("-" * 50 + "\nAfter interaction:\nfinal SQL: {}".format(hyp.sql))
Hypothesis.print_hypotheses([hyp])
print("final evaluation: ")
# Saving for the official evaluation later.
results1 = {}
results1["query"] = hyp.sql_i
results1["table_id"] = tb[0]["id"]
results1["nlu"] = nlu[0]
results.append(results1)
pr_sc = [hyp.sql_i['sel']]
pr_sa = [hyp.sql_i['agg']]
pr_wn = [len(hyp.sql_i['conds'])]
pr_wc = [[col for col, _, _ in hyp.sql_i['conds']]]
pr_wo = [[op for _, op, _ in hyp.sql_i['conds']]]
pr_sql_i = [hyp.sql_i]
pr_sql_q = [hyp.sql]
# Follosing variables are just for the consistency with no-EG case.
pr_wvi = None # not used
pr_wv_str = None
pr_wv_str_wp = None
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_wvi1_list, \
cnt_lx1_list, cnt_x1_list, cnt_list1, g_ans, pr_ans = agent.evaluation(
[pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, pr_sql_i],
[g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, sql_i], engine, tb, bool_verbal=True)
# save interaction records
record = {'nl': t[0]['question'], 'true_sql': g_sql_q[0], 'true_sql_i': "{}".format(sql_i[0]),
'sql': hyp.sql, 'sql_i': "{}".format(hyp.sql_i),
'dec_seq': "{}".format(hyp.dec_seq), 'tag_seq': "{}".format(hyp.tag_seq),
'logprob': "{}".format(hyp.logprob), #test time without dropout
'lx_correct': int(sum(cnt_lx1_list)), 'x_correct': int(sum(cnt_x1_list)),
'exit': bool_exit, 'q_counter': user.q_counter,
'questioned_indices': user.questioned_pointers,
'questioned_tags': "{}".format(user.questioned_tags)}
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
record.update({'logprob_list': "{}".format(hyp.logprob_list),
'test_tag_seq': "{}".format(hyp.test_tag_seq)})
interaction_records.append(record)
time_spent += (time.time() - start_time)
if bool_exit:
count_exit += 1
# stat
ave_loss += 0.
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
current_cnt = [cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
cnt_list.append(cnt_list1)
# report
if detail:
report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_q, pr_ans,
cnt_list1, current_cnt)
ave_loss /= cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wn = cnt_wn / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wvi = cnt_wvi / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
if not bool_interaction:
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
return acc, results, cnt_list, interaction_records
# stats
q_count = sum([item['q_counter'] for item in interaction_records])
dist_q_count = sum([len(set(item['questioned_indices'])) for item in interaction_records])
print("#questions: {}, #questions per example: {:.3f} (exclude options: {:.3f}).".format(
q_count, q_count * 1.0 / len(interaction_records), dist_q_count * 1.0 / len(interaction_records)))
print("#exit: {}".format(count_exit))
print("Avg time spent: {:.3f}".format((time_spent / len(interaction_records))))
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
return acc, results, cnt_list, interaction_records
def train_fast(train_loader, train_table, model, model_bert, opt, bert_config, tokenizer,
max_seq_length, num_target_layers, accumulate_gradients=1, check_grad=True,
st_pos=0, opt_bert=None, path_db=None, dset_name='train'):
model.train()
model_bert.train()
ave_loss = 0
cnt = 0 # count the # of examples
cnt_sc = 0 # count the # of correct predictions of select column
cnt_sa = 0 # of selectd aggregation
cnt_wn = 0 # of where number
cnt_wc = 0 # of where column
cnt_wo = 0 # of where operator
cnt_wv = 0 # of where-value
cnt_wvi = 0 # of where-value index (on question tokens)
