def test_save_load_list():
ListClass = build_composite_list(
input_type=Tuple(MatrixContinuousDense(), CategoricalVector()),
output_type=List(CategoricalVector()),
)
algorithm = ListClass(DummyAlgorithm)
fp = BytesIO()
Pickler(fp).dump(algorithm)
fp.seek(0)
algorithm2 = Unpickler(fp).load()
assert repr(algorithm) == repr(algorithm2)
def automl_fit(
input: Path,
output: Path = Path("automl.bin"),
target: str = None,
ignore_cols: List[int] = typer.Option([]),
evaluation_timeout: int = 5 * Min,
memory_limit: int = 4 * Gb,
search_timeout: int = 60 * 60,
pop_size: int = 20,
iterations: int = 100,
random_state: int = None,
format: str = None,
):
"""
🏃 Train an AutoML instance on a dataset.
"""
try:
dataset = _load_dataset(format, input, ignore_cols)
except ValueError as e:
logger.error(f"⚠️ Error: {str(e)}")
return
if target is None:
target = dataset.columns[-1]
columns = [c for c in dataset.columns if c != target]
X = dataset[columns].values
y = dataset[target].values
automl = AutoML(
output=CategoricalVector(),
search_kwargs=dict(
evaluation_timeout=evaluation_timeout,
memory_limit=memory_limit,
search_timeout=search_timeout,
pop_size=pop_size,
),
random_state=random_state,
search_iterations=iterations,
)
console.print(f"🏃 Training on {len(dataset)} items.")
automl.fit(X, y, logger=RichLogger())
with output.open("wb") as fp:
automl.save(fp)
console.print(f"💾 Saving model to [green]{output.absolute()}[/].")
def run_automl(X, y, name, input=None, output=CategoricalVector()):
telegram = TelegramLogger(
token=os.environ["TOKEN"],
channel="@autogoal_board",
name=name,
)
console = ConsoleLogger()
progress = ProgressLogger()
automl = AutoML(
search_iterations=1000,
metalearning_log=True,
search_kwargs=dict(search_timeout=2 * 60 * 60, pop_size=50),
errors="ignore",
input=input,
output=output,
cross_validation_steps=1,
)
automl.fit(X, y, logger=[telegram, console, progress])
# in the `contrib` library, i.e., anything that could be potentially used
# to solve an AutoML problem.
for cls in find_classes():
print("Using: %s" % cls.__name__)
# ## Experimentation
# Instantiate the classifier.
# Note that the input and output types here are defined to match the problem statement,
# i.e., text classification.
classifier = AutoML(
search_algorithm=PESearch,
input=List(Sentence()),
output=CategoricalVector(),
search_iterations=args.iterations,
score_metric=f1_score,
search_kwargs=dict(
pop_size=args.popsize,
search_timeout=args.global_timeout,
evaluation_timeout=args.timeout,
memory_limit=args.memory * 1024**3,
),
)
# This custom logger is used for debugging purposes, to be able later to recover
# the best pipelines and all the errors encountered in the experimentation process.
class CustomLogger(Logger):
def run(self, input: DenseMatrix()) -> CategoricalVector():
# implementation of the algorithm
pass
def run(
self, input: Tuple(Sentence(), List(Tuple(Entity(), Entity(), Category())))
) -> Tuple(List(Vector()), CategoricalVector()):
pass
def run(
self, input: Tuple(Tensor3(),
CategoricalVector())) -> CategoricalVector():
return super().run(input)
def run(
self, input: Tuple(MatrixContinuousDense(), CategoricalVector())
) -> CategoricalVector():
return super().run(input)
def run(
self, input: Tuple(MatrixContinuousDense(), CategoricalVector())
) -> CategoricalVector():
X, y = input
return y
