# -*- coding: utf-8 -*-
# Natural Language Toolkit: Transformation-based learning
# Copyright (C) 2001-2015 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.

def demo_repr_rule_format():
    Exemplify repr(Rule) (see also str(Rule) and Rule.format("verbose"))

def demo_str_rule_format():
    Exemplify repr(Rule) (see also str(Rule) and Rule.format("verbose"))

def demo_verbose_rule_format():
    Exemplify Rule.format("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

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

    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

def demo_serialize_tagger():
    Serializes the learned tagger to a file in pickle format; reloads it
    and validates the process.

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(
    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))
        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:
        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))
        print("Tagging the test data")
        taggedtest = brill_tagger.tag_sents(testing_data)
        if template_stats:

    # 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")
            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:
    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
        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])

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__':