数据指标波动假设检验统计

eda/recommended_indexs/hypothesis_test.py

@@ -18,6 +18,7 @@ def get_yesterday_date():
 	yesterday = yesterday.strftime("%Y-%m-%d")
 	return yesterday
 yesterday=get_yesterday_date
+print(yesterday)
 #自动获取10日前的日期
 def get_somedate():
 	#自动获取10日前的日期,如"2018-07-28"
@@ -29,127 +30,128 @@ def get_somedate():
 	someday = someday.strftime("%Y-%m-%d")
 	return someday
 ten_days=get_somedate()
-
-#获取最近10天的数据
-def DATA_recently(x,y,z,q):
-    sql_cid = "select {0}/{1} as {2} from {3} \
-    where stat_date >= ten_days group by stat_date".format(x,y,z,q)
-    CVR_DATA_recently = con_sql(sql_cid)[0][0]
-    return CVR_DATA_recently
-
-#获取固定时间的10天的数据
-def DATA_fixed(x,y,z,q):
-    sql_cid = "select {0}/{1} as {2} from {3} \
-    where stat_date >= " "  and stat_date<" " group by stat_date".format(x,y,z,q)
-    CVR_DATA_fixed = con_sql(sql_cid)[0][0]
-    return CVR_DATA_fixed
-
-#新用户cvr
-x_crv_new=DATA_recently("diary_meigou_newUser","diary_clk_newUser","CVR_new","diary_meigou_crv")
-y_crv_new=DATA_fixed("diary_meigou_newUser","diary_clk_newUser","CVR_new","diary_meigou_crv")
-
-#老用户cvr
-x_crv_old=DATA_recently("diary_meigou_oldUser","diary_clk_oldUser","CVR_old","diary_meigou_crv")
-y_crv_old=DATA_fixed("diary_meigou_oldUser","diary_clk_oldUser","CVR_old","diary_meigou_crv")
-
-#新用户ct-cvr
-x_ctcrv_new=DATA_recently("diary_meigou_newUser","diary_exp_newUser","CT_CVR_new","diary_meigou_crv")
-y_ctcrv_new=DATA_fixed("diary_meigou_newUser","diary_exp_newUser","CT_CVR_new","diary_meigou_crv")
-
-#老用户ct-cvr
-x_ctcrv_old=DATA_recently("diary_meigou_oldUser","diary_exp_oldUser","CT_CVR_old","diary_meigou_crv")
-y_ctcrv_old=DATA_fixed("diary_meigou_oldUser","diary_exp_oldUser","CT_CVR_old","diary_meigou_crv")
-
-#新用户ctr(page_view)
-x_ctr_new=DATA_recently("clk_count_newUser_all","imp_count_newUser_all","ctr_new","bug_Recommendation_strategy_newUser")
-y_ctr_new=DATA_fixed("clk_count_newUser_all","imp_count_newUser_all","ctr_new","bug_Recommendation_strategy_newUser")
-
-#老用户ctr(page_view)
-x_ctr_old=DATA_recently("clk_count_oldUser_all","imp_count_oldUser_all","ctr_old","bug_Recommendation_strategy_temp")
-y_ctr_old=DATA_fixed("clk_count_oldUser_all","imp_count_oldUser_all","ctr_old","bug_Recommendation_strategy_temp")
-
-#新用户ctr(on_click_diary_card)
-x_ctr_new_o=DATA_recently("clk_count_newUser_all_a","imp_count_newUser_all","ctr_new","on_click_diary_card")
-y_ctr_new_o=DATA_fixed("clk_count_newUser_all_a","imp_count_newUser_all","ctr_new","on_click_diary_card")
-
-#老用户ctr(on_click_diary_card)
-x_ctr_old_o=DATA_recently("clk_count_oldUser_all_a","imp_count_oldUser_all","ctr_old","on_click_diary_card")
-y_ctr_old_o=DATA_fixed("clk_count_oldUser_all_a","imp_count_oldUser_all","ctr_old","on_click_diary_card")
-
-
-
-
-def t_test(x,y): #进行t检验
-
-    #策略前的数据，赋值给x,策略后的数据赋值给y,均采用10日内数据
-    x=[2,4,2,3,4,2,3]
-    y=[4,5,6,3,4,5,6]
-
-    #检验数据方差是否齐性
-    a=levene(x,y)
-    p_value=a[1] #结果若p_value>0.05,则认为两组数据方差是相等的，否则两组数据方差是不等的
-
-    if p_value>0.05: #认为数据方差具有齐性，equal_var=ture
-        t_test=ttest_ind(x,y,equal_var=True)
-        t_p_value=t_test[1]
-        if t_p_value>0.05:
