项目实战-使用PySpark处理文本多分类问题
原文链接：https://cloud.tencent.com/developer/article/1096712
在大神创作的基础上，学习了一些新知识，并加以注释。
TARGET：将旧金山犯罪记录（San Francisco Crime Description）分类到33个类目中
源代码及数据集：之后提交。
一、载入数据集data
1 import time 2 from pyspark.sql import SQLContext 3 from pyspark import SparkContext 4 # 利用spark的csv库直接载入csv格式的数据 5 sc = SparkContext() 6 sqlContext = SQLContext(sc) 7 data = sqlContext.read.format('com.databricks.spark.csv').options(header='true', 8 inferschema='true').load('train.csv') 9 # 选10000条数据集，减少运行时间 10 data = data.sample(False, 0.01, 100) 11 print(data.count())
结果：
8703
1.1 除去与需求无关的列
1 # 除去一些不要的列，并展示前五行 2 drop_list = ['Dates', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y'] 3 data = data.select([column for column in data.columns if column not in drop_list]) 4 data.show(5)
1.2 显示数据结构
1 # 利用printSchema()方法显示数据的结构 2 data.printSchema()
结果：
root |-- Category: string (nullable = true) |-- Descript: string (nullable = true)
1.3 查看犯罪类型最多的前20个
1 # 包含数量最多的20类犯罪 2 from pyspark.sql.functions import col 3 data.groupBy('Category').count().orderBy(col('count').desc()).show()
结果：
+--------------------+-----+ | Category|count| +--------------------+-----+ | LARCENY/THEFT| 1725| | OTHER OFFENSES| 1230| | NON-CRIMINAL| 962| | ASSAULT| 763| | VEHICLE THEFT| 541| | DRUG/NARCOTIC| 494| | VANDALISM| 447| | WARRANTS| 406| | BURGLARY| 347| | SUSPICIOUS OCC| 295| | MISSING PERSON| 284| | ROBBERY| 225| | FRAUD| 159| | SECONDARY CODES| 124| |FORGERY/COUNTERFE...| 109| | WEAPON LAWS| 86| | TRESPASS| 63| | PROSTITUTION| 59| | DISORDERLY CONDUCT| 54| | DRUNKENNESS| 52| +--------------------+-----+ only showing top 20 rows
1.4 查看犯罪描述最多的前20个
1 # 包含犯罪数量最多的20个描述 2 data.groupBy('Descript').count().orderBy(col('count').desc()).show()
结果：
+--------------------+-----+ | Descript|count| +--------------------+-----+ |GRAND THEFT FROM ...| 569| | LOST PROPERTY| 323| | BATTERY| 301| | STOLEN AUTOMOBILE| 262| |DRIVERS LICENSE, ...| 244| |AIDED CASE, MENTA...| 223| | WARRANT ARREST| 222| |PETTY THEFT FROM ...| 216| |SUSPICIOUS OCCURR...| 211| |MALICIOUS MISCHIE...| 184| | TRAFFIC VIOLATION| 168| |THREATS AGAINST LIFE| 154| |PETTY THEFT OF PR...| 152| | FOUND PROPERTY| 138| |MALICIOUS MISCHIE...| 138| |ENROUTE TO OUTSID...| 121| |GRAND THEFT OF PR...| 115| |MISCELLANEOUS INV...| 101| | DOMESTIC VIOLENCE| 99| | FOUND PERSON| 98| +--------------------+-----+ only showing top 20 rows
二、对犯罪描述进行分词
2.1 对Descript分词，先切分单词，再删除停用词
流程和scikit-learn版本的很相似，包含3个步骤：
1.regexTokenizer: 利用正则切分单词
2.stopwordsRemover: 移除停用词
3.countVectors: 构建词频向量
RegexTokenizer：基于正则的方式进行文档切分成单词组
inputCol: 输入字段
outputCol: 输出字段
pattern： 匹配模式，根据匹配到的内容切分单词
CountVectorizer：构建词频向量
covabSize: 限制的词频数
minDF：如果是float，则表示出现的百分比小于minDF,不会被当做关键词
如果是int，则表示出现是次数小于minDF，不会被当做关键词
1 from pyspark.ml.feature import RegexTokenizer, StopWordsRemover, CountVectorizer 2 from pyspark.ml.classification import LogisticRegression 3 4 # 正则切分单词 5 # inputCol:输入字段名 6 # outputCol:输出字段名 7 regexTokenizer = RegexTokenizer(inputCol='Descript', outputCol='words', pattern='\\W') 8 # 停用词 9 add_stopwords = ['http', 'https', 'amp', 'rt', 't', 'c', 'the'] 10 stopwords_remover = StopWordsRemover(inputCol='words', outputCol='filtered').setStopWords(add_stopwords) 11 # 构建词频向量 12 count_vectors = CountVectorizer(inputCol='filtered', outputCol='features', vocabSize=10000, minDF=5)
2.2 对分词后的词频率排序，最频繁出现的设置为0
StringIndexer
StringIndexer将一列字符串label编码为一列索引号，根据label出现的频率排序，最频繁出现的label的index为0
该例子中，label会被编码成从0-32的整数，最频繁的label被编码成0
Pipeline是基于DataFrame的高层API，可以方便用户构建和调试机器学习流水线，可以使得多个机器学习算法顺序执行，达到高效的数据处理的目的。
fit():将DataFrame转换成一个Transformer的算法，将label列转化为特征向量
transform(): 将特征向量作为新列添加到DataFrame
