python实现 随机森林（RF）的参数寻优

# -*- coding: utf-8 -*-
#RandomForestClassifier
import math
import matplotlib as mpl
import warnings
import numpy as np
from sklearn import tree
from sklearn import ensemble
from sklearn import metrics
from sklearn.metrics import auc 
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import classification_report
from sklearn.cross_validation import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.externals import joblib
import matplotlib.pyplot as plt
plt.switch_backend('agg') 
warnings.filterwarnings('ignore')


# 数据读入,划分训练与验证 
def split_data(file_name,N):  #uniprot_10_1_1_lst.txt
	data = []
	labels = []
	with open(file_name,'r') as ifile:   #-*-# file's name #-*-#
		for line in ifile:
			tokens = line.strip().split(',')
			#print('tokens:',tokens)
			data.append([int(tk) for tk in tokens[:-1]])
			labels.append(tokens[-1])

	x = np.array(data)
	labels = np.array(labels)
	y = np.zeros(labels.shape)
	y[labels=='disorder']=1  #label is 'non-disorder' and 'disorder'
	#拆分Train-Valid , # test_size = N
	x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = N,random_state=0) 
	return x_train,x_test,y_train,y_test


# 参数(n_estimators and  max_features) 寻优
with open('10-1000-2-20-scores.csv','w') as f_scores:
	scores = []
	for n in range(10,1000,10):
		for m in range(2,20): 
			list_empty = []
			clf2 = RandomForestClassifier(n_estimators = n, max_features = m,
					max_depth=None,min_samples_split=2, random_state=0)
			scores2 = cross_val_score(clf2,x_train,y_train,cv=5,scoring='accuracy')
			str_w = str(n)+','+str(m)+','+str(scores2.mean())
			print(str_w)
			f_scores.write(str_w)
			f_scores.write('\n')

1.首先对原始数据进行数据集划分，分别得到训练与验证数据集（及其标签）

2.建立RF，通过爬网格，，以ACC为标准，进行参数寻优

 

# for example #

数据格式：

运行结果如下：