Sono disponibili altri esempi AWS SDK nel repository [AWS Doc SDK](https://github.com/awsdocs/aws-doc-sdk-examples) Examples. GitHub
Le traduzioni sono generate tramite traduzione automatica. In caso di conflitto tra il contenuto di una traduzione e la versione originale in Inglese, quest'ultima prevarrà.
# Scenari per l'utilizzo di Amazon Comprehend AWS SDKs
I seguenti esempi di codice mostrano come implementare scenari comuni in Amazon Comprehend con. AWS SDKs Questi scenari illustrano come eseguire attività specifiche chiamando più funzioni all’interno di Amazon Comprehend o in combinazione con altri Servizi AWS. Ogni scenario include un collegamento al codice sorgente completo, dove è possibile trovare le istruzioni su come configurare ed eseguire il codice.
Gli scenari sono relativi a un livello intermedio di esperienza per aiutarti a comprendere le azioni di servizio nel contesto.
**Topics**
+ [Creazione di un'app in streaming Amazon Transcribe](comprehend_example_cross_TranscriptionStreamingApp_section.md)
+ [Creazione di un chatbot Amazon Lex](comprehend_example_cross_LexChatbotLanguages_section.md)
+ [Creare un’applicazione di messaggistica](comprehend_example_cross_SQSMessageApp_section.md)
+ [Crea un’applicazione per analizzare il feedback dei clienti](comprehend_example_cross_FSA_section.md)
+ [Rilevare gli elementi del documento](comprehend_example_comprehend_Usage_DetectApis_section.md)
+ [Rilevamento di entità nel testo estratto da un’immagine](comprehend_example_cross_TextractComprehendDetectEntities_section.md)
+ [Eseguire un processo di modellazione di argomenti in base a dati di esempio](comprehend_example_comprehend_Usage_TopicModeler_section.md)
+ [Addestrare un classificatore personalizzato e classificare i documenti](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
# Creazione di un’app in streaming Amazon Transcribe
L’esempio di codice seguente mostra come creare un’applicazione che registra, trascrive e traduce l’audio in tempo reale e invia tramite e-mail i risultati.
------
#### [ JavaScript ]
**SDK per JavaScript (v3)**
Mostra come utilizzare Amazon Transcribe per creare un’applicazione che registra, trascrive e traduce l’audio in tempo reale e invia i risultati per e-mail tramite Amazon Simple Email Service (Amazon SES).
Per il codice sorgente completo e le istruzioni su come configurarlo ed eseguirlo, guarda l'esempio completo su. [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/transcribe-streaming-app)
**Servizi utilizzati in questo esempio**
+ Amazon Comprehend
+ Amazon SES
+ Amazon Transcribe
+ Amazon Translate
------
# Creare un chatbot Amazon Lex per coinvolgere i visitatori del sito web
Gli esempi di codice seguenti mostrano come creare un chatbot per coinvolgere i visitatori del sito web.
------
#### [ Java ]
**SDK per Java 2.x**
Mostra come utilizzare l’API Amazon Lex per creare un chatbot all’interno di un’applicazione web per coinvolgere i visitatori del sito web.
Per il codice sorgente completo e le istruzioni su come configurarlo ed eseguirlo, guarda l'esempio completo su [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_lex_chatbot).
**Servizi utilizzati in questo esempio**
+ Amazon Comprehend
+ Amazon Lex
+ Amazon Translate
------
#### [ JavaScript ]
**SDK per JavaScript (v3)**
Mostra come utilizzare l’API Amazon Lex per creare un chatbot all’interno di un’applicazione web per coinvolgere i visitatori del sito web.
Per il codice sorgente completo e le istruzioni su come configurarlo ed eseguirlo, consulta l'esempio completo [Costruire un chatbot Amazon Lex](https://docs.aws.amazon.com/sdk-for-javascript/v3/developer-guide/lex-bot-example.html) nella guida per gli AWS SDK per JavaScript sviluppatori.
**Servizi utilizzati in questo esempio**
+ Amazon Comprehend
+ Amazon Lex
+ Amazon Translate
------
# Creare un’applicazione web che invia e recupera i messaggi utilizzando Amazon SQS
Gli esempi di codice seguenti mostrano come creare un’applicazione di messaggistica utilizzando Amazon SQS.
