PySpark之机器学习库ML(分类、聚类、回归)

淩亂°似流年 2022-04-24 05:56 485阅读 0赞

import findspark
    findspark.init()
    
    from pyspark.sql.session import SparkSession
    spark = SparkSession.builder.appName('LIXU').getOrCreate()

# 案例 1 使用逻辑回归预测婴幼儿存活 #

1、加载数据

数据的下载地址：[http://www.tomdrabas.com/data/LearningPySpark/births\_transformed.csv.gz][http_www.tomdrabas.com_data_LearningPySpark_births_transformed.csv.gz]

import pyspark.sql.types as typ
    
    labels = [
                ('INFANT_ALIVE_AT_REPORT', typ.IntegerType()),
                ('BIRTH_PLACE', typ.StringType()),
                ('MOTHER_AGE_YEARS', typ.IntegerType()),
                ('FATHER_COMBINED_AGE', typ.IntegerType()),
                ('CIG_BEFORE', typ.IntegerType()),
                ('CIG_1_TRI', typ.IntegerType()),
                ('CIG_2_TRI', typ.IntegerType()),
                ('CIG_3_TRI', typ.IntegerType()),
                ('MOTHER_HEIGHT_IN', typ.IntegerType()),
                ('MOTHER_PRE_WEIGHT', typ.IntegerType()),
                ('MOTHER_DELIVERY_WEIGHT', typ.IntegerType()),
                ('MOTHER_WEIGHT_GAIN', typ.IntegerType()),
                ('DIABETES_PRE', typ.IntegerType()),
                ('DIABETES_GEST', typ.IntegerType()),
                ('HYP_TENS_PRE', typ.IntegerType()),
                ('HYP_TENS_GEST', typ.IntegerType()),
                ('PREV_BIRTH_PRETERM', typ.IntegerType())
            ]
    
    schema = typ.StructType([typ.StructField(e[0], e[1], False) for e in labels])
    
    births = spark.read.csv('./births_transformed.csv.gz', header=True, schema=schema)
    
    births.head(1)

[Row(INFANT_ALIVE_AT_REPORT=0, BIRTH_PLACE='1', MOTHER_AGE_YEARS=29, FATHER_COMBINED_AGE=99, CIG_BEFORE=0, CIG_1_TRI=0, CIG_2_TRI=0, CIG_3_TRI=0, MOTHER_HEIGHT_IN=99, MOTHER_PRE_WEIGHT=999, MOTHER_DELIVERY_WEIGHT=999, MOTHER_WEIGHT_GAIN=99, DIABETES_PRE=0, DIABETES_GEST=0, HYP_TENS_PRE=0, HYP_TENS_GEST=0, PREV_BIRTH_PRETERM=0)]

2、创建transformers

import pyspark.ml.feature as ft
    
    births = births.withColumn('BIRTH_PLACE_INT', births['BIRTH_PLACE'].cast(typ.IntegerType())) # 将字符“1”转换成数字1
    encoder = ft.OneHotEncoder(inputCol = 'BIRTH_PLACE_INT', outputCol = 'BIRTH_PLACE_VEC') # onehot处理
    featuresCreator = ft.VectorAssembler(inputCols = [col[0] for col in labels[2:]] + [encoder.getOutputCol()], outputCol = 'features') # 将特征合并成features

3、创建逻辑回归模型

import pyspark.ml.classification as cl
    
    logistic = cl.LogisticRegression(maxIter = 10, regParam = 0.01, labelCol = 'INFANT_ALIVE_AT_REPORT')

4、创建pipeline

from pyspark.ml import Pipeline
    
    pipeline = Pipeline(stages = [encoder, featuresCreator, logistic])

5、训练模型

births_train, births_test = births.randomSplit([0.7, 0.3], seed = 666)
    model = pipeline.fit(births_train)
    test_model = model.transform(births_test)
    test_model.take(1)

[Row(INFANT_ALIVE_AT_REPORT=0, BIRTH_PLACE='1', MOTHER_AGE_YEARS=13, FATHER_COMBINED_AGE=99, CIG_BEFORE=0, CIG_1_TRI=0, CIG_2_TRI=0, CIG_3_TRI=0, MOTHER_HEIGHT_IN=66, MOTHER_PRE_WEIGHT=133, MOTHER_DELIVERY_WEIGHT=135, MOTHER_WEIGHT_GAIN=2, DIABETES_PRE=0, DIABETES_GEST=0, HYP_TENS_PRE=0, HYP_TENS_GEST=0, PREV_BIRTH_PRETERM=0, BIRTH_PLACE_INT=1, BIRTH_PLACE_VEC=SparseVector(9, {1: 1.0}), features=SparseVector(24, {0: 13.0, 1: 99.0, 6: 66.0, 7: 133.0, 8: 135.0, 9: 2.0, 16: 1.0}), rawPrediction=DenseVector([1.0573, -1.0573]), probability=DenseVector([0.7422, 0.2578]), prediction=0.0)]

