Source code for orangecontrib.text.corpus

import os
import warnings
from collections import Counter, defaultdict
from copy import copy
from numbers import Integral
from itertools import chain
from typing import Union, Optional, List, Tuple
from warnings import warn

import nltk
import numpy as np
import scipy.sparse as sp
from gensim import corpora

from import (
from Orange.preprocess.transformation import Identity
from import get_unique_names

    from orangewidget.utils.signals import summarize, PartialSummary
    # import to check if Table summary is available - if summarize_by_name does
    # not exist Orange (3.28) does not support automated summaries
    from Orange.widgets.utils.state_summary import summarize_by_name
except ImportError:
    summarize, PartialSummary = None, None

def get_sample_corpora_dir():
    path = os.path.dirname(__file__)
    directory = os.path.join(path, 'datasets')
    return os.path.abspath(directory)

def _check_arrays(*arrays):
    for a in arrays:
        if not (a is None or isinstance(a, np.ndarray) or sp.issparse(a)):
            raise TypeError('Argument {} should be of type np.array, sparse or None.'.format(a))

    lengths = set(a.shape[0] for a in arrays if a is not None)
    if len(lengths) > 1:
        raise ValueError('Leading dimension mismatch')

    return lengths.pop() if len(lengths) else 0

[docs]class Corpus(Table): """Internal class for storing a corpus.""" def __new__(cls, *args, **kwargs): if args and isinstance(args[0], Domain) or "domain" in kwargs: warn( "Signature of Corpus constructor when called with numpy " "arrays will change in the future to be equal to Corpus.from_numpy. " "To avoid issues use Corpus.from_numpy instead.", FutureWarning, ) # __init__ had a different signature than from_numpy params = ["domain", "X", "Y", "metas", "W", "text_features", "ids"] kwargs.update({param: arg for param, arg in zip(params, args)}) # in old signature it can happen that X is missing n_doc = _check_arrays( kwargs.get("X", None), kwargs.get("Y", None), kwargs.get("metas", None) ) if "X" not in kwargs: kwargs["X"] = np.empty((n_doc, 0)) return cls.from_numpy(**kwargs) return super().__new__(cls, *args, **kwargs) def _setup_corpus(self, text_features: List[Variable] = None) -> None: """ Parameters ---------- text_features meta attributes that are used for text mining. Infer them if None. """ self.text_features = [] # list of text features for mining self._tokens = None self._dictionary = None self.ngram_range = (1, 1) self.attributes = {} self._pos_tags = None from orangecontrib.text.preprocess import PreprocessorList self.__used_preprocessor = PreprocessorList([]) # required for compute values self._titles: Optional[np.ndarray] = None self._pp_documents = None # preprocessed documents if text_features is None: self._infer_text_features() else: self.set_text_features(text_features) self._set_unique_titles() @property def used_preprocessor(self): return self.__used_preprocessor # type: PreprocessorList @used_preprocessor.setter def used_preprocessor(self, pp): from orangecontrib.text.preprocess import PreprocessorList, Preprocessor if isinstance(pp, PreprocessorList): self.__used_preprocessor = PreprocessorList(list(pp.preprocessors)) elif isinstance(pp, Preprocessor): self.__used_preprocessor.preprocessors.append(pp) else: raise NotImplementedError def _find_identical_feature(self, feature: Variable) -> Optional[Variable]: """ Find a renamed feature in the domain which is identical to a feature. Parameters ---------- feature A variable to find an identical variable in the domain. Returns ------- Variable which is identical to a feature (have different name but has Identity(feature) in compute value. """ for var in chain(self.domain.variables, self.domain.metas): if ( var == feature or isinstance(var.compute_value, Identity) and var.compute_value.variable == feature ): return var return None
[docs] def set_text_features(self, feats: Optional[List[Variable]]) -> None: """ Select which meta-attributes to include when mining text. Parameters ---------- feats List of text features to include. If None infer them. """ if feats is not None: feats = copy(feats) # copy to not edit passed array inplace for i, f in enumerate(feats): if f not in chain(self.domain.variables, self.domain.metas): # if not exact feature in the domain, it may be renamed # find identity - renamed feature id_feat = self._find_identical_feature(f) if id_feat is not None: feats[i] = id_feat else: raise ValueError('Feature "{}" not found.'.format(f)) if len(set(feats)) != len(feats): raise ValueError('Text features must be unique.') if feats != self.text_features: # when new features are same than before it is not required # to invalidate tokens self.text_features = feats self._tokens = None # invalidate tokens else: self._infer_text_features()
