def test_train_predict_prob_XGBC_yeast3():
inputer = Inputers(
description_filepath="../../descriptions/pre/inputers/yeast3.yaml")
yeast3 = inputer.transform()
y = yeast3[inputer.target].values
X = yeast3[yeast3.columns.difference([inputer.target])]
learner = Learners(
description_filepath="../../descriptions/learners/XGBC.yaml")
learner.train(X, y, checkpoint="yeast3LGBMC.ckp")
assert learner.predict_proba(X).shape == (1484, 2)
def test_train_predict_prob_XGBC_creditdard():
inputer = Inputers(
description_filepath="../../descriptions/pre/inputers/creditcard.yaml")
creditdard = inputer.transform()
y = creditdard[inputer.target].values
X = creditdard[creditdard.columns.difference([inputer.target])]
learner = Learners(
description_filepath="../../descriptions/learners/XGBC.yaml")
learner.train(X, y, checkpoint="creditdardLGBMC.ckp")
assert learner.predict_proba(X).shape == (284807, 2)
def test_train_predict_prob_LGBC_wine():
inputer = Inputers(
description_filepath="../../descriptions/pre/inputers/wine.yaml")
wine = inputer.transform()
y = wine[inputer.target].values
X = wine[wine.columns.difference([inputer.target])]
learner = Learners(
description_filepath="../../descriptions/learners/LGBC.yaml")
learner.train(X, y, target=inputer.target, checkpoint="wineLGBMC.ckp")
assert learner.predict_proba(X).shape == (178, 3)
def test_train_predict_prob_LGBC():
inputer = Inputers(description_filepath=
"../../descriptions/pre/inputers/pima-diabetes.yaml")
diabetes = inputer.transform()
y = diabetes[inputer.target].values
X = diabetes[diabetes.columns.difference([inputer.target])]
learner = Learners(
description_filepath="../../descriptions/learners/LGBC.yaml")
learner.train(X, y, checkpoint="diabetesLGBMC.ckp")
assert learner.predict_proba(X).shape == (768, 2)
def test_learn_model_name():
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/RFC.yaml")
learner.train(X, y, checkpoint="RandomForest.ckp")
assert learner.model_name == "RandomForest"
def test_learn_train_predict_XGBC():
inputer = Inputers(
description_filepath="../../descriptions/pre/inputers/wine.yaml")
dataset = inputer.transform()
y = dataset[inputer.target].values
X = dataset[dataset.columns.difference([inputer.target])]
learner = Learners(
description_filepath="../../descriptions/learners/XGBC.yaml")
learner.train(X, y, checkpoint="LGBMClassifier.ckp")
assert learner.predict(X).shape == (178, )
def test_learn_train_predict_prob_LGBC():
inputer = Inputers(
description_filepath="../../descriptions/pre/inputers/otto_group.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="otto_group_LGBMC.ckp")
assert learner.predict(X).shape == (61878, )
def test_learn_train_kw_target_pima():
inputer = Inputers(
description_filepath="../../descriptions/pre/inputers/wine.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="diabetesRandomForest1.ckp")
assert learner.trained == True
def test_learn_train_no_y():
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])]
o = Learners(
description_filepath="../../descriptions/learners/RFC.sm.yaml")
with pytest.raises(IndexError):
o.train(X) == o
def test_evaluate_otto_group():
inputer = Inputers(
description_filepath="../../descriptions/pre/inputers/otto_group.yaml")
dataset = inputer.transform()
y = dataset[inputer.target].values
X = dataset[dataset.columns.difference([inputer.target])]
learner = Learners(
description_filepath="../../descriptions/learners/XGBC.yaml")
learner.train(X, y, checkpoint="pima_LGBMC.ckp")
assert len(learner.evaluate(X, y).keys()) == 7
def test_predict_Prob_error():
inputer = Inputers(description_filepath=
"../../descriptions/pre/inputers/pima-diabetes.yaml")
diabetes = inputer.transform()
learner = Learners(
description_filepath="../../descriptions/learners/LGBC.yaml")
# learner.train(
# diabetes, target=inputer.target, checkpoint="diabetesLGBMC.ckp"
# )
X = diabetes
X_train = X[X.columns.difference([inputer.target])]
with pytest.raises(PasoError):
assert learner.predict_proba(X_train).shape == (768, 2)
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
def test_learn_train_kw_target_iris(flower):
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])]
o = Learners(
description_filepath="../../descriptions/learners/RFC.sm.yaml")
assert inputer.target == "Outcome"
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_cross_validaters():
learner = Learners(
description_filepath="../../descriptions/learners/LGBC.yaml")
assert len(learner.cross_validaters()) == 3
def test_learners():
learner = Learners(
description_filepath="../../descriptions/learners/LGBC.yaml")
assert len(learner.learners()) == 16
def test_learn_train_description_file_not_exist(flower):
o = Learners(
description_filepath="../../descriptions/learners/RandomForest.yaml")
with pytest.raises(PasoError):
o.train(flower) == o