predictions = model.transform(testData)
时间: 2024-02-29 12:55:36 浏览: 25
`model.transform(testData)`是一个Spark MLlib中的函数,用于对测试数据进行预测。其中,`model`是一个已经训练好的模型,`testData`是测试数据集,通常是一个DataFrame,包含了模型需要用到的特征列。返回结果是一个包含预测结果的DataFrame,其中包括原始特征列、预测结果列以及其它相关的统计信息。
在你提供的代码中,`model.transform(testData)`将会对`testData`进行预测,并将预测结果存储在`predictions`中。`predictions`是一个包含预测结果的DataFrame,其中包括原始特征列、预测结果列、预测概率列以及其它相关的统计信息。
相关问题
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/art/a.txt
首先,我们需要导入必要的包和模块,并创建SparkContext:
```python
from pyspark.conf import SparkConf
from pyspark.context import SparkContext
from pyspark.sql import SparkSession
from pyspark.ml.feature import HashingTF, IDF, Tokenizer
from pyspark.ml.classification import LinearSVC
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
conf = SparkConf().setAppName('SVM for News Classification')
sc = SparkContext.getOrCreate(conf=conf)
spark = SparkSession(sc)
```
接下来,我们需要读取数据集。由于数据集是多个按照类别分类的文件夹,我们可以使用textFile()函数递归读取整个目录:
```python
data = sc.textFile('/project/art/a.txt', minPartitions=4)
```
由于数据集是中文文本,我们需要使用Tokenizer对文本进行分词,然后使用HashingTF和IDF对文本进行特征提取和向量化:
```python
tokenizer = Tokenizer(inputCol='text', outputCol='words')
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol='rawFeatures')
idf = IDF(inputCol=hashingTF.getOutputCol(), outputCol='features')
words = tokenizer.transform(data)
rawFeatures = hashingTF.transform(words)
features = idf.fit(rawFeatures).transform(rawFeatures)
```
接下来,我们需要将数据集划分为训练集和测试集:
```python
(trainingData, testData) = features.randomSplit([0.7, 0.3], seed=42)
```
然后,我们可以使用LinearSVC算法进行训练:
```python
svm = LinearSVC(maxIter=10, regParam=0.1)
model = svm.fit(trainingData)
```
最后,我们可以使用MulticlassClassificationEvaluator评估模型的性能:
```python
predictions = model.transform(testData)
evaluator = MulticlassClassificationEvaluator(predictionCol='prediction', labelCol='label', metricName='accuracy')
accuracy = evaluator.evaluate(predictions)
print('Test Accuracy = %.2f%%' % (accuracy * 100))
```
完整代码如下:
```python
from pyspark.conf import SparkConf
from pyspark.context import SparkContext
from pyspark.sql import SparkSession
from pyspark.ml.feature import HashingTF, IDF, Tokenizer
from pyspark.ml.classification import LinearSVC
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
conf = SparkConf().setAppName('SVM for News Classification')
sc = SparkContext.getOrCreate(conf=conf)
spark = SparkSession(sc)
data = sc.textFile('/project/art/a.txt', minPartitions=4)
tokenizer = Tokenizer(inputCol='text', outputCol='words')
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol='rawFeatures')
idf = IDF(inputCol=hashingTF.getOutputCol(), outputCol='features')
words = tokenizer.transform(data)
rawFeatures = hashingTF.transform(words)
features = idf.fit(rawFeatures).transform(rawFeatures)
(trainingData, testData) = features.randomSplit([0.7, 0.3], seed=42)
svm = LinearSVC(maxIter=10, regParam=0.1)
model = svm.fit(trainingData)
predictions = model.transform(testData)
evaluator = MulticlassClassificationEvaluator(predictionCol='prediction', labelCol='label', metricName='accuracy')
accuracy = evaluator.evaluate(predictions)
print('Test Accuracy = %.2f%%' % (accuracy * 100))
```
利用pyspark实现SVM
要使用PySpark实现SVM,需要进行以下步骤:
1. 导入必要的库和模块
```python
from pyspark.ml.classification import LinearSVC
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
```
2. 创建Spark会话
```python
spark = SparkSession.builder.appName("SVMExample").getOrCreate()
```
3. 加载数据集
```python
data = spark.read.format("libsvm").load("path/to/data")
```
4. 准备训练数据
将数据集分成训练集和测试集:
```python
(trainingData, testData) = data.randomSplit([0.7, 0.3], seed=1234)
```
5. 准备特征向量
```python
assembler = VectorAssembler(inputCols=data.columns[:-1], outputCol="features")
trainingData = assembler.transform(trainingData)
testData = assembler.transform(testData)
```
6. 训练模型
```python
svm = LinearSVC(maxIter=10, regParam=0.1)
model = svm.fit(trainingData)
```
7. 预测结果
```python
predictions = model.transform(testData)
```
8. 评估模型
使用多分类分类评估器来评估模型的性能:
```python
evaluator = MulticlassClassificationEvaluator(predictionCol="prediction")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))
```
完整代码:
```python
from pyspark.ml.classification import LinearSVC
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("SVMExample").getOrCreate()
data = spark.read.format("libsvm").load("path/to/data")
(trainingData, testData) = data.randomSplit([0.7, 0.3], seed=1234)
assembler = VectorAssembler(inputCols=data.columns[:-1], outputCol="features")
trainingData = assembler.transform(trainingData)
testData = assembler.transform(testData)
svm = LinearSVC(maxIter=10, regParam=0.1)
model = svm.fit(trainingData)
predictions = model.transform(testData)
evaluator = MulticlassClassificationEvaluator(predictionCol="prediction")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))
spark.stop()
```
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