def setUp(self):
self._neighbourhood_sizes = (2, 3)
self._num_example_things = 2
self._builder = array.ArrayConverter(self._neighbourhood_sizes)
self._expected_dims = [self._num_example_things] + list(
reversed(self._neighbourhood_sizes)) + [1]
def __init__(
self,
neighbour_sample_sizes,
features_size,
example_concepts_features_size,
aggregated_size,
embedding_size,
schema_encoding_transaction,
batch_size,
embedding_normalisation=tf.nn.l2_normalize,
neighbour_sampling_method=ordered.ordered_sample,
neighbour_sampling_limit_factor=1,
formatters={'neighbour_value_date': preprocess.datetime_to_unixtime},
features_to_exclude=()):
self._embedding_normalisation = embedding_normalisation
self.embedding_size = embedding_size
self.aggregated_size = aggregated_size
self.neighbour_sample_sizes = neighbour_sample_sizes
self.feature_sizes = [features_size] * len(self.neighbour_sample_sizes)
self.feature_sizes[-1] = example_concepts_features_size
print(f'feature sizes: {self.feature_sizes}')
self._schema_encoding_transaction = schema_encoding_transaction
self._encode = encode.Encoder(self._schema_encoding_transaction)
self.batch_size = batch_size
self._formatters = formatters
self._features_to_exclude = features_to_exclude
traversal_samplers = []
for sample_size in neighbour_sample_sizes:
traversal_samplers.append(
sample.Sampler(sample_size,
neighbour_sampling_method,
limit=int(sample_size *
neighbour_sampling_limit_factor)))
self._array_builder = array.ArrayConverter(neighbour_sample_sizes)
self._context_builder = builder.ContextBuilder(traversal_samplers)
self._embed = embed.Embedder(
self.feature_sizes,
self.aggregated_size,
self.embedding_size,
self.neighbour_sample_sizes,
normalisation=self._embedding_normalisation)
features_to_exclude = {
feat_name: None
for feat_name in self._features_to_exclude
}
self.neighbourhood_dataset, self.array_placeholders = preprocess.build_dataset(
self.neighbour_sample_sizes, **features_to_exclude)
def setUp(self):
import grakn
entity_query = "match $x isa company, has name 'Google'; get;"
uri = "localhost:48555"
keyspace = "test_schema"
client = grakn.Grakn(uri=uri)
session = client.session(keyspace=keyspace)
self._tx = session.transaction(grakn.TxType.WRITE)
neighbour_sample_sizes = (6, 4, 4)
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_things = [
answermap.get('x')
for answermap in list(self._tx.query(entity_query))
]
things = [
neighbour.build_thing(grakn_thing) for grakn_thing in grakn_things
]
context_builder = builder.ContextBuilder(samplers)
neighbourhood_depths = [
context_builder.build(self._tx, thing) for thing in things
]
neighbour_roles = builder.convert_thing_contexts_to_neighbours(
neighbourhood_depths)
# flat = builder.flatten_tree(neighbour_roles)
# [builder.collect_to_tree(neighbourhood_depth) for neighbourhood_depth in neighbourhood_depths]
################################################################################################################
# Context Array Building
################################################################################################################
self._array_builder = array.ArrayConverter(neighbour_sample_sizes)
self._context_arrays = self._array_builder.convert_to_array(
neighbour_roles)
def setUp(self):
self._neighbour_sample_sizes = (2, 3)
neighbourhood_depths = [mock.mock_traversal_output()]
neighbour_roles = builder.convert_thing_contexts_to_neighbours(
neighbourhood_depths)
################################################################################################################
# Context Array Building
################################################################################################################
self._array_builder = array.ArrayConverter(
self._neighbour_sample_sizes)
self._context_arrays = self._array_builder.convert_to_array(
neighbour_roles)
def test_date(self):
neighbour_roles = [
builder.Neighbour(
None, None,
builder.ThingContext(
neighbour.Thing("1",
"start-date",
"attribute",
data_type='date',
value=datetime.datetime(day=1,
month=1,
year=2018)),
mock.gen([]))),
]
self._array_builder = array.ArrayConverter((2, 3))
self._context_arrays = self._array_builder.convert_to_array(
neighbour_roles)