Source code for nltk.tag.brill_trainer

# -*- coding: utf-8 -*-
# Natural Language Toolkit: Transformation-based learning
#
# Copyright (C) 2001-2013 NLTK Project
# Author: Marcus Uneson <marcus.uneson@gmail.com>
#   based on previous (nltk2) version by
#   Christopher Maloof, Edward Loper, Steven Bird
# URL: <http://nltk.org/>
# For license information, see  LICENSE.TXT

from __future__ import print_function, division

import bisect
import textwrap
from collections import defaultdict

from nltk.tag import untag, BrillTagger

######################################################################
#  Brill Tagger Trainer
######################################################################


[docs]class BrillTaggerTrainer(object): """ A trainer for tbl taggers. """ def __init__(self, initial_tagger, templates, trace=0, deterministic=None, ruleformat="str"): """ Construct a Brill tagger from a baseline tagger and a set of templates :param initial_tagger: the baseline tagger :type initial_tagger: Tagger :param templates: templates to be used in training :type templates: list of Templates :param trace: verbosity level :type trace: int :param deterministic: if True, adjudicate ties deterministically :type deterministic: bool :param ruleformat: format of reported Rules :type ruleformat: str :return: An untrained BrillTagger :rtype: BrillTagger """ if deterministic is None: deterministic = (trace > 0) self._initial_tagger = initial_tagger self._templates = templates self._trace = trace self._deterministic = deterministic self._ruleformat = ruleformat self._tag_positions = None """Mapping from tags to lists of positions that use that tag.""" self._rules_by_position = None """Mapping from positions to the set of rules that are known to occur at that position. Position is (sentnum, wordnum). Initially, this will only contain positions where each rule applies in a helpful way; but when we examine a rule, we'll extend this list to also include positions where each rule applies in a harmful or neutral way.""" self._positions_by_rule = None """Mapping from rule to position to effect, specifying the effect that each rule has on the overall score, at each position. Position is (sentnum, wordnum); and effect is -1, 0, or 1. As with _rules_by_position, this mapping starts out only containing rules with positive effects; but when we examine a rule, we'll extend this mapping to include the positions where the rule is harmful or neutral.""" self._rules_by_score = None """Mapping from scores to the set of rules whose effect on the overall score is upper bounded by that score. Invariant: rulesByScore[s] will contain r iff the sum of _positions_by_rule[r] is s.""" self._rule_scores = None """Mapping from rules to upper bounds on their effects on the overall score. This is the inverse mapping to _rules_by_score. Invariant: ruleScores[r] = sum(_positions_by_rule[r])""" self._first_unknown_position = None """Mapping from rules to the first position where we're unsure if the rule applies. This records the next position we need to check to see if the rule messed anything up.""" # Training
[docs] def train(self, train_sents, max_rules=200, min_score=2, min_acc=None): """ Trains the Brill tagger on the corpus *train_sents*, producing at most *max_rules* transformations, each of which reduces the net number of errors in the corpus by at least *min_score*, and each of which has accuracy not lower than *min_acc*. #imports >>> from nltk.tbl.template import Template >>> from nltk.tag.brill import Pos, Word >>> from nltk.tag import untag, RegexpTagger, BrillTaggerTrainer #some data >>> from nltk.corpus import treebank >>> training_data = treebank.tagged_sents()[:100] >>> baseline_data = treebank.tagged_sents()[100:200] >>> gold_data = treebank.tagged_sents()[200:300] >>> testing_data = [untag(s) for s in gold_data] >>> backoff = RegexpTagger([ ... (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), # cardinal numbers ... (r'(The|the|A|a|An|an)$', 'AT'), # articles ... (r'.*able$', 'JJ'), # adjectives ... (r'.*ness$', 'NN'), # nouns formed from adjectives ... (r'.*ly$', 'RB'), # adverbs ... (r'.*s$', 'NNS'), # plural nouns ... (r'.*ing$', 'VBG'), # gerunds ... (r'.*ed$', 'VBD'), # past tense verbs ... (r'.