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