Azure AI Anomaly Detector SDK for Java Acceptance Criteria

SDK: com.azure:azure-ai-anomalydetectorRepository: https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/anomalydetector/azure-ai-anomalydetectorPurpose: Skill testing acceptance criteria for validating generated code correctness

1. Correct Import Patterns

1.1 Client Imports

✅ CORRECT: Client Builder and Clients

java

import com.azure.ai.anomalydetector.AnomalyDetectorClientBuilder;
import com.azure.ai.anomalydetector.MultivariateClient;
import com.azure.ai.anomalydetector.MultivariateAsyncClient;
import com.azure.ai.anomalydetector.UnivariateClient;
import com.azure.ai.anomalydetector.UnivariateAsyncClient;

✅ CORRECT: Authentication

java

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.core.credential.AzureKeyCredential;

1.2 Model Imports

✅ CORRECT: Detection Models

java

import com.azure.ai.anomalydetector.models.TimeSeriesPoint;
import com.azure.ai.anomalydetector.models.TimeGranularity;
import com.azure.ai.anomalydetector.models.UnivariateDetectionOptions;
import com.azure.ai.anomalydetector.models.UnivariateEntireDetectionResult;
import com.azure.ai.anomalydetector.models.UnivariateLastDetectionResult;
import com.azure.ai.anomalydetector.models.UnivariateChangePointDetectionOptions;
import com.azure.ai.anomalydetector.models.UnivariateChangePointDetectionResult;

✅ CORRECT: Multivariate Models

java

import com.azure.ai.anomalydetector.models.ModelInfo;
import com.azure.ai.anomalydetector.models.AnomalyDetectionModel;
import com.azure.ai.anomalydetector.models.MultivariateBatchDetectionOptions;
import com.azure.ai.anomalydetector.models.MultivariateDetectionResult;
import com.azure.ai.anomalydetector.models.MultivariateLastDetectionOptions;
import com.azure.ai.anomalydetector.models.MultivariateLastDetectionResult;
import com.azure.ai.anomalydetector.models.VariableValues;

1.3 Anti-Patterns (ERRORS)

❌ INCORRECT: Wrong import paths

java

// WRONG - Using old package names
import com.azure.cognitiveservices.anomalydetector.AnomalyDetectorClient;

// WRONG - Models not in main package
import com.azure.ai.anomalydetector.TimeSeriesPoint;

// WRONG - Using non-existent classes
import com.azure.ai.anomalydetector.AnomalyDetectorClient;

2. Client Creation Patterns

2.1 ✅ CORRECT: Builder with API Key

java

String endpoint = System.getenv("AZURE_ANOMALY_DETECTOR_ENDPOINT");
String key = System.getenv("AZURE_ANOMALY_DETECTOR_API_KEY");

UnivariateClient univariateClient = new AnomalyDetectorClientBuilder()
    .credential(new AzureKeyCredential(key))
    .endpoint(endpoint)
    .buildUnivariateClient();

MultivariateClient multivariateClient = new AnomalyDetectorClientBuilder()
    .credential(new AzureKeyCredential(key))
    .endpoint(endpoint)
    .buildMultivariateClient();

2.2 ✅ CORRECT: Builder with DefaultAzureCredential

java

UnivariateClient client = new AnomalyDetectorClientBuilder()
    .credential(new DefaultAzureCredentialBuilder().build())
    .endpoint(endpoint)
    .buildUnivariateClient();

2.3 ✅ CORRECT: Async Clients

java

UnivariateAsyncClient asyncClient = new AnomalyDetectorClientBuilder()
    .credential(new DefaultAzureCredentialBuilder().build())
    .endpoint(endpoint)
    .buildUnivariateAsyncClient();

MultivariateAsyncClient multivariateAsyncClient = new AnomalyDetectorClientBuilder()
    .credential(new DefaultAzureCredentialBuilder().build())
    .endpoint(endpoint)
    .buildMultivariateAsyncClient();

2.4 Anti-Patterns (ERRORS)

❌ INCORRECT: Hardcoded credentials

java

// WRONG - hardcoded endpoint and key
UnivariateClient client = new AnomalyDetectorClientBuilder()
    .endpoint("https://myresource.cognitiveservices.azure.com")
    .credential(new AzureKeyCredential("hardcoded-key"))
    .buildUnivariateClient();