cnt_lx = 0 # of logical form acc
cnt_x = 0 # of execution acc
# # Engine for SQL querying.
# engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
for iB, t in enumerate(train_loader):
cnt += len(t)
if cnt < st_pos:
continue
if len(t[0]) == 25:
input_ids, input_mask, segment_ids, tokens, tb, sql_i, hds, i_nlu, i_hds, l_n, l_hpu_batch, l_hs, \
nlu, nlu_t, nlu_tt, t_to_tt_idx, tt_to_t_idx, g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wvi, g_wvi_corenlp = \
list(zip(*t))
weight_sa = weight_sc = weight_wn = weight_wc = weight_wo = weight_wvi = None
else:
assert len(t[0]) == 31
input_ids, input_mask, segment_ids, tokens, tb, sql_i, hds, i_nlu, i_hds, l_n, l_hpu_batch, l_hs, \
nlu, nlu_t, nlu_tt, t_to_tt_idx, tt_to_t_idx, g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wvi, g_wvi_corenlp, \
weight_sc, weight_sa, weight_wn, weight_wc, weight_wo, weight_wvi = \
list(zip(*t))
l_hpu = [hpu1 for l_hpu1 in l_hpu_batch for hpu1 in l_hpu1]
# bert encoding
all_input_ids = torch.tensor(input_ids, dtype=torch.long).to(device)
all_input_mask = torch.tensor(input_mask, dtype=torch.long).to(device)
all_segment_ids = torch.tensor(segment_ids, dtype=torch.long).to(device)
wemb_n, wemb_h = get_wemb_bert_fast(bert_config, model_bert, i_hds, l_n, l_hpu, l_hs,
all_input_ids, all_segment_ids, all_input_mask,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
# score
s_sc, s_sa, s_wn, s_wc, s_wo, s_wv = model(wemb_n, l_n, wemb_h, l_hpu, l_hs,
g_sc=g_sc, g_sa=g_sa, g_wn=g_wn, g_wc=g_wc, g_wvi=g_wvi)
# Calculate loss & step
loss = Loss_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi,
weight_sc=weight_sc, weight_sa=weight_sa, weight_wn=weight_wn,
weight_wc=weight_wc, weight_wo=weight_wo, weight_wvi=weight_wvi)
# Calculate gradient
if iB % accumulate_gradients == 0: # mode
# at start, perform zero_grad
if opt:
opt.zero_grad()
if opt_bert:
opt_bert.zero_grad()
loss.backward()
if accumulate_gradients == 1:
if opt:
opt.step()
if opt_bert:
opt_bert.step()
elif iB % accumulate_gradients == (accumulate_gradients - 1):
# at the final, take step with accumulated graident
loss.backward()
if opt:
opt.step()
if opt_bert:
opt_bert.step()
else:
# at intermediate stage, just accumulates the gradients
loss.backward()
# Prediction
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi = pred_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, )
pr_wv_str, pr_wv_str_wp = convert_pr_wvi_to_string(pr_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
# Sort pr_wc:
# Sort pr_wc when training the model as pr_wo and pr_wvi are predicted using ground-truth where-column (g_wc)
# In case of 'dev' or 'test', it is not necessary as the ground-truth is not used during inference.
pr_wc_sorted = sort_pr_wc(pr_wc, g_wc)
pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_wn, pr_wc_sorted, pr_wo, pr_wv_str, nlu)
# Cacluate accuracy
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, \
cnt_wc1_list, cnt_wo1_list, \
cnt_wvi1_list, cnt_wv1_list = get_cnt_sw_list(g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi,
sql_i, pr_sql_i,
mode='train')
cnt_lx1_list = get_cnt_lx_list(cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list,
cnt_wo1_list, cnt_wv1_list)
