导入pyspark.conf,pyspark.sparkcontext,pyspark.mllib,实现SVM对于新闻的分类。数据集为多个按照类别分类的文件夹，每个文件夹下为新闻的中文正文内容，采用tf-idf对数据集进行清洗和处理，得到RDD。路径为/project/类别/文本

首先，我们需要安装配置Spark和Python环境。接下来，我们可以按照以下步骤实现SVM对于新闻的分类：

1. 导入必要的库和模块

from pyspark.conf import SparkConf
from pyspark.mllib.feature import HashingTF, IDF
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import SVMWithSGD
from pyspark.sql import SparkSession
import os

2. 创建SparkSession对象

conf = SparkConf().setAppName('News Classification')
spark = SparkSession.builder.config(conf=conf).getOrCreate()
sc = spark.sparkContext

3. 定义函数将文本文件转换为LabeledPoint格式

def get_labeled_point(file_path, category):
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()
    words = content.strip().split(' ')
    tf = HashingTF().transform(words)
    idf = IDF().fit(tf)
    tfidf = idf.transform(tf)
    return LabeledPoint(category, tfidf)

4. 加载数据集并将数据集转换为LabeledPoint格式

data = []
for category in os.listdir('/project'):
    category_path = os.path.join('/project', category)
    for file_name in os.listdir(category_path):
        file_path = os.path.join(category_path, file_name)
        data.append(get_labeled_point(file_path, category))
rdd = sc.parallelize(data)

5. 划分训练集和测试集

training, test = rdd.randomSplit([0.6, 0.4])

6. 训练SVM模型

model = SVMWithSGD.train(training, iterations=100)

7. 对测试集进行预测并计算准确率

prediction_and_labels = test.map(lambda x: (model.predict(x.features), x.label))
accuracy = prediction_and_labels.filter(lambda x: x[0] == x[1]).count() / float(test.count())
print("Accuracy:", accuracy)

完整代码如下：

from pyspark.conf import SparkConf
from pyspark.mllib.feature import HashingTF, IDF
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import SVMWithSGD
from pyspark.sql import SparkSession
import os

def get_labeled_point(file_path, category):
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()
    words = content.strip().split(' ')
    tf = HashingTF().transform(words)
    idf = IDF().fit(tf)
    tfidf = idf.transform(tf)
    return LabeledPoint(category, tfidf)

conf = SparkConf().setAppName('News Classification')
spark = SparkSession.builder.config(conf=conf).getOrCreate()
sc = spark.sparkContext

data = []
for category in os.listdir('/project'):
    category_path = os.path.join('/project', category)
    for file_name in os.listdir(category_path):
        file_path = os.path.join(category_path, file_name)
        data.append(get_labeled_point(file_path, category))
rdd = sc.parallelize(data)

training, test = rdd.randomSplit([0.6, 0.4])

model = SVMWithSGD.train(training, iterations=100)

prediction_and_labels = test.map(lambda x: (model.predict(x.features), x.label))
accuracy = prediction_and_labels.filter(lambda x: x[0] == x[1]).count() / float(test.count())
print("Accuracy:", accuracy)