def get_dates_location(self, locationid):
min_max_date_list = []
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = """
select max(date),min(date) from trends where trend in
(select t1.trend as trend from
(select count(*) as c,trend from trends where
locationid = %s group by trend)as t1 order by c desc limit 15)
and locationid = %s
"""
cursor.execute(query, (locationid, locationid))
min_date_column = 1
max_date_column = 0
for row in cursor:
min_max_date_dict = {}
min_max_date_dict["min_date"] = str(row[min_date_column])
min_max_date_dict["max_date"] = str(row[max_date_column])
min_max_date_list.append(min_max_date_dict)
except Exception:
traceback.format_exc()
return min_max_date_list
def get_dates_location(self,locationid):
min_max_date_list = []
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = """
select max(date),min(date) from trends where trend in
(select t1.trend as trend from
(select count(*) as c,trend from trends where
locationid = %s group by trend)as t1 order by c desc limit 15)
and locationid = %s
"""
cursor.execute(query,(locationid,locationid))
min_date_column = 1
max_date_column = 0
for row in cursor:
min_max_date_dict = {}
min_max_date_dict["min_date"] = str(row[min_date_column])
min_max_date_dict["max_date"] = str(row[max_date_column])
min_max_date_list.append(min_max_date_dict)
except Exception:
traceback.format_exc()
return min_max_date_list
def build(self):
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = 'select id,entities,trend from organized_tweets'
cursor.execute(query)
id_column = 0
entities_column = 1
trend_column = 2
with open('copy_from.txt', 'w') as f:
for row in cursor:
tweet_id = row[id_column]
trend = row[trend_column]
hashtag_array = row[entities_column]
json_array = json.loads(hashtag_array)
hashtag_list = [hashtag["text"] for hashtag in json_array]
hashtag_list_unique = list(set(hashtag_list))
print 'Writing data to table for the tweet_id ' + tweet_id
for hashtag in hashtag_list_unique:
f.write(tweet_id + '\t' + hashtag.encode('utf-8') + '\t' +
trend + '\n')
with open('copy_from.txt') as f:
cursor.copy_from(f, 'id_entity', columns=('id', 'entity', 'trend'))
conn.commit()
os.remove('copy_from.txt')
def get_trends(self, location_id, start_date, end_date):
trends_list = []
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = """
select c,trend from
(select count(*) as c,trend from trends where
locationid = %s and date between %s and %s
and id in(select trendid from tweets)
group by trend)
as t1 order by c desc limit 15
"""
cursor.execute(query, (location_id, start_date, end_date))
trend_column = 1
count_column = 0
for row in cursor:
trend_count = {}
trend_count["trend"] = row[trend_column]
trend_count["count"] = row[count_column]
trends_list.append(trend_count)
except Exception as e:
print e
return trends_list
def get_data(self,locationid):
entity_trend_dict = {}
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = """select t1.entity,t2.trend from
(select id,entity from id_entity ) as t1
inner join
(select id,trend from organized_tweets where trend in
(select trend from
(select count(*) as c,trend from
trends where locationid = %s group by trend)as t_in order
by c desc limit 15))as t2
on
t1.id = t2.id"""
cursor.execute(query,(locationid,))
entity_column = 0
trend_column = 1
for row in cursor:
id = row[trend_column]
if id in entity_id_dict.keys():
entity_list = entity_id_dict[id]
entity_list.append(row[entity_column])
entity_id_dict[id] = entity_list
else:
entity_id_dict[id] = [row[entity_column]]
except Exception:
print traceback.format_exc()
return entity_trend_dict
def build(self):
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = 'select id,entities,trend from organized_tweets'
cursor.execute(query)
id_column = 0
entities_column = 1
trend_column = 2
with open('copy_from.txt','w') as f:
for row in cursor:
tweet_id = row[id_column]
trend = row[trend_column]
hashtag_array = row[entities_column]
json_array = json.loads(hashtag_array)
hashtag_list = [hashtag["text"] for hashtag in json_array]
hashtag_list_unique = list(set(hashtag_list))
print 'Writing data to table for the tweet_id ' +tweet_id
for hashtag in hashtag_list_unique:
f.write(tweet_id + '\t' + hashtag.encode('utf-8') + '\t' + trend + '\n')
with open('copy_from.txt') as f:
cursor.copy_from(f, 'id_entity', columns=('id', 'entity','trend'))
conn.commit()
os.remove('copy_from.txt')
def get_total_documents(self):
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = 'select count(distinct(id)) from "IdEntity" '
cursor.execute(query)
count_of_distinct_id_column = 0
total_documents_count = 0
for row in cursor:
total_documents_count = row[count_of_distinct_id_column]
return total_documents_count
def get_sentiments(self): conn = PostgresConnector().get_connection() cursor = conn.cursor() query = """ select text from organized_tweets limit 10 """ cursor.execute(query) for row in cursor: text = row[0] blob = TextBlob(text,analyzer=NaiveBayesAnalyzer()) print blob.sentiment
def get_total_documents(self): conn = PostgresConnector().get_connection() cursor = conn.cursor() query = 'select count(distinct(id)) from "IdEntity" ' cursor.execute(query) count_of_distinct_id_column = 0 total_documents_count = 0 for row in cursor: total_documents_count = row[count_of_distinct_id_column] return total_documents_count
def get_locations(self):
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = 'SELECT id,city from location'
cursor.execute(query)
id_column = 0
city_column = 1
locations_list = []
for row in cursor:
id_location = {}
id_location["geoid"] = row[id_column]
id_location["city"] = row[city_column]
locations_list.append(id_location)
return locations_list
def build_tf(self):
# using group by first get tf score for each entity
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = 'select count(id),entity from "IdEntity" group by entity'
cursor.execute(query)
count_of_id_column = 0
entities_column = 1
entity_id_dict = {}
for row in cursor:
count_of_id = row[count_of_id_column]
entity = row[entities_column]
entity_id_dict[entity] = count_of_id
return entity_id_dict
def get_locations(self):
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = 'SELECT id,city from location';
cursor.execute(query)
id_column = 0
city_column = 1
locations_list = []
for row in cursor:
id_location = {}
id_location["geoid"] = row[id_column]
id_location["city"] = row[city_column]
locations_list.append(id_location)
return locations_list
def build_tf(self):
# using group by first get tf score for each entity
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = 'select count(id),entity from "IdEntity" group by entity'
cursor.execute(query)
count_of_id_column = 0
entities_column = 1
entity_id_dict = {}
for row in cursor:
count_of_id = row[count_of_id_column]
entity = row[entities_column]
entity_id_dict[entity] = count_of_id
return entity_id_dict
def train_model():
np.random.seed(123)
with open(os.path.join("..", "query_pull_1000v3.pkl"), 'rb') as f:
query_pull = pickle.load(f)
connector = PostgresConnector()
env = DatabaseIndexesEnv(n=COLUMNS_AMOUNT,
table_name=table_name,
query_pull=query_pull,
batch_size=BATCH_SIZE,
connector=connector,
k=3,
max_episodes=1000)
# Get the environment and extract the number of actions.
env.seed(123)
# Next, we build a very simple model.
model = build_model()
print(model.summary())
# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
# even the metrics!
dqn = initialize_agent(model)
# Okay, now it's time to learn something! We visualize the training here for show, but this
# slows down training quite a lot. You can always safely abort the training prematurely using
# Ctrl + C.
dqn.fit(env, nb_steps=50000, visualize=False, verbose=0, callbacks=[CustomEpisodeLogger()])
# After training is done, we save the final weights.
dqn.save_weights('dqn_{}_weights_6_4_2_1_2000_episodes_estimated.h5f'.format(ENV_NAME), overwrite=True)
# Finally, evaluate our algorithm for 5 episodes.
dqn.test(env, nb_episodes=5, visualize=False)
def __init__(self,
prefix="entity",
window_size=2,
entities_only=True,
port=5436,
log_file=os.path.join(os.path.dirname(__file__), "logs/SchemaCreator.log"),
log_level=logging.INFO,
log_verbose=True
):
"""
Set up.
