2024年大数据最新【Spark ML】（二）Spark ML 分类算法_spark分类算法(1)，2024年最新吊打面试官-大数据开发中高级面试题

作者：寸_铁 | 2024-06-19 17:19:32

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spark ml

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由于文件比较多，这里只是将部分目录截图出来，全套包含大厂面经、学习笔记、源码讲义、实战项目、大纲路线、讲解视频，并且后续会持续更新

需要这份系统化资料的朋友，可以戳这里获取

fMeasure = trainingSummary.fMeasureByThreshold
maxFMeasure = fMeasure.groupBy().max('F-Measure').select('max(F-Measure)').head()
bestThreshold = fMeasure.where(fMeasure['F-Measure'] == maxFMeasure['max(F-Measure)']) \
    .select('threshold').head()['threshold']
lr.setThreshold(bestThreshold)

spark.stop()
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日志信息：



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objectiveHistory:
0.683314913574
…
±–±-------------------+
|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


#### 1.2 多分类LR


示例代码如下：



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-- coding: utf-8 --

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

if name == “__main__”:
spark = SparkSession
.builder
.appName(“MulticlassLogisticRegressionWithElasticNet”)
.getOrCreate()

training = spark \
    .read \
    .format("libsvm") \
    .load("../data/mllib/sample\_multiclass\_classification\_data.txt")

lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
lrModel = lr.fit(training)

# 系数和截距项
print("Coefficients: \n" + str(lrModel.coefficientMatrix))
print("Intercept: " + str(lrModel.interceptVector))

trainingSummary = lrModel.summary

# 获得每次迭代的优化目标(损失 + 惩罚项)
objectiveHistory = trainingSummary.objectiveHistory
print("objectiveHistory:")
for objective in objectiveHistory:
    print(objective)

# 可以查看每个类的FPR & TPR
print("False positive rate by label:")
for i, rate in enumerate(trainingSummary.falsePositiveRateByLabel):
    print("label %d: %s" % (i, rate))

print("True positive rate by label:")
for i, rate in enumerate(trainingSummary.truePositiveRateByLabel):
    print("label %d: %s" % (i, rate))

print("Precision by label:")
for i, prec in enumerate(trainingSummary.precisionByLabel):
    print("label %d: %s" % (i, prec))

print("Recall by label:")
for i, rec in enumerate(trainingSummary.recallByLabel):
    print("label %d: %s" % (i, rec))

print("F-measure by label:")
for i, f in enumerate(trainingSummary.fMeasureByLabel()):
    print("label %d: %s" % (i, f))

accuracy = trainingSummary.accuracy
falsePositiveRate = trainingSummary.weightedFalsePositiveRate
truePositiveRate = trainingSummary.weightedTruePositiveRate
fMeasure = trainingSummary.weightedFMeasure()
precision = trainingSummary.weightedPrecision
recall = trainingSummary.weightedRecall
print("Accuracy: %s\nFPR: %s\nTPR: %s\nF-measure: %s\nPrecision: %s\nRecall: %s"
      % (accuracy, falsePositiveRate, truePositiveRate, fMeasure, precision, recall))

spark.stop()
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结果如下：



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Coefficients:
3 X 4 CSRMatrix
(0,3) 0.3176
(1,2) -0.7804
(1,3) -0.377
Intercept: [0.05165231659832854,-0.12391224990853622,0.07225993331020768]
objectiveHistory:
1.09861228867
…
False positive rate by label:
label 0: 0.22
label 1: 0.05
label 2: 0.0
True positive rate by label:
label 0: 1.0
label 1: 1.0
label 2: 0.46
Precision by label:
label 0: 0.694444444444
label 1: 0.909090909091
label 2: 1.0
Recall by label:
label 0: 1.0
label 1: 1.0
label 2: 0.46
F-measure by label:
label 0: 0.819672131148
label 1: 0.952380952381
label 2: 0.630136986301
Accuracy: 0.82
FPR: 0.09
TPR: 0.82
F-measure: 0.800730023277
Precision: 0.867845117845
Recall: 0.82


### 2. 决策树分类器


举例


以LibSVM格式加载数据集，将其拆分为训练集和测试集，在第一个数据集上训练，然后在保留的测试集上进行评估。 我们使用两个特征变换器(transformers)来准备数据; 这些帮助标记和分类特征的索引类别，向决策树算法可识别的DataFrame添加元数据。



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-- coding: utf-8 --

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

if name == “__main__”:
spark = SparkSession
.builder
.appName(“DecisionTreeClassificationExample”)
.getOrCreate()

data = spark.read.format("libsvm").load("../data/mllib/sample\_libsvm\_data.txt")

# 对于整个数据集，将label转换为索引
labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
# 自动识别数据集中的分类特征，并且进行矢量化处理;设定maxCategories，以便将具有> 4个不同值的特性视为连续的。
featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)

# 切分训练集和测试集
(trainingData, testData) = data.randomSplit([0.7, 0.3])

# 训练一颗决策树
dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")

# 连接indexers和决策树
pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])

model = pipeline.fit(trainingData)

# 进行预测
predictions = model.transform(testData)
predictions.select("prediction", "indexedLabel", "features").show(5)

# 计算测试误差
evaluator = MulticlassClassificationEvaluator(
    labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g " % (1.0 - accuracy))

treeModel = model.stages[2]
print(treeModel)

spark.stop()
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结果如下：



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±---------±-----------±-------------------+
|prediction|indexedLabel| features|
±---------±-----------±-------------------+
| 1.0| 1.0|(692,[98,99,100,1…|
| 1.0| 1.0|(692,[121,122,123…|
| 1.0| 1.0|(692,[122,123,148…|
| 1.0| 1.0|(692,[124,125,126…|
| 1.0| 1.0|(692,[126,127,128…|
±---------±-----------±-------------------+
only showing top 5 rows