[docs] def set_title_variable( self, title_variable: Union[StringVariable, str, None] ) -> None: """ Set the title attribute. Only one column can be a title attribute. Parameters ---------- title_variable Variable that need to be set as a title variable. If it is None, do not set a variable. """ for a in self.domain.variables + self.domain.metas: a.attributes.pop("title", None) if title_variable and title_variable in self.domain: self.domain[title_variable].attributes["title"] = True self._set_unique_titles()
def _set_unique_titles(self): """ Define self._titles variable as a list of titles (a title for each document). It is used to have an unique title for each document. In case when the document have the same title as the other document we put a number beside. """ if self.domain is None: return attrs = [attr for attr in chain(self.domain.variables, self.domain.metas) if attr.attributes.get('title', False)] if attrs: self._titles = np.array(self._unique_titles( self.documents_from_features(attrs))) else: self._titles = np.array([ 'Document {}'.format(i + 1) for i in range(len(self))]) @staticmethod def _unique_titles(titles: List[str]) -> List[str]: """ Function adds numbers to the non-unique values fo the title. Parameters ---------- titles List of titles - not necessary unique Returns ------- List with unique titles. """ counts = Counter(titles) cur_appearances = defaultdict(int) new_titles = [] for t in titles: if counts[t] > 1: cur_appearances[t] += 1 t += f" ({cur_appearances[t]})" new_titles.append(t) return new_titles def _infer_text_features(self): """ Infer which text features to use. If nothing was provided in the file header, use the first text feature. """ include_feats = [] first = None for attr in self.domain.metas: if attr.is_string: if first is None: first = attr incl = attr.attributes.get('include', False) # variable attributes can be boolean from Orange 3.29 # they are string in older versions # incl == True, since without == string "False" would be True if incl == "True" or incl == True: include_feats.append(attr) if len(include_feats) == 0 and first: include_feats.append(first) self.set_text_features(include_feats)
[docs] def extend_attributes( self, X, feature_names, feature_values=None, compute_values=None, var_attrs=None, sparse=False, rename_existing=False ): """ Append features to corpus. If `feature_values` argument is present, features will be Discrete else Continuous. Args: X (numpy.ndarray or scipy.sparse.csr_matrix): Features values to append feature_names (list): List of string containing feature names feature_values (list): A list of possible values for Discrete features. compute_values (list): Compute values for corresponding features. var_attrs (dict): Additional attributes appended to variable.attributes. sparse (bool): Whether the features should be marked as sparse. rename_existing (bool): When true and names are not unique rename exiting features; if false rename new features """ def _rename_features(additional_names: List) -> Tuple[List, List, List]: cur_attr = list(self.domain.attributes) cur_class = self.domain.class_var cur_meta = list(self.domain.metas) if rename_existing: current_vars = ( cur_attr + ( [cur_class] if cur_class else []) + cur_meta ) current_names = [ for a in current_vars] new_names = get_unique_names( additional_names, current_names, equal_numbers=False ) renamed_vars = [ var.renamed(n) for var, n in zip(current_vars, new_names) ] cur_attr = renamed_vars[:len(cur_attr)] cur_class = renamed_vars[len(cur_attr)] if cur_class else None cur_meta = renamed_vars[-len(cur_meta):] return cur_attr, cur_class, cur_meta if sp.issparse(self.X) or sp.issparse(X): X = sp.hstack((self.X, X)).tocsr() else: X = np.hstack((self.X, X)) if compute_values is None: compute_values = [None] * X.shape[1] if feature_values is None: feature_values = [None] * X.shape[1] # rename existing variables if required curr_attributes, curr_class_var, curr_metas = _rename_features( feature_names ) if not rename_existing: # rename new feature names if required feature_names = get_unique_names( self.domain, feature_names, equal_numbers=False ) additional_attributes = [] for f, values, cv in zip(feature_names, feature_values, compute_values): if values is not None: var = DiscreteVariable(f, values=values, compute_value=cv) else: var = ContinuousVariable(f, compute_value=cv) var.sparse = sparse # don't pass this to constructor so this works with Orange < 3.8.0 if isinstance(var_attrs, dict): var.attributes.update(var_attrs) additional_attributes.append(var) new_domain = Domain( attributes=curr_attributes + additional_attributes, class_vars=curr_class_var, metas=curr_metas ) c = Corpus.from_numpy( new_domain, X, self.Y.copy(), self.metas.copy(), self.W.copy(), text_features=copy(self.text_features), ) Corpus.retain_preprocessing(self, c) return c
