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0000000000000000000000000000000000000000..e9f66503756a0768ece53179cf3ff8f231c2aab1 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/classify/maxent.py @@ -0,0 +1,1569 @@ +# Natural Language Toolkit: Maximum Entropy Classifiers +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Edward Loper +# Dmitry Chichkov (TypedMaxentFeatureEncoding) +# URL: +# For license information, see LICENSE.TXT + +""" +A classifier model based on maximum entropy modeling framework. This +framework considers all of the probability distributions that are +empirically consistent with the training data; and chooses the +distribution with the highest entropy. A probability distribution is +"empirically consistent" with a set of training data if its estimated +frequency with which a class and a feature vector value co-occur is +equal to the actual frequency in the data. + +Terminology: 'feature' +====================== +The term *feature* is usually used to refer to some property of an +unlabeled token. For example, when performing word sense +disambiguation, we might define a ``'prevword'`` feature whose value is +the word preceding the target word. However, in the context of +maxent modeling, the term *feature* is typically used to refer to a +property of a "labeled" token. In order to prevent confusion, we +will introduce two distinct terms to disambiguate these two different +concepts: + + - An "input-feature" is a property of an unlabeled token. + - A "joint-feature" is a property of a labeled token. + +In the rest of the ``nltk.classify`` module, the term "features" is +used to refer to what we will call "input-features" in this module. + +In literature that describes and discusses maximum entropy models, +input-features are typically called "contexts", and joint-features +are simply referred to as "features". + +Converting Input-Features to Joint-Features +------------------------------------------- +In maximum entropy models, joint-features are required to have numeric +values. Typically, each input-feature ``input_feat`` is mapped to a +set of joint-features of the form: + +| joint_feat(token, label) = { 1 if input_feat(token) == feat_val +| { and label == some_label +| { +| { 0 otherwise + +For all values of ``feat_val`` and ``some_label``. This mapping is +performed by classes that implement the ``MaxentFeatureEncodingI`` +interface. +""" +try: + import numpy +except ImportError: + pass + +import os +import tempfile +from collections import defaultdict + +from nltk.classify.api import ClassifierI +from nltk.classify.megam import call_megam, parse_megam_weights, write_megam_file +from nltk.classify.tadm import call_tadm, parse_tadm_weights, write_tadm_file +from nltk.classify.util import CutoffChecker, accuracy, log_likelihood +from nltk.data import gzip_open_unicode +from nltk.probability import DictionaryProbDist +from nltk.util import OrderedDict + +__docformat__ = "epytext en" + +###################################################################### +# { Classifier Model +###################################################################### + + +class MaxentClassifier(ClassifierI): + """ + A maximum entropy classifier (also known as a "conditional + exponential classifier"). This classifier is parameterized by a + set of "weights", which are used to combine the joint-features + that are generated from a featureset by an "encoding". In + particular, the encoding maps each ``(featureset, label)`` pair to + a vector. The probability of each label is then computed using + the following equation:: + + dotprod(weights, encode(fs,label)) + prob(fs|label) = --------------------------------------------------- + sum(dotprod(weights, encode(fs,l)) for l in labels) + + Where ``dotprod`` is the dot product:: + + dotprod(a,b) = sum(x*y for (x,y) in zip(a,b)) + """ + + def __init__(self, encoding, weights, logarithmic=True): + """ + Construct a new maxent classifier model. Typically, new + classifier models are created using the ``train()`` method. + + :type encoding: MaxentFeatureEncodingI + :param encoding: An encoding that is used to convert the + featuresets that are given to the ``classify`` method into + joint-feature vectors, which are used by the maxent + classifier model. + + :type weights: list of float + :param weights: The feature weight vector for this classifier. + + :type logarithmic: bool + :param logarithmic: If false, then use non-logarithmic weights. + """ + self._encoding = encoding + self._weights = weights + self._logarithmic = logarithmic + # self._logarithmic = False + assert encoding.length() == len(weights) + + def labels(self): + return self._encoding.labels() + + def set_weights(self, new_weights): + """ + Set the feature weight vector for this classifier. + :param new_weights: The new feature weight vector. + :type new_weights: list of float + """ + self._weights = new_weights + assert self._encoding.length() == len(new_weights) + + def weights(self): + """ + :return: The feature weight vector for this classifier. + :rtype: list of float + """ + return self._weights + + def classify(self, featureset): + return self.prob_classify(featureset).max() + + def prob_classify(self, featureset): + prob_dict = {} + for label in self._encoding.labels(): + feature_vector = self._encoding.encode(featureset, label) + + if self._logarithmic: + total = 0.0 + for (f_id, f_val) in feature_vector: + total += self._weights[f_id] * f_val + prob_dict[label] = total + + else: + prod = 1.0 + for (f_id, f_val) in feature_vector: + prod *= self._weights[f_id] ** f_val + prob_dict[label] = prod + + # Normalize the dictionary to give a probability distribution + return DictionaryProbDist(prob_dict, log=self._logarithmic, normalize=True) + + def explain(self, featureset, columns=4): + """ + Print a table showing the effect of each of the features in + the given feature set, and how they combine to determine the + probabilities of each label for that featureset. + """ + descr_width = 50 + TEMPLATE = " %-" + str(descr_width - 2) + "s%s%8.3f" + + pdist = self.prob_classify(featureset) + labels = sorted(pdist.samples(), key=pdist.prob, reverse=True) + labels = labels[:columns] + print( + " Feature".ljust(descr_width) + + "".join("%8s" % (("%s" % l)[:7]) for l in labels) + ) + print(" " + "-" * (descr_width - 2 + 8 * len(labels))) + sums = defaultdict(int) + for i, label in enumerate(labels): + feature_vector = self._encoding.encode(featureset, label) + feature_vector.sort( + key=lambda fid__: abs(self._weights[fid__[0]]), reverse=True + ) + for (f_id, f_val) in feature_vector: + if self._logarithmic: + score = self._weights[f_id] * f_val + else: + score = self._weights[f_id] ** f_val + descr = self._encoding.describe(f_id) + descr = descr.split(" and label is ")[0] # hack + descr += " (%s)" % f_val # hack + if len(descr) > 47: + descr = descr[:44] + "..." + print(TEMPLATE % (descr, i * 8 * " ", score)) + sums[label] += score + print(" " + "-" * (descr_width - 1 + 8 * len(labels))) + print( + " TOTAL:".ljust(descr_width) + "".join("%8.3f" % sums[l] for l in labels) + ) + print( + " PROBS:".ljust(descr_width) + + "".join("%8.3f" % pdist.prob(l) for l in labels) + ) + + def most_informative_features(self, n=10): + """ + Generates the ranked list of informative features from most to least. + """ + if hasattr(self, "_most_informative_features"): + return self._most_informative_features[:n] + else: + self._most_informative_features = sorted( + list(range(len(self._weights))), + key=lambda fid: abs(self._weights[fid]), + reverse=True, + ) + return self._most_informative_features[:n] + + def show_most_informative_features(self, n=10, show="all"): + """ + :param show: all, neg, or pos (for negative-only or positive-only) + :type show: str + :param n: The no. of top features + :type n: int + """ + # Use None the full list of ranked features. + fids = self.most_informative_features(None) + if show == "pos": + fids = [fid for fid in fids if self._weights[fid] > 0] + elif show == "neg": + fids = [fid for fid in fids if self._weights[fid] < 0] + for fid in fids[:n]: + print(f"{self._weights[fid]:8.3f} {self._encoding.describe(fid)}") + + def __repr__(self): + return "" % ( + len(self._encoding.labels()), + self._encoding.length(), + ) + + #: A list of the algorithm names that are accepted for the + #: ``train()`` method's ``algorithm`` parameter. + ALGORITHMS = ["GIS", "IIS", "MEGAM", "TADM"] + + @classmethod + def train( + cls, + train_toks, + algorithm=None, + trace=3, + encoding=None, + labels=None, + gaussian_prior_sigma=0, + **cutoffs, + ): + """ + Train a new maxent classifier based on the given corpus of + training samples. This classifier will have its weights + chosen to maximize entropy while remaining empirically + consistent with the training corpus. + + :rtype: MaxentClassifier + :return: The new maxent classifier + + :type train_toks: list + :param train_toks: Training data, represented as a list of + pairs, the first member of which is a featureset, + and the second of which is a classification label. + + :type algorithm: str + :param algorithm: A case-insensitive string, specifying which + algorithm should be used to train the classifier. The + following algorithms are currently available. + + - Iterative Scaling Methods: Generalized Iterative Scaling (``'GIS'``), + Improved Iterative Scaling (``'IIS'``) + - External Libraries (requiring megam): + LM-BFGS algorithm, with training performed by Megam (``'megam'``) + + The default algorithm is ``'IIS'``. + + :type trace: int + :param trace: The level of diagnostic tracing output to produce. + Higher values produce more verbose output. + :type encoding: MaxentFeatureEncodingI + :param encoding: A feature encoding, used to convert featuresets + into feature vectors. If none is specified, then a + ``BinaryMaxentFeatureEncoding`` will be built based on the + features that are attested in the training corpus. + :type labels: list(str) + :param labels: The set of possible labels. If none is given, then + the set of all labels attested in the training data will be + used instead. + :param gaussian_prior_sigma: The sigma value for a gaussian + prior on model weights. Currently, this is supported by + ``megam``. For other algorithms, its value is ignored. + :param cutoffs: Arguments specifying various conditions under + which the training should be halted. (Some of the cutoff + conditions are not supported by some algorithms.) + + - ``max_iter=v``: Terminate after ``v`` iterations. + - ``min_ll=v``: Terminate after the negative average + log-likelihood drops under ``v``. + - ``min_lldelta=v``: Terminate if a single iteration improves + log likelihood by less than ``v``. + """ + if algorithm is None: + algorithm = "iis" + for key in cutoffs: + if key not in ( + "max_iter", + "min_ll", + "min_lldelta", + "max_acc", + "min_accdelta", + "count_cutoff", + "norm", + "explicit", + "bernoulli", + ): + raise TypeError("Unexpected keyword arg %r" % key) + algorithm = algorithm.lower() + if algorithm == "iis": + return train_maxent_classifier_with_iis( + train_toks, trace, encoding, labels, **cutoffs + ) + elif algorithm == "gis": + return train_maxent_classifier_with_gis( + train_toks, trace, encoding, labels, **cutoffs + ) + elif algorithm == "megam": + return train_maxent_classifier_with_megam( + train_toks, trace, encoding, labels, gaussian_prior_sigma, **cutoffs + ) + elif algorithm == "tadm": + kwargs = cutoffs + kwargs["trace"] = trace + kwargs["encoding"] = encoding + kwargs["labels"] = labels + kwargs["gaussian_prior_sigma"] = gaussian_prior_sigma + return TadmMaxentClassifier.train(train_toks, **kwargs) + else: + raise ValueError("Unknown algorithm %s" % algorithm) + + +#: Alias for MaxentClassifier. +ConditionalExponentialClassifier = MaxentClassifier + + +###################################################################### +# { Feature Encodings +###################################################################### + + +class MaxentFeatureEncodingI: + """ + A mapping that converts a set of input-feature values to a vector + of joint-feature values, given a label. This conversion is + necessary to translate featuresets into a format that can be used + by maximum entropy models. + + The set of joint-features used by a given encoding is fixed, and + each index in the generated joint-feature vectors corresponds to a + single joint-feature. The length of the generated joint-feature + vectors is therefore constant (for a given encoding). + + Because the joint-feature vectors generated by + ``MaxentFeatureEncodingI`` are typically very sparse, they are + represented as a list of ``(index, value)`` tuples, specifying the + value of each non-zero joint-feature. + + Feature encodings are generally created using the ``train()`` + method, which generates an appropriate encoding based on the + input-feature values and labels that are present in a given + corpus. + """ + + def encode(self, featureset, label): + """ + Given a (featureset, label) pair, return the corresponding + vector of joint-feature values. This vector is represented as + a list of ``(index, value)`` tuples, specifying the value of + each non-zero joint-feature. + + :type featureset: dict + :rtype: list(tuple(int, int)) + """ + raise NotImplementedError() + + def length(self): + """ + :return: The size of the fixed-length joint-feature vectors + that are generated by this encoding. + :rtype: int + """ + raise NotImplementedError() + + def labels(self): + """ + :return: A list of the \"known labels\" -- i.e., all labels + ``l`` such that ``self.encode(fs,l)`` can be a nonzero + joint-feature vector for some value of ``fs``. + :rtype: list + """ + raise NotImplementedError() + + def describe(self, fid): + """ + :return: A string describing the value of the joint-feature + whose index in the generated feature vectors is ``fid``. + :rtype: str + """ + raise NotImplementedError() + + def train(cls, train_toks): + """ + Construct and return new feature encoding, based on a given + training corpus ``train_toks``. + + :type train_toks: list(tuple(dict, str)) + :param train_toks: Training data, represented as a list of + pairs, the first member of which is a feature dictionary, + and the second of which is a classification label. + """ + raise NotImplementedError() + + +class FunctionBackedMaxentFeatureEncoding(MaxentFeatureEncodingI): + """ + A feature encoding that calls a user-supplied function to map a + given featureset/label pair to a sparse joint-feature vector. + """ + + def __init__(self, func, length, labels): + """ + Construct a new feature encoding based on the given function. + + :type func: (callable) + :param func: A function that takes two arguments, a featureset + and a label, and returns the sparse joint feature vector + that encodes them:: + + func(featureset, label) -> feature_vector + + This sparse joint feature vector (``feature_vector``) is a + list of ``(index,value)`` tuples. + + :type length: int + :param length: The size of the fixed-length joint-feature + vectors that are generated by this encoding. + + :type labels: list + :param labels: A list of the \"known labels\" for this + encoding -- i.e., all labels ``l`` such that + ``self.encode(fs,l)`` can be a nonzero joint-feature vector + for some value of ``fs``. + """ + self._length = length + self._func = func + self._labels = labels + + def encode(self, featureset, label): + return self._func(featureset, label) + + def length(self): + return self._length + + def labels(self): + return self._labels + + def describe(self, fid): + return "no description available" + + +class BinaryMaxentFeatureEncoding(MaxentFeatureEncodingI): + """ + A feature encoding that generates vectors containing a binary + joint-features of the form: + + | joint_feat(fs, l) = { 1 if (fs[fname] == fval) and (l == label) + | { + | { 0 otherwise + + Where ``fname`` is the name of an input-feature, ``fval`` is a value + for that input-feature, and ``label`` is a label. + + Typically, these features are constructed based on a training + corpus, using the ``train()`` method. This method will create one + feature for each combination of ``fname``, ``fval``, and ``label`` + that occurs at least once in the training corpus. + + The ``unseen_features`` parameter can be used to add "unseen-value + features", which are used whenever an input feature has a value + that was not encountered in the training corpus. These features + have the form: + + | joint_feat(fs, l) = { 1 if is_unseen(fname, fs[fname]) + | { and l == label + | { + | { 0 otherwise + + Where ``is_unseen(fname, fval)`` is true if the encoding does not + contain any joint features that are true when ``fs[fname]==fval``. + + The ``alwayson_features`` parameter can be used to add "always-on + features", which have the form:: + + | joint_feat(fs, l) = { 1 if (l == label) + | { + | { 0 otherwise + + These always-on features allow the maxent model to directly model + the prior probabilities of each label. + """ + + def __init__(self, labels, mapping, unseen_features=False, alwayson_features=False): + """ + :param labels: A list of the \"known labels\" for this encoding. + + :param mapping: A dictionary mapping from ``(fname,fval,label)`` + tuples to corresponding joint-feature indexes. These + indexes must be the set of integers from 0...len(mapping). + If ``mapping[fname,fval,label]=id``, then + ``self.encode(..., fname:fval, ..., label)[id]`` is 1; + otherwise, it is 0. + + :param unseen_features: If true, then include unseen value + features in the generated joint-feature vectors. + + :param alwayson_features: If true, then include always-on + features in the generated joint-feature vectors. + """ + if set(mapping.values()) != set(range(len(mapping))): + raise ValueError( + "Mapping values must be exactly the " + "set of integers from 0...len(mapping)" + ) + + self._labels = list(labels) + """A list of attested labels.""" + + self._mapping = mapping + """dict mapping from (fname,fval,label) -> fid""" + + self._length = len(mapping) + """The length of generated joint feature vectors.""" + + self._alwayson = None + """dict mapping from label -> fid""" + + self._unseen = None + """dict mapping from fname -> fid""" + + if alwayson_features: + self._alwayson = { + label: i + self._length for (i, label) in enumerate(labels) + } + self._length += len(self._alwayson) + + if unseen_features: + fnames = {fname for (fname, fval, label) in mapping} + self._unseen = {fname: i + self._length for (i, fname) in enumerate(fnames)} + self._length += len(fnames) + + def encode(self, featureset, label): + # Inherit docs. + encoding = [] + + # Convert input-features to joint-features: + for fname, fval in featureset.items(): + # Known feature name & value: + if (fname, fval, label) in self._mapping: + encoding.append((self._mapping[fname, fval, label], 1)) + + # Otherwise, we might want to fire an "unseen-value feature". + elif self._unseen: + # Have we seen this fname/fval combination with any label? + for label2 in self._labels: + if (fname, fval, label2) in self._mapping: + break # we've seen this fname/fval combo + # We haven't -- fire the unseen-value feature + else: + if fname in self._unseen: + encoding.append((self._unseen[fname], 1)) + + # Add always-on features: + if self._alwayson and label in self._alwayson: + encoding.append((self._alwayson[label], 1)) + + return encoding + + def describe(self, f_id): + # Inherit docs. + if not isinstance(f_id, int): + raise TypeError("describe() expected an int") + try: + self._inv_mapping + except AttributeError: + self._inv_mapping = [-1] * len(self._mapping) + for (info, i) in self._mapping.items(): + self._inv_mapping[i] = info + + if f_id < len(self._mapping): + (fname, fval, label) = self._inv_mapping[f_id] + return f"{fname}=={fval!r} and label is {label!r}" + elif self._alwayson and f_id in self._alwayson.values(): + for (label, f_id2) in self._alwayson.items(): + if f_id == f_id2: + return "label is %r" % label + elif self._unseen and f_id in self._unseen.values(): + for (fname, f_id2) in self._unseen.items(): + if f_id == f_id2: + return "%s is unseen" % fname + else: + raise ValueError("Bad feature id") + + def labels(self): + # Inherit docs. + return self._labels + + def length(self): + # Inherit docs. + return self._length + + @classmethod + def train(cls, train_toks, count_cutoff=0, labels=None, **options): + """ + Construct and return new feature encoding, based on a given + training corpus ``train_toks``. See the class description + ``BinaryMaxentFeatureEncoding`` for a description of the + joint-features that will be included in this encoding. + + :type train_toks: list(tuple(dict, str)) + :param train_toks: Training data, represented as a list of + pairs, the first member of which is a feature dictionary, + and the second of which is a classification label. + + :type count_cutoff: int + :param count_cutoff: A cutoff value that is used to discard + rare joint-features. If a joint-feature's value is 1 + fewer than ``count_cutoff`` times in the training corpus, + then that joint-feature is not included in the generated + encoding. + + :type labels: list + :param labels: A list of labels that should be used by the + classifier. If not specified, then the set of labels + attested in ``train_toks`` will be used. + + :param options: Extra parameters for the constructor, such as + ``unseen_features`` and ``alwayson_features``. + """ + mapping = {} # maps (fname, fval, label) -> fid + seen_labels = set() # The set of labels we've encountered + count = defaultdict(int) # maps (fname, fval) -> count + + for (tok, label) in train_toks: + if labels and label not in labels: + raise ValueError("Unexpected label %s" % label) + seen_labels.add(label) + + # Record each of the features. + for (fname, fval) in tok.items(): + + # If a count cutoff is given, then only add a joint + # feature once the corresponding (fname, fval, label) + # tuple exceeds that cutoff. + count[fname, fval] += 1 + if count[fname, fval] >= count_cutoff: + if (fname, fval, label) not in mapping: + mapping[fname, fval, label] = len(mapping) + + if labels is None: + labels = seen_labels + return cls(labels, mapping, **options) + + +class GISEncoding(BinaryMaxentFeatureEncoding): + """ + A binary feature encoding which adds one new joint-feature to the + joint-features defined by ``BinaryMaxentFeatureEncoding``: a + correction feature, whose value is chosen to ensure that the + sparse vector always sums to a constant non-negative number. This + new feature is used to ensure two preconditions for the GIS + training algorithm: + + - At least one feature vector index must be nonzero for every + token. + - The feature vector must sum to a constant non-negative number + for every token. + """ + + def __init__( + self, labels, mapping, unseen_features=False, alwayson_features=False, C=None + ): + """ + :param C: The correction constant. The value of the correction + feature is based on this value. In particular, its value is + ``C - sum([v for (f,v) in encoding])``. + :seealso: ``BinaryMaxentFeatureEncoding.__init__`` + """ + BinaryMaxentFeatureEncoding.__init__( + self, labels, mapping, unseen_features, alwayson_features + ) + if C is None: + C = len({fname for (fname, fval, label) in mapping}) + 1 + self._C = C + + @property + def C(self): + """The non-negative constant that all encoded feature vectors + will sum to.""" + return self._C + + def encode(self, featureset, label): + # Get the basic encoding. + encoding = BinaryMaxentFeatureEncoding.encode(self, featureset, label) + base_length = BinaryMaxentFeatureEncoding.length(self) + + # Add a correction feature. + total = sum(v for (f, v) in encoding) + if total >= self._C: + raise ValueError("Correction feature is not high enough!") + encoding.append((base_length, self._C - total)) + + # Return the result + return encoding + + def length(self): + return BinaryMaxentFeatureEncoding.length(self) + 1 + + def describe(self, f_id): + if f_id == BinaryMaxentFeatureEncoding.length(self): + return "Correction feature (%s)" % self._C + else: + return BinaryMaxentFeatureEncoding.describe(self, f_id) + + +class TadmEventMaxentFeatureEncoding(BinaryMaxentFeatureEncoding): + def __init__(self, labels, mapping, unseen_features=False, alwayson_features=False): + self._mapping = OrderedDict(mapping) + self._label_mapping = OrderedDict() + BinaryMaxentFeatureEncoding.__init__( + self, labels, self._mapping, unseen_features, alwayson_features + ) + + def encode(self, featureset, label): + encoding = [] + for feature, value in featureset.items(): + if (feature, label) not in self._mapping: + self._mapping[(feature, label)] = len(self._mapping) + if value not in self._label_mapping: + if not isinstance(value, int): + self._label_mapping[value] = len(self._label_mapping) + else: + self._label_mapping[value] = value + encoding.append( + (self._mapping[(feature, label)], self._label_mapping[value]) + ) + return encoding + + def labels(self): + return self._labels + + def describe(self, fid): + for (feature, label) in self._mapping: + if self._mapping[(feature, label)] == fid: + return (feature, label) + + def length(self): + return len(self._mapping) + + @classmethod + def train(cls, train_toks, count_cutoff=0, labels=None, **options): + mapping = OrderedDict() + if not labels: + labels = [] + + # This gets read twice, so compute the values in case it's lazy. + train_toks = list(train_toks) + + for (featureset, label) in train_toks: + if label not in labels: + labels.append(label) + + for (featureset, label) in train_toks: + for label in labels: + for feature in featureset: + if (feature, label) not in mapping: + mapping[(feature, label)] = len(mapping) + + return cls(labels, mapping, **options) + + +class TypedMaxentFeatureEncoding(MaxentFeatureEncodingI): + """ + A feature encoding that generates vectors containing integer, + float and binary joint-features of the form: + + Binary (for string and boolean features): + + | joint_feat(fs, l) = { 1 if (fs[fname] == fval) and (l == label) + | { + | { 0 otherwise + + Value (for integer and float features): + + | joint_feat(fs, l) = { fval if (fs[fname] == type(fval)) + | { and (l == label) + | { + | { not encoded otherwise + + Where ``fname`` is the name of an input-feature, ``fval`` is a value + for that input-feature, and ``label`` is a label. + + Typically, these features are constructed based on a training + corpus, using the ``train()`` method. + + For string and boolean features [type(fval) not in (int, float)] + this method will create one feature for each combination of + ``fname``, ``fval``, and ``label`` that occurs at least once in the + training corpus. + + For integer and float features [type(fval) in (int, float)] this + method will create one feature for each combination of ``fname`` + and ``label`` that occurs at least once in the training corpus. + + For binary features the ``unseen_features`` parameter can be used + to add "unseen-value features", which are used whenever an input + feature has a value that was not encountered in the training + corpus. These features have the form: + + | joint_feat(fs, l) = { 1 if is_unseen(fname, fs[fname]) + | { and l == label + | { + | { 0 otherwise + + Where ``is_unseen(fname, fval)`` is true if the encoding does not + contain any joint features that are true when ``fs[fname]==fval``. + + The ``alwayson_features`` parameter can be used to add "always-on + features", which have the form: + + | joint_feat(fs, l) = { 1 if (l == label) + | { + | { 0 otherwise + + These always-on features allow the maxent model to directly model + the prior probabilities of each label. + """ + + def __init__(self, labels, mapping, unseen_features=False, alwayson_features=False): + """ + :param labels: A list of the \"known labels\" for this encoding. + + :param mapping: A dictionary mapping from ``(fname,fval,label)`` + tuples to corresponding joint-feature indexes. These + indexes must be the set of integers from 0...len(mapping). + If ``mapping[fname,fval,label]=id``, then + ``self.encode({..., fname:fval, ...``, label)[id]} is 1; + otherwise, it is 0. + + :param unseen_features: If true, then include unseen value + features in the generated joint-feature vectors. + + :param alwayson_features: If true, then include always-on + features in the generated joint-feature vectors. + """ + if set(mapping.values()) != set(range(len(mapping))): + raise ValueError( + "Mapping values must be exactly the " + "set of integers from 0...len(mapping)" + ) + + self._labels = list(labels) + """A list of attested labels.""" + + self._mapping = mapping + """dict mapping from (fname,fval,label) -> fid""" + + self._length = len(mapping) + """The length of generated joint feature vectors.""" + + self._alwayson = None + """dict mapping from label -> fid""" + + self._unseen = None + """dict mapping from fname -> fid""" + + if alwayson_features: + self._alwayson = { + label: i + self._length for (i, label) in enumerate(labels) + } + self._length += len(self._alwayson) + + if unseen_features: + fnames = {fname for (fname, fval, label) in mapping} + self._unseen = {fname: i + self._length for (i, fname) in enumerate(fnames)} + self._length += len(fnames) + + def encode(self, featureset, label): + # Inherit docs. + encoding = [] + + # Convert input-features to joint-features: + for fname, fval in featureset.items(): + if isinstance(fval, (int, float)): + # Known feature name & value: + if (fname, type(fval), label) in self._mapping: + encoding.append((self._mapping[fname, type(fval), label], fval)) + else: + # Known feature name & value: + if (fname, fval, label) in self._mapping: + encoding.append((self._mapping[fname, fval, label], 1)) + + # Otherwise, we might want to fire an "unseen-value feature". + elif self._unseen: + # Have we seen this fname/fval combination with any label? + for label2 in self._labels: + if (fname, fval, label2) in self._mapping: + break # we've seen this fname/fval combo + # We haven't -- fire the unseen-value feature + else: + if fname in self._unseen: + encoding.append((self._unseen[fname], 1)) + + # Add always-on features: + if self._alwayson and label in self._alwayson: + encoding.append((self._alwayson[label], 1)) + + return encoding + + def describe(self, f_id): + # Inherit docs. + if not isinstance(f_id, int): + raise TypeError("describe() expected an int") + try: + self._inv_mapping + except AttributeError: + self._inv_mapping = [-1] * len(self._mapping) + for (info, i) in self._mapping.items(): + self._inv_mapping[i] = info + + if f_id < len(self._mapping): + (fname, fval, label) = self._inv_mapping[f_id] + return f"{fname}=={fval!r} and label is {label!r}" + elif self._alwayson and f_id in self._alwayson.values(): + for (label, f_id2) in self._alwayson.items(): + if f_id == f_id2: + return "label is %r" % label + elif self._unseen and f_id in self._unseen.values(): + for (fname, f_id2) in self._unseen.items(): + if f_id == f_id2: + return "%s is unseen" % fname + else: + raise ValueError("Bad feature id") + + def labels(self): + # Inherit docs. + return self._labels + + def length(self): + # Inherit docs. + return self._length + + @classmethod + def train(cls, train_toks, count_cutoff=0, labels=None, **options): + """ + Construct and return new feature encoding, based on a given + training corpus ``train_toks``. See the class description + ``TypedMaxentFeatureEncoding`` for a description of the + joint-features that will be included in this encoding. + + Note: recognized feature values types are (int, float), over + types are interpreted as regular binary features. + + :type train_toks: list(tuple(dict, str)) + :param train_toks: Training data, represented as a list of + pairs, the first member of which is a feature dictionary, + and the second of which is a classification label. + + :type count_cutoff: int + :param count_cutoff: A cutoff value that is used to discard + rare joint-features. If a joint-feature's value is 1 + fewer than ``count_cutoff`` times in the training corpus, + then that joint-feature is not included in the generated + encoding. + + :type labels: list + :param labels: A list of labels that should be used by the + classifier. If not specified, then the set of labels + attested in ``train_toks`` will be used. + + :param options: Extra parameters for the constructor, such as + ``unseen_features`` and ``alwayson_features``. + """ + mapping = {} # maps (fname, fval, label) -> fid + seen_labels = set() # The set of labels we've encountered + count = defaultdict(int) # maps (fname, fval) -> count + + for (tok, label) in train_toks: + if labels and label not in labels: + raise ValueError("Unexpected label %s" % label) + seen_labels.add(label) + + # Record each of the features. + for (fname, fval) in tok.items(): + if type(fval) in (int, float): + fval = type(fval) + # If a count cutoff is given, then only add a joint + # feature once the corresponding (fname, fval, label) + # tuple exceeds that cutoff. + count[fname, fval] += 1 + if count[fname, fval] >= count_cutoff: + if (fname, fval, label) not in mapping: + mapping[fname, fval, label] = len(mapping) + + if labels is None: + labels = seen_labels + return cls(labels, mapping, **options) + + +###################################################################### +# { Classifier Trainer: Generalized Iterative Scaling +###################################################################### + + +def train_maxent_classifier_with_gis( + train_toks, trace=3, encoding=None, labels=None, **cutoffs +): + """ + Train a new ``ConditionalExponentialClassifier``, using the given + training samples, using the Generalized Iterative Scaling + algorithm. This ``ConditionalExponentialClassifier`` will encode + the model that maximizes entropy from all the models that are + empirically consistent with ``train_toks``. + + :see: ``train_maxent_classifier()`` for parameter descriptions. + """ + cutoffs.setdefault("max_iter", 100) + cutoffchecker = CutoffChecker(cutoffs) + + # Construct an encoding from the training data. + if encoding is None: + encoding = GISEncoding.train(train_toks, labels=labels) + + if not hasattr(encoding, "C"): + raise TypeError( + "The GIS algorithm requires an encoding that " + "defines C (e.g., GISEncoding)." + ) + + # Cinv is the inverse of the sum of each joint feature vector. + # This controls the learning rate: higher Cinv (or lower C) gives + # faster learning. + Cinv = 1.0 / encoding.C + + # Count how many times each feature occurs in the training data. + empirical_fcount = calculate_empirical_fcount(train_toks, encoding) + + # Check for any features that are not attested in train_toks. + unattested = set(numpy.nonzero(empirical_fcount == 0)[0]) + + # Build the classifier. Start with weight=0 for each attested + # feature, and weight=-infinity for each unattested feature. + weights = numpy.zeros(len(empirical_fcount), "d") + for fid in unattested: + weights[fid] = numpy.NINF + classifier = ConditionalExponentialClassifier(encoding, weights) + + # Take the log of the empirical fcount. + log_empirical_fcount = numpy.log2(empirical_fcount) + del empirical_fcount + + if trace > 0: + print(" ==> Training (%d iterations)" % cutoffs["max_iter"]) + if trace > 2: + print() + print(" Iteration Log Likelihood Accuracy") + print(" ---------------------------------------") + + # Train the classifier. + try: + while True: + if trace > 2: + ll = cutoffchecker.ll or log_likelihood(classifier, train_toks) + acc = cutoffchecker.acc or accuracy(classifier, train_toks) + iternum = cutoffchecker.iter + print(" %9d %14.5f %9.3f" % (iternum, ll, acc)) + + # Use the model to estimate the number of times each + # feature should occur in the training data. + estimated_fcount = calculate_estimated_fcount( + classifier, train_toks, encoding + ) + + # Take the log of estimated fcount (avoid taking log(0).) + for fid in unattested: + estimated_fcount[fid] += 1 + log_estimated_fcount = numpy.log2(estimated_fcount) + del estimated_fcount + + # Update the classifier weights + weights = classifier.weights() + weights += (log_empirical_fcount - log_estimated_fcount) * Cinv + classifier.set_weights(weights) + + # Check the log-likelihood & accuracy cutoffs. + if cutoffchecker.check(classifier, train_toks): + break + + except KeyboardInterrupt: + print(" Training stopped: keyboard interrupt") + except: + raise + + if trace > 2: + ll = log_likelihood(classifier, train_toks) + acc = accuracy(classifier, train_toks) + print(f" Final {ll:14.5f} {acc:9.3f}") + + # Return the classifier. + return classifier + + +def calculate_empirical_fcount(train_toks, encoding): + fcount = numpy.zeros(encoding.length(), "d") + + for tok, label in train_toks: + for (index, val) in encoding.encode(tok, label): + fcount[index] += val + + return fcount + + +def calculate_estimated_fcount(classifier, train_toks, encoding): + fcount = numpy.zeros(encoding.length(), "d") + + for tok, label in train_toks: + pdist = classifier.prob_classify(tok) + for label in pdist.samples(): + prob = pdist.prob(label) + for (fid, fval) in encoding.encode(tok, label): + fcount[fid] += prob * fval + + return fcount + + +###################################################################### +# { Classifier Trainer: Improved Iterative Scaling +###################################################################### + + +def train_maxent_classifier_with_iis( + train_toks, trace=3, encoding=None, labels=None, **cutoffs +): + """ + Train a new ``ConditionalExponentialClassifier``, using the given + training samples, using the Improved Iterative Scaling algorithm. + This ``ConditionalExponentialClassifier`` will encode the model + that maximizes entropy from all the models that are empirically + consistent with ``train_toks``. + + :see: ``train_maxent_classifier()`` for parameter descriptions. + """ + cutoffs.setdefault("max_iter", 100) + cutoffchecker = CutoffChecker(cutoffs) + + # Construct an encoding from the training data. + if encoding is None: + encoding = BinaryMaxentFeatureEncoding.train(train_toks, labels=labels) + + # Count how many times each feature occurs in the training data. + empirical_ffreq = calculate_empirical_fcount(train_toks, encoding) / len(train_toks) + + # Find the nf map, and related variables nfarray and nfident. + # nf is the sum of the features for a given labeled text. + # nfmap compresses this sparse set of values to a dense list. + # nfarray performs the reverse operation. nfident is + # nfarray multiplied by an identity matrix. + nfmap = calculate_nfmap(train_toks, encoding) + nfarray = numpy.array(sorted(nfmap, key=nfmap.__getitem__), "d") + nftranspose = numpy.reshape(nfarray, (len(nfarray), 1)) + + # Check for any features that are not attested in train_toks. + unattested = set(numpy.nonzero(empirical_ffreq == 0)[0]) + + # Build the classifier. Start with weight=0 for each attested + # feature, and weight=-infinity for each unattested feature. + weights = numpy.zeros(len(empirical_ffreq), "d") + for fid in unattested: + weights[fid] = numpy.NINF + classifier = ConditionalExponentialClassifier(encoding, weights) + + if trace > 0: + print(" ==> Training (%d iterations)" % cutoffs["max_iter"]) + if trace > 2: + print() + print(" Iteration Log Likelihood Accuracy") + print(" ---------------------------------------") + + # Train the classifier. + try: + while True: + if trace > 2: + ll = cutoffchecker.ll or log_likelihood(classifier, train_toks) + acc = cutoffchecker.acc or accuracy(classifier, train_toks) + iternum = cutoffchecker.iter + print(" %9d %14.5f %9.3f" % (iternum, ll, acc)) + + # Calculate the deltas for this iteration, using Newton's method. + deltas = calculate_deltas( + train_toks, + classifier, + unattested, + empirical_ffreq, + nfmap, + nfarray, + nftranspose, + encoding, + ) + + # Use the deltas to update our weights. + weights = classifier.weights() + weights += deltas + classifier.set_weights(weights) + + # Check the log-likelihood & accuracy cutoffs. + if cutoffchecker.check(classifier, train_toks): + break + + except KeyboardInterrupt: + print(" Training stopped: keyboard interrupt") + except: + raise + + if trace > 2: + ll = log_likelihood(classifier, train_toks) + acc = accuracy(classifier, train_toks) + print(f" Final {ll:14.5f} {acc:9.3f}") + + # Return the classifier. + return classifier + + +def calculate_nfmap(train_toks, encoding): + """ + Construct a map that can be used to compress ``nf`` (which is + typically sparse). + + *nf(feature_vector)* is the sum of the feature values for + *feature_vector*. + + This represents the number of features that are active for a + given labeled text. This method finds all values of *nf(t)* + that are attested for at least one token in the given list of + training tokens; and constructs a dictionary mapping these + attested values to a continuous range *0...N*. For example, + if the only values of *nf()* that were attested were 3, 5, and + 7, then ``_nfmap`` might return the dictionary ``{3:0, 5:1, 7:2}``. + + :return: A map that can be used to compress ``nf`` to a dense + vector. + :rtype: dict(int -> int) + """ + # Map from nf to indices. This allows us to use smaller arrays. + nfset = set() + for tok, _ in train_toks: + for label in encoding.labels(): + nfset.add(sum(val for (id, val) in encoding.encode(tok, label))) + return {nf: i for (i, nf) in enumerate(nfset)} + + +def calculate_deltas( + train_toks, + classifier, + unattested, + ffreq_empirical, + nfmap, + nfarray, + nftranspose, + encoding, +): + r""" + Calculate the update values for the classifier weights for + this iteration of IIS. These update weights are the value of + ``delta`` that solves the equation:: + + ffreq_empirical[i] + = + SUM[fs,l] (classifier.prob_classify(fs).prob(l) * + feature_vector(fs,l)[i] * + exp(delta[i] * nf(feature_vector(fs,l)))) + + Where: + - *(fs,l)* is a (featureset, label) tuple from ``train_toks`` + - *feature_vector(fs,l)* = ``encoding.encode(fs,l)`` + - *nf(vector)* = ``sum([val for (id,val) in vector])`` + + This method uses Newton's method to solve this equation for + *delta[i]*. In particular, it starts with a guess of + ``delta[i]`` = 1; and iteratively updates ``delta`` with: + + | delta[i] -= (ffreq_empirical[i] - sum1[i])/(-sum2[i]) + + until convergence, where *sum1* and *sum2* are defined as: + + | sum1[i](delta) = SUM[fs,l] f[i](fs,l,delta) + | sum2[i](delta) = SUM[fs,l] (f[i](fs,l,delta).nf(feature_vector(fs,l))) + | f[i](fs,l,delta) = (classifier.prob_classify(fs).prob(l) . + | feature_vector(fs,l)[i] . + | exp(delta[i] . nf(feature_vector(fs,l)))) + + Note that *sum1* and *sum2* depend on ``delta``; so they need + to be re-computed each iteration. + + The variables ``nfmap``, ``nfarray``, and ``nftranspose`` are + used to generate a dense encoding for *nf(ltext)*. This + allows ``_deltas`` to calculate *sum1* and *sum2* using + matrices, which yields a significant performance improvement. + + :param train_toks: The set of training tokens. + :type train_toks: list(tuple(dict, str)) + :param classifier: The current classifier. + :type classifier: ClassifierI + :param ffreq_empirical: An array containing the empirical + frequency for each feature. The *i*\ th element of this + array is the empirical frequency for feature *i*. + :type ffreq_empirical: sequence of float + :param unattested: An array that is 1 for features that are + not attested in the training data; and 0 for features that + are attested. In other words, ``unattested[i]==0`` iff + ``ffreq_empirical[i]==0``. + :type unattested: sequence of int + :param nfmap: A map that can be used to compress ``nf`` to a dense + vector. + :type nfmap: dict(int -> int) + :param nfarray: An array that can be used to uncompress ``nf`` + from a dense vector. + :type nfarray: array(float) + :param nftranspose: The transpose of ``nfarray`` + :type nftranspose: array(float) + """ + # These parameters control when we decide that we've + # converged. It probably should be possible to set these + # manually, via keyword arguments to train. + NEWTON_CONVERGE = 1e-12 + MAX_NEWTON = 300 + + deltas = numpy.ones(encoding.length(), "d") + + # Precompute the A matrix: + # A[nf][id] = sum ( p(fs) * p(label|fs) * f(fs,label) ) + # over all label,fs s.t. num_features[label,fs]=nf + A = numpy.zeros((len(nfmap), encoding.length()), "d") + + for tok, label in train_toks: + dist = classifier.prob_classify(tok) + + for label in encoding.labels(): + # Generate the feature vector + feature_vector = encoding.encode(tok, label) + # Find the number of active features + nf = sum(val for (id, val) in feature_vector) + # Update the A matrix + for (id, val) in feature_vector: + A[nfmap[nf], id] += dist.prob(label) * val + A /= len(train_toks) + + # Iteratively solve for delta. Use the following variables: + # - nf_delta[x][y] = nfarray[x] * delta[y] + # - exp_nf_delta[x][y] = exp(nf[x] * delta[y]) + # - nf_exp_nf_delta[x][y] = nf[x] * exp(nf[x] * delta[y]) + # - sum1[i][nf] = sum p(fs)p(label|fs)f[i](label,fs) + # exp(delta[i]nf) + # - sum2[i][nf] = sum p(fs)p(label|fs)f[i](label,fs) + # nf exp(delta[i]nf) + for rangenum in range(MAX_NEWTON): + nf_delta = numpy.outer(nfarray, deltas) + exp_nf_delta = 2**nf_delta + nf_exp_nf_delta = nftranspose * exp_nf_delta + sum1 = numpy.sum(exp_nf_delta * A, axis=0) + sum2 = numpy.sum(nf_exp_nf_delta * A, axis=0) + + # Avoid division by zero. + for fid in unattested: + sum2[fid] += 1 + + # Update the deltas. + deltas -= (ffreq_empirical - sum1) / -sum2 + + # We can stop once we converge. + n_error = numpy.sum(abs(ffreq_empirical - sum1)) / numpy.sum(abs(deltas)) + if n_error < NEWTON_CONVERGE: + return deltas + + return deltas + + +###################################################################### +# { Classifier Trainer: megam +###################################################################### + +# [xx] possible extension: add support for using implicit file format; +# this would need to put requirements on what encoding is used. But +# we may need this for other maxent classifier trainers that require +# implicit formats anyway. +def train_maxent_classifier_with_megam( + train_toks, trace=3, encoding=None, labels=None, gaussian_prior_sigma=0, **kwargs +): + """ + Train a new ``ConditionalExponentialClassifier``, using the given + training samples, using the external ``megam`` library. This + ``ConditionalExponentialClassifier`` will encode the model that + maximizes entropy from all the models that are empirically + consistent with ``train_toks``. + + :see: ``train_maxent_classifier()`` for parameter descriptions. + :see: ``nltk.classify.megam`` + """ + + explicit = True + bernoulli = True + if "explicit" in kwargs: + explicit = kwargs["explicit"] + if "bernoulli" in kwargs: + bernoulli = kwargs["bernoulli"] + + # Construct an encoding from the training data. + if encoding is None: + # Count cutoff can also be controlled by megam with the -minfc + # option. Not sure where the best place for it is. + count_cutoff = kwargs.get("count_cutoff", 0) + encoding = BinaryMaxentFeatureEncoding.train( + train_toks, count_cutoff, labels=labels, alwayson_features=True + ) + elif labels is not None: + raise ValueError("Specify encoding or labels, not both") + + # Write a training file for megam. + try: + fd, trainfile_name = tempfile.mkstemp(prefix="nltk-") + with open(trainfile_name, "w") as trainfile: + write_megam_file( + train_toks, encoding, trainfile, explicit=explicit, bernoulli=bernoulli + ) + os.close(fd) + except (OSError, ValueError) as e: + raise ValueError("Error while creating megam training file: %s" % e) from e + + # Run megam on the training file. + options = [] + options += ["-nobias", "-repeat", "10"] + if explicit: + options += ["-explicit"] + if not bernoulli: + options += ["-fvals"] + if gaussian_prior_sigma: + # Lambda is just the precision of the Gaussian prior, i.e. it's the + # inverse variance, so the parameter conversion is 1.0/sigma**2. + # See https://users.umiacs.umd.edu/~hal/docs/daume04cg-bfgs.pdf + inv_variance = 1.0 / gaussian_prior_sigma**2 + else: + inv_variance = 0 + options += ["-lambda", "%.2f" % inv_variance, "-tune"] + if trace < 3: + options += ["-quiet"] + if "max_iter" in kwargs: + options += ["-maxi", "%s" % kwargs["max_iter"]] + if "ll_delta" in kwargs: + # [xx] this is actually a perplexity delta, not a log + # likelihood delta + options += ["-dpp", "%s" % abs(kwargs["ll_delta"])] + if hasattr(encoding, "cost"): + options += ["-multilabel"] # each possible la + options += ["multiclass", trainfile_name] + stdout = call_megam(options) + # print('./megam_i686.opt ', ' '.join(options)) + # Delete the training file + try: + os.remove(trainfile_name) + except OSError as e: + print(f"Warning: unable to delete {trainfile_name}: {e}") + + # Parse the generated weight vector. + weights = parse_megam_weights(stdout, encoding.length(), explicit) + + # Convert from base-e to base-2 weights. + weights *= numpy.log2(numpy.e) + + # Build the classifier + return MaxentClassifier(encoding, weights) + + +###################################################################### +# { Classifier Trainer: tadm +###################################################################### + + +class TadmMaxentClassifier(MaxentClassifier): + @classmethod + def train(cls, train_toks, **kwargs): + algorithm = kwargs.get("algorithm", "tao_lmvm") + trace = kwargs.get("trace", 3) + encoding = kwargs.get("encoding", None) + labels = kwargs.get("labels", None) + sigma = kwargs.get("gaussian_prior_sigma", 0) + count_cutoff = kwargs.get("count_cutoff", 0) + max_iter = kwargs.get("max_iter") + ll_delta = kwargs.get("min_lldelta") + + # Construct an encoding from the training data. + if not encoding: + encoding = TadmEventMaxentFeatureEncoding.train( + train_toks, count_cutoff, labels=labels + ) + + trainfile_fd, trainfile_name = tempfile.mkstemp( + prefix="nltk-tadm-events-", suffix=".gz" + ) + weightfile_fd, weightfile_name = tempfile.mkstemp(prefix="nltk-tadm-weights-") + + trainfile = gzip_open_unicode(trainfile_name, "w") + write_tadm_file(train_toks, encoding, trainfile) + trainfile.close() + + options = [] + options.extend(["-monitor"]) + options.extend(["-method", algorithm]) + if sigma: + options.extend(["-l2", "%.6f" % sigma**2]) + if max_iter: + options.extend(["-max_it", "%d" % max_iter]) + if ll_delta: + options.extend(["-fatol", "%.6f" % abs(ll_delta)]) + options.extend(["-events_in", trainfile_name]) + options.extend(["-params_out", weightfile_name]) + if trace < 3: + options.extend(["2>&1"]) + else: + options.extend(["-summary"]) + + call_tadm(options) + + with open(weightfile_name) as weightfile: + weights = parse_tadm_weights(weightfile) + + os.remove(trainfile_name) + os.remove(weightfile_name) + + # Convert from base-e to base-2 weights. + weights *= numpy.log2(numpy.e) + + # Build the classifier + return cls(encoding, weights) + + +###################################################################### +# { Demo +###################################################################### +def demo(): + from nltk.classify.util import names_demo + + classifier = names_demo(MaxentClassifier.train) + + +if __name__ == "__main__": + demo() diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/classify/svm.py b/llmeval-env/lib/python3.10/site-packages/nltk/classify/svm.py new file mode 100644 index 0000000000000000000000000000000000000000..65b852c2748b0df620a4bc4340197ea1026e6728 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/classify/svm.py @@ -0,0 +1,17 @@ +# Natural Language Toolkit: SVM-based classifier +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Leon Derczynski +# +# URL: +# For license information, see LICENSE.TXT +""" +nltk.classify.svm was deprecated. For classification based +on support vector machines SVMs use nltk.classify.scikitlearn +(or `scikit-learn `_ directly). +""" + + +class SvmClassifier: + def __init__(self, *args, **kwargs): + raise NotImplementedError(__doc__) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/classify/tadm.py b/llmeval-env/lib/python3.10/site-packages/nltk/classify/tadm.py new file mode 100644 index 0000000000000000000000000000000000000000..f8eb4b3daa2b7e904856b3fa2b4f16378427715f --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/classify/tadm.py @@ -0,0 +1,122 @@ +# Natural Language Toolkit: Interface to TADM Classifier +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Joseph Frazee +# URL: +# For license information, see LICENSE.TXT + +import subprocess +import sys + +from nltk.internals import find_binary + +try: + import numpy +except ImportError: + pass + +_tadm_bin = None + + +def config_tadm(bin=None): + global _tadm_bin + _tadm_bin = find_binary( + "tadm", bin, env_vars=["TADM"], binary_names=["tadm"], url="http://tadm.sf.net" + ) + + +def write_tadm_file(train_toks, encoding, stream): + """ + Generate an input file for ``tadm`` based on the given corpus of + classified tokens. + + :type train_toks: list(tuple(dict, str)) + :param train_toks: Training data, represented as a list of + pairs, the first member of which is a feature dictionary, + and the second of which is a classification label. + :type encoding: TadmEventMaxentFeatureEncoding + :param encoding: A feature encoding, used to convert featuresets + into feature vectors. + :type stream: stream + :param stream: The stream to which the ``tadm`` input file should be + written. + """ + # See the following for a file format description: + # + # https://sf.net/forum/forum.php?thread_id=1391502&forum_id=473054 + # https://sf.net/forum/forum.php?thread_id=1675097&forum_id=473054 + labels = encoding.labels() + for featureset, label in train_toks: + length_line = "%d\n" % len(labels) + stream.write(length_line) + for known_label in labels: + v = encoding.encode(featureset, known_label) + line = "%d %d %s\n" % ( + int(label == known_label), + len(v), + " ".join("%d %d" % u for u in v), + ) + stream.write(line) + + +def parse_tadm_weights(paramfile): + """ + Given the stdout output generated by ``tadm`` when training a + model, return a ``numpy`` array containing the corresponding weight + vector. + """ + weights = [] + for line in paramfile: + weights.append(float(line.strip())) + return numpy.array(weights, "d") + + +def call_tadm(args): + """ + Call the ``tadm`` binary with the given arguments. + """ + if isinstance(args, str): + raise TypeError("args should be a list of strings") + if _tadm_bin is None: + config_tadm() + + # Call tadm via a subprocess + cmd = [_tadm_bin] + args + p = subprocess.Popen(cmd, stdout=sys.stdout) + (stdout, stderr) = p.communicate() + + # Check the return code. + if p.returncode != 0: + print() + print(stderr) + raise OSError("tadm command failed!") + + +def names_demo(): + from nltk.classify.maxent import TadmMaxentClassifier + from nltk.classify.util import names_demo + + classifier = names_demo(TadmMaxentClassifier.train) + + +def encoding_demo(): + import sys + + from nltk.classify.maxent import TadmEventMaxentFeatureEncoding + + tokens = [ + ({"f0": 1, "f1": 1, "f3": 1}, "A"), + ({"f0": 1, "f2": 1, "f4": 1}, "B"), + ({"f0": 2, "f2": 1, "f3": 1, "f4": 1}, "A"), + ] + encoding = TadmEventMaxentFeatureEncoding.train(tokens) + write_tadm_file(tokens, encoding, sys.stdout) + print() + for i in range(encoding.length()): + print("%s --> %d" % (encoding.describe(i), i)) + print() + + +if __name__ == "__main__": + encoding_demo() + names_demo() diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/classify/weka.py b/llmeval-env/lib/python3.10/site-packages/nltk/classify/weka.py new file mode 100644 index 0000000000000000000000000000000000000000..b02505f0139bc4e0c516d0384b6c9d3224297dbd --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/classify/weka.py @@ -0,0 +1,377 @@ +# Natural Language Toolkit: Interface to Weka Classsifiers +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Edward Loper +# URL: +# For license information, see LICENSE.TXT + +""" +Classifiers that make use of the external 'Weka' package. +""" + +import os +import re +import subprocess +import tempfile +import time +import zipfile +from sys import stdin + +from nltk.classify.api import ClassifierI +from nltk.internals import config_java, java +from nltk.probability import DictionaryProbDist + +_weka_classpath = None +_weka_search = [ + ".", + "/usr/share/weka", + "/usr/local/share/weka", + "/usr/lib/weka", + "/usr/local/lib/weka", +] + + +def config_weka(classpath=None): + global _weka_classpath + + # Make sure java's configured first. + config_java() + + if classpath is not None: + _weka_classpath = classpath + + if _weka_classpath is None: + searchpath = _weka_search + if "WEKAHOME" in os.environ: + searchpath.insert(0, os.environ["WEKAHOME"]) + + for path in searchpath: + if os.path.exists(os.path.join(path, "weka.jar")): + _weka_classpath = os.path.join(path, "weka.jar") + version = _check_weka_version(_weka_classpath) + if version: + print(f"[Found Weka: {_weka_classpath} (version {version})]") + else: + print("[Found Weka: %s]" % _weka_classpath) + _check_weka_version(_weka_classpath) + + if _weka_classpath is None: + raise LookupError( + "Unable to find weka.jar! Use config_weka() " + "or set the WEKAHOME environment variable. " + "For more information about Weka, please see " + "https://www.cs.waikato.ac.nz/ml/weka/" + ) + + +def _check_weka_version(jar): + try: + zf = zipfile.ZipFile(jar) + except (SystemExit, KeyboardInterrupt): + raise + except: + return None + try: + try: + return zf.read("weka/core/version.txt") + except KeyError: + return None + finally: + zf.close() + + +class WekaClassifier(ClassifierI): + def __init__(self, formatter, model_filename): + self._formatter = formatter + self._model = model_filename + + def prob_classify_many(self, featuresets): + return self._classify_many(featuresets, ["-p", "0", "-distribution"]) + + def classify_many(self, featuresets): + return self._classify_many(featuresets, ["-p", "0"]) + + def _classify_many(self, featuresets, options): + # Make sure we can find java & weka. + config_weka() + + temp_dir = tempfile.mkdtemp() + try: + # Write the test data file. + test_filename = os.path.join(temp_dir, "test.arff") + self._formatter.write(test_filename, featuresets) + + # Call weka to classify the data. + cmd = [ + "weka.classifiers.bayes.NaiveBayes", + "-l", + self._model, + "-T", + test_filename, + ] + options + (stdout, stderr) = java( + cmd, + classpath=_weka_classpath, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + + # Check if something went wrong: + if stderr and not stdout: + if "Illegal options: -distribution" in stderr: + raise ValueError( + "The installed version of weka does " + "not support probability distribution " + "output." + ) + else: + raise ValueError("Weka failed to generate output:\n%s" % stderr) + + # Parse weka's output. + return self.parse_weka_output(stdout.decode(stdin.encoding).split("\n")) + + finally: + for f in os.listdir(temp_dir): + os.remove(os.path.join(temp_dir, f)) + os.rmdir(temp_dir) + + def parse_weka_distribution(self, s): + probs = [float(v) for v in re.split("[*,]+", s) if v.strip()] + probs = dict(zip(self._formatter.labels(), probs)) + return DictionaryProbDist(probs) + + def parse_weka_output(self, lines): + # Strip unwanted text from stdout + for i, line in enumerate(lines): + if line.strip().startswith("inst#"): + lines = lines[i:] + break + + if lines[0].split() == ["inst#", "actual", "predicted", "error", "prediction"]: + return [line.split()[2].split(":")[1] for line in lines[1:] if line.strip()] + elif lines[0].split() == [ + "inst#", + "actual", + "predicted", + "error", + "distribution", + ]: + return [ + self.parse_weka_distribution(line.split()[-1]) + for line in lines[1:] + if line.strip() + ] + + # is this safe:? + elif re.match(r"^0 \w+ [01]\.[0-9]* \?\s*$", lines[0]): + return [line.split()[1] for line in lines if line.strip()] + + else: + for line in lines[:10]: + print(line) + raise ValueError( + "Unhandled output format -- your version " + "of weka may not be supported.\n" + " Header: %s" % lines[0] + ) + + # [xx] full list of classifiers (some may be abstract?): + # ADTree, AODE, BayesNet, ComplementNaiveBayes, ConjunctiveRule, + # DecisionStump, DecisionTable, HyperPipes, IB1, IBk, Id3, J48, + # JRip, KStar, LBR, LeastMedSq, LinearRegression, LMT, Logistic, + # LogisticBase, M5Base, MultilayerPerceptron, + # MultipleClassifiersCombiner, NaiveBayes, NaiveBayesMultinomial, + # NaiveBayesSimple, NBTree, NNge, OneR, PaceRegression, PART, + # PreConstructedLinearModel, Prism, RandomForest, + # RandomizableClassifier, RandomTree, RBFNetwork, REPTree, Ridor, + # RuleNode, SimpleLinearRegression, SimpleLogistic, + # SingleClassifierEnhancer, SMO, SMOreg, UserClassifier, VFI, + # VotedPerceptron, Winnow, ZeroR + + _CLASSIFIER_CLASS = { + "naivebayes": "weka.classifiers.bayes.NaiveBayes", + "C4.5": "weka.classifiers.trees.J48", + "log_regression": "weka.classifiers.functions.Logistic", + "svm": "weka.classifiers.functions.SMO", + "kstar": "weka.classifiers.lazy.KStar", + "ripper": "weka.classifiers.rules.JRip", + } + + @classmethod + def train( + cls, + model_filename, + featuresets, + classifier="naivebayes", + options=[], + quiet=True, + ): + # Make sure we can find java & weka. + config_weka() + + # Build an ARFF formatter. + formatter = ARFF_Formatter.from_train(featuresets) + + temp_dir = tempfile.mkdtemp() + try: + # Write the training data file. + train_filename = os.path.join(temp_dir, "train.arff") + formatter.write(train_filename, featuresets) + + if classifier in cls._CLASSIFIER_CLASS: + javaclass = cls._CLASSIFIER_CLASS[classifier] + elif classifier in cls._CLASSIFIER_CLASS.values(): + javaclass = classifier + else: + raise ValueError("Unknown classifier %s" % classifier) + + # Train the weka model. + cmd = [javaclass, "-d", model_filename, "-t", train_filename] + cmd += list(options) + if quiet: + stdout = subprocess.PIPE + else: + stdout = None + java(cmd, classpath=_weka_classpath, stdout=stdout) + + # Return the new classifier. + return WekaClassifier(formatter, model_filename) + + finally: + for f in os.listdir(temp_dir): + os.remove(os.path.join(temp_dir, f)) + os.rmdir(temp_dir) + + +class ARFF_Formatter: + """ + Converts featuresets and labeled featuresets to ARFF-formatted + strings, appropriate for input into Weka. + + Features and classes can be specified manually in the constructor, or may + be determined from data using ``from_train``. + """ + + def __init__(self, labels, features): + """ + :param labels: A list of all class labels that can be generated. + :param features: A list of feature specifications, where + each feature specification is a tuple (fname, ftype); + and ftype is an ARFF type string such as NUMERIC or + STRING. + """ + self._labels = labels + self._features = features + + def format(self, tokens): + """Returns a string representation of ARFF output for the given data.""" + return self.header_section() + self.data_section(tokens) + + def labels(self): + """Returns the list of classes.""" + return list(self._labels) + + def write(self, outfile, tokens): + """Writes ARFF data to a file for the given data.""" + if not hasattr(outfile, "write"): + outfile = open(outfile, "w") + outfile.write(self.format(tokens)) + outfile.close() + + @staticmethod + def from_train(tokens): + """ + Constructs an ARFF_Formatter instance with class labels and feature + types determined from the given data. Handles boolean, numeric and + string (note: not nominal) types. + """ + # Find the set of all attested labels. + labels = {label for (tok, label) in tokens} + + # Determine the types of all features. + features = {} + for tok, label in tokens: + for (fname, fval) in tok.items(): + if issubclass(type(fval), bool): + ftype = "{True, False}" + elif issubclass(type(fval), (int, float, bool)): + ftype = "NUMERIC" + elif issubclass(type(fval), str): + ftype = "STRING" + elif fval is None: + continue # can't tell the type. + else: + raise ValueError("Unsupported value type %r" % ftype) + + if features.get(fname, ftype) != ftype: + raise ValueError("Inconsistent type for %s" % fname) + features[fname] = ftype + features = sorted(features.items()) + + return ARFF_Formatter(labels, features) + + def header_section(self): + """Returns an ARFF header as a string.""" + # Header comment. + s = ( + "% Weka ARFF file\n" + + "% Generated automatically by NLTK\n" + + "%% %s\n\n" % time.ctime() + ) + + # Relation name + s += "@RELATION rel\n\n" + + # Input attribute specifications + for fname, ftype in self._features: + s += "@ATTRIBUTE %-30r %s\n" % (fname, ftype) + + # Label attribute specification + s += "@ATTRIBUTE %-30r {%s}\n" % ("-label-", ",".join(self._labels)) + + return s + + def data_section(self, tokens, labeled=None): + """ + Returns the ARFF data section for the given data. + + :param tokens: a list of featuresets (dicts) or labelled featuresets + which are tuples (featureset, label). + :param labeled: Indicates whether the given tokens are labeled + or not. If None, then the tokens will be assumed to be + labeled if the first token's value is a tuple or list. + """ + # Check if the tokens are labeled or unlabeled. If unlabeled, + # then use 'None' + if labeled is None: + labeled = tokens and isinstance(tokens[0], (tuple, list)) + if not labeled: + tokens = [(tok, None) for tok in tokens] + + # Data section + s = "\n@DATA\n" + for (tok, label) in tokens: + for fname, ftype in self._features: + s += "%s," % self._fmt_arff_val(tok.get(fname)) + s += "%s\n" % self._fmt_arff_val(label) + + return s + + def _fmt_arff_val(self, fval): + if fval is None: + return "?" + elif isinstance(fval, (bool, int)): + return "%s" % fval + elif isinstance(fval, float): + return "%r" % fval + else: + return "%r" % fval + + +if __name__ == "__main__": + from nltk.classify.util import binary_names_demo_features, names_demo + + def make_classifier(featuresets): + return WekaClassifier.train("/tmp/name.model", featuresets, "C4.5") + + classifier = names_demo(make_classifier, binary_names_demo_features) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/__init__.py b/llmeval-env/lib/python3.10/site-packages/nltk/test/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fa54080263a1ad65b72b1c7aabba8186c77db25d --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/__init__.py @@ -0,0 +1,18 @@ +# Natural Language Toolkit: Unit Tests +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Edward Loper +# URL: +# For license information, see LICENSE.TXT + +""" +Unit tests for the NLTK modules. These tests are intended to ensure +that source code changes don't accidentally introduce bugs. +For instructions, please see: + +../../web/dev/local_testing.rst + +https://github.com/nltk/nltk/blob/develop/web/dev/local_testing.rst + + +""" diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/all.py b/llmeval-env/lib/python3.10/site-packages/nltk/test/all.py new file mode 100644 index 0000000000000000000000000000000000000000..dd0d431e1c2fa356f31076768107b5da1e877bdd --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/all.py @@ -0,0 +1,25 @@ +"""Test suite that runs all NLTK tests. + +This module, `nltk.test.all`, is named as the NLTK ``test_suite`` in the +project's ``setup-eggs.py`` file. Here, we create a test suite that +runs all of our doctests, and return it for processing by the setuptools +test harness. + +""" +import doctest +import os.path +import unittest +from glob import glob + + +def additional_tests(): + # print("here-000000000000000") + # print("-----", glob(os.path.join(os.path.dirname(__file__), '*.doctest'))) + dir = os.path.dirname(__file__) + paths = glob(os.path.join(dir, "*.doctest")) + files = [os.path.basename(path) for path in paths] + return unittest.TestSuite([doctest.DocFileSuite(file) for file in files]) + + +# if os.path.split(path)[-1] != 'index.rst' +# skips time-dependent doctest in index.rst diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/bleu.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/bleu.doctest new file mode 100644 index 0000000000000000000000000000000000000000..d7e6e41f5a17e6f048a7264c72f657615e8567cc --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/bleu.doctest @@ -0,0 +1,29 @@ +========== +BLEU tests +========== + +>>> from nltk.translate import bleu + +If the candidate has no alignment to any of the references, the BLEU score is 0. + +>>> bleu( +... ['The candidate has no alignment to any of the references'.split()], +... 'John loves Mary'.split(), +... (1,), +... ) +0 + +This is an implementation of the smoothing techniques +for segment-level BLEU scores that was presented in +Boxing Chen and Collin Cherry (2014) A Systematic Comparison of +Smoothing Techniques for Sentence-Level BLEU. In WMT14. +http://acl2014.org/acl2014/W14-33/pdf/W14-3346.pdf +>>> from nltk.translate.bleu_score import sentence_bleu,SmoothingFunction + + +>>> sentence_bleu( +... ['It is a place of quiet contemplation .'.split()], +... 'It is .'.split(), +... smoothing_function=SmoothingFunction().method4, +... )*100 +4.4267... diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/bnc.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/bnc.doctest new file mode 100644 index 0000000000000000000000000000000000000000..80e1945d241d3a2f3b20f3550a160a4f70bd2bad --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/bnc.doctest @@ -0,0 +1,60 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + + >>> import os.path + + >>> from nltk.corpus.reader import BNCCorpusReader + >>> import nltk.test + + >>> root = os.path.dirname(nltk.test.__file__) + >>> bnc = BNCCorpusReader(root=root, fileids='FX8.xml') + +Checking the word access. +------------------------- + + >>> len(bnc.words()) + 151 + + >>> bnc.words()[:6] + ['Ah', 'there', 'we', 'are', ',', '.'] + >>> bnc.words(stem=True)[:6] + ['ah', 'there', 'we', 'be', ',', '.'] + + >>> bnc.tagged_words()[:6] + [('Ah', 'INTERJ'), ('there', 'ADV'), ('we', 'PRON'), ('are', 'VERB'), (',', 'PUN'), ('.', 'PUN')] + + >>> bnc.tagged_words(c5=True)[:6] + [('Ah', 'ITJ'), ('there', 'AV0'), ('we', 'PNP'), ('are', 'VBB'), (',', 'PUN'), ('.', 'PUN')] + +Testing access to the sentences. +-------------------------------- + + >>> len(bnc.sents()) + 15 + + >>> bnc.sents()[0] + ['Ah', 'there', 'we', 'are', ',', '.'] + >>> bnc.sents(stem=True)[0] + ['ah', 'there', 'we', 'be', ',', '.'] + + >>> bnc.tagged_sents()[0] + [('Ah', 'INTERJ'), ('there', 'ADV'), ('we', 'PRON'), ('are', 'VERB'), (',', 'PUN'), ('.', 'PUN')] + >>> bnc.tagged_sents(c5=True)[0] + [('Ah', 'ITJ'), ('there', 'AV0'), ('we', 'PNP'), ('are', 'VBB'), (',', 'PUN'), ('.', 'PUN')] + +A not lazy loader. +------------------ + + >>> eager = BNCCorpusReader(root=root, fileids=r'FX8.xml', lazy=False) + + >>> len(eager.words()) + 151 + >>> eager.words(stem=True)[6:17] + ['right', 'abdominal', 'wound', ',', 'she', 'be', 'a', 'wee', 'bit', 'confuse', '.'] + + >>> eager.tagged_words()[6:11] + [('Right', 'ADV'), ('abdominal', 'ADJ'), ('wound', 'SUBST'), (',', 'PUN'), ('she', 'PRON')] + >>> eager.tagged_words(c5=True)[6:17] + [('Right', 'AV0'), ('abdominal', 'AJ0'), ('wound', 'NN1'), (',', 'PUN'), ('she', 'PNP'), ("'s", 'VBZ'), ('a', 'AT0'), ('wee', 'AJ0-NN1'), ('bit', 'NN1'), ('confused', 'VVN-AJ0'), ('.', 'PUN')] + >>> len(eager.sents()) + 15 diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/ccg.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/ccg.doctest new file mode 100644 index 0000000000000000000000000000000000000000..9c1e642c5e32ee0afe4c3d689d6461807a1db738 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/ccg.doctest @@ -0,0 +1,376 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +============================== +Combinatory Categorial Grammar +============================== + +Relative Clauses +---------------- + + >>> from nltk.ccg import chart, lexicon + +Construct a lexicon: + + >>> lex = lexicon.fromstring(''' + ... :- S, NP, N, VP + ... + ... Det :: NP/N + ... Pro :: NP + ... Modal :: S\\NP/VP + ... + ... TV :: VP/NP + ... DTV :: TV/NP + ... + ... the => Det + ... + ... that => Det + ... that => NP + ... + ... I => Pro + ... you => Pro + ... we => Pro + ... + ... chef => N + ... cake => N + ... children => N + ... dough => N + ... + ... will => Modal + ... should => Modal + ... might => Modal + ... must => Modal + ... + ... and => var\\.,var/.,var + ... + ... to => VP[to]/VP + ... + ... without => (VP\\VP)/VP[ing] + ... + ... be => TV + ... cook => TV + ... eat => TV + ... + ... cooking => VP[ing]/NP + ... + ... give => DTV + ... + ... is => (S\\NP)/NP + ... prefer => (S\\NP)/NP + ... + ... which => (N\\N)/(S/NP) + ... + ... persuade => (VP/VP[to])/NP + ... ''') + + >>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet) + >>> for parse in parser.parse("you prefer that cake".split()): + ... chart.printCCGDerivation(parse) + ... break + ... + you prefer that cake + NP ((S\NP)/NP) (NP/N) N + --------------> + NP + ---------------------------> + (S\NP) + --------------------------------< + S + + >>> for parse in parser.parse("that is the cake which you prefer".split()): + ... chart.printCCGDerivation(parse) + ... break + ... + that is the cake which you prefer + NP ((S\NP)/NP) (NP/N) N ((N\N)/(S/NP)) NP ((S\NP)/NP) + ----->T + (S/(S\NP)) + ------------------>B + (S/NP) + ----------------------------------> + (N\N) + ----------------------------------------< + N + ------------------------------------------------> + NP + -------------------------------------------------------------> + (S\NP) + -------------------------------------------------------------------< + S + + +Some other sentences to try: +"that is the cake which we will persuade the chef to cook" +"that is the cake which we will persuade the chef to give the children" + + >>> sent = "that is the dough which you will eat without cooking".split() + >>> nosub_parser = chart.CCGChartParser(lex, chart.ApplicationRuleSet + + ... chart.CompositionRuleSet + chart.TypeRaiseRuleSet) + +Without Substitution (no output) + + >>> for parse in nosub_parser.parse(sent): + ... chart.printCCGDerivation(parse) + +With Substitution: + + >>> for parse in parser.parse(sent): + ... chart.printCCGDerivation(parse) + ... break + ... + that is the dough which you will eat without cooking + NP ((S\NP)/NP) (NP/N) N ((N\N)/(S/NP)) NP ((S\NP)/VP) (VP/NP) ((VP\VP)/VP['ing']) (VP['ing']/NP) + ----->T + (S/(S\NP)) + ------------------------------------->B + ((VP\VP)/NP) + ----------------------------------------------B + ((S\NP)/NP) + ---------------------------------------------------------------->B + (S/NP) + --------------------------------------------------------------------------------> + (N\N) + ---------------------------------------------------------------------------------------< + N + -----------------------------------------------------------------------------------------------> + NP + ------------------------------------------------------------------------------------------------------------> + (S\NP) + ------------------------------------------------------------------------------------------------------------------< + S + + +Conjunction +----------- + + >>> from nltk.ccg.chart import CCGChartParser, ApplicationRuleSet, CompositionRuleSet + >>> from nltk.ccg.chart import SubstitutionRuleSet, TypeRaiseRuleSet, printCCGDerivation + >>> from nltk.ccg import lexicon + +Lexicons for the tests: + + >>> test1_lex = ''' + ... :- S,N,NP,VP + ... I => NP + ... you => NP + ... will => S\\NP/VP + ... cook => VP/NP + ... which => (N\\N)/(S/NP) + ... and => var\\.,var/.,var + ... might => S\\NP/VP + ... eat => VP/NP + ... the => NP/N + ... mushrooms => N + ... parsnips => N''' + >>> test2_lex = ''' + ... :- N, S, NP, VP + ... articles => N + ... the => NP/N + ... and => var\\.,var/.,var + ... which => (N\\N)/(S/NP) + ... I => NP + ... anyone => NP + ... will => (S/VP)\\NP + ... file => VP/NP + ... without => (VP\\VP)/VP[ing] + ... forget => VP/NP + ... reading => VP[ing]/NP + ... ''' + +Tests handling of conjunctions. +Note that while the two derivations are different, they are semantically equivalent. + + >>> lex = lexicon.fromstring(test1_lex) + >>> parser = CCGChartParser(lex, ApplicationRuleSet + CompositionRuleSet + SubstitutionRuleSet) + >>> for parse in parser.parse("I will cook and might eat the mushrooms and parsnips".split()): + ... printCCGDerivation(parse) + I will cook and might eat the mushrooms and parsnips + NP ((S\NP)/VP) (VP/NP) ((_var0\.,_var0)/.,_var0) ((S\NP)/VP) (VP/NP) (NP/N) N ((_var0\.,_var0)/.,_var0) N + ---------------------->B + ((S\NP)/NP) + ---------------------->B + ((S\NP)/NP) + -------------------------------------------------> + (((S\NP)/NP)\.,((S\NP)/NP)) + -----------------------------------------------------------------------< + ((S\NP)/NP) + -------------------------------------> + (N\.,N) + ------------------------------------------------< + N + --------------------------------------------------------> + NP + -------------------------------------------------------------------------------------------------------------------------------> + (S\NP) + -----------------------------------------------------------------------------------------------------------------------------------< + S + I will cook and might eat the mushrooms and parsnips + NP ((S\NP)/VP) (VP/NP) ((_var0\.,_var0)/.,_var0) ((S\NP)/VP) (VP/NP) (NP/N) N ((_var0\.,_var0)/.,_var0) N + ---------------------->B + ((S\NP)/NP) + ---------------------->B + ((S\NP)/NP) + -------------------------------------------------> + (((S\NP)/NP)\.,((S\NP)/NP)) + -----------------------------------------------------------------------< + ((S\NP)/NP) + ------------------------------------------------------------------------------->B + ((S\NP)/N) + -------------------------------------> + (N\.,N) + ------------------------------------------------< + N + -------------------------------------------------------------------------------------------------------------------------------> + (S\NP) + -----------------------------------------------------------------------------------------------------------------------------------< + S + + +Tests handling subject extraction. +Interesting to point that the two parses are clearly semantically different. + + >>> lex = lexicon.fromstring(test2_lex) + >>> parser = CCGChartParser(lex, ApplicationRuleSet + CompositionRuleSet + SubstitutionRuleSet) + >>> for parse in parser.parse("articles which I will file and forget without reading".split()): + ... printCCGDerivation(parse) + articles which I will file and forget without reading + N ((N\N)/(S/NP)) NP ((S/VP)\NP) (VP/NP) ((_var0\.,_var0)/.,_var0) (VP/NP) ((VP\VP)/VP['ing']) (VP['ing']/NP) + -----------------< + (S/VP) + ------------------------------------->B + ((VP\VP)/NP) + ---------------------------------------------- + ((VP/NP)\.,(VP/NP)) + ----------------------------------------------------------------------------------< + (VP/NP) + --------------------------------------------------------------------------------------------------->B + (S/NP) + -------------------------------------------------------------------------------------------------------------------> + (N\N) + -----------------------------------------------------------------------------------------------------------------------------< + N + articles which I will file and forget without reading + N ((N\N)/(S/NP)) NP ((S/VP)\NP) (VP/NP) ((_var0\.,_var0)/.,_var0) (VP/NP) ((VP\VP)/VP['ing']) (VP['ing']/NP) + -----------------< + (S/VP) + ------------------------------------> + ((VP/NP)\.,(VP/NP)) + ---------------------------------------------< + (VP/NP) + ------------------------------------->B + ((VP\VP)/NP) + ----------------------------------------------------------------------------------B + (S/NP) + -------------------------------------------------------------------------------------------------------------------> + (N\N) + -----------------------------------------------------------------------------------------------------------------------------< + N + + +Unicode support +--------------- + +Unicode words are supported. + + >>> from nltk.ccg import chart, lexicon + +Lexicons for the tests: + + >>> lex = lexicon.fromstring(''' + ... :- S, N, NP, PP + ... + ... AdjI :: N\\N + ... AdjD :: N/N + ... AdvD :: S/S + ... AdvI :: S\\S + ... Det :: NP/N + ... PrepNPCompl :: PP/NP + ... PrepNAdjN :: S\\S/N + ... PrepNAdjNP :: S\\S/NP + ... VPNP :: S\\NP/NP + ... VPPP :: S\\NP/PP + ... VPser :: S\\NP/AdjI + ... + ... auto => N + ... bebidas => N + ... cine => N + ... ley => N + ... libro => N + ... ministro => N + ... panadería => N + ... presidente => N + ... super => N + ... + ... el => Det + ... la => Det + ... las => Det + ... un => Det + ... + ... Ana => NP + ... Pablo => NP + ... + ... y => var\\.,var/.,var + ... + ... pero => (S/NP)\\(S/NP)/(S/NP) + ... + ... anunció => VPNP + ... compró => VPNP + ... cree => S\\NP/S[dep] + ... desmintió => VPNP + ... lee => VPNP + ... fueron => VPPP + ... + ... es => VPser + ... + ... interesante => AdjD + ... interesante => AdjI + ... nueva => AdjD + ... nueva => AdjI + ... + ... a => PrepNPCompl + ... en => PrepNAdjN + ... en => PrepNAdjNP + ... + ... ayer => AdvI + ... + ... que => (NP\\NP)/(S/NP) + ... que => S[dep]/S + ... ''') + + >>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet) + >>> for parse in parser.parse(u"el ministro anunció pero el presidente desmintió la nueva ley".split()): + ... printCCGDerivation(parse) # doctest: +SKIP + ... # it fails on python2.7 because of the unicode problem explained in https://github.com/nltk/nltk/pull/1354 + ... break + el ministro anunció pero el presidente desmintió la nueva ley + (NP/N) N ((S\NP)/NP) (((S/NP)\(S/NP))/(S/NP)) (NP/N) N ((S\NP)/NP) (NP/N) (N/N) N + ------------------> + NP + ------------------>T + (S/(S\NP)) + --------------------> + NP + -------------------->T + (S/(S\NP)) + --------------------------------->B + (S/NP) + -----------------------------------------------------------> + ((S/NP)\(S/NP)) + ------------> + N + --------------------> + NP + -------------------- + S diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/ccg_semantics.