#!/usr/bin/env python
#encoding:utf-8
from deepdive import *
import random
from collections import namedtuple
from commonutil import *
import re


Label = namedtuple('Label', 'entity_id, label, type')

@tsv_extractor
@returns(lambda
        entity_id   = "text",
        label   = "int",
        rule_id = "text",
    :[])
# heuristic rules for finding positive/negative examples of transaction relationship mentions
def supervise(
        entity_id="text", entity_begin="int", entity_end="int",
        doc_id="text", sentence_index="int", sentence_text="text",
        tokens="text[]",  pos_tags="text[]", ner_tags="text[]",
    ):

    # Constants
    label = Label(entity_id=entity_id,label=None,type=None)
    MAX_DIST = 10
    TYPE_MENTION = frozenset(["org","company"])

    # Common data objects
    if entity_begin>MAX_DIST:
        begin = entity_begin-MAX_DIST
    else:
        begin = 0
    if len(tokens)-entity_end>MAX_DIST:
        end = entity_end+MAX_DIST
    else:
        end = -1
    front_tokens = tokens[begin:entity_begin]
    end_tokens = tokens[entity_end:end]
    front_ner = ner_tags[begin:entity_begin]
    end_ner = ner_tags[end:entity_end]
    
    
    pattern_person = re.compile("联(\s*|，|,)?系(\s*|，|,)?(人|方式)|项目(经理|负责人)|经办人")
    
    if re.search(pattern_person,"".join(front_tokens)) is not None:
        yield label._replace(label=0,type="match")
    else:
        yield label._replace(label=1,type="not match")