Merge branch 'master' of http://192.168.2.65:3000/BIDI-ML/BIDI_ML_INFO_EXTRACTION

BiddingKG/dl/entityLink/entityLink.py

@@ -3,10 +3,9 @@ Created on 2019年5月21日
 
 @author: User
 '''
-
+import csv
 import re
 import os
-
 import time
 _time = time.time()
 from BiddingKG.dl.common.Utils import *
@@ -63,6 +62,7 @@ def link_entitys(list_entitys,on_value=0.8):
                             _entity.entity_text = _ent.entity_text
 
         # 2021/12/21 替换通过字典识别到的取长度最大的相似实体
+        place_list = get_place_list()
         for _entity in range_entity:
             used_linked_entitys = []
             if not _entity.linked_entitys:
@@ -74,9 +74,54 @@ def link_entitys(list_entitys,on_value=0.8):
                 # print("_entity, _ent", _entity.entity_text, _ent.if_dict_match, _ent.entity_text)
                 if _ent.if_dict_match == 1:
                     if len(_ent.entity_text) > len(_entity.entity_text):
-                        # print("字典替换", _entity.entity_text, "->", _ent.entity_text)
-                        _entity.entity_text = _ent.entity_text
-                        used_linked_entitys.append(_ent)
+                        # 判断两个公司地区相同
+                        match_list_1, match_list_2 = [], []
+                        for place in place_list:
+                            if place[0] in _entity.entity_text:
+                                match_list_1.append(place[0])
+                            if place[0] in _ent.entity_text:
+                                match_list_2.append(place[0])
+
+                        if str(match_list_1) == str(match_list_2):
+                            # print("字典替换", _entity.entity_text, "->", _ent.entity_text)
+                            _entity.origin_entity_text = _entity.entity_text
+                            _entity.entity_text = _ent.entity_text
+                            used_linked_entitys.append(_ent)
+            # print(_entity.origin_entity_text, _entity.entity_text)
+
+
+def get_place_list():
+    with open('../place_info.csv', 'r') as f:
+        reader = csv.reader(f)
+
+        place_list = []
+        for r in reader:
+            place_list.append(r[1:3])
+        place_list = place_list[1:]
+        place_list.append(['台湾', '台湾'])
+        place_list.append(['澳门', '澳门'])
+        place_list.append(['香港', '香港'])
+        place_list.append(['東莞', '東莞'])
+        place_list.append(['廣州', '廣州'])
+        place_list.append(['韩国', '韩国'])
+        place_list.append(['德国', '德国'])
+        place_list.append(['英国', '英国'])
+        place_list.append(['日本', '日本'])
+        place_list.append(['意大利', '意大利'])
+        place_list.append(['新加坡', '新加坡'])
+        place_list.append(['加拿大', '加拿大'])
+        place_list.append(['西班牙', '西班牙'])
+        place_list.append(['澳大利亚', '澳大利亚'])
+        place_list.append(['美国', '美国'])
+    # 去重
+    place_list_str = []
+    for place in place_list:
+        place_list_str.append(str(place))
+    place_list_str = list(set(place_list_str))
+    place_list = []
+    for place in place_list_str:
+        place_list.append(eval(place))
+    return place_list
 
 
 def getEnterprisePath():

BiddingKG/dl/interface/Entitys.py

@@ -172,6 +172,7 @@ class Entity():
         self.if_dict_match = 0  # 2021/12/21 新增，判断公司实体是否由字典识别得到
         self.is_total_money = 0  # 2021/12/29 新增，判断金额是否总价
         self.is_unit_money = 0  # 2021/12/29 新增，判断金额是否单价
+        self.origin_entity_text = ''  # 2022/1/5 新增，记录字典替换的原来的实体名
 
     def set_Role(self,role_label,role_values):
         self.label = int(role_label)

