def test_update_old_solicitations(self):
conn_string = get_db_url()
dal = DataAccessLayer(conn_string)
dal.connect()
with session_scope(dal) as session:
sam_utils.update_old_solicitations(session,
age_cutoff=365,
max_tests=5)
def setUp(self):
self.data = [mock_data_for_db.copy()]
self.dal = DataAccessLayer(conn_string=get_db_url())
self.dal.create_test_postgres_db()
self.dal.connect()
with session_scope(self.dal) as session:
insert_notice_types(session)
self.maxDiff = None
import logging
from utils import get_opps
from utils.predict import Predict
from utils.db.db_utils import get_db_url, session_scope, DataAccessLayer, insert_data_into_solicitations_table, insert_notice_types
from utils.json_log_formatter import CustomJsonFormatter, configureLogger
from utils.sam_utils import update_old_solicitations, opportunity_filter_function, set_psc_code_download_list
import sys
import datetime
import os
logger = logging.getLogger()
configureLogger(logger, stdout_level=logging.INFO)
conn_string = get_db_url()
dal = DataAccessLayer(conn_string)
dal.connect()
def main(limit=None, updateOld=True, opportunity_filter_function=None, target_sol_types="o,k", skip_attachments=False, from_date = 'yesterday', to_date='yesterday'):
try:
if limit:
logger.error("Artifical limit of {} placed on the number of opportunities processed. Should not happen in production.".format(limit))
if not updateOld:
logger.error("Set to NOT update old solicitations. Should not happen in production.".format(limit))
with session_scope(dal) as session:
# make sure that the notice types are configured and committed before going further
insert_notice_types(session)
def setUp(self):
conn_string = get_db_url()
self.dal = DataAccessLayer(conn_string)
self.dal.create_test_postgres_db()
self.dal.connect()
self.main = main
def setUp(self):
conn_string = get_db_url()
self.predicted_nightly_data = {
'AMDCSS': [{
'date':
'0506',
'year':
'18',
'agency':
'department of justice',
'office':
'federal bureau of investigation',
'location':
'procurement section',
'zip':
'20535',
'classcod':
'70',
'naics':
'511210',
'offadd':
'935 pennsylvania avenue, n.w. washington dc 20535',
'subject':
'enterprise business process management software tool',
'solnbr':
'rfp-e-bpm-djf-18-0800-pr-0000828',
'ntype':
'combine',
'contact':
'clark kent, contracting officer, phone 5555555555, email [email protected]',
'desc':
' link to document',
'url':
'url',
'setaside':
'n/a',
'popcountry':
'us',
'popzip':
'20535',
'popaddress':
'935 pennsylvania ave. n.w. washington, dc ',
'attachments': [{
'filename': 'test.txt',
'machine_readable': True,
'text': 'test_text_0',
'url': 'test_url_0',
'prediction': 1,
'decision_boundary': 0,
'validation': None,
'trained': False
}, {
'filename': 'test.txt',
'machine_readable': True,
'text': 'test_text_1',
'url': 'test_url_1',
'prediction': 1,
'decision_boundary': 0,
'validation': None,
'trained': False
}, {
'filename': 'test.txt',
'machine_readable': True,
'text': 'test_text_2',
'url': 'test_url_2',
'prediction': 1,
'decision_boundary': 0,
'validation': None,
'trained': False
}, {
'filename': 'test.txt',
'machine_readable': True,
'text': 'test_text_3',
'url': 'test_url_3',
'prediction': 1,
'decision_boundary': 0,
'validation': None,
'trained': False
}, {
'filename': 'test.txt',
'machine_readable': True,
'text': 'test_text_4',
'url': 'test_url_4',
'prediction': 1,
'decision_boundary': 0,
'validation': None,
'trained': False
}, {
'filename': 'test.txt',
'machine_readable': True,
'text': 'test_text_5',
'url': 'test_url_5',
'prediction': 1,
'decision_boundary': 0,
'validation': None,
'trained': False
}, {
'filename': 'test.txt',
'machine_readable': True,
'text': 'test_text_6',
'url': 'test_url_6',
'prediction': 1,
'decision_boundary': 0,
'validation': None,
'trained': False
}],
'compliant':
0
}],
'MOD': [],
'COMBINE': [{
'date': '0506',
'year': '18',
'agency': 'defense logistics agency',
'office': 'dla acquisition locations',
'location': 'dla aviation - bsm',
'zip': '23297',
'classcod': '66',
'naics': '334511',
'offadd': '334511',
'subject': 'subject',
'solnbr': 'spe4a618t934n',
'respdate': '051418',
'archdate': '06132018',
'contact': '*****@*****.**',
'desc': 'test123',
'url': 'test_url',
'setaside': 'n/a ',
'attachments': [],
'compliant': 0
}],
'PRESOL': []
}
self.predicted_nightly_data_day_two = {
'AMDCSS': [{
'date':
'0506',
'year':
'17',
'agency':
'defense logistics agency',
'office':
'dla acquisition locations',
'location':
'dla aviation - bsm',
'zip':
'23297',
'classcod':
'66',
'naics':
'334511',
'offadd':
'334511',
'subject':
'subject',
'solnbr':
'spe4a618t934n',
'respdate':
'051418',
'archdate':
'06132018',
'contact':
'*****@*****.**',
'desc':
'test123',
'url':
'test_url',
'setaside':
'n/a ',
'attachments': [{
'filename': 'test.txt',
'machine_readable': True,
'text': 'test_text_0',
'url': 'test_url_0',
'prediction': 1,
'decision_boundary': 0,
'validation': None,
'trained': False
}],
'compliant':
0
}]
}
self.dal = DataAccessLayer(conn_string=conn_string)
self.dal.create_test_postgres_db()
self.dal.connect()
self.maxDiff = None
def setUp(self):
self.dal = DataAccessLayer(conn_string=get_db_url())
self.dal.connect()
with session_scope(self.dal) as session:
insert_notice_types(session)