def parse_line(self):
line_number = 1
qline_begin = self.appconfig.get_value('question_lines', 'start')
qline_end = self.appconfig.get_value('question_lines', 'end')
possible_answer_line = self.appconfig.get_value(
'possible_answers_line')
for line in self.readlines():
if line_number == self.appconfig.get_value('paragraph_line'):
para = Paragraph(line, self.appconfig)
para.parse()
yield ("paragraph", para)
elif line_number >= qline_begin and line_number <= qline_end:
line.replace('?', '')
qa = QuestionAnswer(line)
yield ("question_answer", qa)
elif line_number == possible_answer_line:
pa = PossibleAnswers(line, self.appconfig)
pa.parse()
yield ("possible_answers", pa)
else:
raise ValueError('Invalid input, number of lines exceeded')
line_number += 1
def test_init(self):
sentence = '/>あいうえお'
p = Paragraph(sentence)
self.assertEqual(p.sentences, ['あいうえお'])
sentence = 'あいうえお<a href="somewhere">かき</a>くけこ'
p = Paragraph(sentence)
self.assertEqual(p.sentences, ['あいうえおかきくけこ'])
def generate_novel():
characters = [Person(), Person(), Person()]
for i in range(5):
scene_settings.append(generate_setting())
for setting in scene_settings:
paragraph = Paragraph(characters, setting)
paragraph.generate_sentences()
print paragraph
def make_paragraphs(raw_data_path, decorated_data_path, urn, aux_info):
text, info = "", "" # from urn
aux = decorate(info, aux_info)
paragraphs = text.split('\n')
for p in paragraphs:
pg = PG(p, info)
pg.set_aux_info(aux)
pg.save_to_file(decorated_data_path)
def save_section_features_to_db(self, paper_ids, list_authors,
list_authors_id_200):
con = psycopg2.connect("dbname ='%s' user='******' host=/tmp/" %
(self.db_name.lower(), getpass.getuser()))
cur = con.cursor()
index = {}
for m in list_authors_id_200:
index[m] = 0
num_section = 1
chunk_id = 1
for i in range(0, self.num_paper):
tokens_sum = []
for j in range(0, len(list_authors[i])):
novel_id = paper_ids[i]
index[list_authors[i][j]] += 1
raw_novel_text = self.get_raw_text(novel_id)
tokens = nltk.word_tokenize(raw_novel_text.decode('utf-8'))
tokens_sum += tokens[0:self.token_size / len(list_authors[i])]
cur.execute("INSERT INTO section VALUES(%s,%s,%s,%s,%s)", [
i + 1, num_section, raw_novel_text, novel_id,
list_authors[i][j]
])
num_section += 1
paragraphs = self.get_paragraphs(tokens_sum)
for x in range(0, len(paragraphs)):
para = Paragraph("paper_id", para=paragraphs[x])
stylo_list = []
try:
stylo_list = para.get_stylo_list()
except:
raise
print('error')
for y in range(0, 57):
feature_id = y + 1
try:
value = 0 if math.isnan(
stylo_list[y]) else stylo_list[y]
except:
value = 0
cur.execute(
"INSERT INTO features VALUES (%s, %s, %s, %s) " %
(i + 1, chunk_id, feature_id, value))
chunk_id += 1
con.commit()
print("saved section no %s" % (i + 1))
con.close()
cur.close()
def draw(self,page,context):
if not self.reportElement.is_printable(context):
return
P=Paragraph(self.text.data,self.textElement.get_style())
w,h = P.wrap(self.reportElement['width'],self.reportElement['height'])
x,y = page.translate(self.reportElement['x'],self.reportElement['y'])
P.drawOn(page.canvas,x,y)
def test_split_by_dots(self):
p = Paragraph('a')
html_items = p.split_by_dots('abc')
self.assertEqual(html_items, ['abc'])
html_items2 = p.split_by_dots('a！bc')
self.assertEqual(html_items2, ['a', 'bc'])
html_items2 = p.split_by_dots('a。bc')
self.assertEqual(html_items2, ['a', 'bc'])
def test_contains_nomenclature(self):
self.pp.append_ahead(Line('hamster'))
self.pp.append_ahead(
Line('≡ Polyporus mori (Pollini) Fr., Systema Mycologicum 1:'))
self.pp.append_ahead(Line('344 (1821)'))
self.pp.close()
self.assertTrue(self.pp.contains_nomenclature())
pp2 = Paragraph()
pp2.append(Line('Araneosa columellata Long, Mycologia 33 (1941) 353.'))
