class ParticipantMatcher:
def __init__(self, database=None):
daiquiri.setup(level=logging.INFO)
self.logger = daiquiri.getLogger(__name__)
self.database = Database() if not database else database
self.name_resolver = NameResolver(database=self.database)
self.participants = Participants(database=self.database)
self.avg_event_age = {}
def run(self, limit=1000, iters=None):
"""Adds attendees that have not been matched up to a participant
to the look up table. If there are no matches for an attendee, a
new participant id is created."""
n = 0
while True:
missing_attendees = self._get_missing_attendees(limit=limit)
count = len(missing_attendees)
if (iters and n >= iters) or count == 0:
msg = 'Participant matcher has finished processing.'
self.logger.info(msg)
break
msg = 'Iteration {} | Processing {} missing attendees'
self.logger.info(msg.format(n + 1, count))
for i in range(count):
attendee = dict(missing_attendees.loc[i])
if attendee['first_name'] and attendee['last_name']:
self._process_attendee(attendee)
n += 1
def estimate_unknown_ages(self):
"""Finds estimated ages for any participant whose age is
unknown or who has an estimated age."""
unknowns = self._get_unknown_ages()
for i, unknown in enumerate(unknowns):
if i % 1000 == 0:
msg = 'Estimated age for {} participants.'.format(i)
self.logger.info(msg)
estimated_age = self._estimate_participant_age(unknown['id'])
if not estimated_age:
continue
now = datetime.datetime.now()
estimated_birth_date = now - datetime.timedelta(
estimated_age * 365)
estimated_birth_date = "'{}'".format(
str(estimated_birth_date)[:10])
self.database.update_column(table='participant_match',
item_id=unknown['id'],
column='birth_date',
value=estimated_birth_date)
self.database.update_column(table='participant_match',
item_id=unknown['id'],
column='is_birth_date_estimated',
value=True)
def _process_attendee(self, attendee):
"""Adds a link to attendee_to_participant if the attendee has a match.
Otherwise a new participant id is created for the attendee."""
# Cache the average age for the event so it
# doesn't have to pull it from the database each time
event_id = attendee['event_id']
if event_id not in self.avg_event_age:
age = self._get_avg_event_age(event_id)
self.avg_event_age[event_id] = age
else:
age = self.avg_event_age[event_id]
match = self.name_resolver.find_best_match(
first_name=attendee['first_name'],
last_name=attendee['last_name'],
email=attendee['email'],
age=age)
if match:
participant_id = match['id']
else:
# If there is no participant match, a new participant
# is created and added to the database
participant_id = uuid.uuid4().hex
participant = {
'id': participant_id,
'first_name': attendee['first_name'],
'last_name': attendee['last_name'],
'email': attendee['email']
}
self.database.load_item(participant, 'participant_match')
# Insert the attendee to participant match to the database
item = {'id': attendee['id'], 'participant_id': participant_id}
self.database.load_item(item, 'attendee_to_participant')
def _get_missing_attendees(self, limit=1000):
"""Pulls a list of attendees that have not yet been matched
to a participant."""
sql = """
SELECT id, event_id, first_name, last_name, email
FROM {schema}.attendees
WHERE id NOT IN (SELECT id FROM {schema}.attendee_to_participant)
AND first_name IS NOT NULL
AND last_name IS NOT NULL
ORDER BY event_id ASC
LIMIT {limit}
""".format(schema=self.database.schema, limit=limit)
df = pd.read_sql(sql, self.database.connection)
return df
def _get_avg_event_age(self, event_id):
"""Computes the average age of the attendees of an event."""
if not isinstance(event_id, list):
event_id = [str(event_id)]
else:
event_id = [str(x) for x in event_id]
sql = """
SELECT PERCENTILE_CONT(0.5)
WITHIN GROUP
(ORDER BY avg_age) as avg_age
FROM(
SELECT event_id,
PERCENTILE_CONT(0.5)
WITHIN GROUP
(ORDER BY z.age) as avg_age
FROM {schema}.attendees x
INNER JOIN {schema}.attendee_to_participant y
ON x.id = y.id
INNER JOIN {schema}.participants z
ON y.participant_id = z.participant_id
WHERE event_id = ANY(%(event_id)s)
AND z.age IS NOT NULL
GROUP BY event_id
) a
""".format(schema=self.database.schema, event_id=event_id)
df = self.database.fetch_df(sql, params={'event_id': event_id})
avg_age = None
if len(df) > 0:
avg_age = df.loc[0]['avg_age']
return avg_age
def _estimate_participant_age(self, participant_id):
"""Estimates a participants age based on who they've
attended events with."""