:param prefix: (str) Prefix to the table names.
:param port: (int) Used to connect to the Postgres tables.
:param log_file: (os.path) Path to the file containing the logs.
:param log_level: (logging.LEVEL) Specifies the level to be logged.
:param log_verbose: (boolean) Specifies whether or not to look to stdout as well.
"""
self.logger = set_up_logger(__name__, log_file, log_level, log_verbose)
self.window_size = window_size
self.prefix = prefix + "_" + str(self.window_size)
self.entities_only = entities_only
self.names = self.get_names(self.prefix)
self.port = port
self.pc = PostgresConnector(port=port)
self.logger.info("Successfully registered SchemaGenerator.")
def create_table(port):
pc = PostgresConnector(port=port)
with pc as opc:
# add sentence index column but in separate table
print("Starting with ")
pc.cursor.execute("CREATE TABLE sentences_neo4j AS TABLE sentences;")
pc.cursor.execute("ALTER TABLE sentences_neo4j ADD COLUMN id int;")
pc.cursor.execute("""WITH numbered (sid, document_id, sentence_id) AS
(select row_number() OVER() sid, * from sentences_neo4j)
UPDATE sentences_neo4j
SET id = numbered.sid
FROM numbered
WHERE sentences_neo4j.document_id = numbered.document_id AND
sentences_neo4j.sentence_id = numbered.sentence_id;""")
# add term_occurrence index
print("Starting with term occurrences...")
pc.cursor.execute("CREATE TABLE term_occurrence_neo4j AS TABLE term_occurrence;")
pc.cursor.execute("ALTER TABLE term_occurrence_neo4j ADD COLUMN id int;")
pc.cursor.execute("""WITH numbered (sid, document_id, sentence_id, term_id) AS
(select row_number() OVER() sid, * from term_occurrence)
UPDATE term_occurrence_neo4j
SET id = numbered.sid
FROM numbered
WHERE term_occurrence_neo4j.document_id = numbered.document_id AND
term_occurrence_neo4j.sentence_id = numbered.sentence_id AND
term_occurrence_neo4j.term_id = numbered.term_id;""")
def get_tfidf(self, locationid, trend):
tfidf_list = []
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
tfidf_query = """
select entity,tf_idf_score from
(select t4.entity,sum(t4.tf_idf) as tf_idf_score
from
(select t1.id,t1.entity,t2.count_id,t3.count_entity,
(1.0/t3.count_entity)*log((
select count(*) from organized_tweets
where trend = %s
and location_id = %s )/t2.count_id) as tf_idf from
(select id,entity from id_entity where id in
(select id from organized_tweets
where trend = %s
and location_id = %s)) as t1
inner join
(select entity,count(id) as count_id from id_entity where id in
(select id from organized_tweets
where trend = %s
and location_id = %s)group by entity) as t2
on
t1.entity = t2.entity
inner join
(select id,count(entity) as count_entity from id_entity
where id in(select id from organized_tweets
where trend = %s
and location_id = %s )group by id) as t3
on
t1.id = t3.id) as t4 group by entity)as t5 order by
tf_idf_score desc limit 100;
"""
cursor.execute(tfidf_query, (trend, locationid, trend, locationid,
trend, locationid, trend, locationid))
entity_column = 0
tfidf_column = 1
for row in cursor:
entity_tfidf_score = {}
entity_tfidf_score["entity"] = row[entity_column]
entity_tfidf_score["tfidf"] = row[tfidf_column]
tfidf_list.append(entity_tfidf_score)
return tfidf_list
except Exception:
print traceback.format_exc()
def get_tfidf(self,locationid,trend):
tfidf_list = []
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
tfidf_query = """
select entity,tf_idf_score from
(select t4.entity,sum(t4.tf_idf) as tf_idf_score
from
(select t1.id,t1.entity,t2.count_id,t3.count_entity,
(1.0/t3.count_entity)*log((
select count(*) from organized_tweets
where trend = %s
and location_id = %s )/t2.count_id) as tf_idf from
(select id,entity from id_entity where id in
(select id from organized_tweets
where trend = %s
and location_id = %s)) as t1
inner join
(select entity,count(id) as count_id from id_entity where id in
(select id from organized_tweets
where trend = %s
and location_id = %s)group by entity) as t2
on
t1.entity = t2.entity
inner join
(select id,count(entity) as count_entity from id_entity
where id in(select id from organized_tweets
where trend = %s
and location_id = %s )group by id) as t3
on
t1.id = t3.id) as t4 group by entity)as t5 order by
tf_idf_score desc;
"""
cursor.execute(tfidf_query,(trend,locationid,trend,locationid,trend,locationid,trend,locationid))
entity_column = 0
tfidf_column = 1
for row in cursor:
entity_tfidf_score = {}
entity_tfidf_score["entity"] = row[entity_column]
entity_tfidf_score["tfidf"] = row[tfidf_column]
tfidf_list.append(entity_tfidf_score)
return tfidf_list
except Exception :
print traceback.format_exc()
def get_tweets(self, trend, entity):
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query_tweets = """
select text from organized_tweets
where id in (select id from id_entity where
entity = %s) limit 50
"""
cursor.execute(query_tweets, (entity, ))
text_list = []
for row in cursor:
text_dict = {}
text_dict["name"] = row[0]
text_list.append(text_dict)
return text_list
def get_sentiments(self):
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = """
select id,text from organized_tweets
"""
cursor.execute(query)
id_column = 0
text_column = 1
with open("sentiments.tsv","w") as f:
for row in cursor:
text = row[text_column]
blob = TextBlob(text,analyzer=NaiveBayesAnalyzer())
print 'writing for tweet with id ' +str(row[id_column])
f.write(str(row[id_column])+'\t'+str(blob.sentiment.classification)+'\t'+str(blob.sentiment.p_pos)+'\t'+str(blob.sentiment.p_neg)+'\n')
def get_sentiments(self):
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = """
select id,text from organized_tweets
"""
cursor.execute(query)
id_column = 0
text_column = 1
with open("sentiments.tsv", "w") as f:
for row in cursor:
text = row[text_column]
blob = TextBlob(text, analyzer=NaiveBayesAnalyzer())
print 'writing for tweet with id ' + str(row[id_column])
f.write(
str(row[id_column]) + '\t' +
str(blob.sentiment.classification) + '\t' +
str(blob.sentiment.p_pos) + '\t' +
str(blob.sentiment.p_neg) + '\n')
def validate_data(self,vehicle_type,toll_type,date,price,vehicle_no):
if vehicle_type.strip() == '' or toll_type.strip() == '' or price.strip() == '' or date.strip() == '' or vehicle_no.strip() =='':
# figure out what to return here!
raise Exception('input data has nulls')
else:
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = """ INSERT INTO transactions
(vehicle_type,toll_type,timestamp,price,vehicle_no
) values(%s,%s,\'%s\',%s,\'%s\')
""" % (vehicle_type,toll_type,date,float(price),vehicle_no)
#print "Inserting data to table using the query %s" % (query,)
cursor.execute(query)
conn.commit()
return 'Success'
except psycopg2.IntegrityError as e:
raise Exception(' Unique key constraint failed ')
except Exception as e:
print e
raise Exception(' Something else went wrong')
def validate_data(self,vehicle_no,time):
if vehicle_no.strip() == '' and time.strip() == '':