self.assertTrue(pp2.contains_nomenclature())
def setUp(self):
self.pp = Paragraph()
self.pp.append_ahead(
Line(
'Julella sublactea (Nylander) R.C. Harris in Egan, Bryologist 90: 163. 1987;\n'
))
self.pp.append_ahead(
Line(
'Verrucaria sublactea Nylander, Flora 69: 464. 1886. syn. nov.\n'
))
self.pp.close()
def test_split_by_dots(self):
p = Paragraph('a')
html_items = p.split_by_dots('abc')
self.assertEqual(html_items, ['abc'])
html_items2 = p.split_by_dots('a！bc')
self.assertEqual(html_items2, ['a', 'bc'])
html_items2 = p.split_by_dots('a。bc')
self.assertEqual(html_items2, ['a', 'bc'])
def load_docs(docs_file_path):
docs = []
DOMTree = minidom.parse(docs_file_path)
collection = DOMTree.documentElement
docElements = collection.getElementsByTagName("doc")
for docEl in docElements:
paragraphs = []
paragraphElements = docEl.getElementsByTagName("p")
for pEl in paragraphElements:
concepts = {}
conceptElements = pEl.getElementsByTagName("concept")
for cEl in conceptElements:
name = cEl.getAttribute("name")
freq = int(cEl.getAttribute("freq"))
concepts[name] = freq
p = Paragraph(concepts)
paragraphs.append(p)
doc = Document(paragraphs)
docs.append(doc)
return docs
def draw(self,page,context):
if not self.reportElement.is_printable(context):
return
value = self.resolve_expression(self.fieldExpression.data,context)
if value is None and self['isBlankWhenNull']:
value = ''
else:
value = self.format(value)
P=Paragraph(value,self.textElement.get_style())
w,h = P.wrap(self.reportElement['width'], self.reportElement['height'])
x,y = page.translate(self.reportElement['x'],self.reportElement['y'])
P.drawOn(page.canvas,x,y)
def summarize(self, content):
summarized_content_array = []
for paragraph in content:
index = paragraph.index
title = paragraph.title
content = self.summarize_paragraph(paragraph)
p = Paragraph(index, title, content)
summarized_content_array.append(p)
return summarized_content_array
def write_paragraph(self, section_number, content):
""" Writes paragraph to paragraph diction with section as key, content as value"""
if section_number == 0:
title = "Summary"
else:
title = self.get_section(section_number - 1)
paragraph = Paragraph(section_number, title, content)
self.paragraphs.append(paragraph)
def document(self):
current_paragraph = []
paragraphs = []
for line in self._text.splitlines():
line = line.strip()
if line.isupper():
heading = Sentence(line, self._tokenizer, is_heading=True)
current_paragraph.append(heading)
elif not line and current_paragraph:
sentences = self._to_sentences(current_paragraph)
paragraphs.append(Paragraph(sentences))
current_paragraph = []
elif line:
current_paragraph.append(line)
sentences = self._to_sentences(current_paragraph)
paragraphs.append(Paragraph(sentences))
return ObjectDocumentModel(paragraphs)
def summarize(self, content):
summarized_content_array = []
for paragraph in content:
summarized_content = self.get_first_sentance(paragraph.content)
title = paragraph.title
index = paragraph.index
summarized_paragraph = Paragraph(index, title, summarized_content)
summarized_content_array.append(summarized_paragraph)
return summarized_content_array
def read_raw_text(self, raw_text_path):
characters = ''.join(open(raw_text_path).readlines())
begin = 8 # each article begins with ".START\n\n"
pid = 0
while 1:
pc = characters.find('\n\n', begin)
if pc == -1:
break
self.paragraphs.append(Paragraph(begin, pc, pid))
pid += 1
begin = pc + 2 # '\n\n'
for sen in self.sentences:
flag = False
for para in self.paragraphs:
if sen.begin_offset >= para.begin_offset and sen.end_offset <= para.end_offset:
para.sentences.append(sen)
flag = True
break
if not flag:
print >> logs, 'sentence outof paragraph'
def parse_annotated(contents: Iterable[Line]) -> Iterator[Paragraph]:
"""Return paragraphs in annotated block form.
