通过sparkcontext获得txt后缀的数据集，并进行数据划分。使用pyspark mllib实现支持向量机代码，最后进行评估

首先，通过`SparkContext`读取txt文件：

from pyspark import SparkContext

sc = SparkContext("local", "SVMExample")
data = sc.textFile("path/to/data.txt")

接着，将数据集划分为训练集和测试集：

trainingData, testData = data.randomSplit([0.7, 0.3])

然后，使用`pyspark.mllib`中的`SVMWithSGD`训练模型：

from pyspark.mllib.classification import SVMWithSGD
from pyspark.mllib.regression import LabeledPoint

# 将数据集转换为LabeledPoint类型
training = trainingData.map(lambda line: LabeledPoint(label=line.split(',')[0], features=line.split(',')[1:]))

# 训练模型
model = SVMWithSGD.train(training)

最后，使用测试集评估模型：

from pyspark.mllib.evaluation import BinaryClassificationMetrics

# 将测试集转换为LabeledPoint类型
test = testData.map(lambda line: LabeledPoint(label=line.split(',')[0], features=line.split(',')[1:]))

# 预测测试集标签
predictions = model.predict(test.map(lambda x: x.features))

# 计算模型性能指标
metrics = BinaryClassificationMetrics(predictions.zip(test.map(lambda x: x.label)))
print("AUC:", metrics.areaUnderROC)

完整代码如下：

from pyspark import SparkContext
from pyspark.mllib.classification import SVMWithSGD
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.evaluation import BinaryClassificationMetrics

sc = SparkContext("local", "SVMExample")
data = sc.textFile("path/to/data.txt")
trainingData, testData = data.randomSplit([0.7, 0.3])

training = trainingData.map(lambda line: LabeledPoint(label=line.split(',')[0], features=line.split(',')[1:]))
model = SVMWithSGD.train(training)

test = testData.map(lambda line: LabeledPoint(label=line.split(',')[0], features=line.split(',')[1:]))
predictions = model.predict(test.map(lambda x: x.features))
metrics = BinaryClassificationMetrics(predictions.zip(test.map(lambda x: x.label)))

print("AUC:", metrics.areaUnderROC)