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/ccg_semantics.doctest new file mode 100644 index 0000000000000000000000000000000000000000..5350d58d15f6556473a9d8de990d52fc2c97f796 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/ccg_semantics.doctest @@ -0,0 +1,552 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +============================================== +Combinatory Categorial Grammar with semantics +============================================== + +----- +Chart +----- + + + >>> from nltk.ccg import chart, lexicon + >>> from nltk.ccg.chart import printCCGDerivation + +No semantics +------------------- + + >>> lex = lexicon.fromstring(''' + ... :- S, NP, N + ... She => NP + ... has => (S\\NP)/NP + ... books => NP + ... ''', + ... False) + + >>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet) + >>> parses = list(parser.parse("She has books".split())) + >>> print(str(len(parses)) + " parses") + 3 parses + + >>> printCCGDerivation(parses[0]) + She has books + NP ((S\NP)/NP) NP + --------------------> + (S\NP) + -------------------------< + S + + >>> printCCGDerivation(parses[1]) + She has books + NP ((S\NP)/NP) NP + ----->T + (S/(S\NP)) + --------------------> + (S\NP) + -------------------------> + S + + + >>> printCCGDerivation(parses[2]) + She has books + NP ((S\NP)/NP) NP + ----->T + (S/(S\NP)) + ------------------>B + (S/NP) + -------------------------> + S + +Simple semantics +------------------- + + >>> lex = lexicon.fromstring(''' + ... :- S, NP, N + ... She => NP {she} + ... has => (S\\NP)/NP {\\x y.have(y, x)} + ... a => NP/N {\\P.exists z.P(z)} + ... book => N {book} + ... ''', + ... True) + + >>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet) + >>> parses = list(parser.parse("She has a book".split())) + >>> print(str(len(parses)) + " parses") + 7 parses + + >>> printCCGDerivation(parses[0]) + She has a book + NP {she} ((S\NP)/NP) {\x y.have(y,x)} (NP/N) {\P.exists z.P(z)} N {book} + -------------------------------------> + NP {exists z.book(z)} + -------------------------------------------------------------------> + (S\NP) {\y.have(y,exists z.book(z))} + -----------------------------------------------------------------------------< + S {have(she,exists z.book(z))} + + >>> printCCGDerivation(parses[1]) + She has a book + NP {she} ((S\NP)/NP) {\x y.have(y,x)} (NP/N) {\P.exists z.P(z)} N {book} + --------------------------------------------------------->B + ((S\NP)/N) {\P y.have(y,exists z.P(z))} + -------------------------------------------------------------------> + (S\NP) {\y.have(y,exists z.book(z))} + -----------------------------------------------------------------------------< + S {have(she,exists z.book(z))} + + >>> printCCGDerivation(parses[2]) + She has a book + NP {she} ((S\NP)/NP) {\x y.have(y,x)} (NP/N) {\P.exists z.P(z)} N {book} + ---------->T + (S/(S\NP)) {\F.F(she)} + -------------------------------------> + NP {exists z.book(z)} + -------------------------------------------------------------------> + (S\NP) {\y.have(y,exists z.book(z))} + -----------------------------------------------------------------------------> + S {have(she,exists z.book(z))} + + >>> printCCGDerivation(parses[3]) + She has a book + NP {she} ((S\NP)/NP) {\x y.have(y,x)} (NP/N) {\P.exists z.P(z)} N {book} + ---------->T + (S/(S\NP)) {\F.F(she)} + --------------------------------------------------------->B + ((S\NP)/N) {\P y.have(y,exists z.P(z))} + -------------------------------------------------------------------> + (S\NP) {\y.have(y,exists z.book(z))} + -----------------------------------------------------------------------------> + S {have(she,exists z.book(z))} + + >>> printCCGDerivation(parses[4]) + She has a book + NP {she} ((S\NP)/NP) {\x y.have(y,x)} (NP/N) {\P.exists z.P(z)} N {book} + ---------->T + (S/(S\NP)) {\F.F(she)} + ---------------------------------------->B + (S/NP) {\x.have(she,x)} + -------------------------------------> + NP {exists z.book(z)} + -----------------------------------------------------------------------------> + S {have(she,exists z.book(z))} + + >>> printCCGDerivation(parses[5]) + She has a book + NP {she} ((S\NP)/NP) {\x y.have(y,x)} (NP/N) {\P.exists z.P(z)} N {book} + ---------->T + (S/(S\NP)) {\F.F(she)} + --------------------------------------------------------->B + ((S\NP)/N) {\P y.have(y,exists z.P(z))} + ------------------------------------------------------------------->B + (S/N) {\P.have(she,exists z.P(z))} + -----------------------------------------------------------------------------> + S {have(she,exists z.book(z))} + + >>> printCCGDerivation(parses[6]) + She has a book + NP {she} ((S\NP)/NP) {\x y.have(y,x)} (NP/N) {\P.exists z.P(z)} N {book} + ---------->T + (S/(S\NP)) {\F.F(she)} + ---------------------------------------->B + (S/NP) {\x.have(she,x)} + ------------------------------------------------------------------->B + (S/N) {\P.have(she,exists z.P(z))} + -----------------------------------------------------------------------------> + S {have(she,exists z.book(z))} + +Complex semantics +------------------- + + >>> lex = lexicon.fromstring(''' + ... :- S, NP, N + ... She => NP {she} + ... has => (S\\NP)/NP {\\x y.have(y, x)} + ... a => ((S\\NP)\\((S\\NP)/NP))/N {\\P R x.(exists z.P(z) & R(z,x))} + ... book => N {book} + ... ''', + ... True) + + >>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet) + >>> parses = list(parser.parse("She has a book".split())) + >>> print(str(len(parses)) + " parses") + 2 parses + + >>> printCCGDerivation(parses[0]) + She has a book + NP {she} ((S\NP)/NP) {\x y.have(y,x)} (((S\NP)\((S\NP)/NP))/N) {\P R x.(exists z.P(z) & R(z,x))} N {book} + ----------------------------------------------------------------------> + ((S\NP)\((S\NP)/NP)) {\R x.(exists z.book(z) & R(z,x))} + ----------------------------------------------------------------------------------------------------< + (S\NP) {\x.(exists z.book(z) & have(x,z))} + --------------------------------------------------------------------------------------------------------------< + S {(exists z.book(z) & have(she,z))} + + >>> printCCGDerivation(parses[1]) + She has a book + NP {she} ((S\NP)/NP) {\x y.have(y,x)} (((S\NP)\((S\NP)/NP))/N) {\P R x.(exists z.P(z) & R(z,x))} N {book} + ---------->T + (S/(S\NP)) {\F.F(she)} + ----------------------------------------------------------------------> + ((S\NP)\((S\NP)/NP)) {\R x.(exists z.book(z) & R(z,x))} + ----------------------------------------------------------------------------------------------------< + (S\NP) {\x.(exists z.book(z) & have(x,z))} + --------------------------------------------------------------------------------------------------------------> + S {(exists z.book(z) & have(she,z))} + +Using conjunctions +--------------------- + + # TODO: The semantics of "and" should have been more flexible + >>> lex = lexicon.fromstring(''' + ... :- S, NP, N + ... I => NP {I} + ... cook => (S\\NP)/NP {\\x y.cook(x,y)} + ... and => var\\.,var/.,var {\\P Q x y.(P(x,y) & Q(x,y))} + ... eat => (S\\NP)/NP {\\x y.eat(x,y)} + ... the => NP/N {\\x.the(x)} + ... bacon => N {bacon} + ... ''', + ... True) + + >>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet) + >>> parses = list(parser.parse("I cook and eat the bacon".split())) + >>> print(str(len(parses)) + " parses") + 7 parses + + >>> printCCGDerivation(parses[0]) + I cook and eat the bacon + NP {I} ((S\NP)/NP) {\x y.cook(x,y)} ((_var0\.,_var0)/.,_var0) {\P Q x y.(P(x,y) & Q(x,y))} ((S\NP)/NP) {\x y.eat(x,y)} (NP/N) {\x.the(x)} N {bacon} + -------------------------------------------------------------------------------------> + (((S\NP)/NP)\.,((S\NP)/NP)) {\Q x y.(eat(x,y) & Q(x,y))} + -------------------------------------------------------------------------------------------------------------------< + ((S\NP)/NP) {\x y.(eat(x,y) & cook(x,y))} + -------------------------------> + NP {the(bacon)} + --------------------------------------------------------------------------------------------------------------------------------------------------> + (S\NP) {\y.(eat(the(bacon),y) & cook(the(bacon),y))} + ----------------------------------------------------------------------------------------------------------------------------------------------------------< + S {(eat(the(bacon),I) & cook(the(bacon),I))} + + >>> printCCGDerivation(parses[1]) + I cook and eat the bacon + NP {I} ((S\NP)/NP) {\x y.cook(x,y)} ((_var0\.,_var0)/.,_var0) {\P Q x y.(P(x,y) & Q(x,y))} ((S\NP)/NP) {\x y.eat(x,y)} (NP/N) {\x.the(x)} N {bacon} + -------------------------------------------------------------------------------------> + (((S\NP)/NP)\.,((S\NP)/NP)) {\Q x y.(eat(x,y) & Q(x,y))} + -------------------------------------------------------------------------------------------------------------------< + ((S\NP)/NP) {\x y.(eat(x,y) & cook(x,y))} + --------------------------------------------------------------------------------------------------------------------------------------->B + ((S\NP)/N) {\x y.(eat(the(x),y) & cook(the(x),y))} + --------------------------------------------------------------------------------------------------------------------------------------------------> + (S\NP) {\y.(eat(the(bacon),y) & cook(the(bacon),y))} + ----------------------------------------------------------------------------------------------------------------------------------------------------------< + S {(eat(the(bacon),I) & cook(the(bacon),I))} + + >>> printCCGDerivation(parses[2]) + I cook and eat the bacon + NP {I} ((S\NP)/NP) {\x y.cook(x,y)} ((_var0\.,_var0)/.,_var0) {\P Q x y.(P(x,y) & Q(x,y))} ((S\NP)/NP) {\x y.eat(x,y)} (NP/N) {\x.the(x)} N {bacon} + -------->T + (S/(S\NP)) {\F.F(I)} + -------------------------------------------------------------------------------------> + (((S\NP)/NP)\.,((S\NP)/NP)) {\Q x y.(eat(x,y) & Q(x,y))} + -------------------------------------------------------------------------------------------------------------------< + ((S\NP)/NP) {\x y.(eat(x,y) & cook(x,y))} + -------------------------------> + NP {the(bacon)} + --------------------------------------------------------------------------------------------------------------------------------------------------> + (S\NP) {\y.(eat(the(bacon),y) & cook(the(bacon),y))} + ----------------------------------------------------------------------------------------------------------------------------------------------------------> + S {(eat(the(bacon),I) & cook(the(bacon),I))} + + >>> printCCGDerivation(parses[3]) + I cook and eat the bacon + NP {I} ((S\NP)/NP) {\x y.cook(x,y)} ((_var0\.,_var0)/.,_var0) {\P Q x y.(P(x,y) & Q(x,y))} ((S\NP)/NP) {\x y.eat(x,y)} (NP/N) {\x.the(x)} N {bacon} + -------->T + (S/(S\NP)) {\F.F(I)} + -------------------------------------------------------------------------------------> + (((S\NP)/NP)\.,((S\NP)/NP)) {\Q x y.(eat(x,y) & Q(x,y))} + -------------------------------------------------------------------------------------------------------------------< + ((S\NP)/NP) {\x y.(eat(x,y) & cook(x,y))} + --------------------------------------------------------------------------------------------------------------------------------------->B + ((S\NP)/N) {\x y.(eat(the(x),y) & cook(the(x),y))} + --------------------------------------------------------------------------------------------------------------------------------------------------> + (S\NP) {\y.(eat(the(bacon),y) & cook(the(bacon),y))} + ----------------------------------------------------------------------------------------------------------------------------------------------------------> + S {(eat(the(bacon),I) & cook(the(bacon),I))} + + >>> printCCGDerivation(parses[4]) + I cook and eat the bacon + NP {I} ((S\NP)/NP) {\x y.cook(x,y)} ((_var0\.,_var0)/.,_var0) {\P Q x y.(P(x,y) & Q(x,y))} ((S\NP)/NP) {\x y.eat(x,y)} (NP/N) {\x.the(x)} N {bacon} + -------->T + (S/(S\NP)) {\F.F(I)} + -------------------------------------------------------------------------------------> + (((S\NP)/NP)\.,((S\NP)/NP)) {\Q x y.(eat(x,y) & Q(x,y))} + -------------------------------------------------------------------------------------------------------------------< + ((S\NP)/NP) {\x y.(eat(x,y) & cook(x,y))} + --------------------------------------------------------------------------------------------------------------------------->B + (S/NP) {\x.(eat(x,I) & cook(x,I))} + -------------------------------> + NP {the(bacon)} + ----------------------------------------------------------------------------------------------------------------------------------------------------------> + S {(eat(the(bacon),I) & cook(the(bacon),I))} + + >>> printCCGDerivation(parses[5]) + I cook and eat the bacon + NP {I} ((S\NP)/NP) {\x y.cook(x,y)} ((_var0\.,_var0)/.,_var0) {\P Q x y.(P(x,y) & Q(x,y))} ((S\NP)/NP) {\x y.eat(x,y)} (NP/N) {\x.the(x)} N {bacon} + -------->T + (S/(S\NP)) {\F.F(I)} + -------------------------------------------------------------------------------------> + (((S\NP)/NP)\.,((S\NP)/NP)) {\Q x y.(eat(x,y) & Q(x,y))} + -------------------------------------------------------------------------------------------------------------------< + ((S\NP)/NP) {\x y.(eat(x,y) & cook(x,y))} + --------------------------------------------------------------------------------------------------------------------------------------->B + ((S\NP)/N) {\x y.(eat(the(x),y) & cook(the(x),y))} + ----------------------------------------------------------------------------------------------------------------------------------------------->B + (S/N) {\x.(eat(the(x),I) & cook(the(x),I))} + ----------------------------------------------------------------------------------------------------------------------------------------------------------> + S {(eat(the(bacon),I) & cook(the(bacon),I))} + + >>> printCCGDerivation(parses[6]) + I cook and eat the bacon + NP {I} ((S\NP)/NP) {\x y.cook(x,y)} ((_var0\.,_var0)/.,_var0) {\P Q x y.(P(x,y) & Q(x,y))} ((S\NP)/NP) {\x y.eat(x,y)} (NP/N) {\x.the(x)} N {bacon} + -------->T + (S/(S\NP)) {\F.F(I)} + -------------------------------------------------------------------------------------> + (((S\NP)/NP)\.,((S\NP)/NP)) {\Q x y.(eat(x,y) & Q(x,y))} + -------------------------------------------------------------------------------------------------------------------< + ((S\NP)/NP) {\x y.(eat(x,y) & cook(x,y))} + --------------------------------------------------------------------------------------------------------------------------->B + (S/NP) {\x.(eat(x,I) & cook(x,I))} + ----------------------------------------------------------------------------------------------------------------------------------------------->B + (S/N) {\x.(eat(the(x),I) & cook(the(x),I))} + ----------------------------------------------------------------------------------------------------------------------------------------------------------> + S {(eat(the(bacon),I) & cook(the(bacon),I))} + +Tests from published papers +------------------------------ + +An example from "CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank", Hockenmaier and Steedman, 2007, Page 359, https://www.aclweb.org/anthology/J/J07/J07-3004.pdf + + >>> lex = lexicon.fromstring(''' + ... :- S, NP + ... I => NP {I} + ... give => ((S\\NP)/NP)/NP {\\x y z.give(y,x,z)} + ... them => NP {them} + ... money => NP {money} + ... ''', + ... True) + + >>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet) + >>> parses = list(parser.parse("I give them money".split())) + >>> print(str(len(parses)) + " parses") + 3 parses + + >>> printCCGDerivation(parses[0]) + I give them money + NP {I} (((S\NP)/NP)/NP) {\x y z.give(y,x,z)} NP {them} NP {money} + --------------------------------------------------> + ((S\NP)/NP) {\y z.give(y,them,z)} + --------------------------------------------------------------> + (S\NP) {\z.give(money,them,z)} + ----------------------------------------------------------------------< + S {give(money,them,I)} + + >>> printCCGDerivation(parses[1]) + I give them money + NP {I} (((S\NP)/NP)/NP) {\x y z.give(y,x,z)} NP {them} NP {money} + -------->T + (S/(S\NP)) {\F.F(I)} + --------------------------------------------------> + ((S\NP)/NP) {\y z.give(y,them,z)} + --------------------------------------------------------------> + (S\NP) {\z.give(money,them,z)} + ----------------------------------------------------------------------> + S {give(money,them,I)} + + + >>> printCCGDerivation(parses[2]) + I give them money + NP {I} (((S\NP)/NP)/NP) {\x y z.give(y,x,z)} NP {them} NP {money} + -------->T + (S/(S\NP)) {\F.F(I)} + --------------------------------------------------> + ((S\NP)/NP) {\y z.give(y,them,z)} + ---------------------------------------------------------->B + (S/NP) {\y.give(y,them,I)} + ----------------------------------------------------------------------> + S {give(money,them,I)} + + +An example from "CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank", Hockenmaier and Steedman, 2007, Page 359, https://www.aclweb.org/anthology/J/J07/J07-3004.pdf + + >>> lex = lexicon.fromstring(''' + ... :- N, NP, S + ... money => N {money} + ... that => (N\\N)/(S/NP) {\\P Q x.(P(x) & Q(x))} + ... I => NP {I} + ... give => ((S\\NP)/NP)/NP {\\x y z.give(y,x,z)} + ... them => NP {them} + ... ''', + ... True) + + >>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet) + >>> parses = list(parser.parse("money that I give them".split())) + >>> print(str(len(parses)) + " parses") + 3 parses + + >>> printCCGDerivation(parses[0]) + money that I give them + N {money} ((N\N)/(S/NP)) {\P Q x.(P(x) & Q(x))} NP {I} (((S\NP)/NP)/NP) {\x y z.give(y,x,z)} NP {them} + -------->T + (S/(S\NP)) {\F.F(I)} + --------------------------------------------------> + ((S\NP)/NP) {\y z.give(y,them,z)} + ---------------------------------------------------------->B + (S/NP) {\y.give(y,them,I)} + -------------------------------------------------------------------------------------------------> + (N\N) {\Q x.(give(x,them,I) & Q(x))} + ------------------------------------------------------------------------------------------------------------< + N {\x.(give(x,them,I) & money(x))} + + >>> printCCGDerivation(parses[1]) + money that I give them + N {money} ((N\N)/(S/NP)) {\P Q x.(P(x) & Q(x))} NP {I} (((S\NP)/NP)/NP) {\x y z.give(y,x,z)} NP {them} + ----------->T + (N/(N\N)) {\F.F(money)} + -------->T + (S/(S\NP)) {\F.F(I)} + --------------------------------------------------> + ((S\NP)/NP) {\y z.give(y,them,z)} + ---------------------------------------------------------->B + (S/NP) {\y.give(y,them,I)} + -------------------------------------------------------------------------------------------------> + (N\N) {\Q x.(give(x,them,I) & Q(x))} + ------------------------------------------------------------------------------------------------------------> + N {\x.(give(x,them,I) & money(x))} + + >>> printCCGDerivation(parses[2]) + money that I give them + N {money} ((N\N)/(S/NP)) {\P Q x.(P(x) & Q(x))} NP {I} (((S\NP)/NP)/NP) {\x y z.give(y,x,z)} NP {them} + ----------->T + (N/(N\N)) {\F.F(money)} + -------------------------------------------------->B + (N/(S/NP)) {\P x.(P(x) & money(x))} + -------->T + (S/(S\NP)) {\F.F(I)} + --------------------------------------------------> + ((S\NP)/NP) {\y z.give(y,them,z)} + ---------------------------------------------------------->B + (S/NP) {\y.give(y,them,I)} + ------------------------------------------------------------------------------------------------------------> + N {\x.(give(x,them,I) & money(x))} + + +------- +Lexicon +------- + + >>> from nltk.ccg import lexicon + +Parse lexicon with semantics + + >>> print(str(lexicon.fromstring( + ... ''' + ... :- S,NP + ... + ... IntransVsg :: S\\NP[sg] + ... + ... sleeps => IntransVsg {\\x.sleep(x)} + ... eats => S\\NP[sg]/NP {\\x y.eat(x,y)} + ... + ... and => var\\var/var {\\x y.x & y} + ... ''', + ... True + ... ))) + and => ((_var0\_var0)/_var0) {(\x y.x & y)} + eats => ((S\NP['sg'])/NP) {\x y.eat(x,y)} + sleeps => (S\NP['sg']) {\x.sleep(x)} + +Parse lexicon without semantics + + >>> print(str(lexicon.fromstring( + ... ''' + ... :- S,NP + ... + ... IntransVsg :: S\\NP[sg] + ... + ... sleeps => IntransVsg + ... eats => S\\NP[sg]/NP {sem=\\x y.eat(x,y)} + ... + ... and => var\\var/var + ... ''', + ... False + ... ))) + and => ((_var0\_var0)/_var0) + eats => ((S\NP['sg'])/NP) + sleeps => (S\NP['sg']) + +Semantics are missing + + >>> print(str(lexicon.fromstring( + ... ''' + ... :- S,NP + ... + ... eats => S\\NP[sg]/NP + ... ''', + ... True + ... ))) + Traceback (most recent call last): + ... + AssertionError: eats => S\NP[sg]/NP must contain semantics because include_semantics is set to True + + +------------------------------------ +CCG combinator semantics computation +------------------------------------ + + >>> from nltk.sem.logic import * + >>> from nltk.ccg.logic import * + + >>> read_expr = Expression.fromstring + +Compute semantics from function application + + >>> print(str(compute_function_semantics(read_expr(r'\x.P(x)'), read_expr(r'book')))) + P(book) + + >>> print(str(compute_function_semantics(read_expr(r'\P.P(book)'), read_expr(r'read')))) + read(book) + + >>> print(str(compute_function_semantics(read_expr(r'\P.P(book)'), read_expr(r'\x.read(x)')))) + read(book) + +Compute semantics from composition + + >>> print(str(compute_composition_semantics(read_expr(r'\x.P(x)'), read_expr(r'\x.Q(x)')))) + \x.P(Q(x)) + + >>> print(str(compute_composition_semantics(read_expr(r'\x.P(x)'), read_expr(r'read')))) + Traceback (most recent call last): + ... + AssertionError: `read` must be a lambda expression + +Compute semantics from substitution + + >>> print(str(compute_substitution_semantics(read_expr(r'\x y.P(x,y)'), read_expr(r'\x.Q(x)')))) + \x.P(x,Q(x)) + + >>> print(str(compute_substitution_semantics(read_expr(r'\x.P(x)'), read_expr(r'read')))) + Traceback (most recent call last): + ... + AssertionError: `\x.P(x)` must be a lambda expression with 2 arguments + +Compute type-raise semantics + + >>> print(str(compute_type_raised_semantics(read_expr(r'\x.P(x)')))) + \F x.F(P(x)) + + >>> print(str(compute_type_raised_semantics(read_expr(r'\x.F(x)')))) + \F1 x.F1(F(x)) + + >>> print(str(compute_type_raised_semantics(read_expr(r'\x y z.P(x,y,z)')))) + \F x y z.F(P(x,y,z)) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/chat80.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/chat80.doctest new file mode 100644 index 0000000000000000000000000000000000000000..b17a95fb254208823711bb8285c48060a2a6ce3e --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/chat80.doctest @@ -0,0 +1,232 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +======= +Chat-80 +======= + +Chat-80 was a natural language system which allowed the user to +interrogate a Prolog knowledge base in the domain of world +geography. It was developed in the early '80s by Warren and Pereira; see +``_ for a description and +``_ for the source +files. + +The ``chat80`` module contains functions to extract data from the Chat-80 +relation files ('the world database'), and convert then into a format +that can be incorporated in the FOL models of +``nltk.sem.evaluate``. The code assumes that the Prolog +input files are available in the NLTK corpora directory. + +The Chat-80 World Database consists of the following files:: + + world0.pl + rivers.pl + cities.pl + countries.pl + contain.pl + borders.pl + +This module uses a slightly modified version of ``world0.pl``, in which +a set of Prolog rules have been omitted. The modified file is named +``world1.pl``. Currently, the file ``rivers.pl`` is not read in, since +it uses a list rather than a string in the second field. + +Reading Chat-80 Files +===================== + +Chat-80 relations are like tables in a relational database. The +relation acts as the name of the table; the first argument acts as the +'primary key'; and subsequent arguments are further fields in the +table. In general, the name of the table provides a label for a unary +predicate whose extension is all the primary keys. For example, +relations in ``cities.pl`` are of the following form:: + + 'city(athens,greece,1368).' + +Here, ``'athens'`` is the key, and will be mapped to a member of the +unary predicate *city*. + +By analogy with NLTK corpora, ``chat80`` defines a number of 'items' +which correspond to the relations. + + >>> from nltk.sem import chat80 + >>> print(chat80.items) + ('borders', 'circle_of_lat', 'circle_of_long', 'city', ...) + +The fields in the table are mapped to binary predicates. The first +argument of the predicate is the primary key, while the second +argument is the data in the relevant field. Thus, in the above +example, the third field is mapped to the binary predicate +*population_of*, whose extension is a set of pairs such as +``'(athens, 1368)'``. + +An exception to this general framework is required by the relations in +the files ``borders.pl`` and ``contains.pl``. These contain facts of the +following form:: + + 'borders(albania,greece).' + + 'contains0(africa,central_africa).' + +We do not want to form a unary concept out the element in +the first field of these records, and we want the label of the binary +relation just to be ``'border'``/``'contain'`` respectively. + +In order to drive the extraction process, we use 'relation metadata bundles' +which are Python dictionaries such as the following:: + + city = {'label': 'city', + 'closures': [], + 'schema': ['city', 'country', 'population'], + 'filename': 'cities.pl'} + +According to this, the file ``city['filename']`` contains a list of +relational tuples (or more accurately, the corresponding strings in +Prolog form) whose predicate symbol is ``city['label']`` and whose +relational schema is ``city['schema']``. The notion of a ``closure`` is +discussed in the next section. + +Concepts +======== +In order to encapsulate the results of the extraction, a class of +``Concept``\ s is introduced. A ``Concept`` object has a number of +attributes, in particular a ``prefLabel``, an arity and ``extension``. + + >>> c1 = chat80.Concept('dog', arity=1, extension=set(['d1', 'd2'])) + >>> print(c1) + Label = 'dog' + Arity = 1 + Extension = ['d1', 'd2'] + + + +The ``extension`` attribute makes it easier to inspect the output of +the extraction. + + >>> schema = ['city', 'country', 'population'] + >>> concepts = chat80.clause2concepts('cities.pl', 'city', schema) + >>> concepts + [Concept('city'), Concept('country_of'), Concept('population_of')] + >>> for c in concepts: + ... print("%s:\n\t%s" % (c.prefLabel, c.extension[:4])) + city: + ['athens', 'bangkok', 'barcelona', 'berlin'] + country_of: + [('athens', 'greece'), ('bangkok', 'thailand'), ('barcelona', 'spain'), ('berlin', 'east_germany')] + population_of: + [('athens', '1368'), ('bangkok', '1178'), ('barcelona', '1280'), ('berlin', '3481')] + +In addition, the ``extension`` can be further +processed: in the case of the ``'border'`` relation, we check that the +relation is **symmetric**, and in the case of the ``'contain'`` +relation, we carry out the **transitive closure**. The closure +properties associated with a concept is indicated in the relation +metadata, as indicated earlier. + + >>> borders = set([('a1', 'a2'), ('a2', 'a3')]) + >>> c2 = chat80.Concept('borders', arity=2, extension=borders) + >>> print(c2) + Label = 'borders' + Arity = 2 + Extension = [('a1', 'a2'), ('a2', 'a3')] + >>> c3 = chat80.Concept('borders', arity=2, closures=['symmetric'], extension=borders) + >>> c3.close() + >>> print(c3) + Label = 'borders' + Arity = 2 + Extension = [('a1', 'a2'), ('a2', 'a1'), ('a2', 'a3'), ('a3', 'a2')] + +The ``extension`` of a ``Concept`` object is then incorporated into a +``Valuation`` object. + +Persistence +=========== +The functions ``val_dump`` and ``val_load`` are provided to allow a +valuation to be stored in a persistent database and re-loaded, rather +than having to be re-computed each time. + +Individuals and Lexical Items +============================= +As well as deriving relations from the Chat-80 data, we also create a +set of individual constants, one for each entity in the domain. The +individual constants are string-identical to the entities. For +example, given a data item such as ``'zloty'``, we add to the valuation +a pair ``('zloty', 'zloty')``. In order to parse English sentences that +refer to these entities, we also create a lexical item such as the +following for each individual constant:: + + PropN[num=sg, sem=<\P.(P zloty)>] -> 'Zloty' + +The set of rules is written to the file ``chat_pnames.fcfg`` in the +current directory. + +SQL Query +========= + +The ``city`` relation is also available in RDB form and can be queried +using SQL statements. + + >>> import nltk + >>> q = "SELECT City, Population FROM city_table WHERE Country = 'china' and Population > 1000" + >>> for answer in chat80.sql_query('corpora/city_database/city.db', q): + ... print("%-10s %4s" % answer) + canton 1496 + chungking 1100 + mukden 1551 + peking 2031 + shanghai 5407 + tientsin 1795 + +The (deliberately naive) grammar ``sql.fcfg`` translates from English +to SQL: + + >>> nltk.data.show_cfg('grammars/book_grammars/sql0.fcfg') + % start S + S[SEM=(?np + WHERE + ?vp)] -> NP[SEM=?np] VP[SEM=?vp] + VP[SEM=(?v + ?pp)] -> IV[SEM=?v] PP[SEM=?pp] + VP[SEM=(?v + ?ap)] -> IV[SEM=?v] AP[SEM=?ap] + NP[SEM=(?det + ?n)] -> Det[SEM=?det] N[SEM=?n] + PP[SEM=(?p + ?np)] -> P[SEM=?p] NP[SEM=?np] + AP[SEM=?pp] -> A[SEM=?a] PP[SEM=?pp] + NP[SEM='Country="greece"'] -> 'Greece' + NP[SEM='Country="china"'] -> 'China' + Det[SEM='SELECT'] -> 'Which' | 'What' + N[SEM='City FROM city_table'] -> 'cities' + IV[SEM=''] -> 'are' + A[SEM=''] -> 'located' + P[SEM=''] -> 'in' + +Given this grammar, we can express, and then execute, queries in English. + + >>> cp = nltk.parse.load_parser('grammars/book_grammars/sql0.fcfg') + >>> query = 'What cities are in China' + >>> for tree in cp.parse(query.split()): + ... answer = tree.label()['SEM'] + ... q = " ".join(answer) + ... print(q) + ... + SELECT City FROM city_table WHERE Country="china" + + >>> rows = chat80.sql_query('corpora/city_database/city.db', q) + >>> for r in rows: print("%s" % r, end=' ') + canton chungking dairen harbin kowloon mukden peking shanghai sian tientsin + + +Using Valuations +----------------- + +In order to convert such an extension into a valuation, we use the +``make_valuation()`` method; setting ``read=True`` creates and returns +a new ``Valuation`` object which contains the results. + + >>> val = chat80.make_valuation(concepts, read=True) + >>> 'calcutta' in val['city'] + True + >>> [town for (town, country) in val['country_of'] if country == 'india'] + ['bombay', 'calcutta', 'delhi', 'hyderabad', 'madras'] + >>> dom = val.domain + >>> g = nltk.sem.Assignment(dom) + >>> m = nltk.sem.Model(dom, val) + >>> m.evaluate(r'population_of(jakarta, 533)', g) + True diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/childes.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/childes.doctest new file mode 100644 index 0000000000000000000000000000000000000000..d2e1195b1128f97df83e3c7e5ba386583d15242f --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/childes.doctest @@ -0,0 +1,190 @@ +======================= + CHILDES Corpus Readers +======================= + +Read the XML version of the CHILDES corpus. + +Setup +===== + + >>> from nltk.test.childes_fixt import setup_module + >>> setup_module() + +How to use CHILDESCorpusReader +============================== + +Read the CHILDESCorpusReader class and read the CHILDES corpus saved in +the nltk_data directory. + + >>> import nltk + >>> from nltk.corpus.reader import CHILDESCorpusReader + >>> corpus_root = nltk.data.find('corpora/childes/data-xml/Eng-USA-MOR/') + +Reading files in the Valian corpus (Valian, 1991). + + >>> valian = CHILDESCorpusReader(corpus_root, 'Valian/.*.xml') + >>> valian.fileids() + ['Valian/01a.xml', 'Valian/01b.xml', 'Valian/02a.xml', 'Valian/02b.xml',... + +Count the number of files + + >>> len(valian.fileids()) + 43 + +Printing properties of the corpus files. + + >>> corpus_data = valian.corpus(valian.fileids()) + >>> print(corpus_data[0]['Lang']) + eng + >>> for key in sorted(corpus_data[0].keys()): + ... print(key, ": ", corpus_data[0][key]) + Corpus : valian + Date : 1986-03-04 + Id : 01a + Lang : eng + Version : 2.0.1 + {http://www.w3.org/2001/XMLSchema-instance}schemaLocation : http://www.talkbank.org/ns/talkbank http://talkbank.org/software/talkbank.xsd + +Printing information of participants of the corpus. The most common codes for +the participants are 'CHI' (target child), 'MOT' (mother), and 'INV' (investigator). + + >>> corpus_participants = valian.participants(valian.fileids()) + >>> for this_corpus_participants in corpus_participants[:2]: + ... for key in sorted(this_corpus_participants.keys()): + ... dct = this_corpus_participants[key] + ... print(key, ": ", [(k, dct[k]) for k in sorted(dct.keys())]) + CHI : [('age', 'P2Y1M3D'), ('group', 'normal'), ('id', 'CHI'), ('language', 'eng'), ('role', 'Target_Child'), ('sex', 'female')] + INV : [('id', 'INV'), ('language', 'eng'), ('role', 'Investigator')] + MOT : [('id', 'MOT'), ('language', 'eng'), ('role', 'Mother')] + CHI : [('age', 'P2Y1M12D'), ('group', 'normal'), ('id', 'CHI'), ('language', 'eng'), ('role', 'Target_Child'), ('sex', 'female')] + INV : [('id', 'INV'), ('language', 'eng'), ('role', 'Investigator')] + MOT : [('id', 'MOT'), ('language', 'eng'), ('role', 'Mother')] + +printing words. + + >>> valian.words('Valian/01a.xml') + ['at', 'Parent', "Lastname's", 'house', 'with', 'Child', 'Lastname', ... + +printing sentences. + + >>> valian.sents('Valian/01a.xml') + [['at', 'Parent', "Lastname's", 'house', 'with', 'Child', 'Lastname', + 'and', 'it', 'is', 'March', 'fourth', 'I', 'believe', 'and', 'when', + 'was', "Parent's", 'birthday'], ["Child's"], ['oh', "I'm", 'sorry'], + ["that's", 'okay'], ... + +You can specify the participants with the argument *speaker*. + + >>> valian.words('Valian/01a.xml',speaker=['INV']) + ['at', 'Parent', "Lastname's", 'house', 'with', 'Child', 'Lastname', ... + >>> valian.words('Valian/01a.xml',speaker=['MOT']) + ["Child's", "that's", 'okay', 'February', 'first', 'nineteen', ... + >>> valian.words('Valian/01a.xml',speaker=['CHI']) + ['tape', 'it', 'up', 'and', 'two', 'tape', 'players', 'have',... + + +tagged_words() and tagged_sents() return the usual (word,pos) tuple lists. +POS tags in the CHILDES are automatically assigned by MOR and POST programs +(MacWhinney, 2000). + + >>> valian.tagged_words('Valian/01a.xml')[:30] + [('at', 'prep'), ('Parent', 'n:prop'), ("Lastname's", 'n:prop'), ('house', 'n'), + ('with', 'prep'), ('Child', 'n:prop'), ('Lastname', 'n:prop'), ('and', 'coord'), + ('it', 'pro'), ('is', 'v:cop'), ('March', 'n:prop'), ('fourth', 'adj'), + ('I', 'pro:sub'), ('believe', 'v'), ('and', 'coord'), ('when', 'adv:wh'), + ('was', 'v:cop'), ("Parent's", 'n:prop'), ('birthday', 'n'), ("Child's", 'n:prop'), + ('oh', 'co'), ("I'm", 'pro:sub'), ('sorry', 'adj'), ("that's", 'pro:dem'), + ('okay', 'adj'), ('February', 'n:prop'), ('first', 'adj'), + ('nineteen', 'det:num'), ('eighty', 'det:num'), ('four', 'det:num')] + + >>> valian.tagged_sents('Valian/01a.xml')[:10] + [[('at', 'prep'), ('Parent', 'n:prop'), ("Lastname's", 'n:prop'), ('house', 'n'), + ('with', 'prep'), ('Child', 'n:prop'), ('Lastname', 'n:prop'), ('and', 'coord'), + ('it', 'pro'), ('is', 'v:cop'), ('March', 'n:prop'), ('fourth', 'adj'), + ('I', 'pro:sub'), ('believe', 'v'), ('and', 'coord'), ('when', 'adv:wh'), + ('was', 'v:cop'), ("Parent's", 'n:prop'), ('birthday', 'n')], + [("Child's", 'n:prop')], [('oh', 'co'), ("I'm", 'pro:sub'), ('sorry', 'adj')], + [("that's", 'pro:dem'), ('okay', 'adj')], + [('February', 'n:prop'), ('first', 'adj'), ('nineteen', 'det:num'), + ('eighty', 'det:num'), ('four', 'det:num')], + [('great', 'adj')], + [('and', 'coord'), ("she's", 'pro:sub'), ('two', 'det:num'), ('years', 'n'), ('old', 'adj')], + [('correct', 'adj')], + [('okay', 'co')], [('she', 'pro:sub'), ('just', 'adv:int'), ('turned', 'part'), ('two', 'det:num'), + ('a', 'det'), ('month', 'n'), ('ago', 'adv')]] + +When the argument *stem* is true, the word stems (e.g., 'is' -> 'be-3PS') are +used instead of the original words. + + >>> valian.words('Valian/01a.xml')[:30] + ['at', 'Parent', "Lastname's", 'house', 'with', 'Child', 'Lastname', 'and', 'it', 'is', ... + >>> valian.words('Valian/01a.xml',stem=True)[:30] + ['at', 'Parent', 'Lastname', 's', 'house', 'with', 'Child', 'Lastname', 'and', 'it', 'be-3S', ... + +When the argument *replace* is true, the replaced words are used instead of +the original words. + + >>> valian.words('Valian/01a.xml',speaker='CHI')[247] + 'tikteat' + >>> valian.words('Valian/01a.xml',speaker='CHI',replace=True)[247] + 'trick' + +When the argument *relation* is true, the relational relationships in the +sentence are returned. See Sagae et al. (2010) for details of the relational +structure adopted in the CHILDES. + + >>> valian.words('Valian/01a.xml',relation=True)[:10] + [[('at', 'prep', '1|0|ROOT'), ('Parent', 'n', '2|5|VOC'), ('Lastname', 'n', '3|5|MOD'), ('s', 'poss', '4|5|MOD'), ('house', 'n', '5|1|POBJ'), ('with', 'prep', '6|1|JCT'), ('Child', 'n', '7|8|NAME'), ('Lastname', 'n', '8|6|POBJ'), ('and', 'coord', '9|8|COORD'), ('it', 'pro', '10|11|SUBJ'), ('be-3S', 'v', '11|9|COMP'), ('March', 'n', '12|11|PRED'), ('fourth', 'adj', '13|12|MOD'), ('I', 'pro', '15|16|SUBJ'), ('believe', 'v', '16|14|ROOT'), ('and', 'coord', '18|17|ROOT'), ('when', 'adv', '19|20|PRED'), ('be-PAST', 'v', '20|18|COMP'), ('Parent', 'n', '21|23|MOD'), ('s', 'poss', '22|23|MOD'), ('birth', 'n', '23|20|SUBJ')], [('Child', 'n', '1|2|MOD'), ('s', 'poss', '2|0|ROOT')], [('oh', 'co', '1|4|COM'), ('I', 'pro', '3|4|SUBJ'), ('be', 'v', '4|0|ROOT'), ('sorry', 'adj', '5|4|PRED')], [('that', 'pro', '1|2|SUBJ'), ('be', 'v', '2|0|ROOT'), ('okay', 'adj', '3|2|PRED')], [('February', 'n', '1|6|VOC'), ('first', 'adj', '2|6|ENUM'), ('nineteen', 'det', '4|6|ENUM'), ('eighty', 'det', '5|6|ENUM'), ('four', 'det', '6|0|ROOT')], [('great', 'adj', '1|0|ROOT')], [('and', 'coord', '1|0|ROOT'), ('she', 'pro', '2|1|ROOT'), ('be', 'aux', '3|5|AUX'), ('two', 'det', '4|5|QUANT'), ('year-PL', 'n', '5|2|ROOT'), ('old', 'adj', '6|5|MOD')], [('correct', 'adj', '1|0|ROOT')], [('okay', 'co', '1|0|ROOT')], [('she', 'pro', '1|0|ROOT'), ('just', 'adv', '2|3|JCT'), ('turn-PERF', 'part', '3|1|XCOMP'), ('two', 'det', '4|6|QUANT'), ('a', 'det', '5|6|DET'), ('month', 'n', '6|3|OBJ'), ('ago', 'adv', '7|3|JCT')]] + +Printing age. When the argument *month* is true, the age information in +the CHILDES format is converted into the number of months. + + >>> valian.age() + ['P2Y1M3D', 'P2Y1M12D', 'P1Y9M21D', 'P1Y9M28D', 'P2Y1M23D', ... + >>> valian.age('Valian/01a.xml') + ['P2Y1M3D'] + >>> valian.age('Valian/01a.xml',month=True) + [25] + +Printing MLU. The criteria for the MLU computation is broadly based on +Brown (1973). + + >>> valian.MLU() + [2.3574660633484..., 2.292682926829..., 3.492857142857..., 2.961783439490..., + 2.0842696629213..., 3.169811320754..., 3.137404580152..., 3.0578034682080..., + 4.090163934426..., 3.488372093023..., 2.8773584905660..., 3.4792899408284..., + 4.0111940298507..., 3.456790123456..., 4.487603305785..., 4.007936507936..., + 5.25, 5.154696132596..., ...] + + >>> valian.MLU('Valian/01a.xml') + [2.35746606334...] + + +Basic stuff +============================== + +Count the number of words and sentences of each file. + + >>> valian = CHILDESCorpusReader(corpus_root, 'Valian/.*.xml') + >>> for this_file in valian.fileids()[:6]: + ... print(valian.corpus(this_file)[0]['Corpus'], valian.corpus(this_file)[0]['Id']) + ... print("num of words: %i" % len(valian.words(this_file))) + ... print("num of sents: %i" % len(valian.sents(this_file))) + valian 01a + num of words: 3606 + num of sents: 1027 + valian 01b + num of words: 4376 + num of sents: 1274 + valian 02a + num of words: 2673 + num of sents: 801 + valian 02b + num of words: 5020 + num of sents: 1583 + valian 03a + num of words: 2743 + num of sents: 988 + valian 03b + num of words: 4409 + num of sents: 1397 diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/childes_fixt.py b/llmeval-env/lib/python3.10/site-packages/nltk/test/childes_fixt.py new file mode 100644 index 0000000000000000000000000000000000000000..0c3b84fd5f089d55e30f10f8233ec7ce2cb5f1b7 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/childes_fixt.py @@ -0,0 +1,13 @@ +def setup_module(): + import pytest + + import nltk.data + + try: + nltk.data.find("corpora/childes/data-xml/Eng-USA-MOR/") + except LookupError as e: + pytest.skip( + "The CHILDES corpus is not found. " + "It should be manually downloaded and saved/unpacked " + "to [NLTK_Data_Dir]/corpora/childes/" + ) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/chunk.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/chunk.doctest new file mode 100644 index 0000000000000000000000000000000000000000..d67824cb378d99ffaf647bbc7cfc0e481f2cc26b --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/chunk.doctest @@ -0,0 +1,372 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +========== + Chunking +========== + + >>> from nltk.chunk import * + >>> from nltk.chunk.util import * + >>> from nltk.chunk.regexp import * + >>> from nltk import Tree + + >>> tagged_text = "[ The/DT cat/NN ] sat/VBD on/IN [ the/DT mat/NN ] [ the/DT dog/NN ] chewed/VBD ./." + >>> gold_chunked_text = tagstr2tree(tagged_text) + >>> unchunked_text = gold_chunked_text.flatten() + +Chunking uses a special regexp syntax for rules that delimit the chunks. These +rules must be converted to 'regular' regular expressions before a sentence can +be chunked. + + >>> tag_pattern = "
?*" + >>> regexp_pattern = tag_pattern2re_pattern(tag_pattern) + >>> regexp_pattern + '(<(DT)>)?(<(JJ)>)*(<(NN[^\\{\\}<>]*)>)' + +Construct some new chunking rules. + + >>> chunk_rule = ChunkRule(r"<.*>+", "Chunk everything") + >>> strip_rule = StripRule(r"", "Strip on verbs/prepositions") + >>> split_rule = SplitRule("
", "
", + ... "Split successive determiner/noun pairs") + + +Create and score a series of chunk parsers, successively more complex. + + >>> chunk_parser = RegexpChunkParser([chunk_rule], chunk_label='NP') + >>> chunked_text = chunk_parser.parse(unchunked_text) + >>> print(chunked_text) + (S + (NP + The/DT + cat/NN + sat/VBD + on/IN + the/DT + mat/NN + the/DT + dog/NN + chewed/VBD + ./.)) + + >>> chunkscore = ChunkScore() + >>> chunkscore.score(gold_chunked_text, chunked_text) + >>> print(chunkscore.precision()) + 0.0 + + >>> print(chunkscore.recall()) + 0.0 + + >>> print(chunkscore.f_measure()) + 0 + + >>> for chunk in sorted(chunkscore.missed()): print(chunk) + (NP The/DT cat/NN) + (NP the/DT dog/NN) + (NP the/DT mat/NN) + + >>> for chunk in chunkscore.incorrect(): print(chunk) + (NP + The/DT + cat/NN + sat/VBD + on/IN + the/DT + mat/NN + the/DT + dog/NN + chewed/VBD + ./.) + + >>> chunk_parser = RegexpChunkParser([chunk_rule, strip_rule], + ... chunk_label='NP') + >>> chunked_text = chunk_parser.parse(unchunked_text) + >>> print(chunked_text) + (S + (NP The/DT cat/NN) + sat/VBD + on/IN + (NP the/DT mat/NN the/DT dog/NN) + chewed/VBD + ./.) + >>> assert chunked_text == chunk_parser.parse(list(unchunked_text)) + + >>> chunkscore = ChunkScore() + >>> chunkscore.score(gold_chunked_text, chunked_text) + >>> chunkscore.precision() + 0.5 + + >>> print(chunkscore.recall()) + 0.33333333... + + >>> print(chunkscore.f_measure()) + 0.4 + + >>> for chunk in sorted(chunkscore.missed()): print(chunk) + (NP the/DT dog/NN) + (NP the/DT mat/NN) + + >>> for chunk in chunkscore.incorrect(): print(chunk) + (NP the/DT mat/NN the/DT dog/NN) + + >>> chunk_parser = RegexpChunkParser([chunk_rule, strip_rule, split_rule], + ... chunk_label='NP') + >>> chunked_text = chunk_parser.parse(unchunked_text, trace=True) + # Input: +
<.> + # Chunk everything: + {
<.>} + # Strip on verbs/prepositions: + {
} {
} <.> + # Split successive determiner/noun pairs: + {
} {
}{
} <.> + >>> print(chunked_text) + (S + (NP The/DT cat/NN) + sat/VBD + on/IN + (NP the/DT mat/NN) + (NP the/DT dog/NN) + chewed/VBD + ./.) + + >>> chunkscore = ChunkScore() + >>> chunkscore.score(gold_chunked_text, chunked_text) + >>> chunkscore.precision() + 1.0 + + >>> chunkscore.recall() + 1.0 + + >>> chunkscore.f_measure() + 1.0 + + >>> chunkscore.missed() + [] + + >>> chunkscore.incorrect() + [] + + >>> chunk_parser.rules() + [+'>, '>, + ', '
'>] + +Printing parsers: + + >>> print(repr(chunk_parser)) + + >>> print(chunk_parser) + RegexpChunkParser with 3 rules: + Chunk everything + +'> + Strip on verbs/prepositions + '> + Split successive determiner/noun pairs + ', '
'> + +Regression Tests +~~~~~~~~~~~~~~~~ +ChunkParserI +------------ +`ChunkParserI` is an abstract interface -- it is not meant to be +instantiated directly. + + >>> ChunkParserI().parse([]) + Traceback (most recent call last): + . . . + NotImplementedError + + +ChunkString +----------- +ChunkString can be built from a tree of tagged tuples, a tree of +trees, or a mixed list of both: + + >>> t1 = Tree('S', [('w%d' % i, 't%d' % i) for i in range(10)]) + >>> t2 = Tree('S', [Tree('t0', []), Tree('t1', ['c1'])]) + >>> t3 = Tree('S', [('w0', 't0'), Tree('t1', ['c1'])]) + >>> ChunkString(t1) + '> + >>> ChunkString(t2) + '> + >>> ChunkString(t3) + '> + +Other values generate an error: + + >>> ChunkString(Tree('S', ['x'])) + Traceback (most recent call last): + . . . + ValueError: chunk structures must contain tagged tokens or trees + +The `str()` for a chunk string adds spaces to it, which makes it line +up with `str()` output for other chunk strings over the same +underlying input. + + >>> cs = ChunkString(t1) + >>> print(cs) + + >>> cs.xform('', '{}') + >>> print(cs) + {} + +The `_verify()` method makes sure that our transforms don't corrupt +the chunk string. By setting debug_level=2, `_verify()` will be +called at the end of every call to `xform`. + + >>> cs = ChunkString(t1, debug_level=3) + + >>> # tag not marked with <...>: + >>> cs.xform('', 't3') + Traceback (most recent call last): + . . . + ValueError: Transformation generated invalid chunkstring: + t3 + + >>> # brackets not balanced: + >>> cs.xform('', '{') + Traceback (most recent call last): + . . . + ValueError: Transformation generated invalid chunkstring: + { + + >>> # nested brackets: + >>> cs.xform('', '{{}}') + Traceback (most recent call last): + . . . + ValueError: Transformation generated invalid chunkstring: + {{}} + + >>> # modified tags: + >>> cs.xform('', '') + Traceback (most recent call last): + . . . + ValueError: Transformation generated invalid chunkstring: tag changed + + >>> # added tags: + >>> cs.xform('', '') + Traceback (most recent call last): + . . . + ValueError: Transformation generated invalid chunkstring: tag changed + +Chunking Rules +-------------- + +Test the different rule constructors & __repr__ methods: + + >>> r1 = RegexpChunkRule(''+ChunkString.IN_STRIP_PATTERN, + ... '{}', 'chunk and ') + >>> r2 = RegexpChunkRule(re.compile(''+ChunkString.IN_STRIP_PATTERN), + ... '{}', 'chunk and ') + >>> r3 = ChunkRule('', 'chunk and ') + >>> r4 = StripRule('', 'strip and ') + >>> r5 = UnChunkRule('', 'unchunk and ') + >>> r6 = MergeRule('', '', 'merge w/ ') + >>> r7 = SplitRule('', '', 'split from ') + >>> r8 = ExpandLeftRule('', '', 'expand left ') + >>> r9 = ExpandRightRule('', '', 'expand right ') + >>> for rule in r1, r2, r3, r4, r5, r6, r7, r8, r9: + ... print(rule) + (?=[^\\}]*(\\{|$))'->'{}'> + (?=[^\\}]*(\\{|$))'->'{}'> + '> + '> + '> + ', ''> + ', ''> + ', ''> + ', ''> + +`tag_pattern2re_pattern()` complains if the tag pattern looks problematic: + + >>> tag_pattern2re_pattern('{}') + Traceback (most recent call last): + . . . + ValueError: Bad tag pattern: '{}' + +RegexpChunkParser +----------------- + +A warning is printed when parsing an empty sentence: + + >>> parser = RegexpChunkParser([ChunkRule('', '')]) + >>> parser.parse(Tree('S', [])) + Warning: parsing empty text + Tree('S', []) + +RegexpParser +------------ + + >>> parser = RegexpParser(''' + ... NP: {
? * *} # NP + ... P: {} # Preposition + ... V: {} # Verb + ... PP: {

} # PP -> P NP + ... VP: { *} # VP -> V (NP|PP)* + ... ''') + >>> print(repr(parser)) + + >>> print(parser) + chunk.RegexpParser with 5 stages: + RegexpChunkParser with 1 rules: + NP ? * *'> + RegexpChunkParser with 1 rules: + Preposition '> + RegexpChunkParser with 1 rules: + Verb '> + RegexpChunkParser with 1 rules: + PP -> P NP '> + RegexpChunkParser with 1 rules: + VP -> V (NP|PP)* *'> + >>> print(parser.parse(unchunked_text, trace=True)) + # Input: +

<.> + # NP: + {
} {
}{
} <.> + # Input: + <.> + # Preposition: + {} <.> + # Input: +

<.> + # Verb: + {}

{} <.> + # Input: +

<.> + # PP -> P NP: + {

} <.> + # Input: + <.> + # VP -> V (NP|PP)*: + { }{} <.> + (S + (NP The/DT cat/NN) + (VP + (V sat/VBD) + (PP (P on/IN) (NP the/DT mat/NN)) + (NP the/DT dog/NN)) + (VP (V chewed/VBD)) + ./.) + +Test parsing of other rule types: + + >>> print(RegexpParser(''' + ... X: + ... }{ # strip rule + ... }{ # split rule + ... {} # merge rule + ... {} # chunk rule w/ context + ... ''')) + chunk.RegexpParser with 1 stages: + RegexpChunkParser with 4 rules: + strip rule '> + split rule ', ''> + merge rule ', ''> + chunk rule w/ context ', '', ''> + +Illegal patterns give an error message: + + >>> print(RegexpParser('X: {} {}')) + Traceback (most recent call last): + . . . + ValueError: Illegal chunk pattern: {} {} diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/classify.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/classify.doctest new file mode 100644 index 0000000000000000000000000000000000000000..ef1bf7c9563488b6d85bb9098f540c1ce11af34b --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/classify.doctest @@ -0,0 +1,202 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +============= + Classifiers +============= + + >>> from nltk.test.classify_fixt import setup_module + >>> setup_module() + +Classifiers label tokens with category labels (or *class labels*). +Typically, labels are represented with strings (such as ``"health"`` +or ``"sports"``. In NLTK, classifiers are defined using classes that +implement the `ClassifierI` interface, which supports the following operations: + +- self.classify(featureset) +- self.classify_many(featuresets) +- self.labels() +- self.prob_classify(featureset) +- self.prob_classify_many(featuresets) + +NLTK defines several classifier classes: + +- `ConditionalExponentialClassifier` +- `DecisionTreeClassifier` +- `MaxentClassifier` +- `NaiveBayesClassifier` +- `WekaClassifier` + +Classifiers are typically created by training them on a training +corpus. + + +Regression Tests +~~~~~~~~~~~~~~~~ + +We define a very simple training corpus with 3 binary features: ['a', +'b', 'c'], and are two labels: ['x', 'y']. We use a simple feature set so +that the correct answers can be calculated analytically (although we +haven't done this yet for all tests). + + >>> import nltk + >>> train = [ + ... (dict(a=1,b=1,c=1), 'y'), + ... (dict(a=1,b=1,c=1), 'x'), + ... (dict(a=1,b=1,c=0), 'y'), + ... (dict(a=0,b=1,c=1), 'x'), + ... (dict(a=0,b=1,c=1), 'y'), + ... (dict(a=0,b=0,c=1), 'y'), + ... (dict(a=0,b=1,c=0), 'x'), + ... (dict(a=0,b=0,c=0), 'x'), + ... (dict(a=0,b=1,c=1), 'y'), + ... (dict(a=None,b=1,c=0), 'x'), + ... ] + >>> test = [ + ... (dict(a=1,b=0,c=1)), # unseen + ... (dict(a=1,b=0,c=0)), # unseen + ... (dict(a=0,b=1,c=1)), # seen 3 times, labels=y,y,x + ... (dict(a=0,b=1,c=0)), # seen 1 time, label=x + ... ] + +Test the Naive Bayes classifier: + + >>> classifier = nltk.classify.NaiveBayesClassifier.train(train) + >>> sorted(classifier.labels()) + ['x', 'y'] + >>> classifier.classify_many(test) + ['y', 'x', 'y', 'x'] + >>> for pdist in classifier.prob_classify_many(test): + ... print('%.4f %.4f' % (pdist.prob('x'), pdist.prob('y'))) + 0.2500 0.7500 + 0.5833 0.4167 + 0.3571 0.6429 + 0.7000 0.3000 + >>> classifier.show_most_informative_features() + Most Informative Features + c = 0 x : y = 2.3 : 1.0 + c = 1 y : x = 1.8 : 1.0 + a = 1 y : x = 1.7 : 1.0 + a = 0 x : y = 1.0 : 1.0 + b = 0 x : y = 1.0 : 1.0 + b = 1 x : y = 1.0 : 1.0 + +Test the Decision Tree classifier (without None): + + >>> classifier = nltk.classify.DecisionTreeClassifier.train( + ... train[:-1], entropy_cutoff=0, + ... support_cutoff=0) + >>> sorted(classifier.labels()) + ['x', 'y'] + >>> print(classifier) + c=0? .................................................. x + a=0? ................................................ x + a=1? ................................................ y + c=1? .................................................. y + + >>> classifier.classify_many(test) + ['y', 'y', 'y', 'x'] + >>> for pdist in classifier.prob_classify_many(test): + ... print('%.4f %.4f' % (pdist.prob('x'), pdist.prob('y'))) + Traceback (most recent call last): + . . . + NotImplementedError + + +Test the Decision Tree classifier (with None): + + >>> classifier = nltk.classify.DecisionTreeClassifier.train( + ... train, entropy_cutoff=0, + ... support_cutoff=0) + >>> sorted(classifier.labels()) + ['x', 'y'] + >>> print(classifier) + c=0? .................................................. x + a=0? ................................................ x + a=1? ................................................ y + a=None? ............................................. x + c=1? .................................................. y + + + +Test SklearnClassifier, which requires the scikit-learn package. + + >>> from nltk.classify import SklearnClassifier + >>> from sklearn.naive_bayes import BernoulliNB + >>> from sklearn.svm import SVC + >>> train_data = [({"a": 4, "b": 1, "c": 0}, "ham"), + ... ({"a": 5, "b": 2, "c": 1}, "ham"), + ... ({"a": 0, "b": 3, "c": 4}, "spam"), + ... ({"a": 5, "b": 1, "c": 1}, "ham"), + ... ({"a": 1, "b": 4, "c": 3}, "spam")] + >>> classif = SklearnClassifier(BernoulliNB()).train(train_data) + >>> test_data = [{"a": 3, "b": 2, "c": 1}, + ... {"a": 0, "b": 3, "c": 7}] + >>> classif.classify_many(test_data) + ['ham', 'spam'] + >>> classif = SklearnClassifier(SVC(), sparse=False).train(train_data) + >>> classif.classify_many(test_data) + ['ham', 'spam'] + +Test the Maximum Entropy classifier training algorithms; they should all +generate the same results. + + >>> def print_maxent_test_header(): + ... print(' '*11+''.join([' test[%s] ' % i + ... for i in range(len(test))])) + ... print(' '*11+' p(x) p(y)'*len(test)) + ... print('-'*(11+15*len(test))) + + >>> def test_maxent(algorithm): + ... print('%11s' % algorithm, end=' ') + ... try: + ... classifier = nltk.classify.MaxentClassifier.train( + ... train, algorithm, trace=0, max_iter=1000) + ... except Exception as e: + ... print('Error: %r' % e) + ... return + ... + ... for featureset in test: + ... pdist = classifier.prob_classify(featureset) + ... print('%8.2f%6.2f' % (pdist.prob('x'), pdist.prob('y')), end=' ') + ... print() + + >>> print_maxent_test_header(); test_maxent('GIS'); test_maxent('IIS') + test[0] test[1] test[2] test[3] + p(x) p(y) p(x) p(y) p(x) p(y) p(x) p(y) + ----------------------------------------------------------------------- + GIS 0.16 0.84 0.46 0.54 0.41 0.59 0.76 0.24 + IIS 0.16 0.84 0.46 0.54 0.41 0.59 0.76 0.24 + + >>> test_maxent('MEGAM'); test_maxent('TADM') # doctest: +SKIP + MEGAM 0.16 0.84 0.46 0.54 0.41 0.59 0.76 0.24 + TADM 0.16 0.84 0.46 0.54 0.41 0.59 0.76 0.24 + + + +Regression tests for TypedMaxentFeatureEncoding +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + >>> from nltk.classify import maxent + >>> train = [ + ... ({'a': 1, 'b': 1, 'c': 1}, 'y'), + ... ({'a': 5, 'b': 5, 'c': 5}, 'x'), + ... ({'a': 0.9, 'b': 0.9, 'c': 0.9}, 'y'), + ... ({'a': 5.5, 'b': 5.4, 'c': 5.3}, 'x'), + ... ({'a': 0.8, 'b': 1.2, 'c': 1}, 'y'), + ... ({'a': 5.1, 'b': 4.9, 'c': 5.2}, 'x') + ... ] + + >>> test = [ + ... {'a': 1, 'b': 0.8, 'c': 1.2}, + ... {'a': 5.2, 'b': 5.1, 'c': 5} + ... ] + + >>> encoding = maxent.TypedMaxentFeatureEncoding.train( + ... train, count_cutoff=3, alwayson_features=True) + + >>> classifier = maxent.MaxentClassifier.train( + ... train, bernoulli=False, encoding=encoding, trace=0) + + >>> classifier.classify_many(test) + ['y', 'x'] diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/classify_fixt.py b/llmeval-env/lib/python3.10/site-packages/nltk/test/classify_fixt.py new file mode 100644 index 0000000000000000000000000000000000000000..17b037281aff04a7d9a1faf56ccd9b055e1a1071 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/classify_fixt.py @@ -0,0 +1,5 @@ +# most of classify.doctest requires numpy +def setup_module(): + import pytest + + pytest.importorskip("numpy") diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/collections.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/collections.doctest new file mode 100644 index 0000000000000000000000000000000000000000..6dd98358a31a69c881c217cd5cbdbd12a0ee3d21 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/collections.doctest @@ -0,0 +1,31 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +=========== +Collections +=========== + + >>> import nltk + >>> from nltk.collections import * + +Trie +---- + +Trie can be pickled: + + >>> import pickle + >>> trie = nltk.collections.Trie(['a']) + >>> s = pickle.dumps(trie) + >>> pickle.loads(s) + {'a': {True: None}} + +LazyIteratorList +---------------- + +Fetching the length of a LazyIteratorList object does not throw a StopIteration exception: + + >>> lil = LazyIteratorList(i for i in range(1, 11)) + >>> lil[-1] + 10 + >>> len(lil) + 10 diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/concordance.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/concordance.doctest new file mode 100644 index 0000000000000000000000000000000000000000..8dbd81a01818b99681be51491bd3eaadd0c86e38 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/concordance.doctest @@ -0,0 +1,75 @@ +.. Copyright (C) 2001-2016 NLTK Project +.. For license information, see LICENSE.TXT + +================================== +Concordance Example +================================== + +A concordance view shows us every occurrence of a given +word, together with some context. Here we look up the word monstrous +in Moby Dick by entering text1 followed by a period, then the term +concordance, and then placing "monstrous" in parentheses: + +>>> from nltk.corpus import gutenberg +>>> from nltk.text import Text +>>> corpus = gutenberg.words('melville-moby_dick.txt') +>>> text = Text(corpus) + +>>> text.concordance("monstrous") +Displaying 11 of 11 matches: +ong the former , one was of a most monstrous size . ... This came towards us , +ON OF THE PSALMS . " Touching that monstrous bulk of the whale or ork we have r +ll over with a heathenish array of monstrous clubs and spears . Some were thick +d as you gazed , and wondered what monstrous cannibal and savage could ever hav +that has survived the flood ; most monstrous and most mountainous ! That Himmal +they might scout at Moby Dick as a monstrous fable , or still worse and more de +th of Radney .'" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere l +ing Scenes . In connexion with the monstrous pictures of whales , I am strongly +ere to enter upon those still more monstrous stories of them which are to be fo +ght have been rummaged out of this monstrous cabinet there is no telling . But +of Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u + +>>> text.concordance("monstrous") +Displaying 11 of 11 matches: +ong the former , one was of a most monstrous size . ... This came towards us , +ON OF THE PSALMS . " Touching that monstrous bulk of the whale or ork we have r +ll over with a heathenish array of monstrous clubs and spears . Some were thick +... + +We can also search for a multi-word phrase by passing a list of strings: + +>>> text.concordance(["monstrous", "size"]) +Displaying 2 of 2 matches: +the former , one was of a most monstrous size . ... This came towards us , op +Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead upo + +================================= +Concordance List +================================= + +Often we need to store the results of concordance for further usage. +To do so, call the concordance function with the stdout argument set +to false: + +>>> from nltk.corpus import gutenberg +>>> from nltk.text import Text +>>> corpus = gutenberg.words('melville-moby_dick.txt') +>>> text = Text(corpus) +>>> con_list = text.concordance_list("monstrous") +>>> con_list[2].line +'ll over with a heathenish array of monstrous clubs and spears . Some were thick' +>>> len(con_list) +11 + +================================= +Patching Issue #2088 +================================= + +Patching https://github.com/nltk/nltk/issues/2088 +The left slice of the left context should be clip to 0 if the `i-context` < 0. + +>>> from nltk import Text, word_tokenize +>>> jane_eyre = 'Chapter 1\nTHERE was no possibility of taking a walk that day. We had been wandering, indeed, in the leafless shrubbery an hour in the morning; but since dinner (Mrs. Reed, when there was no company, dined early) the cold winter wind had brought with it clouds so sombre, and a rain so penetrating, that further outdoor exercise was now out of the question.' +>>> text = Text(word_tokenize(jane_eyre)) +>>> text.concordance_list('taking')[0].left +['Chapter', '1', 'THERE', 'was', 'no', 'possibility', 'of'] diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/corpus.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/corpus.doctest new file mode 100644 index 0000000000000000000000000000000000000000..4e650d850bbe5327266f159db637f563867ef2b3 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/corpus.doctest @@ -0,0 +1,2196 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +================ + Corpus Readers +================ + +The `nltk.corpus` package defines a collection of *corpus reader* +classes, which can be used to access the contents of a diverse set of +corpora. The list of available corpora is given at: + +https://www.nltk.org/nltk_data/ + +Each corpus reader class is specialized to handle a specific +corpus format. In addition, the `nltk.corpus` package automatically +creates a set of corpus reader instances that can be used to access +the corpora in the NLTK data package. +Section `Corpus Reader Objects`_ ("Corpus Reader Objects") describes +the corpus reader instances that can be used to read the corpora in +the NLTK data package. Section `Corpus Reader Classes`_ ("Corpus +Reader Classes") describes the corpus reader classes themselves, and +discusses the issues involved in creating new corpus reader objects +and new corpus reader classes. Section `Regression Tests`_ +("Regression Tests") contains regression tests for the corpus readers +and associated functions and classes. + +.. contents:: **Table of Contents** + :depth: 4 + :backlinks: none + +--------------------- +Corpus Reader Objects +--------------------- + +Overview +======== + +NLTK includes a diverse set of corpora which can be +read using the ``nltk.corpus`` package. Each corpus is accessed by +means of a "corpus reader" object from ``nltk.corpus``: + + >>> import nltk.corpus + >>> # The Brown corpus: + >>> print(str(nltk.corpus.brown).replace('\\\\','/')) + + >>> # The Penn Treebank Corpus: + >>> print(str(nltk.corpus.treebank).replace('\\\\','/')) + + >>> # The Name Genders Corpus: + >>> print(str(nltk.corpus.names).replace('\\\\','/')) + + >>> # The Inaugural Address Corpus: + >>> print(str(nltk.corpus.inaugural).replace('\\\\','/')) + + +Most corpora consist of a set of files, each containing a document (or +other pieces of text). A list of identifiers for these files is +accessed via the ``fileids()`` method of the corpus reader: + + >>> nltk.corpus.treebank.fileids() + ['wsj_0001.mrg', 'wsj_0002.mrg', 'wsj_0003.mrg', 'wsj_0004.mrg', ...] + >>> nltk.corpus.inaugural.fileids() + ['1789-Washington.txt', '1793-Washington.txt', '1797-Adams.txt', ...] + +Each corpus reader provides a variety of methods to read data from the +corpus, depending on the format of the corpus. For example, plaintext +corpora support methods to read the corpus as raw text, a list of +words, a list of sentences, or a list of paragraphs. + + >>> from nltk.corpus import inaugural + >>> inaugural.raw('1789-Washington.txt') + 'Fellow-Citizens of the Senate ...' + >>> inaugural.words('1789-Washington.txt') + ['Fellow', '-', 'Citizens', 'of', 'the', ...] + >>> inaugural.sents('1789-Washington.txt') + [['Fellow', '-', 'Citizens'...], ['Among', 'the', 'vicissitudes'...]...] + >>> inaugural.paras('1789-Washington.txt') + [[['Fellow', '-', 'Citizens'...]], + [['Among', 'the', 'vicissitudes'...], + ['On', 'the', 'one', 'hand', ',', 'I'...]...]...] + +Each of these reader methods may be given a single document's item +name or a list of document item names. When given a list of document +item names, the reader methods will concatenate together the contents +of the individual documents. + + >>> l1 = len(inaugural.words('1789-Washington.txt')) + >>> l2 = len(inaugural.words('1793-Washington.txt')) + >>> l3 = len(inaugural.words(['1789-Washington.txt', '1793-Washington.txt'])) + >>> print('%s+%s == %s' % (l1, l2, l3)) + 1538+147 == 1685 + +If the reader methods are called without any arguments, they will +typically load all documents in the corpus. + + >>> len(inaugural.words()) + 152901 + +If a corpus contains a README file, it can be accessed with a ``readme()`` method: + + >>> inaugural.readme()[:32] + 'C-Span Inaugural Address Corpus\n' + +Plaintext Corpora +================= + +Here are the first few words from each of NLTK's plaintext corpora: + + >>> nltk.corpus.abc.words() + ['PM', 'denies', 'knowledge', 'of', 'AWB', ...] + >>> nltk.corpus.genesis.words() + ['In', 'the', 'beginning', 'God', 'created', ...] + >>> nltk.corpus.gutenberg.words(fileids='austen-emma.txt') + ['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ...] + >>> nltk.corpus.inaugural.words() + ['Fellow', '-', 'Citizens', 'of', 'the', ...] + >>> nltk.corpus.state_union.words() + ['PRESIDENT', 'HARRY', 'S', '.', 'TRUMAN', "'", ...] + >>> nltk.corpus.webtext.words() + ['Cookie', 'Manager', ':', '"', 'Don', "'", 't', ...] + +Tagged Corpora +============== + +In addition to the plaintext corpora, NLTK's data package also +contains a wide variety of annotated corpora. For example, the Brown +Corpus is annotated with part-of-speech tags, and defines additional +methods ``tagged_*()`` which words as `(word,tag)` tuples, rather +than just bare word strings. + + >>> from nltk.corpus import brown + >>> print(brown.words()) + ['The', 'Fulton', 'County', 'Grand', 'Jury', ...] + >>> print(brown.tagged_words()) + [('The', 'AT'), ('Fulton', 'NP-TL'), ...] + >>> print(brown.sents()) + [['The', 'Fulton', 'County'...], ['The', 'jury', 'further'...], ...] + >>> print(brown.tagged_sents()) + [[('The', 'AT'), ('Fulton', 'NP-TL')...], + [('The', 'AT'), ('jury', 'NN'), ('further', 'RBR')...]...] + >>> print(brown.paras(categories='reviews')) + [[['It', 'is', 'not', 'news', 'that', 'Nathan', 'Milstein'...], + ['Certainly', 'not', 'in', 'Orchestra', 'Hall', 'where'...]], + [['There', 'was', 'about', 'that', 'song', 'something', ...], + ['Not', 'the', 'noblest', 'performance', 'we', 'have', ...], ...], ...] + >>> print(brown.tagged_paras(categories='reviews')) + [[[('It', 'PPS'), ('is', 'BEZ'), ('not', '*'), ...], + [('Certainly', 'RB'), ('not', '*'), ('in', 'IN'), ...]], + [[('There', 'EX'), ('was', 'BEDZ'), ('about', 'IN'), ...], + [('Not', '*'), ('the', 'AT'), ('noblest', 'JJT'), ...], ...], ...] + +Similarly, the Indian Language POS-Tagged Corpus includes samples of +Indian text annotated with part-of-speech tags: + + >>> from nltk.corpus import indian + >>> print(indian.words()) # doctest: +SKIP + ['\xe0\xa6\xae\xe0\xa6\xb9\xe0\xa6\xbf\...', + '\xe0\xa6\xb8\xe0\xa6\xa8\xe0\xa7\x8d\xe0...', ...] + >>> print(indian.tagged_words()) # doctest: +SKIP + [('\xe0\xa6\xae\xe0\xa6\xb9\xe0\xa6\xbf...', 'NN'), + ('\xe0\xa6\xb8\xe0\xa6\xa8\xe0\xa7\x8d\xe0...', 'NN'), ...] + +Several tagged corpora support access to a simplified, universal tagset, e.g. where all nouns +tags are collapsed to a single category ``NOUN``: + + >>> print(brown.tagged_sents(tagset='universal')) + [[('The', 'DET'), ('Fulton', 'NOUN'), ('County', 'NOUN'), ('Grand', 'ADJ'), ('Jury', 'NOUN'), ...], + [('The', 'DET'), ('jury', 'NOUN'), ('further', 'ADV'), ('said', 'VERB'), ('in', 'ADP'), ...]...] + >>> from nltk.corpus import conll2000, switchboard + >>> print(conll2000.tagged_words(tagset='universal')) + [('Confidence', 'NOUN'), ('in', 'ADP'), ...] + +Use ``nltk.app.pos_concordance()`` to access a GUI for searching tagged corpora. + +Chunked Corpora +=============== + +The CoNLL corpora also provide chunk structures, which are encoded as +flat trees. The CoNLL 2000 Corpus includes phrasal chunks; and the +CoNLL 2002 Corpus includes named entity chunks. + + >>> from nltk.corpus import conll2000, conll2002 + >>> print(conll2000.sents()) + [['Confidence', 'in', 'the', 'pound', 'is', 'widely', ...], + ['Chancellor', 'of', 'the', 'Exchequer', ...], ...] + >>> for tree in conll2000.chunked_sents()[:2]: + ... print(tree) + (S + (NP Confidence/NN) + (PP in/IN) + (NP the/DT pound/NN) + (VP is/VBZ widely/RB expected/VBN to/TO take/VB) + (NP another/DT sharp/JJ dive/NN) + if/IN + ...) + (S + Chancellor/NNP + (PP of/IN) + (NP the/DT Exchequer/NNP) + ...) + >>> print(conll2002.sents()) + [['Sao', 'Paulo', '(', 'Brasil', ')', ',', ...], ['-'], ...] + >>> for tree in conll2002.chunked_sents()[:2]: + ... print(tree) + (S + (LOC Sao/NC Paulo/VMI) + (/Fpa + (LOC Brasil/NC) + )/Fpt + ...) + (S -/Fg) + +.. note:: Since the CONLL corpora do not contain paragraph break + information, these readers do not support the ``para()`` method.) + +.. warning:: if you call the conll corpora reader methods without any + arguments, they will return the contents of the entire corpus, + *including* the 'test' portions of the corpus.) + +SemCor is a subset of the Brown corpus tagged with WordNet senses and +named entities. Both kinds of lexical items include multiword units, +which are encoded as chunks (senses and part-of-speech tags pertain +to the entire chunk). + + >>> from nltk.corpus import semcor + >>> semcor.words() + ['The', 'Fulton', 'County', 'Grand', 'Jury', ...] + >>> semcor.chunks() + [['The'], ['Fulton', 'County', 'Grand', 'Jury'], ...] + >>> semcor.sents() + [['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...], + ['The', 'jury', 'further', 'said', ...], ...] + >>> semcor.chunk_sents() + [[['The'], ['Fulton', 'County', 'Grand', 'Jury'], ['said'], ... + ['.']], [['The'], ['jury'], ['further'], ['said'], ... ['.']], ...] + >>> list(map(str, semcor.tagged_chunks(tag='both')[:3])) + ['(DT The)', "(Lemma('group.n.01.group') (NE (NNP Fulton County Grand Jury)))", "(Lemma('state.v.01.say') (VB said))"] + >>> [[str(c) for c in s] for s in semcor.tagged_sents(tag='both')[:2]] + [['(DT The)', "(Lemma('group.n.01.group') (NE (NNP Fulton County Grand Jury)))", ... + '(None .)'], ['(DT The)', ... '(None .)']] + + +The IEER corpus is another chunked corpus. This corpus is unusual in +that each corpus item contains multiple documents. (This reflects the +fact that each corpus file contains multiple documents.) The IEER +corpus defines the `parsed_docs` method, which returns the documents +in a given item as `IEERDocument` objects: + + >>> from nltk.corpus import ieer + >>> ieer.fileids() + ['APW_19980314', 'APW_19980424', 'APW_19980429', + 'NYT_19980315', 'NYT_19980403', 'NYT_19980407'] + >>> docs = ieer.parsed_docs('APW_19980314') + >>> print(docs[0]) + + >>> print(docs[0].docno) + APW19980314.0391 + >>> print(docs[0].doctype) + NEWS STORY + >>> print(docs[0].date_time) + 03/14/1998 10:36:00 + >>> print(docs[0].headline) + (DOCUMENT Kenyans protest tax hikes) + >>> print(docs[0].text) + (DOCUMENT + (LOCATION NAIROBI) + , + (LOCATION Kenya) + ( + (ORGANIZATION AP) + ) + _ + (CARDINAL Thousands) + of + laborers, + ... + on + (DATE Saturday) + ...) + +Parsed Corpora +============== + +The Treebank corpora provide a syntactic parse for each sentence. The +NLTK data package includes a 10% sample of the Penn Treebank (in +``treebank``), as well as the Sinica Treebank (in ``sinica_treebank``). + +Reading the Penn Treebank (Wall Street Journal sample): + + >>> from nltk.corpus import treebank + >>> print(treebank.fileids()) + ['wsj_0001.mrg', 'wsj_0002.mrg', 'wsj_0003.mrg', 'wsj_0004.mrg', ...] + >>> print(treebank.words('wsj_0003.mrg')) + ['A', 'form', 'of', 'asbestos', 'once', 'used', ...] + >>> print(treebank.tagged_words('wsj_0003.mrg')) + [('A', 'DT'), ('form', 'NN'), ('of', 'IN'), ...] + >>> print(treebank.parsed_sents('wsj_0003.mrg')[0]) + (S + (S-TPC-1 + (NP-SBJ + (NP (NP (DT A) (NN form)) (PP (IN of) (NP (NN asbestos)))) + (RRC ...)