Do not apply heuristic methods to divide paragraphs."""
pp = Paragraph()
for line in contents:
pp.append_ahead(line)
if line.contains_start():
(retval, pp) = pp.next_paragraph()
yield retval
continue
if pp.last_line and pp.last_line.end_label() is not None:
(retval, pp) = pp.next_paragraph()
yield retval
continue
def retrieve_forbes_article_data(article, company_list,
company_name_automaton):
article.paragraphs = []
browser.get((article.url))
try:
article_text_section = browser.find_element_by_xpath(
'//*[@id="article-container-0"]/div[2]/div[2]/article-body-container/div/div'
)
article_paragraph_tags = article_text_section.find_elements_by_tag_name(
'p')
except:
return
all_tickers = []
for paragraph in article_paragraph_tags:
if '[+]' in paragraph.text:
continue
tickers = []
for end_index, idx in company_name_automaton.iter(
paragraph.text.upper()):
company_formatted = company_list[idx]
company_formatted = company_formatted[1:]
company_formatted = company_formatted[:-1]
tickers.append(company_formatted)
all_tickers.extend(tickers)
article.paragraphs.append(Paragraph(paragraph.text, tickers))
article.webpage_text = article.webpage_text + paragraph.text
article.tickers = all_tickers
article.tickers = list(dict.fromkeys(article.tickers))
return article
def reset(self):
super().reset()
self.state = 'start'
self.paragraph = Paragraph()
def __init__(self, paragraphs_actions: List[ParagraphsAction] = []):
super().__init__()
self.paragraphs_actions = paragraphs_actions
self.paragraph = Paragraph()
class BlogParser(MachineHTMLParser):
paragraphs_actions: List[ParagraphsAction]
paragraph: Paragraph
def __init__(self, paragraphs_actions: List[ParagraphsAction] = []):
super().__init__()
self.paragraphs_actions = paragraphs_actions
self.paragraph = Paragraph()
# utilities
def parse_file(self, filename: str, rel: Optional[str] = None):
self.reset()
if isinstance(rel, str):
self.paragraph.filename = Path(rel).relative_to(rel)
else:
self.paragraph.filename = filename
super().parse_file(filename)
def parse_date(self, time: str) -> str:
time_ = time.strip()
fmt = '%A, %b %d, %Y' # example: Monday, Jan 1, 2015
try:
return datetime.strptime(time_, fmt).isoformat()
except ValueError:
log.error(f'Invalid date format:"{time_}"')
log.error(f'Invalid date format @ {self.location()}')
return ""
# reduce middleware once paragraph is read
def push_paragraph(self):
reduce(lambda x, f: f(x), self.paragraphs_actions, [self.paragraph])
self.paragraph = self.paragraph.new_paragraph()
# state-machine logic
def reset(self):
super().reset()
self.state = 'start'
self.paragraph = Paragraph()
def validate_transition(self, to_state: State):
return (self.state, to_state) in valid_transitions
def dispatch(self, ms: TransitionData, attrs: Attrs = {}):
if ms == ('start', 'header', 'starttag'):
self.transition('metadata')
elif ms == ('metadata', 'h1', 'starttag'):
self.transition('title')
elif ms == ('metadata', 'Author', 'DATA'):
self.transition('author_1')
elif ms == ('metadata', 'Date', 'DATA'):
self.transition('date_1')
elif ms == ('metadata', 'header', 'endtag'):
self.transition('article')
elif ms == ('title', '*', 'DATA'):
self.paragraph.article_title = ms.tagOrData
elif ms == ('title', 'h1', 'endtag'):
self.transition('metadata')
elif ms == ('author_1', 'p', 'starttag'):
self.transition('author_2')
elif ms == ('author_2', '*', 'DATA'):
self.paragraph.author = ms.tagOrData
self.transition('metadata')
elif ms == ('date_1', 'p', 'starttag'):
self.transition('date_2')
elif ms == ('date_2', '*', 'DATA'):
self.paragraph.date = self.parse_date(ms.tagOrData)
self.transition('metadata')
elif ms == ('article', 'h[23]', 'starttag'):
self.push_paragraph()
self.transition('subtitle')
elif ms == ('subtitle', 'h[23]', 'endtag'):
self.transition('article')
elif ms == ('article', 'article', 'endtag'):
self.push_paragraph()
self.transition('done')
elif ms == ('article', '*', 'DATA'):
self.paragraph.text += ms.tagOrData
# we keep <code> and <p> tags for use in chunking text later
elif ms == ('article', 'p', 'starttag'):
self.paragraph.text += "<p>"
elif ms == ('article', 'p', 'endtag'):
self.paragraph.text += "</p>"
elif ms == ('article', 'code', 'starttag'):
self.paragraph.text += '<code>'
elif ms == ('article', 'code', 'endtag'):
self.paragraph.text += '</code>'
elif ms == ('subtitle', '*', 'DATA'):
self.paragraph.paragraph_title += ms.tagOrData
def endElement(self, name):
if name == 'empty-line':
self.add_empty_line()
elif self.is_body and name != 'body':
data = ''.join(self.cur_data)
if name in ['strong', 'emphasis', 'a', 'style']:
if not self.cur_attr:
##print 'FB2 PARSER ERROR: nested styles?'
