def test_algorithm_hyperparameter_integer_range_valid_range(session):
hyperparameters = [{
"Description":
"Grow a tree with max_leaf_nodes in best-first fashion.",
"Type": "Integer",
"Name": "max_leaf_nodes",
"Range": {
"IntegerParameterRangeSpecification": {
"MinValue": "1",
"MaxValue": "100000"
}
},
"IsTunable": True,
"IsRequired": False,
"DefaultValue": "100",
}]
some_algo = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
some_algo["TrainingSpecification"][
"SupportedHyperParameters"] = hyperparameters
session.sagemaker_client.describe_algorithm = Mock(return_value=some_algo)
estimator = AlgorithmEstimator(
algorithm_arn=
"arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees",
role="SageMakerRole",
train_instance_type="ml.m4.2xlarge",
train_instance_count=1,
sagemaker_session=session,
)
estimator.set_hyperparameters(max_leaf_nodes=1)
estimator.set_hyperparameters(max_leaf_nodes=100000)
def test_algorithm_hyperparameter_continuous_range_invalid_range(sagemaker_session):
hyperparameters = [
{
'Description': 'A continuous hyperparameter',
'Type': 'Continuous',
'Name': 'max_leaf_nodes',
'Range': {
'ContinuousParameterRangeSpecification': {'MinValue': '0.0', 'MaxValue': '1.0'}
},
'IsTunable': True,
'IsRequired': False,
'DefaultValue': '100',
}
]
some_algo = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
some_algo['TrainingSpecification']['SupportedHyperParameters'] = hyperparameters
sagemaker_session.sagemaker_client.describe_algorithm = Mock(return_value=some_algo)
estimator = AlgorithmEstimator(
algorithm_arn='arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees',
role='SageMakerRole',
train_instance_type='ml.m4.2xlarge',
train_instance_count=1,
sagemaker_session=sagemaker_session,
)
with pytest.raises(ValueError):
estimator.set_hyperparameters(max_leaf_nodes=1.1)
with pytest.raises(ValueError):
estimator.set_hyperparameters(max_leaf_nodes=-0.1)
def test_algorithm_hyperparameter_integer_range_valid_range(session):
hyperparameters = [{
'Description':
'Grow a tree with max_leaf_nodes in best-first fashion.',
'Type': 'Integer',
'Name': 'max_leaf_nodes',
'Range': {
'IntegerParameterRangeSpecification': {
'MinValue': '1',
'MaxValue': '100000'
}
},
'IsTunable': True,
'IsRequired': False,
'DefaultValue': '100',
}]
some_algo = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
some_algo['TrainingSpecification'][
'SupportedHyperParameters'] = hyperparameters
session.sagemaker_client.describe_algorithm = Mock(return_value=some_algo)
estimator = AlgorithmEstimator(
algorithm_arn=
'arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees',
role='SageMakerRole',
train_instance_type='ml.m4.2xlarge',
train_instance_count=1,
sagemaker_session=session,
)
estimator.set_hyperparameters(max_leaf_nodes=1)
estimator.set_hyperparameters(max_leaf_nodes=100000)
def test_algorithm_hyperparameter_continuous_range_invalid_range(session):
hyperparameters = [
{
"Description": "A continuous hyperparameter",
"Type": "Continuous",
"Name": "max_leaf_nodes",
"Range": {
"ContinuousParameterRangeSpecification": {"MinValue": "0.0", "MaxValue": "1.0"}
},
"IsTunable": True,
"IsRequired": False,
"DefaultValue": "100",
}
]
some_algo = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
some_algo["TrainingSpecification"]["SupportedHyperParameters"] = hyperparameters
session.sagemaker_client.describe_algorithm = Mock(return_value=some_algo)
estimator = AlgorithmEstimator(
algorithm_arn="arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees",
role="SageMakerRole",
instance_type="ml.m4.2xlarge",
instance_count=1,
sagemaker_session=session,
)
with pytest.raises(ValueError):
estimator.set_hyperparameters(max_leaf_nodes=1.1)
with pytest.raises(ValueError):
estimator.set_hyperparameters(max_leaf_nodes=-0.1)
def test_algorithm_hyperparameter_categorical_range(sagemaker_session):
hyperparameters = [
{
'Description': 'A continuous hyperparameter',
'Type': 'Categorical',
'Name': 'hp1',
'Range': {'CategoricalParameterRangeSpecification': {'Values': ['TF', 'MXNet']}},
'IsTunable': True,
'IsRequired': False,
'DefaultValue': '100',
}
]
some_algo = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
some_algo['TrainingSpecification']['SupportedHyperParameters'] = hyperparameters
sagemaker_session.sagemaker_client.describe_algorithm = Mock(return_value=some_algo)
estimator = AlgorithmEstimator(
algorithm_arn='arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees',
role='SageMakerRole',
train_instance_type='ml.m4.2xlarge',
train_instance_count=1,
sagemaker_session=sagemaker_session,
)
estimator.set_hyperparameters(hp1='MXNet')
estimator.set_hyperparameters(hp1='TF')
with pytest.raises(ValueError):
estimator.set_hyperparameters(hp1='Chainer')
with pytest.raises(ValueError):
estimator.set_hyperparameters(hp1='MxNET')
def test_algorithm_hyperparameter_categorical_range(session):
hyperparameters = [
{
"Description": "A continuous hyperparameter",
"Type": "Categorical",
"Name": "hp1",
"Range": {"CategoricalParameterRangeSpecification": {"Values": ["TF", "MXNet"]}},
"IsTunable": True,
"IsRequired": False,
"DefaultValue": "100",
}
]
some_algo = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
some_algo["TrainingSpecification"]["SupportedHyperParameters"] = hyperparameters