------
#### [ Java ]
**SDK per Java 2.x**
Mostra come utilizzare l’API Amazon SQS per sviluppare una REST API Spring che invia e recupera i messaggi.
Per il codice sorgente completo e le istruzioni su come configurarlo ed eseguirlo, guarda l'esempio completo su [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_message_application).
**Servizi utilizzati in questo esempio**
+ Amazon Comprehend
+ Amazon SQS
------
#### [ Kotlin ]
**SDK per Kotlin**
Mostra come utilizzare l’API Amazon SQS per sviluppare una REST API Spring che invia e recupera i messaggi.
Per il codice sorgente completo e le istruzioni su come configurarlo ed eseguirlo, guarda l'esempio completo su [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_message_application).
**Servizi utilizzati in questo esempio**
+ Amazon Comprehend
+ Amazon SQS
------
# Crea un’applicazione che analizza il feedback dei clienti e sintetizza l’audio
Il seguente esempio di codice spiega come creare un’applicazione che analizza schede dei commenti dei clienti, le traduce dalla loro lingua originale, ne determina la valutazione e genera un file audio dal testo tradotto.
------
#### [ .NET ]
**SDK per .NET**
Questa applicazione di esempio analizza e archivia le schede di feedback dei clienti. In particolare, soddisfa l’esigenza di un hotel fittizio a New York City. L’hotel riceve feedback dagli ospiti in varie lingue sotto forma di schede di commento fisiche. Tale feedback viene caricato nell’app tramite un client Web. Dopo aver caricato l’immagine di una scheda di commento, vengono eseguiti i seguenti passaggi:
+ Il testo viene estratto dall’immagine utilizzando Amazon Textract.
+ Amazon Comprehend determina il sentiment del testo estratto e la sua lingua.
+ Il testo estratto viene tradotto in inglese utilizzando Amazon Translate.
+ Amazon Polly sintetizza un file audio dal testo estratto.
L’app completa può essere implementata con AWS CDK. Per il codice sorgente e le istruzioni di distribuzione, consulta il progetto in [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/FeedbackSentimentAnalyzer).
**Servizi utilizzati in questo esempio**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
#### [ Java ]
**SDK per Java 2.x**
Questa applicazione di esempio analizza e archivia le schede di feedback dei clienti. In particolare, soddisfa l’esigenza di un hotel fittizio a New York City. L’hotel riceve feedback dagli ospiti in varie lingue sotto forma di schede di commento fisiche. Tale feedback viene caricato nell’app tramite un client Web. Dopo aver caricato l’immagine di una scheda di commento, vengono eseguiti i seguenti passaggi:
+ Il testo viene estratto dall’immagine utilizzando Amazon Textract.
+ Amazon Comprehend determina il sentiment del testo estratto e la sua lingua.
+ Il testo estratto viene tradotto in inglese utilizzando Amazon Translate.
+ Amazon Polly sintetizza un file audio dal testo estratto.
L’app completa può essere implementata con AWS CDK. Per il codice sorgente e le istruzioni di distribuzione, consulta il progetto in [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_fsa_app).
**Servizi utilizzati in questo esempio**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
#### [ JavaScript ]
**SDK per JavaScript (v3)**
Questa applicazione di esempio analizza e archivia le schede di feedback dei clienti. In particolare, soddisfa l’esigenza di un hotel fittizio a New York City. L’hotel riceve feedback dagli ospiti in varie lingue sotto forma di schede di commento fisiche. Tale feedback viene caricato nell’app tramite un client Web. Dopo aver caricato l’immagine di una scheda di commento, vengono eseguiti i seguenti passaggi:
+ Il testo viene estratto dall’immagine utilizzando Amazon Textract.
+ Amazon Comprehend determina il sentiment del testo estratto e la sua lingua.
+ Il testo estratto viene tradotto in inglese utilizzando Amazon Translate.
+ Amazon Polly sintetizza un file audio dal testo estratto.
L’app completa può essere implementata con AWS CDK. Per il codice sorgente e le istruzioni di distribuzione, consulta il progetto in. [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/feedback-sentiment-analyzer) I seguenti estratti mostrano come AWS SDK per JavaScript viene utilizzato all'interno delle funzioni Lambda.
```
import {
ComprehendClient,
DetectDominantLanguageCommand,
DetectSentimentCommand,
} from "@aws-sdk/client-comprehend";
/**
* Determine the language and sentiment of the extracted text.
