# -*- coding: utf-8 -*-
# Natural Language Toolkit: Transformation-based learning
#
# Copyright (C) 2001-2016 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, absolute_import, division
import os
import pickle
import random
import time
from nltk.corpus import treebank
from nltk.tbl import error_list, Template
from nltk.tag.brill import Word, Pos
from nltk.tag import BrillTaggerTrainer, RegexpTagger, UnigramTagger
def demo():
"""
Run a demo with defaults. See source comments for details,
or docstrings of any of the more specific demo_* functions.
"""
postag()
def demo_repr_rule_format():
"""
Exemplify repr(Rule) (see also str(Rule) and Rule.format("verbose"))
"""
postag(ruleformat="repr")
def demo_str_rule_format():
"""
Exemplify repr(Rule) (see also str(Rule) and Rule.format("verbose"))
"""
postag(ruleformat="str")
def demo_verbose_rule_format():
"""
Exemplify Rule.format("verbose")
"""
postag(ruleformat="verbose")
def demo_multiposition_feature():
"""
The feature/s of a template takes a list of positions
relative to the current word where the feature should be
looked for, conceptually joined by logical OR. For instance,
Pos([-1, 1]), given a value V, will hold whenever V is found
one step to the left and/or one step to the right.
For contiguous ranges, a 2-arg form giving inclusive end
points can also be used: Pos(-3, -1) is the same as the arg
below.
"""
postag(templates=[Template(Pos([-3,-2,-1]))])
def demo_multifeature_template():
"""
Templates can have more than a single feature.
"""
postag(templates=[Template(Word([0]), Pos([-2,-1]))])
def demo_template_statistics():
"""
Show aggregate statistics per template. Little used templates are
candidates for deletion, much used templates may possibly be refined.
Deleting unused templates is mostly about saving time and/or space:
training is basically O(T) in the number of templates T
(also in terms of memory usage, which often will be the limiting factor).
"""
postag(incremental_stats=True, template_stats=True)
def demo_generated_templates():
"""
Template.expand and Feature.expand are class methods facilitating
generating large amounts of templates. See their documentation for
details.
Note: training with 500 templates can easily fill all available
even on relatively small corpora
"""
wordtpls = Word.expand([-1,0,1], [1,2], excludezero=False)
tagtpls = Pos.expand([-2,-1,0,1], [1,2], excludezero=True)
templates = list(Template.expand([wordtpls, tagtpls], combinations=(1,3)))
print("Generated {0} templates for transformation-based learning".format(len(templates)))
postag(templates=templates, incremental_stats=True, template_stats=True)
def demo_learning_curve():
"""
Plot a learning curve -- the contribution on tagging accuracy of
the individual rules.
Note: requires matplotlib
"""
postag(incremental_stats=True, separate_baseline_data=True, learning_curve_output="learningcurve.png")
def demo_error_analysis():
"""
Writes a file with context for each erroneous word after tagging testing data
"""
postag(error_output="errors.txt")
def demo_serialize_tagger():
"""
Serializes the learned tagger to a file in pickle format; reloads it
and validates the process.
"""
postag(serialize_output="tagger.pcl")
def demo_high_accuracy_rules():
"""
Discard rules with low accuracy. This may hurt performance a bit,
but will often produce rules which are more interesting read to a human.
