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tableQA_single_table.py
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tableQA_single_table.py
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#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
import os
import ast
import re
import json
from icecream import ic
from copy import deepcopy
from itertools import product, combinations
import pandas as pd
import os
import sys
from pyarrow.filesystem import LocalFileSystem
from functools import reduce
import nltk
from nltk import pos_tag, word_tokenize
from collections import namedtuple
from ast import literal_eval
from torch.nn import functional
import numpy as np
import torch
from torch import nn
from torch.nn import init
from torch.nn.utils import rnn as rnn_utils
import math
from icecream import ic
import seaborn as sns
import matplotlib.pyplot as plt
import shutil
#from keybert import KeyBERT
#from bertopic import BERTopic
import sqlite3
import sqlite_utils
from icecream import ic
import jieba
import pandas as pd
import urllib.request
from urllib.parse import quote
from time import sleep
import json
import os
from collections import defaultdict
import re
from functools import reduce, partial
#### used in this condition extract in training.
op_sql_dict = {0:">", 1:"<", 2:"==", 3:"!="}
#### used by clf for intension inference
agg_sql_dict = {0:"", 1:"AVG", 2:"MAX", 3:"MIN", 4:"COUNT", 5:"SUM"}
#### final to combine them (one for 0, and multi for 1 2)
conn_sql_dict = {0:"", 1:"and", 2:"or"}
#### kws and time pattern defination
and_kws = ("且", "而且", "并且", "和", "当中", "同时")
or_kws = ("或", "或者",)
conn_kws = and_kws + or_kws
pattern_list = [u"[年月\.\-\d]+", u"[年月\d]+", u"[年个月\d]+", u"[年月日\d]+"]
time_kws = ("什么时候", "时间", "时候")
sum_count_high_kws = ('多少个', '有几个', '总共') + ('总和','一共',) + ("总数",)
mean_kws = ('平均数', '均值', '平均值', '平均')
max_kws = ('最大', '最多', '最大值', '最高')
min_kws = ('最少', '最小值', '最小', '最低')
sum_count_low_kws = ('个', '总共') + ('总和','加','总','一共','和',) + ("哪些", "查", "数量", "数") + ("几",) + ('多少', "多大") + ("总数",)
max_special_kws = ("以上", "大于")
min_special_kws = ("以下", "小于")
qst_kws = ("多少", "什么", "多大", "哪些", "怎么", "情况", "那些", "哪个")
only_kws_columns = {"城市": "=="}
##### jointbert predict model init start
jointbert_path = "../../featurize/JointBERT"
sys.path.append(jointbert_path)
from model.modeling_jointbert import JointBERT
from model.modeling_jointbert import *
from trainer import *
from main import *
from data_loader import *
pred_parser = argparse.ArgumentParser()
pred_parser.add_argument("--input_file", default="conds_pred/seq.in", type=str, help="Input file for prediction")
pred_parser.add_argument("--output_file", default="conds_pred/sample_pred_out.txt", type=str, help="Output file for prediction")
pred_parser.add_argument("--model_dir", default="bert", type=str, help="Path to save, load model")
pred_parser.add_argument("--batch_size", default=32, type=int, help="Batch size for prediction")
pred_parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
pred_parser_config_dict = dict(map(lambda item:(item.option_strings[0].replace("--", ""), item.default) ,pred_parser.__dict__["_actions"]))
pred_parser_config_dict = dict(filter(lambda t2: t2[0] != "-h", pred_parser_config_dict.items()))
pred_parser_namedtuple = namedtuple("pred_parser_config", pred_parser_config_dict.keys())
for k, v in pred_parser_config_dict.items():
if type(v) == type(""):
exec("pred_parser_namedtuple.{}='{}'".format(k, v))
else:
exec("pred_parser_namedtuple.{}={}".format(k, v))
from predict import *
pred_config = pred_parser_namedtuple
args = get_args(pred_config)
device = get_device(pred_config)
args_parser_namedtuple = namedtuple("args_config", args.keys())
for k, v in args.items():
if type(v) == type(""):
exec("args_parser_namedtuple.{}='{}'".format(k, v))
else:
exec("args_parser_namedtuple.{}={}".format(k, v))
args = args_parser_namedtuple
pred_model = MODEL_CLASSES["bert"][1].from_pretrained(args.model_dir,
args=args,
intent_label_lst=get_intent_labels(args),
slot_label_lst=get_slot_labels(args))
pred_model.to(device)
pred_model.eval()
intent_label_lst = get_intent_labels(args)
slot_label_lst = get_slot_labels(args)
pad_token_label_id = args.ignore_index
tokenizer = load_tokenizer(args)
## jointbert predict model init end
###### one sent conds decomp start
def predict_single_sent(question):
text = " ".join(list(question))
batch = convert_input_file_to_tensor_dataset([text.split(" ")], pred_config, args, tokenizer, pad_token_label_id).tensors
batch = tuple(t.to(device) for t in batch)
inputs = {"input_ids": batch[0],