1 from pyspark.ml import Pipeline 2 from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler 3 label_stringIdx = StringIndexer(inputCol='Category', outputCol='label') 4 pipeline = Pipeline(stages=[regexTokenizer, stopwords_remover, count_vectors, label_stringIdx]) 5 # fit the pipeline to training documents 6 pipeline_fit = pipeline.fit(data) 7 dataset = pipeline_fit.transform(data) 8 dataset.show(5)
结果：
+---------------+--------------------+--------------------+--------------------+--------------------+-----+ | Category| Descript| words| filtered| features|label| +---------------+--------------------+--------------------+--------------------+--------------------+-----+ | LARCENY/THEFT|GRAND THEFT FROM ...|[grand, theft, fr...|[grand, theft, fr...|(309,[0,2,3,4,6],...| 0.0| | VEHICLE THEFT| STOLEN AUTOMOBILE|[stolen, automobile]|[stolen, automobile]|(309,[9,27],[1.0,...| 4.0| | NON-CRIMINAL| FOUND PROPERTY| [found, property]| [found, property]|(309,[5,32],[1.0,...| 2.0| |SECONDARY CODES| JUVENILE INVOLVED|[juvenile, involved]|[juvenile, involved]|(309,[67,218],[1....| 13.0| | OTHER OFFENSES|DRIVERS LICENSE, ...|[drivers, license...|[drivers, license...|(309,[14,23,28,30...| 1.0| +---------------+--------------------+--------------------+--------------------+--------------------+-----+ only showing top 5 rows
三、训练/测试集划分
1 # set seed for reproducibility 2 # 数据集划分训练集和测试集，比例7:3， 设置随机种子100 3 (trainingData, testData) = dataset.randomSplit([0.7, 0.3], seed=100) 4 print('Training Dataset Count:{}'.format(trainingData.count())) 5 print('Test Dataset Count:{}'.format(testData.count()))
结果：
Training Dataset Count:6117 Test Dataset Count:2586
四、模型训练和评价
4.1 以词频作为特征，利用逻辑回归进行分类
模型在测试集上预测和打分，查看10个预测概率值最高的结果：
LogisticRegression：逻辑回归模型
maxIter：最大迭代次数
regParam：正则化参数
elasticNetParam：正则化。0：l1;1:l2
1 start_time = time.time() 2 lr = LogisticRegression(maxIter=20, regParam=0.3, elasticNetParam=0) 3 lrModel = lr.fit(trainingData) 4 predictions = lrModel.transform(testData) 5 # 过滤prediction类别为0数据集 6 predictions.filter(predictions['prediction'] == 0).select('Descript', 'Category', 'probability', 'label', 'prediction').orderBy('probability', accending=False).show(n=10, truncate=30)
结果：
+--------------------------+--------+------------------------------+-----+----------+ | Descript|Category| probability|label|prediction| +--------------------------+--------+------------------------------+-----+----------+ | ARSON OF A VEHICLE| ARSON|[0.1194196587417514,0.10724...| 26.0| 0.0| | ARSON OF A VEHICLE| ARSON|[0.1194196587417514,0.10724...| 26.0| 0.0| | ARSON OF A VEHICLE| ARSON|[0.1194196587417514,0.10724...| 26.0| 0.0| | ATTEMPTED ARSON| ARSON|[0.12978385966276762,0.1084...| 26.0| 0.0| | CREDIT CARD, THEFT OF| FRAUD|[0.21637136655265077,0.0836...| 12.0| 0.0| | CREDIT CARD, THEFT OF| FRAUD|[0.21637136655265077,0.0836...| 12.0| 0.0| | CREDIT CARD, THEFT OF| FRAUD|[0.21637136655265077,0.0836...| 12.0| 0.0| | CREDIT CARD, THEFT OF| FRAUD|[0.21637136655265077,0.0836...| 12.0| 0.0| | CREDIT CARD, THEFT OF| FRAUD|[0.21637136655265077,0.0836...| 12.0| 0.0| |ARSON OF A VACANT BUILDING| ARSON|[0.22897903829071928,0.0980...| 26.0| 0.0| +--------------------------+--------+------------------------------+-----+----------+ only showing top 10 rows
1 from pyspark.ml.evaluation import MulticlassClassificationEvaluator 2 # predictionCol: 预测列的名称 3 evaluator = MulticlassClassificationEvaluator(predictionCol='prediction') 4 # 预测准确率 5 print(evaluator.evaluate(predictions)) 6 end_time = time.time() 7 print(end_time - start_time)
结果：
0.9641817609126011 8.245999813079834
4.2 以TF-ID作为特征，利用逻辑回归进行分类
