def test_processor(feature_size, chunksize):
"""Tests FileProcessor.
:param feature_size: int, size of feature vector.
:param chunksize: int, FileProcessor chunk size.
"""
sep = '\t'
n_rows = 50
feature = 3
with TemporaryDirectory() as dir:
input_path = os.path.join(dir, 'data.tsv')
output_path = os.path.join(dir, 'data_proc.tsv')
data, feature_values = generate_data(n_rows=n_rows,
feature=feature,
feature_size=feature_size,
seed=42)
data.to_csv(input_path, sep=sep, index=False)
reader_params = {
'chunksize': chunksize,
'sep': sep,
}
transformers = (
Standardizer,
MaxIndex,
MaxFeatureAbsMeanDiff,
)
processor = FileProcessor(transformers, reader_params=reader_params)
processor.train(input_path)
processor.process(input_path, output_path)
processed = pd.read_csv(output_path, sep=sep)
# check feature_{i}_stand_{index}
expected_stand = (feature_values -
feature_values.mean(axis=0)) / feature_values.std(axis=0,
ddof=1)
stand = processed.filter(regex=f'feature_{feature}_stand_[0-9]+')
assert np.allclose(expected_stand, stand)
assert np.allclose(stand.mean(axis=0), 0)
assert np.allclose(stand.std(axis=0, ddof=1), 1)
# check max_feature_{i}_index
expected_max = feature_values.max(axis=1)
max_index = processed[f'max_feature_{feature}_index'].values
max_mask = (max_index.reshape(
(-1, 1)) == np.arange(feature_values.shape[1]).reshape((1, -1)))
fact_max = feature_values[max_mask]
assert np.allclose(expected_max, fact_max)
# check max_feature_{i}_abs_mean_diff
expected_max_mean = np.broadcast_to(feature_values.mean(axis=0),
shape=max_mask.shape)[max_mask]
expected_abs_mean_diff = np.abs(expected_max - expected_max_mean)
abs_mean_diff = processed[f'max_feature_{feature}_abs_mean_diff']
assert np.allclose(expected_abs_mean_diff, abs_mean_diff)
def backup(config, database):
"""
Process directoreis to be backed up
"""
logger = logging.getLogger('mylog')
clean_removed(config, database)
if not consistency_check(config, database):
logger.warning('Consistency check detected problems!')
if not query_yes_no('Continue?'):
sys.exit()
dirs = config.get('Backup', 'to_backup').split()
logger.info('Directories to backup: ' + ','.join(dirs))
exclude_dirs = config.get('Backup', 'to_exclude').split()
logger.info('Directories to exclude: ' + ','.join(exclude_dirs))
file_proc = FileProcessor(config, database, encrypt_file)
#Count files to show progress later
total = 0
for directory in dirs:
for subdir, _, files in os.walk(directory):
if subdir in exclude_dirs:
continue
for single_file in files:
fpath = os.path.join(subdir, single_file)
total = total + 1
count = 0
for directory in dirs:
logger.debug('Processing directory' + directory)
for subdir, dirs, files in os.walk(directory):
if subdir in exclude_dirs:
logger.debug('Skipping directory' + subdir)
continue
for single_file in files:
fpath = os.path.join(subdir, single_file)
logger.debug('Processing file ' + fpath)
logger.info(str((count * 100) / total) + ' %')
file_proc.process(fpath)
count = count + 1
class SecondPassCrawler:
session = attr.ib()
navigator = attr.ib(init=False)
file_processor = attr.ib(init=False)
def __attrs_post_init__(self):
self.navigator = SeleniumNavigator(loading_strategy='none')
self.file_processor = FileProcessor()
def exit(self):
self.navigator.close_browser()
self.session.close()
def get_urls(self):
# results = self.session.query(Report).all()
# return (report.url for report in results)
return ['http://media.ethics.ga.gov/search/Campaign/Campaign_ReportOptions.aspx?NameID=16067&FilerID=C2012000744&CDRID=59991']
def add_scrapelog_to_db(self, url, content, dtime):
slog = ScrapeLog(scrape_date=dtime,
raw_data=content,
page_url=url)
try:
self.session.add(slog)
self.session.commit()
except Exception as e:
self.session.rollback()
logging.info(e)
def crawl_download_link(self):
parser = CSVLinkParser(self.navigator.page_source())
parsed_link = parser.parse()
if parsed_link is not None:
logging.info(f'Parsed link: {parsed_link}')
url = self.navigator.get_current_url()
self.navigator.click_link(parsed_link)
logging.info('Clicking download link for csv file.')