return
self.cur_attr.insert(1, len(data))
self.attrs.append(self.cur_attr)
self.cur_attr = []
return
if data and data.strip():
if self.is_title:
self.add_empty_line()
self.content.append(
Paragraph(
'title', data,
attrs=self.attrs,
lang=self.lang,
id=self.cur_id,
byte_index=_parser.CurrentByteIndex,
)
)
elif self.is_epigraph and name == 'p':
self.content.append(
Paragraph(
'epigraph', data,
attrs=self.attrs,
lang=self.lang,
id=self.cur_id,
byte_index=_parser.CurrentByteIndex
)
)
elif self.is_cite and name == 'p':
self.content.append(
Paragraph(
'cite', data,
attrs=self.attrs,
lang=self.lang,
id=self.cur_id,
byte_index=_parser.CurrentByteIndex
)
)
else:
self.content.append(
Paragraph(
name, data,
attrs=self.attrs,
lang=self.lang,
id=self.cur_id,
byte_index=_parser.CurrentByteIndex
)
)
self.prev_paragraph_is_empty = False
self.attrs = []
self.id = None
if name == 'description':
self.is_desc = False
elif name == 'body':
self.is_body = False
elif name == 'epigraph':
self.is_epigraph = False
self.add_empty_line()
elif name == 'cite':
self.is_cite = False
elif name == 'title':
self.is_title = False
self.add_empty_line()
elif name in ('subtitle', 'image', 'poem'):
self.add_empty_line()
elif name == 'lang':
self.lang = ''.join(self.cur_data).strip()
#del self.elem_stack[-1]
self.cur_data = []
self.links = {}
def add_empty_line(self):
if not self.prev_paragraph_is_empty:
self.content.append(Paragraph('empty-line', '',
byte_index=_parser.CurrentByteIndex))
self.prev_paragraph_is_empty = True
def writeCoursesWithDescriptions(ws):
global document
global courses
global coursesWithDescriptions
courseDescriptionOn = 'false'
allparagraphs = []
pertinentParagraphs = []
global number
global default_format
global description_format
writeFile = "pertinentParagraphs.csv"
pHeader = ""
headerIsCourseTitle = 'false'
for p in document.paragraphs:
if p.text.strip() != "":
number = number + 1
paragraph = Paragraph()
paragraph.setNumber(number)
paragraph.setStyle(p.style.name)
paragraph.setText(p.text.strip().rstrip(string.digits))
allparagraphs.append(paragraph)
for i, item in enumerate(allparagraphs):
if item.getStyle() == constant.STYLE_BODY_TEXT:
pPrev = allparagraphs[i - 1]
pHeader = ""
if pPrev.getStyle() == constant.STYLE_NORMAL:
number = number + 1
p2 = Paragraph()
p2.setNumber(number)
p2.setStyle(pPrev.getStyle())
p2.setText(pPrev.getText())
paragraphs2.append(p2)
pHeader = pPrev.getText().strip()
if pHeader != "":
headerIsCourseTitle = HeaderIsCourseTitle(pHeader)
if headerIsCourseTitle == 'true':
number = number + 1
pc = Paragraph()
pc.setNumber(number)
pc.setStyle(item.getStyle())
pc.setHeader(pHeader)
pc.setText(item.getText())
pertinentParagraphs.append(pc)
associatedCourse = Course()
currentCourseDescription = ""
courseTitlePosition = -1
row = 0
col = 0
ws.write(row, col, 'Knowledge Area')
col += 1
ws.write(row, col, 'Course Title')
col += 1
ws.write(row, col, 'Description')
col = 0
row += 1
for i, p in enumerate(pertinentParagraphs):
courseTitleCandidate = p.getHeader().strip()
if p.getHeader().strip(
) != "" and courseDescriptionOn == 'true' and i > courseTitlePosition:
newCourse = Course()
newCourse.setKnowledgeArea(associatedCourse.getKnowledgeArea())
newCourse.setTitle(associatedCourse.getTitle())
newCourse.setDescription(currentCourseDescription)
coursesWithDescriptions.append(newCourse)
courseDescriptionOn = 'false'
ws.write(row, col, associatedCourse.getKnowledgeArea())
col += 1
ws.write(row, col, associatedCourse.getTitle())
col += 1
ws.write(row, col, currentCourseDescription)
col = 0
row += 1
currentCourseDescription = ""
courseTitlePosition = -1
if p.getHeader().strip() != "" and courseDescriptionOn == 'false':
courseDescriptionOn = 'true'
associatedCourse = GetAssociatedCourse(p.getHeader().strip())
courseTitlePosition = i
currentCourseDescription += p.getText().strip()
if courseDescriptionOn == 'true' and i > courseTitlePosition:
currentCourseDescription += p.getText().strip()