6、对模型进行评估

import pyspark.ml.evaluation as ev
    
    evaluator = ev.BinaryClassificationEvaluator(rawPredictionCol = 'probability', labelCol = 'INFANT_ALIVE_AT_REPORT')
    print(evaluator.evaluate(test_model, {
        evaluator.metricName: 'areaUnderROC'}))
    print(evaluator.evaluate(test_model, {
        evaluator.metricName: 'areaUnderPR'}))

0.7401301847095617
    0.7139354342365674

7、保存和加载模型

pipelinePath = ‘./infant\_oneHotEncoder\_Logistic\_Pipeline’

pipeline.write().overwrite().save(pipelinePath)

loadedPipeline = Pipeline.load(pipelinePath)

loadedPipeline.fit(births\_train).transform(births\_test).take(1)

from [pyspark.ml][] import PipelineModel

modelPath = ‘./infant\_oneHotEncoder\_Logistic\_PipelineModel’

model.write().overwrite().save(modelPath)

loadedPipelineModel = PipelineModel.load(modelPath)

test\_loadedModel = loadedPipelineModel.transform(births\_test)

# 案例 2 参数调整 #

1、Grid search

import pyspark.ml.tuning as tune
    
    logistic = cl.LogisticRegression(labelCol="INFANT_ALIVE_AT_REPORT")
    grid = tune.ParamGridBuilder().addGrid(logistic.maxIter, [2, 10, 30]).addGrid(logistic.regParam, [0.01, 0.05, 0.3]).build()
    
    evaluator = ev.BinaryClassificationEvaluator(rawPredictionCol = 'probability', labelCol = 'INFANT_ALIVE_AT_REPORT')
    cv = tune.CrossValidator(estimator = logistic, estimatorParamMaps = grid, evaluator = evaluator)
    
    pipeline = Pipeline(stages = [encoder, featuresCreator])
    data_transformer = pipeline.fit(births_train)
    cvModel = cv.fit(data_transformer.transform(births_train))
    
    data_test = data_transformer.transform(births_test)
    results = cvModel.transform(data_test)
    
    print(evaluator.evaluate(results, {
        evaluator.metricName: 'areaUnderROC'}))
    print(evaluator.evaluate(results, {
        evaluator.metricName: 'areaUnderPR'}))
    
    results = [
        (
            [
                {
        key.name: paramValue} 
                for key, paramValue 
                in zip(
                    params.keys(), 
                    params.values())
            ], metric
        ) 
        for params, metric 
        in zip(
            cvModel.getEstimatorParamMaps(), 
            cvModel.avgMetrics
        )
    ]
    
    print(sorted(results,key=lambda el: el[1],reverse=True)[0])

0.7404799467361349
    0.7158426790526992
    ([{'regParam': 0.01}, {'maxIter': 30}], 0.7384569581670912)

2、Train\_validation splitting

selector = ft.ChiSqSelector(numTopFeatures=5, featuresCol=featuresCreator.getOutputCol(), outputCol='selectedFeatures', labelCol='INFANT_ALIVE_AT_REPORT')
    
    logistic = cl.LogisticRegression(labelCol='INFANT_ALIVE_AT_REPORT',featuresCol='selectedFeatures')
    
    pipeline = Pipeline(stages=[encoder,featuresCreator,selector])
    data_transformer = pipeline.fit(births_train)
    
    tvs = tune.TrainValidationSplit(estimator=logistic, estimatorParamMaps=grid, evaluator=evaluator)
    
    tvsModel = tvs.fit(data_transformer.transform(births_train))
    
    data_train = data_transformer.transform(births_test)
    results = tvsModel.transform(data_train)
    
    print(evaluator.evaluate(results,{
        evaluator.metricName: 'areaUnderROC'}))
    print(evaluator.evaluate(results,{
        evaluator.metricName: 'areaUnderPR'}))

0.7294296314442145
    0.703775950281647

# 案例 3 使用随机森林预测婴幼儿存活 #

import pyspark.sql.functions as func
    births = births.withColumn("INFANT_ALIVE_AT_REPORT", func.col("INFANT_ALIVE_AT_REPORT").cast(typ.DoubleType()))
    births_train, births_test = births.randomSplit([0.7, 0.3], seed = 666)
    
    classifier = cl.RandomForestClassifier(numTrees=5, maxDepth=5, labelCol='INFANT_ALIVE_AT_REPORT')
    pipeline = Pipeline(stages = [encoder, featuresCreator, classifier])
    model = pipeline.fit(births_train)
    test = model.transform(births_test)
    
    evaluator = ev.BinaryClassificationEvaluator(labelCol='INFANT_ALIVE_AT_REPORT')
    print(evaluator.evaluate(test, {
        evaluator.metricName: "areaUnderROC"}))
    print(evaluator.evaluate(test, {
        evaluator.metricName: "areaUnderPR"}))

