pipeline/nltk/classify/util.py

# Natural Language Toolkit: Classifier Utility Functions
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Edward Loper <[email protected]>
#         Steven Bird <[email protected]> (minor additions)
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT

"""

Utility functions and classes for classifiers.

"""

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({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:
    """

    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):
    import random

    from nltk.corpus import names

    # 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):
    import random

    from nltk.corpus import names

    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):
    import random

    from nltk.corpus import senseval

    # 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({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 as e:
        err_msg = str(
            "Please configure your megam binary first, e.g.\n"
            ">>> nltk.config_megam('/usr/bin/local/megam')"
        )
        raise NameError(err_msg) from e