diff options
5 files changed, 37 insertions, 14 deletions
diff --git a/examples/src/main/python/mllib/gaussian_mixture_model.py b/examples/src/main/python/mllib/gaussian_mixture_model.py index 2cb8010cdc..69e836fc1d 100644 --- a/examples/src/main/python/mllib/gaussian_mixture_model.py +++ b/examples/src/main/python/mllib/gaussian_mixture_model.py @@ -62,5 +62,9 @@ if __name__ == "__main__": for i in range(args.k): print(("weight = ", model.weights[i], "mu = ", model.gaussians[i].mu, "sigma = ", model.gaussians[i].sigma.toArray())) + print("\n") + print(("The membership value of each vector to all mixture components (first 100): ", + model.predictSoft(data).take(100))) + print("\n") print(("Cluster labels (first 100): ", model.predict(data).take(100))) sc.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala index 1fce4ba7ef..90b817b23e 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala @@ -58,6 +58,12 @@ object DenseGaussianMixture { (clusters.weights(i), clusters.gaussians(i).mu, clusters.gaussians(i).sigma)) } + println("The membership value of each vector to all mixture components (first <= 100):") + val membership = clusters.predictSoft(data) + membership.take(100).foreach { x => + print(" " + x.mkString(",")) + } + println() println("Cluster labels (first <= 100):") val clusterLabels = clusters.predict(data) clusterLabels.take(100).foreach { x => diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala index 6a3b20c88d..a689b09341 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala @@ -40,5 +40,9 @@ private[python] class GaussianMixtureModelWrapper(model: GaussianMixtureModel) { SerDe.dumps(JavaConverters.seqAsJavaListConverter(modelGaussians).asJava) } + def predictSoft(point: Vector): Vector = { + Vectors.dense(model.predictSoft(point)) + } + def save(sc: SparkContext, path: String): Unit = model.save(sc, path) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala index 16bc45bcb6..42fe27024f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala @@ -75,7 +75,7 @@ class GaussianMixtureModel @Since("1.3.0") ( */ @Since("1.5.0") def predict(point: Vector): Int = { - val r = computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k) + val r = predictSoft(point) r.indexOf(r.max) } diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index d22a7f4c3b..580cb512d8 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -202,16 +202,25 @@ class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader): >>> clusterdata_1 = sc.parallelize(array([-0.1,-0.05,-0.01,-0.1, ... 0.9,0.8,0.75,0.935, - ... -0.83,-0.68,-0.91,-0.76 ]).reshape(6, 2)) + ... -0.83,-0.68,-0.91,-0.76 ]).reshape(6, 2), 2) >>> model = GaussianMixture.train(clusterdata_1, 3, convergenceTol=0.0001, ... maxIterations=50, seed=10) >>> labels = model.predict(clusterdata_1).collect() >>> labels[0]==labels[1] False >>> labels[1]==labels[2] - True + False >>> labels[4]==labels[5] True + >>> model.predict([-0.1,-0.05]) + 0 + >>> softPredicted = model.predictSoft([-0.1,-0.05]) + >>> abs(softPredicted[0] - 1.0) < 0.001 + True + >>> abs(softPredicted[1] - 0.0) < 0.001 + True + >>> abs(softPredicted[2] - 0.0) < 0.001 + True >>> path = tempfile.mkdtemp() >>> model.save(sc, path) @@ -277,26 +286,27 @@ class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader): @since('1.3.0') def predict(self, x): """ - Find the cluster to which the points in 'x' has maximum membership - in this model. + Find the cluster to which the point 'x' or each point in RDD 'x' + has maximum membership in this model. - :param x: RDD of data points. - :return: cluster_labels. RDD of cluster labels. + :param x: vector or RDD of vector represents data points. + :return: cluster label or RDD of cluster labels. """ if isinstance(x, RDD): cluster_labels = self.predictSoft(x).map(lambda z: z.index(max(z))) return cluster_labels else: - raise TypeError("x should be represented by an RDD, " - "but got %s." % type(x)) + z = self.predictSoft(x) + return z.argmax() @since('1.3.0') def predictSoft(self, x): """ - Find the membership of each point in 'x' to all mixture components. + Find the membership of point 'x' or each point in RDD 'x' to all mixture components. - :param x: RDD of data points. - :return: membership_matrix. RDD of array of double values. + :param x: vector or RDD of vector represents data points. + :return: the membership value to all mixture components for vector 'x' + or each vector in RDD 'x'. """ if isinstance(x, RDD): means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians]) @@ -304,8 +314,7 @@ class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader): _convert_to_vector(self.weights), means, sigmas) return membership_matrix.map(lambda x: pyarray.array('d', x)) else: - raise TypeError("x should be represented by an RDD, " - "but got %s." % type(x)) + return self.call("predictSoft", _convert_to_vector(x)).toArray() @classmethod @since('1.5.0') |