D'autres exemples de AWS SDK sont disponibles dans le référentiel [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub .
Les traductions sont fournies par des outils de traduction automatique. En cas de conflit entre le contenu d'une traduction et celui de la version originale en anglais, la version anglaise prévaudra.
# Exemples Amazon Comprehend avec le kit SDK pour Python (Boto3)
Les exemples de code suivants vous montrent comment effectuer des actions et implémenter des scénarios courants à l' AWS SDK pour Python (Boto3) aide d'Amazon Comprehend.
Les *actions* sont des extraits de code de programmes plus larges et doivent être exécutées dans leur contexte. Alors que les actions vous indiquent comment appeler des fonctions de service individuelles, vous pouvez les voir en contexte dans leurs scénarios associés.
Les *scénarios* sont des exemples de code qui vous montrent comment accomplir des tâches spécifiques en appelant plusieurs fonctions au sein d’un même service ou combinés à d’autres Services AWS.
Chaque exemple inclut un lien vers le code source complet, où vous trouverez des instructions sur la configuration et l’exécution du code en contexte.
**Topics**
+ [Actions](#actions)
+ [Scénarios](#scenarios)
## Actions
### `CreateDocumentClassifier`
L'exemple de code suivant montre comment utiliser`CreateDocumentClassifier`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [CreateDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/CreateDocumentClassifier)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DeleteDocumentClassifier`
L'exemple de code suivant montre comment utiliser`DeleteDocumentClassifier`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
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 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
```
+ Pour plus de détails sur l'API, consultez [DeleteDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DeleteDocumentClassifier)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DescribeDocumentClassificationJob`
L'exemple de code suivant montre comment utiliser`DescribeDocumentClassificationJob`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
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 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
```
+ Pour plus de détails sur l'API, consultez [DescribeDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassificationJob)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DescribeDocumentClassifier`
L'exemple de code suivant montre comment utiliser`DescribeDocumentClassifier`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
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 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
```
+ Pour plus de détails sur l'API, consultez [DescribeDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassifier)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DescribeTopicsDetectionJob`
L'exemple de code suivant montre comment utiliser`DescribeTopicsDetectionJob`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
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 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
```
+ Pour plus de détails sur l'API, consultez [DescribeTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeTopicsDetectionJob)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DetectDominantLanguage`
L'exemple de code suivant montre comment utiliser`DetectDominantLanguage`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_languages(self, text):
"""
Detects languages used in a document.
:param text: The document to inspect.
:return: The list of languages along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_dominant_language(Text=text)
languages = response["Languages"]
logger.info("Detected %s languages.", len(languages))
except ClientError:
logger.exception("Couldn't detect languages.")
raise
else:
return languages
```
+ Pour plus de détails sur l'API, consultez [DetectDominantLanguage](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectDominantLanguage)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DetectEntities`
L'exemple de code suivant montre comment utiliser`DetectEntities`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_entities(self, text, language_code):
"""
Detects entities in a document. Entities can be things like people and places
or other common terms.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect entities.")
raise
else:
return entities
```
+ Pour plus de détails sur l'API, consultez [DetectEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectEntities)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DetectKeyPhrases`
L'exemple de code suivant montre comment utiliser`DetectKeyPhrases`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_key_phrases(self, text, language_code):
"""
Detects key phrases in a document. A key phrase is typically a noun and its
modifiers.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of key phrases along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_key_phrases(
Text=text, LanguageCode=language_code
)
phrases = response["KeyPhrases"]
logger.info("Detected %s phrases.", len(phrases))
except ClientError:
logger.exception("Couldn't detect phrases.")
raise
else:
return phrases
```
+ Pour plus de détails sur l'API, consultez [DetectKeyPhrases](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectKeyPhrases)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DetectPiiEntities`
L'exemple de code suivant montre comment utiliser`DetectPiiEntities`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_pii(self, text, language_code):
"""
Detects personally identifiable information (PII) in a document. PII can be
things like names, account numbers, or addresses.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of PII entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_pii_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s PII entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect PII entities.")
raise
else:
return entities
```
+ Pour plus de détails sur l'API, consultez [DetectPiiEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectPiiEntities)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DetectSentiment`
L'exemple de code suivant montre comment utiliser`DetectSentiment`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_sentiment(self, text, language_code):
"""
Detects the overall sentiment expressed in a document. Sentiment can
be positive, negative, neutral, or a mixture.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The sentiments along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_sentiment(
Text=text, LanguageCode=language_code
)
logger.info("Detected primary sentiment %s.", response["Sentiment"])
except ClientError:
logger.exception("Couldn't detect sentiment.")