Test Error = 0.0357143
DecisionTreeClassificationModel (uid=DecisionTreeClassifier_4f508c37c4be93461970) of depth 1 with 3 nodes


### 3. 随机森林分类器


与DT类似的，只不过选择RF来进行训练，示例代码如下：



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-- coding: utf-8 --

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 SparkSession

if name == “__main__”:
spark = SparkSession
.builder
.appName(“RandomForestClassifierExample”)
.getOrCreate()

# 处理方式如DT类似
data = spark.read.format("libsvm").load("../data/mllib/sample\_libsvm\_data.txt")
labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
(trainingData, testData) = data.randomSplit([0.7, 0.3])

# TRAIN RF
rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", numTrees=10)

# 将标签的索引转换为原始标签
labelConverter = IndexToString(inputCol="prediction", outputCol="predictedLabel", labels=labelIndexer.labels)

# 在Pipeline中进行整个训练流程
pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf, labelConverter])

model = pipeline.fit(trainingData)

predictions = model.transform(testData)

predictions.select("predictedLabel", "label", "features").show(5)

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)

spark.stop()
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结果如下：



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±-------------±----±-------------------+
|predictedLabel|label| features|
±-------------±----±-------------------+
| 0.0| 0.0|(692,[98,99,100,1…|
| 0.0| 0.0|(692,[122,123,148…|
| 0.0| 0.0|(692,[124,125,126…|
| 0.0| 0.0|(692,[124,125,126…|
| 0.0| 0.0|(692,[124,125,126…|
±-------------±----±-------------------+
only showing top 5 rows

Test Error = 0.0294118
RandomForestClassificationModel (uid=RandomForestClassifier_421b9fdfb8d0ee9acde3) with 10 trees


### 4. 梯度提升树分类器


如前文类似，选用梯度提升树（Gradient-boosted trees, GBTs）来进行训练，示例代码如下：



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-- coding: utf-8 --

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 SparkSession

if name == “__main__”:
spark = SparkSession
.builder
.appName(“GradientBoostedTreeClassifierExample”)
.getOrCreate()

data = spark.read.format("libsvm").load("../data/mllib/sample\_libsvm\_data.txt")
labelIndex = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
(trainingData, testData) = data.randomSplit([0.7, 0.3])

# train
gbt = GBTClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", maxDepth=10)

# 在管道中进行整个训练流程
pipeline = Pipeline(stages=[labelIndex, featureIndexer, gbt])
model = pipeline.fit(trainingData)

# 预测
predictions = model.transform(testData)
predictions.select("prediction", "indexedLabel", "features").show(5)

# 计算测试误差
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)

spark.stop()
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结果如下：



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±---------±-----------±-------------------+
|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,[122,123,148…|
| 1.0| 1.0|(692,[123,124,125…|
| 1.0| 1.0|(692,[124,125,126…|
±---------±-----------±-------------------+
only showing top 5 rows

Test Error = 0
GBTClassificationModel (uid=GBTClassifier_4a1fa549ada75fa70795) with 20 trees


### 5. 多层感知器分类器


多层感知器分类器（Multilayer perceptron classifier, MLPC）是基于前馈人工神经网络的分类器。 MLPC由多层节点组成。 每层完全连接到网络中的下一层。 输入层中的节点表示输入数据。 所有其他节点通过输入与节点权重www和偏差bbb的线性组合将输入映射到输出，并应用激活函数。 这可以用矩阵形式写入MLPC，K+1层如下：  
 ![在这里插入图片描述](https://img-blog.csdnimg.cn/20200314153637680.png#pic_center)  
 中间层中的节点使用sigmoid函数：


![在这里插入图片描述](https://img-blog.csdnimg.cn/20200314153650701.png#pic_center)  
 输出层中的节点使用softmax函数：  
 ![在这里插入图片描述](https://img-blog.csdnimg.cn/20200314153705547.png#pic_center)  
 输出层中的节点数N对应于类的数量。


MLPC采用反向传播来学习模型。 我们使用逻辑损失函数进行优化，使用L-BFGS作为优化过程。


示例代码如下：



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-- coding: utf-8 --

from future import print_function
from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.sql import SparkSession

if name == “__main__”:
spark = SparkSession.builder.appName(“multilayer_perceptron_classification_example”).getOrCreate()

data = spark.read.format("libsvm").load("../data/mllib/sample\_multiclass\_classification\_data.txt")
(train_data, test_data) = data.randomSplit([0.6, 0.4], seed=2019)

# 输入层为features的大小(4)，输出层为labels的大小(3)
layers = [4, 5, 4, 3]

# train
trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=2019)
model = trainer.fit(train_data)

# 计算在测试集上的准确率
predictions = model.transform(test_data)
predictions.select("prediction", "label", "features").show(5)
evaluator = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction",
                                              metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))
print(model)

spark.stop()
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结果如下：



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±---------±----±-------------------+
|prediction|label| features|
±---------±----±-------------------+
| 0.0| 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],[0.0…|
| 0.0| 0.0|(4,[0,1,2,3],[0.0…|
| 0.0| 0.0|(4,[0,1,2,3],[0.1…|
±---------±----±-------------------+
only showing top 5 rows