BiddingKG/dl/place_info.csv

@@ -0,0 +1,3249 @@
+id,cname,cdesc,parentid,ctype
+1,华北,华北地区,-1,10
+2,北京,北京市,1,20
+3,华南,华南地区,-1,10
+4,广东,广东省,3,20
+5,海南,海南省,3,20
+6,广西,广西省,3,20
+11,华东,华东地区,-1,10
+12,华中,华中地区,-1,10
+13,西南,西南地区,-1,10
+14,东北,东北地区,-1,10
+15,西北,西北地区,-1,10
+16,天津,天津市,1,20
+17,河北,河北省,1,20
+18,山西,山西省,1,20
+19,内蒙古,内蒙古省,1,20
+20,上海,上海市,11,20
+21,江苏,江苏省,11,20
+22,浙江,浙江省,11,20
+23,安徽,安徽省,11,20
+24,福建,福建省,11,20
+25,江西,江西省,11,20
+26,山东,山东省,11,20
+27,河南,河南省,12,20
+28,湖北,湖北省,12,20
+29,湖南,湖南省,12,20
+30,重庆,重庆市,13,20
+31,四川,四川省,13,20
+32,贵州,贵州省,13,20
+33,云南,云南省,13,20
+34,西藏,西藏省,13,20
+35,黑龙江,黑龙江省,14,20
+36,辽宁,辽宁省,14,20
+37,吉林,吉林省,14,20
+38,陕西,陕西省,15,20
+39,甘肃,甘肃省,15,20
+40,青海,青海省,15,20
+41,宁夏,宁夏省,15,20
+42,新疆,新疆省,15,20
+51,全国,全国,-1,20
+56,东城,东城区,2,30
+57,和平,和平,16,30
+58,石家庄,石家庄市,17,30
+59,唐山,唐山市,17,30
+60,秦皇岛,秦皇岛市,17,30
+61,邯郸,邯郸市,17,30
+62,邢台,邢台市,17,30
+63,保定,保定市,17,30
+64,张家口,张家口市,17,30
+65,承德,承德市,17,30
+66,沧州,沧州市,17,30
+67,廊坊,廊坊市,17,30
+68,衡水,衡水市,17,30
+69,太原,太原市,18,30
+70,大同,大同市,18,30
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+1471,扎兰屯,扎兰屯市,86,40
+1472,额尔古纳,额尔古纳市,86,40
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+1498,四子王旗,四子王旗,88,40
+1499,玛沁,玛沁县,372,40
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+1571,梁溪,梁溪区,131,40
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+1582,永靖,永靖县,365,40
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+1586,云龙,云龙区,132,40
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+1593,睢宁,睢宁县,132,40
+1594,邳州,邳州市,132,40
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+1919,陇县,陇县,345,40
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+1938,图们,图们市,342,40
+1939,敦化,敦化市,342,40
+1940,和龙,和龙市,342,40
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+1953,巢湖,巢湖市,154,40
+1954,宁江,宁江区,340,40
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+1956,乾安,乾安县,340,40
+1957,长岭,长岭县,340,40
+1958,前郭尔罗斯蒙古族自治,前郭尔罗斯蒙古族自治县,340,40
+1959,镜湖,镜湖区,155,40
+1960,弋江,弋江区,155,40
+1961,鸠江,鸠江区,155,40
+1962,三山,三山区,155,40
+1963,无为,无为县,155,40
+1964,芜湖,芜湖县,155,40
+1965,繁昌,繁昌县,155,40
+1966,南陵,南陵县,155,40
+1967,洮北,洮北区,341,40
+1968,洮南,洮南市,341,40
+1969,大安,大安市,341,40
+1970,镇赉,镇赉县,341,40
+1971,通榆,通榆县,341,40
+1972,龙子湖,龙子湖区,156,40
+1973,蚌山,蚌山区,156,40
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+1975,淮上,淮上区,156,40
+1976,五河,五河县,156,40
+1977,固镇,固镇县,156,40
+1978,怀远,怀远县,156,40
+1979,浑江,浑江区,339,40
+1980,江源,江源区,339,40
+1981,临江,临江市,339,40
+1982,抚松,抚松县,339,40
+1983,靖宇,靖宇县,339,40
+1984,长白朝鲜族自治,长白朝鲜族自治县,339,40
+1985,大通,大通区,157,40
