随机森林数实现分类

数据预处理看代码地址有操作指南

代码地址  https://github.com/mastercaojie/Machine-Learning

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

import numpy as np  # numpy库

from sklearn import cross_validation

from sklearn.model_selection import cross_val_score  # 交叉检验

import pandas as pd  # 导入pandas

from sklearn.ensemble import RandomForestClassifier

#获取数据

data = pd.read_csv('corrected_new.csv',header=None,delimiter=",")

dataset = np.array(data)

print("数据集shape: ",dataset.shape)

print (70 * '-')  # 打印分隔线

X = dataset[:,0:35]

Y = dataset[:,35]

Label = np.array(['0','1','2','3','4'])

print("输出类别:",Label)

print (70 * '-')  # 打印分隔线

# 划分训练集和测试集

X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.1)

print("训练集个数：",X_train.shape[0])

print("测试集个数：",X_test.shape[0])

print (70 * '-')  # 打印分隔线

#用随机森林

rf = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',

            max_depth=None, max_features='auto', max_leaf_nodes=None,

            min_samples_leaf=1, min_samples_split=2,

            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,

            oob_score=False, random_state=None, verbose=0,

            warm_start=False)

rf.fit(X_train,Y_train)

probability = pd.DataFrame(np.array(rf.predict_proba(X_test)))

result = pd.DataFrame(np.array((rf.predict(X_test))))

score = cross_val_score(rf, X_train,Y_train)

print("训练集精度得分:",score.mean())

score = cross_val_score(rf, X_test,Y_test)

print("测试集精度得分:",score.mean())

showlist = pd.concat([probability,result],axis=1)

print (70 * '-')  # 打印分隔线

print("概率+类别：")

showlist.columns = ['类别0','类别1','类别2','类别3','类别4',"结果"]

prob0 = showlist[showlist['结果'].isin([0])].shape[0]

prob1 = showlist[showlist['结果'].isin([1])].shape[0]

prob2 = showlist[showlist['结果'].isin([2])].shape[0]

prob3 = showlist[showlist['结果'].isin([3])].shape[0]

prob4 = showlist[showlist['结果'].isin([4])].shape[0]

resultlist =[prob0,prob1,prob2,prob3,prob4]

resultlist = pd.DataFrame(resultlist)

print(showlist)

print (70 * '-')  # 打印分隔线

print("统计每个类别的数量：")

resultlist.columns = ['统计']

print(resultlist)

运行结果

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