사용자 지정 Amazon Comprehend 분류기를 훈련시키고 다음을 사용하여 문서를 분류하십시오. AWS SDK

래퍼 등급을 생성하여 Amazon Comprehend 문서 분류기 작업을 직접 호출합니다.

class ComprehendClassifier:
    """Encapsulates an Amazon Comprehend custom classifier."""

    def __init__(self, comprehend_client):
        """
        :param comprehend_client: A Boto3 Comprehend client.
        """
        self.comprehend_client = comprehend_client
        self.classifier_arn = None


    def create(
        self,
        name,
        language_code,
        training_bucket,
        training_key,
        data_access_role_arn,
        mode,
    ):
        """
        Creates a custom classifier. After the classifier is created, it immediately
        starts training on the data found in the specified Amazon S3 bucket. Training
        can take 30 minutes or longer. The `describe_document_classifier` function
        can be used to get training status and returns a status of TRAINED when the
        classifier is ready to use.

        :param name: The name of the classifier.
        :param language_code: The language the classifier can operate on.
        :param training_bucket: The Amazon S3 bucket that contains the training data.
        :param training_key: The prefix used to find training data in the training
                             bucket. If multiple objects have the same prefix, all
                             of them are used.
        :param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
                                     grants Comprehend permission to read from the
                                     training bucket.
        :return: The ARN of the newly created classifier.
        """
        try:
            response = self.comprehend_client.create_document_classifier(
                DocumentClassifierName=name,
                LanguageCode=language_code,
                InputDataConfig={"S3Uri": f"s3://{training_bucket}/{training_key}"},
                DataAccessRoleArn=data_access_role_arn,
                Mode=mode.value,
            )
            self.classifier_arn = response["DocumentClassifierArn"]
            logger.info("Started classifier creation. Arn is: %s.", self.classifier_arn)
        except ClientError:
            logger.exception("Couldn't create classifier %s.", name)
            raise
        else:
            return self.classifier_arn


    def describe(self, classifier_arn=None):
        """
        Gets metadata about a custom classifier, including its current status.

        :param classifier_arn: The ARN of the classifier to look up.
        :return: Metadata about the classifier.
        """
        if classifier_arn is not None:
            self.classifier_arn = classifier_arn
        try:
            response = self.comprehend_client.describe_document_classifier(
                DocumentClassifierArn=self.classifier_arn
            )
            classifier = response["DocumentClassifierProperties"]
            logger.info("Got classifier %s.", self.classifier_arn)
        except ClientError:
            logger.exception("Couldn't get classifier %s.", self.classifier_arn)
            raise
        else:
            return classifier


    def list(self):
        """
        Lists custom classifiers for the current account.

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


    def delete(self):
        """
        Deletes the classifier.
        """
        try:
            self.comprehend_client.delete_document_classifier(
                DocumentClassifierArn=self.classifier_arn
            )
            logger.info("Deleted classifier %s.", self.classifier_arn)
            self.classifier_arn = None
        except ClientError:
            logger.exception("Couldn't deleted classifier %s.", self.classifier_arn)
            raise


    def start_job(
        self,
        job_name,
        input_bucket,
        input_key,
        input_format,
        output_bucket,
        output_key,
        data_access_role_arn,
    ):
        """
        Starts a classification job. The classifier must be trained or the job
        will fail. 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_document_classification_job` to get job status until it
        returns a status of SUCCEEDED.

        :param job_name: The name of the job.
        :param input_bucket: The Amazon S3 bucket that contains input data.
        :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_document_classification_job(
                DocumentClassifierArn=self.classifier_arn,
                JobName=job_name,
                InputDataConfig={
                    "S3Uri": f"s3://{input_bucket}/{input_key}",
                    "InputFormat": input_format.value,
                },
                OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
                DataAccessRoleArn=data_access_role_arn,
            )
            logger.info(
                "Document classification job %s is %s.", job_name, response["JobStatus"]
            )
        except ClientError:
            logger.exception("Couldn't start classification job %s.", job_name)
            raise
        else:
            return response


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

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


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

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

시나리오를 실행하는 클래스를 생성합니다.

