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feat: add filter and timestamp splits #549

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feat: add filter and timestamp splits #549

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5a72c86
initial commit
sirtorry Jul 16, 2021
0a5971e
add tests
sirtorry Jul 16, 2021
4875cd1
fix arrays
sirtorry Jul 16, 2021
9315fea
fix tests
sirtorry Jul 16, 2021
b04dca6
fix test
sirtorry Jul 17, 2021
5b78ee6
change default split behaviour
sirtorry Jul 23, 2021
c0b38f8
modify tests
sirtorry Jul 23, 2021
99aa367
fix tests
sirtorry Jul 27, 2021
2ef9cbd
tests for multi-split
sirtorry Jul 27, 2021
5f8a7b1
split counter bug fix
sirtorry Jul 27, 2021
d43ef98
fixes multi split issue
sirtorry Jul 27, 2021
a45d837
bug fixes
sirtorry Jul 28, 2021
3577e9e
bug fix
sirtorry Jul 28, 2021
864d0e2
fix non-sync tests
sirtorry Jul 29, 2021
e31531f
change yield to return
sirtorry Jul 29, 2021
20ea593
fix tabular test
sirtorry Jul 29, 2021
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913 changes: 745 additions & 168 deletions google/cloud/aiplatform/training_jobs.py
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4 changes: 0 additions & 4 deletions tests/unit/aiplatform/test_automl_forecasting_training_jobs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -241,7 +241,6 @@ def test_run_call_pipeline_service_create(
data_granularity_unit=_TEST_TRAINING_DATA_GRANULARITY_UNIT,
data_granularity_count=_TEST_TRAINING_DATA_GRANULARITY_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
predefined_split_column_name=_TEST_PREDEFINED_SPLIT_COLUMN_NAME,
weight_column=_TEST_TRAINING_WEIGHT_COLUMN,
time_series_attribute_columns=_TEST_TRAINING_TIME_SERIES_ATTRIBUTE_COLUMNS,
context_window=_TEST_TRAINING_CONTEXT_WINDOW,
Expand All @@ -267,9 +266,6 @@ def test_run_call_pipeline_service_create(

true_input_data_config = gca_training_pipeline.InputDataConfig(
fraction_split=true_fraction_split,
predefined_split=gca_training_pipeline.PredefinedSplit(
key=_TEST_PREDEFINED_SPLIT_COLUMN_NAME
),
dataset_id=mock_dataset_time_series.name,
)

Expand Down
59 changes: 44 additions & 15 deletions tests/unit/aiplatform/test_automl_image_training_jobs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,12 @@
_TEST_FRACTION_SPLIT_TRAINING = 0.6
_TEST_FRACTION_SPLIT_VALIDATION = 0.2
_TEST_FRACTION_SPLIT_TEST = 0.2
_TEST_DEFAULT_TRAINING_FRACTION_SPLIT = 0.8
_TEST_DEFAULT_VALIDATION_FRACTION_SPLIT = 0.1
_TEST_DEFAULT_TEST_FRACTION_SPLIT = 0.1
_TEST_FILTER_SPLIT_TRAINING = "train"
_TEST_FILTER_SPLIT_VALIDATION = "validate"
_TEST_FILTER_SPLIT_TEST = "test"

_TEST_MODEL_NAME = (
f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/models/{_TEST_MODEL_ID}"
Expand Down Expand Up @@ -156,6 +162,7 @@ def mock_model_service_get():
def mock_dataset_image():
ds = mock.MagicMock(datasets.ImageDataset)
ds.name = _TEST_DATASET_NAME
ds.metadata_schema_uri = _TEST_METADATA_SCHEMA_URI_IMAGE
ds._latest_future = None
ds._exception = None
ds._gca_resource = gca_dataset.Dataset(
Expand Down Expand Up @@ -257,9 +264,9 @@ def test_run_call_pipeline_service_create(
model_from_job = job.run(
dataset=mock_dataset_image,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
training_fraction_split=_TEST_FRACTION_SPLIT_TRAINING,
validation_fraction_split=_TEST_FRACTION_SPLIT_VALIDATION,
test_fraction_split=_TEST_FRACTION_SPLIT_TEST,
training_filter_split=_TEST_FILTER_SPLIT_TRAINING,
validation_filter_split=_TEST_FILTER_SPLIT_VALIDATION,
test_filter_split=_TEST_FILTER_SPLIT_TEST,
budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS,
disable_early_stopping=_TEST_TRAINING_DISABLE_EARLY_STOPPING,
sync=sync,
Expand All @@ -268,10 +275,10 @@ def test_run_call_pipeline_service_create(
if not sync:
model_from_job.wait()