...)...) + ... + (VP (VBD reported) (SBAR (-NONE- 0) (S (-NONE- *T*-1)))) + (. .)) + +If you have access to a full installation of the Penn Treebank, NLTK +can be configured to load it as well. Download the ``ptb`` package, +and in the directory ``nltk_data/corpora/ptb`` place the ``BROWN`` +and ``WSJ`` directories of the Treebank installation (symlinks work +as well). Then use the ``ptb`` module instead of ``treebank``: + + >>> from nltk.corpus import ptb + >>> print(ptb.fileids()) # doctest: +SKIP + ['BROWN/CF/CF01.MRG', 'BROWN/CF/CF02.MRG', 'BROWN/CF/CF03.MRG', 'BROWN/CF/CF04.MRG', ...] + >>> print(ptb.words('WSJ/00/WSJ_0003.MRG')) # doctest: +SKIP + ['A', 'form', 'of', 'asbestos', 'once', 'used', '*', ...] + >>> print(ptb.tagged_words('WSJ/00/WSJ_0003.MRG')) # doctest: +SKIP + [('A', 'DT'), ('form', 'NN'), ('of', 'IN'), ...] + +...and so forth, like ``treebank`` but with extended fileids. Categories +specified in ``allcats.txt`` can be used to filter by genre; they consist +of ``news`` (for WSJ articles) and names of the Brown subcategories +(``fiction``, ``humor``, ``romance``, etc.): + + >>> ptb.categories() # doctest: +SKIP + ['adventure', 'belles_lettres', 'fiction', 'humor', 'lore', 'mystery', 'news', 'romance', 'science_fiction'] + >>> print(ptb.fileids('news')) # doctest: +SKIP + ['WSJ/00/WSJ_0001.MRG', 'WSJ/00/WSJ_0002.MRG', 'WSJ/00/WSJ_0003.MRG', ...] + >>> print(ptb.words(categories=['humor','fiction'])) # doctest: +SKIP + ['Thirty-three', 'Scotty', 'did', 'not', 'go', 'back', ...] + +As PropBank and NomBank depend on the (WSJ portion of the) Penn Treebank, +the modules ``propbank_ptb`` and ``nombank_ptb`` are provided for access +to a full PTB installation. + +Reading the Sinica Treebank: + + >>> from nltk.corpus import sinica_treebank + >>> print(sinica_treebank.sents()) # doctest: +SKIP + [['\xe4\xb8\x80'], ['\xe5\x8f\x8b\xe6\x83\x85'], ...] + >>> sinica_treebank.parsed_sents()[25] # doctest: +SKIP + Tree('S', + [Tree('NP', + [Tree('Nba', ['\xe5\x98\x89\xe7\x8f\x8d'])]), + Tree('V\xe2\x80\xa7\xe5\x9c\xb0', + [Tree('VA11', ['\xe4\xb8\x8d\xe5\x81\x9c']), + Tree('DE', ['\xe7\x9a\x84'])]), + Tree('VA4', ['\xe5\x93\xad\xe6\xb3\xa3'])]) + +Reading the CoNLL 2007 Dependency Treebanks: + + >>> from nltk.corpus import conll2007 + >>> conll2007.sents('esp.train')[0] # doctest: +SKIP + ['El', 'aumento', 'del', 'índice', 'de', 'desempleo', ...] + >>> conll2007.parsed_sents('esp.train')[0] # doctest: +SKIP + + >>> print(conll2007.parsed_sents('esp.train')[0].tree()) # doctest: +SKIP + (fortaleció + (aumento El (del (índice (de (desempleo estadounidense))))) + hoy + considerablemente + (al + (euro + (cotizaba + , + que + (a (15.35 las GMT)) + se + (en (mercado el (de divisas) (de Fráncfort))) + (a 0,9452_dólares) + (frente_a , (0,9349_dólares los (de (mañana esta))))))) + .) + +Word Lists and Lexicons +======================= + +The NLTK data package also includes a number of lexicons and word +lists. These are accessed just like text corpora. The following +examples illustrate the use of the wordlist corpora: + + >>> from nltk.corpus import names, stopwords, words + >>> words.fileids() + ['en', 'en-basic'] + >>> words.words('en') + ['A', 'a', 'aa', 'aal', 'aalii', 'aam', 'Aani', 'aardvark', 'aardwolf', ...] + + >>> stopwords.fileids() # doctest: +SKIP + ['arabic', 'azerbaijani', 'bengali', 'danish', 'dutch', 'english', 'finnish', 'french', ...] + >>> sorted(stopwords.words('portuguese')) + ['a', 'ao', 'aos', 'aquela', 'aquelas', 'aquele', 'aqueles', ...] + >>> names.fileids() + ['female.txt', 'male.txt'] + >>> names.words('male.txt') + ['Aamir', 'Aaron', 'Abbey', 'Abbie', 'Abbot', 'Abbott', ...] + >>> names.words('female.txt') + ['Abagael', 'Abagail', 'Abbe', 'Abbey', 'Abbi', 'Abbie', ...] + +The CMU Pronunciation Dictionary corpus contains pronunciation +transcriptions for over 100,000 words. It can be accessed as a list +of entries (where each entry consists of a word, an identifier, and a +transcription) or as a dictionary from words to lists of +transcriptions. Transcriptions are encoded as tuples of phoneme +strings. + + >>> from nltk.corpus import cmudict + >>> print(cmudict.entries()[653:659]) + [('acetate', ['AE1', 'S', 'AH0', 'T', 'EY2', 'T']), + ('acetic', ['AH0', 'S', 'EH1', 'T', 'IH0', 'K']), + ('acetic', ['AH0', 'S', 'IY1', 'T', 'IH0', 'K']), + ('aceto', ['AA0', 'S', 'EH1', 'T', 'OW0']), + ('acetochlor', ['AA0', 'S', 'EH1', 'T', 'OW0', 'K', 'L', 'AO2', 'R']), + ('acetone', ['AE1', 'S', 'AH0', 'T', 'OW2', 'N'])] + >>> # Load the entire cmudict corpus into a Python dictionary: + >>> transcr = cmudict.dict() + >>> print([transcr[w][0] for w in 'Natural Language Tool Kit'.lower().split()]) + [['N', 'AE1', 'CH', 'ER0', 'AH0', 'L'], + ['L', 'AE1', 'NG', 'G', 'W', 'AH0', 'JH'], + ['T', 'UW1', 'L'], + ['K', 'IH1', 'T']] + + +WordNet +======= + +Please see the separate WordNet howto. + +FrameNet +======== + +Please see the separate FrameNet howto. + +PropBank +======== + +Please see the separate PropBank howto. + +SentiWordNet +============ + +Please see the separate SentiWordNet howto. + +Categorized Corpora +=================== + +Several corpora included with NLTK contain documents that have been categorized for +topic, genre, polarity, etc. In addition to the standard corpus interface, these +corpora provide access to the list of categories and the mapping between the documents +and their categories (in both directions). Access the categories using the ``categories()`` +method, e.g.: + + >>> from nltk.corpus import brown, movie_reviews, reuters + >>> brown.categories() + ['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor', + 'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction'] + >>> movie_reviews.categories() + ['neg', 'pos'] + >>> reuters.categories() + ['acq', 'alum', 'barley', 'bop', 'carcass', 'castor-oil', 'cocoa', + 'coconut', 'coconut-oil', 'coffee', 'copper', 'copra-cake', 'corn', + 'cotton', 'cotton-oil', 'cpi', 'cpu', 'crude', 'dfl', 'dlr', ...] + +This method has an optional argument that specifies a document or a list +of documents, allowing us to map from (one or more) documents to (one or more) categories: + + >>> brown.categories('ca01') + ['news'] + >>> brown.categories(['ca01','cb01']) + ['editorial', 'news'] + >>> reuters.categories('training/9865') + ['barley', 'corn', 'grain', 'wheat'] + >>> reuters.categories(['training/9865', 'training/9880']) + ['barley', 'corn', 'grain', 'money-fx', 'wheat'] + +We can go back the other way using the optional argument of the ``fileids()`` method: + + >>> reuters.fileids('barley') + ['test/15618', 'test/15649', 'test/15676', 'test/15728', 'test/15871', ...] + +Both the ``categories()`` and ``fileids()`` methods return a sorted list containing +no duplicates. + +In addition to mapping between categories and documents, these corpora permit +direct access to their contents via the categories. Instead of accessing a subset +of a corpus by specifying one or more fileids, we can identify one or more categories, e.g.: + + >>> brown.tagged_words(categories='news') + [('The', 'AT'), ('Fulton', 'NP-TL'), ...] + >>> brown.sents(categories=['editorial','reviews']) + [['Assembly', 'session', 'brought', 'much', 'good'], ['The', 'General', + 'Assembly', ',', 'which', 'adjourns', 'today', ',', 'has', 'performed', + 'in', 'an', 'atmosphere', 'of', 'crisis', 'and', 'struggle', 'from', + 'the', 'day', 'it', 'convened', '.'], ...] + +Note that it is an error to specify both documents and categories. + +In the context of a text categorization system, we can easily test if the +category assigned to a document is correct as follows: + + >>> def classify(doc): return 'news' # Trivial classifier + >>> doc = 'ca01' + >>> classify(doc) in brown.categories(doc) + True + + +Other Corpora +============= + +comparative_sentences +--------------------- +A list of sentences from various sources, especially reviews and articles. Each +line contains one sentence; sentences were separated by using a sentence tokenizer. +Comparative sentences have been annotated with their type, entities, features and +keywords. + + >>> from nltk.corpus import comparative_sentences + >>> comparison = comparative_sentences.comparisons()[0] + >>> comparison.text + ['its', 'fast-forward', 'and', 'rewind', 'work', 'much', 'more', 'smoothly', + 'and', 'consistently', 'than', 'those', 'of', 'other', 'models', 'i', "'ve", + 'had', '.'] + >>> comparison.entity_2 + 'models' + >>> (comparison.feature, comparison.keyword) + ('rewind', 'more') + >>> len(comparative_sentences.comparisons()) + 853 + +opinion_lexicon +--------------- +A list of positive and negative opinion words or sentiment words for English. + + >>> from nltk.corpus import opinion_lexicon + >>> opinion_lexicon.words()[:4] + ['2-faced', '2-faces', 'abnormal', 'abolish'] + +The OpinionLexiconCorpusReader also provides shortcuts to retrieve positive/negative +words: + + >>> opinion_lexicon.negative()[:4] + ['2-faced', '2-faces', 'abnormal', 'abolish'] + +Note that words from `words()` method in opinion_lexicon are sorted by file id, +not alphabetically: + + >>> opinion_lexicon.words()[0:10] + ['2-faced', '2-faces', 'abnormal', 'abolish', 'abominable', 'abominably', + 'abominate', 'abomination', 'abort', 'aborted'] + >>> sorted(opinion_lexicon.words())[0:10] + ['2-faced', '2-faces', 'a+', 'abnormal', 'abolish', 'abominable', 'abominably', + 'abominate', 'abomination', 'abort'] + +ppattach +-------- +The Prepositional Phrase Attachment corpus is a corpus of +prepositional phrase attachment decisions. Each instance in the +corpus is encoded as a ``PPAttachment`` object: + + >>> from nltk.corpus import ppattach + >>> ppattach.attachments('training') + [PPAttachment(sent='0', verb='join', noun1='board', + prep='as', noun2='director', attachment='V'), + PPAttachment(sent='1', verb='is', noun1='chairman', + prep='of', noun2='N.V.', attachment='N'), + ...] + >>> inst = ppattach.attachments('training')[0] + >>> (inst.sent, inst.verb, inst.noun1, inst.prep, inst.noun2) + ('0', 'join', 'board', 'as', 'director') + >>> inst.attachment + 'V' + +product_reviews_1 and product_reviews_2 +--------------------------------------- +These two datasets respectively contain annotated customer reviews of 5 and 9 +products from amazon.com. + + >>> from nltk.corpus import product_reviews_1 + >>> camera_reviews = product_reviews_1.reviews('Canon_G3.txt') + >>> review = camera_reviews[0] + >>> review.sents()[0] + ['i', 'recently', 'purchased', 'the', 'canon', 'powershot', 'g3', 'and', 'am', + 'extremely', 'satisfied', 'with', 'the', 'purchase', '.'] + >>> review.features() + [('canon powershot g3', '+3'), ('use', '+2'), ('picture', '+2'), + ('picture quality', '+1'), ('picture quality', '+1'), ('camera', '+2'), + ('use', '+2'), ('feature', '+1'), ('picture quality', '+3'), ('use', '+1'), + ('option', '+1')] + +It is also possible to reach the same information directly from the stream: + + >>> product_reviews_1.features('Canon_G3.txt') + [('canon powershot g3', '+3'), ('use', '+2'), ...] + +We can compute stats for specific product features: + + >>> n_reviews = len([(feat,score) for (feat,score) in product_reviews_1.features('Canon_G3.txt') if feat=='picture']) + >>> tot = sum([int(score) for (feat,score) in product_reviews_1.features('Canon_G3.txt') if feat=='picture']) + >>> mean = tot / n_reviews + >>> print(n_reviews, tot, mean) + 15 24 1.6 + +pros_cons +--------- +A list of pros/cons sentences for determining context (aspect) dependent +sentiment words, which are then applied to sentiment analysis of comparative +sentences. + + >>> from nltk.corpus import pros_cons + >>> pros_cons.sents(categories='Cons') + [['East', 'batteries', '!', 'On', '-', 'off', 'switch', 'too', 'easy', + 'to', 'maneuver', '.'], ['Eats', '...', 'no', ',', 'GULPS', 'batteries'], + ...] + >>> pros_cons.words('IntegratedPros.txt') + ['Easy', 'to', 'use', ',', 'economical', '!', ...] + +semcor +------ +The Brown Corpus, annotated with WordNet senses. + + >>> from nltk.corpus import semcor + >>> semcor.words('brown2/tagfiles/br-n12.xml') + ['When', 'several', 'minutes', 'had', 'passed', ...] + +senseval +-------- +The Senseval 2 corpus is a word sense disambiguation corpus. Each +item in the corpus corresponds to a single ambiguous word. For each +of these words, the corpus contains a list of instances, corresponding +to occurrences of that word. Each instance provides the word; a list +of word senses that apply to the word occurrence; and the word's +context. + + >>> from nltk.corpus import senseval + >>> senseval.fileids() + ['hard.pos', 'interest.pos', 'line.pos', 'serve.pos'] + >>> senseval.instances('hard.pos') + ... + [SensevalInstance(word='hard-a', + position=20, + context=[('``', '``'), ('he', 'PRP'), ...('hard', 'JJ'), ...], + senses=('HARD1',)), + SensevalInstance(word='hard-a', + position=10, + context=[('clever', 'NNP'), ...('hard', 'JJ'), ('time', 'NN'), ...], + senses=('HARD1',)), ...] + +The following code looks at instances of the word 'interest', and +displays their local context (2 words on each side) and word sense(s): + + >>> for inst in senseval.instances('interest.pos')[:10]: + ... p = inst.position + ... left = ' '.join(w for (w,t) in inst.context[p-2:p]) + ... word = ' '.join(w for (w,t) in inst.context[p:p+1]) + ... right = ' '.join(w for (w,t) in inst.context[p+1:p+3]) + ... senses = ' '.join(inst.senses) + ... print('%20s |%10s | %-15s -> %s' % (left, word, right, senses)) + declines in | interest | rates . -> interest_6 + indicate declining | interest | rates because -> interest_6 + in short-term | interest | rates . -> interest_6 + 4 % | interest | in this -> interest_5 + company with | interests | in the -> interest_5 + , plus | interest | . -> interest_6 + set the | interest | rate on -> interest_6 + 's own | interest | , prompted -> interest_4 + principal and | interest | is the -> interest_6 + increase its | interest | to 70 -> interest_5 + +sentence_polarity +----------------- +The Sentence Polarity dataset contains 5331 positive and 5331 negative processed +sentences. + + >>> from nltk.corpus import sentence_polarity + >>> sentence_polarity.sents() + [['simplistic', ',', 'silly', 'and', 'tedious', '.'], ["it's", 'so', 'laddish', + 'and', 'juvenile', ',', 'only', 'teenage', 'boys', 'could', 'possibly', 'find', + 'it', 'funny', '.'], ...] + >>> sentence_polarity.categories() + ['neg', 'pos'] + >>> sentence_polarity.sents()[1] + ["it's", 'so', 'laddish', 'and', 'juvenile', ',', 'only', 'teenage', 'boys', + 'could', 'possibly', 'find', 'it', 'funny', '.'] + +shakespeare +----------- +The Shakespeare corpus contains a set of Shakespeare plays, formatted +as XML files. These corpora are returned as ElementTree objects: + + >>> from nltk.corpus import shakespeare + >>> from xml.etree import ElementTree + >>> shakespeare.fileids() + ['a_and_c.xml', 'dream.xml', 'hamlet.xml', 'j_caesar.xml', ...] + >>> play = shakespeare.xml('dream.xml') + >>> print(play) + + >>> print('%s: %s' % (play[0].tag, play[0].text)) + TITLE: A Midsummer Night's Dream + >>> personae = [persona.text for persona in + ... play.findall('PERSONAE/PERSONA')] + >>> print(personae) + ['THESEUS, Duke of Athens.', 'EGEUS, father to Hermia.', ...] + >>> # Find and print speakers not listed as personae + >>> names = [persona.split(',')[0] for persona in personae] + >>> speakers = set(speaker.text for speaker in + ... play.findall('*/*/*/SPEAKER')) + >>> print(sorted(speakers.difference(names))) + ['ALL', 'COBWEB', 'DEMETRIUS', 'Fairy', 'HERNIA', 'LYSANDER', + 'Lion', 'MOTH', 'MUSTARDSEED', 'Moonshine', 'PEASEBLOSSOM', + 'Prologue', 'Pyramus', 'Thisbe', 'Wall'] + +subjectivity +------------ +The Subjectivity Dataset contains 5000 subjective and 5000 objective processed +sentences. + + >>> from nltk.corpus import subjectivity + >>> subjectivity.categories() + ['obj', 'subj'] + >>> subjectivity.sents()[23] + ['television', 'made', 'him', 'famous', ',', 'but', 'his', 'biggest', 'hits', + 'happened', 'off', 'screen', '.'] + >>> subjectivity.words(categories='subj') + ['smart', 'and', 'alert', ',', 'thirteen', ...] + +toolbox +------- +The Toolbox corpus distributed with NLTK contains a sample lexicon and +several sample texts from the Rotokas language. The Toolbox corpus +reader returns Toolbox files as XML ElementTree objects. The +following example loads the Rotokas dictionary, and figures out the +distribution of part-of-speech tags for reduplicated words. + +.. doctest: +SKIP + + >>> from nltk.corpus import toolbox + >>> from nltk.probability import FreqDist + >>> from xml.etree import ElementTree + >>> import re + >>> rotokas = toolbox.xml('rotokas.dic') + >>> redup_pos_freqdist = FreqDist() + >>> # Note: we skip over the first record, which is actually + >>> # the header. + >>> for record in rotokas[1:]: + ... lexeme = record.find('lx').text + ... if re.match(r'(.*)\1$', lexeme): + ... redup_pos_freqdist[record.find('ps').text] += 1 + >>> for item, count in redup_pos_freqdist.most_common(): + ... print(item, count) + V 41 + N 14 + ??? 4 + +This example displays some records from a Rotokas text: + +.. doctest: +SKIP + + >>> river = toolbox.xml('rotokas/river.txt', key='ref') + >>> for record in river.findall('record')[:3]: + ... for piece in record: + ... if len(piece.text) > 60: + ... print('%-6s %s...' % (piece.tag, piece.text[:57])) + ... else: + ... print('%-6s %s' % (piece.tag, piece.text)) + ref Paragraph 1 + t ``Viapau oisio ra ovaupasi ... + m viapau oisio ra ovau -pa -si ... + g NEG this way/like this and forget -PROG -2/3.DL... + p NEG ??? CONJ V.I -SUFF.V.3 -SUFF.V... + f ``No ken lus tingting wanema samting papa i bin tok,'' Na... + fe ``Don't forget what Dad said,'' yelled Naomi. + ref 2 + t Osa Ira ora Reviti viapau uvupasiva. + m osa Ira ora Reviti viapau uvu -pa -si ... + g as/like name and name NEG hear/smell -PROG -2/3... + p CONJ N.PN CONJ N.PN NEG V.T -SUFF.V.3 -SUF... + f Tasol Ila na David no bin harim toktok. + fe But Ila and David took no notice. + ref 3 + t Ikaupaoro rokosiva ... + m ikau -pa -oro roko -si -va ... + g run/hurry -PROG -SIM go down -2/3.DL.M -RP ... + p V.T -SUFF.V.3 -SUFF.V.4 ADV -SUFF.V.4 -SUFF.VT.... + f Tupela i bin hariap i go long wara . + fe They raced to the river. + +timit +----- +The NLTK data package includes a fragment of the TIMIT +Acoustic-Phonetic Continuous Speech Corpus. This corpus is broken +down into small speech samples, each of which is available as a wave +file, a phonetic transcription, and a tokenized word list. + + >>> from nltk.corpus import timit + >>> print(timit.utteranceids()) + ['dr1-fvmh0/sa1', 'dr1-fvmh0/sa2', 'dr1-fvmh0/si1466', + 'dr1-fvmh0/si2096', 'dr1-fvmh0/si836', 'dr1-fvmh0/sx116', + 'dr1-fvmh0/sx206', 'dr1-fvmh0/sx26', 'dr1-fvmh0/sx296', ...] + + >>> item = timit.utteranceids()[5] + >>> print(timit.phones(item)) + ['h#', 'k', 'l', 'ae', 's', 'pcl', 'p', 'dh', 'ax', + 's', 'kcl', 'k', 'r', 'ux', 'ix', 'nx', 'y', 'ax', + 'l', 'eh', 'f', 'tcl', 't', 'hh', 'ae', 'n', 'dcl', + 'd', 'h#'] + >>> print(timit.words(item)) + ['clasp', 'the', 'screw', 'in', 'your', 'left', 'hand'] + >>> timit.play(item) # doctest: +SKIP + +The corpus reader can combine the word segmentation information with +the phonemes to produce a single tree structure: + + >>> for tree in timit.phone_trees(item): + ... print(tree) + (S + h# + (clasp k l ae s pcl p) + (the dh ax) + (screw s kcl k r ux) + (in ix nx) + (your y ax) + (left l eh f tcl t) + (hand hh ae n dcl d) + h#) + +The start time and stop time of each phoneme, word, and sentence are +also available: + + >>> print(timit.phone_times(item)) + [('h#', 0, 2190), ('k', 2190, 3430), ('l', 3430, 4326), ...] + >>> print(timit.word_times(item)) + [('clasp', 2190, 8804), ('the', 8804, 9734), ...] + >>> print(timit.sent_times(item)) + [('Clasp the screw in your left hand.', 0, 32154)] + +We can use these times to play selected pieces of a speech sample: + + >>> timit.play(item, 2190, 8804) # 'clasp' # doctest: +SKIP + +The corpus reader can also be queried for information about the +speaker and sentence identifier for a given speech sample: + + >>> print(timit.spkrid(item)) + dr1-fvmh0 + >>> print(timit.sentid(item)) + sx116 + >>> print(timit.spkrinfo(timit.spkrid(item))) + SpeakerInfo(id='VMH0', + sex='F', + dr='1', + use='TRN', + recdate='03/11/86', + birthdate='01/08/60', + ht='5\'05"', + race='WHT', + edu='BS', + comments='BEST NEW ENGLAND ACCENT SO FAR') + + >>> # List the speech samples from the same speaker: + >>> timit.utteranceids(spkrid=timit.spkrid(item)) + ['dr1-fvmh0/sa1', 'dr1-fvmh0/sa2', 'dr1-fvmh0/si1466', ...] + +twitter_samples +--------------- + +Twitter is well-known microblog service that allows public data to be +collected via APIs. NLTK's twitter corpus currently contains a sample of 20k Tweets +retrieved from the Twitter Streaming API. + + >>> from nltk.corpus import twitter_samples + >>> twitter_samples.fileids() + ['negative_tweets.json', 'positive_tweets.json', 'tweets.20150430-223406.json'] + +We follow standard practice in storing full Tweets as line-separated +JSON. These data structures can be accessed via `tweets.docs()`. However, in general it +is more practical to focus just on the text field of the Tweets, which +are accessed via the `strings()` method. + + >>> twitter_samples.strings('tweets.20150430-223406.json')[:5] + ['RT @KirkKus: Indirect cost of the UK being in the EU is estimated to be costing Britain \xa3170 billion per year! #BetterOffOut #UKIP', ...] + +The default tokenizer for Tweets is specialised for 'casual' text, and +the `tokenized()` method returns a list of lists of tokens. + + >>> twitter_samples.tokenized('tweets.20150430-223406.json')[:5] + [['RT', '@KirkKus', ':', 'Indirect', 'cost', 'of', 'the', 'UK', 'being', 'in', ...], + ['VIDEO', ':', 'Sturgeon', 'on', 'post-election', 'deals', 'http://t.co/BTJwrpbmOY'], ...] + +rte +--- +The RTE (Recognizing Textual Entailment) corpus was derived from the +RTE1, RTE2 and RTE3 datasets (dev and test data), and consists of a +list of XML-formatted 'text'/'hypothesis' pairs. + + >>> from nltk.corpus import rte + >>> print(rte.fileids()) + ['rte1_dev.xml', 'rte1_test.xml', 'rte2_dev.xml', ..., 'rte3_test.xml'] + >>> rtepairs = rte.pairs(['rte2_test.xml', 'rte3_test.xml']) + >>> print(rtepairs) + [, , , ...] + +In the gold standard test sets, each pair is labeled according to +whether or not the text 'entails' the hypothesis; the +entailment value is mapped to an integer 1 (True) or 0 (False). + + >>> rtepairs[5] + + >>> rtepairs[5].text + 'His wife Strida won a seat in parliament after forging an alliance + with the main anti-Syrian coalition in the recent election.' + >>> rtepairs[5].hyp + 'Strida elected to parliament.' + >>> rtepairs[5].value + 1 + +The RTE corpus also supports an ``xml()`` method which produces ElementTrees. + + >>> xmltree = rte.xml('rte3_dev.xml') + >>> xmltree # doctest: +SKIP + + >>> xmltree[7].findtext('t') + "Mrs. Bush's approval ratings have remained very high, above 80%, + even as her husband's have recently dropped below 50%." + +verbnet +------- +The VerbNet corpus is a lexicon that divides verbs into classes, based +on their syntax-semantics linking behavior. The basic elements in the +lexicon are verb lemmas, such as 'abandon' and 'accept', and verb +classes, which have identifiers such as 'remove-10.1' and +'admire-31.2-1'. These class identifiers consist of a representative +verb selected from the class, followed by a numerical identifier. The +list of verb lemmas, and the list of class identifiers, can be +retrieved with the following methods: + + >>> from nltk.corpus import verbnet + >>> verbnet.lemmas()[20:25] + ['accelerate', 'accept', 'acclaim', 'accompany', 'accrue'] + >>> verbnet.classids()[:5] + ['accompany-51.7', 'admire-31.2', 'admire-31.2-1', 'admit-65', 'adopt-93'] + +The `classids()` method may also be used to retrieve the classes that +a given lemma belongs to: + + >>> verbnet.classids('accept') + ['approve-77', 'characterize-29.2-1-1', 'obtain-13.5.2'] + +The `classids()` method may additionally be used to retrieve all classes +within verbnet if nothing is passed: + + >>> verbnet.classids() + ['accompany-51.7', 'admire-31.2', 'admire-31.2-1', 'admit-65', 'adopt-93', 'advise-37.9', 'advise-37.9-1', 'allow-64', 'amalgamate-22.2', 'amalgamate-22.2-1', 'amalgamate-22.2-1-1', 'amalgamate-22.2-2', 'amalgamate-22.2-2-1', 'amalgamate-22.2-3', 'amalgamate-22.2-3-1', 'amalgamate-22.2-3-1-1', 'amalgamate-22.2-3-2', 'amuse-31.1', 'animal_sounds-38', 'appeal-31.4', 'appeal-31.4-1', 'appeal-31.4-2', 'appeal-31.4-3', 'appear-48.1.1', 'appoint-29.1', 'approve-77', 'assessment-34', 'assuming_position-50', 'avoid-52', 'banish-10.2', 'battle-36.4', 'battle-36.4-1', 'begin-55.1', 'begin-55.1-1', 'being_dressed-41.3.3', 'bend-45.2', 'berry-13.7', 'bill-54.5', 'body_internal_motion-49', 'body_internal_states-40.6', 'braid-41.2.2', 'break-45.1', 'breathe-40.1.2', 'breathe-40.1.2-1', 'bring-11.3', 'bring-11.3-1', 'build-26.1', 'build-26.1-1', 'bulge-47.5.3', 'bump-18.4', 'bump-18.4-1', 'butter-9.9', 'calibratable_cos-45.6', 'calibratable_cos-45.6-1', 'calve-28', 'captain-29.8', 'captain-29.8-1', 'captain-29.8-1-1', 'care-88', 'care-88-1', 'carry-11.4', 'carry-11.4-1', 'carry-11.4-1-1', 'carve-21.2', 'carve-21.2-1', 'carve-21.2-2', 'change_bodily_state-40.8.4', 'characterize-29.2', 'characterize-29.2-1', 'characterize-29.2-1-1', 'characterize-29.2-1-2', 'chase-51.6', 'cheat-10.6', 'cheat-10.6-1', 'cheat-10.6-1-1', 'chew-39.2', 'chew-39.2-1', 'chew-39.2-2', 'chit_chat-37.6', 'clear-10.3', 'clear-10.3-1', 'cling-22.5', 'coil-9.6', 'coil-9.6-1', 'coloring-24', 'complain-37.8', 'complete-55.2', 'concealment-16', 'concealment-16-1', 'confess-37.10', 'confine-92', 'confine-92-1', 'conjecture-29.5', 'conjecture-29.5-1', 'conjecture-29.5-2', 'consider-29.9', 'consider-29.9-1', 'consider-29.9-1-1', 'consider-29.9-1-1-1', 'consider-29.9-2', 'conspire-71', 'consume-66', 'consume-66-1', 'contiguous_location-47.8', 'contiguous_location-47.8-1', 'contiguous_location-47.8-2', 'continue-55.3', 'contribute-13.2', 'contribute-13.2-1', 'contribute-13.2-1-1', 'contribute-13.2-1-1-1', 'contribute-13.2-2', 'contribute-13.2-2-1', 'convert-26.6.2', 'convert-26.6.2-1', 'cooking-45.3', 'cooperate-73', 'cooperate-73-1', 'cooperate-73-2', 'cooperate-73-3', 'cope-83', 'cope-83-1', 'cope-83-1-1', 'correlate-86', 'correspond-36.1', 'correspond-36.1-1', 'correspond-36.1-1-1', 'cost-54.2', 'crane-40.3.2', 'create-26.4', 'create-26.4-1', 'curtsey-40.3.3', 'cut-21.1', 'cut-21.1-1', 'debone-10.8', 'declare-29.4', 'declare-29.4-1', 'declare-29.4-1-1', 'declare-29.4-1-1-1', 'declare-29.4-1-1-2', 'declare-29.4-1-1-3', 'declare-29.4-2', 'dedicate-79', 'defend-85', 'destroy-44', 'devour-39.4', 'devour-39.4-1', 'devour-39.4-2', 'differ-23.4', 'dine-39.5', 'disappearance-48.2', 'disassemble-23.3', 'discover-84', 'discover-84-1', 'discover-84-1-1', 'dress-41.1.1', 'dressing_well-41.3.2', 'drive-11.5', 'drive-11.5-1', 'dub-29.3', 'dub-29.3-1', 'eat-39.1', 'eat-39.1-1', 'eat-39.1-2', 'enforce-63', 'engender-27', 'entity_specific_cos-45.5', 'entity_specific_modes_being-47.2', 'equip-13.4.2', 'equip-13.4.2-1', 'equip-13.4.2-1-1', 'escape-51.1', 'escape-51.1-1', 'escape-51.1-2', 'escape-51.1-2-1', 'exceed-90', 'exchange-13.6', 'exchange-13.6-1', 'exchange-13.6-1-1', 'exhale-40.1.3', 'exhale-40.1.3-1', 'exhale-40.1.3-2', 'exist-47.1', 'exist-47.1-1', 'exist-47.1-1-1', 'feeding-39.7', 'ferret-35.6', 'fill-9.8', 'fill-9.8-1', 'fit-54.3', 'flinch-40.5', 'floss-41.2.1', 'focus-87', 'forbid-67', 'force-59', 'force-59-1', 'free-80', 'free-80-1', 'fulfilling-13.4.1', 'fulfilling-13.4.1-1', 'fulfilling-13.4.1-2', 'funnel-9.3', 'funnel-9.3-1', 'funnel-9.3-2', 'funnel-9.3-2-1', 'future_having-13.3', 'get-13.5.1', 'get-13.5.1-1', 'give-13.1', 'give-13.1-1', 'gobble-39.3', 'gobble-39.3-1', 'gobble-39.3-2', 'gorge-39.6', 'groom-41.1.2', 'grow-26.2', 'help-72', 'help-72-1', 'herd-47.5.2', 'hiccup-40.1.1', 'hit-18.1', 'hit-18.1-1', 'hold-15.1', 'hold-15.1-1', 'hunt-35.1', 'hurt-40.8.3', 'hurt-40.8.3-1', 'hurt-40.8.3-1-1', 'hurt-40.8.3-2', 'illustrate-25.3', 'image_impression-25.1', 'indicate-78', 'indicate-78-1', 'indicate-78-1-1', 'inquire-37.1.2', 'instr_communication-37.4', 'investigate-35.4', 'judgement-33', 'keep-15.2', 'knead-26.5', 'learn-14', 'learn-14-1', 'learn-14-2', 'learn-14-2-1', 'leave-51.2', 'leave-51.2-1', 'lecture-37.11', 'lecture-37.11-1', 'lecture-37.11-1-1', 'lecture-37.11-2', 'light_emission-43.1', 'limit-76', 'linger-53.1', 'linger-53.1-1', 'lodge-46', 'long-32.2', 'long-32.2-1', 'long-32.2-2', 'manner_speaking-37.3', 'marry-36.2', 'marvel-31.3', 'marvel-31.3-1', 'marvel-31.3-2', 'marvel-31.3-3', 'marvel-31.3-4', 'marvel-31.3-5', 'marvel-31.3-6', 'marvel-31.3-7', 'marvel-31.3-8', 'marvel-31.3-9', 'masquerade-29.6', 'masquerade-29.6-1', 'masquerade-29.6-2', 'matter-91', 'meander-47.7', 'meet-36.3', 'meet-36.3-1', 'meet-36.3-2', 'mine-10.9', 'mix-22.1', 'mix-22.1-1', 'mix-22.1-1-1', 'mix-22.1-2', 'mix-22.1-2-1', 'modes_of_being_with_motion-47.3', 'murder-42.1', 'murder-42.1-1', 'neglect-75', 'neglect-75-1', 'neglect-75-1-1', 'neglect-75-2', 'nonvehicle-51.4.2', 'nonverbal_expression-40.2', 'obtain-13.5.2', 'obtain-13.5.2-1', 'occurrence-48.3', 'order-60', 'order-60-1', 'orphan-29.7', 'other_cos-45.4', 'pain-40.8.1', 'pay-68', 'peer-30.3', 'pelt-17.2', 'performance-26.7', 'performance-26.7-1', 'performance-26.7-1-1', 'performance-26.7-2', 'performance-26.7-2-1', 'pit-10.7', 'pocket-9.10', 'pocket-9.10-1', 'poison-42.2', 'poke-19', 'pour-9.5', 'preparing-26.3', 'preparing-26.3-1', 'preparing-26.3-2', 'price-54.4', 'push-12', 'push-12-1', 'push-12-1-1', 'put-9.1', 'put-9.1-1', 'put-9.1-2', 'put_direction-9.4', 'put_spatial-9.2', 'put_spatial-9.2-1', 'reach-51.8', 'reflexive_appearance-48.1.2', 'refrain-69', 'register-54.1', 'rely-70', 'remove-10.1', 'risk-94', 'risk-94-1', 'roll-51.3.1', 'rummage-35.5', 'run-51.3.2', 'rush-53.2', 'say-37.7', 'say-37.7-1', 'say-37.7-1-1', 'say-37.7-2', 'scribble-25.2', 'search-35.2', 'see-30.1', 'see-30.1-1', 'see-30.1-1-1', 'send-11.1', 'send-11.1-1', 'separate-23.1', 'separate-23.1-1', 'separate-23.1-2', 'settle-89', 'shake-22.3', 'shake-22.3-1', 'shake-22.3-1-1', 'shake-22.3-2', 'shake-22.3-2-1', 'sight-30.2', 'simple_dressing-41.3.1', 'slide-11.2', 'slide-11.2-1-1', 'smell_emission-43.3', 'snooze-40.4', 'sound_emission-43.2', 'sound_existence-47.4', 'spank-18.3', 'spatial_configuration-47.6', 'split-23.2', 'spray-9.7', 'spray-9.7-1', 'spray-9.7-1-1', 'spray-9.7-2', 'stalk-35.3', 'steal-10.5', 'stimulus_subject-30.4', 'stop-55.4', 'stop-55.4-1', 'substance_emission-43.4', 'succeed-74', 'succeed-74-1', 'succeed-74-1-1', 'succeed-74-2', 'suffocate-40.7', 'suspect-81', 'swarm-47.5.1', 'swarm-47.5.1-1', 'swarm-47.5.1-2', 'swarm-47.5.1-2-1', 'swat-18.2', 'talk-37.5', 'tape-22.4', 'tape-22.4-1', 'tell-37.2', 'throw-17.1', 'throw-17.1-1', 'throw-17.1-1-1', 'tingle-40.8.2', 'touch-20', 'touch-20-1', 'transcribe-25.4', 'transfer_mesg-37.1.1', 'transfer_mesg-37.1.1-1', 'transfer_mesg-37.1.1-1-1', 'try-61', 'turn-26.6.1', 'turn-26.6.1-1', 'urge-58', 'vehicle-51.4.1', 'vehicle-51.4.1-1', 'waltz-51.5', 'want-32.1', 'want-32.1-1', 'want-32.1-1-1', 'weather-57', 'weekend-56', 'wink-40.3.1', 'wink-40.3.1-1', 'wipe_instr-10.4.2', 'wipe_instr-10.4.2-1', 'wipe_manner-10.4.1', 'wipe_manner-10.4.1-1', 'wish-62', 'withdraw-82', 'withdraw-82-1', 'withdraw-82-2', 'withdraw-82-3'] + +The primary object in the lexicon is a class record, which is stored +as an ElementTree xml object. The class record for a given class +identifier is returned by the `vnclass()` method: + + >>> verbnet.vnclass('remove-10.1') + + +The `vnclass()` method also accepts "short" identifiers, such as '10.1': + + >>> verbnet.vnclass('10.1') + + +See the Verbnet documentation, or the Verbnet files, for information +about the structure of this xml. As an example, we can retrieve a +list of thematic roles for a given Verbnet class: + + >>> vn_31_2 = verbnet.vnclass('admire-31.2') + >>> for themrole in vn_31_2.findall('THEMROLES/THEMROLE'): + ... print(themrole.attrib['type'], end=' ') + ... for selrestr in themrole.findall('SELRESTRS/SELRESTR'): + ... print('[%(Value)s%(type)s]' % selrestr.attrib, end=' ') + ... print() + Theme + Experiencer [+animate] + Predicate + +The Verbnet corpus also provides a variety of pretty printing +functions that can be used to display the xml contents in a more +concise form. The simplest such method is `pprint()`: + + >>> print(verbnet.pprint('57')) + weather-57 + Subclasses: (none) + Members: blow clear drizzle fog freeze gust hail howl lightning mist + mizzle pelt pour precipitate rain roar shower sleet snow spit spot + sprinkle storm swelter teem thaw thunder + Thematic roles: + * Theme[+concrete +force] + Frames: + Intransitive (Expletive Subject) + Example: It's raining. + Syntax: LEX[it] LEX[[+be]] VERB + Semantics: + * weather(during(E), Weather_type, ?Theme) + NP (Expletive Subject, Theme Object) + Example: It's raining cats and dogs. + Syntax: LEX[it] LEX[[+be]] VERB NP[Theme] + Semantics: + * weather(during(E), Weather_type, Theme) + PP (Expletive Subject, Theme-PP) + Example: It was pelting with rain. + Syntax: LEX[it[+be]] VERB PREP[with] NP[Theme] + Semantics: + * weather(during(E), Weather_type, Theme) + +Verbnet gives us frames that link the syntax and semantics using an example. +These frames are part of the corpus and we can use `frames()` to get a frame +for a given verbnet class. + + >>> frame = verbnet.frames('57') + >>> frame == [{'example': "It's raining.", 'description': {'primary': 'Intransitive', 'secondary': 'Expletive Subject'}, 'syntax': [{'pos_tag': 'LEX', 'modifiers': {'value': 'it', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'LEX', 'modifiers': {'value': '[+be]', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'VERB', 'modifiers': {'value': '', 'selrestrs': [], 'synrestrs': []}}], 'semantics': [{'predicate_value': 'weather', 'arguments': [{'type': 'Event', 'value': 'during(E)'}, {'type': 'VerbSpecific', 'value': 'Weather_type'}, {'type': 'ThemRole', 'value': '?Theme'}], 'negated': False}]}, {'example': "It's raining cats and dogs.", 'description': {'primary': 'NP', 'secondary': 'Expletive Subject, Theme Object'}, 'syntax': [{'pos_tag': 'LEX', 'modifiers': {'value': 'it', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'LEX', 'modifiers': {'value': '[+be]', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'VERB', 'modifiers': {'value': '', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'NP', 'modifiers': {'value': 'Theme', 'selrestrs': [], 'synrestrs': []}}], 'semantics': [{'predicate_value': 'weather', 'arguments': [{'type': 'Event', 'value': 'during(E)'}, {'type': 'VerbSpecific', 'value': 'Weather_type'}, {'type': 'ThemRole', 'value': 'Theme'}], 'negated': False}]}, {'example': 'It was pelting with rain.', 'description': {'primary': 'PP', 'secondary': 'Expletive Subject, Theme-PP'}, 'syntax': [{'pos_tag': 'LEX', 'modifiers': {'value': 'it[+be]', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'VERB', 'modifiers': {'value': '', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'PREP', 'modifiers': {'value': 'with', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'NP', 'modifiers': {'value': 'Theme', 'selrestrs': [], 'synrestrs': []}}], 'semantics': [{'predicate_value': 'weather', 'arguments': [{'type': 'Event', 'value': 'during(E)'}, {'type': 'VerbSpecific', 'value': 'Weather_type'}, {'type': 'ThemRole', 'value': 'Theme'}], 'negated': False}]}] + True + +Verbnet corpus lets us access thematic roles individually using `themroles()`. + + >>> themroles = verbnet.themroles('57') + >>> themroles == [{'modifiers': [{'type': 'concrete', 'value': '+'}, {'type': 'force', 'value': '+'}], 'type': 'Theme'}] + True + +Verbnet classes may also have subclasses sharing similar syntactic and semantic properties +while having differences with the superclass. The Verbnet corpus allows us to access these +subclasses using `subclasses()`. + + >>> print(verbnet.subclasses('9.1')) #Testing for 9.1 since '57' does not have subclasses + ['put-9.1-1', 'put-9.1-2'] + + +nps_chat +-------- + +The NPS Chat Corpus, Release 1.0 consists of over 10,000 posts in age-specific +chat rooms, which have been anonymized, POS-tagged and dialogue-act tagged. + + >>> print(nltk.corpus.nps_chat.words()) + ['now', 'im', 'left', 'with', 'this', 'gay', ...] + >>> print(nltk.corpus.nps_chat.tagged_words()) + [('now', 'RB'), ('im', 'PRP'), ('left', 'VBD'), ...] + >>> print(nltk.corpus.nps_chat.tagged_posts()) + [[('now', 'RB'), ('im', 'PRP'), ('left', 'VBD'), ('with', 'IN'), + ('this', 'DT'), ('gay', 'JJ'), ('name', 'NN')], [(':P', 'UH')], ...] + +We can access the XML elements corresponding to individual posts. These elements +have ``class`` and ``user`` attributes that we can access using ``p.attrib['class']`` +and ``p.attrib['user']``. They also have text content, accessed using ``p.text``. + + >>> print(nltk.corpus.nps_chat.xml_posts()) + [, , ...] + >>> posts = nltk.corpus.nps_chat.xml_posts() + >>> sorted(nltk.FreqDist(p.attrib['class'] for p in posts).keys()) + ['Accept', 'Bye', 'Clarify', 'Continuer', 'Emotion', 'Emphasis', + 'Greet', 'Other', 'Reject', 'Statement', 'System', 'nAnswer', + 'whQuestion', 'yAnswer', 'ynQuestion'] + >>> posts[0].text + 'now im left with this gay name' + +In addition to the above methods for accessing tagged text, we can navigate +the XML structure directly, as follows: + + >>> tokens = posts[0].findall('terminals/t') + >>> [t.attrib['pos'] + "/" + t.attrib['word'] for t in tokens] + ['RB/now', 'PRP/im', 'VBD/left', 'IN/with', 'DT/this', 'JJ/gay', 'NN/name'] + +multext_east +------------ + +The Multext-East Corpus consists of POS-tagged versions of George Orwell's book +1984 in 12 languages: English, Czech, Hungarian, Macedonian, Slovenian, Serbian, +Slovak, Romanian, Estonian, Farsi, Bulgarian and Polish. +The corpus can be accessed using the usual methods for tagged corpora. The tagset +can be transformed from the Multext-East specific MSD tags to the Universal tagset +using the "tagset" parameter of all functions returning tagged parts of the corpus. + + >>> print(nltk.corpus.multext_east.words("oana-en.xml")) + ['It', 'was', 'a', 'bright', ...] + >>> print(nltk.corpus.multext_east.tagged_words("oana-en.xml")) + [('It', '#Pp3ns'), ('was', '#Vmis3s'), ('a', '#Di'), ...] + >>> print(nltk.corpus.multext_east.tagged_sents("oana-en.xml", "universal")) + [[('It', 'PRON'), ('was', 'VERB'), ('a', 'DET'), ...] + + + +--------------------- +Corpus Reader Classes +--------------------- + +NLTK's *corpus reader* classes are used to access the contents of a +diverse set of corpora. Each corpus reader class is specialized to +handle a specific corpus format. Examples include the +`PlaintextCorpusReader`, which handles corpora that consist of a set +of unannotated text files, and the `BracketParseCorpusReader`, which +handles corpora that consist of files containing +parenthesis-delineated parse trees. + +Automatically Created Corpus Reader Instances +============================================= + +When the `nltk.corpus` module is imported, it automatically creates a +set of corpus reader instances that can be used to access the corpora +in the NLTK data distribution. Here is a small sample of those +corpus reader instances: + + >>> import nltk + >>> nltk.corpus.brown + + >>> nltk.corpus.treebank + + >>> nltk.corpus.names + + >>> nltk.corpus.genesis + + >>> nltk.corpus.inaugural + + +This sample illustrates that different corpus reader classes are used +to read different corpora; but that the same corpus reader class may +be used for more than one corpus (e.g., ``genesis`` and ``inaugural``). + +Creating New Corpus Reader Instances +==================================== + +Although the `nltk.corpus` module automatically creates corpus reader +instances for the corpora in the NLTK data distribution, you may +sometimes need to create your own corpus reader. In particular, you +would need to create your own corpus reader if you want... + +- To access a corpus that is not included in the NLTK data + distribution. + +- To access a full copy of a corpus for which the NLTK data + distribution only provides a sample. + +- To access a corpus using a customized corpus reader (e.g., with + a customized tokenizer). + +To create a new corpus reader, you will first need to look up the +signature for that corpus reader's constructor. Different corpus +readers have different constructor signatures, but most of the +constructor signatures have the basic form:: + + SomeCorpusReader(root, files, ...options...) + +Where ``root`` is an absolute path to the directory containing the +corpus data files; ``files`` is either a list of file names (relative +to ``root``) or a regexp specifying which files should be included; +and ``options`` are additional reader-specific options. For example, +we can create a customized corpus reader for the genesis corpus that +uses a different sentence tokenizer as follows: + + >>> # Find the directory where the corpus lives. + >>> genesis_dir = nltk.data.find('corpora/genesis') + >>> # Create our custom sentence tokenizer. + >>> my_sent_tokenizer = nltk.RegexpTokenizer('[^.!?]+') + >>> # Create the new corpus reader object. + >>> my_genesis = nltk.corpus.PlaintextCorpusReader( + ... genesis_dir, r'.*\.txt', sent_tokenizer=my_sent_tokenizer) + >>> # Use the new corpus reader object. + >>> print(my_genesis.sents('english-kjv.txt')[0]) + ['In', 'the', 'beginning', 'God', 'created', 'the', 'heaven', + 'and', 'the', 'earth'] + +If you wish to read your own plaintext corpus, which is stored in the +directory '/usr/share/some-corpus', then you can create a corpus +reader for it with:: + + >>> my_corpus = nltk.corpus.PlaintextCorpusReader( + ... '/usr/share/some-corpus', r'.*\.txt') # doctest: +SKIP + +For a complete list of corpus reader subclasses, see the API +documentation for `nltk.corpus.reader`. + +Corpus Types +============ + +Corpora vary widely in the types of content they include. This is +reflected in the fact that the base class `CorpusReader` only defines +a few general-purpose methods for listing and accessing the files that +make up a corpus. It is up to the subclasses to define *data access +methods* that provide access to the information in the corpus. +However, corpus reader subclasses should be consistent in their +definitions of these data access methods wherever possible. + +At a high level, corpora can be divided into three basic types: + +- A *token corpus* contains information about specific occurrences of + language use (or linguistic tokens), such as dialogues or written + texts. Examples of token corpora are collections of written text + and collections of speech. + +- A *type corpus*, or *lexicon*, contains information about a coherent + set of lexical items (or linguistic types). Examples of lexicons + are dictionaries and word lists. + +- A *language description corpus* contains information about a set of + non-lexical linguistic constructs, such as grammar rules. + +However, many individual corpora blur the distinctions between these +types. For example, corpora that are primarily lexicons may include +token data in the form of example sentences; and corpora that are +primarily token corpora may be accompanied by one or more word lists +or other lexical data sets. + +Because corpora vary so widely in their information content, we have +decided that it would not be wise to use separate corpus reader base +classes for different corpus types. Instead, we simply try to make +the corpus readers consistent wherever possible, but let them differ +where the underlying data itself differs. + +Common Corpus Reader Methods +============================ + +As mentioned above, there are only a handful of methods that all +corpus readers are guaranteed to implement. These methods provide +access to the files that contain the corpus data. Every corpus is +assumed to consist of one or more files, all located in a common root +directory (or in subdirectories of that root directory). The absolute +path to the root directory is stored in the ``root`` property: + + >>> import os + >>> str(nltk.corpus.genesis.root).replace(os.path.sep,'/') + '.../nltk_data/corpora/genesis' + +Each file within the corpus is identified by a platform-independent +identifier, which is basically a path string that uses ``/`` as the +path separator. I.e., this identifier can be converted to a relative +path as follows: + + >>> some_corpus_file_id = nltk.corpus.reuters.fileids()[0] + >>> import os.path + >>> os.path.normpath(some_corpus_file_id).replace(os.path.sep,'/') + 'test/14826' + +To get a list of all data files that make up a corpus, use the +``fileids()`` method. In some corpora, these files will not all contain +the same type of data; for example, for the ``nltk.corpus.timit`` +corpus, ``fileids()`` will return a list including text files, word +segmentation files, phonetic transcription files, sound files, and +metadata files. For corpora with diverse file types, the ``fileids()`` +method will often take one or more optional arguments, which can be +used to get a list of the files with a specific file type: + + >>> nltk.corpus.timit.fileids() + ['dr1-fvmh0/sa1.phn', 'dr1-fvmh0/sa1.txt', 'dr1-fvmh0/sa1.wav', ...] + >>> nltk.corpus.timit.fileids('phn') + ['dr1-fvmh0/sa1.phn', 'dr1-fvmh0/sa2.phn', 'dr1-fvmh0/si1466.phn', ...] + +In some corpora, the files are divided into distinct categories. For +these corpora, the ``fileids()`` method takes an optional argument, +which can be used to get a list of the files within a specific category: + + >>> nltk.corpus.brown.fileids('hobbies') + ['ce01', 'ce02', 'ce03', 'ce04', 'ce05', 'ce06', 'ce07', ...] + +The ``abspath()`` method can be used to find the absolute path to a +corpus file, given its file identifier: + + >>> str(nltk.corpus.brown.abspath('ce06')).replace(os.path.sep,'/') + '.../corpora/brown/ce06' + +The ``abspaths()`` method can be used to find the absolute paths for +one corpus file, a list of corpus files, or (if no fileids are specified), +all corpus files. + +This method is mainly useful as a helper method when defining corpus +data access methods, since data access methods can usually be called +with a string argument (to get a view for a specific file), with a +list argument (to get a view for a specific list of files), or with no +argument (to get a view for the whole corpus). + +Data Access Methods +=================== + +Individual corpus reader subclasses typically extend this basic set of +file-access methods with one or more *data access methods*, which provide +easy access to the data contained in the corpus. The signatures for +data access methods often have the basic form:: + + corpus_reader.some_data access(fileids=None, ...options...) + +Where ``fileids`` can be a single file identifier string (to get a view +for a specific file); a list of file identifier strings (to get a view +for a specific list of files); or None (to get a view for the entire +corpus). Some of the common data access methods, and their return +types, are: + + - I{corpus}.words(): list of str + - I{corpus}.sents(): list of (list of str) + - I{corpus}.paras(): list of (list of (list of str)) + - I{corpus}.tagged_words(): list of (str,str) tuple + - I{corpus}.tagged_sents(): list of (list of (str,str)) + - I{corpus}.tagged_paras(): list of (list of (list of (str,str))) + - I{corpus}.chunked_sents(): list of (Tree w/ (str,str) leaves) + - I{corpus}.parsed_sents(): list of (Tree with str leaves) + - I{corpus}.parsed_paras(): list of (list of (Tree with str leaves)) + - I{corpus}.xml(): A single xml ElementTree + - I{corpus}.raw(): str (unprocessed corpus contents) + +For example, the `words()` method is supported by many different +corpora, and returns a flat list of word strings: + + >>> nltk.corpus.brown.words() + ['The', 'Fulton', 'County', 'Grand', 'Jury', ...] + >>> nltk.corpus.treebank.words() + ['Pierre', 'Vinken', ',', '61', 'years', 'old', ...] + >>> nltk.corpus.conll2002.words() + ['Sao', 'Paulo', '(', 'Brasil', ')', ',', '23', ...] + >>> nltk.corpus.genesis.words() + ['In', 'the', 'beginning', 'God', 'created', ...] + +On the other hand, the `tagged_words()` method is only supported by +corpora that include part-of-speech annotations: + + >>> nltk.corpus.brown.tagged_words() + [('The', 'AT'), ('Fulton', 'NP-TL'), ...] + >>> nltk.corpus.treebank.tagged_words() + [('Pierre', 'NNP'), ('Vinken', 'NNP'), ...] + >>> nltk.corpus.conll2002.tagged_words() + [('Sao', 'NC'), ('Paulo', 'VMI'), ('(', 'Fpa'), ...] + >>> nltk.corpus.genesis.tagged_words() + Traceback (most recent call last): + ... + AttributeError: 'PlaintextCorpusReader' object has no attribute 'tagged_words' + +Although most corpus readers use file identifiers to index their +content, some corpora use different identifiers instead. For example, +the data access methods for the ``timit`` corpus uses *utterance +identifiers* to select which corpus items should be returned: + + >>> nltk.corpus.timit.utteranceids() + ['dr1-fvmh0/sa1', 'dr1-fvmh0/sa2', 'dr1-fvmh0/si1466', ...] + >>> nltk.corpus.timit.words('dr1-fvmh0/sa2') + ["don't", 'ask', 'me', 'to', 'carry', 'an', 'oily', 'rag', 'like', 'that'] + +Attempting to call ``timit``\ 's data access methods with a file +identifier will result in an exception: + + >>> nltk.corpus.timit.fileids() + ['dr1-fvmh0/sa1.phn', 'dr1-fvmh0/sa1.txt', 'dr1-fvmh0/sa1.wav', ...] + >>> nltk.corpus.timit.words('dr1-fvmh0/sa1.txt') # doctest: +SKIP + Traceback (most recent call last): + ... + IOError: No such file or directory: '.../dr1-fvmh0/sa1.txt.wrd' + +As another example, the ``propbank`` corpus defines the ``roleset()`` +method, which expects a roleset identifier, not a file identifier: + + >>> roleset = nltk.corpus.propbank.roleset('eat.01') + >>> from xml.etree import ElementTree as ET + >>> print(ET.tostring(roleset).decode('utf8')) + + + ...... + ... + ... + +Stream Backed Corpus Views +========================== +An important feature of NLTK's corpus readers is that many of them +access the underlying data files using "corpus views." A *corpus +view* is an object that acts like a simple data structure (such as a +list), but does not store the data elements in memory; instead, data +elements are read from the underlying data files on an as-needed +basis. + +By only loading items from the file on an as-needed basis, corpus +views maintain both memory efficiency and responsiveness. The memory +efficiency of corpus readers is important because some corpora contain +very large amounts of data, and storing the entire data set in memory +could overwhelm many machines. The responsiveness is important when +experimenting with corpora in interactive sessions and in in-class +demonstrations. + +The most common corpus view is the `StreamBackedCorpusView`, which +acts as a read-only list of tokens. Two additional corpus view +classes, `ConcatenatedCorpusView` and `LazySubsequence`, make it +possible to create concatenations and take slices of +`StreamBackedCorpusView` objects without actually storing the +resulting list-like object's elements in memory. + +In the future, we may add additional corpus views that act like other +basic data structures, such as dictionaries. + +Writing New Corpus Readers +========================== + +In order to add support for new corpus formats, it is necessary to +define new corpus reader classes. For many corpus formats, writing +new corpus readers is relatively straight-forward. In this section, +we'll describe what's involved in creating a new corpus reader. If +you do create a new corpus reader, we encourage you to contribute it +back to the NLTK project. + +Don't Reinvent the Wheel +------------------------ +Before you start writing a new corpus reader, you should check to be +sure that the desired format can't be read using an existing corpus +reader with appropriate constructor arguments. For example, although +the `TaggedCorpusReader` assumes that words and tags are separated by +``/`` characters by default, an alternative tag-separation character +can be specified via the ``sep`` constructor argument. You should +also check whether the new corpus format can be handled by subclassing +an existing corpus reader, and tweaking a few methods or variables. + +Design +------ +If you decide to write a new corpus reader from scratch, then you +should first decide which data access methods you want the reader to +provide, and what their signatures should be. You should look at +existing corpus readers that process corpora with similar data +contents, and try to be consistent with those corpus readers whenever +possible. + +You should also consider what sets of identifiers are appropriate for +the corpus format. Where it's practical, file identifiers should be +used. However, for some corpora, it may make sense to use additional +sets of identifiers. Each set of identifiers should have a distinct +name (e.g., fileids, utteranceids, rolesets); and you should be consistent +in using that name to refer to that identifier. Do not use parameter +names like ``id``, which leave it unclear what type of identifier is +required. + +Once you've decided what data access methods and identifiers are +appropriate for your corpus, you should decide if there are any +customizable parameters that you'd like the corpus reader to handle. +These parameters make it possible to use a single corpus reader to +handle a wider variety of corpora. The ``sep`` argument for +`TaggedCorpusReader`, mentioned above, is an example of a customizable +corpus reader parameter. + +Implementation +-------------- + +Constructor +~~~~~~~~~~~ +If your corpus reader implements any customizable parameters, then +you'll need to override the constructor. Typically, the new +constructor will first call its base class's constructor, and then +store the customizable parameters. For example, the +`ConllChunkCorpusReader`\ 's constructor is defined as follows: + + >>> def __init__(self, root, fileids, chunk_types, encoding='utf8', + ... tagset=None, separator=None): + ... ConllCorpusReader.__init__( + ... self, root, fileids, ('words', 'pos', 'chunk'), + ... chunk_types=chunk_types, encoding=encoding, + ... tagset=tagset, separator=separator) + +If your corpus reader does not implement any customization parameters, +then you can often just inherit the base class's constructor. + +Data Access Methods +~~~~~~~~~~~~~~~~~~~ + +The most common type of data access method takes an argument +identifying which files to access, and returns a view covering those +files. This argument may be a single file identifier string (to get a +view for a specific file); a list of file identifier strings (to get a +view for a specific list of files); or None (to get a view for the +entire corpus). The method's implementation converts this argument to +a list of path names using the `abspaths()` method, which handles all +three value types (string, list, and None): + + >>> print(str(nltk.corpus.brown.abspaths()).replace('\\\\','/')) + [FileSystemPathPointer('.../corpora/brown/ca01'), + FileSystemPathPointer('.../corpora/brown/ca02'), ...] + >>> print(str(nltk.corpus.brown.abspaths('ce06')).replace('\\\\','/')) + [FileSystemPathPointer('.../corpora/brown/ce06')] + >>> print(str(nltk.corpus.brown.abspaths(['ce06', 'ce07'])).replace('\\\\','/')) + [FileSystemPathPointer('.../corpora/brown/ce06'), + FileSystemPathPointer('.../corpora/brown/ce07')] + +An example of this type of method is the `words()` method, defined by +the `PlaintextCorpusReader` as follows: + + >>> def words(self, fileids=None): + ... return concat([self.CorpusView(fileid, self._read_word_block) + ... for fileid in self.abspaths(fileids)]) + +This method first uses `abspaths()` to convert ``fileids`` to a list of +absolute paths. It then creates a corpus view for each file, using +the `PlaintextCorpusReader._read_word_block()` method to read elements +from the data file (see the discussion of corpus views below). +Finally, it combines these corpus views using the +`nltk.corpus.reader.util.concat()` function. + +When writing a corpus reader for a corpus that is never expected to be +very large, it can sometimes be appropriate to read the files +directly, rather than using a corpus view. For example, the +`WordListCorpusView` class defines its `words()` method as follows: + + >>> def words(self, fileids=None): + ... return concat([[w for w in open(fileid).read().split('\n') if w] + ... for fileid in self.abspaths(fileids)]) + +(This is usually more appropriate for lexicons than for token corpora.) + +If the type of data returned by a data access method is one for which +NLTK has a conventional representation (e.g., words, tagged words, and +parse trees), then you should use that representation. Otherwise, you +may find it necessary to define your own representation. For data +structures that are relatively corpus-specific, it's usually best to +define new classes for these elements. For example, the ``propbank`` +corpus defines the `PropbankInstance` class to store the semantic role +labeling instances described by the corpus; and the ``ppattach`` +corpus defines the `PPAttachment` class to store the prepositional +attachment instances described by the corpus. + +Corpus Views +~~~~~~~~~~~~ +.. (Much of the content for this section is taken from the + StreamBackedCorpusView docstring.) + +The heart of a `StreamBackedCorpusView` is its *block reader* +function, which reads zero or more tokens from a stream, and returns +them as a list. A very simple example of a block reader is: + + >>> def simple_block_reader(stream): + ... return stream.readline().split() + +This simple block reader reads a single line at a time, and returns a +single token (consisting of a string) for each whitespace-separated +substring on the line. A `StreamBackedCorpusView` built from this +block reader will act like a read-only list of all the +whitespace-separated tokens in an underlying file. + +When deciding how to define the block reader for a given corpus, +careful consideration should be given to the size of blocks handled by +the block reader. Smaller block sizes will increase the memory +requirements of the corpus view's internal data structures (by 2 +integers per block). On the other hand, larger block sizes may +decrease performance for random access to the corpus. (But note that +larger block sizes will *not* decrease performance for iteration.) + +Internally, the `StreamBackedCorpusView` class maintains a partial +mapping from token index to file position, with one entry per block. +When a token with a given index *i* is requested, the corpus view +constructs it as follows: + +1. First, it searches the toknum/filepos mapping for the token index + closest to (but less than or equal to) *i*. + +2. Then, starting at the file position corresponding to that index, it + reads one block at a time using the block reader until it reaches + the requested token. + +The toknum/filepos mapping is created lazily: it is initially empty, +but every time a new block is read, the block's initial token is added +to the mapping. (Thus, the toknum/filepos map has one entry per +block.) + +You can create your own corpus view in one of two ways: + +1. Call the `StreamBackedCorpusView` constructor, and provide your + block reader function via the ``block_reader`` argument. + +2. Subclass `StreamBackedCorpusView`, and override the + `read_block()` method. + +The first option is usually easier, but the second option can allow +you to write a single `read_block` method whose behavior can be +customized by different parameters to the subclass's constructor. For +an example of this design pattern, see the `TaggedCorpusView` class, +which is used by `TaggedCorpusView`. + +---------------- +Regression Tests +---------------- + +The following helper functions are used to create and then delete +testing corpora that are stored in temporary directories. These +testing corpora are used to make sure the readers work correctly. + + >>> import tempfile, os.path, textwrap + >>> def make_testcorpus(ext='', **fileids): + ... root = tempfile.mkdtemp() + ... for fileid, contents in fileids.items(): + ... fileid += ext + ... f = open(os.path.join(root, fileid), 'w') + ... f.write(textwrap.dedent(contents)) + ... f.close() + ... return root + >>> def del_testcorpus(root): + ... for fileid in os.listdir(root): + ... os.remove(os.path.join(root, fileid)) + ... os.rmdir(root) + +Plaintext Corpus Reader +======================= +The plaintext corpus reader is used to access corpora that consist of +unprocessed plaintext data. It assumes that paragraph breaks are +indicated by blank lines. Sentences and words can be tokenized using +the default tokenizers, or by custom tokenizers specified as +parameters to the constructor. + + >>> root = make_testcorpus(ext='.txt', + ... a="""\ + ... This is the first sentence. Here is another + ... sentence! And here's a third sentence. + ... + ... This is the second paragraph. Tokenization is currently + ... fairly simple, so the period in Mr. gets tokenized. + ... """, + ... b="""This is the second file.""") + + >>> from nltk.corpus.reader.plaintext import PlaintextCorpusReader + +The list of documents can be specified explicitly, or implicitly (using a +regexp). The ``ext`` argument specifies a file extension. + + >>> corpus = PlaintextCorpusReader(root, ['a.txt', 'b.txt']) + >>> corpus.fileids() + ['a.txt', 'b.txt'] + >>> corpus = PlaintextCorpusReader(root, r'.*\.txt') + >>> corpus.fileids() + ['a.txt', 'b.txt'] + +The directory containing the corpus is corpus.root: + + >>> str(corpus.root) == str(root) + True + +We can get a list of words, or the raw string: + + >>> corpus.words() + ['This', 'is', 'the', 'first', 'sentence', '.', ...] + >>> corpus.raw()[:40] + 'This is the first sentence. Here is ano' + +Check that reading individual documents works, and reading all documents at +once works: + + >>> len(corpus.words()), [len(corpus.words(d)) for d in corpus.fileids()] + (46, [40, 6]) + >>> corpus.words('a.txt') + ['This', 'is', 'the', 'first', 'sentence', '.', ...] + >>> corpus.words('b.txt') + ['This', 'is', 'the', 'second', 'file', '.'] + >>> corpus.words()[:4], corpus.words()[-4:] + (['This', 'is', 'the', 'first'], ['the', 'second', 'file', '.']) + +We're done with the test corpus: + + >>> del_testcorpus(root) + +Test the plaintext corpora that come with nltk: + + >>> from nltk.corpus import abc, genesis, inaugural + >>> from nltk.corpus import state_union, webtext + >>> for corpus in (abc, genesis, inaugural, state_union, + ... webtext): + ... print(str(corpus).replace('\\\\','/')) + ... print(' ', repr(corpus.fileids())[:60]) + ... print(' ', repr(corpus.words()[:10])[:60]) + + ['rural.txt', 'science.txt'] + ['PM', 'denies', 'knowledge', 'of', 'AWB', ... + + ['english-kjv.txt', 'english-web.txt', 'finnish.txt', ... + ['In', 'the', 'beginning', 'God', 'created', 'the', ... + + ['1789-Washington.txt', '1793-Washington.txt', ... + ['Fellow', '-', 'Citizens', 'of', 'the', 'Senate', ... + + ['1945-Truman.txt', '1946-Truman.txt', ... + ['PRESIDENT', 'HARRY', 'S', '.', 'TRUMAN', "'", ... + + ['firefox.txt', 'grail.txt', 'overheard.txt', ... + ['Cookie', 'Manager', ':', '"', 'Don', "'", 't', ... + + +Tagged Corpus Reader +==================== +The Tagged Corpus reader can give us words, sentences, and paragraphs, +each tagged or untagged. All of the read methods can take one item +(in which case they return the contents of that file) or a list of +documents (in which case they concatenate the contents of those files). +By default, they apply to all documents in the corpus. + + >>> root = make_testcorpus( + ... a="""\ + ... This/det is/verb the/det first/adj sentence/noun ./punc + ... Here/det is/verb another/adj sentence/noun ./punc + ... Note/verb that/comp you/pron can/verb use/verb \ + ... any/noun tag/noun set/noun + ... + ... This/det is/verb the/det second/adj paragraph/noun ./punc + ... word/n without/adj a/det tag/noun :/: hello ./punc + ... """, + ... b="""\ + ... This/det is/verb the/det second/adj file/noun ./punc + ... """) + + >>> from nltk.corpus.reader.tagged import TaggedCorpusReader + >>> corpus = TaggedCorpusReader(root, list('ab')) + >>> corpus.fileids() + ['a', 'b'] + >>> str(corpus.root) == str(root) + True + >>> corpus.words() + ['This', 'is', 'the', 'first', 'sentence', '.', ...] + >>> corpus.sents() + [['This', 'is', 'the', 'first', ...], ['Here', 'is', 'another'...], ...] + >>> corpus.paras() + [[['This', ...], ['Here', ...], ...], [['This', ...], ...], ...] + >>> corpus.tagged_words() + [('This', 'DET'), ('is', 'VERB'), ('the', 'DET'), ...] + >>> corpus.tagged_sents() + [[('This', 'DET'), ('is', 'VERB'), ...], [('Here', 'DET'), ...], ...] + >>> corpus.tagged_paras() + [[[('This', 'DET'), ...], ...], [[('This', 'DET'), ...], ...], ...] + >>> corpus.raw()[:40] + 'This/det is/verb the/det first/adj sente' + >>> len(corpus.words()), [len(corpus.words(d)) for d in corpus.fileids()] + (38, [32, 6]) + >>> len(corpus.sents()), [len(corpus.sents(d)) for d in corpus.fileids()] + (6, [5, 1]) + >>> len(corpus.paras()), [len(corpus.paras(d)) for d in corpus.fileids()] + (3, [2, 1]) + >>> print(corpus.words('a')) + ['This', 'is', 'the', 'first', 'sentence', '.', ...] + >>> print(corpus.words('b')) + ['This', 'is', 'the', 'second', 'file', '.'] + >>> del_testcorpus(root) + +The Brown Corpus uses the tagged corpus reader: + + >>> from nltk.corpus import brown + >>> brown.fileids() + ['ca01', 'ca02', 'ca03', 'ca04', 'ca05', 'ca06', 'ca07', ...] + >>> brown.categories() + ['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor', + 'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction'] + >>> print(repr(brown.root).replace('\\\\','/')) + FileSystemPathPointer('.../corpora/brown') + >>> brown.words() + ['The', 'Fulton', 'County', 'Grand', 'Jury', ...] + >>> brown.sents() + [['The', 'Fulton', 'County', 'Grand', ...], ...] + >>> brown.paras() + [[['The', 'Fulton', 'County', ...]], [['The', 'jury', ...]], ...] + >>> brown.tagged_words() + [('The', 'AT'), ('Fulton', 'NP-TL'), ...] + >>> brown.tagged_sents() + [[('The', 'AT'), ('Fulton', 'NP-TL'), ('County', 'NN-TL'), ...], ...] + >>> brown.tagged_paras() + [[[('The', 'AT'), ...]], [[('The', 'AT'), ...]], ...] + +Verbnet Corpus Reader +===================== + +Make sure we're picking up the right number of elements: + + >>> from nltk.corpus import verbnet + >>> len(verbnet.lemmas()) + 3621 + >>> len(verbnet.wordnetids()) + 4953 + >>> len(verbnet.classids()) + 429 + +Selecting classids based on various selectors: + + >>> verbnet.classids(lemma='take') + ['bring-11.3', 'characterize-29.2', 'convert-26.6.2', 'cost-54.2', + 'fit-54.3', 'performance-26.7-2', 'steal-10.5'] + >>> verbnet.classids(wordnetid='lead%2:38:01') + ['accompany-51.7'] + >>> verbnet.classids(fileid='approve-77.xml') + ['approve-77'] + >>> verbnet.classids(classid='admire-31.2') # subclasses + ['admire-31.2-1'] + +vnclass() accepts filenames, long ids, and short ids: + + >>> a = ElementTree.tostring(verbnet.vnclass('admire-31.2.xml')) + >>> b = ElementTree.tostring(verbnet.vnclass('admire-31.2')) + >>> c = ElementTree.tostring(verbnet.vnclass('31.2')) + >>> a == b == c + True + +fileids() can be used to get files based on verbnet class ids: + + >>> verbnet.fileids('admire-31.2') + ['admire-31.2.xml'] + >>> verbnet.fileids(['admire-31.2', 'obtain-13.5.2']) + ['admire-31.2.xml', 'obtain-13.5.2.xml'] + >>> verbnet.fileids('badidentifier') + Traceback (most recent call last): + . . . + ValueError: vnclass identifier 'badidentifier' not found + +longid() and shortid() can be used to convert identifiers: + + >>> verbnet.longid('31.2') + 'admire-31.2' + >>> verbnet.longid('admire-31.2') + 'admire-31.2' + >>> verbnet.shortid('31.2') + '31.2' + >>> verbnet.shortid('admire-31.2') + '31.2' + >>> verbnet.longid('badidentifier') + Traceback (most recent call last): + . . . + ValueError: vnclass identifier 'badidentifier' not found + >>> verbnet.shortid('badidentifier') + Traceback (most recent call last): + . . . + ValueError: vnclass identifier 'badidentifier' not found + +Corpus View Regression Tests +============================ + +Select some corpus files to play with: + + >>> import nltk.data + >>> # A very short file (160 chars): + >>> f1 = nltk.data.find('corpora/inaugural/README') + >>> # A relatively short file (791 chars): + >>> f2 = nltk.data.find('corpora/inaugural/1793-Washington.txt') + >>> # A longer file (32k chars): + >>> f3 = nltk.data.find('corpora/inaugural/1909-Taft.txt') + >>> fileids = [f1, f2, f3] + + +Concatenation +------------- +Check that concatenation works as intended. + + >>> from nltk.corpus.reader.util import * + + >>> c1 = StreamBackedCorpusView(f1, read_whitespace_block, encoding='utf-8') + >>> c2 = StreamBackedCorpusView(f2, read_whitespace_block, encoding='utf-8') + >>> c3 = StreamBackedCorpusView(f3, read_whitespace_block, encoding='utf-8') + >>> c123 = c1+c2+c3 + >>> print(c123) + ['C-Span', 'Inaugural', 'Address', 'Corpus', 'US', ...] + + >>> l1 = f1.open(encoding='utf-8').read().split() + >>> l2 = f2.open(encoding='utf-8').read().split() + >>> l3 = f3.open(encoding='utf-8').read().split() + >>> l123 = l1+l2+l3 + + >>> list(c123) == l123 + True + + >>> (c1+c2+c3)[100] == l123[100] + True + +Slicing +------- +First, do some tests with fairly small slices. These will all +generate tuple values. + + >>> from nltk.util import LazySubsequence + >>> c1 = StreamBackedCorpusView(f1, read_whitespace_block, encoding='utf-8') + >>> l1 = f1.open(encoding='utf-8').read().split() + >>> print(len(c1)) + 21 + >>> len(c1) < LazySubsequence.MIN_SIZE + True + +Choose a list of indices, based on the length, that covers the +important corner cases: + + >>> indices = [-60, -30, -22, -21, -20, -1, + ... 0, 1, 10, 20, 21, 22, 30, 60] + +Test slicing with explicit start & stop value: + + >>> for s in indices: + ... for e in indices: + ... assert list(c1[s:e]) == l1[s:e] + +Test slicing with stop=None: + + >>> for s in indices: + ... assert list(c1[s:]) == l1[s:] + +Test slicing with start=None: + + >>> for e in indices: + ... assert list(c1[:e]) == l1[:e] + +Test slicing with start=stop=None: + + >>> list(c1[:]) == list(l1[:]) + True + +Next, we'll do some tests with much longer slices. These will +generate LazySubsequence objects. + + >>> c3 = StreamBackedCorpusView(f3, read_whitespace_block, encoding='utf-8') + >>> l3 = f3.open(encoding='utf-8').read().split() + >>> print(len(c3)) + 5430 + >>> len(c3) > LazySubsequence.MIN_SIZE*2 + True + +Choose a list of indices, based on the length, that covers the +important corner cases: + + >>> indices = [-12000, -6000, -5431, -5430, -5429, -3000, -200, -1, + ... 0, 1, 200, 3000, 5000, 5429, 5430, 5431, 6000, 12000] + +Test slicing with explicit start & stop value: + + >>> for s in indices: + ... for e in indices: + ... assert list(c3[s:e]) == l3[s:e] + +Test slicing with stop=None: + + >>> for s in indices: + ... assert list(c3[s:]) == l3[s:] + +Test slicing with start=None: + + >>> for e in indices: + ... assert list(c3[:e]) == l3[:e] + +Test slicing with start=stop=None: + + >>> list(c3[:]) == list(l3[:]) + True + +Multiple Iterators +------------------ +If multiple iterators are created for the same corpus view, their +iteration can be interleaved: + + >>> c3 = StreamBackedCorpusView(f3, read_whitespace_block) + >>> iterators = [c3.iterate_from(n) for n in [0,15,30,45]] + >>> for i in range(15): + ... for iterator in iterators: + ... print('%-15s' % next(iterator), end=' ') + ... print() + My a duties in + fellow heavy of a + citizens: weight the proper + Anyone of office sense + who responsibility. upon of + has If which the + taken not, he obligation + the he is which + oath has about the + I no to oath + have conception enter, imposes. + just of or The + taken the he office + must powers is of + feel and lacking an + +SeekableUnicodeStreamReader +=========================== + +The file-like objects provided by the ``codecs`` module unfortunately +suffer from a bug that prevents them from working correctly with +corpus view objects. In particular, although the expose ``seek()`` +and ``tell()`` methods, those methods do not exhibit the expected +behavior, because they are not synchronized with the internal buffers +that are kept by the file-like objects. For example, the ``tell()`` +method will return the file position at the end of the buffers (whose +contents have not yet been returned by the stream); and therefore this +file position can not be used to return to the 'current' location in +the stream (since ``seek()`` has no way to reconstruct the buffers). + +To get around these problems, we define a new class, +`SeekableUnicodeStreamReader`, to act as a file-like interface to +files containing encoded unicode data. This class is loosely based on +the ``codecs.StreamReader`` class. To construct a new reader, we call +the constructor with an underlying stream and an encoding name: + + >>> from io import StringIO, BytesIO + >>> from nltk.data import SeekableUnicodeStreamReader + >>> stream = BytesIO(b"""\ + ... This is a test file. + ... It is encoded in ascii. + ... """.decode('ascii').encode('ascii')) + >>> reader = SeekableUnicodeStreamReader(stream, 'ascii') + +`SeekableUnicodeStreamReader`\ s support all of the normal operations +supplied by a read-only stream. Note that all of the read operations +return ``unicode`` objects (not ``str`` objects). + + >>> reader.read() # read the entire file. + 'This is a test file.\nIt is encoded in ascii.\n' + >>> reader.seek(0) # rewind to the start. + >>> reader.read(5) # read at most 5 bytes. + 'This ' + >>> reader.readline() # read to the end of the line. + 'is a test file.\n' + >>> reader.seek(0) # rewind to the start. + >>> for line in reader: + ... print(repr(line)) # iterate over lines + 'This is a test file.\n' + 'It is encoded in ascii.\n' + >>> reader.seek(0) # rewind to the start. + >>> reader.readlines() # read a list of line strings + ['This is a test file.\n', 'It is encoded in ascii.\n'] + >>> reader.close() + +Size argument to ``read()`` +--------------------------- +The ``size`` argument to ``read()`` specifies the maximum number of +*bytes* to read, not the maximum number of *characters*. Thus, for +encodings that use multiple bytes per character, it may return fewer +characters than the ``size`` argument: + + >>> stream = BytesIO(b"""\ + ... This is a test file. + ... It is encoded in utf-16. + ... """.decode('ascii').encode('utf-16')) + >>> reader = SeekableUnicodeStreamReader(stream, 'utf-16') + >>> reader.read(10) + 'This ' + +If a read block ends in the middle of the byte string encoding a +single character, then that byte string is stored in an internal +buffer, and re-used on the next call to ``read()``. However, if the +size argument is too small to read even a single character, even +though at least one character is available, then the ``read()`` method +will read additional bytes until it can return a single character. +This ensures that the ``read()`` method does not return an empty +string, which could be mistaken for indicating the end of the file. + + >>> reader.seek(0) # rewind to the start. + >>> reader.read(1) # we actually need to read 4 bytes + 'T' + >>> int(reader.tell()) + 4 + +The ``readline()`` method may read more than a single line of text, in +which case it stores the text that it does not return in a buffer. If +this buffer is not empty, then its contents will be included in the +value returned by the next call to ``read()``, regardless of the +``size`` argument, since they are available without reading any new +bytes from the stream: + + >>> reader.seek(0) # rewind to the start. + >>> reader.readline() # stores extra text in a buffer + 'This is a test file.