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RFormula是一个很方便,也很强大的Feature选择(自由组合的)工具。
输入string 进行独热编码(见下面例子country)
输入数值型转换为double(见下面例子hour)
label为string,也用StringIndexer进行编号
RFormula produces a vector column of features and a double or string
column of label. Like when formulas are used in R for linear regression,
string input columns will be one-hot encoded, and numeric columns will be
cast to doubles. If the label column is of type string, it will be first
transformed to double with StringIndexer. If the label column does not exist
in the DataFrame, the output label column will be created from the specified
response variable in the formula.
如输入数据为
+---+-------+----+------+-------+
| id|country|hour|salary|clicked|
+---+-------+----+------+-------+
| 7| US| 18|2500.0| 1.0|
| 8| CA| 12|1500.0| 0.0|
| 9| NZ| 15|1250.0| 0.0|
| 10| US| 10|3200.0| 1.0|
+---+-------+----+------+-------+
使用RFormula:
RFormula formula = new RFormula()
.setFormula("clicked ~ country + hour + salary")
//clicked作为label,~之后的三个为选择的特征
.setFeaturesCol("features")
.setLabelCol("label");
+---------------------+-----+
|features |label|
+---------------------+-----+
|[1.0,0.0,18.0,2500.0]|1.0 |
|[0.0,0.0,12.0,1500.0]|0.0 |
|[0.0,1.0,15.0,1250.0]|0.0 |
|[1.0,0.0,10.0,3200.0]|1.0 |
+---------------------+-----+
[1.0,0.0]为"US"的独热编码,以此类推
ChiSqSelector
参考:
http://www.blogjava.net/zhenandaci/archive/2008/08/31/225966.html
里面举得例子很好理解(原文真的很通俗易懂,直接参考原文吧,瞬间明白的感觉)。
在Spark中似乎非常慢???
ChiSqSelector chiSqSelector=new ChiSqSelector()
.setFeaturesCol("TF")
.setOutputCol("features")
.setLabelCol("label")
.setNumTopFeatures(2);
Dataset<Row> wordsChiSq=chiSqSelector.fit(wordsTF).transform(wordsTF);
中文分词工具比较多,Java,Python版本都有,这里以IKAnalyzer2012+Java版本为例说明。
使用时参考IKAnalyzer2012自带的中文帮助文档(有比较详细的用法)。
IKAnalyzer2012它 的 安 装 部 署 十 分 简 单 , 将 IKAnalyzer2012.jar 部 署 于 项 目 的 lib 目 录 中 ;IKAnalyzer.cfg.xml 与 stopword.dic 文件放置在 class 根目录。
依赖Lucene的类org.wltea.analyzer.luceneorg.wltea.analyzer,分词主类。
//创建分词对象
Analyzer anal=new IKAnalyzer(true);
StringReader reader=new StringReader(row.getString(1));
//分词
TokenStream ts=anal.tokenStream("", reader);
CharTermAttribute term=(CharTermAttribute) ts
.getAttribute(CharTermAttribute.class);
//遍历分词数据
String words="";
while(ts.incrementToken()){
words+=(term.toString()+"|");
}
下面是一个综合的实例子,用到了Spark的一些特征转换API。由于需要处理中文,还需要一个分词器。
(I)首先准备一个数据集,谭松波老师收集的中文情感分析 酒店评论语料
从CSDN上可以下载:http://download.csdn.net/download/x_i_y_u_e/9273533
1、-ChnSentiCorp-Htl-ba-2000: 平衡语料,正负类各1000篇。
2.ChnSentiCorp-Htl-ba-4000: 平衡语料,正负类各2000篇。
3.ChnSentiCorp-Htl-ba-6000: 平衡语料,正负类各3000篇。
4.ChnSentiCorp-Htl-unba-10000: 非平衡语料,正类为7000篇。
(II) 在linux下是乱码的,需要转换:
编码查看:
%file -i neg.9.txt
neg.9.txt: text/plain; charset=iso-8859-1
需要转换为utf8
%iconv -f gb18030 -t utf8 neg.9.txt -o neg.9.o.txt
-f :from -t :to
批量转如下:
(1)复制文件目录 find ChnSen* -type d -exec mkdir -p utf/{} \;
(2)转换 find ChnSen* -type f -exec iconv -f GBK -t UTF-8 {} -o utf/{} \;
ChnSen*是单前文件夹下的目录,utf是输出目录
(III) python 处理文件,合并为一个文件,去掉一条评论中所有换行
# -*- coding: utf-8 -*-
#将所有评论读入到一个文件中,每个原始文件文件为一条评论,中间不要出现换行
#repalce("\r"," "),是将^M(window下产生的 linux不认识的换行)去掉
#输出csv格式
path_out="E:/data/utf/ChnSentiCorp_htl_ba_2000/all.csv"
fw=open(path_out,'w+')
#负样本
for i in range(999):
path1="E:/data/utf/ChnSentiCorp_htl_ba_2000/neg."+str(i+1)+".txt"
fr=open(path1)
lines=fr.readlines()
