def guess_sport(data, full_text):
data_lc = clean_text(str(data)).lower()
print(data_lc)
kickboxing_refs1 = len(re.findall('kickbox', data_lc))
boxing_refs1 = len(re.findall('[\W.,]+box', data_lc))
mma_refs1 = len(re.findall('(mixed martial arts)|(mma)', data_lc))
print(kickboxing_refs1, boxing_refs1, mma_refs1)
if kickboxing_refs1 > boxing_refs1 and kickboxing_refs1 > mma_refs1:
return kickboxing_key
if mma_refs1 > boxing_refs1 and mma_refs1 > kickboxing_refs1:
return mma_key
if boxing_refs1 > mma_refs1 and boxing_refs1 > kickboxing_refs1:
return boxing_key
data_lc = clean_text(str(full_text)).lower()
kickboxing_refs2 = len(re.findall('kickbox', data_lc))
boxing_refs2 = len(re.findall('[\W.,]+box', data_lc))
mma_refs2 = len(re.findall('(mixed martial arts)|(mma)', data_lc))
print(kickboxing_refs2, boxing_refs2, mma_refs2)
if kickboxing_refs2 > boxing_refs2 and kickboxing_refs2 > mma_refs2:
return kickboxing_key
if mma_refs2 > boxing_refs2 and mma_refs2 > kickboxing_refs2:
return mma_key
if boxing_refs2 > mma_refs2 and boxing_refs2 > kickboxing_refs2:
return boxing_key
return other_key
def main():
check_or_create_save_folder(SAVE_FILE_PATH)
# List all scraped files related to ApInfo
for html_file_path in os.listdir(JOB_FOLDER):
# Parse only HTML files
if html_file_path.endswith(".html"):
job_id = re.findall(r'\d+', html_file_path)[0]
json_file_name = "%s-%s.json" % (job_platform, job_id)
save_path = "%s/%s" % (SAVE_FILE_PATH, json_file_name)
# Check if file hasn't already been parsed
if not os.path.isfile(save_path):
try:
htmlfile = open(JOB_FOLDER + "/" + html_file_path)
soup = BeautifulSoup(htmlfile.read())
job_info = soup.find("div",
class_="info-data").text.strip()
date = job_info.rsplit('-', 1)[1].strip()
location = clean_text(job_info.rsplit('-', 1)[0])
city = location.rsplit('-', 1)[0].strip()
state = location.rsplit('-', 1)[1].strip()
job_title = soup.find("div", class_="cargo m-tb").text
job_title = clean_apinfo_jobtitle(job_title)
company = clean_text(
soup.find('div',
class_="texto").contents[3].contents[2])
job_description = clean_text(
soup.find('div', class_="texto").contents[1].text)
data = {
'date': date,
'job_title': job_title,
'company': company,
'location_city': city,
'location_state': state,
'job_description': job_description,
'job_platform': job_platform,
'job_platform_id': job_id
}
save_json_file(save_path, data)
# Log errors to a text file
except Exception as e:
target = open(ERROR_LOG_FILE, "a")
error_details = ""
if job_id:
error_details += "%s: " % job_id
print(job_id)
print(e)
error_details += str(e)
target.write("%s\n" % error_details)
def main():
check_or_create_save_folder(SAVE_FILE_PATH)
# List all scraped files related to ApInfo
for html_file_path in os.listdir(JOB_FOLDER):
# Parse only HTML files
if html_file_path.endswith(".html"):
job_id = re.findall(r'\d+', html_file_path)[0]
json_file_name = "%s-%s.json" % (job_platform, job_id)
save_path = "%s/%s" % (SAVE_FILE_PATH, json_file_name)
# Check if file hasn't already been parsed
if not os.path.isfile(save_path):
try:
htmlfile = open(JOB_FOLDER+"/"+html_file_path)
soup = BeautifulSoup(htmlfile.read())
job_info = soup.find("div", class_="info-data").text.strip()
date = job_info.rsplit('-', 1)[1].strip()
location = clean_text(job_info.rsplit('-', 1)[0])
city = location.rsplit('-', 1)[0].strip()
state = location.rsplit('-', 1)[1].strip()
job_title = soup.find("div", class_="cargo m-tb").text
