class StreamingKMeansModel extends KMeansModel with Logging
StreamingKMeansModel extends MLlib's KMeansModel for streaming algorithms, so it can keep track of a continuously updated weight associated with each cluster, and also update the model by doing a single iteration of the standard k-means algorithm.
The update algorithm uses the "mini-batch" KMeans rule, generalized to incorporate forgetfulness (i.e. decay). The update rule (for each cluster) is:
$$ \begin{align} c_{t+1} &= [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t] \\ n_{t+1} &= n_t * a + m_t \end{align} $$
Where c_t is the previously estimated centroid for that cluster, n_t is the number of points assigned to it thus far, x_t is the centroid estimated on the current batch, and m_t is the number of points assigned to that centroid in the current batch.
The decay factor 'a' scales the contribution of the clusters as estimated thus far, by applying a as a discount weighting on the current point when evaluating new incoming data. If a=1, all batches are weighted equally. If a=0, new centroids are determined entirely by recent data. Lower values correspond to more forgetting.
Decay can optionally be specified by a half life and associated time unit. The time unit can either be a batch of data or a single data point. Considering data arrived at time t, the half life h is defined such that at time t + h the discount applied to the data from t is 0.5. The definition remains the same whether the time unit is given as batches or points.
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- @Since( "1.2.0" )
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- StreamingKMeans.scala
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val
clusterCenters: Array[Vector]
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- StreamingKMeansModel → KMeansModel
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val
clusterWeights: Array[Double]
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def
computeCost(data: RDD[Vector]): Double
Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
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- KMeansModel
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- @Since( "0.8.0" )
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val
distanceMeasure: String
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- KMeansModel
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def
k: Int
Total number of clusters.
Total number of clusters.
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- KMeansModel
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log: Logger
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def
predict(points: JavaRDD[Vector]): JavaRDD[Integer]
Maps given points to their cluster indices.
Maps given points to their cluster indices.
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- KMeansModel
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- @Since( "1.0.0" )
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def
predict(points: RDD[Vector]): RDD[Int]
Maps given points to their cluster indices.
Maps given points to their cluster indices.
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- KMeansModel
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- @Since( "1.0.0" )
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def
predict(point: Vector): Int
Returns the cluster index that a given point belongs to.
Returns the cluster index that a given point belongs to.
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- KMeansModel
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def
save(sc: SparkContext, path: String): Unit
Save this model to the given path.
Save this model to the given path.
This saves:
- human-readable (JSON) model metadata to path/metadata/
- Parquet formatted data to path/data/
The model may be loaded using
Loader.load.- sc
Spark context used to save model data.
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Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.
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- KMeansModel → Saveable
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def
toPMML(): String
Export the model to a String in PMML format
Export the model to a String in PMML format
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Export the model to the OutputStream in PMML format
Export the model to the OutputStream in PMML format
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toPMML(sc: SparkContext, path: String): Unit
Export the model to a directory on a distributed file system in PMML format
Export the model to a directory on a distributed file system in PMML format
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toPMML(localPath: String): Unit
Export the model to a local file in PMML format
Export the model to a local file in PMML format
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val
trainingCost: Double
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- KMeansModel
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def
update(data: RDD[Vector], decayFactor: Double, timeUnit: String): StreamingKMeansModel
Perform a k-means update on a batch of data.
Perform a k-means update on a batch of data.
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