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A small package for feature autoBinning

Project description

auto binning 分箱工具

安装

pip install autoBinning

基础工具 (simpleMethods)

my_list = [1,1,2,2,2,2,3,3,4,5,6,7,8,9,10,10,20,20,20,20,30,30,40,50,60,70,80,90,100]
my_list_y = [1,1,2,2,2,2,1,1,1,2,2,2,1,1]
t = simpleMethods(my_list)
t.equalSize(3)
# 每个分箱样本数平均
print(t.bins) # [  1.           5.33333333  20.         100.        ]
# 等间距划分分箱
t.equalValue(4)
print(t.bins) # [  1.    25.75  50.5   75.25 100.  ]
# 基于numpy histogram分箱
t.equalHist(4)
print(t.bins) # [  1.    25.75  50.5   75.25 100.  ]

基于标签的有监督自动分箱

基于最大woe分裂分箱

按照等距等频分箱(每个分箱样本量相同)得到潜在切分点,计算每个切分点上下的woe,寻找最大的woe变化切分点,

df = pd.read_csv('credit_old.csv')
df = df[['Age','target']]
df = df.dropna()

t = trendSplit(df['Age'], df['target'])
t.fit(sby='woe',minv=0.01,init_split=20,trend='up')
print(t.bins) # [16. 25. 42. 50. 63. 72. 94.]
t.fit(sby='woe',num_split=4,init_split=20,trend='up')
print(t.bins) # [16. 25. 42. 50. 72. 94.]

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基于最大iv分裂分箱

df = pd.read_csv('credit_old.csv')
df = df[['Age','target']]
df = df.dropna()

t = trendSplit(df['Age'], df['target'])
t.fit(sby='iv',minv=0.1)
print(t.bins) # [16.  18.5 82.5 83.5 84.5 85.5 86.5 95. ]
t = trendSplit(df['Age'], df['target'])
t.fit(sby='iv',minv=0.1,init_split=20)
print(t.bins) # [16. 25. 29. 33. 36. 38. 40. 42. 46. 48. 50. 94.]
t = trendSplit(df['Age'], df['target'])
t.fit(sby='iv',num_split=4,init_split=20)
print(t.bins) # [16. 25. 29. 33. 42. 94.]

Project details


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This version

0.1.1

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