"attention_mask": batch[1],
"intent_label_ids": None,
"slot_labels_ids": None}
inputs["token_type_ids"] = batch[2]
outputs = pred_model(**inputs)
_, (intent_logits, slot_logits) = outputs[:2]
intent_preds = intent_logits.detach().cpu().numpy()
slot_preds = slot_logits.detach().cpu().numpy()
intent_preds = np.argmax(intent_preds, axis=1)
slot_preds = np.argmax(slot_preds, axis=2)
all_slot_label_mask = batch[3].detach().cpu().numpy()
slot_label_map = {i: label for i, label in enumerate(slot_label_lst)}
slot_preds_list = [[] for _ in range(slot_preds.shape[0])]
for i in range(slot_preds.shape[0]):
for j in range(slot_preds.shape[1]):
if all_slot_label_mask[i, j] != pad_token_label_id:
slot_preds_list[i].append(slot_label_map[slot_preds[i][j]])
pred_l = []
for words, slot_preds, intent_pred in zip([text.split(" ")], slot_preds_list, intent_preds):
line = ""
for word, pred in zip(words, slot_preds):
if pred == 'O':
line = line + word + " "
else:
line = line + "[{}:{}] ".format(word, pred)
pred_l.append((line, intent_label_lst[intent_pred]))
return pred_l[0]
###@@ conn_kws = ["且", "或", "或者", "和"]
'''
and_kws = ("且", "而且", "并且", "和", "当中", "同时")
or_kws = ("或", "或者",)
conn_kws = and_kws + or_kws
'''
#conn_kws = ("且", "或", "或者", "和") + ("而且", "并且", "当中")
#### some algorithm use in it.
def recurrent_extract(question):
def filter_relation(text):
#kws = ["且", "或", "或者", "和"]
kws = conn_kws
req = text
for kw in sorted(kws, key= lambda x: len(x))[::-1]:
req = req.replace(kw, "")
return req
def produce_plain_text(text):
##### replace tag string from text
kws = ["[", "]", " ", ":B-HEADER", ":I-HEADER", ":B-VALUE", ":I-VALUE"]
plain_text = text
for kw in kws:
plain_text = plain_text.replace(kw, "")
return plain_text
def find_min_commmon_strings(c):
##### {"jack", "ja", "ss", "sss", "ps", ""} -> {"ja", "ss", "ps"}
common_strings = list(filter(lambda x: type(x) == type("") ,
map(lambda t2: t2[0]
if t2[0] in t2[1]
else (t2[1]
if t2[1] in t2[0]
else (t2[0], t2[1])),combinations(c, 2))))
req = set([])
while c:
ele = c.pop()
if all(map(lambda cc: cc not in ele, common_strings)):
req.add(ele)
req = req.union(set(common_strings))
return set(filter(lambda x: x, req))
def extract_scope(scope_text):
def find_max_in(plain_text ,b_chars, i_chars):
chars = "".join(b_chars + i_chars)
while chars and chars not in plain_text:
chars = chars[:-1]
return chars
b_header_chars = re.findall(r"([\w\W]):B-HEADER", scope_text)
i_header_chars = re.findall(r"([\w\W]):I-HEADER", scope_text)
b_value_chars = re.findall(r"([\w\W]):B-VALUE", scope_text)
i_value_chars = re.findall(r"([\w\W]):I-VALUE", scope_text)
if len(b_header_chars) != 1 or len(b_value_chars) != 1:
return None
plain_text = produce_plain_text(scope_text)
header = find_max_in(plain_text, b_header_chars, i_header_chars)
value = find_max_in(plain_text, b_value_chars, i_value_chars)
if (not header) or (not value):
return None
return (header, value)
def find_scope(text):
start_index = text.find("[")
end_index = text.rfind("]")
if start_index == -1 or end_index == -1:
return text
scope_text = text[start_index: end_index + 1]
res_text = filter_relation(text.replace(scope_text, "")).replace(" ", "").strip()
return (scope_text, res_text)
def produce_all_attribute_remove(req):
if not req:
return None
string_or_t2 = find_scope(req[-1][0])
assert type(string_or_t2) in [type(""), type((1,))]
if type(string_or_t2) == type(""):
return string_or_t2
else:
return string_or_t2[-1]
def extract_all_attribute(req):
extract_list = list(map(lambda t2: (t2[0][0], t2[1], t2[0][1]) ,
filter(lambda x: x[0] ,
map(lambda tt2_t2: (extract_scope(tt2_t2[0][0]), tt2_t2[1]) ,
filter(lambda t2_t2: "HEADER" in t2_t2[0][0] and "VALUE" in t2_t2[0][0] ,
filter(lambda string_or_t2_t2: type(string_or_t2_t2[0]) == type((1,)),
map(lambda tttt2: (find_scope(tttt2[0]), tttt2[1]),
req)))))))
return extract_list
def extract_attributes_relation_string(plain_text, all_attributes, res):
if not all_attributes:
return plain_text.replace(res if res else "", "")
def replace_by_one_l_r(text ,t3):
l, _, r = t3
##### produce multi l, r to satisfy string contrain problem
l0, l1 = l, l
r0, r1 = r, r
while l0 and l0 not in text:
l0 = l0[:-1]
while l1 and l1 not in text:
l1 = l1[1:]
while r0 and r0 not in text:
r0 = r0[:-1]
while r1 and r1 not in text:
r1 = r1[1:]
if not l or not r:
return text
conclusion = set([])
for l_, r_ in product([l0, l1], [r0, r1]):
l_r_conclusion = re.findall("({}.*?{})".format(l_, r_), text)
r_l_conclusion = re.findall("({}.*?{})".format(r_, l_), text)
conclusion = conclusion.union(set(l_r_conclusion + r_l_conclusion))