+1986,田家庵,田家庵区,157,40
+1987,谢家集,谢家集区,157,40
+1988,八公山,八公山区,157,40
+1989,潘集,潘集区,157,40
+1990,凤台,凤台县,157,40
+1991,寿县,寿县,157,40
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+1993,二道江,二道江区,338,40
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+3540,清新,清新区,107,40
+3541,英德,英德市,107,40
+3542,连州,连州市,107,40
+3543,佛冈,佛冈县,107,40
+3544,阳山,阳山县,107,40
+3545,连山壮族瑶族自治,连山壮族瑶族自治县,107,40
+3546,连南瑶族自治,连南瑶族自治县,107,40
+3547,天元,天元区,240,40
+3548,荷塘,荷塘区,240,40
+3549,芦淞,芦淞区,240,40
+3550,石峰,石峰区,240,40
+3551,云龙示范,云龙示范区,240,40
+3552,株洲,株洲县,240,40
+3553,攸县,攸县,240,40
+3554,茶陵,茶陵县,240,40
+3555,炎陵,炎陵县,240,40
+3556,醴陵,醴陵市,240,40
+3557,雨湖,雨湖区,241,40
+3558,岳塘,岳塘区,241,40
+3559,湘潭,湘潭县,241,40
+3560,韶山,韶山市,241,40
+3561,湘乡,湘乡市,241,40
+3562,江城,江城区,106,40
+3563,阳东,阳东区,106,40
+3564,阳春,阳春市,106,40
+3565,阳西,阳西县,106,40
+3566,雁峰,雁峰区,242,40
+3567,石鼓,石鼓区,242,40
+3568,珠晖,珠晖区,242,40
+3569,蒸湘,蒸湘区,242,40
+3570,南岳,南岳区,242,40
+3571,衡阳,衡阳县,242,40
+3572,衡南,衡南县,242,40
+3573,衡山,衡山县,242,40
+3574,衡东,衡东县,242,40
+3575,祁东,祁东县,242,40
+3576,耒阳,耒阳市,242,40
+3577,常宁,常宁市,242,40
+3578,源城,源城区,105,40
+3579,龙川,龙川县,105,40
+3580,连平,连平县,105,40
+3581,东源,东源县,105,40
+3582,和平,和平县,105,40
+3583,紫金,紫金县,105,40
+3584,双清,双清区,243,40
+3585,大祥,大祥区,243,40
+3586,北塔,北塔区,243,40
+3587,邵东,邵东县,243,40
+3588,新邵,新邵县,243,40
+3589,邵阳,邵阳县,243,40
+3590,隆回,隆回县,243,40
+3591,洞口,洞口县,243,40
+3592,绥宁,绥宁县,243,40
+3593,新宁,新宁县,243,40
+3594,城步苗族自治,城步苗族自治县,243,40
+3595,武冈,武冈市,243,40
+3596,城区,城区,104,40
+3597,陆丰,陆丰市,104,40
+3598,海丰,海丰县,104,40
+3599,陆河,陆河县,104,40
+3600,岳阳楼,岳阳楼区,244,40
+3601,君山,君山区,244,40
+3602,云溪,云溪区,244,40
+3603,岳阳,岳阳县,244,40
+3604,平江,平江县,244,40
+3605,湘阴,湘阴县,244,40
+3606,华容,华容县,244,40
+3607,汨罗,汨罗市,244,40
+3608,临湘,临湘市,244,40
+3609,武陵,武陵区,245,40
+3610,鼎城,鼎城区,245,40
+3611,安乡,安乡县,245,40
+3612,汉寿,汉寿县,245,40
+3613,澧县,澧县,245,40
+3614,临澧,临澧县,245,40
+3615,桃源,桃源县,245,40
+3616,石门,石门县,245,40
+3617,津市,津市市,245,40
+3618,永定,永定区,246,40
+3619,武陵源,武陵源区,246,40
+3620,慈利,慈利县,246,40
+3621,桑植,桑植县,246,40
+3622,资阳,资阳区,247,40
+3623,赫山,赫山区,247,40
+3624,大通湖,大通湖区,247,40
+3625,安化,安化县,247,40
+3626,桃江,桃江县,247,40
+3627,南县,南县,247,40
+3628,沅江,沅江市,247,40
+3629,娄星,娄星区,251,40
+3630,双峰,双峰县,251,40
+3631,新化,新化县,251,40
+3632,涟源,涟源市,251,40
+3633,冷水江,冷水江市,251,40
+3634,梅江,梅江区,103,40
+3635,梅县,梅县区,103,40
+3636,兴宁,兴宁市,103,40
+3637,大埔,大埔县,103,40
+3638,丰顺,丰顺县,103,40
+3639,五华,五华县,103,40
+3640,平远,平远县,103,40
+3641,蕉岭,蕉岭县,103,40
+3642,惠城,惠城区,102,40
+3643,惠阳,惠阳区,102,40
+3644,博罗,博罗县,102,40
+3645,龙门,龙门县,102,40
+3646,惠东,惠东县,102,40
+3647,苏仙,苏仙区,248,40
+3648,北湖,北湖区,248,40
+3649,安仁,安仁县,248,40
+3650,桂阳,桂阳县,248,40