\n' + >>> print(reader.linebuffer) # examine the buffer contents + ['It is encoded i'] + >>> reader.read(0) # returns the contents of the buffer + 'It is encoded i' + >>> print(reader.linebuffer) # examine the buffer contents + None + +Seek and Tell +------------- +In addition to these basic read operations, +`SeekableUnicodeStreamReader` also supports the ``seek()`` and +``tell()`` operations. However, some care must still be taken when +using these operations. In particular, the only file offsets that +should be passed to ``seek()`` are ``0`` and any offset that has been +returned by ``tell``. + + >>> stream = BytesIO(b"""\ + ... This is a test file. + ... It is encoded in utf-16. + ... """.decode('ascii').encode('utf-16')) + >>> reader = SeekableUnicodeStreamReader(stream, 'utf-16') + >>> reader.read(20) + 'This is a ' + >>> pos = reader.tell(); print(pos) + 22 + >>> reader.read(20) + 'test file.' + >>> reader.seek(pos) # rewind to the position from tell. + >>> reader.read(20) + 'test file.' + +The ``seek()`` and ``tell()`` methods work property even when +``readline()`` is used. + + >>> stream = BytesIO(b"""\ + ... This is a test file. + ... It is encoded in utf-16. + ... """.decode('ascii').encode('utf-16')) + >>> reader = SeekableUnicodeStreamReader(stream, 'utf-16') + >>> reader.readline() + 'This is a test file.\n' + >>> pos = reader.tell(); print(pos) + 44 + >>> reader.readline() + 'It is encoded in utf-16.\n' + >>> reader.seek(pos) # rewind to the position from tell. + >>> reader.readline() + 'It is encoded in utf-16.\n' + + +Squashed Bugs +============= + +svn 5276 fixed a bug in the comment-stripping behavior of +parse_sexpr_block. + + >>> from io import StringIO + >>> from nltk.corpus.reader.util import read_sexpr_block + >>> f = StringIO(b""" + ... (a b c) + ... # This line is a comment. + ... (d e f\ng h)""".decode('ascii')) + >>> print(read_sexpr_block(f, block_size=38, comment_char='#')) + ['(a b c)'] + >>> print(read_sexpr_block(f, block_size=38, comment_char='#')) + ['(d e f\ng h)'] + +svn 5277 fixed a bug in parse_sexpr_block, which would cause it to +enter an infinite loop if a file ended mid-sexpr, or ended with a +token that was not followed by whitespace. A related bug caused +an infinite loop if the corpus ended in an unmatched close paren -- +this was fixed in svn 5279 + + >>> f = StringIO(b""" + ... This file ends mid-sexpr + ... (hello (world""".decode('ascii')) + >>> for i in range(3): print(read_sexpr_block(f)) + ['This', 'file', 'ends', 'mid-sexpr'] + ['(hello (world'] + [] + + >>> f = StringIO(b"This file has no trailing whitespace.".decode('ascii')) + >>> for i in range(3): print(read_sexpr_block(f)) + ['This', 'file', 'has', 'no', 'trailing'] + ['whitespace.'] + [] + + >>> # Bug fixed in 5279: + >>> f = StringIO(b"a b c)".decode('ascii')) + >>> for i in range(3): print(read_sexpr_block(f)) + ['a', 'b'] + ['c)'] + [] + + +svn 5624 & 5265 fixed a bug in ConcatenatedCorpusView, which caused it +to return the wrong items when indexed starting at any index beyond +the first file. + + >>> import nltk + >>> sents = nltk.corpus.brown.sents() + >>> print(sents[6000]) + ['Cholesterol', 'and', 'thyroid'] + >>> print(sents[6000]) + ['Cholesterol', 'and', 'thyroid'] + +svn 5728 fixed a bug in Categorized*CorpusReader, which caused them +to return words from *all* files when just one file was specified. + + >>> from nltk.corpus import reuters + >>> reuters.words('training/13085') + ['SNYDER', '&', 'lt', ';', 'SOI', '>', 'MAKES', ...] + >>> reuters.words('training/5082') + ['SHEPPARD', 'RESOURCES', 'TO', 'MERGE', 'WITH', ...] + +svn 7227 fixed a bug in the qc corpus reader, which prevented +access to its tuples() method + + >>> from nltk.corpus import qc + >>> qc.tuples('test.txt') + [('NUM:dist', 'How far is it from Denver to Aspen ?'), ('LOC:city', 'What county is Modesto , California in ?'), ...] + +Ensure that KEYWORD from `comparative_sents.py` no longer contains a ReDoS vulnerability. + + >>> import re + >>> import time + >>> from nltk.corpus.reader.comparative_sents import KEYWORD + >>> sizes = { + ... "short": 4000, + ... "long": 40000 + ... } + >>> exec_times = { + ... "short": [], + ... "long": [], + ... } + >>> for size_name, size in sizes.items(): + ... for j in range(9): + ... start_t = time.perf_counter() + ... payload = "( " + "(" * size + ... output = KEYWORD.findall(payload) + ... exec_times[size_name].append(time.perf_counter() - start_t) + ... exec_times[size_name] = sorted(exec_times[size_name])[4] # Get the median + +Ideally, the execution time of such a regular expression is linear +in the length of the input. As such, we would expect exec_times["long"] +to be roughly 10 times as big as exec_times["short"]. +With the ReDoS in place, it took roughly 80 times as long. +For now, we accept values below 30 (times as long), due to the potential +for variance. This ensures that the ReDoS has certainly been reduced, +if not removed. + + >>> exec_times["long"] / exec_times["short"] < 30 # doctest: +SKIP + True diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/crubadan.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/crubadan.doctest new file mode 100644 index 0000000000000000000000000000000000000000..8c10781333a47ecc4e4e8ed279440dd7fe589639 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/crubadan.doctest @@ -0,0 +1,65 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +Crubadan Corpus Reader +====================== + +Crubadan is an NLTK corpus reader for ngram files provided +by the Crubadan project. It supports several languages. + + >>> from nltk.corpus import crubadan + >>> crubadan.langs() + ['abk', 'abn',..., 'zpa', 'zul'] + +---------------------------------------- +Language code mapping and helper methods +---------------------------------------- + +The web crawler that generates the 3-gram frequencies works at the +level of "writing systems" rather than languages. Writing systems +are assigned internal 2-3 letter codes that require mapping to the +standard ISO 639-3 codes. For more information, please refer to +the README in nltk_data/crubadan folder after installing it. + +To translate ISO 639-3 codes to "Crubadan Code": + + >>> crubadan.iso_to_crubadan('eng') + 'en' + >>> crubadan.iso_to_crubadan('fra') + 'fr' + >>> crubadan.iso_to_crubadan('aaa') + +In reverse, print ISO 639-3 code if we have the Crubadan Code: + + >>> crubadan.crubadan_to_iso('en') + 'eng' + >>> crubadan.crubadan_to_iso('fr') + 'fra' + >>> crubadan.crubadan_to_iso('aa') + +--------------------------- +Accessing ngram frequencies +--------------------------- + +On initialization the reader will create a dictionary of every +language supported by the Crubadan project, mapping the ISO 639-3 +language code to its corresponding ngram frequency. + +You can access individual language FreqDist and the ngrams within them as follows: + + >>> english_fd = crubadan.lang_freq('eng') + >>> english_fd['the'] + 728135 + +Above accesses the FreqDist of English and returns the frequency of the ngram 'the'. +A ngram that isn't found within the language will return 0: + + >>> english_fd['sometest'] + 0 + +A language that isn't supported will raise an exception: + + >>> crubadan.lang_freq('elvish') + Traceback (most recent call last): + ... + RuntimeError: Unsupported language. diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/data.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/data.doctest new file mode 100644 index 0000000000000000000000000000000000000000..0f54657d00c1e719518ca4f8034c1a91d483835c --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/data.doctest @@ -0,0 +1,387 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +========================================= + Loading Resources From the Data Package +========================================= + + >>> import nltk.data + +Overview +~~~~~~~~ +The `nltk.data` module contains functions that can be used to load +NLTK resource files, such as corpora, grammars, and saved processing +objects. + +Loading Data Files +~~~~~~~~~~~~~~~~~~ +Resources are loaded using the function `nltk.data.load()`, which +takes as its first argument a URL specifying what file should be +loaded. The ``nltk:`` protocol loads files from the NLTK data +distribution: + + >>> tokenizer = nltk.data.load('nltk:tokenizers/punkt/english.pickle') + >>> tokenizer.tokenize('Hello. This is a test. It works!') + ['Hello.', 'This is a test.', 'It works!'] + +It is important to note that there should be no space following the +colon (':') in the URL; 'nltk: tokenizers/punkt/english.pickle' will +not work! + +The ``nltk:`` protocol is used by default if no protocol is specified: + + >>> nltk.data.load('tokenizers/punkt/english.pickle') + + +But it is also possible to load resources from ``http:``, ``ftp:``, +and ``file:`` URLs: + + >>> # Load a grammar from the NLTK webpage. + >>> cfg = nltk.data.load('https://raw.githubusercontent.com/nltk/nltk/develop/nltk/test/toy.cfg') + >>> print(cfg) # doctest: +ELLIPSIS + Grammar with 14 productions (start state = S) + S -> NP VP + PP -> P NP + ... + P -> 'on' + P -> 'in' + + >>> # Load a grammar using an absolute path. + >>> url = 'file:%s' % nltk.data.find('grammars/sample_grammars/toy.cfg') + >>> url.replace('\\', '/') + 'file:...toy.cfg' + >>> print(nltk.data.load(url)) + Grammar with 14 productions (start state = S) + S -> NP VP + PP -> P NP + ... + P -> 'on' + P -> 'in' + +The second argument to the `nltk.data.load()` function specifies the +file format, which determines how the file's contents are processed +before they are returned by ``load()``. The formats that are +currently supported by the data module are described by the dictionary +`nltk.data.FORMATS`: + + >>> for format, descr in sorted(nltk.data.FORMATS.items()): + ... print('{0:<7} {1:}'.format(format, descr)) + cfg A context free grammar. + fcfg A feature CFG. + fol A list of first order logic expressions, parsed with + nltk.sem.logic.Expression.fromstring. + json A serialized python object, stored using the json module. + logic A list of first order logic expressions, parsed with + nltk.sem.logic.LogicParser. Requires an additional logic_parser + parameter + pcfg A probabilistic CFG. + pickle A serialized python object, stored using the pickle + module. + raw The raw (byte string) contents of a file. + text The raw (unicode string) contents of a file. + val A semantic valuation, parsed by + nltk.sem.Valuation.fromstring. + yaml A serialized python object, stored using the yaml module. + +`nltk.data.load()` will raise a ValueError if a bad format name is +specified: + + >>> nltk.data.load('grammars/sample_grammars/toy.cfg', 'bar') + Traceback (most recent call last): + . . . + ValueError: Unknown format type! + +By default, the ``"auto"`` format is used, which chooses a format +based on the filename's extension. The mapping from file extensions +to format names is specified by `nltk.data.AUTO_FORMATS`: + + >>> for ext, format in sorted(nltk.data.AUTO_FORMATS.items()): + ... print('.%-7s -> %s' % (ext, format)) + .cfg -> cfg + .fcfg -> fcfg + .fol -> fol + .json -> json + .logic -> logic + .pcfg -> pcfg + .pickle -> pickle + .text -> text + .txt -> text + .val -> val + .yaml -> yaml + +If `nltk.data.load()` is unable to determine the format based on the +filename's extension, it will raise a ValueError: + + >>> nltk.data.load('foo.bar') + Traceback (most recent call last): + . . . + ValueError: Could not determine format for foo.bar based on its file + extension; use the "format" argument to specify the format explicitly. + +Note that by explicitly specifying the ``format`` argument, you can +override the load method's default processing behavior. For example, +to get the raw contents of any file, simply use ``format="raw"``: + + >>> s = nltk.data.load('grammars/sample_grammars/toy.cfg', 'text') + >>> print(s) + S -> NP VP + PP -> P NP + NP -> Det N | NP PP + VP -> V NP | VP PP + ... + +Making Local Copies +~~~~~~~~~~~~~~~~~~~ +.. This will not be visible in the html output: create a tempdir to + play in. + >>> import tempfile, os + >>> tempdir = tempfile.mkdtemp() + >>> old_dir = os.path.abspath('.') + >>> os.chdir(tempdir) + +The function `nltk.data.retrieve()` copies a given resource to a local +file. This can be useful, for example, if you want to edit one of the +sample grammars. + + >>> nltk.data.retrieve('grammars/sample_grammars/toy.cfg') + Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'toy.cfg' + + >>> # Simulate editing the grammar. + >>> with open('toy.cfg') as inp: + ... s = inp.read().replace('NP', 'DP') + >>> with open('toy.cfg', 'w') as out: + ... _bytes_written = out.write(s) + + >>> # Load the edited grammar, & display it. + >>> cfg = nltk.data.load('file:///' + os.path.abspath('toy.cfg')) + >>> print(cfg) + Grammar with 14 productions (start state = S) + S -> DP VP + PP -> P DP + ... + P -> 'on' + P -> 'in' + +The second argument to `nltk.data.retrieve()` specifies the filename +for the new copy of the file. By default, the source file's filename +is used. + + >>> nltk.data.retrieve('grammars/sample_grammars/toy.cfg', 'mytoy.cfg') + Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'mytoy.cfg' + >>> os.path.isfile('./mytoy.cfg') + True + >>> nltk.data.retrieve('grammars/sample_grammars/np.fcfg') + Retrieving 'nltk:grammars/sample_grammars/np.fcfg', saving to 'np.fcfg' + >>> os.path.isfile('./np.fcfg') + True + +If a file with the specified (or default) filename already exists in +the current directory, then `nltk.data.retrieve()` will raise a +ValueError exception. It will *not* overwrite the file: + + >>> os.path.isfile('./toy.cfg') + True + >>> nltk.data.retrieve('grammars/sample_grammars/toy.cfg') + Traceback (most recent call last): + . . . + ValueError: File '...toy.cfg' already exists! + +.. This will not be visible in the html output: clean up the tempdir. + >>> os.chdir(old_dir) + >>> for f in os.listdir(tempdir): + ... os.remove(os.path.join(tempdir, f)) + >>> os.rmdir(tempdir) + +Finding Files in the NLTK Data Package +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The `nltk.data.find()` function searches the NLTK data package for a +given file, and returns a pointer to that file. This pointer can +either be a `FileSystemPathPointer` (whose `path` attribute gives the +absolute path of the file); or a `ZipFilePathPointer`, specifying a +zipfile and the name of an entry within that zipfile. Both pointer +types define the `open()` method, which can be used to read the string +contents of the file. + + >>> path = nltk.data.find('corpora/abc/rural.txt') + >>> str(path) + '...rural.txt' + >>> print(path.open().read(60).decode()) + PM denies knowledge of AWB kickbacks + The Prime Minister has + +Alternatively, the `nltk.data.load()` function can be used with the +keyword argument ``format="raw"``: + + >>> s = nltk.data.load('corpora/abc/rural.txt', format='raw')[:60] + >>> print(s.decode()) + PM denies knowledge of AWB kickbacks + The Prime Minister has + +Alternatively, you can use the keyword argument ``format="text"``: + + >>> s = nltk.data.load('corpora/abc/rural.txt', format='text')[:60] + >>> print(s) + PM denies knowledge of AWB kickbacks + The Prime Minister has + +Resource Caching +~~~~~~~~~~~~~~~~ + +NLTK uses a weakref dictionary to maintain a cache of resources that +have been loaded. If you load a resource that is already stored in +the cache, then the cached copy will be returned. This behavior can +be seen by the trace output generated when verbose=True: + + >>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg', verbose=True) + <> + >>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg', verbose=True) + <> + +If you wish to load a resource from its source, bypassing the cache, +use the ``cache=False`` argument to `nltk.data.load()`. This can be +useful, for example, if the resource is loaded from a local file, and +you are actively editing that file: + + >>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg',cache=False,verbose=True) + <> + +The cache *no longer* uses weak references. A resource will not be +automatically expunged from the cache when no more objects are using +it. In the following example, when we clear the variable ``feat0``, +the reference count for the feature grammar object drops to zero. +However, the object remains cached: + + >>> del feat0 + >>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg', + ... verbose=True) + <> + +You can clear the entire contents of the cache, using +`nltk.data.clear_cache()`: + + >>> nltk.data.clear_cache() + +Retrieving other Data Sources +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + >>> formulas = nltk.data.load('grammars/book_grammars/background.fol') + >>> for f in formulas: print(str(f)) + all x.(boxerdog(x) -> dog(x)) + all x.(boxer(x) -> person(x)) + all x.-(dog(x) & person(x)) + all x.(married(x) <-> exists y.marry(x,y)) + all x.(bark(x) -> dog(x)) + all x y.(marry(x,y) -> (person(x) & person(y))) + -(Vincent = Mia) + -(Vincent = Fido) + -(Mia = Fido) + +Regression Tests +~~~~~~~~~~~~~~~~ +Create a temp dir for tests that write files: + + >>> import tempfile, os + >>> tempdir = tempfile.mkdtemp() + >>> old_dir = os.path.abspath('.') + >>> os.chdir(tempdir) + +The `retrieve()` function accepts all url types: + + >>> urls = ['https://raw.githubusercontent.com/nltk/nltk/develop/nltk/test/toy.cfg', + ... 'file:%s' % nltk.data.find('grammars/sample_grammars/toy.cfg'), + ... 'nltk:grammars/sample_grammars/toy.cfg', + ... 'grammars/sample_grammars/toy.cfg'] + >>> for i, url in enumerate(urls): + ... nltk.data.retrieve(url, 'toy-%d.cfg' % i) + Retrieving 'https://raw.githubusercontent.com/nltk/nltk/develop/nltk/test/toy.cfg', saving to 'toy-0.cfg' + Retrieving 'file:...toy.cfg', saving to 'toy-1.cfg' + Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'toy-2.cfg' + Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'toy-3.cfg' + +Clean up the temp dir: + + >>> os.chdir(old_dir) + >>> for f in os.listdir(tempdir): + ... os.remove(os.path.join(tempdir, f)) + >>> os.rmdir(tempdir) + +Lazy Loader +----------- +A lazy loader is a wrapper object that defers loading a resource until +it is accessed or used in any way. This is mainly intended for +internal use by NLTK's corpus readers. + + >>> # Create a lazy loader for toy.cfg. + >>> ll = nltk.data.LazyLoader('grammars/sample_grammars/toy.cfg') + + >>> # Show that it's not loaded yet: + >>> object.__repr__(ll) + '' + + >>> # printing it is enough to cause it to be loaded: + >>> print(ll) + + + >>> # Show that it's now been loaded: + >>> object.__repr__(ll) + '' + + + >>> # Test that accessing an attribute also loads it: + >>> ll = nltk.data.LazyLoader('grammars/sample_grammars/toy.cfg') + >>> ll.start() + S + >>> object.__repr__(ll) + '' + +Buffered Gzip Reading and Writing +--------------------------------- +Write performance to gzip-compressed is extremely poor when the files become large. +File creation can become a bottleneck in those cases. + +Read performance from large gzipped pickle files was improved in data.py by +buffering the reads. A similar fix can be applied to writes by buffering +the writes to a StringIO object first. + +This is mainly intended for internal use. The test simply tests that reading +and writing work as intended and does not test how much improvement buffering +provides. + + >>> from io import StringIO + >>> test = nltk.data.BufferedGzipFile('testbuf.gz', 'wb', size=2**10) + >>> ans = [] + >>> for i in range(10000): + ... ans.append(str(i).encode('ascii')) + ... test.write(str(i).encode('ascii')) + >>> test.close() + >>> test = nltk.data.BufferedGzipFile('testbuf.gz', 'rb') + >>> test.read() == b''.join(ans) + True + >>> test.close() + >>> import os + >>> os.unlink('testbuf.gz') + +JSON Encoding and Decoding +-------------------------- +JSON serialization is used instead of pickle for some classes. + + >>> from nltk import jsontags + >>> from nltk.jsontags import JSONTaggedEncoder, JSONTaggedDecoder, register_tag + >>> @jsontags.register_tag + ... class JSONSerializable: + ... json_tag = 'JSONSerializable' + ... + ... def __init__(self, n): + ... self.n = n + ... + ... def encode_json_obj(self): + ... return self.n + ... + ... @classmethod + ... def decode_json_obj(cls, obj): + ... n = obj + ... return cls(n) + ... + >>> JSONTaggedEncoder().encode(JSONSerializable(1)) + '{"!JSONSerializable": 1}' + >>> JSONTaggedDecoder().decode('{"!JSONSerializable": 1}').n + 1 diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/drt.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/drt.doctest new file mode 100644 index 0000000000000000000000000000000000000000..03ba487bcf446483aad8548fc63ad6d06a0e7115 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/drt.doctest @@ -0,0 +1,515 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +================================ + Discourse Representation Theory +================================ + + >>> from nltk.sem import logic + >>> from nltk.inference import TableauProver + +Overview +======== + +A DRS can be created with the ``DRS()`` constructor. This takes two arguments: a list of +discourse referents and list of conditions. . + + >>> from nltk.sem.drt import * + >>> dexpr = DrtExpression.fromstring + >>> man_x = dexpr('man(x)') + >>> walk_x = dexpr('walk(x)') + >>> x = dexpr('x') + >>> print(DRS([x], [man_x, walk_x])) + ([x],[man(x), walk(x)]) + +The ``parse()`` method can also be applied directly to DRS +expressions, which allows them to be specified more +easily. + + >>> drs1 = dexpr('([x],[man(x),walk(x)])') + >>> print(drs1) + ([x],[man(x), walk(x)]) + +DRSs can be *merged* using the ``+`` operator. + + >>> drs2 = dexpr('([y],[woman(y),stop(y)])') + >>> drs3 = drs1 + drs2 + >>> print(drs3) + (([x],[man(x), walk(x)]) + ([y],[woman(y), stop(y)])) + >>> print(drs3.simplify()) + ([x,y],[man(x), walk(x), woman(y), stop(y)]) + +We can embed DRSs as components of an ``implies`` condition. + + >>> s = '([], [(%s -> %s)])' % (drs1, drs2) + >>> print(dexpr(s)) + ([],[(([x],[man(x), walk(x)]) -> ([y],[woman(y), stop(y)]))]) + +The ``fol()`` method converts DRSs into FOL formulae. + + >>> print(dexpr(r'([x],[man(x), walks(x)])').fol()) + exists x.(man(x) & walks(x)) + >>> print(dexpr(r'([],[(([x],[man(x)]) -> ([],[walks(x)]))])').fol()) + all x.(man(x) -> walks(x)) + +In order to visualize a DRS, the ``pretty_format()`` method can be used. + + >>> print(drs3.pretty_format()) + _________ __________ + | x | | y | + (|---------| + |----------|) + | man(x) | | woman(y) | + | walk(x) | | stop(y) | + |_________| |__________| + + +Parse to semantics +------------------ + +.. + >>> logic._counter._value = 0 + +DRSs can be used for building compositional semantics in a feature +based grammar. To specify that we want to use DRSs, the appropriate +logic parser needs be passed as a parameter to ``load_earley()`` + + >>> from nltk.parse import load_parser + >>> from nltk.sem.drt import DrtParser + >>> parser = load_parser('grammars/book_grammars/drt.fcfg', trace=0, logic_parser=DrtParser()) + >>> for tree in parser.parse('a dog barks'.split()): + ... print(tree.label()['SEM'].simplify()) + ... + ([x],[dog(x), bark(x)]) + +Alternatively, a ``FeatStructReader`` can be passed with the ``logic_parser`` set on it + + >>> from nltk.featstruct import FeatStructReader + >>> from nltk.grammar import FeatStructNonterminal + >>> parser = load_parser('grammars/book_grammars/drt.fcfg', trace=0, fstruct_reader=FeatStructReader(fdict_class=FeatStructNonterminal, logic_parser=DrtParser())) + >>> for tree in parser.parse('every girl chases a dog'.split()): + ... print(tree.label()['SEM'].simplify().normalize()) + ... + ([],[(([z1],[girl(z1)]) -> ([z2],[dog(z2), chase(z1,z2)]))]) + + + +Unit Tests +========== + +Parser +------ + + >>> print(dexpr(r'([x,y],[sees(x,y)])')) + ([x,y],[sees(x,y)]) + >>> print(dexpr(r'([x],[man(x), walks(x)])')) + ([x],[man(x), walks(x)]) + >>> print(dexpr(r'\x.([],[man(x), walks(x)])')) + \x.([],[man(x), walks(x)]) + >>> print(dexpr(r'\x.\y.([],[sees(x,y)])')) + \x y.([],[sees(x,y)]) + + >>> print(dexpr(r'([x,y],[(x = y)])')) + ([x,y],[(x = y)]) + >>> print(dexpr(r'([x,y],[(x != y)])')) + ([x,y],[-(x = y)]) + + >>> print(dexpr(r'\x.([],[walks(x)])(john)')) + (\x.([],[walks(x)]))(john) + >>> print(dexpr(r'\R.\x.([],[big(x,R)])(\y.([],[mouse(y)]))')) + (\R x.([],[big(x,R)]))(\y.([],[mouse(y)])) + + >>> print(dexpr(r'(([x],[walks(x)]) + ([y],[runs(y)]))')) + (([x],[walks(x)]) + ([y],[runs(y)])) + >>> print(dexpr(r'(([x,y],[walks(x), jumps(y)]) + (([z],[twos(z)]) + ([w],[runs(w)])))')) + (([x,y],[walks(x), jumps(y)]) + ([z],[twos(z)]) + ([w],[runs(w)])) + >>> print(dexpr(r'((([],[walks(x)]) + ([],[twos(x)])) + ([],[runs(x)]))')) + (([],[walks(x)]) + ([],[twos(x)]) + ([],[runs(x)])) + >>> print(dexpr(r'((([],[walks(x)]) + ([],[runs(x)])) + (([],[threes(x)]) + ([],[fours(x)])))')) + (([],[walks(x)]) + ([],[runs(x)]) + ([],[threes(x)]) + ([],[fours(x)])) + + >>> print(dexpr(r'(([],[walks(x)]) -> ([],[runs(x)]))')) + (([],[walks(x)]) -> ([],[runs(x)])) + + >>> print(dexpr(r'([x],[PRO(x), sees(John,x)])')) + ([x],[PRO(x), sees(John,x)]) + >>> print(dexpr(r'([x],[man(x), -([],[walks(x)])])')) + ([x],[man(x), -([],[walks(x)])]) + >>> print(dexpr(r'([],[(([x],[man(x)]) -> ([],[walks(x)]))])')) + ([],[(([x],[man(x)]) -> ([],[walks(x)]))]) + + >>> print(dexpr(r'DRS([x],[walk(x)])')) + ([x],[walk(x)]) + >>> print(dexpr(r'DRS([x][walk(x)])')) + ([x],[walk(x)]) + >>> print(dexpr(r'([x][walk(x)])')) + ([x],[walk(x)]) + +``simplify()`` +-------------- + + >>> print(dexpr(r'\x.([],[man(x), walks(x)])(john)').simplify()) + ([],[man(john), walks(john)]) + >>> print(dexpr(r'\x.\y.([z],[dog(z),sees(x,y)])(john)(mary)').simplify()) + ([z],[dog(z), sees(john,mary)]) + >>> print(dexpr(r'\R x.([],[big(x,R)])(\y.([],[mouse(y)]))').simplify()) + \x.([],[big(x,\y.([],[mouse(y)]))]) + + >>> print(dexpr(r'(([x],[walks(x)]) + ([y],[runs(y)]))').simplify()) + ([x,y],[walks(x), runs(y)]) + >>> print(dexpr(r'(([x,y],[walks(x), jumps(y)]) + (([z],[twos(z)]) + ([w],[runs(w)])))').simplify()) + ([w,x,y,z],[walks(x), jumps(y), twos(z), runs(w)]) + >>> print(dexpr(r'((([],[walks(x)]) + ([],[runs(x)]) + ([],[threes(x)]) + ([],[fours(x)])))').simplify()) + ([],[walks(x), runs(x), threes(x), fours(x)]) + >>> dexpr(r'([x],[man(x)])+([x],[walks(x)])').simplify() == \ + ... dexpr(r'([x,z1],[man(x), walks(z1)])') + True + >>> dexpr(r'([y],[boy(y), (([x],[dog(x)]) -> ([],[chase(x,y)]))])+([x],[run(x)])').simplify() == \ + ... dexpr(r'([y,z1],[boy(y), (([x],[dog(x)]) -> ([],[chase(x,y)])), run(z1)])') + True + + >>> dexpr(r'\Q.(([x],[john(x),walks(x)]) + Q)(([x],[PRO(x),leaves(x)]))').simplify() == \ + ... dexpr(r'([x,z1],[john(x), walks(x), PRO(z1), leaves(z1)])') + True + + >>> logic._counter._value = 0 + >>> print(dexpr('([],[(([x],[dog(x)]) -> ([e,y],[boy(y), chase(e), subj(e,x), obj(e,y)]))])+([e,x],[PRO(x), run(e), subj(e,x)])').simplify().normalize().normalize()) + ([e02,z5],[(([z3],[dog(z3)]) -> ([e01,z4],[boy(z4), chase(e01), subj(e01,z3), obj(e01,z4)])), PRO(z5), run(e02), subj(e02,z5)]) + +``fol()`` +----------- + + >>> print(dexpr(r'([x,y],[sees(x,y)])').fol()) + exists x y.sees(x,y) + >>> print(dexpr(r'([x],[man(x), walks(x)])').fol()) + exists x.(man(x) & walks(x)) + >>> print(dexpr(r'\x.([],[man(x), walks(x)])').fol()) + \x.(man(x) & walks(x)) + >>> print(dexpr(r'\x y.([],[sees(x,y)])').fol()) + \x y.sees(x,y) + + >>> print(dexpr(r'\x.([],[walks(x)])(john)').fol()) + \x.walks(x)(john) + >>> print(dexpr(r'\R x.([],[big(x,R)])(\y.([],[mouse(y)]))').fol()) + (\R x.big(x,R))(\y.mouse(y)) + + >>> print(dexpr(r'(([x],[walks(x)]) + ([y],[runs(y)]))').fol()) + (exists x.walks(x) & exists y.runs(y)) + + >>> print(dexpr(r'(([],[walks(x)]) -> ([],[runs(x)]))').fol()) + (walks(x) -> runs(x)) + + >>> print(dexpr(r'([x],[PRO(x), sees(John,x)])').fol()) + exists x.(PRO(x) & sees(John,x)) + >>> print(dexpr(r'([x],[man(x), -([],[walks(x)])])').fol()) + exists x.(man(x) & -walks(x)) + >>> print(dexpr(r'([],[(([x],[man(x)]) -> ([],[walks(x)]))])').fol()) + all x.(man(x) -> walks(x)) + + >>> print(dexpr(r'([x],[man(x) | walks(x)])').fol()) + exists x.(man(x) | walks(x)) + >>> print(dexpr(r'P(x) + ([x],[walks(x)])').fol()) + (P(x) & exists x.walks(x)) + +``resolve_anaphora()`` +---------------------- + + >>> from nltk.sem.drt import AnaphoraResolutionException + + >>> print(resolve_anaphora(dexpr(r'([x,y,z],[dog(x), cat(y), walks(z), PRO(z)])'))) + ([x,y,z],[dog(x), cat(y), walks(z), (z = [x,y])]) + >>> print(resolve_anaphora(dexpr(r'([],[(([x],[dog(x)]) -> ([y],[walks(y), PRO(y)]))])'))) + ([],[(([x],[dog(x)]) -> ([y],[walks(y), (y = x)]))]) + >>> print(resolve_anaphora(dexpr(r'(([x,y],[]) + ([],[PRO(x)]))')).simplify()) + ([x,y],[(x = y)]) + >>> try: print(resolve_anaphora(dexpr(r'([x],[walks(x), PRO(x)])'))) + ... except AnaphoraResolutionException as e: print(e) + Variable 'x' does not resolve to anything. + >>> print(resolve_anaphora(dexpr('([e01,z6,z7],[boy(z6), PRO(z7), run(e01), subj(e01,z7)])'))) + ([e01,z6,z7],[boy(z6), (z7 = z6), run(e01), subj(e01,z7)]) + +``equiv()``: +---------------- + + >>> a = dexpr(r'([x],[man(x), walks(x)])') + >>> b = dexpr(r'([x],[walks(x), man(x)])') + >>> print(a.equiv(b, TableauProver())) + True + + +``replace()``: +-------------- + + >>> a = dexpr(r'a') + >>> w = dexpr(r'w') + >>> x = dexpr(r'x') + >>> y = dexpr(r'y') + >>> z = dexpr(r'z') + + +replace bound +------------- + + >>> print(dexpr(r'([x],[give(x,y,z)])').replace(x.variable, a, False)) + ([x],[give(x,y,z)]) + >>> print(dexpr(r'([x],[give(x,y,z)])').replace(x.variable, a, True)) + ([a],[give(a,y,z)]) + +replace unbound +--------------- + + >>> print(dexpr(r'([x],[give(x,y,z)])').replace(y.variable, a, False)) + ([x],[give(x,a,z)]) + >>> print(dexpr(r'([x],[give(x,y,z)])').replace(y.variable, a, True)) + ([x],[give(x,a,z)]) + +replace unbound with bound +-------------------------- + + >>> dexpr(r'([x],[give(x,y,z)])').replace(y.variable, x, False) == \ + ... dexpr('([z1],[give(z1,x,z)])') + True + >>> dexpr(r'([x],[give(x,y,z)])').replace(y.variable, x, True) == \ + ... dexpr('([z1],[give(z1,x,z)])') + True + +replace unbound with unbound +---------------------------- + + >>> print(dexpr(r'([x],[give(x,y,z)])').replace(y.variable, z, False)) + ([x],[give(x,z,z)]) + >>> print(dexpr(r'([x],[give(x,y,z)])').replace(y.variable, z, True)) + ([x],[give(x,z,z)]) + + +replace unbound +--------------- + + >>> print(dexpr(r'([x],[P(x,y,z)])+([y],[Q(x,y,z)])').replace(z.variable, a, False)) + (([x],[P(x,y,a)]) + ([y],[Q(x,y,a)])) + >>> print(dexpr(r'([x],[P(x,y,z)])+([y],[Q(x,y,z)])').replace(z.variable, a, True)) + (([x],[P(x,y,a)]) + ([y],[Q(x,y,a)])) + +replace bound +------------- + + >>> print(dexpr(r'([x],[P(x,y,z)])+([y],[Q(x,y,z)])').replace(x.variable, a, False)) + (([x],[P(x,y,z)]) + ([y],[Q(x,y,z)])) + >>> print(dexpr(r'([x],[P(x,y,z)])+([y],[Q(x,y,z)])').replace(x.variable, a, True)) + (([a],[P(a,y,z)]) + ([y],[Q(a,y,z)])) + +replace unbound with unbound +---------------------------- + + >>> print(dexpr(r'([x],[P(x,y,z)])+([y],[Q(x,y,z)])').replace(z.variable, a, False)) + (([x],[P(x,y,a)]) + ([y],[Q(x,y,a)])) + >>> print(dexpr(r'([x],[P(x,y,z)])+([y],[Q(x,y,z)])').replace(z.variable, a, True)) + (([x],[P(x,y,a)]) + ([y],[Q(x,y,a)])) + +replace unbound with bound on same side +--------------------------------------- + + >>> dexpr(r'([x],[P(x,y,z)])+([y],[Q(x,y,w)])').replace(z.variable, x, False) == \ + ... dexpr(r'(([z1],[P(z1,y,x)]) + ([y],[Q(z1,y,w)]))') + True + >>> dexpr(r'([x],[P(x,y,z)])+([y],[Q(x,y,w)])').replace(z.variable, x, True) == \ + ... dexpr(r'(([z1],[P(z1,y,x)]) + ([y],[Q(z1,y,w)]))') + True + +replace unbound with bound on other side +---------------------------------------- + + >>> dexpr(r'([x],[P(x,y,z)])+([y],[Q(x,y,w)])').replace(w.variable, x, False) == \ + ... dexpr(r'(([z1],[P(z1,y,z)]) + ([y],[Q(z1,y,x)]))') + True + >>> dexpr(r'([x],[P(x,y,z)])+([y],[Q(x,y,w)])').replace(w.variable, x, True) == \ + ... dexpr(r'(([z1],[P(z1,y,z)]) + ([y],[Q(z1,y,x)]))') + True + +replace unbound with double bound +--------------------------------- + + >>> dexpr(r'([x],[P(x,y,z)])+([x],[Q(x,y,w)])').replace(z.variable, x, False) == \ + ... dexpr(r'(([z1],[P(z1,y,x)]) + ([z1],[Q(z1,y,w)]))') + True + >>> dexpr(r'([x],[P(x,y,z)])+([x],[Q(x,y,w)])').replace(z.variable, x, True) == \ + ... dexpr(r'(([z1],[P(z1,y,x)]) + ([z1],[Q(z1,y,w)]))') + True + + +regression tests +---------------- + + >>> d = dexpr('([x],[A(c), ([y],[B(x,y,z,a)])->([z],[C(x,y,z,a)])])') + >>> print(d) + ([x],[A(c), (([y],[B(x,y,z,a)]) -> ([z],[C(x,y,z,a)]))]) + >>> print(d.pretty_format()) + ____________________________________ + | x | + |------------------------------------| + | A(c) | + | ____________ ____________ | + | | y | | z | | + | (|------------| -> |------------|) | + | | B(x,y,z,a) | | C(x,y,z,a) | | + | |____________| |____________| | + |____________________________________| + >>> print(str(d)) + ([x],[A(c), (([y],[B(x,y,z,a)]) -> ([z],[C(x,y,z,a)]))]) + >>> print(d.fol()) + exists x.(A(c) & all y.(B(x,y,z,a) -> exists z.C(x,y,z,a))) + >>> print(d.replace(Variable('a'), DrtVariableExpression(Variable('r')))) + ([x],[A(c), (([y],[B(x,y,z,r)]) -> ([z],[C(x,y,z,r)]))]) + >>> print(d.replace(Variable('x'), DrtVariableExpression(Variable('r')))) + ([x],[A(c), (([y],[B(x,y,z,a)]) -> ([z],[C(x,y,z,a)]))]) + >>> print(d.replace(Variable('y'), DrtVariableExpression(Variable('r')))) + ([x],[A(c), (([y],[B(x,y,z,a)]) -> ([z],[C(x,y,z,a)]))]) + >>> print(d.replace(Variable('z'), DrtVariableExpression(Variable('r')))) + ([x],[A(c), (([y],[B(x,y,r,a)]) -> ([z],[C(x,y,z,a)]))]) + >>> print(d.replace(Variable('x'), DrtVariableExpression(Variable('r')), True)) + ([r],[A(c), (([y],[B(r,y,z,a)]) -> ([z],[C(r,y,z,a)]))]) + >>> print(d.replace(Variable('y'), DrtVariableExpression(Variable('r')), True)) + ([x],[A(c), (([r],[B(x,r,z,a)]) -> ([z],[C(x,r,z,a)]))]) + >>> print(d.replace(Variable('z'), DrtVariableExpression(Variable('r')), True)) + ([x],[A(c), (([y],[B(x,y,r,a)]) -> ([r],[C(x,y,r,a)]))]) + >>> print(d == dexpr('([l],[A(c), ([m],[B(l,m,z,a)])->([n],[C(l,m,n,a)])])')) + True + >>> d = dexpr('([],[([x,y],[B(x,y,h), ([a,b],[dee(x,a,g)])])->([z,w],[cee(x,y,f), ([c,d],[E(x,c,d,e)])])])') + >>> sorted(d.free()) + [Variable('B'), Variable('E'), Variable('e'), Variable('f'), Variable('g'), Variable('h')] + >>> sorted(d.variables()) + [Variable('B'), Variable('E'), Variable('e'), Variable('f'), Variable('g'), Variable('h')] + >>> sorted(d.get_refs(True)) + [Variable('a'), Variable('b'), Variable('c'), Variable('d'), Variable('w'), Variable('x'), Variable('y'), Variable('z')] + >>> sorted(d.conds[0].get_refs(False)) + [Variable('x'), Variable('y')] + >>> print(dexpr('([x,y],[A(x,y), (x=y), ([],[B(x,y)])->([],[C(x,y)]), ([x,y],[D(x,y)])->([],[E(x,y)]), ([],[F(x,y)])->([x,y],[G(x,y)])])').eliminate_equality()) + ([x],[A(x,x), (([],[B(x,x)]) -> ([],[C(x,x)])), (([x,y],[D(x,y)]) -> ([],[E(x,y)])), (([],[F(x,x)]) -> ([x,y],[G(x,y)]))]) + >>> print(dexpr('([x,y],[A(x,y), (x=y)]) -> ([],[B(x,y)])').eliminate_equality()) + (([x],[A(x,x)]) -> ([],[B(x,x)])) + >>> print(dexpr('([x,y],[A(x,y)]) -> ([],[B(x,y), (x=y)])').eliminate_equality()) + (([x,y],[A(x,y)]) -> ([],[B(x,x)])) + >>> print(dexpr('([x,y],[A(x,y), (x=y), ([],[B(x,y)])])').eliminate_equality()) + ([x],[A(x,x), ([],[B(x,x)])]) + >>> print(dexpr('([x,y],[A(x,y), ([],[B(x,y), (x=y)])])').eliminate_equality()) + ([x,y],[A(x,y), ([],[B(x,x)])]) + >>> print(dexpr('([z8 z9 z10],[A(z8), z8=z10, z9=z10, B(z9), C(z10), D(z10)])').eliminate_equality()) + ([z9],[A(z9), B(z9), C(z9), D(z9)]) + + >>> print(dexpr('([x,y],[A(x,y), (x=y), ([],[B(x,y)]), ([x,y],[C(x,y)])])').eliminate_equality()) + ([x],[A(x,x), ([],[B(x,x)]), ([x,y],[C(x,y)])]) + >>> print(dexpr('([x,y],[A(x,y)]) + ([],[B(x,y), (x=y)]) + ([],[C(x,y)])').eliminate_equality()) + ([x],[A(x,x), B(x,x), C(x,x)]) + >>> print(dexpr('([x,y],[B(x,y)])+([x,y],[C(x,y)])').replace(Variable('y'), DrtVariableExpression(Variable('x')))) + (([x,y],[B(x,y)]) + ([x,y],[C(x,y)])) + >>> print(dexpr('(([x,y],[B(x,y)])+([],[C(x,y)]))+([],[D(x,y)])').replace(Variable('y'), DrtVariableExpression(Variable('x')))) + (([x,y],[B(x,y)]) + ([],[C(x,y)]) + ([],[D(x,y)])) + >>> print(dexpr('(([],[B(x,y)])+([],[C(x,y)]))+([],[D(x,y)])').replace(Variable('y'), DrtVariableExpression(Variable('x')))) + (([],[B(x,x)]) + ([],[C(x,x)]) + ([],[D(x,x)])) + >>> print(dexpr('(([],[B(x,y), ([x,y],[A(x,y)])])+([],[C(x,y)]))+([],[D(x,y)])').replace(Variable('y'), DrtVariableExpression(Variable('x'))).normalize()) + (([],[B(z3,z1), ([z2,z3],[A(z3,z2)])]) + ([],[C(z3,z1)]) + ([],[D(z3,z1)])) + + +Parse errors +============ + + >>> def parse_error(drtstring): + ... try: dexpr(drtstring) + ... except logic.LogicalExpressionException as e: print(e) + + >>> parse_error(r'') + End of input found. Expression expected. + + ^ + >>> parse_error(r'(') + End of input found. Expression expected. + ( + ^ + >>> parse_error(r'()') + Unexpected token: ')'. Expression expected. + () + ^ + >>> parse_error(r'([') + End of input found. Expected token ']'. + ([ + ^ + >>> parse_error(r'([,') + ',' is an illegal variable name. Constants may not be quantified. + ([, + ^ + >>> parse_error(r'([x,') + End of input found. Variable expected. + ([x, + ^ + >>> parse_error(r'([]') + End of input found. Expected token '['. + ([] + ^ + >>> parse_error(r'([][') + End of input found. Expected token ']'. + ([][ + ^ + >>> parse_error(r'([][,') + Unexpected token: ','. Expression expected. + ([][, + ^ + >>> parse_error(r'([][]') + End of input found. Expected token ')'. + ([][] + ^ + >>> parse_error(r'([x][man(x)]) |') + End of input found. Expression expected. + ([x][man(x)]) | + ^ + +Pretty Printing +=============== + + >>> dexpr(r"([],[])").pretty_print() + __ + | | + |--| + |__| + + >>> dexpr(r"([],[([x],[big(x), dog(x)]) -> ([],[bark(x)]) -([x],[walk(x)])])").pretty_print() + _____________________________ + | | + |-----------------------------| + | ________ _________ | + | | x | | | | + | (|--------| -> |---------|) | + | | big(x) | | bark(x) | | + | | dog(x) | |_________| | + | |________| | + | _________ | + | | x | | + | __ |---------| | + | | | walk(x) | | + | |_________| | + |_____________________________| + + >>> dexpr(r"([x,y],[x=y]) + ([z],[dog(z), walk(z)])").pretty_print() + _________ _________ + | x y | | z | + (|---------| + |---------|) + | (x = y) | | dog(z) | + |_________| | walk(z) | + |_________| + + >>> dexpr(r"([],[([x],[]) | ([y],[]) | ([z],[dog(z), walk(z)])])").pretty_print() + _______________________________ + | | + |-------------------------------| + | ___ ___ _________ | + | | x | | y | | z | | + | (|---| | |---| | |---------|) | + | |___| |___| | dog(z) | | + | | walk(z) | | + | |_________| | + |_______________________________| + + >>> dexpr(r"\P.\Q.(([x],[]) + P(x) + Q(x))(\x.([],[dog(x)]))").pretty_print() + ___ ________ + \ | x | \ | | + /\ P Q.(|---| + P(x) + Q(x))( /\ x.|--------|) + |___| | dog(x) | + |________| diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/featstruct.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/featstruct.doctest new file mode 100644 index 0000000000000000000000000000000000000000..e6062d4fb31a9894d7cae48d9d834e78e5e37a6a --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/featstruct.doctest @@ -0,0 +1,1229 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +================================== + Feature Structures & Unification +================================== + >>> from nltk.featstruct import FeatStruct + >>> from nltk.sem.logic import Variable, VariableExpression, Expression + +.. note:: For now, featstruct uses the older lambdalogic semantics + module. Eventually, it should be updated to use the new first + order predicate logic module. + +Overview +~~~~~~~~ +A feature structure is a mapping from feature identifiers to feature +values, where feature values can be simple values (like strings or +ints), nested feature structures, or variables: + + >>> fs1 = FeatStruct(number='singular', person=3) + >>> print(fs1) + [ number = 'singular' ] + [ person = 3 ] + +Feature structure may be nested: + + >>> fs2 = FeatStruct(type='NP', agr=fs1) + >>> print(fs2) + [ agr = [ number = 'singular' ] ] + [ [ person = 3 ] ] + [ ] + [ type = 'NP' ] + +Variables are used to indicate that two features should be assigned +the same value. For example, the following feature structure requires +that the feature fs3['agr']['number'] be bound to the same value as the +feature fs3['subj']['number']. + + >>> fs3 = FeatStruct(agr=FeatStruct(number=Variable('?n')), + ... subj=FeatStruct(number=Variable('?n'))) + >>> print(fs3) + [ agr = [ number = ?n ] ] + [ ] + [ subj = [ number = ?n ] ] + +Feature structures are typically used to represent partial information +about objects. A feature name that is not mapped to a value stands +for a feature whose value is unknown (*not* a feature without a +value). Two feature structures that represent (potentially +overlapping) information about the same object can be combined by +*unification*. + + >>> print(fs2.unify(fs3)) + [ agr = [ number = 'singular' ] ] + [ [ person = 3 ] ] + [ ] + [ subj = [ number = 'singular' ] ] + [ ] + [ type = 'NP' ] + +When two inconsistent feature structures are unified, the unification +fails and returns ``None``. + + >>> fs4 = FeatStruct(agr=FeatStruct(person=1)) + >>> print(fs4.unify(fs2)) + None + >>> print(fs2.unify(fs4)) + None + +.. + >>> del fs1, fs2, fs3, fs4 # clean-up + +Feature Structure Types +----------------------- +There are actually two types of feature structure: + +- *feature dictionaries*, implemented by `FeatDict`, act like + Python dictionaries. Feature identifiers may be strings or + instances of the `Feature` class. +- *feature lists*, implemented by `FeatList`, act like Python + lists. Feature identifiers are integers. + +When you construct a feature structure using the `FeatStruct` +constructor, it will automatically decide which type is appropriate: + + >>> type(FeatStruct(number='singular')) + + >>> type(FeatStruct([1,2,3])) + + +Usually, we will just use feature dictionaries; but sometimes feature +lists can be useful too. Two feature lists will unify with each other +only if they have equal lengths, and all of their feature values +match. If you wish to write a feature list that contains 'unknown' +values, you must use variables: + + >>> fs1 = FeatStruct([1,2,Variable('?y')]) + >>> fs2 = FeatStruct([1,Variable('?x'),3]) + >>> fs1.unify(fs2) + [1, 2, 3] + +.. + >>> del fs1, fs2 # clean-up + +Parsing Feature Structure Strings +--------------------------------- +Feature structures can be constructed directly from strings. Often, +this is more convenient than constructing them directly. NLTK can +parse most feature strings to produce the corresponding feature +structures. (But you must restrict your base feature values to +strings, ints, logic expressions (`nltk.sem.logic.Expression`), and a +few other types discussed below). + +Feature dictionaries are written like Python dictionaries, except that +keys are not put in quotes; and square brackets (``[]``) are used +instead of braces (``{}``): + + >>> FeatStruct('[tense="past", agr=[number="sing", person=3]]') + [agr=[number='sing', person=3], tense='past'] + +If a feature value is a single alphanumeric word, then it does not +need to be quoted -- it will be automatically treated as a string: + + >>> FeatStruct('[tense=past, agr=[number=sing, person=3]]') + [agr=[number='sing', person=3], tense='past'] + +Feature lists are written like python lists: + + >>> FeatStruct('[1, 2, 3]') + [1, 2, 3] + +The expression ``[]`` is treated as an empty feature dictionary, not +an empty feature list: + + >>> type(FeatStruct('[]')) + + +Feature Paths +------------- +Features can be specified using *feature paths*, or tuples of feature +identifiers that specify path through the nested feature structures to +a value. + + >>> fs1 = FeatStruct('[x=1, y=[1,2,[z=3]]]') + >>> fs1['y'] + [1, 2, [z=3]] + >>> fs1['y', 2] + [z=3] + >>> fs1['y', 2, 'z'] + 3 + +.. + >>> del fs1 # clean-up + +Reentrance +---------- +Feature structures may contain reentrant feature values. A *reentrant +feature value* is a single feature structure that can be accessed via +multiple feature paths. + + >>> fs1 = FeatStruct(x='val') + >>> fs2 = FeatStruct(a=fs1, b=fs1) + >>> print(fs2) + [ a = (1) [ x = 'val' ] ] + [ ] + [ b -> (1) ] + >>> fs2 + [a=(1)[x='val'], b->(1)] + +As you can see, reentrane is displayed by marking a feature structure +with a unique identifier, in this case ``(1)``, the first time it is +encountered; and then using the special form ``var -> id`` whenever it +is encountered again. You can use the same notation to directly +create reentrant feature structures from strings. + + >>> FeatStruct('[a=(1)[], b->(1), c=[d->(1)]]') + [a=(1)[], b->(1), c=[d->(1)]] + +Reentrant feature structures may contain cycles: + + >>> fs3 = FeatStruct('(1)[a->(1)]') + >>> fs3['a', 'a', 'a', 'a'] + (1)[a->(1)] + >>> fs3['a', 'a', 'a', 'a'] is fs3 + True + +Unification preserves the reentrance relations imposed by both of the +unified feature structures. In the feature structure resulting from +unification, any modifications to a reentrant feature value will be +visible using any of its feature paths. + + >>> fs3.unify(FeatStruct('[a=[b=12], c=33]')) + (1)[a->(1), b=12, c=33] + +.. + >>> del fs1, fs2, fs3 # clean-up + +Feature Structure Equality +-------------------------- +Two feature structures are considered equal if they assign the same +values to all features, *and* they contain the same reentrances. + + >>> fs1 = FeatStruct('[a=(1)[x=1], b->(1)]') + >>> fs2 = FeatStruct('[a=(1)[x=1], b->(1)]') + >>> fs3 = FeatStruct('[a=[x=1], b=[x=1]]') + >>> fs1 == fs1, fs1 is fs1 + (True, True) + >>> fs1 == fs2, fs1 is fs2 + (True, False) + >>> fs1 == fs3, fs1 is fs3 + (False, False) + +Note that this differs from how Python dictionaries and lists define +equality -- in particular, Python dictionaries and lists ignore +reentrance relations. To test two feature structures for equality +while ignoring reentrance relations, use the `equal_values()` method: + + >>> fs1.equal_values(fs1) + True + >>> fs1.equal_values(fs2) + True + >>> fs1.equal_values(fs3) + True + +.. + >>> del fs1, fs2, fs3 # clean-up + +Feature Value Sets & Feature Value Tuples +----------------------------------------- +`nltk.featstruct` defines two new data types that are intended to be +used as feature values: `FeatureValueTuple` and `FeatureValueSet`. +Both of these types are considered base values -- i.e., unification +does *not* apply to them. However, variable binding *does* apply to +any values that they contain. + +Feature value tuples are written with parentheses: + + >>> fs1 = FeatStruct('[x=(?x, ?y)]') + >>> fs1 + [x=(?x, ?y)] + >>> fs1.substitute_bindings({Variable('?x'): 1, Variable('?y'): 2}) + [x=(1, 2)] + +Feature sets are written with braces: + + >>> fs1 = FeatStruct('[x={?x, ?y}]') + >>> fs1 + [x={?x, ?y}] + >>> fs1.substitute_bindings({Variable('?x'): 1, Variable('?y'): 2}) + [x={1, 2}] + +In addition to the basic feature value tuple & set classes, nltk +defines feature value unions (for sets) and feature value +concatenations (for tuples). These are written using '+', and can be +used to combine sets & tuples: + + >>> fs1 = FeatStruct('[x=((1, 2)+?z), z=?z]') + >>> fs1 + [x=((1, 2)+?z), z=?z] + >>> fs1.unify(FeatStruct('[z=(3, 4, 5)]')) + [x=(1, 2, 3, 4, 5), z=(3, 4, 5)] + +Thus, feature value tuples and sets can be used to build up tuples +and sets of values over the course of unification. For example, when +parsing sentences using a semantic feature grammar, feature sets or +feature tuples can be used to build a list of semantic predicates as +the sentence is parsed. + +As was mentioned above, unification does not apply to feature value +tuples and sets. One reason for this that it's impossible to define a +single correct answer for unification when concatenation is used. +Consider the following example: + + >>> fs1 = FeatStruct('[x=(1, 2, 3, 4)]') + >>> fs2 = FeatStruct('[x=(?a+?b), a=?a, b=?b]') + +If unification applied to feature tuples, then the unification +algorithm would have to arbitrarily choose how to divide the tuple +(1,2,3,4) into two parts. Instead, the unification algorithm refuses +to make this decision, and simply unifies based on value. Because +(1,2,3,4) is not equal to (?a+?b), fs1 and fs2 will not unify: + + >>> print(fs1.unify(fs2)) + None + +If you need a list-like structure that unification does apply to, use +`FeatList`. + +.. + >>> del fs1, fs2 # clean-up + +Light-weight Feature Structures +------------------------------- +Many of the functions defined by `nltk.featstruct` can be applied +directly to simple Python dictionaries and lists, rather than to +full-fledged `FeatDict` and `FeatList` objects. In other words, +Python ``dicts`` and ``lists`` can be used as "light-weight" feature +structures. + + >>> # Note: pprint prints dicts sorted + >>> from pprint import pprint + >>> from nltk.featstruct import unify + >>> pprint(unify(dict(x=1, y=dict()), dict(a='a', y=dict(b='b')))) + {'a': 'a', 'x': 1, 'y': {'b': 'b'}} + +However, you should keep in mind the following caveats: + +- Python dictionaries & lists ignore reentrance when checking for + equality between values. But two FeatStructs with different + reentrances are considered nonequal, even if all their base + values are equal. + +- FeatStructs can be easily frozen, allowing them to be used as + keys in hash tables. Python dictionaries and lists can not. + +- FeatStructs display reentrance in their string representations; + Python dictionaries and lists do not. + +- FeatStructs may *not* be mixed with Python dictionaries and lists + (e.g., when performing unification). + +- FeatStructs provide a number of useful methods, such as `walk()` + and `cyclic()`, which are not available for Python dicts & lists. + +In general, if your feature structures will contain any reentrances, +or if you plan to use them as dictionary keys, it is strongly +recommended that you use full-fledged `FeatStruct` objects. + +Custom Feature Values +--------------------- +The abstract base class `CustomFeatureValue` can be used to define new +base value types that have custom unification methods. For example, +the following feature value type encodes a range, and defines +unification as taking the intersection on the ranges: + + >>> from functools import total_ordering + >>> from nltk.featstruct import CustomFeatureValue, UnificationFailure + >>> @total_ordering + ... class Range(CustomFeatureValue): + ... def __init__(self, low, high): + ... assert low <= high + ... self.low = low + ... self.high = high + ... def unify(self, other): + ... if not isinstance(other, Range): + ... return UnificationFailure + ... low = max(self.low, other.low) + ... high = min(self.high, other.high) + ... if low <= high: return Range(low, high) + ... else: return UnificationFailure + ... def __repr__(self): + ... return '(%s>> fs1 = FeatStruct(x=Range(5,8), y=FeatStruct(z=Range(7,22))) + >>> print(fs1.unify(FeatStruct(x=Range(6, 22)))) + [ x = (6>> print(fs1.unify(FeatStruct(x=Range(9, 12)))) + None + >>> print(fs1.unify(FeatStruct(x=12))) + None + >>> print(fs1.unify(FeatStruct('[x=?x, y=[z=?x]]'))) + [ x = (7>> fs1 = FeatStruct(a=1, b=2, c=3) + >>> fs2 = FeatStruct(x=fs1, y='x') + +Feature structures support all dictionary methods (excluding the class +method `dict.fromkeys()`). Non-mutating methods: + + >>> sorted(fs2.keys()) # keys() + ['x', 'y'] + >>> sorted(fs2.values()) # values() + [[a=1, b=2, c=3], 'x'] + >>> sorted(fs2.items()) # items() + [('x', [a=1, b=2, c=3]), ('y', 'x')] + >>> sorted(fs2) # __iter__() + ['x', 'y'] + >>> 'a' in fs2, 'x' in fs2 # __contains__() + (False, True) + >>> fs2.has_key('a'), fs2.has_key('x') # has_key() + (False, True) + >>> fs2['x'], fs2['y'] # __getitem__() + ([a=1, b=2, c=3], 'x') + >>> fs2['a'] # __getitem__() + Traceback (most recent call last): + . . . + KeyError: 'a' + >>> fs2.get('x'), fs2.get('y'), fs2.get('a') # get() + ([a=1, b=2, c=3], 'x', None) + >>> fs2.get('x', 'hello'), fs2.get('a', 'hello') # get() + ([a=1, b=2, c=3], 'hello') + >>> len(fs1), len(fs2) # __len__ + (3, 2) + >>> fs2.copy() # copy() + [x=[a=1, b=2, c=3], y='x'] + >>> fs2.copy() is fs2 # copy() + False + +Note: by default, `FeatStruct.copy()` does a deep copy. Use +`FeatStruct.copy(deep=False)` for a shallow copy. + +.. + >>> del fs1, fs2 # clean-up. + +Dictionary access methods (mutating) +------------------------------------ + >>> fs1 = FeatStruct(a=1, b=2, c=3) + >>> fs2 = FeatStruct(x=fs1, y='x') + +Setting features (`__setitem__()`) + + >>> fs1['c'] = 5 + >>> fs1 + [a=1, b=2, c=5] + >>> fs1['x'] = 12 + >>> fs1 + [a=1, b=2, c=5, x=12] + >>> fs2['x', 'a'] = 2 + >>> fs2 + [x=[a=2, b=2, c=5, x=12], y='x'] + >>> fs1 + [a=2, b=2, c=5, x=12] + +Deleting features (`__delitem__()`) + + >>> del fs1['x'] + >>> fs1 + [a=2, b=2, c=5] + >>> del fs2['x', 'a'] + >>> fs1 + [b=2, c=5] + +`setdefault()`: + + >>> fs1.setdefault('b', 99) + 2 + >>> fs1 + [b=2, c=5] + >>> fs1.setdefault('x', 99) + 99 + >>> fs1 + [b=2, c=5, x=99] + +`update()`: + + >>> fs2.update({'a':'A', 'b':'B'}, c='C') + >>> fs2 + [a='A', b='B', c='C', x=[b=2, c=5, x=99], y='x'] + +`pop()`: + + >>> fs2.pop('a') + 'A' + >>> fs2 + [b='B', c='C', x=[b=2, c=5, x=99], y='x'] + >>> fs2.pop('a') + Traceback (most recent call last): + . . . + KeyError: 'a' + >>> fs2.pop('a', 'foo') + 'foo' + >>> fs2 + [b='B', c='C', x=[b=2, c=5, x=99], y='x'] + +`clear()`: + + >>> fs1.clear() + >>> fs1 + [] + >>> fs2 + [b='B', c='C', x=[], y='x'] + +`popitem()`: + + >>> sorted([fs2.popitem() for i in range(len(fs2))]) + [('b', 'B'), ('c', 'C'), ('x', []), ('y', 'x')] + >>> fs2 + [] + +Once a feature structure has been frozen, it may not be mutated. + + >>> fs1 = FeatStruct('[x=1, y=2, z=[a=3]]') + >>> fs1.freeze() + >>> fs1.frozen() + True + >>> fs1['z'].frozen() + True + + >>> fs1['x'] = 5 + Traceback (most recent call last): + . . . + ValueError: Frozen FeatStructs may not be modified. + >>> del fs1['x'] + Traceback (most recent call last): + . . . + ValueError: Frozen FeatStructs may not be modified. + >>> fs1.clear() + Traceback (most recent call last): + . . . + ValueError: Frozen FeatStructs may not be modified. + >>> fs1.pop('x') + Traceback (most recent call last): + . . . + ValueError: Frozen FeatStructs may not be modified. + >>> fs1.popitem() + Traceback (most recent call last): + . . . + ValueError: Frozen FeatStructs may not be modified. + >>> fs1.setdefault('x') + Traceback (most recent call last): + . . . + ValueError: Frozen FeatStructs may not be modified. + >>> fs1.update(z=22) + Traceback (most recent call last): + . . . + ValueError: Frozen FeatStructs may not be modified. + +.. + >>> del fs1, fs2 # clean-up. + +Feature Paths +------------- +Make sure that __getitem__ with feature paths works as intended: + + >>> fs1 = FeatStruct(a=1, b=2, + ... c=FeatStruct( + ... d=FeatStruct(e=12), + ... f=FeatStruct(g=55, h='hello'))) + >>> fs1[()] + [a=1, b=2, c=[d=[e=12], f=[g=55, h='hello']]] + >>> fs1['a'], fs1[('a',)] + (1, 1) + >>> fs1['c','d','e'] + 12 + >>> fs1['c','f','g'] + 55 + +Feature paths that select unknown features raise KeyError: + + >>> fs1['c', 'f', 'e'] + Traceback (most recent call last): + . . . + KeyError: ('c', 'f', 'e') + >>> fs1['q', 'p'] + Traceback (most recent call last): + . . . + KeyError: ('q', 'p') + +Feature paths that try to go 'through' a feature that's not a feature +structure raise KeyError: + + >>> fs1['a', 'b'] + Traceback (most recent call last): + . . . + KeyError: ('a', 'b') + +Feature paths can go through reentrant structures: + + >>> fs2 = FeatStruct('(1)[a=[b=[c->(1), d=5], e=11]]') + >>> fs2['a', 'b', 'c', 'a', 'e'] + 11 + >>> fs2['a', 'b', 'c', 'a', 'b', 'd'] + 5 + >>> fs2[tuple('abcabcabcabcabcabcabcabcabcabca')] + (1)[b=[c=[a->(1)], d=5], e=11] + +Indexing requires strings, `Feature`\s, or tuples; other types raise a +TypeError: + + >>> fs2[12] + Traceback (most recent call last): + . . . + TypeError: Expected feature name or path. Got 12. + >>> fs2[list('abc')] + Traceback (most recent call last): + . . . + TypeError: Expected feature name or path. Got ['a', 'b', 'c']. + +Feature paths can also be used with `get()`, `has_key()`, and +`__contains__()`. + + >>> fpath1 = tuple('abcabc') + >>> fpath2 = tuple('abcabz') + >>> fs2.get(fpath1), fs2.get(fpath2) + ((1)[a=[b=[c->(1), d=5], e=11]], None) + >>> fpath1 in fs2, fpath2 in fs2 + (True, False) + >>> fs2.has_key(fpath1), fs2.has_key(fpath2) + (True, False) + +.. + >>> del fs1, fs2 # clean-up + +Reading Feature Structures +-------------------------- + +Empty feature struct: + + >>> FeatStruct('[]') + [] + +Test features with integer values: + + >>> FeatStruct('[a=12, b=-33, c=0]') + [a=12, b=-33, c=0] + +Test features with string values. Either single or double quotes may +be used. Strings are evaluated just like python strings -- in +particular, you can use escape sequences and 'u' and 'r' prefixes, and +triple-quoted strings. + + >>> FeatStruct('[a="", b="hello", c="\'", d=\'\', e=\'"\']') + [a='', b='hello', c="'", d='', e='"'] + >>> FeatStruct(r'[a="\\", b="\"", c="\x6f\\y", d="12"]') + [a='\\', b='"', c='o\\y', d='12'] + >>> FeatStruct(r'[b=r"a\b\c"]') + [b='a\\b\\c'] + >>> FeatStruct('[x="""a"""]') + [x='a'] + +Test parsing of reentrant feature structures. + + >>> FeatStruct('[a=(1)[], b->(1)]') + [a=(1)[], b->(1)] + >>> FeatStruct('[a=(1)[x=1, y=2], b->(1)]') + [a=(1)[x=1, y=2], b->(1)] + +Test parsing of cyclic feature structures. + + >>> FeatStruct('[a=(1)[b->(1)]]') + [a=(1)[b->(1)]] + >>> FeatStruct('(1)[a=[b=[c->(1)]]]') + (1)[a=[b=[c->(1)]]] + +Strings of the form "+name" and "-name" may be used to specify boolean +values. + + >>> FeatStruct('[-bar, +baz, +foo]') + [-bar, +baz, +foo] + +None, True, and False are recognized as values: + + >>> FeatStruct('[bar=True, baz=False, foo=None]') + [+bar, -baz, foo=None] + +Special features: + + >>> FeatStruct('NP/VP') + NP[]/VP[] + >>> FeatStruct('?x/?x') + ?x[]/?x[] + >>> print(FeatStruct('VP[+fin, agr=?x, tense=past]/NP[+pl, agr=?x]')) + [ *type* = 'VP' ] + [ ] + [ [ *type* = 'NP' ] ] + [ *slash* = [ agr = ?x ] ] + [ [ pl = True ] ] + [ ] + [ agr = ?x ] + [ fin = True ] + [ tense = 'past' ] + +Here the slash feature gets coerced: + + >>> FeatStruct('[*slash*=a, x=b, *type*="NP"]') + NP[x='b']/a[] + + >>> FeatStruct('NP[sem=]/NP') + NP[sem=]/NP[] + >>> FeatStruct('S[sem=]') + S[sem=] + >>> print(FeatStruct('NP[sem=]/NP')) + [ *type* = 'NP' ] + [ ] + [ *slash* = [ *type* = 'NP' ] ] + [ ] + [ sem = ] + +Playing with ranges: + + >>> from nltk.featstruct import RangeFeature, FeatStructReader + >>> width = RangeFeature('width') + >>> reader = FeatStructReader([width]) + >>> fs1 = reader.fromstring('[*width*=-5:12]') + >>> fs2 = reader.fromstring('[*width*=2:123]') + >>> fs3 = reader.fromstring('[*width*=-7:-2]') + >>> fs1.unify(fs2) + [*width*=(2, 12)] + >>> fs1.unify(fs3) + [*width*=(-5, -2)] + >>> print(fs2.unify(fs3)) # no overlap in width. + None + +The slash feature has a default value of 'False': + + >>> print(FeatStruct('NP[]/VP').unify(FeatStruct('NP[]'), trace=1)) + + Unification trace: + / NP[]/VP[] + |\ NP[] + | + | Unify feature: *type* + | / 'NP' + | |\ 'NP' + | | + | +-->'NP' + | + | Unify feature: *slash* + | / VP[] + | |\ False + | | + X X <-- FAIL + None + +The demo structures from category.py. They all parse, but they don't +do quite the right thing, -- ?x vs x. + + >>> FeatStruct(pos='n', agr=FeatStruct(number='pl', gender='f')) + [agr=[gender='f', number='pl'], pos='n'] + >>> FeatStruct(r'NP[sem=]/NP') + NP[sem=]/NP[] + >>> FeatStruct(r'S[sem=]') + S[sem=] + >>> FeatStruct('?x/?x') + ?x[]/?x[] + >>> FeatStruct('VP[+fin, agr=?x, tense=past]/NP[+pl, agr=?x]') + VP[agr=?x, +fin, tense='past']/NP[agr=?x, +pl] + >>> FeatStruct('S[sem = ]') + S[sem=] + + >>> FeatStruct('S') + S[] + +The parser also includes support for reading sets and tuples. + + >>> FeatStruct('[x={1,2,2,2}, y={/}]') + [x={1, 2}, y={/}] + >>> FeatStruct('[x=(1,2,2,2), y=()]') + [x=(1, 2, 2, 2), y=()] + >>> print(FeatStruct('[x=(1,[z=(1,2,?x)],?z,{/})]')) + [ x = (1, [ z = (1, 2, ?x) ], ?z, {/}) ] + +Note that we can't put a featstruct inside a tuple, because doing so +would hash it, and it's not frozen yet: + + >>> print(FeatStruct('[x={[]}]')) + Traceback (most recent call last): + . . . + TypeError: FeatStructs must be frozen before they can be hashed. + +There's a special syntax for taking the union of sets: "{...+...}". +The elements should only be variables or sets. + + >>> FeatStruct('[x={?a+?b+{1,2,3}}]') + [x={?a+?b+{1, 2, 3}}] + +There's a special syntax for taking the concatenation of tuples: +"(...+...)". The elements should only be variables or tuples. + + >>> FeatStruct('[x=(?a+?b+(1,2,3))]') + [x=(?a+?b+(1, 2, 3))] + +Parsing gives helpful messages if your string contains an error. + + >>> FeatStruct('[a=, b=5]]') + Traceback (most recent call last): + . . . + ValueError: Error parsing feature structure + [a=, b=5]] + ^ Expected value + >>> FeatStruct('[a=12 22, b=33]') + Traceback (most recent call last): + . . . + ValueError: Error parsing feature structure + [a=12 22, b=33] + ^ Expected comma + >>> FeatStruct('[a=5] [b=6]') + Traceback (most recent call last): + . . . + ValueError: Error parsing feature structure + [a=5] [b=6] + ^ Expected end of string + >>> FeatStruct(' *++*') + Traceback (most recent call last): + . . . + ValueError: Error parsing feature structure + *++* + ^ Expected open bracket or identifier + >>> FeatStruct('[x->(1)]') + Traceback (most recent call last): + . . . + ValueError: Error parsing feature structure + [x->(1)] + ^ Expected bound identifier + >>> FeatStruct('[x->y]') + Traceback (most recent call last): + . . . + ValueError: Error parsing feature structure + [x->y] + ^ Expected identifier + >>> FeatStruct('') + Traceback (most recent call last): + . . . + ValueError: Error parsing feature structure + + ^ Expected open bracket or identifier + + +Unification +----------- +Very simple unifications give the expected results: + + >>> FeatStruct().unify(FeatStruct()) + [] + >>> FeatStruct(number='singular').unify(FeatStruct()) + [number='singular'] + >>> FeatStruct().unify(FeatStruct(number='singular')) + [number='singular'] + >>> FeatStruct(number='singular').unify(FeatStruct(person=3)) + [number='singular', person=3] + +Merging nested structures: + + >>> fs1 = FeatStruct('[A=[B=b]]') + >>> fs2 = FeatStruct('[A=[C=c]]') + >>> fs1.unify(fs2) + [A=[B='b', C='c']] + >>> fs2.unify(fs1) + [A=[B='b', C='c']] + +A basic case of reentrant unification + + >>> fs4 = FeatStruct('[A=(1)[B=b], E=[F->(1)]]') + >>> fs5 = FeatStruct("[A=[C='c'], E=[F=[D='d']]]") + >>> fs4.unify(fs5) + [A=(1)[B='b', C='c', D='d'], E=[F->(1)]] + >>> fs5.unify(fs4) + [A=(1)[B='b', C='c', D='d'], E=[F->(1)]] + +More than 2 paths to a value + + >>> fs1 = FeatStruct("[a=[],b=[],c=[],d=[]]") + >>> fs2 = FeatStruct('[a=(1)[], b->(1), c->(1), d->(1)]') + >>> fs1.unify(fs2) + [a=(1)[], b->(1), c->(1), d->(1)] + +fs1[a] gets unified with itself + + >>> fs1 = FeatStruct('[x=(1)[], y->(1)]') + >>> fs2 = FeatStruct('[x=(1)[], y->(1)]') + >>> fs1.unify(fs2) + [x=(1)[], y->(1)] + +Bound variables should get forwarded appropriately + + >>> fs1 = FeatStruct('[A=(1)[X=x], B->(1), C=?cvar, D=?dvar]') + >>> fs2 = FeatStruct('[A=(1)[Y=y], B=(2)[Z=z], C->(1), D->(2)]') + >>> fs1.unify(fs2) + [A=(1)[X='x', Y='y', Z='z'], B->(1), C->(1), D->(1)] + >>> fs2.unify(fs1) + [A=(1)[X='x', Y='y', Z='z'], B->(1), C->(1), D->(1)] + +Cyclic structure created by unification. + + >>> fs1 = FeatStruct('[F=(1)[], G->(1)]') + >>> fs2 = FeatStruct('[F=[H=(2)[]], G->(2)]') + >>> fs3 = fs1.unify(fs2) + >>> fs3 + [F=(1)[H->(1)], G->(1)] + >>> fs3['F'] is fs3['G'] + True + >>> fs3['F'] is fs3['G']['H'] + True + >>> fs3['F'] is fs3['G']['H']['H'] + True + >>> fs3['F'] is fs3['F']['H']['H']['H']['H']['H']['H']['H']['H'] + True + +Cyclic structure created w/ variables. + + >>> fs1 = FeatStruct('[F=[H=?x]]') + >>> fs2 = FeatStruct('[F=?x]') + >>> fs3 = fs1.unify(fs2, rename_vars=False) + >>> fs3 + [F=(1)[H->(1)]] + >>> fs3['F'] is fs3['F']['H'] + True + >>> fs3['F'] is fs3['F']['H']['H'] + True + >>> fs3['F'] is fs3['F']['H']['H']['H']['H']['H']['H']['H']['H'] + True + +Unifying w/ a cyclic feature structure. + + >>> fs4 = FeatStruct('[F=[H=[H=[H=(1)[]]]], K->(1)]') + >>> fs3.unify(fs4) + [F=(1)[H->(1)], K->(1)] + >>> fs4.unify(fs3) + [F=(1)[H->(1)], K->(1)] + +Variable bindings should preserve reentrance. + + >>> bindings = {} + >>> fs1 = FeatStruct("[a=?x]") + >>> fs2 = fs1.unify(FeatStruct("[a=[]]"), bindings) + >>> fs2['a'] is bindings[Variable('?x')] + True + >>> fs2.unify(FeatStruct("[b=?x]"), bindings) + [a=(1)[], b->(1)] + +Aliased variable tests + + >>> fs1 = FeatStruct("[a=?x, b=?x]") + >>> fs2 = FeatStruct("[b=?y, c=?y]") + >>> bindings = {} + >>> fs3 = fs1.unify(fs2, bindings) + >>> fs3 + [a=?x, b=?x, c=?x] + >>> bindings + {Variable('?y'): Variable('?x')} + >>> fs3.unify(FeatStruct("[a=1]")) + [a=1, b=1, c=1] + +If we keep track of the bindings, then we can use the same variable +over multiple calls to unify. + + >>> bindings = {} + >>> fs1 = FeatStruct('[a=?x]') + >>> fs2 = fs1.unify(FeatStruct('[a=[]]'), bindings) + >>> fs2.unify(FeatStruct('[b=?x]'), bindings) + [a=(1)[], b->(1)] + >>> bindings + {Variable('?x'): []} + +.. + >>> del fs1, fs2, fs3, fs4, fs5 # clean-up + +Unification Bindings +-------------------- + + >>> bindings = {} + >>> fs1 = FeatStruct('[a=?x]') + >>> fs2 = FeatStruct('[a=12]') + >>> fs3 = FeatStruct('[b=?x]') + >>> fs1.unify(fs2, bindings) + [a=12] + >>> bindings + {Variable('?x'): 12} + >>> fs3.substitute_bindings(bindings) + [b=12] + >>> fs3 # substitute_bindings didn't mutate fs3. + [b=?x] + >>> fs2.unify(fs3, bindings) + [a=12, b=12] + + >>> bindings = {} + >>> fs1 = FeatStruct('[a=?x, b=1]') + >>> fs2 = FeatStruct('[a=5, b=?x]') + >>> fs1.unify(fs2, bindings) + [a=5, b=1] + >>> sorted(bindings.items()) + [(Variable('?x'), 5), (Variable('?x2'), 1)] + +.. + >>> del fs1, fs2, fs3 # clean-up + +Expressions +----------- + + >>> e = Expression.fromstring('\\P y.P(z,y)') + >>> fs1 = FeatStruct(x=e, y=Variable('z')) + >>> fs2 = FeatStruct(y=VariableExpression(Variable('John'))) + >>> fs1.unify(fs2) + [x=<\P y.P(John,y)>, y=] + +Remove Variables +---------------- + + >>> FeatStruct('[a=?x, b=12, c=[d=?y]]').remove_variables() + [b=12, c=[]] + >>> FeatStruct('(1)[a=[b=?x,c->(1)]]').remove_variables() + (1)[a=[c->(1)]] + +Equality & Hashing +------------------ +The `equal_values` method checks whether two feature structures assign +the same value to every feature. If the optional argument +``check_reentrances`` is supplied, then it also returns false if there +is any difference in the reentrances. + + >>> a = FeatStruct('(1)[x->(1)]') + >>> b = FeatStruct('(1)[x->(1)]') + >>> c = FeatStruct('(1)[x=[x->(1)]]') + >>> d = FeatStruct('[x=(1)[x->(1)]]') + >>> e = FeatStruct('(1)[x=[x->(1), y=1], y=1]') + >>> def compare(x,y): + ... assert x.equal_values(y, True) == y.equal_values(x, True) + ... assert x.equal_values(y, False) == y.equal_values(x, False) + ... if x.equal_values(y, True): + ... assert x.equal_values(y, False) + ... print('equal values, same reentrance') + ... elif x.equal_values(y, False): + ... print('equal values, different reentrance') + ... else: + ... print('different values') + + >>> compare(a, a) + equal values, same reentrance + >>> compare(a, b) + equal values, same reentrance + >>> compare(a, c) + equal values, different reentrance + >>> compare(a, d) + equal values, different reentrance + >>> compare(c, d) + equal values, different reentrance + >>> compare(a, e) + different values + >>> compare(c, e) + different values + >>> compare(d, e) + different values + >>> compare(e, e) + equal values, same reentrance + +Feature structures may not be hashed until they are frozen: + + >>> hash(a) + Traceback (most recent call last): + . . . + TypeError: FeatStructs must be frozen before they can be hashed. + >>> a.freeze() + >>> v = hash(a) + +Feature structures define hash consistently. The following example +looks at the hash value for each (fs1,fs2) pair; if their hash values +are not equal, then they must not be equal. If their hash values are +equal, then display a message, and indicate whether their values are +indeed equal. Note that c and d currently have the same hash value, +even though they are not equal. That is not a bug, strictly speaking, +but it wouldn't be a bad thing if it changed. + + >>> for fstruct in (a, b, c, d, e): + ... fstruct.freeze() + >>> for fs1_name in 'abcde': + ... for fs2_name in 'abcde': + ... fs1 = locals()[fs1_name] + ... fs2 = locals()[fs2_name] + ... if hash(fs1) != hash(fs2): + ... assert fs1 != fs2 + ... else: + ... print('%s and %s have the same hash value,' % + ... (fs1_name, fs2_name)) + ... if fs1 == fs2: print('and are equal') + ... else: print('and are not equal') + a and a have the same hash value, and are equal + a and b have the same hash value, and are equal + b and a have the same hash value, and are equal + b and b have the same hash value, and are equal + c and c have the same hash value, and are equal + c and d have the same hash value, and are not equal + d and c have the same hash value, and are not equal + d and d have the same hash value, and are equal + e and e have the same hash value, and are equal + +.. + >>> del a, b, c, d, e, v # clean-up + +Tracing +------- + + >>> fs1 = FeatStruct('[a=[b=(1)[], c=?x], d->(1), e=[f=?x]]') + >>> fs2 = FeatStruct('[a=(1)[c="C"], e=[g->(1)]]') + >>> fs1.unify(fs2, trace=True) + + Unification trace: + / [a=[b=(1)[], c=?x], d->(1), e=[f=?x]] + |\ [a=(1)[c='C'], e=[g->(1)]] + | + | Unify feature: a + | / [b=[], c=?x] + | |\ [c='C'] + | | + | | Unify feature: a.c + | | / ?x + | | |\ 'C' + | | | + | | +-->Variable('?x') + | | + | +-->[b=[], c=?x] + | Bindings: {?x: 'C'} + | + | Unify feature: e + | / [f=?x] + | |\ [g=[c='C']] + | | + | +-->[f=?x, g=[b=[], c=?x]] + | Bindings: {?x: 'C'} + | + +-->[a=(1)[b=(2)[], c='C'], d->(2), e=[f='C', g->(1)]] + Bindings: {?x: 'C'} + [a=(1)[b=(2)[], c='C'], d->(2), e=[f='C', g->(1)]] + >>> + >>> fs1 = FeatStruct('[a=?x, b=?z, c=?z]') + >>> fs2 = FeatStruct('[a=?y, b=?y, c=?q]') + >>> #fs1.unify(fs2, trace=True) + >>> + +.. + >>> del fs1, fs2 # clean-up + +Unification on Dicts & Lists +---------------------------- +It's possible to do unification on dictionaries: + + >>> from nltk.featstruct import unify + >>> pprint(unify(dict(x=1, y=dict(z=2)), dict(x=1, q=5)), width=1) + {'q': 5, 'x': 1, 'y': {'z': 2}} + +It's possible to do unification on lists as well: + + >>> unify([1, 2, 3], [1, Variable('x'), 3]) + [1, 2, 3] + +Mixing dicts and lists is fine: + + >>> pprint(unify([dict(x=1, y=dict(z=2)),3], [dict(x=1, q=5),3]), + ... width=1) + [{'q': 5, 'x': 1, 'y': {'z': 2}}, 3] + +Mixing dicts and FeatStructs is discouraged: + + >>> unify(dict(x=1), FeatStruct(x=1)) + Traceback (most recent call last): + . . . + ValueError: Mixing FeatStruct objects with Python dicts and lists is not supported. + +But you can do it if you really want, by explicitly stating that both +dictionaries and FeatStructs should be treated as feature structures: + + >>> unify(dict(x=1), FeatStruct(x=1), fs_class=(dict, FeatStruct)) + {'x': 1} + +Finding Conflicts +----------------- + + >>> from nltk.featstruct import conflicts + >>> fs1 = FeatStruct('[a=[b=(1)[c=2], d->(1), e=[f->(1)]]]') + >>> fs2 = FeatStruct('[a=[b=[c=[x=5]], d=[c=2], e=[f=[c=3]]]]') + >>> for path in conflicts(fs1, fs2): + ... print('%-8s: %r vs %r' % ('.'.join(path), fs1[path], fs2[path])) + a.b.c : 2 vs [x=5] + a.e.f.c : 2 vs 3 + +.. + >>> del fs1, fs2 # clean-up + +Retracting Bindings +------------------- + + >>> from nltk.featstruct import retract_bindings + >>> bindings = {} + >>> fs1 = FeatStruct('[a=?x, b=[c=?y]]') + >>> fs2 = FeatStruct('[a=(1)[c=[d=1]], b->(1)]') + >>> fs3 = fs1.unify(fs2, bindings) + >>> print(fs3) + [ a = (1) [ c = [ d = 1 ] ] ] + [ ] + [ b -> (1) ] + >>> pprint(bindings) + {Variable('?x'): [c=[d=1]], Variable('?y'): [d=1]} + >>> retract_bindings(fs3, bindings) + [a=?x, b=?x] + >>> pprint(bindings) + {Variable('?x'): [c=?y], Variable('?y'): [d=1]} + +Squashed Bugs +~~~~~~~~~~~~~ +In svn rev 5167, unifying two feature structures that used the same +variable would cause those variables to become aliased in the output. + + >>> fs1 = FeatStruct('[a=?x]') + >>> fs2 = FeatStruct('[b=?x]') + >>> fs1.unify(fs2) + [a=?x, b=?x2] + +There was a bug in svn revision 5172 that caused `rename_variables` to +rename variables to names that are already used. + + >>> FeatStruct('[a=?x, b=?x2]').rename_variables( + ... vars=[Variable('?x')]) + [a=?x3, b=?x2] + >>> fs1 = FeatStruct('[a=?x]') + >>> fs2 = FeatStruct('[a=?x, b=?x2]') + >>> fs1.unify(fs2) + [a=?x, b=?x2] + +There was a bug in svn rev 5167 that caused us to get the following +example wrong. Basically the problem was that we only followed +'forward' pointers for other, not self, when unifying two feature +structures. (nb: this test assumes that features are unified in +alphabetical order -- if they are not, it might pass even if the bug +is present.) + + >>> fs1 = FeatStruct('[a=[x=1], b=?x, c=?x]') + >>> fs2 = FeatStruct('[a=(1)[], b->(1), c=[x=2]]') + >>> print(fs1.unify(fs2)) + None + +.. + >>> del fs1, fs2 # clean-up diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/framenet.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/framenet.doctest new file mode 100644 index 0000000000000000000000000000000000000000..337c348b923a0d3a95c2576f10da6347e7085e7a --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/framenet.doctest @@ -0,0 +1,288 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +======== +FrameNet +======== + +The FrameNet corpus is a lexical database of English that is both human- +and machine-readable, based on annotating examples of how words are used +in actual texts. FrameNet is based on a theory of meaning called Frame +Semantics, deriving from the work of Charles J. Fillmore and colleagues. +The basic idea is straightforward: that the meanings of most words can +best be understood on the basis of a semantic frame: a description of a +type of event, relation, or entity and the participants in it. For +example, the concept of cooking typically involves a person doing the +cooking (Cook), the food that is to be cooked (Food), something to hold +the food while cooking (Container) and a source of heat +(Heating_instrument). In the FrameNet project, this is represented as a +frame called Apply_heat, and the Cook, Food, Heating_instrument and +Container are called frame elements (FEs). Words that evoke this frame, +such as fry, bake, boil, and broil, are called lexical units (LUs) of +the Apply_heat frame. The job of FrameNet is to define the frames +and to annotate sentences to show how the FEs fit syntactically around +the word that evokes the frame. + +------ +Frames +------ + +A Frame is a script-like conceptual structure that describes a +particular type of situation, object, or event along with the +participants and props that are needed for that Frame. For +example, the "Apply_heat" frame describes a common situation +involving a Cook, some Food, and a Heating_Instrument, and is +evoked by words such as bake, blanch, boil, broil, brown, +simmer, steam, etc. + +We call the roles of a Frame "frame elements" (FEs) and the +frame-evoking words are called "lexical units" (LUs). + +FrameNet includes relations between Frames. Several types of +relations are defined, of which the most important are: + +- Inheritance: An IS-A relation. The child frame is a subtype + of the parent frame, and each FE in the parent is bound to + a corresponding FE in the child. An example is the + "Revenge" frame which inherits from the + "Rewards_and_punishments" frame. + +- Using: The child frame presupposes the parent frame as + background, e.g the "Speed" frame "uses" (or presupposes) + the "Motion" frame; however, not all parent FEs need to be + bound to child FEs. + +- Subframe: The child frame is a subevent of a complex event + represented by the parent, e.g. the "Criminal_process" frame + has subframes of "Arrest", "Arraignment", "Trial", and + "Sentencing". + +- Perspective_on: The child frame provides a particular + perspective on an un-perspectivized parent frame. A pair of + examples consists of the "Hiring" and "Get_a_job" frames, + which perspectivize the "Employment_start" frame from the + Employer's and the Employee's point of view, respectively. + +To get a list of all of the Frames in FrameNet, you can use the +`frames()` function. If you supply a regular expression pattern to the +`frames()` function, you will get a list of all Frames whose names match +that pattern: + + >>> from pprint import pprint + >>> from operator import itemgetter + >>> from nltk.corpus import framenet as fn + >>> from nltk.corpus.reader.framenet import PrettyList + >>> x = fn.frames(r'(?i)crim') + >>> x.sort(key=itemgetter('ID')) + >>> x + [, , ...] + >>> PrettyList(sorted(x, key=itemgetter('ID'))) + [, , ...] + +To get the details of a particular Frame, you can use the `frame()` +function passing in the frame number: + + >>> from pprint import pprint + >>> from nltk.corpus import framenet as fn + >>> f = fn.frame(202) + >>> f.ID + 202 + >>> f.name + 'Arrest' + >>> f.definition + "Authorities charge a Suspect, who is under suspicion of having committed a crime..." + >>> len(f.lexUnit) + 11 + >>> pprint(sorted([x for x in f.FE])) + ['Authorities', + 'Charges', + 'Co-participant', + 'Manner', + 'Means', + 'Offense', + 'Place', + 'Purpose', + 'Source_of_legal_authority', + 'Suspect', + 'Time', + 'Type'] + >>> pprint(f.frameRelations) + [ Child=Arrest>, Component=Arrest>, ...] + +The `frame()` function shown above returns a dict object containing +detailed information about the Frame. See the documentation on the +`frame()` function for the specifics. + +You can also search for Frames by their Lexical Units (LUs). The +`frames_by_lemma()` function returns a list of all frames that contain +LUs in which the 'name' attribute of the LU matches the given regular +expression. Note that LU names are composed of "lemma.POS", where the +"lemma" part can be made up of either a single lexeme (e.g. 