Python pyspark LinearSVC用法及代码示例

本文简要介绍 pyspark.ml.classification.LinearSVC 的用法。

用法: class pyspark.ml.classification.LinearSVC(*, featuresCol='features', labelCol='label', predictionCol='prediction', maxIter=100, regParam=0.0, tol=1e-06, rawPredictionCol='rawPrediction', fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2, maxBlockSizeInMB=0.0)

这个二元分类器使用 OWLQN 优化器优化铰链损失。目前仅支持 L2 正则化。

2.2.0 版中的新函数。

注意：

线性 SVM 分类器

例子：

>>> from pyspark.sql import Row
>>> from pyspark.ml.linalg import Vectors
>>> df = sc.parallelize([
...     Row(label=1.0, features=Vectors.dense(1.0, 1.0, 1.0)),
...     Row(label=0.0, features=Vectors.dense(1.0, 2.0, 3.0))]).toDF()
>>> svm = LinearSVC()
>>> svm.getMaxIter()
100
>>> svm.setMaxIter(5)
LinearSVC...
>>> svm.getMaxIter()
5
>>> svm.getRegParam()
0.0
>>> svm.setRegParam(0.01)
LinearSVC...
>>> svm.getRegParam()
0.01
>>> model = svm.fit(df)
>>> model.setPredictionCol("newPrediction")
LinearSVCModel...
>>> model.getPredictionCol()
'newPrediction'
>>> model.setThreshold(0.5)
LinearSVCModel...
>>> model.getThreshold()
0.5
>>> model.getMaxBlockSizeInMB()
0.0
>>> model.coefficients
DenseVector([0.0, -1.0319, -0.5159])
>>> model.intercept
2.579645978780695
>>> model.numClasses
2
>>> model.numFeatures
3
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, -1.0, -1.0))]).toDF()
>>> model.predict(test0.head().features)
1.0
>>> model.predictRaw(test0.head().features)
DenseVector([-4.1274, 4.1274])
>>> result = model.transform(test0).head()
>>> result.newPrediction
1.0
>>> result.rawPrediction
DenseVector([-4.1274, 4.1274])
>>> svm_path = temp_path + "/svm"
>>> svm.save(svm_path)
>>> svm2 = LinearSVC.load(svm_path)
>>> svm2.getMaxIter()
5
>>> model_path = temp_path + "/svm_model"
>>> model.save(model_path)
>>> model2 = LinearSVCModel.load(model_path)
>>> model.coefficients[0] == model2.coefficients[0]
True
>>> model.intercept == model2.intercept
True
>>> model.transform(test0).take(1) == model2.transform(test0).take(1)
True

相关用法

注：本文由纯净天空筛选整理自spark.apache.org大神的英文原创作品 pyspark.ml.classification.LinearSVC。非经特殊声明，原始代码版权归原作者所有，本译文未经允许或授权，请勿转载或复制。