随机森林模型sklearn_sklearn中的随机森林算法RandomForestClassifier

今天研究了一下随机森林算法RandomForestClassifier的源码，把一些常用的属性找了出来(针对0/1这种对错判断)

# -*- coding: utf-8 -*-

from sklearn.tree import DecisionTreeClassifier

from matplotlib.pyplot import *

from sklearn.cross_validation import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.externals.joblib import Parallel, delayed

from sklearn.tree import export_graphviz

final = open('c:/test/final.dat' , 'r')

data = [line.strip().split('\t') for line in final]

feature = [[float(x) for x in row[3:]] for row in data]

target = [int(row[0]) for row in data]

#拆分训练集和测试集

feature_train, feature_test, target_train, target_test = train_test_split(feature, target, test_size=0.1, random_state=42)

#分类型决策树

clf = RandomForestClassifier(n_estimators = 8)

#训练模型

s = clf.fit(feature_train , target_train)

print s

#评估模型准确率

r = clf.score(feature_test , target_test)

print r

print '判定结果：%s' % clf.predict(feature_test[0])

#print clf.predict_proba(feature_test[0])

print '所有的树:%s' % clf.estimators_

print clf.classes_

print clf.n_classes_

print '各feature的重要性：%s' % clf.feature_importances_

print clf.n_outputs_

def _parallel_helper(obj, methodname, *args, **kwargs):

return getattr(obj, methodname)(*args, **kwargs)

all_proba = Parallel(n_jobs=10, verbose=clf.verbose, backend="threading")(

delayed(_parallel_helper)(e, 'predict_proba', feature_test[0]) for e in clf.estimators_)

print '所有树的判定结果：%s' % all_proba

proba = all_proba[0]

for j in range(1, len(all_proba)):

proba += all_proba[j]

proba /= len(clf.estimators_)

print '数的棵树：%s ， 判不作弊的树比例：%s' % (clf.n_estimators , proba[0,0])

print '数的棵树：%s ， 判作弊的树比例：%s' % (clf.n_estimators , proba[0,1])

#当判作弊的树多余不判作弊的树时，最终结果是判作弊

print '判断结果：%s' % clf.classes_.take(np.argmax(proba, axis=1), axis=0)

#把所有的树都保存到word

for i in xrange(len(clf.estimators_)):

export_graphviz(clf.estimators_[i] , '%d.dot'%i)

来自 “ ITPUB博客 ” ，链接：http://blog.itpub.net/12199764/viewspace-1572056/，如需转载，请注明出处，否则将追究法律责任。