'run') or +multiple lexemes (e.g. 'a little') (see below). + + >>> PrettyList(sorted(fn.frames_by_lemma(r'(?i)a little'), key=itemgetter('ID'))) + [, ] + +------------- +Lexical Units +------------- + +A lexical unit (LU) is a pairing of a word with a meaning. For +example, the "Apply_heat" Frame describes a common situation +involving a Cook, some Food, and a Heating Instrument, and is +_evoked_ by words such as bake, blanch, boil, broil, brown, +simmer, steam, etc. These frame-evoking words are the LUs in the +Apply_heat frame. Each sense of a polysemous word is a different +LU. + +We have used the word "word" in talking about LUs. The reality +is actually rather complex. When we say that the word "bake" is +polysemous, we mean that the lemma "bake.v" (which has the +word-forms "bake", "bakes", "baked", and "baking") is linked to +three different frames: + +- Apply_heat: "Michelle baked the potatoes for 45 minutes." + +- Cooking_creation: "Michelle baked her mother a cake for her birthday." + +- Absorb_heat: "The potatoes have to bake for more than 30 minutes." + +These constitute three different LUs, with different +definitions. + +Multiword expressions such as "given name" and hyphenated words +like "shut-eye" can also be LUs. Idiomatic phrases such as +"middle of nowhere" and "give the slip (to)" are also defined as +LUs in the appropriate frames ("Isolated_places" and "Evading", +respectively), and their internal structure is not analyzed. + +Framenet provides multiple annotated examples of each sense of a +word (i.e. each LU). Moreover, the set of examples +(approximately 20 per LU) illustrates all of the combinatorial +possibilities of the lexical unit. + +Each LU is linked to a Frame, and hence to the other words which +evoke that Frame. This makes the FrameNet database similar to a +thesaurus, grouping together semantically similar words. + +In the simplest case, frame-evoking words are verbs such as +"fried" in: + + "Matilde fried the catfish in a heavy iron skillet." + +Sometimes event nouns may evoke a Frame. For example, +"reduction" evokes "Cause_change_of_scalar_position" in: + + "...the reduction of debt levels to $665 million from $2.6 billion." + +Adjectives may also evoke a Frame. For example, "asleep" may +evoke the "Sleep" frame as in: + + "They were asleep for hours." + +Many common nouns, such as artifacts like "hat" or "tower", +typically serve as dependents rather than clearly evoking their +own frames. + +Details for a specific lexical unit can be obtained using this class's +`lus()` function, which takes an optional regular expression +pattern that will be matched against the name of the lexical unit: + + >>> from pprint import pprint + >>> PrettyList(sorted(fn.lus(r'(?i)a little'), key=itemgetter('ID'))) + [, , ...] + +You can obtain detailed information on a particular LU by calling the +`lu()` function and passing in an LU's 'ID' number: + + >>> from pprint import pprint + >>> from nltk.corpus import framenet as fn + >>> fn.lu(256).name + 'foresee.v' + >>> fn.lu(256).definition + 'COD: be aware of beforehand; predict.' + >>> fn.lu(256).frame.name + 'Expectation' + >>> fn.lu(256).lexemes[0].name + 'foresee' + +Note that LU names take the form of a dotted string (e.g. "run.v" or "a +little.adv") in which a lemma precedes the "." and a part of speech +(POS) follows the dot. The lemma may be composed of a single lexeme +(e.g. "run") or of multiple lexemes (e.g. "a little"). The list of +POSs used in the LUs is: + +v - verb +n - noun +a - adjective +adv - adverb +prep - preposition +num - numbers +intj - interjection +art - article +c - conjunction +scon - subordinating conjunction + +For more detailed information about the info that is contained in the +dict that is returned by the `lu()` function, see the documentation on +the `lu()` function. + +------------------- +Annotated Documents +------------------- + +The FrameNet corpus contains a small set of annotated documents. A list +of these documents can be obtained by calling the `docs()` function: + + >>> from pprint import pprint + >>> from nltk.corpus import framenet as fn + >>> d = fn.docs('BellRinging')[0] + >>> d.corpname + 'PropBank' + >>> d.sentence[49] + full-text sentence (...) in BellRinging: + + + [POS] 17 tags + + [POS_tagset] PENN + + [text] + [annotationSet] + + `` I live in hopes that the ringers themselves will be drawn into + ***** ******* ***** + Desir Cause_t Cause + [1] [3] [2] + + that fuller life . + ****** + Comple + [4] + (Desir=Desiring, Cause_t=Cause_to_make_noise, Cause=Cause_motion, Comple=Completeness) + + + >>> d.sentence[49].annotationSet[1] + annotation set (...): + + [status] MANUAL + + [LU] (6605) hope.n in Desiring + + [frame] (366) Desiring + + [GF] 2 relations + + [PT] 2 phrases + + [text] + [Target] + [FE] + [Noun] + + `` I live in hopes that the ringers themselves will be drawn into + - ^^^^ ^^ ***** ---------------------------------------------- + E supp su Event + + that fuller life . + ----------------- + + (E=Experiencer, su=supp) + + diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/generate.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/generate.doctest new file mode 100644 index 0000000000000000000000000000000000000000..eee322d6d7811e46c5d4c17e7d2daf0ef2e314c2 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/generate.doctest @@ -0,0 +1,78 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +=============================================== +Generating sentences from context-free grammars +=============================================== + +An example grammar: + + >>> from nltk.parse.generate import generate, demo_grammar + >>> from nltk import CFG + >>> grammar = CFG.fromstring(demo_grammar) + >>> print(grammar) + Grammar with 13 productions (start state = S) + S -> NP VP + NP -> Det N + PP -> P NP + VP -> 'slept' + VP -> 'saw' NP + VP -> 'walked' PP + Det -> 'the' + Det -> 'a' + N -> 'man' + N -> 'park' + N -> 'dog' + P -> 'in' + P -> 'with' + +The first 10 generated sentences: + + >>> for sentence in generate(grammar, n=10): + ... print(' '.join(sentence)) + the man slept + the man saw the man + the man saw the park + the man saw the dog + the man saw a man + the man saw a park + the man saw a dog + the man walked in the man + the man walked in the park + the man walked in the dog + +All sentences of max depth 4: + + >>> for sentence in generate(grammar, depth=4): + ... print(' '.join(sentence)) + the man slept + the park slept + the dog slept + a man slept + a park slept + a dog slept + +The number of sentences of different max depths: + + >>> len(list(generate(grammar, depth=3))) + 0 + >>> len(list(generate(grammar, depth=4))) + 6 + >>> len(list(generate(grammar, depth=5))) + 42 + >>> len(list(generate(grammar, depth=6))) + 114 + >>> len(list(generate(grammar))) + 114 + +Infinite grammars will throw a RecursionError when not bounded by some ``depth``: + + >>> grammar = CFG.fromstring(""" + ... S -> A B + ... A -> B + ... B -> "b" | A + ... """) + >>> list(generate(grammar)) + Traceback (most recent call last): + ... + RuntimeError: The grammar has rule(s) that yield infinite recursion! diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/gensim.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/gensim.doctest new file mode 100644 index 0000000000000000000000000000000000000000..65d0c6a53f4ac5d209a8557bc4cec37e98ca1e4d --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/gensim.doctest @@ -0,0 +1,141 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +======================================= +Demonstrate word embedding using Gensim +======================================= + + >>> from nltk.test.gensim_fixt import setup_module + >>> setup_module() + +We demonstrate three functions: +- Train the word embeddings using brown corpus; +- Load the pre-trained model and perform simple tasks; and +- Pruning the pre-trained binary model. + + >>> import gensim + +--------------- +Train the model +--------------- + +Here we train a word embedding using the Brown Corpus: + + >>> from nltk.corpus import brown + >>> train_set = brown.sents()[:10000] + >>> model = gensim.models.Word2Vec(train_set) + +It might take some time to train the model. So, after it is trained, it can be saved as follows: + + >>> model.save('brown.embedding') + >>> new_model = gensim.models.Word2Vec.load('brown.embedding') + +The model will be the list of words with their embedding. We can easily get the vector representation of a word. + + >>> len(new_model.wv['university']) + 100 + +There are some supporting functions already implemented in Gensim to manipulate with word embeddings. +For example, to compute the cosine similarity between 2 words: + + >>> new_model.wv.similarity('university','school') > 0.3 + True + +--------------------------- +Using the pre-trained model +--------------------------- + +NLTK includes a pre-trained model which is part of a model that is trained on 100 billion words from the Google News Dataset. +The full model is from https://code.google.com/p/word2vec/ (about 3 GB). + + >>> from nltk.data import find + >>> word2vec_sample = str(find('models/word2vec_sample/pruned.word2vec.txt')) + >>> model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_sample, binary=False) + +We pruned the model to only include the most common words (~44k words). + + >>> len(model) + 43981 + +Each word is represented in the space of 300 dimensions: + + >>> len(model['university']) + 300 + +Finding the top n words that are similar to a target word is simple. The result is the list of n words with the score. + + >>> model.most_similar(positive=['university'], topn = 3) + [('universities', 0.70039...), ('faculty', 0.67809...), ('undergraduate', 0.65870...)] + +Finding a word that is not in a list is also supported, although, implementing this by yourself is simple. + + >>> model.doesnt_match('breakfast cereal dinner lunch'.split()) + 'cereal' + +Mikolov et al. (2013) figured out that word embedding captures much of syntactic and semantic regularities. For example, +the vector 'King - Man + Woman' is close to 'Queen' and 'Germany - Berlin + Paris' is close to 'France'. + + >>> model.most_similar(positive=['woman','king'], negative=['man'], topn = 1) + [('queen', 0.71181...)] + + >>> model.most_similar(positive=['Paris','Germany'], negative=['Berlin'], topn = 1) + [('France', 0.78840...)] + +We can visualize the word embeddings using t-SNE (https://lvdmaaten.github.io/tsne/). For this demonstration, we visualize the first 1000 words. + +| import numpy as np +| labels = [] +| count = 0 +| max_count = 1000 +| X = np.zeros(shape=(max_count,len(model['university']))) +| +| for term in model.index_to_key: +| X[count] = model[term] +| labels.append(term) +| count+= 1 +| if count >= max_count: break +| +| # It is recommended to use PCA first to reduce to ~50 dimensions +| from sklearn.decomposition import PCA +| pca = PCA(n_components=50) +| X_50 = pca.fit_transform(X) +| +| # Using TSNE to further reduce to 2 dimensions +| from sklearn.manifold import TSNE +| model_tsne = TSNE(n_components=2, random_state=0) +| Y = model_tsne.fit_transform(X_50) +| +| # Show the scatter plot +| import matplotlib.pyplot as plt +| plt.scatter(Y[:,0], Y[:,1], 20) +| +| # Add labels +| for label, x, y in zip(labels, Y[:, 0], Y[:, 1]): +| plt.annotate(label, xy = (x,y), xytext = (0, 0), textcoords = 'offset points', size = 10) +| +| plt.show() + +------------------------------ +Prune the trained binary model +------------------------------ + +Here is the supporting code to extract part of the binary model (GoogleNews-vectors-negative300.bin.gz) from https://code.google.com/p/word2vec/ +We use this code to get the `word2vec_sample` model. + +| import gensim +| # Load the binary model +| model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary = True) +| +| # Only output word that appear in the Brown corpus +| from nltk.corpus import brown +| words = set(brown.words()) +| print(len(words)) +| +| # Output presented word to a temporary file +| out_file = 'pruned.word2vec.txt' +| with open(out_file,'w') as f: +| word_presented = words.intersection(model.index_to_key) +| f.write('{} {}\n'.format(len(word_presented),len(model['word']))) +| +| for word in word_presented: +| f.write('{} {}\n'.format(word, ' '.join(str(value) for value in model[word]))) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/gluesemantics_malt.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/gluesemantics_malt.doctest new file mode 100644 index 0000000000000000000000000000000000000000..66bebd35c553e47457bba3b3e15c6f2233698d85 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/gluesemantics_malt.doctest @@ -0,0 +1,69 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +.. see also: gluesemantics.doctest + +============================================================================== + Glue Semantics +============================================================================== + + >>> from nltk.test.gluesemantics_malt_fixt import setup_module + >>> setup_module() + + >>> from nltk.sem.glue import * + >>> nltk.sem.logic._counter._value = 0 + +-------------------------------- +Initialize the Dependency Parser +-------------------------------- + >>> from nltk.parse.malt import MaltParser + + >>> tagger = RegexpTagger( + ... [('^(John|Mary)$', 'NNP'), + ... ('^(sees|chases)$', 'VB'), + ... ('^(a)$', 'ex_quant'), + ... ('^(every)$', 'univ_quant'), + ... ('^(girl|dog)$', 'NN') + ... ]).tag + >>> depparser = MaltParser(tagger=tagger) + +-------------------- +Automated Derivation +-------------------- + >>> glue = Glue(depparser=depparser) + >>> readings = glue.parse_to_meaning('every girl chases a dog'.split()) + >>> for reading in sorted([r.simplify().normalize() for r in readings], key=str): + ... print(reading.normalize()) + all z1.(girl(z1) -> exists z2.(dog(z2) & chases(z1,z2))) + exists z1.(dog(z1) & all z2.(girl(z2) -> chases(z2,z1))) + + >>> drtglue = DrtGlue(depparser=depparser) + >>> readings = drtglue.parse_to_meaning('every girl chases a dog'.split()) + >>> for reading in sorted([r.simplify().normalize() for r in readings], key=str): + ... print(reading) + ([],[(([z1],[girl(z1)]) -> ([z2],[dog(z2), chases(z1,z2)]))]) + ([z1],[dog(z1), (([z2],[girl(z2)]) -> ([],[chases(z2,z1)]))]) + +-------------- +With inference +-------------- + +Checking for equality of two DRSs is very useful when generating readings of a sentence. +For example, the ``glue`` module generates two readings for the sentence +*John sees Mary*: + + >>> from nltk.sem.glue import DrtGlue + >>> readings = drtglue.parse_to_meaning('John sees Mary'.split()) + >>> for drs in sorted([r.simplify().normalize() for r in readings], key=str): + ... print(drs) + ([z1,z2],[John(z1), Mary(z2), sees(z1,z2)]) + ([z1,z2],[Mary(z1), John(z2), sees(z2,z1)]) + +However, it is easy to tell that these two readings are logically the +same, and therefore one of them is superfluous. We can use the theorem prover +to determine this equivalence, and then delete one of them. A particular +theorem prover may be specified, or the argument may be left off to use the +default. + + >>> readings[0].equiv(readings[1]) + True diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/grammartestsuites.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/grammartestsuites.doctest new file mode 100644 index 0000000000000000000000000000000000000000..2d008b70f6fedd55c537188f2b69f688df873201 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/grammartestsuites.doctest @@ -0,0 +1,109 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +========================== + Test Suites for Grammars +========================== + +Sentences in the test suite are divided into two classes: + +- grammatical (*accept*) and +- ungrammatical (*reject*). + +If a sentence should parse according to the grammar, the value of +``trees`` will be a non-empty list. If a sentence should be rejected +according to the grammar, then the value of ``trees`` will be ``None``. + + >>> from nltk.parse import TestGrammar + >>> germantest1 = {} + >>> germantest1['doc'] = "Tests for person agreement" + >>> germantest1['accept'] = [ + ... 'ich komme', + ... 'ich sehe mich', + ... 'du kommst', + ... 'du siehst mich', + ... 'sie kommt', + ... 'sie sieht mich', + ... 'ihr kommt', + ... 'wir kommen', + ... 'sie kommen', + ... 'du magst mich', + ... 'er mag mich', + ... 'du folgst mir', + ... 'sie hilft mir', + ... ] + >>> germantest1['reject'] = [ + ... 'ich kommt', + ... 'ich kommst', + ... 'ich siehst mich', + ... 'du komme', + ... 'du sehe mich', + ... 'du kommt', + ... 'er komme', + ... 'er siehst mich', + ... 'wir komme', + ... 'wir kommst', + ... 'die Katzen kommst', + ... 'sie komme', + ... 'sie kommst', + ... 'du mag mich', + ... 'er magst mich', + ... 'du folgt mir', + ... 'sie hilfst mir', + ... ] + >>> germantest2 = {} + >>> germantest2['doc'] = "Tests for number agreement" + >>> germantest2['accept'] = [ + ... 'der Hund kommt', + ... 'die Hunde kommen', + ... 'ich komme', + ... 'wir kommen', + ... 'ich sehe die Katzen', + ... 'ich folge den Katzen', + ... 'ich sehe die Katzen', + ... 'ich folge den Katzen', + ... 'wir sehen die Katzen', + ... 'wir folgen den Katzen' + ... ] + >>> germantest2['reject'] = [ + ... 'ich kommen', + ... 'wir komme', + ... 'der Hunde kommt', + ... 'der Hunde kommen', + ... 'die Katzen kommt', + ... 'ich sehe der Hunde', + ... 'ich folge den Hund', + ... 'ich sehen der Hunde', + ... 'ich folgen den Hund', + ... 'wir sehe die Katzen', + ... 'wir folge den Katzen' + ... ] + >>> germantest3 = {} + >>> germantest3['doc'] = "Tests for case government and subcategorization" + >>> germantest3['accept'] = [ + ... 'der Hund sieht mich', + ... 'der Hund kommt', + ... 'ich sehe den Hund', + ... 'ich helfe dem Hund', + ... ] + >>> germantest3['reject'] = [ + ... 'ich sehe', + ... 'ich helfe', + ... 'ich komme den Hund', + ... 'ich sehe den Hund die Katzen', + ... 'du hilfst mich', + ... 'du siehst mir', + ... 'du siehst ich', + ... 'der Hunde kommt mich', + ... 'die Hunde sehe die Hunde', + ... 'der Hund sehe die Hunde', + ... 'ich hilft den Hund', + ... 'ich hilft der Hund', + ... 'ich sehe dem Hund', + ... ] + >>> germantestsuites = [germantest1, germantest2, germantest3] + >>> tester = TestGrammar('grammars/book_grammars/german.fcfg', germantestsuites) + >>> tester.run() + Tests for person agreement: All tests passed! + Tests for number agreement: All tests passed! + Tests for case government and subcategorization: All tests passed! diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/internals.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/internals.doctest new file mode 100644 index 0000000000000000000000000000000000000000..2d09a0b698ea626c1098d5fb374c478d4b73c0fd --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/internals.doctest @@ -0,0 +1,161 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +========================================== + Unit tests for the nltk.utilities module +========================================== + +overridden() +~~~~~~~~~~~~ + >>> from nltk.internals import overridden + +The typical use case is in defining methods for an interface or +abstract base class, in such a way that subclasses don't have to +implement all of the methods: + + >>> class EaterI(object): + ... '''Subclass must define eat() or batch_eat().''' + ... def eat(self, food): + ... if overridden(self.batch_eat): + ... return self.batch_eat([food])[0] + ... else: + ... raise NotImplementedError() + ... def batch_eat(self, foods): + ... return [self.eat(food) for food in foods] + +As long as a subclass implements one method, it will be used to +perform the other method: + + >>> class GoodEater1(EaterI): + ... def eat(self, food): + ... return 'yum' + >>> GoodEater1().eat('steak') + 'yum' + >>> GoodEater1().batch_eat(['steak', 'peas']) + ['yum', 'yum'] + + >>> class GoodEater2(EaterI): + ... def batch_eat(self, foods): + ... return ['yum' for food in foods] + >>> GoodEater2().eat('steak') + 'yum' + >>> GoodEater2().batch_eat(['steak', 'peas']) + ['yum', 'yum'] + +But if a subclass doesn't implement either one, then they'll get an +error when they try to call them. (nb this is better than infinite +recursion): + + >>> class BadEater1(EaterI): + ... pass + >>> BadEater1().eat('steak') + Traceback (most recent call last): + . . . + NotImplementedError + >>> BadEater1().batch_eat(['steak', 'peas']) + Traceback (most recent call last): + . . . + NotImplementedError + +Trying to use the abstract base class itself will also result in an +error: + + >>> class EaterI(EaterI): + ... pass + >>> EaterI().eat('steak') + Traceback (most recent call last): + . . . + NotImplementedError + >>> EaterI().batch_eat(['steak', 'peas']) + Traceback (most recent call last): + . . . + NotImplementedError + +It's ok to use intermediate abstract classes: + + >>> class AbstractEater(EaterI): + ... pass + + >>> class GoodEater3(AbstractEater): + ... def eat(self, food): + ... return 'yum' + ... + >>> GoodEater3().eat('steak') + 'yum' + >>> GoodEater3().batch_eat(['steak', 'peas']) + ['yum', 'yum'] + + >>> class GoodEater4(AbstractEater): + ... def batch_eat(self, foods): + ... return ['yum' for food in foods] + >>> GoodEater4().eat('steak') + 'yum' + >>> GoodEater4().batch_eat(['steak', 'peas']) + ['yum', 'yum'] + + >>> class BadEater2(AbstractEater): + ... pass + >>> BadEater2().eat('steak') + Traceback (most recent call last): + . . . + NotImplementedError + >>> BadEater2().batch_eat(['steak', 'peas']) + Traceback (most recent call last): + . . . + NotImplementedError + +Here's some extra tests: + + >>> class A(object): + ... def f(x): pass + >>> class B(A): + ... def f(x): pass + >>> class C(A): pass + >>> class D(B): pass + + >>> overridden(A().f) + False + >>> overridden(B().f) + True + >>> overridden(C().f) + False + >>> overridden(D().f) + True + +It works for classic classes, too: + + >>> class A: + ... def f(x): pass + >>> class B(A): + ... def f(x): pass + >>> class C(A): pass + >>> class D(B): pass + >>> overridden(A().f) + False + >>> overridden(B().f) + True + >>> overridden(C().f) + False + >>> overridden(D().f) + True + + +read_str() +~~~~~~~~~~~~ + >>> from nltk.internals import read_str + +Test valid scenarios + + >>> read_str("'valid string'", 0) + ('valid string', 14) + +Now test invalid scenarios + + >>> read_str("should error", 0) + Traceback (most recent call last): + ... + nltk.internals.ReadError: Expected open quote at 0 + >>> read_str("'should error", 0) + Traceback (most recent call last): + ... + nltk.internals.ReadError: Expected close quote at 1 diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/lm.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/lm.doctest new file mode 100644 index 0000000000000000000000000000000000000000..9668582b3f90f8bcc5ee48f72b0a41d2da9660e5 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/lm.doctest @@ -0,0 +1,135 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +.. -*- coding: utf-8 -*- + + +Regression Tests +================ + + +Issue 167 +--------- +https://github.com/nltk/nltk/issues/167 + + >>> from nltk.corpus import brown + >>> from nltk.lm.preprocessing import padded_everygram_pipeline + >>> ngram_order = 3 + >>> train_data, vocab_data = padded_everygram_pipeline( + ... ngram_order, + ... brown.sents(categories="news") + ... ) + + >>> from nltk.lm import WittenBellInterpolated + >>> lm = WittenBellInterpolated(ngram_order) + >>> lm.fit(train_data, vocab_data) + + + + +Sentence containing an unseen word should result in infinite entropy because +Witten-Bell is based ultimately on MLE, which cannot handle unseen ngrams. +Crucially, it shouldn't raise any exceptions for unseen words. + + >>> from nltk.util import ngrams + >>> sent = ngrams("This is a sentence with the word aaddvark".split(), 3) + >>> lm.entropy(sent) + inf + +If we remove all unseen ngrams from the sentence, we'll get a non-infinite value +for the entropy. + + >>> sent = ngrams("This is a sentence".split(), 3) + >>> round(lm.entropy(sent), 14) + 10.23701322869105 + + +Issue 367 +--------- +https://github.com/nltk/nltk/issues/367 + +Reproducing Dan Blanchard's example: +https://github.com/nltk/nltk/issues/367#issuecomment-14646110 + + >>> from nltk.lm import Lidstone, Vocabulary + >>> word_seq = list('aaaababaaccbacb') + >>> ngram_order = 2 + >>> from nltk.util import everygrams + >>> train_data = [everygrams(word_seq, max_len=ngram_order)] + >>> V = Vocabulary(['a', 'b', 'c', '']) + >>> lm = Lidstone(0.2, ngram_order, vocabulary=V) + >>> lm.fit(train_data) + +For doctest to work we have to sort the vocabulary keys. + + >>> V_keys = sorted(V) + >>> round(sum(lm.score(w, ("b",)) for w in V_keys), 6) + 1.0 + >>> round(sum(lm.score(w, ("a",)) for w in V_keys), 6) + 1.0 + + >>> [lm.score(w, ("b",)) for w in V_keys] + [0.05, 0.05, 0.8, 0.05, 0.05] + >>> [round(lm.score(w, ("a",)), 4) for w in V_keys] + [0.0222, 0.0222, 0.4667, 0.2444, 0.2444] + + +Here's reproducing @afourney's comment: +https://github.com/nltk/nltk/issues/367#issuecomment-15686289 + + >>> sent = ['foo', 'foo', 'foo', 'foo', 'bar', 'baz'] + >>> ngram_order = 3 + >>> from nltk.lm.preprocessing import padded_everygram_pipeline + >>> train_data, vocab_data = padded_everygram_pipeline(ngram_order, [sent]) + >>> from nltk.lm import Lidstone + >>> lm = Lidstone(0.2, ngram_order) + >>> lm.fit(train_data, vocab_data) + +The vocabulary includes the "UNK" symbol as well as two padding symbols. + + >>> len(lm.vocab) + 6 + >>> word = "foo" + >>> context = ("bar", "baz") + +The raw counts. + + >>> lm.context_counts(context)[word] + 0 + >>> lm.context_counts(context).N() + 1 + +Counts with Lidstone smoothing. + + >>> lm.context_counts(context)[word] + lm.gamma + 0.2 + >>> lm.context_counts(context).N() + len(lm.vocab) * lm.gamma + 2.2 + +Without any backoff, just using Lidstone smoothing, P("foo" | "bar", "baz") should be: +0.2 / 2.2 ~= 0.090909 + + >>> round(lm.score(word, context), 6) + 0.090909 + + +Issue 380 +--------- +https://github.com/nltk/nltk/issues/380 + +Reproducing setup akin to this comment: +https://github.com/nltk/nltk/issues/380#issue-12879030 + +For speed take only the first 100 sentences of reuters. Shouldn't affect the test. + + >>> from nltk.corpus import reuters + >>> sents = reuters.sents()[:100] + >>> ngram_order = 3 + >>> from nltk.lm.preprocessing import padded_everygram_pipeline + >>> train_data, vocab_data = padded_everygram_pipeline(ngram_order, sents) + + >>> from nltk.lm import Lidstone + >>> lm = Lidstone(0.2, ngram_order) + >>> lm.fit(train_data, vocab_data) + >>> lm.score("said", ("",)) < 1 + True diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/logic.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/logic.doctest new file mode 100644 index 0000000000000000000000000000000000000000..1c08675f1ca21ae8826851f7299cb482bf812910 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/logic.doctest @@ -0,0 +1,1096 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +======================= +Logic & Lambda Calculus +======================= + +The `nltk.logic` package allows expressions of First-Order Logic (FOL) to be +parsed into ``Expression`` objects. In addition to FOL, the parser +handles lambda-abstraction with variables of higher order. + +-------- +Overview +-------- + + >>> from nltk.sem.logic import * + +The default inventory of logical constants is the following: + + >>> boolean_ops() + negation - + conjunction & + disjunction | + implication -> + equivalence <-> + >>> equality_preds() + equality = + inequality != + >>> binding_ops() + existential exists + universal all + lambda \ + +---------------- +Regression Tests +---------------- + + +Untyped Logic ++++++++++++++ + +Process logical expressions conveniently: + + >>> read_expr = Expression.fromstring + +Test for equality under alpha-conversion +======================================== + + >>> e1 = read_expr('exists x.P(x)') + >>> print(e1) + exists x.P(x) + >>> e2 = e1.alpha_convert(Variable('z')) + >>> print(e2) + exists z.P(z) + >>> e1 == e2 + True + + + >>> l = read_expr(r'\X.\X.X(X)(1)').simplify() + >>> id = read_expr(r'\X.X(X)') + >>> l == id + True + +Test numerals +============= + + >>> zero = read_expr(r'\F x.x') + >>> one = read_expr(r'\F x.F(x)') + >>> two = read_expr(r'\F x.F(F(x))') + >>> three = read_expr(r'\F x.F(F(F(x)))') + >>> four = read_expr(r'\F x.F(F(F(F(x))))') + >>> succ = read_expr(r'\N F x.F(N(F,x))') + >>> plus = read_expr(r'\M N F x.M(F,N(F,x))') + >>> mult = read_expr(r'\M N F.M(N(F))') + >>> pred = read_expr(r'\N F x.(N(\G H.H(G(F)))(\u.x)(\u.u))') + >>> v1 = ApplicationExpression(succ, zero).simplify() + >>> v1 == one + True + >>> v2 = ApplicationExpression(succ, v1).simplify() + >>> v2 == two + True + >>> v3 = ApplicationExpression(ApplicationExpression(plus, v1), v2).simplify() + >>> v3 == three + True + >>> v4 = ApplicationExpression(ApplicationExpression(mult, v2), v2).simplify() + >>> v4 == four + True + >>> v5 = ApplicationExpression(pred, ApplicationExpression(pred, v4)).simplify() + >>> v5 == two + True + +Overloaded operators also exist, for convenience. + + >>> print(succ(zero).simplify() == one) + True + >>> print(plus(one,two).simplify() == three) + True + >>> print(mult(two,two).simplify() == four) + True + >>> print(pred(pred(four)).simplify() == two) + True + + >>> john = read_expr(r'john') + >>> man = read_expr(r'\x.man(x)') + >>> walk = read_expr(r'\x.walk(x)') + >>> man(john).simplify() + + >>> print(-walk(john).simplify()) + -walk(john) + >>> print((man(john) & walk(john)).simplify()) + (man(john) & walk(john)) + >>> print((man(john) | walk(john)).simplify()) + (man(john) | walk(john)) + >>> print((man(john) > walk(john)).simplify()) + (man(john) -> walk(john)) + >>> print((man(john) < walk(john)).simplify()) + (man(john) <-> walk(john)) + +Python's built-in lambda operator can also be used with Expressions + + >>> john = VariableExpression(Variable('john')) + >>> run_var = VariableExpression(Variable('run')) + >>> run = lambda x: run_var(x) + >>> run(john) + + + +``betaConversionTestSuite.pl`` +------------------------------ + +Tests based on Blackburn & Bos' book, *Representation and Inference +for Natural Language*. + + >>> x1 = read_expr(r'\P.P(mia)(\x.walk(x))').simplify() + >>> x2 = read_expr(r'walk(mia)').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'exists x.(man(x) & ((\P.exists x.(woman(x) & P(x)))(\y.love(x,y))))').simplify() + >>> x2 = read_expr(r'exists x.(man(x) & exists y.(woman(y) & love(x,y)))').simplify() + >>> x1 == x2 + True + >>> x1 = read_expr(r'\a.sleep(a)(mia)').simplify() + >>> x2 = read_expr(r'sleep(mia)').simplify() + >>> x1 == x2 + True + >>> x1 = read_expr(r'\a.\b.like(b,a)(mia)').simplify() + >>> x2 = read_expr(r'\b.like(b,mia)').simplify() + >>> x1 == x2 + True + >>> x1 = read_expr(r'\a.(\b.like(b,a)(vincent))').simplify() + >>> x2 = read_expr(r'\a.like(vincent,a)').simplify() + >>> x1 == x2 + True + >>> x1 = read_expr(r'\a.((\b.like(b,a)(vincent)) & sleep(a))').simplify() + >>> x2 = read_expr(r'\a.(like(vincent,a) & sleep(a))').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'(\a.\b.like(b,a)(mia)(vincent))').simplify() + >>> x2 = read_expr(r'like(vincent,mia)').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'P((\a.sleep(a)(vincent)))').simplify() + >>> x2 = read_expr(r'P(sleep(vincent))').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'\A.A((\b.sleep(b)(vincent)))').simplify() + >>> x2 = read_expr(r'\A.A(sleep(vincent))').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'\A.A(sleep(vincent))').simplify() + >>> x2 = read_expr(r'\A.A(sleep(vincent))').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'(\A.A(vincent)(\b.sleep(b)))').simplify() + >>> x2 = read_expr(r'sleep(vincent)').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'\A.believe(mia,A(vincent))(\b.sleep(b))').simplify() + >>> x2 = read_expr(r'believe(mia,sleep(vincent))').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'(\A.(A(vincent) & A(mia)))(\b.sleep(b))').simplify() + >>> x2 = read_expr(r'(sleep(vincent) & sleep(mia))').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'\A.\B.(\C.C(A(vincent))(\d.probably(d)) & (\C.C(B(mia))(\d.improbably(d))))(\f.walk(f))(\f.talk(f))').simplify() + >>> x2 = read_expr(r'(probably(walk(vincent)) & improbably(talk(mia)))').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'(\a.\b.(\C.C(a,b)(\d.\f.love(d,f))))(jules)(mia)').simplify() + >>> x2 = read_expr(r'love(jules,mia)').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'(\A.\B.exists c.(A(c) & B(c)))(\d.boxer(d),\d.sleep(d))').simplify() + >>> x2 = read_expr(r'exists c.(boxer(c) & sleep(c))').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'\A.Z(A)(\c.\a.like(a,c))').simplify() + >>> x2 = read_expr(r'Z(\c.\a.like(a,c))').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'\A.\b.A(b)(\c.\b.like(b,c))').simplify() + >>> x2 = read_expr(r'\b.(\c.\b.like(b,c)(b))').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'(\a.\b.(\C.C(a,b)(\b.\a.loves(b,a))))(jules)(mia)').simplify() + >>> x2 = read_expr(r'loves(jules,mia)').simplify() + >>> x1 == x2 + True + + >>> x1 = read_expr(r'(\A.\b.(exists b.A(b) & A(b)))(\c.boxer(c))(vincent)').simplify() + >>> x2 = read_expr(r'((exists b.boxer(b)) & boxer(vincent))').simplify() + >>> x1 == x2 + True + +Test Parser +=========== + + >>> print(read_expr(r'john')) + john + >>> print(read_expr(r'x')) + x + >>> print(read_expr(r'-man(x)')) + -man(x) + >>> print(read_expr(r'--man(x)')) + --man(x) + >>> print(read_expr(r'(man(x))')) + man(x) + >>> print(read_expr(r'((man(x)))')) + man(x) + >>> print(read_expr(r'man(x) <-> tall(x)')) + (man(x) <-> tall(x)) + >>> print(read_expr(r'(man(x) <-> tall(x))')) + (man(x) <-> tall(x)) + >>> print(read_expr(r'(man(x) & tall(x) & walks(x))')) + (man(x) & tall(x) & walks(x)) + >>> print(read_expr(r'(man(x) & tall(x) & walks(x))').first) + (man(x) & tall(x)) + >>> print(read_expr(r'man(x) | tall(x) & walks(x)')) + (man(x) | (tall(x) & walks(x))) + >>> print(read_expr(r'((man(x) & tall(x)) | walks(x))')) + ((man(x) & tall(x)) | walks(x)) + >>> print(read_expr(r'man(x) & (tall(x) | walks(x))')) + (man(x) & (tall(x) | walks(x))) + >>> print(read_expr(r'(man(x) & (tall(x) | walks(x)))')) + (man(x) & (tall(x) | walks(x))) + >>> print(read_expr(r'P(x) -> Q(x) <-> R(x) | S(x) & T(x)')) + ((P(x) -> Q(x)) <-> (R(x) | (S(x) & T(x)))) + >>> print(read_expr(r'exists x.man(x)')) + exists x.man(x) + >>> print(read_expr(r'exists x.(man(x) & tall(x))')) + exists x.(man(x) & tall(x)) + >>> print(read_expr(r'exists x.(man(x) & tall(x) & walks(x))')) + exists x.(man(x) & tall(x) & walks(x)) + >>> print(read_expr(r'-P(x) & Q(x)')) + (-P(x) & Q(x)) + >>> read_expr(r'-P(x) & Q(x)') == read_expr(r'(-P(x)) & Q(x)') + True + >>> print(read_expr(r'\x.man(x)')) + \x.man(x) + >>> print(read_expr(r'\x.man(x)(john)')) + \x.man(x)(john) + >>> print(read_expr(r'\x.man(x)(john) & tall(x)')) + (\x.man(x)(john) & tall(x)) + >>> print(read_expr(r'\x.\y.sees(x,y)')) + \x y.sees(x,y) + >>> print(read_expr(r'\x y.sees(x,y)')) + \x y.sees(x,y) + >>> print(read_expr(r'\x.\y.sees(x,y)(a)')) + (\x y.sees(x,y))(a) + >>> print(read_expr(r'\x y.sees(x,y)(a)')) + (\x y.sees(x,y))(a) + >>> print(read_expr(r'\x.\y.sees(x,y)(a)(b)')) + ((\x y.sees(x,y))(a))(b) + >>> print(read_expr(r'\x y.sees(x,y)(a)(b)')) + ((\x y.sees(x,y))(a))(b) + >>> print(read_expr(r'\x.\y.sees(x,y)(a,b)')) + ((\x y.sees(x,y))(a))(b) + >>> print(read_expr(r'\x y.sees(x,y)(a,b)')) + ((\x y.sees(x,y))(a))(b) + >>> print(read_expr(r'((\x.\y.sees(x,y))(a))(b)')) + ((\x y.sees(x,y))(a))(b) + >>> print(read_expr(r'P(x)(y)(z)')) + P(x,y,z) + >>> print(read_expr(r'P(Q)')) + P(Q) + >>> print(read_expr(r'P(Q(x))')) + P(Q(x)) + >>> print(read_expr(r'(\x.exists y.walks(x,y))(x)')) + (\x.exists y.walks(x,y))(x) + >>> print(read_expr(r'exists x.(x = john)')) + exists x.(x = john) + >>> print(read_expr(r'((\P.\Q.exists x.(P(x) & Q(x)))(\x.dog(x)))(\x.bark(x))')) + ((\P Q.exists x.(P(x) & Q(x)))(\x.dog(x)))(\x.bark(x)) + >>> a = read_expr(r'exists c.exists b.A(b,c) & A(b,c)') + >>> b = read_expr(r'(exists c.(exists b.A(b,c))) & A(b,c)') + >>> print(a == b) + True + >>> a = read_expr(r'exists c.(exists b.A(b,c) & A(b,c))') + >>> b = read_expr(r'exists c.((exists b.A(b,c)) & A(b,c))') + >>> print(a == b) + True + >>> print(read_expr(r'exists x.x = y')) + exists x.(x = y) + >>> print(read_expr('A(B)(C)')) + A(B,C) + >>> print(read_expr('(A(B))(C)')) + A(B,C) + >>> print(read_expr('A((B)(C))')) + A(B(C)) + >>> print(read_expr('A(B(C))')) + A(B(C)) + >>> print(read_expr('(A)(B(C))')) + A(B(C)) + >>> print(read_expr('(((A)))(((B))(((C))))')) + A(B(C)) + >>> print(read_expr(r'A != B')) + -(A = B) + >>> print(read_expr('P(x) & x=y & P(y)')) + (P(x) & (x = y) & P(y)) + >>> try: print(read_expr(r'\walk.walk(x)')) + ... except LogicalExpressionException as e: print(e) + 'walk' is an illegal variable name. Constants may not be abstracted. + \walk.walk(x) + ^ + >>> try: print(read_expr(r'all walk.walk(john)')) + ... except LogicalExpressionException as e: print(e) + 'walk' is an illegal variable name. Constants may not be quantified. + all walk.walk(john) + ^ + >>> try: print(read_expr(r'x(john)')) + ... except LogicalExpressionException as e: print(e) + 'x' is an illegal predicate name. Individual variables may not be used as predicates. + x(john) + ^ + + >>> from nltk.sem.logic import LogicParser # hack to give access to custom quote chars + >>> lpq = LogicParser() + >>> lpq.quote_chars = [("'", "'", "\\", False)] + >>> print(lpq.parse(r"(man(x) & 'tall\'s,' (x) & walks (x) )")) + (man(x) & tall's,(x) & walks(x)) + >>> lpq.quote_chars = [("'", "'", "\\", True)] + >>> print(lpq.parse(r"'tall\'s,'")) + 'tall\'s,' + >>> print(lpq.parse(r"'spaced name(x)'")) + 'spaced name(x)' + >>> print(lpq.parse(r"-'tall\'s,'(x)")) + -'tall\'s,'(x) + >>> print(lpq.parse(r"(man(x) & 'tall\'s,' (x) & walks (x) )")) + (man(x) & 'tall\'s,'(x) & walks(x)) + + +Simplify +======== + + >>> print(read_expr(r'\x.man(x)(john)').simplify()) + man(john) + >>> print(read_expr(r'\x.((man(x)))(john)').simplify()) + man(john) + >>> print(read_expr(r'\x.\y.sees(x,y)(john, mary)').simplify()) + sees(john,mary) + >>> print(read_expr(r'\x y.sees(x,y)(john, mary)').simplify()) + sees(john,mary) + >>> print(read_expr(r'\x.\y.sees(x,y)(john)(mary)').simplify()) + sees(john,mary) + >>> print(read_expr(r'\x y.sees(x,y)(john)(mary)').simplify()) + sees(john,mary) + >>> print(read_expr(r'\x.\y.sees(x,y)(john)').simplify()) + \y.sees(john,y) + >>> print(read_expr(r'\x y.sees(x,y)(john)').simplify()) + \y.sees(john,y) + >>> print(read_expr(r'(\x.\y.sees(x,y)(john))(mary)').simplify()) + sees(john,mary) + >>> print(read_expr(r'(\x y.sees(x,y)(john))(mary)').simplify()) + sees(john,mary) + >>> print(read_expr(r'exists x.(man(x) & (\x.exists y.walks(x,y))(x))').simplify()) + exists x.(man(x) & exists y.walks(x,y)) + >>> e1 = read_expr(r'exists x.(man(x) & (\x.exists y.walks(x,y))(y))').simplify() + >>> e2 = read_expr(r'exists x.(man(x) & exists z1.walks(y,z1))') + >>> e1 == e2 + True + >>> print(read_expr(r'(\P Q.exists x.(P(x) & Q(x)))(\x.dog(x))').simplify()) + \Q.exists x.(dog(x) & Q(x)) + >>> print(read_expr(r'((\P.\Q.exists x.(P(x) & Q(x)))(\x.dog(x)))(\x.bark(x))').simplify()) + exists x.(dog(x) & bark(x)) + >>> print(read_expr(r'\P.(P(x)(y))(\a b.Q(a,b))').simplify()) + Q(x,y) + +Replace +======= + + >>> a = read_expr(r'a') + >>> x = read_expr(r'x') + >>> y = read_expr(r'y') + >>> z = read_expr(r'z') + + >>> print(read_expr(r'man(x)').replace(x.variable, a, False)) + man(a) + >>> print(read_expr(r'(man(x) & tall(x))').replace(x.variable, a, False)) + (man(a) & tall(a)) + >>> print(read_expr(r'exists x.man(x)').replace(x.variable, a, False)) + exists x.man(x) + >>> print(read_expr(r'exists x.man(x)').replace(x.variable, a, True)) + exists a.man(a) + >>> print(read_expr(r'exists x.give(x,y,z)').replace(y.variable, a, False)) + exists x.give(x,a,z) + >>> print(read_expr(r'exists x.give(x,y,z)').replace(y.variable, a, True)) + exists x.give(x,a,z) + >>> e1 = read_expr(r'exists x.give(x,y,z)').replace(y.variable, x, False) + >>> e2 = read_expr(r'exists z1.give(z1,x,z)') + >>> e1 == e2 + True + >>> e1 = read_expr(r'exists x.give(x,y,z)').replace(y.variable, x, True) + >>> e2 = read_expr(r'exists z1.give(z1,x,z)') + >>> e1 == e2 + True + >>> print(read_expr(r'\x y z.give(x,y,z)').replace(y.variable, a, False)) + \x y z.give(x,y,z) + >>> print(read_expr(r'\x y z.give(x,y,z)').replace(y.variable, a, True)) + \x a z.give(x,a,z) + >>> print(read_expr(r'\x.\y.give(x,y,z)').replace(z.variable, a, False)) + \x y.give(x,y,a) + >>> print(read_expr(r'\x.\y.give(x,y,z)').replace(z.variable, a, True)) + \x y.give(x,y,a) + >>> e1 = read_expr(r'\x.\y.give(x,y,z)').replace(z.variable, x, False) + >>> e2 = read_expr(r'\z1.\y.give(z1,y,x)') + >>> e1 == e2 + True + >>> e1 = read_expr(r'\x.\y.give(x,y,z)').replace(z.variable, x, True) + >>> e2 = read_expr(r'\z1.\y.give(z1,y,x)') + >>> e1 == e2 + True + >>> print(read_expr(r'\x.give(x,y,z)').replace(z.variable, y, False)) + \x.give(x,y,y) + >>> print(read_expr(r'\x.give(x,y,z)').replace(z.variable, y, True)) + \x.give(x,y,y) + + >>> from nltk.sem import logic + >>> logic._counter._value = 0 + >>> e1 = read_expr('e1') + >>> e2 = read_expr('e2') + >>> print(read_expr('exists e1 e2.(walk(e1) & talk(e2))').replace(e1.variable, e2, True)) + exists e2 e01.(walk(e2) & talk(e01)) + + +Variables / Free +================ + + >>> examples = [r'walk(john)', + ... r'walk(x)', + ... r'?vp(?np)', + ... r'see(john,mary)', + ... r'exists x.walk(x)', + ... r'\x.see(john,x)', + ... r'\x.see(john,x)(mary)', + ... r'P(x)', + ... r'\P.P(x)', + ... r'aa(x,bb(y),cc(z),P(w),u)', + ... r'bo(?det(?n),@x)'] + >>> examples = [read_expr(e) for e in examples] + + >>> for e in examples: + ... print('%-25s' % e, sorted(e.free())) + walk(john) [] + walk(x) [Variable('x')] + ?vp(?np) [] + see(john,mary) [] + exists x.walk(x) [] + \x.see(john,x) [] + (\x.see(john,x))(mary) [] + P(x) [Variable('P'), Variable('x')] + \P.P(x) [Variable('x')] + aa(x,bb(y),cc(z),P(w),u) [Variable('P'), Variable('u'), Variable('w'), Variable('x'), Variable('y'), Variable('z')] + bo(?det(?n),@x) [] + + >>> for e in examples: + ... print('%-25s' % e, sorted(e.constants())) + walk(john) [Variable('john')] + walk(x) [] + ?vp(?np) [Variable('?np')] + see(john,mary) [Variable('john'), Variable('mary')] + exists x.walk(x) [] + \x.see(john,x) [Variable('john')] + (\x.see(john,x))(mary) [Variable('john'), Variable('mary')] + P(x) [] + \P.P(x) [] + aa(x,bb(y),cc(z),P(w),u) [] + bo(?det(?n),@x) [Variable('?n'), Variable('@x')] + + >>> for e in examples: + ... print('%-25s' % e, sorted(e.predicates())) + walk(john) [Variable('walk')] + walk(x) [Variable('walk')] + ?vp(?np) [Variable('?vp')] + see(john,mary) [Variable('see')] + exists x.walk(x) [Variable('walk')] + \x.see(john,x) [Variable('see')] + (\x.see(john,x))(mary) [Variable('see')] + P(x) [] + \P.P(x) [] + aa(x,bb(y),cc(z),P(w),u) [Variable('aa'), Variable('bb'), Variable('cc')] + bo(?det(?n),@x) [Variable('?det'), Variable('bo')] + + >>> for e in examples: + ... print('%-25s' % e, sorted(e.variables())) + walk(john) [] + walk(x) [Variable('x')] + ?vp(?np) [Variable('?np'), Variable('?vp')] + see(john,mary) [] + exists x.walk(x) [] + \x.see(john,x) [] + (\x.see(john,x))(mary) [] + P(x) [Variable('P'), Variable('x')] + \P.P(x) [Variable('x')] + aa(x,bb(y),cc(z),P(w),u) [Variable('P'), Variable('u'), Variable('w'), Variable('x'), Variable('y'), Variable('z')] + bo(?det(?n),@x) [Variable('?det'), Variable('?n'), Variable('@x')] + + + +`normalize` + >>> print(read_expr(r'\e083.(walk(e083, z472) & talk(e092, z938))').normalize()) + \e01.(walk(e01,z3) & talk(e02,z4)) + +Typed Logic ++++++++++++ + + >>> from nltk.sem.logic import LogicParser + >>> tlp = LogicParser(True) + >>> print(tlp.parse(r'man(x)').type) + ? + >>> print(tlp.parse(r'walk(angus)').type) + ? + >>> print(tlp.parse(r'-man(x)').type) + t + >>> print(tlp.parse(r'(man(x) <-> tall(x))').type) + t + >>> print(tlp.parse(r'exists x.(man(x) & tall(x))').type) + t + >>> print(tlp.parse(r'\x.man(x)').type) + + >>> print(tlp.parse(r'john').type) + e + >>> print(tlp.parse(r'\x y.sees(x,y)').type) + > + >>> print(tlp.parse(r'\x.man(x)(john)').type) + ? + >>> print(tlp.parse(r'\x.\y.sees(x,y)(john)').type) + + >>> print(tlp.parse(r'\x.\y.sees(x,y)(john)(mary)').type) + ? + >>> print(tlp.parse(r'\P.\Q.exists x.(P(x) & Q(x))').type) + <,<,t>> + >>> print(tlp.parse(r'\x.y').type) + + >>> print(tlp.parse(r'\P.P(x)').type) + <,?> + + >>> parsed = tlp.parse('see(john,mary)') + >>> print(parsed.type) + ? + >>> print(parsed.function) + see(john) + >>> print(parsed.function.type) + + >>> print(parsed.function.function) + see + >>> print(parsed.function.function.type) + > + + >>> parsed = tlp.parse('P(x,y)') + >>> print(parsed) + P(x,y) + >>> print(parsed.type) + ? + >>> print(parsed.function) + P(x) + >>> print(parsed.function.type) + + >>> print(parsed.function.function) + P + >>> print(parsed.function.function.type) + > + + >>> print(tlp.parse(r'P').type) + ? + + >>> print(tlp.parse(r'P', {'P': 't'}).type) + t + + >>> a = tlp.parse(r'P(x)') + >>> print(a.type) + ? + >>> print(a.function.type) + + >>> print(a.argument.type) + e + + >>> a = tlp.parse(r'-P(x)') + >>> print(a.type) + t + >>> print(a.term.type) + t + >>> print(a.term.function.type) + + >>> print(a.term.argument.type) + e + + >>> a = tlp.parse(r'P & Q') + >>> print(a.type) + t + >>> print(a.first.type) + t + >>> print(a.second.type) + t + + >>> a = tlp.parse(r'(P(x) & Q(x))') + >>> print(a.type) + t + >>> print(a.first.type) + t + >>> print(a.first.function.type) + + >>> print(a.first.argument.type) + e + >>> print(a.second.type) + t + >>> print(a.second.function.type) + + >>> print(a.second.argument.type) + e + + >>> a = tlp.parse(r'\x.P(x)') + >>> print(a.type) + + >>> print(a.term.function.type) + + >>> print(a.term.argument.type) + e + + >>> a = tlp.parse(r'\P.P(x)') + >>> print(a.type) + <,?> + >>> print(a.term.function.type) + + >>> print(a.term.argument.type) + e + + >>> a = tlp.parse(r'(\x.P(x)(john)) & Q(x)') + >>> print(a.type) + t + >>> print(a.first.type) + t + >>> print(a.first.function.type) + + >>> print(a.first.function.term.function.type) + + >>> print(a.first.function.term.argument.type) + e + >>> print(a.first.argument.type) + e + + >>> a = tlp.parse(r'\x y.P(x,y)(john)(mary) & Q(x)') + >>> print(a.type) + t + >>> print(a.first.type) + t + >>> print(a.first.function.type) + + >>> print(a.first.function.function.type) + > + + >>> a = tlp.parse(r'--P') + >>> print(a.type) + t + >>> print(a.term.type) + t + >>> print(a.term.term.type) + t + + >>> tlp.parse(r'\x y.P(x,y)').type + > + >>> tlp.parse(r'\x y.P(x,y)', {'P': '>'}).type + > + + >>> a = tlp.parse(r'\P y.P(john,y)(\x y.see(x,y))') + >>> a.type + + >>> a.function.type + <>,> + >>> a.function.term.term.function.function.type + > + >>> a.argument.type + > + + >>> a = tlp.parse(r'exists c f.(father(c) = f)') + >>> a.type + t + >>> a.term.term.type + t + >>> a.term.term.first.type + e + >>> a.term.term.first.function.type + + >>> a.term.term.second.type + e + +typecheck() + + >>> a = tlp.parse('P(x)') + >>> b = tlp.parse('Q(x)') + >>> a.type + ? + >>> c = a & b + >>> c.first.type + ? + >>> c.typecheck() + {...} + >>> c.first.type + t + + >>> a = tlp.parse('P(x)') + >>> b = tlp.parse('P(x) & Q(x)') + >>> a.type + ? + >>> typecheck([a,b]) + {...} + >>> a.type + t + + >>> e = tlp.parse(r'man(x)') + >>> print(dict((k,str(v)) for k,v in e.typecheck().items()) == {'x': 'e', 'man': ''}) + True + >>> sig = {'man': ''} + >>> e = tlp.parse(r'man(x)', sig) + >>> print(e.function.type) + + >>> print(dict((k,str(v)) for k,v in e.typecheck().items()) == {'x': 'e', 'man': ''}) + True + >>> print(e.function.type) + + >>> print(dict((k,str(v)) for k,v in e.typecheck(sig).items()) == {'x': 'e', 'man': ''}) + True + +findtype() + + >>> print(tlp.parse(r'man(x)').findtype(Variable('man'))) + + >>> print(tlp.parse(r'see(x,y)').findtype(Variable('see'))) + > + >>> print(tlp.parse(r'P(Q(R(x)))').findtype(Variable('Q'))) + ? + +reading types from strings + + >>> Type.fromstring('e') + e + >>> Type.fromstring('') + + >>> Type.fromstring('<,>') + <,> + >>> Type.fromstring('<,?>') + <,?> + +alternative type format + + >>> Type.fromstring('e').str() + 'IND' + >>> Type.fromstring('').str() + '(IND -> ANY)' + >>> Type.fromstring('<,t>').str() + '((IND -> BOOL) -> BOOL)' + +Type.__eq__() + + >>> from nltk.sem.logic import * + + >>> e = ENTITY_TYPE + >>> t = TRUTH_TYPE + >>> a = ANY_TYPE + >>> et = ComplexType(e,t) + >>> eet = ComplexType(e,ComplexType(e,t)) + >>> at = ComplexType(a,t) + >>> ea = ComplexType(e,a) + >>> aa = ComplexType(a,a) + + >>> e == e + True + >>> t == t + True + >>> e == t + False + >>> a == t + False + >>> t == a + False + >>> a == a + True + >>> et == et + True + >>> a == et + False + >>> et == a + False + >>> a == ComplexType(a,aa) + True + >>> ComplexType(a,aa) == a + True + +matches() + + >>> e.matches(t) + False + >>> a.matches(t) + True + >>> t.matches(a) + True + >>> a.matches(et) + True + >>> et.matches(a) + True + >>> ea.matches(eet) + True + >>> eet.matches(ea) + True + >>> aa.matches(et) + True + >>> aa.matches(t) + True + +Type error during parsing +========================= + + >>> try: print(tlp.parse(r'exists x y.(P(x) & P(x,y))')) + ... except InconsistentTypeHierarchyException as e: print(e) + The variable 'P' was found in multiple places with different types. + >>> try: tlp.parse(r'\x y.see(x,y)(\x.man(x))') + ... except TypeException as e: print(e) + The function '\x y.see(x,y)' is of type '>' and cannot be applied to '\x.man(x)' of type ''. Its argument must match type 'e'. + >>> try: tlp.parse(r'\P x y.-P(x,y)(\x.-man(x))') + ... except TypeException as e: print(e) + The function '\P x y.-P(x,y)' is of type '<>,>>' and cannot be applied to '\x.-man(x)' of type ''. Its argument must match type '>'. + + >>> a = tlp.parse(r'-talk(x)') + >>> signature = a.typecheck() + >>> try: print(tlp.parse(r'-talk(x,y)', signature)) + ... except InconsistentTypeHierarchyException as e: print(e) + The variable 'talk' was found in multiple places with different types. + + >>> a = tlp.parse(r'-P(x)') + >>> b = tlp.parse(r'-P(x,y)') + >>> a.typecheck() + {...} + >>> b.typecheck() + {...} + >>> try: typecheck([a,b]) + ... except InconsistentTypeHierarchyException as e: print(e) + The variable 'P' was found in multiple places with different types. + + >>> a = tlp.parse(r'P(x)') + >>> b = tlp.parse(r'P(x,y)') + >>> signature = {'P': ''} + >>> a.typecheck(signature) + {...} + >>> try: typecheck([a,b], signature) + ... except InconsistentTypeHierarchyException as e: print(e) + The variable 'P' was found in multiple places with different types. + +Parse errors +============ + + >>> try: read_expr(r'') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + + ^ + >>> try: read_expr(r'(') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + ( + ^ + >>> try: read_expr(r')') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + ) + ^ + >>> try: read_expr(r'()') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + () + ^ + >>> try: read_expr(r'(P(x) & Q(x)') + ... except LogicalExpressionException as e: print(e) + End of input found. Expected token ')'. + (P(x) & Q(x) + ^ + >>> try: read_expr(r'(P(x) &') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + (P(x) & + ^ + >>> try: read_expr(r'(P(x) | )') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + (P(x) | ) + ^ + >>> try: read_expr(r'P(x) ->') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + P(x) -> + ^ + >>> try: read_expr(r'P(x') + ... except LogicalExpressionException as e: print(e) + End of input found. Expected token ')'. + P(x + ^ + >>> try: read_expr(r'P(x,') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + P(x, + ^ + >>> try: read_expr(r'P(x,)') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + P(x,) + ^ + >>> try: read_expr(r'exists') + ... except LogicalExpressionException as e: print(e) + End of input found. Variable and Expression expected following quantifier 'exists'. + exists + ^ + >>> try: read_expr(r'exists x') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + exists x + ^ + >>> try: read_expr(r'exists x.') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + exists x. + ^ + >>> try: read_expr(r'\ ') + ... except LogicalExpressionException as e: print(e) + End of input found. Variable and Expression expected following lambda operator. + \ + ^ + >>> try: read_expr(r'\ x') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + \ x + ^ + >>> try: read_expr(r'\ x y') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + \ x y + ^ + >>> try: read_expr(r'\ x.') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + \ x. + ^ + >>> try: read_expr(r'P(x)Q(x)') + ... except LogicalExpressionException as e: print(e) + Unexpected token: 'Q'. + P(x)Q(x) + ^ + >>> try: read_expr(r'(P(x)Q(x)') + ... except LogicalExpressionException as e: print(e) + Unexpected token: 'Q'. Expected token ')'. + (P(x)Q(x) + ^ + >>> try: read_expr(r'exists x y') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + exists x y + ^ + >>> try: read_expr(r'exists x y.') + ... except LogicalExpressionException as e: print(e) + End of input found. Expression expected. + exists x y. + ^ + >>> try: read_expr(r'exists x -> y') + ... except LogicalExpressionException as e: print(e) + Unexpected token: '->'. Expression expected. + exists x -> y + ^ + + + >>> try: read_expr(r'A -> ((P(x) & Q(x)) -> Z') + ... except LogicalExpressionException as e: print(e) + End of input found. Expected token ')'. + A -> ((P(x) & Q(x)) -> Z + ^ + >>> try: read_expr(r'A -> ((P(x) &) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> ((P(x) &) -> Z + ^ + >>> try: read_expr(r'A -> ((P(x) | )) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> ((P(x) | )) -> Z + ^ + >>> try: read_expr(r'A -> (P(x) ->) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> (P(x) ->) -> Z + ^ + >>> try: read_expr(r'A -> (P(x) -> Z') + ... except LogicalExpressionException as e: print(e) + End of input found. Expected token ')'. + A -> (P(x) -> Z + ^ + >>> try: read_expr(r'A -> (P(x,) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> (P(x,) -> Z + ^ + >>> try: read_expr(r'A -> (P(x,)) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> (P(x,)) -> Z + ^ + >>> try: read_expr(r'A -> (exists) -> Z') + ... except LogicalExpressionException as e: print(e) + ')' is an illegal variable name. Constants may not be quantified. + A -> (exists) -> Z + ^ + >>> try: read_expr(r'A -> (exists x) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> (exists x) -> Z + ^ + >>> try: read_expr(r'A -> (exists x.) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> (exists x.) -> Z + ^ + >>> try: read_expr(r'A -> (\ ) -> Z') + ... except LogicalExpressionException as e: print(e) + ')' is an illegal variable name. Constants may not be abstracted. + A -> (\ ) -> Z + ^ + >>> try: read_expr(r'A -> (\ x) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> (\ x) -> Z + ^ + >>> try: read_expr(r'A -> (\ x y) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> (\ x y) -> Z + ^ + >>> try: read_expr(r'A -> (\ x.) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> (\ x.) -> Z + ^ + >>> try: read_expr(r'A -> (P(x)Q(x)) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: 'Q'. Expected token ')'. + A -> (P(x)Q(x)) -> Z + ^ + >>> try: read_expr(r'A -> ((P(x)Q(x)) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: 'Q'. Expected token ')'. + A -> ((P(x)Q(x)) -> Z + ^ + >>> try: read_expr(r'A -> (all x y) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> (all x y) -> Z + ^ + >>> try: read_expr(r'A -> (exists x y.) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: ')'. Expression expected. + A -> (exists x y.) -> Z + ^ + >>> try: read_expr(r'A -> (exists x -> y) -> Z') + ... except LogicalExpressionException as e: print(e) + Unexpected token: '->'. Expression expected. + A -> (exists x -> y) -> Z + ^ diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/metrics.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/metrics.doctest new file mode 100644 index 0000000000000000000000000000000000000000..9c51fd82bfdf6cb91185fbb4b40d1a01fdf72086 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/metrics.doctest @@ -0,0 +1,321 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +======= +Metrics +======= + +----- +Setup +----- + + >>> import pytest + >>> _ = pytest.importorskip("numpy") + + +The `nltk.metrics` package provides a variety of *evaluation measures* +which can be used for a wide variety of NLP tasks. + + >>> from nltk.metrics import * + +------------------ +Standard IR Scores +------------------ + +We can use standard scores from information retrieval to test the +performance of taggers, chunkers, etc. + + >>> reference = 'DET NN VB DET JJ NN NN IN DET NN'.split() + >>> test = 'DET VB VB DET NN NN NN IN DET NN'.split() + >>> print(accuracy(reference, test)) + 0.8 + + +The following measures apply to sets: + + >>> reference_set = set(reference) + >>> test_set = set(test) + >>> precision(reference_set, test_set) + 1.0 + >>> print(recall(reference_set, test_set)) + 0.8 + >>> print(f_measure(reference_set, test_set)) + 0.88888888888... + +Measuring the likelihood of the data, given probability distributions: + + >>> from nltk import FreqDist, MLEProbDist + >>> pdist1 = MLEProbDist(FreqDist("aldjfalskfjaldsf")) + >>> pdist2 = MLEProbDist(FreqDist("aldjfalssjjlldss")) + >>> print(log_likelihood(['a', 'd'], [pdist1, pdist2])) + -2.7075187496... + + +---------------- +Distance Metrics +---------------- + +String edit distance (Levenshtein): + + >>> edit_distance("rain", "shine") + 3 + >>> edit_distance_align("shine", "shine") + [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5)] + >>> edit_distance_align("rain", "brainy") + [(0, 0), (0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (4, 6)] + >>> edit_distance_align("", "brainy") + [(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6)] + >>> edit_distance_align("", "") + [(0, 0)] + +Other distance measures: + + >>> s1 = set([1,2,3,4]) + >>> s2 = set([3,4,5]) + >>> binary_distance(s1, s2) + 1.0 + >>> print(jaccard_distance(s1, s2)) + 0.6 + >>> print(masi_distance(s1, s2)) + 0.868 + +---------------------- +Miscellaneous Measures +---------------------- + +Rank Correlation works with two dictionaries mapping keys to ranks. +The dictionaries should have the same set of keys. + + >>> spearman_correlation({'e':1, 't':2, 'a':3}, {'e':1, 'a':2, 't':3}) + 0.5 + +Windowdiff uses a sliding window in comparing two segmentations of the same input (e.g. tokenizations, chunkings). +Segmentations are represented using strings of zeros and ones. + + >>> s1 = "000100000010" + >>> s2 = "000010000100" + >>> s3 = "100000010000" + >>> s4 = "000000000000" + >>> s5 = "111111111111" + >>> windowdiff(s1, s1, 3) + 0.0 + >>> abs(windowdiff(s1, s2, 3) - 0.3) < 1e-6 # windowdiff(s1, s2, 3) == 0.3 + True + >>> abs(windowdiff(s2, s3, 3) - 0.8) < 1e-6 # windowdiff(s2, s3, 3) == 0.8 + True + >>> windowdiff(s1, s4, 3) + 0.5 + >>> windowdiff(s1, s5, 3) + 1.0 + +---------------- +Confusion Matrix +---------------- + + >>> reference = 'This is the reference data. Testing 123. aoaeoeoe' + >>> test = 'Thos iz_the rifirenci data. Testeng 123. aoaeoeoe' + >>> print(ConfusionMatrix(reference, test)) + | . 1 2 3 T _ a c d e f g h i n o r s t z | + --+-------------------------------------------+ + |<8>. . . . . 1 . . . . . . . . . . . . . . | + . | .<2>. . . . . . . . . . . . . . . . . . . | + 1 | . .<1>. . . . . . . . . . . . . . . . . . | + 2 | . . .<1>. . . . . . . . . . . . . . . . . | + 3 | . . . .<1>. . . . . . . . . . . . . . . . | + T | . . . . .<2>. . . . . . . . . . . . . . . | + _ | . . . . . .<.>. . . . . . . . . . . . . . | + a | . . . . . . .<4>. . . . . . . . . . . . . | + c | . . . . . . . .<1>. . . . . . . . . . . . | + d | . . . . . . . . .<1>. . . . . . . . . . . | + e | . . . . . . . . . .<6>. . . 3 . . . . . . | + f | . . . . . . . . . . .<1>. . . . . . . . . | + g | . . . . . . . . . . . .<1>. . . . . . . . | + h | . . . . . . . . . . . . .<2>. . . . . . . | + i | . . . . . . . . . . 1 . . .<1>. 1 . . . . | + n | . . . . . . . . . . . . . . .<2>. . . . . | + o | . . . . . . . . . . . . . . . .<3>. . . . | + r | . . . . . . . . . . . . . . . . .<2>. . . | + s | . . . . . . . . . . . . . . . . . .<2>. 1 | + t | . . . . . . . . . . . . . . . . . . .<3>. | + z | . . . . . . . . . . . . . . . . . . . .<.>| + --+-------------------------------------------+ + (row = reference; col = test) + + + >>> cm = ConfusionMatrix(reference, test) + >>> print(cm.pretty_format(sort_by_count=True)) + | e a i o s t . T h n r 1 2 3 c d f g _ z | + --+-------------------------------------------+ + |<8>. . . . . . . . . . . . . . . . . . 1 . | + e | .<6>. 3 . . . . . . . . . . . . . . . . . | + a | . .<4>. . . . . . . . . . . . . . . . . . | + i | . 1 .<1>1 . . . . . . . . . . . . . . . . | + o | . . . .<3>. . . . . . . . . . . . . . . . | + s | . . . . .<2>. . . . . . . . . . . . . . 1 | + t | . . . . . .<3>. . . . . . . . . . . . . . | + . | . . . . . . .<2>. . . . . . . . . . . . . | + T | . . . . . . . .<2>. . . . . . . . . . . . | + h | . . . . . . . . .<2>. . . . . . . . . . . | + n | . . . . . . . . . .<2>. . . . . . . . . . | + r | . . . . . . . . . . .<2>. . . . . . . . . | + 1 | . . . . . . . . . . . .<1>. . . . . . . . | + 2 | . . . . . . . . . . . . .<1>. . . . . . . | + 3 | . . . . . . . . . . . . . .<1>. . . . . . | + c | . . . . . . . . . . . . . . .<1>. . . . . | + d | . . . . . . . . . . . . . . . .<1>. . . . | + f | . . . . . . . . . . . . . . . . .<1>. . . | + g | . . . . . . . . . . . . . . . . . .<1>. . | + _ | . . . . . . . . . . . . . . . . . . .<.>. | + z | . . . . . . . . . . . . . . . . . . . .<.>| + --+-------------------------------------------+ + (row = reference; col = test) + + + >>> print(cm.pretty_format(sort_by_count=True, truncate=10)) + | e a i o s t . T h | + --+---------------------+ + |<8>. . . . . . . . . | + e | .<6>. 3 . . . . . . | + a | . .<4>. . . . . . . | + i | . 1 .<1>1 . . . . . | + o | . . . .<3>. . . . . | + s | . . . . .<2>. . . . | + t | . . . . . .<3>. . . | + . | . . . . . . .<2>. . | + T | . . . . . . . .<2>. | + h | . . . . . . . . .<2>| + --+---------------------+ + (row = reference; col = test) + + + >>> print(cm.pretty_format(sort_by_count=True, truncate=10, values_in_chart=False)) + | 1 | + | 1 2 3 4 5 6 7 8 9 0 | + ---+---------------------+ + 1 |<8>. . . . . . . . . | + 2 | .<6>. 3 . . . . . . | + 3 | . .<4>. . . . . . . | + 4 | . 1 .<1>1 . . . . . | + 5 | . . . .<3>. . . . . | + 6 | . . . . .<2>. . . . | + 7 | . . . . . .<3>. . . | + 8 | . . . . . . .<2>. . | + 9 | . . . . . . . .<2>. | + 10 | . . . . . . . . .<2>| + ---+---------------------+ + (row = reference; col = test) + Value key: + 1: + 2: e + 3: a + 4: i + 5: o + 6: s + 7: t + 8: . + 9: T + 10: h + + +For "e", the number of true positives should be 6, while the number of false negatives is 3. +So, the recall ought to be 6 / (6 + 3): + + >>> cm.recall("e") # doctest: +ELLIPSIS + 0.666666... + +For "e", the false positive is just 1, so the precision should be 6 / (6 + 1): + + >>> cm.precision("e") # doctest: +ELLIPSIS + 0.857142... + +The f-measure with default value of ``alpha = 0.5`` should then be: + +* *1/(alpha/p + (1-alpha)/r) =* +* *1/(0.5/p + 0.5/r) =* +* *2pr / (p + r) =* +* *2 * 0.857142... * 0.666666... / (0.857142... + 0.666666...) =* +* *0.749999...* + + >>> cm.f_measure("e") # doctest: +ELLIPSIS + 0.749999... + +-------------------- +Association measures +-------------------- + +These measures are useful to determine whether the coocurrence of two random +events is meaningful. They are used, for instance, to distinguish collocations +from other pairs of adjacent words. + +We bring some examples of bigram association calculations from Manning and +Schutze's SNLP, 2nd Ed. chapter 5. + + >>> n_new_companies, n_new, n_companies, N = 8, 15828, 4675, 14307668 + >>> bam = BigramAssocMeasures + >>> bam.raw_freq(20, (42, 20), N) == 20. / N + True + >>> bam.student_t(n_new_companies, (n_new, n_companies), N) + 0.999... + >>> bam.chi_sq(n_new_companies, (n_new, n_companies), N) + 1.54... + >>> bam.likelihood_ratio(150, (12593, 932), N) + 1291... + +For other associations, we ensure the ordering of the measures: + + >>> bam.mi_like(20, (42, 20), N) > bam.mi_like(20, (41, 27), N) + True + >>> bam.pmi(20, (42, 20), N) > bam.pmi(20, (41, 27), N) + True + >>> bam.phi_sq(20, (42, 20), N) > bam.phi_sq(20, (41, 27), N) + True + >>> bam.poisson_stirling(20, (42, 20), N) > bam.poisson_stirling(20, (41, 27), N) + True + >>> bam.jaccard(20, (42, 20), N) > bam.jaccard(20, (41, 27), N) + True + >>> bam.dice(20, (42, 20), N) > bam.dice(20, (41, 27), N) + True + >>> bam.fisher(20, (42, 20), N) > bam.fisher(20, (41, 27), N) # doctest: +SKIP + False + +For trigrams, we have to provide more count information: + + >>> n_w1_w2_w3 = 20 + >>> n_w1_w2, n_w1_w3, n_w2_w3 = 35, 60, 40 + >>> pair_counts = (n_w1_w2, n_w1_w3, n_w2_w3) + >>> n_w1, n_w2, n_w3 = 100, 200, 300 + >>> uni_counts = (n_w1, n_w2, n_w3) + >>> N = 14307668 + >>> tam = TrigramAssocMeasures + >>> tam.raw_freq(n_w1_w2_w3, pair_counts, uni_counts, N) == 1. * n_w1_w2_w3 / N + True + >>> uni_counts2 = (n_w1, n_w2, 100) + >>> tam.student_t(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.student_t(n_w1_w2_w3, pair_counts, uni_counts, N) + True + >>> tam.chi_sq(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.chi_sq(n_w1_w2_w3, pair_counts, uni_counts, N) + True + >>> tam.mi_like(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.mi_like(n_w1_w2_w3, pair_counts, uni_counts, N) + True + >>> tam.pmi(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.pmi(n_w1_w2_w3, pair_counts, uni_counts, N) + True + >>> tam.likelihood_ratio(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.likelihood_ratio(n_w1_w2_w3, pair_counts, uni_counts, N) + True + >>> tam.poisson_stirling(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.poisson_stirling(n_w1_w2_w3, pair_counts, uni_counts, N) + True + >>> tam.jaccard(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.jaccard(n_w1_w2_w3, pair_counts, uni_counts, N) + True + + +For fourgrams, we have to provide more count information: + + >>> n_w1_w2_w3_w4 = 5 + >>> n_w1_w2, n_w1_w3, n_w2_w3 = 35, 60, 40 + >>> n_w1_w2_w3, n_w2_w3_w4 = 20, 10 + >>> pair_counts = (n_w1_w2, n_w1_w3, n_w2_w3) + >>> triplet_counts = (n_w1_w2_w3, n_w2_w3_w4) + >>> n_w1, n_w2, n_w3, n_w4 = 100, 200, 300, 400 + >>> uni_counts = (n_w1, n_w2, n_w3, n_w4) + >>> N = 14307668 + >>> qam = QuadgramAssocMeasures + >>> qam.raw_freq(n_w1_w2_w3_w4, pair_counts, triplet_counts, uni_counts, N) == 1. * n_w1_w2_w3_w4 / N + True diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/misc.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/misc.doctest new file mode 100644 index 0000000000000000000000000000000000000000..7b88b6d742e0917ee841187e3c792754c88dfbf2 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/misc.doctest @@ -0,0 +1,118 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +-------------------------------------------------------------------------------- +Unit tests for the miscellaneous sort functions. +-------------------------------------------------------------------------------- + + >>> from copy import deepcopy + >>> from nltk.misc.sort import * + +A (very) small list of unsorted integers. + + >>> test_data = [12, 67, 7, 28, 92, 56, 53, 720, 91, 57, 20, 20] + +Test each sorting method - each method returns the number of operations +required to sort the data, and sorts in-place (desctructively - hence the need +for multiple copies). + + >>> sorted_data = deepcopy(test_data) + >>> selection(sorted_data) + 66 + + >>> sorted_data + [7, 12, 20, 20, 28, 53, 56, 57, 67, 91, 92, 720] + + >>> sorted_data = deepcopy(test_data) + >>> bubble(sorted_data) + 30 + + >>> sorted_data + [7, 12, 20, 20, 28, 53, 56, 57, 67, 91, 92, 720] + + >>> sorted_data = deepcopy(test_data) + >>> merge(sorted_data) + 30 + + >>> sorted_data + [7, 12, 20, 20, 28, 53, 56, 57, 67, 91, 92, 720] + + >>> sorted_data = deepcopy(test_data) + >>> quick(sorted_data) + 13 + + >>> sorted_data + [7, 12, 20, 20, 28, 53, 56, 57, 67, 91, 92, 720] + +-------------------------------------------------------------------------------- +Unit tests for Wordfinder class +-------------------------------------------------------------------------------- + + >>> import random + + >>> # The following is not enough for reproducibility under Python 2/3 + >>> # (see https://bugs.python.org/issue9025) so this test is skipped. + >>> random.seed(12345) + + >>> from nltk.misc import wordfinder + >>> wordfinder.word_finder() # doctest: +SKIP + Word Finder + + J V L A I R O T A T I S I V O D E R E T + H U U B E A R O E P O C S O R E T N E P + A D A U Z E E S R A P P A L L M E N T R + C X A D Q S Z T P E O R S N G P J A D E + I G Y K K T I A A R G F I D T E L C N S + R E C N B H T R L T N N B W N T A O A I + A Y I L O E I A M E I A A Y U R P L L D + G L T V S T S F E A D I P H D O O H N I + R L S E C I N I L R N N M E C G R U E A + A A Y G I C E N L L E O I G Q R T A E L + M R C E T I S T A E T L L E U A E N R L + O U O T A S E E C S O O N H Y P A T G Y + E M H O M M D R E S F P U L T H C F N V + L A C A I M A M A N L B R U T E D O M I + O R I L N E E E E E U A R S C R Y L I P + H T R K E S N N M S I L A S R E V I N U + T X T A A O U T K S E T A R R E S I B J + A E D L E L J I F O O R P E L K N I R W + K H A I D E Q O P R I C K T I M B E R P + Z K D O O H G N I H T U R V E Y D R O P + + 1: INTERCHANGER + 2: TEARLESSNESS + 3: UNIVERSALISM + 4: DESENSITIZER + 5: INTERMENTION + 6: TRICHOCYSTIC + 7: EXTRAMURALLY + 8: VEGETOALKALI + 9: PALMELLACEAE + 10: AESTHETICISM + 11: PETROGRAPHER + 12: VISITATORIAL + 13: OLEOMARGARIC + 14: WRINKLEPROOF + 15: PRICKTIMBER + 16: PRESIDIALLY + 17: SCITAMINEAE + 18: ENTEROSCOPE + 19: APPALLMENT + 20: TURVEYDROP + 21: THINGHOOD + 22: BISERRATE + 23: GREENLAND + 24: BRUTEDOM + 25: POLONIAN + 26: ACOLHUAN + 27: LAPORTEA + 28: TENDING + 29: TEREDO + 30: MESOLE + 31: UNLIMP + 32: OSTARA + 33: PILY + 34: DUNT + 35: ONYX + 36: KATH + 37: JUNE diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/portuguese_en_fixt.py b/llmeval-env/lib/python3.10/site-packages/nltk/test/portuguese_en_fixt.py new file mode 100644 index 0000000000000000000000000000000000000000..1e86682b0810ef1299cf353ae606db9f9e9114d7 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/portuguese_en_fixt.py @@ -0,0 +1,4 @@ +def setup_module(): + import pytest + + pytest.skip("portuguese_en.doctest imports nltk.examples.pt which doesn't exist!") diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/probability.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/probability.doctest new file mode 100644 index 0000000000000000000000000000000000000000..f8f385dec2bf207684558068525b3ab9c9719d6b --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/probability.doctest @@ -0,0 +1,306 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +=========== +Probability +=========== + + >>> from nltk.test.probability_fixt import setup_module + >>> setup_module() + + >>> import nltk + >>> from nltk.probability import * + +FreqDist +-------- + + >>> text1 = ['no', 'good', 'fish', 'goes', 'anywhere', 'without', 'a', 'porpoise', '!'] + >>> text2 = ['no', 'good', 'porpoise', 'likes', 'to', 'fish', 'fish', 'anywhere', '.'] + + >>> fd1 = nltk.FreqDist(text1) + >>> fd1 == nltk.FreqDist(text1) + True + +Note that items are sorted in order of decreasing frequency; two items of the same frequency appear in indeterminate order. + + >>> import itertools + >>> both = nltk.FreqDist(text1 + text2) + >>> both_most_common = both.most_common() + >>> list(itertools.chain(*(sorted(ys) for k, ys in itertools.groupby(both_most_common, key=lambda t: t[1])))) + [('fish', 3), ('anywhere', 2), ('good', 2), ('no', 2), ('porpoise', 2), ('!', 1), ('.', 1), ('a', 1), ('goes', 1), ('likes', 1), ('to', 1), ('without', 1)] + + >>> both == fd1 + nltk.FreqDist(text2) + True + >>> fd1 == nltk.FreqDist(text1) # But fd1 is unchanged + True + + >>> fd2 = nltk.FreqDist(text2) + >>> fd1.update(fd2) + >>> fd1 == both + True + + >>> fd1 = nltk.FreqDist(text1) + >>> fd1.update(text2) + >>> fd1 == both + True + + >>> fd1 = nltk.FreqDist(text1) + >>> fd2 = nltk.FreqDist(fd1) + >>> fd2 == fd1 + True + +``nltk.FreqDist`` can be pickled: + + >>> import pickle + >>> fd1 = nltk.FreqDist(text1) + >>> pickled = pickle.dumps(fd1) + >>> fd1 == pickle.loads(pickled) + True + +Mathematical operations: + + >>> FreqDist('abbb') + FreqDist('bcc') + FreqDist({'b': 4, 'c': 2, 'a': 1}) + >>> FreqDist('abbbc') - FreqDist('bccd') + FreqDist({'b': 2, 'a': 1}) + >>> FreqDist('abbb') | FreqDist('bcc') + FreqDist({'b': 3, 'c': 2, 'a': 1}) + >>> FreqDist('abbb') & FreqDist('bcc') + FreqDist({'b': 1}) + +ConditionalFreqDist +------------------- + + >>> cfd1 = ConditionalFreqDist() + >>> cfd1[1] = FreqDist('abbbb') + >>> cfd1[2] = FreqDist('xxxxyy') + >>> cfd1 + + + >>> cfd2 = ConditionalFreqDist() + >>> cfd2[1] = FreqDist('bbccc') + >>> cfd2[2] = FreqDist('xxxyyyzz') + >>> cfd2[3] = FreqDist('m') + >>> cfd2 + + + >>> r = cfd1 + cfd2 + >>> [(i,r[i]) for i in r.conditions()] + [(1, FreqDist({'b': 6, 'c': 3, 'a': 1})), (2, FreqDist({'x': 7, 'y': 5, 'z': 2})), (3, FreqDist({'m': 1}))] + + >>> r = cfd1 - cfd2 + >>> [(i,r[i]) for i in r.conditions()] + [(1, FreqDist({'b': 2, 'a': 1})), (2, FreqDist({'x': 1}))] + + >>> r = cfd1 | cfd2 + >>> [(i,r[i]) for i in r.conditions()] + [(1, FreqDist({'b': 4, 'c': 3, 'a': 1})), (2, FreqDist({'x': 4, 'y': 3, 'z': 2})), (3, FreqDist({'m': 1}))] + + >>> r = cfd1 & cfd2 + >>> [(i,r[i]) for i in r.conditions()] + [(1, FreqDist({'b': 2})), (2, FreqDist({'x': 3, 'y': 2}))] + +Testing some HMM estimators +--------------------------- + +We extract a small part (500 sentences) of the Brown corpus + + >>> corpus = nltk.corpus.brown.tagged_sents(categories='adventure')[:500] + >>> print(len(corpus)) + 500 + +We create a HMM trainer - note that we need the tags and symbols +from the whole corpus, not just the training corpus + + >>> from nltk.util import unique_list + >>> tag_set = unique_list(tag for sent in corpus for (word,tag) in sent) + >>> print(len(tag_set)) + 92 + >>> symbols = unique_list(word for sent in corpus for (word,tag) in sent) + >>> print(len(symbols)) + 1464 + >>> trainer = nltk.tag.HiddenMarkovModelTrainer(tag_set, symbols) + +We divide the corpus into 90% training and 10% testing + + >>> train_corpus = [] + >>> test_corpus = [] + >>> for i in range(len(corpus)): + ... if i % 10: + ... train_corpus += [corpus[i]] + ... else: + ... test_corpus += [corpus[i]] + >>> print(len(train_corpus)) + 450 + >>> print(len(test_corpus)) + 50 + +And now we can test the estimators + + >>> def train_and_test(est): + ... hmm = trainer.train_supervised(train_corpus, estimator=est) + ... print('%.2f%%' % (100 * hmm.accuracy(test_corpus))) + +Maximum Likelihood Estimation +----------------------------- +- this resulted in an initialization error before r7209 + + >>> mle = lambda fd, bins: MLEProbDist(fd) + >>> train_and_test(mle) + 22.75% + +Laplace (= Lidstone with gamma==1) + + >>> train_and_test(LaplaceProbDist) + 66.04% + +Expected Likelihood Estimation (= Lidstone with gamma==0.5) + + >>> train_and_test(ELEProbDist) + 73.01% + +Lidstone Estimation, for gamma==0.1, 0.5 and 1 +(the later two should be exactly equal to MLE and ELE above) + + >>> def lidstone(gamma): + ... return lambda fd, bins: LidstoneProbDist(fd, gamma, bins) + >>> train_and_test(lidstone(0.1)) + 82.51% + >>> train_and_test(lidstone(0.5)) + 73.01% + >>> train_and_test(lidstone(1.0)) + 66.04% + +Witten Bell Estimation +---------------------- +- This resulted in ZeroDivisionError before r7209 + + >>> train_and_test(WittenBellProbDist) + 88.12% + +Good Turing Estimation + + >>> gt = lambda fd, bins: SimpleGoodTuringProbDist(fd, bins=1e5) + >>> train_and_test(gt) + 86.93% + +Kneser Ney Estimation +--------------------- +Since the Kneser-Ney distribution is best suited for trigrams, we must adjust +our testing accordingly. + + >>> corpus = [[((x[0],y[0],z[0]),(x[1],y[1],z[1])) + ... for x, y, z in nltk.trigrams(sent)] + ... for sent in corpus[:100]] + +We will then need to redefine the rest of the training/testing variables + + >>> tag_set = unique_list(tag for sent in corpus for (word,tag) in sent) + >>> len(tag_set) + 906 + + >>> symbols = unique_list(word for sent in corpus for (word,tag) in sent) + >>> len(symbols) + 1341 + + >>> trainer = nltk.tag.HiddenMarkovModelTrainer(tag_set, symbols) + >>> train_corpus = [] + >>> test_corpus = [] + + >>> for i in range(len(corpus)): + ... if i % 10: + ... train_corpus += [corpus[i]] + ... else: + ... test_corpus += [corpus[i]] + + >>> len(train_corpus) + 90 + >>> len(test_corpus) + 10 + + >>> kn = lambda fd, bins: KneserNeyProbDist(fd) + >>> train_and_test(kn) + 0.86% + +Remains to be added: +- Tests for HeldoutProbDist, CrossValidationProbDist and MutableProbDist + +Squashed bugs +------------- + +Issue 511: override pop and popitem to invalidate the cache + + >>> fd = nltk.FreqDist('a') + >>> list(fd.keys()) + ['a'] + >>> fd.pop('a') + 1 + >>> list(fd.keys()) + [] + +Issue 533: access cumulative frequencies with no arguments + + >>> fd = nltk.FreqDist('aab') + >>> list(fd._cumulative_frequencies(['a'])) + [2.0] + >>> list(fd._cumulative_frequencies(['a', 'b'])) + [2.0, 3.0] + +Issue 579: override clear to reset some variables + + >>> fd = FreqDist('aab') + >>> fd.clear() + >>> fd.N() + 0 + +Issue 351: fix fileids method of CategorizedCorpusReader to inadvertently +add errant categories + + >>> from nltk.corpus import brown + >>> brown.fileids('blah') + Traceback (most recent call last): + ... + ValueError: Category blah not found + >>> brown.categories() + ['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor', 'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction'] + +Issue 175: add the unseen bin to SimpleGoodTuringProbDist by default +otherwise any unseen events get a probability of zero, i.e., +they don't get smoothed + + >>> from nltk import SimpleGoodTuringProbDist, FreqDist + >>> fd = FreqDist({'a':1, 'b':1, 'c': 2, 'd': 3, 'e': 4, 'f': 4, 'g': 4, 'h': 5, 'i': 5, 'j': 6, 'k': 6, 'l': 6, 'm': 7, 'n': 7, 'o': 8, 'p': 9, 'q': 10}) + >>> p = SimpleGoodTuringProbDist(fd) + >>> p.prob('a') + 0.017649766667026317... + >>> p.prob('o') + 0.08433050215340411... + >>> p.prob('z') + 0.022727272727272728... + >>> p.prob('foobar') + 0.022727272727272728... + +``MLEProbDist``, ``ConditionalProbDist'', ``DictionaryConditionalProbDist`` and +``ConditionalFreqDist`` can be pickled: + + >>> import pickle + >>> pd = MLEProbDist(fd) + >>> sorted(pd.samples()) == sorted(pickle.loads(pickle.dumps(pd)).samples()) + True + >>> dpd = DictionaryConditionalProbDist({'x': pd}) + >>> unpickled = pickle.loads(pickle.dumps(dpd)) + >>> dpd['x'].prob('a') + 0.011363636... + >>> dpd['x'].prob('a') == unpickled['x'].prob('a') + True + >>> cfd = nltk.probability.ConditionalFreqDist() + >>> cfd['foo']['hello'] += 1 + >>> cfd['foo']['hello'] += 1 + >>> cfd['bar']['hello'] += 1 + >>> cfd2 = pickle.loads(pickle.dumps(cfd)) + >>> cfd2 == cfd + True + >>> cpd = ConditionalProbDist(cfd, SimpleGoodTuringProbDist) + >>> cpd2 = pickle.loads(pickle.dumps(cpd)) + >>> cpd['foo'].prob('hello') == cpd2['foo'].prob('hello') + True diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/probability_fixt.py b/llmeval-env/lib/python3.10/site-packages/nltk/test/probability_fixt.py new file mode 100644 index 0000000000000000000000000000000000000000..a67809384d3780fa9d1b3efcf4ca51e10cd4be00 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/probability_fixt.py @@ -0,0 +1,8 @@ +# probability.doctest uses HMM which requires numpy; +# skip probability.doctest if numpy is not available + + +def setup_module(): + import pytest + + pytest.importorskip("numpy") diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/propbank.