Spark机器学习基础三

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监督学习

0.线性回归（加L1、L2正则化）

from __future__ import print_function from pyspark.ml.regression import LinearRegression from pyspark.sql import SparkSessionspark = SparkSession\ .builder\ .appName("LinearRegressionWithElasticNet")\ .getOrCreate()# 加载数据 training = spark.read.format("libsvm").load("sample_linear_regression_data.txt")# 线性回归 lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)# 拟合模型 lrModel = lr.fit(training)# 输出系数和截距 print("Coefficients: %s" % str(lrModel.coefficients)) print("Intercept: %s" % str(lrModel.intercept))# 模型信息总结输出 trainingSummary = lrModel.summary print("numIterations: %d" % trainingSummary.totalIterations) print("objectiveHistory: %s" % str(trainingSummary.objectiveHistory)) trainingSummary.residuals.show() print("RMSE: %f" % trainingSummary.rootMeanSquaredError) print("r2: %f" % trainingSummary.r2)spark.stop()

Coefficients: [0.0,0.32292516677405936,-0.3438548034562218,1.9156017023458414,0.05288058680386263,0.765962720459771,0.0,-0.15105392669186682,-0.21587930360904642,0.22025369188813426] Intercept: 0.1598936844239736 numIterations: 7 objectiveHistory: [0.49999999999999994, 0.4967620357443381, 0.4936361664340463, 0.4936351537897608, 0.4936351214177871, 0.49363512062528014, 0.4936351206216114] +--------------------+ | residuals| +--------------------+ | -9.889232683103197| | 0.5533794340053554| | -5.204019455758823| | -20.566686715507508| | -9.4497405180564| | -6.909112502719486| | -10.00431602969873| | 2.062397807050484| | 3.1117508432954772| | -15.893608229419382| | -5.036284254673026| | 6.483215876994333| | 12.429497299109002| | -20.32003219007654| | -2.0049838218725005| | -17.867901734183793| | 7.646455887420495| | -2.2653482182417406| |-0.10308920436195645| | -1.380034070385301| +--------------------+ only showing top 20 rowsRMSE: 10.189077 r2: 0.022861

1.广义线性模型

from __future__ import print_function from pyspark.sql import SparkSession from pyspark.ml.regression import GeneralizedLinearRegressionspark = SparkSession\ .builder\ .appName("GeneralizedLinearRegressionExample")\ .getOrCreate()# 加载数据 dataset = spark.read.format("libsvm").load("sample_linear_regression_data.txt")# 广义线性模型 glr = GeneralizedLinearRegression(family="gaussian", link="identity", maxIter=10, regParam=0.3)# 拟合模型 model = glr.fit(dataset)# 输出系数和截距 print("Coefficients: " + str(model.coefficients)) print("Intercept: " + str(model.intercept))# 模型信息总结与输出 summary = model.summary print("Coefficient Standard Errors: " + str(summary.coefficientStandardErrors)) print("T Values: " + str(summary.tValues)) print("P Values: " + str(summary.pValues)) print("Dispersion: " + str(summary.dispersion)) print("Null Deviance: " + str(summary.nullDeviance)) print("Residual Degree Of Freedom Null: " + str(summary.residualDegreeOfFreedomNull)) print("Deviance: " + str(summary.deviance)) print("Residual Degree Of Freedom: " + str(summary.residualDegreeOfFreedom)) print("AIC: " + str(summary.aic)) print("Deviance Residuals: ") summary.residuals().show()spark.stop()

