[SPARK-17744][ML] Parity check between the ml and mllib test suites for NB

## What changes were proposed in this pull request?
1,parity check and add missing test suites for ml's NB
2,remove some unused imports

## How was this patch tested?
 manual tests in spark-shell

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #15312 from zhengruifeng/nb_test_parity.

7 files changed, 70 insertions, 12 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala
index 6cefa7086c..7d8e4adcc2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala
@@ -25,7 +25,7 @@ import org.apache.spark.ml.linalg.Vector
 /**
  * :: Experimental ::
  *
- * Class that represents the features and labels of a data point.
+ * Class that represents the features and label of a data point.
  *
  * @param label Label for this data point.
  * @param features List of features for this data point.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala
index 1e59d71a70..05e034d90f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala
@@ -25,7 +25,7 @@ import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util._
 import org.apache.spark.sql.Dataset
-import org.apache.spark.sql.types.{DoubleType, StructType}
+import org.apache.spark.sql.types.StructType
 
 /**
  * Params for [[QuantileDiscretizer]].
diff --git a/mllib/src/main/scala/org/apache/spark/ml/python/MLSerDe.scala b/mllib/src/main/scala/org/apache/spark/ml/python/MLSerDe.scala
index 4b805e1454..da62f8518e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/python/MLSerDe.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/python/MLSerDe.scala
@@ -19,17 +19,12 @@ package org.apache.spark.ml.python
 
 import java.io.OutputStream
 import java.nio.{ByteBuffer, ByteOrder}
-import java.util.{ArrayList => JArrayList}
-
-import scala.collection.JavaConverters._
 
 import net.razorvine.pickle._
 
-import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.python.SerDeUtil
 import org.apache.spark.ml.linalg._
 import org.apache.spark.mllib.api.python.SerDeBase
-import org.apache.spark.rdd.RDD
 
 /**
  * SerDe utility functions for pyspark.ml.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index ce355938ec..bb01f9d5a3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -21,7 +21,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 import org.json4s.{DefaultFormats, JObject}
 import org.json4s.JsonDSL._
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.feature.LabeledPoint
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 7fddfd9b10..536c58f998 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -37,7 +37,6 @@ import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.evaluation.RegressionMetrics
-import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
 import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.util.MLUtils
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
index 597428d036..e934e5ea42 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
@@ -22,10 +22,10 @@ import scala.util.Random
 import breeze.linalg.{DenseVector => BDV, Vector => BV}
 import breeze.stats.distributions.{Multinomial => BrzMultinomial}
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.ml.classification.NaiveBayes.{Bernoulli, Multinomial}
 import org.apache.spark.ml.classification.NaiveBayesSuite._
-import org.apache.spark.ml.feature.{Instance, LabeledPoint}
+import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
@@ -106,6 +106,11 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with Defa
     }
   }
 
+  test("model types") {
+    assert(Multinomial === "multinomial")
+    assert(Bernoulli === "bernoulli")
+  }
+
   test("params") {
     ParamsSuite.checkParams(new NaiveBayes)
     val model = new NaiveBayesModel("nb", pi = Vectors.dense(Array(0.2, 0.8)),
@@ -228,6 +233,66 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with Defa
     validateProbabilities(featureAndProbabilities, model, "bernoulli")
   }
 
+  test("detect negative values") {
+    val dense = spark.createDataFrame(Seq(
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(0.0, Vectors.dense(-1.0)),
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(1.0, Vectors.dense(0.0))))
+    intercept[SparkException] {
+      new NaiveBayes().fit(dense)
+    }
+    val sparse = spark.createDataFrame(Seq(
+      LabeledPoint(1.0, Vectors.sparse(1, Array(0), Array(1.0))),
+      LabeledPoint(0.0, Vectors.sparse(1, Array(0), Array(-1.0))),
+      LabeledPoint(1.0, Vectors.sparse(1, Array(0), Array(1.0))),
+      LabeledPoint(1.0, Vectors.sparse(1, Array.empty, Array.empty))))
+    intercept[SparkException] {
+      new NaiveBayes().fit(sparse)
+    }
+    val nan = spark.createDataFrame(Seq(
+      LabeledPoint(1.0, Vectors.sparse(1, Array(0), Array(1.0))),
+      LabeledPoint(0.0, Vectors.sparse(1, Array(0), Array(Double.NaN))),
+      LabeledPoint(1.0, Vectors.sparse(1, Array(0), Array(1.0))),
+      LabeledPoint(1.0, Vectors.sparse(1, Array.empty, Array.empty))))
+    intercept[SparkException] {
+      new NaiveBayes().fit(nan)
+    }
+  }
+
+  test("detect non zero or one values in Bernoulli") {
+    val badTrain = spark.createDataFrame(Seq(
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(0.0, Vectors.dense(2.0)),
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(1.0, Vectors.dense(0.0))))
+
+    intercept[SparkException] {
+      new NaiveBayes().setModelType(Bernoulli).setSmoothing(1.0).fit(badTrain)
+    }
+
+    val okTrain = spark.createDataFrame(Seq(
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(0.0, Vectors.dense(0.0)),
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(0.0, Vectors.dense(0.0)),
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(1.0, Vectors.dense(1.0))))
+
+    val model = new NaiveBayes().setModelType(Bernoulli).setSmoothing(1.0).fit(okTrain)
+
+    val badPredict = spark.createDataFrame(Seq(
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(1.0, Vectors.dense(2.0)),
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(1.0, Vectors.dense(0.0))))
+
+    intercept[SparkException] {
+      model.transform(badPredict).collect()
+    }
+  }
+
   test("read/write") {
     def checkModelData(model: NaiveBayesModel, model2: NaiveBayesModel): Unit = {
       assert(model.pi === model2.pi)
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 505e7bffd1..ea60fab029 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -16,7 +16,6 @@
 #
 
 import operator
-import warnings
 
 from pyspark import since, keyword_only
 from pyspark.ml import Estimator, Model