job_title = clean_apinfo_jobtitle(job_title)
company = clean_text(soup.find('div', class_="texto").contents[3].contents[2])
job_description = clean_text(soup.find('div', class_="texto").contents[1].text)
data = {
'date': date,
'job_title': job_title,
'company': company,
'location_city': city,
'location_state': state,
'job_description': job_description,
'job_platform': job_platform,
'job_platform_id': job_id
}
save_json_file(save_path, data)
# Log errors to a text file
except Exception as e:
target = open(ERROR_LOG_FILE, "a")
error_details = ""
if job_id:
error_details += "%s: " % job_id
print(job_id)
print(e)
error_details += str(e)
target.write("%s\n" % error_details)
def extract_general_method(s):
split_method = str(s).split('(')
if len(split_method) >= 2:
clean_method = clean_text(split_method[0])
else:
clean_method = clean_text(s)
if 'tko' in clean_method or 'ko' in clean_method:
return 'ko'
elif 'submission' in clean_method:
return 'submission'
elif 'decision' in clean_method:
return 'decision'
else:
return 'other'
def main():
check_or_create_save_folder(SAVE_FILE_PATH)
# List all scraped files related to Trampos
for json_file_path in os.listdir(JOB_FOLDER):
# Parse only JSON files
if json_file_path.endswith(".json"):
job_id = re.findall(r'\d+', json_file_path)[0]
json_file_name = "%s-%s.json" % (job_platform, job_id)
save_path = "%s/%s" % (SAVE_FILE_PATH, json_file_name)
# Check if file hasn't already been parsed
if not os.path.isfile(save_path):
try:
with open("%s/%s" %
(JOB_FOLDER, json_file_path)) as json_data:
job_data = json.load(json_data)['opportunity']
data = {}
data['date'] = get_date(job_data['published_at'])
if 'city' in job_data and 'state' in job_data:
data['city'] = clean_text(job_data['city'])
data['state'] = clean_text(job_data['state'])
elif 'home_office' in job_data:
data['home_office'] = True
data['job_title'] = clean_text(job_data['name'])
if 'company' in job_data and job_data['company']:
data['company'] = clean_text(
job_data['company']['name'])
data['job_description'] = "%s %s %s" % (
clean_text(job_data['description']),
clean_text(job_data['prerequisite']),
clean_text(job_data['desirable']))
data['job_platform'] = job_platform
data['job_platform_id'] = job_id
save_json_file(save_path, data)
# Log errors to a text file
except Exception as e:
target = open(ERROR_LOG_FILE, "a")
error_details = ""
if job_id:
error_details += "%s: " % job_id
print(job_id)
print(e)
error_details += str(e)
target.write("%s\n" % error_details)
def transform(self, documents):
documents = [tokenize(d) for d in documents]
documents = [d[:self.max_page_size] for d in documents]
documents = [' '.join(d) for d in documents]
if self.encoding_type in ['tfidf', 'count', 'binary']:
return self.vectorizer.transform(documents).toarray()
if self.encoding_type == 'lda':
documents_tokenized = [tokenize(i) for i in documents]
other_corpus = [
self.common_dictionary.doc2bow(i) for i in documents_tokenized
]
results = []
for i in other_corpus:
result = self.vectorizer[i]
result = vectorize_topic_models(result, self.num_of_topics)
results.append(result)
return np.array(results)
if self.encoding_type in ['doc2vec']:
documents_tokenized = [tokenize(i) for i in documents]
results = []
for i in documents_tokenized:
if i:
try:
results.append(self.vectorizer[i][0])
except KeyError:
results.append([0 for _ in range(self.encoding_size)])
else:
results.append([0 for _ in range(self.encoding_size)])
