def test_asserting_errors(self):
c = Corpus.from_file('book-excerpts')
too_large_x = np.vstack((c.X, c.X))
with self.assertRaises(ValueError):
Corpus.from_numpy(c.domain, too_large_x, c.Y, c.metas, c.W,
c.text_features)
with self.assertRaises(ValueError):
c.set_text_features([StringVariable('foobar')])
with self.assertRaises(ValueError):
c.set_text_features([c.domain.metas[0], c.domain.metas[0]])
def main():
from Orange.data import Table, Domain, ContinuousVariable, StringVariable
words = 'hey~mr. tallyman tally~me banana daylight come and me wanna go home'
words = np.array([w.replace('~', ' ') for w in words.split()], dtype=object, ndmin=2).T
weights = np.random.random((len(words), 1))
data = np.zeros((len(words), 0))
metas = []
for i, w in enumerate(weights.T):
data = np.column_stack((data, words, w))
metas = metas + [StringVariable('Topic' + str(i)),
ContinuousVariable('weights')]
domain = Domain([], metas=metas)
table = Table.from_numpy(domain,
X=np.zeros((len(words), 0)),
metas=data)
app = QtGui.QApplication([''])
w = OWWordCloud()
w.on_topics_change(table)
domain = Domain([], metas=[StringVariable('text')])
data = Corpus.from_numpy(domain, X=np.zeros((1, 0)), metas=np.array([[' '.join(words.flat)]]))
w.on_corpus_change(data)
w.show()
app.exec()
def main():
from Orange.data import Table, Domain, ContinuousVariable, StringVariable
words = 'hey~mr. tallyman tally~me banana daylight come and me wanna go home'
words = np.array([w.replace('~', ' ') for w in words.split()], dtype=object, ndmin=2).T
weights = np.random.random((len(words), 1))
data = np.zeros((len(words), 0))
metas = []
for i, w in enumerate(weights.T):
data = np.column_stack((data, words, w))
metas = metas + [StringVariable('Topic' + str(i)),
ContinuousVariable('weights')]
domain = Domain([], metas=metas)
table = Table.from_numpy(domain,
X=np.zeros((len(words), 0)),
metas=data)
app = QtGui.QApplication([''])
w = OWWordCloud()
w.on_topics_change(table)
domain = Domain([], metas=[StringVariable('text')])
data = Corpus.from_numpy(domain, X=np.zeros((1, 0)), metas=np.array([[' '.join(words.flat)]]))
w.on_corpus_change(data)
w.show()
app.exec()
def test_set_title_from_domain(self):
"""
When we setup domain from data (e.g. from_numpy) _title variable
must be set.
"""
domain = Domain([], metas=[StringVariable("title"), StringVariable("a")])
metas = [["title1", "a"], ["title2", "b"]]
corpus = Corpus.from_numpy(
domain, X=np.empty((2, 0)), metas=np.array(metas)
)
assert_array_equal(["Document 1", "Document 2"], corpus.titles)
domain["title"].attributes["title"] = True
corpus = Corpus.from_numpy(
domain, X=np.empty((2, 0)), metas=np.array(metas)
)
assert_array_equal(["title1", "title2"], corpus.titles)
def test_titles_from_rows(self):
domain = Domain([],
metas=[StringVariable("title"), StringVariable("a")])
metas = [["title1", "a"], ["title2", "b"], ["titles3", "c"]]
corpus = Corpus.from_numpy(
domain, X=np.empty((3, 0)), metas=np.array(metas)
)
corpus = Corpus.from_table_rows(corpus, [0, 2])
assert_array_equal(["Document 1", "Document 3"], corpus.titles)
def test_init_preserve_shape_of_empty_x(self):
c = Corpus.from_file('book-excerpts')
d = c.domain
new_domain = Domain((ContinuousVariable('c1'), ), d.class_vars,
d.metas)
empty_X = csr_matrix((len(c), 1))
new = Corpus.from_numpy(new_domain, X=empty_X, Y=c.Y, metas=c.metas)
self.assertEqual(empty_X.nnz, 0)
self.assertEqual(new.X.shape, empty_X.shape)
def test_corpus_from_numpy(self):
domain = Domain([],
metas=[StringVariable("title"),
StringVariable("a")])
corpus = Corpus.from_numpy(domain,
np.empty((2, 0)),
metas=np.array([["title1", "a"],
["title2", "b"]]))
self.assertEqual(2, len(corpus))
assert_array_equal(["Document 1", "Document 2"], corpus.titles)
self.assertListEqual([StringVariable("title")], corpus.text_features)
self.assertIsNone(corpus._tokens)