+3651,永兴,永兴县,248,40
+3652,宜章,宜章县,248,40
+3653,嘉禾,嘉禾县,248,40
+3654,临武,临武县,248,40
+3655,汝城,汝城县,248,40
+3656,桂东,桂东县,248,40
+3657,资兴,资兴市,248,40
+3658,茂南,茂南区,100,40
+3659,电白,电白区,100,40
+3660,高州,高州市,100,40
+3661,化州,化州市,100,40
+3662,信宜,信宜市,100,40
+3663,零陵,零陵区,249,40
+3664,冷水滩,冷水滩区,249,40
+3665,道县,道县,249,40
+3666,祁阳,祁阳县,249,40
+3667,东安,东安县,249,40
+3668,双牌,双牌县,249,40
+3669,江永,江永县,249,40
+3670,宁远,宁远县,249,40
+3671,蓝山,蓝山县,249,40
+3672,新田,新田县,249,40
+3673,江华瑶族自治,江华瑶族自治县,249,40
+3674,金洞管理,金洞管理区,249,40
+3675,回龙圩管理,回龙圩管理区,249,40
+3676,江海,江海区,98,40
+3677,蓬江,蓬江区,98,40
+3678,新会,新会区,98,40
+3679,台山,台山市,98,40
+3680,鹤山,鹤山市,98,40
+3681,开平,开平市,98,40
+3682,恩平,恩平市,98,40
+3683,鹤城,鹤城区,250,40
+3684,中方,中方县,250,40
+3685,沅陵,沅陵县,250,40
+3686,辰溪,辰溪县,250,40
+3687,溆浦,溆浦县,250,40
+3688,会同,会同县,250,40
+3689,麻阳苗族自治,麻阳苗族自治县,250,40
+3690,新晃侗族自治,新晃侗族自治县,250,40
+3691,芷江侗族自治,芷江侗族自治县,250,40
+3692,靖州苗族侗族自治,靖州苗族侗族自治县,250,40
+3693,通道侗族自治,通道侗族自治县,250,40
+3694,洪江,洪江市,250,40
+3695,洪江管理,洪江管理区,250,40
+3696,吉首,吉首市,252,40
+3697,泸溪,泸溪县,252,40
+3698,凤凰,凤凰县,252,40
+3699,花垣,花垣县,252,40
+3700,保靖,保靖县,252,40
+3701,古丈,古丈县,252,40
+3702,永顺,永顺县,252,40
+3703,龙山,龙山县,252,40
+3704,端州,端州区,101,40
+3705,鼎湖,鼎湖区,101,40
+3706,高要,高要区,101,40
+3707,四会,四会市,101,40
+3708,广宁,广宁县,101,40
+3709,怀集,怀集县,101,40
+3710,封开,封开县,101,40
+3711,德庆,德庆县,101,40
+3712,赤坎,赤坎区,99,40
+3713,霞山,霞山区,99,40
+3714,坡头,坡头区,99,40
+3715,麻章,麻章区,99,40
+3716,廉江,廉江市,99,40
+3717,吴川,吴川市,99,40
+3718,雷州,雷州市,99,40
+3719,遂溪,遂溪县,99,40
+3720,徐闻,徐闻县,99,40
+3721,浈江,浈江区,93,40
+3722,武江,武江区,93,40
+3723,曲江,曲江区,93,40
+3724,乐昌,乐昌市,93,40
+3725,南雄,南雄市,93,40
+3726,始兴,始兴县,93,40
+3727,仁化,仁化县,93,40
+3728,翁源,翁源县,93,40
+3729,新丰,新丰县,93,40
+3730,乳源瑶族自治,乳源瑶族自治县,93,40
+3731,禅城,禅城区,97,40
+3732,高明,高明区,97,40
+3733,三水,三水区,97,40
+3734,南海,南海区,97,40
+3735,顺德,顺德区,97,40
+3736,金平,金平区,96,40
+3737,龙湖,龙湖区,96,40
+3738,濠江,濠江区,96,40
+3739,潮南,潮南区,96,40
+3740,潮阳,潮阳区,96,40
+3741,澄海,澄海区,96,40
+3742,南澳,南澳县,96,40
+3743,香洲,香洲区,95,40
+3744,斗门,斗门区,95,40
+3745,金湾,金湾区,95,40
+3746,福田,福田区,94,40
+3747,罗湖,罗湖区,94,40
+3748,南山,南山区,94,40
+3749,盐田,盐田区,94,40
+3750,宝安,宝安区,94,40
+3751,龙岗,龙岗区,94,40
+3752,越秀,越秀区,92,40
+3753,荔湾,荔湾区,92,40
+3754,海珠,海珠区,92,40
+3755,天河,天河区,92,40
+3756,白云,白云区,92,40
+3757,黄埔,黄埔区,92,40
+3758,花都,花都区,92,40
+3759,番禺,番禺区,92,40
+3760,南沙,南沙区,92,40
+3761,增城,增城区,92,40
+3762,从化,从化区,92,40
+3766,梁平,梁平,30,30
+3769,丰都,丰都,30,30
+3770,城口,城口,30,30
+3771,垫江,垫江,30,30
+3772,忠县,忠县,30,30
+3773,云阳,云阳,30,30
+3774,奉节,奉节,30,30
+3775,巫山,巫山,30,30
+3776,巫溪,巫溪,30,30
+3777,石柱,石柱,30,30
+3778,秀山,秀山,30,30
+3779,武隆,武隆,30,30
+3780,酉阳,酉阳,30,30
+3781,彭水,彭水,30,30

