Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[FEATURE] Improve test coverage for MLEngine Class #3675

Open
wants to merge 2 commits into
base: main
Choose a base branch
from
Open

[FEATURE] Improve test coverage for MLEngine Class #3675

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
3648777
[FEATURE] Improve test coverage for MLEngine Class
akolarkunnu Mar 19, 2025
9e863cf
Merge branch 'opensearch-project:main' into MLEngineTest
akolarkunnu Mar 21, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
84 changes: 80 additions & 4 deletions ml-algorithms/src/test/java/org/opensearch/ml/engine/MLEngineTest.java
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,8 +15,10 @@

import java.io.IOException;
import java.nio.file.Path;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
import java.util.UUID;

Expand All @@ -43,6 +45,7 @@
import org.opensearch.ml.common.dataset.MLInputDataset;
import org.opensearch.ml.common.input.Input;
import org.opensearch.ml.common.input.MLInput;
import org.opensearch.ml.common.input.execute.metricscorrelation.MetricsCorrelationInput;
import org.opensearch.ml.common.input.execute.samplecalculator.LocalSampleCalculatorInput;
import org.opensearch.ml.common.input.parameter.MLAlgoParams;
import org.opensearch.ml.common.input.parameter.clustering.KMeansParams;
Expand Down Expand Up @@ -254,6 +257,17 @@ public void trainAndPredictWithKmeans() {
assertEquals(dataSize, output.getPredictionResult().size());
}

@Test
public void trainAndPredictWithMetricsCorrelationThrowsException() {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("Unsupported algorithm: METRICS_CORRELATION");
int dataSize = 100;
DataFrame dataFrame = constructTestDataFrame(dataSize);
MLInputDataset inputData = new DataFrameInputDataset(dataFrame);
Input input = new MLInput(FunctionName.METRICS_CORRELATION, null, inputData);
mlEngine.trainAndPredict(input);
}

@Test
public void trainAndPredictWithInvalidInput() {
exceptionRule.expect(IllegalArgumentException.class);
Expand All @@ -272,6 +286,18 @@ public void executeLocalSampleCalculator() throws Exception {
mlEngine.execute(input, listener);
}

@Test
public void executeWithMetricsCorrelationThrowsException() throws Exception {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("Unsupported executable function: METRICS_CORRELATION");
List<float[]> inputData = new ArrayList<>();
inputData.add(new float[] { 1.0f, 2.0f, 3.0f, 4.0f });
inputData.add(new float[] { 1.0f, 2.0f, 3.0f, 4.0f });
inputData.add(new float[] { 1.0f, 2.0f, 3.0f, 4.0f });
Input input = MetricsCorrelationInput.builder().inputData(inputData).build();
mlEngine.execute(input, null);
}

@Test
public void executeWithInvalidInput() throws Exception {
exceptionRule.expect(IllegalArgumentException.class);
Expand Down Expand Up @@ -355,15 +381,36 @@ public void getRegisterModelPath_ReturnsCorrectPath() {
}

@Test
public void getDeployModelPath_ReturnsCorrectPath() {
public void getPathAPIs_ReturnsCorrectPath() {
String modelId = "deployedModel";

// Use the actual base path from the mlEngine instance
Path basePath = mlEngine.getMlCachePath().getParent();
Path expectedPath = basePath.resolve("ml_cache").resolve("models_cache").resolve(MLEngine.DEPLOY_MODEL_FOLDER).resolve(modelId);
Path actualPath = mlEngine.getDeployModelPath(modelId);
Path modelsCachePath = basePath.resolve("ml_cache").resolve("models_cache");
Path expectedDeployModelRootPath = modelsCachePath.resolve(MLEngine.DEPLOY_MODEL_FOLDER);
assertEquals(expectedDeployModelRootPath.toString(), mlEngine.getDeployModelRootPath().toString());
Path expectedDeployModelPath = expectedDeployModelRootPath.resolve(modelId);
assertEquals(expectedDeployModelPath.toString(), mlEngine.getDeployModelPath(modelId).toString());

assertEquals(expectedPath.toString(), actualPath.toString());
String expectedDeployModelZipPath = expectedDeployModelRootPath.resolve(modelId).resolve("myModel") + ".zip";
assertEquals(expectedDeployModelZipPath, mlEngine.getDeployModelZipPath(modelId, "myModel"));
Path expectedDeployModelChunkPath = expectedDeployModelRootPath.resolve(modelId).resolve("chunks").resolve("1");
assertEquals(expectedDeployModelChunkPath.toString(), mlEngine.getDeployModelChunkPath(modelId, 1).toString());

assertEquals(
"https://artifacts.opensearch.org/models/ml-models/model_listing/pre_trained_models.json",
mlEngine.getPrebuiltModelMetaListPath()
);

Path expectedRegisterRootPath = modelsCachePath.resolve(MLEngine.REGISTER_MODEL_FOLDER);
assertEquals(expectedRegisterRootPath.toString(), mlEngine.getRegisterModelRootPath().toString());
Path expectedRegisterModelPath = expectedRegisterRootPath.resolve(modelId);
assertEquals(expectedRegisterModelPath.toString(), mlEngine.getRegisterModelPath(modelId).toString());

Path expectedMdelCacheRootPath = modelsCachePath.resolve("models");
assertEquals(expectedMdelCacheRootPath.toString(), mlEngine.getModelCacheRootPath().toString());
Path expectedMdelCachePath = expectedMdelCacheRootPath.resolve(modelId);
assertEquals(expectedMdelCachePath.toString(), mlEngine.getModelCachePath(modelId).toString());
}

@Test
Expand Down Expand Up @@ -444,4 +491,33 @@ public void testGetConnectorCredential() throws IOException {
assertEquals(decryptedCredential.get("key"), "test_key_value");
assertEquals(decryptedCredential.get("region"), "test region");
}

@Test
public void testGetConnectorCredentialWithoutRegion() throws IOException {
String encryptedValue = mlEngine.encrypt("test_key_value", null);
String test_connector_string = "{\"name\":\"test_connector_name\",\"version\":\"1\","
+ "\"description\":\"this is a test connector\",\"protocol\":\"http\","
+ "\"parameters\":{},\"credential\":{\"key\":\""
+ encryptedValue
+ "\"},"
+ "\"actions\":[{\"action_type\":\"PREDICT\",\"method\":\"POST\",\"url\":\"https://test.com\","
+ "\"headers\":{\"api_key\":\"${credential.key}\"},"
+ "\"request_body\":\"{\\\"input\\\": \\\"${parameters.input}\\\"}\"}],"
+ "\"retry_backoff_millis\":10,\"retry_timeout_seconds\":10,\"max_retry_times\":-1,\"retry_backoff_policy\":\"constant\"}}";

XContentParser parser = XContentType.JSON
.xContent()
.createParser(
new NamedXContentRegistry(new SearchModule(Settings.EMPTY, Collections.emptyList()).getNamedXContents()),
null,
test_connector_string
);
parser.nextToken();

HttpConnector connector = new HttpConnector("http", parser);
Map<String, String> decryptedCredential = mlEngine.getConnectorCredential(connector);
assertNotNull(decryptedCredential);
assertEquals("test_key_value", decryptedCredential.get("key"));
assertEquals(null, decryptedCredential.get("region"));
}
}
Loading