# figure out what to return here!
raise Exception('input data has nulls')
else:
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = ""
if vehicle_no.strip() != '' and time.strip() != '':
query = """ SELECT * FROM transactions where vehicle_no = \'%s\'
and timestamp = \'%s\' order by timestamp """ % (vehicle_no,time)
elif vehicle_no.strip() != '' :
query = """ select t2.vehicle_type,t2.toll_type,t1.timestamp,t1.price,t1.vehicle_no from
(SELECT * FROM transactions where vehicle_no =\'%s\') as t1
inner join
(select * from master_data) as t2
on
t2.vehicle_type_id = t1.vehicle_type
and
t2.toll_type_id = t1.toll_type """ % (vehicle_no,)
else:
query = """ SELECT * FROM transactions where timestamp = \'%s\'
order by timestamp """ % (time,)
print "selecting data from the table using the query %s" % (query,)
cursor.execute(query)
search_list = []
for row in cursor:
elements = {}
elements['vehicle_type'] = row[0]
elements['toll_type'] = row[1]
elements['time'] = str(row[2])
elements['price'] = row[3]
elements['vehicle_no'] = row[4]
search_list.append(elements)
return search_list
except Exception:
print traceback.format_exc()
def build(self):
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = 'select id,hashtags from "organizedTweets" '
cursor.execute(query)
id_column = 0
entities_column = 1
entity_id_dict = {}
for row in cursor:
tweet_id = row[id_column]
hashtag_array = row[entities_column]
hashtag_list = [hashtag['text'] for hashtag in hashtag_array]
for entity in hashtag_list:
if entity in entity_id_dict.keys():
id_list = entity_id_dict[entity]
id_list.append(tweet_id)
entity_id_dict[entity] = id_list
else:
id_list = []
id_list.append(tweet_id)
entity_id_dict[entity] = id_list
return entity_id_dict
def build(self):
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = 'select id,hashtags from "organizedTweets" '
cursor.execute(query)
id_column = 0
entities_column = 1
entity_id_dict = {}
for row in cursor:
tweet_id = row[id_column]
hashtag_array = row[entities_column]
hashtag_list = [hashtag['text'] for hashtag in hashtag_array]
for entity in hashtag_list:
if entity in entity_id_dict.keys():
id_list = entity_id_dict[entity]
id_list.append(tweet_id)
entity_id_dict[entity] = id_list
else:
id_list = []
id_list.append(tweet_id)
entity_id_dict[entity] = id_list
return entity_id_dict
def get_data(self):
out_list = []
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = """ SELECT vehicle_type,vehicle_type_id,
toll_type,toll_type_id,price
from master_data"""
cursor.execute(query)
resultset = cursor.fetchall()
out_list = []
vehicle_dict = {}
for row in resultset:
row_data = {}
row_value_dict = {}
id = row[1]
vehicle_name = row[0]
journey_list = []
if id not in vehicle_dict.keys():
vehicle_dict_out = {}
vehicle_dict[id] = 1
for row_in in resultset:
journey_type = {}
if row_in[1] == id:
journey_type['toll_type'] = row_in[2]
journey_type['toll_id'] = str(row_in[3])
journey_type['price'] = str(row_in[4])
journey_list.append(journey_type)
vehicle_dict_out['vehicle_type'] = vehicle_name
vehicle_dict_out['vehicle_type_id'] = id
vehicle_dict_out['journey'] = journey_list
out_list.append(vehicle_dict_out)
return out_list
except Exception as e:
print e
raise Exception(' Something went wrong while retrieving data')
def get_trends(self,location_id,start_date,end_date):
trends_list = []
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query = """
select c,trend from
(select count(*) as c,trend from trends where
locationid = %s and date between %s and %s group by trend)as t1 order by c desc limit 15
"""
cursor.execute(query,(location_id,start_date,end_date))
trend_column = 1
count_column = 0
for row in cursor:
trend_count = {}
trend_count["trend"] = row[trend_column]
trend_count["count"] = row[count_column]
trends_list.append(trend_count)
except Exception as e:
print e
return trends_list
def test_dqn_against_heuristic(self):
np.random.seed(123)
with open(path.join("..", "query_pull_1000v3.pkl"), 'rb') as f:
query_pull = pickle.load(f)[0:5]
workload = np.random.choice(query_pull, const.BATCH_SIZE)
env = DatabaseIndexesEnv(n=const.COLUMNS_AMOUNT,
table_name=table_name,
query_pull=query_pull,
batch_size=const.BATCH_SIZE,
connector=PostgresConnector(),
k=3,
max_episodes=1)
dqn = load_agent(
path.join("..", "dqn_specific_{}.h5f".format(ENV_NAME)))
results = dqn.test(env, nb_episodes=1)
print(results)
print(env.state)
print(predict_on_workload(workload))
def test_cache(self):
np.random.seed(123)
with open("..\query_pull_1000v3.pkl", 'rb') as f:
query_pull = pickle.load(f)
register(
id='DatabaseIndexesEnv-v0',
entry_point='dbenv:DatabaseIndexesEnv',
kwargs={'n': const.COLUMNS_AMOUNT,
'table_name': "test_table",
'query_pull': query_pull,
'batch_size': 2,
'connector': PostgresConnector(),
'k': 3,
'max_episodes': 1}
)
env = gym.make('DatabaseIndexesEnv-v0')
env.step(0)
env.step(1)
env.step(2)
print(env.cache)
def train_model():
np.random.seed(123)
with open("query_pull_1000v2.pkl", 'rb') as f:
query_pull = pickle.load(f)[0:5]
register(id=ENV_NAME,
entry_point='dbenv:DatabaseIndexesEnv',
kwargs={
'n': COLUMNS_AMOUNT,
'table_name': table_name,
'query_pull': query_pull,
'batch_size': BATCH_SIZE,
'connector': PostgresConnector(),
'k': 3,
'max_episodes': episodes
})
# Get the environment and extract the number of actions.
env = gym.make(ENV_NAME)
env.seed(123)
# Next, we build a very simple model.
model = build_model()
print(model.summary())
# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
# even the metrics!
dqn = initialize_agent(model)
# Okay, now it's time to learn something! We visualize the training here for show, but this
# slows down training quite a lot. You can always safely abort the training prematurely using
# Ctrl + C.
dqn.fit(env, nb_steps=episodes, visualize=False, verbose=2)
# After training is done, we save the final weights.
dqn.save_weights('dqn_specific_{}.h5f'.format(ENV_NAME),
overwrite=True)
# Finally, evaluate our algorithm for 5 episodes.
dqn.test(env, nb_episodes=5, visualize=False)
result = []
print("Number of empty hyperedges:")
for i, prefix in enumerate(prefixes):
with pc as open_pc:
table = prefix + "hyperedges"
open_pc.cursor.execute(
"SELECT (SELECT MAX(edge_id) from {}) - "
"(SELECT count(distinct edge_id) from {}) as diff".format(
table, table))
result.append(open_pc.cursor.fetchall()[0][0])
print("Results for {}: {}".format(table, result[i]))
if __name__ == "__main__":
prefixes = ["", "entity_"]
pc = PostgresConnector(port=5435)
print()
get_document_table_length(prefixes, pc)
print()
get_sentence_table_length(prefixes, pc)
print()
get_hyperedge_table_length(prefixes, pc)
print()
analyze_edge_size(prefixes, pc)
print()
analyze_term_frequency(prefixes, pc)
print()
get_number_of_empty_edges(prefixes, pc)
def __init__(self): self.connector = PostgresConnector(CONFIG_FILE_NAME, CONFIG_SECTION_NAME) self.SQL_constructor = PostgresSQLConstructor()
def get_indexes_qagent(index_amount, queries, Log=False):
connector = PostgresConnector()
if not table_exists(connector, table_name):
create_table_2(connector)
load_table(connector)
# make results repeatable
np.random.seed(123)
# gym configuration
query_batch = list()
env = gym.make('DatabaseIndexesEnv-v0')
env.set_indices_num(index_amount)
current_query_idx = 0
query_batch = list()
for workload in range(1):
exploration_rate = 1.0 # represents the exploration rate to be decayed by the time
initial_lr = 1.0 # Learning rate
query_batch = list()
Q_table_c.Q_table = {}
query_batch = list()
workload_selectivity_l = list()
# 1. generate the queries per workload
# 2. generate the cummlative selectivity per workload
start = timer()
for i in range(current_query_idx, current_query_idx + num_queries_batch):
query_batch.append(queries[i]['query'])
workload_selectivity_l.append(list(map(lambda x: x, queries[i]['sf_array'])))
current_query_idx += num_queries_batch
workload_selectivity = np.prod(workload_selectivity_l, axis=0).tolist()
max_workload_selectivity = max(workload_selectivity)
env.set_query_batch(query_batch)
actions_taken = list()
# as a heuristic: the indices with the lowest selectivity
selectivity_indices = heapq.nsmallest(3, range(len(workload_selectivity)), workload_selectivity.__getitem__)
if Log:
print("Entering the q learning ..... the process can take time.")