1 from pyspark.ml.feature import HashingTF, IDF 2 start_time = time.time() 3 # numFeatures: 最大特征数 4 hashingTF = HashingTF(inputCol='filtered', outputCol='rawFeatures', numFeatures=10000) 5 # minDocFreq：过滤的最少文档数量 6 idf = IDF(inputCol='rawFeatures', outputCol='features', minDocFreq=5) 7 pipeline = Pipeline(stages=[regexTokenizer, stopwords_remover, hashingTF, idf, label_stringIdx]) 8 pipeline_fit = pipeline.fit(data) 9 dataset = pipeline_fit.transform(data) 10 (trainingData, testData) = dataset.randomSplit([0.7, 0.3], seed=100) 11 12 lr = LogisticRegression(maxIter=20, regParam=0.3, elasticNetParam=0) 13 lr_model = lr.fit(trainingData) 14 predictions = lr_model.transform(testData) 15 predictions.filter(predictions['prediction'] == 0).select('Descript', 'Category', 'probability', 'label', 'prediction').\ 16 orderBy('probability', ascending=False).show(n=10, truncate=30)
结果：
+----------------------------+-------------+------------------------------+-----+----------+ | Descript| Category| probability|label|prediction| +----------------------------+-------------+------------------------------+-----+----------+ |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.865376337558355,0.018892...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.865376337558355,0.018892...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.865376337558355,0.018892...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.865376337558355,0.018892...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.865376337558355,0.018892...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.865376337558355,0.018892...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.865376337558355,0.018892...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.865376337558355,0.018892...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.865376337558355,0.018892...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.865376337558355,0.018892...| 0.0| 0.0| +----------------------------+-------------+------------------------------+-----+----------+ only showing top 10 rows
1 evaluator = MulticlassClassificationEvaluator(predictionCol='prediction') 2 print(evaluator.evaluate(predictions)) 3 end_time = time.time() 4 print(end_time - start_time)
结果：
0.9653361434618551 12.998999834060669
4.3 交叉验证
用交叉验证来优化参数，这里针对基于词频特征的逻辑回归模型进行优化
1 from pyspark.ml.tuning import ParamGridBuilder, CrossValidator 2 start_time = time.time() 3 pipeline = Pipeline(stages=[regexTokenizer, stopwords_remover, count_vectors, label_stringIdx]) 4 pipeline_fit = pipeline.fit(data) 5 (trainingData, testData) = dataset.randomSplit([0.7, 0.3], seed=100) 6 lr = LogisticRegression(maxIter=20, regParam=0.3, elasticNetParam=0) 7 # 为交叉验证创建参数 8 # ParamGridBuilder：用于基于网格搜索的模型选择的参数网格的生成器 9 # addGrid：将网格中给定参数设置为固定值 10 # parameter：正则化参数 11 # maxIter：迭代次数 12 # numFeatures：特征值 13 paramGrid = (ParamGridBuilder() 14 .addGrid(lr.regParam, [0.1, 0.3, 0.5]) 15 .addGrid(lr.elasticNetParam, [0.0, 0.1, 0.2]) 16 .addGrid(lr.maxIter, [10, 20, 50]) 17 # .addGrid(idf.numFeatures, [10, 100, 1000]) 18 .build()) 19 20 # 创建五折交叉验证 21 # estimator：要交叉验证的估计器 22 # estimatorParamMaps：网格搜索的最优参数 23 # evaluator：评估器 24 # numFolds：交叉次数 25 cv = CrossValidator(estimator=lr,\ 26 estimatorParamMaps=paramGrid,\ 27 evaluator=evaluator,\ 28 numFolds=5) 29 cv_model = cv.fit(trainingData) 30 predictions = cv_model.transform(testData) 31 32 # 模型评估 33 evaluator = MulticlassClassificationEvaluator(predictionCol='prediction') 34 print(evaluator.evaluate(predictions)) 35 end_time = time.time() 36 print(end_time - start_time)
结果：
0.9807684755923513 368.97300004959106
4.4 朴素贝叶斯
1 from pyspark.ml.classification import NaiveBayes 2 start_time = time.time() 3 # smoothing：平滑参数 4 nb = NaiveBayes(smoothing=1) 5 model = nb.fit(trainingData) 6 predictions = model.transform(testData) 7 predictions.filter(predictions['prediction'] == 0) \ 8 .select('Descript', 'Category', 'probability', 'label', 'prediction') \ 9 .orderBy('probability', ascending=False) \ 10 .show(n=10, truncate=30)
