text: # Natural Language Toolkit: Texts
  #
  # Copyright (C) 2001-2020 NLTK Project
  # Author: Steven Bird <stevenbird1@gmail.com>
  #         Edward Loper <edloper@gmail.com>
  # URL: <http://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, Counter, namedtuple
  from functools import reduce
  import re
  import sys
  
  from nltk.lm import MLE
  from nltk.lm.preprocessing import padded_everygram_pipeline
  from nltk.probability import FreqDist
  from nltk.probability import ConditionalFreqDist as CFD
  from nltk.util import tokenwrap, LazyConcatenation
  from nltk.metrics import f_measure, BigramAssocMeasures
  from nltk.collocations import BigramCollocationFinder
  from nltk.tokenize import sent_tokenize
  
  ConcordanceLine = namedtuple(
      "ConcordanceLine",
      ["left", "query", "right", "offset", "left_print", "right_print", "line"],
  )
  
  
  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"""
          left = tokens[i - 1].lower() if i != 0 else "*START*"
          right = tokens[i + 1].lower() if i != len(tokens) - 1 else "*END*"
          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:
                      scores[w] += (
                          self._context_to_words[c][word] * self._context_to_words[c][w]
                      )
          return sorted(scores, key=scores.get, reverse=True)[: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 find_concordance(self, word, width=80):
          """
          Find all concordance lines given the query word.
  
          Provided with a list of words, these will be found as a phrase.
          """
          if isinstance(word, list):
              phrase = word
          else:
              phrase = [word]
  
          half_width = (width - len(' '.join(phrase)) - 2) // 2
          context = width // 4  # approx number of words of context
  
          # Find the instances of the word to create the ConcordanceLine
          concordance_list = []
          offsets = self.offsets(phrase[0])
          for i, word in enumerate(phrase[1:]):
              word_offsets = {offset - i - 1 for offset in self.offsets(word)}
              offsets = sorted(word_offsets.intersection(offsets))
          if offsets:
              for i in offsets:
                  query_word = " ".join(self._tokens[i : i + len(phrase)])
                  # Find the context of query word.
                  left_context = self._tokens[max(0, i - context) : i]
                  right_context = self._tokens[i + len(phrase) : i + context]
                  # Create the pretty lines with the query_word in the middle.
                  left_print = " ".join(left_context)[-half_width:]
                  right_print = " ".join(right_context)[:half_width]
                  # The WYSIWYG line of the concordance.
                  line_print = " ".join([left_print, query_word, right_print])
                  # Create the ConcordanceLine
                  concordance_line = ConcordanceLine(
                      left_context,
                      query_word,
                      right_context,
                      i,
                      left_print,
                      right_print,
                      line_print,
                  )
                  concordance_list.append(concordance_line)
          return concordance_list
  
      def print_concordance(self, word, width=80, lines=25):
          """
          Print concordance lines given the query word.
          :param word: The target word or phrase (a list of strings)
          :type word: str or list
          :param lines: The number of lines to display (default=25)
          :type lines: int
          :param width: The width of each line, in characters (default=80)
          :type width: int
          :param save: The option to save the concordance.
          :type save: bool
          """
          concordance_list = self.find_concordance(word, width=width)
  
          if not concordance_list:
              print("no matches")
          else:
              lines = min(lines, len(concordance_list))
              print("Displaying {} of {} matches:".format(lines, len(concordance_list)))
              for i, concordance_line in enumerate(concordance_list[:lines]):
                  print(concordance_line.line)
  
  
  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 non-capturing 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
          >>> print('hack'); from nltk.book import text1, text5, text9
          hack...
          >>> 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(str(tok) for tok in tokens[1:end])
          else:
              self.name = " ".join(str(tok) for tok in tokens[:8]) + "..."
  
      # ////////////////////////////////////////////////////////////
      # Support item & slice access
      # ////////////////////////////////////////////////////////////
  
      def __getitem__(self, i):
          return self.tokens[i]
  
      def __len__(self):
          return len(self.tokens)
  
      # ////////////////////////////////////////////////////////////
      # Interactive console methods
      # ////////////////////////////////////////////////////////////
  
      def concordance(self, word, width=79, lines=25):
          """
          Prints a concordance for ``word`` with the specified context window.
          Word matching is not case-sensitive.
  
          :param word: The target word or phrase (a list of strings)
          :type word: str or list
          :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
  
          :seealso: ``ConcordanceIndex``
          """
          if "_concordance_index" not in self.__dict__:
              self._concordance_index = ConcordanceIndex(
                  self.tokens, key=lambda s: s.lower()
              )
  
          return self._concordance_index.print_concordance(word, width, lines)
  
      def concordance_list(self, word, width=79, lines=25):
          """
          Generate a concordance for ``word`` with the specified context window.
          Word matching is not case-sensitive.
  
