def test_interpolation(self):
context = Parameters.from_mapping(
yaml.safe_load(self.WRITING_REFERENCE))
loader = YAMLParametersLoader()
self.assertEqual(
loader._interpolate(
Parameters.from_mapping(
yaml.safe_load(self.MULTIPLE_INTERPOLATION_REFERENCE)),
context,
)._data,
immutabledict([
("pear", "raspberry"),
("banana", "raspberry"),
("apple", "raspberry"),
("the_ultimate_fruit", "raspberry"),
]),
)
self.assertEqual(
loader._interpolate(
Parameters.from_mapping(
yaml.safe_load(
self.MULTIPLE_INTERPOLATION_REFERENCE_NEEDING_CONTEXT)
),
context,
)._data,
immutabledict([
("pear", "raspberry/world"),
("banana", "raspberry/world"),
("apple", "raspberry/world"),
("the_ultimate_fruit", "raspberry/world"),
# the actual pair ("hello", "world") should not be present
]),
)
self.assertEqual(
loader._interpolate(
Parameters.from_mapping(
yaml.safe_load(self.NESTED_INTERPOLATION)),
context,
).as_nested_dicts(),
{
"key": 2,
"key2": "fooo",
"key3": {
"lalala": "fooo",
"meep": 2,
"list": [1, 2, 3]
},
},
)
with self.assertRaisesRegex(
ParameterInterpolationError,
r"These interpolated parameters form at least one graph cycle that must be fixed: "
r"\('b', 'c'\)",
):
loader._interpolate(
Parameters.from_mapping(
yaml.safe_load('a: "%b%"\nb: "%c%"\nc: "%b%"')),
context,
)
def main(params: Parameters):
curriculum_repository_path = params.creatable_directory(
CURRICULUM_REPOSITORY_PATH_PARAMETER
)
language_mode = params.enum(
LANGUAGE_MODE_PARAMETER, LanguageMode, default=LanguageMode.ENGLISH
)
train_curriculum, test_curriculum = curriculum_from_params(
params, language_mode=language_mode
)
strict_curriculum = ExperimentCurriculum(
evaluate_curriculum(train_curriculum), evaluate_curriculum(test_curriculum)
)
write_experiment_curriculum(
curriculum_repository_path,
params,
language_mode,
strict_curriculum,
ignored_parameters=immutableset(
IGNORED_PARAMETERS.union(
{CURRICULUM_REPOSITORY_PATH_PARAMETER, LANGUAGE_MODE_PARAMETER}
)
),
)
def main(params: Parameters):
# create_cas_from_apf(TEST_APF_PATH, TEST_SGM_PATH, OUTPUT_DIR_PATH)
corpus_paths = params.arbitrary_list("corpus_paths")
output_xmi_dir_path = params.creatable_directory("cached_xmi_path")
type_system_path = params.existing_file("type_system_path")
cas_xmi_template_path = params.existing_file("cas_xmi_template_path")
# Load Typesystem
with type_system_path.open('rb') as file:
typesystem = load_typesystem(file)
# Load xmi_template
with cas_xmi_template_path.open('rb') as cas_xmi_file:
cas_template = load_cas_from_xmi(cas_xmi_file, typesystem=typesystem)
for ace_corpus_path in corpus_paths:
print('Processing apf files from: ' + ace_corpus_path)
start_time = time.perf_counter()
for filename in os.listdir(ace_corpus_path):
if filename.endswith(".apf.xml"):
print("Processing " + filename)
create_cas_from_apf(apf_filename=filename,
apf_path=ace_corpus_path + filename,
source_sgm_path=ace_corpus_path + filename.replace(
".apf.xml", ".sgm"),
output_dir_path=output_xmi_dir_path, typesystem=typesystem,
cas_template=cas_template)
elapsed_time = time.perf_counter() - start_time
print(f"Processing Completed. Time elapsed: {elapsed_time:0.4f} seconds")
def add_saga_cluster_to_sites(
sites_catalog: SiteCatalog,
params: Parameters = Parameters.empty()) -> None:
home = params.string("home_dir", default=str(Path.home().absolute()))
data_configuration = params.string("data_configuration",
default="sharedfs")
shared_scratch_dir = params.string(
"saga_shared_scratch", default=f"{home}/workflows/shared-scratch")
saga = Site("saga", arch=Arch.X86_64, os_type=OS.LINUX)
saga.add_directories(
Directory(Directory.SHARED_SCRATCH,
shared_scratch_dir).add_file_servers(
FileServer("file://" + shared_scratch_dir,
Operation.ALL)))
saga.add_env(key="PEGASUS_HOME",
value="/nas/gaia/shared/cluster/pegasus5/pegasus-5.0.0")
# Profiles
saga.add_pegasus_profile(style="glite",
auxillary_local=True,
data_configuration=data_configuration)
saga.add_condor_profile(grid_resource="batch slurm")
sites_catalog.add_sites(saga)
def _build_curriculum_path(
repository: Path,
parameters: Parameters,
