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test_automl_forecasting_training_jobs.py
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test_automl_forecasting_training_jobs.py
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import importlib
import pytest
from unittest import mock
from google.cloud import aiplatform
from google.cloud.aiplatform import datasets
from google.cloud.aiplatform import initializer
from google.cloud.aiplatform import schema
from google.cloud.aiplatform.training_jobs import AutoMLForecastingTrainingJob
from google.cloud.aiplatform_v1.services.model_service import (
client as model_service_client,
)
from google.cloud.aiplatform_v1.services.pipeline_service import (
client as pipeline_service_client,
)
from google.cloud.aiplatform_v1.types import (
dataset as gca_dataset,
model as gca_model,
pipeline_state as gca_pipeline_state,
training_pipeline as gca_training_pipeline,
)
from google.protobuf import json_format
from google.protobuf import struct_pb2
_TEST_BUCKET_NAME = "test-bucket"
_TEST_GCS_PATH_WITHOUT_BUCKET = "path/to/folder"
_TEST_GCS_PATH = f"{_TEST_BUCKET_NAME}/{_TEST_GCS_PATH_WITHOUT_BUCKET}"
_TEST_GCS_PATH_WITH_TRAILING_SLASH = f"{_TEST_GCS_PATH}/"
_TEST_PROJECT = "test-project"
_TEST_DATASET_DISPLAY_NAME = "test-dataset-display-name"
_TEST_DATASET_NAME = "test-dataset-name"
_TEST_DISPLAY_NAME = "test-display-name"
_TEST_TRAINING_CONTAINER_IMAGE = "gcr.io/test-training/container:image"
_TEST_METADATA_SCHEMA_URI_TIMESERIES = schema.dataset.metadata.time_series
_TEST_METADATA_SCHEMA_URI_NONTIMESERIES = schema.dataset.metadata.image
_TEST_TRAINING_COLUMN_TRANSFORMATIONS = [
{"auto": {"column_name": "time"}},
{"auto": {"column_name": "time_series_identifier"}},
{"auto": {"column_name": "target"}},
{"auto": {"column_name": "weight"}},
]
_TEST_TRAINING_TARGET_COLUMN = "target"
_TEST_TRAINING_TIME_COLUMN = "time"
_TEST_TRAINING_TIME_SERIES_IDENTIFIER_COLUMN = "time_series_identifier"
_TEST_TRAINING_TIME_SERIES_ATTRIBUTE_COLUMNS = []
_TEST_TRAINING_UNAVAILABLE_AT_FORECAST_COLUMNS = []
_TEST_TRAINING_AVAILABLE_AT_FORECAST_COLUMNS = []
_TEST_TRAINING_FORECAST_HORIZON = 10
_TEST_TRAINING_DATA_GRANULARITY_UNIT = "day"
_TEST_TRAINING_DATA_GRANULARITY_COUNT = 1
_TEST_TRAINING_CONTEXT_WINDOW = None
_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS = True
_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI = (
"bq://path.to.table"
)
_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION = False
_TEST_TRAINING_QUANTILES = None
_TEST_TRAINING_VALIDATION_OPTIONS = None
_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS = 1000
_TEST_TRAINING_WEIGHT_COLUMN = "weight"
_TEST_TRAINING_OPTIMIZATION_OBJECTIVE_NAME = "minimize-rmse"
_TEST_ADDITIONAL_EXPERIMENTS = ["exp1", "exp2"]
_TEST_TRAINING_TASK_INPUTS_DICT = {
# required inputs
"targetColumn": _TEST_TRAINING_TARGET_COLUMN,
"timeColumn": _TEST_TRAINING_TIME_COLUMN,
"timeSeriesIdentifierColumn": _TEST_TRAINING_TIME_SERIES_IDENTIFIER_COLUMN,
"timeSeriesAttributeColumns": _TEST_TRAINING_TIME_SERIES_ATTRIBUTE_COLUMNS,
"unavailableAtForecastColumns": _TEST_TRAINING_UNAVAILABLE_AT_FORECAST_COLUMNS,
"availableAtForecastColumns": _TEST_TRAINING_AVAILABLE_AT_FORECAST_COLUMNS,
"forecastHorizon": _TEST_TRAINING_FORECAST_HORIZON,
"dataGranularity": {
"unit": _TEST_TRAINING_DATA_GRANULARITY_UNIT,
"quantity": _TEST_TRAINING_DATA_GRANULARITY_COUNT,
