[FEATURE] Improve test coverage for MLEngine Class (#3675)

Improved the test coverage by adding missed use cases.
There is no zero argument constructor, constructor with Input as paremeter and constructor with MLAlgoParams.class as paremeter in the class class org.opensearch.ml.engine.algorithms.metrics_correlation.MetricsCorrelation. So it is impossible to cover remianing potions, it always throws java.lang.NoSuchMethodException: org.opensearch.ml.engine.algorithms.metrics_correlation.MetricsCorrelation.<init>().

Resolves #1376

Signed-off-by: Abdul Muneer Kolarkunnu <[email protected]>

Author: akolarkunnu

ml-algorithms/src/test/java/org/opensearch/ml/engine/MLEngineTest.java

@@ -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;
 
@@ -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;
@@ -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);
@@ -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);
@@ -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
@@ -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"));
+    }
 }