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可解释性实现代码.py
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可解释性实现代码.py
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# coding: utf-8
# In[ ]:
"""
1.提升图/洛伦兹曲线
"""
def plot_lifting(df,score_col,target,bins=10,plt_size=None):
"""
df:数据集，包含最终的得分
score_col:最终分数的字段名
target:目标变量名
bins:分数划分成的等份数
plt_size:绘图尺寸
return:提升图和洛伦兹曲线
"""
score_list = list(df[score_col])
label_list = list(df[target])
items = sorted(zip(score_list,label_list),key = lambda x:x[0])
step = round(df.shape[0]/bins,0)
bad = df[target].sum()
all_badrate = float(1/bins)
all_badrate_list = [all_badrate]*bins
all_badrate_cum = list(np.cumsum(all_badrate_list))
all_badrate_cum.insert(0,0)
score_bin_list=[]
bad_rate_list = []
for i in range(0,bins,1):
index_a = int(i*step)
index_b = int((i+1)*step)
score = [x[0] for x in items[index_a:index_b]]
tup1 = (min(score),)
tup2 = (max(score),)
score_bin = tup1+tup2
score_bin_list.append(score_bin)
label_bin = [x[1] for x in items[index_a:index_b]]
bin_bad = sum(label_bin)
bin_bad_rate = bin_bad/bad
bad_rate_list.append(bin_bad_rate)
bad_rate_cumsum = list(np.cumsum(bad_rate_list))
bad_rate_cumsum.insert(0,0)
plt.figure(figsize=plt_size)
x = score_bin_list
y1 = bad_rate_list
y2 = all_badrate_list
y3 = bad_rate_cumsum
y4 = all_badrate_cum
plt.subplot(1,2,1)
plt.title('提升图')
plt.xticks(np.arange(bins)+0.15,x,rotation=90)
bar_width= 0.3
plt.bar(np.arange(bins),y1,width=bar_width,color='hotpink',label='score_card')
plt.bar(np.arange(bins)+bar_width,y2,width=bar_width,color='seagreen',label='random')
plt.legend(loc='best')
plt.subplot(1,2,2)
plt.title('洛伦兹曲线图')
plt.plot(y3,color='hotpink',label='score_card')
plt.plot(y4,color='seagreen',label='random')
plt.xticks(np.arange(bins+1),rotation=0)
plt.legend(loc='best')
return plt.show()
"""
2.woe可视化
"""
def plot_woe(bin_df,hspace=0.4,wspace=0.4,plt_size=None,plt_num=None,x=None,y=None):
"""
bin_df:list形式，里面存储每个变量的分箱结果
hspace :子图之间的间隔(y轴方向)
wspace :子图之间的间隔(x轴方向)
plt_size :图纸的尺寸
plt_num :子图的数量
x :子图矩阵中一行子图的数量
y :子图矩阵中一列子图的数量
return :每个变量的woe变化趋势图
"""
plt.figure(figsize=plt_size)
plt.subplots_adjust(hspace=hspace,wspace=wspace)
for i,df in zip(range(1,plt_num+1,1),bin_df):
col_name = df.index.name
df = df.reset_index()
plt.subplot(x,y,i)
plt.title(col_name)
sns.barplot(data=df,x=col_name,y='woe')
plt.xlabel('')
plt.xticks(rotation=30)
return plt.show()
"""
3.检查变量的woe是否单调
"""
def woe_monoton(bin_df):
"""
bin_df:list形式，里面存储每个变量的分箱结果
return :
woe_notmonoton_col :woe没有呈单调变化的变量，list形式
woe_judge_df :df形式，每个变量的检验结果
"""
woe_notmonoton_col =[]
col_list = []
woe_judge=[]
for woe_df in bin_df:
col_name = woe_df.index.name
woe_list = list(woe_df.woe)
if woe_df.shape[0]==2:
#print('{}是否单调: True'.format(col_name))
col_list.append(col_name)
woe_judge.append('True')
else:
woe_not_monoton = [(woe_list[i]<woe_list[i+1] and woe_list[i]<woe_list[i-1]) or (woe_list[i]>woe_list[i+1] and woe_list[i]>woe_list[i-1]) for i in range(1,len(woe_list)-1,1)]
if True in woe_not_monoton:
#print('{}是否单调: False'.format(col_name))
woe_notmonoton_col.append(col_name)
col_list.append(col_name)
woe_judge.append('False')
else:
#print('{}是否单调: True'.format(col_name))
col_list.append(col_name)
woe_judge.append('True')
woe_judge_df = pd.DataFrame({'col':col_list,
'judge_monoton':woe_judge})
return woe_notmonoton_col,woe_judge_df
"""
4.某个区间的woe值是否过大（绝对值大于等于1）
"""
def woe_large(bin_df):
"""
bin_df:list形式，里面存储每个变量的分箱结果
return:
woe_large_col: 某个区间woe大于1的变量，list集合
woe_judge_df :df形式，每个变量的检验结果
"""
woe_large_col=[]
col_list =[]
woe_judge =[]
for woe_df in bin_df:
col_name = woe_df.index.name
woe_list = list(woe_df.woe)
woe_large = list(filter(lambda x:abs(x)>=1,woe_list))
if len(woe_large)>0:
col_list.append(col_name)
woe_judge.append('True')
woe_large_col.append(col_name)
else:
col_list.append(col_name)
woe_judge.append('False')
woe_judge_df = pd.DataFrame({'col':col_list,
'judge_large':woe_judge})
return woe_large_col,woe_judge_df