send_email

eda/recommended_indexs/hypothesis_test.py

@@ -36,7 +36,6 @@ def get_somedate():
 	someday = someday.strftime("%Y-%m-%d")
 	return someday
 ten_days=get_somedate()
-# print(ten_days)
 print("===========分割线，T检验最近10日指标与策略前10日指标是否获得显著提升============")
 #获取最近10天的数据
 def DATA_recently(x,y,z,q,t):
@@ -138,7 +137,6 @@ def t_test(x,y): #进行t检验
     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("\n")
@@ -224,7 +222,6 @@ def data_cal(x,y):
 
 def chi_cal(data):
     data['共计'] = data.apply(lambda x: x.sum(), axis=1)
-    # print(data)
     data.loc['共计'] = data.apply(lambda x: x.sum())
     t1=data.iloc[0]
     t2=data.iloc[1]
@@ -249,20 +246,20 @@ def chi_cal(data):
     v=(len(data)-1)*(data.columns.size-1)
     #查表发现阈值为3.84
     if X>3.84:
-        print("数据波动较大，超出正常波动范围，95%可能性属于指标显著变化，请关注")
+        print("数据波动较大，超出正常波动范围，95%可能性属于指标【显著变化，请关注】")
         print("\n")
     else:
-        print("数据波动较小，95%可能性属于正常波动范围")
+        print("数据波动较小，95%可能性属于【正常波动】范围")
         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：")
 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)
+
 chi_ctr_precise_old_yesterday=chi_DATA_yesterday("clk_count_oldUser_all_a","clk_count_oldUser_all_b","imp_count_oldUser_all_precise","on_click_diary_card",yesterday)
 temp2_old=[float(chi_ctr_precise_old_yesterday[i]) for i in range(len(chi_ctr_precise_old_yesterday))]
-# print(temp2)
+
 ctr_tst_old=data_cal(temp1_old,temp2_old)
 chi_cal(ctr_tst_old)
 
@@ -270,10 +267,10 @@ chi_cal(ctr_tst_old)
 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)
+
 chi_ctr_precise_new_yesterday=chi_DATA_yesterday("clk_count_newUser_all_a","clk_count_newUser_all_b","imp_count_newUser_all_precise","on_click_diary_card",yesterday)
 temp2_new=[float(chi_ctr_precise_new_yesterday[i]) for i in range(len(chi_ctr_precise_new_yesterday))]
-# print(temp2)
+
 ctr_tst_new=data_cal(temp1_new,temp2_new)
 chi_cal(ctr_tst_new)
 
