class TestConnection(unittest.TestCase):
def test_mysql(self):
self.connection = DBConnection(TEST_DB['user'],
TEST_DB['pass'],
TEST_DB['host'],
TEST_DB['database'],
vendor=TEST_DB['vendor'])
try:
self.connection.check_connection()
connection_success = True
except:
connection_success = False
self.assertTrue(connection_success)
def test_pgsql(self):
self.connection = DBConnection(TEST_DB_POSTGRES['user'],
TEST_DB_POSTGRES['pass'],
TEST_DB_POSTGRES['host'],
TEST_DB_POSTGRES['database'],
vendor=TEST_DB_POSTGRES['vendor'])
try:
self.connection.check_connection()
connection_success = True
except:
connection_success = False
self.assertTrue(connection_success)
class TestConnection(unittest.TestCase):
def test_mysql(self):
self.connection = DBConnection(
TEST_DB['user'],
TEST_DB['pass'],
TEST_DB['host'],
TEST_DB['database'],
vendor=TEST_DB['vendor']
)
try:
self.connection.check_connection()
connection_success = True
except:
connection_success = False
self.assertTrue(connection_success)
def test_pgsql(self):
self.connection = DBConnection(
TEST_DB_POSTGRES['user'],
TEST_DB_POSTGRES['pass'],
TEST_DB_POSTGRES['host'],
TEST_DB_POSTGRES['database'],
vendor=TEST_DB_POSTGRES['vendor']
)
try:
self.connection.check_connection()
connection_success = True
except:
connection_success = False
self.assertTrue(connection_success)
def setUp(self):
self.connection = DBConnection(TEST_DB['user'],
TEST_DB['pass'],
TEST_DB['host'],
TEST_DB['database'],
vendor=TEST_DB['vendor'])
self.connection_pg = DBConnection(TEST_DB_POSTGRES['user'],
TEST_DB_POSTGRES['pass'],
TEST_DB_POSTGRES['host'],
TEST_DB_POSTGRES['database'],
vendor=TEST_DB_POSTGRES['vendor'])
def test_mysql(self):
self.connection = DBConnection(TEST_DB['user'],
TEST_DB['pass'],
TEST_DB['host'],
TEST_DB['database'],
vendor=TEST_DB['vendor'])
try:
self.connection.check_connection()
connection_success = True
except:
connection_success = False
self.assertTrue(connection_success)
def setUp(self):
# MySQL test db
self.connection = DBConnection(TEST_DB['user'],
TEST_DB['pass'],
TEST_DB['host'],
TEST_DB['database'],
vendor=TEST_DB['vendor'])
self.context = DBContext(self.connection,
target_table='trains',
target_att='direction')
# Postgres test db
self.connection_pg = DBConnection(TEST_DB_POSTGRES['user'],
TEST_DB_POSTGRES['pass'],
TEST_DB_POSTGRES['host'],
TEST_DB_POSTGRES['database'],
vendor=TEST_DB_POSTGRES['vendor'])
self.context_pg = DBContext(self.connection_pg)
self.context_pg.target_table = 'urbanblock'
self.context_pg.target_att = 'class'
def test_mysql(self):
self.connection = DBConnection(
TEST_DB['user'],
TEST_DB['pass'],
TEST_DB['host'],
TEST_DB['database'],
vendor=TEST_DB['vendor']
)
try:
self.connection.check_connection()
connection_success = True
except:
connection_success = False
self.assertTrue(connection_success)
from rdm.db import DBVendor, DBConnection, DBContext, AlephConverter
from rdm.wrappers import Aleph
# Provide connection information
connection = DBConnection(
'ilp', # User
'ilp123', # Password
'workflow.ijs.si', # Host
'ilp', # Database
)
# Define learning context
context = DBContext(connection, target_table='trains', target_att='direction')
# Convert the data and induce features using Aleph
conv = AlephConverter(context, target_att_val='east')
aleph = Aleph()
theory, features = aleph.induce('induce_features', conv.positive_examples(),
conv.negative_examples(),
conv.background_knowledge())
print(theory)
import sys
reload(sys) # Reload does the trick!
sys.setdefaultencoding('UTF8')
import orange
from rdm.db import DBVendor, DBConnection, DBContext, RSDConverter, mapper
from rdm.wrappers import RSD
from rdm.validation import cv_split
from rdm.helpers import arff_to_orange_table
# Provide connection information
connection = DBConnection(
'ilp', # User
'ilp123', # Password
'workflow.ijs.si', # Host
'imdb_top', # Database
vendor=DBVendor.MySQL)
# Define learning context
context = DBContext(connection, target_table='movies', target_att='quality')
# Cross-validation loop
predictions = []
folds = 10
for train_context, test_context in cv_split(context,
folds=folds,
random_seed=0):
# Find features on the train set
conv = RSDConverter(train_context)
rsd = RSD()
algorithm = "nrelaggs"
#hyperparameters
predictor_layers = [(100, ), (50, ), (100, 50)]
loss = 'hinge'
feature_generation = [1., 0.5, 0.75]
feature_selection = [1., 0.5, 0.75]
#for propStar/propDRM
learning_rates = [0.001, 0.01, 0.0001]
num_featuress = [10000, 30000, 50000]
hidden_sizes = [8, 16, 32]
connection = DBConnection(
'guest', # User
'relational', # Password
'relational.fit.cvut.cz', # Host
dataset, # Database
vendor=DBVendor.MySQL)
context = DBContext(connection,
target_table=target_table,
target_att=target_label)
#Sql-File for propStar/propDRM
sql_file = "Data/trains/trains.sql"
if algorithm in ["aleph", "rsd", "treeliker", "wordification", "relaggs"]:
transform(algorithm,
context,
target_attr_value,
seed=1,
