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ffm-baseline
Commit 27276aa6 authored by 王志伟's avatar 王志伟
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数据指标波动假设检验统计

parent 41f521c6
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Showing with 6 additions and 6 deletions
eda/recommended_indexs/hypothesis_test.py
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...@@ -97,19 +97,19 @@ def t_test(x,y): #进行t检验 ...@@ -97,19 +97,19 @@ def t_test(x,y): #进行t检验
if p_value>0.05: #认为数据方差具有齐性，equal_var=ture 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_test=ttest_ind(x,y,equal_var=True)
t_p_value=t_test[1] t_p_value=t_test[1]
print(t_p_value) # print(t_p_value)
if t_p_value>0.05: 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) 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:" % t_p_value)
else: 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) 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:" % t_p_value)
else: #认为数据方差不具有齐性，equal_var=false 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_test = ttest_ind(x, y, equal_var=False)
t_p_value = t_test[1] t_p_value = t_test[1]
print(t_p_value) # print(t_p_value)
if t_p_value > 0.05: 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) 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:" % t_p_value)
else: 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) 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:" % t_p_value)
# #
# ###假设检验，判断是否具有显著性 # ###假设检验，判断是否具有显著性
# #
......
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