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稳定性实现代码.py
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稳定性实现代码.py
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# coding: utf-8
# In[ ]:
"""
1.评分分布表（得分PSI）
"""
def score_psi(df1,df2,id_col,score_col,x,y,step=None):
"""
df1:建模样本的得分,包含用户id,得分
df2:上线样本的得分，包含用户id，得分
id_col:用户id字段名
score_col:得分的字段名
x:划分得分区间的left值
y:划分得分区间的right值
step:步长
return: 得分psi表
"""
df1['score_bin'] = pd.cut(df1[score_col],bins=np.arange(x,y,step))
model_score_group = df1.groupby('score_bin',as_index=False)[id_col].count(). assign(pct=lambda x:x[id_col]/x[id_col].sum()). rename(columns={id_col:'建模样本户数',
'pct':'建模户数占比'})
df2['score_bin'] = pd.cut(df2[score_col],bins=np.arange(x,y,step))
online_score_group = df2.groupby('score_bin',as_index=False)[id_col].count(). assign(pct=lambda x:x[id_col]/x[id_col].sum()). rename(columns={id_col:'线上样本户数',
'pct':'线上户数占比'})
score_compare = pd.merge(model_score_group,online_score_group,on='score_bin',how='inner')
score_compare['占比差异'] = score_compare['线上户数占比'] - score_compare['建模户数占比']
score_compare['占比权重'] = np.log(score_compare['线上户数占比']/score_compare['建模户数占比'])
score_compare['Index']= score_compare['占比差异']*score_compare['占比权重']
score_compare['PSI'] = score_compare['Index'].sum()
return score_compare
"""
2.评分分布图
"""
def plot_score_compare(df,plt_size=None):
fig = plt.figure(figsize=plt_size)
x = df.score_bin
y1 = df.建模户数占比
y2 = df.线上户数占比
width=0.3
plt.title('评分分布对比图')
plt.xlabel('得分区间')
plt.ylabel('用户占比')
plt.xticks(np.arange(len(x))+0.15,x)
plt.bar(np.arange(len(y1)),y1,width=width,color='seagreen',label='建模样本')
plt.bar(np.arange(len(y2))+width,y2,width=width,color='hotpink',label='上线样本')
plt.legend()
return plt.show()
"""
3.变量稳定性分析
"""
def var_stable(score_result,df,var,id_col,score_col,bins):
"""
score_result:评分卡的score明细表，包含区间，用户数，用户占比,得分
var：分析的变量名
df:上线样本变量的得分，包含用户id,变量的value，变量的score
id_col:df的用户id字段名
score_col:df的得分字段名
bins:变量划分的区间
return :变量的稳定性分析表
"""
model_var_group = score_result.loc[score_result.col==var, ['bin','total','totalrate','score']].reset_index(drop=True). rename(columns={'total':'建模用户数',
'totalrate':'建模用户占比',
'score':'得分'})
df['bin'] = pd.cut(df[score_col],bins=bins)
online_var_group = df.groupby('bin',as_index=False)[id_col].count() .assign(pct=lambda x:x[id_col]/x[id_col].sum()) .rename(columns={id_col:'线上用户数',
'pct':'线上用户占比'})
var_stable_df = pd.merge(model_var_group,online_var_group,on='bin',how='inner')
var_stable_df = var_stable_df.iloc[:,[0,3,1,2,4,5]]
var_stable_df['得分'] = var_stable_df['得分'].astype('int64')
var_stable_df['建模样本权重'] = np.abs(var_stable_df['得分']*var_stable_df['建模用户占比'])
var_stable_df['线上样本权重'] = np.abs(var_stable_df['得分']*var_stable_df['线上用户占比'])
var_stable_df['权重差距'] = var_stable_df['线上样本权重'] - var_stable_df['建模样本权重']
return var_stable_df
"""
4.变量偏移分析
"""
def plot_var_shift(df,day_col,score_col,plt_size=None):
"""
df:变量在一段时间内，每个区间上的得分
day_col:时间的字段名（天）
score_col:得分的字段名
plt_size: 绘图尺寸
return:变量区间得分的偏移图
"""
day_list = sorted(set(list(df[day_col])))
score_list = sorted(set(list(df[score_col])))
# 计算每天各个区间得分的占比
prop_day_list = []
for day in day_list:
prop_list = []
for score in score_list:
prop = df[(df[day_col]==day)&(df[score_col]==score)].shape[0]/df[df[day_col]==day].shape[0]
prop_list.append(prop)
prop_day_list.append(prop_list)
# 将得分占比的转化为画图的格式
sub_list = []
for p in prop_day_list:
p_cumsum = list(np.cumsum(p))
p_cumsum = p_cumsum[:-1]
p_cumsum.insert(0,0)
bar1_list = [1]*int(len(p_cumsum))
sub = [bar1_list[i]-p_cumsum[i] for i in range(len(p_cumsum))]
sub_list.append(sub)
array = np.array(sub_list)
stack_prop_list = [] # 面积图的y值
bar_prop_list = [] # 堆积柱状图的y
for i in range(len(score_list)):
bar_prop = array[:,i]
bar_prop_list.append(bar_prop)
stack_prop = []
for j in bar_prop:
a = j
b = j
stack_prop.append(a)
stack_prop.append(b)
stack_prop_list.append(stack_prop)
# 画图的x坐标轴
x_bar = list(range(1,len(day_list)*2,2)) # 堆积柱状图的x值
x_stack = [] # 面积图的x值
for i in x_bar:
c = i-0.5
d = i+0.5
x_stack.append(c)
x_stack.append(d)
# 绘图
fig = plt.figure(figsize=plt_size)
ax1 = fig.add_subplot(1,1,1)
# 先清除x轴的刻度
ax1.xaxis.set_major_formatter(plt.FuncFormatter(''.format))
ax1.set_xticks(range(1,len(day_list)*2,2))
# 将y轴的刻度设置为百分比形式
def to_percent(temp, position):
return '%1.0f'%(100*temp) + '%'
plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(to_percent))
# 自定义x轴刻度标签
for a,b in zip(x_bar,day_list):
ax1.text(a,-0.08,b,ha='center',va='bottom')
# 绘制面积图和堆积柱状图
for i,s in zip(range(len(day_list)),score_list):
ax1.stackplot(x_stack,stack_prop_list[i],alpha=0.25)
ax1.bar(x_bar,bar_prop_list[i],width=1,label='得分:{}'.format(s))
# 添加y轴刻度虚线
ax1.grid(True, 'major', 'y', ls='--', lw=.5, c='black', alpha=.3)
ax1.legend(loc='best')
return plt.show()