},
"transformations": _TEST_TRAINING_COLUMN_TRANSFORMATIONS,
"trainBudgetMilliNodeHours": _TEST_TRAINING_BUDGET_MILLI_NODE_HOURS,
# optional inputs
"weightColumn": _TEST_TRAINING_WEIGHT_COLUMN,
"contextWindow": _TEST_TRAINING_CONTEXT_WINDOW,
"exportEvaluatedDataItemsConfig": {
"destinationBigqueryUri": _TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI,
"overrideExistingTable": _TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION,
},
"quantiles": _TEST_TRAINING_QUANTILES,
"validationOptions": _TEST_TRAINING_VALIDATION_OPTIONS,
"optimizationObjective": _TEST_TRAINING_OPTIMIZATION_OBJECTIVE_NAME,
}
_TEST_TRAINING_TASK_INPUTS = json_format.ParseDict(
_TEST_TRAINING_TASK_INPUTS_DICT, struct_pb2.Value(),
)
_TEST_TRAINING_TASK_INPUTS_WITH_ADDITIONAL_EXPERIMENTS = json_format.ParseDict(
{
**_TEST_TRAINING_TASK_INPUTS_DICT,
"additionalExperiments": _TEST_ADDITIONAL_EXPERIMENTS,
},
struct_pb2.Value(),
)
_TEST_DATASET_NAME = "test-dataset-name"
_TEST_MODEL_DISPLAY_NAME = "model-display-name"
_TEST_TRAINING_FRACTION_SPLIT = 0.8
_TEST_VALIDATION_FRACTION_SPLIT = 0.1
_TEST_TEST_FRACTION_SPLIT = 0.1
_TEST_PREDEFINED_SPLIT_COLUMN_NAME = "split"
_TEST_OUTPUT_PYTHON_PACKAGE_PATH = "gs://test/ouput/python/trainer.tar.gz"
_TEST_MODEL_NAME = "projects/my-project/locations/us-central1/models/12345"
_TEST_PIPELINE_RESOURCE_NAME = (
"projects/my-project/locations/us-central1/trainingPipeline/12345"
)
@pytest.fixture
def mock_pipeline_service_create():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "create_training_pipeline"
) as mock_create_training_pipeline:
mock_create_training_pipeline.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
model_to_upload=gca_model.Model(name=_TEST_MODEL_NAME),
)
yield mock_create_training_pipeline
@pytest.fixture
def mock_pipeline_service_get():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "get_training_pipeline"
) as mock_get_training_pipeline:
mock_get_training_pipeline.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
model_to_upload=gca_model.Model(name=_TEST_MODEL_NAME),
)
yield mock_get_training_pipeline
@pytest.fixture
def mock_pipeline_service_create_and_get_with_fail():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "create_training_pipeline"
) as mock_create_training_pipeline:
mock_create_training_pipeline.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_RUNNING,
)
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "get_training_pipeline"
) as mock_get_training_pipeline:
mock_get_training_pipeline.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_FAILED,
)
yield mock_create_training_pipeline, mock_get_training_pipeline
@pytest.fixture
def mock_model_service_get():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as mock_get_model:
mock_get_model.return_value = gca_model.Model()
yield mock_get_model
@pytest.fixture
def mock_dataset_time_series():
ds = mock.MagicMock(datasets.TimeSeriesDataset)
ds.name = _TEST_DATASET_NAME
ds._latest_future = None
ds._exception = None
ds._gca_resource = gca_dataset.Dataset(
display_name=_TEST_DATASET_DISPLAY_NAME,
metadata_schema_uri=_TEST_METADATA_SCHEMA_URI_TIMESERIES,
labels={},
name=_TEST_DATASET_NAME,
metadata={},
)
return ds
@pytest.fixture
def mock_dataset_nontimeseries():
ds = mock.MagicMock(datasets.ImageDataset)
ds.name = _TEST_DATASET_NAME
ds._latest_future = None
ds._exception = None
ds._gca_resource = gca_dataset.Dataset(