content, dtime = self.file_processor.process()
self.add_scrapelog_to_db(url, content, dtime)
self.file_processor.delete_csv()
def crawl_view_contributions_ids(self):
logging.info(f'Current page: {self.navigator.get_current_url()}')
parser = ContributionsViewParser(self.navigator.page_source())
parsed_link = parser.parse()
if parsed_link is not None:
logging.info(f'Parsed link: {parsed_link}')
self.navigator.click_link(parsed_link)
self.navigator.wait_for_csv_link()
self.crawl_download_link()
def crawl(self):
urls = self.get_urls()
for url in urls:
logging.info(f'Current url: {url}')
self.navigator.navigate(url)
self.navigator.wait_for_contributions_id()
self.crawl_view_contributions_ids()
header = True
for seed in tqdm(range(100), desc='generating data'):
data, _ = generate_data(
n_rows=100_000,
feature=feature,
feature_size=512,
seed=seed
)
data.to_csv(
input_path,
sep=sep,
index=False,
mode='a',
header=header
)
header = False
reader_params = {
'chunksize': 10_000,
'sep': sep,
}
transformers = (
Standardizer,
MaxIndex,
MaxFeatureAbsMeanDiff,
)
processor = FileProcessor(transformers, reader_params=reader_params)
processor.train(input_path)
processor.process(input_path, output_path)
class SecondPassCrawler:
session = attr.ib()
navigator = attr.ib(init=False)
file_processor = attr.ib(init=False)
letter = attr.ib()
def __attrs_post_init__(self):
logging.info('attrs post init called')
self.navigator = SeleniumNavigator(loading_strategy='none',
letter=self.letter)
self.file_processor = FileProcessor(letter=self.letter)
def exit(self):
self.navigator.close_browser()
self.session.close()
def get_urls(self):
_ids = self.session.query(Candidate).filter(
Candidate.Lastname.ilike('zorn')).all()
#ids_ = \
#self.session.query(Candidate).filter(Lastname.like("%z%")).all()
reports = []
for _id in _ids:
results = \
self.session.query(Report).filter_by(CandidateId=_id.CandidateId).all()
logging.info(results)
for result in results:
reports.append((result.CandidateId, result.Url))
return reports
#return ['http://media.ethics.ga.gov/search/Campaign/Campaign_ReportOptions.aspx?NameID=16067&FilerID=C2012000744&CDRID=59991']
def add_scrapelog_to_db(self, _id, url, content, dtime):
slog = ScrapeLog(CandidateId=_id,
ScrapeDate=dtime,
RawData=content,
PageURL=url)
try:
self.session.add(slog)
self.session.commit()
except Exception as e:
self.session.rollback()
logging.info(e)
def crawl_download_link(self, _id):
parser = CSVLinkParser(self.navigator.page_source())
parsed_link = parser.parse()
if parsed_link is not None:
logging.info(f'Parsed link: {parsed_link}')
url = self.navigator.get_current_url()
self.navigator.click_link(parsed_link)
logging.info('Clicking download link for csv file.')
content, dtime = self.file_processor.process()
logging.info('Adding scrapelog to database')
self.add_scrapelog_to_db(_id, url, content, dtime)
self.file_processor.delete_csv()
def crawl_view_contributions_ids(self, _id):
logging.info(f'Current page: {self.navigator.get_current_url()}')
parser = ContributionsViewParser(self.navigator.page_source())
parsed_link = parser.parse()
if parsed_link is not None:
logging.info(f'Parsed link: {parsed_link}')
self.navigator.click_link(parsed_link)
self.navigator.wait_for_csv_link()
self.crawl_download_link(_id)
def crawl(self):
for _id, url in self.get_urls():
logging.info(f'Current url: {url}')
self.navigator.navigate(url)
self.navigator.wait_for_contributions_id()
self.crawl_view_contributions_ids(_id)
parser.add_argument(
'-o', '--output',
default=os.path.join(DATA_DIR, OUTPUT_FILE),
help='Path to file, where processed data will be stored.',
)
parser.add_argument(
'-c', '--chunk_size',
default=128,
help='Rows to process at a time.',
)
args = parser.parse_args()
if __name__ == '__main__':
train_path = args.train
test_path = args.input
output_path = args.output
reader_params = {
'chunksize': args.chunk_size,
'sep': SEPARATOR,
}
transformers = (
Standardizer,
MaxIndex,
MaxFeatureAbsMeanDiff,
)
processor = FileProcessor(transformers, reader_params=reader_params)
processor.train(train_path)
processor.process(test_path, output_path)
