Spark机器学习基础-监督学习

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 SparkSession

spark = SparkSession
    .builder
    .appName("LinearRegressionWithElasticNet")
    .getOrCreate()

# 加载数据
training = spark.read.format("libsvm")
    .load("data/mllib/sample_linear_regression_data.txt")

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()

 结果：

Coefficients: [0.0,0.322925166774,-0.343854803456,1.91560170235,0.0528805868039,0.76596272046,0.0,-0.151053926692,-0.215879303609,0.220253691888]
Intercept: 0.159893684424
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 rows

RMSE: 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 GeneralizedLinearRegression


spark = SparkSession
    .builder
    .appName("GeneralizedLinearRegressionExample")
    .getOrCreate()

# 加载数据
dataset = spark.read.format("libsvm")
    .load("data/mllib/sample_linear_regression_data.txt")

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()

　　结果：

Coefficients: [0.0105418280813,0.800325310056,-0.784516554142,2.36798871714,0.501000208986,1.12223511598,-0.292682439862,-0.498371743232,-0.603579718068,0.672555006719]
Intercept: 0.145921761452
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: 105.609883568
Null Deviance: 53229.3654339
Residual Degree Of Freedom Null: 500
Deviance: 51748.8429484
Residual Degree Of Freedom: 490
AIC: 3769.18958718
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 SparkSession

spark = SparkSession 
    .builder 
    .appName("LogisticRegressionSummary") 
    .getOrCreate()

# 加载数据
training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")

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()

　　结果：

objectiveHistory:
0.683314913574
0.666287575147
0.621706854603
0.612726524589
0.60603479868
0.603175068757
0.596962153484
0.594074303198
0.590608924334
0.589472457649
0.588218777573
+---+--------------------+
|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 rows

areaUnderROC: 1.0

from __future__ import print_function
from pyspark.ml.classification import LogisticRegression
from pyspark.sql import SparkSession

spark = SparkSession
    .builder
    .appName("LogisticRegressionWithElasticNet")
    .getOrCreate()

# 加载数据
training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")

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()

　　结果：

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],[-7.35398352419e-05,-9.10273850559e-05,-0.000194674305469,-0.000203006424735,-3.14761833149e-05,-6.84297760266e-05,1.58836268982e-05,1.40234970914e-05,0.00035432047525,0.000114432728982,0.000100167123837,0.00060141093038,0.000284024817912,-0.000115410847365,0.000385996886313,0.000635019557424,-0.000115064123846,-0.00015271865865,0.000280493380899,0.000607011747119,-0.000200845966325,-0.000142107557929,0.000273901034116,0.00027730456245,-9.83802702727e-05,-0.000380852244352,-0.000253151980086,0.000277477147708,-0.000244361976392,-0.00153947446876,-0.000230733284113])
Intercept: 0.224563159613
Multinomial coefficients: DenseMatrix([[ 0.,  0.,  0., ...,  0.,  0.,  0.],
             [ 0.,  0.,  0., ...,  0.,  0.,  0.]])
Multinomial intercepts: [-0.120658794459,0.120658794459]

3.多分类逻辑回归

from __future__ import print_function
from pyspark.ml.classification import LogisticRegression
from pyspark.sql import SparkSession

spark = SparkSession 
    .builder 
    .appName("MulticlassLogisticRegressionWithElasticNet") 
    .getOrCreate()

# 加载数据
training = spark 
    .read 
    .format("libsvm") 
    .load("data/mllib/sample_multiclass_classification_data.txt")

lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)

# 拟合模型
lrModel = lr.fit(training)

# 输出系数
print("Coefficients: 
" + str(lrModel.coefficientMatrix))
print("Intercept: " + str(lrModel.interceptVector))

# 预测结果
lrModel.transform(training).show()

spark.stop()

　　结果：

Coefficients: 
DenseMatrix([[ 0.        ,  0.        ,  0.        ,  0.31764832],
             [ 0.        ,  0.        , -0.78039435, -0.37696114],
             [ 0.        ,  0.        ,  0.        ,  0.        ]])
Intercept: [0.0516523165983,-0.123912249909,0.0722599333102]
+-----+--------------------+--------------------+--------------------+----------+
|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 SparkSession

spark = SparkSession
    .builder.appName("multilayer_perceptron_classification_example").getOrCreate()

# 加载数据
data = spark.read.format("libsvm")
    .load("data/mllib/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()

　　结果：

+-----+--------------------+----------+
|label|            features|prediction|
+-----+--------------------+----------+
|  0.0|(4,[0,1,2,3],[-0....|       2.0|
|  0.0|(4,[0,1,2,3],[-0....|       0.0|
|  0.0|(4,[0,1,2,3],[-0....|       0.0|
|  0.0|(4,[0,1,2,3],[-0....|       2.0|
|  0.0|(4,[0,1,2,3],[-0....|       2.0|
|  0.0|(4,[0,1,2,3],[-1....|       2.0|
|  0.0|(4,[0,1,2,3],[0.1...|       0.0|
|  0.0|(4,[0,1,2,3],[0.2...|       0.0|
|  0.0|(4,[0,1,2,3],[0.3...|       0.0|
|  0.0|(4,[0,1,2,3],[0.3...|       0.0|
|  0.0|(4,[0,1,2,3],[0.3...|       0.0|
|  0.0|(4,[0,1,2,3],[0.4...|       0.0|
|  0.0|(4,[0,1,2,3],[0.5...|       0.0|
|  0.0|(4,[0,1,2,3],[0.7...|       0.0|
|  0.0|(4,[0,1,2,3],[0.8...|       0.0|
|  0.0|(4,[0,1,2,3],[1.0...|       0.0|
|  0.0|(4,[0,2,3],[0.166...|       0.0|
|  0.0|(4,[0,2,3],[0.388...|       0.0|
|  1.0|(4,[0,1,2,3],[-0....|       1.0|
|  1.0|(4,[0,1,2,3],[-0....|       1.0|
+-----+--------------------+----------+
only showing top 20 rows

Test set accuracy = 0.901960784314

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 SparkSession

spark = SparkSession
    .builder
    .appName("DecisionTreeClassificationExample")
    .getOrCreate()

# 加载数据
data = spark.read.format("libsvm").load("data/mllib/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.
# 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 = 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()

　　结果：

+----------+------------+--------------------+
|prediction|indexedLabel|            features|
+----------+------------+--------------------+
|       1.0|         1.0|(692,[98,99,100,1...|
|       1.0|         1.0|(692,[100,101,102...|
|       1.0|         1.0|(692,[123,124,125...|
|       1.0|         1.0|(692,[124,125,126...|
|       1.0|         1.0|(692,[125,126,127...|
+----------+------------+--------------------+
only showing top 5 rows

Test Error = 0.0333333 
DecisionTreeClassificationModel (uid=DecisionTreeClassifier_4bf3a2017c8143b08d57) of depth 1 with 3 nodes