def test_initialize_models():
test_tablename, _ = create_dha(path='data/dha_missing.csv')
engine = Engine(seed=0)
num_models = 5
engine.initialize_models(test_tablename, num_models)
model_ids = engine.persistence_layer.get_model_ids(test_tablename)
assert sorted(model_ids) == range(num_models)
for i in range(num_models):
model = engine.persistence_layer.get_models(test_tablename, i)
assert model['iterations'] == 0
def test_initialize_models():
test_tablename, _ = create_dha(path='data/dha_missing.csv')
engine = Engine(seed=0)
num_models = 5
engine.initialize_models(test_tablename, num_models)
model_ids = engine.persistence_layer.get_model_ids(test_tablename)
assert sorted(model_ids) == range(num_models)
for i in range(num_models):
model = engine.persistence_layer.get_models(test_tablename, i)
assert model['iterations'] == 0
def test_infer():
# TODO: whereclauses
test_tablename, _ = create_dha(path='data/dha_missing.csv')
# dha_missing has missing qual_score in first 5 rows, and missing name in rows 6-10.
engine = Engine(seed=0)
engine.initialize_models(test_tablename, 20)
functions = bql.bql_statement.parseString('infer name, qual_score from test',
parseAll=True).functions
whereclause = None
limit = float('inf')
order_by = False
numsamples = 30
confidence = 0
infer_result = engine.infer(test_tablename, functions, confidence, whereclause, limit,
numsamples, order_by)
assert 'column_labels' in infer_result
assert 'data' in infer_result
assert infer_result['column_labels'] == ['key', 'name', 'qual_score']
# 307 is the total number of rows in the dataset.
assert(len(infer_result['data']) == 307 and
len(infer_result['data'][0]) == len(infer_result['column_labels']))
assert type(infer_result['data'][0][0]) == numpy.string_ # type of key is int
t = type(infer_result['data'][0][1])
assert (t == unicode) or (t == numpy.string_) # type of name is string
assert type(infer_result['data'][0][2]) == float # type of qual_score is float
all_possible_names = [infer_result['data'][row][1] for row in range(5) + range(10, 307)]
all_observed_qual_scores = [infer_result['data'][row][2] for row in range(5, 307)]
for row in range(5):
inferred_name = infer_result['data'][row+5][1]
inferred_qual_score = infer_result['data'][row][2]
assert inferred_name in all_possible_names
assert type(inferred_qual_score) == type(1.2)
assert inferred_qual_score > min(all_observed_qual_scores)
assert inferred_qual_score < max(all_observed_qual_scores)
# Now, try infer with higher confidence, and make sure that name isn't inferred anymore.
confidence = 0.9
infer_result = engine.infer(test_tablename, functions, confidence, whereclause, limit,
numsamples, order_by)
for row in range(5):
# TODO: what do missing values look like? these should be missing
inferred_name = infer_result['data'][row+5][1]
inferred_qual_score = infer_result['data'][row][2]
assert numpy.isnan(inferred_name)
assert numpy.isnan(inferred_qual_score)
def test_infer():
# TODO: whereclauses
test_tablename, _ = create_dha(path='data/dha_missing.csv')
# dha_missing has missing qual_score in first 5 rows, and missing name in rows 6-10.
engine = Engine(seed=0)
engine.initialize_models(test_tablename, 20)
functions = bql.bql_statement.parseString('infer name, qual_score from test',
parseAll=True).functions
whereclause = None
limit = float('inf')
order_by = False
numsamples = 30
confidence = 0
infer_result = engine.infer(test_tablename, functions, confidence, whereclause, limit,
numsamples, order_by)
assert 'column_labels' in infer_result
assert 'data' in infer_result
assert infer_result['column_labels'] == ['key', 'name', 'qual_score']
# 307 is the total number of rows in the dataset.
assert(len(infer_result['data']) == 307 and
len(infer_result['data'][0]) == len(infer_result['column_labels']))
assert type(infer_result['data'][0][0]) == numpy.string_ # type of key is int
t = type(infer_result['data'][0][1])
assert (t == unicode) or (t == numpy.string_) # type of name is string
assert type(infer_result['data'][0][2]) == float # type of qual_score is float
all_possible_names = [infer_result['data'][row][1] for row in range(5) + range(10, 307)]
all_observed_qual_scores = [infer_result['data'][row][2] for row in range(5, 307)]
for row in range(5):
inferred_name = infer_result['data'][row+5][1]
inferred_qual_score = infer_result['data'][row][2]
assert inferred_name in all_possible_names
assert type(inferred_qual_score) == type(1.2)
assert inferred_qual_score > min(all_observed_qual_scores)
assert inferred_qual_score < max(all_observed_qual_scores)
# Now, try infer with higher confidence, and make sure that name isn't inferred anymore.
confidence = 0.9
infer_result = engine.infer(test_tablename, functions, confidence, whereclause, limit,
numsamples, order_by)
for row in range(5):
# TODO: what do missing values look like? these should be missing
inferred_name = infer_result['data'][row+5][1]
inferred_qual_score = infer_result['data'][row][2]
assert numpy.isnan(inferred_name)
assert numpy.isnan(inferred_qual_score)
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
#
from twisted.web import server, resource # iweb
# from twisted.web.resource import EncodingResourceWrapper
# from twisted.internet import ssl
# import traceback
from twisted.internet import reactor
from jsonrpc.server import ServerEvents, JSON_RPC
from bayesdb.engine import Engine
engine = Engine()
from bayesdb.client import Client
client = Client()
class ExampleServer(ServerEvents):
# inherited hooks
def log(self, responses, txrequest, error):
print(txrequest.code, end=' ')
if isinstance(responses, list):
for response in responses:
msg = self._get_msg(response)
print(txrequest, msg)
else:
msg = self._get_msg(responses)
def setup_function(function):
global test_tablenames
test_tablenames = []
global engine
engine = Engine()
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import time
import inspect
import psycopg2
import pickle
import os
from bayesdb.engine import Engine
engine = Engine('local')
def run_test(hostname='localhost', middleware_port=8008, online=False):
URI = 'http://' + hostname + ':%d' % middleware_port
cur_dir = os.path.dirname(os.path.abspath(__file__))
test_tablenames = ['dha_small', 'anneal_small']
for tablename in test_tablenames:
table_csv = open('%s/../../www/data/%s.csv' % (cur_dir, tablename),
'r').read()
run_test_with(tablename, table_csv, URI, online)
def run_test_with(tablename,
table_csv,
