xgboost实例代码

# -*- coding: utf-8 -*-
import xgboost as xgb
import csv
import jieba
jieba.load_userdict('wordDict.txt')
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
 
 
# 读取训练集
def readtrain():
    with open('Train.csv', 'rb') as csvfile:
        reader = csv.reader(csvfile)
        column1 = [row for row in reader]
    content_train = [i[1] for i in column1[1:]] # 第一列为文本内容，并去除列名
    opinion_train = [i[2] for i in column1[1:]] # 第二列为类别，并去除列名
    print '训练集有 %s 条句子' % len(content_train)
    train = [content_train, opinion_train]
    return train
 
 
# 将utf8的列表转换成unicode
def changeListCode(b):
    a = []
    for i in b:
        a.append(i.decode('utf8'))
    return a
 
 
# 对列表进行分词并用空格连接
def segmentWord(cont):
    c = []
    for i in cont:
        a = list(jieba.cut(i))
        b = " ".join(a)
        c.append(b)
    return c
 
 
# 类别用数字表示：pos:2,neu:1,neg:0
def transLabel(labels):
    for i in range(len(labels)):
        if labels[i] == 'pos':
            labels[i] = 2
        elif labels[i] == 'neu':
            labels[i] = 1
        elif labels[i] == 'neg':
            labels[i] = 0
        else: print "label无效：",labels[i]
    return labels
 
 
train = readtrain()
content = segmentWord(train[0])
opinion = transLabel(train[1])  # 需要用数字表示类别
opinion = np.array(opinion)     # 需要numpy格式
 
 
train_content = content[:7000]
train_opinion = opinion[:7000]
test_content = content[7000:]
test_opinion = opinion[7000:]
 
 
vectorizer = CountVectorizer()
tfidftransformer = TfidfTransformer()
tfidf = tfidftransformer.fit_transform(vectorizer.fit_transform(train_content))
weight = tfidf.toarray()
print tfidf.shape
test_tfidf = tfidftransformer.transform(vectorizer.transform(test_content))
test_weight = test_tfidf.toarray()
print test_weight.shape
 
 
dtrain = xgb.DMatrix(weight, label=train_opinion)
dtest = xgb.DMatrix(test_weight, label=test_opinion)  # label可以不要，此处需要是为了测试效果
param = {'max_depth':6, 'eta':0.5, 'eval_metric':'merror', 'silent':1, 'objective':'multi:softmax', 'num_class':3}  # 参数
evallist  = [(dtrain,'train'), (dtest,'test')]  # 这步可以不要，用于测试效果
num_round = 50  # 循环次数
bst = xgb.train(param, dtrain, num_round, evallist)
preds = bst.predict(dtest)