SPARK-4156

examples/src/main/scala/org/apache/spark/examples/mllib/DenseGmmEM.scala

@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.clustering.GaussianMixtureModel
+import org.apache.spark.mllib.clustering.GMMExpectationMaximization
+import org.apache.spark.mllib.linalg.Vectors
+
+object DenseGmmEM {
+  def main(args: Array[String]): Unit = {
+    if( args.length != 3 ) {
+      println("usage: DenseGmmEM <input file> <k> <delta>")
+    } else {
+      run(args(0), args(1).toInt, args(2).toDouble)
+    }
+  }
+
+  def run(inputFile: String, k: Int, tol: Double) {
+    val conf = new SparkConf().setAppName("Spark EM Sample")
+    val ctx  = new SparkContext(conf)
+
+    val data = ctx.textFile(inputFile).map(line =>
+        Vectors.dense(line.trim.split(' ').map(_.toDouble))).cache()
+
+    val clusters = GMMExpectationMaximization.train(data, k)
+
+    for(i <- 0 until clusters.k) {
+      println("w=%f mu=%s sigma=\n%s\n" format (clusters.w(i), clusters.mu(i), clusters.sigma(i)))
+    }
+  }
+}

mllib/src/main/scala/org/apache/spark/mllib/clustering/GMMExpectationMaximization.scala