@@ -281,40 +278,40 @@ chi_cal(ctr_tst_new)
 print("【3】老用户CVR数据波动假设检验结果：")
 chi_cvr_old_recently=chi_DATA_recently_all("diary_meigou_oldUser","diary_clk_oldUser","diary_meigou_crv",five_days,yesterday)
 cvr_old=[float(str(Decimal(chi_cvr_old_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_cvr_old_recently))]
-# print(temp1)
+
 chi_cvr_old_yesterday=chi_DATA_yesterday_all("diary_meigou_oldUser","diary_clk_oldUser","diary_meigou_crv",yesterday)
 cvr_old2=[float(chi_cvr_old_yesterday[i]) for i in range(len(chi_cvr_old_yesterday))]
-# print(temp2)
+
 cvr_tst_old=data_cal(cvr_old,cvr_old2)
 chi_cal(cvr_tst_old)
 #老用户美购转化数据
 print("【4】新用户CVR数据波动假设检验结果：")
 chi_cvr_new_recently=chi_DATA_recently_all("diary_meigou_newUser","diary_clk_newUser","diary_meigou_crv",five_days,yesterday)
 cvr_new=[float(str(Decimal(chi_cvr_new_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_cvr_new_recently))]
-# print(temp1)
+
 chi_cvr_new_yesterday=chi_DATA_yesterday_all("diary_meigou_newUser","diary_clk_newUser","diary_meigou_crv",yesterday)
 cvr_new2=[float(chi_cvr_new_yesterday[i]) for i in range(len(chi_cvr_new_yesterday))]
-# print(temp2)
+
 cvr_tst_new=data_cal(cvr_new,cvr_new2)
 chi_cal(cvr_tst_new)
 #老用户美购转化数据
 print("【5】老用户CT-CVR数据波动假设检验结果：")
 chi_ctcvr_old_recently=chi_DATA_recently_all("diary_meigou_oldUser","diary_exp_oldUser","diary_meigou_crv",five_days,yesterday)
 ctcvr_old=[float(str(Decimal(chi_ctcvr_old_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_ctcvr_old_recently))]
-# print(temp1)
+
 chi_ctcvr_old_yesterday=chi_DATA_yesterday_all("diary_meigou_oldUser","diary_exp_oldUser","diary_meigou_crv",yesterday)
 ctcvr_old2=[float(chi_ctcvr_old_yesterday[i]) for i in range(len(chi_ctcvr_old_yesterday))]
-# print(temp2)
+
 ctcvr_tst_old=data_cal(ctcvr_old,ctcvr_old2)
 chi_cal(ctcvr_tst_old)
 #老用户美购转化数据
 print("【6】新用户CT-CVR数据波动假设检验结果：")
 chi_ctcvr_new_recently=chi_DATA_recently_all("diary_meigou_newUser","diary_exp_newUser","diary_meigou_crv",five_days,yesterday)
 ctcvr_new=[float(str(Decimal(chi_ctcvr_new_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_ctcvr_new_recently))]
-# print(temp1)
+
 chi_ctcvr_new_yesterday=chi_DATA_yesterday_all("diary_meigou_newUser","diary_exp_newUser","diary_meigou_crv",yesterday)
 ctcvr_new2=[float(chi_ctcvr_new_yesterday[i]) for i in range(len(chi_ctcvr_new_yesterday))]
-# print(temp2)
+
 ctcvr_tst_new=data_cal(ctcvr_new,ctcvr_new2)
 chi_cal(ctcvr_tst_new)
 
@@ -404,11 +401,39 @@ print("【8-2】老用户精准曝光CTR数据波动5日内均值:{}%".format(me
 print("\n")
 
 
-
-# print(chi_ctr_precise_recently)
-# print(chi_ctr_precise_yesterday)
-print("============================分割线===================================")
-#保存文件
+# print("============================分割线===================================")
+#根据新老用户进行区分
+# print("============================新用户各指标假设检验结果分析===================================")
+# #新用户cvr假设检验
+# print("【1】新用户CVR假设检验结果：")
+# crv_new_ttest1=t_test(x_crv_new,y_crv_new)
+# #新用户ct_cvr假设检验
+# print("【3】新用户CT-CVR假设检验结果：")
+# ctcrv_new_ttest1=t_test(x_ctcrv_new,y_ctcrv_new)
+# #新用户ctr假设检验
+# print("【5】新用户CTR假设检验结果：")
+# ctr_new_ttest1=t_test(x_ctr_new,y_ctr_new)
+# #新用户ctr(on_click_diary_card)假设检验
+# print("【7】新用户CTR假设检验(日记本列表ctr)（on_click_diary_card）结果：")
+# ctr_new_o_ttest1=t_test(x_ctr_new_o,y_ctr_new_o)
+#
+#
+#
+#
+#
+# print("============================老用户各指标假设检验结果分析===================================")
+# #老用户cvr假设检验
+# print("【2】老用户CVR假设检验结果：")
+# crv_old_ttest1=t_test(x_crv_old,y_crv_old)
+# # #老用户ct_cvr假设检验
+# print("【4】老用户CT-CVR假设检验结果：")
+# ctcrv_old_ttest1=t_test(x_ctcrv_old,y_ctcrv_old)
+# #老用户ctr假设检验
+# print("【6】老用户CTR假设检验结果：")
+# ctr_old_ttest1=t_test(x_ctr_old,y_ctr_old)
+# #老用户ctr(on_click_diary_card)假设检验
+# print("【8】老用户CTR假设检验(日记本列表ctr)（on_click_diary_card）结果：")
+# ctr_old_o_ttest1=t_test(x_ctr_old_o,y_ctr_old_o)
 
