CODEWITHSUNDEEP
pythonmachine-learningpysparkcluster-analysisk-means
DataBach
DataBach

Reputation: 1645

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

I created a kmeans algorithm with Pypsark. Now, I want to also extract the cluster centers. How do I include it in the pipeline? This is the code that I have so far, but it throws me an error 'AttributeError: 'PipelineModel' object has no attribute 'clusterCenters'. How can it be fixed?

#### 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()

Upvotes: 2

Views: 2193

Answers (1)

E.ZY.
E.ZY.

Reputation: 725

dummy data

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"])

your code

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)

just change the last line

model.stages[0].clusterCenters()

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

Upvotes: 3

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