display_name=_TEST_DATASET_DISPLAY_NAME,
metadata_schema_uri=_TEST_METADATA_SCHEMA_URI_NONTIMESERIES,
labels={},
name=_TEST_DATASET_NAME,
metadata={},
)
return ds
class TestAutoMLForecastingTrainingJob:
def setup_method(self):
importlib.reload(initializer)
importlib.reload(aiplatform)
def teardown_method(self):
initializer.global_pool.shutdown(wait=True)
@pytest.mark.parametrize("sync", [True, False])
def test_run_call_pipeline_service_create(
self,
mock_pipeline_service_create,
mock_pipeline_service_get,
mock_dataset_time_series,
mock_model_service_get,
sync,
):
aiplatform.init(project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME)
job = AutoMLForecastingTrainingJob(
display_name=_TEST_DISPLAY_NAME,
optimization_objective=_TEST_TRAINING_OPTIMIZATION_OBJECTIVE_NAME,
column_transformations=_TEST_TRAINING_COLUMN_TRANSFORMATIONS,
)
model_from_job = job.run(
dataset=mock_dataset_time_series,
target_column=_TEST_TRAINING_TARGET_COLUMN,
time_column=_TEST_TRAINING_TIME_COLUMN,
time_series_identifier_column=_TEST_TRAINING_TIME_SERIES_IDENTIFIER_COLUMN,
unavailable_at_forecast_columns=_TEST_TRAINING_UNAVAILABLE_AT_FORECAST_COLUMNS,
available_at_forecast_columns=_TEST_TRAINING_AVAILABLE_AT_FORECAST_COLUMNS,
forecast_horizon=_TEST_TRAINING_FORECAST_HORIZON,
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,
budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS,
export_evaluated_data_items=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS,
export_evaluated_data_items_bigquery_destination_uri=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI,
export_evaluated_data_items_override_destination=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION,
quantiles=_TEST_TRAINING_QUANTILES,
validation_options=_TEST_TRAINING_VALIDATION_OPTIONS,
sync=sync,
)
if not sync:
model_from_job.wait()
true_fraction_split = gca_training_pipeline.FractionSplit(
training_fraction=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction=_TEST_TEST_FRACTION_SPLIT,
)
true_managed_model = gca_model.Model(display_name=_TEST_MODEL_DISPLAY_NAME)
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,
)
true_training_pipeline = gca_training_pipeline.TrainingPipeline(
display_name=_TEST_DISPLAY_NAME,
training_task_definition=schema.training_job.definition.automl_forecasting,
training_task_inputs=_TEST_TRAINING_TASK_INPUTS,
model_to_upload=true_managed_model,
input_data_config=true_input_data_config,
)
mock_pipeline_service_create.assert_called_once_with(
parent=initializer.global_config.common_location_path(),
training_pipeline=true_training_pipeline,
)
assert job._gca_resource is mock_pipeline_service_get.return_value
mock_model_service_get.assert_called_once_with(name=_TEST_MODEL_NAME)
assert model_from_job._gca_resource is mock_model_service_get.return_value
assert job.get_model()._gca_resource is mock_model_service_get.return_value
assert not job.has_failed
assert job.state == gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
@pytest.mark.usefixtures("mock_pipeline_service_get")
@pytest.mark.parametrize("sync", [True, False])
def test_run_call_pipeline_if_no_model_display_name(
self,
mock_pipeline_service_create,
mock_dataset_time_series,
mock_model_service_get,
sync,
):
aiplatform.init(project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME)
job = AutoMLForecastingTrainingJob(
display_name=_TEST_DISPLAY_NAME,
