def test_construct(self):
examples = types.TfxType(type_name='ExamplesPath')
model_exports = types.TfxType(type_name='ModelExportPath')
evaluator = component.Evaluator(
examples=channel.as_channel([examples]),
model_exports=channel.as_channel([model_exports]))
self.assertEqual('ModelEvalPath', evaluator.outputs.output.type_name)
def test_do(self):
input_data_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
'testdata')
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create input dict.
input_base = types.TfxType(type_name='ExternalPath')
input_base.uri = os.path.join(input_data_dir, 'external/csv/')
input_dict = {'input-base': [input_base]}
# Create output dict.
train_examples = types.TfxType(type_name='ExamplesPath', split='train')
train_examples.uri = os.path.join(output_data_dir, 'train')
eval_examples = types.TfxType(type_name='ExamplesPath', split='eval')
eval_examples.uri = os.path.join(output_data_dir, 'eval')
output_dict = {'examples': [train_examples, eval_examples]}
# Run executor.
csv_example_gen = executor.Executor()
csv_example_gen.Do(input_dict, output_dict, {})
# Check CSV example gen outputs.
train_output_file = os.path.join(train_examples.uri,
'data_tfrecord-00000-of-00001.gz')
eval_output_file = os.path.join(eval_examples.uri,
'data_tfrecord-00000-of-00001.gz')
self.assertTrue(tf.gfile.Exists(train_output_file))
self.assertTrue(tf.gfile.Exists(eval_output_file))
self.assertGreater(
tf.gfile.GFile(train_output_file).size(),
tf.gfile.GFile(eval_output_file).size())
def test_channel_as_channel_success(self):
instance_a = types.TfxType('MyTypeName')
instance_b = types.TfxType('MyTypeName')
chnl_original = channel.Channel(
'MyTypeName', static_artifact_collection=[instance_a, instance_b])
chnl_result = channel.as_channel(chnl_original)
self.assertEqual(chnl_original, chnl_result)
def testDo(self, mock_client):
# Mock query result schema for _BigQueryConverter.
mock_client.return_value.query.return_value.result.return_value.schema = self._schema
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create output dict.
train_examples = types.TfxType(type_name='ExamplesPath', split='train')
train_examples.uri = os.path.join(output_data_dir, 'train')
eval_examples = types.TfxType(type_name='ExamplesPath', split='eval')
eval_examples.uri = os.path.join(output_data_dir, 'eval')
output_dict = {'examples': [train_examples, eval_examples]}
# Run executor.
big_query_example_gen = executor.Executor()
big_query_example_gen.Do({}, output_dict, self._exec_properties)
# Check BigQuery example gen outputs.
train_output_file = os.path.join(train_examples.uri,
'data_tfrecord-00000-of-00001.gz')
eval_output_file = os.path.join(eval_examples.uri,
'data_tfrecord-00000-of-00001.gz')
self.assertTrue(tf.gfile.Exists(train_output_file))
self.assertTrue(tf.gfile.Exists(eval_output_file))
self.assertGreater(
tf.gfile.GFile(train_output_file).size(),
tf.gfile.GFile(eval_output_file).size())
def test_construct(self):
train_examples = types.TfxType(type_name='ExamplesPath', split='train')
eval_examples = types.TfxType(type_name='ExamplesPath', split='eval')
statistics_gen = component.StatisticsGen(
input_data=channel.as_channel([train_examples, eval_examples]))
self.assertEqual('ExampleStatisticsPath',
statistics_gen.outputs.output.type_name)
def setUp(self):
self._source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
tf.gfile.MakeDirs(self._output_data_dir)
self._model_export = types.TfxType(type_name='ModelExportPath')
self._model_export.uri = os.path.join(self._source_data_dir,
'trainer/current/')
self._model_blessing = types.TfxType(type_name='ModelBlessingPath')
self._input_dict = {
'model_export': [self._model_export],
'model_blessing': [self._model_blessing],
}
self._model_push = types.TfxType(type_name='ModelPushPath')
self._model_push.uri = os.path.join(self._output_data_dir, 'model_push')
tf.gfile.MakeDirs(self._model_push.uri)
self._output_dict = {
'model_push': [self._model_push],
}
self._serving_model_dir = os.path.join(self._output_data_dir,
'serving_model_dir')
tf.gfile.MakeDirs(self._serving_model_dir)
self._exec_properties = {
'push_destination':
json_format.MessageToJson(
pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=self._serving_model_dir))),
}
self._executor = executor.Executor()
def test_valid_channel(self):
instance_a = types.TfxType('MyTypeName')
instance_b = types.TfxType('MyTypeName')
chnl = channel.Channel(
'MyTypeName', static_artifact_collection=[instance_a, instance_b])
self.assertEqual(chnl.type_name, 'MyTypeName')
self.assertItemsEqual(chnl.get(), [instance_a, instance_b])
def test_fetch_previous_result(self):
with Metadata(
connection_config=self._connection_config, logger=self._logger) as m:
# Create an 'previous' execution.
exec_properties = {'log_root': 'path'}
eid = m.prepare_execution('Test', exec_properties)
input_artifact = types.TfxType(type_name='ExamplesPath')
m.publish_artifacts([input_artifact])
output_artifact = types.TfxType(type_name='ExamplesPath')
input_dict = {'input': [input_artifact]}
output_dict = {'output': [output_artifact]}
m.publish_execution(eid, input_dict, output_dict)
# Test previous_run.
self.assertEqual(None, m.previous_run('Test', input_dict, {}))
self.assertEqual(None, m.previous_run('Test', {}, exec_properties))
self.assertEqual(None, m.previous_run('Test2', input_dict,
exec_properties))
self.assertEqual(eid, m.previous_run('Test', input_dict, exec_properties))
# Test fetch_previous_result_artifacts.
new_output_artifact = types.TfxType(type_name='ExamplesPath')
self.assertNotEqual(types.ARTIFACT_STATE_PUBLISHED,
new_output_artifact.state)
new_output_dict = {'output': [new_output_artifact]}
updated_output_dict = m.fetch_previous_result_artifacts(
new_output_dict, eid)
previous_artifact = output_dict['output'][-1].artifact
current_artifact = updated_output_dict['output'][-1].artifact
self.assertEqual(types.ARTIFACT_STATE_PUBLISHED,
current_artifact.properties['state'].string_value)
self.assertEqual(previous_artifact.id, current_artifact.id)
self.assertEqual(previous_artifact.type_id, current_artifact.type_id)
def test_invalid_channel_type(self):
instance_a = types.TfxType('MyTypeName')
instance_b = types.TfxType('MyTypeName')
with self.assertRaises(ValueError):
channel.Channel(
'AnotherTypeName',
static_artifact_collection=[instance_a, instance_b])
def setUp(self):
self._source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create input dict.
eval_examples = types.TfxType(type_name='ExamplesPath', split='eval')
eval_examples.uri = os.path.join(self._source_data_dir,
'csv_example_gen/eval/')
model = types.TfxType(type_name='ModelExportPath')
model.uri = os.path.join(self._source_data_dir, 'trainer/current/')
self._input_dict = {
'examples': [eval_examples],
'model': [model],
}
# Create output dict.
self._blessing = types.TfxType('ModelBlessingPath')
self._blessing.uri = os.path.join(output_data_dir, 'blessing')
self._output_dict = {
'blessing': [self._blessing]
}
# Create context
self._tmp_dir = os.path.join(output_data_dir, '.temp')
self._context = executor.Executor.Context(tmp_dir=self._tmp_dir,
unique_id='2')
def test_do(self):
source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
train_stats_artifact = types.TfxType('ExampleStatsPath', split='train')
train_stats_artifact.uri = os.path.join(source_data_dir,
'statistics_gen/train/')
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
schema_output = types.TfxType('SchemaPath')
schema_output.uri = os.path.join(output_data_dir, 'schema_output')
input_dict = {
'stats': [train_stats_artifact],
}
output_dict = {
'output': [schema_output],
}
exec_properties = {}
schema_gen_executor = executor.Executor()
schema_gen_executor.Do(input_dict, output_dict, exec_properties)
self.assertNotEqual(0, len(tf.gfile.ListDirectory(schema_output.uri)))
def setUp(self):
self._mock_metadata = tf.test.mock.Mock()
self._input_dict = {
'input_data': [types.TfxType(type_name='InputType')],
}
input_dir = os.path.join(
os.environ.get('TEST_TMP_DIR', self.get_temp_dir()),
self._testMethodName, 'input_dir')
