def test_learner_cross_validate_LGBC():
inputer = Inputers(description_filepath=
"../../descriptions/pre/inputers/pima-diabetes.yaml")
dataset = inputer.transform()
y = dataset[inputer.target].values
X = dataset[dataset.columns.difference([inputer.target])]
learner = Learners(
description_filepath="../../descriptions/learners/LGBC.yaml")
learner.train(X, y, checkpoint="pima_LGBMC.ckp")
learner.cross_validate(
X,
y,
cv_description_filepath=
"../../descriptions/learners/Cross_validation_classification.yaml",
)
assert len(learner.evaluate(X, y).keys()) == 9
def test_learner_cross_validate_RFC_iris_multiclass_evaluate_AO():
inputer = Inputers(
description_filepath="../../descriptions/pre/inputers/iris.yaml")
dataset = inputer.transform()
y = dataset[inputer.target].values
X = dataset[dataset.columns.difference([inputer.target])]
learner = Learners(
description_filepath="../../descriptions/learners/RFC.yaml")
learner.train(X, y, checkpoint="iris)RC.ckp")
learner.cross_validate(
X,
y,
cv_description_filepath=
"../../descriptions/learners/Cross_validation_classification.yaml",
)
assert learner.evaluate(X, y)["accuracy"] == 1.0
def test_learner_cross_validate_RFC_iris_milticlass_evaluate_test_accuracy():
inputer = Inputers(
description_filepath="../../descriptions/pre/inputers/iris.yaml")
dataset = inputer.transform()
y = dataset[inputer.target].values
X = dataset[dataset.columns.difference([inputer.target])]
learner = Learners(
description_filepath="../../descriptions/learners/RFC.yaml")
learner.train(X, y, checkpoint="pima_LGBMC.ckp")
score = learner.cross_validate(
X,
y,
cv_description_filepath=
"../../descriptions/learners/Cross_validation_classification.yaml",
)
assert score["mean"]["test_accuracy"] >= 0.95