"""
postag(num_sents=3000, min_acc=0.96, min_score=10)
def postag(
templates=None,
tagged_data=None,
num_sents=1000,
max_rules=300,
min_score=3,
min_acc=None,
train=0.8,
trace=3,
randomize=False,
ruleformat="str",
incremental_stats=False,
template_stats=False,
error_output=None,
serialize_output=None,
learning_curve_output=None,
learning_curve_take=300,
baseline_backoff_tagger=None,
separate_baseline_data=False,
cache_baseline_tagger=None):
"""
Brill Tagger Demonstration
:param templates: how many sentences of training and testing data to use
:type templates: list of Template
:param tagged_data: maximum number of rule instances to create
:type tagged_data: C{int}
:param num_sents: how many sentences of training and testing data to use
:type num_sents: C{int}
:param max_rules: maximum number of rule instances to create
:type max_rules: C{int}
:param min_score: the minimum score for a rule in order for it to be considered
:type min_score: C{int}
:param min_acc: the minimum score for a rule in order for it to be considered
:type min_acc: C{float}
:param train: the fraction of the the corpus to be used for training (1=all)
:type train: C{float}
:param trace: the level of diagnostic tracing output to produce (0-4)
:type trace: C{int}
:param randomize: whether the training data should be a random subset of the corpus
:type randomize: C{bool}
:param ruleformat: rule output format, one of "str", "repr", "verbose"
:type ruleformat: C{str}
:param incremental_stats: if true, will tag incrementally and collect stats for each rule (rather slow)
:type incremental_stats: C{bool}
:param template_stats: if true, will print per-template statistics collected in training and (optionally) testing
:type template_stats: C{bool}
:param error_output: the file where errors will be saved
:type error_output: C{string}
:param serialize_output: the file where the learned tbl tagger will be saved
:type serialize_output: C{string}
:param learning_curve_output: filename of plot of learning curve(s) (train and also test, if available)
:type learning_curve_output: C{string}
:param learning_curve_take: how many rules plotted
:type learning_curve_take: C{int}
:param baseline_backoff_tagger: the file where rules will be saved
:type baseline_backoff_tagger: tagger
:param separate_baseline_data: use a fraction of the training data exclusively for training baseline
:type separate_baseline_data: C{bool}
:param cache_baseline_tagger: cache baseline tagger to this file (only interesting as a temporary workaround to get
deterministic output from the baseline unigram tagger between python versions)
:type cache_baseline_tagger: C{string}
Note on separate_baseline_data: if True, reuse training data both for baseline and rule learner. This
is fast and fine for a demo, but is likely to generalize worse on unseen data.
Also cannot be sensibly used for learning curves on training data (the baseline will be artificially high).
"""
# defaults
baseline_backoff_tagger = baseline_backoff_tagger or REGEXP_TAGGER
if templates is None:
from nltk.tag.brill import describe_template_sets, brill24
# some pre-built template sets taken from typical systems or publications are
# available. Print a list with describe_template_sets()
# for instance:
templates = brill24()
(training_data, baseline_data, gold_data, testing_data) = \
_demo_prepare_data(tagged_data, train, num_sents, randomize, separate_baseline_data)
# creating (or reloading from cache) a baseline tagger (unigram tagger)
# this is just a mechanism for getting deterministic output from the baseline between
# python versions
if cache_baseline_tagger:
if not os.path.exists(cache_baseline_tagger):
baseline_tagger = UnigramTagger(baseline_data, backoff=baseline_backoff_tagger)
with open(cache_baseline_tagger, 'w') as print_rules:
pickle.dump(baseline_tagger, print_rules)
print("Trained baseline tagger, pickled it to {0}".format(cache_baseline_tagger))
with open(cache_baseline_tagger, "r") as print_rules:
baseline_tagger= pickle.load(print_rules)
print("Reloaded pickled tagger from {0}".format(cache_baseline_tagger))
else:
baseline_tagger = UnigramTagger(baseline_data, backoff=baseline_backoff_tagger)
print("Trained baseline tagger")
if gold_data:
print(" Accuracy on test set: {0:0.4f}".format(baseline_tagger.evaluate(gold_data)))
# creating a Brill tagger
tbrill = time.time()
trainer = BrillTaggerTrainer(baseline_tagger, templates, trace, ruleformat=ruleformat)
print("Training tbl tagger...")
