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create_training_pipeline_tabular_forecasting_sample_test.py
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create_training_pipeline_tabular_forecasting_sample_test.py
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# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
import os
from uuid import uuid4
from google.cloud import aiplatform
import pytest
import cancel_training_pipeline_sample
import create_training_pipeline_tabular_forecasting_sample
import delete_training_pipeline_sample
import helpers
PROJECT_ID = os.getenv("BUILD_SPECIFIC_GCLOUD_PROJECT")
DATASET_ID = "3003302817130610688" # COVID Dataset
DISPLAY_NAME = f"temp_create_training_pipeline_test_{uuid4()}"
TARGET_COLUMN = "deaths"
PREDICTION_TYPE = "forecasting"
@pytest.fixture
def shared_state():
state = {}
yield state
@pytest.fixture(scope="function", autouse=True)
def teardown(shared_state):
yield
training_pipeline_id = shared_state["training_pipeline_name"].split("/")[-1]
# Stop the training pipeline
cancel_training_pipeline_sample.cancel_training_pipeline_sample(
project=PROJECT_ID, training_pipeline_id=training_pipeline_id
)
client_options = {"api_endpoint": "us-central1-aiplatform.googleapis.com"}
pipeline_client = aiplatform.gapic.PipelineServiceClient(
client_options=client_options
)
# Waiting for training pipeline to be in CANCELLED state
helpers.wait_for_job_state(
get_job_method=pipeline_client.get_training_pipeline,
name=shared_state["training_pipeline_name"],
)
# Delete the training pipeline
delete_training_pipeline_sample.delete_training_pipeline_sample(
project=PROJECT_ID, training_pipeline_id=training_pipeline_id
)
@pytest.mark.skip(reason="https://github.com/googleapis/java-aiplatform/issues/420")
def test_ucaip_generated_create_training_pipeline_sample(capsys, shared_state):
create_training_pipeline_tabular_forecasting_sample.create_training_pipeline_tabular_forecasting_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
dataset_id=DATASET_ID,
model_display_name="permanent_tabular_forecasting_model",
target_column=TARGET_COLUMN,
time_series_identifier_column="county",
time_column="date",
static_columns=["state_name"],
time_variant_past_only_columns=["deaths"],
time_variant_past_and_future_columns=["date"],
forecast_window_end=10,
)
out, _ = capsys.readouterr()
assert "response:" in out
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)