language_mode: LanguageMode,
*,
ignored_parameters: AbstractSet[str] = IGNORED_PARAMETERS,
) -> Path:
curriculum_file_path: Path = repository / LANGUAGE_MODE_TO_NAME[
language_mode]
unignored = immutableset(parameter
for parameter, _ in parameters.namespaced_items()
if parameter not in ignored_parameters)
if not unignored.issubset(_PARAMETER_ORDER):
unrecognized_parameters = unignored.difference(_PARAMETER_ORDER)
raise RuntimeError(
f"No defined order for parameters: {unrecognized_parameters}")
if "curriculum" not in unignored:
raise RuntimeError(
"Expected curriculum name, but none present in parameters.")
for parameter in iter(_PARAMETER_ORDER):
value = parameters.get_optional(parameter, object)
curriculum_file_path = curriculum_file_path / f"{value}_{parameter}"
return curriculum_file_path / _EXPERIMENT_CURRICULUM_FILE_NAME
def from_parameters(params: Parameters) -> Optional["SpackConfiguration"]:
if SpackConfiguration.SPACK_ENVIRONMENT_PARAM in params:
if SpackConfiguration.SPACK_PACKAGES_PARAM in params:
raise RuntimeError(
f"{SpackConfiguration.SPACK_ENVIRONMENT_PARAM} "
f"and {SpackConfiguration.SPACK_PACKAGES_PARAM} are mutually exclusive"
)
return SpackConfiguration(
spack_root=params.existing_directory(
SpackConfiguration.SPACK_ROOT_PARAM),
spack_environment=params.string(
SpackConfiguration.SPACK_ENVIRONMENT_PARAM),
)
elif SpackConfiguration.SPACK_PACKAGES_PARAM in params:
if SpackConfiguration.SPACK_ENVIRONMENT_PARAM in params:
raise RuntimeError(
f"{SpackConfiguration.SPACK_ENVIRONMENT_PARAM} "
f"and {SpackConfiguration.SPACK_PACKAGES_PARAM} are mutually exclusive"
)
return SpackConfiguration(
spack_root=params.existing_directory(
SpackConfiguration.SPACK_ROOT_PARAM),
spack_packages=[
SpackPackage.parse(package_specifier)
for package_specifier in params.arbitrary_list(
SpackConfiguration.SPACK_PACKAGES_PARAM)
],
)
else:
return None
def test_double_context_fail(self):
# cannot specify both deprecated context argument and new included_context argument
with self.assertRaises(ParameterError):
YAMLParametersLoader().load(
f='foo: "foo"',
context=Parameters.empty(),
included_context=Parameters.empty(),
)
def from_parameters(params: Parameters) -> Optional["CondaConfiguration"]:
if CondaConfiguration.CONDA_ENVIRONMENT_PARAM in params:
return CondaConfiguration(
conda_base_path=params.existing_directory("conda_base_path"),
conda_environment=params.string(
CondaConfiguration.CONDA_ENVIRONMENT_PARAM),
)
else:
return None
def from_parameters(params: Parameters) -> ResourceRequest:
return SlurmResourceRequest(
partition=params.string("partition"),
num_cpus=params.optional_positive_integer("num_cpus"),
num_gpus=params.optional_integer("num_gpus"),
memory=MemoryAmount.parse(params.string("memory"))
if "memory" in params
else None,
job_time_in_minutes=params.optional_integer("job_time_in_minutes"),
)
def from_parameters(params: Parameters) -> "WorkflowBuilder":
wb = WorkflowBuilder(
name=params.string("workflow_name", default="Workflow"),
created_by=params.string("workflow_created",
default="Default Constructor"),
workflow_directory=params.creatable_directory(
"workflow_directory"),
default_site=params.string("site"),
conda_script_generator=CondaJobScriptGenerator.from_parameters(
params),
docker_script_generator=DockerJobScriptGenerator.from_parameters(
params),
namespace=params.string("namespace"),
default_resource_request=ResourceRequest.from_parameters(params),
data_configuration=params.string("data_configuration",
default="sharedfs"),
experiment_name=params.string("experiment_name", default=""),
)
if params.boolean("include_nas", default=True):
add_local_nas_to_sites(
wb._sites_catalog,
params # pylint: disable=protected-access
)
if params.boolean("include_saga", default=True):
add_saga_cluster_to_sites(
wb._sites_catalog,
params # pylint: disable=protected-access
)
configure_saga_properities(
wb._properties,
params # pylint: disable=protected-access
)
return wb
def main(params: Parameters):
params.assert_exactly_one_present(
[_NUM_SLICES_PARAM, _EXPLICIT_SPLIT_PARAM])