结果：
+----------------------+-------------+------------------------------+-----+----------+ | Descript| Category| probability|label|prediction| +----------------------+-------------+------------------------------+-----+----------+ | PETTY THEFT BICYCLE|LARCENY/THEFT|[1.0,1.236977662838925E-20,...| 0.0| 0.0| | PETTY THEFT BICYCLE|LARCENY/THEFT|[1.0,1.236977662838925E-20,...| 0.0| 0.0| | PETTY THEFT BICYCLE|LARCENY/THEFT|[1.0,1.236977662838925E-20,...| 0.0| 0.0| |GRAND THEFT PICKPOCKET|LARCENY/THEFT|[1.0,7.699728277574397E-24,...| 0.0| 0.0| |GRAND THEFT PICKPOCKET|LARCENY/THEFT|[1.0,7.699728277574397E-24,...| 0.0| 0.0| |GRAND THEFT PICKPOCKET|LARCENY/THEFT|[1.0,7.699728277574397E-24,...| 0.0| 0.0| |GRAND THEFT PICKPOCKET|LARCENY/THEFT|[1.0,7.699728277574397E-24,...| 0.0| 0.0| |GRAND THEFT PICKPOCKET|LARCENY/THEFT|[1.0,7.699728277574397E-24,...| 0.0| 0.0| |GRAND THEFT PICKPOCKET|LARCENY/THEFT|[1.0,7.699728277574397E-24,...| 0.0| 0.0| |GRAND THEFT PICKPOCKET|LARCENY/THEFT|[1.0,7.699728277574397E-24,...| 0.0| 0.0| +----------------------+-------------+------------------------------+-----+----------+ only showing top 10 rows
1 evaluator = MulticlassClassificationEvaluator(predictionCol='prediction') 2 print(evaluator.evaluate(predictions)) 3 end_time = time.time() 4 print(end_time - start_time)
结果：
0.977432832447723 5.371000051498413
4.5 随机森林
1 from pyspark.ml.classification import RandomForestClassifier 2 start_time = time.time() 3 # numTree：训练树的个数 4 # maxDepth：最大深度 5 # maxBins：连续特征离散化的最大分类数 6 rf = RandomForestClassifier(labelCol='label', \ 7 featuresCol='features', \ 8 numTrees=100, \ 9 maxDepth=4, \ 10 maxBins=32) 11 # Train model with Training Data 12 rfModel = rf.fit(trainingData) 13 predictions = rfModel.transform(testData) 14 predictions.filter(predictions['prediction'] == 0) \ 15 .select('Descript','Category','probability','label','prediction') \ 16 .orderBy('probability', ascending=False) \ 17 .show(n = 10, truncate = 30)
结果：
+----------------------------+-------------+------------------------------+-----+----------+ | Descript| Category| probability|label|prediction| +----------------------------+-------------+------------------------------+-----+----------+ |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.33206188381818563,0.1168...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.33206188381818563,0.1168...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.33206188381818563,0.1168...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.33206188381818563,0.1168...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.33206188381818563,0.1168...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.33206188381818563,0.1168...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.33206188381818563,0.1168...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.33206188381818563,0.1168...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.33206188381818563,0.1168...| 0.0| 0.0| |PETTY THEFT FROM LOCKED AUTO|LARCENY/THEFT|[0.33206188381818563,0.1168...| 0.0| 0.0| +----------------------------+-------------+------------------------------+-----+----------+ only showing top 10 rows
1 evaluator = MulticlassClassificationEvaluator(predictionCol='prediction') 2 print(evaluator.evaluate(predictions)) 3 end_time = time.time() 4 print(end_time - start_time)
结果：
0.27929770811242954 36.63699984550476
上面的结果可以看出：随机森林是优秀的、鲁棒的通用模型，但对于高维稀疏数据来说，并不是一个很好的选择。
明显，选择使用交叉验证的逻辑回归。
但是选择交叉验证的逻辑回归时需要注意一点：由于使用了交叉验证，训练时间会过长，在实际的应用场景中要根据业务选择最合适的模型。
