深度学习--项目提炼1

一、步骤

1、处理数据：避免原数据被感染，将原数据copy，标记缺失值，用热力图，去掉多余的字段

2、清洗数据：copy处理的数据，然后进行删除字段和删除重复的字段

3、划分测试、训练集：copy 测试集和训练集，再给缺失的值里面填充中位数、众数

处理数据

 1 #求补集（对应索引不一致的数据）
 2 ls = ["tom","name",3]
 3 ls2 = [7,"name2",3]
 4 print(np.setxor1d(ls,ls2)) #['7' 'name' 'name2' 'tom']
 5 
 6 
 7 # 把a转化成b
 8 a = ['NY8Y9', 'N2N29', 'N3N39', 'N4N49', 'N5N59', 'N6N64']
 9 b = ['N1819', 'N2029','N3039','N4049', 'N5059','N6064']
10 dic = dict(zip(a,b))
11 def tran(x):
12     if x in dic:
13         return dic[x]
14     else:
15         return x
16 data_01.columns = data_01.columns.map(tran)

 

 1 # 把英文列转成中文列
 2 dic = {k:v for k,v in feature_dict[['变量名','变量说明']].values} #{"英文":"中文"}
 3 def chinese(x):
 4     y = x.copy()
 5     #将输入进来的字段名通过字典映射的方式去对应
 6     y.columns = pd.Series(y.columns).map(dic)
 7     return y
 8 #得到列表
 9 feature_dict.变量名[:5].tolist()
10 
11 #将0_4列取出来并进行翻译
12 data0_4 = chinese(data_01[feature_dict.变量名[:5].tolist()])
13 data0_4.head()

 

 1 # 检测字段是否有空值
 2 def fre(x):
 3     for i in x.columns:
 4         print("字段名:",i)
 5         print("----------")
 6         print("字段数据类型:",x[i].dtype)
 7         print("----------------------------")
 8         print(x[i].value_counts()) #出现的频数
 9         print("----------------------------")
10         print("缺失值的个数:",x[i].isnull().sum())
11         print("------------------------------------------------\n\n")

 

1 # 条形图
2 import seaborn as sns
3 
4 #中文编码
5 sns.set_style("darkgrid",{"font.sans-serif":['simhei','Droid Sans Fallback']})
6 #sns.set()
7 plt.figure(1,figsize=(6,2))
8 sns.countplot(y='是否response',data=data0_4)
9 plt.show()

 

 

 

1 #根据年龄   概率密度图
2 sns.kdeplot(data0_4.年龄[data0_4.是否response==1],label='购买')
3 sns.kdeplot(data0_4.年龄[data0_4.是否response==0],label='不购买')
4 sns.kdeplot(data0_4.年龄.dropna(),label='所有人')
5 
6 plt.xlim([60,90])
7 plt.xlabel('Age')
8 plt.ylabel('Density'）

 

 

 

 1 #0 1 转码
 2 def zero_one(x):
 3     for i in x.columns:
 4         if x[i].dtype == 'object':
 5             dic = dict(zip(list(x[i].value_counts().index),range(x[i].nunique())))
 6             x[i] = x[i].map(dic)
 7     return x
 8 x[i].value_counts()# Y:20,N:10
 9 x[i].value_counts().index# 0,1
10 x[i].nunique()# 不重复的值 N，Y
11 
12 zero_one(data23_35).corr() #相关系数
# 转码 方法二
from sklearn.preprocessing import OrdinalEncoder


#fit_transform 直接转
new_arr = OrdinalEncoder().fit_transform(Xtrain_02)


 


import matplotlib.pyplot as plt
import seaborn as sns

#画一个热力图
sns.heatmap(zero_one(data23_35).corr(),cmap='Blues')

#形式2

a = chinese(data_01[["c210apvt","c210blu","c210bpvt","c210mob","c210wht","zhip19"]])


sns.heatmap(a.corr(),cmap='Blues')






 


 




 1 # 相关系数高的列
 2 def higt_cor(x,y=0.65):
 3     data_cor = (x.corr()>y)
 4     a=[]
 5     
 6     for i in data_cor.columns:
 7         if data_cor[i].sum()>=2:
 8             a.append(i)
 9 
10     return a  #这些是我们要考虑删除的




 