true_fraction_split = gca_training_pipeline.FractionSplit(
training_fraction=_TEST_FRACTION_SPLIT_TRAINING,
validation_fraction=_TEST_FRACTION_SPLIT_VALIDATION,
test_fraction=_TEST_FRACTION_SPLIT_TEST,
true_filter_split = gca_training_pipeline.FilterSplit(
training_filter=_TEST_FILTER_SPLIT_TRAINING,
validation_filter=_TEST_FILTER_SPLIT_VALIDATION,
test_filter=_TEST_FILTER_SPLIT_TEST,
)

true_managed_model = gca_model.Model(
Expand All @@ -281,7 +288,7 @@ def test_run_call_pipeline_service_create(
)

true_input_data_config = gca_training_pipeline.InputDataConfig(
fraction_split=true_fraction_split, dataset_id=mock_dataset_image.name,
filter_split=true_filter_split, dataset_id=mock_dataset_image.name,
)

true_training_pipeline = gca_training_pipeline.TrainingPipeline(
Expand Down Expand Up @@ -324,9 +331,6 @@ def test_run_call_pipeline_if_no_model_display_name(

model_from_job = job.run(
dataset=mock_dataset_image,
training_fraction_split=_TEST_FRACTION_SPLIT_TRAINING,
validation_fraction_split=_TEST_FRACTION_SPLIT_VALIDATION,
test_fraction_split=_TEST_FRACTION_SPLIT_TEST,
budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS,
disable_early_stopping=_TEST_TRAINING_DISABLE_EARLY_STOPPING,
)
Expand All @@ -335,9 +339,9 @@ def test_run_call_pipeline_if_no_model_display_name(
model_from_job.wait()

true_fraction_split = gca_training_pipeline.FractionSplit(
training_fraction=_TEST_FRACTION_SPLIT_TRAINING,
validation_fraction=_TEST_FRACTION_SPLIT_VALIDATION,
test_fraction=_TEST_FRACTION_SPLIT_TEST,
training_fraction=_TEST_DEFAULT_TRAINING_FRACTION_SPLIT,
validation_fraction=_TEST_DEFAULT_VALIDATION_FRACTION_SPLIT,
test_fraction=_TEST_DEFAULT_TEST_FRACTION_SPLIT,
)

# Test that if defaults to the job display name
Expand All @@ -346,7 +350,7 @@ def test_run_call_pipeline_if_no_model_display_name(
)

true_input_data_config = gca_training_pipeline.InputDataConfig(
fraction_split=true_fraction_split, dataset_id=mock_dataset_image.name,
fraction_split=true_fraction_split, dataset_id=mock_dataset_image.name
)

true_training_pipeline = gca_training_pipeline.TrainingPipeline(
Expand Down Expand Up @@ -386,13 +390,38 @@ def test_run_called_twice_raises(self, mock_dataset_image, sync):

with pytest.raises(RuntimeError):
job.run(
dataset=mock_dataset_image,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
sync=sync,
)

@pytest.mark.usefixtures(
"mock_pipeline_service_create",
"mock_pipeline_service_get",
"mock_model_service_get",
)
@pytest.mark.parametrize("sync", [True, False])
def test_run_with_two_split_raises(
self, mock_dataset_image, sync,
):
aiplatform.init(project=_TEST_PROJECT)

job = training_jobs.AutoMLImageTrainingJob(display_name=_TEST_DISPLAY_NAME,)

with pytest.raises(ValueError):
model_from_job = job.run(
dataset=mock_dataset_image,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
training_fraction_split=_TEST_FRACTION_SPLIT_TRAINING,
validation_fraction_split=_TEST_FRACTION_SPLIT_VALIDATION,
test_fraction_split=_TEST_FRACTION_SPLIT_TEST,
training_filter_split=_TEST_FILTER_SPLIT_TRAINING,
validation_filter_split=_TEST_FILTER_SPLIT_VALIDATION,
test_filter_split=_TEST_FILTER_SPLIT_TEST,
sync=sync,
)
if not sync:
model_from_job.wait()

@pytest.mark.parametrize("sync", [True, False])
def test_run_raises_if_pipeline_fails(
Expand Down
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