Esegui un processo di modellazione tematica di Amazon Comprehend su dati di esempio utilizzando un AWS SDK

Crea una classe wrapper per richiamare le azioni di modellazione degli argomenti di Amazon Comprehend.

class ComprehendTopicModeler:
    """Encapsulates a Comprehend topic modeler."""

    def __init__(self, comprehend_client):
        """
        :param comprehend_client: A Boto3 Comprehend client.
        """
        self.comprehend_client = comprehend_client


    def start_job(
        self,
        job_name,
        input_bucket,
        input_key,
        input_format,
        output_bucket,
        output_key,
        data_access_role_arn,
    ):
        """
        Starts a topic modeling job. Input is read from the specified Amazon S3
        input bucket and written to the specified output bucket. Output data is stored
        in a tar archive compressed in gzip format. The job runs asynchronously, so you
        can call `describe_topics_detection_job` to get job status until it
        returns a status of SUCCEEDED.

        :param job_name: The name of the job.
        :param input_bucket: An Amazon S3 bucket that contains job input.
        :param input_key: The prefix used to find input data in the input
                             bucket. If multiple objects have the same prefix, all
                             of them are used.
        :param input_format: The format of the input data, either one document per
                             file or one document per line.
        :param output_bucket: The Amazon S3 bucket where output data is written.
        :param output_key: The prefix prepended to the output data.
        :param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
                                     grants Comprehend permission to read from the
                                     input bucket and write to the output bucket.
        :return: Information about the job, including the job ID.
        """
        try:
            response = self.comprehend_client.start_topics_detection_job(
                JobName=job_name,
                DataAccessRoleArn=data_access_role_arn,
                InputDataConfig={
                    "S3Uri": f"s3://{input_bucket}/{input_key}",
                    "InputFormat": input_format.value,
                },
                OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
            )
            logger.info("Started topic modeling job %s.", response["JobId"])
        except ClientError:
            logger.exception("Couldn't start topic modeling job.")
            raise
        else:
            return response


    def describe_job(self, job_id):
        """
        Gets metadata about a topic modeling job.

        :param job_id: The ID of the job to look up.
        :return: Metadata about the job.
        """
        try:
            response = self.comprehend_client.describe_topics_detection_job(
                JobId=job_id
            )
            job = response["TopicsDetectionJobProperties"]
            logger.info("Got topic detection job %s.", job_id)
        except ClientError:
            logger.exception("Couldn't get topic detection job %s.", job_id)
            raise
        else:
            return job


    def list_jobs(self):
        """
        Lists topic modeling jobs for the current account.

        :return: The list of jobs.
        """
        try:
            response = self.comprehend_client.list_topics_detection_jobs()
            jobs = response["TopicsDetectionJobPropertiesList"]
            logger.info("Got %s topic detection jobs.", len(jobs))
        except ClientError:
            logger.exception("Couldn't get topic detection jobs.")
            raise
        else:
            return jobs

Usa la classe wrapper per eseguire un lavoro di modellazione di argomenti e ottenere dati sul lavoro.

def usage_demo():
    print("-" * 88)
    print("Welcome to the Amazon Comprehend topic modeling demo!")
    print("-" * 88)

    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

    input_prefix = "input/"
    output_prefix = "output/"
    demo_resources = ComprehendDemoResources(
        boto3.resource("s3"), boto3.resource("iam")
    )
    topic_modeler = ComprehendTopicModeler(boto3.client("comprehend"))

    print("Setting up storage and security resources needed for the demo.")
    demo_resources.setup("comprehend-topic-modeler-demo")
    print("Copying sample data from public bucket into input bucket.")
    demo_resources.bucket.copy(
        {"Bucket": "public-sample-us-west-2", "Key": "TopicModeling/Sample.txt"},
        f"{input_prefix}sample.txt",
    )

    print("Starting topic modeling job on sample data.")
    job_info = topic_modeler.start_job(
        "demo-topic-modeling-job",
        demo_resources.bucket.name,
        input_prefix,
        JobInputFormat.per_line,
        demo_resources.bucket.name,
        output_prefix,
        demo_resources.data_access_role.arn,
    )

    print(
        f"Waiting for job {job_info['JobId']} to complete. This typically takes "
        f"20 - 30 minutes."
    )
    job_waiter = JobCompleteWaiter(topic_modeler.comprehend_client)
    job_waiter.wait(job_info["JobId"])

    job = topic_modeler.describe_job(job_info["JobId"])
    print(f"Job {job['JobId']} complete:")
    pprint(job)

    print(
        f"Getting job output data from the output Amazon S3 bucket: "
        f"{job['OutputDataConfig']['S3Uri']}."
    )
    job_output = demo_resources.extract_job_output(job)
    lines = 10
    print(f"First {lines} lines of document topics output:")
    pprint(job_output["doc-topics.csv"]["data"][:lines])
    print(f"First {lines} lines of terms output:")
    pprint(job_output["topic-terms.csv"]["data"][:lines])

    print("Cleaning up resources created for the demo.")
    demo_resources.cleanup()

    print("Thanks for watching!")
    print("-" * 88)