with byte_key_value_linear_source_from_params(params) as input_source:
if _NUM_SLICES_PARAM in params:
_split_into_even_slices(input_source, params)
elif _EXPLICIT_SPLIT_PARAM in params:
_explicit_split(input_source, params)
else:
raise RuntimeError("No known split parameter specified.")
def main(params: Parameters):
input_file_path = params.existing_file("input_file")
output_file_path = params.creatable_file("output_file")
logging.info("Reading from input file: %s",
str(input_file_path.absolute()))
with input_file_path.open() as input_file:
nums = [int(x.strip()) for x in input_file if x.strip() != ""]
nums.sort()
output_file_path.write_text("\n".join(immutableset([str(x)
for x in nums])))
def test_namespace_prefix(self):
assert Parameters.from_mapping({
"hello": {
"world": {
"foo": "bar"
}
}
}).namespace("hello").namespace("world").namespace_prefix == ("hello",
"world")
assert Parameters.empty(
namespace_prefix=("foo", )).namespace_prefix == ("foo", )
# test it works even for empty parameters
assert Parameters.empty().namespace_or_empty("foo").namespace_or_empty(
"bar").namespace_prefix == ("foo", "bar")
def main(params: Parameters):
input_file_path = params.existing_file("input_file")
output_file_path = params.creatable_file("output_file")
x = params.integer("x")
logging.info("Reading from input file: %s",
str(input_file_path.absolute()))
with input_file_path.open() as input_file:
with output_file_path.open("w") as output_file:
for num in input_file:
output_file.write(f"{int(num)*x}\n")
logging.info("Writing to output file: %s", str(input_file_path.absolute()))
# Pause so that we can examine the job on the SAGA cluster
time.sleep(30)
def from_parameters(params: Parameters) -> "SlurmPythonRunner":
return SlurmPythonRunner(
conda_config=CondaConfiguration.from_parameters(params),
spack_config=SpackConfiguration.from_parameters(params),
log_base_directory=params.creatable_directory(
"log_directory").absolute(),
)
def test_optional_existing_directory(self):
test_dir = Path(tempfile.mkdtemp()).absolute()
existing_dir_path = test_dir / "existing_directory"
existing_dir_path.mkdir(parents=True, exist_ok=True)
non_existing_dir_path = test_dir / "non_existent_directory"
a_file = test_dir / "a_file"
a_file.touch()
params = Parameters.from_mapping({
"directory_which_exists":
str(existing_dir_path.absolute()),
"directory_which_does_not_exist":
non_existing_dir_path,
"a_file":
a_file,
})
# noinspection PyTypeChecker
self.assertEqual(
os.path.realpath(existing_dir_path),
os.path.realpath(
params.optional_existing_directory("directory_which_exists")),
)
self.assertEqual(None,
params.optional_existing_directory("missing_param"))
with self.assertRaises(ParameterError):
params.optional_existing_directory(
"directory_which_does_not_exist")
with self.assertRaises(ParameterError):
params.optional_existing_directory("a_file")
shutil.rmtree(test_dir)
def test_optional_creatable_empty_directory(self):
test_dir = Path(tempfile.mkdtemp()).absolute()
existing_dir_path = test_dir / "existing_directory"
existing_dir_path.mkdir(parents=True, exist_ok=True)
non_existing_dir_path = test_dir / "non_existent_directory"
a_file = existing_dir_path / "a_file"
a_file.touch()
params = Parameters.from_mapping({
"directory_which_exists":
str(existing_dir_path.absolute()),
"directory_which_does_not_exist":
str(non_existing_dir_path.absolute()),
"a_file":
a_file,
})
self.assertEqual(
None, params.optional_creatable_empty_directory("missing_param"))
self.assertEqual(
os.path.realpath(non_existing_dir_path),
os.path.realpath(
params.optional_creatable_empty_directory(
"directory_which_does_not_exist")),
)
with self.assertRaises(ParameterError):
params.optional_creatable_empty_directory("a_file")
with self.assertRaises(ParameterError):
params.optional_creatable_empty_directory("directory_which_exists")
self.assertEqual(
os.path.realpath(existing_dir_path),
os.path.realpath(
params.optional_creatable_empty_directory(
"directory_which_exists", delete=True)),
)
def test_optional_creatable_file(self):