# valid input artifacts must have a uri pointing to an existing directory.
for key, input_list in self._input_dict.items():
for index, artifact in enumerate(input_list):
artifact.id = index + 1
uri = os.path.join(input_dir, key, str(artifact.id), '')
artifact.uri = uri
tf.gfile.MakeDirs(uri)
self._output_dict = {
'output_data': [types.TfxType(type_name='OutputType')],
}
self._exec_properties = {
'key': 'value',
}
self._base_output_dir = os.path.join(
os.environ.get('TEST_TMP_DIR', self.get_temp_dir()),
self._testMethodName, 'base_output_dir')
self._driver_options = base_driver.DriverOptions(
worker_name='worker_name',
base_output_dir=self._base_output_dir,
enable_cache=True)
self._execution_id = 100
log_root = os.path.join(self._base_output_dir, 'log_dir')
logger_config = logging_utils.LoggerConfig(log_root=log_root)
self._logger = logging_utils.get_logger(logger_config)
def setUp(self):
self._source_data_dir = os.path.join(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))),
'components', 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
tf.gfile.MakeDirs(self._output_data_dir)
self._model_export = types.TfxType(type_name='ModelExportPath')
self._model_export.uri = os.path.join(self._source_data_dir,
'trainer/current/')
self._model_blessing = types.TfxType(type_name='ModelBlessingPath')
self._input_dict = {
'model_export': [self._model_export],
'model_blessing': [self._model_blessing],
}
self._model_push = types.TfxType(type_name='ModelPushPath')
self._model_push.uri = os.path.join(self._output_data_dir,
'model_push')
tf.gfile.MakeDirs(self._model_push.uri)
self._output_dict = {
'model_push': [self._model_push],
}
self._exec_properties = {
'custom_config': {
'ai_platform_serving_args': {
'model_name': 'model_name',
'project_id': 'project_id'
},
},
}
self._executor = Executor()
def test_do(self):
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create output dict.
train_examples = types.TfxType(type_name='ExamplesPath', split='train')
train_examples.uri = os.path.join(output_data_dir, 'train')
eval_examples = types.TfxType(type_name='ExamplesPath', split='eval')
eval_examples.uri = os.path.join(output_data_dir, 'eval')
output_dict = {'examples': [train_examples, eval_examples]}
# Run executor.
example_gen = TestExampleGenExecutor()
example_gen.Do({}, output_dict, {})
# Check example gen outputs.
train_output_file = os.path.join(train_examples.uri,
'data_tfrecord-00000-of-00001.gz')
eval_output_file = os.path.join(eval_examples.uri,
'data_tfrecord-00000-of-00001.gz')
self.assertTrue(tf.gfile.Exists(train_output_file))
self.assertTrue(tf.gfile.Exists(eval_output_file))
self.assertGreater(
tf.gfile.GFile(train_output_file).size(),
tf.gfile.GFile(eval_output_file).size())
def test_do(self):
source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create input dict.
train_examples = types.TfxType(type_name='ExamplesPath', split='train')
train_examples.uri = os.path.join(
source_data_dir, 'transform/transformed_examples/train/')
eval_examples = types.TfxType(type_name='ExamplesPath', split='eval')
eval_examples.uri = os.path.join(
source_data_dir, 'transform/transformed_examples/eval/')
transform_output = types.TfxType(type_name='TransformPath')
transform_output.uri = os.path.join(source_data_dir,
'transform/transform_output/')
schema = types.TfxType(type_name='ExamplesPath')
schema.uri = os.path.join(source_data_dir, 'schema_gen/')
input_dict = {
'transformed_examples': [train_examples, eval_examples],
'transform_output': [transform_output],
'schema': [schema],
}
# Create output dict.
model_exports = types.TfxType(type_name='ModelExportPath')
model_exports.uri = os.path.join(output_data_dir, 'model_export_path')
output_dict = {'output': [model_exports]}
# Create exec properties.
module_file_path = os.path.join(source_data_dir, 'module_file',
'trainer_module.py')
exec_properties = {
'train_args':
json_format.MessageToJson(trainer_pb2.TrainArgs(num_steps=1000)),
'eval_args':
json_format.MessageToJson(trainer_pb2.EvalArgs(num_steps=500)),
'module_file':
module_file_path,
'warm_starting':
False,
}
# Run executor.
pipeline = beam.Pipeline()
evaluator = executor.Executor(pipeline)
evaluator.Do(input_dict=input_dict,
output_dict=output_dict,
exec_properties=exec_properties)