optimization_objective=_TEST_TRAINING_OPTIMIZATION_OBJECTIVE_NAME,
column_transformations=_TEST_TRAINING_COLUMN_TRANSFORMATIONS,
)
model_from_job = job.run(
dataset=mock_dataset_time_series,
target_column=_TEST_TRAINING_TARGET_COLUMN,
time_column=_TEST_TRAINING_TIME_COLUMN,
time_series_identifier_column=_TEST_TRAINING_TIME_SERIES_IDENTIFIER_COLUMN,
unavailable_at_forecast_columns=_TEST_TRAINING_UNAVAILABLE_AT_FORECAST_COLUMNS,
available_at_forecast_columns=_TEST_TRAINING_AVAILABLE_AT_FORECAST_COLUMNS,
forecast_horizon=_TEST_TRAINING_FORECAST_HORIZON,
data_granularity_unit=_TEST_TRAINING_DATA_GRANULARITY_UNIT,
data_granularity_count=_TEST_TRAINING_DATA_GRANULARITY_COUNT,
weight_column=_TEST_TRAINING_WEIGHT_COLUMN,
time_series_attribute_columns=_TEST_TRAINING_TIME_SERIES_ATTRIBUTE_COLUMNS,
context_window=_TEST_TRAINING_CONTEXT_WINDOW,
budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS,
export_evaluated_data_items=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS,
export_evaluated_data_items_bigquery_destination_uri=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI,
export_evaluated_data_items_override_destination=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION,
quantiles=_TEST_TRAINING_QUANTILES,
validation_options=_TEST_TRAINING_VALIDATION_OPTIONS,
sync=sync,
)
if not sync:
model_from_job.wait()
true_fraction_split = gca_training_pipeline.FractionSplit(
training_fraction=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction=_TEST_TEST_FRACTION_SPLIT,
)
# Test that if defaults to the job display name
true_managed_model = gca_model.Model(display_name=_TEST_DISPLAY_NAME)
true_input_data_config = gca_training_pipeline.InputDataConfig(
fraction_split=true_fraction_split,
dataset_id=mock_dataset_time_series.name,
)
true_training_pipeline = gca_training_pipeline.TrainingPipeline(
display_name=_TEST_DISPLAY_NAME,
training_task_definition=schema.training_job.definition.automl_forecasting,
training_task_inputs=_TEST_TRAINING_TASK_INPUTS,
model_to_upload=true_managed_model,
input_data_config=true_input_data_config,
)
mock_pipeline_service_create.assert_called_once_with(
parent=initializer.global_config.common_location_path(),
training_pipeline=true_training_pipeline,
)
@pytest.mark.usefixtures("mock_pipeline_service_get")
@pytest.mark.parametrize("sync", [True, False])
def test_run_call_pipeline_if_set_additional_experiments(
self,
mock_pipeline_service_create,
mock_dataset_time_series,
mock_model_service_get,
sync,
):
aiplatform.init(project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME)
job = AutoMLForecastingTrainingJob(
display_name=_TEST_DISPLAY_NAME,
optimization_objective=_TEST_TRAINING_OPTIMIZATION_OBJECTIVE_NAME,
column_transformations=_TEST_TRAINING_COLUMN_TRANSFORMATIONS,
)
job._add_additional_experiments(_TEST_ADDITIONAL_EXPERIMENTS)
model_from_job = job.run(
dataset=mock_dataset_time_series,
target_column=_TEST_TRAINING_TARGET_COLUMN,
time_column=_TEST_TRAINING_TIME_COLUMN,
time_series_identifier_column=_TEST_TRAINING_TIME_SERIES_IDENTIFIER_COLUMN,
unavailable_at_forecast_columns=_TEST_TRAINING_UNAVAILABLE_AT_FORECAST_COLUMNS,
available_at_forecast_columns=_TEST_TRAINING_AVAILABLE_AT_FORECAST_COLUMNS,
forecast_horizon=_TEST_TRAINING_FORECAST_HORIZON,
data_granularity_unit=_TEST_TRAINING_DATA_GRANULARITY_UNIT,
data_granularity_count=_TEST_TRAINING_DATA_GRANULARITY_COUNT,
weight_column=_TEST_TRAINING_WEIGHT_COLUMN,