          :param word: The target word or phrase (a list of strings)
          :type word: str or list
          :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
  
          :seealso: ``ConcordanceIndex``
          """
          if "_concordance_index" not in self.__dict__:
              self._concordance_index = ConcordanceIndex(
                  self.tokens, key=lambda s: s.lower()
              )
          return self._concordance_index.find_concordance(word, width)[:lines]
  
      def collocation_list(self, num=20, window_size=2):
          """
          Return collocations derived from the text, ignoring stopwords.
  
              >>> from nltk.book import text4
              >>> text4.collocation_list()[:2]
              [('United', 'States'), ('fellow', 'citizens')]
  
          :param num: The maximum number of collocations to return.
          :type num: int
          :param window_size: The number of tokens spanned by a collocation (default=2)
          :type window_size: int
          :rtype: list(tuple(str, str))
          """
          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 = list(finder.nbest(bigram_measures.likelihood_ratio, num))
          return self._collocations
  
      def collocations(self, num=20, window_size=2):
          """
          Print collocations derived from the text, ignoring stopwords.
  
              >>> from nltk.book import text4
              >>> text4.collocations() # doctest: +ELLIPSIS
              United States; fellow citizens; four years; ...
  
          :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
          """
  
          collocation_strings = [
              w1 + " " + w2 for w1, w2 in self.collocation_list(num, window_size)
          ]
          print(tokenwrap(collocation_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 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 = Counter(
                  w
                  for w in wci.conditions()
                  for c in wci[w]
                  if c in contexts and not w == word
              )
              words = [w for w, _ in fd.most_common(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 words: The words used to seed the similarity search
          :type words: 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 = [w for w, _ in fd.most_common(num)]
                  print(tokenwrap(w1 + "_" + w2 for w1, w2 in ranked_contexts))
  
          except ValueError as 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 words: list(str)
          :seealso: nltk.draw.dispersion_plot()
          """
          from nltk.draw import dispersion_plot
  
          dispersion_plot(self, words)
  
      def _train_default_ngram_lm(self, tokenized_sents, n=3):
          train_data, padded_sents = padded_everygram_pipeline(n, tokenized_sents)
          model = MLE(order=n)
          model.fit(train_data, padded_sents)
          return model
  
      def generate(self, length=100, text_seed=None, random_seed=42):
          """
          Print random text, generated using a trigram language model.
          See also `help(nltk.lm)`.
  
          :param length: The length of text to generate (default=100)
          :type length: int
  
          :param text_seed: Generation can be conditioned on preceding context.
          :type text_seed: list(str)
  
          :param random_seed: A random seed or an instance of `random.Random`. If provided,
          makes the random sampling part of generation reproducible. (default=42)
          :type random_seed: int
  
          """
          # Create the model when using it the first time.
          self._tokenized_sents = [
              sent.split(" ") for sent in sent_tokenize(" ".join(self.tokens))
          ]
          if not hasattr(self, "_trigram_model"):
              print("Building ngram index...", file=sys.stderr)
              self._trigram_model = self._train_default_ngram_lm(
                  self._tokenized_sents, n=3
              )
  
          generated_tokens = []
  
          assert length > 0, "The `length` must be more than 0."
          while len(generated_tokens) < length:
              for idx, token in enumerate(
                  self._trigram_model.generate(
                      length, text_seed=text_seed, random_seed=random_seed
                  )
              ):
                  if token == "<s>":
                      continue
                  if token == "</s>":
                      break
                  generated_tokens.append(token)
              random_seed += 1
  
          prefix = " ".join(text_seed) + " " if text_seed else ""
          output_str = prefix + tokenwrap(generated_tokens[:length])
          print(output_str)
          return output_str
  
      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.
  
          >>> print('hack'); from nltk.book import text1, text5, text9
          hack...
          >>> 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(r"\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 -= 1
          left = tokens[j] if j != 0 else "*START*"
  
          # Right context
          j = i + 1
          while j < len(tokens) and not self._CONTEXT_RE.match(tokens[j]):
              j += 1
          right = tokens[j] if j != len(tokens) else "*END*"
  
          return (left, right)
  
      # ////////////////////////////////////////////////////////////
      # String Display
      # ////////////////////////////////////////////////////////////
  
      def __str__(self):
          return "<Text: %s>" % self.name
  
      def __repr__(self):
          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
      >>> print('hack'); from nltk.book import text1, text2, text3
      hack...
      >>> 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):
          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):
          """ The frequency of the term in text. """
          return text.count(term) / len(text)
  
      def idf(self, term):
          """ 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([True for text in self._texts if term in text])
              if len(self._texts) == 0:
                  raise ValueError("IDF undefined for empty document collection")
              idf = log(len(self._texts) / matches) if matches else 0.0
              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",
  ]
  