python - 随机森林 （初）

 https://blog.youkuaiyun.com/jklcl/article/details/77447656 （详细）

import numpy as np
import scipy.stats as ss
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.externals.six import StringIO
from sklearn.tree import DecisionTreeClassifier,export_graphviz
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score,recall_score,f1_score
from sklearn.externals import joblib
 
df=pd.DataFrame({"A":ss.norm.rvs(size=100),"B":np.random.randint(low=0,high=10,size=100),\
     "C":np.random.uniform(0, 1, size=100),"D":np.random.randint(low=0,high=2,size=100)})
 
label = df["D"]
df=df.drop("D",axis=1)
 
##切分训练测试、验证集 （6：2：2）
X_tt,X_validation,Y_tt,Y_validation=train_test_split(df.values,label.values,test_size=0.2,random_state=0)
X_train,X_test,Y_train,Y_test=train_test_split(X_tt,Y_tt,test_size=0.25,random_state=0)
   
### tree models
 
tree_clf = RandomForestClassifier()   ### RandomForestClassifier()类 
tree_clf.fit(X_train, Y_train)                    ### 使用训练集数据，构建tree
 
 
xy_lst = [(X_test, Y_test),(X_validation,Y_validation)]
for i in range(len(xy_lst)):
    x_part = xy_lst[i][0]
    y_part = xy_lst[i][1]
    Y_pred = tree_clf.predict(x_part)                ## 使用验证集的数据进行预测
    print("ACC:", accuracy_score(y_part,Y_pred))  ### sklearn.metrics 评估
    print("REC:", recall_score(y_part,Y_pred))
    print("F-Score:", f1_score(y_part,Y_pred))
    pass
 
### 存储models
joblib.dump(tree_clf,"tree_clf")    ### joblib 保存模型