test_dir = Path(tempfile.mkdtemp()).absolute()
existing_dir_path = test_dir / "existing_directory"
existing_dir_path.mkdir(parents=True, exist_ok=True)
non_existing_dir_path = test_dir / "non_existent_directory"
a_file = existing_dir_path / "a_file"
a_file.touch()
non_existing_file = test_dir / "b_file"
params = Parameters.from_mapping({
"directory_which_exists":
str(existing_dir_path.absolute()),
"directory_which_does_not_exist":
str(non_existing_dir_path.absolute()),
"a_file":
str(a_file.absolute()),
"non_existing_file":
str(non_existing_file.absolute()),
})
self.assertEqual(None, params.optional_creatable_file("missing_param"))
self.assertEqual(
os.path.realpath(non_existing_file),
os.path.realpath(
params.optional_creatable_file("non_existing_file")),
)
def test_optionals_when_present(self):
params = Parameters.from_mapping({
"list": [1, 2, 3, ["a", "b", "c"]],
"boolean": True,
"float": 0.5,
"integer": 42,
"negative_int": -5,
"namespace": {
"fred": "meep"
},
"string": "foo",
})
assert params.optional_arbitrary_list("list") == [
1, 2, 3, ["a", "b", "c"]
]
assert params.optional_boolean("boolean")
assert params.optional_floating_point("float") == 0.5
assert params.optional_integer("integer") == 42
assert params.optional_positive_integer("integer") == 42
with self.assertRaises(ParameterError):
params.optional_positive_integer("negative_int")
assert params.optional_namespace("namespace").as_nested_dicts() == {
"fred": "meep"
}
assert params.optional_string("string") == "foo"
def split_key_value_store(
input_store: KeyValueStore,
*,
num_parts: int,
random_seed: Optional[int] = None) -> Tuple[KeyValueStore]:
"""
Splits *input_store* into *num_parts* pieces of nearly equal size.
Some of the resulting key-value stores may be empty.
"""
if num_parts <= 0:
raise RuntimeError("Number of parts must be positive")
split_locator = input_store.locator / "split"
split_output_dir = directory_for(split_locator)
param_args = {
"input": input_store.input_parameters(),
"num_slices": num_parts,
"output_dir": split_output_dir,
}
if random_seed:
param_args["random_seed"] = random_seed
split_job = run_python_on_parameters(
split_locator,
split_entry_point,
Parameters.from_mapping(param_args),
depends_on=input_store,
)
return tuple(
ZipKeyValueStore(
path=split_output_dir / f"{slice_index}.zip",
depends_on=split_job,
locator=split_locator / str(slice_index),
) for slice_index in range(num_parts))
def downsample(input_store: KeyValueStore,
*,
limit: int,
output_locator: Optional[Locator] = None) -> KeyValueStore:
"""
Convince function to run `vistautils.scripts.downsample_key_value_store` as a Pegasus Job
"""
if not output_locator:
output_locator = input_store.locator / f"downsampled-{limit}"
output_zip_path = directory_for(output_locator) / "downsampled.zip"
downsample_job = run_python_on_parameters(
output_locator,
downsample_key_value_store,
Parameters.from_mapping({
"input": input_store.input_parameters(),
"output_zip_path": output_zip_path,
"num_to_sample": limit,
"random_seed": 0,
}),
depends_on=input_store,
)
return ZipKeyValueStore(
path=output_zip_path,
locator=output_locator,
depends_on=[input_store.depends_on, downsample_job],
)
def test_simple_dax(tmp_path):
params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"scavenge",
})
workflow_builder = WorkflowBuilder.from_parameters(params)
assert workflow_builder.name == "Test"
assert workflow_builder.created_by == "Testing"
assert (workflow_builder._workflow_directory # pylint:disable=protected-access
== tmp_path / "working")
assert workflow_builder._namespace == "test" # pylint:disable=protected-access
assert workflow_builder._default_site == "saga" # pylint:disable=protected-access
assert workflow_builder.default_resource_request # pylint:disable=protected-access
assert workflow_builder._job_graph is not None # pylint:disable=protected-access
def byte_key_value_sink_from_params(
params: Parameters,
*,
output_namespace: str = "output",
eval_context: Optional[Dict] = None,
) -> KeyValueSink[str, bytes]:
"""
Get a binary key-value sink based on parameters.