*
* @param {{ source_text: string}} extractTextOutput
*/
export const handler = async (extractTextOutput) => {
const comprehendClient = new ComprehendClient({});
const detectDominantLanguageCommand = new DetectDominantLanguageCommand({
Text: extractTextOutput.source_text,
});
// The source language is required for sentiment analysis and
// translation in the next step.
const { Languages } = await comprehendClient.send(
detectDominantLanguageCommand,
);
const languageCode = Languages[0].LanguageCode;
const detectSentimentCommand = new DetectSentimentCommand({
Text: extractTextOutput.source_text,
LanguageCode: languageCode,
});
const { Sentiment } = await comprehendClient.send(detectSentimentCommand);
return {
sentiment: Sentiment,
language_code: languageCode,
};
};
```
```
import {
DetectDocumentTextCommand,
TextractClient,
} from "@aws-sdk/client-textract";
/**
* Fetch the S3 object from the event and analyze it using Amazon Textract.
*
* @param {import("@types/aws-lambda").EventBridgeEvent<"Object Created">} eventBridgeS3Event
*/
export const handler = async (eventBridgeS3Event) => {
const textractClient = new TextractClient();
const detectDocumentTextCommand = new DetectDocumentTextCommand({
Document: {
S3Object: {
Bucket: eventBridgeS3Event.bucket,
Name: eventBridgeS3Event.object,
},
},
});
// Textract returns a list of blocks. A block can be a line, a page, word, etc.
// Each block also contains geometry of the detected text.
// For more information on the Block type, see https://docs.aws.amazon.com/textract/latest/dg/API_Block.html.
const { Blocks } = await textractClient.send(detectDocumentTextCommand);
// For the purpose of this example, we are only interested in words.
const extractedWords = Blocks.filter((b) => b.BlockType === "WORD").map(
(b) => b.Text,
);
return extractedWords.join(" ");
};
```
```
import { PollyClient, SynthesizeSpeechCommand } from "@aws-sdk/client-polly";
import { S3Client } from "@aws-sdk/client-s3";
import { Upload } from "@aws-sdk/lib-storage";
/**
* Synthesize an audio file from text.
*
* @param {{ bucket: string, translated_text: string, object: string}} sourceDestinationConfig
*/
export const handler = async (sourceDestinationConfig) => {
const pollyClient = new PollyClient({});
const synthesizeSpeechCommand = new SynthesizeSpeechCommand({
Engine: "neural",
Text: sourceDestinationConfig.translated_text,
VoiceId: "Ruth",
OutputFormat: "mp3",
});
const { AudioStream } = await pollyClient.send(synthesizeSpeechCommand);
const audioKey = `${sourceDestinationConfig.object}.mp3`;
// Store the audio file in S3.
const s3Client = new S3Client();
const upload = new Upload({
client: s3Client,
params: {
Bucket: sourceDestinationConfig.bucket,
Key: audioKey,
Body: AudioStream,
ContentType: "audio/mp3",
},
});
await upload.done();
return audioKey;
};
```
```
import {
TranslateClient,
TranslateTextCommand,
} from "@aws-sdk/client-translate";
/**
* Translate the extracted text to English.
*
* @param {{ extracted_text: string, source_language_code: string}} textAndSourceLanguage
*/
export const handler = async (textAndSourceLanguage) => {
const translateClient = new TranslateClient({});
const translateCommand = new TranslateTextCommand({
SourceLanguageCode: textAndSourceLanguage.source_language_code,
TargetLanguageCode: "en",
Text: textAndSourceLanguage.extracted_text,
});
const { TranslatedText } = await translateClient.send(translateCommand);
return { translated_text: TranslatedText };
};
```
**Servizi utilizzati in questo esempio**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
#### [ Ruby ]
**SDK per Ruby**
Questa applicazione di esempio analizza e archivia le schede di feedback dei clienti. In particolare, soddisfa l’esigenza di un hotel fittizio a New York City. L’hotel riceve feedback dagli ospiti in varie lingue sotto forma di schede di commento fisiche. Tale feedback viene caricato nell’app tramite un client Web. Dopo aver caricato l’immagine di una scheda di commento, vengono eseguiti i seguenti passaggi:
+ Il testo viene estratto dall’immagine utilizzando Amazon Textract.
+ Amazon Comprehend determina il sentiment del testo estratto e la sua lingua.