self.assertListEqual([], corpus.used_preprocessor.preprocessors)
def _create_corpus(self) -> Corpus:
corpus = None
names = ["name", "path", "content"] if not self.is_conllu else [
"name", "path", "utterance", "content"
]
data = []
category_data = []
text_categories = list(set(t.category for t in self._text_data))
values = list(set(text_categories))
category_var = DiscreteVariable.make("category", values=values)
for textdata in self._text_data:
datum = [
# some characters are written as decomposed (č is char c
# and separate char for caron), with NFC normalization we
# normalize them to be written as precomposed (č is one
# unicode char - 0x10D)
# https://docs.python.org/3/library/unicodedata.html#unicodedata.normalize
normalize('NFC', textdata.name),
normalize('NFC', textdata.path),
normalize('NFC', textdata.content)
]
if self.is_conllu:
datum.insert(2, normalize('NFC', textdata.doc_id))
data.append(datum)
category_data.append(category_var.to_val(textdata.category))
if len(text_categories) > 1:
category_data = np.array(category_data)
else:
category_var = []
category_data = np.empty((len(data), 0))
domain = Domain([], category_var,
[StringVariable.make(name) for name in names])
domain["name"].attributes["title"] = True
data = np.array(data, dtype=object)
if len(data):
corpus = Corpus.from_numpy(domain,
X=np.empty((len(category_data), 0)),
Y=category_data,
metas=data,
text_features=[domain.metas[-1]])
return corpus
def _corpus_from_records(records, includes_metadata):
"""Receives PubMed records and transforms them into a corpus.
Args:
records (list): A list of PubMed entries.
includes_metadata (list): A list of text fields to include.
Returns:
corpus: The output Corpus.
"""
meta_vars = []
time_var = None
for field_name, _ in includes_metadata:
if field_name == PUBMED_FIELD_DATE:
time_var = TimeVariable(field_name)
meta_vars.append(time_var)
else:
meta_vars.append(StringVariable.make(field_name))
if field_name == PUBMED_FIELD_TITLE:
meta_vars[-1].attributes["title"] = True
meta_values, class_values = _records_to_corpus_entries(
records,
includes_metadata=includes_metadata,
time_var=time_var,
)
class_vars = [
DiscreteVariable('section',
values=list(map(str, set(filter(None,
class_values)))))
]
domain = Domain([], class_vars=class_vars, metas=meta_vars)
Y = np.array([class_vars[0].to_val(cv) for cv in class_values])[:, None]
return Corpus.from_numpy(domain=domain,
X=np.empty((len(Y), 0)),
Y=Y,
metas=meta_values)
def get_data(self):
domain = Domain([],
metas=[
StringVariable("Conc. {}".format(self.word)),
StringVariable("Document")
])
data = []
docs = []
for row in range(self.rowCount()):
txt = []
for column in range(self.columnCount()):
index = self.index(row, column)
txt.append(str(self.data(index)))
data.append([" ".join(txt)])
docs.append([self.corpus.titles[self.word_index[row][0]]])
conc = (np.array(np.hstack(
(data, docs)), dtype=object) if data else np.empty((0, 2)))
return Corpus.from_numpy(
domain,
X=np.empty((len(conc), 0)),
metas=conc,
text_features=[domain.metas[0]],
)
"""
input: Corpus preprocessed with Preprocess Text. Tokenizer is set to Sentences.
output: Corpus where sentences are now documents.
requires: Text add-on
"""
import numpy as np
from Orange.data import Domain, StringVariable
from orangecontrib.text.corpus import Corpus
tokens = in_data.tokens
title = [i for i in in_data.domain.metas if "title" in i.attributes][0]
new_domain = Domain(attributes=[], metas=[StringVariable('Sentences'),
title)
titles = []
content = []
for i, doc in enumerate(tokens):
for t in doc:
titles.append(in_data[i][title.name].value)
content.append(t)
metas = np.column_stack((content, titles))
out_data = Corpus.from_numpy(domain=new_domain, X=np.empty((len(content), 0)),
metas=metas)
out_data.set_text_features([StringVariable('Sentences')])
out_data.set_title_variable(title)