Coefficients: [0.010541828081257216,0.8003253100560949,-0.7845165541420371,2.3679887171421914,0.5010002089857577,1.1222351159753026,-0.2926824398623296,-0.49837174323213035,-0.6035797180675657,0.6725550067187461] Intercept: 0.14592176145232041 Coefficient Standard Errors: [0.7950428434287478, 0.8049713176546897, 0.7975916824772489, 0.8312649247659919, 0.7945436200517938, 0.8118992572197593, 0.7919506385542777, 0.7973378214726764, 0.8300714999626418, 0.7771333489686802, 0.463930109648428] T Values: [0.013259446542269243, 0.9942283563442594, -0.9836067393599172, 2.848657084633759, 0.6305509179635714, 1.382234441029355, -0.3695715687490668, -0.6250446546128238, -0.7271418403049983, 0.8654306337661122, 0.31453393176593286] P Values: [0.989426199114056, 0.32060241580811044, 0.3257943227369877, 0.004575078538306521, 0.5286281628105467, 0.16752945248679119, 0.7118614002322872, 0.5322327097421431, 0.467486325282384, 0.3872259825794293, 0.753249430501097] Dispersion: 105.60988356821714 Null Deviance: 53229.3654338832 Residual Degree Of Freedom Null: 500 Deviance: 51748.8429484264 Residual Degree Of Freedom: 490 AIC: 3769.1895871765314 Deviance Residuals: +-------------------+ | devianceResiduals| +-------------------+ |-10.974359174246889| | 0.8872320138420559| | -4.596541837478908| |-20.411667435019638| |-10.270419345342642| |-6.0156058956799905| |-10.663939415849267| | 2.1153960525024713| | 3.9807132379137675| |-17.225218272069533| | -4.611647633532147| | 6.4176669407698546| | 11.407137945300537| | -20.70176540467664| | -2.683748540510967| |-16.755494794232536| | 8.154668342638725| |-1.4355057987358848| |-0.6435058688185704| | -1.13802589316832| +-------------------+ only showing top 20 rows

2.逻辑回归

from __future__ import print_function from pyspark.ml.classification import LogisticRegression from pyspark.sql import SparkSessionspark = SparkSession \ .builder \ .appName("LogisticRegressionSummary") \ .getOrCreate()# 加载数据 training = spark.read.format("libsvm").load("sample_libsvm_data.txt")# 逻辑回归 lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)# 拟合模型 lrModel = lr.fit(training)# 模型信息总结与输出 trainingSummary = lrModel.summary# 输出每一轮的损失函数值 objectiveHistory = trainingSummary.objectiveHistory print("objectiveHistory:") for objective in objectiveHistory: print(objective)# ROC曲线 trainingSummary.roc.show() print("areaUnderROC: " + str(trainingSummary.areaUnderROC))# Set the model threshold to maximize F-Measure #fMeasure = trainingSummary.fMeasureByThreshold #maxFMeasure = fMeasure.groupBy(['threshold']).max('F-Measure').select('max(F-Measure)') #bestThreshold = fMeasure.where(fMeasure['F-Measure'] == maxFMeasure.select('max(F-Measure)')['max(F-Measure)']).select('threshold')['threshold'] #lr.setThreshold(bestThreshold)spark.stop()

objectiveHistory: 0.6833149135741672 0.6662875751473734 0.6217068546034618 0.6127265245887887 0.6060347986802873 0.6031750687571562 0.5969621534836274 0.5940743031983118 0.5906089243339022 0.5894724576491042 0.5882187775729587 +---+--------------------+ |FPR| TPR| +---+--------------------+ |0.0| 0.0| |0.0|0.017543859649122806| |0.0| 0.03508771929824561| |0.0| 0.05263157894736842| |0.0| 0.07017543859649122| |0.0| 0.08771929824561403| |0.0| 0.10526315789473684| |0.0| 0.12280701754385964| |0.0| 0.14035087719298245| |0.0| 0.15789473684210525| |0.0| 0.17543859649122806| |0.0| 0.19298245614035087| |0.0| 0.21052631578947367| |0.0| 0.22807017543859648| |0.0| 0.24561403508771928| |0.0| 0.2631578947368421| |0.0| 0.2807017543859649| |0.0| 0.2982456140350877| |0.0| 0.3157894736842105| |0.0| 0.3333333333333333| +---+--------------------+ only showing top 20 rowsareaUnderROC: 1.0

