# Natural Language Toolkit: Texts
#
# Copyright (C) 2001-2010 NLTK Project
# Author: Steven Bird <sb@csse.unimelb.edu.au>
#         Edward Loper <edloper@gradient.cis.upenn.edu>
# URL: <http://www.nltk.org/>
# For license information, see LICENSE.TXT

from math import log
import re

from nltk.probability import FreqDist, LidstoneProbDist
from nltk.probability import ConditionalFreqDist as CFD
from nltk.compat import defaultdict
from nltk.util import tokenwrap, LazyConcatenation
from nltk.model import NgramModel
from nltk.metrics import f_measure, BigramAssocMeasures
from nltk.collocations import BigramCollocationFinder


class ContextIndex(object):
    """
    A bidirectional index between words and their 'contexts' in a text.
    The context of a word is usually defined to be the words that occur
    in a fixed window around the word; but other definitions may also
    be used by providing a custom context function.
    """
    @staticmethod
    def _default_context(tokens, i):
        """One left token and one right token, normalized to lowercase"""
        if i == 0: left = '*START*'
        else: left = tokens[i-1].lower()
        if i == len(tokens) - 1: right = '*END*'
        else: right = tokens[i+1].lower()
        return (left, right)
        
    def __init__(self, tokens, context_func=None, filter=None, key=lambda x:x):
        self._key = key
        self._tokens = tokens
        if not context_func:
            self._context_func = self._default_context
        if filter:
            tokens = [t for t in tokens if filter(t)]
        self._word_to_contexts = CFD((self._key(w), self._context_func(tokens, i))
                                     for i, w in enumerate(tokens))
        self._context_to_words = CFD((self._context_func(tokens, i), self._key(w))
                                     for i, w in enumerate(tokens))

    def tokens(self):
        """
        @rtype: C{list} of token
        @return: The document that this context index was
            created from.  
        """
        return self._tokens

    def word_similarity_dict(self, word):
        """
        Return a dictionary mapping from words to 'similarity scores,'
        indicating how often these two words occur in the same
        context.  
        """
        word = self._key(word)
        word_contexts = set(self._word_to_contexts[word])

        scores = {}
        for w, w_contexts in self._word_to_contexts.items():
            scores[w] = f_measure(word_contexts, set(w_contexts))

        return scores

    def similar_words(self, word, n=20):
        scores = defaultdict(int)
        for c in self._word_to_contexts[self._key(word)]:
            for w in self._context_to_words[c]:
                if w != word:
                    print w, c, self._context_to_words[c][word], self._context_to_words[c][w]  
                    scores[w] += self._context_to_words[c][word] * self._context_to_words[c][w]  
        return sorted(scores, key=scores.get)[:n]

    def common_contexts(self, words, fail_on_unknown=False):
        """
        Find contexts where the specified words can all appear; and
        return a frequency distribution mapping each context to the
        number of times that context was used.
        
        @param words: The words used to seed the similarity search
        @type words: C{str} 
        @param fail_on_unknown: If true, then raise a value error if
            any of the given words do not occur at all in the index.
        """
        words = [self._key(w) for w in words]
        contexts = [set(self._word_to_contexts[w]) for w in words]
        empty = [words[i] for i in range(len(words)) if not contexts[i]]
        common = reduce(set.intersection, contexts)
        if empty and fail_on_unknown:
            raise ValueError("The following word(s) were not found:",
                             " ".join(words))
        elif not common:
            # nothing in common -- just return an empty freqdist.
            return FreqDist()
        else:
            fd = FreqDist(c for w in words
                          for c in self._word_to_contexts[w]
                          if c in common)
            return fd

class ConcordanceIndex(object):
    """
    An index that can be used to look up the offset locations at which
    a given word occurs in a document.
    """
    def __init__(self, tokens, key=lambda x:x):
        """
        Construct a new concordance index.

