def test_ols_with_simple_dataset():
# construct a simple datset
dataset = create_simple_dataset()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.OLSModel(preprocessors=[],
input_names=input_names,
response_names=response_names)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.946672107012
assert result.score > 0.94
assert result.score < 0.95
def test_kriging_with_boston_dataset():
# load boston dataset
dataset = load_boston()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.KrigingModel(preprocessors=[],
input_names=input_names,
response_names=response_names)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.787001297651
assert result.score > 0.78
assert result.score < 0.79
def test_kernelridgeregression_with_boston_dataset():
# load boston dataset
dataset = load_boston()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.KernelRidgeRegression(preprocessors=[],
input_names=input_names,
response_names=response_names)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.556351440256
assert result.score > 0.55
assert result.score < 0.56
def test_kneighbors_with_boston_dataset():
# load boston dataset
dataset = load_boston()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.KNeighborsModel(preprocessors=[],
input_names=input_names,
response_names=response_names)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.364547876566
assert result.score > 0.36
assert result.score < 0.37
def test_kneighbors_with_simple_dataset():
# construct a simple datset
dataset = create_simple_dataset()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.KNeighborsModel(preprocessors=[],
input_names=input_names,
response_names=response_names)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.998574286629
assert result.score > 0.99
assert result.score < 0.999
def test_ols_with_boston_dataset():
# load boston dataset
dataset = load_boston()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.OLSModel(
preprocessors=[PolynomialFeatures(degree=2)],
input_names=input_names,
response_names=response_names)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.682539990982
assert result.score > 0.68
assert result.score < 0.69
def test_kernelridgeregression_with_simple_dataset():
# construct a simple datset
dataset = create_simple_dataset()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.KernelRidgeRegression(preprocessors=[],
input_names=input_names,
response_names=response_names)
#metamodel = metamodels.DecisionTreeRegression(preprocessors=[], input_names=input_names, response_names=response_names)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# 0.881282222886
assert result.score > 0.88
assert result.score < 0.89
def test_decisiontree_with_boston_dataset():
# load boston dataset
dataset = load_boston()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.DecisionTreeRegression(
preprocessors=[],
input_names=input_names,
response_names=response_names)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.594024623862
assert result.score > 0.5
assert result.score < 0.65
def test_decisiontree_with_simple_dataset():
# construct a simple datset
dataset = create_simple_dataset()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.DecisionTreeRegression(
preprocessors=[],
input_names=input_names,
response_names=response_names)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.998615575778
assert result.score > 0.998
assert result.score < 0.999
def test_elasticnet_with_boston_dataset():
# load boston dataset
dataset = load_boston()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.ElasticNetModel(preprocessors=[],
input_names=input_names,
response_names=response_names,
cv=5)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.477073081177
assert result.score > 0.47
assert result.score < 0.48
def test_elasticnet_with_simple_dataset():
# construct a simple datset
dataset = create_simple_dataset()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.ElasticNetModel(preprocessors=[],
input_names=input_names,
response_names=response_names,
cv=5)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.939611874972
assert result.score > 0.93
assert result.score < 0.94