def parse_sparse(filename, vocab_filename):
    """
    Parse a file that's in libSVM format. In libSVM format each line of the text
    file represents a document in bag of words format:

    num_unique_words_in_doc word_id:count another_id:count

    The word_ids have 0-based indexing, i.e. 0 corresponds to the first
    word in the vocab filename.

    Parameters
    ----------
    filename : str
        The name of the file to parse.

    vocab_filename : str
        A list of words that are used for this data set.

    Returns
    -------
    out : SArray
        Each element represents a document in bag-of-words format.

    Examples
    --------
    If we have two documents:
    1. "It was the best of times, it was the worst of times"
    2. "It was the age of wisdom, it was the age of foolishness"

    Then the vocabulary file might contain the unique words, with a word
    on each line, in the following order:
    it, was, the, best, of, times, worst, age, wisdom, foolishness

    In this case, the file in libSVM format would have two lines:
    7 0:2 1:2 2:2 3:1 4:2 5:1 6:1
    7 0:2 1:2 2:2 7:2 8:1 9:1 10:1

    The following command will parse the above two files into an SArray
    of type dict.

    >>> file = 'https://static.turi.com/datasets/text/ap.dat'
    >>> vocab = 'https://static.turi.com/datasets/text/ap.vocab.txt'
    >>> docs = turicreate.text_analytics.parse_sparse(file, vocab)
    """
    vocab = _turicreate.SFrame.read_csv(vocab_filename, header=None)['X1']
    vocab = list(vocab)

    docs = _turicreate.SFrame.read_csv(filename, header=None)

    # Remove first word
    docs = docs['X1'].apply(lambda x: x.split(' ')[1:])

    # Helper function that checks whether we get too large a word id
    def get_word(word_id):
        assert int(word_id) < len(vocab), \
                "Text data contains integers that are larger than the \
                 size of the provided vocabulary."
        return vocab[word_id]

    def make_dict(pairs):
        pairs = [z.split(':') for z in pairs]
        ret = {}
        for k, v in pairs:
            ret[get_word(int(k))] = int(v)
        return ret

    # Split word_id and count and make into a dictionary
    docs = docs.apply(lambda x: make_dict(x))

    return docs
def parse_sparse ( filename , vocab_filename ) : vocab = _turicreate . SFrame . read_csv ( vocab_filename , header = None ) [ 'X1' ] vocab = list ( vocab ) docs = _turicreate . SFrame . read_csv ( filename , header = None ) docs = docs [ 'X1' ] . apply ( lambda x : x . split ( ' ' ) [ 1 : ] ) def get_word ( word_id ) : assert int ( word_id ) < len ( vocab ) , "Text data contains integers that are larger than the \
                 size of the provided vocabulary." return vocab [ word_id ] def make_dict ( pairs ) : pairs = [ z . split ( ':' ) for z in pairs ] ret = { } for k , v in pairs : ret [ get_word ( int ( k ) ) ] = int ( v ) return ret docs = docs . apply ( lambda x : make_dict ( x ) ) return docs
Parse a file that's in libSVM format. In libSVM format each line of the text
    file represents a document in bag of words format:

    num_unique_words_in_doc word_id:count another_id:count

    The word_ids have 0-based indexing, i.e. 0 corresponds to the first
    word in the vocab filename.

    Parameters
    ----------
    filename : str
        The name of the file to parse.

    vocab_filename : str
        A list of words that are used for this data set.

    Returns
    -------
    out : SArray
        Each element represents a document in bag-of-words format.

    Examples
    --------
    If we have two documents:
    1. "It was the best of times, it was the worst of times"
    2. "It was the age of wisdom, it was the age of foolishness"

    Then the vocabulary file might contain the unique words, with a word
    on each line, in the following order:
    it, was, the, best, of, times, worst, age, wisdom, foolishness

    In this case, the file in libSVM format would have two lines:
    7 0:2 1:2 2:2 3:1 4:2 5:1 6:1
    7 0:2 1:2 2:2 7:2 8:1 9:1 10:1

    The following command will parse the above two files into an SArray
    of type dict.

    >>> file = 'https://static.turi.com/datasets/text/ap.dat'
    >>> vocab = 'https://static.turi.com/datasets/text/ap.vocab.txt'
    >>> docs = turicreate.text_analytics.parse_sparse(file, vocab)