Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15413#discussion_r94084799
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala ---
@@ -323,27 +326,95 @@ class GaussianMixture @Since("2.0.0") (
@Since("2.0.0")
def setSeed(value: Long): this.type = set(seed, value)
+ /**
+ * Number of samples per cluster to use when initializing Gaussians.
+ */
+ private val numSamples = 5
+
@Since("2.0.0")
override def fit(dataset: Dataset[_]): GaussianMixtureModel = {
transformSchema(dataset.schema, logging = true)
- val rdd: RDD[OldVector] = dataset.select(col($(featuresCol))).rdd.map {
- case Row(point: Vector) => OldVectors.fromML(point)
- }
- val instr = Instrumentation.create(this, rdd)
+ val sc = dataset.sparkSession.sparkContext
+ val numClusters = $(k)
+
+ val instances: RDD[Vector] =
dataset.select(col($(featuresCol))).rdd.map {
+ case Row(features: Vector) => features
+ }.cache()
+
+ // Extract the number of features.
+ val numFeatures = instances.first().size
+
+ val instr = Instrumentation.create(this, instances)
instr.logParams(featuresCol, predictionCol, probabilityCol, k,
maxIter, seed, tol)
- val algo = new MLlibGM()
- .setK($(k))
- .setMaxIterations($(maxIter))
- .setSeed($(seed))
- .setConvergenceTol($(tol))
- val parentModel = algo.run(rdd)
- val gaussians = parentModel.gaussians.map { case g =>
- new MultivariateGaussian(g.mu.asML, g.sigma.asML)
+ val shouldDistributeGaussians =
GaussianMixture.shouldDistributeGaussians(
+ numClusters, numFeatures)
+
+ // TODO: SPARK-15785 Support users supplied initial GMM.
+ val (weights, gaussians) = initRandom(instances, numClusters,
numFeatures)
+
+ var logLikelihood = Double.MinValue
+ var logLikelihoodPrev = 0.0
+
+ var iter = 0
+ while (iter < $(maxIter) && math.abs(logLikelihood -
logLikelihoodPrev) > $(tol)) {
+
+ val bcWeights = instances.sparkContext.broadcast(weights)
+ val bcGaussians = instances.sparkContext.broadcast(gaussians)
+
+ // aggregate the cluster contribution for all sample points
+ val sums = instances.treeAggregate(
+ new ExpectationAggregator(numFeatures, bcWeights, bcGaussians))(
+ seqOp = (c, v) => (c, v) match {
+ case (aggregator, instance) => aggregator.add(instance)
+ },
+ combOp = (c1, c2) => (c1, c2) match {
+ case (aggregator1, aggregator2) => aggregator1.merge(aggregator2)
+ })
+
+ bcWeights.destroy(blocking = false)
+ bcGaussians.destroy(blocking = false)
+
+ /*
+ Create new distributions based on the partial assignments
+ (often referred to as the "M" step in literature)
+ */
+ val sumWeights = sums.weights.sum
+
+ if (shouldDistributeGaussians) {
+ val numPartitions = math.min(numClusters, 1024)
+ val tuples = Seq.tabulate(numClusters) { i =>
+ (sums.means(i), sums.covs(i), sums.weights(i))
+ }
+ val (ws, gs) = sc.parallelize(tuples, numPartitions).map { case
(mean, cov, weight) =>
+ GaussianMixture.updateWeightsAndGaussians(mean, cov, weight,
sumWeights)
+ }.collect().unzip
+ Array.copy(ws.toArray, 0, weights, 0, ws.length)
+ Array.copy(gs.toArray, 0, gaussians, 0, gs.length)
+ } else {
+ var i = 0
+ while (i < numClusters) {
+ val (weight, gaussian) =
GaussianMixture.updateWeightsAndGaussians(
+ sums.means(i), sums.covs(i), sums.weights(i), sumWeights)
+ weights(i) = weight
+ gaussians(i) = gaussian
+ i += 1
+ }
+ }
+
+ logLikelihoodPrev = logLikelihood // current becomes previous
+ logLikelihood = sums.logLikelihood // this is the freshly computed
log-likelihood
+ iter += 1
}
- val model = copyValues(new GaussianMixtureModel(uid,
parentModel.weights, gaussians))
- .setParent(this)
+
+ val gaussianDists = gaussians.map { case (mean, covVec) =>
+ val cov = new DenseMatrix(numFeatures, numFeatures,
+ GaussianMixture.unpackUpperTriangularMatrix(numFeatures,
covVec.values))
+ new MultivariateGaussian(mean, cov)
+ }
+
+ val model = copyValues(new GaussianMixtureModel(uid, weights,
gaussianDists)).setParent(this)
--- End diff --
Below here, we call logNumFeatures. This isn't part of your PR, but could
you move it earlier since numFeatures is available before running the algorithm?
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