0.7671165748668931
    0.7367360611074735

import pyspark.ml.tuning as tune
    
    clf = cl.RandomForestClassifier(labelCol="INFANT_ALIVE_AT_REPORT")
    grid = tune.ParamGridBuilder().addGrid(clf.numTrees, [2, 5, 8]).addGrid(clf.maxDepth, [3, 5, 7]).build()
    
    evaluator = ev.BinaryClassificationEvaluator(rawPredictionCol = 'probability', labelCol = 'INFANT_ALIVE_AT_REPORT')
    cv = tune.CrossValidator(estimator = clf, estimatorParamMaps = grid, evaluator = evaluator)
    
    pipeline = Pipeline(stages = [encoder, featuresCreator])
    data_transformer = pipeline.fit(births_train)
    cvModel = cv.fit(data_transformer.transform(births_train))
    
    data_test = data_transformer.transform(births_test)
    results = cvModel.transform(data_test)
    
    print(evaluator.evaluate(results, {
        evaluator.metricName: 'areaUnderROC'}))
    print(evaluator.evaluate(results, {
        evaluator.metricName: 'areaUnderPR'}))
    
    results = [
        (
            [
                {
        key.name: paramValue} 
                for key, paramValue 
                in zip(
                    params.keys(), 
                    params.values())
            ], metric
        ) 
        for params, metric 
        in zip(
            cvModel.getEstimatorParamMaps(), 
            cvModel.avgMetrics
        )
    ]
    
    print(sorted(results,key=lambda el: el[1],reverse=True)[0])

0.7780783438870142
    0.758032350342556
    ([{'maxDepth': 7}, {'numTrees': 8}], 0.7751605479831878)

# 案例 4 KMeans #

import pyspark.ml.clustering as clus
    kmeans = clus.KMeans(k = 5, featuresCol = 'features')
    pipeline = Pipeline(stages = [encoder, featuresCreator, kmeans])
    model = pipeline.fit(births_train)
    test = model.transform(births_test)
    test.groupBy('prediction').agg({
        '*' : 'count', 'MOTHER_HEIGHT_IN' : 'avg'}).collect()

[Row(prediction=1, avg(MOTHER_HEIGHT_IN)=67.69473684210526, count(1)=475),
     Row(prediction=3, avg(MOTHER_HEIGHT_IN)=66.64658634538152, count(1)=249),
     Row(prediction=4, avg(MOTHER_HEIGHT_IN)=64.43472584856397, count(1)=2298),
     Row(prediction=2, avg(MOTHER_HEIGHT_IN)=83.91154791154791, count(1)=407),
     Row(prediction=0, avg(MOTHER_HEIGHT_IN)=64.31597357170618, count(1)=10292)]