events = self.participants.get_participant_events(participant_id)
if len(events) == 0:
return None
else:
event_id = [x['event_id'] for x in events]
age = self._get_avg_event_age(event_id)
return age
def _get_unknown_ages(self):
"""Pulls all participant ids that have a null date or
and estimated date."""
sql = """
SELECT id
FROM {schema}.participant_match
WHERE is_birth_date_estimated = TRUE
OR birth_date IS NULL
""".format(schema=self.database.schema)
df = pd.read_sql(sql, self.database.connection)
results = self.database.to_json(df)
return results
class FakeNews:
"""A class for generating and uploading fake Shir Connect data."""
def __init__(self, database=None):
daiquiri.setup(level=logging.INFO)
self.logger = daiquiri.getLogger(__name__)
self.database = Database() if not database else database
self.faker = Faker()
self.postal_codes = None
def build_fake_data(self):
""" Generates fake data for the database. """
self.fake_names()
self.fake_events()
self.fake_venues()
self.database.refresh_views()
def fake_events(self):
"""Generates fake events for the events table."""
event_ids = self._get_events()
prefixes = [x for x in conf.EVENT_GROUPS]
prefixes.append(None)
for i, event_id in enumerate(event_ids):
if i % 1000 == 0:
msg = 'Generated fake names for {} events.'.format(i)
self.logger.info(msg)
fake_name = self._random_name(max_size=5)
fake_name = fake_name.replace("'", '')
np.random.shuffle(prefixes)
prefix = prefixes[0]
if prefix:
fake_name = prefix + ': ' + fake_name
fake_descr = self._random_paragraph(max_size=15)
self.database.update_column(table='events',
item_id=event_id,
column='fake_name',
value="'{}'".format(fake_name))
self.database.update_column(table='events',
item_id=event_id,
column='fake_description',
value="'{}'".format(fake_descr))
def fake_venues(self):
"""Generates fake venues for the venues table."""
venue_ids = self._get_venues()
for i, venue_id in enumerate(venue_ids):
if i % 1000 == 0:
msg = 'Generated fake names for {} venues.'.format(i)
self.logger.info(msg)
fake_name = self._random_name(max_size=2)
self.database.update_column(table='venues',
item_id=venue_id,
column='fake_name',
value="'{}'".format(fake_name))
def fake_names(self):
"""Generates fake names for the attendees, members, and orders table. """
participant_ids = self._get_participants()
for i, participant_id in enumerate(participant_ids):
if i % 1000 == 0:
msg = 'Generated fake names for {} participants.'.format(i)
self.logger.info(msg)
fake_first_name = "'{}'".format(self.faker.first_name())
fake_last_name = "'{}'".format(self.faker.last_name())
fake_email = "'{}'".format(self.faker.email())
self.database.update_column(table='participant_match',
item_id=participant_id,
column='fake_first_name',
value=fake_first_name)
self.database.update_column(table='participant_match',
item_id=participant_id,
column='fake_nickname',
value=fake_first_name)
self.database.update_column(table='participant_match',
item_id=participant_id,
column='fake_last_name',
value=fake_last_name)
self.database.update_column(table='participant_match',
item_id=participant_id,
column='fake_email',
value=fake_email)
def fake_birthday(self, min_age=18, max_age=85):
"""Generates a fake birthday that can be used in the demo version.
