PySpark: AttributeError: 'PipelineModel' object has no attribute 'clusterCenters'

Question

我用 Pypsark 创建了一个 kmeans 算法。现在，我还想提取聚类中心。如何将其包含在管道中？这是我到目前为止的代码，但它向我抛出错误“AttributeError：'PipelineModel' object has no attribute 'clusterCenters'。如何修复？

#### model K-Means ###

from pyspark.ml.clustering import KMeans, KMeansModel

kmeans = KMeans() \
          .setK(3) \
          .setFeaturesCol("scaledFeatures")\
          .setPredictionCol("cluster")

# Chain indexer and tree in a Pipeline
pipeline = Pipeline(stages=[kmeans])

model = pipeline.fit(matrix_normalized)

cluster = model.transform(matrix_normalized)

#get cluster centers
centers = model.clusterCenters()

Answer 1

虚拟数据

from pyspark.ml.linalg import Vectors
from pyspark.ml.clustering import KMeans, KMeansModel
from pyspark.ml.pipeline import Pipeline


data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
        (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
matrix_normalized = spark.createDataFrame(data, ["scaledFeatures"])

你的代码

kmeans = KMeans() \
          .setK(3) \
          .setFeaturesCol("scaledFeatures")\
          .setPredictionCol("cluster")

# Chain indexer and tree in a Pipeline
pipeline = Pipeline(stages=[kmeans])

model = pipeline.fit(matrix_normalized)

cluster = model.transform(matrix_normalized)

只需更改最后一行

model.stages[0].clusterCenters()

[array([0.5, 0.5]), array([8., 9.]), array([9., 8.])]