# lx stands for logical form accuracy
# # Execution accuracy test.
# cnt_x1_list, g_ans, pr_ans = get_cnt_x_list(engine, tb, g_sc, g_sa, sql_i, pr_sc, pr_sa, pr_sql_i)
# statistics
ave_loss += loss.item()
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_lx += sum(cnt_lx1_list)
# cnt_x += sum(cnt_x1_list)
ave_loss /= cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wn = cnt_wn / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wvi = cnt_wv / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
aux_out = 1
return acc, aux_out
def test_fast(data_loader, data_table, model, model_bert, bert_config, tokenizer, max_seq_length,
num_target_layers, detail=False, st_pos=0, cnt_tot=1, EG=False, beam_size=4,
path_db=None, dset_name='test', bool_ex=False):
model.eval()
model_bert.eval()
ave_loss = 0
cnt = 0
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
cnt_list = []
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
results = []
for iB, t in enumerate(data_loader):
cnt += len(t)
if cnt < st_pos:
continue
input_ids, input_mask, segment_ids, tokens, tb, sql_i, hds, i_nlu, i_hds, l_n, l_hpu_batch, l_hs, \
nlu, nlu_t, nlu_tt, t_to_tt_idx, tt_to_t_idx, g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wvi, g_wvi_corenlp = \
list(zip(*t))
l_hpu = [hpu1 for l_hpu1 in l_hpu_batch for hpu1 in l_hpu1]
try:
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
for b in range(len(nlu)):
results1 = {}
results1["error"] = "Skip happened"
results1["nlu"] = nlu[b]
results1["table_id"] = tb[b]["id"]
results.append(results1)
continue
# bert encoding
all_input_ids = torch.tensor(input_ids, dtype=torch.long).to(device)
all_input_mask = torch.tensor(input_mask, dtype=torch.long).to(device)
all_segment_ids = torch.tensor(segment_ids, dtype=torch.long).to(device)
wemb_n, wemb_h = get_wemb_bert_fast(bert_config, model_bert, i_hds, l_n, l_hpu, l_hs,
all_input_ids, all_segment_ids, all_input_mask,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
# model specific part
# score
if not EG:
# No Execution guided decoding
s_sc, s_sa, s_wn, s_wc, s_wo, s_wv = model(wemb_n, l_n, wemb_h, l_hpu, l_hs)
# get loss & step
loss = Loss_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi)
# prediction
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi = pred_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, )
pr_wv_str, pr_wv_str_wp = convert_pr_wvi_to_string(pr_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
# g_sql_i = generate_sql_i(g_sc, g_sa, g_wn, g_wc, g_wo, g_wv_str, nlu)
pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, nlu)
else:
# Execution guided decoding
prob_sca, prob_w, prob_wn_w, pr_sc, pr_sa, pr_wn, pr_sql_i = model.beam_forward(wemb_n, l_n, wemb_h, l_hpu,
l_hs, engine, tb,
nlu_t, nlu_tt,
tt_to_t_idx, nlu,
beam_size=beam_size)
# sort and generate
pr_wc, pr_wo, pr_wv, pr_sql_i = sort_and_generate_pr_w(pr_sql_i)
# Follosing variables are just for the consistency with no-EG case.
pr_wvi = None # not used
pr_wv_str = None
pr_wv_str_wp = None
loss = torch.tensor([0])
g_sql_q = generate_sql_q(sql_i, tb)
pr_sql_q = generate_sql_q(pr_sql_i, tb)
# Saving for the official evaluation later.
for b, pr_sql_i1 in enumerate(pr_sql_i):
results1 = {}
results1["query"] = pr_sql_i1
results1["table_id"] = tb[b]["id"]
results1["nlu"] = nlu[b]
results.append(results1)
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, \
cnt_wc1_list, cnt_wo1_list, \
cnt_wvi1_list, cnt_wv1_list = get_cnt_sw_list(g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi,
sql_i, pr_sql_i,
mode='test')
cnt_lx1_list = get_cnt_lx_list(cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list,
cnt_wo1_list, cnt_wv1_list)
# Execution accura y test
cnt_x1_list = []
g_ans = pr_ans = None
# lx stands for logical form accuracy
# Execution accuracy test.
if bool_ex:
cnt_x1_list, g_ans, pr_ans = get_cnt_x_list(engine, tb, g_sc, g_sa, sql_i, pr_sc, pr_sa, pr_sql_i)
# stat
ave_loss += loss.item()
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
current_cnt = [cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
cnt_list1 = [cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_lx1_list,
cnt_x1_list]
cnt_list.append(cnt_list1)
# report
if detail:
report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_q, pr_ans,
cnt_list1, current_cnt)
ave_loss /= cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wn = cnt_wn / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wvi = cnt_wvi / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
return acc, results, cnt_list
def run_epochs(model, model_bert, opt, opt_bert, bert_config, tokenizer, path_wikisql, model_path,
train_loader, train_table, dev_loader, dev_table, test_loader, test_table,
early_stop_ep=None, bool_eval=True, startime_time=None):
# some args
tepoch = 100
accumulate_gradients = 4
assert bool_eval
print("## Actual tepoch %d, accumulate_gradients %d " % (tepoch, accumulate_gradients))
print("## Early stop epoch: {}".format(early_stop_ep))