-            print("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:%f" % t_p_value)
-        else:
-            print("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:%f" % t_p_value)
-    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]
-        if t_p_value > 0.05:
-            print("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:%f" % t_p_value)
-        else:
-            print("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:%f" % t_p_value)
-
-###假设检验，判断是否具有显著性
-
-#新用户cvr假设检验
-crv_new_ttest=t_test(x_crv_new,y_crv_new)
-#老用户cvr假设检验
-crv_old_ttest=t_test(x_crv_old,y_crv_old)
-
-#新用户ct_cvr假设检验
-ctcrv_new_ttest=t_test(x_ctcrv_new,y_ctcrv_new)
-#老用户ct_cvr假设检验
-ctcrv_old_ttest=t_test(x_ctcrv_old,y_ctcrv_old)
-
-
-#新用户ctr假设检验
-ctr_new_ttest=t_test(x_ctr_new,y_ctr_new)
-#老用户ctr假设检验
-ctr_old_ttest=t_test(x_ctr_old,y_ctr_old)
-
-##新用户ctr(on_click_diary_card)假设检验
-ctr_new_o_ttest=t_test(x_ctr_new_o,y_ctr_new_o)
-#老用户ctr(on_click_diary_card)假设检验
-ctr_old_o_ttest=t_test(x_ctr_old_o,y_ctr_old_o)
-
-
-###############推荐策略不变的情况下数据假设检验##############
-
-#1 计算每日指标卡方检验
-
-#自动获取5日前的日期
-def get_fivedate():
-	#自动获取10日前的日期,如"2018-07-28"
-	"""
-	:rtype : str
-	"""
-	today = datetime.date.today()
-	someday = today - datetime.timedelta(days=5)
-	someday = someday.strftime("%Y-%m-%d")
-	return someday
-five_days=get_fivedate()
-
-#获取最近5天的数据
-def chi_DATA_recently(x,y,z):
-    sql_cid = "select AVG({0}),AVG({1}) from {2} \
-    where stat_date >= five_days and stat_date<yesterday union all select {0},{1} from {2} where stat_date=yesterday}".format(x,y,z)
-    CVR_DATA_recently = con_sql(sql_cid)[0][0]
-    return CVR_DATA_recently
+print(ten_days)
+
+# #获取最近10天的数据
+# def DATA_recently(x,y,z,q):
+#     sql_cid = "select {0}/{1} as {2} from {3} \
+#     where stat_date >= ten_days group by stat_date".format(x,y,z,q)
+#     CVR_DATA_recently = con_sql(sql_cid)[0][0]
+#     return CVR_DATA_recently
+#
+# #获取固定时间的10天的数据
+# def DATA_fixed(x,y,z,q):
+#     sql_cid = "select {0}/{1} as {2} from {3} \
+#     where stat_date >= " "  and stat_date<" " group by stat_date".format(x,y,z,q)
+#     CVR_DATA_fixed = con_sql(sql_cid)[0][0]
+#     return CVR_DATA_fixed
+#
+# #新用户cvr
+# x_crv_new=DATA_recently("diary_meigou_newUser","diary_clk_newUser","CVR_new","diary_meigou_crv")
+# y_crv_new=DATA_fixed("diary_meigou_newUser","diary_clk_newUser","CVR_new","diary_meigou_crv")
+#
+# #老用户cvr
+# x_crv_old=DATA_recently("diary_meigou_oldUser","diary_clk_oldUser","CVR_old","diary_meigou_crv")
+# y_crv_old=DATA_fixed("diary_meigou_oldUser","diary_clk_oldUser","CVR_old","diary_meigou_crv")
+#
+# #新用户ct-cvr
+# x_ctcrv_new=DATA_recently("diary_meigou_newUser","diary_exp_newUser","CT_CVR_new","diary_meigou_crv")
+# y_ctcrv_new=DATA_fixed("diary_meigou_newUser","diary_exp_newUser","CT_CVR_new","diary_meigou_crv")
+#
+# #老用户ct-cvr
+# x_ctcrv_old=DATA_recently("diary_meigou_oldUser","diary_exp_oldUser","CT_CVR_old","diary_meigou_crv")
+# y_ctcrv_old=DATA_fixed("diary_meigou_oldUser","diary_exp_oldUser","CT_CVR_old","diary_meigou_crv")
+#
+# #新用户ctr(page_view)
+# x_ctr_new=DATA_recently("clk_count_newUser_all","imp_count_newUser_all","ctr_new","bug_Recommendation_strategy_newUser")
