import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from modules.word_embedding import WordEmbedding
from modules.aggregator_predict import AggPredictor
from modules.selection_predict import SelPredictor
from modules.sqlnet_condition_predict import SQLNetCondPredictor
from modules.select_number import SelNumPredictor
from modules.where_relation import WhereRelationPredictor
class SQLNet(nn.Module):
def __init__(self, word_emb, N_word, N_h=100, N_depth=2,
gpu=False, use_ca=True, trainable_emb=False):
super(SQLNet, self).__init__()
self.use_ca = use_ca
self.trainable_emb = trainable_emb
self.gpu = gpu
self.N_h = N_h
self.N_depth = N_depth
self.max_col_num = 45
self.max_tok_num = 200
self.SQL_TOK = ['<UNK>', '<END>', 'WHERE', 'AND', 'OR', '==', '>', '<', '!=', '<BEG>']
self.COND_OPS = ['>', '<', '==', '!=']
# Word embedding
self.embed_layer = WordEmbedding(word_emb, N_word, gpu, self.SQL_TOK, our_model=True, trainable=trainable_emb)
# Predict the number of selected columns
self.sel_num = SelNumPredictor(N_word, N_h, N_depth, use_ca=use_ca)
#Predict which columns are selected
self.sel_pred = SelPredictor(N_word, N_h, N_depth, self.max_tok_num, use_ca=use_ca)
#Predict aggregation functions of corresponding selected columns
self.agg_pred = AggPredictor(N_word, N_h, N_depth, use_ca=use_ca)
#Predict number of conditions, condition columns, condition operations and condition values
self.cond_pred = SQLNetCondPredictor(N_word, N_h, N_depth, self.max_col_num, self.max_tok_num, use_ca, gpu)
# Predict condition relationship, like 'and', 'or'
self.where_rela_pred = WhereRelationPredictor(N_word, N_h, N_depth, use_ca=use_ca)
self.CE = nn.CrossEntropyLoss()
self.softmax = nn.Softmax(dim=-1)
self.log_softmax = nn.LogSoftmax()
self.bce_logit = nn.BCEWithLogitsLoss()
if gpu:
self.cuda()
def generate_gt_where_seq_test(self, q, gt_cond_seq):
ret_seq = []
for cur_q, ans in zip(q, gt_cond_seq):
temp_q = u"".join(cur_q)
cur_q = [u'<BEG>'] + cur_q + [u'<END>']
record = []
record_cond = []
for cond in ans:
if cond[2] not in temp_q:
record.append((False, cond[2]))
else:
record.append((True, cond[2]))
for idx, item in enumerate(record):
temp_ret_seq = []
if item[0]:
temp_ret_seq.append(0)
temp_ret_seq.extend(list(range(temp_q.index(item[1])+1,temp_q.index(item[1])+len(item[1])+1)))
temp_ret_seq.append(len(cur_q)-1)
else:
temp_ret_seq.append([0,len(cur_q)-1])
record_cond.append(temp_ret_seq)
ret_seq.append(record_cond)
return ret_seq
def forward(self, q, col, col_num, gt_where = None, gt_cond=None, reinforce=False, gt_sel=None, gt_sel_num=None):
B = len(q)
sel_num_score = None
agg_score = None
sel_score = None
cond_score = None
#Predict aggregator
if self.trainable_emb:
x_emb_var, x_len = self.agg_embed_layer.gen_x_batch(q, col)
col_inp_var, col_name_len, col_len = self.agg_embed_layer.gen_col_batch(col)
max_x_len = max(x_len)
agg_score = self.agg_pred(x_emb_var, x_len, col_inp_var,
col_name_len, col_len, col_num, gt_sel=gt_sel)
x_emb_var, x_len = self.sel_embed_layer.gen_x_batch(q, col)
col_inp_var, col_name_len, col_len = self.sel_embed_layer.gen_col_batch(col)
max_x_len = max(x_len)
sel_score = self.sel_pred(x_emb_var, x_len, col_inp_var,
col_name_len, col_len, col_num)
x_emb_var, x_len = self.cond_embed_layer.gen_x_batch(q, col)
