ubunu安裝libsvm

英雄王不二發表於2017-05-08

libsvm安裝參考網站：http://www.cnblogs.com/liangxw1987/archive/2012/11/26/2788850.html

關於xgboost操作libsvm參考網站：http://blog.csdn.net/john159151/article/details/45549143

還有一種工具叫phraug；可以使csv到libsvm格式之間轉換

第一步：從官網下載libsvm安裝檔案

官網地址：http://www.csie.ntu.edu.tw/~cjlin/libsvm/

$ wget -r -O libsvm+tar.gz "http://www.csie.ntu.edu.tw/~cjlin/cgi-bin/libsvm.cgi?+http://www.csie.ntu.edu.tw/~cjlin/libsvm+tar.gz"
$ tar -zxvf libsvm+tar.gz

第二步：進入libsvm-3.22目錄

在***/libsvm-3.22目錄下執行
$ make lib   #生成libsvm.so.2檔案
在***/libsvm-3.22/python目錄下執行

$ make
將***/libsvm-3.22/python目錄下的*.py和***/libsvm-3.22目錄下的libsvm.so.2檔案copy到python環境包裡 

$ sudo cp *.py /usr/lib/python2.7/dist-packages/  
$ cd ..  
$ sudo cp libsvm.so.2 /usr/lib/python2.7/

第三部：檢查是否安裝成功

python檔案中嘗試引入包

import svm
import svmutil

關於libsvm

他的資料格式為
說明一點：訓練資料DataFrame格式一共有多少行，libsvm就有多少行。indexi表示那一列特徵。
<label> <index1>:<value1> <index2>:<value2> .......
比如
1 1:2.927699e+01 2:1.072510e+02 3:1.149632e-01 4:1.077885e+02
一個簡單的dataFrame轉libsvm檔案的例子
#-*- coding:utf-8 -*-
import numpy as np
import pandas as pd
from sklearn.datasets import dump_svmlight_file
from svmutil import svm_read_problem
# dataFrame2libsvm
df = pd.DataFrame()
df['feature1'] = np.random.rand(10,)
df['feature2'] = np.random.rand(10,)
df['feature3'] = np.random.rand(10,)
df['label'] = map(lambda x: -1 if x < 0.5 else 1, np.random.rand(10,))
train_x = df[ np.setdiff1d(df.columns,['label']) ]
train_y = df.label
print df
dump_svmlight_file(train_x,train_y,'./data/smvlight.libsvm',zero_based=True,multilabel=False)
train_y, train_x = svm_read_problem('./data/smvlight.libsvm') #注意返回引數中第一個list是label
print train_y  # type = list
print train_x  # type = list
輸出結果
tensorflow@NoNo:~/py_workspace/code_test$ python libsvm_test.py 
   feature1  feature2  feature3  label
0  0.658051  0.231439  0.959484     -1
1  0.946773  0.162317  0.019349      1
2  0.492598  0.538605  0.487779      1
3  0.878717  0.180026  0.317419      1
4  0.274376  0.757067  0.763130     -1
5  0.028619  0.756345  0.384797      1
6  0.297084  0.037591  0.170282     -1
7  0.690053  0.772461  0.771781     -1
8  0.552999  0.006163  0.194889     -1
9  0.121132  0.784318  0.213316      1
[-1.0, 1.0, 1.0, 1.0, -1.0, 1.0, -1.0, -1.0, -1.0, 1.0]
[{0: 0.6580514571148101, 1: 0.2314389067431816, 2: 0.9594842762467438}, 
{0: 0.9467733677685749, 1: 0.1623167175795766, 2: 0.01934886005436343}, 
{0: 0.4925978580764161, 1: 0.5386046263984983, 2: 0.4877788468915214}, 
{0: 0.8787172500390132, 1: 0.1800264331531124, 2: 0.3174192448003894}, 
{0: 0.2743762486602979, 1: 0.7570674932547728, 2: 0.763130398639558}, 
{0: 0.02861937544524562, 1: 0.7563446388879407, 2: 0.3847974464441019}, 
{0: 0.2970842179706645, 1: 0.03759119176879699, 2: 0.1702815123822912}, 
{0: 0.6900527245965673, 1: 0.7724610030039325, 2: 0.7717805678493267}, 
{0: 0.552998651635158, 1: 0.006163155707424983, 2: 0.1948893427641173}, 
{0: 0.1211315661039534, 1: 0.7843176563346573, 2: 0.2133161959586511}]









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