def test_polimorphic_interface():
interface = algorithm(MatrixContinuousDense(), MatrixContinuousDense())
assert interface.is_compatible(ExactAlgorithm)
assert interface.is_compatible(HigherInputAlgorithm)
interface = algorithm(MatrixContinuousDense(), MatrixContinuous())
assert interface.is_compatible(LowerOutputAlgorithm)
def test_simple_pipeline_graph():
graph = build_pipeline_graph(input=MatrixContinuousDense()
,output= MatrixContinuousDense()
,registry=[ExactAlgorithm, HigherInputAlgorithm, LowerOutputAlgorithm]).graph
assert_graph(graph, 3, 3, 6)
graph = build_pipeline_graph(input=List(Text())
,output= Document()
,registry=[WordToWordAlgorithm,
TextToWordAlgorithm,
WordToWordListAlgorithm,
WordListToSentenceAlgorithm,
WordListToSentenceAlgorithm,
SentenceListToDocumentAlgorithm,
TextListToDocumentAlgorithm]).graph
assert_graph(graph, 2, 2, 12)
graph = build_pipeline_graph(input=List(Word())
,output=Document()
,registry=[WordToWordAlgorithm,
TextToWordAlgorithm,
WordToWordListAlgorithm,
WordListToSentenceAlgorithm,
WordListToSentenceAlgorithm,
SentenceListToDocumentAlgorithm,
TextListToDocumentAlgorithm]).graph
assert_graph(graph, 2, 1, 10)
def test_save_load_tuple():
TupleClass = build_composite_tuple(
1,
input_type=Tuple(MatrixContinuousDense(), CategoricalVector()),
output_type=Tuple(MatrixContinuousDense(), CategoricalVector()),
)
algorithm = TupleClass(DummyAlgorithm)
fp = BytesIO()
Pickler(fp).dump(algorithm)
fp.seek(0)
algorithm2 = Unpickler(fp).load()
assert repr(algorithm) == repr(algorithm2)
# 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)
def run(self, input: List(MatrixContinuousDense())) -> Tensor3():
return np.vstack([np.expand_dims(m, axis=0) for m in input])
def run(self, input: List(ContinuousVector())) -> MatrixContinuousDense():
return np.vstack(input)
def run(
self, input: Tuple(List(MatrixContinuousDense()), List(List(Postag())))
) -> List(List(Postag())):
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
def run(self, input: List(Flags())) -> MatrixContinuousDense():
return super().run(input)
def test_matrix_hierarchy():
assert conforms(MatrixContinuous(), Matrix())
assert conforms(MatrixContinuousSparse(), Matrix())
assert conforms(MatrixContinuousDense(), Matrix())
assert conforms(MatrixContinuousSparse(), MatrixContinuous())
assert conforms(MatrixContinuousDense(), MatrixContinuous())
def run(self, input: MatrixContinuousDense()) -> MatrixContinuousDense():
pass