*', 'NN') # nouns (default) ... ]) >>> baseline = backoff #see NOTE1 >>> baseline.evaluate(gold_data) #doctest: +ELLIPSIS 0.2450142... #templates >>> Template._cleartemplates() #clear any templates created in earlier tests >>> templates = [Template(Pos([-1])), Template(Pos([-1]), Word([0]))] #construct a BrillTaggerTrainer >>> tt = BrillTaggerTrainer(baseline, templates, trace=3) >>> tagger1 = tt.train(training_data, max_rules=10) TBL train (fast) (seqs: 100; tokens: 2417; tpls: 2; min score: 2; min acc: None) Finding initial useful rules... Found 845 useful rules. <BLANKLINE> B | S F r O | Score = Fixed - Broken c i o t | R Fixed = num tags changed incorrect -> correct o x k h | u Broken = num tags changed correct -> incorrect r e e e | l Other = num tags changed incorrect -> incorrect e d n r | e ------------------+------------------------------------------------------- 132 132 0 0 | AT->DT if Pos:NN@[-1] 85 85 0 0 | NN->, if Pos:NN@[-1] & Word:,@[0] 69 69 0 0 | NN->. if Pos:NN@[-1] & Word:.@[0] 51 51 0 0 | NN->IN if Pos:NN@[-1] & Word:of@[0] 47 63 16 161 | NN->IN if Pos:NNS@[-1] 33 33 0 0 | NN->TO if Pos:NN@[-1] & Word:to@[0] 26 26 0 0 | IN->. if Pos:NNS@[-1] & Word:.@[0] 24 24 0 0 | IN->, if Pos:NNS@[-1] & Word:,@[0] 22 27 5 24 | NN->-NONE- if Pos:VBD@[-1] 17 17 0 0 | NN->CC if Pos:NN@[-1] & Word:and@[0] >>> tagger1.rules()[1:3] (Rule('001', 'NN', ',', [(Pos([-1]),'NN'), (Word([0]),',')]), Rule('001', 'NN', '.', [(Pos([-1]),'NN'), (Word([0]),'.')])) >>> train_stats = tagger1.train_stats() >>> [train_stats[stat] for stat in ['initialerrors', 'finalerrors', 'rulescores']] [1775, 1269, [132, 85, 69, 51, 47, 33, 26, 24, 22, 17]] >>> tagger1.print_template_statistics(printunused=False) TEMPLATE STATISTICS (TRAIN) 2 templates, 10 rules) TRAIN ( 2417 tokens) initial 1775 0.2656 final: 1269 0.4750 #ID | Score (train) | #Rules | Template -------------------------------------------- 001 | 305 0.603 | 7 0.700 | Template(Pos([-1]),Word([0])) 000 | 201 0.397 | 3 0.300 | Template(Pos([-1])) <BLANKLINE> <BLANKLINE> >>> tagger1.evaluate(gold_data) # doctest: +ELLIPSIS 0.43996... >>> tagged, test_stats = tagger1.batch_tag_incremental(testing_data, gold_data) >>> tagged[33][12:] == [('foreign', 'IN'), ('debt', 'NN'), ('of', 'IN'), ('$', 'NN'), ('64', 'CD'), ... ('billion', 'NN'), ('*U*', 'NN'), ('--', 'NN'), ('the', 'DT'), ('third-highest', 'NN'), ('in', 'NN'), ... ('the', 'DT'), ('developing', 'VBG'), ('world', 'NN'), ('.', '.')] True >>> [test_stats[stat] for stat in ['initialerrors', 'finalerrors', 'rulescores']] [1855, 1376, [100, 85, 67, 58, 27, 36, 27, 16, 31, 32]] # a high-accuracy tagger >>> tagger2 = tt.train(training_data, max_rules=10, min_acc=0.99) TBL train (fast) (seqs: 100; tokens: 2417; tpls: 2; min score: 2; min acc: 0.99) Finding initial useful rules... Found 845 useful rules. <BLANKLINE> B | S F r O | Score = Fixed - Broken c i o t | R Fixed = num tags changed incorrect -> correct o x k h | u Broken = num tags changed correct -> incorrect r e e e | l Other = num tags changed incorrect -> incorrect e d n r | e ------------------+------------------------------------------------------- 132 132 0 0 | AT->DT if Pos:NN@[-1] 85 85 0 0 | NN->, if Pos:NN@[-1] & Word:,@[0] 69 69 0 0 | NN->. if Pos:NN@[-1] & Word:.@[0] 51 51 0 0 | NN->IN if Pos:NN@[-1] & Word:of@[0] 36 36 0 0 | NN->TO if Pos:NN@[-1] & Word:to@[0] 26 26 0 0 | NN->. if Pos:NNS@[-1] & Word:.@[0] 24 24 0 0 | NN->, if Pos:NNS@[-1] & Word:,@[0] 19 19 0 6 | NN->VB if Pos:TO@[-1] 18 18 0 0 | CD->-NONE- if Pos:NN@[-1] & Word:0@[0] 18 18 0 0 | NN->CC if Pos:NN@[-1] & Word:and@[0] >>> tagger2.evaluate(gold_data) # doctest: +ELLIPSIS 0.44159544... >>> tagger2.rules()[2:4] (Rule('001', 'NN', '.', [(Pos([-1]),'NN'), (Word([0]),'.')]), Rule('001', 'NN', 'IN', [(Pos([-1]),'NN'), (Word([0]),'of')])) # NOTE1: (!!FIXME) A far better baseline uses nltk.tag.UnigramTagger, # with a RegexpTagger only as backoff. For instance, # >>> baseline = UnigramTagger(baseline_data, backoff=backoff) # However, as of Nov 2013, nltk.tag.UnigramTagger does not yield consistent results # between python versions. The simplistic backoff above is a workaround to make doctests # get consistent input. :param train_sents: training data :type train_sents: list(list(tuple)) :param max_rules: output at most max_rules rules :type max_rules: int :param min_score: stop training when no rules better than min_score can be found :type min_score: int :param min_acc: discard any rule with lower accuracy than min_acc :type min_acc: float or None :return: the learned tagger :rtype: BrillTagger """ # FIXME: several tests are a bit too dependent on tracing format # FIXME: tests in trainer.fast and trainer.brillorig are exact duplicates # Basic idea: Keep track of the rules that apply at each position. # And keep track of the positions to which each rule applies. # Create a new copy of the training corpus, and run the # initial tagger on it. We will progressively update this # test corpus to look more like the training corpus. test_sents = [list(self._initial_tagger.tag(untag(sent))) for sent in train_sents] # Collect some statistics on the training process trainstats = {} trainstats['min_acc'] = min_acc trainstats['min_score'] = min_score trainstats['tokencount'] = sum(len(t) for t in test_sents) trainstats['sequencecount'] = len(test_sents) trainstats['templatecount'] = len(self._templates) trainstats['rulescores'] = [] trainstats['initialerrors'] = sum( tag[1] != truth[1] for paired in zip(test_sents, train_sents) for (tag, truth) in zip(*paired) ) trainstats['initialacc'] = 1 - trainstats['initialerrors']/trainstats['tokencount'] if self._trace > 0: print("TBL train (fast) (seqs: {sequencecount}; tokens: {tokencount}; " "tpls: {templatecount}; min score: {min_score}; min acc: {min_acc})".format(**trainstats)) # Initialize our mappings. This will find any errors made # by the initial tagger, and use those to generate repair # rules, which are added to the rule mappings. if self._trace: print("Finding initial useful rules...") self._init_mappings(test_sents, train_sents) if self._trace: print((" Found %d useful rules." % len(self._rule_scores))) # Let the user know what we're up to. if self._trace > 2: self._trace_header() elif self._trace == 1: print("Selecting rules...") # Repeatedly select the best rule, and add it to `rules`. rules = [] try: while (len(rules) < max_rules): # Find the best rule, and add it to our rule list. rule = self._best_rule(train_sents, test_sents, min_score, min_acc) if rule: rules.append(rule) score = self._rule_scores[rule] trainstats['rulescores'].append(score) else: break # No more good rules left! # Report the rule that we found. if self._trace > 1: self._trace_rule(rule) # Apply the new rule at the relevant sites self._apply_rule(rule, test_sents) # Update _tag_positions[rule.original_tag] and # _tag_positions[rule.replacement_tag] for the affected # positions (i.e., self._positions_by_rule[rule]). self._update_tag_positions(rule) # Update rules that were affected by the change. self._update_rules(rule, train_sents, test_sents) # The user can cancel training manually: except KeyboardInterrupt: print("Training stopped manually -- %d rules found" % len(rules)) # Discard our tag position mapping & rule mappings. self._clean() trainstats['finalerrors'] = trainstats['initialerrors'] - sum(trainstats['rulescores']) trainstats['finalacc'] = 1 - trainstats['finalerrors']/trainstats['tokencount'] # Create and return a tagger from the rules we found. return BrillTagger(self._initial_tagger, rules, trainstats)
def _init_mappings(self, test_sents, train_sents): """ Initialize the tag position mapping & the rule related mappings. For each error in test_sents, find new rules that would correct them, and add them to the rule mappings. """ self._tag_positions = defaultdict(list) self._rules_by_position = defaultdict(set) self._positions_by_rule = defaultdict(dict) self._rules_by_score = defaultdict(set) self._rule_scores = defaultdict(int) self._first_unknown_position = defaultdict(int) # Scan through the corpus, initializing the tag_positions # mapping and all the rule-related mappings. for sentnum, sent in enumerate(test_sents): for wordnum, (word, tag) in enumerate(sent): # Initialize tag_positions self._tag_positions[tag].append((sentnum, wordnum)) # If it's an error token, update the rule-related mappings. correct_tag = train_sents[sentnum][wordnum][1] if tag != correct_tag: for rule in