[SPARK-14434][ML]:User guide doc and examples for GaussianMixture in spark.ml

docs/ml-clustering.md

@@ -148,3 +148,85 @@ Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.
 {% include_example python/ml/bisecting_k_means_example.py %}
 </div>
 </div>
+
+## Gaussian Mixture Model (GMM)
+
+A [Gaussian Mixture Model](http://en.wikipedia.org/wiki/Mixture_model#Multivariate_Gaussian_mixture_model)
+represents a composite distribution whereby points are drawn from one of *k* Gaussian sub-distributions,
+each with its own probability. The `spark.ml` implementation uses the
+[expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
+algorithm to induce the maximum-likelihood model given a set of samples.
+
+`GaussianMixture` is implemented as an `Estimator` and generates a `GaussianMixtureModel` as the base
+model.
+
+### Input Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>featuresCol</td>
+      <td>Vector</td>
+      <td>"features"</td>
+      <td>Feature vector</td>
+    </tr>
+  </tbody>
+</table>
+
+### Output Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>predictionCol</td>
+      <td>Int</td>
+      <td>"prediction"</td>
+      <td>Predicted cluster center</td>
+    </tr>
+    <tr>
+      <td>probabilityCol</td>
+      <td>Vector</td>
+      <td>"probability"</td>
+      <td>Probability of each cluster</td>
+    </tr>
+  </tbody>
+</table>
+
+### Example
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.GaussianMixture) for more details.
+
+{% include_example scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala %}
+</div>
+
+<div data-lang="java" markdown="1">
+Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/GaussianMixture.html) for more details.
+
+{% include_example java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java %}
+</div>
+
+<div data-lang="python" markdown="1">
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.GaussianMixture) for more details.
+
+{% include_example python/ml/gaussian_mixture_example.py %}
+</div>
+</div>

examples/src/main/java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java

@@ -0,0 +1,64 @@
+/*
+ * 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.ml;
+
+// $example on$
+import org.apache.spark.ml.clustering.GaussianMixture;
+import org.apache.spark.ml.clustering.GaussianMixtureModel;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+// $example off$
+import org.apache.spark.sql.SparkSession;
+
+
+/**
+ * An example demonstrating Gaussian Mixture Model.
+ * Run with
+ * <pre>
+ * bin/run-example ml.JavaGaussianMixtureExample
+ * </pre>
+ */
+public class JavaGaussianMixtureExample {
+
+  public static void main(String[] args) {
+
+    // Creates a SparkSession 
+    SparkSession spark = SparkSession
+            .builder()
+            .appName("JavaGaussianMixtureExample")
+            .getOrCreate();
+
+    // $example on$
+    // Loads data
+    Dataset<Row> dataset = spark.read().format("libsvm").load("data/mllib/sample_kmeans_data.txt");
+
+    // Trains a GaussianMixture model
+    GaussianMixture gmm = new GaussianMixture()
+      .setK(2);
+    GaussianMixtureModel model = gmm.fit(dataset);
+
+    // Output the parameters of the mixture model
+    for (int i = 0; i < model.getK(); i++) {
+      System.out.printf("weight=%f\nmu=%s\nsigma=\n%s\n",
+              model.weights()[i], model.gaussians()[i].mean(), model.gaussians()[i].cov());
+    }
+    // $example off$
+
+    spark.stop();
+  }
+}

examples/src/main/python/ml/gaussian_mixture_example.py

@@ -0,0 +1,48 @@
+#
+# 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.
+#
+
+from __future__ import print_function
+
+# $example on$
+from pyspark.ml.clustering import GaussianMixture
+# $example off$
+from pyspark.sql import SparkSession
+
+"""
+A simple example demonstrating Gaussian Mixture Model (GMM).
+Run with:
+  bin/spark-submit examples/src/main/python/ml/gaussian_mixture_example.py
+"""
+
+if __name__ == "__main__":
+    spark = SparkSession\
+        .builder\
+        .appName("PythonGuassianMixtureExample")\
+        .getOrCreate()
+
+    # $example on$
+    # loads data
+    dataset = spark.read.format("libsvm").load("data/mllib/sample_kmeans_data.txt")
+
+    gmm = GaussianMixture().setK(2)
+    model = gmm.fit(dataset)
+
+    print("Gaussians: ")
+    model.gaussiansDF.show()
+    # $example off$
+
+    spark.stop()

examples/src/main/scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala

@@ -0,0 +1,58 @@
+/*
+ * 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.ml
+
+// scalastyle:off println
+
+// $example on$
+import org.apache.spark.ml.clustering.GaussianMixture
+import org.apache.spark.sql.SparkSession
+// $example off$
+
+/**
+ * An example demonstrating Gaussian Mixture Model (GMM).
+ * Run with
+ * {{{
+ * bin/run-example ml.GaussianMixtureExample
+ * }}}
+ */
+object GaussianMixtureExample {
+  def main(args: Array[String]): Unit = {
+    // Creates a SparkSession
+    val spark = SparkSession.builder.appName(s"${this.getClass.getSimpleName}").getOrCreate()
+
+    // $example on$
+    // Loads data
+    val dataset = spark.read.format("libsvm").load("data/mllib/sample_kmeans_data.txt")
+
+    // Trains Gaussian Mixture Model
+    val gmm = new GaussianMixture()
+      .setK(2)
+    val model = gmm.fit(dataset)
+
+    // output parameters of mixture model model
+    for (i <- 0 until model.getK) {
+      println("weight=%f\nmu=%s\nsigma=\n%s\n" format
+        (model.weights(i), model.gaussians(i).mean, model.gaussians(i).cov))
+    }
+    // $example off$
+
+    spark.stop()
+  }
+}
+// scalastyle:on println