##### because use produce multi must choose the shortest elements from them
## to prevent "relation word" also be replaced.
conclusion_filtered = find_min_commmon_strings(conclusion)
conclusion = conclusion_filtered
req_text = text
for c in conclusion:
req_text = req_text.replace(c, "")
return req_text
req_text_ = plain_text
for t3 in all_attributes:
req_text_ = replace_by_one_l_r(req_text_, t3)
return req_text_.replace(res, "")
req = []
t2 = predict_single_sent(question)
req.append(t2)
while "[" in t2[0]:
scope = find_scope(t2[0])
if type(scope) == type(""):
break
else:
assert type(scope) == type((1,))
scope_text, res_text = scope
#ic(req)
t2 = predict_single_sent(res_text)
req.append(t2)
req = list(filter(lambda tt2: "HEADER" in tt2[0] and "VALUE" in tt2[0] , req))
res = produce_all_attribute_remove(req)
#ic(req)
all_attributes = extract_all_attribute(req)
# plain_text = produce_plain_text(scope_text)
return all_attributes, res, extract_attributes_relation_string(produce_plain_text(req[0][0] if req else ""), all_attributes, res)
def rec_more_time(decomp):
assert type(decomp) == type((1,)) and len(decomp) == 3
assert not decomp[0]
res, relation_string = decomp[1:]
new_decomp = recurrent_extract(relation_string)
#### stop if rec not help by new_decomp[1] != decomp[1]
if not new_decomp[0] and new_decomp[1] != decomp[1]:
return rec_more_time(new_decomp)
return (new_decomp[0], res, new_decomp[1])
### one sent conds decomp end
##### data source start
train_path = "../TableQA/TableQA/train"
def data_loader(table_json_path = os.path.join(train_path ,"train.tables.json"),
json_path = os.path.join(train_path ,"train.json"),
req_table_num = 1):
assert os.path.exists(table_json_path)
assert os.path.exists(json_path)
json_df = pd.read_json(json_path, lines = True)
all_tables = pd.read_json(table_json_path, lines = True)
if req_table_num is not None:
assert type(req_table_num) == type(0) and req_table_num > 0 and req_table_num <= all_tables.shape[0]
else:
req_table_num = all_tables.shape[0]
for i in range(req_table_num):
#one_table = all_tables.iloc[i]["table"]
#one_table_df = pd.read_sql("select * from `{}`".format(one_table), train_tables_dump_engine)
one_table_s = all_tables.iloc[i]
one_table_df = pd.DataFrame(one_table_s["rows"], columns = one_table_s["header"])
yield one_table_df, json_df[json_df["table_id"] == one_table_s["id"]]
## data source end
###### string toolkit start
def findMaxSubString(str1, str2):
"""
"""
maxSub = 0
maxSubString = ""
str1_len = len(str1)
str2_len = len(str2)
for i in range(str1_len):
str1_pos = i
for j in range(str2_len):
str2_pos = j
str1_pos = i
if str1[str1_pos] != str2[str2_pos]:
continue
else:
while (str1_pos < str1_len) and (str2_pos < str2_len):
if str1[str1_pos] == str2[str2_pos]:
str1_pos = str1_pos + 1
str2_pos = str2_pos + 1
else:
break
sub_len = str2_pos - j
if maxSub < sub_len:
maxSub = sub_len
maxSubString = str2[j:str2_pos]
return maxSubString
def find_min_commmon_strings(c):
##### {"jack", "ja", "ss", "sss", "ps", ""} -> {"ja", "ss", "ps"}
common_strings = list(filter(lambda x: type(x) == type("") ,
map(lambda t2: t2[0]
if t2[0] in t2[1]
else (t2[1]
if t2[1] in t2[0]
else (t2[0], t2[1])),combinations(c, 2))))
req = set([])
while c:
ele = c.pop()
if all(map(lambda cc: cc not in ele, common_strings)):
req.add(ele)
req = req.union(set(common_strings))
return set(filter(lambda x: x, req))
## string toolkit end
###### datetime column match start
#### only use object dtype to extract
def time_template_extractor(rows_filtered, pattern = u"[年月\.\-\d]+"):
#re_words = re.compile(u"[年月\.\-\d]+")
re_words = re.compile(pattern)
nest_collection = pd.DataFrame(rows_filtered).applymap(lambda x: tuple(sorted(list(re.findall(re_words, x))))).values.tolist()
def flatten_collection(c):
if not c:
return c
if type(c[0]) == type(""):
return c
else:
c = list(c)