print(workload_selectivity)
env.clear_cache()
for episode in range(NUM_EPISODES):
state = env.reset()
actions_taken = list()
# decay the exploration as the number of episodes grows, the Q table becomes more mature
eps = exploration_rate / np.sqrt(episode + 1)
eps = max(eps, min_exp_rate)
episode_total_reward = 0
episode_total_qreward = 0
episode_strategy = []
eta = max(min_lr, initial_lr * (0.85 ** (episode // 100)))
## now the learning comes
for kk in range(3):
# do exploration, i.e., choose a random actions
# make sure the last step is exploitation unless the state is new
if episode == 0:
episode_strategy.append("explore")
action = selectivity_indices[kk]
Q_table_c.Q_table[state_to_int(state)] = {}
Q_table_c.Q_table[state_to_int(state)][action] = 0
elif (is_new_state(state) or (np.random.uniform(0, 1) < eps)) and episode != NUM_EPISODES - 1:
episode_strategy.append("explore")
# generate only actions that matches something with selectivity.
action = env.action_space.sample()
# high selectivity, not a good option for an index
while workload_selectivity[action] >= max_workload_selectivity:
action = env.action_space.sample()
if is_new_state(state):
Q_table_c.Q_table[state_to_int(state)] = {}
if action not in Q_table_c.Q_table[state_to_int(state)]:
Q_table_c.Q_table[state_to_int(state)][action] = 0
else:
# else exploit choose the maximum value from the Q table
episode_strategy.append("exploit")
action = get_action_maximum_reward(state)[0]
actions_taken.append(action)
state_old_int = state_to_int(state)
state_new, reward, done, _ = env.step(action)
episode_total_reward += reward
next_action = 0
next_action_q_value = 0
if is_new_state(state_new):
next_action = env.action_space.sample()
while (action == next_action or workload_selectivity[next_action] >= max_workload_selectivity):
next_action = env.action_space.sample()
next_action_q_value = 0
else:
next_action, next_action_q_value = get_action_maximum_reward(state_new)
Q_table_c.Q_table[state_old_int][action] += eta * (reward + GAMMA * next_action_q_value -
Q_table_c.Q_table[state_old_int][action])
episode_total_qreward += Q_table_c.Q_table[state_old_int][action]
state, action = state_new, next_action
actions_taken_s = ','.join(str(e) for e in actions_taken)
if Log:
print(
"episode num = '{0}', episode_total_immediate_rewards = '{1}', episode_total_reward = '{2}', current_state = '{3}', actions_taken = '{4}', strategy = {5}"
.format(episode, float(episode_total_reward), float(episode_total_qreward),
state_to_string(state), actions_taken_s,
episode_strategy))
return actions_taken
def update_organized_tweets(self):
tweet_id_dict = {}
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query_location = 'select id from location'
cursor.execute(query_location)
location_column = 0
for row_location in cursor:
query = """
select id,trend from trends
where trend in(select trend from (select count(*) as c,trend from
trends where locationid = %s group by trend)as t1 order
by c desc limit 15)
"""
cursor = conn.cursor()
location_id = row_location[location_column]
cursor.execute(query,(location_id,))
trend_id_column = 0
trend_name_column = 1
trend_count = 0
for row in cursor:
trend_count = trend_count + 1
trend_id = row[trend_id_column]
trend_name = row[trend_name_column]
print 'Processing for trend ' +trend_id+' , ' +str(trend_count)
query_tweets = 'select tweets from tweets where trendId = \''+str(trend_id)+'\''
cursor_tweets = conn.cursor()
cursor_tweets.execute(query_tweets)
tweets_column = 0
with open(trend_name+'.txt','w') as f:
# rows of tweets array
for tweets_row in cursor_tweets:
tweets_json_array = tweets_row[tweets_column]
# tweets in a tweets array
for json_in in tweets_json_array:
id = json_in['id']
tweet_id_exists = tweet_id_dict.get(id)
if tweet_id_exists is None:
#print jsonIn
tweet_id_dict[id] = 1
geo = 'none' if json_in['geo'] is None else 'none' #json['geo']
retweeted = json_in['retweeted']
in_reply_to_screen_name = 'none' if json_in['in_reply_to_screen_name'] is None else json_in['in_reply_to_screen_name']
truncated = 'none' if json_in['truncated'] is None else json_in['truncated']
source = json_in['source']
created_at = json_in['created_at']
place = 'none' if json_in['place'] is None else 'none'#json['place']
user_id = json_in['user']['id']
text = json_in['text'].strip()
#text = " ".join(str(text).split())
text = str(filter(lambda x: x in string.printable,text))
#text = text.encode('utf-16')
text = re.sub('\s+',' ',text)
text = text.replace('\\','')
entities = json_in['entities']['hashtags']
user_mentions = json_in['entities']['user_mentions']
user_mentions = []
retweet_count = json_in['retweet_count']
favorite_count = json_in['favorite_count']
# if len(entities) > 0:
# for entity in entities:
# for k,v in entity.items():
# if k in 'text':
# entity_list = {}
# new_v = entity[k]
# new_v = str(new_v.encode('utf-8'))
# new_v = filter(lambda x: x in string.printable,new_v)
# #print id,check,new_v,len(new_v)
# if len(new_v) > 0:
# entity[k] = new_v
# else:
# entity[k] = ''
#print id,geo,retweeted ,in_reply_to_screen_name ,truncated ,source ,created_at ,place ,user_id ,text ,entities ,user_mentions,retweet_count,favorite_count
f.write(str(id)+'\t'+str(geo)+'\t'+str(retweeted)+'\t'+str(in_reply_to_screen_name.encode('utf-8'))+'\t'+str(truncated)+'\t'+str(source.encode('utf-8'))+'\t'+str(created_at.encode('utf-8'))+'\t'+str(place)+'\t'+str(user_id)+'\t'+text+'\t'+str(json.dumps(entities))+'\t'+str(user_mentions)+'\t'+str(retweet_count)+'\t'+str(favorite_count)+'\t'+str(trend_name)+'\t'+str(location_id)+'\n')
else:
continue
# array of tweets json ends here
#break
# total number of tweets rows for a given trend ends here
#break
print 'Writing to table'
with open(trend_name+'.txt') as f:
cursor_write = conn.cursor()
cursor_write.copy_from(f,'organized_tweets',columns=('id','geo','retweeted','in_reply_to_screen_name','truncated','source','created_at','place','user_id','text','entities','user_mentions','retweet_count','favorite_count','trend','location_id'))
conn.commit()
os.remove(trend_name+'.txt')
# all trends finish here
#break
except Exception :
print traceback.format_exc()
def run_qlearning():
connector = PostgresConnector()
query_pull = generate_query_pull('.query_pull', queries_amount, [4, 6],
table_column_types, table_column_names,
table_name, connector)