# Check outputs.
self.assertTrue(
tf.gfile.Exists(os.path.join(model_exports.uri, 'eval_model_dir')))
self.assertTrue(
tf.gfile.Exists(
os.path.join(model_exports.uri, 'serving_model_dir')))
def test_construct(self):
examples = types.TfxType(type_name='ExamplesPath')
model = types.TfxType(type_name='ModelExportPath')
model_validator = component.ModelValidator(
examples=channel.as_channel([examples]),
model=channel.as_channel([model]))
self.assertEqual('ModelBlessingPath',
model_validator.outputs.blessing.type_name)
def test_construct(self):
example_vadalitor = component.ExampleValidator(
stats=channel.as_channel([
types.TfxType(type_name='ExampleStatisticsPath', split='eval')
]),
schema=channel.as_channel([types.TfxType(type_name='SchemaPath')]),
)
self.assertEqual('ExampleValidationPath',
example_vadalitor.outputs.output.type_name)
def setUp(self):
self.input_one = types.TfxType('INPUT_ONE')
self.input_one.source = airflow_component._OrchestrationSource(
'input_one_key', 'input_one_component_id')
self.output_one = types.TfxType('OUTPUT_ONE')
self.output_one.source = airflow_component._OrchestrationSource(
'output_one_key', 'output_one_component_id')
self.input_one_json = json.dumps([self.input_one.json_dict()])
self.output_one_json = json.dumps([self.output_one.json_dict()])
self._logger_config = logging_utils.LoggerConfig()
def test_execution(self):
with Metadata(
connection_config=self._connection_config,
logger=self._logger) as m:
# Test prepare_execution.
exec_properties = {}
eid = m.prepare_execution('Test', exec_properties)
[execution] = m.store.get_executions()
self.assertProtoEquals(
"""
id: 1
type_id: 1
properties {
key: "state"
value {
string_value: "new"
}
}""", execution)
# Test publish_execution.
input_artifact = types.TfxType(type_name='ExamplesPath')
m.publish_artifacts([input_artifact])
output_artifact = types.TfxType(type_name='ExamplesPath')
input_dict = {'input': [input_artifact]}
output_dict = {'output': [output_artifact]}
m.publish_execution(eid, input_dict, output_dict)
# Make sure artifacts in output_dict are published.
self.assertEqual(types.ARTIFACT_STATE_PUBLISHED, output_artifact.state)
# Make sure execution state are changed.
[execution] = m.store.get_executions_by_id([eid])
self.assertEqual('complete', execution.properties['state'].string_value)
# Make sure events are published.
events = m.store.get_events_by_execution_ids([eid])
self.assertEqual(2, len(events))
self.assertEqual(input_artifact.id, events[0].artifact_id)
self.assertEqual(metadata_store_pb2.Event.DECLARED_INPUT, events[0].type)
self.assertProtoEquals(
"""
steps {
key: "input"
}
steps {
index: 0
}""", events[0].path)
self.assertEqual(output_artifact.id, events[1].artifact_id)
self.assertEqual(metadata_store_pb2.Event.DECLARED_OUTPUT, events[1].type)
self.assertProtoEquals(
"""
steps {
key: "output"
}
steps {
index: 0
}""", events[1].path)
def test_construct_with_slice_spec(self):
examples = types.TfxType(type_name='ExamplesPath')
model_exports = types.TfxType(type_name='ModelExportPath')
evaluator = component.Evaluator(
examples=channel.as_channel([examples]),
model_exports=channel.as_channel([model_exports]),
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
self.assertEqual('ModelEvalPath', evaluator.outputs.output.type_name)
def test_construct(self):
transformed_examples = types.TfxType(type_name='ExamplesPath')
transform_output = types.TfxType(type_name='TransformPath')
schema = types.TfxType(type_name='SchemaPath')
trainer = component.Trainer(
module_file='/path/to/module/file',
transformed_examples=channel.as_channel([transformed_examples]),
transform_output=channel.as_channel([transform_output]),
schema=channel.as_channel([schema]),
train_args=trainer_pb2.TrainArgs(num_steps=100),
eval_args=trainer_pb2.EvalArgs(num_steps=50))
self.assertEqual('ModelExportPath', trainer.outputs.output.type_name)
def testDo(self, mock_client):
# Mock query result schema for _BigQueryConverter.
mock_client.return_value.query.return_value.result.return_value.schema = self._schema
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create output dict.