1 # 图
2 sns.countplot(x='N2NCY',hue='resp_flag',data=data_01)
3 plt.xlabel('县的大小')
4 plt.ylabel('购买数量')






 


 




1 # 图
2 sns.countplot(x='STATE_NAME',hue='resp_flag',data=data_01)
3 plt.xlabel('所处的省份')
4 plt.ylabel('购买数量')






 


 




# 删除
del_col = ["KBM_INDV_ID","U18","POEP","AART","AHCH","AASN","COLLEGE",
 "INVE","c210cip","c210hmi","c210hva","c210kses","c210blu","c210bpvt","c210poo","KBM_INDV_ID","meda"]
data_02 = data_02.drop(columns=del_col)






1 # 删除重复值
2 data_02.drop_duplicates().shape




划分训练集与测试集


一定要先划分数据集再填充、转码




 1 from sklearn.model_selection import train_test_split
 2 
 3 y = data_02.pop('resp_flag') #标签
 4 X = data_02  #特征
 5 Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,y,test_size=0.3,random_state=100)
 6 Xtrain_01=Xtrain.copy()
 7 Xtest_01=Xtest.copy()
 8 Ytrain_01=Ytrain.copy()
 9 Ytest_01=Ytest.copy()
10 fil = ["age","c210mah","c210b200","c210psu","c210wht","ilor"]
11 Xtrain_01[fil].median()
12 dic = dict(zip(Xtrain_01[fil].median().index,Xtrain_01[fil].median()))
13 #向训练集填充中位数
14 Xtrain_01 = Xtrain_01.fillna(dic) 
15 #向训练集填充众数
16 mod = ["N1819","ASKN","MOBPLUS","N2NCY","LIVEWELL","HOMSTAT","HINSUB"]
17 dic_mod = dict(zip(Xtrain_01[mod].mode().columns,Xtrain_01[mod].iloc[0,:]))
18 Xtrain_01 = Xtrain_01.fillna(dic_mod) 
19 # 替换填充
20 Xtrain_01['N6064'] = Xtrain_01['N6064'].replace('0','N')  #0  替换成  N
21 # 检验每个字段（列）是否有空值
22 Xtrain_01.isnull().sum()[Xtrain_01.isnull().sum()!=0]




 转码




1 # 筛出object列
2 # 方法一
3 for i in data_01.columns:
4     if data_01[i].dtype == 'object':
5         print(i)
6 
7 # 方法二
8 object_tr =data_01.describe(include='O').columns #include= “O“ 是object类型






1 encod_col = pd.read_excel('保险案例数据字典_清洗.xlsx',sheet_name=2)
2 encod_col.head()






 


 




1 # 查看Xtrain_01中object类型
2 object_tr =Xtrain_01.describe(include='O').columns






1 #检查一下转码的目标是否出现
2 np.setdiff1d(object_tr,encod_col['变量名']) # 将所有obj类型的列筛选出来，看看在不在需要转码的列（encod_col）里




 


0-1 转码总结




 1 #获取需要转码的字段
 2 encod_col = pd.read_excel('保险案例数据字典_清洗.xlsx',sheet_name=2)
 3 
 4 # 查看Xtest_01中object类型
 5 object_tr =Xtest_01.describe(include='O').columns
 6 
 7 #检查一下转码的目标是否出现
 8 np.setdiff1d(object_tr,encod_col['变量名'])
 9 
10 #0-1 转码
11 # 获取0-1 转码的变量名
12 z_0_list = encod_col[encod_col['转']=='0-1'].变量名 # 要转码的列
13 
14 Xtest_02 = Xtest_01[z_0_list]
15 
16 #sklearn的预处理模块
17 from sklearn.preprocessing import OrdinalEncoder
18 
19 #fit_transform 直接转
20 new_arr = OrdinalEncoder().fit_transform(Xtest_02)
21 # columns 设置表头为原来的   index 索引也是原来
22 Xtest_02 = pd.DataFrame(data=new_arr,columns=Xtest_02.columns,index=Xtest_02.index)
23 
24 Xtest_01[z_0_list] = Xtest_02
25 
26 Xtest_01.head()