from __future__ import print_function from pyspark.ml.classification import LogisticRegression from pyspark.sql import SparkSessionspark = SparkSession\ .builder\ .appName("LogisticRegressionWithElasticNet")\ .getOrCreate()# 加载数据 training = spark.read.format("libsvm").load("sample_libsvm_data.txt")# 逻辑回归 lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)# 拟合模型 lrModel = lr.fit(training)# 系数与截距 print("Coefficients: " + str(lrModel.coefficients)) print("Intercept: " + str(lrModel.intercept))# 多项式逻辑回归 mlr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8, family="multinomial")# 拟合模型 mlrModel = mlr.fit(training)# 输出系数 print("Multinomial coefficients: " + str(mlrModel.coefficientMatrix)) print("Multinomial intercepts: " + str(mlrModel.interceptVector))spark.stop()

Coefficients: (692,[244,263,272,300,301,328,350,351,378,379,405,406,407,428,433,434,455,456,461,462,483,484,489,490,496,511,512,517,539,540,568],[-7.353983524188197e-05,-9.102738505589466e-05,-0.00019467430546904298,-0.00020300642473486668,-3.1476183314863995e-05,-6.842977602660743e-05,1.5883626898239883e-05,1.4023497091372047e-05,0.00035432047524968605,0.00011443272898171087,0.00010016712383666666,0.0006014109303795481,0.0002840248179122762,-0.00011541084736508837,0.000385996886312906,0.000635019557424107,-0.00011506412384575676,-0.00015271865864986808,0.0002804933808994214,0.0006070117471191634,-0.0002008459663247437,-0.0001421075579290126,0.0002739010341160883,0.00027730456244968115,-9.838027027269332e-05,-0.0003808522443517704,-0.00025315198008555033,0.00027747714770754307,-0.0002443619763919199,-0.0015394744687597765,-0.00023073328411331293]) Intercept: 0.22456315961250325 Multinomial coefficients: 2 X 692 CSRMatrix (0,244) 0.0 (0,263) 0.0001 (0,272) 0.0001 (0,300) 0.0001 (0,350) -0.0 (0,351) -0.0 (0,378) -0.0 (0,379) -0.0 (0,405) -0.0 (0,406) -0.0006 (0,407) -0.0001 (0,428) 0.0001 (0,433) -0.0 (0,434) -0.0007 (0,455) 0.0001 (0,456) 0.0001 .. .. Multinomial intercepts: [-0.12065879445860686,0.12065879445860686]

3.多分类逻辑回归

Coefficients: 3 X 4 CSRMatrix (0,3) 0.3176 (1,2) -0.7804 (1,3) -0.377 Intercept: [0.05165231659832854,-0.12391224990853622,0.07225993331020768] +-----+--------------------+--------------------+--------------------+----------+ |label| features| rawPrediction| probability|prediction| +-----+--------------------+--------------------+--------------------+----------+ | 1.0|(4,[0,1,2,3],[-0....|[-0.2130545101220...|[0.19824091021950...| 1.0| | 1.0|(4,[0,1,2,3],[-0....|[-0.2395254151479...|[0.18250386256254...| 1.0| | 1.0|(4,[0,1,2,3],[-0....|[-0.2130545101220...|[0.18980556250236...| 1.0| | 1.0|(4,[0,1,2,3],[-0....|[-0.2395254151479...|[0.19632523546632...| 1.0| | 0.0|(4,[0,1,2,3],[0.1...|[0.21047647616023...|[0.43750398183438...| 0.0| | 1.0|(4,[0,2,3],[-0.83...|[-0.2395254151479...|[0.18250386256254...| 1.0| | 2.0|(4,[0,1,2,3],[-1....|[0.07812299927036...|[0.37581775428218...| 0.0| | 2.0|(4,[0,1,2,3],[-1....|[0.05165230377890...|[0.35102739153795...| 2.0| | 1.0|(4,[0,1,2,3],[-0....|[-0.2659960025254...|[0.17808226409449...| 1.0| | 0.0|(4,[0,2,3],[0.611...|[0.18400588878268...|[0.44258017540583...| 0.0| | 0.0|(4,[0,1,2,3],[0.2...|[0.23694706353777...|[0.44442301486604...| 0.0| | 1.0|(4,[0,1,2,3],[-0....|[-0.2659960025254...|[0.17539206930356...| 1.0| | 1.0|(4,[0,1,2,3],[-0....|[-0.2395254151479...|[0.18250386256254...| 1.0| | 2.0|(4,[0,1,2,3],[-0....|[0.05165230377890...|[0.35371124645092...| 2.0| | 2.0|(4,[0,1,2,3],[-0....|[-0.0277597631826...|[0.32360705108265...| 2.0| | 2.0|(4,[0,1,2,3],[-0....|[0.02518163392628...|[0.33909561029444...| 2.0| | 1.0|(4,[0,2,3],[-0.94...|[-0.2395254151479...|[0.17976563656243...| 1.0| | 2.0|(4,[0,1,2,3],[-0....|[-0.0012891758050...|[0.32994371314262...| 2.0| | 0.0|(4,[0,1,2,3],[0.1...|[0.10459380900173...|[0.39691355784123...| 0.0| | 2.0|(4,[0,1,2,3],[-0....|[0.02518163392628...|[0.34718685710751...| 2.0| +-----+--------------------+--------------------+--------------------+----------+ only showing top 20 rows

