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[SPARK-7104][MLlib] Support model save/load in Python's Word2Vec #6821

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[SPARK-7104][MLlib] Support model save/load in Python's Word2Vec #6821

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1d468ef
[SPARK-7104][MLlib] Support model save/load in Python's Word2Vec
yu-iskw Jun 15, 2015
cb21653
Add an explicit type for `Word2VecModelWrapper.save`
yu-iskw Jun 15, 2015
0ef58b6
Use rmtree, instead of removedirs
yu-iskw Jul 2, 2015
975136b
Organize import
yu-iskw Jul 2, 2015
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3 changes: 3 additions & 0 deletions mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
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Expand Up @@ -28,6 +28,7 @@ import scala.reflect.ClassTag

import net.razorvine.pickle._

import org.apache.spark.SparkContext
import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
import org.apache.spark.api.python.SerDeUtil
import org.apache.spark.mllib.classification._
Expand Down Expand Up @@ -641,6 +642,8 @@ private[python] class PythonMLLibAPI extends Serializable {
def getVectors: JMap[String, JList[Float]] = {
model.getVectors.map({case (k, v) => (k, v.toList.asJava)}).asJava
}

def save(sc: SparkContext, path: String): Unit = model.save(sc, path)
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Is this necessary to do? Isn't the wrapper written in JavaSaveable sufficient?

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Ah I see that _java_model is not Word2VecModel but a wrapper to it. Sorry about this comment.

}

/**
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21 changes: 20 additions & 1 deletion python/pyspark/mllib/feature.py
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Expand Up @@ -36,6 +36,7 @@
from pyspark.mllib.linalg import (
Vector, Vectors, DenseVector, SparseVector, _convert_to_vector)
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.util import JavaLoader, JavaSaveable

__all__ = ['Normalizer', 'StandardScalerModel', 'StandardScaler',
'HashingTF', 'IDFModel', 'IDF', 'Word2Vec', 'Word2VecModel',
Expand Down Expand Up @@ -416,7 +417,7 @@ def fit(self, dataset):
return IDFModel(jmodel)


class Word2VecModel(JavaVectorTransformer):
class Word2VecModel(JavaVectorTransformer, JavaSaveable, JavaLoader):
"""
class for Word2Vec model
"""
Expand Down Expand Up @@ -455,6 +456,12 @@ def getVectors(self):
"""
return self.call("getVectors")

@classmethod
def load(cls, sc, path):
jmodel = sc._jvm.org.apache.spark.mllib.feature \
.Word2VecModel.load(sc._jsc.sc(), path)
return Word2VecModel(jmodel)


@ignore_unicode_prefix
class Word2Vec(object):
Expand Down Expand Up @@ -488,6 +495,18 @@ class Word2Vec(object):
>>> syms = model.findSynonyms(vec, 2)
>>> [s[0] for s in syms]
[u'b', u'c']

>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> model.save(sc, path)
>>> sameModel = Word2VecModel.load(sc, path)
>>> model.transform("a") == sameModel.transform("a")
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It has been advised by @mengxr to keep doctests and testing for correctness separately. (#6499 (comment))
It might be better to just write sameModel.transform("a") and write a small test to test model and sameModel give same results using getVectors maybe?

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What I meant was this test is not for correctness. I wanted to check whether a saved model works or not.

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Sure, but I think it would be better to just write
sameModel.transform(a) separately and the output.

However, I have no objections either !

True
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except OSError:
... pass
"""
def __init__(self):
"""
Expand Down