[ML] RegressionIT: Fix hyperparameters for regression tests and unmute the test (elastic#135541)

This PR fixes the flaky test muted in elastic#93228 by fixing hyperparameters to the values that always work. Since the test is for alias fields and not for the training algorithm, fixing the hyperparameters is not dangerous.

Closes elastic#93228

Author: valeriy42

x-pack/plugin/ml/qa/native-multi-node-tests/src/javaRestTest/java/org/elasticsearch/xpack/ml/integration/RegressionIT.java

@@ -27,12 +27,8 @@
 import org.elasticsearch.xpack.core.ml.dataframe.analyses.BoostedTreeParams;
 import org.elasticsearch.xpack.core.ml.dataframe.analyses.Regression;
 import org.elasticsearch.xpack.core.ml.inference.TrainedModelConfig;
-import org.elasticsearch.xpack.core.ml.inference.TrainedModelDefinition;
 import org.elasticsearch.xpack.core.ml.inference.preprocessing.OneHotEncoding;
 import org.elasticsearch.xpack.core.ml.inference.preprocessing.PreProcessor;
-import org.elasticsearch.xpack.core.ml.inference.trainedmodel.ensemble.Ensemble;
-import org.elasticsearch.xpack.core.ml.inference.trainedmodel.metadata.Hyperparameters;
-import org.elasticsearch.xpack.core.ml.inference.trainedmodel.metadata.TrainedModelMetadata;
 import org.hamcrest.Matchers;
 import org.junit.After;
 
@@ -540,7 +536,6 @@ public void testWithDatastream() throws Exception {
         );
     }
 
-    @AwaitsFix(bugUrl = "https://github.com/elastic/elasticsearch/issues/93228")
     public void testAliasFields() throws Exception {
         // The goal of this test is to assert alias fields are included in the analytics job.
         // We have a simple dataset with two integer fields: field_1 and field_2.
@@ -585,10 +580,32 @@ public void testAliasFields() throws Exception {
         // Very infrequently this test may fail as the algorithm underestimates the
         // required number of trees for this simple problem. This failure is irrelevant
         // for non-trivial real-world problem and improving estimation of the number of trees
-        // would introduce unnecessary overhead. Hence, to reduce the noise from this test we fix the seed.
+        // would introduce unnecessary overhead. Hence, to reduce the noise from this test we fix the seed
+        // and use the hyperparameters that are known to work.
         long seed = 1000L; // fix seed
 
-        Regression regression = new Regression("field_2", BoostedTreeParams.builder().build(), null, 90.0, seed, null, null, null, null);
+        Regression regression = new Regression(
+            "field_2",
+            BoostedTreeParams.builder()
+                .setDownsampleFactor(0.7520841625652861)
+                .setAlpha(547.9095715556235)
+                .setLambda(3.3008189603590044)
+                .setGamma(1.6082763366825203)
+                .setSoftTreeDepthLimit(4.733224114945455)
+                .setSoftTreeDepthTolerance(0.15)
+                .setEta(0.12371209659057758)
+                .setEtaGrowthRatePerTree(1.0618560482952888)
+                .setMaxTrees(30)
+                .setFeatureBagFraction(0.8)
+                .build(),
+            null,
+            90.0,
+            seed,
+            null,
+            null,
+            null,
+            null
+        );
         DataFrameAnalyticsConfig config = new DataFrameAnalyticsConfig.Builder().setId(jobId)
             .setSource(new DataFrameAnalyticsSource(new String[] { sourceIndex }, null, null, Collections.emptyMap()))
             .setDest(new DataFrameAnalyticsDest(destIndex, null))
@@ -604,19 +621,6 @@ public void testAliasFields() throws Exception {
 
         waitUntilAnalyticsIsStopped(jobId);
 
-        // obtain addition information for investigation of #90599
-        String modelId = getModelId(jobId);
-        TrainedModelMetadata modelMetadata = getModelMetadata(modelId);
-        assertThat(modelMetadata.getHyperparameters().size(), greaterThan(0));
-        StringBuilder hyperparameters = new StringBuilder(); // used to investigate #90599
-        for (Hyperparameters hyperparameter : modelMetadata.getHyperparameters()) {
-            hyperparameters.append(hyperparameter.hyperparameterName).append(": ").append(hyperparameter.value).append("\n");
-        }
-        TrainedModelDefinition modelDefinition = getModelDefinition(modelId);
-        Ensemble ensemble = (Ensemble) modelDefinition.getTrainedModel();
-        int numberTrees = ensemble.getModels().size();
-
-        StringBuilder targetsPredictions = new StringBuilder(); // used to investigate #90599
         assertResponse(prepareSearch(sourceIndex).setSize(totalDocCount), sourceData -> {
             double predictionErrorSum = 0.0;
             for (SearchHit hit : sourceData.getHits()) {
@@ -629,19 +633,12 @@ public void testAliasFields() throws Exception {
                 int featureValue = (int) destDoc.get("field_1");
                 double predictionValue = (double) resultsObject.get(predictionField);
                 predictionErrorSum += Math.abs(predictionValue - 2 * featureValue);
-
-                // collect the log of targets and predictions for debugging #90599
-                targetsPredictions.append(2 * featureValue).append(", ").append(predictionValue).append("\n");
             }
             // We assert on the mean prediction error in order to reduce the probability
             // the test fails compared to asserting on the prediction of each individual doc.
             double meanPredictionError = predictionErrorSum / sourceData.getHits().getHits().length;
             String str = "Failure: failed for seed %d inferenceEntityId %s numberTrees %d\n";
-            assertThat(
-                Strings.format(str, seed, modelId, numberTrees) + targetsPredictions + hyperparameters,
-                meanPredictionError,
-                lessThanOrEqualTo(3.0)
-            );
+            assertThat(meanPredictionError, lessThanOrEqualTo(3.0));
         });
 
         assertProgressComplete(jobId);