raise
else:
return response
```
+ Pour plus de détails sur l'API, consultez [DetectSentiment](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSentiment)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DetectSyntax`
L'exemple de code suivant montre comment utiliser`DetectSyntax`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_syntax(self, text, language_code):
"""
Detects syntactical elements of a document. Syntax tokens are portions of
text along with their use as parts of speech, such as nouns, verbs, and
interjections.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of syntax tokens along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_syntax(
Text=text, LanguageCode=language_code
)
tokens = response["SyntaxTokens"]
logger.info("Detected %s syntax tokens.", len(tokens))
except ClientError:
logger.exception("Couldn't detect syntax.")
raise
else:
return tokens
```
+ Pour plus de détails sur l'API, consultez [DetectSyntax](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSyntax)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListDocumentClassificationJobs`
L'exemple de code suivant montre comment utiliser`ListDocumentClassificationJobs`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
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 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
```
+ Pour plus de détails sur l'API, consultez [ListDocumentClassificationJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassificationJobs)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListDocumentClassifiers`
L'exemple de code suivant montre comment utiliser`ListDocumentClassifiers`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
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 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
```
+ Pour plus de détails sur l'API, consultez [ListDocumentClassifiers](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassifiers)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListTopicsDetectionJobs`
L'exemple de code suivant montre comment utiliser`ListTopicsDetectionJobs`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
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 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
```
+ Pour plus de détails sur l'API, consultez [ListTopicsDetectionJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListTopicsDetectionJobs)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `StartDocumentClassificationJob`
L'exemple de code suivant montre comment utiliser`StartDocumentClassificationJob`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
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 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
```
+ Pour plus de détails sur l'API, consultez [StartDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartDocumentClassificationJob)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `StartTopicsDetectionJob`
L'exemple de code suivant montre comment utiliser`StartTopicsDetectionJob`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [StartTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartTopicsDetectionJob)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
## Scénarios
### Détection des éléments du document
L’exemple de code suivant illustre comment :
+ détecter les langues, les entités et les expressions clés d’un document ;
+ détecter les données d’identification personnelle (PII) d’un document ;
+ détecter le sentiment d’un document ;
+ détecter les éléments de syntaxe dans un document.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Créez une classe qui encapsule les actions Amazon Comprehend.
```
import logging
from pprint import pprint
import boto3
from botocore.exceptions import ClientError
logger = logging.getLogger(__name__)
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_languages(self, text):
"""
Detects languages used in a document.
:param text: The document to inspect.
:return: The list of languages along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_dominant_language(Text=text)
languages = response["Languages"]
logger.info("Detected %s languages.", len(languages))
except ClientError:
logger.exception("Couldn't detect languages.")
raise
else:
return languages
def detect_entities(self, text, language_code):
"""
Detects entities in a document. Entities can be things like people and places
or other common terms.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect entities.")
raise
else:
return entities
def detect_key_phrases(self, text, language_code):
"""
Detects key phrases in a document. A key phrase is typically a noun and its
modifiers.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of key phrases along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_key_phrases(
Text=text, LanguageCode=language_code
)
phrases = response["KeyPhrases"]
logger.info("Detected %s phrases.", len(phrases))
except ClientError:
logger.exception("Couldn't detect phrases.")
raise
else:
return phrases
def detect_pii(self, text, language_code):
"""
Detects personally identifiable information (PII) in a document. PII can be
things like names, account numbers, or addresses.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of PII entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_pii_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s PII entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect PII entities.")
raise
else:
return entities
def detect_sentiment(self, text, language_code):
"""
Detects the overall sentiment expressed in a document. Sentiment can
be positive, negative, neutral, or a mixture.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The sentiments along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_sentiment(
Text=text, LanguageCode=language_code
)
logger.info("Detected primary sentiment %s.", response["Sentiment"])
except ClientError:
logger.exception("Couldn't detect sentiment.")
raise
else:
return response
def detect_syntax(self, text, language_code):
"""
Detects syntactical elements of a document. Syntax tokens are portions of
text along with their use as parts of speech, such as nouns, verbs, and
interjections.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of syntax tokens along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_syntax(
Text=text, LanguageCode=language_code
)
tokens = response["SyntaxTokens"]
logger.info("Detected %s syntax tokens.", len(tokens))
except ClientError:
logger.exception("Couldn't detect syntax.")
raise
else:
return tokens
```
Appelez des fonctions sur la classe d’encapsuleur pour détecter des entités, des phrases, etc. dans un document.