4.多层感知器(MLP)

from __future__ import print_function from pyspark.ml.classification import MultilayerPerceptronClassifier from pyspark.ml.evaluation import MulticlassClassificationEvaluator from pyspark.sql import SparkSessionspark = SparkSession\ .builder.appName("multilayer_perceptron_classification_example").getOrCreate()# 加载数据 data = spark.read.format("libsvm").load("sample_multiclass_classification_data.txt")# 切分训练集和测试集 splits = data.randomSplit([0.6, 0.4], 1234) train = splits[0] test = splits[1]# 输入、隐层、隐层、输出个数 layers = [4, 5, 4, 3]# 创建多层感知器 trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234)# 训练模型 model = trainer.fit(train)# 预测和计算准确度 result = model.transform(test) result.show() predictionAndLabels = result.select("prediction", "label") evaluator = MulticlassClassificationEvaluator(metricName="accuracy") print("Test set accuracy = " + str(evaluator.evaluate(predictionAndLabels)))spark.stop()

+-----+--------------------+--------------------+--------------------+----------+ |label| features| rawPrediction| probability|prediction| +-----+--------------------+--------------------+--------------------+----------+ | 0.0|(4,[0,1,2,3],[-0....|[-29.588369001638...|[2.63020383878084...| 2.0| | 0.0|(4,[0,1,2,3],[-0....|[125.657894478296...|[1.0,1.4484875476...| 0.0| | 0.0|(4,[0,1,2,3],[-0....|[126.190155254739...|[1.0,5.1578089761...| 0.0| | 0.0|(4,[0,1,2,3],[-0....|[-26.984478255346...|[4.23003198458660...| 2.0| | 0.0|(4,[0,1,2,3],[-0....|[-29.588369001638...|[2.63020383878084...| 2.0| | 0.0|(4,[0,1,2,3],[-1....|[-29.588368732563...|[2.63020459374897...| 2.0| | 0.0|(4,[0,1,2,3],[0.1...|[126.190175711705...|[1.0,5.1572549882...| 0.0| | 0.0|(4,[0,1,2,3],[0.2...|[126.190175994586...|[1.0,5.1572473280...| 0.0| | 0.0|(4,[0,1,2,3],[0.3...|[126.190175994586...|[1.0,5.1572473280...| 0.0| | 0.0|(4,[0,1,2,3],[0.3...|[126.190175994586...|[1.0,5.1572473280...| 0.0| | 0.0|(4,[0,1,2,3],[0.3...|[126.190175994586...|[1.0,5.1572473280...| 0.0| | 0.0|(4,[0,1,2,3],[0.4...|[126.190175994586...|[1.0,5.1572473280...| 0.0| | 0.0|(4,[0,1,2,3],[0.5...|[126.190175994586...|[1.0,5.1572473280...| 0.0| | 0.0|(4,[0,1,2,3],[0.7...|[126.190175994586...|[1.0,5.1572473280...| 0.0| | 0.0|(4,[0,1,2,3],[0.8...|[126.190175994586...|[1.0,5.1572473280...| 0.0| | 0.0|(4,[0,1,2,3],[1.0...|[126.190175994592...|[1.0,5.1572473278...| 0.0| | 0.0|(4,[0,2,3],[0.166...|[126.190175994583...|[1.0,5.1572473280...| 0.0| | 0.0|(4,[0,2,3],[0.388...|[126.190175994586...|[1.0,5.1572473280...| 0.0| | 1.0|(4,[0,1,2,3],[-0....|[-122.71364090590...|[1.47439846164393...| 1.0| | 1.0|(4,[0,1,2,3],[-0....|[-122.71364090590...|[1.47439846164393...| 1.0| +-----+--------------------+--------------------+--------------------+----------+ only showing top 20 rowsTest set accuracy = 0.9019607843137255