@property def documents(self): """ Returns a list of strings representing documents — created by joining selected text features. """ return self.documents_from_features(self.text_features) @property def pp_documents(self): """ Preprocessed documents (transformed). """ return self._pp_documents or self.documents @pp_documents.setter def pp_documents(self, documents): self._pp_documents = documents @property def titles(self): """ Returns a list of titles. """ assert self._titles is not None return self._titles
[docs] def documents_from_features(self, feats): """ Args: feats (list): A list fo features to join. Returns: a list of strings constructed by joining feats. """ # create a Table where feats are in metas data = Table.from_table(Domain([], [], [ for i in feats], source=self.domain), self) # When we use only features coming from sparse X data.metas is sparse. # Transform it to dense. if sp.issparse(data.metas): data.metas = data.metas.toarray() return [' '.join(f.str_val(val) for f, val in zip(data.domain.metas, row)) for row in data.metas]
[docs] def store_tokens(self, tokens, dictionary=None): """ Args: tokens (list): List of lists containing tokens. """ self._tokens = np.array(tokens, dtype=object) self._dictionary = dictionary or corpora.Dictionary(self.tokens)
@property def tokens(self): """ np.ndarray: A list of lists containing tokens. If tokens are not yet present, run default preprocessor and return tokens. """ if self._tokens is None: return self._base_tokens()[0] return self._tokens
[docs] def has_tokens(self): """ Return whether corpus is preprocessed or not. """ return self._tokens is not None
def _base_tokens(self): from orangecontrib.text.preprocess import BASE_TRANSFORMER, \ BASE_TOKENIZER, PreprocessorList # don't use anything that requires NLTK data to assure async download base_preprocessors = PreprocessorList([BASE_TRANSFORMER, BASE_TOKENIZER]) corpus = base_preprocessors(self) return corpus.tokens, corpus.dictionary @property def dictionary(self): """ corpora.Dictionary: A token to id mapper. """ if self._dictionary is None: return self._base_tokens()[1] return self._dictionary @property def pos_tags(self): """ np.ndarray: A list of lists containing POS tags. If there are no POS tags available, return None. """ if self._pos_tags is None: return None return np.array(self._pos_tags, dtype=object) @pos_tags.setter def pos_tags(self, pos_tags): self._pos_tags = pos_tags def ngrams_iterator(self, join_with=' ', include_postags=False): if self.pos_tags is None: include_postags = False if include_postags: data = zip(self.tokens, self.pos_tags) else: data = self.tokens if join_with is None: processor = lambda doc, n: nltk.ngrams(doc, n) elif include_postags: processor = lambda doc, n: (join_with.join(token + '_' + tag for token, tag in ngram) for ngram in nltk.ngrams(zip(*doc), n)) else: processor = lambda doc, n: (join_with.join(ngram) for ngram in nltk.ngrams(doc, n)) return (list(chain(*(processor(doc, n) for n in range(self.ngram_range[0], self.ngram_range[1]+1)))) for doc in data) @property def ngrams(self): """generator: Ngram representations of documents.""" return self.ngrams_iterator(join_with=' ')
[docs] def copy(self): """Return a copy of the table.""" c = super().copy() # since tokens and dictionary are considered immutable copies are not needed c._setup_corpus(copy(self.text_features)) c._tokens = self._tokens c._dictionary = self._dictionary c.ngram_range = self.ngram_range c.pos_tags = self.pos_tags = c.used_preprocessor = self.used_preprocessor c._titles = self._titles c._pp_documents = self._pp_documents return c
[docs] @staticmethod def from_documents(documents, name, attributes=None, class_vars=None, metas=None, title_indices=None): """ Create corpus from documents. Args: documents (list): List of documents. name (str): Name of the corpus attributes (list): List of tuples (Variable, getter) for attributes. class_vars (list): List of tuples (Variable, getter) for class vars. metas (list): List of tuples (Variable, getter) for metas. title_indices (list): List of indices into domain corresponding to features which will be used as titles. Returns: Corpus. """ attributes = attributes or [] class_vars = class_vars or [] metas = metas or [] title_indices = title_indices or [] domain = Domain(attributes=[attr