This should be passed a parameter namespace. If the "type" field is present, it should
be the name of a class or method. If a class, its static `from_parameters` method will be
called with these parameters and should return a `KeyValueSink[str, bytes]`. If a callable,
it will be called with these parameters (and should also return a KeyValueSink[str, bytes].
The type 'zip' is a shortcut for a key-value zip file. 'directory' is a shortcut for
writing the output files to the specified directory.
If additional imports are needed to resolve 'type', they can be specified as a Python list in
the `import` field.
If no type is specified, a 'directory' sink will be created.
"""
# to be sure the default special values can be evaluated, we want to include this module
# itself in the evaluation context. We combine it with eval_context, giving priority to
# the context specified by the user
effective_context = dict(globals())
effective_context.update(eval_context or {})
return params.object_from_parameters( # type: ignore
output_namespace,
KeyValueSink,
special_factories=_BYTE_KEY_VALUE_SINK_SPECIAL_VALUES,
default_factory=_DirectoryBytesKeyValueSink,
context=effective_context,
factory_namespace_param_name="type",
)
def test_dax_with_categories(tmp_path):
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"gaia",
"home_dir":
str(tmp_path),
})
initialize_vista_pegasus_wrapper(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping({
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4
})
multiply_job_category = "arithmetic"
run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
category=multiply_job_category,
)
# Check that the multiply job has the appropriate category set in the DAX file
dax_file = write_workflow_description()
assert dax_file.exists()
assert _job_in_dax_has_category(dax_file, multiply_job_name,
multiply_job_category)
assert not _job_in_dax_has_category(dax_file, multiply_job_name,
"an-arbitrary-category")
def create_logger(params: Parameters) -> "LearningProgressHtmlLogger":
output_dir = params.creatable_directory("experiment_group_dir")
experiment_name = params.string("experiment")
include_links_to_images = params.optional_boolean("include_image_links")
num_pretty_descriptions = params.positive_integer(
"num_pretty_descriptions", default=3
)
sort_by_length = params.boolean(
"sort_learner_descriptions_by_length", default=False
)
logging_dir = output_dir / experiment_name
logging_dir.mkdir(parents=True, exist_ok=True)
output_html_path = str(logging_dir / "index.html")
if include_links_to_images is None:
include_links_to_images = False
logging.info("Experiment will be logged to %s", output_html_path)
with open(output_html_path, "w") as outfile:
html_dumper = CurriculumToHtmlDumper()
outfile.write(f"<head>\n\t<style>{CSS}\n\t</style>\n</head>")
outfile.write(f"\n<body>\n\t<h1>{experiment_name}</h1>")
# A JavaScript function to allow toggling perception information
outfile.write(
"""
<script>
function myFunction(id) {
var x = document.getElementById(id);
if (x.style.display === "none") {
x.style.display = "block";
} else {
x.style.display = "none";
}
}
</script>
"""
)
return LearningProgressHtmlLogger(
outfile_dir=output_html_path,
html_dumper=html_dumper,
include_links_to_images=include_links_to_images,
num_pretty_descriptions=num_pretty_descriptions,
sort_by_length=sort_by_length,
)
def log_experiment_entry_point(params: Parameters) -> None:
experiment_name = params.string("experiment")
debug_log_dir = params.optional_creatable_directory("debug_log_directory")
graph_logger: Optional[HypothesisLogger]
if debug_log_dir:
logging.info("Debug graphs will be written to %s", debug_log_dir)
graph_logger = HypothesisLogger(debug_log_dir,
enable_graph_rendering=True)
else:
graph_logger = None
logger = LearningProgressHtmlLogger.create_logger(params)
language_mode = params.enum("language_mode",