5.决策树分类

from __future__ import print_function from pyspark.ml import Pipeline from pyspark.ml.classification import DecisionTreeClassifier from pyspark.ml.feature import StringIndexer, VectorIndexer from pyspark.ml.evaluation import MulticlassClassificationEvaluator from pyspark.sql import SparkSessionspark = SparkSession\ .builder\ .appName("DecisionTreeClassificationExample")\ .getOrCreate()# 加载数据 data = spark.read.format("libsvm").load("sample_libsvm_data.txt")# 将分类值转换为类别索引 labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)# 用于为向量数据集中的分类特征列建立索引 featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)# 将数据划分为培训和测试集 (trainingData, testData) = data.randomSplit([0.7, 0.3])# 训练决策树模型 dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")# Chain indexers and tree in a Pipeline pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])# Train model. This also runs the indexers. model = pipeline.fit(trainingData)# Make predictions. predictions = model.transform(testData)# Select example rows to display. predictions.select("prediction", "indexedLabel", "features").show(5)# Select (prediction, true label) and compute test error evaluator = MulticlassClassificationEvaluator(labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy") accuracy = evaluator.evaluate(predictions) print("Test Error = %g " % (1.0 - accuracy))treeModel = model.stages[2] # summary only print(treeModel)spark.stop()

+----------+------------+--------------------+ |prediction|indexedLabel| features| +----------+------------+--------------------+ | 1.0| 1.0|(692,[95,96,97,12...| | 1.0| 1.0|(692,[98,99,100,1...| | 1.0| 1.0|(692,[123,124,125...| | 1.0| 1.0|(692,[124,125,126...| | 1.0| 1.0|(692,[124,125,126...| +----------+------------+--------------------+ only showing top 5 rowsTest Error = 0.0833333 DecisionTreeClassificationModel (uid=DecisionTreeClassifier_e2935d804502) of depth 1 with 3 nodes

6.决策树回归

from __future__ import print_function from pyspark.ml import Pipeline from pyspark.ml.regression import DecisionTreeRegressor from pyspark.ml.feature import VectorIndexer from pyspark.ml.evaluation import RegressionEvaluator from pyspark.sql import SparkSessionspark = SparkSession\ .builder\ .appName("DecisionTreeRegressionExample")\ .getOrCreate()# 加载数据 data = spark.read.format("libsvm").load("sample_libsvm_data.txt")# Automatically identify categorical features, and index them. # We specify maxCategories so features with > 4 distinct values are treated as continuous. featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])# Train a DecisionTree model. dt = DecisionTreeRegressor(featuresCol="indexedFeatures")# Chain indexer and tree in a Pipeline pipeline = Pipeline(stages=[featureIndexer, dt])# Train model. This also runs the indexer. model = pipeline.fit(trainingData)# Make predictions. predictions = model.transform(testData)# Select example rows to display. predictions.select("prediction", "label", "features").show(5)# Select (prediction, true label) and compute test error evaluator = RegressionEvaluator(labelCol="label", predictionCol="prediction", metricName="rmse") rmse = evaluator.evaluate(predictions) print("Root Mean Squared Error (RMSE) on test data = %g" % rmse)treeModel = model.stages[1] # summary only print(treeModel)spark.stop()