for attr, _ in attributes], class_vars=[attr for attr, _ in class_vars], metas=[attr for attr, _ in metas]) for ind in title_indices: domain[ind].attributes['title'] = True def to_val(attr, val): if isinstance(attr, DiscreteVariable): attr.val_from_str_add(val) return attr.to_val(val) if documents: X = np.array([[to_val(attr, func(doc)) for attr, func in attributes] for doc in documents], dtype=np.float64) Y = np.array([[to_val(attr, func(doc)) for attr, func in class_vars] for doc in documents], dtype=np.float64) metas = np.array([[to_val(attr, func(doc)) for attr, func in metas] for doc in documents], dtype=object) else: # assure shapes match the number of columns X = np.empty((0, len(attributes))) Y = np.empty((0, len(class_vars))) metas = np.empty((0, len(metas))) corpus = Corpus.from_numpy( domain=domain, X=X, Y=Y, metas=metas, text_features=[] ) = name return corpus
def __getitem__(self, key): c = super().__getitem__(key) if isinstance(c, (Corpus, RowInstance)): Corpus.retain_preprocessing(self, c, key) return c
[docs] @classmethod def from_table(cls, domain, source, row_indices=...): c = super().from_table(domain, source, row_indices) c._setup_corpus() Corpus.retain_preprocessing(source, c, row_indices) return c
[docs] @classmethod def from_numpy( cls, domain, X, Y=None, metas=None, W=None, attributes=None, ids=None, text_features=None, ): t = super().from_numpy( domain, X, Y=Y, metas=metas, W=W, attributes=attributes, ids=ids ) # t is corpus but corpus specific attributes were not set yet t._setup_corpus(text_features=text_features) return t
@classmethod def from_list(cls, domain, rows, weights=None): t = super().from_list(domain, rows, weights) # t is corpus but corpus specific attributes were not set yet t._setup_corpus() return t
[docs] @classmethod def from_table_rows(cls, source, row_indices): c = super().from_table_rows(source, row_indices) # t is corpus but corpus specific attributes were not set yet c._setup_corpus() if hasattr(source, "_titles"): # covering case when from_table_rows called by from_table c._titles = source._titles[row_indices] return c
[docs] @classmethod def from_file(cls, filename): if not os.path.exists(filename): # check the default location abs_path = os.path.join(get_sample_corpora_dir(), filename) if not abs_path.endswith('.tab'): abs_path += '.tab' if os.path.exists(abs_path): filename = abs_path table = super().from_file(filename) if not isinstance(table, Corpus): # when loading regular file result of super().from_file is Table - need # to be transformed to Corpus, when loading pickle it is Corpus already name = table = cls.from_numpy(table.domain, table.X, table.Y, table.metas, table.W, attributes=table.attributes) = name return table
[docs] @staticmethod def retain_preprocessing(orig, new, key=...): """ Set preprocessing of 'new' object to match the 'orig' object. """ if isinstance(orig, Corpus): if isinstance(key, tuple): # get row selection key = key[0] if orig._tokens is not None: # retain preprocessing if isinstance(key, Integral): new._tokens = np.array([orig._tokens[key]]) new.pos_tags = None if orig.pos_tags is None else np.array( [orig.pos_tags[key]]) elif isinstance(key, list) or isinstance(key, np.ndarray) \ or isinstance(key, slice) or isinstance(key, range): new._tokens = orig._tokens[key] new.pos_tags = None if orig.pos_tags is None else orig.pos_tags[key] elif key is Ellipsis: new._tokens = orig._tokens new.pos_tags = orig.pos_tags else: raise TypeError('Indexing by type {} not supported.'.format(type(key))) new._dictionary = orig._dictionary if isinstance(new, Corpus): # _find_identical_feature returns non when feature not found # filter this Nones from list new.text_features = list(filter(None, [ new._find_identical_feature(tf) for tf in orig.text_features ])) else: new.text_features = [ tf for tf in orig.text_features if tf in set(new.domain.metas) ] new._titles = orig._titles[key] new.ngram_range = orig.ngram_range new.attributes = orig.attributes new.used_preprocessor = orig.used_preprocessor else: # orig is not Corpus new._set_unique_titles() new._infer_text_features()
if summarize: # summarize is not available in older versions of orange-widget-base # skip if not available @summarize.register(Corpus) def summarize_(corpus: Corpus) -> PartialSummary: """ Provides automated input and output summaries for Corpus """ table_summary = summarize.dispatch(Table)(corpus) extras = ( ( f"<br/><nobr>Tokens: {sum(map(len, corpus.tokens))}, " f"Types: {len(corpus.dictionary)}</nobr>" ) if corpus.has_tokens() else "<br/><nobr>Corpus is not preprocessed</nobr>" ) return PartialSummary(table_summary.summary, table_summary.details + extras)