col_inp_var, col_name_len, col_len = self.cond_embed_layer.gen_col_batch(col)
max_x_len = max(x_len)
cond_score = self.cond_pred(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num, gt_where, gt_cond, reinforce=reinforce)
where_rela_score = None
else:
x_emb_var, x_len = self.embed_layer.gen_x_batch(q, col)
col_inp_var, col_name_len, col_len = self.embed_layer.gen_col_batch(col)
sel_num_score = self.sel_num(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num)
# x_emb_var: embedding of each question
# x_len: length of each question
# col_inp_var: embedding of each header
# col_name_len: length of each header
# col_len: number of headers in each table, array type
# col_num: number of headers in each table, list type
if gt_sel_num:
pr_sel_num = gt_sel_num
else:
pr_sel_num = np.argmax(sel_num_score.data.cpu().numpy(), axis=1)
sel_score = self.sel_pred(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num)
if gt_sel:
pr_sel = gt_sel
else:
num = np.argmax(sel_num_score.data.cpu().numpy(), axis=1)
sel = sel_score.data.cpu().numpy()
pr_sel = [list(np.argsort(-sel[b])[:num[b]]) for b in range(len(num))]
agg_score = self.agg_pred(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num, gt_sel=pr_sel, gt_sel_num=pr_sel_num)
where_rela_score = self.where_rela_pred(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num)
cond_score = self.cond_pred(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num, gt_where, gt_cond, reinforce=reinforce)
return (sel_num_score, sel_score, agg_score, cond_score, where_rela_score)
def loss(self, score, truth_num, gt_where):
sel_num_score, sel_score, agg_score, cond_score, where_rela_score = score
B = len(truth_num)
loss = 0
# Evaluate select number
sel_num_truth = map(lambda x:x[0], truth_num)
sel_num_truth = torch.from_numpy(np.array(sel_num_truth))
if self.gpu:
sel_num_truth = Variable(sel_num_truth.cuda())
else:
sel_num_truth = Variable(sel_num_truth)
loss += self.CE(sel_num_score, sel_num_truth)
# Evaluate select column
T = len(sel_score[0])
truth_prob = np.zeros((B,T), dtype=np.float32)
for b in range(B):
truth_prob[b][list(truth_num[b][1])] = 1
data = torch.from_numpy(truth_prob)
if self.gpu:
sel_col_truth_var = Variable(data.cuda())
else:
sel_col_truth_var = Variable(data)
sigm = nn.Sigmoid()
sel_col_prob = sigm(sel_score)
bce_loss = -torch.mean(
3*(sel_col_truth_var * torch.log(sel_col_prob+1e-10)) +
(1-sel_col_truth_var) * torch.log(1-sel_col_prob+1e-10)
)
loss += bce_loss
# Evaluate select aggregation
for b in range(len(truth_num)):
data = torch.from_numpy(np.array(truth_num[b][2]))
if self.gpu:
sel_agg_truth_var = Variable(data.cuda())
else:
sel_agg_truth_var = Variable(data)
sel_agg_pred = agg_score[b, :len(truth_num[b][1])]
loss += (self.CE(sel_agg_pred, sel_agg_truth_var)) / len(truth_num)
cond_num_score, cond_col_score, cond_op_score, cond_str_score = cond_score
# Evaluate the number of conditions
cond_num_truth = map(lambda x:x[3], truth_num)
data = torch.from_numpy(np.array(cond_num_truth))
if self.gpu:
try:
cond_num_truth_var = Variable(data.cuda())
except:
print "cond_num_truth_var error"
print data
exit(0)
else:
cond_num_truth_var = Variable(data)
loss += self.CE(cond_num_score, cond_num_truth_var)