self._find_rules(sent, wordnum, correct_tag): self._update_rule_applies(rule, sentnum, wordnum, train_sents) def _clean(self): self._tag_positions = None self._rules_by_position = None self._positions_by_rule = None self._rules_by_score = None self._rule_scores = None self._first_unknown_position = None def _find_rules(self, sent, wordnum, new_tag): """ Use the templates to find rules that apply at index *wordnum* in the sentence *sent* and generate the tag *new_tag*. """ for template in self._templates: for rule in template.applicable_rules(sent, wordnum, new_tag): yield rule def _update_rule_applies(self, rule, sentnum, wordnum, train_sents): """ Update the rule data tables to reflect the fact that *rule* applies at the position *(sentnum, wordnum)*. """ pos = sentnum, wordnum # If the rule is already known to apply here, ignore. # (This only happens if the position's tag hasn't changed.) if pos in self._positions_by_rule[rule]: return # Update self._positions_by_rule. correct_tag = train_sents[sentnum][wordnum][1] if rule.replacement_tag == correct_tag: self._positions_by_rule[rule][pos] = 1 elif rule.original_tag == correct_tag: self._positions_by_rule[rule][pos] = -1 else: # was wrong, remains wrong self._positions_by_rule[rule][pos] = 0 # Update _rules_by_position self._rules_by_position[pos].add(rule) # Update _rule_scores. old_score = self._rule_scores[rule] self._rule_scores[rule] += self._positions_by_rule[rule][pos] # Update _rules_by_score. self._rules_by_score[old_score].discard(rule) self._rules_by_score[self._rule_scores[rule]].add(rule) def _update_rule_not_applies(self, rule, sentnum, wordnum): """ Update the rule data tables to reflect the fact that *rule* does not apply at the position *(sentnum, wordnum)*. """ pos = sentnum, wordnum # Update _rule_scores. old_score = self._rule_scores[rule] self._rule_scores[rule] -= self._positions_by_rule[rule][pos] # Update _rules_by_score. self._rules_by_score[old_score].discard(rule) self._rules_by_score[self._rule_scores[rule]].add(rule) # Update _positions_by_rule del self._positions_by_rule[rule][pos] self._rules_by_position[pos].remove(rule) # Optional addition: if the rule now applies nowhere, delete # all its dictionary entries. def _best_rule(self, train_sents, test_sents, min_score, min_acc): """ Find the next best rule. This is done by repeatedly taking a rule with the highest score and stepping through the corpus to see where it applies. When it makes an error (decreasing its score) it's bumped down, and we try a new rule with the highest score. When we find a rule which has the highest score *and* which has been tested against the entire corpus, we can conclude that it's the next best rule. """ for max_score in sorted(self._rules_by_score.keys(), reverse=True): if len(self._rules_by_score) == 0: return None if max_score < min_score or max_score <= 0: return None best_rules = list(self._rules_by_score[max_score]) if self._deterministic: best_rules.sort(key=repr) for rule in best_rules: positions = self._tag_positions[rule.original_tag] unk = self._first_unknown_position.get(rule, (0, -1)) start = bisect.bisect_left(positions, unk) for i in range(start, len(positions)): sentnum, wordnum = positions[i] if rule.applies(test_sents[sentnum], wordnum): self._update_rule_applies(rule, sentnum, wordnum, train_sents) if self._rule_scores[rule] < max_score: self._first_unknown_position[rule] = (sentnum, wordnum+1) break # The update demoted the rule. if self._rule_scores[rule] == max_score: self._first_unknown_position[rule] = (len(train_sents) + 1, 0) # optimization: if no min_acc threshold given, don't bother computing accuracy if min_acc is None: return rule else: changes = self._positions_by_rule[rule].values() num_fixed = len([c for c in changes if c == 1]) num_broken = len([c for c in changes if c == -1]) # acc here is fixed/(fixed+broken); could also be # fixed/(fixed+broken+other) == num_fixed/len(changes) acc = num_fixed/(num_fixed+num_broken) if acc >= min_acc: return rule # else: rule too inaccurate, discard and try next # We demoted (or skipped due to < min_acc, if that was given) # all the rules with score==max_score. assert min_acc is not None or not self._rules_by_score[max_score] if not