def test_preprocess_task_for_model(self):
task = FITBTask.from_gml_files(self.test_gml_files)
task_filepath = os.path.join(self.output_dataset_dir, 'FITBTask.pkl')
task.save(task_filepath)
FITBCharCNN.preprocess_task(
task=task,
output_dir=self.output_dataset_dir,
n_jobs=30,
data_encoder='new',
data_encoder_kwargs=dict(max_name_encoding_length=10),
instance_to_datapoints_kwargs=dict(max_nodes_per_graph=100))
self.assertNotIn(
'jobs.txt', os.listdir(self.output_dataset_dir),
"The jobs.txt file from process_graph_to_datapoints_with_xargs didn't get deleted"
)
self.assertTrue(
all(len(i) > 10 for i in os.listdir(self.output_dataset_dir)),
"Hacky check for if pickled jobs didn't get deleted")
reencoding_dir = os.path.join(self.output_dataset_dir, 're-encoding')
os.mkdir(reencoding_dir)
data_encoder = FITBCharCNN.DataEncoder.load(
os.path.join(self.output_dataset_dir,
'FITBCharCNNDataEncoder.pkl'))
self.assertCountEqual(
data_encoder.all_edge_types,
list(all_edge_types) +
['reverse_{}'.format(i) for i in all_edge_types],
"DataEncoder found weird edge types")
FITBCharCNN.preprocess_task(task=task,
output_dir=reencoding_dir,
n_jobs=30,
data_encoder=data_encoder)
orig_datapoints = []
for file in os.listdir(self.output_dataset_dir):
if file not in [
'FITBCharCNNDataEncoder.pkl', 'FITBTask.pkl', 're-encoding'
]:
with open(os.path.join(self.output_dataset_dir, file),
'rb') as f:
dp = pickle.load(f)
self.assertCountEqual(
dp.edges.keys(),
list(all_edge_types) +
['reverse_{}'.format(i) for i in all_edge_types],
'We lost some edge types')
orig_datapoints.append(
(dp.node_types, dp.node_names, dp.label,
dp.origin_file, dp.encoder_hash, dp.edges.keys()))
reencoded_datapoints = []
for file in os.listdir(reencoding_dir):
with open(os.path.join(reencoding_dir, file), 'rb') as f:
dp = pickle.load(f)
reencoded_datapoints.append(
(dp.node_types, dp.node_names, dp.label, dp.origin_file,
dp.encoder_hash, dp.edges.keys()))
self.assertCountEqual(orig_datapoints, reencoded_datapoints)
def test_encode(self):
de = FITBCharCNNDataEncoder(self.task.graphs_and_instances,
excluded_edge_types=frozenset(),
instance_to_datapoints_kwargs=dict(),
max_name_encoding_length=self.max_name_encoding_length)
for graph, instances in self.task.graphs_and_instances:
for instance in tqdm(instances):
dporig = FITBCharCNN.instance_to_datapoint(graph, instance, de, max_nodes_per_graph=50)
dp = deepcopy(dporig)
de.encode(dp)
self.assertEqual(list(dp.edges.keys()), sorted(list(de.all_edge_types)),
"Not all adjacency matrices were created")
for edge_type, adj_mat in dp.edges.items():
np.testing.assert_equal(adj_mat.todense(),
dporig.subgraph.get_adjacency_matrix(edge_type).todense())
self.assertIsInstance(adj_mat, sp.sparse.coo_matrix,
"Encoding produces adjacency matrix of wrong type")
self.assertEqual(len(dporig.node_types), len(dp.node_types),
"Type for some node got lost during encoding")
self.assertEqual([len(i) for i in dporig.node_types], [len(i) for i in dp.node_types],
"Some type for some node got lost during encoding")
for i in range(len(dp.node_types)):
for j in range(len(dp.node_types[i])):
self.assertEqual(dp.node_types[i][j], de.all_node_types[dporig.node_types[i][j]],
"Some node type got encoded wrong")
self.assertEqual(tuple(dporig.node_names), dp.node_names)
self.assertEqual(tuple(dporig.label), dp.label)
def test_batchify_and_unbatchify_are_inverses(self):
FITBCharCNN.preprocess_task(self.task,
output_dir=self.output_dataset_dir,
n_jobs=30,
data_encoder='new',
data_encoder_kwargs=dict(max_name_encoding_length=self.max_name_encoding_length),
instance_to_datapoints_kwargs=dict(max_nodes_per_graph=20))
with open(os.path.join(self.output_dataset_dir, '{}.pkl'.format(FITBCharCNN.DataEncoder.__name__)),
'rb') as f:
de = pickle.load(f)
model = FITBCharCNNGGNN(data_encoder=de,
hidden_size=17,