# Evaluate the columns of conditions
T = len(cond_col_score[0])
truth_prob = np.zeros((B, T), dtype=np.float32)
for b in range(B):
if len(truth_num[b][4]) > 0:
truth_prob[b][list(truth_num[b][4])] = 1
data = torch.from_numpy(truth_prob)
if self.gpu:
cond_col_truth_var = Variable(data.cuda())
else:
cond_col_truth_var = Variable(data)
sigm = nn.Sigmoid()
cond_col_prob = sigm(cond_col_score)
bce_loss = -torch.mean(
3*(cond_col_truth_var * torch.log(cond_col_prob+1e-10)) +
(1-cond_col_truth_var) * torch.log(1-cond_col_prob+1e-10) )
loss += bce_loss
# Evaluate the operator of conditions
for b in range(len(truth_num)):
if len(truth_num[b][5]) == 0:
continue
data = torch.from_numpy(np.array(truth_num[b][5]))
if self.gpu:
cond_op_truth_var = Variable(data.cuda())
else:
cond_op_truth_var = Variable(data)
cond_op_pred = cond_op_score[b, :len(truth_num[b][5])]
try:
loss += (self.CE(cond_op_pred, cond_op_truth_var) / len(truth_num))
except:
print cond_op_pred
print cond_op_truth_var
exit(0)
#Evaluate the strings of conditions
for b in range(len(gt_where)):
for idx in range(len(gt_where[b])):
cond_str_truth = gt_where[b][idx]
if len(cond_str_truth) == 1:
continue
data = torch.from_numpy(np.array(cond_str_truth[1:]))
if self.gpu:
cond_str_truth_var = Variable(data.cuda())
else:
cond_str_truth_var = Variable(data)
str_end = len(cond_str_truth)-1
cond_str_pred = cond_str_score[b, idx, :str_end]
loss += (self.CE(cond_str_pred, cond_str_truth_var) \
/ (len(gt_where) * len(gt_where[b])))
# Evaluate condition relationship, and / or
where_rela_truth = map(lambda x:x[6], truth_num)
data = torch.from_numpy(np.array(where_rela_truth))
if self.gpu:
try:
where_rela_truth = Variable(data.cuda())
except:
print "where_rela_truth error"
print data
exit(0)
else:
where_rela_truth = Variable(data)
loss += self.CE(where_rela_score, where_rela_truth)
return loss
def check_acc(self, vis_info, pred_queries, gt_queries):
def gen_cond_str(conds, header):
if len(conds) == 0:
return 'None'
cond_str = []
for cond in conds:
cond_str.append(header[cond[0]] + ' ' +
self.COND_OPS[cond[1]] + ' ' + unicode(cond[2]).lower())
return 'WHERE ' + ' AND '.join(cond_str)
tot_err = sel_num_err = agg_err = sel_err = 0.0
cond_num_err = cond_col_err = cond_op_err = cond_val_err = cond_rela_err = 0.0
for b, (pred_qry, gt_qry) in enumerate(zip(pred_queries, gt_queries)):
good = True
sel_pred, agg_pred, where_rela_pred = pred_qry['sel'], pred_qry['agg'], pred_qry['cond_conn_op']
sel_gt, agg_gt, where_rela_gt = gt_qry['sel'], gt_qry['agg'], gt_qry['cond_conn_op']
if where_rela_gt != where_rela_pred:
good = False
cond_rela_err += 1
if len(sel_pred) != len(sel_gt):
good = False
sel_num_err += 1
pred_sel_dict = {k:v for k,v in zip(list(sel_pred), list(agg_pred))}
gt_sel_dict = {k:v for k,v in zip(sel_gt, agg_gt)}
if set(sel_pred) != set(sel_gt):
good = False
sel_err += 1
agg_pred = [pred_sel_dict[x] for x in sorted(pred_sel_dict.keys())]
agg_gt = [gt_sel_dict[x] for x in sorted(gt_sel_dict.keys())]
if agg_pred != agg_gt:
good = False
agg_err += 1
cond_pred = pred_qry['conds']
cond_gt = gt_qry['conds']
if len(cond_pred) != len(cond_gt):
good = False
cond_num_err += 1
else:
cond_op_pred, cond_op_gt = {}, {}
cond_val_pred, cond_val_gt = {}, {}
for p, g in zip(cond_pred, cond_gt):
