def test_cars():
X, y = cars.load()
run_automl_test(
dataset=(X, y),
input=(MatrixContinuousDense, Supervised[VectorCategorical]),
output=VectorCategorical,
search_timeout=60,
evaluation_timeout=5,
expected_fitness=0.9,
)
# import high-level API
from autogoal.ml import AutoML
from autogoal.kb import MatrixContinuousDense, CategoricalVector
# load data
from autogoal.datasets import cars
X, y = cars.load()
# instantiate AutoML class
automl = AutoML(
input=MatrixContinuousDense(),
output=CategoricalVector(),
# ... other parameters and constraints
)
# fit the model
automl.fit(X, y)
# save the best model
with open("model.bin", "wb") as fp:
automl.save(fp)