```
def usage_demo():
print("-" * 88)
print("Welcome to the Amazon Comprehend detection demo!")
print("-" * 88)
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
comp_detect = ComprehendDetect(boto3.client("comprehend"))
with open("detect_sample.txt") as sample_file:
sample_text = sample_file.read()
demo_size = 3
print("Sample text used for this demo:")
print("-" * 88)
print(sample_text)
print("-" * 88)
print("Detecting languages.")
languages = comp_detect.detect_languages(sample_text)
pprint(languages)
lang_code = languages[0]["LanguageCode"]
print("Detecting entities.")
entities = comp_detect.detect_entities(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(entities[:demo_size])
print("Detecting key phrases.")
phrases = comp_detect.detect_key_phrases(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(phrases[:demo_size])
print("Detecting personally identifiable information (PII).")
pii_entities = comp_detect.detect_pii(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(pii_entities[:demo_size])
print("Detecting sentiment.")
sentiment = comp_detect.detect_sentiment(sample_text, lang_code)
print(f"Sentiment: {sentiment['Sentiment']}")
print("SentimentScore:")
pprint(sentiment["SentimentScore"])
print("Detecting syntax elements.")
syntax_tokens = comp_detect.detect_syntax(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(syntax_tokens[:demo_size])
print("Thanks for watching!")
print("-" * 88)
```
+ Pour plus de détails sur l’API, consultez les rubriques suivantes dans la *Référence des API du kit AWS SDK for Python (Boto3)*.
+ [DetectDominantLanguage](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectDominantLanguage)
+ [DetectEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectEntities)
+ [DetectKeyPhrases](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectKeyPhrases)
+ [DetectPiiEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectPiiEntities)
+ [DetectSentiment](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSentiment)
+ [DetectSyntax](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSyntax)
### Détecter des entités dans un texte extrait à partir d’une image
L’exemple de code suivant montre comment utiliser Amazon Comprehend pour détecter des entités dans du texte extrait par Amazon Textract à partir d’une image stockée dans Amazon S3.
**Kit SDK for Python (Boto3)**
Montre comment utiliser le AWS SDK pour Python (Boto3) dans un bloc-notes Jupyter pour détecter des entités dans du texte extrait d'une image. Cet exemple utilise Amazon Textract pour extraire le texte d’une image stockée dans Amazon Simple Storage Service (Amazon S3) et Amazon Comprehend pour détecter les entités dans le texte extrait.
Cet exemple est un carnet Jupyter et doit être exécuté dans un environnement qui peut accueillir des carnets. Pour savoir comment exécuter cet exemple à l'aide d'Amazon SageMaker AI, consultez les instructions du [TextractAndComprehendNotebookfichier .ipynb](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook/TextractAndComprehendNotebook.ipynb).
Pour obtenir le code source complet et les instructions de configuration et d'exécution, consultez l'exemple complet sur [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook#readme).
**Les services utilisés dans cet exemple**
+ Amazon Comprehend
+ Amazon S3
+ Amazon Textract
### Exécution d’une tâche de modélisation de rubrique sur des exemples de données
L’exemple de code suivant illustre comment :
+ Exécutez une tâche de modélisation de rubriques Amazon Comprehend sur des exemples de données.
+ Obtenez des informations sur la tâche.
+ Extrayez des données de sortie des tâches à partir d’Amazon S3.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Créez une classe d’encapsuleur pour appeler les actions de modélisation de sujets 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
```
Utilisez la classe d’encapsuleur pour exécuter une tâche de modélisation de rubrique et obtenir des données de tâche.
```
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)
```
+ Pour plus de détails sur l’API, consultez les rubriques suivantes dans la *Référence des API du kit AWS SDK for Python (Boto3)*.
+ [DescribeTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeTopicsDetectionJob)
+ [ListTopicsDetectionJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListTopicsDetectionJobs)
+ [StartTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartTopicsDetectionJob)
### Entraînement d’un classificateur personnalisé et classification des documents
L’exemple de code suivant illustre comment :
+ créer un classificateur multi-étiquette Amazon Comprehend ;
+ entraîner le classificateur sur des échantillons de données ;
+ exécuter une tâche de classification sur un deuxième jeu de données ;
+ extraire les données de sortie des tâches à partir d’Amazon S3.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Créez une classe d’encapsuleur pour appeler les actions du classificateur de documents Amazon Comprehend.
```
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
```
Créez une classe qui aide à exécuter le scénario.
```
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
```
Formez un classificateur à un ensemble de GitHub problèmes associés à des étiquettes connues, puis envoyez un deuxième ensemble de GitHub problèmes au classificateur afin qu'il puisse les étiqueter.
```
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)
```
+ Pour plus de détails sur l’API, consultez les rubriques suivantes dans la *Référence des API du kit AWS SDK for Python (Boto3)*.
+ [CreateDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/CreateDocumentClassifier)
+ [DeleteDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DeleteDocumentClassifier)
+ [DescribeDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassificationJob)
+ [DescribeDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassifier)
+ [ListDocumentClassificationJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassificationJobs)
+ [ListDocumentClassifiers](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassifiers)
+ [StartDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartDocumentClassificationJob)