def setUpClass(cls):
client = grakn.client.GraknClient(uri="localhost:48555")
cls.session = client.session(keyspace="test_schema")
entity_query = "match $x isa company, has name 'Google'; get;"
cls._tx = cls.session.transaction().write()
neighbour_sample_sizes = (4, 3)
sampling_method = ordered.ordered_sample
samplers = []
for sample_size in neighbour_sample_sizes:
samplers.append(
samp.Sampler(sample_size,
sampling_method,
limit=sample_size * 2))
grakn_thing = next(cls._tx.query(entity_query)).get('x')
thing = neighbour.build_thing(grakn_thing)
context_builder = builder.ContextBuilder(samplers)
cls.context = context_builder.build(cls._tx, thing)
def test_integration(self):
client = grakn.client.GraknClient(uri=TEST_URI)
session = client.session(keyspace=TEST_KEYSPACE)
tx = session.transaction().write()
print("================= THINGS ======================")
te = ex.TraversalExecutor(tx)
schema_concept_types = te.get_schema_concept_types(
encode.GET_THING_TYPES_QUERY,
include_implicit=True,
include_metatypes=False)
labels = trv.labels_from_types(schema_concept_types)
print(list(labels))
schema_concept_types = te.get_schema_concept_types(
encode.GET_THING_TYPES_QUERY,
include_implicit=True,
include_metatypes=False)
super_types = trv.get_sups_labels_per_type(schema_concept_types,
include_self=True,
include_metatypes=False)
print("==== super types ====")
[print(type, super_types) for type, super_types in super_types.items()]
print("================= ROLES ======================")
schema_concept_types = te.get_schema_concept_types(
encode.GET_ROLE_TYPES_QUERY,
include_implicit=True,
include_metatypes=False)
labels = trv.labels_from_types(schema_concept_types)
print(list(labels))
schema_concept_types = te.get_schema_concept_types(
encode.GET_ROLE_TYPES_QUERY,
include_implicit=True,
include_metatypes=False)
super_types = trv.get_sups_labels_per_type(schema_concept_types,
include_self=True,
include_metatypes=False)
print("==== super types ====")
[print(type, super_types) for type, super_types in super_types.items()]
import grakn.client
training_keyspace= "dm_graph"
neighbour_sample_sizes= [7,2,2]
features_size= 10
example_things_features_size= 5
aggregated_size= 20
embedding_size= 32
batch_size= 10
learning_rate= .1
num_classes=
URI = "172.16.253.242:48555"
client = grakn.client.GraknClient(uri=URI)
session = client.session(keyspace=training_keyspace)
transaction = session.transaction().write()
kgcn = model.KGCN(neighbour_sample_sizes,
features_size,
example_things_features_size,
aggregated_size,
embedding_size,
transaction,
batch_size)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
classifier = classify.SupervisedKGCNMultiClassSingleLabelClassifier(kgcn,
optimizer,
num_classes,
def setUp(self):
client = grakn.client.GraknClient(uri=TEST_URI)
session = client.session(keyspace=TEST_KEYSPACE)
self._tx = session.transaction().write()
def test_encode(self):
client = grakn.client.GraknClient(uri=TEST_URI)
session = client.session(keyspace=TEST_KEYSPACE)
tx = session.transaction().write()
encoder = encode.Encoder(tx)
placeholders = [{
'role_type':
tf.placeholder(dtype=tf.string, shape=(None, 1)),
'role_direction':
tf.placeholder(dtype=tf.int64, shape=(None, 1)),
'neighbour_type':
tf.placeholder(dtype=tf.string, shape=(None, 1)),
'neighbour_data_type':
tf.placeholder(dtype=tf.string, shape=(None, 1)),
'neighbour_value_long':
tf.placeholder(dtype=tf.int64, shape=(None, 1)),
'neighbour_value_double':
tf.placeholder(dtype=tf.float32, shape=(None, 1)),
'neighbour_value_boolean':
tf.placeholder(dtype=tf.int64, shape=(None, 1)),
'neighbour_value_date':
tf.placeholder(dtype=tf.int64, shape=(None, 1)),
'neighbour_value_string':
tf.placeholder(dtype=tf.string, shape=(None, 1))
}]
encoded_output = encoder(placeholders)
example_arrays = {
'role_type':
np.full((4, 1), fill_value='employee', dtype=np.dtype('U50')),
'role_direction':
np.full((4, 1), fill_value=0, dtype=np.int),
'neighbour_type':
np.full((4, 1), fill_value='person', dtype=np.dtype('U50')),
'neighbour_data_type':
np.full((4, 1), fill_value='', dtype=np.dtype('U10')),
'neighbour_value_long':
np.full((4, 1), fill_value=0, dtype=np.int),
'neighbour_value_double':
np.full((4, 1), fill_value=0.0, dtype=np.float),
'neighbour_value_boolean':
np.full((4, 1), fill_value=0, dtype=np.int),
'neighbour_value_date':
np.full((4, 1), fill_value=0, dtype=np.int),
'neighbour_value_string':
np.full((4, 1), fill_value='', dtype=np.dtype('U50'))
}
feed_dict = {
placeholder: example_arrays[placeholder_name]
for placeholder_name, placeholder in placeholders[0].items()
}
init_global = tf.global_variables_initializer()
init_tables = tf.tables_initializer()
tf_session = tf.Session()
tf_session.run(init_global)
tf_session.run(init_tables)
tf_session.run(encoded_output, feed_dict=feed_dict)
def setUp(self):
client = grakn.client.GraknClient(uri="localhost:48555")
session = client.session(keyspace="test_schema")
self._tx = session.transaction().write()
def setUpClass(cls):
client = grakn.client.GraknClient(uri=TEST_URI)
cls.session = client.session(keyspace=cls.keyspace)