luojiehua
/
BIDI_ML_INFO_EXTRACTION


			
				
					
						
						
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							#coding:utf8

import codecs
import psycopg2

def importPredict():
    
    file = "predict.txt"
    
    conn = psycopg2.connect(dbname="BiddingKM_test_10000",user="postgres",password="postgres",host="192.168.2.101")
    
    cursor = conn.cursor()
    
    cursor.execute(" delete from dl_predict ")
    
    
    with codecs.open(file,"r",encoding="utf8") as f:
        while(True):
            line = f.readline()
            if not line:
                break
            line_split = line.split("\t")
            entity_id=line_split[0]
            id = line_split[1]
            expectation = line_split[2]
            dl_predict = line_split[4]
            sql = " insert into dl_predict(entity_id,id,expectation,dl_predict) values('"+str(entity_id)+"',"+str(id)+","+str(expectation)+","+str(dl_predict)+")"
            cursor.execute(sql)
        f.close()
    conn.commit()
    conn.close()
    
def exportResult():
    
    exporttable = " is_thirdtenderer "
    column = " third_tenderer "
    conn = psycopg2.connect(dbname="BiddingKM_test_10000",user="postgres",password="postgres",host="192.168.2.101")
    
    cursor = conn.cursor()
    
    sql = "select distinct B.doc_id from "+exporttable+" A,entity_mention B,dd_graph_variables_holdout D where A.id=D.variable_id and A.entity_id=B.entity_id"
    cursor.execute(sql)
    rows = cursor.fetchall()
    with codecs.open("testCompare"+exporttable+".html","w",encoding="utf8") as f:
        f.write('<html><head>\
        <meta http-equiv="Content-Type"\
        content="text/html; charset=UTF-8">\
        </head>\
        <body bgcolor="#FFFFFF">\
        <table border="1">\
        <tr>\
        <td>id</td>\
        <td>sumvalue</td>\
        <td>sum期望</td>\
        <td>ddvalue</td>\
        <td>dd期望</td>\
        <td>dlvalue<</td>\
        <td>dl期望</td>\
        <td>标注第一候选</td>\
        <td>相比</td>\
        </tr>')
        for row in rows:
            f.write("<tr>")
            doc_id = row[0]
            sql = " select A.entity_text,B.expectation,B.entity_id from entity_mention A,dl_predict B where A.doc_id='"+doc_id+"' and A.entity_id=B.entity_id order by B.expectation desc limit 1"
            cursor.execute(sql)
            dd_row = cursor.fetchall()[0]
            dd_entity = dd_row[0]
            dd_expectation = dd_row[1]
            sql = " select A.entity_text,B.dl_predict,B.entity_id from entity_mention A,dl_predict B where A.doc_id='"+doc_id+"' and A.entity_id=B.entity_id order by B.dl_predict desc limit 1"
            cursor.execute(sql)
            dl_row = cursor.fetchall()[0]
            dl_entity = dl_row[0]
            dl_expectation = dl_row[1]
            
            sql = " select A.entity_text,B.dl_predict*0.6+B.expectation*0.4 from entity_mention A,dl_predict B where A.doc_id='"+doc_id+"' and A.entity_id=B.entity_id order by B.dl_predict*0.5+B.expectation*0.5 desc limit 1"
            cursor.execute(sql)
            row = cursor.fetchall()[0]
            sum_entity = row[0]
            sum_expectation = row[1]
            
            sql = " select "+column+" from articles where id='"+doc_id+"' "
            cursor.execute(sql)
            re_match = cursor.fetchall()[0][0]
            
            if sum_expectation>0.5:
                if sum_entity==re_match:
                    isSame_sum = "#A0"
                else:
                    isSame_sum = "#A1"
            else:
                if sum_entity==re_match:
                    isSame_sum = "#A2"
                else:
                    isSame_sum = "#A3"
                    
            if dd_expectation>0.5:
                if dd_entity==re_match:
                    isSame_dd_re = "#B0"
                else:
                    isSame_dd_re = "#B1"
            else:
                if dd_entity==re_match:
                    isSame_dd_re = "#B2"
                else:
                    isSame_dd_re = "#B3"
            
            if dl_expectation>0.5:
                if dl_entity==re_match:
                    isSame_dl_re = "#C0"
                else:
                    isSame_dl_re = "#C1"
            else:
                if dl_entity==re_match:
                    isSame_dl_re = "#C2"
                else:
                    isSame_dl_re = "#C3"
            
                
            f.write("<td>"+str(doc_id)+"</td>"+"<td>"+str(sum_entity)+"</td>"+"<td>"+str(sum_expectation)+"</td>"+"<td>"+str(dd_entity)+"</td>"+"<td>"+str(dd_expectation)+"</td>"+"<td>"+str(dl_entity)+"</td>"+"<td>"+str(dl_expectation)+"</td>"+"<td>"+str(re_match)+"</td>"+"<td>"+str(isSame_sum+isSame_dd_re+isSame_dl_re)+"</td>")
            f.write("</tr>")
            f.write("\n")
        f.write('</tr>\
        </table>\
        </body>\
        </html>')
        f.flush()
        f.close()
    conn.close()
        
        

if __name__=="__main__":
    importPredict()
    #exportResult()