 
 

eda/recommended_indexs/send_email.py

-##发送邮件
+# ##发送邮件
+#
+# #coding=utf-8
+#
+# import smtplib
+# from email.mime.text import MIMEText
+# from email.utils import formataddr
+# from email.mime.application import MIMEApplication
+# import datetime
+#
+# 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'
+#     pdfApart = MIMEApplication(open(pdfFile, 'rb').read())
+#     pdfApart.add_header('Content-Disposition', 'attachment', filename=pdfFile)
+#     m = MIMEMultipart()
+#     m.attach(pdfApart)
+#     m['Subject'] = '数据指标监控数据（假设检验）'
+#     m['From'] = '王志伟<wangzhiwei@igengmei.com>'
+#
+#
+#     try:
+#         # text = "Hi!\nHow are you?\nHere is the link you wanted:\nhttp://www.baidu.com"
+#         # msg = MIMEText(text, 'plain', 'utf-8')
+#         # msg['From'] = formataddr(["王志伟", my_sender])
+#         # msg['To'] = my_user1
+#         # msg['Subject'] = str(datetime.date.today()) + "-esmm多目标模型训练指标统计"
+#         server = smtplib.SMTP_SSL("smtp.exmail.qq.com", 465)
+#         server.login(my_sender, my_pass)
+#         server.sendmail(my_sender, [my_user1,my_user2,my_user3], m.as_string())
+#         server.quit()
+#     except Exception:
+#         ret=False
+#     return ret
+#
+# ret=mail()
+# if ret:
+#     print("邮件发送成功")
+# else:
+#     print("邮件发送失败")
+
+#####尝试发送邮箱，不带附件
 
 #coding=utf-8
 
 import smtplib
 from email.mime.text import MIMEText
 from email.utils import formataddr
-from email.mime.application import MIMEApplication
 import datetime
 
-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'
+# my_user2='zhangyanzhao@igengmei.com'
 def mail():
-    ret = True
-    pdfFile = 'hypothesis.txt'
-    pdfApart = MIMEApplication(open(pdfFile, 'rb').read())
-    pdfApart.add_header('Content-Disposition', 'attachment', filename=pdfFile)
-
-
-
+    ret=True
     try:
-        text = "Hi!\nHow are you?\nHere is the link you wanted:\nhttp://www.baidu.com"
-        m = MIMEMultipart(text, 'plain', 'utf-8')
-        m.attach(pdfApart)
-        m['Subject'] = '数据指标监控数据（假设检验）'
-        m['From'] = '王志伟<wangzhiwei@igengmei.com>'
-        # msg = MIMEText(text, 'plain', 'utf-8')
-        # msg['From'] = formataddr(["王志伟", my_sender])
-        # msg['To'] = my_user1
-        # msg['Subject'] = str(datetime.date.today()) + "-esmm多目标模型训练指标统计"
-        server = smtplib.SMTP_SSL("smtp.exmail.qq.com", 465)
+        with open('hypothesis.txt') as f:
+            stat_data = f.read()
+            msg=MIMEText(stat_data,'plain','utf-8')
+            msg['From']=formataddr(["王志伟",my_sender])
+            msg['To']=my_user1
+            msg['Subject']= str(datetime.date.today())+"-数据指标监控数据（假设检验）"
+            server=smtplib.SMTP_SSL("smtp.exmail.qq.com", 465)
             server.login(my_sender, my_pass)
-        server.sendmail(my_sender, [my_user1], m.as_string())
+            server.sendmail(my_sender,[my_user1],msg.as_string())
             server.quit()
     except Exception:
         ret=False