time_series_attribute_columns=_TEST_TRAINING_TIME_SERIES_ATTRIBUTE_COLUMNS,
context_window=_TEST_TRAINING_CONTEXT_WINDOW,
budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS,
export_evaluated_data_items=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS,
export_evaluated_data_items_bigquery_destination_uri=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI,
export_evaluated_data_items_override_destination=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION,
quantiles=_TEST_TRAINING_QUANTILES,
validation_options=_TEST_TRAINING_VALIDATION_OPTIONS,
sync=sync,
)
if not sync:
model_from_job.wait()
true_fraction_split = gca_training_pipeline.FractionSplit(
training_fraction=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction=_TEST_TEST_FRACTION_SPLIT,
)
# Test that if defaults to the job display name
true_managed_model = gca_model.Model(display_name=_TEST_DISPLAY_NAME)
true_input_data_config = gca_training_pipeline.InputDataConfig(
fraction_split=true_fraction_split,
dataset_id=mock_dataset_time_series.name,
)
true_training_pipeline = gca_training_pipeline.TrainingPipeline(
display_name=_TEST_DISPLAY_NAME,
training_task_definition=schema.training_job.definition.automl_forecasting,
training_task_inputs=_TEST_TRAINING_TASK_INPUTS_WITH_ADDITIONAL_EXPERIMENTS,
model_to_upload=true_managed_model,
input_data_config=true_input_data_config,
)
mock_pipeline_service_create.assert_called_once_with(
parent=initializer.global_config.common_location_path(),
training_pipeline=true_training_pipeline,
)
@pytest.mark.usefixtures(
"mock_pipeline_service_create",
"mock_pipeline_service_get",
"mock_model_service_get",
)
@pytest.mark.parametrize("sync", [True, False])
def test_run_called_twice_raises(
self, mock_dataset_time_series, sync,
):
aiplatform.init(project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME)
job = AutoMLForecastingTrainingJob(
display_name=_TEST_DISPLAY_NAME,
optimization_objective=_TEST_TRAINING_OPTIMIZATION_OBJECTIVE_NAME,
column_transformations=_TEST_TRAINING_COLUMN_TRANSFORMATIONS,
)
job.run(
dataset=mock_dataset_time_series,
target_column=_TEST_TRAINING_TARGET_COLUMN,
time_column=_TEST_TRAINING_TIME_COLUMN,
time_series_identifier_column=_TEST_TRAINING_TIME_SERIES_IDENTIFIER_COLUMN,
unavailable_at_forecast_columns=_TEST_TRAINING_UNAVAILABLE_AT_FORECAST_COLUMNS,
available_at_forecast_columns=_TEST_TRAINING_AVAILABLE_AT_FORECAST_COLUMNS,
forecast_horizon=_TEST_TRAINING_FORECAST_HORIZON,
data_granularity_unit=_TEST_TRAINING_DATA_GRANULARITY_UNIT,
data_granularity_count=_TEST_TRAINING_DATA_GRANULARITY_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
weight_column=_TEST_TRAINING_WEIGHT_COLUMN,
time_series_attribute_columns=_TEST_TRAINING_TIME_SERIES_ATTRIBUTE_COLUMNS,
context_window=_TEST_TRAINING_CONTEXT_WINDOW,
budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS,
export_evaluated_data_items=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS,
export_evaluated_data_items_bigquery_destination_uri=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI,
export_evaluated_data_items_override_destination=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION,
quantiles=_TEST_TRAINING_QUANTILES,
validation_options=_TEST_TRAINING_VALIDATION_OPTIONS,
sync=sync,
)
with pytest.raises(RuntimeError):
job.run(
dataset=mock_dataset_time_series,
target_column=_TEST_TRAINING_TARGET_COLUMN,
