class Seq2SQL(nn.Module):
def __init__(self,
word_emb,
num_words,
num_hidden=100,
num_layers=2,
use_gpu=True):
super(Seq2SQL, self).__init__()
self.word_emb = word_emb
self.num_words = num_words
self.num_hidden = num_hidden
self.num_layers = num_layers
self.use_gpu = use_gpu
self.max_col_num = 45
self.max_tok_num = 200
self.COND_OPS = ['EQL', 'GT', 'LT']
self.SQL_TOK = ['<UNK>', '<BEG>', '<END>', 'WHERE', 'AND'
] + self.COND_OPS
# GloVe Word Embedding
self.embed_layer = WordEmbedding(word_emb, num_words, self.SQL_TOK,
use_gpu)
# Aggregation Classifier
self.agg_classifier = AggregationClassifier(num_words, num_hidden,
num_layers)
# SELECT Column(s)
self.sel_classifier = SelectClassifier(num_words, num_hidden,
num_layers, self.max_tok_num)
# WHERE Clause
self.whr_classifier = WhereClassifier(num_words, num_hidden,
num_layers, self.max_col_num,
self.max_tok_num, use_gpu)
# run on GPU
if use_gpu:
self.cuda()
def generate_g_s(self, q, col, query):
# data format
# <BEG> WHERE cond1_col cond1_op cond1
# AND cond2_col cond2_op cond2
# AND ... <END>
ret_seq = []
for cur_q, cur_col, cur_query in zip(q, col, query):
connect_col = [
tok for col_tok in cur_col for tok in col_tok + [',']
]
all_toks = self.SQL_TOK + connect_col + [None] + cur_q + [None]
cur_seq = [all_toks.index('<BEG>')]
if 'WHERE' in cur_query:
cur_where_query = cur_query[cur_query.index('WHERE'):]
cur_seq = cur_seq + map(
lambda tok: all_toks.index(tok)
if tok in all_toks else 0, cur_where_query)
cur_seq.append(all_toks.index('<END>'))
ret_seq.append(cur_seq)
return ret_seq
def forward(self,
q,
col,
col_num,
classif_flag,
g_s=None,
reinforce=False):
agg_classif, sel_classif, whr_classif = classif_flag
agg_score, sel_score, whr_score = None, None, None
x_emb_var, x_len = self.embed_layer.gen_x_batch(q, col)
if agg_classif:
agg_score = self.agg_classifier(x_emb_var, x_len)
if sel_classif:
col_inp_var, col_name_len, col_len = self.embed_layer.gen_col_batch(
col)
sel_score = self.sel_classifier(x_emb_var, x_len, col_inp_var,
col_name_len, col_len, col_num)
if whr_classif:
whr_score = self.whr_classifier(x_emb_var,
x_len,
g_s,
reinforce=reinforce)
return (agg_score, sel_score, whr_score)
def loss(self, score, ref_score, classif_flag, g_s):
agg_classif, sel_classif, whr_classif = classif_flag
agg_score, sel_score, whr_score = score
loss = 0
if agg_classif:
agg_ref = torch.from_numpy(np.array(map(lambda x: x[0],
ref_score)))
agg_ref_var = Variable(agg_ref)
if self.use_gpu:
agg_ref_var = agg_ref_var.cuda()
loss += nn.CrossEntropyLoss()(agg_score, agg_ref_var)
if sel_classif:
sel_ref = torch.from_numpy(np.array(map(lambda x: x[1],
ref_score)))
sel_ref_var = Variable(sel_ref)
if self.use_gpu:
sel_ref_var = sel_ref_var.cuda()
loss += nn.CrossEntropyLoss()(sel_score, sel_ref_var)
if whr_classif:
g_s_len = len(g_s)
for s, g_s_i in enumerate(g_s):
whr_ref_var = Variable(torch.from_numpy(np.array(g_s_i[1:])))
if self.use_gpu:
whr_ref_var = whr_ref_var.cuda()
loss += (nn.CrossEntropyLoss()(whr_score[s, :len(g_s_i) - 1],
whr_ref_var) / g_s_len)
return loss
def reinforce_backward(self, score, rewards):
agg_score, sel_score, whr_score = score
cur_reward = rewards[:]
eof = self.SQL_TOK.index('<END>')
for whr_score_t in whr_score[1]:
reward_inp = torch.FloatTensor(cur_reward).unsqueeze(1)
if self.use_gpu:
reward_inp = reward_inp.cuda()
whr_score_t.reinforce(reward_inp)
for b, _ in enumerate(rewards):
if whr_score_t[b].data.cpu().numpy()[0] == eof:
cur_reward[b] = 0
torch.autograd.backward(whr_score[1], [None for _ in whr_score[1]])
return
def check_acc(self, classif_queries, g_s_queries, classif_flag):
agg_classif, sel_classif, whr_classif = classif_flag
tot_err = agg_err = sel_err = whr_err = whr_num_err = whr_col_err = whr_op_err = whr_val_err = 0.0
