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      at c12f908c13 test(workflow-operator): add unit test coverage for Sklearn MLP and probability-calibration descriptors (#5951)

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(texera) 01/01: test(workflow-operator): add unit test coverage for Sklearn MLP and probability-calibration descriptors (#5951)

[gi...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

github-merge-queue[bot] pushed a commit to branch gh-readonly-queue/main/pr-5951-7a38b6cf4c476e6f7ae0b0555fd711b50f4e85ec
in repository https://gitbox.apache.org/repos/asf/texera.git

commit c12f908c13fc16c373bf3c2827687c0d89b2dfed
Author: Xinyuan Lin <xi...@uci.edu>
AuthorDate: Fri Jun 26 01:41:01 2026 -0700

    test(workflow-operator): add unit test coverage for Sklearn MLP and probability-calibration descriptors (#5951)
    
    ### What changes were proposed in this PR?
    
    Pin behavior of two previously-untested Sklearn classifier descriptors
    (multi-layer perceptron and probability calibration) in
    `common/workflow-operator`. No production-code changes. This completes
    unit coverage of the concrete `SklearnClassifierOpDesc` classifier
    family.
    
    | Spec | Source class | Tests |
    | --- | --- | --- |
    | `SklearnMultiLayerPerceptronOpDescSpec` |
    `SklearnMultiLayerPerceptronOpDesc` | 5 |
    | `SklearnProbabilityCalibrationOpDescSpec` |
    `SklearnProbabilityCalibrationOpDesc` | 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
    (`MLPClassifier`/`CalibratedClassifierCV`) 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, #5945, #5946).
    
    ### How was this PR tested?
    
    - `sbt "WorkflowOperator/testOnly *SklearnMultiLayerPerceptronOpDescSpec
    *SklearnProbabilityCalibrationOpDescSpec"` — 10 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])
---
 .../SklearnMultiLayerPerceptronOpDescSpec.scala    | 79 ++++++++++++++++++++++
 .../SklearnProbabilityCalibrationOpDescSpec.scala  | 79 ++++++++++++++++++++++
 2 files changed, 158 insertions(+)

diff --git a/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnMultiLayerPerceptronOpDescSpec.scala b/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnMultiLayerPerceptronOpDescSpec.scala
new file mode 100644
index 0000000000..592f577956
--- /dev/null
+++ b/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnMultiLayerPerceptronOpDescSpec.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 SklearnMultiLayerPerceptronOpDescSpec extends AnyFlatSpec with Matchers {
+
+  "SklearnMultiLayerPerceptronOpDesc.operatorInfo" should
+    "advertise the model name, Sklearn group, and the training/testing port shape" in {
+    val info = (new SklearnMultiLayerPerceptronOpDesc).operatorInfo
+    info.userFriendlyName shouldBe "Multi-layer Perceptron"
+    info.operatorDescription shouldBe "Sklearn Multi-layer Perceptron 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
+  }
+
+  "SklearnMultiLayerPerceptronOpDesc" should "default its config fields" in {
+    val d = new SklearnMultiLayerPerceptronOpDesc
+    d.countVectorizer shouldBe false
+    d.tfidfTransformer shouldBe false
+    d.target shouldBe null
+    d.text shouldBe null
+  }
+
+  "SklearnMultiLayerPerceptronOpDesc.getOutputSchemas" should
+    "emit the model_name/model schema keyed by the declared output port" in {
+    val d = new SklearnMultiLayerPerceptronOpDesc
+    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
+  }
+
+  "SklearnMultiLayerPerceptronOpDesc.generatePythonCode" should "import the configured sklearn estimator" in {
+    val d = new SklearnMultiLayerPerceptronOpDesc
+    d.target = "y"
+    val code = d.generatePythonCode()
+    code should include("from sklearn.neural_network import MLPClassifier")
+    code should include("make_pipeline")
+    code should include("Multi-layer Perceptron")
+  }
+
+  "SklearnMultiLayerPerceptronOpDesc" should "round-trip its config fields through the polymorphic base" in {
+    val d = new SklearnMultiLayerPerceptronOpDesc
+    d.target = "label"
+    d.countVectorizer = true
+    val json = objectMapper.writeValueAsString(d)
+    json should include("\"operatorType\":\"SklearnMultiLayerPerceptron\"")
+    val restored = objectMapper.readValue(json, classOf[LogicalOp])
+    restored shouldBe a[SklearnMultiLayerPerceptronOpDesc]
+    val r = restored.asInstanceOf[SklearnMultiLayerPerceptronOpDesc]
+    r.target shouldBe "label"
+    r.countVectorizer shouldBe true
+  }
+}
diff --git a/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnProbabilityCalibrationOpDescSpec.scala b/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnProbabilityCalibrationOpDescSpec.scala
new file mode 100644
index 0000000000..2eab3102cb
--- /dev/null
+++ b/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnProbabilityCalibrationOpDescSpec.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 SklearnProbabilityCalibrationOpDescSpec extends AnyFlatSpec with Matchers {
+
+  "SklearnProbabilityCalibrationOpDesc.operatorInfo" should
+    "advertise the model name, Sklearn group, and the training/testing port shape" in {
+    val info = (new SklearnProbabilityCalibrationOpDesc).operatorInfo
+    info.userFriendlyName shouldBe "Probability Calibration"
+    info.operatorDescription shouldBe "Sklearn Probability Calibration 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
+  }
+
+  "SklearnProbabilityCalibrationOpDesc" should "default its config fields" in {
+    val d = new SklearnProbabilityCalibrationOpDesc
+    d.countVectorizer shouldBe false
+    d.tfidfTransformer shouldBe false
+    d.target shouldBe null
+    d.text shouldBe null
+  }
+
+  "SklearnProbabilityCalibrationOpDesc.getOutputSchemas" should
+    "emit the model_name/model schema keyed by the declared output port" in {
+    val d = new SklearnProbabilityCalibrationOpDesc
+    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
+  }
+
+  "SklearnProbabilityCalibrationOpDesc.generatePythonCode" should "import the configured sklearn estimator" in {
+    val d = new SklearnProbabilityCalibrationOpDesc
+    d.target = "y"
+    val code = d.generatePythonCode()
+    code should include("from sklearn.calibration import CalibratedClassifierCV")
+    code should include("make_pipeline")
+    code should include("Probability Calibration")
+  }
+
+  "SklearnProbabilityCalibrationOpDesc" should "round-trip its config fields through the polymorphic base" in {
+    val d = new SklearnProbabilityCalibrationOpDesc
+    d.target = "label"
+    d.countVectorizer = true
+    val json = objectMapper.writeValueAsString(d)
+    json should include("\"operatorType\":\"SklearnProbabilityCalibration\"")
+    val restored = objectMapper.readValue(json, classOf[LogicalOp])
+    restored shouldBe a[SklearnProbabilityCalibrationOpDesc]
+    val r = restored.asInstanceOf[SklearnProbabilityCalibrationOpDesc]
+    r.target shouldBe "label"
+    r.countVectorizer shouldBe true
+  }
+}