モデルの混同行列の表示

モデルの混同行列を表示するには (SDK）

1. まだインストールしていない場合は、 をインストールして設定します。 AWS CLI と AWS SDKs。詳細については、「ステップ 4: をセットアップする AWS CLI また、 AWS SDKs」を参照してください。

2. 次のコードを使用してモデルの混同行列を表示します。次のコマンドライン引数を指定します。

   • project_name - 使用するプロジェクトの名前。プロジェクト名は、Amazon Rekognition Custom Labels コンソールのプロジェクトページから取得できます。

   • version_name - 使用するモデルのバージョン。バージョン名は、Amazon Rekognition Custom Labels コンソールのプロジェクト詳細ページから取得できます。

   # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
   # SPDX-License-Identifier: Apache-2.0
   
   """
   Purpose
   
   Shows how to display the confusion matrix for an Amazon Rekognition Custom labels image
   classification model.
   """
   
   
   import json
   import argparse
   import logging
   import boto3
   import pandas as pd
   from botocore.exceptions import ClientError
   
   
   logger = logging.getLogger(__name__)
   
   
   def get_model_summary_location(rek_client, project_name, version_name):
       """
       Get the summary file location for a model.
   
       :param rek_client: A Boto3 Rekognition client.
       :param project_arn: The Amazon Resource Name (ARN) of the project that contains the model.
       :param model_arn: The Amazon Resource Name (ARN) of the model.
       :return: The location of the model summary file.
       """
   
       try:
           logger.info(
               "Getting summary file for model %s in project %s.", version_name, project_name)
   
           summary_location = ""
   
           # Get the project ARN from the project name.
           response = rek_client.describe_projects(ProjectNames=[project_name])
   
           assert len(response['ProjectDescriptions']) > 0, \
               f"Project {project_name} not found."
   
           project_arn = response['ProjectDescriptions'][0]['ProjectArn']
   
           # Get the summary file location for the model.
           describe_response = rek_client.describe_project_versions(ProjectArn=project_arn,
                                                                    VersionNames=[version_name])
           assert len(describe_response['ProjectVersionDescriptions']) > 0, \
               f"Model {version_name} not found."
   
           model=describe_response['ProjectVersionDescriptions'][0]
   
           evaluation_results=model['EvaluationResult']
   
           summary_location=(f"s3://{evaluation_results['Summary']['S3Object']['Bucket']}"
                               f"/{evaluation_results['Summary']['S3Object']['Name']}")
   
           return summary_location
   
       except ClientError as err:
           logger.exception(
               "Couldn't get summary file location: %s", err.response['Error']['Message'])
           raise
   
   
   def show_confusion_matrix(summary):
       """
       Shows the confusion matrix for an Amazon Rekognition Custom Labels
       image classification model.
       :param summary: The summary file JSON object.
       """
       pd.options.display.float_format = '{:.0%}'.format
   
       # Load the model summary JSON into a DataFrame.
   
       summary_df = pd.DataFrame(
           summary['AggregatedEvaluationResults']['ConfusionMatrix'])
   
       # Get the confusion matrix.
       confusion_matrix = summary_df.pivot_table(index='PredictedLabel',
                                                 columns='GroundTruthLabel',
                                                 fill_value=0.0).astype(float)
   
       # Display the confusion matrix.
       print(confusion_matrix)
   
   
   def get_summary(s3_resource, summary):
       """
       Gets the summary file.
       : return: The summary file in bytes.
       """
       try:
           summary_bucket, summary_key = summary.replace(
               "s3://", "").split("/", 1)
   
           bucket = s3_resource.Bucket(summary_bucket)
           obj = bucket.Object(summary_key)
           body = obj.get()['Body'].read()
           logger.info(
               "Got summary file '%s' from bucket '%s'.",
               obj.key, obj.bucket_name)
       except ClientError:
           logger.exception(
               "Couldn't get summary file '%s' from bucket '%s'.",
               obj.key, obj.bucket_name)
           raise
       else:
           return body
   
   
   def add_arguments(parser):
       """
       Adds command line arguments to the parser.
       : param parser: The command line parser.
       """
   
       parser.add_argument(
           "project_name", help="The ARN of the project in which the model resides."
       )
       parser.add_argument(
           "version_name", help="The version of the model that you want to describe."
       )
   
   
   def main():
       """
       Entry point for script.
       """
   
       logging.basicConfig(level=logging.INFO,
                           format="%(levelname)s: %(message)s")
   
       try:
   
           # Get the command line arguments.
           parser = argparse.ArgumentParser(usage=argparse.SUPPRESS)
           add_arguments(parser)
           args = parser.parse_args()
   
           print(
               f"Showing confusion matrix for: {args.version_name} for project {args.project_name}.")
   
           session = boto3.Session(profile_name='custom-labels-access')
           rekognition_client = session.client("rekognition")
           s3_resource = session.resource('s3')
   
           # Get the summary file for the model.
           summary_location = get_model_summary_location(rekognition_client, args.project_name,
                                                         args.version_name
                                                         )
           summary = json.loads(get_summary(s3_resource, summary_location))
   
           # Check that the confusion matrix is available.
           assert 'ConfusionMatrix' in summary['AggregatedEvaluationResults'], \
               "Confusion matrix not found in summary. Is the model a classification model?"
   
           # Show the confusion matrix.
           show_confusion_matrix(summary)
           print("Done")
   
       except ClientError as err:
           logger.exception("Problem showing confusion matrix: %s", err)
           print(f"Problem describing model: {err}")
   
       except AssertionError as err:
           logger.exception(
               "Error: %s.\n", err)
           print(
               f"Error: {err}\n")
   
   
   if __name__ == "__main__":
       main()