#!/usr/bin/env python

# Python version of the evaluation script from CoNLL'00-

# Intentional differences:
# - accept any space as delimiter by default
# - optional file argument (default STDIN)
# - option to set boundary (-b argument)
# - LaTeX output (-l argument) not supported
# - raw tags (-r argument) not supported

import sys
import re

from collections import defaultdict, namedtuple

from conlldata import FormatError

ANY_SPACE = '<SPACE>'

Metrics = namedtuple('Metrics', 'tp fp fn prec rec fscore')

class EvalCounts(object):
    def __init__(self):
        self.correct_chunk = 0    # number of correctly identified chunks
        self.correct_tags = 0     # number of correct chunk tags
        self.found_correct = 0    # number of chunks in corpus
        self.found_guessed = 0    # number of identified chunks
        self.token_counter = 0    # token counter (ignores sentence breaks)

        # counts by type
        self.t_correct_chunk = defaultdict(int)
        self.t_found_correct = defaultdict(int)
        self.t_found_guessed = defaultdict(int)

def parse_args(argv):
    import argparse
    parser = argparse.ArgumentParser(
        description='evaluate tagging results using CoNLL criteria',
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    arg = parser.add_argument
    arg('-b', '--boundary', metavar='STR', default='-X-',
        help='sentence boundary')
    arg('-d', '--delimiter', metavar='CHAR', default=ANY_SPACE,
        help='character delimiting items in input')
    arg('-o', '--otag', metavar='CHAR', default='O',
        help='alternative outside tag')
    arg('file', nargs='?', default=None)
    return parser.parse_args(argv)

def parse_tag(t):
    m = re.match(r'^([^-]*)-(.*)$', t)
    return m.groups() if m else (t, '')

def evaluate(iterable, options=None):
    """Evaluate performance based on (token, true, predicted) sequence."""

    if options is None:
        options = parse_args([])    # use defaults

    counts = EvalCounts()
    in_correct = False        # currently processed chunks is correct until now
    last_correct = 'O'        # previous chunk tag in corpus
    last_correct_type = ''    # type of previously identified chunk tag
    last_guessed = 'O'        # previously identified chunk tag
    last_guessed_type = ''    # type of previous chunk tag in corpus

    for token, true, predicted in iterable:
        guessed, guessed_type = parse_tag(predicted)
        correct, correct_type = parse_tag(true)

        if token == options.boundary:
            guessed = 'O'

        end_correct = end_of_chunk(last_correct, correct,
                                   last_correct_type, correct_type)
        end_guessed = end_of_chunk(last_guessed, guessed,
                                   last_guessed_type, guessed_type)
        start_correct = start_of_chunk(last_correct, correct,
                                       last_correct_type, correct_type)
        start_guessed = start_of_chunk(last_guessed, guessed,
                                       last_guessed_type, guessed_type)

        if in_correct:
            if (end_correct and end_guessed and
                last_guessed_type == last_correct_type):
                in_correct = False
                counts.correct_chunk += 1
                counts.t_correct_chunk[last_correct_type] += 1
            elif (end_correct != end_guessed or guessed_type != correct_type):
                in_correct = False

        if start_correct and start_guessed and guessed_type == correct_type:
            in_correct = True

        if start_correct:
            counts.found_correct += 1
            counts.t_found_correct[correct_type] += 1
        if start_guessed:
            counts.found_guessed += 1
            counts.t_found_guessed[guessed_type] += 1
        if token != options.boundary:
            if correct == guessed and guessed_type == correct_type:
                counts.correct_tags += 1
            counts.token_counter += 1

        last_guessed = guessed
        last_correct = correct
        last_guessed_type = guessed_type
        last_correct_type = correct_type

    if in_correct:
        counts.correct_chunk += 1
        counts.t_correct_chunk[last_correct_type] += 1

    return counts

def evaluate_sentences(sentences):
    """Evaluate sentence sequences of (true, predicted) tags."""

    # evaluate() only uses the token text to identify sentence (and
    # document) boundaries, so if the tags are already grouped into
    # sentences, the token texts are not required. Here, dummy tokens
    # are used for everything except the (inserted) boundaries.

    dummy, boundary = '', parse_args([]).boundary    # default boundary
    text_true_pred = []
    for sentence in sentences:
        for true, pred in sentence:
            text_true_pred.append((dummy, true, pred))
        text_true_pred.append((boundary, 'O', 'O'))
    return evaluate(text_true_pred)

def evaluate_stream(flo, args):
    return evaluate(parse(flo, args), args)

def parse(iterable, options=None):
    """Parse CoNLL format, yield (token, true, predicted) sequence."""

    if options is None:
        options = parse_args([])    # use defaults

    num_features = None       # number of features per line

    for line in iterable:
        line = line.rstrip('\r\n')

        if options.delimiter == ANY_SPACE:
            features = line.split()
        else:
            features = line.split(options.delimiter)

        if num_features is None:
            num_features = len(features)
        elif num_features != len(features) and len(features) != 0:
            raise FormatError('unexpected number of features: %d (%d)' %
                              (len(features), num_features))

        if len(features) == 0 or features[0] == options.boundary:
            features = [options.boundary, 'O', 'O']
        if len(features) < 3:
            raise FormatError('unexpected number of features in line %s' %
                              line)

        predicted = features.pop()
        true = features.pop()
        token = features.pop(0)

        yield (token, true, predicted)

