Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/2819#discussion_r18932968
--- Diff: python/pyspark/mllib/feature.py ---
@@ -95,90 +360,46 @@ class Word2Vec(object):
>>> sentence = "a b " * 100 + "a c " * 10
>>> localDoc = [sentence, sentence]
>>> doc = sc.parallelize(localDoc).map(lambda line: line.split(" "))
- >>> model = Word2Vec().setVectorSize(10).setSeed(42L).fit(doc)
+ >>> model = Word2Vec(vectorSize=10).fit(doc)
+
>>> syms = model.findSynonyms("a", 2)
- >>> str(syms[0][0])
- 'b'
- >>> str(syms[1][0])
- 'c'
- >>> len(syms)
- 2
+ >>> [s[0] for s in syms]
+ [u'b', u'c']
>>> vec = model.transform("a")
- >>> len(vec)
- 10
>>> syms = model.findSynonyms(vec, 2)
- >>> str(syms[0][0])
- 'b'
- >>> str(syms[1][0])
- 'c'
- >>> len(syms)
- 2
+ >>> [s[0] for s in syms]
+ [u'b', u'c']
"""
- def __init__(self):
+ def __init__(self, vectorSize=100, learningRate=0.025, numPartitions=1,
+ numIterations=1, seed=42L):
"""
Construct Word2Vec instance
- """
- self.vectorSize = 100
- self.learningRate = 0.025
- self.numPartitions = 1
- self.numIterations = 1
- self.seed = 42L
- def setVectorSize(self, vectorSize):
- """
- Sets vector size (default: 100).
+ :param vectorSize: vector size (default: 100).
+ :param learningRate: initial learning rate (default: 0.025).
+ :param numPartitions: number of partitions (default: 1). Use
+ a small number for accuracy.
+ :param numIterations: number of iterations (default: 1), which
should
+ be smaller than or equal to number of
partitions.
"""
--- End diff --
It's good to have same interface crossing languages, but sometimes it looks
wired to having the API that is designed for Java.
I'd like to simply the Python API a little bit (without introducing
confusing), then Python programmer can feel better (in this case). We can find
several similar cases in APIs of pyspark.RDD.
Does it make sense?
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