def get_matrix(self,locationid): try: conn = PostgresConnector().get_connection() cursor = conn.cursor() query = """ WITH TREND_COUNT_TT AS (SELECT TREND,COUNT(*) AS TREND_COUNT FROM TRENDS WHERE LOCATIONID = %s GROUP BY TREND), TOP_TRENDS_TT AS (SELECT TREND FROM TREND_COUNT_TT ORDER BY TREND_COUNT DESC LIMIT 15), IDS_FOR_TOP_TRENDS_TT AS (SELECT ID FROM ORGANIZED_TWEETS WHERE TREND IN (SELECT TREND FROM TOP_TRENDS_TT) AND LOCATION_ID = '2295420'), --SELECT * FROM IDS_FOR_TOP_TRENDS_TT ID_ENTITY_TOP_TRENDS_TT AS (SELECT TREND,ID,ENTITY FROM ID_ENTITY WHERE ID IN (SELECT ID FROM IDS_FOR_TOP_TRENDS_TT)), TREND_ENTITY_TF_IDF_SUM_TT AS (SELECT TREND,ENTITY,COUNT(ID) TF_IDF_SUM FROM ID_ENTITY WHERE ID IN (SELECT ID FROM IDS_FOR_TOP_TRENDS_TT) GROUP BY TREND,ENTITY), --SELECT * FROM TREND_ENTITY_TF_IDF_SUM_TT TREND_TF_IDF_SQ_SUM_TT AS (SELECT TREND, SUM(TF_IDF_SUM*TF_IDF_SUM) AS TF_IDF_SQ_SUM FROM TREND_ENTITY_TF_IDF_SUM_TT GROUP BY TREND), COSINE_DIST_NUM_TT AS (SELECT T1.TREND AS TREND1,T2.TREND AS TREND2, SUM(T1.TF_IDF_SUM*T2.TF_IDF_SUM) AS COSINE_NUM FROM TREND_ENTITY_TF_IDF_SUM_TT AS T1 INNER JOIN TREND_ENTITY_TF_IDF_SUM_TT AS T2 ON T2.TREND>T1.TREND AND T1.ENTITY = T2.ENTITY GROUP BY T1.TREND,T2.TREND), COSINE_DIST_TT AS (SELECT TREND1,TREND2, COSINE_NUM/(SQRT(T2.TF_IDF_SQ_SUM)*SQRT(T3.TF_IDF_SQ_SUM)) AS COSIND_DIST FROM COSINE_DIST_NUM_TT AS T1 INNER JOIN TREND_TF_IDF_SQ_SUM_TT AS T2 ON T1.TREND1=T2.TREND INNER JOIN TREND_TF_IDF_SQ_SUM_TT AS T3 ON T1.TREND2=T3.TREND) SELECT * FROM COSINE_DIST_TT ORDER BY TREND1,TREND2; """ cursor.execute(query,(locationid,)) trend1_column = 0 trend2_column = 1 distance_value_column = 2 trends_list = [] row_counter = 0 max_columns = 15 column_iteration = 1 distance_matrix = [[0 for x in xrange(max_columns)] for x in xrange(max_columns)] for row in cursor: trend1 = row[trend1_column] trend2 = row[trend2_column] if trend1 not in trends_list: trends_list.append(trend1) if trend2 not in trends_list: trends_list.append(trend2) # this is to check 0,0 1,1 and so on distance_matrix[row_counter][row_counter] = 0 # this populates 1,2 and 2,1 and so on # this avoid 2 loops distance_matrix[row_counter][column_iteration] = row[distance_value_column] distance_matrix[column_iteration][row_counter] = row[distance_value_column] column_iteration = column_iteration + 1 if column_iteration == max_columns: row_counter = row_counter + 1 column_iteration = row_counter + 1 return distance_matrix,trends_list except Exception: print traceback.format_exc()
def __init__(self,
num_distinct_documents=5000,
replace_entities=True,
max_term_length=127,
remove_stopwords=True,
custom_stopwords=[
',', '.', '-', '\xa0', '“', '”', '"', '\n', '—', ':', '?',
'I', '(', ')'
],
analyze=False,
document_tabe_name="documents",
sentence_table_name="sentences",
sentence_fields=OrderedDict({
"doc_id": "document_id",
"sen_id": "sentence_id",
"content": "sentence_text"
}),
term_table_name="terms",
term_sql_format=("term_id", "term_text", "is_entity"),
term_occurrence_table_name="term_occurrence",
term_occurrence_sql_format=("document_id", "sentence_id",
"term_id"),
entity_table_name="entities",
entity_sql_format=("entity_id", "entity_type"),
database="postgres",
user="******",
password="******",
host="127.0.0.1",
port=5435,
log_file=os.path.join(os.path.dirname(__file__),
"logs/TermGenerator.log"),
log_level=logging.INFO,
log_verbose=True):
"""
Initializes various parameters, registers logger and MongoConnector, and sets up the limit.
:param num_distinct_documents: (int) The number of distinct documents retrieved from the queries.
For performance reasons, this should be limited during debugging/development.
0 (Zero) represents no limit, in accordance with the MongoDB standard for .limit().
:param replace_entities: (boolean) Whether or not the entities in the text should be replaced/recognised.
The reason for this is that single terms might be merged together to one term, i.e. first and last name:
"Dennis" "Aumiller" would be two separate terms in the traditional splitting (replace_entities=False),
whereas - if set to true - "Dennis Aumiller" would represent only one entity.
:param max_term_length: (int) Indicator of how long the terms are supposed to be (varchar property in table).
:param remove_stopwords: (boolean) Determines whether or not stop words are removed. Currently, we are still
deciding on the final set, but likely either one (or both) of NLTK and SpaCy's stop word lists.
:param custom_stopwords: (list of strings) Additional words that will not be considered at adding-time.
:param analyze: (boolean) Whether or not to include analytically relevant metrics.
:param document_tabe_name: (str) Name of the table where the document information is stored.
:param sentence_table_name: (str) Name of the table where the sentence information will be stored.
:param sentence_fields: (OrderedDict) Structure of input to output values from MongoDB to postgres for the
sentence table and its fields.
:param term_table_name: (str) Name of the Postgres tables for the terms.
:param term_sql_format: (tuple) Since those are generated locally, only a tuple of the PostgresColumns suffices.
:param term_occurrence_table_name: (str) Name of the Postgres table for the term occurrences
:param term_occurrence_sql_format: (tuple) Same as term_sql_format, but for the term occurrences.
:param entity_table_name: (str) (Not implemented yet) Name of the table for the entity meta information.
:param entity_sql_format: (str) Same as term_sql_format, but for entities.
:param database: (str) database name.
:param user: (str) User name to get access to the Postgres database.
:param password: (str) Corresponding user password.
:param host: (IP) IP address (in string format) for the host of the postgres database.
:param port: (integer) Port at which to access the database.
"""
# set up logger
self.logger = set_up_logger(__name__, log_file, log_level, log_verbose)
self.logger.info("Successfully registered logger to TermGenerator.")
# register a MongoConnector
self.mc = MongoConnector()
self.logger.info(
"Successfully registered MongoConnector to TermGenerator.")
# PostgresConnector
self.pc = PostgresConnector(database, user, password, host, port)
self.logger.info(
"Successfully registered PostgresConnector to DocumentGenerator.")
self.num_distinct_documents = num_distinct_documents
# do this earlier since we need it already for the distinct documents.
self.document_table_name = document_tabe_name
# get the distinct IDs for the documents so we can match against them later
# since we have removed parts of the document collection, we have to make sure to get this from Postgres.
self.logger.info("Parsing relevant documents from Postgres...")
with self.pc as open_pc:
open_pc.cursor.execute("SELECT document_id FROM {}".format(
self.document_table_name))
self.first_distinct_documents = list(open_pc.cursor.fetchall())
# extract from the tuple structure
self.first_distinct_documents = [
el[0] for el in self.first_distinct_documents
]
self.logger.info("Retrieved all relevant documents from Postgres.")
