def __init__(self, load=True): warnings.warn("PerceptronTagger is deprecated. Use " " the textblob-aptagger extension instead", category=DeprecationWarning) self.model = AveragedPerceptron() self.tagdict = {} self.classes = set() if load: self.load(self.AP_MODEL_LOC)
class PerceptronTagger(BaseTagger): '''Greedy Averaged Perceptron tagger, as implemented by Matthew Honnibal. Requires that ``trontagger.pickle`` exists in the text/en package directory. The pickle file can be obtained from the Github Releases page for TextBlob. .. note:: This class is deprecated as of version ``0.7.0``. It is now maintained as a TextBlob extension, ``textblob-aptagger``. .. deprecated:: 0.7.0 Install the ``textblob-aptagger`` extension instead. ''' START = ['-START-', '-START2-'] END = ['-END-', '-END2-'] AP_MODEL_LOC = os.path.join(os.path.dirname(__file__), 'trontagger.pickle') def __init__(self, load=True): warnings.warn("PerceptronTagger is deprecated. Use " " the textblob-aptagger extension instead", category=DeprecationWarning) self.model = AveragedPerceptron() self.tagdict = {} self.classes = set() if load: self.load(self.AP_MODEL_LOC) def tag(self, corpus, tokenize=True): '''Tags a string `corpus`.''' # Assume untokenized corpus has \n between sentences and ' ' between words s_split = nltk.sent_tokenize if tokenize else lambda t: t.split('\n') w_split = nltk.word_tokenize if tokenize else lambda s: s.split() def split_sents(corpus): for s in s_split(corpus): yield w_split(s) prev, prev2 = self.START tokens = [] for words in split_sents(corpus): context = self.START + [self._normalize(w) for w in words] + self.END for i, word in enumerate(words): tag = self.tagdict.get(word) if not tag: features = self._get_features(i, word, context, prev, prev2) tag = self.model.predict(features) tokens.append((word, tag)) prev2 = prev prev = tag return tokens def train(self, sentences, save_loc=None, nr_iter=5): '''Train a model from sentences, and save it at ``save_loc``. ``nr_iter`` controls the number of Perceptron training iterations. :param sentences: A list of (words, tags) tuples. :param save_loc: If not ``None``, saves a pickled model in this location. :param nr_iter: Number of training iterations. ''' self._make_tagdict(sentences) self.model.classes = self.classes prev, prev2 = self.START for iter_ in range(nr_iter): c = 0 n = 0 for words, tags in sentences: context = self.START + [self._normalize(w) for w in words] \ + self.END for i, word in enumerate(words): guess = self.tagdict.get(word) if not guess: feats = self._get_features(i, word, context, prev, prev2) guess = self.model.predict(feats) self.model.update(tags[i], guess, feats) prev2 = prev prev = guess c += guess == tags[i] n += 1 random.shuffle(sentences) logging.info("Iter {0}: {1}/{2}={3}".format(iter_, c, n, _pc(c, n))) self.model.average_weights() # Pickle as a binary file if save_loc is not None: pickle.dump((self.model.weights, self.tagdict, self.classes), open(save_loc, 'wb'), -1) return None def load(self, loc): '''Load a pickled model.''' try: w_td_c = pickle.load(open(loc, 'rb')) except IOError: package_dir = text.PACKAGE_DIR msg = ("Missing trontagger.pickle. Download it from the TextBlob " "release page: https://github.com/sloria/TextBlob/releases" " and add it to your textblob package " "directory: {0}".format(os.path.join(package_dir, 'en'))) raise MissingCorpusException(msg) self.model.weights, self.tagdict, self.classes = w_td_c self.model.classes = self.classes return None def _normalize(self, word): '''Normalization used in pre-processing. - All words are lower cased - Digits in the range 1800-2100 are represented as !YEAR; - Other digits are represented as !DIGITS :rtype: str ''' if '-' in word and word[0] != '-': return '!HYPHEN' elif word.isdigit() and len(word) == 4: return '!YEAR' elif word[0].isdigit(): return '!DIGITS' else: return word.lower() def _get_features(self, i, word, context, prev, prev2): '''Map tokens into a feature representation, implemented as a {hashable: float} dict. If the features change, a new model must be trained. ''' def add(name, *args): features[' '.join((name,) + tuple(args))] += 1 i += len(self.START) features = defaultdict(int) # It's useful to have a constant feature, which acts sort of like a prior add('bias') add('i suffix', word[-3:]) add('i pref1', word[0]) add('i-1 tag', prev) add('i-2 tag', prev2) add('i tag+i-2 tag', prev, prev2) add('i word', context[i]) add('i-1 tag+i word', prev, context[i]) add('i-1 word', context[i-1]) add('i-1 suffix', context[i-1][-3:]) add('i-2 word', context[i-2]) add('i+1 word', context[i+1]) add('i+1 suffix', context[i+1][-3:]) add('i+2 word', context[i+2]) return features def _make_tagdict(self, sentences): '''Make a tag dictionary for single-tag words.''' counts = defaultdict(lambda: defaultdict(int)) for words, tags in sentences: for word, tag in zip(words, tags): counts[word][tag] += 1 self.classes.add(tag) freq_thresh = 20 ambiguity_thresh = 0.97 for word, tag_freqs in counts.items(): tag, mode = max(tag_freqs.items(), key=lambda item: item[1]) n = sum(tag_freqs.values()) # Don't add rare words to the tag dictionary # Only add quite unambiguous words if n >= freq_thresh and (float(mode) / n) >= ambiguity_thresh: self.tagdict[word] = tag