LanguageMode,
default=LanguageMode.ENGLISH)
(training_instance_groups,
test_instance_groups) = curriculum_from_params(params, language_mode)
execute_experiment(
Experiment(
name=experiment_name,
training_stages=training_instance_groups,
learner_factory=learner_factory_from_params(
params, graph_logger, language_mode),
pre_example_training_observers=[
logger.pre_observer(),
CandidateAccuracyObserver("pre-acc-observer"),
],
post_example_training_observers=[logger.post_observer()],
test_instance_groups=test_instance_groups,
test_observers=[logger.test_observer()],
sequence_chooser=RandomChooser.for_seed(0),
),
log_path=params.optional_creatable_directory("hypothesis_log_dir"),
log_hypotheses_every_n_examples=params.integer(
"log_hypothesis_every_n_steps", default=250),
log_learner_state=params.boolean("log_learner_state", default=True),
learner_logging_path=params.optional_creatable_directory(
"experiment_group_dir"),
starting_point=params.integer("starting_point", default=-1),
point_to_log=params.integer("point_to_log", default=0),
load_learner_state=params.optional_existing_file("learner_state_path"),
)
def from_parameters(params: Parameters) -> KeyValueSink[str, bytes]:
"""
Create a key-value sink writing to a zip file.
Right now, these uses all the defaults for `KeyValueSink.zip_bytes_sink`. In the
future, we might examine other parameters to allow greater customization.
"""
return KeyValueSink.zip_bytes_sink(params.creatable_file("path"))
def main(params: Parameters):
with byte_key_value_source_from_params(params) as input_source:
keys = list(input_source.keys())
num_to_sample = min(params.positive_integer(_NUM_TO_SAMPLE_PARAM),
len(keys))
random.shuffle(
keys,
random=random.Random(params.integer(_RANDOM_SEED_PARAM,
default=0)).random,
)
keys_to_keep = keys[:num_to_sample]
output_zip_path = params.creatable_file("output_zip_path")
logging.info("Downsampling %s files to %s", num_to_sample,
output_zip_path)
with KeyValueSink.zip_bytes_sink(output_zip_path) as out:
for key in keys_to_keep:
out.put(key, input_source[key])
def test_not_clearing_ckpts(monkeypatch, tmp_path):
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"scavenge",
"home_dir":
str(tmp_path),
})
initialize_vista_pegasus_wrapper(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping({
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4
})
multiple_dir = directory_for(multiply_job_name)
checkpointed_multiply_file = multiple_dir / "___ckpt"
checkpointed_multiply_file.touch()
multiply_output_file.touch()
run_python_on_parameters(multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[])
monkeypatch.setattr("builtins.input", lambda _: "n")
write_workflow_description()
assert checkpointed_multiply_file.exists()
def build_relation_learner_factory(
params: Parameters, beam_size: int,
language_mode: LanguageMode) -> Optional[TemplateLearner]:
learner_type = params.string("learner_type",
valid_options=["subset", "pursuit", "none"],
default="subset")
ontology, _, _ = ONTOLOGY_STR_TO_ONTOLOGY[params.string(
"ontology",
valid_options=ONTOLOGY_STR_TO_ONTOLOGY.keys(),
default="phase2")]
if learner_type == "subset":
return SubsetRelationLearnerNew(ontology=ontology,
beam_size=beam_size,
language_mode=language_mode)
elif learner_type == "pursuit":
rng = random.Random()
rng.seed(params.integer("random_seed", default=0))
return PursuitRelationLearnerNew(
learning_factor=params.floating_point("learning_factor"),
graph_match_confirmation_threshold=params.floating_point(
"graph_match_confirmation_threshold"),
lexicon_entry_threshold=params.floating_point(
"lexicon_entry_threshold"),
rng=rng,
smoothing_parameter=params.floating_point("smoothing_parameter"),
ontology=ontology,
language_mode=language_mode,
)
elif learner_type == "none":
# We don't want to include this learner type.
return None
else:
raise RuntimeError("Relation learner type invalid ")