train_examples = types.TfxType(type_name='ExamplesPath', split='train')
train_examples.uri = os.path.join(output_data_dir, 'train')
eval_examples = types.TfxType(type_name='ExamplesPath', split='eval')
eval_examples.uri = os.path.join(output_data_dir, 'eval')
output_dict = {'examples': [train_examples, eval_examples]}
# Create exe properties.
exec_properties = {
'input':
json_format.MessageToJson(
example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(
name='bq', pattern='SELECT i, f, s FROM `fake`'),
])),
'output':
json_format.MessageToJson(
example_gen_pb2.Output(
split_config=example_gen_pb2.SplitConfig(splits=[
example_gen_pb2.SplitConfig.Split(name='train',
hash_buckets=2),
example_gen_pb2.SplitConfig.Split(name='eval',
hash_buckets=1)
])))
}
# Run executor.
big_query_example_gen = executor.Executor()
big_query_example_gen.Do({}, output_dict, exec_properties)
# Check BigQuery example gen outputs.
train_output_file = os.path.join(train_examples.uri,
'data_tfrecord-00000-of-00001.gz')
eval_output_file = os.path.join(eval_examples.uri,
'data_tfrecord-00000-of-00001.gz')
self.assertTrue(tf.gfile.Exists(train_output_file))
self.assertTrue(tf.gfile.Exists(eval_output_file))
self.assertGreater(
tf.gfile.GFile(train_output_file).size(),
tf.gfile.GFile(eval_output_file).size())
def test_fetch_last_blessed_model(self):
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
log_root = os.path.join(output_data_dir, 'log_root')
# Mock metadata.
mock_metadata = tf.test.mock.Mock()
model_validator_driver = driver.Driver(log_root, mock_metadata)
# No blessed model.
mock_metadata.get_all_artifacts.return_value = []
self.assertEqual((None, None),
model_validator_driver._fetch_last_blessed_model())
# Mock blessing artifacts.
artifacts = []
for span in [4, 3, 2, 1]:
model_blessing = types.TfxType(type_name='ModelBlessingPath')
model_blessing.span = span
model_blessing.set_string_custom_property('current_model',
'uri-%d' % span)
model_blessing.set_int_custom_property('current_model_id', span)
# Only odd spans are "blessed"
model_blessing.set_int_custom_property('blessed', span % 2)
artifacts.append(model_blessing.artifact)
mock_metadata.get_all_artifacts.return_value = artifacts
self.assertEqual(('uri-3', 3),
model_validator_driver._fetch_last_blessed_model())
def setUp(self):
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create output dict.
train_examples = types.TfxType(type_name='ExamplesPath', split='train')
train_examples.uri = os.path.join(output_data_dir, 'train')
eval_examples = types.TfxType(type_name='ExamplesPath', split='eval')
eval_examples.uri = os.path.join(output_data_dir, 'eval')
self._output_dict = {'examples': [train_examples, eval_examples]}
self._train_output_file = os.path.join(
train_examples.uri, 'data_tfrecord-00000-of-00001.gz')
self._eval_output_file = os.path.join(
eval_examples.uri, 'data_tfrecord-00000-of-00001.gz')
def test_do(self):
source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create input dict.
train_examples = types.TfxType(type_name='ExamplesPath', split='train')
eval_examples = types.TfxType(type_name='ExamplesPath', split='eval')
eval_examples.uri = os.path.join(source_data_dir,
'csv_example_gen/eval/')
model_exports = types.TfxType(type_name='ModelExportPath')
model_exports.uri = os.path.join(source_data_dir, 'trainer/current/')
input_dict = {
'examples': [train_examples, eval_examples],
'model_exports': [model_exports],
}
# Create output dict.
eval_output = types.TfxType('ModelEvalPath')
eval_output.uri = os.path.join(output_data_dir, 'eval_output')
output_dict = {'output': [eval_output]}
# Create exec proterties.
exec_properties = {
'feature_slicing_spec':
json_format.MessageToJson(
evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour']),
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_day', 'trip_miles']),
]))
}
# Run executor.
evaluator = executor.Executor()
evaluator.Do(input_dict, output_dict, exec_properties)
# Check evaluator outputs.
self.assertTrue(
tf.gfile.Exists(os.path.join(eval_output.uri, 'eval_config')))
self.assertTrue(
tf.gfile.Exists(os.path.join(eval_output.uri, 'metrics')))
self.assertTrue(tf.gfile.Exists(os.path.join(eval_output.uri,
'plots')))
def setUp(self):
input_data_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'testdata')