ws.set_column(0, 0, 35, default_format)
ws.set_column(1, 1, 50, default_format)
ws.set_column(2, 2, 120, description_format)
def writeDocStyles():
global number
writeFile = "docStyles_fall.csv"
wrawFile = "docStylesRaw_fall.csv"
global document
with open(writeFile, 'w') as output:
fieldnames = ['number', 'style', 'text']
writer = csv.DictWriter(output, fieldnames=fieldnames)
writer.writeheader()
for p in document.paragraphs:
if p.text.strip() != "":
number = number + 1
paragraph = Paragraph()
runCnt = 0
for r in p.runs:
runCnt += 1
run = Run()
run.setBold(str(r.bold))
run.setFont(r.font.name)
run.setItalic(str(r.font.italic))
paragraph.setRuns(run)
paragraph.setNumber(number)
paragraph.setStyle(p.style.name)
paragraph.setText(p.text.strip().rstrip(string.digits))
paragraph.setRunCount(runCnt)
paragraphs1.append(paragraph)
number = 0
i = 0
for i, item in enumerate(paragraphs1):
if item.getStyle() == constant.STYLE_BODY_TEXT:
pPrev = paragraphs1[i - 1]
if pPrev.getStyle() == constant.STYLE_NORMAL:
number = number + 1
p2 = Paragraph()
p2.setNumber(number)
p2.setStyle(pPrev.getStyle())
p2.setText(pPrev.getText())
paragraphs2.append(p2)
number = number + 1
pc = Paragraph()
pc.setNumber(number)
pc.setStyle(item.getStyle())
pc.setText(item.getText().strip())
paragraphs2.append(pc)
for paragraph in paragraphs2:
writer.writerow({
'number': paragraph.getNumber(),
'style': paragraph.getStyle(),
'text': paragraph.getText()
})
with open(wrawFile, "w") as rawoutput:
fieldnames = ['number', 'style', 'runs', 'font', 'bold', 'text']
rwriter = csv.DictWriter(rawoutput, fieldnames=fieldnames)
rwriter.writeheader()
for paragraph in paragraphs1:
for run in paragraph.getRuns():
rwriter.writerow({
'number': paragraph.getNumber(),
'style': paragraph.getStyle(),
'runs': paragraph.getRunCount(),
'font': run.getFont(),
'bold': run.getBold(),
'text': paragraph.getText()
})
def parse_paragraphs(contents: Iterable[Line]) -> Iterator[Paragraph]:
pp = Paragraph()
for line in contents:
pp.append_ahead(line)
next_pp = pp.split_at_nomenclature()
if next_pp:
if not pp.is_empty():
yield pp
(retval, pp) = next_pp.next_paragraph()
yield retval
continue
# New document triggers a new paragraph.
if pp.last_line and pp.last_line.filename != line.filename:
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# Page break triggers a new paragraph.
if line.startswith(' '):
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# Page break is a whole paragraph.
if pp.is_page_header():
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# Leading tab triggers a new paragraph.
if line.startswith('\t'):
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# Tables start with a few long lines and
# continue to grow as long as we have short lines.
if pp.is_table():
if line.is_short(pp.short_line):
continue
else:
if pp.is_all_long():
continue
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# Blocks of blank lines are a paragraph.
if pp.is_blank():
if line.is_blank():
continue
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# Figures end with a blank line, or period or colon at the end
# of a line.
if pp.is_figure():
if (not line.is_blank() and
not pp.detect_period() and
not pp.endswith(':')):
continue
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# Leading hyphen triggers a new paragraph.
if line.startswith('-'):
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# A table starts a new paragraph.
if pp.next_line.is_table():
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# Synonymy reference ends a taxon.
if pp.last_line and pp.last_line.search(r'\([Ss]yn.*\)$'):
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# A taxon ends in nov., nov. comb., nov. sp., ined.,
# emend. (followed by emender), or nom. sanct.
if pp.last_line and pp.last_line.search(
r'(nov\.|nov\.\s?(comb\.|sp\.)|[(]?in\.?\s?ed\.[)]?|'
r'[(]?nom\.\s?sanct\.[)]?|emend\..*)$'
):
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# A short line ends a paragraph.