哑变量 总结




 1 #获取哑变量转码的变量
 2 o_h_list = encod_col[encod_col['转']=='哑变量'].变量名
 3 
 4 o_h_01 = ['c210mys','LIVEWELL'] #非字符型的变量
 5 o_h_02 = [i for i in o_h_list if i not in o_h_01] #字符类型的变量
 6 
 7 #先转o_h_02 字符类型
 8 Xtest_02 = Xtest_01.copy()
 9 Xtest_02 = pd.get_dummies(chinese(Xtest_02[o_h_02]))
10 
11 #w我们再转 o_h_01  非字符
12 Xtest_03 = Xtest_01.copy()
13 #转成字符类型
14 Xtest_03 = Xtest_03[o_h_01].astype(str)
15 #转化覆盖
16 Xtest_03 = pd.get_dummies(chinese(Xtest_03[o_h_01]))
17 
18 
19 # Xtrain_04 删除原转码的字段
20 Xtest_04 = Xtest_01.copy()
21 Xtest_04 = chinese(Xtest_04.drop(columns=o_h_01+o_h_02))
22 
23 
24 #将 Xtest_04  Xtest_02 Xtest_03 合并
25 Xtest_05 = pd.concat([Xtest_04,Xtest_02,Xtest_03],axis=1)
26 Xtest_05.shape




 


初步建模




1 from sklearn.tree import DecisionTreeClassifier
2 from sklearn.model_selection import cross_val_score
3 
4 clf = DecisionTreeClassifier(random_state=420,class_weight='balanced')
5 cvs = cross_val_score(clf,Xtrain_05,Ytrain)
6 cvs.mean() # 平均分 0.5993914576104883




网格搜索找最优参数




 1 from sklearn.model_selection import GridSearchCV
 2 
 3 #测试参数
 4 param_test = {
 5              'splitter':('best','random'),
 6              'criterion':('gini','entropy'), #基尼  信息熵
 7              'max_depth':range(3,15) #最大深度
 8              #,min_samples_leaf:(1,50,5)
 9 }
10 
11 gsearch= GridSearchCV(estimator=clf, #对应模型
12                 param_grid=param_test,#要找最优的参数
13                 scoring='roc_auc',#准确度评估标准 
14                 n_jobs=-1,# 并行数  个数   -1:跟CPU核数一致
15                 cv = 5,#交叉验证 5折
16                 iid=False,# 默认是True  与各个样本的分布一致 
17                 verbose=2#输出训练过程
18                 )
19 
20 gsearch.fit(Xtrain_05,Ytrain_01)
21 #优化期间观察到的最高评分
22 gsearch.best_score_
23 #0.691856415170639
# 最优参数


gsearch.best_params_


#{'criterion': 'entropy', 'max_depth': 6, 'splitter': 'best'}




模型评估




 1 from sklearn.metrics import accuracy_score #准确率
 2 from sklearn.metrics import precision_score #精准率
 3 from sklearn.metrics import recall_score #召回率
 4 from sklearn.metrics import roc_curve
 5 
 6 y_pre = gsearch.predict(Xtest_05)
 7 accuracy_score(y_pre,Ytest) 
 8 #0.6090076335877863
 9 
10 precision_score(y_pre,Ytest)
11 #0.748152359295054
12 
13 recall_score(y_pre,Ytest
14 #0.5100116264048572
15 
16 fpr,tpr,thresholds = roc_curve(y_pre,Ytest) #roc参数
17 import matplotlib.pyplot as plt
18 plt.plot(fpr,tpr,c='b',label='roc曲线')
19 plt.plot(fpr,fpr,c='r',ls='--')






 


 


 


 输出规则




 1 #最优参数
 2 #{'criterion': 'entropy', 'max_depth': 6, 'splitter': 'best'}
 3 from sklearn.tree import DecisionTreeClassifier
 4 from sklearn import tree
 5 
 6 import graphviz
 7 
 8 #将最优参数放到分类器
 9 clf = DecisionTreeClassifier(criterion='entropy',max_depth=6,splitter='best')
10 clf = clf.fit(Xtrain_05,Ytrain)
11 
12 
13 features = Xtrain_05.columns
14 dot_data = tree.export_graphviz(clf,
15                      feature_names=features,
16                      class_names=['Not Buy','Buy'],
17                      filled=True,
18                      rounded=True,
19                      leaves_parallel=False)
20 
21 graph= graphviz.Source(dot_data)




 






		

		
posted @ 
2021-11-26 16:52 
东方不败--Never 
阅读(93) 
评论(0) 
编辑 
收藏 
举报