我正在将Spark 1.5.1与MLLib一起使用。我使用MLLib建立了一个随机森林模型,现在使用该模型进行预测。我可以使用.predict函数找到预测类别(0.0或1.0)。但是,我找不到用于检索概率的函数(请参阅随附的屏幕截图)。我以为星火1.5.1随机森林会提供可能性,我在这里错过了什么吗?

scala - Spark 1.5.1,MLLIb随机森林概率-LMLPHP

最佳答案

不幸的是,该功能在较旧的Spark MLlib 1.5.1中不可用。

但是,您可以在Spark MLlib 2.x的最新管道API中以RandomForestClassifier的形式找到它:



import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
import org.apache.spark.mllib.util.MLUtils

// Load and parse the data file, converting it to a DataFrame.
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF

// Index labels, adding metadata to the label column.
// Fit on whole dataset to include all labels in index.
val labelIndexer = new StringIndexer()
  .setInputCol("label")
  .setOutputCol("indexedLabel").fit(data)

// Automatically identify categorical features, and index them.
// Set maxCategories so features with > 4 distinct values are treated as continuous.
val featureIndexer = new VectorIndexer()
  .setInputCol("features")
  .setOutputCol("indexedFeatures")
  .setMaxCategories(4).fit(data)

// Split the data into training and test sets (30% held out for testing)
val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

// Train a RandomForest model.
val rf = new RandomForestClassifier()
  .setLabelCol(labelIndexer.getOutputCol)
  .setFeaturesCol(featureIndexer.getOutputCol)
  .setNumTrees(10)

// Convert indexed labels back to original labels.
val labelConverter = new IndexToString()
  .setInputCol("prediction")
  .setOutputCol("predictedLabel")
  .setLabels(labelIndexer.labels)

// Chain indexers and forest in a Pipeline
val pipeline = new Pipeline()
  .setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))

// Fit model. This also runs the indexers.
val model = pipeline.fit(trainingData)

// Make predictions.
val predictions = model.transform(testData)
// predictions: org.apache.spark.sql.DataFrame = [label: double, features: vector, indexedLabel: double, indexedFeatures: vector, rawPrediction: vector, probability: vector, prediction: double, predictedLabel: string]

predictions.show(10)
// +-----+--------------------+------------+--------------------+-------------+-----------+----------+--------------+
// |label|            features|indexedLabel|     indexedFeatures|rawPrediction|probability|prediction|predictedLabel|
// +-----+--------------------+------------+--------------------+-------------+-----------+----------+--------------+
// |  0.0|(692,[124,125,126...|         1.0|(692,[124,125,126...|   [0.0,10.0]|  [0.0,1.0]|       1.0|           0.0|
// |  0.0|(692,[124,125,126...|         1.0|(692,[124,125,126...|    [1.0,9.0]|  [0.1,0.9]|       1.0|           0.0|
// |  0.0|(692,[129,130,131...|         1.0|(692,[129,130,131...|    [1.0,9.0]|  [0.1,0.9]|       1.0|           0.0|
// |  0.0|(692,[154,155,156...|         1.0|(692,[154,155,156...|    [1.0,9.0]|  [0.1,0.9]|       1.0|           0.0|
// |  0.0|(692,[154,155,156...|         1.0|(692,[154,155,156...|    [1.0,9.0]|  [0.1,0.9]|       1.0|           0.0|
// |  0.0|(692,[181,182,183...|         1.0|(692,[181,182,183...|    [1.0,9.0]|  [0.1,0.9]|       1.0|           0.0|
// |  1.0|(692,[99,100,101,...|         0.0|(692,[99,100,101,...|    [4.0,6.0]|  [0.4,0.6]|       1.0|           0.0|
// |  1.0|(692,[123,124,125...|         0.0|(692,[123,124,125...|   [10.0,0.0]|  [1.0,0.0]|       0.0|           1.0|
// |  1.0|(692,[124,125,126...|         0.0|(692,[124,125,126...|   [10.0,0.0]|  [1.0,0.0]|       0.0|           1.0|
// |  1.0|(692,[125,126,127...|         0.0|(692,[125,126,127...|   [10.0,0.0]|  [1.0,0.0]|       0.0|           1.0|
// +-----+--------------------+------------+--------------------+-------------+-----------+----------+--------------+
// only showing top 10 rows


注意:此示例来自Spark MLlib的ML - Random forest classifier的官方文档。

这是一些输出列的一些解释:


predictionCol代表预测的标签。
rawPredictionCol表示一个长度为#类的Vector,在进行预测的树节点上有训练实例标签的计数(仅适用于分类)。
probabilityCol表示长度#类的概率向量,其等于归一化为多项分布的rawPrediction(仅适用于分类)。

08-25 04:46