class ClassifierDemo:
    """
    Encapsulates functions used to run the demonstration.
    """

    def __init__(self, demo_resources):
        """
        :param demo_resources: A ComprehendDemoResources class that manages resources
                               for the demonstration.
        """
        self.demo_resources = demo_resources
        self.training_prefix = "training/"
        self.input_prefix = "input/"
        self.input_format = JobInputFormat.per_line
        self.output_prefix = "output/"

    def setup(self):
        """Creates AWS resources used by the demo."""
        self.demo_resources.setup("comprehend-classifier-demo")

    def cleanup(self):
        """Deletes AWS resources used by the demo."""
        self.demo_resources.cleanup()

    @staticmethod
    def _sanitize_text(text):
        """Removes characters that cause errors for the document parser."""
        return text.replace("\r", " ").replace("\n", " ").replace(",", ";")

    @staticmethod
    def _get_issues(query, issue_count):
        """
        Gets issues from GitHub using the specified query parameters.

        :param query: The query string used to request issues from the GitHub API.
        :param issue_count: The number of issues to retrieve.
        :return: The list of issues retrieved from GitHub.
        """
        issues = []
        logger.info("Requesting issues from %s?%s.", GITHUB_SEARCH_URL, query)
        response = requests.get(f"{GITHUB_SEARCH_URL}?{query}&per_page={issue_count}")
        if response.status_code == 200:
            issue_page = response.json()["items"]
            logger.info("Got %s issues.", len(issue_page))
            issues = [
                {
                    "title": ClassifierDemo._sanitize_text(issue["title"]),
                    "body": ClassifierDemo._sanitize_text(issue["body"]),
                    "labels": {label["name"] for label in issue["labels"]},
                }
                for issue in issue_page
            ]
        else:
            logger.error(
                "GitHub returned error code %s with message %s.",
                response.status_code,
                response.json(),
            )
        logger.info("Found %s issues.", len(issues))
        return issues

    def get_training_issues(self, training_labels):
        """
        Gets issues used for training the custom classifier. Training issues are
        closed issues from the Boto3 repo that have known labels. Comprehend
        requires a minimum of ten training issues per label.

        :param training_labels: The issue labels to use for training.
        :return: The set of issues used for training.
        """
        issues = []
        per_label_count = 15
        for label in training_labels:
            issues += self._get_issues(
                f"q=type:issue+repo:boto/boto3+state:closed+label:{label}",
                per_label_count,
            )
            for issue in issues:
                issue["labels"] = issue["labels"].intersection(training_labels)
        return issues

    def get_input_issues(self, training_labels):
        """
        Gets input issues from GitHub. For demonstration purposes, input issues
        are open issues from the Boto3 repo with known labels, though in practice
        any issue could be submitted to the classifier for labeling.

        :param training_labels: The set of labels to query for.
        :return: The set of issues used for input.
        """
        issues = []
        per_label_count = 5
        for label in training_labels:
            issues += self._get_issues(
                f"q=type:issue+repo:boto/boto3+state:open+label:{label}",
                per_label_count,
            )
        return issues

    def upload_issue_data(self, issues, training=False):
        """
        Uploads issue data to an Amazon S3 bucket, either for training or for input.
        The data is first put into the format expected by Comprehend. For training,
        the set of pipe-delimited labels is prepended to each document. For
        input, labels are not sent.

        :param issues: The set of issues to upload to Amazon S3.
        :param training: Indicates whether the issue data is used for training or
                         input.
        """
        try:
            obj_key = (
                self.training_prefix if training else self.input_prefix
            ) + "issues.txt"
            if training:
                issue_strings = [
                    f"{'|'.join(issue['labels'])},{issue['title']} {issue['body']}"
                    for issue in issues
                ]
            else:
                issue_strings = [
                    f"{issue['title']} {issue['body']}" for issue in issues
                ]
            issue_bytes = BytesIO("\n".join(issue_strings).encode("utf-8"))
            self.demo_resources.bucket.upload_fileobj(issue_bytes, obj_key)
            logger.info(
                "Uploaded data as %s to bucket %s.",
                obj_key,
                self.demo_resources.bucket.name,
            )
        except ClientError:
            logger.exception(
                "Couldn't upload data to bucket %s.", self.demo_resources.bucket.name
            )
            raise

    def extract_job_output(self, job):
        """Extracts job output from Amazon S3."""
        return self.demo_resources.extract_job_output(job)

    @staticmethod
    def reconcile_job_output(input_issues, output_dict):
        """
        Reconciles job output with the list of input issues. Because the input issues
        have known labels, these can be compared with the labels added by the
        classifier to judge the accuracy of the output.

