Source code for tests.system.amazon.aws.example_comprehend_document_classifier

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from __future__ import annotations

import os
from datetime import datetime

from airflow import DAG, settings
from airflow.decorators import task, task_group
from airflow.models import Connection
from airflow.models.baseoperator import chain
from airflow.providers.amazon.aws.hooks.comprehend import ComprehendHook
from airflow.providers.amazon.aws.operators.comprehend import (
    ComprehendCreateDocumentClassifierOperator,
)
from airflow.providers.amazon.aws.operators.s3 import (
    S3CreateBucketOperator,
    S3CreateObjectOperator,
    S3DeleteBucketOperator,
)
from airflow.providers.amazon.aws.sensors.comprehend import (
    ComprehendCreateDocumentClassifierCompletedSensor,
)
from airflow.providers.amazon.aws.transfers.http_to_s3 import HttpToS3Operator
from airflow.utils.trigger_rule import TriggerRule

from providers.tests.system.amazon.aws.utils import SystemTestContextBuilder

[docs]ROLE_ARN_KEY = "ROLE_ARN"
[docs]sys_test_context_task = SystemTestContextBuilder().add_variable(ROLE_ARN_KEY).build()
[docs]DAG_ID = "example_comprehend_document_classifier"
[docs]ANNOTATION_BUCKET_KEY = "training-labels/label.csv"
[docs]TRAINING_DATA_PREFIX = "training-docs"
# To create a custom document classifier, we need a minimum of 10 documents for each label. # for testing purpose, we will generate 10 copies of each document referenced below.
[docs]PUBLIC_DATA_SOURCES = [ { "fileName": "discharge-summary.pdf", "endpoint": "aws-samples/amazon-comprehend-examples/blob/master/building-custom-classifier/sample-docs/discharge-summary.pdf?raw=true", }, { "fileName": "doctors-notes.pdf", "endpoint": "aws-samples/amazon-comprehend-examples/blob/master/building-custom-classifier/sample-docs/doctors-notes.pdf?raw=true", }, ]
# Annotations file won't allow headers # label,document name,page number
[docs]ANNOTATIONS = """DISCHARGE_SUMMARY,discharge-summary-0.pdf,1 DISCHARGE_SUMMARY,discharge-summary-1.pdf,1 DISCHARGE_SUMMARY,discharge-summary-2.pdf,1 DISCHARGE_SUMMARY,discharge-summary-3.pdf,1 DISCHARGE_SUMMARY,discharge-summary-4.pdf,1 DISCHARGE_SUMMARY,discharge-summary-5.pdf,1 DISCHARGE_SUMMARY,discharge-summary-6.pdf,1 DISCHARGE_SUMMARY,discharge-summary-7.pdf,1 DISCHARGE_SUMMARY,discharge-summary-8.pdf,1 DISCHARGE_SUMMARY,discharge-summary-9.pdf,1 DOCTOR_NOTES,doctors-notes-0.pdf,1 DOCTOR_NOTES,doctors-notes-1.pdf,1 DOCTOR_NOTES,doctors-notes-2.pdf,1 DOCTOR_NOTES,doctors-notes-3.pdf,1 DOCTOR_NOTES,doctors-notes-4.pdf,1 DOCTOR_NOTES,doctors-notes-5.pdf,1 DOCTOR_NOTES,doctors-notes-6.pdf,1 DOCTOR_NOTES,doctors-notes-7.pdf,1 DOCTOR_NOTES,doctors-notes-8.pdf,1 DOCTOR_NOTES,doctors-notes-9.pdf,1"""
@task_group
[docs]def document_classifier_workflow(): # [START howto_operator_create_document_classifier] create_document_classifier = ComprehendCreateDocumentClassifierOperator( task_id="create_document_classifier", document_classifier_name=classifier_name, input_data_config=input_data_configurations, output_data_config=output_data_configurations, mode="MULTI_CLASS", data_access_role_arn=test_context[ROLE_ARN_KEY], language_code="en", document_classifier_kwargs=document_classifier_kwargs, ) # [END howto_operator_create_document_classifier] create_document_classifier.wait_for_completion = False # [START howto_sensor_create_document_classifier] await_create_document_classifier = ComprehendCreateDocumentClassifierCompletedSensor( task_id="await_create_document_classifier", document_classifier_arn=create_document_classifier.output ) # [END howto_sensor_create_document_classifier] @task(trigger_rule=TriggerRule.ALL_DONE) def delete_classifier(document_classifier_arn: str): ComprehendHook().conn.delete_document_classifier(DocumentClassifierArn=document_classifier_arn) chain( create_document_classifier, await_create_document_classifier, delete_classifier(create_document_classifier.output), )