for classif_qry, g_s_qry in zip(classif_queries, g_s_queries):
agg_err_inc = 1 if agg_classif and classif_qry['agg'] != g_s_qry[
'agg'] else 0
agg_err += agg_err_inc
sel_err_inc = 1 if sel_classif and classif_qry['sel'] != g_s_qry[
'sel'] else 0
sel_err += sel_err_inc
if whr_classif:
flag = True
whr_classifier = classif_qry['conds']
whr_g_s = g_s_qry['conds']
if len(whr_classifier) != len(whr_g_s):
flag = False
whr_num_err += 1
elif set(x[0]
for x in whr_classifier) != set(x[0]
for x in whr_g_s):
flag = False
whr_col_err += 1
if flag:
for whr_class_i in whr_classifier:
g_s_idx = tuple(x[0]
for x in whr_g_s).index(whr_class_i[0])
if flag and whr_g_s[g_s_idx][1] != whr_class_i[1]:
flag = False
whr_op_err += 1
break
if flag:
for whr_class_i in whr_classifier:
g_s_idx = tuple(x[0]
for x in whr_g_s).index(whr_class_i[0])
if flag and unicode(whr_g_s[g_s_idx][2]).lower() != \
unicode(whr_class_i[2]).lower():
flag = False
whr_val_err += 1
break
if not flag:
whr_err += 1
if agg_err_inc > 0 or sel_err_inc > 0 or not flag:
tot_err += 1
return np.array((agg_err, sel_err, whr_err)), tot_err
def gen_query(self,
score,
q,
col,
raw_q,
raw_col,
classif_flag,
reinforce=False,
verbose=False):
def merge_tokens(tok_list, raw_tok_str):
tok_str = raw_tok_str.lower()
special = {
'-LRB-': '(',
'-RRB-': ')',
'-LSB-': '[',
'-RSB-': ']',
'``': '"',
'\'\'': '"',
'--': u'\u2013'
}
ret = ''
double_quote_pair_track = 0
for raw_tok in tok_list:
if not raw_tok:
continue
tok = special.get(raw_tok, raw_tok)
if tok == '"':
double_quote_pair_track = 1 - double_quote_pair_track
if double_quote_pair_track:
ret = ret + ' '
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[0] not in string.ascii_lowercase) and (
tok[0] not in string.digits) and (tok[0] not in '$('):
pass
elif (ret[-1] not in ['(', '/', u'\u2013', '#', '$', '&']) and \
(ret[-1] != '"' or not double_quote_pair_track):
ret = ret + ' '
ret = ret + tok
return ret.strip()
agg_classif, sel_classif, whr_classif = classif_flag
agg_score, sel_score, whr_score = score
ret_queries = []
batch_len = len(agg_score) if agg_classif else len(
sel_score) if sel_classif else len(
whr_score[0]) if reinforce else len(whr_score)
for b in range(batch_len):
cur_query = {}
if agg_classif:
cur_query['agg'] = np.argmax(agg_score[b].data.cpu().numpy())
if sel_classif:
cur_query['sel'] = np.argmax(sel_score[b].data.cpu().numpy())
if whr_classif:
cur_query['conds'] = []
all_toks = self.SQL_TOK + [
x for toks in col[b] for x in toks + [',']
] + [''] + q[b] + ['']
whr_toks = []
if reinforce:
for choices in whr_score[1]:
if choices[b].data.cpu().numpy()[0] < len(all_toks):
whr_val = all_toks[choices[b].data.cpu().numpy()
[0]]
else:
whr_val = '<UNK>'
if whr_val == '<END>':
break
whr_toks.append(whr_val)
else:
for where_score in whr_score[b].data.cpu().numpy():
whr_tok = np.argmax(where_score)
whr_val = all_toks[whr_tok]
if whr_val == '<END>':
break
whr_toks.append(whr_val)
if verbose:
print whr_toks
if len(whr_toks) > 0:
whr_toks = whr_toks[1:]
st = 0
while st < len(whr_toks):
cur_cond = [None, None, None]
ed = len(whr_toks) if 'AND' not in whr_toks[st:] \
else whr_toks[st:].index('AND') + st
if 'EQL' in whr_toks[st:ed]:
op = whr_toks[st:ed].index('EQL') + st
cur_cond[1] = 0
elif 'GT' in whr_toks[st:ed]:
op = whr_toks[st:ed].index('GT') + st
cur_cond[1] = 1
elif 'LT' in whr_toks[st:ed]:
op = whr_toks[st:ed].index('LT') + st
cur_cond[1] = 2
else:
op = st
cur_cond[1] = 0
sel_col = whr_toks[st:op]
to_idx = [x.lower() for x in raw_col[b]]
classif_col = merge_tokens(sel_col, raw_q[b] + ' || ' + \
' || '.join(raw_col[b]))
if classif_col in to_idx:
cur_cond[0] = to_idx.index(classif_col)
else:
cur_cond[0] = 0
cur_cond[2] = merge_tokens(whr_toks[op + 1:ed], raw_q[b])
cur_query['conds'].append(cur_cond)
st = ed + 1