type_emb_size=5,
name_emb_size=7,
n_msg_pass_iters=1)
model.collect_params().initialize('Xavier', ctx=mx.cpu())
datapoints = [os.path.join(self.output_dataset_dir, i) for i in os.listdir(self.output_dataset_dir) if
'Encoder.pkl' not in i]
batch_size = 64
for b in tqdm(range(int(math.ceil(len(datapoints) / batch_size)))):
batchdpspaths = datapoints[batch_size * b: batch_size * (b + 1)]
batchdps = [de.load_datapoint(b) for b in batchdpspaths]
batchified = model.batchify(batchdpspaths, ctx=mx.cpu())
unbatchified = model.unbatchify(batchified, model(batchified.data))
self.assertEqual(len(batchdps), len(unbatchified), "We lost some datapoints somewhere")
self.assertEqual(sum(len(dp.node_names) for dp in batchdps), sum(batchified.data.batch_sizes).asscalar())
self.assertEqual(sum(len(dp.node_types) for dp in batchdps), sum(batchified.data.batch_sizes).asscalar())
for adj_mat in batchified.data.edges.values():
self.assertEqual(adj_mat.shape, (
sum(len(dp.node_names) for dp in batchdps), sum(len(dp.node_names) for dp in batchdps)),
"Batchified adjacency matrix is wrong size")
for i, (dp, (prediction, label)) in enumerate(zip(batchdps, unbatchified)):
self.assertEqual(len(dp.node_types), len(dp.node_names),
"node_types and node_names arrays are different lengths")
self.assertEqual(len(dp.node_types), batchified.data.batch_sizes[i],
"batch_sizes doesn't match datapoint's array size")
self.assertEqual(prediction.shape, label.shape, "Prediction and one-hot label don't match size")
self.assertEqual(sum(prediction), 1, "Made more than one prediction for this datapoint")
for j in range(len(label)):
if j in dp.label:
self.assertEqual(label[j], 1, "Something didn't get one-hotted")
else:
self.assertEqual(label[j], 0, "Something got one-hotted that shouldn't have")
def test_preprocess_task_existing_encoding_basic_functionality(self):
FITBCharCNN.preprocess_task(
self.task,
output_dir=self.output_dataset_dir,
n_jobs=30,
data_encoder='new',
data_encoder_kwargs=dict(
max_name_encoding_length=self.max_name_encoding_length),
instance_to_datapoints_kwargs=dict(max_nodes_per_graph=20))
de = FITBCharCNNDataEncoder.load(
os.path.join(self.output_dataset_dir,
'{}.pkl'.format(FITBCharCNNDataEncoder.__name__)))
FITBCharCNN.preprocess_task(
self.task,
output_dir=self.output_dataset_dir,
n_jobs=30,
data_encoder=de,
data_encoder_kwargs=dict(
excluded_edge_types=syntax_only_excluded_edge_types,
max_name_encoding_length=self.max_name_encoding_length))
with self.assertRaises(AssertionError):
de = BaseDataEncoder(dict(), frozenset())
FITBCharCNN.preprocess_task(
self.task,
output_dir=self.output_dataset_dir,
n_jobs=30,
data_encoder=de,
data_encoder_kwargs=dict(
excluded_edge_types=syntax_only_excluded_edge_types,
max_name_encoding_length=self.max_name_encoding_length))
def test_preprocess_task_existing_encoding_basic_functionality_excluded_edges(self):
FITBCharCNN.preprocess_task(self.task, output_dir=self.output_dataset_dir, n_jobs=30, data_encoder='new',
excluded_edge_types=syntax_only_excluded_edge_types,
data_encoder_kwargs=dict(
max_name_encoding_length=self.max_name_encoding_length),
instance_to_datapoints_kwargs=dict(max_nodes_per_graph=20))
de = FITBCharCNNDataEncoder.load(
os.path.join(self.output_dataset_dir, '{}.pkl'.format(FITBCharCNNDataEncoder.__name__)))
self.assertEqual(de.excluded_edge_types, syntax_only_excluded_edge_types)
self.assertCountEqual(de.all_edge_types,
list(syntax_only_edge_types) + ['reverse_' + i for i in syntax_only_edge_types])
datapoints = [os.path.join(self.output_dataset_dir, i) for i in os.listdir(self.output_dataset_dir) if
i != 'FITBCharCNNDataEncoder.pkl']
for dp in datapoints:
datapoint = de.load_datapoint(dp)
for e in datapoint.edges.keys():
if e.startswith('reverse_'):
self.assertIn(e[8:], syntax_only_edge_types)
else:
self.assertIn(e, syntax_only_edge_types)