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/propbank.doctest new file mode 100644 index 0000000000000000000000000000000000000000..d7f9a98a4be23e050676205d11a776b30f7eb499 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/propbank.doctest @@ -0,0 +1,176 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +======== +PropBank +======== + +The PropBank Corpus provides predicate-argument annotation for the +entire Penn Treebank. Each verb in the treebank is annotated by a single +instance in PropBank, containing information about the location of +the verb, and the location and identity of its arguments: + + >>> from nltk.corpus import propbank + >>> pb_instances = propbank.instances() + >>> print(pb_instances) + [, + , ...] + +Each propbank instance defines the following member variables: + + - Location information: `fileid`, `sentnum`, `wordnum` + - Annotator information: `tagger` + - Inflection information: `inflection` + - Roleset identifier: `roleset` + - Verb (aka predicate) location: `predicate` + - Argument locations and types: `arguments` + +The following examples show the types of these arguments: + + >>> inst = pb_instances[103] + >>> (inst.fileid, inst.sentnum, inst.wordnum) + ('wsj_0004.mrg', 8, 16) + >>> inst.tagger + 'gold' + >>> inst.inflection + + >>> infl = inst.inflection + >>> infl.form, infl.tense, infl.aspect, infl.person, infl.voice + ('v', 'p', '-', '-', 'a') + >>> inst.roleset + 'rise.01' + >>> inst.predicate + PropbankTreePointer(16, 0) + >>> inst.arguments + ((PropbankTreePointer(0, 2), 'ARG1'), + (PropbankTreePointer(13, 1), 'ARGM-DIS'), + (PropbankTreePointer(17, 1), 'ARG4-to'), + (PropbankTreePointer(20, 1), 'ARG3-from')) + +The location of the predicate and of the arguments are encoded using +`PropbankTreePointer` objects, as well as `PropbankChainTreePointer` +objects and `PropbankSplitTreePointer` objects. A +`PropbankTreePointer` consists of a `wordnum` and a `height`: + + >>> print(inst.predicate.wordnum, inst.predicate.height) + 16 0 + +This identifies the tree constituent that is headed by the word that +is the `wordnum`\ 'th token in the sentence, and whose span is found +by going `height` nodes up in the tree. This type of pointer is only +useful if we also have the corresponding tree structure, since it +includes empty elements such as traces in the word number count. The +trees for 10% of the standard PropBank Corpus are contained in the +`treebank` corpus: + + >>> tree = inst.tree + + >>> from nltk.corpus import treebank + >>> assert tree == treebank.parsed_sents(inst.fileid)[inst.sentnum] + + >>> inst.predicate.select(tree) + Tree('VBD', ['rose']) + >>> for (argloc, argid) in inst.arguments: + ... print('%-10s %s' % (argid, argloc.select(tree).pformat(500)[:50])) + ARG1 (NP-SBJ (NP (DT The) (NN yield)) (PP (IN on) (NP ( + ARGM-DIS (PP (IN for) (NP (NN example))) + ARG4-to (PP-DIR (TO to) (NP (CD 8.04) (NN %))) + ARG3-from (PP-DIR (IN from) (NP (CD 7.90) (NN %))) + +Propbank tree pointers can be converted to standard tree locations, +which are usually easier to work with, using the `treepos()` method: + + >>> treepos = inst.predicate.treepos(tree) + >>> print (treepos, tree[treepos]) + (4, 0) (VBD rose) + +In some cases, argument locations will be encoded using +`PropbankChainTreePointer`\ s (for trace chains) or +`PropbankSplitTreePointer`\ s (for discontinuous constituents). Both +of these objects contain a single member variable, `pieces`, +containing a list of the constituent pieces. They also define the +method `select()`, which will return a tree containing all the +elements of the argument. (A new head node is created, labeled +"*CHAIN*" or "*SPLIT*", since the argument is not a single constituent +in the original tree). Sentence #6 contains an example of an argument +that is both discontinuous and contains a chain: + + >>> inst = pb_instances[6] + >>> inst.roleset + 'expose.01' + >>> argloc, argid = inst.arguments[2] + >>> argloc + + >>> argloc.pieces + [, PropbankTreePointer(27, 0)] + >>> argloc.pieces[0].pieces + ... + [PropbankTreePointer(22, 1), PropbankTreePointer(24, 0), + PropbankTreePointer(25, 1)] + >>> print(argloc.select(inst.tree)) + (*CHAIN* + (*SPLIT* (NP (DT a) (NN group)) (IN of) (NP (NNS workers))) + (-NONE- *)) + +The PropBank Corpus also provides access to the frameset files, which +define the argument labels used by the annotations, on a per-verb +basis. Each frameset file contains one or more predicates, such as +'turn' or 'turn_on', each of which is divided into coarse-grained word +senses called rolesets. For each roleset, the frameset file provides +descriptions of the argument roles, along with examples. + + >>> expose_01 = propbank.roleset('expose.01') + >>> turn_01 = propbank.roleset('turn.01') + >>> print(turn_01) + + >>> for role in turn_01.findall("roles/role"): + ... print(role.attrib['n'], role.attrib['descr']) + 0 turner + 1 thing turning + m direction, location + + >>> from xml.etree import ElementTree + >>> print(ElementTree.tostring(turn_01.find('example')).decode('utf8').strip()) + + + John turned the key in the lock. + + John + turned + the key + in the lock + + +Note that the standard corpus distribution only contains 10% of the +treebank, so the parse trees are not available for instances starting +at 9353: + + >>> inst = pb_instances[9352] + >>> inst.fileid + 'wsj_0199.mrg' + >>> print(inst.tree) + (S (NP-SBJ (NNP Trinity)) (VP (VBD said) (SBAR (-NONE- 0) ...)) + >>> print(inst.predicate.select(inst.tree)) + (VB begin) + + >>> inst = pb_instances[9353] + >>> inst.fileid + 'wsj_0200.mrg' + >>> print(inst.tree) + None + >>> print(inst.predicate.select(inst.tree)) + Traceback (most recent call last): + . . . + ValueError: Parse tree not available + +However, if you supply your own version of the treebank corpus (by +putting it before the nltk-provided version on `nltk.data.path`, or +by creating a `ptb` directory as described above and using the +`propbank_ptb` module), then you can access the trees for all +instances. + +A list of the verb lemmas contained in PropBank is returned by the +`propbank.verbs()` method: + + >>> propbank.verbs() + ['abandon', 'abate', 'abdicate', 'abet', 'abide', ...] diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/relextract.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/relextract.doctest new file mode 100644 index 0000000000000000000000000000000000000000..83c08d4bb0dfaed9efe430ff6b114de999445fd0 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/relextract.doctest @@ -0,0 +1,263 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +====================== +Information Extraction +====================== + +Information Extraction standardly consists of three subtasks: + +#. Named Entity Recognition + +#. Relation Extraction + +#. Template Filling + +Named Entities +~~~~~~~~~~~~~~ + +The IEER corpus is marked up for a variety of Named Entities. A Named +Entity (more strictly, a Named Entity mention) is a name of an +entity belonging to a specified class. For example, the Named Entity +classes in IEER include PERSON, LOCATION, ORGANIZATION, DATE and so +on. Within NLTK, Named Entities are represented as subtrees within a +chunk structure: the class name is treated as node label, while the +entity mention itself appears as the leaves of the subtree. This is +illustrated below, where we have show an extract of the chunk +representation of document NYT_19980315.064: + + >>> from nltk.corpus import ieer + >>> docs = ieer.parsed_docs('NYT_19980315') + >>> tree = docs[1].text + >>> print(tree) + (DOCUMENT + ... + ``It's + a + chance + to + think + about + first-level + questions,'' + said + Ms. + (PERSON Cohn) + , + a + partner + in + the + (ORGANIZATION McGlashan & Sarrail) + firm + in + (LOCATION San Mateo) + , + (LOCATION Calif.) + ...) + +Thus, the Named Entity mentions in this example are *Cohn*, *McGlashan & +Sarrail*, *San Mateo* and *Calif.*. + +The CoNLL2002 Dutch and Spanish data is treated similarly, although in +this case, the strings are also POS tagged. + + >>> from nltk.corpus import conll2002 + >>> for doc in conll2002.chunked_sents('ned.train')[27]: + ... print(doc) + ('Het', 'Art') + (ORG Hof/N van/Prep Cassatie/N) + ('verbrak', 'V') + ('het', 'Art') + ('arrest', 'N') + ('zodat', 'Conj') + ('het', 'Pron') + ('moest', 'V') + ('worden', 'V') + ('overgedaan', 'V') + ('door', 'Prep') + ('het', 'Art') + ('hof', 'N') + ('van', 'Prep') + ('beroep', 'N') + ('van', 'Prep') + (LOC Antwerpen/N) + ('.', 'Punc') + +Relation Extraction +~~~~~~~~~~~~~~~~~~~ + +Relation Extraction standardly consists of identifying specified +relations between Named Entities. For example, assuming that we can +recognize ORGANIZATIONs and LOCATIONs in text, we might want to also +recognize pairs *(o, l)* of these kinds of entities such that *o* is +located in *l*. + +The `sem.relextract` module provides some tools to help carry out a +simple version of this task. The `tree2semi_rel()` function splits a chunk +document into a list of two-member lists, each of which consists of a +(possibly empty) string followed by a `Tree` (i.e., a Named Entity): + + >>> from nltk.sem import relextract + >>> pairs = relextract.tree2semi_rel(tree) + >>> for s, tree in pairs[18:22]: + ... print('("...%s", %s)' % (" ".join(s[-5:]),tree)) + ("...about first-level questions,'' said Ms.", (PERSON Cohn)) + ("..., a partner in the", (ORGANIZATION McGlashan & Sarrail)) + ("...firm in", (LOCATION San Mateo)) + ("...,", (LOCATION Calif.)) + +The function `semi_rel2reldict()` processes triples of these pairs, i.e., +pairs of the form ``((string1, Tree1), (string2, Tree2), (string3, +Tree3))`` and outputs a dictionary (a `reldict`) in which ``Tree1`` is +the subject of the relation, ``string2`` is the filler +and ``Tree3`` is the object of the relation. ``string1`` and ``string3`` are +stored as left and right context respectively. + + >>> reldicts = relextract.semi_rel2reldict(pairs) + >>> for k, v in sorted(reldicts[0].items()): + ... print(k, '=>', v) + filler => of messages to their own ``Cyberia'' ... + lcon => transactions.'' Each week, they post + objclass => ORGANIZATION + objsym => white_house + objtext => White House + rcon => for access to its planned + subjclass => CARDINAL + subjsym => hundreds + subjtext => hundreds + untagged_filler => of messages to their own ``Cyberia'' ... + +The next example shows some of the values for two `reldict`\ s +corresponding to the ``'NYT_19980315'`` text extract shown earlier. + + >>> for r in reldicts[18:20]: + ... print('=' * 20) + ... print(r['subjtext']) + ... print(r['filler']) + ... print(r['objtext']) + ==================== + Cohn + , a partner in the + McGlashan & Sarrail + ==================== + McGlashan & Sarrail + firm in + San Mateo + +The function `relextract()` allows us to filter the `reldict`\ s +according to the classes of the subject and object named entities. In +addition, we can specify that the filler text has to match a given +regular expression, as illustrated in the next example. Here, we are +looking for pairs of entities in the IN relation, where IN has +signature . + + >>> import re + >>> IN = re.compile(r'.*\bin\b(?!\b.+ing\b)') + >>> for fileid in ieer.fileids(): + ... for doc in ieer.parsed_docs(fileid): + ... for rel in relextract.extract_rels('ORG', 'LOC', doc, corpus='ieer', pattern = IN): + ... print(relextract.rtuple(rel)) + [ORG: 'Christian Democrats'] ', the leading political forces in' [LOC: 'Italy'] + [ORG: 'AP'] ') _ Lebanese guerrillas attacked Israeli forces in southern' [LOC: 'Lebanon'] + [ORG: 'Security Council'] 'adopted Resolution 425. Huge yellow banners hung across intersections in' [LOC: 'Beirut'] + [ORG: 'U.N.'] 'failures in' [LOC: 'Africa'] + [ORG: 'U.N.'] 'peacekeeping operation in' [LOC: 'Somalia'] + [ORG: 'U.N.'] 'partners on a more effective role in' [LOC: 'Africa'] + [ORG: 'AP'] ') _ A bomb exploded in a mosque in central' [LOC: 'San`a'] + [ORG: 'Krasnoye Sormovo'] 'shipyard in the Soviet city of' [LOC: 'Gorky'] + [ORG: 'Kelab Golf Darul Ridzuan'] 'in' [LOC: 'Perak'] + [ORG: 'U.N.'] 'peacekeeping operation in' [LOC: 'Somalia'] + [ORG: 'WHYY'] 'in' [LOC: 'Philadelphia'] + [ORG: 'McGlashan & Sarrail'] 'firm in' [LOC: 'San Mateo'] + [ORG: 'Freedom Forum'] 'in' [LOC: 'Arlington'] + [ORG: 'Brookings Institution'] ', the research group in' [LOC: 'Washington'] + [ORG: 'Idealab'] ', a self-described business incubator based in' [LOC: 'Los Angeles'] + [ORG: 'Open Text'] ', based in' [LOC: 'Waterloo'] + ... + +The next example illustrates a case where the pattern is a disjunction +of roles that a PERSON can occupy in an ORGANIZATION. + + >>> roles = r""" + ... (.*( + ... analyst| + ... chair(wo)?man| + ... commissioner| + ... counsel| + ... director| + ... economist| + ... editor| + ... executive| + ... foreman| + ... governor| + ... head| + ... lawyer| + ... leader| + ... librarian).*)| + ... manager| + ... partner| + ... president| + ... producer| + ... professor| + ... researcher| + ... spokes(wo)?man| + ... writer| + ... ,\sof\sthe?\s* # "X, of (the) Y" + ... """ + >>> ROLES = re.compile(roles, re.VERBOSE) + >>> for fileid in ieer.fileids(): + ... for doc in ieer.parsed_docs(fileid): + ... for rel in relextract.extract_rels('PER', 'ORG', doc, corpus='ieer', pattern=ROLES): + ... print(relextract.rtuple(rel)) + [PER: 'Kivutha Kibwana'] ', of the' [ORG: 'National Convention Assembly'] + [PER: 'Boban Boskovic'] ', chief executive of the' [ORG: 'Plastika'] + [PER: 'Annan'] ', the first sub-Saharan African to head the' [ORG: 'United Nations'] + [PER: 'Kiriyenko'] 'became a foreman at the' [ORG: 'Krasnoye Sormovo'] + [PER: 'Annan'] ', the first sub-Saharan African to head the' [ORG: 'United Nations'] + [PER: 'Mike Godwin'] ', chief counsel for the' [ORG: 'Electronic Frontier Foundation'] + ... + +In the case of the CoNLL2002 data, we can include POS tags in the +query pattern. This example also illustrates how the output can be +presented as something that looks more like a clause in a logical language. + + >>> de = """ + ... .* + ... ( + ... de/SP| + ... del/SP + ... ) + ... """ + >>> DE = re.compile(de, re.VERBOSE) + >>> rels = [rel for doc in conll2002.chunked_sents('esp.train') + ... for rel in relextract.extract_rels('ORG', 'LOC', doc, corpus='conll2002', pattern = DE)] + >>> for r in rels[:10]: + ... print(relextract.clause(r, relsym='DE')) + DE('tribunal_supremo', 'victoria') + DE('museo_de_arte', 'alcorc\xf3n') + DE('museo_de_bellas_artes', 'a_coru\xf1a') + DE('siria', 'l\xedbano') + DE('uni\xf3n_europea', 'pek\xedn') + DE('ej\xe9rcito', 'rogberi') + DE('juzgado_de_instrucci\xf3n_n\xfamero_1', 'san_sebasti\xe1n') + DE('psoe', 'villanueva_de_la_serena') + DE('ej\xe9rcito', 'l\xedbano') + DE('juzgado_de_lo_penal_n\xfamero_2', 'ceuta') + >>> vnv = """ + ... ( + ... is/V| + ... was/V| + ... werd/V| + ... wordt/V + ... ) + ... .* + ... van/Prep + ... """ + >>> VAN = re.compile(vnv, re.VERBOSE) + >>> for doc in conll2002.chunked_sents('ned.train'): + ... for r in relextract.extract_rels('PER', 'ORG', doc, corpus='conll2002', pattern=VAN): + ... print(relextract.clause(r, relsym="VAN")) + VAN("cornet_d'elzius", 'buitenlandse_handel') + VAN('johan_rottiers', 'kardinaal_van_roey_instituut') + VAN('annie_lennox', 'eurythmics') diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/resolution.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/resolution.doctest new file mode 100644 index 0000000000000000000000000000000000000000..f1cf70090d5bed3b868f5e427f5c3f5d045073d4 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/resolution.doctest @@ -0,0 +1,222 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +========================= +Resolution Theorem Prover +========================= + + >>> from nltk.inference.resolution import * + >>> from nltk.sem import logic + >>> from nltk.sem.logic import * + >>> logic._counter._value = 0 + >>> read_expr = logic.Expression.fromstring + + >>> P = read_expr('P') + >>> Q = read_expr('Q') + >>> R = read_expr('R') + >>> A = read_expr('A') + >>> B = read_expr('B') + >>> x = read_expr('x') + >>> y = read_expr('y') + >>> z = read_expr('z') + +------------------------------- +Test most_general_unification() +------------------------------- + >>> print(most_general_unification(x, x)) + {} + >>> print(most_general_unification(A, A)) + {} + >>> print(most_general_unification(A, x)) + {x: A} + >>> print(most_general_unification(x, A)) + {x: A} + >>> print(most_general_unification(x, y)) + {x: y} + >>> print(most_general_unification(P(x), P(A))) + {x: A} + >>> print(most_general_unification(P(x,B), P(A,y))) + {x: A, y: B} + >>> print(most_general_unification(P(x,B), P(B,x))) + {x: B} + >>> print(most_general_unification(P(x,y), P(A,x))) + {x: A, y: x} + >>> print(most_general_unification(P(Q(x)), P(y))) + {y: Q(x)} + +------------ +Test unify() +------------ + >>> print(Clause([]).unify(Clause([]))) + [] + >>> print(Clause([P(x)]).unify(Clause([-P(A)]))) + [{}] + >>> print(Clause([P(A), Q(x)]).unify(Clause([-P(x), R(x)]))) + [{R(A), Q(A)}] + >>> print(Clause([P(A), Q(x), R(x,y)]).unify(Clause([-P(x), Q(y)]))) + [{Q(y), Q(A), R(A,y)}] + >>> print(Clause([P(A), -Q(y)]).unify(Clause([-P(x), Q(B)]))) + [{}] + >>> print(Clause([P(x), Q(x)]).unify(Clause([-P(A), -Q(B)]))) + [{-Q(B), Q(A)}, {-P(A), P(B)}] + >>> print(Clause([P(x,x), Q(x), R(x)]).unify(Clause([-P(A,z), -Q(B)]))) + [{-Q(B), Q(A), R(A)}, {-P(A,z), R(B), P(B,B)}] + + >>> a = clausify(read_expr('P(A)')) + >>> b = clausify(read_expr('A=B')) + >>> print(a[0].unify(b[0])) + [{P(B)}] + +------------------------- +Test is_tautology() +------------------------- + >>> print(Clause([P(A), -P(A)]).is_tautology()) + True + >>> print(Clause([-P(A), P(A)]).is_tautology()) + True + >>> print(Clause([P(x), -P(A)]).is_tautology()) + False + >>> print(Clause([Q(B), -P(A), P(A)]).is_tautology()) + True + >>> print(Clause([-Q(A), P(R(A)), -P(R(A)), Q(x), -R(y)]).is_tautology()) + True + >>> print(Clause([P(x), -Q(A)]).is_tautology()) + False + +------------------------- +Test subsumes() +------------------------- + >>> print(Clause([P(A), Q(B)]).subsumes(Clause([P(A), Q(B)]))) + True + >>> print(Clause([-P(A)]).subsumes(Clause([P(A)]))) + False + >>> print(Clause([P(A), Q(B)]).subsumes(Clause([Q(B), P(A)]))) + True + >>> print(Clause([P(A), Q(B)]).subsumes(Clause([Q(B), R(A), P(A)]))) + True + >>> print(Clause([P(A), R(A), Q(B)]).subsumes(Clause([Q(B), P(A)]))) + False + >>> print(Clause([P(x)]).subsumes(Clause([P(A)]))) + True + >>> print(Clause([P(A)]).subsumes(Clause([P(x)]))) + True + +------------ +Test prove() +------------ + >>> print(ResolutionProverCommand(read_expr('man(x)')).prove()) + False + >>> print(ResolutionProverCommand(read_expr('(man(x) -> man(x))')).prove()) + True + >>> print(ResolutionProverCommand(read_expr('(man(x) -> --man(x))')).prove()) + True + >>> print(ResolutionProverCommand(read_expr('-(man(x) & -man(x))')).prove()) + True + >>> print(ResolutionProverCommand(read_expr('(man(x) | -man(x))')).prove()) + True + >>> print(ResolutionProverCommand(read_expr('(man(x) -> man(x))')).prove()) + True + >>> print(ResolutionProverCommand(read_expr('-(man(x) & -man(x))')).prove()) + True + >>> print(ResolutionProverCommand(read_expr('(man(x) | -man(x))')).prove()) + True + >>> print(ResolutionProverCommand(read_expr('(man(x) -> man(x))')).prove()) + True + >>> print(ResolutionProverCommand(read_expr('(man(x) <-> man(x))')).prove()) + True + >>> print(ResolutionProverCommand(read_expr('-(man(x) <-> -man(x))')).prove()) + True + >>> print(ResolutionProverCommand(read_expr('all x.man(x)')).prove()) + False + >>> print(ResolutionProverCommand(read_expr('-all x.some y.F(x,y) & some x.all y.(-F(x,y))')).prove()) + False + >>> print(ResolutionProverCommand(read_expr('some x.all y.sees(x,y)')).prove()) + False + + >>> p1 = read_expr('all x.(man(x) -> mortal(x))') + >>> p2 = read_expr('man(Socrates)') + >>> c = read_expr('mortal(Socrates)') + >>> ResolutionProverCommand(c, [p1,p2]).prove() + True + + >>> p1 = read_expr('all x.(man(x) -> walks(x))') + >>> p2 = read_expr('man(John)') + >>> c = read_expr('some y.walks(y)') + >>> ResolutionProverCommand(c, [p1,p2]).prove() + True + + >>> p = read_expr('some e1.some e2.(believe(e1,john,e2) & walk(e2,mary))') + >>> c = read_expr('some e0.walk(e0,mary)') + >>> ResolutionProverCommand(c, [p]).prove() + True + +------------ +Test proof() +------------ + >>> p1 = read_expr('all x.(man(x) -> mortal(x))') + >>> p2 = read_expr('man(Socrates)') + >>> c = read_expr('mortal(Socrates)') + >>> logic._counter._value = 0 + >>> tp = ResolutionProverCommand(c, [p1,p2]) + >>> tp.prove() + True + >>> print(tp.proof()) + [1] {-mortal(Socrates)} A + [2] {-man(z2), mortal(z2)} A + [3] {man(Socrates)} A + [4] {-man(Socrates)} (1, 2) + [5] {mortal(Socrates)} (2, 3) + [6] {} (1, 5) + + +------------------ +Question Answering +------------------ +One answer + + >>> p1 = read_expr('father_of(art,john)') + >>> p2 = read_expr('father_of(bob,kim)') + >>> p3 = read_expr('all x.all y.(father_of(x,y) -> parent_of(x,y))') + >>> c = read_expr('all x.(parent_of(x,john) -> ANSWER(x))') + >>> logic._counter._value = 0 + >>> tp = ResolutionProverCommand(None, [p1,p2,p3,c]) + >>> sorted(tp.find_answers()) + [] + >>> print(tp.proof()) # doctest: +SKIP + [1] {father_of(art,john)} A + [2] {father_of(bob,kim)} A + [3] {-father_of(z3,z4), parent_of(z3,z4)} A + [4] {-parent_of(z6,john), ANSWER(z6)} A + [5] {parent_of(art,john)} (1, 3) + [6] {parent_of(bob,kim)} (2, 3) + [7] {ANSWER(z6), -father_of(z6,john)} (3, 4) + [8] {ANSWER(art)} (1, 7) + [9] {ANSWER(art)} (4, 5) + + +Multiple answers + + >>> p1 = read_expr('father_of(art,john)') + >>> p2 = read_expr('mother_of(ann,john)') + >>> p3 = read_expr('all x.all y.(father_of(x,y) -> parent_of(x,y))') + >>> p4 = read_expr('all x.all y.(mother_of(x,y) -> parent_of(x,y))') + >>> c = read_expr('all x.(parent_of(x,john) -> ANSWER(x))') + >>> logic._counter._value = 0 + >>> tp = ResolutionProverCommand(None, [p1,p2,p3,p4,c]) + >>> sorted(tp.find_answers()) + [, ] + >>> print(tp.proof()) # doctest: +SKIP + [ 1] {father_of(art,john)} A + [ 2] {mother_of(ann,john)} A + [ 3] {-father_of(z3,z4), parent_of(z3,z4)} A + [ 4] {-mother_of(z7,z8), parent_of(z7,z8)} A + [ 5] {-parent_of(z10,john), ANSWER(z10)} A + [ 6] {parent_of(art,john)} (1, 3) + [ 7] {parent_of(ann,john)} (2, 4) + [ 8] {ANSWER(z10), -father_of(z10,john)} (3, 5) + [ 9] {ANSWER(art)} (1, 8) + [10] {ANSWER(z10), -mother_of(z10,john)} (4, 5) + [11] {ANSWER(ann)} (2, 10) + [12] {ANSWER(art)} (5, 6) + [13] {ANSWER(ann)} (5, 7) + diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/sentiment.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/sentiment.doctest new file mode 100644 index 0000000000000000000000000000000000000000..d7899edb8dd95b5a69e9fa5ecfb47a1e8ff0a2ef --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/sentiment.doctest @@ -0,0 +1,236 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +=================== +Sentiment Analysis +=================== + + >>> from nltk.classify import NaiveBayesClassifier + >>> from nltk.corpus import subjectivity + >>> from nltk.sentiment import SentimentAnalyzer + >>> from nltk.sentiment.util import * + + >>> n_instances = 100 + >>> subj_docs = [(sent, 'subj') for sent in subjectivity.sents(categories='subj')[:n_instances]] + >>> obj_docs = [(sent, 'obj') for sent in subjectivity.sents(categories='obj')[:n_instances]] + >>> len(subj_docs), len(obj_docs) + (100, 100) + +Each document is represented by a tuple (sentence, label). The sentence is tokenized, +so it is represented by a list of strings: + + >>> subj_docs[0] + (['smart', 'and', 'alert', ',', 'thirteen', 'conversations', 'about', 'one', + 'thing', 'is', 'a', 'small', 'gem', '.'], 'subj') + +We separately split subjective and objective instances to keep a balanced uniform +class distribution in both train and test sets. + + >>> train_subj_docs = subj_docs[:80] + >>> test_subj_docs = subj_docs[80:100] + >>> train_obj_docs = obj_docs[:80] + >>> test_obj_docs = obj_docs[80:100] + >>> training_docs = train_subj_docs+train_obj_docs + >>> testing_docs = test_subj_docs+test_obj_docs + + >>> sentim_analyzer = SentimentAnalyzer() + >>> all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs]) + +We use simple unigram word features, handling negation: + + >>> unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4) + >>> len(unigram_feats) + 83 + >>> sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats) + +We apply features to obtain a feature-value representation of our datasets: + + >>> training_set = sentim_analyzer.apply_features(training_docs) + >>> test_set = sentim_analyzer.apply_features(testing_docs) + +We can now train our classifier on the training set, and subsequently output the +evaluation results: + + >>> trainer = NaiveBayesClassifier.train + >>> classifier = sentim_analyzer.train(trainer, training_set) + Training classifier + >>> for key,value in sorted(sentim_analyzer.evaluate(test_set).items()): + ... print('{0}: {1}'.format(key, value)) + Evaluating NaiveBayesClassifier results... + Accuracy: 0.8 + F-measure [obj]: 0.8 + F-measure [subj]: 0.8 + Precision [obj]: 0.8 + Precision [subj]: 0.8 + Recall [obj]: 0.8 + Recall [subj]: 0.8 + + +Vader +------ + + >>> from nltk.sentiment.vader import SentimentIntensityAnalyzer + >>> sentences = ["VADER is smart, handsome, and funny.", # positive sentence example + ... "VADER is smart, handsome, and funny!", # punctuation emphasis handled correctly (sentiment intensity adjusted) + ... "VADER is very smart, handsome, and funny.", # booster words handled correctly (sentiment intensity adjusted) + ... "VADER is VERY SMART, handsome, and FUNNY.", # emphasis for ALLCAPS handled + ... "VADER is VERY SMART, handsome, and FUNNY!!!",# combination of signals - VADER appropriately adjusts intensity + ... "VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!",# booster words & punctuation make this close to ceiling for score + ... "The book was good.", # positive sentence + ... "The book was kind of good.", # qualified positive sentence is handled correctly (intensity adjusted) + ... "The plot was good, but the characters are uncompelling and the dialog is not great.", # mixed negation sentence + ... "A really bad, horrible book.", # negative sentence with booster words + ... "At least it isn't a horrible book.", # negated negative sentence with contraction + ... ":) and :D", # emoticons handled + ... "", # an empty string is correctly handled + ... "Today sux", # negative slang handled + ... "Today sux!", # negative slang with punctuation emphasis handled + ... "Today SUX!", # negative slang with capitalization emphasis + ... "Today kinda sux! But I'll get by, lol" # mixed sentiment example with slang and constrastive conjunction "but" + ... ] + >>> paragraph = "It was one of the worst movies I've seen, despite good reviews. \ + ... Unbelievably bad acting!! Poor direction. VERY poor production. \ + ... The movie was bad. Very bad movie. VERY bad movie. VERY BAD movie. VERY BAD movie!" + + >>> from nltk import tokenize + >>> lines_list = tokenize.sent_tokenize(paragraph) + >>> sentences.extend(lines_list) + + >>> tricky_sentences = [ + ... "Most automated sentiment analysis tools are shit.", + ... "VADER sentiment analysis is the shit.", + ... "Sentiment analysis has never been good.", + ... "Sentiment analysis with VADER has never been this good.", + ... "Warren Beatty has never been so entertaining.", + ... "I won't say that the movie is astounding and I wouldn't claim that \ + ... the movie is too banal either.", + ... "I like to hate Michael Bay films, but I couldn't fault this one", + ... "I like to hate Michael Bay films, BUT I couldn't help but fault this one", + ... "It's one thing to watch an Uwe Boll film, but another thing entirely \ + ... to pay for it", + ... "The movie was too good", + ... "This movie was actually neither that funny, nor super witty.", + ... "This movie doesn't care about cleverness, wit or any other kind of \ + ... intelligent humor.", + ... "Those who find ugly meanings in beautiful things are corrupt without \ + ... being charming.", + ... "There are slow and repetitive parts, BUT it has just enough spice to \ + ... keep it interesting.", + ... "The script is not fantastic, but the acting is decent and the cinematography \ + ... is EXCELLENT!", + ... "Roger Dodger is one of the most compelling variations on this theme.", + ... "Roger Dodger is one of the least compelling variations on this theme.", + ... "Roger Dodger is at least compelling as a variation on the theme.", + ... "they fall in love with the product", + ... "but then it breaks", + ... "usually around the time the 90 day warranty expires", + ... "the twin towers collapsed today", + ... "However, Mr. Carter solemnly argues, his client carried out the kidnapping \ + ... under orders and in the ''least offensive way possible.''" + ... ] + >>> sentences.extend(tricky_sentences) + >>> for sentence in sentences: + ... sid = SentimentIntensityAnalyzer() + ... print(sentence) + ... ss = sid.polarity_scores(sentence) + ... for k in sorted(ss): + ... print('{0}: {1}, '.format(k, ss[k]), end='') + ... print() + VADER is smart, handsome, and funny. + compound: 0.8316, neg: 0.0, neu: 0.254, pos: 0.746, + VADER is smart, handsome, and funny! + compound: 0.8439, neg: 0.0, neu: 0.248, pos: 0.752, + VADER is very smart, handsome, and funny. + compound: 0.8545, neg: 0.0, neu: 0.299, pos: 0.701, + VADER is VERY SMART, handsome, and FUNNY. + compound: 0.9227, neg: 0.0, neu: 0.246, pos: 0.754, + VADER is VERY SMART, handsome, and FUNNY!!! + compound: 0.9342, neg: 0.0, neu: 0.233, pos: 0.767, + VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!! + compound: 0.9469, neg: 0.0, neu: 0.294, pos: 0.706, + The book was good. + compound: 0.4404, neg: 0.0, neu: 0.508, pos: 0.492, + The book was kind of good. + compound: 0.3832, neg: 0.0, neu: 0.657, pos: 0.343, + The plot was good, but the characters are uncompelling and the dialog is not great. + compound: -0.7042, neg: 0.327, neu: 0.579, pos: 0.094, + A really bad, horrible book. + compound: -0.8211, neg: 0.791, neu: 0.209, pos: 0.0, + At least it isn't a horrible book. + compound: 0.431, neg: 0.0, neu: 0.637, pos: 0.363, + :) and :D + compound: 0.7925, neg: 0.0, neu: 0.124, pos: 0.876, + + compound: 0.0, neg: 0.0, neu: 0.0, pos: 0.0, + Today sux + compound: -0.3612, neg: 0.714, neu: 0.286, pos: 0.0, + Today sux! + compound: -0.4199, neg: 0.736, neu: 0.264, pos: 0.0, + Today SUX! + compound: -0.5461, neg: 0.779, neu: 0.221, pos: 0.0, + Today kinda sux! But I'll get by, lol + compound: 0.5249, neg: 0.138, neu: 0.517, pos: 0.344, + It was one of the worst movies I've seen, despite good reviews. + compound: -0.7584, neg: 0.394, neu: 0.606, pos: 0.0, + Unbelievably bad acting!! + compound: -0.6572, neg: 0.686, neu: 0.314, pos: 0.0, + Poor direction. + compound: -0.4767, neg: 0.756, neu: 0.244, pos: 0.0, + VERY poor production. + compound: -0.6281, neg: 0.674, neu: 0.326, pos: 0.0, + The movie was bad. + compound: -0.5423, neg: 0.538, neu: 0.462, pos: 0.0, + Very bad movie. + compound: -0.5849, neg: 0.655, neu: 0.345, pos: 0.0, + VERY bad movie. + compound: -0.6732, neg: 0.694, neu: 0.306, pos: 0.0, + VERY BAD movie. + compound: -0.7398, neg: 0.724, neu: 0.276, pos: 0.0, + VERY BAD movie! + compound: -0.7616, neg: 0.735, neu: 0.265, pos: 0.0, + Most automated sentiment analysis tools are shit. + compound: -0.5574, neg: 0.375, neu: 0.625, pos: 0.0, + VADER sentiment analysis is the shit. + compound: 0.6124, neg: 0.0, neu: 0.556, pos: 0.444, + Sentiment analysis has never been good. + compound: -0.3412, neg: 0.325, neu: 0.675, pos: 0.0, + Sentiment analysis with VADER has never been this good. + compound: 0.5228, neg: 0.0, neu: 0.703, pos: 0.297, + Warren Beatty has never been so entertaining. + compound: 0.5777, neg: 0.0, neu: 0.616, pos: 0.384, + I won't say that the movie is astounding and I wouldn't claim that the movie is too banal either. + compound: 0.4215, neg: 0.0, neu: 0.851, pos: 0.149, + I like to hate Michael Bay films, but I couldn't fault this one + compound: 0.3153, neg: 0.157, neu: 0.534, pos: 0.309, + I like to hate Michael Bay films, BUT I couldn't help but fault this one + compound: -0.1531, neg: 0.277, neu: 0.477, pos: 0.246, + It's one thing to watch an Uwe Boll film, but another thing entirely to pay for it + compound: -0.2541, neg: 0.112, neu: 0.888, pos: 0.0, + The movie was too good + compound: 0.4404, neg: 0.0, neu: 0.58, pos: 0.42, + This movie was actually neither that funny, nor super witty. + compound: -0.6759, neg: 0.41, neu: 0.59, pos: 0.0, + This movie doesn't care about cleverness, wit or any other kind of intelligent humor. + compound: -0.1338, neg: 0.265, neu: 0.497, pos: 0.239, + Those who find ugly meanings in beautiful things are corrupt without being charming. + compound: -0.3553, neg: 0.314, neu: 0.493, pos: 0.192, + There are slow and repetitive parts, BUT it has just enough spice to keep it interesting. + compound: 0.4678, neg: 0.079, neu: 0.735, pos: 0.186, + The script is not fantastic, but the acting is decent and the cinematography is EXCELLENT! + compound: 0.7565, neg: 0.092, neu: 0.607, pos: 0.301, + Roger Dodger is one of the most compelling variations on this theme. + compound: 0.2944, neg: 0.0, neu: 0.834, pos: 0.166, + Roger Dodger is one of the least compelling variations on this theme. + compound: -0.1695, neg: 0.132, neu: 0.868, pos: 0.0, + Roger Dodger is at least compelling as a variation on the theme. + compound: 0.2263, neg: 0.0, neu: 0.84, pos: 0.16, + they fall in love with the product + compound: 0.6369, neg: 0.0, neu: 0.588, pos: 0.412, + but then it breaks + compound: 0.0, neg: 0.0, neu: 1.0, pos: 0.0, + usually around the time the 90 day warranty expires + compound: 0.0, neg: 0.0, neu: 1.0, pos: 0.0, + the twin towers collapsed today + compound: -0.2732, neg: 0.344, neu: 0.656, pos: 0.0, + However, Mr. Carter solemnly argues, his client carried out the kidnapping under orders and in the ''least offensive way possible.'' + compound: -0.5859, neg: 0.23, neu: 0.697, pos: 0.074, diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/sentiwordnet.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/sentiwordnet.doctest new file mode 100644 index 0000000000000000000000000000000000000000..8cab0d9590c71b37b1319e5f21b388168777c476 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/sentiwordnet.doctest @@ -0,0 +1,41 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +====================== +SentiWordNet Interface +====================== + +SentiWordNet can be imported like this: + + >>> from nltk.corpus import sentiwordnet as swn + +------------ +SentiSynsets +------------ + + >>> breakdown = swn.senti_synset('breakdown.n.03') + >>> print(breakdown) + + >>> breakdown.pos_score() + 0.0 + >>> breakdown.neg_score() + 0.25 + >>> breakdown.obj_score() + 0.75 + + +------ +Lookup +------ + + >>> list(swn.senti_synsets('slow')) + [SentiSynset('decelerate.v.01'), SentiSynset('slow.v.02'), + SentiSynset('slow.v.03'), SentiSynset('slow.a.01'), + SentiSynset('slow.a.02'), SentiSynset('dense.s.04'), + SentiSynset('slow.a.04'), SentiSynset('boring.s.01'), + SentiSynset('dull.s.08'), SentiSynset('slowly.r.01'), + SentiSynset('behind.r.03')] + + >>> happy = swn.senti_synsets('happy', 'a') + + >>> all = swn.all_senti_synsets() diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/setup_fixt.py b/llmeval-env/lib/python3.10/site-packages/nltk/test/setup_fixt.py new file mode 100644 index 0000000000000000000000000000000000000000..e7f3a27464b1875107354eb01e1fe9467c653539 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/setup_fixt.py @@ -0,0 +1,26 @@ +from nltk.internals import find_binary, find_jar + + +def check_binary(binary: str, **args): + """Skip a test via `pytest.skip` if the `binary` executable is not found. + Keyword arguments are passed to `nltk.internals.find_binary`.""" + import pytest + + try: + find_binary(binary, **args) + except LookupError: + pytest.skip(f"Skipping test because the {binary} binary was not found.") + + +def check_jar(name_pattern: str, **args): + """Skip a test via `pytest.skip` if the `name_pattern` jar is not found. + Keyword arguments are passed to `nltk.internals.find_jar`. + + TODO: Investigate why the CoreNLP tests that rely on this check_jar failed + on the CI. https://github.com/nltk/nltk/pull/3060#issuecomment-1268355108 + """ + import pytest + + pytest.skip( + "Skipping test because the doctests requiring jars are inconsistent on the CI." + ) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/simple.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/simple.doctest new file mode 100644 index 0000000000000000000000000000000000000000..5cff34f2b3aab1dcfed64ffa93a63e3ce3c40c35 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/simple.doctest @@ -0,0 +1,83 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +================= +EasyInstall Tests +================= + +This file contains some simple tests that will be run by EasyInstall in +order to test the installation when NLTK-Data is absent. + + +------------ +Tokenization +------------ + + >>> from nltk.tokenize import wordpunct_tokenize + >>> s = ("Good muffins cost $3.88\nin New York. Please buy me\n" + ... "two of them.\n\nThanks.") + >>> wordpunct_tokenize(s) + ['Good', 'muffins', 'cost', '$', '3', '.', '88', 'in', 'New', 'York', '.', + 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.'] + +------- +Metrics +------- + + >>> from nltk.metrics import precision, recall, f_measure + >>> reference = 'DET NN VB DET JJ NN NN IN DET NN'.split() + >>> test = 'DET VB VB DET NN NN NN IN DET NN'.split() + >>> reference_set = set(reference) + >>> test_set = set(test) + >>> precision(reference_set, test_set) + 1.0 + >>> print(recall(reference_set, test_set)) + 0.8 + >>> print(f_measure(reference_set, test_set)) + 0.88888888888... + +------------------ +Feature Structures +------------------ + + >>> from nltk import FeatStruct + >>> fs1 = FeatStruct(PER=3, NUM='pl', GND='fem') + >>> fs2 = FeatStruct(POS='N', AGR=fs1) + >>> print(fs2) + [ [ GND = 'fem' ] ] + [ AGR = [ NUM = 'pl' ] ] + [ [ PER = 3 ] ] + [ ] + [ POS = 'N' ] + >>> print(fs2['AGR']) + [ GND = 'fem' ] + [ NUM = 'pl' ] + [ PER = 3 ] + >>> print(fs2['AGR']['PER']) + 3 + +------- +Parsing +------- + + >>> from nltk.parse.recursivedescent import RecursiveDescentParser + >>> from nltk.grammar import CFG + >>> grammar = CFG.fromstring(""" + ... S -> NP VP + ... PP -> P NP + ... NP -> 'the' N | N PP | 'the' N PP + ... VP -> V NP | V PP | V NP PP + ... N -> 'cat' | 'dog' | 'rug' + ... V -> 'chased' + ... P -> 'on' + ... """) + >>> rd = RecursiveDescentParser(grammar) + >>> sent = 'the cat chased the dog on the rug'.split() + >>> for t in rd.parse(sent): + ... print(t) + (S + (NP the (N cat)) + (VP (V chased) (NP the (N dog) (PP (P on) (NP the (N rug)))))) + (S + (NP the (N cat)) + (VP (V chased) (NP the (N dog)) (PP (P on) (NP the (N rug))))) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/stem.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/stem.doctest new file mode 100644 index 0000000000000000000000000000000000000000..c2c40a66d4202e13b46eb81424b7902637c7f942 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/stem.doctest @@ -0,0 +1,105 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +========== + Stemmers +========== + +Overview +~~~~~~~~ + +Stemmers remove morphological affixes from words, leaving only the +word stem. + + >>> from nltk.stem import * + +Unit tests for the Porter stemmer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + >>> from nltk.stem.porter import * + +Create a new Porter stemmer. + + >>> stemmer = PorterStemmer() + +Test the stemmer on various pluralised words. + + >>> plurals = ['caresses', 'flies', 'dies', 'mules', 'denied', + ... 'died', 'agreed', 'owned', 'humbled', 'sized', + ... 'meeting', 'stating', 'siezing', 'itemization', + ... 'sensational', 'traditional', 'reference', 'colonizer', + ... 'plotted'] + + >>> singles = [stemmer.stem(plural) for plural in plurals] + + >>> print(' '.join(singles)) + caress fli die mule deni die agre own humbl size meet + state siez item sensat tradit refer colon plot + + +Unit tests for Snowball stemmer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + >>> from nltk.stem.snowball import SnowballStemmer + +See which languages are supported. + + >>> print(" ".join(SnowballStemmer.languages)) + arabic danish dutch english finnish french german hungarian italian + norwegian porter portuguese romanian russian spanish swedish + +Create a new instance of a language specific subclass. + + >>> stemmer = SnowballStemmer("english") + +Stem a word. + + >>> print(stemmer.stem("running")) + run + +Decide not to stem stopwords. + + >>> stemmer2 = SnowballStemmer("english", ignore_stopwords=True) + >>> print(stemmer.stem("having")) + have + >>> print(stemmer2.stem("having")) + having + +The 'english' stemmer is better than the original 'porter' stemmer. + + >>> print(SnowballStemmer("english").stem("generously")) + generous + >>> print(SnowballStemmer("porter").stem("generously")) + gener + +.. note:: + + Extra stemmer tests can be found in `nltk.test.unit.test_stem`. + +Unit tests for ARLSTem Stemmer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + >>> from nltk.stem.arlstem import ARLSTem + +Create a Stemmer instance. + + >>> stemmer = ARLSTem() + +Stem a word. + + >>> stemmer.stem('يعمل') + 'عمل' + +Unit tests for ARLSTem2 Stemmer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + >>> from nltk.stem.arlstem2 import ARLSTem2 + +Create a Stemmer instance. + + >>> stemmer = ARLSTem2() + +Stem a word. + + >>> stemmer.stem('يعمل') + 'عمل' diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/tag.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/tag.doctest new file mode 100644 index 0000000000000000000000000000000000000000..505f622dfbc5eb0798ce775774df9c08a135c01f --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/tag.doctest @@ -0,0 +1,475 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +Evaluation of Taggers +===================== + +Evaluating the standard NLTK PerceptronTagger using Accuracy, +Precision, Recall and F-measure for each of the tags. + + >>> from nltk.tag import PerceptronTagger + >>> from nltk.corpus import treebank + >>> tagger = PerceptronTagger() + >>> gold_data = treebank.tagged_sents()[10:20] + >>> print(tagger.accuracy(gold_data)) # doctest: +ELLIPSIS + 0.885931... + + >>> print(tagger.evaluate_per_tag(gold_data)) + Tag | Prec. | Recall | F-measure + -------+--------+--------+----------- + '' | 1.0000 | 1.0000 | 1.0000 + , | 1.0000 | 1.0000 | 1.0000 + -NONE- | 0.0000 | 0.0000 | 0.0000 + . | 1.0000 | 1.0000 | 1.0000 + : | 1.0000 | 1.0000 | 1.0000 + CC | 1.0000 | 1.0000 | 1.0000 + CD | 0.7647 | 1.0000 | 0.8667 + DT | 1.0000 | 1.0000 | 1.0000 + IN | 1.0000 | 1.0000 | 1.0000 + JJ | 0.5882 | 0.8333 | 0.6897 + JJR | 1.0000 | 1.0000 | 1.0000 + JJS | 1.0000 | 1.0000 | 1.0000 + NN | 0.7647 | 0.9630 | 0.8525 + NNP | 0.8929 | 1.0000 | 0.9434 + NNS | 1.0000 | 1.0000 | 1.0000 + POS | 1.0000 | 1.0000 | 1.0000 + PRP | 1.0000 | 1.0000 | 1.0000 + RB | 0.8000 | 1.0000 | 0.8889 + RBR | 0.0000 | 0.0000 | 0.0000 + TO | 1.0000 | 1.0000 | 1.0000 + VB | 1.0000 | 1.0000 | 1.0000 + VBD | 0.8571 | 0.9231 | 0.8889 + VBG | 1.0000 | 1.0000 | 1.0000 + VBN | 0.8333 | 0.5556 | 0.6667 + VBP | 0.5714 | 0.8000 | 0.6667 + VBZ | 1.0000 | 1.0000 | 1.0000 + WP | 1.0000 | 1.0000 | 1.0000 + `` | 1.0000 | 1.0000 | 1.0000 + + +List only the 10 most common tags: + + >>> print(tagger.evaluate_per_tag(gold_data, truncate=10, sort_by_count=True)) + Tag | Prec. | Recall | F-measure + -------+--------+--------+----------- + IN | 1.0000 | 1.0000 | 1.0000 + DT | 1.0000 | 1.0000 | 1.0000 + NN | 0.7647 | 0.9630 | 0.8525 + NNP | 0.8929 | 1.0000 | 0.9434 + NNS | 1.0000 | 1.0000 | 1.0000 + -NONE- | 0.0000 | 0.0000 | 0.0000 + CD | 0.7647 | 1.0000 | 0.8667 + VBD | 0.8571 | 0.9231 | 0.8889 + JJ | 0.5882 | 0.8333 | 0.6897 + , | 1.0000 | 1.0000 | 1.0000 + + +Similarly, we can display the confusion matrix for this tagger. + + >>> print(tagger.confusion(gold_data)) + | - | + | N | + | O | + | N J J N N P P R V V V V V | + | ' E C C D I J J J N N N O R R B T V B B B B B W ` | + | ' , - . : C D T N J R S N P S S P B R O B D G N P Z P ` | + -------+-------------------------------------------------------------------------------------+ + '' | <3> . . . . . . . . . . . . . . . . . . . . . . . . . . . | + , | .<11> . . . . . . . . . . . . . . . . . . . . . . . . . . | + -NONE- | . . <.> . . . 4 . . 4 . . 7 2 . . . 1 . . . . . . 3 . . . | + . | . . .<10> . . . . . . . . . . . . . . . . . . . . . . . . | + : | . . . . <1> . . . . . . . . . . . . . . . . . . . . . . . | + CC | . . . . . <5> . . . . . . . . . . . . . . . . . . . . . . | + CD | . . . . . .<13> . . . . . . . . . . . . . . . . . . . . . | + DT | . . . . . . .<28> . . . . . . . . . . . . . . . . . . . . | + IN | . . . . . . . .<34> . . . . . . . . . . . . . . . . . . . | + JJ | . . . . . . . . .<10> . . . 1 . . . . 1 . . . . . . . . . | + JJR | . . . . . . . . . . <1> . . . . . . . . . . . . . . . . . | + JJS | . . . . . . . . . . . <1> . . . . . . . . . . . . . . . . | + NN | . . . . . . . . . 1 . .<26> . . . . . . . . . . . . . . . | + NNP | . . . . . . . . . . . . .<25> . . . . . . . . . . . . . . | + NNS | . . . . . . . . . . . . . .<22> . . . . . . . . . . . . . | + POS | . . . . . . . . . . . . . . . <1> . . . . . . . . . . . . | + PRP | . . . . . . . . . . . . . . . . <3> . . . . . . . . . . . | + RB | . . . . . . . . . . . . . . . . . <4> . . . . . . . . . . | + RBR | . . . . . . . . . . . . . . . . . . <.> . . . . . . . . . | + TO | . . . . . . . . . . . . . . . . . . . <2> . . . . . . . . | + VB | . . . . . . . . . . . . . . . . . . . . <1> . . . . . . . | + VBD | . . . . . . . . . . . . . . . . . . . . .<12> . 1 . . . . | + VBG | . . . . . . . . . . . . . . . . . . . . . . <3> . . . . . | + VBN | . . . . . . . . . 2 . . . . . . . . . . . 2 . <5> . . . . | + VBP | . . . . . . . . . . . . 1 . . . . . . . . . . . <4> . . . | + VBZ | . . . . . . . . . . . . . . . . . . . . . . . . . <2> . . | + WP | . . . . . . . . . . . . . . . . . . . . . . . . . . <3> . | + `` | . . . . . . . . . . . . . . . . . . . . . . . . . . . <3>| + -------+-------------------------------------------------------------------------------------+ + (row = reference; col = test) + + +Brill Trainer with evaluation +============================= + + >>> # Perform the relevant imports. + >>> from nltk.tbl.template import Template + >>> from nltk.tag.brill import Pos, Word + >>> from nltk.tag import untag, RegexpTagger, BrillTaggerTrainer, UnigramTagger + + >>> # Load some data + >>> from nltk.corpus import treebank + >>> training_data = treebank.tagged_sents()[:100] + >>> baseline_data = treebank.tagged_sents()[100:200] + >>> gold_data = treebank.tagged_sents()[200:300] + >>> testing_data = [untag(s) for s in gold_data] + + >>> backoff = 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) + ... ]) + +We've now created a simple ``RegexpTagger``, which tags according to the regular expression +rules it has been supplied. This tagger in and of itself does not have a great accuracy. + + >>> backoff.accuracy(gold_data) #doctest: +ELLIPSIS + 0.245014... + +Neither does a simple ``UnigramTagger``. This tagger is trained on some data, +and will then first try to match unigrams (i.e. tokens) of the sentence it has +to tag to the learned data. + + >>> unigram_tagger = UnigramTagger(baseline_data) + >>> unigram_tagger.accuracy(gold_data) #doctest: +ELLIPSIS + 0.581196... + +The lackluster accuracy here can be explained with the following example: + + >>> unigram_tagger.tag(["I", "would", "like", "this", "sentence", "to", "be", "tagged"]) + [('I', 'NNP'), ('would', 'MD'), ('like', None), ('this', 'DT'), ('sentence', None), + ('to', 'TO'), ('be', 'VB'), ('tagged', None)] + +As you can see, many tokens are tagged as ``None``, as these tokens are OOV (out of vocabulary). +The ``UnigramTagger`` has never seen them, and as a result they are not in its database of known terms. + +In practice, a ``UnigramTagger`` is exclusively used in conjunction with a *backoff*. Our real +baseline which will use such a backoff. We'll create a ``UnigramTagger`` like before, but now +the ``RegexpTagger`` will be used as a backoff for the situations where the ``UnigramTagger`` +encounters an OOV token. + + >>> baseline = UnigramTagger(baseline_data, backoff=backoff) + >>> baseline.accuracy(gold_data) #doctest: +ELLIPSIS + 0.7537647... + +That is already much better. We can investigate the performance further by running +``evaluate_per_tag``. This method will output the *Precision*, *Recall* and *F-measure* +of each tag. + + >>> print(baseline.evaluate_per_tag(gold_data, sort_by_count=True)) + Tag | Prec. | Recall | F-measure + -------+--------+--------+----------- + NNP | 0.9674 | 0.2738 | 0.4269 + NN | 0.4111 | 0.9136 | 0.5670 + IN | 0.9383 | 0.9580 | 0.9480 + DT | 0.9819 | 0.8859 | 0.9314 + JJ | 0.8167 | 0.2970 | 0.4356 + NNS | 0.7393 | 0.9630 | 0.8365 + -NONE- | 1.0000 | 0.8345 | 0.9098 + , | 1.0000 | 1.0000 | 1.0000 + . | 1.0000 | 1.0000 | 1.0000 + VBD | 0.6429 | 0.8804 | 0.7431 + CD | 1.0000 | 0.9872 | 0.9935 + CC | 1.0000 | 0.9355 | 0.9667 + VB | 0.7778 | 0.3684 | 0.5000 + VBN | 0.9375 | 0.3000 | 0.4545 + RB | 0.7778 | 0.7447 | 0.7609 + TO | 1.0000 | 1.0000 | 1.0000 + VBZ | 0.9643 | 0.6429 | 0.7714 + VBG | 0.6415 | 0.9444 | 0.7640 + PRP$ | 1.0000 | 1.0000 | 1.0000 + PRP | 1.0000 | 0.5556 | 0.7143 + MD | 1.0000 | 1.0000 | 1.0000 + VBP | 0.6471 | 0.5789 | 0.6111 + POS | 1.0000 | 1.0000 | 1.0000 + $ | 1.0000 | 0.8182 | 0.9000 + '' | 1.0000 | 1.0000 | 1.0000 + : | 1.0000 | 1.0000 | 1.0000 + WDT | 0.4000 | 0.2000 | 0.2667 + `` | 1.0000 | 1.0000 | 1.0000 + JJR | 1.0000 | 0.5000 | 0.6667 + NNPS | 0.0000 | 0.0000 | 0.0000 + RBR | 1.0000 | 1.0000 | 1.0000 + -LRB- | 0.0000 | 0.0000 | 0.0000 + -RRB- | 0.0000 | 0.0000 | 0.0000 + RP | 0.6667 | 0.6667 | 0.6667 + EX | 0.5000 | 0.5000 | 0.5000 + JJS | 0.0000 | 0.0000 | 0.0000 + WP | 1.0000 | 1.0000 | 1.0000 + PDT | 0.0000 | 0.0000 | 0.0000 + AT | 0.0000 | 0.0000 | 0.0000 + + +It's clear that although the precision of tagging `"NNP"` is high, the recall is very low. +With other words, we're missing a lot of cases where the true label is `"NNP"`. We can see +a similar effect with `"JJ"`. + +We can also see a very expected result: The precision of `"NN"` is low, while the recall +is high. If a term is OOV (i.e. ``UnigramTagger`` defers it to ``RegexpTagger``) and +``RegexpTagger`` doesn't have a good rule for it, then it will be tagged as `"NN"`. So, +we catch almost all tokens that are truly labeled as `"NN"`, but we also tag as `"NN"` +for many tokens that shouldn't be `"NN"`. + +This method gives us some insight in what parts of the tagger needs more attention, and why. +However, it doesn't tell us what the terms with true label `"NNP"` or `"JJ"` are actually +tagged as. +To help that, we can create a confusion matrix. + + >>> print(baseline.confusion(gold_data)) + | - | + | - N - | + | L O R N P | + | R N R J J N N N P P P R R V V V V V W | + | ' B E B A C C D E I J J J M N N P N D O R P R B R T V B B B B B D W ` | + | $ ' , - - - . : T C D T X N J R S D N P S S T S P $ B R P O B D G N P Z T P ` | + -------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+ + $ | <9> . . . . . . . . . . . . . . . . . 2 . . . . . . . . . . . . . . . . . . . . | + '' | . <10> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | + , | . .<115> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | + -LRB- | . . . <.> . . . . . . . . . . . . . . 3 . . . . . . . . . . . . . . . . . . . . | + -NONE- | . . . .<121> . . . . . . . . . . . . . 24 . . . . . . . . . . . . . . . . . . . . | + -RRB- | . . . . . <.> . . . . . . . . . . . . 3 . . . . . . . . . . . . . . . . . . . . | + . | . . . . . .<100> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | + : | . . . . . . . <10> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | + AT | . . . . . . . . <.> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | + CC | . . . . . . . . . <58> . . . . . . . . 4 . . . . . . . . . . . . . . . . . . . . | + CD | . . . . . . . . . . <77> . . . . . . . 1 . . . . . . . . . . . . . . . . . . . . | + DT | . . . . . . . . 1 . .<163> . 4 . . . . 13 . . . . . . . . . . . . . . . . . 3 . . | + EX | . . . . . . . . . . . . <1> . . . . . 1 . . . . . . . . . . . . . . . . . . . . | + IN | . . . . . . . . . . . . .<228> . . . . 8 . . . . . . . . . . . . . 2 . . . . . . | + JJ | . . . . . . . . . . . . . . <49> . . . 86 2 . 4 . . . . 6 . . . . 12 3 . 3 . . . . | + JJR | . . . . . . . . . . . . . . . <3> . . 3 . . . . . . . . . . . . . . . . . . . . | + JJS | . . . . . . . . . . . . . . . . <.> . 2 . . . . . . . . . . . . . . . . . . . . | + MD | . . . . . . . . . . . . . . . . . <19> . . . . . . . . . . . . . . . . . . . . . | + NN | . . . . . . . . . . . . . . 9 . . .<296> . . 5 . . . . . . . . 5 . 9 . . . . . . | + NNP | . . . . . . . . . . . 2 . . . . . . 199 <89> . 26 . . . . 2 . . . . 2 5 . . . . . . | + NNPS | . . . . . . . . . . . . . . . . . . . 1 <.> 3 . . . . . . . . . . . . . . . . . | + NNS | . . . . . . . . . . . . . . . . . . 5 . .<156> . . . . . . . . . . . . . 1 . . . | + PDT | . . . . . . . . . . . 1 . . . . . . . . . . <.> . . . . . . . . . . . . . . . . | + POS | . . . . . . . . . . . . . . . . . . . . . . . <14> . . . . . . . . . . . . . . . | + PRP | . . . . . . . . . . . . . . . . . . 10 . . 2 . . <15> . . . . . . . . . . . . . . | + PRP$ | . . . . . . . . . . . . . . . . . . . . . . . . . <28> . . . . . . . . . . . . . | + RB | . . . . . . . . . . . . 1 4 . . . . 6 . . . . . . . <35> . 1 . . . . . . . . . . | + RBR | . . . . . . . . . . . . . . . . . . . . . . . . . . . <4> . . . . . . . . . . . | + RP | . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . <2> . . . . . . . . . . | + TO | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . <47> . . . . . . . . . | + VB | . . . . . . . . . . . . . . 2 . . . 30 . . . . . . . 1 . . . <21> . . . 3 . . . . | + VBD | . . . . . . . . . . . . . . . . . . 10 . . . . . . . . . . . . <81> . 1 . . . . . | + VBG | . . . . . . . . . . . . . . . . . . 2 . . . . . . . . . . . . . <34> . . . . . . | + VBN | . . . . . . . . . . . . . . . . . . 4 . . . . . . . . . . . . 31 . <15> . . . . . | + VBP | . . . . . . . . . . . . . . . . . . 7 . . . . . . . . . . . 1 . . . <11> . . . . | + VBZ | . . . . . . . . . . . . . . . . . . . . . 15 . . . . . . . . . . . . . <27> . . . | + WDT | . . . . . . . . . . . . . 7 . . . . 1 . . . . . . . . . . . . . . . . . <2> . . | + WP | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . <2> . | + `` | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . <10>| + -------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+ + (row = reference; col = test) + + +Once again we can see that `"NN"` is the default if the tagger isn't sure. Beyond that, +we can see why the recall for `"NNP"` is so low: these tokens are often tagged as `"NN"`. +This effect can also be seen for `"JJ"`, where the majority of tokens that ought to be +tagged as `"JJ"` are actually tagged as `"NN"` by our tagger. + +This tagger will only serve as a baseline for the ``BrillTaggerTrainer``, which uses +templates to attempt to improve the performance of the tagger. + + >>> # Set up templates + >>> Template._cleartemplates() #clear any templates created in earlier tests + >>> templates = [Template(Pos([-1])), Template(Pos([-1]), Word([0]))] + + >>> # Construct a BrillTaggerTrainer + >>> tt = BrillTaggerTrainer(baseline, templates, trace=3) + >>> tagger1 = tt.train(training_data, max_rules=10) + TBL train (fast) (seqs: 100; tokens: 2417; tpls: 2; min score: 2; min acc: None) + Finding initial useful rules... + Found 618 useful rules. + + B | + S F r O | Score = Fixed - Broken + c i o t | R Fixed = num tags changed incorrect -> correct + o x k h | u Broken = num tags changed correct -> incorrect + r e e e | l Other = num tags changed incorrect -> incorrect + e d n r | e + ------------------+------------------------------------------------------- + 13 14 1 4 | NN->VB if Pos:TO@[-1] + 8 8 0 0 | NN->VB if Pos:MD@[-1] + 7 10 3 22 | NN->IN if Pos:NNS@[-1] + 5 5 0 0 | NN->VBP if Pos:PRP@[-1] + 5 5 0 0 | VBD->VBN if Pos:VBZ@[-1] + 5 5 0 0 | NNS->NN if Pos:IN@[-1] & Word:asbestos@[0] + 4 4 0 0 | NN->-NONE- if Pos:WP@[-1] + 4 4 0 3 | NN->NNP if Pos:-NONE-@[-1] + 4 6 2 2 | NN->NNP if Pos:NNP@[-1] + 4 4 0 0 | NNS->VBZ if Pos:PRP@[-1] + + >>> tagger1.rules()[1:3] + (Rule('000', 'NN', 'VB', [(Pos([-1]),'MD')]), Rule('000', 'NN', 'IN', [(Pos([-1]),'NNS')])) + + >>> tagger1.print_template_statistics(printunused=False) + TEMPLATE STATISTICS (TRAIN) 2 templates, 10 rules) + TRAIN ( 2417 tokens) initial 555 0.7704 final: 496 0.7948 + #ID | Score (train) | #Rules | Template + -------------------------------------------- + 000 | 54 0.915 | 9 0.900 | Template(Pos([-1])) + 001 | 5 0.085 | 1 0.100 | Template(Pos([-1]),Word([0])) + + + + >>> tagger1.accuracy(gold_data) # doctest: +ELLIPSIS + 0.769230... + + >>> print(tagger1.evaluate_per_tag(gold_data, sort_by_count=True)) + Tag | Prec. | Recall | F-measure + -------+--------+--------+----------- + NNP | 0.8298 | 0.3600 | 0.5021 + NN | 0.4435 | 0.8364 | 0.5797 + IN | 0.8476 | 0.9580 | 0.8994 + DT | 0.9819 | 0.8859 | 0.9314 + JJ | 0.8167 | 0.2970 | 0.4356 + NNS | 0.7464 | 0.9630 | 0.8410 + -NONE- | 1.0000 | 0.8414 | 0.9139 + , | 1.0000 | 1.0000 | 1.0000 + . | 1.0000 | 1.0000 | 1.0000 + VBD | 0.6723 | 0.8696 | 0.7583 + CD | 1.0000 | 0.9872 | 0.9935 + CC | 1.0000 | 0.9355 | 0.9667 + VB | 0.8103 | 0.8246 | 0.8174 + VBN | 0.9130 | 0.4200 | 0.5753 + RB | 0.7778 | 0.7447 | 0.7609 + TO | 1.0000 | 1.0000 | 1.0000 + VBZ | 0.9667 | 0.6905 | 0.8056 + VBG | 0.6415 | 0.9444 | 0.7640 + PRP$ | 1.0000 | 1.0000 | 1.0000 + PRP | 1.0000 | 0.5556 | 0.7143 + MD | 1.0000 | 1.0000 | 1.0000 + VBP | 0.6316 | 0.6316 | 0.6316 + POS | 1.0000 | 1.0000 | 1.0000 + $ | 1.0000 | 0.8182 | 0.9000 + '' | 1.0000 | 1.0000 | 1.0000 + : | 1.0000 | 1.0000 | 1.0000 + WDT | 0.4000 | 0.2000 | 0.2667 + `` | 1.0000 | 1.0000 | 1.0000 + JJR | 1.0000 | 0.5000 | 0.6667 + NNPS | 0.0000 | 0.0000 | 0.0000 + RBR | 1.0000 | 1.0000 | 1.0000 + -LRB- | 0.0000 | 0.0000 | 0.0000 + -RRB- | 0.0000 | 0.0000 | 0.0000 + RP | 0.6667 | 0.6667 | 0.6667 + EX | 0.5000 | 0.5000 | 0.5000 + JJS | 0.0000 | 0.0000 | 0.0000 + WP | 1.0000 | 1.0000 | 1.0000 + PDT | 0.0000 | 0.0000 | 0.0000 + AT | 0.0000 | 0.0000 | 0.0000 + + + >>> print(tagger1.confusion(gold_data)) + | - | + | - N - | + | L O R N P | + | R N R J J N N N P P P R R V V V V V W | + | ' B E B A C C D E I J J J M N N P N D O R P R B R T V B B B B B D W ` | + | $ ' , - - - . : T C D T X N J R S D N P S S T S P $ B R P O B D G N P Z T P ` | + -------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+ + $ | <9> . . . . . . . . . . . . . . . . . 1 . . . . . . . . . . . 1 . . . . . . . . | + '' | . <10> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | + , | . .<115> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | + -LRB- | . . . <.> . . . . . . . . . 1 . . . . 2 . . . . . . . . . . . . . . . . . . . . | + -NONE- | . . . .<122> . . . . . . . . 1 . . . . 22 . . . . . . . . . . . . . . . . . . . . | + -RRB- | . . . . . <.> . . . . . . . . . . . . 2 1 . . . . . . . . . . . . . . . . . . . | + . | . . . . . .<100> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | + : | . . . . . . . <10> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | + AT | . . . . . . . . <.> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | + CC | . . . . . . . . . <58> . . . . . . . . 2 1 . . . . . . . . . . . . . . 1 . . . . | + CD | . . . . . . . . . . <77> . . . . . . . 1 . . . . . . . . . . . . . . . . . . . . | + DT | . . . . . . . . 1 . .<163> . 5 . . . . 12 . . . . . . . . . . . . . . . . . 3 . . | + EX | . . . . . . . . . . . . <1> . . . . . 1 . . . . . . . . . . . . . . . . . . . . | + IN | . . . . . . . . . . . . .<228> . . . . 8 . . . . . . . . . . . . . 2 . . . . . . | + JJ | . . . . . . . . . . . . . 4 <49> . . . 79 4 . 4 . . . . 6 . . . 1 12 3 . 3 . . . . | + JJR | . . . . . . . . . . . . . 2 . <3> . . 1 . . . . . . . . . . . . . . . . . . . . | + JJS | . . . . . . . . . . . . . . . . <.> . 2 . . . . . . . . . . . . . . . . . . . . | + MD | . . . . . . . . . . . . . . . . . <19> . . . . . . . . . . . . . . . . . . . . . | + NN | . . . . . . . . . . . . . 7 9 . . .<271> 16 . 5 . . . . . . . . 7 . 9 . . . . . . | + NNP | . . . . . . . . . . . 2 . 7 . . . . 163<117> . 26 . . . . 2 . . . 1 2 5 . . . . . . | + NNPS | . . . . . . . . . . . . . . . . . . . 1 <.> 3 . . . . . . . . . . . . . . . . . | + NNS | . . . . . . . . . . . . . . . . . . 5 . .<156> . . . . . . . . . . . . . 1 . . . | + PDT | . . . . . . . . . . . 1 . . . . . . . . . . <.> . . . . . . . . . . . . . . . . | + POS | . . . . . . . . . . . . . . . . . . . . . . . <14> . . . . . . . . . . . . . . . | + PRP | . . . . . . . . . . . . . . . . . . 10 . . 2 . . <15> . . . . . . . . . . . . . . | + PRP$ | . . . . . . . . . . . . . . . . . . . . . . . . . <28> . . . . . . . . . . . . . | + RB | . . . . . . . . . . . . 1 4 . . . . 6 . . . . . . . <35> . 1 . . . . . . . . . . | + RBR | . . . . . . . . . . . . . . . . . . . . . . . . . . . <4> . . . . . . . . . . . | + RP | . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . <2> . . . . . . . . . . | + TO | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . <47> . . . . . . . . . | + VB | . . . . . . . . . . . . . . 2 . . . 4 . . . . . . . 1 . . . <47> . . . 3 . . . . | + VBD | . . . . . . . . . . . . . 1 . . . . 8 1 . . . . . . . . . . . <80> . 2 . . . . . | + VBG | . . . . . . . . . . . . . . . . . . 2 . . . . . . . . . . . . . <34> . . . . . . | + VBN | . . . . . . . . . . . . . . . . . . 4 . . . . . . . . . . . . 25 . <21> . . . . . | + VBP | . . . . . . . . . . . . . 2 . . . . 4 . . . . . . . . . . . 1 . . . <12> . . . . | + VBZ | . . . . . . . . . . . . . . . . . . . . . 13 . . . . . . . . . . . . . <29> . . . | + WDT | . . . . . . . . . . . . . 7 . . . . 1 . . . . . . . . . . . . . . . . . <2> . . | + WP | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . <2> . | + `` | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . <10>| + -------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+ + (row = reference; col = test) + + + >>> tagged, test_stats = tagger1.batch_tag_incremental(testing_data, gold_data) + >>> tagged[33][12:] + [('foreign', 'NN'), ('debt', 'NN'), ('of', 'IN'), ('$', '$'), ('64', 'CD'), + ('billion', 'CD'), ('*U*', '-NONE-'), ('--', ':'), ('the', 'DT'), ('third-highest', 'NN'), + ('in', 'IN'), ('the', 'DT'), ('developing', 'VBG'), ('world', 'NN'), ('.', '.')] + +Regression Tests +~~~~~~~~~~~~~~~~ + +Sequential Taggers +------------------ + +Add tests for: + - make sure backoff is being done correctly. + - make sure ngram taggers don't use previous sentences for context. + - make sure ngram taggers see 'beginning of the sentence' as a + unique context + - make sure regexp tagger's regexps are tried in order + - train on some simple examples, & make sure that the size & the + generated models are correct. + - make sure cutoff works as intended + - make sure that ngram models only exclude contexts covered by the + backoff tagger if the backoff tagger gets that context correct at + *all* locations. + + +Regression Testing for issue #1025 +================================== + +We want to ensure that a RegexpTagger can be created with more than 100 patterns +and does not fail with: "AssertionError: sorry, but this version only supports 100 named groups" + + >>> from nltk.tag import RegexpTagger + >>> patterns = [(str(i), 'NNP',) for i in range(200)] + >>> tagger = RegexpTagger(patterns) + +Regression Testing for issue #2483 +================================== + +Ensure that tagging with pos_tag (PerceptronTagger) does not throw an IndexError +when attempting tagging an empty string. What it must return instead is not +strictly defined. + + >>> from nltk.tag import pos_tag + >>> pos_tag(['', 'is', 'a', 'beautiful', 'day']) + [...] diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/tokenize.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/tokenize.doctest new file mode 100644 index 0000000000000000000000000000000000000000..c3f40c8b64820315eb3c809e31ac53517d4dfca8 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/tokenize.doctest @@ -0,0 +1,397 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + + >>> from nltk.tokenize import * + +Regression Tests: NLTKWordTokenizer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Tokenizing some test strings. + + >>> s1 = "On a $50,000 mortgage of 30 years at 8 percent, the monthly payment would be $366.88." + >>> word_tokenize(s1) + ['On', 'a', '$', '50,000', 'mortgage', 'of', '30', 'years', 'at', '8', 'percent', ',', 'the', 'monthly', 'payment', 'would', 'be', '$', '366.88', '.'] + >>> s2 = "\"We beat some pretty good teams to get here,\" Slocum said." + >>> word_tokenize(s2) + ['``', 'We', 'beat', 'some', 'pretty', 'good', 'teams', 'to', 'get', 'here', ',', "''", 'Slocum', 'said', '.'] + >>> s3 = "Well, we couldn't have this predictable, cliche-ridden, \"Touched by an Angel\" (a show creator John Masius worked on) wanna-be if she didn't." + >>> word_tokenize(s3) + ['Well', ',', 'we', 'could', "n't", 'have', 'this', 'predictable', ',', 'cliche-ridden', ',', '``', 'Touched', 'by', 'an', 'Angel', "''", '(', 'a', 'show', 'creator', 'John', 'Masius', 'worked', 'on', ')', 'wanna-be', 'if', 'she', 'did', "n't", '.'] + >>> s4 = "I cannot cannot work under these conditions!" + >>> word_tokenize(s4) + ['I', 'can', 'not', 'can', 'not', 'work', 'under', 'these', 'conditions', '!'] + >>> s5 = "The company spent $30,000,000 last year." + >>> word_tokenize(s5) + ['The', 'company', 'spent', '$', '30,000,000', 'last', 'year', '.'] + >>> s6 = "The company spent 40.75% of its income last year." + >>> word_tokenize(s6) + ['The', 'company', 'spent', '40.75', '%', 'of', 'its', 'income', 'last', 'year', '.'] + >>> s7 = "He arrived at 3:00 pm." + >>> word_tokenize(s7) + ['He', 'arrived', 'at', '3:00', 'pm', '.'] + >>> s8 = "I bought these items: books, pencils, and pens." + >>> word_tokenize(s8) + ['I', 'bought', 'these', 'items', ':', 'books', ',', 'pencils', ',', 'and', 'pens', '.'] + >>> s9 = "Though there were 150, 100 of them were old." + >>> word_tokenize(s9) + ['Though', 'there', 'were', '150', ',', '100', 'of', 'them', 'were', 'old', '.'] + >>> s10 = "There were 300,000, but that wasn't enough." + >>> word_tokenize(s10) + ['There', 'were', '300,000', ',', 'but', 'that', 'was', "n't", 'enough', '.'] + >>> s11 = "It's more'n enough." + >>> word_tokenize(s11) + ['It', "'s", 'more', "'n", 'enough', '.'] + +Gathering the spans of the tokenized strings. + + >>> s = '''Good muffins cost $3.88\nin New (York). Please (buy) me\ntwo of them.\n(Thanks).''' + >>> expected = [(0, 4), (5, 12), (13, 17), (18, 19), (19, 23), + ... (24, 26), (27, 30), (31, 32), (32, 36), (36, 37), (37, 38), + ... (40, 46), (47, 48), (48, 51), (51, 52), (53, 55), (56, 59), + ... (60, 62), (63, 68), (69, 70), (70, 76), (76, 77), (77, 78)] + >>> list(NLTKWordTokenizer().span_tokenize(s)) == expected + True + >>> expected = ['Good', 'muffins', 'cost', '$', '3.88', 'in', + ... 'New', '(', 'York', ')', '.', 'Please', '(', 'buy', ')', + ... 'me', 'two', 'of', 'them.', '(', 'Thanks', ')', '.'] + >>> [s[start:end] for start, end in NLTKWordTokenizer().span_tokenize(s)] == expected + True + + >>> s = '''I said, "I'd like to buy some ''good muffins" which cost $3.88\n each in New (York)."''' + >>> expected = [(0, 1), (2, 6), (6, 7), (8, 9), (9, 10), (10, 12), + ... (13, 17), (18, 20), (21, 24), (25, 29), (30, 32), (32, 36), + ... (37, 44), (44, 45), (46, 51), (52, 56), (57, 58), (58, 62), + ... (64, 68), (69, 71), (72, 75), (76, 77), (77, 81), (81, 82), + ... (82, 83), (83, 84)] + >>> list(NLTKWordTokenizer().span_tokenize(s)) == expected + True + >>> expected = ['I', 'said', ',', '"', 'I', "'d", 'like', 'to', + ... 'buy', 'some', "''", "good", 'muffins', '"', 'which', 'cost', + ... '$', '3.88', 'each', 'in', 'New', '(', 'York', ')', '.', '"'] + >>> [s[start:end] for start, end in NLTKWordTokenizer().span_tokenize(s)] == expected + True + +Testing improvement made to the TreebankWordTokenizer + + >>> sx1 = '\xabNow that I can do.\xbb' + >>> expected = ['\xab', 'Now', 'that', 'I', 'can', 'do', '.', '\xbb'] + >>> word_tokenize(sx1) == expected + True + >>> sx2 = 'The unicode 201C and 201D \u201cLEFT(RIGHT) DOUBLE QUOTATION MARK\u201d is also OPEN_PUNCT and CLOSE_PUNCT.' + >>> expected = ['The', 'unicode', '201C', 'and', '201D', '\u201c', 'LEFT', '(', 'RIGHT', ')', 'DOUBLE', 'QUOTATION', 'MARK', '\u201d', 'is', 'also', 'OPEN_PUNCT', 'and', 'CLOSE_PUNCT', '.'] + >>> word_tokenize(sx2) == expected + True + + +Testing treebank's detokenizer + + >>> from nltk.tokenize.treebank import TreebankWordDetokenizer + >>> detokenizer = TreebankWordDetokenizer() + >>> s = "On a $50,000 mortgage of 30 years at 8 percent, the monthly payment would be $366.88." + >>> detokenizer.detokenize(word_tokenize(s)) + 'On a $50,000 mortgage of 30 years at 8 percent, the monthly payment would be $366.88.' + >>> s = "\"We beat some pretty good teams to get here,\" Slocum said." + >>> detokenizer.detokenize(word_tokenize(s)) + '"We beat some pretty good teams to get here," Slocum said.' + >>> s = "Well, we couldn't have this predictable, cliche-ridden, \"Touched by an Angel\" (a show creator John Masius worked on) wanna-be if she didn't." + >>> detokenizer.detokenize(word_tokenize(s)) + 'Well, we couldn\'t have this predictable, cliche-ridden, "Touched by an Angel" (a show creator John Masius worked on) wanna-be if she didn\'t.' + >>> s = "I cannot cannot work under these conditions!" + >>> detokenizer.detokenize(word_tokenize(s)) + 'I cannot cannot work under these conditions!' + >>> s = "The company spent $30,000,000 last year." + >>> detokenizer.detokenize(word_tokenize(s)) + 'The company spent $30,000,000 last year.' + >>> s = "The company spent 40.75% of its income last year." + >>> detokenizer.detokenize(word_tokenize(s)) + 'The company spent 40.75% of its income last year.' + >>> s = "He arrived at 3:00 pm." + >>> detokenizer.detokenize(word_tokenize(s)) + 'He arrived at 3:00 pm.' + >>> s = "I bought these items: books, pencils, and pens." + >>> detokenizer.detokenize(word_tokenize(s)) + 'I bought these items: books, pencils, and pens.' + >>> s = "Though there were 150, 100 of them were old." + >>> detokenizer.detokenize(word_tokenize(s)) + 'Though there were 150, 100 of them were old.' + >>> s = "There were 300,000, but that wasn't enough." + >>> detokenizer.detokenize(word_tokenize(s)) + "There were 300,000, but that wasn't enough." + >>> s = 'How "are" you?' + >>> detokenizer.detokenize(word_tokenize(s)) + 'How "are" you?' + >>> s = "Hello (world)" + >>> detokenizer.detokenize(word_tokenize(s)) + 'Hello (world)' + >>> s = ' with (many) [kinds] of {parentheses}. "Sometimes it\'s inside (quotes)". ("Sometimes the otherway around").' + >>> detokenizer.detokenize(word_tokenize(s)) + ' with (many) [kinds] of {parentheses}. "Sometimes it\'s inside (quotes)". ("Sometimes the otherway around").' + >>> s = "Sentence ending with (parentheses)" + >>> detokenizer.detokenize(word_tokenize(s)) + 'Sentence ending with (parentheses)' + >>> s = "(Sentence) starting with parentheses." + >>> detokenizer.detokenize(word_tokenize(s)) + '(Sentence) starting with parentheses.' + >>> s = "I've" + >>> detokenizer.detokenize(word_tokenize(s)) + "I've" + >>> s = "Don't" + >>> detokenizer.detokenize(word_tokenize(s)) + "Don't" + >>> s = "I'd" + >>> detokenizer.detokenize(word_tokenize(s)) + "I'd" + + +Sentence tokenization in word_tokenize: + + >>> s11 = "I called Dr. Jones. I called Dr. Jones." + >>> word_tokenize(s11) + ['I', 'called', 'Dr.', 'Jones', '.', 'I', 'called', 'Dr.', 'Jones', '.'] + >>> s12 = ("Ich muss unbedingt daran denken, Mehl, usw. fur einen " + ... "Kuchen einzukaufen. Ich muss.") + >>> word_tokenize(s12) + ['Ich', 'muss', 'unbedingt', 'daran', 'denken', ',', 'Mehl', ',', 'usw', + '.', 'fur', 'einen', 'Kuchen', 'einzukaufen', '.', 'Ich', 'muss', '.'] + >>> word_tokenize(s12, 'german') + ['Ich', 'muss', 'unbedingt', 'daran', 'denken', ',', 'Mehl', ',', 'usw.', + 'fur', 'einen', 'Kuchen', 'einzukaufen', '.', 'Ich', 'muss', '.'] + + +Regression Tests: Regexp Tokenizer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Some additional test strings. + + >>> s = ("Good muffins cost $3.88\nin New York. Please buy me\n" + ... "two of them.\n\nThanks.") + >>> s2 = ("Alas, it has not rained today. When, do you think, " + ... "will it rain again?") + >>> s3 = ("

Although this is not the case here, we must " + ... "not relax our vigilance!