BiddingKG/dl/table_head/loss.py

@@ -2,12 +2,12 @@ import tensorflow as tf
 import keras as K
 
 
-def focal_loss(gamma=2., alpha=.5):
-    def focal_loss_fixed(y_true, y_pred):
+def focal_loss(gamma=3., alpha=.25):
+    def f_loss(y_true, y_pred):
         pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
         pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
         return - K.backend.sum(alpha * K.backend.pow(1. - pt_1, gamma)
                                * K.backend.log(K.backend.epsilon()+pt_1))\
                - K.backend.sum((1-alpha) * K.backend.pow(pt_0, gamma)
                                * K.backend.log(1. - pt_0 + K.backend.epsilon()))
-    return focal_loss_fixed
+    return f_loss

BiddingKG/dl/table_head/metrics.py

@@ -0,0 +1,85 @@
+from keras import backend as K
+
+
+def mcor(y_true, y_pred):
+    # matthews_correlation
+    y_pred_pos = K.round(K.clip(y_pred, 0, 1))
+    y_pred_neg = 1 - y_pred_pos
+
+    y_pos = K.round(K.clip(y_true, 0, 1))
+    y_neg = 1 - y_pos
+
+    tp = K.sum(y_pos * y_pred_pos)
+    tn = K.sum(y_neg * y_pred_neg)
+
+    fp = K.sum(y_neg * y_pred_pos)
+    fn = K.sum(y_pos * y_pred_neg)
+
+    numerator = (tp * tn - fp * fn)
+    denominator = K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
+    return numerator / (denominator + K.epsilon())
+
+
+def precision(y_true, y_pred):
+    """Precision metric.
+
+    Only computes a batch-wise average of precision.
+
+    Computes the precision, a metric for multi-label classification of
+    how many selected items are relevant.
+    """
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+    _precision = true_positives / (predicted_positives + K.epsilon())
+    return _precision
+
+
+def recall(y_true, y_pred):
+    """Recall metric.
+
+    Only computes a batch-wise average of recall.
+
+    Computes the recall, a metric for multi-label classification of
+    how many relevant items are selected.
+    """
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+    _recall = true_positives / (possible_positives + K.epsilon())
+    return _recall
+
+
+def f1(y_true, y_pred):
+    def recall(y_true, y_pred):
+        """Recall metric.
+
+        Only computes a batch-wise average of recall.
+
+        Computes the recall, a metric for multi-label classification of
+        how many relevant items are selected.
+        """
+        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+        _recall = true_positives / (possible_positives + K.epsilon())
+        return _recall
+
+    def precision(y_true, y_pred):
+        """Precision metric.
+
+        Only computes a batch-wise average of precision.
+
+        Computes the precision, a metric for multi-label classification of
+        how many selected items are relevant.
+        """
+        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+        _precision = true_positives / (predicted_positives + K.epsilon())
+        return _precision
+    _precision = precision(y_true, y_pred)
+    _recall = recall(y_true, y_pred)
+    return 2*((_precision*_recall)/(_precision+_recall+K.epsilon()))
+
+
+#you can use it like this
+# model.compile(loss='binary_crossentropy',
+#               optimizer= "adam",
+#               metrics=[mcor,recall, f1])

BiddingKG/dl/table_head/models/model.py

@@ -1,6 +1,8 @@
+import sys
+import os
+sys.path.append(os.path.abspath("../.."))
 from keras import layers, models
-from keras.layers import Lambda
-
+import tensorflow as tf
 from BiddingKG.dl.table_head.models.my_average_pooling import MyAveragePooling1D
 from BiddingKG.dl.table_head.models.self_attention import SeqSelfAttention
 
@@ -9,31 +11,34 @@ def get_model(input_shape, output_shape):
     # Input
     input_1 = layers.Input(shape=input_shape[1:], dtype="float32")
     input_2 = layers.Input(shape=input_shape[1:], dtype="float32")
+    input_3 = layers.Input(shape=input_shape[1:], dtype="float32")
 
     # Embedding
-    embed_1 = layers.Embedding(input_dim=6624, output_dim=32,
-                               input_length=input_shape[1], mask_zero=True)(input_1)
-    embed_2 = layers.Embedding(input_dim=6624, output_dim=32,
-                               input_length=input_shape[1], mask_zero=True)(input_2)
+    # embed_1 = layers.Embedding(input_dim=6624, output_dim=32,
+    #                            input_length=input_shape[1], mask_zero=True)(input_1)
+    # embed_2 = layers.Embedding(input_dim=6624, output_dim=32,
+    #                            input_length=input_shape[1], mask_zero=True)(input_2)
 
     # Bi-LSTM
-    bi_lstm_1 = layers.Bidirectional(layers.LSTM(16, return_sequences=True))(embed_1)
-    bi_lstm_2 = layers.Bidirectional(layers.LSTM(16, return_sequences=True))(embed_2)
+    bi_lstm_1 = layers.Bidirectional(layers.LSTM(16, return_sequences=True))(input_1)
+    bi_lstm_2 = layers.Bidirectional(layers.LSTM(16, return_sequences=True))(input_2)
+    bi_lstm_3 = layers.Bidirectional(layers.LSTM(16, return_sequences=True))(input_3)
 
     # Self-Attention
     self_attention_1 = SeqSelfAttention(attention_activation='sigmoid')(bi_lstm_1)
     self_attention_2 = SeqSelfAttention(attention_activation='sigmoid')(bi_lstm_2)
+    self_attention_3 = SeqSelfAttention(attention_activation='sigmoid')(bi_lstm_3)
 
     # Concat
-    concat = layers.concatenate([self_attention_1, self_attention_2])
+    concat = layers.concatenate([self_attention_1, self_attention_2, self_attention_3])
 
     # Dense + Softmax
-    output = layers.Dense(output_shape[0], activation="softmax")(concat)
+    output = layers.Dense(output_shape[0], activation="sigmoid")(concat)
 
     # mask mean pooling
-    output = MyAveragePooling1D(axis=1)(output)
+    output = MyAveragePooling1D(axis=1, name='output')(output)
 
