# Natural Language Toolkit: Categorized Sentences Corpus Reader
#
# Copyright (C) 2001-2016 NLTK Project
# Author: Pierpaolo Pantone <24alsecondo@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

"""
CorpusReader structured for corpora that contain one instance on each row.
This CorpusReader is specifically used for the Subjectivity Dataset and the
Sentence Polarity Dataset.

- Subjectivity Dataset information -

Authors: Bo Pang and Lillian Lee.
Url: http://www.cs.cornell.edu/people/pabo/movie-review-data

Distributed with permission.

Related papers:

- Bo Pang and Lillian Lee. "A Sentimental Education: Sentiment Analysis Using
    Subjectivity Summarization Based on Minimum Cuts". Proceedings of the ACL,
    2004.

- Sentence Polarity Dataset information -

Authors: Bo Pang and Lillian Lee.
Url: http://www.cs.cornell.edu/people/pabo/movie-review-data

Related papers:

- Bo Pang and Lillian Lee. "Seeing stars: Exploiting class relationships for
    sentiment categorization with respect to rating scales". Proceedings of the
    ACL, 2005.
"""

from nltk.corpus.reader.api import *
from nltk.tokenize import *

class CategorizedSentencesCorpusReader(CategorizedCorpusReader, CorpusReader):
    """
    A reader for corpora in which each row represents a single instance, mainly
    a sentence. Istances are divided into categories based on their file identifiers
    (see CategorizedCorpusReader).
    Since many corpora allow rows that contain more than one sentence, it is
    possible to specify a sentence tokenizer to retrieve all sentences instead
    than all rows.

    Examples using the Subjectivity Dataset:

    >>> from nltk.corpus import subjectivity
    >>> subjectivity.sents()[23]
    ['television', 'made', 'him', 'famous', ',', 'but', 'his', 'biggest', 'hits',
    'happened', 'off', 'screen', '.']
    >>> subjectivity.categories()
    ['obj', 'subj']
    >>> subjectivity.words(categories='subj')
    ['smart', 'and', 'alert', ',', 'thirteen', ...]

    Examples using the Sentence Polarity Dataset:

    >>> from nltk.corpus import sentence_polarity
    >>> sentence_polarity.sents()
    [['simplistic', ',', 'silly', 'and', 'tedious', '.'], ["it's", 'so', 'laddish',
    'and', 'juvenile', ',', 'only', 'teenage', 'boys', 'could', 'possibly', 'find',
    'it', 'funny', '.'], ...]
    >>> sentence_polarity.categories()
    ['neg', 'pos']
    """

    CorpusView = StreamBackedCorpusView

    def __init__(self, root, fileids, word_tokenizer=WhitespaceTokenizer(),
                 sent_tokenizer=None, encoding='utf8', **kwargs):
        """
        :param root: The root directory for the corpus.
        :param fileids: a list or regexp specifying the fileids in the corpus.
        :param word_tokenizer: a tokenizer for breaking sentences or paragraphs
            into words. Default: `WhitespaceTokenizer`
        :param sent_tokenizer: a tokenizer for breaking paragraphs into sentences.
        :param encoding: the encoding that should be used to read the corpus.
        :param kwargs: additional parameters passed to CategorizedCorpusReader.
        """

        CorpusReader.__init__(self, root, fileids, encoding)
        CategorizedCorpusReader.__init__(self, kwargs)
        self._word_tokenizer = word_tokenizer
        self._sent_tokenizer = sent_tokenizer

    def _resolve(self, fileids, categories):
        if fileids is not None and categories is not None:
            raise ValueError('Specify fileids or categories, not both')
        if categories is not None:
            return self.fileids(categories)
        else:
            return fileids

    def raw(self, fileids=None, categories=None):
        """
        :param fileids: a list or regexp specifying the fileids that have to be
            returned as a raw string.
        :param categories: a list specifying the categories whose files have to
            be returned as a raw string.
        :return: the given file(s) as a single string.
        :rtype: str
        """
        fileids = self._resolve(fileids, categories)
        if fileids is None:
            fileids = self._fileids
        elif isinstance(fileids, string_types):
            fileids = [fileids]
        return concat([self.open(f).read() for f in fileids])

    def readme(self):
        """
        Return the contents of the corpus Readme.txt file.
        """
        return self.open("README").read()

    def sents(self, fileids=None, categories=None):
        """
        Return all sentences in the corpus or in the specified file(s).

        :param fileids: a list or regexp specifying the ids of the files whose
            sentences have to be returned.
        :param categories: a list specifying the categories whose sentences have
            to be returned.
        :return: the given file(s) as a list of sentences.
            Each sentence is tokenized using the specified word_tokenizer.
        :rtype: list(list(str))
        """
        fileids = self._resolve(fileids, categories)
        if fileids is None:
            fileids = self._fileids
        elif isinstance(fileids, compat.string_types):
            fileids = [fileids]
        return concat([self.CorpusView(path, self._read_sent_block, encoding=enc)
            for (path, enc, fileid) in self.abspaths(fileids, True, True)])

    def words(self, fileids=None, categories=None):
        """
        Return all words and punctuation symbols in the corpus or in the specified
        file(s).

        :param fileids: a list or regexp specifying the ids of the files whose
            words have to be returned.
        :param categories: a list specifying the categories whose words have to
            be returned.
        :return: the given file(s) as a list of words and punctuation symbols.
        :rtype: list(str)
        """
        fileids = self._resolve(fileids, categories)
        if fileids is None:
            fileids = self._fileids
        elif isinstance(fileids, compat.string_types):
            fileids = [fileids]
        return concat([self.CorpusView(path, self._read_word_block, encoding=enc)
            for (path, enc, fileid) in self.abspaths(fileids, True, True)])

    def _read_sent_block(self, stream):
        sents = []
        for i in range(20): # Read 20 lines at a time.
            line = stream.readline()
            if not line:
                continue
            if self._sent_tokenizer:
                sents.extend([self._word_tokenizer.tokenize(sent)
                              for sent in self._sent_tokenizer.tokenize(line)])
            else:
                sents.append(self._word_tokenizer.tokenize(line))
        return sents

    def _read_word_block(self, stream):
        words = []
        for sent in self._read_sent_block(stream):
            words.extend(sent)
        return words
