[SPARK-13089][ML] [Doc] spark.ml Naive Bayes user guide and examples

docs/ml-classification-regression.md

@@ -302,6 +302,40 @@ Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/OneVsRe
 </div>
 </div>
 
+## Naive Bayes
+
+[Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) are a family of simple 
+probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence 
+assumptions between the features. The spark.ml implementation currently supports both [multinomial
+naive Bayes](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html)
+and [Bernoulli naive Bayes](http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html).
+More information can be found in the section on [Naive Bayes in MLlib](mllib-naive-bayes.html#naive-bayes-sparkmllib).
+
+**Example**
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.NaiveBayes) for more details.
+
+{% include_example scala/org/apache/spark/examples/ml/NaiveBayesExample.scala %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/NaiveBayes.html) for more details.
+
+{% include_example java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.NaiveBayes) for more details.
+
+{% include_example python/ml/naive_bayes_example.py %}
+</div>
+</div>
+
 
 # Regression
 

examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.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;
+
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaSparkContext;
+// $example on$
+import org.apache.spark.ml.classification.NaiveBayes;
+import org.apache.spark.ml.classification.NaiveBayesModel;
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
+// $example off$
+
+/**
+ * An example for Naive Bayes Classification.
+ */
+public class JavaNaiveBayesExample {
+
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("JavaNaiveBayesExample");
+    JavaSparkContext jsc = new JavaSparkContext(conf);
+    SQLContext jsql = new SQLContext(jsc);
+
+    // $example on$
+    // Load training data
+    Dataset<Row> dataFrame = jsql.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
+    // Split the data into train and test
+    Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
+    Dataset<Row> train = splits[0];
+    Dataset<Row> test = splits[1];
+
+    // create the trainer and set its parameters
+    NaiveBayes nb = new NaiveBayes();
+    // train the model
+    NaiveBayesModel model = nb.fit(train);
+    // compute precision on the test set
+    Dataset<Row> result = model.transform(test);
+    Dataset<Row> predictionAndLabels = result.select("prediction", "label");
+    MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
+      .setMetricName("precision");
+    System.out.println("Precision = " + evaluator.evaluate(predictionAndLabels));
+    // $example off$
+
+    jsc.stop();
+  }
+}

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

@@ -0,0 +1,53 @@
+#
+# 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
+
+from pyspark import SparkContext
+from pyspark.sql import SQLContext
+# $example on$
+from pyspark.ml.classification import NaiveBayes
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+# $example off$
+
+if __name__ == "__main__":
+
+    sc = SparkContext(appName="naive_bayes_example")
+    sqlContext = SQLContext(sc)
+
+    # $example on$
+    # Load training data
+    data = sqlContext.read.format("libsvm") \
+        .load("data/mllib/sample_libsvm_data.txt")
+    # Split the data into train and test
+    splits = data.randomSplit([0.6, 0.4], 1234)
+    train = splits[0]
+    test = splits[1]
+
+    # create the trainer and set its parameters
+    nb = NaiveBayes(smoothing=1.0, modelType="multinomial")
+
+    # train the model
+    model = nb.fit(train)
+    # compute precision on the test set
+    result = model.transform(test)
+    predictionAndLabels = result.select("prediction", "label")
+    evaluator = MulticlassClassificationEvaluator(metricName="precision")
+    print("Precision:" + str(evaluator.evaluate(predictionAndLabels)))
+    # $example off$
+
+    sc.stop()

examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.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.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.ml
+
+import org.apache.spark.{SparkConf, SparkContext}
+// $example on$
+import org.apache.spark.ml.classification.{NaiveBayes}
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+// $example off$
+import org.apache.spark.sql.SQLContext
+
+object NaiveBayesExample {
+  def main(args: Array[String]): Unit = {
+    val conf = new SparkConf().setAppName("NaiveBayesExample")
+    val sc = new SparkContext(conf)
+    val sqlContext = new SQLContext(sc)
+    // $example on$
+    // Load the data stored in LIBSVM format as a DataFrame.
+    val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
+
+    // Split the data into training and test sets (30% held out for testing)
+    val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+    // Train a NaiveBayes model.
+    val model = new NaiveBayes()
+      .fit(trainingData)
+
+    // Select example rows to display.
+    val predictions = model.transform(testData)
+    predictions.show()
+
+    // Select (prediction, true label) and compute test error
+    val evaluator = new MulticlassClassificationEvaluator()
+      .setLabelCol("label")
+      .setPredictionCol("prediction")
+      .setMetricName("precision")
+    val precision = evaluator.evaluate(predictions)
+    println("Precision:" + precision)
+    // $example off$
+  }
+}
+// scalastyle:on println