def test_fuzzy_match_with_st_saint_discrepancy(
example_IRS_service_data,
example_IRS_search_object_with_spelled_out_saint, mock_mongo_client):
insert_services(example_IRS_service_data, mock_mongo_client.shelter,
'tmpIRS')
refresh_ngrams(mock_mongo_client.shelter, 'tmpIRS')
name = example_IRS_search_object_with_spelled_out_saint['name']
zip_code = example_IRS_search_object_with_spelled_out_saint['zip']
with pytest.raises(NotImplementedError):
locate_potential_duplicate(
name, zip_code, mock_mongo_client.shelter, 'tmpIRS'
) # Ensure this throws error, b/c if not, we can stop using the real connection below
def main(config, client, check_collection, dump_collection, dupe_collection):
scraped_update_date = scrape_updated_date()
try:
stored_update_date = client['data-sources'].find_one(
{"name": "irs_exempt_organizations"})['last_updated']
stored_update_date = datetime.strptime(str(stored_update_date),
'%Y-%m-%d %H:%M:%S').date()
if check_site_for_new_date(stored_update_date):
logging.info('No new update detected. Exiting script...')
return
except KeyError:
pass
logging.info('updating scraped update date in data-sources collection')
client['data_sources'].update_one(
{"name": "irs_exempt_organizations"},
{'$set': {
'last_updated': str(scraped_update_date)
}})
code_dict = config['NTEE_codes']
df = grab_data(config, code_dict)
logging.info('purging EIN duplicates')
if client[dump_collection].estimated_document_count() > 0:
df = purge_EIN_duplicates(df, client, dump_collection, dupe_collection)
if client[check_collection].estimated_document_count() == 0:
# No need to check for duplicates in an empty collection
insert_services(df.to_dict('records'), client, dump_collection)
else:
logging.info('refreshing ngrams')
refresh_ngrams(client, check_collection)
found_duplicates = []
logging.info('checking for duplicates in the services collection')
for i in tqdm(range(len(df))):
dc = locate_potential_duplicate(df.loc[i, 'name'], df.loc[i,
'zip'],
client, check_collection)
if dc is not False:
if check_similarity(df.loc[i, 'name'], dc):
found_duplicates.append(i)
duplicate_df = df.loc[found_duplicates].reset_index(drop=True)
logging.info(
f'inserting {duplicate_df.shape[0]} services dupes into the dupe collection'
)
if len(duplicate_df) > 0:
insert_services(duplicate_df.to_dict('records'), client,
dupe_collection)
df = df.drop(found_duplicates).reset_index(drop=True)
logging.info(f'final df shape: {df.shape}')
if len(df) > 0:
insert_services(df.to_dict('records'), client, dump_collection)
def main_scraper(self, client: MongoClient) -> None:
"""Base function for ingesting raw data, preparing it and depositing it in MongoDB
Args:
client (MongoClient): connection to the MongoDB instance
scraper_config (ScraperConfig): instance of the ScraperConfig class
"""
df = self.grab_data()
if client[self.dump_collection].estimated_document_count() > 0:
logging.info(
f'purging duplicates from existing {self.source} collection')
df = self.purge_collection_duplicates(df, client)
if client[self.check_collection].estimated_document_count() == 0:
# No need to check for duplicates in an empty collection
insert_services(df.to_dict('records'), client,
self.dump_collection)
else:
logging.info('refreshing ngrams')
refresh_ngrams(client, self.check_collection)
found_duplicates = []
logging.info('checking for duplicates in the services collection')
for i in tqdm(range(len(df))):
dc = locate_potential_duplicate(df.loc[i, 'name'],
df.loc[i, 'zip'], client,
self.check_collection)
if dc is not False:
if check_similarity(df.loc[i, 'name'], dc):
found_duplicates.append(i)
duplicate_df = df.loc[found_duplicates].reset_index(drop=True)
if len(duplicate_df) > 0:
logging.info(
f'inserting services dupes into the {self.source} dupe collection'
)
insert_services(duplicate_df.to_dict('records'), client,
self.dupe_collection)
df = df.drop(found_duplicates).reset_index(drop=True)
logging.info(f'final df shape: {df.shape}')
if len(df) > 0:
insert_services(df.to_dict('records'), client,
self.dump_collection)
logging.info(
'updating scraped update date in data-sources collection')
client['data_sources'].update_one(
{"name": self.data_source_collection_name}, {
'$set': {
'last_updated':
datetime.strftime(datetime.now(), '%m/%d/%Y')
}
})
def test_fuzzy_match(example_IRS_service_data,
example_IRS_search_object_with_spelled_out_saint,
mock_config_object):
client = get_mongo_client()
if 'pytest_fuzzy_test' in client.list_collection_names():
client.drop_collection('pytest_fuzzy_test')
client.create_collection('pytest_fuzzy_test')
insert_services(example_IRS_service_data, client, 'pytest_fuzzy_test')
refresh_ngrams(client, 'pytest_fuzzy_test')
name = example_IRS_search_object_with_spelled_out_saint['name']
zip_code = example_IRS_search_object_with_spelled_out_saint['zip']
dc = locate_potential_duplicate(name, zip_code, client,
'pytest_fuzzy_test')
client.drop_collection('pytest_fuzzy_test')
assert dc == 'ST FERIOLE ISLAND PARK'
def main(config, client, check_collection, dump_collection, dupe_collection):
scraped_update_date = scrape_updated_date()
try:
stored_update_date = retrieve_last_scraped_date(date)
if stored_update_date and scraped_update_date <= stored_update_date:
logger.info('No new update detected. Exiting script...')