return flatten_collection(reduce(lambda a, b: a + b, map(list ,c)))
return flatten_collection(nest_collection)
###@@ pattern_list
#pattern_list = [u"[年月\.\-\d]+", u"[年月\d]+", u"[年个月\d]+", u"[年月日\d]+"]
def justify_column_as_datetime(df, threshold = 0.8, time_template_extractor = lambda x: x):
object_columns = list(map(lambda tt2: tt2[0] ,filter(lambda t2: t2[1].name == "object" ,dict(df.dtypes).items())))
time_columns = []
for col in object_columns:
input_ = df[[col]].applymap(lambda x: "~" if type(x) != type("") else x)
output_ = time_template_extractor(input_.values.tolist())
input_ = input_.iloc[:, 0].values.tolist()
time_evidence_cnt = sum(map(lambda t2: t2[0].strip() == t2[1].strip() and t2[0] and t2[1] and t2[0] != "~" and t2[1] != "~",zip(input_, output_)))
if time_evidence_cnt > 0 and time_evidence_cnt / df.shape[0] >= threshold:
#### use evidence ratio because may have some noise in data
time_columns.append(col)
return time_columns
def justify_column_as_datetime_reduce(df, threshold = 0.8, time_template_extractor_list = list(map(lambda p: partial(time_template_extractor, pattern = p), pattern_list))):
return sorted(reduce(lambda a, b: a.union(b) ,map(lambda func: set(justify_column_as_datetime(df, threshold, func)), time_template_extractor_list)))
## datetime column match end
##### choose question column have a reduce function call below (choose_res_by_kws)
##### this is a tiny first version
###@@ time_kws = ("什么时候", "时间", "时候")
#time_kws = ("什么时候", "时间", "时候")
#####
def choose_question_column(decomp, header, df):
assert type(decomp) == type((1,)) and type(header) == type([])
time_columns = justify_column_as_datetime_reduce(df)
_, res, _ = decomp
if type(res) != type(""):
return None
#ic(res)
##### should add time kws to it.
#time_kws = ("什么时候", "时间", "时候")
if any(map(lambda t_kw: t_kw in res, time_kws)):
if len(time_columns) == 1:
return time_columns[0]
else:
'''
return sorted(map(lambda t_col: (t_col ,len(findMaxSubString(t_col, res)) / len(t_col)), time_columns),
key = lambda t2: t2[1])[::-1][0][0]
'''
sort_list = sorted(map(lambda t_col: (t_col ,len(findMaxSubString(t_col, res)) / len(t_col)), time_columns),
key = lambda t2: t2[1])[::-1]
if sort_list:
if sort_list[0]:
return sort_list[0][0]
return None
c_res_common_dict = dict(filter(lambda t2: t2[1] ,map(lambda c: (c ,findMaxSubString(c, res)), header)))
common_ratio_c_dict = dict(map(lambda t2: (t2[0], len(t2[1]) / len(t2[0])), c_res_common_dict.items()))
common_ratio_res_dict = dict(map(lambda t2: (t2[0], len(t2[1]) / len(res)), c_res_common_dict.items()))
#ic(decomp)
#ic(common_ratio_c_dict)
#ic(common_ratio_res_dict)
if not common_ratio_c_dict or not common_ratio_res_dict:
return None
dict_0_max_key = sorted(common_ratio_c_dict.items(), key = lambda t2: t2[1])[::-1][0][0]
dict_1_max_key = sorted(common_ratio_res_dict.items(), key = lambda t2: t2[1])[::-1][0][0]
return dict_0_max_key if dict_0_max_key == dict_1_max_key else None
##### agg-classifier start
'''
sum_count_high_kws = ('多少个', '有几个', '总共') + ('总和','一共',) + ("总数",)
mean_kws = ('平均数', '均值', '平均值', '平均')
max_kws = ('最大', '最多', '最大值', '最高')
min_kws = ('最少', '最小值', '最小', '最低')
sum_count_low_kws = ('个', '总共') + ('总和','加','总','一共','和',) + ("哪些", "查", "数量", "数") + ("几",) + ('多少', "多大") + ("总数",)
max_special_kws = ("以上", "大于")
min_special_kws = ("以下", "小于")
'''
###@@ sum_count_high_kws = ('多少个', '有几个', '总共') + ('总和','一共',) + ("总数",)
###@@ mean_kws = ('平均数', '均值', '平均值', '平均')
###@@ max_kws = ('最大', '最多', '最大值', '最高')
###@@ min_kws = ('最少', '最小值', '最小', '最低')
###@@ sum_count_low_kws = ('个', '总共') + ('总和','加','总','一共','和',) + ("哪些", "查", "数量", "数") + ("几",) + ('多少', "多大") + ("总数",)
###@@ max_special_kws = ("以上", "大于")
###@@ min_special_kws = ("以下", "小于")
def simple_label_func(s, drop_header = True):
text_tokens =s.question_cut
header = list(map(lambda x: x[:x.find("(")] if (not x.startswith("(") and x.endswith(")")) else x ,s.header.split(",")))