-    model = models.Model(inputs=[input_1, input_2], outputs=output)
+    model = models.Model(inputs=[input_1, input_2, input_3], outputs=output)
 
     model.summary()
     return model

BiddingKG/dl/table_head/pre_process.py

@@ -1,9 +1,8 @@
-import sys
-import os
-sys.path.append(os.path.abspath("../.."))
 import psycopg2
 import numpy as np
 
+from BiddingKG.dl.common.Utils import embedding_word
+
 
 def get_sentence_index_list(sentence, dict_path='utils/ppocr_keys_v1.txt'):
     with open(dict_path, 'r') as f:
@@ -47,14 +46,14 @@ def get_data_from_sql(dim=10):
     select table_text, pre_label, post_label, id
     from label_table_head_info 
     where update_user <> 'test27' and table_box_cnt >= 4 and table_box_cnt <= 200 
-    limit 1000;
+    ;
     """
     # sql = """
     # select table_text, pre_label, post_label, id
     # from label_table_head_info
     # where id = 843
     # """
-    result_list = postgresql_util(sql, limit=10000)
+    result_list = postgresql_util(sql, limit=1000000)
 
     all_data_list = []
     all_data_label_list = []
@@ -90,22 +89,54 @@ def get_data_from_sql(dim=10):
         else:
             data_list, data_label_list = [], []
 
-        for data in data_list:
-            # 中文字符映射为index
-            data[0] = get_sentence_index_list(data[0])
-            data[1] = get_sentence_index_list(data[1])
-
-            # 维度不够，填充掩码0
-            if len(data[0]) < dim:
-                data[0] = data[0] + [0]*(dim-len(data[0]))
-            elif len(data[0]) > dim:
-                data[0] = data[0][:dim]
-            if len(data[1]) < dim:
-                data[1] = data[1] + [0]*(dim-len(data[1]))
-            elif len(data[1]) > dim:
-                data[1] = data[1][:dim]
         all_data_list += data_list
         all_data_label_list += data_label_list
+
+    print("len(all_data_list)", len(all_data_list))
+
+        #
+        # new_data_list = []
+        # for data in data_list:
+        #     # 中文字符映射为index
+        #     # data[0] = get_sentence_index_list(data[0])
+        #     # data[1] = get_sentence_index_list(data[1])
+        #     # 维度不够，填充掩码0
+        #     # if len(data[0]) < dim:
+        #     #     data[0] = data[0] + [0]*(dim-len(data[0]))
+        #     # elif len(data[0]) > dim:
+        #     #     data[0] = data[0][:dim]
+        #     # if len(data[1]) < dim:
+        #     #     data[1] = data[1] + [0]*(dim-len(data[1]))
+        #     # elif len(data[1]) > dim:
+        #     #     data[1] = data[1][:dim]
+        #
+        #     # 中文字符映射为Embedding
+        #     data = embedding_word(data, input_shape)
+        #     new_data_list.append(data)
+        #
+        # new_data_list = np.array(new_data_list)
+        # data_label_list = np.array(data_label_list)
+        # if np.array(new_data_list).shape[1:] == input_shape:
+        #     all_data_list.append(new_data_list)
+        #     all_data_label_list.append(data_label_list)
+
+    # # 防止concat太慢
+    # split_len = 1000
+    # _len = int(len(all_data_list) / split_len)
+    # all_data_list_1 = []
+    # all_data_list_2 = []
+    # for i in range(_len):
+    #     if i == _len - 1:
+    #         array1 = np.concatenate(all_data_list[i*split_len:])
+    #         array2 = np.concatenate(all_data_label_list[i*split_len:])
+    #     else:
+    #         array1 = np.concatenate(all_data_list[i*split_len:i*split_len+split_len])
+    #         array2 = np.concatenate(all_data_label_list[i*split_len:i*split_len+split_len])
+    #     all_data_list_1.append(array1)
+    #     all_data_list_2.append(array2)
+    # all_data_list = np.concatenate(all_data_list_1)
+    # all_data_label_list = np.concatenate(all_data_list_2)
+
     return all_data_list, all_data_label_list
 
 
@@ -121,6 +152,13 @@ def table_process(text_list, label_list, _id):
         row = text_list[i]
         row_label = label_list[i]
 
+        if i > 0:
+            last_row = text_list[i-1]
+            last_row_label = label_list[i-1]
+        else:
+            last_row = []
+            last_row_label = []
+
         if i < len(text_list) - 1:
             next_row = text_list[i+1]
             next_row_label = label_list[i+1]
@@ -132,47 +170,86 @@ def table_process(text_list, label_list, _id):
             col = row[j]
             col_label = row_label[j]
 