return
except KeyError:
pass
logger.info('updating last scraped date in data-sources collection')
client['data-sources'].update_one({"name": "irs"}, {
'$set': {
'last_scraped':
datetime.now(timezone('UTC')).replace(microsecond=0).isoformat()
}
},
upsert=True)
code_dict = config['NTEE_codes']
df = grab_data(config, code_dict)
logger.info('purging EIN duplicates')
if client[dump_collection].estimated_document_count() > 0:
df = purge_EIN_duplicates(df, client, dump_collection, dupe_collection)
if client[check_collection].estimated_document_count() == 0:
# No need to check for duplicates in an empty collection
insert_services(df.to_dict('records'), client, dump_collection)
else:
logger.info('refreshing ngrams')
refresh_ngrams(client, check_collection)
found_duplicates = []
logger.info('checking for duplicates in the services collection')
for i in tqdm(range(len(df))):
dc = locate_potential_duplicate(df.loc[i, 'name'], df.loc[i,
'zip'],
client, check_collection)
if dc is not False:
if check_similarity(df.loc[i, 'name'], dc):
found_duplicates.append(i)
duplicate_df = df.loc[found_duplicates].reset_index(drop=True)
logger.info(
f'inserting {duplicate_df.shape[0]} services dupes into the dupe collection'
)
if len(duplicate_df) > 0:
insert_services(duplicate_df.to_dict('records'), client,
dupe_collection)
df = df.drop(found_duplicates).reset_index(drop=True)
logger.info(f'final df shape: {df.shape}')
if len(df) > 0:
insert_services(df.to_dict('records'), client, dump_collection)
def test_fuzzy_match(example_IRS_service_data,
example_IRS_search_object_with_spelled_out_saint,
mock_config_object):
client = MongoClient(
"mongodb+srv://" + os.environ.get('DBUSERNAME') + ":" +
os.environ.get('PW') +
"@shelter-rm3lc.azure.mongodb.net/shelter?retryWrites=true&w=majority"
)['shelter']
if 'pytest_fuzzy_test' in client.list_collection_names():
client.drop_collection('pytest_fuzzy_test')
client.create_collection('pytest_fuzzy_test')
insert_services(example_IRS_service_data, client, 'pytest_fuzzy_test')
refresh_ngrams(client, 'pytest_fuzzy_test')
name = example_IRS_search_object_with_spelled_out_saint['name']
zip_code = example_IRS_search_object_with_spelled_out_saint['zip']
dc = locate_potential_duplicate(name, zip_code, client,
'pytest_fuzzy_test')
client.drop_collection('pytest_fuzzy_test')
assert dc == 'ST FERIOLE ISLAND PARK'
def main_scraper(self, client: MongoClient) -> None:
"""Base function for ingesting raw data, preparing it and depositing it in MongoDB
Args:
client (MongoClient): connection to the MongoDB instance
scraper_config (ScraperConfig): instance of the ScraperConfig class
"""
if not self.is_new_data_available(client):
logger.info('No new data. Goodbye...')
return
df = self.grab_data()
if client[self.dump_collection].estimated_document_count() > 0:
logger.info(
f'purging duplicates from existing {self.source} collection')
df = self.purge_collection_duplicates(df, client)
if self.groupby_columns is not None:
df = self.aggregate_service_summary(df)
if client[self.check_collection].estimated_document_count() == 0:
# No need to check for duplicates in an empty collection
insert_services(df.to_dict('records'), client,
self.dump_collection)
else:
logger.info('refreshing ngrams')
refresh_ngrams(client, self.check_collection)
found_duplicates = []
logger.info('checking for duplicates in the services collection')
for i in tqdm(range(len(df))):
dc = locate_potential_duplicate(df.loc[i, 'name'],
df.loc[i, 'zip'], client,
self.check_collection)
if dc is not False:
if check_similarity(df.loc[i, 'name'], dc):
found_duplicates.append(i)
duplicate_df = df.loc[found_duplicates].reset_index(drop=True)
if len(duplicate_df) > 0:
logger.info(
f'inserting services dupes into the {self.source} dupe collection'
)
insert_services(duplicate_df.to_dict('records'), client,
self.dupe_collection)
df = df.drop(found_duplicates).reset_index(drop=True)
logger.info(f'final df shape: {df.shape}')
self.add_required_fields(df)
if len(df) > 0:
insert_services(df.to_dict('records'), client,
self.dump_collection)
logger.info(
'updating last scraped date in data-sources collection')
client['data-sources'].update_one(
{"name": self.data_source_collection_name}, {
'$set': {
'last_scraped':
datetime.now(timezone('UTC')).replace(
microsecond=0).isoformat()
}
},
upsert=True)