return np.array(results)
if self.encoding_type in ['fasttext']:
documents_clean = [clean_text(i) for i in documents]
results = []
for i in documents_clean:
if i:
results.append(self.vectorizer.get_sentence_vector(i))
# results.append(self.vectorizer[i])
else:
results.append(
np.array([0 for _ in range(self.encoding_size)]))
return np.array(results)
def scrape_fighter(next_url):
sections_dict = dict()
r = requests.get(next_url)
soup = BeautifulSoup(r.text)
stats_table_card = soup.find('table', {'class': 'infobox vcard'})
general_stats = get_general_info(stats_table_card)
mw_parser_output = soup.find('div', {'class': 'mw-parser-output'})
if mw_parser_output:
page_items = mw_parser_output.find_all(['h2', 'h3', 'table'])
sections = dict()
active_key = None
for i in page_items:
if i.name == 'h2':
active_key = clean_text(i.get_text()).lower()
sections[active_key] = []
if active_key:
sections[active_key].append(i)
for i in sections:
if 'mma' in i.lower() or 'boxing' in i.lower() or 'record' in i.lower():
if i not in mma_page_section_names and i not in boxing_page_section_names and i not in ['Possibly missed key: Amateur kickboxing career[edit]', 'Possibly missed key: Professional boxing career[edit]', 'Possibly missed key: Mixed martial arts career[edit]', 'Possibly missed key: Kickboxing record (Incomplete)[edit]']:
print('Possibly missed key: {0}'.format(i))
active_key2 = None
for c1, i in enumerate(sections):
if i in mma_page_section_names:
sections_dict.setdefault(mma_key, dict())
sections_dict[mma_key].setdefault(exhibition_key, list())
sections_dict[mma_key].setdefault(pro_key, list())
sections_dict[mma_key].setdefault(amateur_key, list())
active_key1 = mma_key
elif i in boxing_page_section_names:
sections_dict.setdefault(boxing_key, dict())
sections_dict[boxing_key].setdefault(exhibition_key, list())
sections_dict[boxing_key].setdefault(pro_key, list())
sections_dict[boxing_key].setdefault(amateur_key, list())
active_key1 = boxing_key
# elif i in kickboxing_page_section_names:
# sections_dict.setdefault(kickboxing_key, dict())
# sections_dict[kickboxing_key].setdefault(exhibition_key, list())
# sections_dict[kickboxing_key].setdefault(pro_key, list())
# sections_dict[kickboxing_key].setdefault(amateur_key, list())
# active_key1 = kickboxing_key
elif i in mma_amateur_section:
sections_dict.setdefault(mma_key, dict())
sections_dict[mma_key].setdefault(amateur_key, list())
active_key1 = mma_key
active_key2 = amateur_key
elif i in mma_amateur_section:
sections_dict.setdefault(mma_key, dict())
sections_dict[mma_key].setdefault(amateur_key, list())
active_key1 = mma_key
active_key2 = amateur_key
elif i in mma_pro_section:
sections_dict.setdefault(mma_key, dict())
sections_dict[mma_key].setdefault(pro_key, list())
active_key1 = mma_key
active_key2 = pro_key
elif i in professional_boxing_section_names:
sections_dict.setdefault(boxing_key, dict())
sections_dict[boxing_key].setdefault(pro_key, list())
active_key1 = boxing_key
active_key2 = pro_key
elif i in professional_record_names:
sport_type = guess_sport(stats_table_card, r.text)
print('guessing sport: {0} {1}'.format(next_url, sport_type))
active_key1 = sport_type
sections_dict.setdefault(sport_type, dict())
sections_dict[sport_type].setdefault(amateur_key, list())
sections_dict[sport_type].setdefault(pro_key, list())
sections_dict[sport_type].setdefault(amateur_key, list())
else:
continue
