text.py 22.4 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623
# Natural Language Toolkit: Texts
#
# Copyright (C) 2001-2012 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

"""
This module brings together a variety of NLTK functionality for
text analysis, and provides simple, interactive interfaces.
Functionality includes: concordancing, collocation discovery,
regular expression search over tokenized strings, and
distributional similarity.
"""

from math import log
from collections import defaultdict
import re

from nltk.probability import FreqDist, LidstoneProbDist
from nltk.probability import ConditionalFreqDist as CFD
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 context_func:
            self._context_func = context_func
        else:
            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: list(str)
        :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: 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 occurrence.
        :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 ``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: list(str)
        :return: The document that this concordance index was
            created from.
        """
        return self._tokens

    def offsets(self, word):
        """
        :rtype: list(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 ``word`` with the specified context window.

        :param word: The target word
        :type word: str
        :param width: The width of each line, in characters (default=80)
        :type width: int
        :param lines: The number of lines to display (default=25)
        :type lines: 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, self._tokens[i], 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.,
    ``'<the><window><is><still><open>'``.  The regular expression
    passed to the ``findall()`` method is modified to treat angle
    brackets as nongrouping parentheses, in addition to matching the
    token boundaries; and to have ``'.'`` 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.

        >>> from nltk.text import TokenSearcher
        >>> from nltk.book import text1, text5, text9
        >>> 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: 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 ``Text`` class, and use the appropriate analysis function or
    class directly instead.

    A ``Text`` is typically initialized from a given document or
    corpus.  E.g.:

    >>> import nltk.corpus
    >>> from nltk.text import Text
    >>> moby = Text(nltk.corpus.gutenberg.words('melville-moby_dick.txt'))

    """
    # This defeats lazy loading, but makes things faster.  This
    # *shouldn't* 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: sequence of 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 ``word`` with the specified context window.
        Word matching is not case-sensitive.
        :seealso: ``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: find_collocations
        :param num: The maximum number of collocations to print.
        :type num: int
        :param window_size: The number of tokens spanned by a collocation (default=2)
        :type window_size: 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: int
        :seealso: 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=estimator)
        text = self._trigram_model.generate(length)
        print tokenwrap(text)

    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: str
        :param num: The number of words to generate (default=20)
        :type num: int
        :seealso: 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: str
        :param num: The number of words to generate (default=20)
        :type num: int
        :seealso: 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: str
        :seealso: nltk.draw.dispersion_plot()
        """
        from nltk.draw import dispersion_plot
        dispersion_plot(self, words)

    def plot(self, *args):
        """
        See documentation for FreqDist.plot()
        :seealso: nltk.prob.FreqDist.plot()
        """
        self.vocab().plot(*args)

    def vocab(self):
        """
        :seealso: 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.

        >>> from nltk.book import text1, text5, text9
        >>> 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: 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-normalized.  Skip over
        non-sentence-final punctuation.  Used by the ``ContextIndex``
        that is created for ``similar()`` and ``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 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:

    >>> import nltk.corpus
    >>> from nltk.text import TextCollection
    >>> from nltk.book import text1, text2, text3
    >>> 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.fileids()]

        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"]