") + + >>> regexp_tokenize(s2, r'[,\.\?!"]\s*', gaps=False) + [', ', '. ', ', ', ', ', '?'] + >>> regexp_tokenize(s2, r'[,\.\?!"]\s*', gaps=True) + ['Alas', 'it has not rained today', 'When', 'do you think', + 'will it rain again'] + +Take care to avoid using capturing groups: + + >>> regexp_tokenize(s3, r'', gaps=False) + ['

', '', '', '

'] + >>> regexp_tokenize(s3, r'', gaps=False) + ['

', '', '', '

'] + >>> regexp_tokenize(s3, r'', gaps=True) + ['Although this is ', 'not', + ' the case here, we must not relax our vigilance!'] + +Named groups are capturing groups, and confuse the tokenizer: + + >>> regexp_tokenize(s3, r'b|p)>', gaps=False) + ['p', 'b', 'b', 'p'] + >>> regexp_tokenize(s3, r'b|p)>', gaps=True) + ['p', 'Although this is ', 'b', 'not', 'b', + ' the case here, we must not relax our vigilance!', 'p'] + +Make sure that nested groups don't confuse the tokenizer: + + >>> regexp_tokenize(s2, r'(?:h|r|l)a(?:s|(?:i|n0))', gaps=False) + ['las', 'has', 'rai', 'rai'] + >>> regexp_tokenize(s2, r'(?:h|r|l)a(?:s|(?:i|n0))', gaps=True) + ['A', ', it ', ' not ', 'ned today. When, do you think, will it ', + 'n again?'] + +Back-references require capturing groups, and these are not supported: + + >>> regexp_tokenize("aabbbcccc", r'(.)\1') + ['a', 'b', 'c', 'c'] + +A simple sentence tokenizer '\.(\s+|$)' + + >>> regexp_tokenize(s, pattern=r'\.(?:\s+|$)', gaps=True) + ['Good muffins cost $3.88\nin New York', + 'Please buy me\ntwo of them', 'Thanks'] + + +Regression Tests: TweetTokenizer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +TweetTokenizer is a tokenizer specifically designed for micro-blogging tokenization tasks. + + >>> from nltk.tokenize import TweetTokenizer + >>> tknzr = TweetTokenizer() + >>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--" + >>> tknzr.tokenize(s0) + ['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--'] + >>> s1 = "@Joyster2012 @CathStaincliffe Good for you, girl!! Best wishes :-)" + >>> tknzr.tokenize(s1) + ['@Joyster2012', '@CathStaincliffe', 'Good', 'for', 'you', ',', 'girl', '!', '!', 'Best', 'wishes', ':-)'] + >>> s2 = "3Points for #DreamTeam Gooo BAILEY! :) #PBB737Gold @PBBabscbn" + >>> tknzr.tokenize(s2) + ['3Points', 'for', '#DreamTeam', 'Gooo', 'BAILEY', '!', ':)', '#PBB737Gold', '@PBBabscbn'] + >>> s3 = "@Insanomania They do... Their mentality doesn't :(" + >>> tknzr.tokenize(s3) + ['@Insanomania', 'They', 'do', '...', 'Their', 'mentality', "doesn't", ':('] + >>> s4 = "RT @facugambande: Ya por arrancar a grabar !!! #TirenTirenTiren vamoo !!" + >>> tknzr.tokenize(s4) + ['RT', '@facugambande', ':', 'Ya', 'por', 'arrancar', 'a', 'grabar', '!', '!', '!', '#TirenTirenTiren', 'vamoo', '!', '!'] + >>> tknzr = TweetTokenizer(reduce_len=True) + >>> s5 = "@crushinghes the summer holidays are great but I'm so bored already :(" + >>> tknzr.tokenize(s5) + ['@crushinghes', 'the', 'summer', 'holidays', 'are', 'great', 'but', "I'm", 'so', 'bored', 'already', ':('] + +It is possible to specify `strip_handles` and `reduce_len` parameters for a TweetTokenizer instance. Setting `strip_handles` to True, the tokenizer will remove Twitter handles (e.g. usernames). Setting `reduce_len` to True, repeated character sequences of length 3 or greater will be replaced with sequences of length 3. + + >>> tknzr = TweetTokenizer(strip_handles=True, reduce_len=True) + >>> s6 = '@remy: This is waaaaayyyy too much for you!!!!!!' + >>> tknzr.tokenize(s6) + [':', 'This', 'is', 'waaayyy', 'too', 'much', 'for', 'you', '!', '!', '!'] + >>> s7 = '@_willy65: No place for @chuck tonight. Sorry.' + >>> tknzr.tokenize(s7) + [':', 'No', 'place', 'for', 'tonight', '.', 'Sorry', '.'] + >>> s8 = '@mar_tin is a great developer. Contact him at mar_tin@email.com.' + >>> tknzr.tokenize(s8) + ['is', 'a', 'great', 'developer', '.', 'Contact', 'him', 'at', 'mar_tin@email.com', '.'] + +The `preserve_case` parameter (default: True) allows to convert uppercase tokens to lowercase tokens. Emoticons are not affected: + + >>> tknzr = TweetTokenizer(preserve_case=False) + >>> s9 = "@jrmy: I'm REALLY HAPPYYY about that! NICEEEE :D :P" + >>> tknzr.tokenize(s9) + ['@jrmy', ':', "i'm", 'really', 'happyyy', 'about', 'that', '!', 'niceeee', ':D', ':P'] + +It should not hang on long sequences of the same punctuation character. + + >>> tknzr = TweetTokenizer() + >>> s10 = "Photo: Aujourd'hui sur http://t.co/0gebOFDUzn Projet... http://t.co/bKfIUbydz2.............................. http://fb.me/3b6uXpz0L" + >>> tknzr.tokenize(s10) + ['Photo', ':', "Aujourd'hui", 'sur', 'http://t.co/0gebOFDUzn', 'Projet', '...', 'http://t.co/bKfIUbydz2', '...', 'http://fb.me/3b6uXpz0L'] + +Tokenizing multiple sentences at once: + + >>> tknzr = TweetTokenizer() + >>> sentences = [ + ... "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--", + ... "@jrmy: I'm REALLY HAPPYYY about that! NICEEEE :D :P", + ... "@_willy65: No place for @chuck tonight. Sorry." + ... ] + >>> tknzr.tokenize_sents(sentences) # doctest: +NORMALIZE_WHITESPACE + [['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--'], + ['@jrmy', ':', "I'm", 'REALLY', 'HAPPYYY', 'about', 'that', '!', 'NICEEEE', ':D', ':P'], + ['@_willy65', ':', 'No', 'place', 'for', '@chuck', 'tonight', '.', 'Sorry', '.']] + + +Regression Tests: PunktSentenceTokenizer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The sentence splitter should remove whitespace following the sentence boundary. + + >>> pst = PunktSentenceTokenizer() + >>> pst.tokenize('See Section 3). Or Section 2). ') + ['See Section 3).', 'Or Section 2).'] + >>> pst.tokenize('See Section 3.) Or Section 2.) ') + ['See Section 3.)', 'Or Section 2.)'] + >>> pst.tokenize('See Section 3.) Or Section 2.) ', realign_boundaries=False) + ['See Section 3.', ') Or Section 2.', ')'] + + +Two instances of PunktSentenceTokenizer should not share PunktParameters. + + >>> pst = PunktSentenceTokenizer() + >>> pst2 = PunktSentenceTokenizer() + >>> pst._params is pst2._params + False + +Testing mutable default arguments for https://github.com/nltk/nltk/pull/2067 + + >>> from nltk.tokenize.punkt import PunktBaseClass, PunktTrainer, PunktSentenceTokenizer + >>> from nltk.tokenize.punkt import PunktLanguageVars, PunktParameters + >>> pbc = PunktBaseClass(lang_vars=None, params=None) + >>> type(pbc._params) + + >>> type(pbc._lang_vars) + + >>> pt = PunktTrainer(lang_vars=None) + >>> type(pt._lang_vars) + + >>> pst = PunktSentenceTokenizer(lang_vars=None) + >>> type(pst._lang_vars) + + +Testing that inputs can start with dots. + + >>> pst = PunktSentenceTokenizer(lang_vars=None) + >>> pst.tokenize(". This input starts with a dot. This used to cause issues.") + ['.', 'This input starts with a dot.', 'This used to cause issues.'] + +Regression Tests: align_tokens +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Post-hoc alignment of tokens with a source string + + >>> from nltk.tokenize.util import align_tokens + >>> list(align_tokens([''], "")) + [(0, 0)] + >>> list(align_tokens([''], " ")) + [(0, 0)] + >>> list(align_tokens([], "")) + [] + >>> list(align_tokens([], " ")) + [] + >>> list(align_tokens(['a'], "a")) + [(0, 1)] + >>> list(align_tokens(['abc', 'def'], "abcdef")) + [(0, 3), (3, 6)] + >>> list(align_tokens(['abc', 'def'], "abc def")) + [(0, 3), (4, 7)] + >>> list(align_tokens(['ab', 'cd'], "ab cd ef")) + [(0, 2), (3, 5)] + >>> list(align_tokens(['ab', 'cd', 'ef'], "ab cd ef")) + [(0, 2), (3, 5), (6, 8)] + >>> list(align_tokens(['ab', 'cd', 'efg'], "ab cd ef")) + Traceback (most recent call last): + .... + ValueError: substring "efg" not found in "ab cd ef" + >>> list(align_tokens(['ab', 'cd', 'ef', 'gh'], "ab cd ef")) + Traceback (most recent call last): + .... + ValueError: substring "gh" not found in "ab cd ef" + >>> list(align_tokens(['The', 'plane', ',', 'bound', 'for', 'St', 'Petersburg', ',', 'crashed', 'in', 'Egypt', "'s", 'Sinai', 'desert', 'just', '23', 'minutes', 'after', 'take-off', 'from', 'Sharm', 'el-Sheikh', 'on', 'Saturday', '.'], "The plane, bound for St Petersburg, crashed in Egypt's Sinai desert just 23 minutes after take-off from Sharm el-Sheikh on Saturday.")) + [(0, 3), (4, 9), (9, 10), (11, 16), (17, 20), (21, 23), (24, 34), (34, 35), (36, 43), (44, 46), (47, 52), (52, 54), (55, 60), (61, 67), (68, 72), (73, 75), (76, 83), (84, 89), (90, 98), (99, 103), (104, 109), (110, 119), (120, 122), (123, 131), (131, 132)] + + +Regression Tests: MWETokenizer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Pickle an MWETokenizer + + >>> from nltk.tokenize import MWETokenizer + >>> import pickle + + >>> tokenizer = MWETokenizer([('hors', "d'oeuvre")], separator='+') + >>> p = pickle.dumps(tokenizer) + >>> unpickeled = pickle.loads(p) + >>> unpickeled.tokenize("An hors d'oeuvre tonight, sir?".split()) + ['An', "hors+d'oeuvre", 'tonight,', 'sir?'] + + +Regression Tests: TextTilingTokenizer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +TextTilingTokenizer tokenizes text into coherent subtopic chunks based upon Hearst's TextTiling algorithm. + + >>> from nltk.tokenize import TextTilingTokenizer + >>> from nltk.corpus import brown + >>> tt = TextTilingTokenizer() + >>> tt.tokenize(brown.raw()[0:1000]) + ["\n\n\tThe/at Fulton/np-tl County/nn-tl Grand/jj-tl Jury/nn-tl said/vbd Friday/nr an/at investigation/nn of/in Atlanta's/np$ recent/jj primary/nn election/nn produced/vbd ``/`` no/at evidence/nn ''/'' that/cs any/dti irregularities/nns took/vbd place/nn ./.\n\n\n\tThe/at jury/nn further/rbr said/vbd in/in term-end/nn presentments/nns that/cs the/at City/nn-tl Executive/jj-tl Committee/nn-tl ,/, which/wdt had/hvd over-all/jj charge/nn of/in the/at election/nn ,/, ``/`` deserves/vbz the/at praise/nn and/cc thanks/nns of/in the/at City/nn-tl of/in-tl Atlanta/np-tl ''/'' for/in the/at manner/nn in/in which/wdt the/at election/nn was/bedz conducted/vbn ./.\n\n\n\tThe/at September-October/np term/nn jury/nn had/hvd been/ben charged/vbn by/in Fulton/np-tl Superior/jj-tl Court/nn-tl Judge/nn-tl Durwood/np Pye/np to/to investigate/vb reports/nns of/in possible/jj ``/`` irregularities/nns ''/'' in/in the/at hard-fought/jj primary/nn which/wdt was/bedz won/vbn by/in Mayor-nominate/nn-tl Ivan/np Allen/np Jr./"] + +Test that `ValueError` exceptions are raised when illegal arguments are used. + + >>> TextTilingTokenizer(similarity_method='foo').tokenize(brown.raw()[0:1000]) + Traceback (most recent call last): + ... + ValueError: Similarity method foo not recognized + >>> TextTilingTokenizer(smoothing_method='bar').tokenize(brown.raw()[0:1000]) + Traceback (most recent call last): + ... + ValueError: Smoothing method bar not recognized diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/toolbox.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/toolbox.doctest new file mode 100644 index 0000000000000000000000000000000000000000..0dcf8495ad83460e081d47007ee5439aa54e097e --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/toolbox.doctest @@ -0,0 +1,306 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +=============================== +Unit test cases for ``toolbox`` +=============================== + + >>> from nltk import toolbox + +-------------------------- +``toolbox.StandardFormat`` +-------------------------- + + >>> f = toolbox.StandardFormat() + +``toolbox.StandardFormat.open()`` +--------------------------------- + >>> import os, tempfile + >>> (fd, fname) = tempfile.mkstemp() + >>> tf = os.fdopen(fd, "w") + >>> _ = tf.write('\\lx a value\n\\lx another value\n') + >>> tf.close() + >>> f = toolbox.StandardFormat() + >>> f.open(fname) + >>> list(f.fields()) + [('lx', 'a value'), ('lx', 'another value')] + >>> f.close() + >>> os.unlink(fname) + +``toolbox.StandardFormat.open_string()`` +---------------------------------------- + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx a value\n\\lx another value\n') + >>> list(f.fields()) + [('lx', 'a value'), ('lx', 'another value')] + >>> f.close() + +``toolbox.StandardFormat.close()`` +---------------------------------- + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx a value\n\\lx another value\n') + >>> list(f.fields()) + [('lx', 'a value'), ('lx', 'another value')] + >>> f.close() + +``toolbox.StandardFormat.line_num`` +--------------------------------------- + +``StandardFormat.line_num`` contains the line number of the last line returned: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx a value\n\\lx another value\n\\lx a third value\n') + >>> line_nums = [] + >>> for l in f.raw_fields(): + ... line_nums.append(f.line_num) + >>> line_nums + [1, 2, 3] + +``StandardFormat.line_num`` contains the line number of the last line returned: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx two\nlines\n\\lx three\nlines\n\n\\lx two\nlines\n') + >>> line_nums = [] + >>> for l in f.raw_fields(): + ... line_nums.append(f.line_num) + >>> line_nums + [2, 5, 7] + +``StandardFormat.line_num`` doesn't exist before opening or after closing +a file or string: + + >>> f = toolbox.StandardFormat() + >>> f.line_num + Traceback (most recent call last): + ... + AttributeError: 'StandardFormat' object has no attribute 'line_num' + >>> f.open_string('\\lx two\nlines\n\\lx three\nlines\n\n\\lx two\nlines\n') + >>> line_nums = [] + >>> for l in f.raw_fields(): + ... line_nums.append(f.line_num) + >>> line_nums + [2, 5, 7] + >>> f.close() + >>> f.line_num + Traceback (most recent call last): + ... + AttributeError: 'StandardFormat' object has no attribute 'line_num' + +``toolbox.StandardFormat.raw_fields()`` +--------------------------------------- +``raw_fields()`` returns an iterator over tuples of two strings representing the +marker and its value. The marker is given without the backslash and the value +without its trailing newline: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx a value\n\\lx another value\n') + >>> list(f.raw_fields()) + [('lx', 'a value'), ('lx', 'another value')] + +an empty file returns nothing: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('') + >>> list(f.raw_fields()) + [] + +file with only a newline returns WHAT SHOULD IT RETURN???: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\n') + >>> list(f.raw_fields()) + [(None, '')] + +file with only one field should be parsed ok: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx one value\n') + >>> list(f.raw_fields()) + [('lx', 'one value')] + +file without a trailing newline should be parsed ok: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx a value\n\\lx another value') + >>> list(f.raw_fields()) + [('lx', 'a value'), ('lx', 'another value')] + +trailing white space is preserved except for the final newline: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx trailing space \n\\lx trailing tab\t\n\\lx extra newline\n\n') + >>> list(f.raw_fields()) + [('lx', 'trailing space '), ('lx', 'trailing tab\t'), ('lx', 'extra newline\n')] + +line wrapping is preserved: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx a value\nmore of the value\nand still more\n\\lc another val\n') + >>> list(f.raw_fields()) + [('lx', 'a value\nmore of the value\nand still more'), ('lc', 'another val')] + +file beginning with a multiline record should be parsed ok: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx a value\nmore of the value\nand still more\n\\lc another val\n') + >>> list(f.raw_fields()) + [('lx', 'a value\nmore of the value\nand still more'), ('lc', 'another val')] + +file ending with a multiline record should be parsed ok: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lc a value\n\\lx another value\nmore of the value\nand still more\n') + >>> list(f.raw_fields()) + [('lc', 'a value'), ('lx', 'another value\nmore of the value\nand still more')] + +file beginning with a BOM should be parsed ok: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\xef\xbb\xbf\\lx a value\n\\lx another value\n') + >>> list(f.raw_fields()) + [('lx', 'a value'), ('lx', 'another value')] + +file beginning with two BOMs should ignore only the first one: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\xef\xbb\xbf\xef\xbb\xbf\\lx a value\n\\lx another value\n') + >>> list(f.raw_fields()) + [(None, '\xef\xbb\xbf\\lx a value'), ('lx', 'another value')] + +should not ignore a BOM not at the beginning of the file: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx a value\n\xef\xbb\xbf\\lx another value\n') + >>> list(f.raw_fields()) + [('lx', 'a value\n\xef\xbb\xbf\\lx another value')] + +``toolbox.StandardFormat.fields()`` +----------------------------------- +trailing white space is not preserved: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx trailing space \n\\lx trailing tab\t\n\\lx extra newline\n\n') + >>> list(f.fields()) + [('lx', 'trailing space'), ('lx', 'trailing tab'), ('lx', 'extra newline')] + +multiline fields are unwrapped: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\lx a value\nmore of the value\nand still more\n\\lc another val\n') + >>> list(f.fields()) + [('lx', 'a value more of the value and still more'), ('lc', 'another val')] + +markers +------- +A backslash in the first position on a new line indicates the start of a +marker. The backslash is not part of the marker: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\mk a value\n') + >>> list(f.fields()) + [('mk', 'a value')] + +If the backslash occurs later in the line it does not indicate the start +of a marker: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\mk a value\n \\mk another one\n') + >>> list(f.raw_fields()) + [('mk', 'a value\n \\mk another one')] + +There is no specific limit to the length of a marker: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\this_is_an_extremely_long_marker value\n') + >>> list(f.fields()) + [('this_is_an_extremely_long_marker', 'value')] + +A marker can contain any non white space character: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\`~!@#$%^&*()_-=+[{]}\\|,<.>/?;:"0123456789 value\n') + >>> list(f.fields()) + [('`~!@#$%^&*()_-=+[{]}\\|,<.>/?;:"0123456789', 'value')] + +A marker is terminated by any white space character: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\mk a value\n\\mk\tanother one\n\\mk\rthird one\n\\mk\ffourth one') + >>> list(f.fields()) + [('mk', 'a value'), ('mk', 'another one'), ('mk', 'third one'), ('mk', 'fourth one')] + +Consecutive whitespace characters (except newline) are treated the same as one: + + >>> f = toolbox.StandardFormat() + >>> f.open_string('\\mk \t\r\fa value\n') + >>> list(f.fields()) + [('mk', 'a value')] + +----------------------- +``toolbox.ToolboxData`` +----------------------- + + >>> db = toolbox.ToolboxData() + +``toolbox.ToolboxData.parse()`` +------------------------------- +check that normal parsing works: + + >>> from xml.etree import ElementTree + >>> td = toolbox.ToolboxData() + >>> s = """\\_sh v3.0 400 Rotokas Dictionary + ... \\_DateStampHasFourDigitYear + ... + ... \\lx kaa + ... \\ps V.A + ... \\ge gag + ... \\gp nek i pas + ... + ... \\lx kaa + ... \\ps V.B + ... \\ge strangle + ... \\gp pasim nek + ... """ + >>> td.open_string(s) + >>> tree = td.parse(key='lx') + >>> tree.tag + 'toolbox_data' + >>> ElementTree.tostring(list(tree)[0]).decode('utf8') + '
<_sh>v3.0 400 Rotokas Dictionary<_DateStampHasFourDigitYear />
' + >>> ElementTree.tostring(list(tree)[1]).decode('utf8') + 'kaaV.Agagnek i pas' + >>> ElementTree.tostring(list(tree)[2]).decode('utf8') + 'kaaV.Bstranglepasim nek' + +check that guessing the key marker works: + + >>> from xml.etree import ElementTree + >>> td = toolbox.ToolboxData() + >>> s = """\\_sh v3.0 400 Rotokas Dictionary + ... \\_DateStampHasFourDigitYear + ... + ... \\lx kaa + ... \\ps V.A + ... \\ge gag + ... \\gp nek i pas + ... + ... \\lx kaa + ... \\ps V.B + ... \\ge strangle + ... \\gp pasim nek + ... """ + >>> td.open_string(s) + >>> tree = td.parse() + >>> ElementTree.tostring(list(tree)[0]).decode('utf8') + '
<_sh>v3.0 400 Rotokas Dictionary<_DateStampHasFourDigitYear />
' + >>> ElementTree.tostring(list(tree)[1]).decode('utf8') + 'kaaV.Agagnek i pas' + >>> ElementTree.tostring(list(tree)[2]).decode('utf8') + 'kaaV.Bstranglepasim nek' + +----------------------- +``toolbox`` functions +----------------------- + +``toolbox.to_sfm_string()`` +------------------------------- diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/translate.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/translate.doctest new file mode 100644 index 0000000000000000000000000000000000000000..fd8eb4c1b50ac9f24fcc18cd12cb614f2b2feda9 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/translate.doctest @@ -0,0 +1,240 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +.. -*- coding: utf-8 -*- + +========= +Alignment +========= + +Corpus Reader +------------- + + >>> from nltk.corpus import comtrans + >>> words = comtrans.words('alignment-en-fr.txt') + >>> for word in words[:6]: + ... print(word) + Resumption + of + the + session + I + declare + >>> als = comtrans.aligned_sents('alignment-en-fr.txt')[0] + >>> als + AlignedSent(['Resumption', 'of', 'the', 'session'], + ['Reprise', 'de', 'la', 'session'], + Alignment([(0, 0), (1, 1), (2, 2), (3, 3)])) + + +Alignment Objects +----------------- + +Aligned sentences are simply a mapping between words in a sentence: + + >>> print(" ".join(als.words)) + Resumption of the session + >>> print(" ".join(als.mots)) + Reprise de la session + >>> als.alignment + Alignment([(0, 0), (1, 1), (2, 2), (3, 3)]) + + +Usually we look at them from the perspective of a source to a target language, +but they are easily inverted: + + >>> als.invert() + AlignedSent(['Reprise', 'de', 'la', 'session'], + ['Resumption', 'of', 'the', 'session'], + Alignment([(0, 0), (1, 1), (2, 2), (3, 3)])) + + +We can create new alignments, but these need to be in the correct range of +the corresponding sentences: + + >>> from nltk.translate import Alignment, AlignedSent + >>> als = AlignedSent(['Reprise', 'de', 'la', 'session'], + ... ['Resumption', 'of', 'the', 'session'], + ... Alignment([(0, 0), (1, 4), (2, 1), (3, 3)])) + Traceback (most recent call last): + ... + IndexError: Alignment is outside boundary of mots + + +You can set alignments with any sequence of tuples, so long as the first two +indexes of the tuple are the alignment indices: + + >>> als.alignment = Alignment([(0, 0), (1, 1), (2, 2, "boat"), (3, 3, False, (1,2))]) + + >>> Alignment([(0, 0), (1, 1), (2, 2, "boat"), (3, 3, False, (1,2))]) + Alignment([(0, 0), (1, 1), (2, 2, 'boat'), (3, 3, False, (1, 2))]) + + +Alignment Algorithms +-------------------- + +EM for IBM Model 1 +~~~~~~~~~~~~~~~~~~ + +Here is an example from Koehn, 2010: + + >>> from nltk.translate import IBMModel1 + >>> corpus = [AlignedSent(['the', 'house'], ['das', 'Haus']), + ... AlignedSent(['the', 'book'], ['das', 'Buch']), + ... AlignedSent(['a', 'book'], ['ein', 'Buch'])] + >>> em_ibm1 = IBMModel1(corpus, 20) + >>> print(round(em_ibm1.translation_table['the']['das'], 1)) + 1.0 + >>> print(round(em_ibm1.translation_table['book']['das'], 1)) + 0.0 + >>> print(round(em_ibm1.translation_table['house']['das'], 1)) + 0.0 + >>> print(round(em_ibm1.translation_table['the']['Buch'], 1)) + 0.0 + >>> print(round(em_ibm1.translation_table['book']['Buch'], 1)) + 1.0 + >>> print(round(em_ibm1.translation_table['a']['Buch'], 1)) + 0.0 + >>> print(round(em_ibm1.translation_table['book']['ein'], 1)) + 0.0 + >>> print(round(em_ibm1.translation_table['a']['ein'], 1)) + 1.0 + >>> print(round(em_ibm1.translation_table['the']['Haus'], 1)) + 0.0 + >>> print(round(em_ibm1.translation_table['house']['Haus'], 1)) + 1.0 + >>> print(round(em_ibm1.translation_table['book'][None], 1)) + 0.5 + +And using an NLTK corpus. We train on only 10 sentences, since it is so slow: + + >>> from nltk.corpus import comtrans + >>> com_ibm1 = IBMModel1(comtrans.aligned_sents()[:10], 20) + >>> print(round(com_ibm1.translation_table['bitte']['Please'], 1)) + 0.2 + >>> print(round(com_ibm1.translation_table['Sitzungsperiode']['session'], 1)) + 1.0 + + +Evaluation +---------- +The evaluation metrics for alignments are usually not interested in the +contents of alignments but more often the comparison to a "gold standard" +alignment that has been been constructed by human experts. For this reason we +often want to work just with raw set operations against the alignment points. +This then gives us a very clean form for defining our evaluation metrics. + +.. Note:: + The AlignedSent class has no distinction of "possible" or "sure" + alignments. Thus all alignments are treated as "sure". + +Consider the following aligned sentence for evaluation: + + >>> my_als = AlignedSent(['Resumption', 'of', 'the', 'session'], + ... ['Reprise', 'de', 'la', 'session'], + ... Alignment([(0, 0), (3, 3), (1, 2), (1, 1), (1, 3)])) + +Precision +~~~~~~~~~ +``precision = |A∩P| / |A|`` + +**Precision** is probably the most well known evaluation metric and it is implemented +in `nltk.metrics.scores.precision`_. Since precision is simply interested in the +proportion of correct alignments, we calculate the ratio of the number of our +test alignments (*A*) that match a possible alignment (*P*), over the number of +test alignments provided. There is no penalty for missing a possible alignment +in our test alignments. An easy way to game this metric is to provide just one +test alignment that is in *P* [OCH2000]_. + +Here are some examples: + + >>> from nltk.metrics import precision + >>> als.alignment = Alignment([(0,0), (1,1), (2,2), (3,3)]) + >>> precision(Alignment([]), als.alignment) + 0.0 + >>> precision(Alignment([(0,0), (1,1), (2,2), (3,3)]), als.alignment) + 1.0 + >>> precision(Alignment([(0,0), (3,3)]), als.alignment) + 0.5 + >>> precision(Alignment.fromstring('0-0 3-3'), als.alignment) + 0.5 + >>> precision(Alignment([(0,0), (1,1), (2,2), (3,3), (1,2), (2,1)]), als.alignment) + 1.0 + >>> precision(als.alignment, my_als.alignment) + 0.6 + + +.. _nltk.metrics.scores.precision: + https://www.nltk.org/api/nltk.metrics.html#nltk.metrics.scores.precision + + +Recall +~~~~~~ +``recall = |A∩S| / |S|`` + +**Recall** is another well known evaluation metric that has a set based +implementation in NLTK as `nltk.metrics.scores.recall`_. Since recall is +simply interested in the proportion of found alignments, we calculate the +ratio of the number of our test alignments (*A*) that match a sure alignment +(*S*) over the number of sure alignments. There is no penalty for producing +a lot of test alignments. An easy way to game this metric is to include every +possible alignment in our test alignments, regardless if they are correct or +not [OCH2000]_. + +Here are some examples: + + >>> from nltk.metrics import recall + >>> print(recall(Alignment([]), als.alignment)) + None + >>> recall(Alignment([(0,0), (1,1), (2,2), (3,3)]), als.alignment) + 1.0 + >>> recall(Alignment.fromstring('0-0 3-3'), als.alignment) + 1.0 + >>> recall(Alignment([(0,0), (3,3)]), als.alignment) + 1.0 + >>> recall(Alignment([(0,0), (1,1), (2,2), (3,3), (1,2), (2,1)]), als.alignment) + 0.66666... + >>> recall(als.alignment, my_als.alignment) + 0.75 + + +.. _nltk.metrics.scores.recall: + https://www.nltk.org/api/nltk.metrics.html#nltk.metrics.scores.recall + + +Alignment Error Rate (AER) +~~~~~~~~~~~~~~~~~~~~~~~~~~ +``AER = 1 - (|A∩S| + |A∩P|) / (|A| + |S|)`` + +**Alignment Error Rate** is commonly used metric for assessing sentence +alignments. It combines precision and recall metrics together such that a +perfect alignment must have all of the sure alignments and may have some +possible alignments [MIHALCEA2003]_ [KOEHN2010]_. + +.. Note:: + [KOEHN2010]_ defines the AER as ``AER = (|A∩S| + |A∩P|) / (|A| + |S|)`` + in his book, but corrects it to the above in his online errata. This is + in line with [MIHALCEA2003]_. + +Here are some examples: + + >>> from nltk.translate import alignment_error_rate + >>> alignment_error_rate(Alignment([]), als.alignment) + 1.0 + >>> alignment_error_rate(Alignment([(0,0), (1,1), (2,2), (3,3)]), als.alignment) + 0.0 + >>> alignment_error_rate(als.alignment, my_als.alignment) + 0.333333... + >>> alignment_error_rate(als.alignment, my_als.alignment, + ... als.alignment | Alignment([(1,2), (2,1)])) + 0.222222... + + +.. [OCH2000] Och, F. and Ney, H. (2000) + *Statistical Machine Translation*, EAMT Workshop + +.. [MIHALCEA2003] Mihalcea, R. and Pedersen, T. (2003) + *An evaluation exercise for word alignment*, HLT-NAACL 2003 + +.. [KOEHN2010] Koehn, P. (2010) + *Statistical Machine Translation*, Cambridge University Press diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/tree.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/tree.doctest new file mode 100644 index 0000000000000000000000000000000000000000..7b6748bd4abdde316b92b38789c777b2209c3da0 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/tree.doctest @@ -0,0 +1,1223 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +=============================== + Unit tests for nltk.tree.Tree +=============================== + + >>> from nltk.tree import * + +Some trees to run tests on: + + >>> dp1 = Tree('dp', [Tree('d', ['the']), Tree('np', ['dog'])]) + >>> dp2 = Tree('dp', [Tree('d', ['the']), Tree('np', ['cat'])]) + >>> vp = Tree('vp', [Tree('v', ['chased']), dp2]) + >>> tree = Tree('s', [dp1, vp]) + >>> print(tree) + (s (dp (d the) (np dog)) (vp (v chased) (dp (d the) (np cat)))) + +The node label is accessed using the `label()` method: + + >>> dp1.label(), dp2.label(), vp.label(), tree.label() + ('dp', 'dp', 'vp', 's') + + >>> print(tree[1,1,1,0]) + cat + +The `treepositions` method returns a list of the tree positions of +subtrees and leaves in a tree. By default, it gives the position of +every tree, subtree, and leaf, in prefix order: + + >>> print(tree.treepositions()) + [(), (0,), (0, 0), (0, 0, 0), (0, 1), (0, 1, 0), (1,), (1, 0), (1, 0, 0), (1, 1), (1, 1, 0), (1, 1, 0, 0), (1, 1, 1), (1, 1, 1, 0)] + +In addition to `str` and `repr`, several methods exist to convert a +tree object to one of several standard tree encodings: + + >>> print(tree.pformat_latex_qtree()) + \Tree [.s + [.dp [.d the ] [.np dog ] ] + [.vp [.v chased ] [.dp [.d the ] [.np cat ] ] ] ] + +There is also a fancy ASCII art representation: + + >>> tree.pretty_print() + s + ________|_____ + | vp + | _____|___ + dp | dp + ___|___ | ___|___ + d np v d np + | | | | | + the dog chased the cat + + >>> tree.pretty_print(unicodelines=True, nodedist=4) + s + ┌──────────────┴────────┐ + │ vp + │ ┌────────┴──────┐ + dp │ dp + ┌──────┴──────┐ │ ┌──────┴──────┐ + d np v d np + │ │ │ │ │ + the dog chased the cat + +Trees can be initialized from treebank strings: + + >>> tree2 = Tree.fromstring('(S (NP I) (VP (V enjoyed) (NP my cookie)))') + >>> print(tree2) + (S (NP I) (VP (V enjoyed) (NP my cookie))) + +Trees can be compared for equality: + + >>> tree == Tree.fromstring(str(tree)) + True + >>> tree2 == Tree.fromstring(str(tree2)) + True + >>> tree == tree2 + False + >>> tree == Tree.fromstring(str(tree2)) + False + >>> tree2 == Tree.fromstring(str(tree)) + False + + >>> tree != Tree.fromstring(str(tree)) + False + >>> tree2 != Tree.fromstring(str(tree2)) + False + >>> tree != tree2 + True + >>> tree != Tree.fromstring(str(tree2)) + True + >>> tree2 != Tree.fromstring(str(tree)) + True + + >>> tree < tree2 or tree > tree2 + True + +Tree Parsing +============ + +The class method `Tree.fromstring()` can be used to parse trees, and it +provides some additional options. + + >>> tree = Tree.fromstring('(S (NP I) (VP (V enjoyed) (NP my cookie)))') + >>> print(tree) + (S (NP I) (VP (V enjoyed) (NP my cookie))) + +When called on a subclass of `Tree`, it will create trees of that +type: + + >>> tree = ImmutableTree.fromstring('(VP (V enjoyed) (NP my cookie))') + >>> print(tree) + (VP (V enjoyed) (NP my cookie)) + >>> print(type(tree)) + + >>> tree[1] = 'x' + Traceback (most recent call last): + . . . + ValueError: ImmutableTree may not be modified + >>> del tree[0] + Traceback (most recent call last): + . . . + ValueError: ImmutableTree may not be modified + +The ``brackets`` parameter can be used to specify two characters that +should be used as brackets: + + >>> print(Tree.fromstring('[S [NP I] [VP [V enjoyed] [NP my cookie]]]', + ... brackets='[]')) + (S (NP I) (VP (V enjoyed) (NP my cookie))) + >>> print(Tree.fromstring(' >>', + ... brackets='<>')) + (S (NP I) (VP (V enjoyed) (NP my cookie))) + +If ``brackets`` is not a string, or is not exactly two characters, +then `Tree.fromstring` raises an exception: + + >>> Tree.fromstring(' >', brackets='') + Traceback (most recent call last): + . . . + TypeError: brackets must be a length-2 string + >>> Tree.fromstring(' >', brackets='<<>>') + Traceback (most recent call last): + . . . + TypeError: brackets must be a length-2 string + >>> Tree.fromstring(' >', brackets=12) + Traceback (most recent call last): + . . . + TypeError: brackets must be a length-2 string + >>> Tree.fromstring('<>', brackets=('<<','>>')) + Traceback (most recent call last): + . . . + TypeError: brackets must be a length-2 string + +(We may add support for multi-character brackets in the future, in +which case the ``brackets=('<<','>>')`` example would start working.) + +Whitespace brackets are not permitted: + + >>> Tree.fromstring('(NP my cookie\n', brackets='(\n') + Traceback (most recent call last): + . . . + TypeError: whitespace brackets not allowed + +If an invalid tree is given to Tree.fromstring, then it raises a +ValueError, with a description of the problem: + + >>> Tree.fromstring('(NP my cookie) (NP my milk)') + Traceback (most recent call last): + . . . + ValueError: Tree.fromstring(): expected 'end-of-string' but got '(NP' + at index 15. + "...y cookie) (NP my mil..." + ^ + >>> Tree.fromstring(')NP my cookie(') + Traceback (most recent call last): + . . . + ValueError: Tree.fromstring(): expected '(' but got ')' + at index 0. + ")NP my coo..." + ^ + >>> Tree.fromstring('(NP my cookie))') + Traceback (most recent call last): + . . . + ValueError: Tree.fromstring(): expected 'end-of-string' but got ')' + at index 14. + "...my cookie))" + ^ + >>> Tree.fromstring('my cookie)') + Traceback (most recent call last): + . . . + ValueError: Tree.fromstring(): expected '(' but got 'my' + at index 0. + "my cookie)" + ^ + >>> Tree.fromstring('(NP my cookie') + Traceback (most recent call last): + . . . + ValueError: Tree.fromstring(): expected ')' but got 'end-of-string' + at index 13. + "... my cookie" + ^ + >>> Tree.fromstring('') + Traceback (most recent call last): + . . . + ValueError: Tree.fromstring(): expected '(' but got 'end-of-string' + at index 0. + "" + ^ + +Trees with no children are supported: + + >>> print(Tree.fromstring('(S)')) + (S ) + >>> print(Tree.fromstring('(X (Y) (Z))')) + (X (Y ) (Z )) + +Trees with an empty node label and no children are supported: + + >>> print(Tree.fromstring('()')) + ( ) + >>> print(Tree.fromstring('(X () ())')) + (X ( ) ( )) + +Trees with an empty node label and children are supported, but only if the +first child is not a leaf (otherwise, it will be treated as the node label). + + >>> print(Tree.fromstring('((A) (B) (C))')) + ( (A ) (B ) (C )) + >>> print(Tree.fromstring('((A) leaf)')) + ( (A ) leaf) + >>> print(Tree.fromstring('(((())))')) + ( ( ( ( )))) + +The optional arguments `read_node` and `read_leaf` may be used to +transform the string values of nodes or leaves. + + >>> print(Tree.fromstring('(A b (C d e) (F (G h i)))', + ... read_node=lambda s: '<%s>' % s, + ... read_leaf=lambda s: '"%s"' % s)) + ( "b" ( "d" "e") ( ( "h" "i"))) + +These transformation functions are typically used when the node or +leaf labels should be parsed to a non-string value (such as a feature +structure). If node and leaf labels need to be able to include +whitespace, then you must also use the optional `node_pattern` and +`leaf_pattern` arguments. + + >>> from nltk.featstruct import FeatStruct + >>> tree = Tree.fromstring('([cat=NP] [lex=the] [lex=dog])', + ... read_node=FeatStruct, read_leaf=FeatStruct) + >>> tree.set_label(tree.label().unify(FeatStruct('[num=singular]'))) + >>> print(tree) + ([cat='NP', num='singular'] [lex='the'] [lex='dog']) + +The optional argument ``remove_empty_top_bracketing`` can be used to +remove any top-level empty bracketing that occurs. + + >>> print(Tree.fromstring('((S (NP I) (VP (V enjoyed) (NP my cookie))))', + ... remove_empty_top_bracketing=True)) + (S (NP I) (VP (V enjoyed) (NP my cookie))) + +It will not remove a top-level empty bracketing with multiple children: + + >>> print(Tree.fromstring('((A a) (B b))')) + ( (A a) (B b)) + + +Tree.fromlist() +--------------- +The class method `Tree.fromlist()` can be used to parse trees +that are expressed as nested lists, such as those produced by +the tree() function from the wordnet module. + + >>> from nltk.corpus import wordnet as wn + >>> t=Tree.fromlist(wn.synset('dog.n.01').tree(lambda s:s.hypernyms())) + >>> print(t.height()) + 14 + >>> print(t.leaves()) + ["Synset('entity.n.01')", "Synset('entity.n.01')"] + >>> t.pretty_print() + Synset('dog.n.01') + _________________|__________________ + Synset('canine.n. | + 02') | + | | + Synset('carnivor | + e.n.01') | + | | + Synset('placenta | + l.n.01') | + | | + Synset('mammal.n. | + 01') | + | | + Synset('vertebra | + te.n.01') | + | | + Synset('chordate. Synset('domestic + n.01') _animal.n.01') + | | + Synset('animal.n. Synset('animal.n. + 01') 01') + | | + Synset('organism. Synset('organism. + n.01') n.01') + | | + Synset('living_t Synset('living_t + hing.n.01') hing.n.01') + | | + Synset('whole.n. Synset('whole.n. + 02') 02') + | | + Synset('object.n. Synset('object.n. + 01') 01') + | | + Synset('physical Synset('physical + _entity.n.01') _entity.n.01') + | | + Synset('entity.n. Synset('entity.n. + 01') 01') + + + +Parented Trees +============== +`ParentedTree` is a subclass of `Tree` that automatically maintains +parent pointers for single-parented trees. Parented trees can be +created directly from a node label and a list of children: + + >>> ptree = ( + ... ParentedTree('VP', [ + ... ParentedTree('VERB', ['saw']), + ... ParentedTree('NP', [ + ... ParentedTree('DET', ['the']), + ... ParentedTree('NOUN', ['dog'])])])) + >>> print(ptree) + (VP (VERB saw) (NP (DET the) (NOUN dog))) + +Parented trees can be created from strings using the classmethod +`ParentedTree.fromstring`: + + >>> ptree = ParentedTree.fromstring('(VP (VERB saw) (NP (DET the) (NOUN dog)))') + >>> print(ptree) + (VP (VERB saw) (NP (DET the) (NOUN dog))) + >>> print(type(ptree)) + + +Parented trees can also be created by using the classmethod +`ParentedTree.convert` to convert another type of tree to a parented +tree: + + >>> tree = Tree.fromstring('(VP (VERB saw) (NP (DET the) (NOUN dog)))') + >>> ptree = ParentedTree.convert(tree) + >>> print(ptree) + (VP (VERB saw) (NP (DET the) (NOUN dog))) + >>> print(type(ptree)) + + +.. clean-up: + + >>> del tree + +`ParentedTree`\ s should never be used in the same tree as `Tree`\ s +or `MultiParentedTree`\ s. Mixing tree implementations may result in +incorrect parent pointers and in `TypeError` exceptions: + + >>> # Inserting a Tree in a ParentedTree gives an exception: + >>> ParentedTree('NP', [ + ... Tree('DET', ['the']), Tree('NOUN', ['dog'])]) + Traceback (most recent call last): + . . . + TypeError: Can not insert a non-ParentedTree into a ParentedTree + + >>> # inserting a ParentedTree in a Tree gives incorrect parent pointers: + >>> broken_tree = Tree('NP', [ + ... ParentedTree('DET', ['the']), ParentedTree('NOUN', ['dog'])]) + >>> print(broken_tree[0].parent()) + None + +Parented Tree Methods +------------------------ +In addition to all the methods defined by the `Tree` class, the +`ParentedTree` class adds six new methods whose values are +automatically updated whenever a parented tree is modified: `parent()`, +`parent_index()`, `left_sibling()`, `right_sibling()`, `root()`, and +`treeposition()`. + +The `parent()` method contains a `ParentedTree`\ 's parent, if it has +one; and ``None`` otherwise. `ParentedTree`\ s that do not have +parents are known as "root trees." + + >>> for subtree in ptree.subtrees(): + ... print(subtree) + ... print(' Parent = %s' % subtree.parent()) + (VP (VERB saw) (NP (DET the) (NOUN dog))) + Parent = None + (VERB saw) + Parent = (VP (VERB saw) (NP (DET the) (NOUN dog))) + (NP (DET the) (NOUN dog)) + Parent = (VP (VERB saw) (NP (DET the) (NOUN dog))) + (DET the) + Parent = (NP (DET the) (NOUN dog)) + (NOUN dog) + Parent = (NP (DET the) (NOUN dog)) + +The `parent_index()` method stores the index of a tree in its parent's +child list. If a tree does not have a parent, then its `parent_index` +is ``None``. + + >>> for subtree in ptree.subtrees(): + ... print(subtree) + ... print(' Parent Index = %s' % subtree.parent_index()) + ... assert (subtree.parent() is None or + ... subtree.parent()[subtree.parent_index()] is subtree) + (VP (VERB saw) (NP (DET the) (NOUN dog))) + Parent Index = None + (VERB saw) + Parent Index = 0 + (NP (DET the) (NOUN dog)) + Parent Index = 1 + (DET the) + Parent Index = 0 + (NOUN dog) + Parent Index = 1 + +Note that ``ptree.parent().index(ptree)`` is *not* equivalent to +``ptree.parent_index()``. In particular, ``ptree.parent().index(ptree)`` +will return the index of the first child of ``ptree.parent()`` that is +equal to ``ptree`` (using ``==``); and that child may not be +``ptree``: + + >>> on_and_on = ParentedTree('CONJP', [ + ... ParentedTree('PREP', ['on']), + ... ParentedTree('COJN', ['and']), + ... ParentedTree('PREP', ['on'])]) + >>> second_on = on_and_on[2] + >>> print(second_on.parent_index()) + 2 + >>> print(second_on.parent().index(second_on)) + 0 + +The methods `left_sibling()` and `right_sibling()` can be used to get a +parented tree's siblings. If a tree does not have a left or right +sibling, then the corresponding method's value is ``None``: + + >>> for subtree in ptree.subtrees(): + ... print(subtree) + ... print(' Left Sibling = %s' % subtree.left_sibling()) + ... print(' Right Sibling = %s' % subtree.right_sibling()) + (VP (VERB saw) (NP (DET the) (NOUN dog))) + Left Sibling = None + Right Sibling = None + (VERB saw) + Left Sibling = None + Right Sibling = (NP (DET the) (NOUN dog)) + (NP (DET the) (NOUN dog)) + Left Sibling = (VERB saw) + Right Sibling = None + (DET the) + Left Sibling = None + Right Sibling = (NOUN dog) + (NOUN dog) + Left Sibling = (DET the) + Right Sibling = None + +A parented tree's root tree can be accessed using the `root()` +method. This method follows the tree's parent pointers until it +finds a tree without a parent. If a tree does not have a parent, then +it is its own root: + + >>> for subtree in ptree.subtrees(): + ... print(subtree) + ... print(' Root = %s' % subtree.root()) + (VP (VERB saw) (NP (DET the) (NOUN dog))) + Root = (VP (VERB saw) (NP (DET the) (NOUN dog))) + (VERB saw) + Root = (VP (VERB saw) (NP (DET the) (NOUN dog))) + (NP (DET the) (NOUN dog)) + Root = (VP (VERB saw) (NP (DET the) (NOUN dog))) + (DET the) + Root = (VP (VERB saw) (NP (DET the) (NOUN dog))) + (NOUN dog) + Root = (VP (VERB saw) (NP (DET the) (NOUN dog))) + +The `treeposition()` method can be used to find a tree's treeposition +relative to its root: + + >>> for subtree in ptree.subtrees(): + ... print(subtree) + ... print(' Tree Position = %s' % (subtree.treeposition(),)) + ... assert subtree.root()[subtree.treeposition()] is subtree + (VP (VERB saw) (NP (DET the) (NOUN dog))) + Tree Position = () + (VERB saw) + Tree Position = (0,) + (NP (DET the) (NOUN dog)) + Tree Position = (1,) + (DET the) + Tree Position = (1, 0) + (NOUN dog) + Tree Position = (1, 1) + +Whenever a parented tree is modified, all of the methods described +above (`parent()`, `parent_index()`, `left_sibling()`, `right_sibling()`, +`root()`, and `treeposition()`) are automatically updated. For example, +if we replace ``ptree``\ 's subtree for the word "dog" with a new +subtree for "cat," the method values for both the "dog" subtree and the +"cat" subtree get automatically updated: + + >>> # Replace the dog with a cat + >>> dog = ptree[1,1] + >>> cat = ParentedTree('NOUN', ['cat']) + >>> ptree[1,1] = cat + + >>> # the noun phrase is no longer the dog's parent: + >>> print(dog.parent(), dog.parent_index(), dog.left_sibling()) + None None None + >>> # dog is now its own root. + >>> print(dog.root()) + (NOUN dog) + >>> print(dog.treeposition()) + () + + >>> # the cat's parent is now the noun phrase: + >>> print(cat.parent()) + (NP (DET the) (NOUN cat)) + >>> print(cat.parent_index()) + 1 + >>> print(cat.left_sibling()) + (DET the) + >>> print(cat.root()) + (VP (VERB saw) (NP (DET the) (NOUN cat))) + >>> print(cat.treeposition()) + (1, 1) + +ParentedTree Regression Tests +----------------------------- +Keep track of all trees that we create (including subtrees) using this +variable: + + >>> all_ptrees = [] + +Define a helper function to create new parented trees: + + >>> def make_ptree(s): + ... ptree = ParentedTree.convert(Tree.fromstring(s)) + ... all_ptrees.extend(t for t in ptree.subtrees() + ... if isinstance(t, Tree)) + ... return ptree + +Define a test function that examines every subtree in all_ptrees; and +checks that all six of its methods are defined correctly. If any +ptrees are passed as arguments, then they are printed. + + >>> def pcheck(*print_ptrees): + ... for ptree in all_ptrees: + ... # Check ptree's methods. + ... if ptree.parent() is not None: + ... i = ptree.parent_index() + ... assert ptree.parent()[i] is ptree + ... if i > 0: + ... assert ptree.left_sibling() is ptree.parent()[i-1] + ... if i < (len(ptree.parent())-1): + ... assert ptree.right_sibling() is ptree.parent()[i+1] + ... assert len(ptree.treeposition()) > 0 + ... assert (ptree.treeposition() == + ... ptree.parent().treeposition() + (ptree.parent_index(),)) + ... assert ptree.root() is not ptree + ... assert ptree.root() is not None + ... assert ptree.root() is ptree.parent().root() + ... assert ptree.root()[ptree.treeposition()] is ptree + ... else: + ... assert ptree.parent_index() is None + ... assert ptree.left_sibling() is None + ... assert ptree.right_sibling() is None + ... assert ptree.root() is ptree + ... assert ptree.treeposition() == () + ... # Check ptree's children's methods: + ... for i, child in enumerate(ptree): + ... if isinstance(child, Tree): + ... # pcheck parent() & parent_index() methods + ... assert child.parent() is ptree + ... assert child.parent_index() == i + ... # pcheck sibling methods + ... if i == 0: + ... assert child.left_sibling() is None + ... else: + ... assert child.left_sibling() is ptree[i-1] + ... if i == len(ptree)-1: + ... assert child.right_sibling() is None + ... else: + ... assert child.right_sibling() is ptree[i+1] + ... if print_ptrees: + ... print('ok!', end=' ') + ... for ptree in print_ptrees: print(ptree) + ... else: + ... print('ok!') + +Run our test function on a variety of newly-created trees: + + >>> pcheck(make_ptree('(A)')) + ok! (A ) + >>> pcheck(make_ptree('(A (B (C (D) (E f)) g) h)')) + ok! (A (B (C (D ) (E f)) g) h) + >>> pcheck(make_ptree('(A (B) (C c) (D d d) (E e e e))')) + ok! (A (B ) (C c) (D d d) (E e e e)) + >>> pcheck(make_ptree('(A (B) (C (c)) (D (d) (d)) (E (e) (e) (e)))')) + ok! (A (B ) (C (c )) (D (d ) (d )) (E (e ) (e ) (e ))) + +Run our test function after performing various tree-modification +operations: + +**__delitem__()** + + >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> e = ptree[0,0,1] + >>> del ptree[0,0,1]; pcheck(ptree); pcheck(e) + ok! (A (B (C (D ) (Q p)) g) h) + ok! (E f) + >>> del ptree[0,0,0]; pcheck(ptree) + ok! (A (B (C (Q p)) g) h) + >>> del ptree[0,1]; pcheck(ptree) + ok! (A (B (C (Q p))) h) + >>> del ptree[-1]; pcheck(ptree) + ok! (A (B (C (Q p)))) + >>> del ptree[-100] + Traceback (most recent call last): + . . . + IndexError: index out of range + >>> del ptree[()] + Traceback (most recent call last): + . . . + IndexError: The tree position () may not be deleted. + + >>> # With slices: + >>> ptree = make_ptree('(A (B c) (D e) f g (H i) j (K l))') + >>> b = ptree[0] + >>> del ptree[0:0]; pcheck(ptree) + ok! (A (B c) (D e) f g (H i) j (K l)) + >>> del ptree[:1]; pcheck(ptree); pcheck(b) + ok! (A (D e) f g (H i) j (K l)) + ok! (B c) + >>> del ptree[-2:]; pcheck(ptree) + ok! (A (D e) f g (H i)) + >>> del ptree[1:3]; pcheck(ptree) + ok! (A (D e) (H i)) + >>> ptree = make_ptree('(A (B c) (D e) f g (H i) j (K l))') + >>> del ptree[5:1000]; pcheck(ptree) + ok! (A (B c) (D e) f g (H i)) + >>> del ptree[-2:1000]; pcheck(ptree) + ok! (A (B c) (D e) f) + >>> del ptree[-100:1]; pcheck(ptree) + ok! (A (D e) f) + >>> ptree = make_ptree('(A (B c) (D e) f g (H i) j (K l))') + >>> del ptree[1:-2:2]; pcheck(ptree) + ok! (A (B c) f (H i) j (K l)) + +**__setitem__()** + + >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> d, e, q = ptree[0,0] + >>> ptree[0,0,0] = 'x'; pcheck(ptree); pcheck(d) + ok! (A (B (C x (E f) (Q p)) g) h) + ok! (D ) + >>> ptree[0,0,1] = make_ptree('(X (Y z))'); pcheck(ptree); pcheck(e) + ok! (A (B (C x (X (Y z)) (Q p)) g) h) + ok! (E f) + >>> ptree[1] = d; pcheck(ptree) + ok! (A (B (C x (X (Y z)) (Q p)) g) (D )) + >>> ptree[-1] = 'x'; pcheck(ptree) + ok! (A (B (C x (X (Y z)) (Q p)) g) x) + >>> ptree[-100] = 'y' + Traceback (most recent call last): + . . . + IndexError: index out of range + >>> ptree[()] = make_ptree('(X y)') + Traceback (most recent call last): + . . . + IndexError: The tree position () may not be assigned to. + + >>> # With slices: + >>> ptree = make_ptree('(A (B c) (D e) f g (H i) j (K l))') + >>> b = ptree[0] + >>> ptree[0:0] = ('x', make_ptree('(Y)')); pcheck(ptree) + ok! (A x (Y ) (B c) (D e) f g (H i) j (K l)) + >>> ptree[2:6] = (); pcheck(ptree); pcheck(b) + ok! (A x (Y ) (H i) j (K l)) + ok! (B c) + >>> ptree[-2:] = ('z', 'p'); pcheck(ptree) + ok! (A x (Y ) (H i) z p) + >>> ptree[1:3] = [make_ptree('(X)') for x in range(10)]; pcheck(ptree) + ok! (A x (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) z p) + >>> ptree[5:1000] = []; pcheck(ptree) + ok! (A x (X ) (X ) (X ) (X )) + >>> ptree[-2:1000] = ['n']; pcheck(ptree) + ok! (A x (X ) (X ) n) + >>> ptree[-100:1] = [make_ptree('(U v)')]; pcheck(ptree) + ok! (A (U v) (X ) (X ) n) + >>> ptree[-1:] = (make_ptree('(X)') for x in range(3)); pcheck(ptree) + ok! (A (U v) (X ) (X ) (X ) (X ) (X )) + >>> ptree[1:-2:2] = ['x', 'y']; pcheck(ptree) + ok! (A (U v) x (X ) y (X ) (X )) + +**append()** + + >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> ptree.append('x'); pcheck(ptree) + ok! (A (B (C (D ) (E f) (Q p)) g) h x) + >>> ptree.append(make_ptree('(X (Y z))')); pcheck(ptree) + ok! (A (B (C (D ) (E f) (Q p)) g) h x (X (Y z))) + +**extend()** + + >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> ptree.extend(['x', 'y', make_ptree('(X (Y z))')]); pcheck(ptree) + ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z))) + >>> ptree.extend([]); pcheck(ptree) + ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z))) + >>> ptree.extend(make_ptree('(X)') for x in range(3)); pcheck(ptree) + ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z)) (X ) (X ) (X )) + +**insert()** + + >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> ptree.insert(0, make_ptree('(X (Y z))')); pcheck(ptree) + ok! (A (X (Y z)) (B (C (D ) (E f) (Q p)) g) h) + >>> ptree.insert(-1, make_ptree('(X (Y z))')); pcheck(ptree) + ok! (A (X (Y z)) (B (C (D ) (E f) (Q p)) g) (X (Y z)) h) + >>> ptree.insert(-4, make_ptree('(X (Y z))')); pcheck(ptree) + ok! (A (X (Y z)) (X (Y z)) (B (C (D ) (E f) (Q p)) g) (X (Y z)) h) + >>> # Note: as with ``list``, inserting at a negative index that + >>> # gives a position before the start of the list does *not* + >>> # raise an IndexError exception; it just inserts at 0. + >>> ptree.insert(-400, make_ptree('(X (Y z))')); pcheck(ptree) + ok! (A + (X (Y z)) + (X (Y z)) + (X (Y z)) + (B (C (D ) (E f) (Q p)) g) + (X (Y z)) + h) + +**pop()** + + >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> ptree[0,0].pop(1); pcheck(ptree) + ParentedTree('E', ['f']) + ok! (A (B (C (D ) (Q p)) g) h) + >>> ptree[0].pop(-1); pcheck(ptree) + 'g' + ok! (A (B (C (D ) (Q p))) h) + >>> ptree.pop(); pcheck(ptree) + 'h' + ok! (A (B (C (D ) (Q p)))) + >>> ptree.pop(-100) + Traceback (most recent call last): + . . . + IndexError: index out of range + +**remove()** + + >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> e = ptree[0,0,1] + >>> ptree[0,0].remove(ptree[0,0,1]); pcheck(ptree); pcheck(e) + ok! (A (B (C (D ) (Q p)) g) h) + ok! (E f) + >>> ptree[0,0].remove(make_ptree('(Q p)')); pcheck(ptree) + ok! (A (B (C (D )) g) h) + >>> ptree[0,0].remove(make_ptree('(Q p)')) + Traceback (most recent call last): + . . . + ValueError: ParentedTree('Q', ['p']) is not in list + >>> ptree.remove('h'); pcheck(ptree) + ok! (A (B (C (D )) g)) + >>> ptree.remove('h'); + Traceback (most recent call last): + . . . + ValueError: 'h' is not in list + >>> # remove() removes the first subtree that is equal (==) to the + >>> # given tree, which may not be the identical tree we give it: + >>> ptree = make_ptree('(A (X x) (Y y) (X x))') + >>> x1, y, x2 = ptree + >>> ptree.remove(ptree[-1]); pcheck(ptree) + ok! (A (Y y) (X x)) + >>> print(x1.parent()); pcheck(x1) + None + ok! (X x) + >>> print(x2.parent()) + (A (Y y) (X x)) + +Test that a tree can not be given multiple parents: + + >>> ptree = make_ptree('(A (X x) (Y y) (Z z))') + >>> ptree[0] = ptree[1] + Traceback (most recent call last): + . . . + ValueError: Can not insert a subtree that already has a parent. + >>> pcheck() + ok! + +[more to be written] + +Shallow copying can be tricky for Tree and several of its subclasses. +For shallow copies of Tree, only the root node is reconstructed, while +all the children are shared between the two trees. Modify the children +of one tree - and the shallowly copied tree will also update. + + >>> from nltk.tree import Tree, ParentedTree, MultiParentedTree + >>> tree = Tree.fromstring("(TOP (S (NP (NNP Bell,)) (NP (NP (DT a) (NN company)) (SBAR (WHNP (WDT which)) (S (VP (VBZ is) (VP (VBN based) (PP (IN in) (NP (NNP LA,)))))))) (VP (VBZ makes) (CC and) (VBZ distributes) (NP (NN computer))) (. products.)))") + >>> copy_tree = tree.copy(deep=False) + >>> tree == copy_tree # Ensure identical labels and nodes + True + >>> id(copy_tree[0]) == id(tree[0]) # Ensure shallow copy - the children are the same objects in memory + True + +For ParentedTree objects, this behaviour is not possible. With a shallow +copy, the children of the root node would be reused for both the original +and the shallow copy. For this to be possible, some children would need +to have multiple parents. As this is forbidden for ParentedTree objects, +attempting to make a shallow copy will cause a warning, and a deep copy +is made instead. + + >>> ptree = ParentedTree.fromstring("(TOP (S (NP (NNP Bell,)) (NP (NP (DT a) (NN company)) (SBAR (WHNP (WDT which)) (S (VP (VBZ is) (VP (VBN based) (PP (IN in) (NP (NNP LA,)))))))) (VP (VBZ makes) (CC and) (VBZ distributes) (NP (NN computer))) (. products.)))") + >>> copy_ptree = ptree.copy(deep=False) + >>> copy_ptree == ptree # Ensure identical labels and nodes + True + >>> id(copy_ptree[0]) != id(ptree[0]) # Shallow copying isn't supported - it defaults to deep copy. + True + +For MultiParentedTree objects, the issue of only allowing one parent that +can be seen for ParentedTree objects is no more. Shallow copying a +MultiParentedTree gives the children of the root node two parents: +the original and the newly copied root. + + >>> mptree = MultiParentedTree.fromstring("(TOP (S (NP (NNP Bell,)) (NP (NP (DT a) (NN company)) (SBAR (WHNP (WDT which)) (S (VP (VBZ is) (VP (VBN based) (PP (IN in) (NP (NNP LA,)))))))) (VP (VBZ makes) (CC and) (VBZ distributes) (NP (NN computer))) (. products.)))") + >>> len(mptree[0].parents()) + 1 + >>> copy_mptree = mptree.copy(deep=False) + >>> copy_mptree == mptree # Ensure identical labels and nodes + True + >>> len(mptree[0].parents()) + 2 + >>> len(copy_mptree[0].parents()) + 2 + +Shallow copying a MultiParentedTree is similar to creating a second root +which is identically labeled as the root on which the copy method was called. + + +ImmutableParentedTree Regression Tests +-------------------------------------- + + >>> iptree = ImmutableParentedTree.convert(ptree) + >>> type(iptree) + + >>> del iptree[0] + Traceback (most recent call last): + . . . + ValueError: ImmutableParentedTree may not be modified + >>> iptree.set_label('newnode') + Traceback (most recent call last): + . . . + ValueError: ImmutableParentedTree may not be modified + + +MultiParentedTree Regression Tests +---------------------------------- +Keep track of all trees that we create (including subtrees) using this +variable: + + >>> all_mptrees = [] + +Define a helper function to create new parented trees: + + >>> def make_mptree(s): + ... mptree = MultiParentedTree.convert(Tree.fromstring(s)) + ... all_mptrees.extend(t for t in mptree.subtrees() + ... if isinstance(t, Tree)) + ... return mptree + +Define a test function that examines every subtree in all_mptrees; and +checks that all six of its methods are defined correctly. If any +mptrees are passed as arguments, then they are printed. + + >>> def mpcheck(*print_mptrees): + ... def has(seq, val): # uses identity comparison + ... for item in seq: + ... if item is val: return True + ... return False + ... for mptree in all_mptrees: + ... # Check mptree's methods. + ... if len(mptree.parents()) == 0: + ... assert len(mptree.left_siblings()) == 0 + ... assert len(mptree.right_siblings()) == 0 + ... assert len(mptree.roots()) == 1 + ... assert mptree.roots()[0] is mptree + ... assert mptree.treepositions(mptree) == [()] + ... left_siblings = right_siblings = () + ... roots = {id(mptree): 1} + ... else: + ... roots = dict((id(r), 0) for r in mptree.roots()) + ... left_siblings = mptree.left_siblings() + ... right_siblings = mptree.right_siblings() + ... for parent in mptree.parents(): + ... for i in mptree.parent_indices(parent): + ... assert parent[i] is mptree + ... # check left siblings + ... if i > 0: + ... for j in range(len(left_siblings)): + ... if left_siblings[j] is parent[i-1]: + ... del left_siblings[j] + ... break + ... else: + ... assert 0, 'sibling not found!' + ... # check ight siblings + ... if i < (len(parent)-1): + ... for j in range(len(right_siblings)): + ... if right_siblings[j] is parent[i+1]: + ... del right_siblings[j] + ... break + ... else: + ... assert 0, 'sibling not found!' + ... # check roots + ... for root in parent.roots(): + ... assert id(root) in roots, 'missing root' + ... roots[id(root)] += 1 + ... # check that we don't have any unexplained values + ... assert len(left_siblings)==0, 'unexpected sibling' + ... assert len(right_siblings)==0, 'unexpected sibling' + ... for v in roots.values(): assert v>0, roots #'unexpected root' + ... # check treepositions + ... for root in mptree.roots(): + ... for treepos in mptree.treepositions(root): + ... assert root[treepos] is mptree + ... # Check mptree's children's methods: + ... for i, child in enumerate(mptree): + ... if isinstance(child, Tree): + ... # mpcheck parent() & parent_index() methods + ... assert has(child.parents(), mptree) + ... assert i in child.parent_indices(mptree) + ... # mpcheck sibling methods + ... if i > 0: + ... assert has(child.left_siblings(), mptree[i-1]) + ... if i < len(mptree)-1: + ... assert has(child.right_siblings(), mptree[i+1]) + ... if print_mptrees: + ... print('ok!', end=' ') + ... for mptree in print_mptrees: print(mptree) + ... else: + ... print('ok!') + +Run our test function on a variety of newly-created trees: + + >>> mpcheck(make_mptree('(A)')) + ok! (A ) + >>> mpcheck(make_mptree('(A (B (C (D) (E f)) g) h)')) + ok! (A (B (C (D ) (E f)) g) h) + >>> mpcheck(make_mptree('(A (B) (C c) (D d d) (E e e e))')) + ok! (A (B ) (C c) (D d d) (E e e e)) + >>> mpcheck(make_mptree('(A (B) (C (c)) (D (d) (d)) (E (e) (e) (e)))')) + ok! (A (B ) (C (c )) (D (d ) (d )) (E (e ) (e ) (e ))) + >>> subtree = make_mptree('(A (B (C (D) (E f)) g) h)') + +Including some trees that contain multiple parents: + + >>> mpcheck(MultiParentedTree('Z', [subtree, subtree])) + ok! (Z (A (B (C (D ) (E f)) g) h) (A (B (C (D ) (E f)) g) h)) + +Run our test function after performing various tree-modification +operations (n.b., these are the same tests that we ran for +`ParentedTree`, above; thus, none of these trees actually *uses* +multiple parents.) + +**__delitem__()** + + >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> e = mptree[0,0,1] + >>> del mptree[0,0,1]; mpcheck(mptree); mpcheck(e) + ok! (A (B (C (D ) (Q p)) g) h) + ok! (E f) + >>> del mptree[0,0,0]; mpcheck(mptree) + ok! (A (B (C (Q p)) g) h) + >>> del mptree[0,1]; mpcheck(mptree) + ok! (A (B (C (Q p))) h) + >>> del mptree[-1]; mpcheck(mptree) + ok! (A (B (C (Q p)))) + >>> del mptree[-100] + Traceback (most recent call last): + . . . + IndexError: index out of range + >>> del mptree[()] + Traceback (most recent call last): + . . . + IndexError: The tree position () may not be deleted. + + >>> # With slices: + >>> mptree = make_mptree('(A (B c) (D e) f g (H i) j (K l))') + >>> b = mptree[0] + >>> del mptree[0:0]; mpcheck(mptree) + ok! (A (B c) (D e) f g (H i) j (K l)) + >>> del mptree[:1]; mpcheck(mptree); mpcheck(b) + ok! (A (D e) f g (H i) j (K l)) + ok! (B c) + >>> del mptree[-2:]; mpcheck(mptree) + ok! (A (D e) f g (H i)) + >>> del mptree[1:3]; mpcheck(mptree) + ok! (A (D e) (H i)) + >>> mptree = make_mptree('(A (B c) (D e) f g (H i) j (K l))') + >>> del mptree[5:1000]; mpcheck(mptree) + ok! (A (B c) (D e) f g (H i)) + >>> del mptree[-2:1000]; mpcheck(mptree) + ok! (A (B c) (D e) f) + >>> del mptree[-100:1]; mpcheck(mptree) + ok! (A (D e) f) + >>> mptree = make_mptree('(A (B c) (D e) f g (H i) j (K l))') + >>> del mptree[1:-2:2]; mpcheck(mptree) + ok! (A (B c) f (H i) j (K l)) + +**__setitem__()** + + >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> d, e, q = mptree[0,0] + >>> mptree[0,0,0] = 'x'; mpcheck(mptree); mpcheck(d) + ok! (A (B (C x (E f) (Q p)) g) h) + ok! (D ) + >>> mptree[0,0,1] = make_mptree('(X (Y z))'); mpcheck(mptree); mpcheck(e) + ok! (A (B (C x (X (Y z)) (Q p)) g) h) + ok! (E f) + >>> mptree[1] = d; mpcheck(mptree) + ok! (A (B (C x (X (Y z)) (Q p)) g) (D )) + >>> mptree[-1] = 'x'; mpcheck(mptree) + ok! (A (B (C x (X (Y z)) (Q p)) g) x) + >>> mptree[-100] = 'y' + Traceback (most recent call last): + . . . + IndexError: index out of range + >>> mptree[()] = make_mptree('(X y)') + Traceback (most recent call last): + . . . + IndexError: The tree position () may not be assigned to. + + >>> # With slices: + >>> mptree = make_mptree('(A (B c) (D e) f g (H i) j (K l))') + >>> b = mptree[0] + >>> mptree[0:0] = ('x', make_mptree('(Y)')); mpcheck(mptree) + ok! (A x (Y ) (B c) (D e) f g (H i) j (K l)) + >>> mptree[2:6] = (); mpcheck(mptree); mpcheck(b) + ok! (A x (Y ) (H i) j (K l)) + ok! (B c) + >>> mptree[-2:] = ('z', 'p'); mpcheck(mptree) + ok! (A x (Y ) (H i) z p) + >>> mptree[1:3] = [make_mptree('(X)') for x in range(10)]; mpcheck(mptree) + ok! (A x (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) z p) + >>> mptree[5:1000] = []; mpcheck(mptree) + ok! (A x (X ) (X ) (X ) (X )) + >>> mptree[-2:1000] = ['n']; mpcheck(mptree) + ok! (A x (X ) (X ) n) + >>> mptree[-100:1] = [make_mptree('(U v)')]; mpcheck(mptree) + ok! (A (U v) (X ) (X ) n) + >>> mptree[-1:] = (make_mptree('(X)') for x in range(3)); mpcheck(mptree) + ok! (A (U v) (X ) (X ) (X ) (X ) (X )) + >>> mptree[1:-2:2] = ['x', 'y']; mpcheck(mptree) + ok! (A (U v) x (X ) y (X ) (X )) + +**append()** + + >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> mptree.append('x'); mpcheck(mptree) + ok! (A (B (C (D ) (E f) (Q p)) g) h x) + >>> mptree.append(make_mptree('(X (Y z))')); mpcheck(mptree) + ok! (A (B (C (D ) (E f) (Q p)) g) h x (X (Y z))) + +**extend()** + + >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> mptree.extend(['x', 'y', make_mptree('(X (Y z))')]); mpcheck(mptree) + ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z))) + >>> mptree.extend([]); mpcheck(mptree) + ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z))) + >>> mptree.extend(make_mptree('(X)') for x in range(3)); mpcheck(mptree) + ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z)) (X ) (X ) (X )) + +**insert()** + + >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> mptree.insert(0, make_mptree('(X (Y z))')); mpcheck(mptree) + ok! (A (X (Y z)) (B (C (D ) (E f) (Q p)) g) h) + >>> mptree.insert(-1, make_mptree('(X (Y z))')); mpcheck(mptree) + ok! (A (X (Y z)) (B (C (D ) (E f) (Q p)) g) (X (Y z)) h) + >>> mptree.insert(-4, make_mptree('(X (Y z))')); mpcheck(mptree) + ok! (A (X (Y z)) (X (Y z)) (B (C (D ) (E f) (Q p)) g) (X (Y z)) h) + >>> # Note: as with ``list``, inserting at a negative index that + >>> # gives a position before the start of the list does *not* + >>> # raise an IndexError exception; it just inserts at 0. + >>> mptree.insert(-400, make_mptree('(X (Y z))')); mpcheck(mptree) + ok! (A + (X (Y z)) + (X (Y z)) + (X (Y z)) + (B (C (D ) (E f) (Q p)) g) + (X (Y z)) + h) + +**pop()** + + >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> mptree[0,0].pop(1); mpcheck(mptree) + MultiParentedTree('E', ['f']) + ok! (A (B (C (D ) (Q p)) g) h) + >>> mptree[0].pop(-1); mpcheck(mptree) + 'g' + ok! (A (B (C (D ) (Q p))) h) + >>> mptree.pop(); mpcheck(mptree) + 'h' + ok! (A (B (C (D ) (Q p)))) + >>> mptree.pop(-100) + Traceback (most recent call last): + . . . + IndexError: index out of range + +**remove()** + + >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)') + >>> e = mptree[0,0,1] + >>> mptree[0,0].remove(mptree[0,0,1]); mpcheck(mptree); mpcheck(e) + ok! (A (B (C (D ) (Q p)) g) h) + ok! (E f) + >>> mptree[0,0].remove(make_mptree('(Q p)')); mpcheck(mptree) + ok! (A (B (C (D )) g) h) + >>> mptree[0,0].remove(make_mptree('(Q p)')) + Traceback (most recent call last): + . . . + ValueError: MultiParentedTree('Q', ['p']) is not in list + >>> mptree.remove('h'); mpcheck(mptree) + ok! (A (B (C (D )) g)) + >>> mptree.remove('h'); + Traceback (most recent call last): + . . . + ValueError: 'h' is not in list + >>> # remove() removes the first subtree that is equal (==) to the + >>> # given tree, which may not be the identical tree we give it: + >>> mptree = make_mptree('(A (X x) (Y y) (X x))') + >>> x1, y, x2 = mptree + >>> mptree.remove(mptree[-1]); mpcheck(mptree) + ok! (A (Y y) (X x)) + >>> print([str(p) for p in x1.parents()]) + [] + >>> print([str(p) for p in x2.parents()]) + ['(A (Y y) (X x))'] + + +ImmutableMultiParentedTree Regression Tests +------------------------------------------- + + >>> imptree = ImmutableMultiParentedTree.convert(mptree) + >>> type(imptree) + + >>> del imptree[0] + Traceback (most recent call last): + . . . + ValueError: ImmutableMultiParentedTree may not be modified + >>> imptree.set_label('newnode') + Traceback (most recent call last): + . . . + ValueError: ImmutableMultiParentedTree may not be modified + + +ProbabilisticTree Regression Tests +---------------------------------- + + >>> prtree = ProbabilisticTree("S", [ProbabilisticTree("NP", ["N"], prob=0.3)], prob=0.6) + >>> print(prtree) + (S (NP N)) (p=0.6) + >>> import copy + >>> prtree == copy.deepcopy(prtree) == prtree.copy(deep=True) == prtree.copy() + True + >>> prtree[0] is prtree.copy()[0] + True + >>> prtree[0] is prtree.copy(deep=True)[0] + False + + >>> imprtree = ImmutableProbabilisticTree.convert(prtree) + >>> type(imprtree) + + >>> del imprtree[0] + Traceback (most recent call last): + . . . + ValueError: ImmutableProbabilisticTree may not be modified + >>> imprtree.set_label('newnode') + Traceback (most recent call last): + . . . + ValueError: ImmutableProbabilisticTree may not be modified + + +Squashed Bugs +============= + +This used to discard the ``(B b)`` subtree (fixed in svn 6270): + + >>> print(Tree.fromstring('((A a) (B b))')) + ( (A a) (B b)) + +Pickling ParentedTree instances didn't work for Python 3.7 onwards (See #2478) + + >>> import pickle + >>> tree = ParentedTree.fromstring('(S (NN x) (NP x) (NN x))') + >>> print(tree) + (S (NN x) (NP x) (NN x)) + + >>> pickled = pickle.dumps(tree) + >>> tree_loaded = pickle.loads(pickled) + >>> print(tree_loaded) + (S (NN x) (NP x) (NN x)) + +ParentedTree used to be impossible to (deep)copy. (See #1324) + + >>> from nltk.tree import ParentedTree + >>> import copy + >>> tree = ParentedTree.fromstring("(TOP (S (NP (NNP Bell,)) (NP (NP (DT a) (NN company)) (SBAR (WHNP (WDT which)) (S (VP (VBZ is) (VP (VBN based) (PP (IN in) (NP (NNP LA,)))))))) (VP (VBZ makes) (CC and) (VBZ distributes) (NP (NN computer))) (. products.)))") + >>> tree == copy.deepcopy(tree) == copy.copy(tree) == tree.copy(deep=True) == tree.copy() + True diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/treeprettyprinter.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/treeprettyprinter.doctest new file mode 100644 index 0000000000000000000000000000000000000000..b85c6d1e251d7e6e95a68fbeaf88eb8dd20fed00 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/treeprettyprinter.doctest @@ -0,0 +1,177 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +========================================================= + Unit tests for nltk.tree.prettyprinter.TreePrettyPrinter +========================================================= + + >>> from nltk.tree import Tree, TreePrettyPrinter + +Tree nr 2170 from nltk.corpus.treebank: + + >>> tree = Tree.fromstring( + ... '(S (NP-SBJ (PRP I)) (VP (VBP feel) (ADJP-PRD (RB pretty) ' + ... '(JJ good)) (PP-CLR (IN about) (NP (PRP it)))) (. .))') + >>> tpp = TreePrettyPrinter(tree) + >>> print(tpp.text()) + S + __________________________|_____________________ + | VP | + | ____________________|___________ | + | | | PP-CLR | + | | | _____|_____ | + NP-SBJ | ADJP-PRD | NP | + | | _______|______ | | | + PRP VBP RB JJ IN PRP . + | | | | | | | + I feel pretty good about it . + + >>> print(tpp.text(unicodelines=True)) + S + ┌──────────────────────────┼─────────────────────┐ + │ VP │ + │ ┌─────────────┬──────┴───────────┐ │ + │ │ │ PP-CLR │ + │ │ │ ┌─────┴─────┐ │ + NP-SBJ │ ADJP-PRD │ NP │ + │ │ ┌───────┴──────┐ │ │ │ + PRP VBP RB JJ IN PRP . + │ │ │ │ │ │ │ + I feel pretty good about it . + +A tree with long labels: + + >>> tree = Tree.fromstring( + ... '(sentence (plural-noun-phrase (plural-noun Superconductors)) ' + ... '(verb-phrase (plural-verb conduct) ' + ... '(noun-phrase (singular-noun electricity))))') + >>> tpp = TreePrettyPrinter(tree) + >>> print(tpp.text(abbreviate=8, nodedist=2)) + sentence + __________|__________ + | verb-phr. + | __________|__________ + plural-n. | noun-phr. + | | | + plural-n. plural-v. singular. + | | | + Supercon. conduct electric. + + >>> print(tpp.text(maxwidth=8, nodedist=2)) + sentence + _________|________ + | verb- + | phrase + | ________|_________ + plural- | noun- + noun- | phrase + phrase | | + | | | + plural- plural- singular- + noun verb noun + | | | + Supercon conduct electric + ductors ity + +A discontinuous tree: + + >>> tree = Tree.fromstring( + ... '(top (punct 8) (smain (noun 0) (verb 1) (inf (verb 5) (inf (verb 6) ' + ... '(conj (inf (pp (prep 2) (np (det 3) (noun 4))) (verb 7)) (inf (verb 9)) ' + ... '(vg 10) (inf (verb 11)))))) (punct 12))', read_leaf=int) + >>> sentence = ('Ze had met haar moeder kunnen gaan winkelen ,' + ... ' zwemmen of terrassen .'.split()) + >>> tpp = TreePrettyPrinter(tree, sentence) + >>> print(tpp.text()) + top + _____|______________________________________________ + smain | | + _______________________________|_____ | | + | | inf | | + | | _____|____ | | + | | | inf | | + | | | ____|_____ | | + | | | | conj | | + | | _____ | ___ | _________|______ | __________________ | + | | inf | | | | | | | + | | _________|_____ | ___ | _________ | | | | | + | | pp | | | | | | | | + | | ____|____ | | | | | | | | + | | | np | | | | inf | inf | + | | | ____|____ | | | | | | | | + noun verb prep det noun verb verb verb punct verb vg verb punct + | | | | | | | | | | | | | + Ze had met haar moeder kunnen gaan winkelen , zwemmen of terrassen . + + >>> print(tpp.text(unicodelines=True)) + top + ┌─────┴──────────────────┬───────────────────────────┐ + smain │ │ + ┌────┬──────────────────────────┴─────┐ │ │ + │ │ inf │ │ + │ │ ┌─────┴────┐ │ │ + │ │ │ inf │ │ + │ │ │ ┌────┴─────┐ │ │ + │ │ │ │ conj │ │ + │ │ ┌───── │ ─── │ ─────────┴────── │ ─────┬─────┬──────┐ │ + │ │ inf │ │ │ │ │ │ │ + │ │ ┌─────────┴───── │ ─── │ ─────────┐ │ │ │ │ │ + │ │ pp │ │ │ │ │ │ │ │ + │ │ ┌────┴────┐ │ │ │ │ │ │ │ │ + │ │ │ np │ │ │ │ inf │ inf │ + │ │ │ ┌────┴────┐ │ │ │ │ │ │ │ │ + noun verb prep det noun verb verb verb punct verb vg verb punct + │ │ │ │ │ │ │ │ │ │ │ │ │ + Ze had met haar moeder kunnen gaan winkelen , zwemmen of terrassen . + +Importing TreePrettyPrinter +--------------------------- + +First of all, a simple tree will be constructed:: + + >>> from nltk.tree import Tree + >>> tree = Tree.fromstring('(S (NP Mary) (VP walks))') + +We'll use this sample tree to show that the method of importing `TreePrettyPrinter` work correctly: + +- Recommended:: + + >>> from nltk.tree import TreePrettyPrinter + >>> print(TreePrettyPrinter(tree).text()) + S + ____|____ + NP VP + | | + Mary walks + +- Alternative but valid options:: + + >>> from nltk import TreePrettyPrinter + >>> print(TreePrettyPrinter(tree).text()) + S + ____|____ + NP VP + | | + Mary walks + + >>> from nltk.tree.prettyprinter import TreePrettyPrinter + >>> print(TreePrettyPrinter(tree).text()) + S + ____|____ + NP VP + | | + Mary walks + +- Deprecated, do not use:: + + >>> from nltk.treeprettyprinter import TreePrettyPrinter + >>> print(TreePrettyPrinter(tree).text()) + S + ____|____ + NP VP + | | + Mary walks + + This method will throw a DeprecationWarning:: + + Import `TreePrettyPrinter` using `from nltk.tree import TreePrettyPrinter` instead. diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/util.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/util.doctest new file mode 100644 index 0000000000000000000000000000000000000000..b5dce4e3ce1f2ab30ee1083d969c666889e6c4ec --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/util.doctest @@ -0,0 +1,47 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +================= +Utility functions +================= + + >>> from nltk.util import * + >>> from nltk.tree import Tree + + >>> print_string("This is a long string, therefore it should break", 25) + This is a long string, + therefore it should break + + >>> re_show("[a-z]+", "sdf123") + {sdf}123 + + >>> tree = Tree(5, + ... [Tree(4, [Tree(2, [1, 3])]), + ... Tree(8, [Tree(6, [7]), 9])]) + >>> for x in breadth_first(tree): + ... if isinstance(x, int): print(x) + ... else: print(x.label()) + 5 + 4 + 8 + 2 + 6 + 9 + 1 + 3 + 7 + >>> for x in breadth_first(tree, maxdepth=2): + ... if isinstance(x, int): print(x) + ... else: print(x.label()) + 5 + 4 + 8 + 2 + 6 + 9 + + >>> invert_dict({1: 2}) + defaultdict(<... 'list'>, {2: 1}) + + >>> invert_dict({1: [3, 4, 5]}) + defaultdict(<... 'list'>, {3: [1], 4: [1], 5: [1]}) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/wordnet.