[jira] [Created] (FLINK-1736) Add CountVectorizer to machine learning library

[Shang Yuanchun (Jira)]

Till Rohrmann created FLINK-1736:
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             Summary: Add CountVectorizer to machine learning library
                 Key: FLINK-1736
                 URL: https://issues.apache.org/jira/browse/FLINK-1736
             Project: Flink
          Issue Type: Improvement
          Components: Machine Learning Library
            Reporter: Till Rohrmann


A {{CountVectorizer}} feature extractor [1] assigns each occurring word in a corpus an unique identifier. With this mapping it can vectorize models such as bag of words or ngrams in a efficient way. The unique identifier assigned to a word acts as the index of a vector. The number of word occurrences is represented as a vector value at a specific index.

The advantage of the {{CountVectorizer}} compared to the FeatureHasher is that the mapping of words to indices can be obtained which makes it easier to understand the resulting feature vectors.

The {{CountVectorizer}} could be generalized to support arbitrary feature values.

The {{CountVectorizer}} should be implemented as a {{Transfomer}}.

Resources:
[1] [http://scikit-learn.org/stable/modules/feature_extraction.html#common-vectorizer-usage]



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