# additionally restrict if we want only a number of documents.
if self.num_distinct_documents != 0:
self.logger.info(
"Non-zero limit detected. Limiting to the first N entries.")
self.first_distinct_documents = self.first_distinct_documents[:self
.
num_distinct_documents]
self.replace_entities = replace_entities
self.analyze = analyze
self.max_term_length = max_term_length
self.nlp = spacy.load("en")
# construct dictionary with the entries per document/sentence id pair. Thus, we can later check whether
# there are any entities in the current sentence with higher efficiency.
self.occurrence_dict = {}
self.occurring_entities = []
# start building the term dictionary/set, as well as an occurence map. Since terms will be "post-processed",
# it is first created as a list and later cast to Counter and set.
self.terms = [] # cast into a set later on.
self.term_in_sentence = set()
self.term_id = {}
self.term_is_entity = {}
if self.analyze:
self.term_count = Counter()
self.entity_count = Counter()
self.entities = []
self.sentences = []
self.processed_sentences = []
# Postgres tables
if not sentence_fields:
self.logger.error("No sentence fields specified!")
self.sentence_table_name = sentence_table_name
self.sentence_fields = sentence_fields
if not term_sql_format:
self.logger.error("No term fields specified!")
self.term_table_name = term_table_name
self.term_sql_format = ", ".join(term_sql_format)
if not term_occurrence_sql_format:
self.logger.error("No term occurrence fields specified!")
self.term_occurrence_table_name = term_occurrence_table_name
self.term_occurrence_sql_format = ", ".join(term_occurrence_sql_format)
if not entity_sql_format:
self.logger.error("No entity fields specified!")
self.entity_table_name = entity_table_name
self.entity_sql_format = ", ".join(entity_sql_format)
# value retrieving parse:
self.sentence_values_to_retrieve = {
key: 1
for key in self.sentence_fields.keys()
}
# suppress _id if not present:
if "_id" not in self.sentence_values_to_retrieve.keys():
self.sentence_values_to_retrieve["_id"] = 0
self.sentence_sql_format = ", ".join(
[value for value in self.sentence_fields.values()])
# create union of stop words, and add potentially custom stop words
self.remove_stopwords = remove_stopwords
self.removed_counter = 0
self.stopwords = STOP_WORDS.union(set(stopwords.words("english")))
# add custom stopwords.
for word in custom_stopwords:
self.stopwords.add(word)
self.logger.info("Successfully initialized TermGenerator.")
def __init__(self,
window_size=2,
limit_edges=False,
entities_only=False,
document_table_name="documents",
sentence_table_name="sentences",
entity_table_name="entities",
term_table_name="terms",
term_occurrence_table_name="term_occurrence",
hyperedge_table_name="hyperedges",
hyperedge_format=("edge_id", "term_id", "pos"),
hyperedge_document_table_name="hyperedge_document",
hyperedge_document_format=("edge_id", "document_id"),
hyperedge_sentence_table_name="hyperedge_sentences",
hyperedge_sentence_format=("edge_id", "document_id",
"sentence_id", "pos"),
database="postgres",
user="******",
password="******",
host="127.0.0.1",
port=5435,
log_file=os.path.join(os.path.dirname(__file__),
"logs/HyperedgeGenerator.log"),
log_level=logging.INFO,
log_verbose=True):
"""
Initializes hyper edge generator class.
:param window_size: (int) Number of sentences in each direction that will determine the context window size
of the algorithm.
:param limit_edges: (boolean) Experimental: Should limit the maximum number of terms per hyperedge. This would
only be useful in context with other theoretical results.
:param entities_only: (boolean) Indicating whether or not we should only take into account entity terms,
and not the entirety of all term occurrences for the edges.
:param document_table_name: (str) Name of the table where documents are stored.
:param sentence_table_name: (str) Name of the table containing the sentences and their content.
:param entity_table_name: (str) Name of the table containing the entity information and their properties.
:param term_table_name: (str) Name of the table containing the terms and meta data.
:param term_occurrence_table_name: (str) Name of the table containing term occurrence data.
:param hyperedge_table_name: (str) Name of the table containing the general hyper edge information.
:param hyperedge_format: (str) Table structure of hyper edge table.
:param hyperedge_document_table_name: (str) Name of the table containing the document classification.
:param hyperedge_document_format: (str) Table structure of hyper edge document table.
:param hyperedge_sentence_table_name: (str) Name of the tale containing the hyper edge sentence data.
:param hyperedge_sentence_format: (str) Table structure of the hyper edge sentence table.
:param database: (str) database name.
:param user: (str) User name to get access to the Postgres database.
:param password: (str) Corresponding user password.
:param host: (IP) IP address (in string format) for the host of the postgres database.
:param port: (integer) Port at which to access the database.
:param log_file: (os.path) Path to the file containing the logs.
:param log_level: (logging.LEVEL) Specifies the level to be logged.
:param log_verbose: (boolean) Specifies whether or not to look to stdout as well.
"""
self.logger = set_up_logger(__name__, log_file, log_level, log_verbose)
self.logger.info(
"Successfully registered logger to HyperedgeGenerator.")
# important for hyperedges
self.window_size = window_size
self.limit_edges = limit_edges
self.entities_only = entities_only
# table names
self.document_table_name = document_table_name
self.sentence_table_name = sentence_table_name
self.entity_table_name = entity_table_name
self.term_table_name = term_table_name
self.term_occurrence_table_name = term_occurrence_table_name
self.hyperedge_table_name = hyperedge_table_name
self.hyperedge_document_table_name = hyperedge_document_table_name
self.hyperedge_sentence_table_name = hyperedge_sentence_table_name
self.hyperedge_format = ", ".join([el for el in hyperedge_format])
self.hyperedge_document_format = ", ".join(
[el for el in hyperedge_document_format])
self.hyperedge_sentence_format = ",".join(
[el for el in hyperedge_sentence_format])
self.pc = PostgresConnector(database, user, password, host, port)
self.logger.info(
"Successfully registered PostgresConnector to HyperedgeGenerator.")
self.hyperedge = []
self.hyperedge_sentence = []
self.hyperedge_document = []
self.all_hyperedges = []
self.all_hyperedge_sentences = []
# set up the "hyper edge ID counter", which is simply consecutive from 1.
with self.pc as open_pc:
if not check_table_existence(self.logger, open_pc,
self.hyperedge_table_name):
return 0
self.logger.info("Retrieving current hyper edge ID key...")
open_pc.cursor.execute(
"SELECT COUNT(DISTINCT h.edge_id) FROM {} as h".format(
self.hyperedge_table_name))
# either start with 1 or get the current maximum
self.hyperedge_ID = max(1, open_pc.cursor.fetchone()[0])
# assert Q_table_c.Q_table[state_to_int(state)], "This state has no corresponding action: %r" % state_to_int(state)
max_reward = float('-inf')
int_state = state_to_int(state)
actions_rewards_dict = Q_table_c.Q_table[int_state]
for key, val in actions_rewards_dict.items():
if val > max_reward:
max_reward = val
max_key = key
return max_key, max_reward
register(
id='DatabaseIndexesEnv-v0',
entry_point='dbenvm:DatabaseIndexesEnv',
kwargs={'n': len(table_column_names), 'table_name': table_name, 'query_batch': list(),
'connector': PostgresConnector(), 'k': 3}
)
def get_indexes_qagent(index_amount, queries, Log=False):
connector = PostgresConnector()
if not table_exists(connector, table_name):
create_table_2(connector)
load_table(connector)
# make results repeatable
np.random.seed(123)
# gym configuration
query_batch = list()
env = gym.make('DatabaseIndexesEnv-v0')
def __init__(self,
fields=OrderedDict({
"_id": "document_id",
"title": "title",
"feedName": "feedName",
"category": "category",
"feedURL": "feedURL",
"published": "published"
}),
num_distinct_documents=0,
document_table_name="documents",
database="postgres",
user="******",
password="******",
host="127.0.0.1",
port=5435,
log_file=os.path.join(os.path.dirname(__file__),
"logs/DocumentGenerator.log"),
log_level=logging.INFO,
log_verbose=True):
"""
Initializes context, and sets up documents that will be parsed.