        :param input_issues: The list of issues used as input.
        :param output_dict: The dictionary of data that is output by the classifier.
        :return: The list of reconciled input and output data.
        """
        reconciled = []
        for archive in output_dict.values():
            for line in archive["data"]:
                in_line = int(line["Line"])
                in_labels = input_issues[in_line]["labels"]
                out_labels = {
                    label["Name"]
                    for label in line["Labels"]
                    if float(label["Score"]) > 0.3
                }
                reconciled.append(
                    f"{line['File']}, line {in_line} has labels {in_labels}.\n"
                    f"\tClassifier assigned {out_labels}."
                )
        logger.info("Reconciled input and output labels.")
        return reconciled

알려진 레이블이 있는 일련의 GitHub 문제에 대해 분류기를 학습시킨 다음, 분류기에 두 번째 GitHub 이슈 세트를 전송하여 레이블을 지정할 수 있도록 합니다.

def usage_demo():
    print("-" * 88)
    print("Welcome to the Amazon Comprehend custom document classifier demo!")
    print("-" * 88)

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

    comp_demo = ClassifierDemo(
        ComprehendDemoResources(boto3.resource("s3"), boto3.resource("iam"))
    )
    comp_classifier = ComprehendClassifier(boto3.client("comprehend"))
    classifier_trained_waiter = ClassifierTrainedWaiter(
        comp_classifier.comprehend_client
    )
    training_labels = {"bug", "feature-request", "dynamodb", "s3"}

    print("Setting up storage and security resources needed for the demo.")
    comp_demo.setup()

    print("Getting training data from GitHub and uploading it to Amazon S3.")
    training_issues = comp_demo.get_training_issues(training_labels)
    comp_demo.upload_issue_data(training_issues, True)

    classifier_name = "doc-example-classifier"
    print(f"Creating document classifier {classifier_name}.")
    comp_classifier.create(
        classifier_name,
        "en",
        comp_demo.demo_resources.bucket.name,
        comp_demo.training_prefix,
        comp_demo.demo_resources.data_access_role.arn,
        ClassifierMode.multi_label,
    )
    print(
        f"Waiting until {classifier_name} is trained. This typically takes "
        f"30–40 minutes."
    )
    classifier_trained_waiter.wait(comp_classifier.classifier_arn)

    print(f"Classifier {classifier_name} is trained:")
    pprint(comp_classifier.describe())

    print("Getting input data from GitHub and uploading it to Amazon S3.")
    input_issues = comp_demo.get_input_issues(training_labels)
    comp_demo.upload_issue_data(input_issues)

    print("Starting classification job on input data.")
    job_info = comp_classifier.start_job(
        "issue_classification_job",
        comp_demo.demo_resources.bucket.name,
        comp_demo.input_prefix,
        comp_demo.input_format,
        comp_demo.demo_resources.bucket.name,
        comp_demo.output_prefix,
        comp_demo.demo_resources.data_access_role.arn,
    )
    print(f"Waiting for job {job_info['JobId']} to complete.")
    job_waiter = JobCompleteWaiter(comp_classifier.comprehend_client)
    job_waiter.wait(job_info["JobId"])

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

    print(
        f"Getting job output data from Amazon S3: "
        f"{job['OutputDataConfig']['S3Uri']}."
    )
    job_output = comp_demo.extract_job_output(job)
    print("Job output:")
    pprint(job_output)

    print("Reconciling job output with labels from GitHub:")
    reconciled_output = comp_demo.reconcile_job_output(input_issues, job_output)
    print(*reconciled_output, sep="\n")

    answer = input(f"Do you want to delete the classifier {classifier_name} (y/n)? ")
    if answer.lower() == "y":
        print(f"Deleting {classifier_name}.")
        comp_classifier.delete()

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

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