Corpus

class orangecontrib.text.corpus.Corpus(X=None, Y=None, metas=None, domain=None, text_features=None)

Internal class for storing a corpus.

__init__(X=None, Y=None, metas=None, domain=None, text_features=None)

Parameters:

• X (numpy.ndarray) – attributes
• Y (numpy.ndarray) – class variables
• metas (numpy.ndarray) – meta attributes; e.g. text
• domain (Orange.data.Domain) – the domain for this Corpus
• text_features (list) – meta attributes that are used for text mining. Infer them if None.

static __new__(*args, **kwargs)

Bypass Table.__new__.

copy()

Return a copy of the table.

dictionary

corpora.Dictionary: A token to id mapper.

documents

Returns: a list of strings representing documents — created by joining

selected text features.

documents_from_features(feats)

Parameters:feats (list) – A list fo features to join.

Returns: a list of strings constructed by joining feats.

extend_attributes(X, feature_names, var_attrs=None)

Append features to corpus.

Parameters:

• X (numpy.ndarray or scipy.sparse.csr_matrix) – Features to append
• feature_names (list) – List of string containing feature names
• var_attrs (dict) – Additional attributes appended to variable.attributes.

extend_corpus(metadata, Y)

Append documents to corpus.

Parameters:

• metadata (numpy.ndarray) – Meta data
• Y (numpy.ndarray) – Class variables

has_tokens()

Return whether corpus is preprocessed or not.

ngrams

generator: Ngram representations of documents.

set_text_features(feats)

Select which meta-attributes to include when mining text.

Parameters:feats (list) – list of text features to include.

store_tokens(tokens, dictionary=None)

Parameters:tokens (list) – List of lists containing tokens.

tokens

np.ndarray: A list of lists containing tokens. If tokens are not yet present, run default preprocessor and save tokens.