+----------+-----+--------------------+ |prediction|label| features| +----------+-----+--------------------+ | 0.0| 0.0|(692,[95,96,97,12...| | 0.0| 0.0|(692,[98,99,100,1...| | 0.0| 0.0|(692,[122,123,124...| | 0.0| 0.0|(692,[123,124,125...| | 0.0| 0.0|(692,[124,125,126...| +----------+-----+--------------------+ only showing top 5 rowsRoot Mean Squared Error (RMSE) on test data = 0.179605 DecisionTreeRegressionModel (uid=DecisionTreeRegressor_3c2c5981fa9b) of depth 1 with 3 nodes

7.随机森林分类

from __future__ import print_function from pyspark.ml import Pipeline from pyspark.ml.classification import RandomForestClassifier from pyspark.ml.feature import IndexToString, StringIndexer, VectorIndexer from pyspark.ml.evaluation import MulticlassClassificationEvaluator from pyspark.sql import SparkSessionspark = SparkSession\ .builder\ .appName("RandomForestClassifierExample")\ .getOrCreate()# 加载数据 data = spark.read.format("libsvm").load("sample_libsvm_data.txt")# Index labels, adding metadata to the label column. # Fit on whole dataset to include all labels in index. labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)# Automatically identify categorical features, and index them. # Set maxCategories so features with > 4 distinct values are treated as continuous. featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])# Train a RandomForest model. rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", numTrees=10)# Convert indexed labels back to original labels. labelConverter = IndexToString(inputCol="prediction", outputCol="predictedLabel",labels=labelIndexer.labels)# Chain indexers and forest in a Pipeline pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf, labelConverter])# Train model. This also runs the indexers. model = pipeline.fit(trainingData)# Make predictions. predictions = model.transform(testData)# Select example rows to display. predictions.select("predictedLabel", "label", "features").show(5)# Select (prediction, true label) and compute test error evaluator = MulticlassClassificationEvaluator(labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy") accuracy = evaluator.evaluate(predictions) print("Test Error = %g" % (1.0 - accuracy))rfModel = model.stages[2] print(rfModel) # summary onlyspark.stop()

+--------------+-----+--------------------+ |predictedLabel|label| features| +--------------+-----+--------------------+ | 0.0| 0.0|(692,[122,123,124...| | 0.0| 0.0|(692,[123,124,125...| | 0.0| 0.0|(692,[124,125,126...| | 0.0| 0.0|(692,[126,127,128...| | 0.0| 0.0|(692,[126,127,128...| +--------------+-----+--------------------+ only showing top 5 rowsTest Error = 0.0344828 RandomForestClassificationModel (uid=RandomForestClassifier_5669b6efb44b) with 10 trees

8.随机森林回归

from __future__ import print_function from pyspark.ml import Pipeline from pyspark.ml.regression import RandomForestRegressor from pyspark.ml.feature import VectorIndexer from pyspark.ml.evaluation import RegressionEvaluator from pyspark.sql import SparkSessionspark = SparkSession\ .builder\ .appName("RandomForestRegressorExample")\ .getOrCreate()# 加载数据 data = spark.read.format("libsvm").load("sample_libsvm_data.txt")# Automatically identify categorical features, and index them. # Set maxCategories so features with > 4 distinct values are treated as continuous. featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])# Train a RandomForest model. rf = RandomForestRegressor(featuresCol="indexedFeatures")# Chain indexer and forest in a Pipeline pipeline = Pipeline(stages=[featureIndexer, rf])# Train model. This also runs the indexer. model = pipeline.fit(trainingData)# Make predictions. predictions = model.transform(testData)# Select example rows to display. predictions.select("prediction", "label", "features").show(5)# Select (prediction, true label) and compute test error evaluator = RegressionEvaluator(labelCol="label", predictionCol="prediction", metricName="rmse") rmse = evaluator.evaluate(predictions) print("Root Mean Squared Error (RMSE) on test data = %g" % rmse)rfModel = model.stages[1] print(rfModel) # summary onlyspark.stop()