fw.write("0.0,")#label
for line in lines:
#repalce("\r"," "),是将^M(window下产生的 linux不认识的换行)去掉
#replace(",",""),是为输出CSV格式做准备
line=line.replace("\r",'').strip().replace(",","")
fw.write(line)
fw.write("\n")
fr.close()
#正样本
for i in range(999):
path2="E:/data/utf/ChnSentiCorp_htl_ba_2000/pos."+str(i+1)+".txt"
fr=open(path2)
fw.write("1.0,")#label
lines=fr.readlines()
for line in lines:
line=line.replace("\r",'').strip().replace(",","")
fw.write(line)
fw.write("\n")
fr.close()
fw.close
可参考论文(只是分词工具不同):
基于 Spark 的文本情感分析
http://www.ibm.com/developerworks/cn/cognitive/library/cc-1606-spark-seniment-analysis/index.html
思路是一样的,不过我是用Java实现的,写起来远远不如Python简洁。
//初步完整的流程,还需要进一步优化
//IKAnalyzer2012分词->TF-IDF特征->NaiveBayes ML
package my.spark.ml.practice.classification;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.ml.classification.NaiveBayes;
import org.apache.spark.ml.classification.NaiveBayesModel;
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
import org.apache.spark.ml.feature.HashingTF;
import org.apache.spark.ml.feature.IDF;
import org.apache.spark.ml.feature.IDFModel;
import org.apache.spark.ml.feature.RegexTokenizer;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoder;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import java.io.IOException;
import java.io.StringReader;
import org.apache.log4j.Level;
import org.apache.log4j.Logger;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
//引用IKAnalyzer2012的类
import org.wltea.analyzer.lucene.IKAnalyzer;
import my.spark.ml.practice.classification.LabelValue;;
//文本处理,酒店评论
public class myHotelTextClassifer3 {
public static void main(String[] args) throws IOException {
SparkSession spark=SparkSession
.builder()
.appName("Chinese Text Processing")
.master("local[4]")
.config("spark.sql.warehouse.dir",
"file///:G:/Projects/Java/Spark/spark-warehouse" )
.getOrCreate();
//屏蔽日志
Logger.getLogger("org.apache.spark").setLevel(Level.WARN);
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF);
//--------------------------(0)读入数据,数据预处理--------------------------------
//原始数据文件包含两行,一行是label,一行是sentence,csv格式的
Dataset<Row> raw=spark.read().format("csv")
.load("E:/data/utf/ChnSentiCorp_htl_ba_2000/all.csv");
//去掉空值,不然一定后面一定抛出nullpointer异常
//distinct数据去重 ,并打乱数据的顺序:不然数据是先负样本后正样本有规律排列的,预测效果偏高
Dataset<Row> rawFilterNaN=raw
.filter(raw.col("_c0").isNotNull())
.filter(raw.col("_c1").isNotNull())
.distinct();
//--------------------------(1)分词----------------------------------------
//为Map自定义了Class LabelValue,见后面
Encoder<LabelValue> LongStringEncoder=Encoders.bean(LabelValue.class);
Dataset<LabelValue> wordsDF=rawFilterNa.map(new MapFunction<Row,LabelValue>() {
@Override
public LabelValue call(Row row) throws Exception {
if (row!=null) {
LabelValue ret = new LabelValue();
double Label=1.0;
if (row.getString(0).equals("0.0")) {
Label=0.0;
}else{
Label=1.0;
}
ret.setLabel(Label);
//-------------KAnalyzer分词--------------------
//创建分词对象
Analyzer anal=new IKAnalyzer(true);
StringReader reader=new StringReader(row.getString(1));
//分词
TokenStream ts=anal.tokenStream("", reader);
CharTermAttribute term=(CharTermAttribute) ts
.getAttribute(CharTermAttribute.class);
//遍历分词数据
String words="";
while(ts.incrementToken()){
words+=(term.toString()+"|");
}
ret.setValue(words);
reader.close();
return ret;
}
else {
return null;
}
}