        @param tokens: The document (list of tokens) that this
            concordance index was created from.  This list can be used
            to access the context of a given word occurance.
        @param key: A function that maps each token to a normalized
            version that will be used as a key in the index.  E.g., if
            you use C{key=lambda s:s.lower()}, then the index will be
            case-insensitive.
        """
        self._tokens = tokens
        """The document (list of tokens) that this concordance index
           was created from."""
        
        self._key = key
        """Function mapping each token to an index key (or None)."""
        
        self._offsets = defaultdict(list)
        """Dictionary mapping words (or keys) to lists of offset
           indices."""
        
        # Initialize the index (self._offsets)
        for index, word in enumerate(tokens):
            word = self._key(word)
            self._offsets[word].append(index)

    def tokens(self):
        """
        @rtype: C{list} of token
        @return: The document that this concordance index was
            created from.  
        """
        return self._tokens

    def offsets(self, word):
        """
        @rtype: C{list} of C{int}
        @return: A list of the offset positions at which the given
            word occurs.  If a key function was specified for the
            index, then given word's key will be looked up.
        """
        word = self._key(word)
        return self._offsets[word]

    def __repr__(self):
        return '<ConcordanceIndex for %d tokens (%d types)>' % (
            len(self._tokens), len(self._offsets))

    def print_concordance(self, word, width=75, lines=25):
        """
        Print a concordance for C{word} with the specified context window.
        
        @param word: The target word
        @type word: C{str}
        @param width: The width of each line, in characters (default=80)
        @type width: C{int}
        @param lines: The number of lines to display (default=25)
        @type lines: C{int}
        """
        half_width = (width - len(word) - 2) / 2
        context = width/4 # approx number of words of context
        
        offsets = self.offsets(word)
        if offsets:
            lines = min(lines, len(offsets))
            print "Displaying %s of %s matches:" % (lines, len(offsets))
            for i in offsets:
                if lines <= 0:
                    break
                left = (' ' * half_width +
                        ' '.join(self._tokens[i-context:i]))
                right = ' '.join(self._tokens[i+1:i+context])
                left = left[-half_width:]
                right = right[:half_width]
                print left, word, right
                lines -= 1
        else:
            print "No matches"

class TokenSearcher(object):
    """
    A class that makes it easier to use regular expressions to search
    over tokenized strings.  The tokenized string is converted to a
    string where tokens are marked with angle brackets -- e.g.,
    C{'<the><window><is><still><open>'}.  The regular expression
    passed to the L{findall()} method is modified to treat angle
    brackets as nongrouping parentheses, in addition to matching the
    token boundaries; and to have C{'.'} not match the angle brackets.
    """
    def __init__(self, tokens):
        self._raw = ''.join('<'+w+'>' for w in tokens) 

    def findall(self, regexp):
        """
        Find instances of the regular expression in the text.
        The text is a list of tokens, and a regexp pattern to match
        a single token must be surrounded by angle brackets.  E.g.
        
        >>> ts.findall("<.*><.*><bro>")
        ['you rule bro', ['telling you bro; u twizted bro
        >>> ts.findall("<a>(<.*>)<man>")
        monied; nervous; dangerous; white; white; white; pious; queer; good;
        mature; white; Cape; great; wise; wise; butterless; white; fiendish;
        pale; furious; better; certain; complete; dismasted; younger; brave;
        brave; brave; brave
        >>> text9.findall("<th.*>{3,}")
        thread through those; the thought that; that the thing; the thing
        that; that that thing; through these than through; them that the;
        through the thick; them that they; thought that the
        
        @param regexp: A regular expression
        @type regexp: C{str}
        """
        # preprocess the regular expression
        regexp = re.sub(r'\s', '', regexp)
        regexp = re.sub(r'<', '(?:<(?:', regexp)
        regexp = re.sub(r'>', ')>)', regexp)
        regexp = re.sub(r'(?<!\\)\.', '[^>]', regexp)

        # perform the search
        hits = re.findall(regexp, self._raw)

        # Sanity check
        for h in hits:
            if not h.startswith('<') and h.endswith('>'):
                raise ValueError('Bad regexp for TokenSearcher.findall')
            
