technical_drawings_extraction/venv/lib/python3.7/site-packages/nltk/classify/util.py

# Natural Language Toolkit: Classifier Utility Functions
#
# Copyright (C) 2001-2019 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
#         Steven Bird <stevenbird1@gmail.com> (minor additions)
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

"""
Utility functions and classes for classifiers.
"""
from __future__ import print_function, division

import math

# from nltk.util import Deprecated
import nltk.classify.util  # for accuracy & log_likelihood
from nltk.util import LazyMap

######################################################################
# { Helper Functions
######################################################################

# alternative name possibility: 'map_featurefunc()'?
# alternative name possibility: 'detect_features()'?
# alternative name possibility: 'map_featuredetect()'?
# or.. just have users use LazyMap directly?
def apply_features(feature_func, toks, labeled=None):
    """
    Use the ``LazyMap`` class to construct a lazy list-like
    object that is analogous to ``map(feature_func, toks)``.  In
    particular, if ``labeled=False``, then the returned list-like
    object's values are equal to::

        [feature_func(tok) for tok in toks]

    If ``labeled=True``, then the returned list-like object's values
    are equal to::

        [(feature_func(tok), label) for (tok, label) in toks]

    The primary purpose of this function is to avoid the memory
    overhead involved in storing all the featuresets for every token
    in a corpus.  Instead, these featuresets are constructed lazily,
    as-needed.  The reduction in memory overhead can be especially
    significant when the underlying list of tokens is itself lazy (as
    is the case with many corpus readers).

    :param feature_func: The function that will be applied to each
        token.  It should return a featureset -- i.e., a dict
        mapping feature names to feature values.
    :param toks: The list of tokens to which ``feature_func`` should be
        applied.  If ``labeled=True``, then the list elements will be
        passed directly to ``feature_func()``.  If ``labeled=False``,
        then the list elements should be tuples ``(tok,label)``, and
        ``tok`` will be passed to ``feature_func()``.
    :param labeled: If true, then ``toks`` contains labeled tokens --
        i.e., tuples of the form ``(tok, label)``.  (Default:
        auto-detect based on types.)
    """
    if labeled is None:
        labeled = toks and isinstance(toks[0], (tuple, list))
    if labeled:

        def lazy_func(labeled_token):
            return (feature_func(labeled_token[0]), labeled_token[1])

        return LazyMap(lazy_func, toks)
    else:
        return LazyMap(feature_func, toks)


def attested_labels(tokens):
    """
    :return: A list of all labels that are attested in the given list
        of tokens.
    :rtype: list of (immutable)
    :param tokens: The list of classified tokens from which to extract
        labels.  A classified token has the form ``(token, label)``.
    :type tokens: list
    """
    return tuple(set(label for (tok, label) in tokens))


def log_likelihood(classifier, gold):
    results = classifier.prob_classify_many([fs for (fs, l) in gold])
    ll = [pdist.prob(l) for ((fs, l), pdist) in zip(gold, results)]
    return math.log(sum(ll) / len(ll))


def accuracy(classifier, gold):
    results = classifier.classify_many([fs for (fs, l) in gold])
    correct = [l == r for ((fs, l), r) in zip(gold, results)]
    if correct:
        return sum(correct) / len(correct)
    else:
        return 0


class CutoffChecker(object):
    """
    A helper class that implements cutoff checks based on number of
    iterations and log likelihood.

    Accuracy cutoffs are also implemented, but they're almost never
    a good idea to use.
    """

    def __init__(self, cutoffs):
        self.cutoffs = cutoffs.copy()
        if 'min_ll' in cutoffs:
            cutoffs['min_ll'] = -abs(cutoffs['min_ll'])
        if 'min_lldelta' in cutoffs:
            cutoffs['min_lldelta'] = abs(cutoffs['min_lldelta'])
        self.ll = None
        self.acc = None
        self.iter = 1

    def check(self, classifier, train_toks):
        cutoffs = self.cutoffs
        self.iter += 1
        if 'max_iter' in cutoffs and self.iter >= cutoffs['max_iter']:
            return True  # iteration cutoff.

        new_ll = nltk.classify.util.log_likelihood(classifier, train_toks)
        if math.isnan(new_ll):
            return True

        if 'min_ll' in cutoffs or 'min_lldelta' in cutoffs:
            if 'min_ll' in cutoffs and new_ll >= cutoffs['min_ll']:
                return True  # log likelihood cutoff
            if (
                'min_lldelta' in cutoffs
                and self.ll
                and ((new_ll - self.ll) <= abs(cutoffs['min_lldelta']))
            ):
                return True  # log likelihood delta cutoff
            self.ll = new_ll

        if 'max_acc' in cutoffs or 'min_accdelta' in cutoffs:
            new_acc = nltk.classify.util.log_likelihood(classifier, train_toks)
            if 'max_acc' in cutoffs and new_acc >= cutoffs['max_acc']:
                return True  # log likelihood cutoff
            if (
                'min_accdelta' in cutoffs
                and self.acc
                and ((new_acc - self.acc) <= abs(cutoffs['min_accdelta']))
            ):
                return True  # log likelihood delta cutoff
            self.acc = new_acc

            return False  # no cutoff reached.