#### not contain samples may not match in fuzzy-match, special column mapping in finance,
### or "3" to "三"
'''
fit_collection = ('多少个', '有几个', '总共') + ('总和','一共',) + ('平均数', '均值', '平均值', '平均') + ('最大', '最多', '最大值', '最高') + ('最少', '最小值', '最小', '最低')
'''
fit_collection = sum_count_high_kws + mean_kws + max_kws + min_kws
fit_header = []
for c in header:
for kw in fit_collection:
if kw in c:
start_idx = c.find(kw)
end_idx = start_idx + len(kw)
fit_header.append(c[start_idx: end_idx])
if not drop_header:
header = []
fit_header = []
input_ = "".join(text_tokens)
for c in header + fit_header:
if c in fit_collection:
continue
input_ = input_.replace(c, "")
c0, c1 = c, c
while c0 and c0 not in fit_collection and len(c0) >= 4:
c0 = c0[1:]
if c0 in fit_collection:
break
input_ = input_.replace(c0, "")
while c1 and c1 not in fit_collection and len(c1) >= 4:
c1 = c1[:-1]
if c1 in fit_collection:
break
input_ = input_.replace(c1, "")
#ic(input_)
text_tokens = list(jieba.cut(input_))
#cat_6_collection_high_level = ('多少个', '有几个', '总共') + ('总和','一共',) + ("哪些", "查", "数量")
#cat_6_collection_high_level = ('多少个', '有几个', '总共') + ('总和','一共',)
##### 高置信度部分 (作为是否构成使用特殊规则的判断标准)
#### case 2 部分 (高置信度有效匹配)
#cat_6_collection_high_level = ('多少个', '有几个', '总共') + ('总和','一共',)
#cat_6_collection_high_level = ('多少个', '有几个', '总共') + ('总和','一共',) + ("总数",)
cat_6_collection_high_level = sum_count_high_kws
if any(map(lambda high_level_token: high_level_token in "".join(text_tokens), cat_6_collection_high_level)):
return 6
#### 够深 够宽 规则部分, change order by header, if header have kws in , lower order
if any(map(lambda kw: kw in text_tokens, mean_kws)):
return 1
if any(map(lambda kw: kw in text_tokens, max_kws)):
return 2
if any(map(lambda kw: kw in text_tokens, min_kws)):
return 3
##### 低置信度部分
#### case 2 部分 (低置信度尾部匹配)
cat_6_collection = sum_count_low_kws
if any(map(lambda kw: kw in text_tokens, cat_6_collection)):
return 6
if any(map(lambda token: "几" in token, text_tokens)):
return 6
#### special case 部分
if any(map(lambda kw: kw in text_tokens, max_special_kws)):
return 2
if any(map(lambda kw: kw in text_tokens, min_special_kws)):
return 3
#### 无效匹配
return 0
def simple_special_func(s, drop_header = True):
text_tokens =s.question_cut
header = list(map(lambda x: x[:x.find("(")] if (not x.startswith("(") and x.endswith(")")) else x ,s.header.split(",")))
#### not contain samples may not match in fuzzy-match, special column mapping in finance,
### or "3" to "三"
fit_collection = sum_count_high_kws + mean_kws + max_kws + min_kws
fit_header = []
for c in header:
for kw in fit_collection:
if kw in c:
start_idx = c.find(kw)
end_idx = start_idx + len(kw)
fit_header.append(c[start_idx: end_idx])
input_ = "".join(text_tokens)
if not drop_header:
header = []
fit_header = []
for c in header + fit_header:
if c in fit_collection:
continue
input_ = input_.replace(c, "")
c0, c1 = c, c
while c0 and c0 not in fit_collection and len(c0) >= 4:
c0 = c0[1:]
if c0 in fit_collection:
break
input_ = input_.replace(c0, "")
while c1 and c1 not in fit_collection and len(c1) >= 4:
c1 = c1[:-1]
if c1 in fit_collection:
break
input_ = input_.replace(c1, "")
#ic(input_)
text_tokens = list(jieba.cut(input_))
#ic(text_tokens)
#cat_6_collection_high_level = ('多少个', '有几个', '总共') + ('总和','一共',) + ("哪些", "查", "数量")
#cat_6_collection_high_level = ('多少个', '有几个', '总共') + ('总和','一共',)
#### case 2 部分 (高置信度有效匹配)
cat_6_collection_high_level = sum_count_high_kws
if any(map(lambda high_level_token: high_level_token in "".join(text_tokens), cat_6_collection_high_level)):
return 6
#### 够深 够宽 规则部分, change order by header, if header have kws in , lower order
if any(map(lambda kw: kw in text_tokens, mean_kws)):
return 1
if any(map(lambda kw: kw in text_tokens, max_kws)):
return 2
if any(map(lambda kw: kw in text_tokens, min_kws)):
return 3
return 0