@@ -0,0 +1,246 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.clustering
+
+import breeze.linalg.{DenseVector => BreezeVector, DenseMatrix => BreezeMatrix}
+import breeze.linalg.{Transpose, det, inv}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Matrices, Vector, Vectors}
+import org.apache.spark.{Accumulator, AccumulatorParam, SparkContext}
+import org.apache.spark.SparkContext.DoubleAccumulatorParam
+
+/**
+ * Expectation-Maximization for multivariate Gaussian Mixture Models.
+ * 
+ */
+object GMMExpectationMaximization {
+  /**
+   * Trains a GMM using the given parameters
+   * 
+   * @param data training points stores as RDD[Vector]
+   * @param k the number of Gaussians in the mixture
+   * @param maxIterations the maximum number of iterations to perform
+   * @param delta change in log-likelihood at which convergence is considered achieved
+   */
+  def train(data: RDD[Vector], k: Int, maxIterations: Int, delta: Double): GaussianMixtureModel = {
+    new GMMExpectationMaximization().setK(k)
+      .setMaxIterations(maxIterations)
+      .setDelta(delta)
+      .run(data)
+  }
+
+  /**
+   * Trains a GMM using the given parameters
+   * 
+   * @param data training points stores as RDD[Vector]
+   * @param k the number of Gaussians in the mixture
+   * @param maxIterations the maximum number of iterations to perform
+   */
+  def train(data: RDD[Vector], k: Int, maxIterations: Int): GaussianMixtureModel = {
+    new GMMExpectationMaximization().setK(k).setMaxIterations(maxIterations).run(data)
+  }
+
+  /**
+   * Trains a GMM using the given parameters
+   * 
+   * @param data training points stores as RDD[Vector]
+   * @param k the number of Gaussians in the mixture
+   */
+  def train(data: RDD[Vector], k: Int): GaussianMixtureModel = {
+    new GMMExpectationMaximization().setK(k).run(data)
+  }
+}
+
+/**
+ * This class performs multivariate Gaussian expectation maximization.  It will 
+ * maximize the log-likelihood for a mixture of k Gaussians, iterating until
+ * the log-likelihood changes by less than delta, or until it has reached
+ * the max number of iterations.  
+ */
+class GMMExpectationMaximization private (
+    private var k: Int, 
+    private var delta: Double, 
+    private var maxIterations: Int) extends Serializable {
+
+  // Type aliases for convenience
+  private type DenseDoubleVector = BreezeVector[Double]
+  private type DenseDoubleMatrix = BreezeMatrix[Double]
+
+  // A default instance, 2 Gaussians, 100 iterations, 0.01 log-likelihood threshold
+  def this() = this(2, 0.01, 100)
+
+  /** Set the number of Gaussians in the mixture model.  Default: 2 */
+  def setK(k: Int): this.type = {
+    this.k = k
+    this
+  }
+
+  /** Set the maximum number of iterations to run. Default: 100 */
+  def setMaxIterations(maxIterations: Int): this.type = {
+    this.maxIterations = maxIterations
+    this
+  }
+
+  /**
+   * Set the largest change in log-likelihood at which convergence is 
+   * considered to have occurred.
+   */
+  def setDelta(delta: Double): this.type = {
+    this.delta = delta
+    this
+  }
+
+  /** Machine precision value used to ensure matrix conditioning */
+  private val eps = math.pow(2.0, -52)
+
+  /** Perform expectation maximization */
+  def run(data: RDD[Vector]): GaussianMixtureModel = {
+    val ctx = data.sparkContext
+
+    // we will operate on the data as breeze data
+    val breezeData = data.map{ u => u.toBreeze.toDenseVector }.cache()
+
+    // Get length of the input vectors
+    val d = breezeData.first.length 
+
+    // For each Gaussian, we will initialize the mean as some random
+    // point from the data.  (This could be improved)
+    val samples = breezeData.takeSample(true, k, scala.util.Random.nextInt)
+
+    // C will be array of (weight, mean, covariance) tuples
+    // we start with uniform weights, a random mean from the data, and
+    // identity matrices for covariance 
+    var C = (0 until k).map(i => (1.0/k, 
+                                  samples(i), 
+                                  BreezeMatrix.eye[Double](d))).toArray
+
+    val acc_w     = new Array[Accumulator[Double]](k)
+    val acc_mu    = new Array[Accumulator[DenseDoubleVector]](k)
+    val acc_sigma = new Array[Accumulator[DenseDoubleMatrix]](k)
+
+    var llh = Double.MinValue // current log-likelihood 
+    var llhp = 0.0            // previous log-likelihood
+
+    var i, iter = 0
+    do {
+      // reset accumulators
+      for(i <- 0 until k){
+        acc_w(i)     = ctx.accumulator(0.0)
+        acc_mu(i)    = ctx.accumulator(
+                      BreezeVector.zeros[Double](d))(DenseDoubleVectorAccumulatorParam)
+        acc_sigma(i) = ctx.accumulator(
+                      BreezeMatrix.zeros[Double](d,d))(DenseDoubleMatrixAccumulatorParam)
+      }
+
+      val log_likelihood = ctx.accumulator(0.0)
+
+      // broadcast the current weights and distributions to all nodes
+      val dists = ctx.broadcast((0 until k).map(i => 
+                                  new MultivariateGaussian(C(i)._2, C(i)._3)).toArray)
+      val weights = ctx.broadcast((0 until k).map(i => C(i)._1).toArray)
+
+      // calculate partial assignments for each sample in the data
+      // (often referred to as the "E" step in literature)
+      breezeData.foreach(x => {  
+        val p = (0 until k).map(i => 
+          eps + weights.value(i) * dists.value(i).pdf(x)).toArray
+        val norm = sum(p)
+
+        log_likelihood += math.log(norm)  
+
+        // accumulate weighted sums  
+        for(i <- 0 until k){
+          p(i) /= norm
+          acc_w(i) += p(i)
+          acc_mu(i) += x * p(i)
+          acc_sigma(i) += x * new Transpose(x) * p(i)
+        }  
+      })
+
+      // Collect the computed sums
+      val W = (0 until k).map(i => acc_w(i).value).toArray
+      val MU = (0 until k).map(i => acc_mu(i).value).toArray
+      val SIGMA = (0 until k).map(i => acc_sigma(i).value).toArray
+
+      // Create new distributions based on the partial assignments
+      // (often referred to as the "M" step in literature)
+      C = (0 until k).map(i => {
+            val weight = W(i) / sum(W)
+            val mu = MU(i) / W(i)
+            val sigma = SIGMA(i) / W(i) - mu * new Transpose(mu)
+            (weight, mu, sigma)
+          }).toArray
+
+      llhp = llh; // current becomes previous
+      llh = log_likelihood.value // this is the freshly computed log-likelihood
+      iter += 1
+    } while(iter < maxIterations && Math.abs(llh-llhp) > delta)
+
+    // Need to convert the breeze matrices to MLlib matrices
+    val weights = (0 until k).map(i => C(i)._1).toArray
+    val means   = (0 until k).map(i => Vectors.fromBreeze(C(i)._2)).toArray
+    val sigmas  = (0 until k).map(i => Matrices.fromBreeze(C(i)._3)).toArray
+    new GaussianMixtureModel(weights, means, sigmas)
+  }
+
+  /** Sum the values in array of doubles */
+  private def sum(x : Array[Double]) : Double = {
+    var s : Double = 0.0
+    x.foreach(u => s += u)
+    s
+  }
+
+  /** AccumulatorParam for Dense Breeze Vectors */
+  private object DenseDoubleVectorAccumulatorParam extends AccumulatorParam[DenseDoubleVector] {
+    def zero(initialVector : DenseDoubleVector) : DenseDoubleVector = {
+      BreezeVector.zeros[Double](initialVector.length)
+    }
+
+    def addInPlace(a : DenseDoubleVector, b : DenseDoubleVector) : DenseDoubleVector = {
+      a += b
+    }
+  }
+
+  /** AccumulatorParam for Dense Breeze Matrices */
+  private object DenseDoubleMatrixAccumulatorParam extends AccumulatorParam[DenseDoubleMatrix] {
+    def zero(initialVector : DenseDoubleMatrix) : DenseDoubleMatrix = {
+      BreezeMatrix.zeros[Double](initialVector.rows, initialVector.cols)
+    }
+
+    def addInPlace(a : DenseDoubleMatrix, b : DenseDoubleMatrix) : DenseDoubleMatrix = {
+      a += b
+    }
+  }  
+
+  /** 
+   * Utility class to implement the density function for multivariate Gaussian distribution.
+   * Breeze provides this functionality, but it requires the Apache Commons Math library,
+   * so this class is here so-as to not introduce a new dependency in Spark.
+   */
+  private class MultivariateGaussian(val mu : DenseDoubleVector, val sigma : DenseDoubleMatrix) 
+      extends Serializable {
+    private val sigma_inv_2 = inv(sigma) * -0.5
+    private val U = math.pow(2.0*math.Pi, -mu.length/2.0) * math.pow(det(sigma), -0.5)
+
+    def pdf(x : DenseDoubleVector) : Double = {
+      val delta = x - mu
+      val delta_t = new Transpose(delta)
+      U * math.exp(delta_t * sigma_inv_2 * delta)
+    }
+  }
+}

mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala

@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.clustering
+
+import org.apache.spark.mllib.linalg.Matrix
+import org.apache.spark.mllib.linalg.Vector
+
+/**
+ * Multivariate Gaussian mixture model consisting of k Gaussians, where points are drawn 
+ * from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are the respective 
+ * mean and covariance for each Gaussian distribution i=1..k. 
+ */
+class GaussianMixtureModel(val w: Array[Double], val mu: Array[Vector], val sigma: Array[Matrix]) {
+
+  /** Number of gaussians in mixture */
+  def k: Int = w.length;
+}