来自 mllib 的梯度提升树中的类型错误

Question

我尝试对一些混合类型的数据运行梯度提升树算法：

[('feature1', 'bigint'),
 ('feature2', 'int'),
 ('label', 'double')]

我尝试了以下

from pyspark.mllib.tree import GradientBoostedTrees, GradientBoostedTreesModel
from pyspark.ml.feature import VectorAssembler
from pyspark.mllib.linalg import Vector as MLLibVector, Vectors as MLLibVectors
from pyspark.mllib.regression import LabeledPoint

vectorAssembler = VectorAssembler(inputCols = ["feature1", "feature2"], outputCol = "features")

data_assembled = vectorAssembler.transform(data)
data_assembled = data_assembled.select(['features', 'label'])
data_assembled = data_assembled.select(F.col("features"), F.col("label"))\
  .rdd\
  .map(lambda row: LabeledPoint(MLLibVectors.fromML(row.label), MLLibVectors.fromML(row.features)))

(trainingData, testData) = data_assembled.randomSplit([0.9, 0.1])

model = GradientBoostedTrees.trainRegressor(trainingData,
                                            categoricalFeaturesInfo={}, numIterations=100)

但是我收到以下错误：

TypeError: Unsupported vector type <class 'float'>

但我的类型实际上都不是浮动的。此外，如果相关，feature2 是二进制的。

Answer 1

我最终避免了 mllib 实现，而是使用了 Spark ML：

from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import GBTRegressor

vectorAssembler = VectorAssembler(inputCols = ["feature1", "feature2"], outputCol = "features")

data_assembled = vectorAssembler.transform(data)
data_assembled = data_assembled.select(F.col("label"), F.col("features"))

(trainingData, testData) = data_assembled.randomSplit([0.7, 0.3])

gbt_model = GBTRegressor(featuresCol="features", maxIter=10).fit(trainingData)

Python 没有 LabeledPoint 对象所需的双精度类型，因此我假设 pyspark 的映射导致转换为浮点数。