+            # 超出表格置为None, 0
+            if j > 0:
+                last_col = row[j-1]
+                last_col_label = row_label[j-1]
+            else:
+                last_col = None
+                last_col_label = 0
+
             if j < len(row) - 1:
                 next_col = row[j+1]
                 next_col_label = row_label[j+1]
             else:
-                next_col = ""
-                next_col_label = ""
+                next_col = None
+                next_col_label = 0
+
+            if last_row:
+                last_row_col = last_row[j]
+                last_row_col_label = last_row_label[j]
+            else:
+                last_row_col = None
+                last_row_col_label = 0
 
             if next_row:
                 next_row_col = next_row[j]
                 next_row_col_label = next_row_label[j]
             else:
-                next_row_col = ""
-                next_row_col_label = ""
-
-            if next_col:
-                if col != next_col:
-                    data_list.append([col, next_col])
-                    data_label_list.append([int(col_label), int(next_col_label)])
-            if next_row_col:
-                if col != next_row_col:
-                    data_list.append([col, next_row_col])
-                    data_label_list.append([int(col_label), int(next_row_col_label)])
+                next_row_col = None
+                next_row_col_label = 0
+
+            # 三元组有一对不相等就作为数据
+            # if col != next_col or col != last_col:
+            data_list.append([last_col, col, next_col])
+            data_label_list.append([int(last_col_label), int(col_label),
+                                    int(next_col_label)])
+
+            # if col != next_row_col or col != last_row_col:
+            data_list.append([last_row_col, col, next_row_col])
+            data_label_list.append([int(last_row_col_label), int(col_label),
+                                    int(next_row_col_label)])
 
     return data_list, data_label_list
 
 
-def get_data_from_file():
-    data_path = 'train_data/data.txt'
-    data_label_path = 'train_data/data_label.txt'
+def get_data_from_file(file_type):
+    if file_type == 'np':
+        data_path = 'train_data/data_3.npy'
+        data_label_path = 'train_data/data_label_3.npy'
 
-    with open(data_path, 'r') as f:
-        data_list = f.readlines()
-    with open(data_label_path, 'r') as f:
-        data_label_list = f.readlines()
+        array1 = np.load(data_path)
+        array2 = np.load(data_label_path)
+        return array1, array2
+    elif file_type == 'txt':
+        data_path = 'train_data/data.txt'
+        data_label_path = 'train_data/data_label.txt'
 
-    for i in range(len(data_list)):
-        data_list[i] = eval(data_list[i][:-1])
-        data_label_list[i] = eval(data_label_list[i][:-1])
+        with open(data_path, 'r') as f:
+            data_list = f.readlines()
+        with open(data_label_path, 'r') as f:
+            data_label_list = f.readlines()
+
+        # for i in range(len(data_list)):
+        #     data_list[i] = eval(data_list[i][:-1])
+        #     data_label_list[i] = eval(data_label_list[i][:-1])
+        return data_list, data_label_list
+    else:
+        print("file type error! only np and txt supported")
+        raise Exception
 
-    print(len(data_list))
-    return data_list, data_label_list
+
+def processed_save_to_np():
+    array1, array2 = get_data_from_sql()
+    np.save('train_data/data_3.npy', array1)
+    np.save('train_data/data_label_3.npy', array2)
+
+    # with open('train_data/data.txt', 'w') as f:
+    #     for line in list1:
+    #         f.write(str(line) + "\n")
+    # with open('train_data/data_label.txt', 'w') as f:
+    #     for line in list2:
+    #         f.write(str(line) + "\n")
 
 
 def processed_save_to_txt():
@@ -185,5 +262,65 @@ def processed_save_to_txt():
             f.write(str(line) + "\n")
 
 
+def data_balance():
+    array1, array2 = get_data_from_file()
+    data_list = array2.tolist()
+    all_cnt = len(data_list)
+    cnt_0 = 0
+    cnt_1 = 0
+    for data in data_list:
+        if data[0] == 1 or data[1] == 1:
+            cnt_1 += 1
+        else:
+            cnt_0 += 1
+    print("all_cnt", all_cnt)
+    print("label has 1", cnt_1)
+    print("label all 0", cnt_0)
+
+
+def test_embedding():
+    output_shape = (2, 1, 60)
+    data = [[None], [None]]
+    result = embedding_word(data, output_shape)
+    print(result)
+
+
+def my_data_loader(data_list, data_label_list, batch_size):
+    data_num = len(data_list)
+
+    # 定义Embedding输出
+    output_shape = (3, 10, 60)
+
+    # batch循环取数据
+    i = 0
+    while True:
+        new_data_list = []
+        new_data_label_list = []
+        for j in range(batch_size):
+            if i >= data_num:
+                i = 0
+
+            # 中文字符映射为Embedding
+            data = eval(data_list[i][:-1])
+            data_label = eval(data_label_list[i][:-1])
+            data = embedding_word(data, output_shape)
+            if data.shape == output_shape:
+                new_data_list.append(data)
+                new_data_label_list.append(data_label)
+            i += 1
+
+        new_data_list = np.array(new_data_list)
+        new_data_label_list = np.array(new_data_label_list)
+        X = new_data_list
+        Y = new_data_label_list
+        # (table_num, 3 sentences, dim characters, embedding) -> (3, table_num, dim, embedding)
+        X = np.transpose(X, (1, 0, 2, 3))
+        # print("input_1", X[0].shape, "input_2", X[1].shape, "input_3", X[2].shape, "Y", Y.shape)
+        yield {'input_1': X[0], 'input_2': X[1], 'input_3': X[2]}, {'output': Y}
+
+
 if __name__ == '__main__':
-    get_data_from_file()
+    processed_save_to_txt()
+    # data_balance()
+
+    # test_embedding()

