StreamingKMeansModel¶

class pyspark.mllib.clustering.StreamingKMeansModel(clusterCenters: List[VectorLike], clusterWeights: VectorLike)¶

Clustering model which can perform an online update of the centroids.

The update formula for each centroid is given by

c_t+1 = ((c_t * n_t * a) + (x_t * m_t)) / (n_t + m_t)
n_t+1 = n_t * a + m_t

where

c_t: Centroid at the n_th iteration.
n_t: Number of samples (or) weights associated with the centroid at the n_th iteration.
x_t: Centroid of the new data closest to c_t.
m_t: Number of samples (or) weights of the new data closest to c_t
c_t+1: New centroid.
n_t+1: New number of weights.
a: Decay Factor, which gives the forgetfulness.

Parameters

clusterCenterslist of pyspark.mllib.linalg.Vector or covertible: Initial cluster centers.
clusterWeightspyspark.mllib.linalg.Vector or covertible: List of weights assigned to each cluster.

Notes

If a is set to 1, it is the weighted mean of the previous and new data. If it set to zero, the old centroids are completely forgotten.

Examples

>>> initCenters = [[0.0, 0.0], [1.0, 1.0]]
>>> initWeights = [1.0, 1.0]
>>> stkm = StreamingKMeansModel(initCenters, initWeights)
>>> data = sc.parallelize([[-0.1, -0.1], [0.1, 0.1],
...                        [0.9, 0.9], [1.1, 1.1]])
>>> stkm = stkm.update(data, 1.0, "batches")
>>> stkm.centers
array([[ 0.,  0.],
       [ 1.,  1.]])
>>> stkm.predict([-0.1, -0.1])
0
>>> stkm.predict([0.9, 0.9])
1
>>> stkm.clusterWeights
[3.0, 3.0]
>>> decayFactor = 0.0
>>> data = sc.parallelize([DenseVector([1.5, 1.5]), DenseVector([0.2, 0.2])])
>>> stkm = stkm.update(data, 0.0, "batches")
>>> stkm.centers
array([[ 0.2,  0.2],
       [ 1.5,  1.5]])
>>> stkm.clusterWeights
[1.0, 1.0]
>>> stkm.predict([0.2, 0.2])
0
>>> stkm.predict([1.5, 1.5])
1

Methods

`computeCost`(rdd)	Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
`load`(sc, path)	Load a model from the given path.
`predict`(x)	Find the cluster that each of the points belongs to in this model.
`save`(sc, path)	Save this model to the given path.
`update`(data, decayFactor, timeUnit)	Update the centroids, according to data

Attributes

`clusterCenters`	Get the cluster centers, represented as a list of NumPy arrays.
`clusterWeights`	Return the cluster weights.
`k`	Total number of clusters.

Methods Documentation

computeCost(rdd: pyspark.rdd.RDD[VectorLike]) → float¶

Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

Parameters

rdd:pyspark.RDD: The RDD of points to compute the cost on.

classmethod load(sc: pyspark.context.SparkContext, path: str) → pyspark.mllib.clustering.KMeansModel ¶: Load a model from the given path.

predict(x: Union[VectorLike, pyspark.rdd.RDD[VectorLike]]) → Union[int, pyspark.rdd.RDD[int]]¶

Find the cluster that each of the points belongs to in this model.

Parameters

xpyspark.mllib.linalg.Vector or pyspark.RDD: A data point (or RDD of points) to determine cluster index. pyspark.mllib.linalg.Vector can be replaced with equivalent objects (list, tuple, numpy.ndarray).

Returns

int or pyspark.RDD of int: Predicted cluster index or an RDD of predicted cluster indices if the input is an RDD.

save(sc: pyspark.context.SparkContext, path: str) → None¶: Save this model to the given path.

update(data: pyspark.rdd.RDD[VectorLike], decayFactor: float, timeUnit: str) → StreamingKMeansModel¶

Update the centroids, according to data

Parameters

datapyspark.RDD: RDD with new data for the model update.
decayFactorfloat: Forgetfulness of the previous centroids.
timeUnitstr: Can be “batches” or “points”. If points, then the decay factor is raised to the power of number of new points and if batches, then decay factor will be used as is.

Attributes Documentation

clusterCenters¶: Get the cluster centers, represented as a list of NumPy arrays.

clusterWeights¶: Return the cluster weights.

k¶: Total number of clusters.

StreamingKMeans

LDA