def simple_total_label_func(s):
is_special = False if simple_special_func(s) == 0 else True
if not is_special:
return 0
return simple_label_func(s)
## agg-classifier end
##### main block of process start
def split_by_cond(s, extract_return = True):
def recurrent_extract_cond(text):
#return np.asarray(recurrent_extract(text)[0])
#return recurrent_extract(text)[0]
return np.asarray(list(recurrent_extract(text)[0]))
question = s["question"]
res = s["rec_decomp"][1]
if question is None:
question = ""
if res is None:
res = ""
common_res = findMaxSubString(question, res)
#cond_kws = ("或", "而且", "并且", "当中")
#cond_kws = ("或", "而且" "并且" "当中")
cond_kws = conn_kws
condition_part = question.replace(common_res, "")
fit_kws = set([])
for kw in cond_kws:
if kw in condition_part and not condition_part.startswith(kw) and not condition_part.endswith(kw):
fit_kws.add(kw)
if not fit_kws:
will_return = ([condition_part.replace(" ", "") + " " + common_res], "")
if extract_return:
#return (list(map(recurrent_extract_cond, will_return[0])), will_return[1])
will_return = (np.asarray(list(map(recurrent_extract_cond, will_return[0]))) , will_return[1])
#will_return = (np.concatenate(list(filter(lambda x: x.size ,map(np.asarray ,will_return[0].tolist()))), axis = 0), will_return[1])
#will_return = (np.concatenate(list(map(np.asarray ,will_return[0].tolist())), axis = 0), will_return[1])
input_ = list(filter(lambda x: x.size ,map(np.asarray ,will_return[0].tolist())))
if input_:
will_return = (np.concatenate(input_, axis = 0), will_return[1])
else:
will_return = (np.empty((0, 3)), will_return[1])
will_return = will_return[0].reshape((-1, 3)) if will_return[0].size else np.empty((0, 3))
return will_return
fit_kw = sorted(fit_kws, key = len)[::-1][0]
condition_part_splits = condition_part.split(fit_kw)
#if fit_kw in ("或",):
if fit_kw in or_kws:
fit_kw = "or"
#elif fit_kw in ("而且", "并且", "当中",):
elif fit_kw in and_kws:
fit_kw = "and"
else:
fit_kw = ""
will_return = (list(map(lambda cond_: cond_.replace(" ", "") + " " + common_res, condition_part_splits)), fit_kw)
if extract_return:
#return (list(map(recurrent_extract_cond, will_return[0])), will_return[1])
will_return = (np.asarray(list(map(recurrent_extract_cond, will_return[0]))), will_return[1])
#ic(will_return[0])
#will_return = (np.concatenate(list(map(np.asarray ,will_return[0].tolist())), axis = 0), will_return[1])
input_ = list(filter(lambda x: x.size ,map(np.asarray ,will_return[0].tolist())))
if input_:
will_return = (np.concatenate(input_, axis = 0), will_return[1])
else:
will_return = (np.empty((0, 3)), will_return[1])
#ic(will_return[0])
will_return = will_return[0].reshape((-1, 3)) if will_return[0].size else np.empty((0, 3))
return will_return
def filter_total_conds(s, df, condition_fit_num = 0):
assert condition_fit_num >= 0 and type(condition_fit_num) == type(0)
df = df.copy()
#### some col not as float with only "None" as cell, also transform them into float
df = df.applymap(lambda x: np.nan if x in ["None", None, "/"] else x)
def justify_column_as_float(s):
if "float" in str(s.dtype):
return True
if all(s.map(type).map(lambda tx: "float" in str(tx))):
return True
return False
float_cols = list(map(lambda tt2: tt2[0],filter(lambda t2: t2[1] ,df.apply(justify_column_as_float, axis = 0).to_dict().items())))
for f_col in float_cols:
df[f_col] = df[f_col].astype(np.float64)
###
header = df.columns.tolist()
units_cols = filter(lambda c: "(" in c and c.endswith(")"), df.columns.tolist())
if not float_cols:
float_discribe_df = pd.DataFrame()
else:
float_discribe_df = df[float_cols].describe()
def call_eval(val):
try:
return literal_eval(val)
except:
return val
#### find condition column same as question_column
def find_cond_col(res, header):
#ic(res, header)
c_res_common_dict = dict(filter(lambda t2: t2[1] ,map(lambda c: (c ,findMaxSubString(c, res)), header)))
#ic(c_res_common_dict)