Also establishes the PostgresConnector that will later be used to push the retrieved documents.
:param fields: (OrderedDict) Key-value pairs that indicate a mapping of fields that should be retrieved (key),
and the respective field it should be called in the SQL table. Ordered because SQL tables are.
:param num_distinct_documents: (int) As the name indicates, the number of distinct articles that should be used.
Mainly for debugging purposes. 0 means all documents will be used, in accordance with MongoDB standards.
:param document_table_name: (str) Name of the Postgres table that should contain the documents
:param database: (str) database name.
:param user: (str) User name to get access to the Postgres database.
:param password: (str) Corresponding user password.
:param host: (IP) IP address (in string format) for the host of the postgres database.
:param port: (integer) Port at which to access the database.
:param log_file: (os.path) Path to the file containing the logs.
:param log_level: (logging.LEVEL) Specifies the level to be logged.
:param log_verbose: (boolean) Specifies whether or not to look to stdout as well.
"""
# set up logger
self.logger = set_up_logger(__name__, log_file, log_level, log_verbose)
self.logger.info(
"Successfully registered logger to DocumentGenerator.")
# register a MongoConnector
self.mc = MongoConnector()
self.logger.info(
"Successfully registered MongoConnector to DocumentGenerator.")
self.num_distinct_documents = num_distinct_documents
# get the distinct IDs for the documents so we can match against them later
if self.num_distinct_documents != 0:
self.logger.info(
"Non-zero limit detected. Fetching first N distinct document IDs now..."
)
with self.mc as open_mc:
documents = open_mc.client[open_mc.news].articles
self.first_documents = list(documents.find().limit(
self.num_distinct_documents))
# for small enough number, and large enough document collection, this is more efficient:
self.first_documents = [
el["_id"] for el in self.first_documents
]
self.logger.info(
"Successfully registered relevant document IDs.")
else:
# needed to avoid later conflicts
self.first_documents = []
# set up PostgresConnector. Since we only use these once, I don't see any reason to store the connection
# details locally again.
self.pc = PostgresConnector(database, user, password, host, port)
self.logger.info(
"Successfully registered PostgresConnector to DocumentGenerator.")
# format them into a reasonable format
self.fields = fields
if not self.fields:
self.logger.error("No fields for MongoDB table specified!")
self.values_to_retrieve = {key: 1 for key in self.fields.keys()}
# suppress _id if not wanted, as it is returned by default.
if "_id" not in self.values_to_retrieve.keys():
self.values_to_retrieve["_id"] = 0
# TODO
self.sql_format = ", ".join([value for value in self.fields.values()])
self.document_table_name = document_table_name
# preparation for later. According to PEP8
self.data = []
self.logger.info("Successfully set up DocumentGenerator.")
import psycopg2 as db
import csv
import os
import sys
sys.path.append(os.path.abspath("../lib/"))
from query_helper import comm_helper
from PostgresConnector import PostgresConnector
# ports = list(range(5435, 5440))
port = 5436
windows = [5, 10]
t = comm_helper("postgres", "", "127.0.0.1", str(port))
pc = PostgresConnector(port=port)
def query_and_write(filename, query, header):
with pc as opc:
print("Start querying table {}".format(filename))
if os.path.isfile(filename):
os.remove(filename)
opc.cursor.execute(query)
# This only happens for documents
print("Start writing table {}.".format(filename))
with open(filename, 'w') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(header)
while True:
data = opc.cursor.fetchmany(65536)
def update_organized_tweets(self):
tweet_id_dict = {}
try:
conn = PostgresConnector().get_connection()
cursor = conn.cursor()
query_location = 'select id from location'
cursor.execute(query_location)
location_column = 0
for row_location in cursor:
query = """
select id,trend from trends
where trend in(select trend from (select count(*) as c,trend from
trends where locationid = %s group by trend)as t1 order
by c desc limit 80)
"""
cursor = conn.cursor()
location_id = row_location[location_column]
cursor.execute(query, (location_id, ))
trend_id_column = 0
trend_name_column = 1
trend_count = 0
for row in cursor:
trend_count = trend_count + 1
trend_id = row[trend_id_column]
trend_name = row[trend_name_column]
print 'Processing for trend ' + trend_id + ' , ' + str(
trend_count)
query_tweets = 'select tweets from tweets where trendId = \'' + str(
trend_id) + '\''
cursor_tweets = conn.cursor()
cursor_tweets.execute(query_tweets)
tweets_column = 0
with open(trend_name + '.txt', 'w') as f:
# rows of tweets array
for tweets_row in cursor_tweets:
tweets_json_array = tweets_row[tweets_column]
# tweets in a tweets array
for json_in in tweets_json_array:
id = json_in['id']
tweet_id_exists = tweet_id_dict.get(id)
if tweet_id_exists is None:
#print jsonIn
tweet_id_dict[id] = 1
geo = 'none' if json_in[
'geo'] is None else 'none' #json['geo']
retweeted = json_in['retweeted']
in_reply_to_screen_name = 'none' if json_in[
'in_reply_to_screen_name'] is None else json_in[
'in_reply_to_screen_name']
truncated = 'none' if json_in[
'truncated'] is None else json_in[
'truncated']
source = json_in['source']
created_at = json_in['created_at']
place = 'none' if json_in[
'place'] is None else 'none' #json['place']
user_id = json_in['user']['id']
text = json_in['text'].strip()
#text = " ".join(str(text).split())
text = str(
filter(lambda x: x in string.printable,
text))
#text = text.encode('utf-16')
text = re.sub('\s+', ' ', text)
text = text.replace('\\', '')
entities = json_in['entities']['hashtags']
user_mentions = json_in['entities'][
'user_mentions']
user_mentions = []
retweet_count = json_in['retweet_count']
favorite_count = json_in['favorite_count']
# if len(entities) > 0:
# for entity in entities:
# for k,v in entity.items():
# if k in 'text':
# entity_list = {}
# new_v = entity[k]
# new_v = str(new_v.encode('utf-8'))
# new_v = filter(lambda x: x in string.printable,new_v)
# #print id,check,new_v,len(new_v)
# if len(new_v) > 0:
# entity[k] = new_v
# else:
# entity[k] = ''
#print id,geo,retweeted ,in_reply_to_screen_name ,truncated ,source ,created_at ,place ,user_id ,text ,entities ,user_mentions,retweet_count,favorite_count
f.write(
str(id) + '\t' + str(geo) + '\t' +
str(retweeted) + '\t' + str(
in_reply_to_screen_name.encode(
'utf-8')) + '\t' +
str(truncated) + '\t' +
str(source.encode('utf-8')) + '\t' +
str(created_at.encode('utf-8')) +
'\t' + str(place) + '\t' +
str(user_id) + '\t' + text + '\t' +
str(json.dumps(entities)) + '\t' +
str(user_mentions) + '\t' +
str(retweet_count) + '\t' +
str(favorite_count) + '\t' +
str(trend_name) + '\t' +
str(location_id) + '\n')
else:
continue
# array of tweets json ends here
#break
# total number of tweets rows for a given trend ends here
#break
print 'Writing to table'
with open(trend_name + '.txt') as f:
cursor_write = conn.cursor()
cursor_write.copy_from(
f,
'organized_tweets',
columns=('id', 'geo', 'retweeted',
