[ML][Inference] Adds validations for model PUT

client/rest-high-level/src/test/java/org/elasticsearch/client/ml/inference/TrainedModelDefinitionTests.java

@@ -32,6 +32,7 @@
 
 import java.io.IOException;
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.Collections;
 import java.util.List;
 import java.util.function.Predicate;
@@ -70,7 +71,7 @@ public static TrainedModelDefinition.Builder createRandomBuilder(TargetType targ
                         TargetMeanEncodingTests.createRandom()))
                         .limit(numberOfProcessors)
                         .collect(Collectors.toList()))
-            .setTrainedModel(randomFrom(TreeTests.buildRandomTree(Collections.emptyList(), 6, targetType),
+            .setTrainedModel(randomFrom(TreeTests.buildRandomTree(Arrays.asList("foo", "bar"), 6, targetType),
                 EnsembleTests.createRandom(targetType)));
     }
 

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/TrainedModelConfig.java

@@ -535,6 +535,9 @@ public Builder validate(boolean forCreation) {
                     break;
                 }
             }
+            if (input != null && input.getFieldNames().isEmpty()) {
+                validationException = addValidationError("[input.field_names] must not be empty", validationException);
+            }
             if (forCreation) {
                 validationException = checkIllegalSetting(version, VERSION.getPreferredName(), validationException);
                 validationException = checkIllegalSetting(createdBy, CREATED_BY.getPreferredName(), validationException);

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/trainedmodel/ensemble/Ensemble.java

@@ -250,6 +250,10 @@ public int hashCode() {
 
     @Override
     public void validate() {
+        if (this.models.isEmpty()) {
+            throw ExceptionsHelper.badRequestException("[{}] must not be empty", TRAINED_MODELS.getPreferredName());
+        }
+
         if (outputAggregator.compatibleWith(targetType) == false) {
             throw ExceptionsHelper.badRequestException(
                 "aggregate_output [{}] is not compatible with target_type [{}]",

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/trainedmodel/tree/Tree.java

@@ -253,6 +253,9 @@ public static Builder builder() {
 
     @Override
     public void validate() {
+        if (featureNames.isEmpty()) {
+            throw ExceptionsHelper.badRequestException("[{}] must not be empty for tree model", FEATURE_NAMES.getPreferredName());
+        }
         checkTargetType();
         detectMissingNodes();
         detectCycle();

x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/inference/trainedmodel/ensemble/EnsembleTests.java

@@ -26,6 +26,7 @@
 import java.io.IOException;
 import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.Collections;
 import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
@@ -202,6 +203,14 @@ public void testEnsembleWithTargetTypeAndLabelsMismatch() {
         assertThat(ex.getMessage(), equalTo(msg));
     }
 
+    public void testEnsembleWithEmptyModels() {
+        List<String> featureNames = Arrays.asList("foo", "bar");
+        ElasticsearchException ex = expectThrows(ElasticsearchException.class, () -> {
+            Ensemble.builder().setTrainedModels(Collections.emptyList()).setFeatureNames(featureNames).build().validate();
+        });
+        assertThat(ex.getMessage(), equalTo("[trained_models] must not be empty"));
+    }
+
     public void testClassificationProbability() {
         List<String> featureNames = Arrays.asList("foo", "bar");
         Tree tree1 = Tree.builder()

x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/inference/trainedmodel/tree/TreeTests.java

@@ -339,6 +339,21 @@ public void testTreeWithTargetTypeAndLabelsMismatch() {
         assertThat(ex.getMessage(), equalTo(msg));
     }
 
+    public void testTreeWithEmptyFeatureNames() {
+        String msg = "[feature_names] must not be empty for tree model";
+        ElasticsearchException ex = expectThrows(ElasticsearchException.class, () -> {
+            Tree.builder()
+                .setRoot(TreeNode.builder(0)
+                    .setLeftChild(1)
+                    .setSplitFeature(1)
+                    .setThreshold(randomDouble()))
+                .setFeatureNames(Collections.emptyList())
+                .build()
+                .validate();
+        });
+        assertThat(ex.getMessage(), equalTo(msg));
+    }
+
     public void testOperationsEstimations() {
         Tree tree = buildRandomTree(Arrays.asList("foo", "bar", "baz"), 5);
         assertThat(tree.estimatedNumOperations(), equalTo(7L));

x-pack/plugin/ml/qa/ml-with-security/build.gradle

@@ -136,6 +136,9 @@ integTest.runner {
     'ml/inference_crud/Test delete with missing model',
     'ml/inference_crud/Test get given missing trained model',
     'ml/inference_crud/Test get given expression without matches and allow_no_match is false',
+    'ml/inference_crud/Test put ensemble with empty models',
+    'ml/inference_crud/Test put ensemble with tree where tree has empty feature-names',
+    'ml/inference_crud/Test put model with empty input.field_names',
     'ml/inference_stats_crud/Test get stats given missing trained model',
     'ml/inference_stats_crud/Test get stats given expression without matches and allow_no_match is false',
     'ml/jobs_crud/Test cannot create job with existing categorizer state document',

x-pack/plugin/src/test/resources/rest-api-spec/test/ml/inference_crud.yml

@@ -171,3 +171,97 @@ setup:
         allow_no_match: false
   - match: { count: 1 }
   - match: { trained_model_configs.0.model_id: "lang_ident_model_1" }
+---
+"Test put ensemble with empty models":
+  - do:
+      catch: /\[trained_models\] must not be empty/
+      ml.put_trained_model:
+        model_id: "missing_model_ensembles"
+        body: >
+          {
+            "input": {
+              "field_names": "fieldy_mc_fieldname"
+            },
+            "definition": {
+              "trained_model": {
+                "ensemble": {
+                  "feature_names": [],
+                  "trained_models": []
+                }
+              }
+            }
+          }
+---
+"Test put ensemble with tree where tree has empty feature-names":
+  - do:
+      catch: /\[feature_names\] must not be empty/
+      ml.put_trained_model:
+        model_id: "ensemble_tree_missing_feature_names"
+        body: >
+          {
+            "input": {
+              "field_names": "fieldy_mc_fieldname"
+            },
+            "definition": {
+              "trained_model": {
+                "ensemble": {
+                  "feature_names": [],
+                  "trained_models": [
+                     {
+                        "tree": {
+                           "feature_names": [],
+                           "tree_structure": [
+                             {
+                               "node_index": 0,
+                               "split_feature": 0,
+                               "split_gain": 12.0,
+                               "threshold": 10.0,
+                               "decision_type": "lte",
+                               "default_left": true,
+                               "left_child": 1,
+                               "right_child": 2
+                             }]
+                        }
+                     }
+                  ]
+                }
+              }
+            }
+          }
+---
+"Test put model with empty input.field_names":
+  - do:
+      catch: /\[input\.field_names\] must not be empty/
+      ml.put_trained_model:
+        model_id: "missing_model_ensembles"
+        body: >
+          {
+            "input": {
+              "field_names": []
+            },
+            "definition": {
+              "trained_model": {
+                "ensemble": {
+                  "feature_names": [],
+                  "trained_models": [
+                     {
+                        "tree": {
+                           "feature_names": [],
+                           "tree_structure": [
+                             {
+                               "node_index": 0,
+                               "split_feature": 0,
+                               "split_gain": 12.0,
+                               "threshold": 10.0,
+                               "decision_type": "lte",
+                               "default_left": true,
+                               "left_child": 1,
+                               "right_child": 2
+                             }]
+                        }
+                     }
+                  ]
+                }
+              }
+            }
+          }