######################################################################
# { Demos
######################################################################


def names_demo_features(name):
    features = {}
    features['alwayson'] = True
    features['startswith'] = name[0].lower()
    features['endswith'] = name[-1].lower()
    for letter in 'abcdefghijklmnopqrstuvwxyz':
        features['count(%s)' % letter] = name.lower().count(letter)
        features['has(%s)' % letter] = letter in name.lower()
    return features


def binary_names_demo_features(name):
    features = {}
    features['alwayson'] = True
    features['startswith(vowel)'] = name[0].lower() in 'aeiouy'
    features['endswith(vowel)'] = name[-1].lower() in 'aeiouy'
    for letter in 'abcdefghijklmnopqrstuvwxyz':
        features['count(%s)' % letter] = name.lower().count(letter)
        features['has(%s)' % letter] = letter in name.lower()
        features['startswith(%s)' % letter] = letter == name[0].lower()
        features['endswith(%s)' % letter] = letter == name[-1].lower()
    return features


def names_demo(trainer, features=names_demo_features):
    from nltk.corpus import names
    import random

    # Construct a list of classified names, using the names corpus.
    namelist = [(name, 'male') for name in names.words('male.txt')] + [
        (name, 'female') for name in names.words('female.txt')
    ]

    # Randomly split the names into a test & train set.
    random.seed(123456)
    random.shuffle(namelist)
    train = namelist[:5000]
    test = namelist[5000:5500]

    # Train up a classifier.
    print('Training classifier...')
    classifier = trainer([(features(n), g) for (n, g) in train])

    # Run the classifier on the test data.
    print('Testing classifier...')
    acc = accuracy(classifier, [(features(n), g) for (n, g) in test])
    print('Accuracy: %6.4f' % acc)

    # For classifiers that can find probabilities, show the log
    # likelihood and some sample probability distributions.
    try:
        test_featuresets = [features(n) for (n, g) in test]
        pdists = classifier.prob_classify_many(test_featuresets)
        ll = [pdist.logprob(gold) for ((name, gold), pdist) in zip(test, pdists)]
        print('Avg. log likelihood: %6.4f' % (sum(ll) / len(test)))
        print()
        print('Unseen Names      P(Male)  P(Female)\n' + '-' * 40)
        for ((name, gender), pdist) in list(zip(test, pdists))[:5]:
            if gender == 'male':
                fmt = '  %-15s *%6.4f   %6.4f'
            else:
                fmt = '  %-15s  %6.4f  *%6.4f'
            print(fmt % (name, pdist.prob('male'), pdist.prob('female')))
    except NotImplementedError:
        pass

    # Return the classifier
    return classifier


def partial_names_demo(trainer, features=names_demo_features):
    from nltk.corpus import names
    import random

    male_names = names.words('male.txt')
    female_names = names.words('female.txt')

    random.seed(654321)
    random.shuffle(male_names)
    random.shuffle(female_names)

    # Create a list of male names to be used as positive-labeled examples for training
    positive = map(features, male_names[:2000])

    # Create a list of male and female names to be used as unlabeled examples
    unlabeled = map(features, male_names[2000:2500] + female_names[:500])

    # Create a test set with correctly-labeled male and female names
    test = [(name, True) for name in male_names[2500:2750]] + [
        (name, False) for name in female_names[500:750]
    ]

    random.shuffle(test)

    # Train up a classifier.
    print('Training classifier...')
    classifier = trainer(positive, unlabeled)

    # Run the classifier on the test data.
    print('Testing classifier...')
    acc = accuracy(classifier, [(features(n), m) for (n, m) in test])
    print('Accuracy: %6.4f' % acc)

    # For classifiers that can find probabilities, show the log
    # likelihood and some sample probability distributions.
    try:
        test_featuresets = [features(n) for (n, m) in test]
        pdists = classifier.prob_classify_many(test_featuresets)
        ll = [pdist.logprob(gold) for ((name, gold), pdist) in zip(test, pdists)]
        print('Avg. log likelihood: %6.4f' % (sum(ll) / len(test)))
        print()
        print('Unseen Names      P(Male)  P(Female)\n' + '-' * 40)
        for ((name, is_male), pdist) in zip(test, pdists)[:5]:
            if is_male == True:
                fmt = '  %-15s *%6.4f   %6.4f'
            else:
                fmt = '  %-15s  %6.4f  *%6.4f'
            print(fmt % (name, pdist.prob(True), pdist.prob(False)))
    except NotImplementedError:
        pass

    # Return the classifier
    return classifier


_inst_cache = {}


def wsd_demo(trainer, word, features, n=1000):
    from nltk.corpus import senseval
    import random

    # Get the instances.
    print('Reading data...')
    global _inst_cache
    if word not in _inst_cache:
        _inst_cache[word] = [(i, i.senses[0]) for i in senseval.instances(word)]
    instances = _inst_cache[word][:]
    if n > len(instances):
        n = len(instances)
    senses = list(set(l for (i, l) in instances))
    print('  Senses: ' + ' '.join(senses))

    # Randomly split the names into a test & train set.
    print('Splitting into test & train...')
    random.seed(123456)
    random.shuffle(instances)
    train = instances[: int(0.8 * n)]
    test = instances[int(0.8 * n) : n]

    # Train up a classifier.
    print('Training classifier...')
    classifier = trainer([(features(i), l) for (i, l) in train])

    # Run the classifier on the test data.
    print('Testing classifier...')
    acc = accuracy(classifier, [(features(i), l) for (i, l) in test])
    print('Accuracy: %6.4f' % acc)

    # For classifiers that can find probabilities, show the log
    # likelihood and some sample probability distributions.
    try:
        test_featuresets = [features(i) for (i, n) in test]
        pdists = classifier.prob_classify_many(test_featuresets)
        ll = [pdist.logprob(gold) for ((name, gold), pdist) in zip(test, pdists)]
        print('Avg. log likelihood: %6.4f' % (sum(ll) / len(test)))
    except NotImplementedError:
        pass

    # Return the classifier
    return classifier


def check_megam_config():
    """
    Checks whether the MEGAM binary is configured.
    """
    try:
        _megam_bin
    except NameError:
        err_msg = str(
            "Please configure your megam binary first, e.g.\n"
            ">>> nltk.config_megam('/usr/bin/local/megam')"
        )
        raise NameError(err_msg)