time_column=_TEST_TRAINING_TIME_COLUMN,
time_series_identifier_column=_TEST_TRAINING_TIME_SERIES_IDENTIFIER_COLUMN,
unavailable_at_forecast_columns=_TEST_TRAINING_UNAVAILABLE_AT_FORECAST_COLUMNS,
available_at_forecast_columns=_TEST_TRAINING_AVAILABLE_AT_FORECAST_COLUMNS,
forecast_horizon=_TEST_TRAINING_FORECAST_HORIZON,
data_granularity_unit=_TEST_TRAINING_DATA_GRANULARITY_UNIT,
data_granularity_count=_TEST_TRAINING_DATA_GRANULARITY_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
weight_column=_TEST_TRAINING_WEIGHT_COLUMN,
time_series_attribute_columns=_TEST_TRAINING_TIME_SERIES_ATTRIBUTE_COLUMNS,
context_window=_TEST_TRAINING_CONTEXT_WINDOW,
budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS,
export_evaluated_data_items=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS,
export_evaluated_data_items_bigquery_destination_uri=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI,
export_evaluated_data_items_override_destination=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION,
quantiles=_TEST_TRAINING_QUANTILES,
validation_options=_TEST_TRAINING_VALIDATION_OPTIONS,
sync=sync,
)
@pytest.mark.parametrize("sync", [True, False])
def test_run_raises_if_pipeline_fails(
self,
mock_pipeline_service_create_and_get_with_fail,
mock_dataset_time_series,
sync,
):
aiplatform.init(project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME)
job = AutoMLForecastingTrainingJob(
display_name=_TEST_DISPLAY_NAME,
optimization_objective=_TEST_TRAINING_OPTIMIZATION_OBJECTIVE_NAME,
column_transformations=_TEST_TRAINING_COLUMN_TRANSFORMATIONS,
)
with pytest.raises(RuntimeError):
job.run(
dataset=mock_dataset_time_series,
target_column=_TEST_TRAINING_TARGET_COLUMN,
time_column=_TEST_TRAINING_TIME_COLUMN,
time_series_identifier_column=_TEST_TRAINING_TIME_SERIES_IDENTIFIER_COLUMN,
unavailable_at_forecast_columns=_TEST_TRAINING_UNAVAILABLE_AT_FORECAST_COLUMNS,
available_at_forecast_columns=_TEST_TRAINING_AVAILABLE_AT_FORECAST_COLUMNS,
forecast_horizon=_TEST_TRAINING_FORECAST_HORIZON,
data_granularity_unit=_TEST_TRAINING_DATA_GRANULARITY_UNIT,
data_granularity_count=_TEST_TRAINING_DATA_GRANULARITY_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
weight_column=_TEST_TRAINING_WEIGHT_COLUMN,
time_series_attribute_columns=_TEST_TRAINING_TIME_SERIES_ATTRIBUTE_COLUMNS,
context_window=_TEST_TRAINING_CONTEXT_WINDOW,
budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS,
export_evaluated_data_items=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS,
export_evaluated_data_items_bigquery_destination_uri=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI,
export_evaluated_data_items_override_destination=_TEST_TRAINING_EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION,
quantiles=_TEST_TRAINING_QUANTILES,
validation_options=_TEST_TRAINING_VALIDATION_OPTIONS,
sync=sync,
)
if not sync:
job.wait()
with pytest.raises(RuntimeError):
job.get_model()
def test_raises_before_run_is_called(self, mock_pipeline_service_create):
aiplatform.init(project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME)
job = AutoMLForecastingTrainingJob(
display_name=_TEST_DISPLAY_NAME,
optimization_objective=_TEST_TRAINING_OPTIMIZATION_OBJECTIVE_NAME,
column_transformations=_TEST_TRAINING_COLUMN_TRANSFORMATIONS,
)
with pytest.raises(RuntimeError):
job.get_model()
with pytest.raises(RuntimeError):
job.has_failed
with pytest.raises(RuntimeError):
job.state