for c2, j in enumerate(sections[i]):
data_type = get_table_type(j)
# print(data_type)
if data_type == other_key:
continue
if data_type == data_table_key and c2 < 2:
active_key2 = pro_key
if data_type in [pro_key, amateur_key, exhibition_key]:
active_key2 = data_type
if data_type == data_table_key and active_key2:
sections_dict[active_key1][active_key2].append(j)
active_key2 = None
return sections_dict, general_stats
def extract_table(f_url, tables_dict, general_stats):
dfs = []
new_urls = []
fighter_id = str(uuid.uuid5(uuid.NAMESPACE_DNS, f_url))
sport_keys = tables_dict.keys()
for s in sport_keys:
types_of_events = tables_dict[s].keys()
for t in types_of_events:
print(s, t)
for j in tables_dict[s][t]:
print(1)
tr_tags = j.find_all('tr')
header_index = get_row_num_of_headers(j)
opponent_col_name = [i.get_text().strip() for i in tr_tags[header_index].find_all(['th', 'td']) if 'opponent' in i.get_text().strip().lower()][0]
index_of_opponent = [c for c, i in enumerate(tr_tags[header_index].find_all(['th', 'td'])) if opponent_col_name.strip() == i.get_text().strip()][0]
id_mapping = dict()
for k in tr_tags[header_index + 1:]:
opponent_cell = k.find_all('td')[index_of_opponent]
opponent_a_tag = opponent_cell.find_all('a')
opponent_name = opponent_cell.get_text().strip()
opponent_rel_links = [k2['href'] for k2 in opponent_a_tag if k2['href'] not in links_to_avoid]
if opponent_rel_links:
opponent_rel_link = opponent_rel_links[-1]
opponent_abs_link = urljoin(base_url, opponent_rel_link)
new_urls.append(opponent_abs_link)
opponent_id = str(uuid.uuid5(uuid.NAMESPACE_DNS, opponent_abs_link))
has_wiki = 1
else:
opponent_id = str(uuid.uuid4())
has_wiki = 0
id_mapping[opponent_name] = {'id': opponent_id, 'has_wiki': has_wiki}
df = pd.read_html(str(j), header=header_index)[0]
df['fighter_id'] = fighter_id
df['opponent_id'] = df.apply(lambda x: id_mapping.get(x[opponent_col_name], {'id':str(uuid.uuid4())})['id'], axis = 1)
df['opponent_has_wiki'] = df.apply(lambda x: id_mapping.get(x[opponent_col_name], {'has_wiki':0})['has_wiki'], axis = 1)
df['sport'] = s
df['event_type'] = t
for g in general_stats:
df[g] = general_stats[g]
df = df.applymap(lambda x: clean_text(x))
dfs.append(df)
if dfs:
df = pd.concat(dfs)
else:
df = pd.DataFrame()
return df, new_urls
def main():
check_or_create_save_folder(SAVE_FILE_PATH)
# List all scraped files related to Ceviu
for html_file_path in os.listdir(JOB_FOLDER):
# Parse only HTML files
if html_file_path.endswith(".html"):
job_id = re.findall(r'\d+', html_file_path)[0]
json_file_name = "%s-%s.json" % (job_platform, job_id)
save_path = "%s/%s" % (SAVE_FILE_PATH, json_file_name)
# Check if file hasn't already been parsed
if not os.path.isfile(save_path):
try:
htmlfile = open(JOB_FOLDER + "/" + html_file_path)
soup = BeautifulSoup(htmlfile.read())
job_info = soup.find(
"p", class_="codigo-data-vaga").text.strip()
date = job_info.rsplit('Data: ', 1)[1].strip()
location = soup.find('div', class_="localizacao-vaga").text
location = clean_text(location)
city = re.search('Localizacao (.*)/', location,
re.IGNORECASE).group(1)
state = re.search('/(.*)', location,
re.IGNORECASE).group(1)
job_title = soup.find("h2", class_="titulo-vaga").text
job_title = clean_text(job_title)
company = None
if soup.find('a', class_="nome-empresa"):
company = soup.find('a', class_="nome-empresa").text
company = clean_text(company)
job_description = soup.find('div',
class_='descricao-vaga').text