        # postprocess the output
        hits = [h[1:-1].split('><') for h in hits]
        return hits

class Text(object):
    """
    A wrapper around a sequence of simple (string) tokens, which is
    intended to support initial exploration of texts (via the
    interactive console).  Its methods perform a variety of analyses
    on the text's contexts (e.g., counting, concordancing, collocation
    discovery), and display the results.  If you wish to write a
    program which makes use of these analyses, then you should bypass
    the C{Text} class, and use the appropriate analysis function or
    class directly instead.

    C{Text}s are typically initialized from a given document or
    corpus.  E.g.:
    
    >>> moby = Text(nltk.corpus.gutenberg.words('melville-moby_dick.txt'))
    """
    # This defeats lazy loading, but makes things faster.  This
    # *shouldnt* be necessary because the corpus view *should* be
    # doing intelligent caching, but without this it's running slow.
    # Look into whether the caching is working correctly.
    _COPY_TOKENS = True
    
    def __init__(self, tokens, name=None):
        """
        Create a Text object.
        
        @param tokens: The source text.
        @type tokens: C{sequence} of C{str}
        """
        if self._COPY_TOKENS:
            tokens = list(tokens)
        self.tokens = tokens
        
        if name:
            self.name = name
        elif ']' in tokens[:20]:
            end = tokens[:20].index(']')
            self.name = " ".join(map(str, tokens[1:end]))
        else:
            self.name = " ".join(map(str, tokens[:8])) + "..."

    #////////////////////////////////////////////////////////////
    # Support item & slice access
    #////////////////////////////////////////////////////////////
    
    def __getitem__(self, i):
        if isinstance(i, slice):
            return self.tokens[i.start:i.stop]
        else:
            return self.tokens[i]

    def __len__(self):
        return len(self.tokens)
    
    #////////////////////////////////////////////////////////////
    # Interactive console methods
    #////////////////////////////////////////////////////////////
    
    def concordance(self, word, width=79, lines=25):
        """
        Print a concordance for C{word} with the specified context window.
        @seealso: L{ConcordanceIndex}
        """
        if '_concordance_index' not in self.__dict__:
            print "Building index..."
            self._concordance_index = ConcordanceIndex(self.tokens,
                                                       key=lambda s:s.lower())
            
        self._concordance_index.print_concordance(word, width, lines)
    
    def collocations(self, num=20, window_size=2):
        """
        Print collocations derived from the text, ignoring stopwords.

        @seealso: L{find_collocations}
        @param num: The maximum number of collocations to print.
        @type num: C{int}
        @param window_size: The number of tokens spanned by a collocation (default=2)
        @type window_size: C{int}
        """
        if not ('_collocations' in self.__dict__ and self._num == num and self._window_size == window_size):
            self._num = num
            self._window_size = window_size

            print "Building collocations list"
            from nltk.corpus import stopwords
            ignored_words = stopwords.words('english')
            finder = BigramCollocationFinder.from_words(self.tokens, window_size) 
            finder.apply_freq_filter(2)
            finder.apply_word_filter(lambda w: len(w) < 3 or w.lower() in ignored_words)
            bigram_measures = BigramAssocMeasures()
            self._collocations = finder.nbest(bigram_measures.likelihood_ratio, num)
        colloc_strings = [w1+' '+w2 for w1, w2 in self._collocations]
        print tokenwrap(colloc_strings, separator="; ")

    def count(self, word):
        """
        Count the number of times this word appears in the text.
        """
        return self.tokens.count(word)

    def index(self, word):
        """
        Find the index of the first occurrence of the word in the text.
        """
        return self.tokens.index(word)

    def readability(self, method):
        # code from nltk_contrib.readability
        raise NotImplementedError
    
    def generate(self, length=100):
        """
        Print random text, generated using a trigram language model.
        