'in_reply_to_screen_name', 'truncated',
'source', 'created_at', 'place',
'user_id', 'text', 'entities',
'user_mentions', 'retweet_count',
'favorite_count', 'trend', 'location_id'))
conn.commit()
os.remove(trend_name + '.txt')
# all trends finish here
#break
except Exception:
print traceback.format_exc()
subjects = [(1, 'Accounting & Finance'), (2, 'Art & Design'),
(3, 'Architecture'), (4, 'Manufacturing Engineering'), (5, 'Law'),
(6, 'Economics & Econometrics'), (7, 'Medicine'),
(8, 'Business & Management Studies'),
(9, 'Engineering & Technology'), (10, 'Computer Science')]
#Дані для занесення в бд
subjects_to_teachers = [(1, 1, 1), (2, 2, 2), (3, 3, 3), (4, 4, 4), (5, 5, 5),
(6, 6, 6), (7, 7, 7), (8, 8, 8), (9, 9, 9),
(10, 10, 10), (11, 11, 1), (12, 12, 2), (13, 13, 3),
(14, 14, 4), (15, 15, 5), (16, 16, 6), (17, 17, 7),
(18, 18, 8), (19, 19, 9), (20, 20, 10)]
#Під'єднання до БД
sqlite = SqliteConnector()
Postgres = PostgresConnector()
MySql = MySqlConnector()
# Функція для створення БД1
def createDB():
MySql.dropAllTables()
MySql.createDatabase()
MySql.executemany("INSERT INTO faculties VALUES (%s,%s)", faculties)
MySql.executemany("INSERT INTO department VALUES (%s,%s,%s)", departments)
MySql.executemany("INSERT INTO teachers VALUES (%s,%s,%s,%s,%s)", teachers)
MySql.executemany("INSERT INTO subject VALUES (%s,%s)", subjects)
MySql.executemany("INSERT INTO subjects_to_teachers VALUES (%s,%s,%s)",
subjects_to_teachers)
class PostgresQuerier(object):
def __init__(self):
self.connector = PostgresConnector(CONFIG_FILE_NAME, CONFIG_SECTION_NAME)
self.SQL_constructor = PostgresSQLConstructor()
def GetVersion(self):
SQL = self.SQL_constructor.GetVersion()
self.connector.ExecuteSQL(SQL)
return self.connector.GetResults(1)
def GetCurrentDate(self):
SQL = self.SQL_constructor.GetCurrentDate()
self.connector.ExecuteSQL(SQL)
return self.connector.GetResults(1)
def _SetDatestyleGerman(self):
SQL = "SET DATESTYLE=%s;";
data = ("German",)
self.connector.ExecuteSQL(SQL, data)
return "Datestyle set to German"
def SelectColumnsFromTable(self, columns, table, limit=100):
SQL_select = self.SQL_constructor.GetSELECTColumns(columns)
SQL_from = self.SQL_constructor.GetFROMTable(table)
SQL_limit = self.SQL_constructor.GetLIMIT(limit)
SQL = SQL_select + SQL_from + SQL_limit
self.connector.ExecuteSQL(SQL)
return self.connector.GetAllResults()
def FindWordsInColumnsInTableThenBoldAndRank(self, search_words, columns, table, operation="OR"):
search_phrase = self.SQL_constructor.ConstructSearchPhrase(search_words, operation)
headline_columns = self.SQL_constructor.GetHeadlineColumnsWithPhrase(columns, search_phrase)
rank_column = self.SQL_constructor.Getts_rankColumnByPhraseWithLengthPenaltyWithAlias(columns[0], search_phrase, 1, "rank")
headline_columns.append(rank_column)
vector_query_pairs = [self.SQL_constructor.GetVectorColumnHasQueryPhrase(x, search_phrase) for x in columns]
SQL_select = self.SQL_constructor.GetSELECTColumns(headline_columns)
SQL_from = self.SQL_constructor.GetFROMTable(table)
SQL_where = self.SQL_constructor.GetWHERE()
SQL_conditions = " OR".join(vector_query_pairs)
SQL_order = self.SQL_constructor.GetORDERByAliasInDirection("rank", "DESC")
SQL = SQL_select + SQL_from + SQL_where + SQL_conditions + SQL_order
self.connector.ExecuteSQL(SQL)
return self.connector.GetAllResults()
def FindWordsInDocumentInTableThenBoldAndRank(self, search_words, document, table, operation="OR"):
search_phrase = self.SQL_constructor.ConstructSearchPhrase(search_words, operation)
document_columns = ["title", "categories", "summary", "description"]
headline_columns = self.SQL_constructor.GetHeadlineColumnsWithPhrase(document_columns, search_phrase)
rank_column = self.SQL_constructor.Getts_rankDocumentByPhraseWithLengthPenaltyWithAlias(document, search_phrase, 1, "rank")
headline_columns.append(rank_column)
SQL_select = self.SQL_constructor.GetSELECTColumns(headline_columns)
SQL_from = self.SQL_constructor.GetFROMTable(table)
SQL_where = self.SQL_constructor.GetWHERE()
SQL_conditions = self.SQL_constructor.GetDocumentHasQueryPhrase(document, search_phrase)
SQL_order = self.SQL_constructor.GetORDERByAliasInDirection("rank", "DESC")
SQL = SQL_select + SQL_from + SQL_where + SQL_conditions + SQL_order
self.connector.ExecuteSQL(SQL)
return self.connector.GetAllResults()
def SuggestBasedOnPhraseInColumnInTable(self, phrase, column, table):
similarity_function = self.SQL_constructor.GetSimilarityOfColumnAndStringWithAlias(column, phrase, "sameness")
SQL_select = self.SQL_constructor.GetSELECTColumns([column, similarity_function])
SQL_from = self.SQL_constructor.GetFROMTable(table)
SQL_order = self.SQL_constructor.GetORDERByAliasInDirection("sameness", "DESC")
SQL = SQL_select + SQL_from + SQL_order
self.connector.ExecuteSQL(SQL)
return self.connector.GetResults(5)
def PivotMovies(self, start_datetime, stop_datetime, granulation):
iso_start_datetime = start_datetime.isoformat(" ")
iso_end_datetime = stop_datetime.isoformat(" ")
diff = stop_datetime - start_datetime
days, seconds = diff.days, diff.seconds
hours = days * 24 + seconds // 3600
if granulation == "hour":
format = ["time_interval" + str(x) + " bigint" for x in range(hours + 1)]
elif granulation == "day":
format = ["time_interval" + str(x) + " bigint" for x in range(days + 1)]
else:
raise Exception("WARNING: incorrect granulation; possible granulation are (hour, day)")
format.insert(0, "query character varying(255)")
sub_query = ("select query,"
" date_trunc(''" + granulation + "'', date_and_time) as periods,"
" count(*)"
" from queries"
" where date_and_time >= ''" + iso_start_datetime + "'' and"
" date_and_time <= ''" + iso_end_datetime + "''"
" group by query, periods"
" order by query, periods;")
sequence = ("select d"
" from generate_series(''" + iso_start_datetime + "''::timestamp,"
"''" + iso_end_datetime + "'',"
"''1 " + granulation + "'')"
" d;")
SQL = ("select * from crosstab('" + sub_query + "',"
"'" + sequence + "')"
"as ct(" + ", ".join(format) + ");")
self.connector.ExecuteSQL(SQL)
return self.connector.GetAllResults()
def InsertIntoMovies(self, title, categories="No categories.", summary="No summary.", description="No description."):
table = "movies"
columns = ["title", "categories", "summary", "description"]
values = [title, categories, summary, description]
SQL_insert = self.SQL_constructor.GetINSERTIntoTableColumns(table, columns)
SQL_values = self.SQL_constructor.GetVALUES(values)
SQL = SQL_insert + SQL_values
self.connector.ExecuteSQL(SQL)
self.connector.CommitTransaction()
return "Insertion successful."
def _GetTableColumnsAfterIndex(self, table, index=0):
return [x[0] for x in self.DescribeTable(table)][index:]
def Prototype(self):
SQL = ("SELECT title, similarity (title, 'The Dencing Master')"
"FROM movies"
"WHERE title % 'The Dencing Master'"
)
self.connector.ExecuteSQL(SQL)
return self.connector.GetAllResults()
def DescribeTable(self, table):
SQL = "SELECT column_name FROM information_schema.columns WHERE table_name = '" + table + "'";
self.connector.ExecuteSQL(SQL)
return self.connector.GetAllResults()
def Close(self):
self.connector.Close()