common_ratio_c_dict = dict(map(lambda t2: (t2[0], len(t2[1]) / len(t2[0])), c_res_common_dict.items()))
common_ratio_res_dict = dict(map(lambda t2: (t2[0], len(t2[1]) / len(res)), c_res_common_dict.items()))
if not common_ratio_c_dict or not common_ratio_res_dict:
return None
dict_0_max_key = sorted(common_ratio_c_dict.items(), key = lambda t2: t2[1])[::-1][0][0]
dict_1_max_key = sorted(common_ratio_res_dict.items(), key = lambda t2: t2[1])[::-1][0][0]
return dict_0_max_key if dict_0_max_key == dict_1_max_key else None
###
#### type comptatible in column type and value type, and fit_num match
def filter_cond_col(cond_t3):
assert type(cond_t3) == type((1,)) and len(cond_t3) == 3
col, _, value = cond_t3
if type(value) == type(""):
value = call_eval(value)
if col not in df.columns.tolist():
return False
#### type key value comp
if col in float_cols and type(value) not in (type(0), type(0.0)):
return False
if col not in float_cols and type(value) in (type(0), type(0.0)):
return False
#### string value not in corr column
if col not in float_cols and type(value) not in (type(0), type(0.0)):
if type(value) == type(""):
if value not in df[col].tolist():
return False
if type(value) in (type(0), type(0.0)):
if col in float_discribe_df.columns.tolist():
if condition_fit_num > 0:
if value >= float_discribe_df[col].loc["min"] and value <= float_discribe_df[col].loc["max"]:
return True
else:
return False
else:
assert condition_fit_num == 0
return True
if condition_fit_num > 0:
if value in df[col].tolist():
return True
else:
return False
else:
assert condition_fit_num == 0
return True
return True
###
#### condtions with same column may have conflict, choose the nearest one by stats in float or
### common_string as find_cond_col do.
def same_column_cond_filter(cond_list, sort_stats = "mean"):
#ic(cond_list)
if len(cond_list) == len(set(map(lambda t3: t3[0] ,cond_list))):
return cond_list
req = defaultdict(list)
for t3 in cond_list:
req[t3[0]].append(t3[1:])
def t2_list_sort(col_name ,t2_list):
if not t2_list:
return None
t2 = None
if col_name in float_cols:
t2 = sorted(t2_list, key = lambda t2: np.abs(t2[1] - float_discribe_df[col_name].loc[sort_stats]))[0]
else:
if all(map(lambda t2: type(t2[1]) == type("") ,t2_list)):
col_val_cnt_df = df[col_name].value_counts().reset_index()
col_val_cnt_df.columns = ["val", "cnt"]
#col_val_cnt_df["val"].map(lambda x: sorted(filter(lambda tt2: tt2[-1] ,map(lambda t2: (t2 ,len(findMaxSubString(x, t2[1]))), t2_list)),
# key = lambda ttt2: -1 * ttt2[-1])[0])
t2_list_map_to_column_val = list(filter(lambda x: x[1] ,map(lambda t2: (t2[0] ,find_cond_col(t2[1], list(set(col_val_cnt_df["val"].values.tolist())))), t2_list)))
if t2_list_map_to_column_val:
#### return max length fit val in column
t2 = sorted(t2_list_map_to_column_val, key = lambda t2: -1 * len(t2[1]))[0]
if t2 is None and t2_list:
t2 = t2_list[0]
return t2
cond_list_filtered = list(map(lambda ttt2: (ttt2[0], ttt2[1][0], ttt2[1][1]) ,
filter(lambda tt2: tt2[1] ,map(lambda t2: (t2[0] ,t2_list_sort(t2[0], t2[1])), req.items()))))
return cond_list_filtered
###
total_conds_map_to_column = list(map(lambda t3: (find_cond_col(t3[0], header), t3[1], t3[2]), s["total_conds"]))
total_conds_map_to_column_filtered = list(filter(filter_cond_col, total_conds_map_to_column))
total_conds_map_to_column_filtered = sorted(set(map(lambda t3: (t3[0], t3[1], call_eval(t3[2]) if type(t3[2]) == type("") else t3[2]), total_conds_map_to_column_filtered)))
#ic(total_conds_map_to_column_filtered)
cp_cond_list = list(filter(lambda t3: t3[1] in (">", "<"), total_conds_map_to_column_filtered))
eq_cond_list = list(filter(lambda t3: t3[1] in ("==", "!="), total_conds_map_to_column_filtered))
cp_cond_list_filtered = same_column_cond_filter(cp_cond_list)
#total_conds_map_to_column_filtered = same_column_cond_filter(total_conds_map_to_column_filtered)
