test(workflow-operator): add unit test coverage for Sklearn SVM and neighbor classifier descriptors (#5945)

### What changes were proposed in this PR?

Pin behavior of four previously-untested Sklearn support-vector and
neighbor classifier descriptors in `common/workflow-operator`. No
production-code changes.

| Spec | Source class | Tests |
| --- | --- | --- |
| `SklearnSVMOpDescSpec` | `SklearnSVMOpDesc` | 5 |
| `SklearnLinearSVMOpDescSpec` | `SklearnLinearSVMOpDesc` | 5 |
| `SklearnKNNOpDescSpec` | `SklearnKNNOpDesc` | 5 |
| `SklearnNearestCentroidOpDescSpec` | `SklearnNearestCentroidOpDesc` |
5 |

**Behavior pinned**

| Surface | Contract |
| --- | --- |
| `operatorInfo` | exact model name + `Sklearn <name> Operator`
description; Sklearn group; training/testing input ports + one blocking
output |
| field defaults | `countVectorizer`/`tfidfTransformer` `false`;
`target`/`text` `null` |
| `getOutputSchemas` | `model_name` (STRING) + `model` (BINARY) keyed by
the declared output port |
| `generatePythonCode` | imports the matching sklearn estimator and
builds the `make_pipeline` model |
| Round-trip | config fields preserved through the polymorphic
`LogicalOp` base, with the correct `operatorType` discriminator |

### Any related issues, documentation, discussions?

Part of the ongoing `workflow-operator` unit-test coverage effort
(follow-up to the Sklearn classifier coverage in #5925, #5939, #5940,
#5941).

### How was this PR tested?

- `sbt "WorkflowOperator/testOnly *SklearnSVMOpDescSpec
*SklearnLinearSVMOpDescSpec *SklearnKNNOpDescSpec
*SklearnNearestCentroidOpDescSpec"` — 20 tests, all green
- `sbt "WorkflowOperator/Test/scalafmtCheck"` and `sbt
"WorkflowOperator/scalafixAll --check"` — clean
- CI to confirm

### Was this PR authored or co-authored using generative AI tooling?

Generated-by: Claude Code (Opus 4.8 [1M context])

Author: aglinxinyuan

common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnKNNOpDescSpec.scala

@@ -0,0 +1,79 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.texera.amber.operator.sklearn
+
+import org.apache.texera.amber.core.tuple.AttributeType
+import org.apache.texera.amber.operator.LogicalOp
+import org.apache.texera.amber.operator.metadata.OperatorGroupConstants
+import org.apache.texera.amber.util.JSONUtils.objectMapper
+import org.scalatest.flatspec.AnyFlatSpec
+import org.scalatest.matchers.should.Matchers
+
+class SklearnKNNOpDescSpec extends AnyFlatSpec with Matchers {
+
+  "SklearnKNNOpDesc.operatorInfo" should
+    "advertise the model name, Sklearn group, and the training/testing port shape" in {
+    val info = (new SklearnKNNOpDesc).operatorInfo
+    info.userFriendlyName shouldBe "K-nearest Neighbors"
+    info.operatorDescription shouldBe "Sklearn K-nearest Neighbors Operator"
+    info.operatorGroupName shouldBe OperatorGroupConstants.SKLEARN_GROUP
+    info.inputPorts.map(_.displayName) shouldBe List("training", "testing")
+    info.outputPorts should have length 1
+    info.outputPorts.head.blocking shouldBe true
+  }
+
+  "SklearnKNNOpDesc" should "default its config fields" in {
+    val d = new SklearnKNNOpDesc
+    d.countVectorizer shouldBe false
+    d.tfidfTransformer shouldBe false
+    d.target shouldBe null
+    d.text shouldBe null
+  }
+
+  "SklearnKNNOpDesc.getOutputSchemas" should
+    "emit the model_name/model schema keyed by the declared output port" in {
+    val d = new SklearnKNNOpDesc
+    val schema = d.getOutputSchemas(Map.empty)(d.operatorInfo.outputPorts.head.id)
+    schema.getAttribute("model_name").getType shouldBe AttributeType.STRING
+    schema.getAttribute("model").getType shouldBe AttributeType.BINARY
+  }
+
+  "SklearnKNNOpDesc.generatePythonCode" should "import the configured sklearn estimator" in {
+    val d = new SklearnKNNOpDesc
+    d.target = "y"
+    val code = d.generatePythonCode()
+    code should include("from sklearn.neighbors import KNeighborsClassifier")
+    code should include("make_pipeline")
+    code should include("K-nearest Neighbors")
+  }
+
+  "SklearnKNNOpDesc" should "round-trip its config fields through the polymorphic base" in {
+    val d = new SklearnKNNOpDesc
+    d.target = "label"
+    d.countVectorizer = true
+    val json = objectMapper.writeValueAsString(d)
+    json should include("\"operatorType\":\"SklearnKNN\"")
+    val restored = objectMapper.readValue(json, classOf[LogicalOp])
+    restored shouldBe a[SklearnKNNOpDesc]
+    val r = restored.asInstanceOf[SklearnKNNOpDesc]
+    r.target shouldBe "label"
+    r.countVectorizer shouldBe true
+  }
+}

common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnLinearSVMOpDescSpec.scala

@@ -0,0 +1,79 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.texera.amber.operator.sklearn
+
+import org.apache.texera.amber.core.tuple.AttributeType
+import org.apache.texera.amber.operator.LogicalOp
+import org.apache.texera.amber.operator.metadata.OperatorGroupConstants
+import org.apache.texera.amber.util.JSONUtils.objectMapper
+import org.scalatest.flatspec.AnyFlatSpec
+import org.scalatest.matchers.should.Matchers
+
+class SklearnLinearSVMOpDescSpec extends AnyFlatSpec with Matchers {
+
+  "SklearnLinearSVMOpDesc.operatorInfo" should
+    "advertise the model name, Sklearn group, and the training/testing port shape" in {
+    val info = (new SklearnLinearSVMOpDesc).operatorInfo
+    info.userFriendlyName shouldBe "Linear Support Vector Machine"
+    info.operatorDescription shouldBe "Sklearn Linear Support Vector Machine Operator"
+    info.operatorGroupName shouldBe OperatorGroupConstants.SKLEARN_GROUP
+    info.inputPorts.map(_.displayName) shouldBe List("training", "testing")
+    info.outputPorts should have length 1
+    info.outputPorts.head.blocking shouldBe true
+  }
+
+  "SklearnLinearSVMOpDesc" should "default its config fields" in {
+    val d = new SklearnLinearSVMOpDesc
+    d.countVectorizer shouldBe false
+    d.tfidfTransformer shouldBe false
+    d.target shouldBe null
+    d.text shouldBe null
+  }
+
+  "SklearnLinearSVMOpDesc.getOutputSchemas" should
+    "emit the model_name/model schema keyed by the declared output port" in {
+    val d = new SklearnLinearSVMOpDesc
+    val schema = d.getOutputSchemas(Map.empty)(d.operatorInfo.outputPorts.head.id)
+    schema.getAttribute("model_name").getType shouldBe AttributeType.STRING
+    schema.getAttribute("model").getType shouldBe AttributeType.BINARY
+  }
+
+  "SklearnLinearSVMOpDesc.generatePythonCode" should "import the configured sklearn estimator" in {
+    val d = new SklearnLinearSVMOpDesc
+    d.target = "y"
+    val code = d.generatePythonCode()
+    code should include("from sklearn.svm import LinearSVC")
+    code should include("make_pipeline")
+    code should include("Linear Support Vector Machine")
+  }
+
+  "SklearnLinearSVMOpDesc" should "round-trip its config fields through the polymorphic base" in {
+    val d = new SklearnLinearSVMOpDesc
+    d.target = "label"
+    d.countVectorizer = true
+    val json = objectMapper.writeValueAsString(d)
+    json should include("\"operatorType\":\"SklearnLinearSVM\"")
+    val restored = objectMapper.readValue(json, classOf[LogicalOp])
+    restored shouldBe a[SklearnLinearSVMOpDesc]
+    val r = restored.asInstanceOf[SklearnLinearSVMOpDesc]
+    r.target shouldBe "label"
+    r.countVectorizer shouldBe true
+  }
+}

common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnNearestCentroidOpDescSpec.scala

@@ -0,0 +1,79 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.texera.amber.operator.sklearn
+
+import org.apache.texera.amber.core.tuple.AttributeType
+import org.apache.texera.amber.operator.LogicalOp
+import org.apache.texera.amber.operator.metadata.OperatorGroupConstants
+import org.apache.texera.amber.util.JSONUtils.objectMapper
+import org.scalatest.flatspec.AnyFlatSpec
+import org.scalatest.matchers.should.Matchers
+
+class SklearnNearestCentroidOpDescSpec extends AnyFlatSpec with Matchers {
+
+  "SklearnNearestCentroidOpDesc.operatorInfo" should
+    "advertise the model name, Sklearn group, and the training/testing port shape" in {
+    val info = (new SklearnNearestCentroidOpDesc).operatorInfo
+    info.userFriendlyName shouldBe "Nearest Centroid"
+    info.operatorDescription shouldBe "Sklearn Nearest Centroid Operator"
+    info.operatorGroupName shouldBe OperatorGroupConstants.SKLEARN_GROUP
+    info.inputPorts.map(_.displayName) shouldBe List("training", "testing")
+    info.outputPorts should have length 1
+    info.outputPorts.head.blocking shouldBe true
+  }
+
+  "SklearnNearestCentroidOpDesc" should "default its config fields" in {
+    val d = new SklearnNearestCentroidOpDesc
+    d.countVectorizer shouldBe false
+    d.tfidfTransformer shouldBe false
+    d.target shouldBe null
+    d.text shouldBe null
+  }
+
+  "SklearnNearestCentroidOpDesc.getOutputSchemas" should
+    "emit the model_name/model schema keyed by the declared output port" in {
+    val d = new SklearnNearestCentroidOpDesc
+    val schema = d.getOutputSchemas(Map.empty)(d.operatorInfo.outputPorts.head.id)
+    schema.getAttribute("model_name").getType shouldBe AttributeType.STRING
+    schema.getAttribute("model").getType shouldBe AttributeType.BINARY
+  }
+
+  "SklearnNearestCentroidOpDesc.generatePythonCode" should "import the configured sklearn estimator" in {
+    val d = new SklearnNearestCentroidOpDesc
+    d.target = "y"
+    val code = d.generatePythonCode()
+    code should include("from sklearn.neighbors import NearestCentroid")
+    code should include("make_pipeline")
+    code should include("Nearest Centroid")
+  }
+
+  "SklearnNearestCentroidOpDesc" should "round-trip its config fields through the polymorphic base" in {
+    val d = new SklearnNearestCentroidOpDesc
+    d.target = "label"
+    d.countVectorizer = true
+    val json = objectMapper.writeValueAsString(d)
+    json should include("\"operatorType\":\"SklearnNearestCentroid\"")
+    val restored = objectMapper.readValue(json, classOf[LogicalOp])
+    restored shouldBe a[SklearnNearestCentroidOpDesc]
+    val r = restored.asInstanceOf[SklearnNearestCentroidOpDesc]
+    r.target shouldBe "label"
+    r.countVectorizer shouldBe true
+  }
+}

common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnSVMOpDescSpec.scala

@@ -0,0 +1,79 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.texera.amber.operator.sklearn
+
+import org.apache.texera.amber.core.tuple.AttributeType
+import org.apache.texera.amber.operator.LogicalOp
+import org.apache.texera.amber.operator.metadata.OperatorGroupConstants
+import org.apache.texera.amber.util.JSONUtils.objectMapper
+import org.scalatest.flatspec.AnyFlatSpec
+import org.scalatest.matchers.should.Matchers
+
+class SklearnSVMOpDescSpec extends AnyFlatSpec with Matchers {
+
+  "SklearnSVMOpDesc.operatorInfo" should
+    "advertise the model name, Sklearn group, and the training/testing port shape" in {
+    val info = (new SklearnSVMOpDesc).operatorInfo
+    info.userFriendlyName shouldBe "Support Vector Machine"
+    info.operatorDescription shouldBe "Sklearn Support Vector Machine Operator"
+    info.operatorGroupName shouldBe OperatorGroupConstants.SKLEARN_GROUP
+    info.inputPorts.map(_.displayName) shouldBe List("training", "testing")
+    info.outputPorts should have length 1
+    info.outputPorts.head.blocking shouldBe true
+  }
+
+  "SklearnSVMOpDesc" should "default its config fields" in {
+    val d = new SklearnSVMOpDesc
+    d.countVectorizer shouldBe false
+    d.tfidfTransformer shouldBe false
+    d.target shouldBe null
+    d.text shouldBe null
+  }
+
+  "SklearnSVMOpDesc.getOutputSchemas" should
+    "emit the model_name/model schema keyed by the declared output port" in {
+    val d = new SklearnSVMOpDesc
+    val schema = d.getOutputSchemas(Map.empty)(d.operatorInfo.outputPorts.head.id)
+    schema.getAttribute("model_name").getType shouldBe AttributeType.STRING
+    schema.getAttribute("model").getType shouldBe AttributeType.BINARY
+  }
+
+  "SklearnSVMOpDesc.generatePythonCode" should "import the configured sklearn estimator" in {
+    val d = new SklearnSVMOpDesc
+    d.target = "y"
+    val code = d.generatePythonCode()
+    code should include("from sklearn.svm import SVC")
+    code should include("make_pipeline")
+    code should include("Support Vector Machine")
+  }
+
+  "SklearnSVMOpDesc" should "round-trip its config fields through the polymorphic base" in {
+    val d = new SklearnSVMOpDesc
+    d.target = "label"
+    d.countVectorizer = true
+    val json = objectMapper.writeValueAsString(d)
+    json should include("\"operatorType\":\"SklearnSVM\"")
+    val restored = objectMapper.readValue(json, classOf[LogicalOp])
+    restored shouldBe a[SklearnSVMOpDesc]
+    val r = restored.asInstanceOf[SklearnSVMOpDesc]
+    r.target shouldBe "label"
+    r.countVectorizer shouldBe true
+  }
+}