ret_queries.append(cur_query)
return ret_queries
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 = ['>', '<', '==', '!=']
# 词向量，可选择自己训练或者使用训练好的词向量，这里选用加载好的词向量
self.embed_layer = WordEmbedding(word_emb,
N_word,
gpu,
self.SQL_TOK,
our_model=True,
trainable=trainable_emb)
# 预测列数目
self.sel_num = SelNumPredictor(N_word, N_h, N_depth, use_ca=use_ca)
# 预测那个列被选中了
self.sel_pred = SelPredictor(N_word,
N_h,
N_depth,
self.max_tok_num,
use_ca=use_ca)
# 预测相应选定列的聚合函数
self.agg_pred = AggPredictor(N_word, N_h, N_depth, use_ca=use_ca)
# 预测条件数、条件列、条件操作和条件值
self.cond_pred = SQLNetCondPredictor(N_word, N_h, N_depth,
self.max_col_num,
self.max_tok_num, use_ca, gpu)
# 预测条件关系，如“and”、“or”
self.where_rela_pred = WhereRelationPredictor(N_word,
N_h,
N_depth,
use_ca=use_ca)
self.CE = nn.CrossEntropyLoss() #交叉熵损失函数
self.softmax = nn.Softmax(dim=-1)
self.log_softmax = nn.LogSoftmax()
self.bce_logit = nn.BCEWithLogitsLoss()
if gpu:
self.cuda()
# q：问题，gt_cond_seq：三元组 目的：要选择那一列
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>'] # 在每个问题前加<BEG>和结尾加<END>
record = [] #如果条件值在问题中，标记（TRUE，条件值）
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))) #获取条件值的索引
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
#q：问题，col：表头名字，col_num：有几个表头列，gt_where：conds中条件值不出现在问题中，gt_conds：conds，gt_sel：选择那列，gt_sel_num：选择几列
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) #batch_size的大小
sel_num_score = None
agg_score = None
sel_score = None
cond_score = None
#预测聚合函数
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
) #x_len：batch中每个问题的长度，[x_emb_var：batch_size，max_seq_len，word_embedding_size]
col_inp_var, col_name_len, col_len = self.embed_layer.gen_col_batch(
col) #列名向量化，长度，几个列
sel_num_score = self.sel_num(
x_emb_var, x_len, col_inp_var, col_name_len, col_len,
col_num) #[16,4]对问题的编码经过lstm，linear，softmax之后乘以编码
# 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) #【16,19】
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) #【16,4,6】
where_rela_score = self.where_rela_pred(x_emb_var, x_len,
col_inp_var, col_name_len,
col_len, col_num) #【16,3】
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) #4=>[16,5]
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 = list(map(lambda x: x[0], truth_num)) #聚合函数个数
sel_num_truth = torch.from_numpy(
np.array(sel_num_truth)).long() #.astype(float))
if self.gpu:
sel_num_truth = Variable(sel_num_truth.cuda())
else:
sel_num_truth = Variable(sel_num_truth)
#选择几个列的损失
loss += self.CE(sel_num_score, sel_num_truth)
# Evaluate select column选择哪个列的损失
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)
) #这儿采用bceloss：-w*[y*log(x)+(1-y)*log(1-x)]
loss += bce_loss
# Evaluate select aggregation选择聚合函数的损失交叉熵
for b in range(len(truth_num)):
data = torch.from_numpy(np.array(truth_num[b][2])) #真实的聚合函数
if self.gpu:
sel_agg_truth_var = Variable(data.cuda())
else:
sel_agg_truth_var = Variable(data.long())
sel_agg_pred = agg_score[b, :len(truth_num[b][1])] #聚合函数共六种
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预测多少个conds的损失交叉熵
cond_num_truth = list(map(lambda x: x[3], truth_num))
data = torch.from_numpy(np.array(cond_num_truth).astype(float)).long()
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评估条件列
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 #条件列
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评估操作条件
for b in range(len(truth_num)):
if len(truth_num[b][5]) == 0: #条件类型
continue
data = torch.from_numpy(np.array(truth_num[b][5])).long()
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评估条件串
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:])).long()
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评估条件关系
where_rela_truth = list(map(lambda x: x[6], truth_num))
data = torch.from_numpy(np.array(where_rela_truth)).long()
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
