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[SPARK-15009][PYTHON][ML] Construct a CountVectorizerModel from a vocabulary list #16770

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[SPARK-15009][PYTHON][ML] Construct a CountVectorizerModel from a vocabulary list #16770

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01e5a4b
Added class method to construct CountVectorizerModel from vocab, not …
BryanCutler Feb 1, 2017
e94dde3
updated CountVectorizerModel to use common param base class
BryanCutler Mar 6, 2018
8860641
Added exception for checking default values of param base classes
BryanCutler Mar 6, 2018
5220ff1
use explicity vocab list in doc test, add test for empty vocab and us…
BryanCutler Mar 14, 2018
7e05da4
select default col in test
BryanCutler Mar 14, 2018
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Added class method to construct CountVectorizerModel from vocab, not … 
…yet working because missing param _copyValues from estimator to model
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@BryanCutler
BryanCutler committed Mar 5, 2018
commit 01e5a4bf8e7951e8fe2a5cc80bcd856ba2da7ee8
31 changes: 29 additions & 2 deletions python/pyspark/ml/feature.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
if sys.version > '3':
basestring = str

from pyspark import since, keyword_only
from pyspark import since, keyword_only, SparkContext
from pyspark.rdd import ignore_unicode_prefix
from pyspark.ml.linalg import _convert_to_vector
from pyspark.ml.param.shared import *
Expand Down Expand Up @@ -437,6 +437,16 @@ class CountVectorizer(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable,
>>> loadedModel = CountVectorizerModel.load(modelPath)
>>> loadedModel.vocabulary == model.vocabulary
True
>>> fromVocabModel = CountVectorizerModel.fromVocabulary(model.vocabulary,
... inputCol="raw", outputCol="vectors")
>>> fromVocabModel.transform(df).show(truncate=False)
+-----+---------------+-------------------------+
|label|raw |vectors |
+-----+---------------+-------------------------+
|0 |[a, b, c] |(3,[0,1,2],[1.0,1.0,1.0])|
|1 |[a, b, b, c, a]|(3,[0,1,2],[2.0,2.0,1.0])|
+-----+---------------+-------------------------+
...

.. versionadded:: 1.6.0
"""
Expand Down Expand Up @@ -550,13 +560,30 @@ def _create_model(self, java_model):
return CountVectorizerModel(java_model)


class CountVectorizerModel(JavaModel, JavaMLReadable, JavaMLWritable):
class CountVectorizerModel(JavaModel, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
"""
Model fitted by :py:class:`CountVectorizer`.

.. versionadded:: 1.6.0
"""

@classmethod
@since("2.2.0")
def fromVocabulary(cls, vocab, inputCol, outputCol=None):
"""
Construct the model directly from a vocabulary list, requires
an active SparkContext.
"""
sc = SparkContext._active_spark_context
java_class = sc._gateway.jvm.java.lang.String
jvocab = CountVectorizerModel._new_java_array(vocab, java_class)
model = CountVectorizerModel._create_from_java_class(
"org.apache.spark.ml.feature.CountVectorizerModel", jvocab)
model.setInputCol(inputCol)
if outputCol is not None:
model.setOutputCol(outputCol)
return model

@property
@since("1.6.0")
def vocabulary(self):
Expand Down