@task_group
[docs]def copy_data_to_s3(bucket: str, sources: list[dict], prefix: str, number_of_copies=1): """ Copy some sample data to S3 using HttpToS3Operator. :param bucket: Name of the Amazon S3 bucket to send the data. :param prefix: Folder to store the files :param number_of_copies: Number of files to create for a document from the sources :param sources: Public available data locations """ """ EX: If number_of_copies is 2, sources has file name 'file.pdf', and prefix is 'training-docs'. Will generate two copies and upload to s3: - training-docs/file-0.pdf - training-docs/file-1.pdf """ http_to_s3_configs = [ { "endpoint": source["endpoint"], "s3_key": f"{prefix}/{os.path.splitext(os.path.basename(source['fileName']))[0]}-{counter}{os.path.splitext(os.path.basename(source['fileName']))[1]}", } for counter in range(number_of_copies) for source in sources ] @task def create_connection(conn_id): conn = Connection( conn_id=conn_id, conn_type="http", host="https://github.com/", ) session = settings.Session() session.add(conn) session.commit() @task(trigger_rule=TriggerRule.ALL_DONE) def delete_connection(conn_id): session = settings.Session() conn_to_details = session.query(Connection).filter(Connection.conn_id == conn_id).first() session.delete(conn_to_details) session.commit() http_to_s3_task = HttpToS3Operator.partial( task_id="http_to_s3_task", http_conn_id=http_conn_id, s3_bucket=bucket, ).expand_kwargs(http_to_s3_configs) chain(create_connection(http_conn_id), http_to_s3_task, delete_connection(http_conn_id))
with DAG( dag_id=DAG_ID, schedule="@once", start_date=datetime(2021, 1, 1), tags=["example"], catchup=False, ) as dag:
[docs] test_context = sys_test_context_task()
env_id = test_context["ENV_ID"] classifier_name = f"{env_id}-custom-document-classifier" bucket_name = f"{env_id}-comprehend-document-classifier" http_conn_id = f"{env_id}-git" input_data_configurations = { "S3Uri": f"s3://{bucket_name}/{ANNOTATION_BUCKET_KEY}", "DataFormat": "COMPREHEND_CSV", "DocumentType": "SEMI_STRUCTURED_DOCUMENT", "Documents": {"S3Uri": f"s3://{bucket_name}/{TRAINING_DATA_PREFIX}/"}, "DocumentReaderConfig": { "DocumentReadAction": "TEXTRACT_DETECT_DOCUMENT_TEXT", "DocumentReadMode": "SERVICE_DEFAULT", }, } output_data_configurations = {"S3Uri": f"s3://{bucket_name}/output/"} document_classifier_kwargs = {"VersionName": "v1"} create_bucket = S3CreateBucketOperator( task_id="create_bucket", bucket_name=bucket_name, ) upload_annotation_file = S3CreateObjectOperator( task_id="upload_annotation_file", s3_bucket=bucket_name, s3_key=ANNOTATION_BUCKET_KEY, data=ANNOTATIONS.encode("utf-8"), ) delete_bucket = S3DeleteBucketOperator( task_id="delete_bucket", trigger_rule=TriggerRule.ALL_DONE, bucket_name=bucket_name, force_delete=True, ) chain( test_context, create_bucket, upload_annotation_file, copy_data_to_s3( bucket=bucket_name, sources=PUBLIC_DATA_SOURCES, prefix=TRAINING_DATA_PREFIX, number_of_copies=10 ), # TEST BODY document_classifier_workflow(), # TEST TEARDOWN delete_bucket, ) from dev.tests_common.test_utils.watcher import watcher # This test needs watcher in order to properly mark success/failure # when "tearDown" task with trigger rule is part of the DAG list(dag.tasks) >> watcher() from dev.tests_common.test_utils.system_tests import get_test_run # noqa: E402 # Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)

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