def setUpClass(cls):
run_environment(apis=['http'])
config.update(json.loads(Path(CONFIG_PATH).read_text()))
data = fetch(f'select * from test_data.{TEST_DATA_TABLE} limit 50',
as_dict=True)
cls.external_datasource_csv_path = make_test_csv(
EXTERNAL_DS_NAME, data)
def setUpClass(cls):
mdb, datastore = run_environment(
config,
apis=['mysql'],
override_integration_config={
'default_clickhouse': {
'publish': True
}
},
override_api_config={'mysql': {
'ssl': False
}},
mindsdb_database=MINDSDB_DATABASE)
cls.mdb = mdb
models = cls.mdb.get_models()
models = [x['name'] for x in models]
if TEST_PREDICTOR_NAME in models:
cls.mdb.delete_model(TEST_PREDICTOR_NAME)
query('create database if not exists test_data')
if not USE_EXTERNAL_DB_SERVER:
test_csv_path = Path(DATASETS_PATH).joinpath(
TEST_DATASET).joinpath('data.csv')
if TEST_DATA_TABLE not in cls.get_tables_in(cls, 'test_data'):
print('creating test data table...')
upload_csv(
query=query,
columns_map=DATASETS_COLUMN_TYPES[TEST_DATASET],
db_types_map=DB_TYPES_MAP,
table_name=TEST_DATA_TABLE,
csv_path=test_csv_path,
template=
'create table test_data.%s (%s) ENGINE = MergeTree() ORDER BY days_on_market PARTITION BY location'
)
ds = datastore.get_datasource(EXTERNAL_DS_NAME)
if ds is not None:
datastore.delete_datasource(EXTERNAL_DS_NAME)
data = fetch(f'select * from test_data.{TEST_DATA_TABLE} limit 50')
external_datasource_csv = make_test_csv(EXTERNAL_DS_NAME, data)
datastore.save_datasource(EXTERNAL_DS_NAME, 'file', 'test.csv',
external_datasource_csv)
def setUpClass(cls):
mdb, datastore = run_environment(
config,
apis=['mysql'],
override_integration_config={'default_mssql': {
'publish': True
}},
mindsdb_database=MINDSDB_DATABASE)
cls.mdb = mdb
models = cls.mdb.get_models()
models = [x['name'] for x in models]
if TEST_PREDICTOR_NAME in models:
cls.mdb.delete_model(TEST_PREDICTOR_NAME)
data = fetch(
f'select * from test_data.{TEST_DATA_TABLE} order by rental_price offset 0 rows fetch next 50 rows only'
)
external_datasource_csv = make_test_csv(EXTERNAL_DS_NAME, data)
datastore.save_datasource(EXTERNAL_DS_NAME, 'file', 'test.csv',
external_datasource_csv)
def setUpClass(cls):
mdb, datastore = run_environment(config,
apis=['mysql', 'http'],
mindsdb_database=MINDSDB_DATABASE)
cls.mdb = mdb
query('create database if not exists test_data')
if not USE_EXTERNAL_DB_SERVER:
test_csv_path = Path(DATASETS_PATH).joinpath(
TEST_DATASET).joinpath('data.csv')
if TEST_DATA_TABLE not in cls.get_tables_in(cls, 'test_data'):
print('creating test data table...')