Parameters
----------
min_age : int
the minimum age of the random birthday
max_age : int
the maximum age of the random birthday
Returns
-------
birthday : datetime.datetime
"""
start_date = "-{}y".format(max_age)
end_date = "-{}y".format(min_age)
birthday = self.faker.date_between(start_date=start_date,
end_date=end_date)
return birthday
def fake_membership_date(self, max_age=30):
"""Generates a fake membership date for the database
Parameters
----------
max_age : int
the maximum age of the random birthday
Returns
-------
birthday : datetime.datetime
"""
start_date = "-{}y".format(max_age)
membership_date = self.faker.date_between(start_date=start_date,
end_date="today")
return membership_date #
def fake_members(self, num_members):
"""Generates fake members and loads them into the databse
Parameters
----------
num_members : int
the number of fake members to create
Returns
-------
Loads the fake members into the demo database
"""
for i in range(num_members):
member = self._fake_member(i)
self.database.load_item(member, 'members')
def _fake_member(self, identifier):
"""Generates a fake member with all of the attributes required in the
member table"""
member = {}
member['id'] = identifier
member['household_id'] = identifier
member['member_family'] = 'Y'
# Generate a male or female name with 50% probability
female = np.random.random() < .5
if female:
first_name = self.faker.first_name_female()
gender = 'F'
else:
first_name = self.faker.first_name_male()
gender = 'M'
member['first_name'] = first_name
member['nickname'] = first_name
member['last_name'] = self.faker.last_name()
member['gender'] = gender
member['email'] = self.faker.email()
member['postal_code'] = self._postal_code()
member['birth_date'] = self.fake_birthday()
membership_date = self.fake_membership_date()
member['membership_date'] = membership_date
# Make the member Jewish with 90% probability
jewish = np.random.random() < .9
religion = 'Jewish' if jewish else 'Not Jewish'
member['member_religion'] = religion
member['resignation_date'] = self._resignation_date(membership_date)
reasons = ['Too far', 'Finished bar mitzvah', 'Moved', 'Lost interest']
if member['resignation_date']:
resignation_reason = np.random.choice(reasons)
else:
resignation_reason = None
member['resignation_reason'] = resignation_reason
active = np.random.random() < .9
member['active_member'] = active and not member['resignation_date']
membership_types = ['MEMFAM', 'MEMIND', 'STAFF']
member['member_type'] = np.random.choice(membership_types,
p=[.75, .2, .05])
return member
def _resignation_date(self, membership_date, prob=.1):
"""Generates a fake resignation date with probability prob."""
# Make the member resign with 10% probability
resigned = np.random.random() < .1
resignation_date = None
if resigned:
today = datetime.datetime.now()
mem_dt = datetime.datetime.combine(membership_date,
datetime.datetime.min.time())
max_age = int(np.floor((today - mem_dt).days / 365))
if max_age > 0:
resignation_date = self.fake_membership_date(max_age)
return resignation_date
def _get_participants(self):
"""Pulls a list of participants who need fake names."""
sql = """
SELECT id
FROM {schema}.participant_match
WHERE fake_first_name IS NULL
OR fake_last_name IS NULL
OR fake_nickname IS NULL
""".format(schema=self.database.schema)
df = pd.read_sql(sql, self.database.connection)
participant_ids = list(df['id'].unique())
return participant_ids
def _postal_code(self):
"""Generates a fake postal code, weighted by numerical.
distance from the default postal_code."""
if not isinstance(self.postal_codes, pd.DataFrame):
default = conf.DEFAULT_LOCATION['postal_code']
postal_codes = self.database.read_table('geometries', ['id'])
postal_codes['weights'] = (postal_codes['id'].astype(int) -
default)**2
postal_codes.sort_values('weights', ascending=True, inplace=True)
postal_codes.reset_index(drop=True, inplace=True)
postal_codes = postal_codes[:100]
postal_codes['weights'] = (postal_codes['weights'].max() -
postal_codes['weights'])
postal_codes['weights'] = (postal_codes['weights'] /
postal_codes['weights'].sum())
self.postal_codes = postal_codes
return np.random.choice(self.postal_codes['id'],
p=self.postal_codes['weights'])
def _get_events(self):
"""Pulls a list of events that need fake names."""
sql = """
SELECT id
FROM {schema}.events
WHERE fake_name IS NULL
""".format(schema=self.database.schema)
df = pd.read_sql(sql, self.database.connection)
event_ids = list(df['id'].unique())
return event_ids
def _get_venues(self):
"""Pulls a list of events that need fake names."""
sql = """
SELECT id
FROM {schema}.venues
WHERE fake_name IS NULL
""".format(schema=self.database.schema)
df = pd.read_sql(sql, self.database.connection)
venue_ids = list(df['id'].unique())
return venue_ids
def _random_name(self, max_size=2):
"""Generates a random name for events and venues."""
phrase = self.faker.catch_phrase().split()
short_phrase = ' '.join(phrase[:max_size])
letters = list(short_phrase)
random.shuffle(letters)
name = ''.join(letters)
return name.title()
def _random_paragraph(self, max_size=15):
"""Generates a random sentence for event descriptions."""
sentences = self.faker.sentences(max_size)
scrambled_sentences = []
for sentence in sentences:
sentence_list = list(sentence)
random.shuffle(sentence_list)
scrambled_sentence = ''.join(sentence_list).replace('.', '')
scrambled_sentences.append(scrambled_sentence.capitalize())
paragraph = '. '.join(scrambled_sentences)
return paragraph