job_description = clean_text(job_description)
job_description = re.sub("Descricao da vaga ", "",
job_description)
job_description = re.sub("Vaga Patrocinada ", "",
job_description)
data = {
'date': date,
'job_title': job_title,
'location_city': city,
'location_state': state,
'job_description': job_description,
'job_platform': job_platform,
'job_platform_id': job_id
}
if company:
data['company'] = company
save_json_file(save_path, data)
# Log errors to a text file
except Exception as e:
target = open(ERROR_LOG_FILE, "a")
error_details = ""
if job_id:
error_details += "%s: " % job_id
print(job_id)
print(e)
error_details += str(e)
target.write("%s\n" % error_details)
def clean_name(s):
s_split = str(s).split('"')
if len(s_split) >= 3:
return clean_text(s_split[0] + s_split[-1])
return clean_text(s)
def extract_details(s):
split_method = str(s).split('(')
if len(split_method) >= 2:
clean_method = clean_text(' '.join([i for i in split_method[1:]]))
return clean_method
def extract_method(s):
split_method = str(s).split('(')
if len(split_method) >= 2:
clean_method = clean_text(split_method[0])
return clean_method
def main():
check_or_create_save_folder(SAVE_FILE_PATH)
# List all scraped files related to Ceviu
for html_file_path in os.listdir(JOB_FOLDER):
# Parse only HTML files
if html_file_path.endswith(".html"):
job_id = re.findall(r'\d+', html_file_path)[0]
json_file_name = "%s-%s.json" % (job_platform, job_id)
save_path = "%s/%s" % (SAVE_FILE_PATH, json_file_name)
# Check if file hasn't already been parsed
if not os.path.isfile(save_path):
try:
htmlfile = open(JOB_FOLDER+"/"+html_file_path)
soup = BeautifulSoup(htmlfile.read())
job_info = soup.find("p", class_="codigo-data-vaga").text.strip()
date = job_info.rsplit('Data: ', 1)[1].strip()
location = soup.find('div', class_="localizacao-vaga").text
location = clean_text(location)
city = re.search('Localizacao (.*)/', location, re.IGNORECASE).group(1)
state = re.search('/(.*)', location, re.IGNORECASE).group(1)
job_title = soup.find("h2", class_="titulo-vaga").text
job_title = clean_text(job_title)
company = None
if soup.find('a', class_="nome-empresa"):
company = soup.find('a', class_="nome-empresa").text
company = clean_text(company)
job_description = soup.find('div', class_='descricao-vaga').text
job_description = clean_text(job_description)
job_description = re.sub("Descricao da vaga ", "", job_description)
job_description = re.sub("Vaga Patrocinada ", "", job_description)
data = {
'date': date,
'job_title': job_title,
'location_city': city,
'location_state': state,
'job_description': job_description,
'job_platform': job_platform,
'job_platform_id': job_id
}
if company:
data['company'] = company
save_json_file(save_path, data)
# Log errors to a text file
except Exception as e:
target = open(ERROR_LOG_FILE, "a")
error_details = ""
if job_id:
error_details += "%s: " % job_id
print(job_id)
print(e)
error_details += str(e)
target.write("%s\n" % error_details)
def main():
check_or_create_save_folder(SAVE_FILE_PATH)
# List all scraped files related to NetCarreiras
for html_file_path in os.listdir(JOB_FOLDER):
# Parse only HTML files
if html_file_path.endswith(".html"):
job_id = re.findall(r'\d+', html_file_path)[0]
json_file_name = "%s-%s.json" % (job_platform, job_id)
save_path = "%s/%s" % (SAVE_FILE_PATH, json_file_name)
# Check if file hasn't already been parsed
if not os.path.isfile(save_path):
try:
htmlfile = open("%s/%s" % (JOB_FOLDER, html_file_path))
soup = BeautifulSoup(htmlfile.read())
date = soup.find('div', class_="profile").contents[3].text
date = clean_text(date)
location = soup.find('div', {'id': "location"}).text
location = clean_text(location)
city = re.search('(.*) -', location, re.IGNORECASE).group(1)
state = re.search('- (.*) \(', location, re.IGNORECASE).group(1)
job_title = soup.find("h1").text
job_title = clean_text(job_title)
company = None
if soup.find_all('a', href=re.compile('^vagas-na-(.*)')):
company = soup.find_all('a', href=re.compile('^vagas-na-(.*)'))[0].text
company = clean_text(company)
job_description = soup.find('article').contents[11].text
job_description = clean_text(job_description)
data = {
'date': date,
'job_title': job_title,
'location_city': city,
'location_state': state,
'job_description': job_description,
'job_platform': job_platform,
'job_platform_id': job_id
}
if company:
data['company'] = company
save_json_file(save_path, data)
# Log errors to a text file
except Exception as e:
target = open(ERROR_LOG_FILE, "a")
error_details = ""
if job_id:
error_details += "%s: " % job_id
print(job_id)
print(e)
error_details += str(e)
target.write("%s\n" % error_details)
def fit(self, documents):
documents = [tokenize(d) for d in documents]
documents = [d[:self.max_page_size] for d in documents]
documents = [' '.join(d) for d in documents]
if self.encoding_type in ['tfidf', 'count', 'binary']:
if self.encoding_type == 'tfidf':
self.vectorizer = CountVectorizer(
ngram_range=(self.min_n_gram, self.max_n_gram),
max_features=self.max_vocab_size,
binary=False,
max_df=self.max_df,
analyzer=self.tokenizer_level)
self.vectorizer.fit(documents)
if self.encoding_type == 'count':
self.vectorizer = CountVectorizer(
ngram_range=(self.min_n_gram, self.max_n_gram),
max_features=self.max_vocab_size,
binary=False,
max_df=self.max_df,
analyzer=self.tokenizer_level)
self.vectorizer.fit(documents)
if self.encoding_type == 'binary':
self.vectorizer = CountVectorizer(
ngram_range=(self.min_n_gram, self.max_n_gram),
max_features=self.max_vocab_size,
binary=False,
max_df=self.max_df,
analyzer=self.tokenizer_level)
self.vectorizer.fit(documents)
with open(self.save_file_loc, 'wb') as f:
pickle.dump(self.vectorizer, f)
if self.encoding_type == 'lda':
documents_tokenized = [tokenize(i) for i in documents]
self.common_dictionary = Dictionary(documents_tokenized)
common_corpus = [
self.common_dictionary.doc2bow(text)
for text in documents_tokenized
]
self.vectorizer = ldamodel.LdaModel(common_corpus,
id2word=self.common_dictionary,
num_topics=self.num_of_topics,
passes=self.vectorizer_epochs)
self.vectorizer.save(self.save_file_loc)
if self.encoding_type == 'doc2vec':
tagged_documents = [
TaggedDocument(tokenize(doc), [i])
for i, doc in enumerate(documents)
]
self.vectorizer = Doc2Vec(tagged_documents,
vector_size=self.encoding_size,
window=2,
min_count=1,
workers=4,
epochs=self.vectorizer_epochs,
max_vocab_size=100000)
self.vectorizer.delete_temporary_training_data(
keep_doctags_vectors=True, keep_inference=True)
self.vectorizer.save(self.save_file_loc)
if self.encoding_type == 'fasttext':
with open(self.fasttext_training_file_location, 'w') as f:
for i in documents:
f.write(clean_text(i) + '\n')
self.vectorizer = fasttext.train_unsupervised(
self.fasttext_training_file_location,
model=self.fasttext_algorithm,
dim=self.encoding_size)
self.vectorizer.save_model(self.save_file_loc)
