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test(workflow-operator): add unit test coverage for Sklearn linear classifier descriptors (#5941)
### What changes were proposed in this PR? Pin behavior of four previously-untested Sklearn linear classifier descriptors in `common/workflow-operator`. No production-code changes. | Spec | Source class | Tests | | --- | --- | --- | | `SklearnLogisticRegressionOpDescSpec` | `SklearnLogisticRegressionOpDesc` | 5 | | `SklearnLogisticRegressionCVOpDescSpec` | `SklearnLogisticRegressionCVOpDesc` | 5 | | `SklearnPerceptronOpDescSpec` | `SklearnPerceptronOpDesc` | 5 | | `SklearnPassiveAggressiveOpDescSpec` | `SklearnPassiveAggressiveOpDesc` | 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 Naive Bayes coverage in #5925). ### How was this PR tested? - `sbt "WorkflowOperator/testOnly *SklearnLogisticRegressionOpDescSpec *SklearnLogisticRegressionCVOpDescSpec *SklearnPerceptronOpDescSpec *SklearnPassiveAggressiveOpDescSpec"` — 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])
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‎common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnLogisticRegressionCVOpDescSpec.scala‎

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

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

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

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

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

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

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

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