send_email

eda/recommended_indexs/hypothesis_test.py

@@ -10,7 +10,6 @@ import smtplib
 from email.mime.text import MIMEText
 from email.utils import formataddr
 
-f=open('/srv/apps/ffm-baseline/eda/recommended_indexs/test.txt','w')
 
 #########推荐策略前后统计指标假设检验（t检验）###############
 
@@ -131,20 +130,20 @@ def t_test(x,y): #进行t检验
         t_p_value=t_test[1]
         # print(t_p_value)
         if t_p_value>0.05:
-            print("95%置信度认为策略前后两组数据无显著性差异，即该指标没有显著提升，p_value:{}" .format(t_p_value))
+            print("95%置信度认为策略前后两组数据【无显著性差异】，即该指标没有显著提升，p_value:{}" .format(t_p_value))
             print("\n")
         else:
-            print("95%置信度认为策略前后两组数据有显著性差异，即该指标获得显著提升，p_value:{}" .format(t_p_value))
+            print("95%置信度认为策略前后两组数据【有显著性差异】，即该指标获得显著提升，p_value:{}" .format(t_p_value))
             print("\n")
     else: #认为数据方差不具有齐性，equal_var=false
         t_test = ttest_ind(x, y, equal_var=False)
         t_p_value = t_test[1]
         # print(t_p_value)
         if t_p_value > 0.05:
-            print("95%置信度认为策略前后两组数据无显著性差异，即该指标没有显著提升，p_value:{}" .format(t_p_value))
+            print("95%置信度认为策略前后两组数据【无显著性差异】，即该指标没有显著提升，p_value:{}" .format(t_p_value))
             print("\n")
         else:
-            print("95%置信度认为策略前后两组数据有显著性差异，即该指标获得显著提升，p_value:{}" .format(t_p_value))
+            print("95%置信度认为策略前后两组数据【有显著性差异】，即该指标获得显著提升，p_value:{}" .format(t_p_value))
             print("\n")
 #
 # ###假设检验，判断是否具有显著性
@@ -257,7 +256,7 @@ def chi_cal(data):
         print("\n")
 
 #老用户精准点击曝光数据（首页精选日记本列表on_click_diary_card）
-print("【1】（精准曝光）首页精选日记本列表老用户ctr数据波动假设检验结果：")
+print("【1】（精准曝光）首页精选日记本列表老用户CTR数据波动假设检验结果：")
 chi_ctr_precise_old_recently=chi_DATA_recently("clk_count_oldUser_all_a","clk_count_oldUser_all_b","imp_count_oldUser_all_precise","on_click_diary_card",five_days,yesterday)
 temp1_old=[float(str(Decimal(chi_ctr_precise_old_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_ctr_precise_old_recently))]
 # print(temp1)
@@ -268,7 +267,7 @@ ctr_tst_old=data_cal(temp1_old,temp2_old)
 chi_cal(ctr_tst_old)
 
 #新用户精准点击曝光数据（首页精选日记本列表on_click_diary_card）
-print("【2】（精准曝光）首页精选日记本列表新用户ctr数据波动假设检验结果：")
+print("【2】（精准曝光）首页精选日记本列表新用户CTR数据波动假设检验结果：")
 chi_ctr_precise_new_recently=chi_DATA_recently("clk_count_newUser_all_a","clk_count_newUser_all_b","imp_count_newUser_all_precise","on_click_diary_card",five_days,yesterday)
 temp1_new=[float(str(Decimal(chi_ctr_precise_new_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_ctr_precise_new_recently))]
 # print(temp1)

eda/recommended_indexs/send_email.py

@@ -13,6 +13,8 @@ from email.mime.multipart import MIMEMultipart
 my_sender='wangzhiwei@igengmei.com'
 my_pass = 'RiKEcsHAgesCZ7yd'
 my_user1='wangzhiwei@igengmei.com'
+my_user2='gaoyazhe@igengmei.com'
+my_user3='huangkai@igengmei.com'
 def mail():
     ret = True
     pdfFile = 'hypothesis.txt'