+# y_ctr_new=DATA_fixed("clk_count_newUser_all","imp_count_newUser_all","ctr_new","bug_Recommendation_strategy_newUser")
+#
+# #老用户ctr(page_view)
+# x_ctr_old=DATA_recently("clk_count_oldUser_all","imp_count_oldUser_all","ctr_old","bug_Recommendation_strategy_temp")
+# y_ctr_old=DATA_fixed("clk_count_oldUser_all","imp_count_oldUser_all","ctr_old","bug_Recommendation_strategy_temp")
+#
+# #新用户ctr(on_click_diary_card)
+# x_ctr_new_o=DATA_recently("clk_count_newUser_all_a","imp_count_newUser_all","ctr_new","on_click_diary_card")
+# y_ctr_new_o=DATA_fixed("clk_count_newUser_all_a","imp_count_newUser_all","ctr_new","on_click_diary_card")
+#
+# #老用户ctr(on_click_diary_card)
+# x_ctr_old_o=DATA_recently("clk_count_oldUser_all_a","imp_count_oldUser_all","ctr_old","on_click_diary_card")
+# y_ctr_old_o=DATA_fixed("clk_count_oldUser_all_a","imp_count_oldUser_all","ctr_old","on_click_diary_card")
+#
+#
+#
+#
+# def t_test(x,y): #进行t检验
+#
+#     #策略前的数据，赋值给x,策略后的数据赋值给y,均采用10日内数据
+#     x=[2,4,2,3,4,2,3]
+#     y=[4,5,6,3,4,5,6]
+#
+#     #检验数据方差是否齐性
+#     a=levene(x,y)
+#     p_value=a[1] #结果若p_value>0.05,则认为两组数据方差是相等的，否则两组数据方差是不等的
+#
+#     if p_value>0.05: #认为数据方差具有齐性，equal_var=ture
+#         t_test=ttest_ind(x,y,equal_var=True)
+#         t_p_value=t_test[1]
+#         if t_p_value>0.05:
+#             print("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:%f" % t_p_value)
+#         else:
+#             print("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:%f" % t_p_value)
+#     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]
+#         if t_p_value > 0.05:
+#             print("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:%f" % t_p_value)
+#         else:
+#             print("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:%f" % t_p_value)
+#
+# ###假设检验，判断是否具有显著性
+#
+# #新用户cvr假设检验
+# crv_new_ttest=t_test(x_crv_new,y_crv_new)
+# #老用户cvr假设检验
+# crv_old_ttest=t_test(x_crv_old,y_crv_old)
+#
+# #新用户ct_cvr假设检验
+# ctcrv_new_ttest=t_test(x_ctcrv_new,y_ctcrv_new)
+# #老用户ct_cvr假设检验
+# ctcrv_old_ttest=t_test(x_ctcrv_old,y_ctcrv_old)
+#
+#
+# #新用户ctr假设检验
+# ctr_new_ttest=t_test(x_ctr_new,y_ctr_new)
+# #老用户ctr假设检验
+# ctr_old_ttest=t_test(x_ctr_old,y_ctr_old)
+#
+# ##新用户ctr(on_click_diary_card)假设检验
+# ctr_new_o_ttest=t_test(x_ctr_new_o,y_ctr_new_o)
+# #老用户ctr(on_click_diary_card)假设检验
+# ctr_old_o_ttest=t_test(x_ctr_old_o,y_ctr_old_o)
+#
+#
+# ###############推荐策略不变的情况下数据假设检验##############
+#
+# #1 计算每日指标卡方检验
+#
+# #自动获取5日前的日期
+# def get_fivedate():
+# 	#自动获取10日前的日期,如"2018-07-28"
+# 	"""
+# 	:rtype : str
+# 	"""
+# 	today = datetime.date.today()
+# 	someday = today - datetime.timedelta(days=5)
+# 	someday = someday.strftime("%Y-%m-%d")
+# 	return someday
+# five_days=get_fivedate()
+#
+# #获取最近5天的数据
+# def chi_DATA_recently(x,y,z):
+#     sql_cid = "select AVG({0}),AVG({1}) from {2} \
+#     where stat_date >= five_days and stat_date<yesterday union all select {0},{1} from {2} where stat_date=yesterday}".format(x,y,z)
+#     CVR_DATA_recently = con_sql(sql_cid)[0][0]
+#     return CVR_DATA_recently
 
 
 # chi_cvr_new=