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fix: use name_from_base in auto_ml.py but unique_name_from_base in tests. #1182

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Merged
merged 4 commits into from
Dec 17, 2019
Merged

fix: use name_from_base in auto_ml.py but unique_name_from_base in tests. #1182

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6b5f005
fix: use name_from_base in auto_ml.py but unique_name_from_base in te…
Dec 17, 2019
6d80520
add missing arg
Dec 17, 2019
c4a5479
Merge branch 'master' into auto_ml
chuyang-deng Dec 17, 2019
a0c705a
Merge branch 'master' into auto_ml
laurenyu Dec 17, 2019
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4 changes: 2 additions & 2 deletions src/sagemaker/automl/automl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
from sagemaker.automl.candidate_estimator import CandidateEstimator
from sagemaker.job import _Job
from sagemaker.session import Session
from sagemaker.utils import unique_name_from_base
from sagemaker.utils import name_from_base


class AutoML(object):
Expand Down Expand Up @@ -381,7 +381,7 @@ def _prepare_for_auto_ml_job(self, job_name=None):
else:
base_name = "automl"
# CreateAutoMLJob API validates that member length less than or equal to 32
self.current_job_name = unique_name_from_base(base_name, max_length=32)
self.current_job_name = name_from_base(base_name, max_length=32)

if self.output_path is None:
self.output_path = "s3://{}/".format(self.sagemaker_session.default_bucket())
Expand Down
44 changes: 25 additions & 19 deletions tests/unit/sagemaker/automl/test_auto_ml.py
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Expand Up @@ -13,13 +13,14 @@
from __future__ import absolute_import

import pytest
from mock import Mock
from mock import Mock, patch
from sagemaker import AutoML, AutoMLJob, AutoMLInput, CandidateEstimator

MODEL_DATA = "s3://bucket/model.tar.gz"
MODEL_IMAGE = "mi"
ENTRY_POINT = "blah.py"

TIMESTAMP = "2017-11-06-14:14:15.671"
BUCKET_NAME = "mybucket"
INSTANCE_COUNT = 1
INSTANCE_TYPE = "ml.c5.2xlarge"
Expand All @@ -31,6 +32,7 @@
DEFAULT_OUTPUT_PATH = "s3://{}/".format(BUCKET_NAME)
LOCAL_DATA_PATH = "file://data"
DEFAULT_MAX_CANDIDATES = 500
DEFAULT_JOB_NAME = "automl-{}".format(TIMESTAMP)

JOB_NAME = "default-job-name"
JOB_NAME_2 = "banana-auto-ml-job"
Expand Down Expand Up @@ -281,34 +283,38 @@ def test_auto_ml_additional_optional_params(sagemaker_session):
}


def test_auto_ml_default_fit(sagemaker_session):
@patch("time.strftime", return_value=TIMESTAMP)
def test_auto_ml_default_fit(strftime, sagemaker_session):
auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
inputs = DEFAULT_S3_INPUT_DATA
auto_ml.fit(inputs)
sagemaker_session.auto_ml.assert_called_once()
_, args = sagemaker_session.auto_ml.call_args
assert args["input_config"] == [
{
"DataSource": {
"S3DataSource": {"S3DataType": "S3Prefix", "S3Uri": DEFAULT_S3_INPUT_DATA}
assert args == {
"input_config": [
{
"DataSource": {
"S3DataSource": {"S3DataType": "S3Prefix", "S3Uri": DEFAULT_S3_INPUT_DATA}
},
"TargetAttributeName": TARGET_ATTRIBUTE_NAME,
}
],
"output_config": {"S3OutputPath": DEFAULT_OUTPUT_PATH},
"auto_ml_job_config": {
"CompletionCriteria": {"MaxCandidates": DEFAULT_MAX_CANDIDATES},
"SecurityConfig": {
"EnableInterContainerTrafficEncryption": ENCRYPT_INTER_CONTAINER_TRAFFIC
},
"TargetAttributeName": TARGET_ATTRIBUTE_NAME,
}
]
assert args["output_config"] == {"S3OutputPath": DEFAULT_OUTPUT_PATH}
assert args["auto_ml_job_config"] == {
"CompletionCriteria": {"MaxCandidates": DEFAULT_MAX_CANDIDATES},
"SecurityConfig": {
"EnableInterContainerTrafficEncryption": ENCRYPT_INTER_CONTAINER_TRAFFIC
},
"role": ROLE,
"job_name": DEFAULT_JOB_NAME,
"problem_type": None,
"job_objective": None,
"generate_candidate_definitions_only": GENERATE_CANDIDATE_DEFINITIONS_ONLY,
"tags": None,
}
assert args["role"] == ROLE
assert args["problem_type"] is None
assert args["job_objective"] is None
assert args["generate_candidate_definitions_only"] == GENERATE_CANDIDATE_DEFINITIONS_ONLY
assert args["tags"] is None


def test_auto_ml_local_input(sagemaker_session):
Expand Down