# Create input dict.
input_base = types.TfxType(type_name='ExternalPath')
input_base.uri = os.path.join(input_data_dir, 'external/csv/')
self._input_dict = {'input-base': [input_base]}
def test_tfxtype_deprecated(self):
with mock.patch.object(tf_logging, 'warning'):
warn_mock = mock.MagicMock()
tf_logging.warning = warn_mock
types.TfxType('FakeType')
warn_mock.assert_called_once()
self.assertIn('TfxType has been renamed to TfxArtifact',
warn_mock.call_args[0][5])
def test_construct(self):
source_data_dir = os.path.join(os.path.dirname(__file__), 'testdata',
'taxi')
preprocessing_fn_file = os.path.join(source_data_dir, 'module',
'preprocess.py')
transform = component.Transform(
input_data=channel.as_channel([
types.TfxType(type_name='ExamplesPath', split='train'),
types.TfxType(type_name='ExamplesPath', split='eval'),
]),
schema=channel.as_channel([types.TfxType(type_name='SchemaPath')]),
module_file=preprocessing_fn_file,
)
self.assertEqual('TransformPath',
transform.outputs.transform_output.type_name)
self.assertEqual('ExamplesPath',
transform.outputs.transformed_examples.type_name)
def test_tfx_type(self):
instance = types.TfxType('MyTypeName', split='eval')
# Test property getters.
self.assertEqual('', instance.uri)
self.assertEqual(0, instance.id)
self.assertEqual(0, instance.type_id)
self.assertEqual('MyTypeName', instance.type_name)
self.assertIsNone(instance.state)
self.assertEqual('eval', instance.split)
self.assertIsNone(instance.span)
# Test property setters.
instance.uri = '/tmp/uri2'
self.assertEqual('/tmp/uri2', instance.uri)
instance.id = 1
self.assertEqual(1, instance.id)
instance.type_id = 2
self.assertEqual(2, instance.type_id)
instance.state = types.ARTIFACT_STATE_DELETED
self.assertEqual(types.ARTIFACT_STATE_DELETED, instance.state)
instance.split = ''
self.assertEqual('', instance.split)
instance.span = 20190101
self.assertEqual(20190101, instance.span)
instance.set_int_custom_property('int_key', 20)
self.assertEqual(20,
instance.artifact.custom_properties['int_key'].int_value)
instance.set_string_custom_property('string_key', 'string_value')
self.assertEqual(
'string_value',
instance.artifact.custom_properties['string_key'].string_value)
self.assertEqual('MyTypeName:/tmp/uri2.1', str(instance))
# Test json serialization.
json_dict = instance.json_dict()
s = json.dumps(json_dict)
other_instance = types.TfxType.parse_from_json_dict(json.loads(s))
self.assertEqual(instance.artifact, other_instance.artifact)
self.assertEqual(instance.artifact_type, other_instance.artifact_type)
# Test pickling
dumped_instance = pickle.dumps(instance)
loaded_instance = pickle.loads(dumped_instance)
self.assertEqual(instance.artifact, loaded_instance.artifact)
self.assertEqual(instance.artifact_type, loaded_instance.artifact_type)
self.assertIsNone(instance.source)
instance.source = 'hello_world'
self.assertEqual('hello_world', instance.source)
def testDo(self):
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create output dict.
train_examples = types.TfxType(type_name='ExamplesPath', split='train')
train_examples.uri = os.path.join(output_data_dir, 'train')
eval_examples = types.TfxType(type_name='ExamplesPath', split='eval')
eval_examples.uri = os.path.join(output_data_dir, 'eval')
output_dict = {'examples': [train_examples, eval_examples]}
# Create exec proterties.
exec_properties = {
'input':
json_format.MessageToJson(
example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(name='tfrecord',
pattern='tfrecord/*'),
])),
'output':
json_format.MessageToJson(
example_gen_pb2.Output(
split_config=example_gen_pb2.SplitConfig(splits=[
example_gen_pb2.SplitConfig.Split(name='train',
hash_buckets=2),
example_gen_pb2.SplitConfig.Split(name='eval',
hash_buckets=1)
])))
}
# Run executor.
import_example_gen = executor.Executor()
import_example_gen.Do(self._input_dict, output_dict, exec_properties)
# Check import_example_gen outputs.
train_output_file = os.path.join(train_examples.uri,
'data_tfrecord-00000-of-00001.gz')
eval_output_file = os.path.join(eval_examples.uri,
'data_tfrecord-00000-of-00001.gz')
self.assertTrue(tf.gfile.Exists(train_output_file))
self.assertTrue(tf.gfile.Exists(eval_output_file))
self.assertGreater(
tf.gfile.GFile(train_output_file).size(),
tf.gfile.GFile(eval_output_file).size())