        @param length: The length of text to generate (default=100)
        @type length: C{int}
        @seealso: L{NgramModel}
        """
        if '_trigram_model' not in self.__dict__:
            print "Building ngram index..."
            estimator = lambda fdist, bins: LidstoneProbDist(fdist, 0.2)
            self._trigram_model = NgramModel(3, self, estimator)
        text = self._trigram_model.generate(length)
        print tokenwrap(text)

    def search(self, pattern):
        """
        Search for instances of the regular expression pattern in the text.
        
        @seealso: L{TokenSearcher}
        """
        if '_token_searcher' not in self.__dict__:
            print "Loading data..."
            self._token_searcher = TokenSearcher(self.tokens)
            
        self._token_searcher.findall(pattern)
    
    def similar(self, word, num=20):
        """
        Distributional similarity: find other words which appear in the
        same contexts as the specified word; list most similar words first.
        
        @param word: The word used to seed the similarity search
        @type word: C{str} 
        @param num: The number of words to generate (default=20)
        @type num: C{int}
        @seealso: L{ContextIndex.similar_words()}
        """
        if '_word_context_index' not in self.__dict__:
            print 'Building word-context index...'
            self._word_context_index = ContextIndex(self.tokens,
                                                    filter=lambda x:x.isalpha(),
                                                    key=lambda s:s.lower())

#        words = self._word_context_index.similar_words(word, num)

        word = word.lower()
        wci = self._word_context_index._word_to_contexts
        if word in wci.conditions():
            contexts = set(wci[word])
            fd = FreqDist(w for w in wci.conditions() for c in wci[w]
                          if c in contexts and not w == word)
            words = fd.keys()[:num]
            print tokenwrap(words)
        else:
            print "No matches"
            
    
    def common_contexts(self, words, num=20):
        """
        Find contexts where the specified words appear; list
        most frequent common contexts first.
        
        @param word: The word used to seed the similarity search
        @type word: C{str} 
        @param num: The number of words to generate (default=20)
        @type num: C{int}
        @seealso: L{ContextIndex.common_contexts()}
        """
        if '_word_context_index' not in self.__dict__:
            print 'Building word-context index...'
            self._word_context_index = ContextIndex(self.tokens,
                                                    key=lambda s:s.lower())

        try:
            fd = self._word_context_index.common_contexts(words, True)
            if not fd:
                print "No common contexts were found"
            else:
                ranked_contexts = fd.keys()[:num]
                print tokenwrap(w1+"_"+w2 for w1,w2 in ranked_contexts)
                
        except ValueError, e:
            print e
            
    def dispersion_plot(self, words):
        """
        Produce a plot showing the distribution of the words through the text.
        Requires pylab to be installed.
        
        @param words: The words to be plotted
        @type word: C{str}
        @seealso: L{nltk.draw.dispersion_plot()}
        """
        from nltk.draw import dispersion_plot
        dispersion_plot(self, words)

    def plot(self, *args):
        """
        See documentation for FreqDist.plot()
        @seealso: L{nltk.prob.FreqDist.plot()}
        """
        self.vocab().plot(*args)
    
    def vocab(self):
        """
        @seealso: L{nltk.prob.FreqDist}
        """
        if "_vocab" not in self.__dict__:
            print "Building vocabulary index..."
            self._vocab = FreqDist(self)
        return self._vocab

    def findall(self, regexp):
        """
        Find instances of the regular expression in the text.
        The text is a list of tokens, and a regexp pattern to match
        a single token must be surrounded by angle brackets.  E.g.
        