cond_op_pred[p[0]] = p[1]
cond_val_pred[p[0]] = p[2]
cond_op_gt[g[0]] = g[1]
cond_val_gt[g[0]] = g[2]
if set(cond_op_pred.keys()) != set(cond_op_gt.keys()):
cond_col_err += 1
good=False
where_op_pred = [cond_op_pred[x] for x in sorted(cond_op_pred.keys())]
where_op_gt = [cond_op_gt[x] for x in sorted(cond_op_gt.keys())]
if where_op_pred != where_op_gt:
cond_op_err += 1
good=False
where_val_pred = [cond_val_pred[x] for x in sorted(cond_val_pred.keys())]
where_val_gt = [cond_val_gt[x] for x in sorted(cond_val_gt.keys())]
if where_val_pred != where_val_gt:
cond_val_err += 1
good=False
if not good:
tot_err += 1
return np.array((sel_num_err, sel_err, agg_err, cond_num_err, cond_col_err, cond_op_err, cond_val_err , cond_rela_err)), tot_err
def gen_query(self, score, q, col, raw_q, reinforce=False, verbose=False):
"""
:param score:
:param q: token-questions
:param col: token-headers
:param raw_q: original question sequence
:return:
"""
def merge_tokens(tok_list, raw_tok_str):
tok_str = raw_tok_str# .lower()
alphabet = 'abcdefghijklmnopqrstuvwxyz0123456789$('
special = {'-LRB-':'(',
'-RRB-':')',
'-LSB-':'[',
'-RSB-':']',
'``':'"',
'\'\'':'"',
'--':u'\u2013'}
ret = ''
double_quote_appear = 0
for raw_tok in tok_list:
if not raw_tok:
continue
tok = special.get(raw_tok, raw_tok)
if tok == '"':
double_quote_appear = 1 - double_quote_appear
if len(ret) == 0:
pass
elif len(ret) > 0 and ret + ' ' + tok in tok_str:
ret = ret + ' '
elif len(ret) > 0 and ret + tok in tok_str:
pass
elif tok == '"':
if double_quote_appear:
ret = ret + ' '
# elif tok[0] not in alphabet:
# pass
elif (ret[-1] not in ['(', '/', u'\u2013', '#', '$', '&']) \
and (ret[-1] != '"' or not double_quote_appear):
ret = ret + ' '
ret = ret + tok
return ret.strip()
sel_num_score, sel_score, agg_score, cond_score, where_rela_score = score
# [64,4,6], [64,14], ..., [64,4]
sel_num_score = sel_num_score.data.cpu().numpy()
sel_score = sel_score.data.cpu().numpy()
agg_score = agg_score.data.cpu().numpy()
where_rela_score = where_rela_score.data.cpu().numpy()
ret_queries = []
B = len(agg_score)
cond_num_score,cond_col_score,cond_op_score,cond_str_score =\
[x.data.cpu().numpy() for x in cond_score]
for b in range(B):
cur_query = {}
cur_query['sel'] = []
cur_query['agg'] = []
sel_num = np.argmax(sel_num_score[b])
max_col_idxes = np.argsort(-sel_score[b])[:sel_num]
# find the most-probable columns' indexes
max_agg_idxes = np.argsort(-agg_score[b])[:sel_num]
cur_query['sel'].extend([int(i) for i in max_col_idxes])
cur_query['agg'].extend([i[0] for i in max_agg_idxes])
cur_query['cond_conn_op'] = np.argmax(where_rela_score[b])
cur_query['conds'] = []
cond_num = np.argmax(cond_num_score[b])
all_toks = ['<BEG>'] + q[b] + ['<END>']
max_idxes = np.argsort(-cond_col_score[b])[:cond_num]
for idx in range(cond_num):
cur_cond = []
cur_cond.append(max_idxes[idx]) # where-col
cur_cond.append(np.argmax(cond_op_score[b][idx])) # where-op
cur_cond_str_toks = []
for str_score in cond_str_score[b][idx]:
str_tok = np.argmax(str_score[:len(all_toks)])
str_val = all_toks[str_tok]
if str_val == '<END>':
break
cur_cond_str_toks.append(str_val)
cur_cond.append(merge_tokens(cur_cond_str_toks, raw_q[b]))
cur_query['conds'].append(cur_cond)
ret_queries.append(cur_query)
return ret_queries