def test_component_basic(self):
input_channel = channel.Channel(type_name='InputType')
component = _BasicComponent(folds=10, input=input_channel)
self.assertIs(input_channel, component.inputs.input)
self.assertIsInstance(component.outputs.output, channel.Channel)
self.assertEqual(component.outputs.output.type_name, 'OutputType')
def test_type_check_success(self):
chnl = channel.Channel('MyTypeName')
chnl.type_check('MyTypeName')
def test_type_check_fail(self):
chnl = channel.Channel('MyTypeName')
with self.assertRaises(TypeError):
chnl.type_check('AnotherTypeName')
def __init__(
self,
examples: channel.Channel = None,
transformed_examples: channel.Channel = None,
transform_output: Optional[channel.Channel] = None,
schema: channel.Channel = None,
module_file: Text = None,
train_args: trainer_pb2.TrainArgs = None,
eval_args: trainer_pb2.EvalArgs = None,
custom_config: Optional[Dict[Text, Any]] = None,
executor_class: Optional[Type[base_executor.BaseExecutor]] = None,
output: Optional[channel.Channel] = None,
name: Optional[Text] = None):
"""Construct a Trainer component.
Args:
examples: A Channel of 'ExamplesPath' type, serving as the source of
examples that are used in training.
transformed_examples: Deprecated field. Please set 'examples' instead.
transform_output: An optional Channel of 'TransformPath' type, serving as
the input transform graph if present.
schema: A Channel of 'SchemaPath' type, serving as the schema of training
and eval data.
module_file: A python module file containing UDF model definition.
train_args: A trainer_pb2.TrainArgs instance, containing args used for
training. Current only num_steps is available.
eval_args: A trainer_pb2.EvalArgs instance, containing args used for eval.
Current only num_steps is available.
custom_config: A dict which contains the training job parameters to be
passed to Google Cloud ML Engine. For the full set of parameters
supported by Google Cloud ML Engine, refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#Job
executor_class: Optional custom executor class.
output: Optional 'ModelExportPath' channel for result of exported models.
name: Optional unique name. Necessary iff multiple Trainer components are
declared in the same pipeline.
"""
output = output or channel.Channel(
type_name='ModelExportPath',
artifacts=[types.Artifact('ModelExportPath')])
assert bool(examples) ^ bool(
transformed_examples
), 'Exactly one of example or transformed_example should be set.'
if transformed_examples:
assert bool(
transform_output
), 'If transformed_examples is set, transform_output should be set too.'
examples = examples or transformed_examples
transform_output_channel = channel.as_channel(
transform_output) if transform_output else None
spec = TrainerSpec(examples=channel.as_channel(examples),
transform_output=transform_output_channel,
schema=channel.as_channel(schema),
train_args=train_args,
eval_args=eval_args,
module_file=module_file,
custom_config=custom_config,
output=output)
super(Trainer, self).__init__(spec=spec,
custom_executor_class=executor_class,
name=name)
def test_invalid_channel_type(self):
instance_a = types.Artifact('MyTypeName')
instance_b = types.Artifact('MyTypeName')
with self.assertRaises(ValueError):
channel.Channel('AnotherTypeName',
artifacts=[instance_a, instance_b])
def __init__(self, name: Text, spec_kwargs: Dict[Text, Any]):
spec = _FakeComponentSpec(output=channel.Channel(type_name=name),
**spec_kwargs)
super(_FakeComponent, self).__init__(spec=spec, name=name)
def __init__(self, stats: str, schema: str):
component = example_validator_component.ExampleValidator(
channel.Channel('ExampleStatisticsPath'),
channel.Channel('SchemaPath'))
super().__init__(component, {"stats": stats, "schema": schema})
def __init__(self, stats: str):
component = schema_gen_component.SchemaGen(
channel.Channel('ExampleStatisticsPath'))
super().__init__(component, {"stats": stats})
def __init__(self, input_data: str):
component = statistics_gen_component.StatisticsGen(
channel.Channel('ExamplesPath'))
super().__init__(component, {"input_data": input_data})