❌ INCORRECT: Missing required parameters

java

// WRONG - missing endpoint
UnivariateClient client = new AnomalyDetectorClientBuilder()
    .credential(new AzureKeyCredential(key))
    .buildUnivariateClient();

3. Univariate Detection Patterns

3.1 ✅ CORRECT: Batch Detection

java

List<TimeSeriesPoint> series = List.of(
    new TimeSeriesPoint(OffsetDateTime.parse("2023-01-01T00:00:00Z"), 1.0),
    new TimeSeriesPoint(OffsetDateTime.parse("2023-01-02T00:00:00Z"), 2.5)
    // ... minimum 12 points required
);

UnivariateDetectionOptions options = new UnivariateDetectionOptions(series)
    .setGranularity(TimeGranularity.DAILY)
    .setSensitivity(95);

UnivariateEntireDetectionResult result = univariateClient.detectUnivariateEntireSeries(options);

for (int i = 0; i < result.getIsAnomaly().size(); i++) {
    if (result.getIsAnomaly().get(i)) {
        System.out.printf("Anomaly at index %d%n", i);
    }
}

3.2 ✅ CORRECT: Last Point Detection

java

UnivariateLastDetectionResult result = univariateClient.detectUnivariateLastPoint(options);

if (result.isAnomaly()) {
    System.out.println("Latest point is an anomaly!");
}

3.3 ✅ CORRECT: Change Point Detection

java

UnivariateChangePointDetectionOptions changeOptions = 
    new UnivariateChangePointDetectionOptions(series, TimeGranularity.DAILY);

UnivariateChangePointDetectionResult result = 
    univariateClient.detectUnivariateChangePoint(changeOptions);

for (int i = 0; i < result.getIsChangePoint().size(); i++) {
    if (result.getIsChangePoint().get(i)) {
        System.out.printf("Change point at index %d%n", i);
    }
}

3.4 Anti-Patterns (ERRORS)

❌ INCORRECT: Insufficient data points

java

// WRONG - less than 12 data points
List<TimeSeriesPoint> series = List.of(
    new TimeSeriesPoint(OffsetDateTime.now(), 1.0)
);
univariateClient.detectUnivariateEntireSeries(new UnivariateDetectionOptions(series));

4. Multivariate Detection Patterns

4.1 ✅ CORRECT: Train Model

java

ModelInfo modelInfo = new ModelInfo()
    .setDataSource("https://storage.blob.core.windows.net/container/data.zip?sasToken")
    .setStartTime(OffsetDateTime.parse("2023-01-01T00:00:00Z"))
    .setEndTime(OffsetDateTime.parse("2023-06-01T00:00:00Z"))
    .setSlidingWindow(200)
    .setDisplayName("MyModel");

AnomalyDetectionModel model = multivariateClient.trainMultivariateModel(modelInfo);
String modelId = model.getModelId();

4.2 ✅ CORRECT: Batch Detection

java

MultivariateBatchDetectionOptions detectionOptions = new MultivariateBatchDetectionOptions()
    .setDataSource("https://storage.blob.core.windows.net/container/inference.zip?sasToken")
    .setStartTime(OffsetDateTime.parse("2023-07-01T00:00:00Z"))
    .setEndTime(OffsetDateTime.parse("2023-07-31T00:00:00Z"))
    .setTopContributorCount(10);

MultivariateDetectionResult result = 
    multivariateClient.detectMultivariateBatchAnomaly(modelId, detectionOptions);

4.3 ✅ CORRECT: Last Point Detection

java

MultivariateLastDetectionOptions lastOptions = new MultivariateLastDetectionOptions()
    .setVariables(List.of(
        new VariableValues("variable1", List.of("timestamp1"), List.of(1.0f)),
        new VariableValues("variable2", List.of("timestamp1"), List.of(2.5f))
    ))
    .setTopContributorCount(5);

MultivariateLastDetectionResult result = 
    multivariateClient.detectMultivariateLastAnomaly(modelId, lastOptions);