if pp.last_line and pp.last_line.is_short(pp.short_line):
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
# A blank line ends a paragraph.
if line.is_blank():
(retval, pp) = pp.next_paragraph()
if not retval.is_empty():
yield retval
continue
def main():
args = define_args()
Paragraph.set_reinterpretations(args.reinterpret)
if args.dump_files:
print('\ntraining_files:', args.training_files)
print('\nevaluate_files:', args.evaluate_files)
classifiers = [
BernoulliNB(),
RandomForestClassifier(n_estimators=100, n_jobs=-1),
AdaBoostClassifier(),
BaggingClassifier(),
ExtraTreesClassifier(),
GradientBoostingClassifier(),
DecisionTreeClassifier(),
CalibratedClassifierCV(),
DummyClassifier(),
PassiveAggressiveClassifier(max_iter=5, tol=None),
RidgeClassifier(),
RidgeClassifierCV(),
SGDClassifier(max_iter=5, tol=-np.infty),
OneVsRestClassifier(SVC(kernel='linear')),
OneVsRestClassifier(LogisticRegression()),
KNeighborsClassifier()
]
vectorizers = [
CountVectorizer(),
TfidfVectorizer(),
HashingVectorizer()
]
fast_classifiers = [
BernoulliNB(),
RandomForestClassifier(n_estimators=100, n_jobs=-1),
AdaBoostClassifier(),
# BaggingClassifier(),
ExtraTreesClassifier(),
GradientBoostingClassifier(),
DecisionTreeClassifier(),
CalibratedClassifierCV(),
DummyClassifier(),
PassiveAggressiveClassifier(max_iter=5, tol=None),
RidgeClassifier(),
# RidgeClassifierCV(),
SGDClassifier(max_iter=5, tol=-np.infty),
# OneVsRestClassifier(SVC(kernel='linear')),
OneVsRestClassifier(LogisticRegression()),
# KNeighborsClassifier() # Actually not slow, but we run out of memory.
]
fast_vectorizers = [
CountVectorizer(),
TfidfVectorizer(),
# HashingVectorizer()
]
if args.fast:
classifiers = fast_classifiers
vectorizers = fast_vectorizers
try:
i = [c.__class__.__name__ for c in classifiers].index(args.classifier)
except ValueError:
raise ValueError('Unknown classifier %s' % args.classifier)
classifier = classifiers[i]
try:
i = [v.__class__.__name__ for v in vectorizers].index(args.vectorizer)
except ValueError:
raise ValueError('Unknown vectorizer %s' % args.vectorizer)
vectorizer = vectorizers[i]
if args.training_files:
contents = read_files(args.training_files)
if args.annotated_paragraphs:
phase1 = parse_annotated(contents)
else:
phase1 = parse_paragraphs(contents)
if 1 in args.dump_phase:
print('Phase 1')
print('=======')
phase1 = list(phase1)
print(repr(phase1))
if 1 == max(args.dump_phase):
sys.exit(0)
if args.keep_interstitials:
phase2 = phase1
else:
phase2 = remove_interstitials(phase1)
phase1 = None # Potentially recover memory.
if 2 in args.dump_phase:
print('Phase 2')
print('=======')
phase2 = list(phase2)
print(repr(phase2))
if 2 == max(args.dump_phase):
sys.exit(0)
# All labels need to be resolved for this phase. The easiest way
# to assure this is to convert to list.
phase3 = target_classes(
list(phase2),
default=Label('Misc-exposition'),
keep=[Label(l) for l in args.labels]
)
if args.dump_input:
phase3 = list(phase3)
if args.output_annotated:
if not args.output_labels:
print('\n'.join([pp.as_annotated() for pp in phase3]))
else:
print('\n'.join([pp.as_annotated()
for pp in phase3
if pp.top_label() in args.output_labels]))
else:
print('\n'.join([str(pp) for pp in phase3]))
phase2 = None
if 3 in args.dump_phase:
print('Phase 3')
print('=======')
phase3 = list(phase3)
print(repr(phase3))
if 3 == max(args.dump_phase):
sys.exit(0)
phase3 = list(phase3)
sample_size = len(phase3)
if args.group_paragraphs:
writer = csv.DictWriter(sys.stdout, fieldnames=Taxon.FIELDNAMES)
writer.writeheader()
for taxon in group_paragraphs(phase3):
for d in taxon.dictionaries():
writer.writerow(d)
sys.exit(0)
np.random.seed(SEED)
cutoff = int(sample_size * 0.70)
permutation = np.random.permutation(phase3)
phase3 = None
learn = paragraph.to_dataframe(permutation[:cutoff], args.suppress_text)
test = paragraph.to_dataframe(permutation[cutoff:], args.suppress_text)
if args.test_classifiers:
perform(
classifiers,
vectorizers,
learn,
test
)
sys.exit(0)
if args.test_classifiers_by_label:
perform_confusion_matrix(
classifiers,
vectorizers,
learn,
test,
emit_csv=args.csv
)
sys.exit(0)
# train or load models
if args.load_vectorizer:
vectorizer = joblib.load(args.load_vectorizer)
classifier = joblib.load(args.load_classifier)
else:
vectorize_text = vectorizer.fit_transform(learn.v2)
classifier.fit(vectorize_text, learn.v1)