}, LongStringEncoder);
//--------------------------(1)-2 RegexTokenizer分词器-----------------------------
RegexTokenizer regexTokenizer = new RegexTokenizer()
.setInputCol("value")
.setOutputCol("words")
.setPattern("\\|");
Dataset<Row> wordsDF2 = regexTokenizer.transform(wordsDF);
//--------------------------(2) HashingTF训练词频矩阵---------------------------------
HashingTF tf=new HashingTF()
.setInputCol("words")
.setOutputCol("TF");
Dataset<Row> wordsTF=tf.transform(wordsDF2).select("TF","label");
wordsTF.show();wordsTF.printSchema();
Dataset<Row> wordsTF2=wordsTF
.filter(wordsTF.col("TF").isNotNull())
.filter(wordsTF.col("label").isNotNull());
//------------------------- (4)计算 TF-IDF 矩阵--------------------------------------
IDFModel idf=new IDF()
.setInputCol("TF")
.setOutputCol("features")
.fit(wordsTF2);
Dataset<Row> wordsTfidf=idf.transform(wordsTF2);
//----------------------------(5)NaiveBayesModel ML---------------------
Dataset<Row>[] split=wordsTfidf.randomSplit(new double[]{0.6,0.4});
Dataset<Row> training=split[0];
Dataset<Row> test=split[1];
NaiveBayes naiveBayes=new NaiveBayes()
.setLabelCol("label")
.setFeaturesCol("features");
NaiveBayesModel naiveBayesModel=naiveBayes.fit(training);
Dataset<Row> predictDF=naiveBayesModel.transform(test);
//自定义计算accuracy
double total=(double) predictDF.count();
Encoder<Double> doubleEncoder=Encoders.DOUBLE();
Dataset<Double> accuracyDF=predictDF.map(new MapFunction<Row,Double>() {
@Override
public Double call(Row row) throws Exception {
if((double)row.get(1)==(double)row.get(5)){return 1.0;}
else {return 0.0;}
}
}, doubleEncoder);
accuracyDF.createOrReplaceTempView("view");
double correct=(double) spark.sql("SELECT value FROM view WHERE value=1.0").count();
System.out.println("accuracy "+(correct/total));
//计算areaUnderRoc
double areaUnderRoc=new BinaryClassificationEvaluator()
.setLabelCol("label")
.setRawPredictionCol("prediction")
.evaluate(predictDF);
//(areaUnderROC|areaUnderPR) (default: areaUnderROC)
System.out.println("areaUnderRoc "+areaUnderRoc);
}
}
//结果分析
//accuracy 0.7957860615883307
//areaUnderRoc 0.7873761854583772
//应该还有提升的空间
//Class LabelValue
package my.spark.ml.practice.classification;
import java.io.Serializable;
public class LabelValue implements Serializable {
private String value;
private double label;
public String getValue() {
return value;
}
public void setValue(String value) {
this.value = value;
}
public double getLabel() {
return label;
}
public void setLabel(double label) {
this.label = label;
}
}
先使用word2vect,然后将词产生的向量作为特征,分别用随机森林,GBTC,
LogisticRegression,其中GBTC效果最好。但是普遍不如Naive-Bayes,可能还需要某些地方进行改进。
//转换为词向量,并进行标准化
Word2Vec word2Vec=new Word2Vec()
.setInputCol("words")
.setOutputCol("vect")
.setVectorSize(10);
Dataset<Row> vect=word2Vec
.fit(wordsDF2)
.transform(wordsDF2);
//vect.show();vect.printSchema();
//正则化
Dataset<Row> vect2=new MinMaxScaler()
.setInputCol("vect")
.setOutputCol("features")
.setMax(1.0)
.setMin(0.0)
.fit(vect)
.transform(vect);
//vect2.show();vect2.printSchema();
//GBTC分类
GBTClassifier gbtc=new GBTClassifier()
.setLabelCol("label")
.setFeaturesCol("vect")
.setMaxDepth(10)
.setMaxIter(10)
.setStepSize(0.1);
Dataset<Row> predictDF=gbtc.fit(training0).transform(test0);
//其余代码是一样的,可以尝试不同的参数组合。
后续还将完成 去停用词、SVM训练
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