多层感知机预测

功能介绍

基于多层感知机模型，进行分类预测。

参数说明

名称 中文名称 描述 类型 是否必须？ 默认值
vectorCol 向量列名 向量列对应的列名，默认值是null String null
predictionCol 预测结果列名 预测结果列名 String ✓
predictionDetailCol 预测详细信息列名 预测详细信息列名 String
reservedCols 算法保留列名 算法保留列 String[] null

脚本示例

脚本代码

  1. URL = "https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/iris.csv"
  2. SCHEMA_STR = "sepal_length double, sepal_width double, petal_length double, petal_width double, category string";
  3. data = CsvSourceBatchOp().setFilePath(URL).setSchemaStr(SCHEMA_STR)
  5. classifier = MultilayerPerceptronClassifier()\
  6.             .setFeatureCols(Iris.getFeatureColNames())\
  7.             .setLabelCol(Iris.getLabelColName())\
  8.             .setLayers([4, 5, 3])\
  9.             .setMaxIter(100)\
  10.             .setPredictionCol("pred_label")\
  11.             .setPredictionDetailCol("pred_detail")
  13. classifier.fit(data).transform(Iris.getStreamData()).print();

脚本运行结果

  1. 6.3000|3.3000|6.0000|2.5000|Iris-virginica|Iris-virginica|{"Iris-virginica":0.9433614954932688,"Iris-versicolor":0.056638504506731226,"Iris-setosa":3.008568854761749E-175}
  2. 5.6000|2.8000|4.9000|2.0000|Iris-virginica|Iris-virginica|{"Iris-virginica":0.9433614954932688,"Iris-versicolor":0.056638504506731226,"Iris-setosa":3.008568854761749E-175}
  3. 5.0000|3.3000|1.4000|0.2000|Iris-setosa|Iris-setosa|{"Iris-virginica":8.4E-323,"Iris-versicolor":4.0138401486628416E-173,"Iris-setosa":1.0}
  4. 5.8000|2.7000|5.1000|1.9000|Iris-virginica|Iris-virginica|{"Iris-virginica":0.9433614954932688,"Iris-versicolor":0.056638504506731226,"Iris-setosa":3.008568854761749E-175}
  5. 7.0000|3.2000|4.7000|1.4000|Iris-versicolor|Iris-versicolor|{"Iris-virginica":5.31328185381337E-80,"Iris-versicolor":1.0,"Iris-setosa":6.407249280059006E-44}