# Dump trained models.
if args.dump_vectorizer:
joblib.dump(vectorizer, args.dump_vectorizer)
if args.dump_classifier:
joblib.dump(classifier, args.dump_classifier)
if args.evaluate_files:
phase4 = []
# predict
if args.keep_interstitials:
evaluated = (
parse_paragraphs(read_files(args.evaluate_files)))
else:
evaluated = remove_interstitials(
parse_paragraphs(read_files(args.evaluate_files)))
for pp in evaluated:
text = str(pp)
vectorize_text = vectorizer.transform([text])
predict = classifier.predict(vectorize_text)[0]
if args.insert_nomenclature and pp.contains_nomenclature():
predict = 'Nomenclature'
phase4.append(pp.replace_labels(labels=[Label(predict)]))
if args.output_annotated:
if not args.output_labels:
print('\n'.join([pp.as_annotated() for pp in phase4]))
else:
print('\n'.join([pp.as_annotated()
for pp in phase4
if pp.top_label() in args.output_labels]))
if 4 in args.dump_phase:
print('Phase 4')
print('=======')
print(repr(phase4))
if 4 == max(args.dump_phase):
sys.exit(0)
def ExtractCourseDescriptions(document, courses):
courseDescriptionOn = 'false'
allparagraphs = []
pertinentParagraphs = []
number = 0
pHeader = ""
headerIsCourseTitle = 'false'
partialTitle = []
fulltitle = ""
for p in document.paragraphs:
if p.text.strip() != "":
pText = p.text.strip()
lastPos = len(pText)
if pText[lastPos - 1] == ":":
partialTitle.append(pText)
else:
if len(partialTitle) > 0:
partialTitle.append(p.text.strip())
fulltitle = ' '.join(partialTitle)
else:
fulltitle = pText
number = number + 1
paragraph = Paragraph()
paragraph.setNumber(number)
paragraph.setStyle(p.style.name)
paragraph.setText(p.text.strip().rstrip(string.digits))
allparagraphs.append(paragraph)
for i, item in enumerate(allparagraphs):
if item.getStyle() == constant.STYLE_BODY_TEXT:
pPrev = allparagraphs[i - 1]
pHeader = ""
if pPrev.getStyle() == constant.STYLE_NORMAL:
number = number + 1
p2 = Paragraph()
p2.setNumber(number)
p2.setStyle(pPrev.getStyle())
p2.setText(pPrev.getText())
paragraphs2.append(p2)
pHeader = pPrev.getText().strip()
if pHeader != "":
headerIsCourseTitle = HeaderIsCourseTitle(pHeader)
if headerIsCourseTitle == 'true':
number = number + 1
pc = Paragraph()
pc.setNumber(number)
pc.setStyle(item.getStyle())
pc.setHeader(pHeader)
pc.setText(item.getText())
pertinentParagraphs.append(pc)
associatedCourse = Course()
currentCourseDescription = ""
courseTitlePosition = -1
for i, p in enumerate(pertinentParagraphs):
courseTitleCandidate = p.getHeader().strip()
#print("courseTitleCandidate: {}".format(courseTitleCandidate))
if p.getHeader().strip(
) != "" and courseDescriptionOn == 'true' and i > courseTitlePosition:
associatedCourse.setDescription(currentCourseDescription)
courseDescriptionOn = 'false'
currentCourseDescription = ""
courseTitlePosition = -1
if p.getHeader().strip() != "" and courseDescriptionOn == 'false':
courseDescriptionOn = 'true'
associatedCourse = GetAssociatedCourse(courses,
p.getHeader().strip())
#print("return from getAssociatedCourse(): {}".format(associatedCourse.getTitle()))
courseTitlePosition = i
currentCourseDescription += p.getText().strip()
if courseDescriptionOn == 'true' and i > courseTitlePosition:
currentCourseDescription += p.getText().strip()
img = PIL.Image.open(join(img_dir, img_list[page]))
print('image loaded')
img: Image
width = img.width
name_font = load_font(
join(
current_dir,
try_get(settings, 'name_font', 'fonts/FZY3JW.TTF',
quiet=True)),
int(get_best_font_size(width) * name_scale_factor))
word_font = load_font(
join(
current_dir,
try_get(settings, 'word_font', 'fonts/FZY3JW.TTF',
quiet=True)), int(get_best_font_size(width)))
paragraph = Paragraph(width - border_width * 2)
for i in range(length):
name = names[i]
word = words[i]
if name != '':
name_color = try_get(name_color_dict,
name,
default_text_color,
quiet=True)
name_block = TextBlock(name + ': ', name_font, name_color)
paragraph.add_text_block(name_block)
else:
name_color = default_text_color
word_block = TextBlock(word, word_font, name_color)
def ExtractCourseDescriptions(document: Document, courses: List[Course]) -> None:
"""
Extract the course descriptions from the document.
