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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import absolute_import
import pytest
from mock import Mock, patch
from sagemaker import image_uris
from sagemaker.amazon.randomcutforest import RandomCutForest, RandomCutForestPredictor
from sagemaker.amazon.amazon_estimator import RecordSet
ROLE = "myrole"
INSTANCE_COUNT = 1
INSTANCE_TYPE = "ml.c4.xlarge"
NUM_SAMPLES_PER_TREE = 20
NUM_TREES = 50
EVAL_METRICS = ["accuracy", "precision_recall_fscore"]
COMMON_TRAIN_ARGS = {
"role": ROLE,
"instance_count": INSTANCE_COUNT,
"instance_type": INSTANCE_TYPE,
}
ALL_REQ_ARGS = dict(**COMMON_TRAIN_ARGS)
REGION = "us-west-2"
BUCKET_NAME = "Some-Bucket"
DESCRIBE_TRAINING_JOB_RESULT = {"ModelArtifacts": {"S3ModelArtifacts": "s3://bucket/model.tar.gz"}}
ENDPOINT_DESC = {"EndpointConfigName": "test-endpoint"}
ENDPOINT_CONFIG_DESC = {"ProductionVariants": [{"ModelName": "model-1"}, {"ModelName": "model-2"}]}
@pytest.fixture()
def sagemaker_session():
boto_mock = Mock(name="boto_session", region_name=REGION)
sms = Mock(
name="sagemaker_session",
boto_session=boto_mock,
region_name=REGION,
config=None,
local_mode=False,
)
sms.boto_region_name = REGION
sms.default_bucket = Mock(name="default_bucket", return_value=BUCKET_NAME)
sms.sagemaker_client.describe_training_job = Mock(
name="describe_training_job", return_value=DESCRIBE_TRAINING_JOB_RESULT
)
sms.sagemaker_client.describe_endpoint = Mock(return_value=ENDPOINT_DESC)
sms.sagemaker_client.describe_endpoint_config = Mock(return_value=ENDPOINT_CONFIG_DESC)
return sms
def test_init_required_positional(sagemaker_session):
randomcutforest = RandomCutForest(
ROLE,
INSTANCE_COUNT,
INSTANCE_TYPE,
NUM_SAMPLES_PER_TREE,
NUM_TREES,
EVAL_METRICS,
sagemaker_session=sagemaker_session,
)
assert randomcutforest.role == ROLE
assert randomcutforest.instance_count == INSTANCE_COUNT
assert randomcutforest.instance_type == INSTANCE_TYPE
assert randomcutforest.num_trees == NUM_TREES
assert randomcutforest.num_samples_per_tree == NUM_SAMPLES_PER_TREE
assert randomcutforest.eval_metrics == EVAL_METRICS
def test_init_required_named(sagemaker_session):
randomcutforest = RandomCutForest(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert randomcutforest.role == COMMON_TRAIN_ARGS["role"]
assert randomcutforest.instance_count == INSTANCE_COUNT
assert randomcutforest.instance_type == COMMON_TRAIN_ARGS["instance_type"]
def test_all_hyperparameters(sagemaker_session):
randomcutforest = RandomCutForest(
sagemaker_session=sagemaker_session,
num_trees=NUM_TREES,
num_samples_per_tree=NUM_SAMPLES_PER_TREE,
eval_metrics=EVAL_METRICS,
**ALL_REQ_ARGS,
)
assert randomcutforest.hyperparameters() == dict(
num_samples_per_tree=str(NUM_SAMPLES_PER_TREE),
num_trees=str(NUM_TREES),
eval_metrics='["accuracy", "precision_recall_fscore"]',
)
def test_image(sagemaker_session):
randomcutforest = RandomCutForest(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert image_uris.retrieve("randomcutforest", REGION) == randomcutforest.training_image_uri()
@pytest.mark.parametrize("iterable_hyper_parameters, value", [("eval_metrics", 0)])
def test_iterable_hyper_parameters_type(sagemaker_session, iterable_hyper_parameters, value):
with pytest.raises(TypeError):
test_params = ALL_REQ_ARGS.copy()
test_params.update({iterable_hyper_parameters: value})
RandomCutForest(sagemaker_session=sagemaker_session, **test_params)
@pytest.mark.parametrize(
"optional_hyper_parameters, value",
[("num_trees", "string"), ("num_samples_per_tree", "string")],
)
def test_optional_hyper_parameters_type(sagemaker_session, optional_hyper_parameters, value):
with pytest.raises(ValueError):
test_params = ALL_REQ_ARGS.copy()
test_params.update({optional_hyper_parameters: value})
RandomCutForest(sagemaker_session=sagemaker_session, **test_params)
@pytest.mark.parametrize(
"optional_hyper_parameters, value",
[
("num_trees", 49),
("num_trees", 1001),
("num_samples_per_tree", 0),
("num_samples_per_tree", 2049),
],
)
def test_optional_hyper_parameters_value(sagemaker_session, optional_hyper_parameters, value):
with pytest.raises(ValueError):
test_params = ALL_REQ_ARGS.copy()
test_params.update({optional_hyper_parameters: value})
RandomCutForest(sagemaker_session=sagemaker_session, **test_params)
PREFIX = "prefix"
FEATURE_DIM = 10
MAX_FEATURE_DIM = 10000
MINI_BATCH_SIZE = 1000
@patch("sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit")
def test_call_fit(base_fit, sagemaker_session):
randomcutforest = RandomCutForest(
base_job_name="randomcutforest", sagemaker_session=sagemaker_session, **ALL_REQ_ARGS
)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
randomcutforest.fit(data, MINI_BATCH_SIZE)
base_fit.assert_called_once()
assert len(base_fit.call_args[0]) == 2
assert base_fit.call_args[0][0] == data
assert base_fit.call_args[0][1] == MINI_BATCH_SIZE
def test_prepare_for_training_no_mini_batch_size(sagemaker_session):
randomcutforest = RandomCutForest(
base_job_name="randomcutforest", sagemaker_session=sagemaker_session, **ALL_REQ_ARGS
)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
randomcutforest._prepare_for_training(data)
assert randomcutforest.mini_batch_size == MINI_BATCH_SIZE
def test_prepare_for_training_wrong_type_mini_batch_size(sagemaker_session):
randomcutforest = RandomCutForest(
base_job_name="randomcutforest", sagemaker_session=sagemaker_session, **ALL_REQ_ARGS
)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
with pytest.raises((TypeError, ValueError)):
randomcutforest._prepare_for_training(data, 1234)
def test_prepare_for_training_feature_dim_greater_than_max_allowed(sagemaker_session):
randomcutforest = RandomCutForest(
base_job_name="randomcutforest", sagemaker_session=sagemaker_session, **ALL_REQ_ARGS
)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=MAX_FEATURE_DIM + 1,
channel="train",
)
with pytest.raises((TypeError, ValueError)):
randomcutforest._prepare_for_training(data)
def test_model_image(sagemaker_session):
randomcutforest = RandomCutForest(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
randomcutforest.fit(data, MINI_BATCH_SIZE)
model = randomcutforest.create_model()
assert image_uris.retrieve("randomcutforest", REGION) == model.image_uri
def test_predictor_type(sagemaker_session):
randomcutforest = RandomCutForest(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
randomcutforest.fit(data, MINI_BATCH_SIZE)
model = randomcutforest.create_model()
predictor = model.deploy(1, INSTANCE_TYPE)
assert isinstance(predictor, RandomCutForestPredictor)
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