FITBCharCNN.preprocess_task(self.task, output_dir=self.output_dataset_dir, n_jobs=30, data_encoder=de,
excluded_edge_types=syntax_only_excluded_edge_types,
data_encoder_kwargs=dict(
max_name_encoding_length=self.max_name_encoding_length))
with self.assertRaises(AssertionError):
de = BaseDataEncoder(dict(), frozenset())
FITBCharCNN.preprocess_task(self.task, output_dir=self.output_dataset_dir, n_jobs=30, data_encoder=de,
excluded_edge_types=syntax_only_excluded_edge_types,
data_encoder_kwargs=dict(
max_name_encoding_length=self.max_name_encoding_length))
def extra_graph_processing(graph, instances, data_encoder):
graph, instances = FITBCharCNN.extra_graph_processing(
graph, instances, data_encoder)
for node, data in list(graph.nodes):
if graph.is_variable_node(node):
node_subtokens = data_encoder.name_to_subtokens(
data['identifier'])
for st in node_subtokens:
st_node, _ = graph.add_node(
st, identifier=st, type=data_encoder.subtoken_flag)
graph.add_edge(node,
st_node,
type=data_encoder.subtoken_edge_type)
graph.add_edge(
st_node,
node,
type=data_encoder.subtoken_reverse_edge_type)
return graph, instances
def test_instance_to_datapoint(self):
for excluded_edge_types in [syntax_only_excluded_edge_types, frozenset()]:
de = FITBCharCNN.DataEncoder(self.task.graphs_and_instances,
excluded_edge_types=excluded_edge_types,
instance_to_datapoints_kwargs=dict(),
max_name_encoding_length=self.max_name_encoding_length)
for graph, instances in tqdm(self.task.graphs_and_instances):
FITBCharCNN.fix_up_edges(graph, instances, excluded_edge_types)
FITBCharCNN.extra_graph_processing(graph, instances, de)
for instance in instances:
dp = FITBCharCNN.instance_to_datapoint(graph, instance, de, max_nodes_per_graph=100)
self.assertEqual(type(dp), FITBCharCNNDataPoint)
self.assertEqual(len(dp.subgraph.nodes), len(dp.node_types))
self.assertEqual(len(dp.subgraph.nodes), len(dp.node_names))
fill_in_nodes = [i for i in dp.subgraph.nodes_that_represent_variables if
i[1]['identifier'] == de.fill_in_flag]
self.assertEqual(len(fill_in_nodes), 1, "Zero or more than one variable got flagged")
fill_in_idx = fill_in_nodes[0][0]
self.assertEqual(dp.node_names[fill_in_idx], de.fill_in_flag, "Variable flagged wrong")
self.assertEqual(dp.node_types[fill_in_idx], [de.fill_in_flag], "Variable flagged wrong")
self.assertEqual(len([i for i in dp.node_names if i == de.fill_in_flag]), 1,
"Zero or more than one variable got flagged")
self.assertEqual(len([i for i in dp.node_types if i == [de.fill_in_flag]]), 1,
"Zero of more than one variable got flagged")
for et in too_useful_edge_types:
self.assertNotIn(et, [e[3]['type'] for e in dp.subgraph.all_adjacent_edges(fill_in_idx)])
self.assertEqual(len(instance[1]), len(
[n for n, d in dp.subgraph.nodes if 'other_use' in d.keys() and d['other_use'] == True]),
"Wrong number of other uses in label")
for i, (name, types) in enumerate(zip(dp.node_names, dp.node_types)):
self.assertEqual(type(name), str)
self.assertGreater(len(name), 0)
self.assertEqual(type(types), list)
self.assertGreaterEqual(len(types), 1)
if dp.subgraph.is_variable_node(i):
if name != de.fill_in_flag:
self.assertCountEqual(set(re.split(r'[,.]', dp.subgraph[i]['reference'])), types)
self.assertEqual(name, dp.subgraph[i]['identifier'])
else:
self.assertEqual(name, de.fill_in_flag)
else:
self.assertEqual(name, de.internal_node_flag)
self.assertEqual(len(types), 1)
for i in dp.label:
del dp.subgraph[i]['other_use']
self.assertCountEqual([dp.subgraph[i] for i in dp.label], [graph[i] for i in instance[1]])
de.encode(dp)
self.assertIn('AST', dp.edges.keys())
self.assertIn('NEXT_TOKEN', dp.edges.keys())
de.save_datapoint(dp, self.output_dataset_dir)
def test_preprocess_task_type_check_basic_functionality(self):
task = Task
with self.assertRaises(AssertionError):
FITBCharCNN.preprocess_task(task)