# 案例 5 LDA模型 #

text_data = spark.createDataFrame([
        ['''To make a computer do anything, you have to write a 
        computer program. To write a computer program, you have 
        to tell the computer, step by step, exactly what you want 
        it to do. The computer then "executes" the program, 
        following each step mechanically, to accomplish the end 
        goal. When you are telling the computer what to do, you 
        also get to choose how it's going to do it. That's where 
        computer algorithms come in. The algorithm is the basic 
        technique used to get the job done. Let's follow an 
        example to help get an understanding of the algorithm 
        concept.'''],
        ['''Laptop computers use batteries to run while not 
        connected to mains. When we overcharge or overheat 
        lithium ion batteries, the materials inside start to 
        break down and produce bubbles of oxygen, carbon dioxide, 
        and other gases. Pressure builds up, and the hot battery 
        swells from a rectangle into a pillow shape. Sometimes 
        the phone involved will operate afterwards. Other times 
        it will die. And occasionally—kapow! To see what's 
        happening inside the battery when it swells, the CLS team 
        used an x-ray technology called computed tomography.'''],
        ['''This technology describes a technique where touch 
        sensors can be placed around any side of a device 
        allowing for new input sources. The patent also notes 
        that physical buttons (such as the volume controls) could 
        be replaced by these embedded touch sensors. In essence 
        Apple could drop the current buttons and move towards 
        touch-enabled areas on the device for the existing UI. It 
        could also open up areas for new UI paradigms, such as 
        using the back of the smartphone for quick scrolling or 
        page turning.'''],
        ['''The National Park Service is a proud protector of 
        America’s lands. Preserving our land not only safeguards 
        the natural environment, but it also protects the 
        stories, cultures, and histories of our ancestors. As we 
        face the increasingly dire consequences of climate 
        change, it is imperative that we continue to expand 
        America’s protected lands under the oversight of the 
        National Park Service. Doing so combats climate change 
        and allows all American’s to visit, explore, and learn 
        from these treasured places for generations to come. It 
        is critical that President Obama acts swiftly to preserve 
        land that is at risk of external threats before the end 
        of his term as it has become blatantly clear that the 
        next administration will not hold the same value for our 
        environment over the next four years.'''],
        ['''The National Park Foundation, the official charitable 
        partner of the National Park Service, enriches America’s 
        national parks and programs through the support of 
        private citizens, park lovers, stewards of nature, 
        history enthusiasts, and wilderness adventurers. 
        Chartered by Congress in 1967, the Foundation grew out of 
        a legacy of park protection that began over a century 
        ago, when ordinary citizens took action to establish and 
        protect our national parks. Today, the National Park 
        Foundation carries on the tradition of early park 
        advocates, big thinkers, doers and dreamers—from John 
        Muir and Ansel Adams to President Theodore Roosevelt.'''],
        ['''Australia has over 500 national parks. Over 28 
        million hectares of land is designated as national 
        parkland, accounting for almost four per cent of 
        Australia's land areas. In addition, a further six per 
        cent of Australia is protected and includes state 
        forests, nature parks and conservation reserves.National 
        parks are usually large areas of land that are protected 
        because they have unspoilt landscapes and a diverse 
        number of native plants and animals. This means that 
        commercial activities such as farming are prohibited and 
        human activity is strictly monitored.''']
    ], ['documents'])
    
    tokenizer = ft.RegexTokenizer(inputCol='documents', outputCol='input_arr', pattern='\s+|[,.\"]')
    stopwords = ft.StopWordsRemover(inputCol=tokenizer.getOutputCol(), outputCol='input_stop')
    stringIndexer = ft.CountVectorizer(inputCol=stopwords.getOutputCol(), outputCol='input_indexed')
    tokenized = stopwords.transform(tokenizer.transform(text_data))
    stringIndexer.fit(tokenized).transform(tokenized).select('input_indexed').take(2)
    
    clustering = clus.LDA(k=2, optimizer='online', featuresCol=stringIndexer.getOutputCol())
    pipeline = Pipeline(stages = [tokenizer, stopwords, stringIndexer, clustering])
    topics = pipeline.fit(text_data).transform(text_data)
    topics.select('topicDistribution').collect()

[Row(topicDistribution=DenseVector([0.7338, 0.2662])),
     Row(topicDistribution=DenseVector([0.0127, 0.9873])),
     Row(topicDistribution=DenseVector([0.0191, 0.9809])),
     Row(topicDistribution=DenseVector([0.9886, 0.0114])),
     Row(topicDistribution=DenseVector([0.9896, 0.0104])),
     Row(topicDistribution=DenseVector([0.9794, 0.0206]))]

## 案例 6 GBDT拟合MOTHER\_WEIGHT\_GAIN ##

features = ['MOTHER_AGE_YEARS','MOTHER_HEIGHT_IN',
                'MOTHER_PRE_WEIGHT','DIABETES_PRE',
                'DIABETES_GEST','HYP_TENS_PRE', 
                'HYP_TENS_GEST', 'PREV_BIRTH_PRETERM',
                'CIG_BEFORE','CIG_1_TRI', 'CIG_2_TRI', 
                'CIG_3_TRI'
               ]
    
    featuresCreator = ft.VectorAssembler(inputCols=[col for col in features[1:]], outputCol='features')
    selector = ft.ChiSqSelector(numTopFeatures=6, outputCol='selectedFeatures', labelCol='MOTHER_WEIGHT_GAIN')
    
    import pyspark.ml.regression as reg
    regressor = reg.GBTRegressor(maxIter=15, maxDepth=3, labelCol='MOTHER_WEIGHT_GAIN')
    
    pipeline = Pipeline(stages = [featuresCreator, selector, regressor])
    
    weightGain = pipeline.fit(births_train)
    evaluator = ev.RegressionEvaluator(predictionCol="prediction", labelCol="MOTHER_WEIGHT_GAIN")
    print(evaluator.evaluate(weightGain.transform(births_test),{
        evaluator.metricName: 'r2'}))

0.4886585890292995

[http_www.tomdrabas.com_data_LearningPySpark_births_transformed.csv.gz]: http://www.tomdrabas.com/data/LearningPySpark/births_transformed.csv.gz
[pyspark.ml]: http://pyspark.ml