+----------+-----+--------------------+ |prediction|label| features| +----------+-----+--------------------+ | 0.05| 0.0|(692,[95,96,97,12...| | 0.0| 0.0|(692,[98,99,100,1...| | 0.05| 0.0|(692,[123,124,125...| | 0.0| 0.0|(692,[124,125,126...| | 0.05| 0.0|(692,[124,125,126...| +----------+-----+--------------------+ only showing top 5 rowsRoot Mean Squared Error (RMSE) on test data = 0.111163 RandomForestRegressionModel (uid=RandomForestRegressor_d557129aa0ea) with 20 trees

9.梯度增强树分类

from __future__ import print_function from pyspark.ml import Pipeline from pyspark.ml.classification import GBTClassifier from pyspark.ml.feature import StringIndexer, VectorIndexer from pyspark.ml.evaluation import MulticlassClassificationEvaluator from pyspark.sql import SparkSessionspark = SparkSession\ .builder\ .appName("GradientBoostedTreeClassifierExample")\ .getOrCreate()# 加载数据 data = spark.read.format("libsvm").load("sample_libsvm_data.txt")# Index labels, adding metadata to the label column. # Fit on whole dataset to include all labels in index. labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) # Automatically identify categorical features, and index them. # Set maxCategories so features with > 4 distinct values are treated as continuous. featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])# Train a GBT model. gbt = GBTClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", maxIter=10)# Chain indexers and GBT in a Pipeline pipeline = Pipeline(stages=[labelIndexer, featureIndexer, gbt])# Train model. This also runs the indexers. model = pipeline.fit(trainingData)# Make predictions. predictions = model.transform(testData)# Select example rows to display. predictions.select("prediction", "indexedLabel", "features").show(5)# Select (prediction, true label) and compute test error evaluator = MulticlassClassificationEvaluator(labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy") accuracy = evaluator.evaluate(predictions) print("Test Error = %g" % (1.0 - accuracy))gbtModel = model.stages[2] print(gbtModel) # summary onlyspark.stop()

+----------+------------+--------------------+ |prediction|indexedLabel| features| +----------+------------+--------------------+ | 1.0| 1.0|(692,[95,96,97,12...| | 1.0| 1.0|(692,[100,101,102...| | 1.0| 1.0|(692,[122,123,124...| | 1.0| 1.0|(692,[123,124,125...| | 1.0| 1.0|(692,[124,125,126...| +----------+------------+--------------------+ only showing top 5 rowsTest Error = 0.125 GBTClassificationModel (uid=GBTClassifier_4a28928cccd2) with 10 trees

10.梯度增强树回归

from __future__ import print_function from pyspark.ml import Pipeline from pyspark.ml.regression import GBTRegressor from pyspark.ml.feature import VectorIndexer from pyspark.ml.evaluation import RegressionEvaluator from pyspark.sql import SparkSessionspark = SparkSession\ .builder\ .appName("GradientBoostedTreeRegressorExample")\ .getOrCreate()# 加载数据 data = spark.read.format("libsvm").load("sample_libsvm_data.txt")# Automatically identify categorical features, and index them. # Set maxCategories so features with > 4 distinct values are treated as continuous. featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])# Train a GBT model. gbt = GBTRegressor(featuresCol="indexedFeatures", maxIter=10)# Chain indexer and GBT in a Pipeline pipeline = Pipeline(stages=[featureIndexer, gbt])# Train model. This also runs the indexer. model = pipeline.fit(trainingData)# Make predictions. predictions = model.transform(testData)# Select example rows to display. predictions.select("prediction", "label", "features").show(5)# Select (prediction, true label) and compute test error evaluator = RegressionEvaluator(labelCol="label", predictionCol="prediction", metricName="rmse") rmse = evaluator.evaluate(predictions) print("Root Mean Squared Error (RMSE) on test data = %g" % rmse)gbtModel = model.stages[1] print(gbtModel) # summary onlyspark.stop()