5. Error Handling

5.1 ✅ CORRECT: HTTP Exception Handling

java

import com.azure.core.exception.HttpResponseException;

try {
    univariateClient.detectUnivariateEntireSeries(options);
} catch (HttpResponseException e) {
    System.err.println("HTTP Status: " + e.getResponse().getStatusCode());
    System.err.println("Error: " + e.getMessage());
}

5.2 Anti-Patterns (ERRORS)

❌ INCORRECT: Empty catch blocks

java

// WRONG - swallowing exceptions
try {
    univariateClient.detectUnivariateEntireSeries(options);
} catch (Exception e) {
    // Do nothing
}

6. Best Practices Checklist

[ ] Use DefaultAzureCredentialBuilder for production authentication
[ ] Use environment variables for endpoint and API key configuration
[ ] Provide at least 12 data points for univariate detection
[ ] Match TimeGranularity to actual data frequency
[ ] Use async clients for high-concurrency scenarios
[ ] Handle HttpResponseException appropriately
[ ] Clean up multivariate models when no longer needed

Azure AI Anomaly Detector SDK for Java Acceptance Criteria ​

1. Correct Import Patterns ​

1.1 Client Imports ​

✅ CORRECT: Client Builder and Clients ​

✅ CORRECT: Authentication ​

1.2 Model Imports ​

✅ CORRECT: Detection Models ​

✅ CORRECT: Multivariate Models ​

1.3 Anti-Patterns (ERRORS) ​

❌ INCORRECT: Wrong import paths ​

2. Client Creation Patterns ​

2.1 ✅ CORRECT: Builder with API Key ​

2.2 ✅ CORRECT: Builder with DefaultAzureCredential ​

2.3 ✅ CORRECT: Async Clients ​

2.4 Anti-Patterns (ERRORS) ​

❌ INCORRECT: Hardcoded credentials ​

❌ INCORRECT: Missing required parameters ​

3. Univariate Detection Patterns ​

3.1 ✅ CORRECT: Batch Detection ​

3.2 ✅ CORRECT: Last Point Detection ​

3.3 ✅ CORRECT: Change Point Detection ​

3.4 Anti-Patterns (ERRORS) ​

❌ INCORRECT: Insufficient data points ​

4. Multivariate Detection Patterns ​

4.1 ✅ CORRECT: Train Model ​

4.2 ✅ CORRECT: Batch Detection ​

4.3 ✅ CORRECT: Last Point Detection ​

5. Error Handling ​

5.1 ✅ CORRECT: HTTP Exception Handling ​

5.2 Anti-Patterns (ERRORS) ​

❌ INCORRECT: Empty catch blocks ​

6. Best Practices Checklist ​

Azure AI Anomaly Detector SDK for Java Acceptance Criteria

1. Correct Import Patterns

1.1 Client Imports

✅ CORRECT: Client Builder and Clients

✅ CORRECT: Authentication

1.2 Model Imports

✅ CORRECT: Detection Models

✅ CORRECT: Multivariate Models

1.3 Anti-Patterns (ERRORS)

❌ INCORRECT: Wrong import paths

2. Client Creation Patterns

2.1 ✅ CORRECT: Builder with API Key

2.2 ✅ CORRECT: Builder with DefaultAzureCredential

2.3 ✅ CORRECT: Async Clients

2.4 Anti-Patterns (ERRORS)

❌ INCORRECT: Hardcoded credentials

❌ INCORRECT: Missing required parameters

3. Univariate Detection Patterns

3.1 ✅ CORRECT: Batch Detection

3.2 ✅ CORRECT: Last Point Detection

3.3 ✅ CORRECT: Change Point Detection

3.4 Anti-Patterns (ERRORS)

❌ INCORRECT: Insufficient data points

4. Multivariate Detection Patterns

4.1 ✅ CORRECT: Train Model

4.2 ✅ CORRECT: Batch Detection

4.3 ✅ CORRECT: Last Point Detection

5. Error Handling

5.1 ✅ CORRECT: HTTP Exception Handling

5.2 Anti-Patterns (ERRORS)

❌ INCORRECT: Empty catch blocks

6. Best Practices Checklist