self._rules_by_score[max_score]: del self._rules_by_score[max_score] def _apply_rule(self, rule, test_sents): """ Update *test_sents* by applying *rule* everywhere where its conditions are met. """ update_positions = set(self._positions_by_rule[rule]) new_tag = rule.replacement_tag if self._trace > 3: self._trace_apply(len(update_positions)) # Update test_sents. for (sentnum, wordnum) in update_positions: text = test_sents[sentnum][wordnum][0] test_sents[sentnum][wordnum] = (text, new_tag) def _update_tag_positions(self, rule): """ Update _tag_positions to reflect the changes to tags that are made by *rule*. """ # Update the tag index. for pos in self._positions_by_rule[rule]: # Delete the old tag. old_tag_positions = self._tag_positions[rule.original_tag] old_index = bisect.bisect_left(old_tag_positions, pos) del old_tag_positions[old_index] # Insert the new tag. new_tag_positions = self._tag_positions[rule.replacement_tag] bisect.insort_left(new_tag_positions, pos) def _update_rules(self, rule, train_sents, test_sents): """ Check if we should add or remove any rules from consideration, given the changes made by *rule*. """ # Collect a list of all positions that might be affected. neighbors = set() for sentnum, wordnum in self._positions_by_rule[rule]: for template in self._templates: n = template.get_neighborhood(test_sents[sentnum], wordnum) neighbors.update([(sentnum, i) for i in n]) # Update the rules at each position. num_obsolete = num_new = num_unseen = 0 for sentnum, wordnum in neighbors: test_sent = test_sents[sentnum] correct_tag = train_sents[sentnum][wordnum][1] # Check if the change causes any rule at this position to # stop matching; if so, then update our rule mappings # accordingly. old_rules = set(self._rules_by_position[sentnum, wordnum]) for old_rule in old_rules: if not old_rule.applies(test_sent, wordnum): num_obsolete += 1 self._update_rule_not_applies(old_rule, sentnum, wordnum) # Check if the change causes our templates to propose any # new rules for this position. for template in self._templates: for new_rule in template.applicable_rules(test_sent, wordnum, correct_tag): if new_rule not in old_rules: num_new += 1 if new_rule not in self._rule_scores: num_unseen += 1 old_rules.add(new_rule) self._update_rule_applies(new_rule, sentnum, wordnum, train_sents) # We may have caused other rules to match here, that are # not proposed by our templates -- in particular, rules # that are harmful or neutral. We therefore need to # update any rule whose first_unknown_position is past # this rule. for new_rule, pos in self._first_unknown_position.items(): if pos > (sentnum, wordnum): if new_rule not in old_rules: num_new += 1 if new_rule.applies(test_sent, wordnum): self._update_rule_applies(new_rule, sentnum, wordnum, train_sents) if self._trace > 3: self._trace_update_rules(num_obsolete, num_new, num_unseen) # Tracing def _trace_header(self): print(""" B | S F r O | Score = Fixed - Broken c i o t | R Fixed = num tags changed incorrect -> correct o x k h | u Broken = num tags changed correct -> incorrect r e e e | l Other = num tags changed incorrect -> incorrect e d n r | e ------------------+------------------------------------------------------- """.rstrip()) def _trace_rule(self, rule): assert self._rule_scores[rule] == sum(self._positions_by_rule[rule].values()) changes = self._positions_by_rule[rule].values() num_fixed = len([c for c in changes if c == 1]) num_broken = len([c for c in changes if c == -1]) num_other = len([c for c in changes if c == 0]) score = self._rule_scores[rule] rulestr = rule.format(self._ruleformat) if self._trace > 2: print('%4d%4d%4d%4d |' % (score, num_fixed, num_broken, num_other), end=' ') print(textwrap.fill(rulestr, initial_indent=' '*20, width=79, subsequent_indent=' '*18+'| ').strip()) else: print(rulestr) def _trace_apply(self, num_updates): prefix = ' '*18+'|' print(prefix) print(prefix, 'Applying rule to %d positions.' % num_updates) def _trace_update_rules(self, num_obsolete, num_new, num_unseen): prefix = ' '*18+'|' print(prefix, 'Updated rule tables:') print(prefix, (' - %d rule applications removed' % num_obsolete)) print(prefix, (' - %d rule applications added (%d novel)' % (num_new, num_unseen))) print(prefix)