session.sagemaker_client.describe_algorithm = Mock(return_value=some_algo)
estimator = AlgorithmEstimator(
algorithm_arn="arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees",
role="SageMakerRole",
train_instance_type="ml.m4.2xlarge",
train_instance_count=1,
sagemaker_session=session,
)
estimator.set_hyperparameters(hp1="MXNet")
estimator.set_hyperparameters(hp1="TF")
with pytest.raises(ValueError):
estimator.set_hyperparameters(hp1="Chainer")
with pytest.raises(ValueError):
estimator.set_hyperparameters(hp1="MxNET")
def test_algorithm_required_hyperparameters_are_provided(session):
hyperparameters = [
{
"Description": "A categorical hyperparameter",
"Type": "Categorical",
"Name": "hp1",
"Range": {
"CategoricalParameterRangeSpecification": {
"Values": ["TF", "MXNet"]
}
},
"IsTunable": True,
"IsRequired": True,
},
{
"Name": "hp2",
"Description": "A categorical hyperparameter",
"Type": "Categorical",
"IsTunable": False,
"IsRequired": True,
},
{
"Name": "free_text_hp1",
"Description": "You can write anything here",
"Type": "FreeText",
"IsTunable": False,
"IsRequired": True,
},
]
some_algo = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
some_algo["TrainingSpecification"][
"SupportedHyperParameters"] = hyperparameters
session.sagemaker_client.describe_algorithm = Mock(return_value=some_algo)
estimator = AlgorithmEstimator(
algorithm_arn=
"arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees",
role="SageMakerRole",
train_instance_type="ml.m4.2xlarge",
train_instance_count=1,
sagemaker_session=session,
)
# All 3 Hyperparameters are provided
estimator.set_hyperparameters(hp1="TF", hp2="TF2", free_text_hp1="Hello!")
def test_algorithm_required_hyperparameters_not_provided(session):
hyperparameters = [
{
"Description": "A continuous hyperparameter",
"Type": "Categorical",
"Name": "hp1",
"Range": {
"CategoricalParameterRangeSpecification": {
"Values": ["TF", "MXNet"]
}
},
"IsTunable": True,
"IsRequired": True,
},
{
"Name": "hp2",
"Description": "A continuous hyperparameter",
"Type": "Categorical",
"IsTunable": False,
"IsRequired": True,
},
]
some_algo = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
some_algo["TrainingSpecification"][
"SupportedHyperParameters"] = hyperparameters
session.sagemaker_client.describe_algorithm = Mock(return_value=some_algo)
estimator = AlgorithmEstimator(
algorithm_arn=
"arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees",
role="SageMakerRole",
train_instance_type="ml.m4.2xlarge",
train_instance_count=1,
sagemaker_session=session,
)
# hp1 is required and was not provided
with pytest.raises(ValueError):
estimator.set_hyperparameters(hp2="TF2")
# Calling fit with unset required hyperparameters should fail
# this covers the use case of not calling set_hyperparameters() explicitly
with pytest.raises(ValueError):
estimator.fit({"training": "s3://some/place"})
def test_algorithm_required_hyperparameters_are_provided(sagemaker_session):
hyperparameters = [{
'Description': 'A categorical hyperparameter',
'Type': 'Categorical',
'Name': 'hp1',
'Range': {
'CategoricalParameterRangeSpecification': {
'Values': ['TF', 'MXNet']
}
},
'IsTunable': True,
'IsRequired': True,
}, {
'Name': 'hp2',
'Description': 'A categorical hyperparameter',
'Type': 'Categorical',
'IsTunable': False,
'IsRequired': True
}, {
'Name': 'free_text_hp1',
'Description': 'You can write anything here',
'Type': 'FreeText',
'IsTunable': False,
'IsRequired': True
}]
some_algo = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
some_algo['TrainingSpecification'][
'SupportedHyperParameters'] = hyperparameters
sagemaker_session.sagemaker_client.describe_algorithm = Mock(
return_value=some_algo)
estimator = AlgorithmEstimator(
algorithm_arn=
'arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees',
role='SageMakerRole',
train_instance_type='ml.m4.2xlarge',
train_instance_count=1,
sagemaker_session=sagemaker_session,
)
# All 3 Hyperparameters are provided
estimator.set_hyperparameters(hp1='TF', hp2='TF2', free_text_hp1='Hello!')
def test_algorithm_required_hyperparameters_not_provided(sagemaker_session):
hyperparameters = [{
'Description': 'A continuous hyperparameter',
'Type': 'Categorical',
'Name': 'hp1',
'Range': {
'CategoricalParameterRangeSpecification': {
'Values': ['TF', 'MXNet']
}
},
'IsTunable': True,
'IsRequired': True,
}, {
'Name': 'hp2',
'Description': 'A continuous hyperparameter',
'Type': 'Categorical',
'IsTunable': False,
'IsRequired': True
}]
some_algo = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
some_algo['TrainingSpecification'][
'SupportedHyperParameters'] = hyperparameters
sagemaker_session.sagemaker_client.describe_algorithm = Mock(
return_value=some_algo)
estimator = AlgorithmEstimator(
algorithm_arn=
'arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees',
role='SageMakerRole',
train_instance_type='ml.m4.2xlarge',
train_instance_count=1,
sagemaker_session=sagemaker_session,
)
# hp1 is required and was not provided
with pytest.raises(ValueError):
estimator.set_hyperparameters(hp2='TF2')
# Calling fit with unset required hyperparameters should fail
# this covers the use case of not calling set_hyperparameters() explicitly
with pytest.raises(ValueError):
estimator.fit({'training': 's3://some/place'})
