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ffm-baseline
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a2f61993
Commit
a2f61993
authored
Feb 19, 2019
by
王志伟
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数据指标波动假设检验统计
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63d0b99e
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hypothesis_test.py
eda/recommended_indexs/hypothesis_test.py
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eda/recommended_indexs/hypothesis_test.py
View file @
a2f61993
...
...
@@ -86,36 +86,33 @@ print(y_crv_new)
#
#
#
# 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)
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
#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
#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
...
...
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