return cp_cond_list_filtered + eq_cond_list
#return total_conds_map_to_column_filtered
###@@ only_kws_columns = {"城市": "=="}
#### this function only use to cond can not extract by JointBert,
### may because not contain column string in question such as "城市" or difficult to extract kw
### define kw column as all cells in series are string type.
### this function support config relation to column and if future
### want to auto extract relation, this may can be done by head string or tail string by edit pattern "\w?{}\w?"
### "是" or "不是" can be extract in this manner.
def augment_kw_in_question(question_df, df, only_kws_in_string = []):
#### keep only_kws_in_string empty to maintain all condition
question_df = question_df.copy()
#df = df.copy()
def call_eval(val):
try:
return literal_eval(val)
except:
return val
df = df.copy()
df = df.applymap(call_eval)
#### some col not as float with only "None" as cell, also transform them into float
df = df.applymap(lambda x: np.nan if x in ["None", None, "/"] else x)
def justify_column_as_float(s):
if "float" in str(s.dtype):
return True
if all(s.map(type).map(lambda tx: "float" in str(tx))):
return True
return False
float_cols = list(map(lambda tt2: tt2[0],filter(lambda t2: t2[1] ,df.apply(justify_column_as_float, axis = 0).to_dict().items())))
#obj_cols = set(df.columns.tolist()).difference(set(float_cols))
def justify_column_as_kw(s):
if all(s.map(type).map(lambda tx: "str" in str(tx))):
return True
return False
obj_cols = list(map(lambda tt2: tt2[0],filter(lambda t2: t2[1] ,df.apply(justify_column_as_kw, axis = 0).to_dict().items())))
obj_cols = list(set(obj_cols).difference(set(float_cols)))
if only_kws_columns:
obj_cols = list(set(obj_cols).intersection(set(only_kws_columns.keys())))
#replace_format = "{}是{}"
#kw_augmented_df = pd.DataFrame(df[obj_cols].apply(lambda s: list(map(lambda val :(val,replace_format.format(s.name, val)), s.tolist())), axis = 0).values.tolist())
#kw_augmented_df.columns = obj_cols
kw_augmented_df = df[obj_cols].copy()
#ic(kw_augmented_df)
def extract_question_kws(question):
if not kw_augmented_df.size:
return []
req = defaultdict(set)
for ridx, r in kw_augmented_df.iterrows():
for k, v in dict(r).items():
if v in question:
pattern = "\w?{}\w?".format(v)
all_match = re.findall(pattern, question)
#req = req.union(set(all_match))
#req[v] = req[v].union(set(all_match))
key = "{}~{}".format(k, v)
req[key] = req[key].union(set(all_match))
#ic(k, v)
#question = question.replace(v[0], v[1])
#return question.replace(replace_format.format("","") * 2, replace_format.format("",""))
#req = list(req)
if only_kws_in_string:
req = list(map(lambda tt2: tt2[0] ,filter(lambda t2: sum(map(lambda kw: sum(map(lambda t: kw in t ,t2[1])), only_kws_in_string)), req.items())))
else:
req = list(set(req.keys()))
def req_to_t3(req_string, relation = "=="):
assert "~" in req_string
left, right = req_string.split("~")
if left in only_kws_columns:
relation = only_kws_columns[left]
return (left, relation, right)
if not req:
return []
return list(map(req_to_t3, req))
#return req
question_df["question_kw_conds"] = question_df["question"].map(extract_question_kws)
return question_df
def choose_question_column_by_rm_conds(s, df):
question = s.question
total_conds_filtered = s.total_conds_filtered
#cond_kws = ("或", "而且", "并且", "当中")
cond_kws = conn_kws
stopwords = ("是", )
#ic(total_conds_filtered)
def construct_res(question):
for k, _, v in total_conds_filtered:
if "(" in k:
k = k[:k.find("(")]
#ic(k)
question = question.replace(str(k), "")
question = question.replace(str(v), "")
for w in cond_kws + stopwords:
question = question.replace(w, "")
return question
res = construct_res(question)
decomp = (None, res, None)
return choose_question_column(decomp, df.columns.tolist(), df)
def split_qst_by_kw(question, kw = "的"):
return question.split(kw)
#qst_kws = ("多少", "什么", "多大", "哪些", "怎么", "情况", "那些", "哪个")
###@@ qst_kws = ("多少", "什么", "多大", "哪些", "怎么", "情况", "那些", "哪个")
def choose_res_by_kws(question):
#kws = ["的","多少", '是']
question = question.replace(" ", "")
#kws = ["的","或者","或", "且","并且","同时"]
kws = ("的",) + conn_kws
kws = list(kws)
def qst_kw_filter(text):
#qst_kws = ("多少", "什么", "多大", "哪些", "怎么", "情况", "那些", "哪个")
if any(map(lambda kw: kw in text, qst_kws)):