+ Il testo estratto viene tradotto in inglese utilizzando Amazon Translate.
+ Amazon Polly sintetizza un file audio dal testo estratto.
L’app completa può essere implementata con AWS CDK. Per il codice sorgente e le istruzioni di distribuzione, consulta il progetto in. [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/ruby/cross_service_examples/feedback_sentiment_analyzer)
**Servizi utilizzati in questo esempio**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
# Rileva gli elementi del documento con Amazon Comprehend e un SDK AWS
L’esempio di codice seguente mostra come:
+ Rilevare lingue, entità e frasi chiave in un documento.
+ Rilevare le informazioni di identificazione personale (PII) in un documento.
+ Rilevare il sentiment di un documento.
+ Rilevare gli elementi di sintassi in un documento.
------
#### [ Python ]
**SDK per Python (Boto3)**
C'è altro da fare. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel [Repository di esempi di codice AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Crea una classe per il wrapping delle azioni di 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
```
Chiama le funzioni della classe wrapper per rilevare entità, frasi e altri elementi in un documento.
```
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)
```
+ Per informazioni dettagliate sull’API, consulta i seguenti argomenti nella *documentazione di riferimento dell’API AWS SDK per 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)
------
# Rileva le entità nel testo estratto da un'immagine utilizzando un SDK AWS
L’esempio di codice seguente mostra come utilizzare Amazon Comprehend per rilevare le entità nel testo estratto da Amazon Textract da un’immagine archiviata in Amazon S3.
------
#### [ Python ]
**SDK per Python (Boto3)**
Mostra come utilizzarlo AWS SDK per Python (Boto3) in un notebook Jupyter per rilevare entità nel testo estratto da un'immagine. In questo esempio viene utilizzato Amazon Textract per estrarre il testo da un’immagine archiviata in Amazon Simple Storage Service (Amazon S3) e Amazon Comprehend per rilevare le entità nel testo estratto.
Questo esempio è un notebook Jupyter e deve essere eseguito in un ambiente in grado di ospitare notebook. Per istruzioni su come eseguire l'esempio utilizzando Amazon SageMaker AI, consulta le istruzioni in [TextractAndComprehendNotebook.ipynb](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook/TextractAndComprehendNotebook.ipynb).
Per il codice sorgente completo e le istruzioni su come configurarlo ed eseguirlo, guarda l'esempio completo su. [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook#readme)
**Servizi utilizzati in questo esempio**
+ Amazon Comprehend
+ Simple Storage Service (Amazon S3)
+ Amazon Textract
------
# Esegui un processo di modellazione tematica di Amazon Comprehend su dati di esempio utilizzando un SDK AWS
L’esempio di codice seguente mostra come:
+ Eseguire un processo di modellazione di argomenti di Amazon Comprehend in base a dati di esempio.
+ Ottenere informazioni sul processo.
+ Estrarre i dati di output del processo da Amazon S3.
------
#### [ Python ]
**SDK per Python (Boto3)**
C'è altro da fare. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel [Repository di esempi di codice AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Crea una classe wrapper per chiamare le azioni di modellazione di 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
```
Utilizza la classe wrapper per eseguire un processo di modellazione di argomenti e ottenere i dati del processo.
```
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)
```
+ Per informazioni dettagliate sull’API, consulta i seguenti argomenti nella *documentazione di riferimento dell’API AWS SDK per 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)
------
# Addestra un classificatore Amazon Comprehend personalizzato e classifica i documenti utilizzando un SDK AWS
L’esempio di codice seguente mostra come:
+ Creare un classificatore multietichetta Amazon Comprehend.
+ Addestrare il classificatore in base a dati di esempio.
+ Eseguire un processo di classificazione su un secondo set di dati.
+ Estrarre i dati di output del processo da Amazon S3.
------
#### [ Python ]
**SDK per Python (Boto3)**
C'è altro da fare. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel [Repository di esempi di codice AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Crea una classe wrapper per chiamare le azioni del classificatore di documenti 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
```
Creare una classe a supporto dell’esecuzione dello scenario.
```
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
```
Addestra un classificatore su una serie di GitHub problemi con etichette note, quindi invia un secondo set di GitHub problemi al classificatore in modo che possano essere etichettati.
```
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)
```
+ Per informazioni dettagliate sull’API, consulta i seguenti argomenti nella *documentazione di riferimento dell’API AWS SDK per 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)
------