[SPARK-9533] [PYSPARK] [ML] Add missing methods in Word2Vec ML

After https://github.com/apache/spark/pull/7263 it is pretty straightforward to Python wrappers.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7930 from MechCoder/spark-9533 and squashes the following commits:

1bea394 [MechCoder] make getVectors a lazy val
5522756 [MechCoder] [SPARK-9533] [PySpark] [ML] Add missing methods in Word2Vec ML

mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala

@@ -153,7 +153,7 @@ class Word2VecModel private[ml] (
    * Returns a dataframe with two fields, "word" and "vector", with "word" being a String and
    * and the vector the DenseVector that it is mapped to.
    */
-  val getVectors: DataFrame = {
+  @transient lazy val getVectors: DataFrame = {
     val sc = SparkContext.getOrCreate()
     val sqlContext = SQLContext.getOrCreate(sc)
     import sqlContext.implicits._

python/pyspark/ml/feature.py

@@ -15,11 +15,16 @@
 # limitations under the License.
 #
 
+import sys
+if sys.version > '3':
+    basestring = str
+
 from pyspark.rdd import ignore_unicode_prefix
 from pyspark.ml.param.shared import *
 from pyspark.ml.util import keyword_only
 from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer
 from pyspark.mllib.common import inherit_doc
+from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', 'OneHotEncoder',
            'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel',
@@ -954,6 +959,23 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has
     >>> sent = ("a b " * 100 + "a c " * 10).split(" ")
     >>> doc = sqlContext.createDataFrame([(sent,), (sent,)], ["sentence"])
     >>> model = Word2Vec(vectorSize=5, seed=42, inputCol="sentence", outputCol="model").fit(doc)
+    >>> model.getVectors().show()
+    +----+--------------------+
+    |word|              vector|
+    +----+--------------------+
+    |   a|[-0.3511952459812...|
+    |   b|[0.29077222943305...|
+    |   c|[0.02315592765808...|
+    +----+--------------------+
+    ...
+    >>> model.findSynonyms("a", 2).show()
+    +----+-------------------+
+    |word|         similarity|
+    +----+-------------------+
+    |   b|0.29255685145799626|
+    |   c|-0.5414068302988307|
+    +----+-------------------+
+    ...
     >>> model.transform(doc).head().model
     DenseVector([-0.0422, -0.5138, -0.2546, 0.6885, 0.276])
     """
@@ -1047,6 +1069,24 @@ class Word2VecModel(JavaModel):
     Model fitted by Word2Vec.
     """
 
+    def getVectors(self):
+        """
+        Returns the vector representation of the words as a dataframe
+        with two fields, word and vector.
+        """
+        return self._call_java("getVectors")
+
+    def findSynonyms(self, word, num):
+        """
+        Find "num" number of words closest in similarity to "word".
+        word can be a string or vector representation.
+        Returns a dataframe with two fields word and similarity (which
+        gives the cosine similarity).
+        """
+        if not isinstance(word, basestring):
+            word = _convert_to_vector(word)
+        return self._call_java("findSynonyms", word, num)
+
 
 @inherit_doc
 class PCA(JavaEstimator, HasInputCol, HasOutputCol):