brill_tagger = trainer.train(training_data, max_rules, min_score, min_acc)
print("Trained tbl tagger in {0:0.2f} seconds".format(time.time() - tbrill))
if gold_data:
print(" Accuracy on test set: %.4f" % brill_tagger.evaluate(gold_data))
# printing the learned rules, if learned silently
if trace == 1:
print("\nLearned rules: ")
for (ruleno, rule) in enumerate(brill_tagger.rules(),1):
print("{0:4d} {1:s}".format(ruleno, rule.format(ruleformat)))
# printing template statistics (optionally including comparison with the training data)
# note: if not separate_baseline_data, then baseline accuracy will be artificially high
if incremental_stats:
print("Incrementally tagging the test data, collecting individual rule statistics")
(taggedtest, teststats) = brill_tagger.batch_tag_incremental(testing_data, gold_data)
print(" Rule statistics collected")
if not separate_baseline_data:
print("WARNING: train_stats asked for separate_baseline_data=True; the baseline "
"will be artificially high")
trainstats = brill_tagger.train_stats()
if template_stats:
brill_tagger.print_template_statistics(teststats)
if learning_curve_output:
_demo_plot(learning_curve_output, teststats, trainstats, take=learning_curve_take)
print("Wrote plot of learning curve to {0}".format(learning_curve_output))
else:
print("Tagging the test data")
taggedtest = brill_tagger.tag_sents(testing_data)
if template_stats:
brill_tagger.print_template_statistics()
# writing error analysis to file
if error_output is not None:
with open(error_output, 'w') as f:
f.write('Errors for Brill Tagger %r\n\n' % serialize_output)
f.write(u'\n'.join(error_list(gold_data, taggedtest)).encode('utf-8') + '\n')
print("Wrote tagger errors including context to {0}".format(error_output))
# serializing the tagger to a pickle file and reloading (just to see it works)
if serialize_output is not None:
taggedtest = brill_tagger.tag_sents(testing_data)
with open(serialize_output, 'w') as print_rules:
pickle.dump(brill_tagger, print_rules)
print("Wrote pickled tagger to {0}".format(serialize_output))
with open(serialize_output, "r") as print_rules:
brill_tagger_reloaded = pickle.load(print_rules)
print("Reloaded pickled tagger from {0}".format(serialize_output))
taggedtest_reloaded = brill_tagger.tag_sents(testing_data)
if taggedtest == taggedtest_reloaded:
print("Reloaded tagger tried on test set, results identical")
else:
print("PROBLEM: Reloaded tagger gave different results on test set")
def _demo_prepare_data(tagged_data, train, num_sents, randomize, separate_baseline_data):
# train is the proportion of data used in training; the rest is reserved
# for testing.
if tagged_data is None:
print("Loading tagged data from treebank... ")
tagged_data = treebank.tagged_sents()
if num_sents is None or len(tagged_data) <= num_sents:
num_sents = len(tagged_data)
if randomize:
random.seed(len(tagged_data))
random.shuffle(tagged_data)
cutoff = int(num_sents * train)
training_data = tagged_data[:cutoff]
gold_data = tagged_data[cutoff:num_sents]
testing_data = [[t[0] for t in sent] for sent in gold_data]
if not separate_baseline_data:
baseline_data = training_data
else:
bl_cutoff = len(training_data) // 3
(baseline_data, training_data) = (training_data[:bl_cutoff], training_data[bl_cutoff:])
(trainseqs, traintokens) = corpus_size(training_data)
(testseqs, testtokens) = corpus_size(testing_data)
(bltrainseqs, bltraintokens) = corpus_size(baseline_data)
print("Read testing data ({0:d} sents/{1:d} wds)".format(testseqs, testtokens))
print("Read training data ({0:d} sents/{1:d} wds)".format(trainseqs, traintokens))
print("Read baseline data ({0:d} sents/{1:d} wds) {2:s}".format(
bltrainseqs, bltraintokens, "" if separate_baseline_data else "[reused the training set]"))
return (training_data, baseline_data, gold_data, testing_data)
def _demo_plot(learning_curve_output, teststats, trainstats=None, take=None):
testcurve = [teststats['initialerrors']]
for rulescore in teststats['rulescores']:
testcurve.append(testcurve[-1] - rulescore)
testcurve = [1 - x/teststats['tokencount'] for x in testcurve[:take]]
traincurve = [trainstats['initialerrors']]
for rulescore in trainstats['rulescores']:
traincurve.append(traincurve[-1] - rulescore)
traincurve = [1 - x/trainstats['tokencount'] for x in traincurve[:take]]
import matplotlib.pyplot as plt
r = list(range(len(testcurve)))
plt.plot(r, testcurve, r, traincurve)
plt.axis([None, None, None, 1.0])
plt.savefig(learning_curve_output)
NN_CD_TAGGER = RegexpTagger(
[(r'^-?[0-9]+(.[0-9]+)?$', 'CD'),
(r'.*', 'NN')])
REGEXP_TAGGER = RegexpTagger(
[(r'^-?[0-9]+(.[0-9]+)?$', '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)
])
def corpus_size(seqs):
return (len(seqs), sum(len(x) for x in seqs))
if __name__ == '__main__':
demo_learning_curve()