+----------+-----+--------------------+ |prediction|label| features| +----------+-----+--------------------+ | 1.0| 0.0|(692,[98,99,100,1...| | 0.0| 0.0|(692,[122,123,124...| | 0.0| 0.0|(692,[123,124,125...| | 0.0| 0.0|(692,[124,125,126...| | 0.0| 0.0|(692,[124,125,126...| +----------+-----+--------------------+ only showing top 5 rowsRoot Mean Squared Error (RMSE) on test data = 0.2 GBTRegressionModel (uid=GBTRegressor_a891b3f3210d) with 10 trees

11.机器学习模板与交叉验证

from __future__ import print_function from pyspark.ml import Pipeline from pyspark.ml.classification import LogisticRegression from pyspark.ml.evaluation import BinaryClassificationEvaluator from pyspark.ml.feature import HashingTF, Tokenizer from pyspark.ml.tuning import CrossValidator, ParamGridBuilder from pyspark.sql import SparkSessionspark = SparkSession\ .builder\ .appName("CrossValidatorExample")\ .getOrCreate()# $example on$ # Prepare training documents, which are labeled. training = spark.createDataFrame([ (0, "a b c d e spark", 1.0), (1, "b d", 0.0), (2, "spark f g h", 1.0), (3, "hadoop mapreduce", 0.0), (4, "b spark who", 1.0), (5, "g d a y", 0.0), (6, "spark fly", 1.0), (7, "was mapreduce", 0.0), (8, "e spark program", 1.0), (9, "a e c l", 0.0), (10, "spark compile", 1.0), (11, "hadoop software", 0.0) ], ["id", "text", "label"])# Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr. tokenizer = Tokenizer(inputCol="text", outputCol="words") hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") lr = LogisticRegression(maxIter=10) pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])# We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance. # This will allow us to jointly choose parameters for all Pipeline stages. # A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. # We use a ParamGridBuilder to construct a grid of parameters to search over. # With 3 values for hashingTF.numFeatures and 2 values for lr.regParam, # this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from. paramGrid = ParamGridBuilder() \ .addGrid(hashingTF.numFeatures, [10, 100, 1000]) \ .addGrid(lr.regParam, [0.1, 0.01]) \ .build()crossval = CrossValidator(estimator=pipeline, estimatorParamMaps=paramGrid, evaluator=BinaryClassificationEvaluator(), numFolds=2) # use 3+ folds in practice# Run cross-validation, and choose the best set of parameters. cvModel = crossval.fit(training)# Prepare test documents, which are unlabeled. test = spark.createDataFrame([ (4, "spark i j k"), (5, "l m n"), (6, "mapreduce spark"), (7, "apache hadoop") ], ["id", "text"])# Make predictions on test documents. cvModel uses the best model found (lrModel). prediction = cvModel.transform(test) selected = prediction.select("id", "text", "probability", "prediction") for row in selected.collect(): print(row)spark.stop()

Row(id=4, text='spark i j k', probability=DenseVector([0.2581, 0.7419]), prediction=1.0) Row(id=5, text='l m n', probability=DenseVector([0.9186, 0.0814]), prediction=0.0) Row(id=6, text='mapreduce spark', probability=DenseVector([0.432, 0.568]), prediction=1.0) Row(id=7, text='apache hadoop', probability=DenseVector([0.6766, 0.3234]), prediction=0.0)