Write them to the appropriate Course records in the
database.
:param document: a Python-Docx Document object
:param courses: a List of Course objects
"""
courseDescriptionOn = False
allparagraphs = []
pertinentParagraphs = []
number = 0
pHeader = ""
hIsCourseTitle = False
partialTitle = []
global courseNameStyles
for p in document.paragraphs:
if p.text.strip() != "":
pText = p.text.strip()
lastPos = len(pText)
if pText[lastPos-1] == ":":
partialTitle.append(pText)
else:
if len(partialTitle) > 0:
partialTitle.append(p.text.strip())
number = number + 1
paragraph = Paragraph()
paragraph.setNumber(number)
paragraph.setStyle(p.style.name)
paragraph.setText(p.text.strip().rstrip(string.digits))
allparagraphs.append(paragraph)
for i, item in enumerate(allparagraphs):
if item.getStyle() == constant.STYLE_BODY_TEXT:
pPrev = allparagraphs[i-1]
pHeader = ""
if pPrev.getStyle() in courseNameStyles:
number = number + 1
p2 = Paragraph()
p2.setNumber(number)
p2.setStyle(pPrev.getStyle())
p2.setText(pPrev.getText())
paragraphs2.append(p2)
pHeader = pPrev.getText().strip()
if pHeader != "":
hIsCourseTitle = HeaderIsCourseTitle(pHeader)
if hIsCourseTitle:
number = number + 1
pc = Paragraph()
pc.setNumber(number)
pc.setStyle(item.getStyle())
pc.setHeader(pHeader)
pc.setText(item.getText())
pertinentParagraphs.append(pc)
associatedCourse = Course()
currentCourseDescription = ""
courseTitlePosition = -1
for i, p in enumerate(pertinentParagraphs):
#if p.getHeader().strip() != "":
#print("paragraph header: {}".format(p.getHeader().strip()))
if p.getHeader().strip() != "" and courseDescriptionOn and i > courseTitlePosition:
associatedCourse.setDescription(currentCourseDescription)
courseDescriptionOn = False
currentCourseDescription = ""
courseTitlePosition = -1
if p.getHeader().strip() != "" and not courseDescriptionOn:
courseDescriptionOn = True
associatedCourse = GetAssociatedCourse(courses, p.getHeader().replace("'","").strip())
courseTitlePosition = i
currentCourseDescription += p.getText().strip()
if courseDescriptionOn and i > courseTitlePosition:
currentCourseDescription += p.getText().strip()
if __name__ == '__main__':
stop_words = set(stopwords.words('english'))
stemmer = PorterStemmer()
documents = []
vocabulary = set()
files = os.listdir('corpus')
for i, file in enumerate(files):
with open('corpus/' + file, encoding="utf8", errors='ignore') as f:
raw = f.read()
paras = paragraph_tokenizer(raw)
paragraphs = []
for j, para in enumerate(paras):
# Preprocessing
tokens = preprocessor(para)
_id = (i, j)
paragraph = Paragraph(_id, tokens)
paragraphs.append(paragraph)
for term in tokens:
vocabulary.add(term)
document = Document(i, paragraphs)
documents.append(document)
# Length of vocabulary
vocabularyLength = len(vocabulary)
# print(vocabularyLength)
# Creating the inverted index
indexer = Indexer(documents)
# Take filename as input for processing
inputDocument = str(sys.argv[1])