def uniq(iterable):
  seen = set()
  return [i for i in iterable if not (i in seen or seen.add(i))]

def calculate_metrics(correct, guessed, total):
    tp, fp, fn = correct, guessed-correct, total-correct
    p = 0 if tp + fp == 0 else 1.*tp / (tp + fp)
    r = 0 if tp + fn == 0 else 1.*tp / (tp + fn)
    f = 0 if p + r == 0 else 2 * p * r / (p + r)
    return Metrics(tp, fp, fn, p, r, f)

def metrics(counts):
    c = counts
    overall = calculate_metrics(
        c.correct_chunk, c.found_guessed, c.found_correct
    )
    by_type = {}
    for t in uniq(c.t_found_correct.keys() + c.t_found_guessed.keys()):
        by_type[t] = calculate_metrics(
            c.t_correct_chunk[t], c.t_found_guessed[t], c.t_found_correct[t]
        )
    return overall, by_type

def report(counts, out=None):
    if out is None:
        out = sys.stdout

    overall, by_type = metrics(counts)

    c = counts
    out.write('processed %d tokens with %d phrases; ' %
              (c.token_counter, c.found_correct))
    out.write('found: %d phrases; correct: %d.\n' %
              (c.found_guessed, c.correct_chunk))

    if c.token_counter > 0:
        out.write('accuracy: %6.2f%%; ' % 
                  (100.*c.correct_tags/c.token_counter))
        out.write('precision: %6.2f%%; ' % (100.*overall.prec))
        out.write('recall: %6.2f%%; ' % (100.*overall.rec))
        out.write('FB1: %6.2f\n' % (100.*overall.fscore))

    for i, m in sorted(by_type.items()):
        out.write('%17s: ' % i)
        out.write('precision: %6.2f%%; ' % (100.*m.prec))
        out.write('recall: %6.2f%%; ' % (100.*m.rec))
        out.write('FB1: %6.2f  %d\n' % (100.*m.fscore, c.t_found_guessed[i]))

def evaluation_summary(gold_sentences, pred_sentences):
    if len(gold_sentences) != len(pred_sentences):
        raise ValueError('count mismatch')
    gold = [t for s in gold_sentences for t in s]
    pred = [t for s in pred_sentences for t in s]
    acc = 1.*sum(int(g == p) for g, p in zip(gold, pred))/len(gold)
    sentences = [
        [ (g, p) for g, p in zip(gold_sentences[i], pred_sentences[i]) ]
        for i in range(len(gold_sentences))
    ]
    counts = evaluate_sentences(sentences)
    overall, by_type = metrics(counts)
    nlen = max(len(name) for name in by_type.keys())
    summaries = [(
        'acc: {acc:.2%} f: {m.fscore:.2%} ' +
        '(p:{m.prec:.1%} r:{m.rec:.1%} tp:{m.tp} fp:{m.fp} fn:{m.fn})'
    ).format(acc=acc, m=overall)]
    for name, r in sorted(by_type.items()):
        summaries.append((
            '{name:{nlen}} f: {m.fscore:.2%} ' +
            '(p:{m.prec:.1%} r:{m.rec:.1%} tp:{m.tp} fp:{m.fp} fn:{m.fn})'
        ).format(name=name, nlen=nlen, m=r))
    return '\n'.join(summaries)

def end_of_chunk(prev_tag, tag, prev_type, type_):
    # check if a chunk ended between the previous and current word
    # arguments: previous and current chunk tags, previous and current types
    chunk_end = False

    if prev_tag == 'E': chunk_end = True
    if prev_tag == 'S': chunk_end = True

    if prev_tag == 'B' and tag == 'B': chunk_end = True
    if prev_tag == 'B' and tag == 'S': chunk_end = True
    if prev_tag == 'B' and tag == 'O': chunk_end = True
    if prev_tag == 'I' and tag == 'B': chunk_end = True
    if prev_tag == 'I' and tag == 'S': chunk_end = True
    if prev_tag == 'I' and tag == 'O': chunk_end = True

    if prev_tag != 'O' and prev_tag != '.' and prev_type != type_:
        chunk_end = True

    # these chunks are assumed to have length 1
    if prev_tag == ']': chunk_end = True
    if prev_tag == '[': chunk_end = True

    return chunk_end

def start_of_chunk(prev_tag, tag, prev_type, type_):
    # check if a chunk started between the previous and current word
    # arguments: previous and current chunk tags, previous and current types
    chunk_start = False

    if tag == 'B': chunk_start = True
    if tag == 'S': chunk_start = True

    if prev_tag == 'E' and tag == 'E': chunk_start = True
    if prev_tag == 'E' and tag == 'I': chunk_start = True
    if prev_tag == 'S' and tag == 'E': chunk_start = True
    if prev_tag == 'S' and tag == 'I': chunk_start = True
    if prev_tag == 'O' and tag == 'E': chunk_start = True
    if prev_tag == 'O' and tag == 'I': chunk_start = True

    if tag != 'O' and tag != '.' and prev_type != type_:
        chunk_start = True

    # these chunks are assumed to have length 1
    if tag == '[': chunk_start = True
    if tag == ']': chunk_start = True

    return chunk_start

def main(argv):
    args = parse_args(argv[1:])

    if args.file is None:
        counts = evaluate_stream(sys.stdin, args)
    else:
        with open(args.file) as f:
            counts = evaluate_stream(f, args)
    report(counts)

if __name__ == '__main__':
    sys.exit(main(sys.argv))
