Added save/load for NaiveBayes

mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala

@@ -147,6 +147,7 @@ class LogisticRegressionModel (
       clazz = this.getClass.getName, version = Exportable.latestVersion)
     val metadataRDD: DataFrame = sc.parallelize(Seq(metadata))
     metadataRDD.toJSON.saveAsTextFile(path + "/metadata")
+
     // Create Parquet data.
     val data = LogisticRegressionModel.Data(weights, intercept, threshold)
     val dataRDD: DataFrame = sc.parallelize(Seq(data))

mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala

@@ -18,6 +18,8 @@
 package org.apache.spark.mllib.classification
 
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum}
+import org.apache.spark.sql.catalyst.ScalaReflection
+import org.apache.spark.sql.types.{ArrayType, DataType, DoubleType, StructField, StructType}
 
 import org.apache.spark.{SparkContext, SparkException, Logging}
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector}
@@ -79,6 +81,7 @@ class NaiveBayesModel private[mllib] (
       clazz = this.getClass.getName, version = Exportable.latestVersion)
     val metadataRDD: DataFrame = sc.parallelize(Seq(metadata))
     metadataRDD.toJSON.saveAsTextFile(path + "/metadata")
+
     // Create Parquet data.
     val data = NaiveBayesModel.Data(labels, pi, theta)
     val dataRDD: DataFrame = sc.parallelize(Seq(data))
@@ -117,11 +120,12 @@ object NaiveBayesModel extends Importable[NaiveBayesModel] {
     assert(dataArray.size == 1, s"Unable to load NaiveBayesModel data from: ${path + "/data"}")
     val data = dataArray(0)
     assert(data.size == 3, s"Unable to load NaiveBayesModel data from: ${path + "/data"}")
-    val nb = data match {
-      case Row(labels: Seq[Double], pi: Seq[Double], theta: Seq[Seq[Double]]) =>
-        new NaiveBayesModel(labels.toArray, pi.toArray, theta.map(_.toArray).toArray)
-    }
-    nb
+    // Check schema explicitly since erasure makes it hard to use match-case for checking.
+    Importable.checkSchema[Data](dataRDD.schema)
+    val labels = data.getAs[Seq[Double]](0).toArray
+    val pi = data.getAs[Seq[Double]](1).toArray
+    val theta = data.getAs[Seq[Seq[Double]]](2).map(_.toArray).toArray
+    new NaiveBayesModel(labels, pi, theta)
   }
 }
 

mllib/src/main/scala/org/apache/spark/mllib/util/modelImportExport.scala

@@ -17,8 +17,13 @@
 
 package org.apache.spark.mllib.util
 
+import scala.reflect.runtime.universe.TypeTag
+
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.sql.catalyst.ScalaReflection
+import org.apache.spark.sql.types.{DataType, StructType, StructField}
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
 
 /**
  * :: DeveloperApi ::
@@ -46,11 +51,7 @@ trait Exportable {
 
 }
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
-object Exportable {
+private[mllib] object Exportable {
 
   /** Current version of model import/export format. */
   val latestVersion: String = "1.0"
@@ -79,34 +80,32 @@ trait Importable[Model <: Exportable] {
 
 }
 
-/*
-/**
- * :: DeveloperApi ::
- *
- * Trait for models and transformers which may be saved as files.
- * This should be inherited by the class which implements model instances.
- *
- * This specializes [[Exportable]] for local models which can be stored on a single machine.
- * This provides helper functionality, but developers can choose to use [[Exportable]] instead,
- * even for local models.
- */
-@DeveloperApi
-trait LocalExportable {
+private[mllib] object Importable {
 
   /**
-   * Save this model to the given path.
-   *
-   * This saves:
-   *  - human-readable (JSON) model metadata to path/metadata/
-   *  - Parquet formatted data to path/data/
+   * Check the schema of loaded model data.
    *
-   * The model may be loaded using [[Importable.load]].
+   * This checks every field in the expected schema to make sure that a field with the same
+   * name and DataType appears in the loaded schema.  Note that this does NOT check metadata
+   * or containsNull.
    *
-   * @param sc  Spark context used to save model data.
-   * @param path  Path specifying the directory in which to save this model.
-   *              This directory and any intermediate directory will be created if needed.
+   * @param loadedSchema  Schema for model data loaded from file.
+   * @tparam Data  Expected data type from which an expected schema can be derived.
    */
-  def save(sc: SparkContext, path: String): Unit
+  def checkSchema[Data: TypeTag](loadedSchema: StructType): Unit = {
+    // Check schema explicitly since erasure makes it hard to use match-case for checking.
+    val expectedFields: Array[StructField] =
+      ScalaReflection.schemaFor[Data].dataType.asInstanceOf[StructType].fields
+    val loadedFields: Map[String, DataType] =
+      loadedSchema.map(field => field.name -> field.dataType).toMap
+    expectedFields.foreach { field =>
+      assert(loadedFields.contains(field.name), s"Unable to parse model data." +
+        s"  Expected field with name ${field.name} was missing in loaded schema:" +
+        s" ${loadedFields.mkString(", ")}")
+      assert(loadedFields(field.name) == field.dataType,
+        s"Unable to parse model data.  Expected field $field but found field" +
+          s" with different type: ${loadedFields(field.name)}")
+    }
+  }
 
 }
-*/