        >>> text5.findall("<.*><.*><bro>")
        you rule bro; telling you bro; u twizted bro
        >>> text1.findall("<a>(<.*>)<man>")
        monied; nervous; dangerous; white; white; white; pious; queer; good;
        mature; white; Cape; great; wise; wise; butterless; white; fiendish;
        pale; furious; better; certain; complete; dismasted; younger; brave;
        brave; brave; brave
        >>> text9.findall("<th.*>{3,}")
        thread through those; the thought that; that the thing; the thing
        that; that that thing; through these than through; them that the;
        through the thick; them that they; thought that the
        
        @param regexp: A regular expression
        @type regexp: C{str}
        """
        
        if "_token_searcher" not in self.__dict__:
            self._token_searcher = TokenSearcher(self)

        hits = self._token_searcher.findall(regexp)
        hits = [' '.join(h) for h in hits]
        print tokenwrap(hits, "; ") 

    #////////////////////////////////////////////////////////////
    # Helper Methods
    #////////////////////////////////////////////////////////////

    _CONTEXT_RE = re.compile('\w+|[\.\!\?]')
    def _context(self, tokens, i):
        """
        One left & one right token, both case-normalied.  Skip over
        non-sentence-final punctuation.  Used by the L{ContextIndex}
        that is created for L{similar()} and L{common_contexts()}.
        """
        # Left context
        j = i-1
        while j>=0 and not self._CONTEXT_RE.match(tokens[j]):
            j = j-1
        if j == 0: left = '*START*'
        else: left = tokens[j]
        # Right context
        j = i+1
        while j<len(tokens) and not self._CONTEXT_RE.match(tokens[j]):
            j = j+1
        if j == len(tokens): right = '*END*'
        else: right = tokens[j]
        return (left, right)
            
    #////////////////////////////////////////////////////////////
    # String Display
    #////////////////////////////////////////////////////////////
    
    def __repr__(self):
        """
        @return: A string representation of this C{FreqDist}.
        @rtype: string
        """
        return '<Text: %s>' % self.name


# Prototype only; this approach will be slow to load
class TextCollection(Text):
    """A collection of texts, which can be loaded with list of texts, or
    with a corpus consisting of one or more texts, and which supports
    counting, concordancing, collocation discovery, etc.  Initialize a
    TextCollection as follows:
    
    >>> gutenberg = TextCollection(nltk.corpus.gutenberg)
    >>> mytexts = TextCollection([text1, text2, text3])
    
    Iterating over a TextCollection produces all the tokens of all the
    texts in order.
    """    
    def __init__(self, source, name=None):
        if hasattr(source, 'words'): # bridge to the text corpus reader
            source = [source.words(f) for f in source.files()]

        self._texts = source
        Text.__init__(self, LazyConcatenation(source))
        self._idf_cache = {}
    
    def tf(self, term, text, method=None):
        """ The frequency of the term in text. """
        return float(text.count(term)) / len(text)

    def idf(self, term, method=None):
        """ The number of texts in the corpus divided by the
        number of texts that the term appears in. 
        If a term does not appear in the corpus, 0.0 is returned. """
        # idf values are cached for performance.
        idf = self._idf_cache.get(term)
        if idf is None: 
            matches = len(list(True for text in self._texts if term in text))
            if not matches:
                # FIXME Should this raise some kind of error instead?
                idf = 0.0
            else:
                idf = log(float(len(self._texts)) / matches)
            self._idf_cache[term] = idf
        return idf

    def tf_idf(self, term, text):
        return self.tf(term, text) * self.idf(term)
    
def demo():
    from nltk.corpus import brown
    text = Text(brown.words(categories='news'))
    print text
    print
    print "Concordance:"
    text.concordance('news')
    print
    print "Distributionally similar words:"
    text.similar('news')
    print
    print "Collocations:"
    text.collocations()
    print
    print "Automatically generated text:"
    text.generate()
    print
    print "Dispersion plot:"
    text.dispersion_plot(['news', 'report', 'said', 'announced'])
    print
    print "Vocabulary plot:"
    text.plot(50)
    print
    print "Indexing:"
    print "text[3]:", text[3]
    print "text[3:5]:", text[3:5]
    print "text.vocab()['news']:", text.vocab()['news']
                         
if __name__ == '__main__':
    demo()

__all__ = ["ContextIndex",
           "ConcordanceIndex",
           "TokenSearcher",
           "Text",
           "TextCollection"]