upload_csv(query=query,
columns_map=DATASETS_COLUMN_TYPES[TEST_DATASET],
db_types_map=DB_TYPES_MAP,
table_name=TEST_DATA_TABLE,
csv_path=test_csv_path)
data = fetch(f'select * from test_data.{TEST_DATA_TABLE} limit 50',
as_dict=True)
cls.external_datasource_csv_path = make_test_csv(
EXTERNAL_DS_NAME, data)
def setUpClass(cls):
mdb, datastore = run_environment(
config,
apis=['http', 'mysql'],
override_integration_config={'default_mariadb': {
'publish': True
}},
mindsdb_database=MINDSDB_DATABASE)
cls.mdb = mdb
models = cls.mdb.get_models()
models = [x['name'] for x in models]
if TEST_PREDICTOR_NAME in models:
cls.mdb.delete_model(TEST_PREDICTOR_NAME)
if not USE_EXTERNAL_DB_SERVER:
query('create database if not exists test_data')
test_tables = fetch('show tables from test_data', as_dict=False)
test_tables = [x[0] for x in test_tables]
if TEST_DATA_TABLE not in test_tables:
test_csv_path = Path(DATASETS_PATH).joinpath(
TEST_DATASET).joinpath('data.csv')
upload_csv(query=query,
columns_map=DATASETS_COLUMN_TYPES[TEST_DATASET],
db_types_map=DB_TYPES_MAP,
table_name=TEST_DATA_TABLE,
csv_path=test_csv_path)
ds = datastore.get_datasource(EXTERNAL_DS_NAME)
if ds is not None:
datastore.delete_datasource(EXTERNAL_DS_NAME)
data = fetch(f'select * from test_data.{TEST_DATA_TABLE} limit 50')
external_datasource_csv = make_test_csv(EXTERNAL_DS_NAME, data)
datastore.save_datasource(EXTERNAL_DS_NAME, 'file', 'test.csv',
external_datasource_csv)
def setUpClass(cls):
mdb, datastore = run_environment(
config,
apis=['mysql'],
override_integration_config={'default_mssql': {
'publish': True
}},
mindsdb_database=MINDSDB_DATABASE)
cls.mdb = mdb
models = cls.mdb.get_models()
models = [x['name'] for x in models]
if TEST_PREDICTOR_NAME in models:
cls.mdb.delete_model(TEST_PREDICTOR_NAME)
test_csv_path = Path(DATASETS_PATH).joinpath('home_rentals').joinpath(
'data.csv')
res = query(
"SELECT name FROM master.dbo.sysdatabases where name = 'mindsdb_test'",
fetch=True)
if len(res) == 0:
query("create database mindsdb_test")
res = query('''
select * from sys.schemas where name = 'mindsdb_schema';
''',
fetch=True)
if len(res) == 0:
query('''
create schema [mindsdb_schema];
''')
if not USE_EXTERNAL_DB_SERVER:
# show tables from test
test_tables = query(f'''
select 1 from sysobjects where name='{TEST_DATA_TABLE}' and xtype='U';
''',
fetch=True)
if len(test_tables) == 0:
print('creating test data table...')
query(f'''
CREATE TABLE mindsdb_schema.{TEST_DATA_TABLE} (
number_of_rooms int,
number_of_bathrooms int,
sqft int,
location varchar(100),
days_on_market int,
initial_price int,
neighborhood varchar(100),
rental_price int
)
''')
with open(test_csv_path) as f:
csvf = csv.reader(f)
i = 0
for row in csvf:
if i > 0:
number_of_rooms = int(row[0])
number_of_bathrooms = int(row[1])
sqft = int(float(row[2].replace(',', '.')))
location = str(row[3])
days_on_market = int(row[4])
initial_price = int(row[5])
neighborhood = str(row[6])
rental_price = int(float(row[7]))
query(f'''
INSERT INTO mindsdb_schema.{TEST_DATA_TABLE} VALUES (
{number_of_rooms},
{number_of_bathrooms},
{sqft},
'{location}',
{days_on_market},
{initial_price},
'{neighborhood}',
{rental_price}
)''')
i += 1
if i % 100 == 0:
print(i)
print('done')
ds = datastore.get_datasource(EXTERNAL_DS_NAME)
if ds is not None:
datastore.delete_datasource(EXTERNAL_DS_NAME)
data = query(f'select * from test_data.{TEST_DATA_TABLE} limit 50',
fetch=True,
as_dict=True)
external_datasource_csv = make_test_csv(EXTERNAL_DS_NAME, data)
datastore.save_datasource(EXTERNAL_DS_NAME, 'file', 'test.csv',
external_datasource_csv)