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/wordnet.doctest new file mode 100644 index 0000000000000000000000000000000000000000..0249e6564a73155051050700d76c0014b4086b0e --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/wordnet.doctest @@ -0,0 +1,828 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +================= +WordNet Interface +================= + +WordNet is just another NLTK corpus reader, and can be imported like this: + + >>> from nltk.corpus import wordnet + +For more compact code, we recommend: + + >>> from nltk.corpus import wordnet as wn + +----- +Words +----- + +Look up a word using ``synsets()``; this function has an optional ``pos`` argument +which lets you constrain the part of speech of the word: + + >>> wn.synsets('dog') + [Synset('dog.n.01'), Synset('frump.n.01'), Synset('dog.n.03'), Synset('cad.n.01'), + Synset('frank.n.02'), Synset('pawl.n.01'), Synset('andiron.n.01'), Synset('chase.v.01')] + >>> wn.synsets('dog', pos=wn.VERB) + [Synset('chase.v.01')] + +The other parts of speech are ``NOUN``, ``ADJ`` and ``ADV``. +A synset is identified with a 3-part name of the form: word.pos.nn: + + >>> wn.synset('dog.n.01') + Synset('dog.n.01') + >>> print(wn.synset('dog.n.01').definition()) + a member of the genus Canis (probably descended from the common wolf) that has been domesticated by man since prehistoric times; occurs in many breeds + >>> len(wn.synset('dog.n.01').examples()) + 1 + >>> print(wn.synset('dog.n.01').examples()[0]) + the dog barked all night + >>> wn.synset('dog.n.01').lemmas() + [Lemma('dog.n.01.dog'), Lemma('dog.n.01.domestic_dog'), Lemma('dog.n.01.Canis_familiaris')] + >>> [str(lemma.name()) for lemma in wn.synset('dog.n.01').lemmas()] + ['dog', 'domestic_dog', 'Canis_familiaris'] + >>> wn.lemma('dog.n.01.dog').synset() + Synset('dog.n.01') + +The WordNet corpus reader gives access to the Open Multilingual +WordNet, using ISO-639 language codes. These languages are not +loaded by default, but only lazily, when needed. + + >>> wn.langs() + ['eng'] + + >>> wn.synsets(b'\xe7\x8a\xac'.decode('utf-8'), lang='jpn') + [Synset('dog.n.01'), Synset('spy.n.01')] + + >>> wn.synset('spy.n.01').lemma_names('jpn') + ['いぬ', 'まわし者', 'スパイ', '回し者', '回者', '密偵', + '工作員', '廻し者', '廻者', '探', '探り', '犬', '秘密捜査員', + '諜報員', '諜者', '間者', '間諜', '隠密'] + + >>> sorted(wn.langs()) + ['als', 'arb', 'bul', 'cat', 'cmn', 'dan', 'ell', 'eng', 'eus', + 'fin', 'fra', 'glg', 'heb', 'hrv', 'ind', 'isl', 'ita', 'ita_iwn', + 'jpn', 'lit', 'nld', 'nno', 'nob', 'pol', 'por', 'ron', 'slk', + 'slv', 'spa', 'swe', 'tha', 'zsm'] + + >>> wn.synset('dog.n.01').lemma_names('ita') + ['Canis_familiaris', 'cane'] + >>> wn.lemmas('cane', lang='ita') + [Lemma('dog.n.01.cane'), Lemma('cramp.n.02.cane'), Lemma('hammer.n.01.cane'), Lemma('bad_person.n.01.cane'), + Lemma('incompetent.n.01.cane')] + >>> sorted(wn.synset('dog.n.01').lemmas('dan')) + [Lemma('dog.n.01.hund'), Lemma('dog.n.01.k\xf8ter'), + Lemma('dog.n.01.vovhund'), Lemma('dog.n.01.vovse')] + + >>> sorted(wn.synset('dog.n.01').lemmas('por')) + [Lemma('dog.n.01.cachorra'), Lemma('dog.n.01.cachorro'), Lemma('dog.n.01.cadela'), Lemma('dog.n.01.c\xe3o')] + + >>> dog_lemma = wn.lemma(b'dog.n.01.c\xc3\xa3o'.decode('utf-8'), lang='por') + >>> dog_lemma + Lemma('dog.n.01.c\xe3o') + >>> dog_lemma.lang() + 'por' + >>> len(list(wordnet.all_lemma_names(pos='n', lang='jpn'))) + 66031 + +The synonyms of a word are returned as a nested list of synonyms of the different senses of +the input word in the given language, since these different senses are not mutual synonyms: + + >>> wn.synonyms('car') + [['auto', 'automobile', 'machine', 'motorcar'], ['railcar', 'railroad_car', 'railway_car'], ['gondola'], ['elevator_car'], ['cable_car']] + >>> wn.synonyms('coche', lang='spa') + [['auto', 'automóvil', 'carro', 'máquina', 'turismo', 'vehículo'], ['automotor', 'vagón'], ['vagón', 'vagón_de_pasajeros']] + + +------- +Synsets +------- + +`Synset`: a set of synonyms that share a common meaning. + + >>> dog = wn.synset('dog.n.01') + >>> dog.hypernyms() + [Synset('canine.n.02'), Synset('domestic_animal.n.01')] + >>> dog.hyponyms() + [Synset('basenji.n.01'), Synset('corgi.n.01'), Synset('cur.n.01'), Synset('dalmatian.n.02'), ...] + >>> dog.member_holonyms() + [Synset('canis.n.01'), Synset('pack.n.06')] + >>> dog.root_hypernyms() + [Synset('entity.n.01')] + >>> wn.synset('dog.n.01').lowest_common_hypernyms(wn.synset('cat.n.01')) + [Synset('carnivore.n.01')] + +Each synset contains one or more lemmas, which represent a specific +sense of a specific word. + +Note that some relations are defined by WordNet only over Lemmas: + + >>> good = wn.synset('good.a.01') + >>> good.antonyms() + Traceback (most recent call last): + File "", line 1, in + AttributeError: 'Synset' object has no attribute 'antonyms' + >>> good.lemmas()[0].antonyms() + [Lemma('bad.a.01.bad')] + +The relations that are currently defined in this way are `antonyms`, +`derivationally_related_forms` and `pertainyms`. + +If you know the byte offset used to identify a synset in the original +Princeton WordNet data file, you can use that to instantiate the synset +in NLTK: + + >>> wn.synset_from_pos_and_offset('n', 4543158) + Synset('wagon.n.01') + +Likewise, instantiate a synset from a known sense key: + >>> wn.synset_from_sense_key("driving%1:04:03::") + Synset('drive.n.06') + + +------ +Lemmas +------ + + >>> eat = wn.lemma('eat.v.03.eat') + >>> eat + Lemma('feed.v.06.eat') + >>> print(eat.key()) + eat%2:34:02:: + >>> eat.count() + 4 + >>> wn.lemma_from_key(eat.key()) + Lemma('feed.v.06.eat') + >>> wn.lemma_from_key(eat.key()).synset() + Synset('feed.v.06') + >>> wn.lemma_from_key('feebleminded%5:00:00:retarded:00') + Lemma('backward.s.03.feebleminded') + >>> for lemma in wn.synset('eat.v.03').lemmas(): + ... print(lemma, lemma.count()) + ... + Lemma('feed.v.06.feed') 3 + Lemma('feed.v.06.eat') 4 + >>> for lemma in wn.lemmas('eat', 'v'): + ... print(lemma, lemma.count()) + ... + Lemma('eat.v.01.eat') 61 + Lemma('eat.v.02.eat') 13 + Lemma('feed.v.06.eat') 4 + Lemma('eat.v.04.eat') 0 + Lemma('consume.v.05.eat') 0 + Lemma('corrode.v.01.eat') 0 + >>> wn.lemma('jump.v.11.jump') + Lemma('jump.v.11.jump') + +Lemmas can also have relations between them: + + >>> vocal = wn.lemma('vocal.a.01.vocal') + >>> vocal.derivationally_related_forms() + [Lemma('vocalize.v.02.vocalize')] + >>> vocal.pertainyms() + [Lemma('voice.n.02.voice')] + >>> vocal.antonyms() + [Lemma('instrumental.a.01.instrumental')] + +The three relations above exist only on lemmas, not on synsets. + +----------- +Verb Frames +----------- + + >>> wn.synset('think.v.01').frame_ids() + [5, 9] + >>> for lemma in wn.synset('think.v.01').lemmas(): + ... print(lemma, lemma.frame_ids()) + ... print(" | ".join(lemma.frame_strings())) + ... + Lemma('think.v.01.think') [5, 9] + Something think something Adjective/Noun | Somebody think somebody + Lemma('think.v.01.believe') [5, 9] + Something believe something Adjective/Noun | Somebody believe somebody + Lemma('think.v.01.consider') [5, 9] + Something consider something Adjective/Noun | Somebody consider somebody + Lemma('think.v.01.conceive') [5, 9] + Something conceive something Adjective/Noun | Somebody conceive somebody + >>> wn.synset('stretch.v.02').frame_ids() + [8] + >>> for lemma in wn.synset('stretch.v.02').lemmas(): + ... print(lemma, lemma.frame_ids()) + ... print(" | ".join(lemma.frame_strings())) + ... + Lemma('stretch.v.02.stretch') [8, 2] + Somebody stretch something | Somebody stretch + Lemma('stretch.v.02.extend') [8] + Somebody extend something + + +---------- +Similarity +---------- + + >>> dog = wn.synset('dog.n.01') + >>> cat = wn.synset('cat.n.01') + + >>> hit = wn.synset('hit.v.01') + >>> slap = wn.synset('slap.v.01') + + +``synset1.path_similarity(synset2):`` +Return a score denoting how similar two word senses are, based on the +shortest path that connects the senses in the is-a (hypernym/hypnoym) +taxonomy. The score is in the range 0 to 1. By default, there is now +a fake root node added to verbs so for cases where previously a path +could not be found---and None was returned---it should return a value. +The old behavior can be achieved by setting simulate_root to be False. +A score of 1 represents identity i.e. comparing a sense with itself +will return 1. + + >>> dog.path_similarity(cat) + 0.2... + + >>> hit.path_similarity(slap) + 0.142... + + >>> wn.path_similarity(hit, slap) + 0.142... + + >>> print(hit.path_similarity(slap, simulate_root=False)) + None + + >>> print(wn.path_similarity(hit, slap, simulate_root=False)) + None + +``synset1.lch_similarity(synset2):`` +Leacock-Chodorow Similarity: +Return a score denoting how similar two word senses are, based on the +shortest path that connects the senses (as above) and the maximum depth +of the taxonomy in which the senses occur. The relationship is given +as -log(p/2d) where p is the shortest path length and d the taxonomy +depth. + + >>> dog.lch_similarity(cat) + 2.028... + + >>> hit.lch_similarity(slap) + 1.312... + + >>> wn.lch_similarity(hit, slap) + 1.312... + + >>> print(hit.lch_similarity(slap, simulate_root=False)) + None + + >>> print(wn.lch_similarity(hit, slap, simulate_root=False)) + None + +``synset1.wup_similarity(synset2):`` +Wu-Palmer Similarity: +Return a score denoting how similar two word senses are, based on the +depth of the two senses in the taxonomy and that of their Least Common +Subsumer (most specific ancestor node). Note that at this time the +scores given do **not** always agree with those given by Pedersen's Perl +implementation of Wordnet Similarity. + +The LCS does not necessarily feature in the shortest path connecting the +two senses, as it is by definition the common ancestor deepest in the +taxonomy, not closest to the two senses. Typically, however, it will so +feature. Where multiple candidates for the LCS exist, that whose +shortest path to the root node is the longest will be selected. Where +the LCS has multiple paths to the root, the longer path is used for +the purposes of the calculation. + + >>> dog.wup_similarity(cat) + 0.857... + + >>> hit.wup_similarity(slap) + 0.25 + + >>> wn.wup_similarity(hit, slap) + 0.25 + + >>> print(hit.wup_similarity(slap, simulate_root=False)) + None + + >>> print(wn.wup_similarity(hit, slap, simulate_root=False)) + None + +``wordnet_ic`` +Information Content: +Load an information content file from the wordnet_ic corpus. + + >>> from nltk.corpus import wordnet_ic + >>> brown_ic = wordnet_ic.ic('ic-brown.dat') + >>> semcor_ic = wordnet_ic.ic('ic-semcor.dat') + +Or you can create an information content dictionary from a corpus (or +anything that has a words() method). + + >>> from nltk.corpus import genesis + >>> genesis_ic = wn.ic(genesis, False, 0.0) + +``synset1.res_similarity(synset2, ic):`` +Resnik Similarity: +Return a score denoting how similar two word senses are, based on the +Information Content (IC) of the Least Common Subsumer (most specific +ancestor node). Note that for any similarity measure that uses +information content, the result is dependent on the corpus used to +generate the information content and the specifics of how the +information content was created. + + >>> dog.res_similarity(cat, brown_ic) + 7.911... + >>> dog.res_similarity(cat, genesis_ic) + 7.204... + +``synset1.jcn_similarity(synset2, ic):`` +Jiang-Conrath Similarity +Return a score denoting how similar two word senses are, based on the +Information Content (IC) of the Least Common Subsumer (most specific +ancestor node) and that of the two input Synsets. The relationship is +given by the equation 1 / (IC(s1) + IC(s2) - 2 * IC(lcs)). + + >>> dog.jcn_similarity(cat, brown_ic) + 0.449... + >>> dog.jcn_similarity(cat, genesis_ic) + 0.285... + +``synset1.lin_similarity(synset2, ic):`` +Lin Similarity: +Return a score denoting how similar two word senses are, based on the +Information Content (IC) of the Least Common Subsumer (most specific +ancestor node) and that of the two input Synsets. The relationship is +given by the equation 2 * IC(lcs) / (IC(s1) + IC(s2)). + + >>> dog.lin_similarity(cat, semcor_ic) + 0.886... + + +--------------------- +Access to all Synsets +--------------------- + +Iterate over all the noun synsets: + + >>> for synset in list(wn.all_synsets('n'))[:10]: + ... print(synset) + ... + Synset('entity.n.01') + Synset('physical_entity.n.01') + Synset('abstraction.n.06') + Synset('thing.n.12') + Synset('object.n.01') + Synset('whole.n.02') + Synset('congener.n.03') + Synset('living_thing.n.01') + Synset('organism.n.01') + Synset('benthos.n.02') + +Get all synsets for this word, possibly restricted by POS: + + >>> wn.synsets('dog') + [Synset('dog.n.01'), Synset('frump.n.01'), Synset('dog.n.03'), Synset('cad.n.01'), ...] + >>> wn.synsets('dog', pos='v') + [Synset('chase.v.01')] + +Walk through the noun synsets looking at their hypernyms: + + >>> from itertools import islice + >>> for synset in islice(wn.all_synsets('n'), 5): + ... print(synset, synset.hypernyms()) + ... + Synset('entity.n.01') [] + Synset('physical_entity.n.01') [Synset('entity.n.01')] + Synset('abstraction.n.06') [Synset('entity.n.01')] + Synset('thing.n.12') [Synset('physical_entity.n.01')] + Synset('object.n.01') [Synset('physical_entity.n.01')] + + +------ +Morphy +------ + +Look up forms not in WordNet, with the help of Morphy: + + >>> wn.morphy('denied', wn.NOUN) + >>> print(wn.morphy('denied', wn.VERB)) + deny + >>> wn.synsets('denied', wn.NOUN) + [] + >>> wn.synsets('denied', wn.VERB) + [Synset('deny.v.01'), Synset('deny.v.02'), Synset('deny.v.03'), Synset('deny.v.04'), + Synset('deny.v.05'), Synset('traverse.v.03'), Synset('deny.v.07')] + +Morphy uses a combination of inflectional ending rules and exception +lists to handle a variety of different possibilities: + + >>> print(wn.morphy('dogs')) + dog + >>> print(wn.morphy('churches')) + church + >>> print(wn.morphy('aardwolves')) + aardwolf + >>> print(wn.morphy('abaci')) + abacus + >>> print(wn.morphy('book', wn.NOUN)) + book + >>> wn.morphy('hardrock', wn.ADV) + >>> wn.morphy('book', wn.ADJ) + >>> wn.morphy('his', wn.NOUN) + >>> + +--------------- +Synset Closures +--------------- + +Compute transitive closures of synsets + + >>> dog = wn.synset('dog.n.01') + >>> hypo = lambda s: s.hyponyms() + >>> hyper = lambda s: s.hypernyms() + >>> list(dog.closure(hypo, depth=1)) == dog.hyponyms() + True + >>> list(dog.closure(hyper, depth=1)) == dog.hypernyms() + True + >>> list(dog.closure(hypo)) + [Synset('basenji.n.01'), Synset('corgi.n.01'), Synset('cur.n.01'), + Synset('dalmatian.n.02'), Synset('great_pyrenees.n.01'), + Synset('griffon.n.02'), Synset('hunting_dog.n.01'), Synset('lapdog.n.01'), + Synset('leonberg.n.01'), Synset('mexican_hairless.n.01'), + Synset('newfoundland.n.01'), Synset('pooch.n.01'), Synset('poodle.n.01'), ...] + >>> list(dog.closure(hyper)) + [Synset('canine.n.02'), Synset('domestic_animal.n.01'), Synset('carnivore.n.01'), Synset('animal.n.01'), + Synset('placental.n.01'), Synset('organism.n.01'), Synset('mammal.n.01'), Synset('living_thing.n.01'), + Synset('vertebrate.n.01'), Synset('whole.n.02'), Synset('chordate.n.01'), Synset('object.n.01'), + Synset('physical_entity.n.01'), Synset('entity.n.01')] + + +---------------- +Regression Tests +---------------- + +Bug 85: morphy returns the base form of a word, if it's input is given +as a base form for a POS for which that word is not defined: + + >>> wn.synsets('book', wn.NOUN) + [Synset('book.n.01'), Synset('book.n.02'), Synset('record.n.05'), Synset('script.n.01'), Synset('ledger.n.01'), Synset('book.n.06'), Synset('book.n.07'), Synset('koran.n.01'), Synset('bible.n.01'), Synset('book.n.10'), Synset('book.n.11')] + >>> wn.synsets('book', wn.ADJ) + [] + >>> wn.morphy('book', wn.NOUN) + 'book' + >>> wn.morphy('book', wn.ADJ) + >>> + +Bug 160: wup_similarity breaks when the two synsets have no common hypernym + + >>> t = wn.synsets('picasso')[0] + >>> m = wn.synsets('male')[1] + >>> t.wup_similarity(m) + 0.631... + +Issue #2278: wup_similarity not commutative when comparing a noun and a verb. +Patch #2650 resolved this error. As a result, the output of the following use of wup_similarity no longer returns None. + + >>> t = wn.synsets('titan')[1] + >>> s = wn.synsets('say', wn.VERB)[0] + >>> t.wup_similarity(s) + 0.142... + +Bug 21: "instance of" not included in LCS (very similar to bug 160) + + >>> a = wn.synsets("writings")[0] + >>> b = wn.synsets("scripture")[0] + >>> brown_ic = wordnet_ic.ic('ic-brown.dat') + >>> a.jcn_similarity(b, brown_ic) + 0.175... + +Bug 221: Verb root IC is zero + + >>> from nltk.corpus.reader.wordnet import information_content + >>> s = wn.synsets('say', wn.VERB)[0] + >>> information_content(s, brown_ic) + 4.623... + +Bug 161: Comparison between WN keys/lemmas should not be case sensitive + + >>> k = wn.synsets("jefferson")[0].lemmas()[0].key() + >>> wn.lemma_from_key(k) + Lemma('jefferson.n.01.Jefferson') + >>> wn.lemma_from_key(k.upper()) + Lemma('jefferson.n.01.Jefferson') + +Bug 99: WordNet root_hypernyms gives incorrect results + + >>> from nltk.corpus import wordnet as wn + >>> for s in wn.all_synsets(wn.NOUN): + ... if s.root_hypernyms()[0] != wn.synset('entity.n.01'): + ... print(s, s.root_hypernyms()) + ... + >>> + +Bug 382: JCN Division by zero error + + >>> tow = wn.synset('tow.v.01') + >>> shlep = wn.synset('shlep.v.02') + >>> from nltk.corpus import wordnet_ic + >>> brown_ic = wordnet_ic.ic('ic-brown.dat') + >>> tow.jcn_similarity(shlep, brown_ic) + 1...e+300 + +Bug 428: Depth is zero for instance nouns + + >>> s = wn.synset("lincoln.n.01") + >>> s.max_depth() > 0 + True + +Bug 429: Information content smoothing used old reference to all_synsets + + >>> genesis_ic = wn.ic(genesis, True, 1.0) + +Bug 430: all_synsets used wrong pos lookup when synsets were cached + + >>> for ii in wn.all_synsets(): pass + >>> for ii in wn.all_synsets(): pass + +Bug 470: shortest_path_distance ignored instance hypernyms + + >>> google = wordnet.synsets("google")[0] + >>> earth = wordnet.synsets("earth")[0] + >>> google.wup_similarity(earth) + 0.1... + +Bug 484: similarity metrics returned -1 instead of None for no LCS + + >>> t = wn.synsets('fly', wn.VERB)[0] + >>> s = wn.synsets('say', wn.VERB)[0] + >>> print(s.shortest_path_distance(t)) + None + >>> print(s.path_similarity(t, simulate_root=False)) + None + >>> print(s.lch_similarity(t, simulate_root=False)) + None + >>> print(s.wup_similarity(t, simulate_root=False)) + None + +Bug 427: "pants" does not return all the senses it should + + >>> from nltk.corpus import wordnet + >>> wordnet.synsets("pants",'n') + [Synset('bloomers.n.01'), Synset('pant.n.01'), Synset('trouser.n.01'), Synset('gasp.n.01')] + +Bug 482: Some nouns not being lemmatised by WordNetLemmatizer().lemmatize + + >>> from nltk.stem.wordnet import WordNetLemmatizer + >>> WordNetLemmatizer().lemmatize("eggs", pos="n") + 'egg' + >>> WordNetLemmatizer().lemmatize("legs", pos="n") + 'leg' + +Bug 284: instance hypernyms not used in similarity calculations + + >>> wn.synset('john.n.02').lch_similarity(wn.synset('dog.n.01')) + 1.335... + >>> wn.synset('john.n.02').wup_similarity(wn.synset('dog.n.01')) + 0.571... + >>> wn.synset('john.n.02').res_similarity(wn.synset('dog.n.01'), brown_ic) + 2.224... + >>> wn.synset('john.n.02').jcn_similarity(wn.synset('dog.n.01'), brown_ic) + 0.075... + >>> wn.synset('john.n.02').lin_similarity(wn.synset('dog.n.01'), brown_ic) + 0.252... + >>> wn.synset('john.n.02').hypernym_paths() + [[Synset('entity.n.01'), ..., Synset('john.n.02')]] + +Issue 541: add domains to wordnet + + >>> wn.synset('code.n.03').topic_domains() + [Synset('computer_science.n.01')] + >>> wn.synset('pukka.a.01').region_domains() + [Synset('india.n.01')] + >>> wn.synset('freaky.a.01').usage_domains() + [Synset('slang.n.02')] + +Issue 629: wordnet failures when python run with -O optimizations + + >>> # Run the test suite with python -O to check this + >>> wn.synsets("brunch") + [Synset('brunch.n.01'), Synset('brunch.v.01')] + +Issue 395: wordnet returns incorrect result for lowest_common_hypernyms of chef and policeman + + >>> wn.synset('policeman.n.01').lowest_common_hypernyms(wn.synset('chef.n.01')) + [Synset('person.n.01')] + +Bug https://github.com/nltk/nltk/issues/1641: Non-English lemmas containing capital letters cannot be looked up using wordnet.lemmas() or wordnet.synsets() + + >>> wn.lemmas('Londres', lang='fra') + [Lemma('united_kingdom.n.01.Londres'), Lemma('london.n.01.Londres'), Lemma('london.n.02.Londres')] + >>> wn.lemmas('londres', lang='fra') + [Lemma('united_kingdom.n.01.Londres'), Lemma('london.n.01.Londres'), Lemma('london.n.02.Londres')] + +Patch-1 https://github.com/nltk/nltk/pull/2065 Adding 3 functions (relations) to WordNet class + + >>> wn.synsets("computer_science")[0].in_topic_domains()[2] + Synset('access_time.n.01') + >>> wn.synsets("France")[0].in_region_domains()[18] + Synset('french.n.01') + >>> wn.synsets("slang")[1].in_usage_domains()[18] + Synset('can-do.s.01') + +Issue 2721: WordNetCorpusReader.ic() does not add smoothing to N + + >>> class FakeCorpus: + ... def words(self): return ['word'] + ... + >>> fake_ic = wn.ic(FakeCorpus(), False, 1.0) + >>> word = wn.synset('word.n.01') + >>> information_content(word, fake_ic) > 0 + True + +Issue 3077: Incorrect part-of-speech filtering in all_synsets + + >>> next(wn.all_synsets(pos="a")) + Synset('able.a.01') + >>> next(wn.all_synsets(pos="s")) + Synset('emergent.s.02') + >>> wn.add_omw() + >>> next(wn.all_synsets(lang="hrv")) + Synset('able.a.01') + >>> next(wn.all_synsets(lang="hrv", pos="n")) + Synset('entity.n.01') + >>> next(wn.all_synsets(lang="hrv", pos="v")) + Synset('breathe.v.01') + >>> next(wn.all_synsets(lang="hrv", pos="s")) + Synset('ideological.s.02') + >>> next(wn.all_synsets(lang="hrv", pos="a")) + Synset('able.a.01') + + +------------------------------------------------ +Endlessness vs. intractability in relation trees +------------------------------------------------ + +1. Endlessness +-------------- + +Until NLTK v. 3.5, the ``tree()`` function looped forever on symmetric +relations (verb_groups, attributes, and most also_sees). But in +the current version, ``tree()`` now detects and discards these cycles: + + >>> from pprint import pprint + >>> pprint(wn.synset('bound.a.01').tree(lambda s:s.also_sees())) + [Synset('bound.a.01'), + [Synset('unfree.a.02'), + [Synset('confined.a.02'), + [Synset('restricted.a.01'), [Synset('classified.a.02')]]], + [Synset('dependent.a.01')], + [Synset('restricted.a.01'), + [Synset('classified.a.02')], + [Synset('confined.a.02')]]]] + +Specifying the "cut_mark" parameter increases verbosity, so that the cycles +are mentioned in the output, together with the level where they occur: + + >>> pprint(wn.synset('bound.a.01').tree(lambda s:s.also_sees(),cut_mark='...')) + [Synset('bound.a.01'), + [Synset('unfree.a.02'), + "Cycle(Synset('bound.a.01'),-3,...)", + [Synset('confined.a.02'), + [Synset('restricted.a.01'), + [Synset('classified.a.02')], + "Cycle(Synset('confined.a.02'),-5,...)", + "Cycle(Synset('unfree.a.02'),-5,...)"], + "Cycle(Synset('unfree.a.02'),-4,...)"], + [Synset('dependent.a.01'), "Cycle(Synset('unfree.a.02'),-4,...)"], + [Synset('restricted.a.01'), + [Synset('classified.a.02')], + [Synset('confined.a.02'), + "Cycle(Synset('restricted.a.01'),-5,...)", + "Cycle(Synset('unfree.a.02'),-5,...)"], + "Cycle(Synset('unfree.a.02'),-4,...)"]]] + + +2. Intractability +----------------- + +However, even after discarding the infinite cycles, some trees can remain +intractable, due to combinatorial explosion in a relation. This happens in +WordNet, because the ``also_sees()`` relation has a big Strongly Connected +Component (_SCC_) consisting in 758 synsets, where any member node is +transitively connected by the same relation, to all other members of the +same SCC. This produces intractable relation trees for each of these 758 +synsets, i. e. trees that are too big to compute or display on any computer. + +For example, the synset 'concrete.a.01' is a member of the largest SCC, +so its ``also_sees()`` tree is intractable, and can normally only be handled +by limiting the ``depth`` parameter to display a small number of levels: + + >>> from pprint import pprint + >>> pprint(wn.synset('concrete.a.01').tree(lambda s:s.also_sees(),cut_mark='...',depth=2)) + [Synset('concrete.a.01'), + [Synset('practical.a.01'), + "Cycle(Synset('concrete.a.01'),0,...)", + [Synset('possible.a.01'), '...'], + [Synset('realistic.a.01'), '...'], + [Synset('serviceable.a.01'), '...']], + [Synset('real.a.01'), + "Cycle(Synset('concrete.a.01'),0,...)", + [Synset('genuine.a.01'), '...'], + [Synset('realistic.a.01'), '...'], + [Synset('sincere.a.01'), '...']], + [Synset('tangible.a.01'), "Cycle(Synset('concrete.a.01'),0,...)"]] + + +2.1 First solution: ``acyclic_tree()`` +...................................... + +On the other hand, the new ``acyclic_tree()`` function is able to also handle +the intractable cases. The ``also_sees()`` acyclic tree of 'concrete.a.01' is +several hundred lines long, so here is a simpler example, concerning a much +smaller SCC: counting only five members, the SCC that includes 'bound.a.01' +is tractable with the normal ``tree()`` function, as seen above. + +But while ``tree()`` only prunes redundancy within local branches, ``acyclic_tree()`` +prunes the tree globally, thus discarding any additional redundancy, and +produces a tree that includes all reachable nodes (i.e., a **spanning tree**). +This tree is **minimal** because it includes the reachable nodes only once, +but it is not necessarily a **Minimum Spanning Tree** (MST), because the +Depth-first search strategy does not guarantee that nodes are reached +through the lowest number of links (as Breadth-first search would). + + >>> pprint(wn.synset('bound.a.01').acyclic_tree(lambda s:s.also_sees())) + [Synset('bound.a.01'), + [Synset('unfree.a.02'), + [Synset('confined.a.02'), + [Synset('restricted.a.01'), [Synset('classified.a.02')]]], + [Synset('dependent.a.01')]]] + +Again, specifying the ``cut_mark`` parameter increases verbosity, so that the +cycles are mentioned in the output, together with the level where they occur: + + >>> pprint(wn.synset('bound.a.01').acyclic_tree(lambda s:s.also_sees(),cut_mark='...')) + [Synset('bound.a.01'), + [Synset('unfree.a.02'), + "Cycle(Synset('bound.a.01'),-3,...)", + [Synset('confined.a.02'), + [Synset('restricted.a.01'), + [Synset('classified.a.02')], + "Cycle(Synset('confined.a.02'),-5,...)", + "Cycle(Synset('unfree.a.02'),-5,...)"], + "Cycle(Synset('unfree.a.02'),-4,...)"], + [Synset('dependent.a.01'), "Cycle(Synset('unfree.a.02'),-4,...)"], + "Cycle(Synset('restricted.a.01'),-3,...)"]] + + +2.2 Better solution: mst() +.......................... + +A Minimum Spanning Tree (MST) spans all the nodes of a relation subgraph once, +while guaranteeing that each node is reached through the shortest path possible. +In unweighted relation graphs like WordNet, a MST can be computed very efficiently +in linear time, using Breadth-First Search (BFS). Like acyclic_tree(), the new +``unweighted_minimum_spanning_tree()`` function (imported in the Wordnet +module as ``mst``) handles intractable trees, such as the example discussed above: +``wn.synset('concrete.a.01').mst(lambda s:s.also_sees())``. + +But, while the also_sees() acyclic_tree of 'bound.a.01' reaches +'classified.a.02' through four links, using depth-first search as seen above +(bound.a.01 > unfree.a.02 > confined.a.02 > restricted.a.01 > classified.a.02), +in the following MST, the path to 'classified.a.02' is the shortest possible, +consisting only in three links (bound.a.01 > unfree.a.02 > restricted.a.01 > +classified.a.02): + + >>> pprint(wn.synset('bound.a.01').mst(lambda s:s.also_sees())) + [Synset('bound.a.01'), + [Synset('unfree.a.02'), + [Synset('confined.a.02')], + [Synset('dependent.a.01')], + [Synset('restricted.a.01'), [Synset('classified.a.02')]]]] + + +---------------------------------------------------------------- +Loading alternative Wordnet versions +---------------------------------------------------------------- + + >>> print("Wordnet {}".format(wn.get_version())) + Wordnet 3.0 + + >>> from nltk.corpus import wordnet31 as wn31 + >>> print("Wordnet {}".format(wn31.get_version())) + Wordnet 3.1 + + >>> print(wn.synset('restrain.v.01').hyponyms()) + [Synset('confine.v.03'), Synset('control.v.02'), Synset('hold.v.36'), Synset('inhibit.v.04')] + + >>> print(wn31.synset('restrain.v.01').hyponyms()) + [Synset('enchain.v.01'), Synset('fetter.v.01'), Synset('ground.v.02'), Synset('impound.v.02'), Synset('pen_up.v.01'), Synset('pinion.v.01'), Synset('pound.v.06'), Synset('tie_down.v.01')] + + >>> print(wn31.synset('restrain.v.04').hyponyms()) + [Synset('baffle.v.03'), Synset('confine.v.02'), Synset('control.v.02'), Synset('hold.v.36'), Synset('rule.v.07'), Synset('swallow.v.06'), Synset('wink.v.04')] + + +------------- +Teardown test +------------- + + >>> from nltk.corpus import wordnet + >>> wordnet._unload() diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/wordnet_lch.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/wordnet_lch.doctest new file mode 100644 index 0000000000000000000000000000000000000000..877626fe9236f494d8fa9fb4609925049103be2b --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/wordnet_lch.doctest @@ -0,0 +1,53 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +=============================== +WordNet Lowest Common Hypernyms +=============================== + +Wordnet's lowest_common_hypernyms() method is based used to locate the +lowest single hypernym that is shared by two given words: + + >>> from nltk.corpus import wordnet as wn + >>> wn.synset('kin.n.01').lowest_common_hypernyms(wn.synset('mother.n.01')) + [Synset('relative.n.01')] + + >>> wn.synset('policeman.n.01').lowest_common_hypernyms(wn.synset('chef.n.01')) + [Synset('person.n.01')] + +This method generally returns a single result, but in some cases, more than one +valid LCH is possible: + + >>> wn.synset('body.n.09').lowest_common_hypernyms(wn.synset('sidereal_day.n.01')) + [Synset('attribute.n.02'), Synset('measure.n.02')] + +In some cases, lowest_common_hypernyms() can return one of the synsets which was +passed to it as an argument: + + >>> wn.synset('woman.n.01').lowest_common_hypernyms(wn.synset('girlfriend.n.02')) + [Synset('woman.n.01')] + +In NLTK 3.0a2 the behavior of lowest_common_hypernyms() was changed to give more +accurate results in a small set of cases, generally when dealing with nouns describing +social roles or jobs. To emulate the pre v3.0a2 behavior, you can set the use_min_depth=True +flag: + + >>> wn.synset('policeman.n.01').lowest_common_hypernyms(wn.synset('chef.n.01')) + [Synset('person.n.01')] + >>> wn.synset('policeman.n.01').lowest_common_hypernyms(wn.synset('chef.n.01'), use_min_depth=True) + [Synset('organism.n.01')] + +In some cases use_min_depth=True may return more or fewer results than the default +behavior: + + >>> wn.synset('woman.n.01').lowest_common_hypernyms(wn.synset('girlfriend.n.02')) + [Synset('woman.n.01')] + >>> wn.synset('woman.n.01').lowest_common_hypernyms(wn.synset('girlfriend.n.02'), use_min_depth=True) + [Synset('organism.n.01'), Synset('woman.n.01')] + +In the general case, however, they tend to return the same results: + + >>> wn.synset('body.n.09').lowest_common_hypernyms(wn.synset('sidereal_day.n.01')) + [Synset('attribute.n.02'), Synset('measure.n.02')] + >>> wn.synset('body.n.09').lowest_common_hypernyms(wn.synset('sidereal_day.n.01'), use_min_depth=True) + [Synset('attribute.n.02'), Synset('measure.n.02')] diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/test/wsd.doctest b/llmeval-env/lib/python3.10/site-packages/nltk/test/wsd.doctest new file mode 100644 index 0000000000000000000000000000000000000000..e4445c51d038f01e73214f370c220471530f859d --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/test/wsd.doctest @@ -0,0 +1,68 @@ +.. Copyright (C) 2001-2023 NLTK Project +.. For license information, see LICENSE.TXT + +.. -*- coding: utf-8 -*- + +========================= +Word Sense Disambiguation +========================= + + +Lesk Algorithm +-------------- + + +Performs the classic Lesk algorithm for Word Sense Disambiguation (WSD) using +a the definitions of the ambiguous word. + +Given an ambiguous word and the context in which the word occurs, Lesk returns +a Synset with the highest number of overlapping words between the context +sentence and different definitions from each Synset. + + >>> from nltk.wsd import lesk + >>> sent = ['I', 'went', 'to', 'the', 'bank', 'to', 'deposit', 'money', '.'] + + >>> print(lesk(sent, 'bank', 'n')) + Synset('savings_bank.n.02') + + >>> print(lesk(sent, 'bank')) + Synset('savings_bank.n.02') + +The definitions for "bank" are: + + >>> from nltk.corpus import wordnet as wn + >>> for ss in wn.synsets('bank'): + ... print(ss, ss.definition()) + ... + Synset('bank.n.01') sloping land (especially the slope beside a body of water) + Synset('depository_financial_institution.n.01') a financial institution that accepts deposits and channels the money into lending activities + Synset('bank.n.03') a long ridge or pile + Synset('bank.n.04') an arrangement of similar objects in a row or in tiers + Synset('bank.n.05') a supply or stock held in reserve for future use (especially in emergencies) + Synset('bank.n.06') the funds held by a gambling house or the dealer in some gambling games + Synset('bank.n.07') a slope in the turn of a road or track; the outside is higher than the inside in order to reduce the effects of centrifugal force + Synset('savings_bank.n.02') a container (usually with a slot in the top) for keeping money at home + Synset('bank.n.09') a building in which the business of banking transacted + Synset('bank.n.10') a flight maneuver; aircraft tips laterally about its longitudinal axis (especially in turning) + Synset('bank.v.01') tip laterally + Synset('bank.v.02') enclose with a bank + Synset('bank.v.03') do business with a bank or keep an account at a bank + Synset('bank.v.04') act as the banker in a game or in gambling + Synset('bank.v.05') be in the banking business + Synset('deposit.v.02') put into a bank account + Synset('bank.v.07') cover with ashes so to control the rate of burning + Synset('trust.v.01') have confidence or faith in + +Test disambiguation of POS tagged `able`. + + >>> [(s, s.pos()) for s in wn.synsets('able')] + [(Synset('able.a.01'), 'a'), (Synset('able.s.02'), 's'), (Synset('able.s.03'), 's'), (Synset('able.s.04'), 's')] + >>> sent = 'people should be able to marry a person of their choice'.split() + >>> lesk(sent, 'able') + Synset('able.s.04') + >>> lesk(sent, 'able', pos='a') + Synset('able.a.01') + +Test behavior if there is are no matching senses. + + >>> lesk('John loves Mary'.split(), 'loves', synsets=[]) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/twitter/__init__.py b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cd14ffb4703bf38bb349cc19cca2d97b6df29f77 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/__init__.py @@ -0,0 +1,35 @@ +# Natural Language Toolkit: Twitter +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Ewan Klein +# URL: +# For license information, see LICENSE.TXT + +""" +NLTK Twitter Package + +This package contains classes for retrieving Tweet documents using the +Twitter API. + +""" +try: + import twython +except ImportError: + import warnings + + warnings.warn( + "The twython library has not been installed. " + "Some functionality from the twitter package will not be available." + ) +else: + from nltk.twitter.util import Authenticate, credsfromfile + from nltk.twitter.twitterclient import ( + Streamer, + Query, + Twitter, + TweetViewer, + TweetWriter, + ) + + +from nltk.twitter.common import json2csv diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/twitter/api.py b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/api.py new file mode 100644 index 0000000000000000000000000000000000000000..71248b176340abd0d0d7d51e8ed68700f7948e13 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/api.py @@ -0,0 +1,145 @@ +# Natural Language Toolkit: Twitter API +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Ewan Klein +# Lorenzo Rubio +# URL: +# For license information, see LICENSE.TXT + +""" +This module provides an interface for TweetHandlers, and support for timezone +handling. +""" + +import time as _time +from abc import ABCMeta, abstractmethod +from datetime import datetime, timedelta, timezone, tzinfo + + +class LocalTimezoneOffsetWithUTC(tzinfo): + """ + This is not intended to be a general purpose class for dealing with the + local timezone. In particular: + + * it assumes that the date passed has been created using + `datetime(..., tzinfo=Local)`, where `Local` is an instance of + the object `LocalTimezoneOffsetWithUTC`; + * for such an object, it returns the offset with UTC, used for date comparisons. + + Reference: https://docs.python.org/3/library/datetime.html + """ + + STDOFFSET = timedelta(seconds=-_time.timezone) + + if _time.daylight: + DSTOFFSET = timedelta(seconds=-_time.altzone) + else: + DSTOFFSET = STDOFFSET + + def utcoffset(self, dt): + """ + Access the relevant time offset. + """ + return self.DSTOFFSET + + +LOCAL = LocalTimezoneOffsetWithUTC() + + +class BasicTweetHandler(metaclass=ABCMeta): + """ + Minimal implementation of `TweetHandler`. + + Counts the number of Tweets and decides when the client should stop + fetching them. + """ + + def __init__(self, limit=20): + self.limit = limit + self.counter = 0 + + """ + A flag to indicate to the client whether to stop fetching data given + some condition (e.g., reaching a date limit). + """ + self.do_stop = False + + """ + Stores the id of the last fetched Tweet to handle pagination. + """ + self.max_id = None + + def do_continue(self): + """ + Returns `False` if the client should stop fetching Tweets. + """ + return self.counter < self.limit and not self.do_stop + + +class TweetHandlerI(BasicTweetHandler): + """ + Interface class whose subclasses should implement a handle method that + Twitter clients can delegate to. + """ + + def __init__(self, limit=20, upper_date_limit=None, lower_date_limit=None): + """ + :param int limit: The number of data items to process in the current\ + round of processing. + + :param tuple upper_date_limit: The date at which to stop collecting\ + new data. This should be entered as a tuple which can serve as the\ + argument to `datetime.datetime`.\ + E.g. `date_limit=(2015, 4, 1, 12, 40)` for 12:30 pm on April 1 2015. + + :param tuple lower_date_limit: The date at which to stop collecting\ + new data. See `upper_data_limit` for formatting. + """ + BasicTweetHandler.__init__(self, limit) + + self.upper_date_limit = None + self.lower_date_limit = None + if upper_date_limit: + self.upper_date_limit = datetime(*upper_date_limit, tzinfo=LOCAL) + if lower_date_limit: + self.lower_date_limit = datetime(*lower_date_limit, tzinfo=LOCAL) + + self.startingup = True + + @abstractmethod + def handle(self, data): + """ + Deal appropriately with data returned by the Twitter API + """ + + @abstractmethod + def on_finish(self): + """ + Actions when the tweet limit has been reached + """ + + def check_date_limit(self, data, verbose=False): + """ + Validate date limits. + """ + if self.upper_date_limit or self.lower_date_limit: + date_fmt = "%a %b %d %H:%M:%S +0000 %Y" + tweet_date = datetime.strptime(data["created_at"], date_fmt).replace( + tzinfo=timezone.utc + ) + if (self.upper_date_limit and tweet_date > self.upper_date_limit) or ( + self.lower_date_limit and tweet_date < self.lower_date_limit + ): + if self.upper_date_limit: + message = "earlier" + date_limit = self.upper_date_limit + else: + message = "later" + date_limit = self.lower_date_limit + if verbose: + print( + "Date limit {} is {} than date of current tweet {}".format( + date_limit, message, tweet_date + ) + ) + self.do_stop = True diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/twitter/common.py b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/common.py new file mode 100644 index 0000000000000000000000000000000000000000..d9428724cfa8cae69e14d899cb73eee5607475d0 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/common.py @@ -0,0 +1,270 @@ +# Natural Language Toolkit: Twitter client +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Ewan Klein +# Lorenzo Rubio +# URL: +# For license information, see LICENSE.TXT + +""" +Utility functions for the `twitterclient` module which do not require +the `twython` library to have been installed. +""" +import csv +import gzip +import json + +from nltk.internals import deprecated + +HIER_SEPARATOR = "." + + +def extract_fields(tweet, fields): + """ + Extract field values from a full tweet and return them as a list + + :param json tweet: The tweet in JSON format + :param list fields: The fields to be extracted from the tweet + :rtype: list(str) + """ + out = [] + for field in fields: + try: + _add_field_to_out(tweet, field, out) + except TypeError as e: + raise RuntimeError( + "Fatal error when extracting fields. Cannot find field ", field + ) from e + return out + + +def _add_field_to_out(json, field, out): + if _is_composed_key(field): + key, value = _get_key_value_composed(field) + _add_field_to_out(json[key], value, out) + else: + out += [json[field]] + + +def _is_composed_key(field): + return HIER_SEPARATOR in field + + +def _get_key_value_composed(field): + out = field.split(HIER_SEPARATOR) + # there could be up to 3 levels + key = out[0] + value = HIER_SEPARATOR.join(out[1:]) + return key, value + + +def _get_entity_recursive(json, entity): + if not json: + return None + elif isinstance(json, dict): + for key, value in json.items(): + if key == entity: + return value + # 'entities' and 'extended_entities' are wrappers in Twitter json + # structure that contain other Twitter objects. See: + # https://dev.twitter.com/overview/api/entities-in-twitter-objects + + if key == "entities" or key == "extended_entities": + candidate = _get_entity_recursive(value, entity) + if candidate is not None: + return candidate + return None + elif isinstance(json, list): + for item in json: + candidate = _get_entity_recursive(item, entity) + if candidate is not None: + return candidate + return None + else: + return None + + +def json2csv( + fp, outfile, fields, encoding="utf8", errors="replace", gzip_compress=False +): + """ + Extract selected fields from a file of line-separated JSON tweets and + write to a file in CSV format. + + This utility function allows a file of full tweets to be easily converted + to a CSV file for easier processing. For example, just TweetIDs or + just the text content of the Tweets can be extracted. + + Additionally, the function allows combinations of fields of other Twitter + objects (mainly the users, see below). + + For Twitter entities (e.g. hashtags of a Tweet), and for geolocation, see + `json2csv_entities` + + :param str infile: The name of the file containing full tweets + + :param str outfile: The name of the text file where results should be\ + written + + :param list fields: The list of fields to be extracted. Useful examples\ + are 'id_str' for the tweetID and 'text' for the text of the tweet. See\ + for a full list of fields.\ + e. g.: ['id_str'], ['id', 'text', 'favorite_count', 'retweet_count']\ + Additionally, it allows IDs from other Twitter objects, e. g.,\ + ['id', 'text', 'user.id', 'user.followers_count', 'user.friends_count'] + + :param error: Behaviour for encoding errors, see\ + https://docs.python.org/3/library/codecs.html#codec-base-classes + + :param gzip_compress: if `True`, output files are compressed with gzip + """ + (writer, outf) = _outf_writer(outfile, encoding, errors, gzip_compress) + # write the list of fields as header + writer.writerow(fields) + # process the file + for line in fp: + tweet = json.loads(line) + row = extract_fields(tweet, fields) + writer.writerow(row) + outf.close() + + +@deprecated("Use open() and csv.writer() directly instead.") +def outf_writer_compat(outfile, encoding, errors, gzip_compress=False): + """Get a CSV writer with optional compression.""" + return _outf_writer(outfile, encoding, errors, gzip_compress) + + +def _outf_writer(outfile, encoding, errors, gzip_compress=False): + if gzip_compress: + outf = gzip.open(outfile, "wt", newline="", encoding=encoding, errors=errors) + else: + outf = open(outfile, "w", newline="", encoding=encoding, errors=errors) + writer = csv.writer(outf) + return (writer, outf) + + +def json2csv_entities( + tweets_file, + outfile, + main_fields, + entity_type, + entity_fields, + encoding="utf8", + errors="replace", + gzip_compress=False, +): + """ + Extract selected fields from a file of line-separated JSON tweets and + write to a file in CSV format. + + This utility function allows a file of full Tweets to be easily converted + to a CSV file for easier processing of Twitter entities. For example, the + hashtags or media elements of a tweet can be extracted. + + It returns one line per entity of a Tweet, e.g. if a tweet has two hashtags + there will be two lines in the output file, one per hashtag + + :param tweets_file: the file-like object containing full Tweets + + :param str outfile: The path of the text file where results should be\ + written + + :param list main_fields: The list of fields to be extracted from the main\ + object, usually the tweet. Useful examples: 'id_str' for the tweetID. See\ + for a full list of fields. + e. g.: ['id_str'], ['id', 'text', 'favorite_count', 'retweet_count'] + If `entity_type` is expressed with hierarchy, then it is the list of\ + fields of the object that corresponds to the key of the entity_type,\ + (e.g., for entity_type='user.urls', the fields in the main_fields list\ + belong to the user object; for entity_type='place.bounding_box', the\ + files in the main_field list belong to the place object of the tweet). + + :param list entity_type: The name of the entity: 'hashtags', 'media',\ + 'urls' and 'user_mentions' for the tweet object. For a user object,\ + this needs to be expressed with a hierarchy: `'user.urls'`. For the\ + bounding box of the Tweet location, use `'place.bounding_box'`. + + :param list entity_fields: The list of fields to be extracted from the\ + entity. E.g. `['text']` (of the Tweet) + + :param error: Behaviour for encoding errors, see\ + https://docs.python.org/3/library/codecs.html#codec-base-classes + + :param gzip_compress: if `True`, output files are compressed with gzip + """ + + (writer, outf) = _outf_writer(outfile, encoding, errors, gzip_compress) + header = get_header_field_list(main_fields, entity_type, entity_fields) + writer.writerow(header) + for line in tweets_file: + tweet = json.loads(line) + if _is_composed_key(entity_type): + key, value = _get_key_value_composed(entity_type) + object_json = _get_entity_recursive(tweet, key) + if not object_json: + # this can happen in the case of "place" + continue + object_fields = extract_fields(object_json, main_fields) + items = _get_entity_recursive(object_json, value) + _write_to_file(object_fields, items, entity_fields, writer) + else: + tweet_fields = extract_fields(tweet, main_fields) + items = _get_entity_recursive(tweet, entity_type) + _write_to_file(tweet_fields, items, entity_fields, writer) + outf.close() + + +def get_header_field_list(main_fields, entity_type, entity_fields): + if _is_composed_key(entity_type): + key, value = _get_key_value_composed(entity_type) + main_entity = key + sub_entity = value + else: + main_entity = None + sub_entity = entity_type + + if main_entity: + output1 = [HIER_SEPARATOR.join([main_entity, x]) for x in main_fields] + else: + output1 = main_fields + output2 = [HIER_SEPARATOR.join([sub_entity, x]) for x in entity_fields] + return output1 + output2 + + +def _write_to_file(object_fields, items, entity_fields, writer): + if not items: + # it could be that the entity is just not present for the tweet + # e.g. tweet hashtag is always present, even as [], however + # tweet media may not be present + return + if isinstance(items, dict): + # this happens e.g. for "place" of a tweet + row = object_fields + # there might be composed keys in de list of required fields + entity_field_values = [x for x in entity_fields if not _is_composed_key(x)] + entity_field_composed = [x for x in entity_fields if _is_composed_key(x)] + for field in entity_field_values: + value = items[field] + if isinstance(value, list): + row += value + else: + row += [value] + # now check required dictionaries + for d in entity_field_composed: + kd, vd = _get_key_value_composed(d) + json_dict = items[kd] + if not isinstance(json_dict, dict): + raise RuntimeError( + """Key {} does not contain a dictionary + in the json file""".format( + kd + ) + ) + row += [json_dict[vd]] + writer.writerow(row) + return + # in general it is a list + for item in items: + row = object_fields + extract_fields(item, entity_fields) + writer.writerow(row) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/twitter/twitter_demo.py b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/twitter_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..554bdfef511190b28504f9ded8dc8a6098e16ed9 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/twitter_demo.py @@ -0,0 +1,306 @@ +# Natural Language Toolkit: Twitter client +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Ewan Klein +# Lorenzo Rubio +# URL: +# For license information, see LICENSE.TXT + +""" +Examples to demo the :py:mod:`twitterclient` code. + +These demo functions should all run, with the following caveats: + +* You must have obtained API keys from Twitter, and installed them according to + the instructions in the `twitter HOWTO `_. + +* If you are on a slow network, some of the calls to the Twitter API may + timeout. + +* If you are being rate limited while searching, you will receive a 420 + error response. + +* Your terminal window / console must be able to display UTF-8 encoded characters. + +For documentation about the Twitter APIs, see `The Streaming APIs Overview +`_ and `The REST APIs Overview +`_. + +For error codes see Twitter's +`Error Codes and Responses ` +""" + +import datetime +import json +from functools import wraps +from io import StringIO + +from nltk.twitter import ( + Query, + Streamer, + TweetViewer, + TweetWriter, + Twitter, + credsfromfile, +) + +SPACER = "###################################" + + +def verbose(func): + """Decorator for demo functions""" + + @wraps(func) + def with_formatting(*args, **kwargs): + print() + print(SPACER) + print("Using %s" % (func.__name__)) + print(SPACER) + return func(*args, **kwargs) + + return with_formatting + + +def yesterday(): + """ + Get yesterday's datetime as a 5-tuple. + """ + date = datetime.datetime.now() + date -= datetime.timedelta(days=1) + date_tuple = date.timetuple()[:6] + return date_tuple + + +def setup(): + """ + Initialize global variables for the demos. + """ + global USERIDS, FIELDS + + USERIDS = ["759251", "612473", "15108702", "6017542", "2673523800"] + # UserIDs corresponding to\ + # @CNN, @BBCNews, @ReutersLive, @BreakingNews, @AJELive + FIELDS = ["id_str"] + + +@verbose +def twitterclass_demo(): + """ + Use the simplified :class:`Twitter` class to write some tweets to a file. + """ + tw = Twitter() + print("Track from the public stream\n") + tw.tweets(keywords="love, hate", limit=10) # public stream + print(SPACER) + print("Search past Tweets\n") + tw = Twitter() + tw.tweets(keywords="love, hate", stream=False, limit=10) # search past tweets + print(SPACER) + print( + "Follow two accounts in the public stream" + + " -- be prepared to wait a few minutes\n" + ) + tw = Twitter() + tw.tweets(follow=["759251", "6017542"], stream=True, limit=5) # public stream + + +@verbose +def sampletoscreen_demo(limit=20): + """ + Sample from the Streaming API and send output to terminal. + """ + oauth = credsfromfile() + client = Streamer(**oauth) + client.register(TweetViewer(limit=limit)) + client.sample() + + +@verbose +def tracktoscreen_demo(track="taylor swift", limit=10): + """ + Track keywords from the public Streaming API and send output to terminal. + """ + oauth = credsfromfile() + client = Streamer(**oauth) + client.register(TweetViewer(limit=limit)) + client.filter(track=track) + + +@verbose +def search_demo(keywords="nltk"): + """ + Use the REST API to search for past tweets containing a given keyword. + """ + oauth = credsfromfile() + client = Query(**oauth) + for tweet in client.search_tweets(keywords=keywords, limit=10): + print(tweet["text"]) + + +@verbose +def tweets_by_user_demo(user="NLTK_org", count=200): + """ + Use the REST API to search for past tweets by a given user. + """ + oauth = credsfromfile() + client = Query(**oauth) + client.register(TweetWriter()) + client.user_tweets(user, count) + + +@verbose +def lookup_by_userid_demo(): + """ + Use the REST API to convert a userID to a screen name. + """ + oauth = credsfromfile() + client = Query(**oauth) + user_info = client.user_info_from_id(USERIDS) + for info in user_info: + name = info["screen_name"] + followers = info["followers_count"] + following = info["friends_count"] + print(f"{name}, followers: {followers}, following: {following}") + + +@verbose +def followtoscreen_demo(limit=10): + """ + Using the Streaming API, select just the tweets from a specified list of + userIDs. + + This is will only give results in a reasonable time if the users in + question produce a high volume of tweets, and may even so show some delay. + """ + oauth = credsfromfile() + client = Streamer(**oauth) + client.register(TweetViewer(limit=limit)) + client.statuses.filter(follow=USERIDS) + + +@verbose +def streamtofile_demo(limit=20): + """ + Write 20 tweets sampled from the public Streaming API to a file. + """ + oauth = credsfromfile() + client = Streamer(**oauth) + client.register(TweetWriter(limit=limit, repeat=False)) + client.statuses.sample() + + +@verbose +def limit_by_time_demo(keywords="nltk"): + """ + Query the REST API for Tweets about NLTK since yesterday and send + the output to terminal. + + This example makes the assumption that there are sufficient Tweets since + yesterday for the date to be an effective cut-off. + """ + date = yesterday() + dt_date = datetime.datetime(*date) + oauth = credsfromfile() + client = Query(**oauth) + client.register(TweetViewer(limit=100, lower_date_limit=date)) + + print(f"Cutoff date: {dt_date}\n") + + for tweet in client.search_tweets(keywords=keywords): + print("{} ".format(tweet["created_at"]), end="") + client.handler.handle(tweet) + + +@verbose +def corpusreader_demo(): + """ + Use `TwitterCorpusReader` tp read a file of tweets, and print out + + * some full tweets in JSON format; + * some raw strings from the tweets (i.e., the value of the `text` field); and + * the result of tokenising the raw strings. + + """ + from nltk.corpus import twitter_samples as tweets + + print() + print("Complete tweet documents") + print(SPACER) + for tweet in tweets.docs("tweets.20150430-223406.json")[:1]: + print(json.dumps(tweet, indent=1, sort_keys=True)) + + print() + print("Raw tweet strings:") + print(SPACER) + for text in tweets.strings("tweets.20150430-223406.json")[:15]: + print(text) + + print() + print("Tokenized tweet strings:") + print(SPACER) + for toks in tweets.tokenized("tweets.20150430-223406.json")[:15]: + print(toks) + + +@verbose +def expand_tweetids_demo(): + """ + Given a file object containing a list of Tweet IDs, fetch the + corresponding full Tweets, if available. + + """ + ids_f = StringIO( + """\ + 588665495492124672 + 588665495487909888 + 588665495508766721 + 588665495513006080 + 588665495517200384 + 588665495487811584 + 588665495525588992 + 588665495487844352 + 588665495492014081 + 588665495512948737""" + ) + oauth = credsfromfile() + client = Query(**oauth) + hydrated = client.expand_tweetids(ids_f) + + for tweet in hydrated: + id_str = tweet["id_str"] + print(f"id: {id_str}") + text = tweet["text"] + if text.startswith("@null"): + text = "[Tweet not available]" + print(text + "\n") + + +ALL = [ + twitterclass_demo, + sampletoscreen_demo, + tracktoscreen_demo, + search_demo, + tweets_by_user_demo, + lookup_by_userid_demo, + followtoscreen_demo, + streamtofile_demo, + limit_by_time_demo, + corpusreader_demo, + expand_tweetids_demo, +] + +""" +Select demo functions to run. E.g. replace the following line with "DEMOS = +ALL[8:]" to execute only the final three demos. +""" +DEMOS = ALL[:] + +if __name__ == "__main__": + setup() + + for demo in DEMOS: + demo() + + print("\n" + SPACER) + print("All demos completed") + print(SPACER) diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/twitter/twitterclient.py b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/twitterclient.py new file mode 100644 index 0000000000000000000000000000000000000000..d556738e0849faf35454166cec8a5949fcca93dc --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/twitterclient.py @@ -0,0 +1,564 @@ +# Natural Language Toolkit: Twitter client +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Ewan Klein +# Lorenzo Rubio +# URL: +# For license information, see LICENSE.TXT + + +""" +NLTK Twitter client + +This module offers methods for collecting and processing Tweets. Most of the +functionality depends on access to the Twitter APIs, and this is handled via +the third party Twython library. + +If one of the methods below returns an integer, it is probably a `Twitter +error code `_. For +example, the response of '420' means that you have reached the limit of the +requests you can currently make to the Twitter API. Currently, `rate limits +for the search API `_ are +divided into 15 minute windows. +""" + +import datetime +import gzip +import itertools +import json +import os +import time + +import requests +from twython import Twython, TwythonStreamer +from twython.exceptions import TwythonError, TwythonRateLimitError + +from nltk.twitter.api import BasicTweetHandler, TweetHandlerI +from nltk.twitter.util import credsfromfile, guess_path + + +class Streamer(TwythonStreamer): + """ + Retrieve data from the Twitter Streaming API. + + The streaming API requires + `OAuth 1.0 `_ authentication. + """ + + def __init__(self, app_key, app_secret, oauth_token, oauth_token_secret): + + self.handler = None + self.do_continue = True + TwythonStreamer.__init__( + self, app_key, app_secret, oauth_token, oauth_token_secret + ) + + def register(self, handler): + """ + Register a method for handling Tweets. + + :param TweetHandlerI handler: method for viewing + """ + self.handler = handler + + def on_success(self, data): + """ + :param data: response from Twitter API + """ + if self.do_continue: + if self.handler is not None: + if "text" in data: + self.handler.counter += 1 + self.handler.handle(data) + self.do_continue = self.handler.do_continue() + else: + raise ValueError("No data handler has been registered.") + else: + self.disconnect() + self.handler.on_finish() + + def on_error(self, status_code, data): + """ + :param status_code: The status code returned by the Twitter API + :param data: The response from Twitter API + + """ + print(status_code) + + def sample(self): + """ + Wrapper for 'statuses / sample' API call + """ + while self.do_continue: + + # Stream in an endless loop until limit is reached. See twython + # issue 288: https://github.com/ryanmcgrath/twython/issues/288 + # colditzjb commented on 9 Dec 2014 + + try: + self.statuses.sample() + except requests.exceptions.ChunkedEncodingError as e: + if e is not None: + print(f"Error (stream will continue): {e}") + continue + + def filter(self, track="", follow="", lang="en"): + """ + Wrapper for 'statuses / filter' API call + """ + while self.do_continue: + # Stream in an endless loop until limit is reached + + try: + if track == "" and follow == "": + msg = "Please supply a value for 'track', 'follow'" + raise ValueError(msg) + self.statuses.filter(track=track, follow=follow, lang=lang) + except requests.exceptions.ChunkedEncodingError as e: + if e is not None: + print(f"Error (stream will continue): {e}") + continue + + +class Query(Twython): + """ + Retrieve data from the Twitter REST API. + """ + + def __init__(self, app_key, app_secret, oauth_token, oauth_token_secret): + """ + :param app_key: (optional) Your applications key + :param app_secret: (optional) Your applications secret key + :param oauth_token: (optional) When using **OAuth 1**, combined with + oauth_token_secret to make authenticated calls + :param oauth_token_secret: (optional) When using **OAuth 1** combined + with oauth_token to make authenticated calls + """ + self.handler = None + self.do_continue = True + Twython.__init__(self, app_key, app_secret, oauth_token, oauth_token_secret) + + def register(self, handler): + """ + Register a method for handling Tweets. + + :param TweetHandlerI handler: method for viewing or writing Tweets to a file. + """ + self.handler = handler + + def expand_tweetids(self, ids_f, verbose=True): + """ + Given a file object containing a list of Tweet IDs, fetch the + corresponding full Tweets from the Twitter API. + + The API call `statuses/lookup` will fail to retrieve a Tweet if the + user has deleted it. + + This call to the Twitter API is rate-limited. See + for details. + + :param ids_f: input file object consisting of Tweet IDs, one to a line + :return: iterable of Tweet objects in JSON format + """ + ids = [line.strip() for line in ids_f if line] + + if verbose: + print(f"Counted {len(ids)} Tweet IDs in {ids_f}.") + + # The Twitter endpoint takes lists of up to 100 ids, so we chunk the + # ids. + id_chunks = [ids[i : i + 100] for i in range(0, len(ids), 100)] + + chunked_tweets = (self.lookup_status(id=chunk) for chunk in id_chunks) + + return itertools.chain.from_iterable(chunked_tweets) + + def _search_tweets(self, keywords, limit=100, lang="en"): + """ + Assumes that the handler has been informed. Fetches Tweets from + search_tweets generator output and passses them to handler + + :param str keywords: A list of query terms to search for, written as\ + a comma-separated string. + :param int limit: Number of Tweets to process + :param str lang: language + """ + while True: + tweets = self.search_tweets( + keywords=keywords, limit=limit, lang=lang, max_id=self.handler.max_id + ) + for tweet in tweets: + self.handler.handle(tweet) + if not (self.handler.do_continue() and self.handler.repeat): + break + self.handler.on_finish() + + def search_tweets( + self, + keywords, + limit=100, + lang="en", + max_id=None, + retries_after_twython_exception=0, + ): + """ + Call the REST API ``'search/tweets'`` endpoint with some plausible + defaults. See `the Twitter search documentation + `_ for more information + about admissible search parameters. + + :param str keywords: A list of query terms to search for, written as\ + a comma-separated string + :param int limit: Number of Tweets to process + :param str lang: language + :param int max_id: id of the last tweet fetched + :param int retries_after_twython_exception: number of retries when\ + searching Tweets before raising an exception + :rtype: python generator + """ + if not self.handler: + # if no handler is provided, `BasicTweetHandler` provides minimum + # functionality for limiting the number of Tweets retrieved + self.handler = BasicTweetHandler(limit=limit) + + count_from_query = 0 + if max_id: + self.handler.max_id = max_id + else: + results = self.search( + q=keywords, count=min(100, limit), lang=lang, result_type="recent" + ) + count = len(results["statuses"]) + if count == 0: + print("No Tweets available through REST API for those keywords") + return + count_from_query = count + self.handler.max_id = results["statuses"][count - 1]["id"] - 1 + + for result in results["statuses"]: + yield result + self.handler.counter += 1 + if self.handler.do_continue() == False: + return + + # Pagination loop: keep fetching Tweets until the desired count is + # reached while dealing with Twitter rate limits. + retries = 0 + while count_from_query < limit: + try: + mcount = min(100, limit - count_from_query) + results = self.search( + q=keywords, + count=mcount, + lang=lang, + max_id=self.handler.max_id, + result_type="recent", + ) + except TwythonRateLimitError as e: + print(f"Waiting for 15 minutes -{e}") + time.sleep(15 * 60) # wait 15 minutes + continue + except TwythonError as e: + print(f"Fatal error in Twython request -{e}") + if retries_after_twython_exception == retries: + raise e + retries += 1 + + count = len(results["statuses"]) + if count == 0: + print("No more Tweets available through rest api") + return + count_from_query += count + # the max_id is also present in the Tweet metadata + # results['search_metadata']['next_results'], but as part of a + # query and difficult to fetch. This is doing the equivalent + # (last tweet id minus one) + self.handler.max_id = results["statuses"][count - 1]["id"] - 1 + + for result in results["statuses"]: + yield result + self.handler.counter += 1 + if self.handler.do_continue() == False: + return + + def user_info_from_id(self, userids): + """ + Convert a list of userIDs into a variety of information about the users. + + See . + + :param list userids: A list of integer strings corresponding to Twitter userIDs + :rtype: list(json) + """ + return [self.show_user(user_id=userid) for userid in userids] + + def user_tweets(self, screen_name, limit, include_rts="false"): + """ + Return a collection of the most recent Tweets posted by the user + + :param str user: The user's screen name; the initial '@' symbol\ + should be omitted + :param int limit: The number of Tweets to recover; 200 is the maximum allowed + :param str include_rts: Whether to include statuses which have been\ + retweeted by the user; possible values are 'true' and 'false' + """ + data = self.get_user_timeline( + screen_name=screen_name, count=limit, include_rts=include_rts + ) + for item in data: + self.handler.handle(item) + + +class Twitter: + """ + Wrapper class with restricted functionality and fewer options. + """ + + def __init__(self): + self._oauth = credsfromfile() + self.streamer = Streamer(**self._oauth) + self.query = Query(**self._oauth) + + def tweets( + self, + keywords="", + follow="", + to_screen=True, + stream=True, + limit=100, + date_limit=None, + lang="en", + repeat=False, + gzip_compress=False, + ): + """ + Process some Tweets in a simple manner. + + :param str keywords: Keywords to use for searching or filtering + :param list follow: UserIDs to use for filtering Tweets from the public stream + :param bool to_screen: If `True`, display the tweet texts on the screen,\ + otherwise print to a file + + :param bool stream: If `True`, use the live public stream,\ + otherwise search past public Tweets + + :param int limit: The number of data items to process in the current\ + round of processing. + + :param tuple date_limit: The date at which to stop collecting\ + new data. This should be entered as a tuple which can serve as the\ + argument to `datetime.datetime`.\ + E.g. `date_limit=(2015, 4, 1, 12, 40)` for 12:30 pm on April 1 2015. + Note that, in the case of streaming, this is the maximum date, i.e.\ + a date in the future; if not, it is the minimum date, i.e. a date\ + in the past + + :param str lang: language + + :param bool repeat: A flag to determine whether multiple files should\ + be written. If `True`, the length of each file will be set by the\ + value of `limit`. Use only if `to_screen` is `False`. See also + :py:func:`handle`. + + :param gzip_compress: if `True`, output files are compressed with gzip. + """ + if stream: + upper_date_limit = date_limit + lower_date_limit = None + else: + upper_date_limit = None + lower_date_limit = date_limit + + if to_screen: + handler = TweetViewer( + limit=limit, + upper_date_limit=upper_date_limit, + lower_date_limit=lower_date_limit, + ) + else: + handler = TweetWriter( + limit=limit, + upper_date_limit=upper_date_limit, + lower_date_limit=lower_date_limit, + repeat=repeat, + gzip_compress=gzip_compress, + ) + + if to_screen: + handler = TweetViewer(limit=limit) + else: + if stream: + upper_date_limit = date_limit + lower_date_limit = None + else: + upper_date_limit = None + lower_date_limit = date_limit + + handler = TweetWriter( + limit=limit, + upper_date_limit=upper_date_limit, + lower_date_limit=lower_date_limit, + repeat=repeat, + gzip_compress=gzip_compress, + ) + + if stream: + self.streamer.register(handler) + if keywords == "" and follow == "": + self.streamer.sample() + else: + self.streamer.filter(track=keywords, follow=follow, lang=lang) + else: + self.query.register(handler) + if keywords == "": + raise ValueError("Please supply at least one keyword to search for.") + else: + self.query._search_tweets(keywords, limit=limit, lang=lang) + + +class TweetViewer(TweetHandlerI): + """ + Handle data by sending it to the terminal. + """ + + def handle(self, data): + """ + Direct data to `sys.stdout` + + :return: return ``False`` if processing should cease, otherwise return ``True``. + :rtype: bool + :param data: Tweet object returned by Twitter API + """ + text = data["text"] + print(text) + + self.check_date_limit(data) + if self.do_stop: + return + + def on_finish(self): + print(f"Written {self.counter} Tweets") + + +class TweetWriter(TweetHandlerI): + """ + Handle data by writing it to a file. + """ + + def __init__( + self, + limit=2000, + upper_date_limit=None, + lower_date_limit=None, + fprefix="tweets", + subdir="twitter-files", + repeat=False, + gzip_compress=False, + ): + """ + The difference between the upper and lower date limits depends on + whether Tweets are coming in an ascending date order (i.e. when + streaming) or descending date order (i.e. when searching past Tweets). + + :param int limit: number of data items to process in the current\ + round of processing. + + :param tuple upper_date_limit: The date at which to stop collecting new\ + data. This should be entered as a tuple which can serve as the\ + argument to `datetime.datetime`. E.g. `upper_date_limit=(2015, 4, 1, 12,\ + 40)` for 12:30 pm on April 1 2015. + + :param tuple lower_date_limit: The date at which to stop collecting new\ + data. See `upper_data_limit` for formatting. + + :param str fprefix: The prefix to use in creating file names for Tweet\ + collections. + + :param str subdir: The name of the directory where Tweet collection\ + files should be stored. + + :param bool repeat: flag to determine whether multiple files should be\ + written. If `True`, the length of each file will be set by the value\ + of `limit`. See also :py:func:`handle`. + + :param gzip_compress: if `True`, output files are compressed with gzip. + """ + self.fprefix = fprefix + self.subdir = guess_path(subdir) + self.gzip_compress = gzip_compress + self.fname = self.timestamped_file() + self.repeat = repeat + self.output = None + TweetHandlerI.__init__(self, limit, upper_date_limit, lower_date_limit) + + def timestamped_file(self): + """ + :return: timestamped file name + :rtype: str + """ + subdir = self.subdir + fprefix = self.fprefix + if subdir: + if not os.path.exists(subdir): + os.mkdir(subdir) + + fname = os.path.join(subdir, fprefix) + fmt = "%Y%m%d-%H%M%S" + timestamp = datetime.datetime.now().strftime(fmt) + if self.gzip_compress: + suffix = ".gz" + else: + suffix = "" + outfile = f"{fname}.{timestamp}.json{suffix}" + return outfile + + def handle(self, data): + """ + Write Twitter data as line-delimited JSON into one or more files. + + :return: return `False` if processing should cease, otherwise return `True`. + :param data: tweet object returned by Twitter API + """ + if self.startingup: + if self.gzip_compress: + self.output = gzip.open(self.fname, "w") + else: + self.output = open(self.fname, "w") + print(f"Writing to {self.fname}") + + json_data = json.dumps(data) + if self.gzip_compress: + self.output.write((json_data + "\n").encode("utf-8")) + else: + self.output.write(json_data + "\n") + + self.check_date_limit(data) + if self.do_stop: + return + + self.startingup = False + + def on_finish(self): + print(f"Written {self.counter} Tweets") + if self.output: + self.output.close() + + def do_continue(self): + if self.repeat == False: + return TweetHandlerI.do_continue(self) + + if self.do_stop: + # stop for a functional cause (e.g. date limit) + return False + + if self.counter == self.limit: + # repeat is True, thus close output file and + # create a new one + self._restart_file() + return True + + def _restart_file(self): + self.on_finish() + self.fname = self.timestamped_file() + self.startingup = True + self.counter = 0 diff --git a/llmeval-env/lib/python3.10/site-packages/nltk/twitter/util.py b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/util.py new file mode 100644 index 0000000000000000000000000000000000000000..adfa08853867280da85642778c3e9fb89a532574 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/nltk/twitter/util.py @@ -0,0 +1,147 @@ +# Natural Language Toolkit: Twitter client +# +# Copyright (C) 2001-2023 NLTK Project +# Author: Ewan Klein +# Lorenzo Rubio +# URL: +# For license information, see LICENSE.TXT + +""" +Authentication utilities to accompany `twitterclient`. +""" + +import os +import pprint + +from twython import Twython + + +def credsfromfile(creds_file=None, subdir=None, verbose=False): + """ + Convenience function for authentication + """ + return Authenticate().load_creds( + creds_file=creds_file, subdir=subdir, verbose=verbose + ) + + +class Authenticate: + """ + Methods for authenticating with Twitter. + """ + + def __init__(self): + self.creds_file = "credentials.txt" + self.creds_fullpath = None + + self.oauth = {} + try: + self.twitter_dir = os.environ["TWITTER"] + self.creds_subdir = self.twitter_dir + except KeyError: + self.twitter_dir = None + self.creds_subdir = None + + def load_creds(self, creds_file=None, subdir=None, verbose=False): + """ + Read OAuth credentials from a text file. + + File format for OAuth 1:: + + app_key=YOUR_APP_KEY + app_secret=YOUR_APP_SECRET + oauth_token=OAUTH_TOKEN + oauth_token_secret=OAUTH_TOKEN_SECRET + + + File format for OAuth 2:: + + app_key=YOUR_APP_KEY + app_secret=YOUR_APP_SECRET + access_token=ACCESS_TOKEN + + :param str file_name: File containing credentials. ``None`` (default) reads + data from `TWITTER/'credentials.txt'` + """ + if creds_file is not None: + self.creds_file = creds_file + + if subdir is None: + if self.creds_subdir is None: + msg = ( + "Supply a value to the 'subdir' parameter or" + + " set the TWITTER environment variable." + ) + raise ValueError(msg) + else: + self.creds_subdir = subdir + + self.creds_fullpath = os.path.normpath( + os.path.join(self.creds_subdir, self.creds_file) + ) + + if not os.path.isfile(self.creds_fullpath): + raise OSError(f"Cannot find file {self.creds_fullpath}") + + with open(self.creds_fullpath) as infile: + if verbose: + print(f"Reading credentials file {self.creds_fullpath}") + + for line in infile: + if "=" in line: + name, value = line.split("=", 1) + self.oauth[name.strip()] = value.strip() + + self._validate_creds_file(verbose=verbose) + + return self.oauth + + def _validate_creds_file(self, verbose=False): + """Check validity of a credentials file.""" + oauth1 = False + oauth1_keys = ["app_key", "app_secret", "oauth_token", "oauth_token_secret"] + oauth2 = False + oauth2_keys = ["app_key", "app_secret", "access_token"] + if all(k in self.oauth for k in oauth1_keys): + oauth1 = True + elif all(k in self.oauth for k in oauth2_keys): + oauth2 = True + + if not (oauth1 or oauth2): + msg = f"Missing or incorrect entries in {self.creds_file}\n" + msg += pprint.pformat(self.oauth) + raise ValueError(msg) + elif verbose: + print(f'Credentials file "{self.creds_file}" looks good') + + +def add_access_token(creds_file=None): + """ + For OAuth 2, retrieve an access token for an app and append it to a + credentials file. + """ + if creds_file is None: + path = os.path.dirname(__file__) + creds_file = os.path.join(path, "credentials2.txt") + oauth2 = credsfromfile(creds_file=creds_file) + app_key = oauth2["app_key"] + app_secret = oauth2["app_secret"] + + twitter = Twython(app_key, app_secret, oauth_version=2) + access_token = twitter.obtain_access_token() + tok = f"access_token={access_token}\n" + with open(creds_file, "a") as infile: + print(tok, file=infile) + + +def guess_path(pth): + """ + If the path is not absolute, guess that it is a subdirectory of the + user's home directory. + + :param str pth: The pathname of the directory where files of tweets should be written + """ + if os.path.isabs(pth): + return pth + else: + return os.path.expanduser(os.path.join("~", pth))