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()
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 _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
def set_corpus(self, data=None):
self.corpus = data
# create preprocessed corpus upon setting data to avoid preprocessing
# at each method run
pp_list = [
preprocess.LowercaseTransformer(),
preprocess.WordPunctTokenizer()
]
self.pp_corpus = PreprocessorList(pp_list)(self.corpus)
self.commit()
def test_used_preprocessors(self):
corpus1 = self.corpus.copy()
for pp in self.pp_list:
corpus1 = pp(corpus1)
self.assertEqual(len(self.corpus.used_preprocessor.preprocessors), 0)
self.assertEqual(len(corpus1.used_preprocessor.preprocessors), 5)
self.assertEqual([8, 10, 6, 8, 9, 7, 7, 10, 4],
list(map(len, corpus1._tokens)))
corpus2 = PreprocessorList(self.pp_list)(self.corpus)
self.assertEqual(corpus1, corpus2)
def __init__(self,
domain=None,
X=None,
Y=None,
metas=None,
W=None,
text_features=None,
ids=None):
"""
Args:
domain (Orange.data.Domain): the domain for this Corpus
X (numpy.ndarray): attributes
Y (numpy.ndarray): class variables
metas (numpy.ndarray): meta attributes; e.g. text
W (numpy.ndarray): instance weights
text_features (list): meta attributes that are used for
text mining. Infer them if None.
ids (numpy.ndarray): Indices
"""
super().__init__()
n_doc = _check_arrays(X, Y, metas)
with self.unlocked_reference():
self.X = X if X is not None else np.zeros((n_doc, 0))
self.Y = Y if Y is not None else np.zeros((n_doc, 0))
self.metas = metas if metas is not None else np.zeros((n_doc, 0))
self.W = W if W is not None else np.zeros((n_doc, 0))
self.domain = domain
self.text_features = [] # list of text features for mining
self._tokens = None
self._dictionary = None
self._ngrams_corpus = 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 domain is not None and text_features is None:
self._infer_text_features()
elif domain is not None:
self.set_text_features(text_features)
if ids is not None:
self.ids = ids
else:
Table._init_ids(self)
self._set_unique_titles()
def set_corpus(self, data=None):
self.corpus = data
self.pp_corpus = None
if self.corpus is not None:
if not self.corpus.has_tokens():
# create preprocessed corpus upon setting data to avoid
# preprocessing at each method run
pp_list = [
preprocess.LowercaseTransformer(),
preprocess.WordPunctTokenizer()
]
self.pp_corpus = PreprocessorList(pp_list)(self.corpus)
else:
self.pp_corpus = self.corpus
self.commit.now()
def preprocess_only_words(corpus: Corpus) -> Corpus:
"""
Apply the preprocessor that splits words, transforms them to lower case
(and removes punctuations).
Parameters
----------
corpus
Corpus on which the preprocessor will be applied.
Returns
-------
Preprocessed corpus. Result of pre-processing is saved in tokens/ngrams.
"""
p = PreprocessorList(
[LowercaseTransformer(),
# by default regexp keeps only words (no punctuations, no spaces)
RegexpTokenizer()]
)
return p(corpus)
def test_result(self):
pp = PreprocessorList([BASE_TRANSFORMER, RegexpTokenizer()])
corpus = pp(Corpus.from_file("book-excerpts")[::3])
vect = BowVectorizer()
corpus_vect = vect.transform(corpus)
words = ["beheld", "events", "dragged", "basin", "visit", "have"]
d = Domain([corpus_vect.domain[w] for w in words])
corpus_vect = corpus_vect.transform(d)
self.send_signal(self.widget.Inputs.data, corpus_vect)
self.send_signal(self.widget.Inputs.selected_data, corpus_vect[:1])
self.wait_until_finished(timeout=100000)
np.testing.assert_array_almost_equal(
self.widget.results.p_values,
[0.02128, 1, 0.04255, 0.06383, 0.08511, 0.97872],
decimal=5,
)
np.testing.assert_array_almost_equal(
self.widget.results.fdr_values,
[0.12766, 1, 0.12766, 0.12766, 0.12766, 1],
decimal=5,
)
def test_apply_preprocessors(self):
corpus = PreprocessorList(self.pp_list)(self.corpus)
self.assertEqual([8, 10, 6, 8, 9, 7, 7, 10, 4],
list(map(len, corpus._tokens)))
self.assertEqual(len(corpus.used_preprocessor.preprocessors), 5)