BiddingKG/dl/table_head/train.py

@@ -1,38 +1,44 @@
 import sys
 import os
-sys.path.append(os.path.abspath("../.."))
-from keras import optimizers
-from tensorflow.contrib.metrics import f1_score
-from tensorflow.python.ops.metrics_impl import precision, recall
+sys.path.append(os.path.abspath("../../.."))
+os.environ['KERAS_BACKEND'] = 'tensorflow'
+import keras
+from BiddingKG.dl.table_head.metrics import precision, recall, f1
+from keras import optimizers, Model
 from BiddingKG.dl.table_head.models.model import get_model
 from BiddingKG.dl.table_head.loss import focal_loss
-from keras.callbacks import ModelCheckpoint
-from BiddingKG.dl.table_head.pre_process import get_data_from_file
+from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
+from BiddingKG.dl.table_head.pre_process import get_data_from_file, get_data_from_sql, my_data_loader
 import numpy as np
+from keras import backend as K
 
 
-input_shape = (2, 10)
-output_shape = (2,)
-pretrained_path = ""
+input_shape = (3, 10, 60)
+output_shape = (3,)
+batch_size = 1024
+epochs = 1000
+pretrained_path = "checkpoints/best.hdf5"
 checkpoint_path = "checkpoints/"
 PRETRAINED = False
 CHECKPOINT = False
 
 
 def train():
+    # GPU available
+    print("gpus", K.tensorflow_backend._get_available_gpus())
+
     # Data
-    data_x, data_y = get_data_from_file()
-    data_x = np.array(data_x)
-    data_y = np.array(data_y)
+    data_x, data_y = get_data_from_file('txt')
+    print("finish read data", len(data_x))
 
     # Split -> Train, Test
     split_size = int(len(data_x)*0.1)
     test_x, test_y = data_x[:split_size], data_y[:split_size]
     train_x, train_y = data_x[split_size:], data_y[split_size:]
 
-    # (table_num, 2 sentences, dim characters) -> (2, table_num, dim)
-    train_x = np.transpose(train_x, (1, 0, 2))
-    test_x = np.transpose(test_x, (1, 0, 2))
+    # Data Loader
+    train_data_loader = my_data_loader(train_x, train_y, batch_size=batch_size)
+    test_data_loader = my_data_loader(test_x, test_y, batch_size=batch_size)
 
     # Model
     model = get_model(input_shape, output_shape)
@@ -47,18 +53,40 @@ def train():
     else:
         print("no checkpoint")
 
-    filepath = '{epoch:02d}-{val_loss:.2f}.h5'
-    checkpoint = ModelCheckpoint(checkpoint_path+filepath+".hdf5", monitor=focal_loss(),
-                                 verbose=1, save_best_only=True, mode='min')
-    model.compile(optimizer=optimizers.Adam(lr=0.0005), loss=focal_loss(),
-                  metrics=[focal_loss()])
+    filepath = 'e-{epoch:02d}-loss-{val_loss:.2f}'
+    checkpoint = ModelCheckpoint(checkpoint_path+filepath+".hdf5", monitor='val_f1',
+                                 verbose=1, save_best_only=True, mode='max')
+
+    model.compile(optimizer=optimizers.Adam(lr=0.0005), loss='binary_crossentropy',
+                  metrics=['binary_crossentropy', 'acc',
+                           precision, recall, f1])
+
+    rlu = ReduceLROnPlateau(monitor='val_f1', factor=0.1, patience=5,
+                            verbose=1, mode='max', cooldown=0, min_lr=0)
+
+    model.fit_generator(train_data_loader,
+                        steps_per_epoch=max(1, len(train_x) // batch_size),
+                        callbacks=[checkpoint, rlu],
+                        validation_data=test_data_loader,
+                        validation_steps=max(1, len(test_x) // batch_size),
+                        epochs=epochs)
+
+    # model.fit(x=[train_x[0], train_x[1], train_x[2]], y=train_y,
+    #           validation_data=([test_x[0], test_x[1], test_x[2]], test_y),
+    #           epochs=epochs, batch_size=256, shuffle=True,
+    #           callbacks=[checkpoint, rlu])
+
+    return model, test_x
+
+
+def print_layer_output(model, data):
+    middle_layer = Model(inputs=model.inputs,
+                         outputs=model.get_layer('input_2').output)
 
-    print(train_x.shape, train_y.shape)
-    model.fit(x=[train_x[0], train_x[1]], y=train_y,
-              validation_data=([test_x[0], test_x[1]], test_y),
-              epochs=100, batch_size=128, shuffle=True,
-              callbacks=[checkpoint])
+    middle_layer_output = middle_layer.predict([data[0], data[1]])
+    print(middle_layer_output)
+    return
 
 
 if __name__ == '__main__':
-    train()
+    model, data = train()