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amandelpieamandelpie
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Move the Torch wrapper to the separate module (#821)
* Added an initial solution * Just renamed some weird names * Removed unused settings * Added minimal readme.md and fix some typos * Fixed some names * Fixed some names * Fixed gradle * Handle engine not found exception from DJL * Handle engine not found exception from DJL * Handle engine not found exception from DJL * Handle engine not found exception from DJL * Changed the setter * Removed the comment * Changed the visibility level for some dependencies
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‎settings.gradle

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@@ -18,6 +18,7 @@ include 'utbot-sample'
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include 'utbot-fuzzers'
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include 'utbot-junit-contest'
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include 'utbot-analytics'
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include 'utbot-analytics-torch'
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include 'utbot-cli'
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include 'utbot-api'
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include 'utbot-instrumentation'

‎utbot-analytics-torch/build.gradle

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apply from: "${parent.projectDir}/gradle/include/jvm-project.gradle"
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configurations {
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torchmodels
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}
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def osName = System.getProperty('os.name').toLowerCase().split()[0]
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if (osName == "mac") osName = "macosx"
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String classifier = osName + "-x86_64"
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evaluationDependsOn(':utbot-framework')
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compileTestJava.dependsOn tasks.getByPath(':utbot-framework:testClasses')
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dependencies {
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api project(':utbot-analytics')
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testImplementation project(':utbot-sample')
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testImplementation group: 'junit', name: 'junit', version: junit4_version
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implementation group: 'org.bytedeco', name: 'arpack-ng', version: "3.7.0-1.5.4", classifier: "$classifier"
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implementation group: 'org.bytedeco', name: 'openblas', version: "0.3.10-1.5.4", classifier: "$classifier"
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implementation group: 'org.bytedeco', name: 'javacpp', version: javacpp_version, classifier: "$classifier"
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implementation group: 'org.jsoup', name: 'jsoup', version: jsoup_version
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implementation "ai.djl:api:$djl_api_version"
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implementation "ai.djl.pytorch:pytorch-engine:$djl_api_version"
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implementation "ai.djl.pytorch:pytorch-native-auto:$pytorch_native_version"
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testImplementation project(':utbot-framework').sourceSets.test.output
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}
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test {
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minHeapSize = "128m"
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maxHeapSize = "3072m"
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jvmArgs '-XX:MaxHeapSize=3072m'
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useJUnitPlatform() {
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excludeTags 'slow', 'IntegrationTest'
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}
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}
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processResources {
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configurations.torchmodels.resolvedConfiguration.resolvedArtifacts.each { artifact ->
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from(zipTree(artifact.getFile())) {
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into "models"
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}
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}
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}
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jar {
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dependsOn classes
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manifest {
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attributes 'Main-Class': 'org.utbot.QualityAnalysisKt'
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}
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dependsOn configurations.runtimeClasspath
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from {
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configurations.runtimeClasspath.collect { it.isDirectory() ? it : zipTree(it) }
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}
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duplicatesStrategy = DuplicatesStrategy.EXCLUDE
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zip64 = true
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}

‎utbot-analytics-torch/readme.md

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To enable support of the `utbot-analytics-torch` models in `utbot-intellij` module the following steps should be made:
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- change the row `api project(':utbot-analytics-torch')` to the `api project(':utbot-analytics-torch')` in the `build.gradle` file in the `utbot-intellij` module
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- change the `pathSelectorType` in the `UtSettings.kt` to the `PathSelectorType.TORCH_SELECTOR`
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- don't forget the put the Torch model in the path ruled by the setting `modelPath` in the `UtSettings.kt`
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NOTE: for Windows you could obtain the error message related to the "engine not found problem" from DJL library during the Torch model initialization.
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The proposed solution from DJL authors includes the installation of the [Microsoft Visual C++ Redistributable.](https://docs.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170)
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But at this moment it doesn't work on Windows at all.

‎utbot-analytics-torch/src/main/kotlin/org/utbot/AnalyticsTorchConfiguration.kt

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package org.utbot
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import org.utbot.analytics.EngineAnalyticsContext
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import org.utbot.features.FeatureExtractorFactoryImpl
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import org.utbot.features.FeatureProcessorWithStatesRepetitionFactory
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import org.utbot.predictors.StateRewardPredictorWithTorchModelsSupportFactoryImpl
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/**
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* The basic configuration of the utbot-analytics-torch module used in utbot-intellij and (as planned) in utbot-cli
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* to implement the hidden configuration initialization to avoid direct calls of this configuration and usage of utbot-analytics-torch imports.
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*
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* @see <a href="https://github.com/UnitTestBot/UTBotJava/issues/725">
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* Issue: Enable utbot-analytics module in utbot-intellij module</a>
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*/
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object AnalyticsTorchConfiguration {
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init {
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EngineAnalyticsContext.featureProcessorFactory = FeatureProcessorWithStatesRepetitionFactory()
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EngineAnalyticsContext.featureExtractorFactory = FeatureExtractorFactoryImpl()
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EngineAnalyticsContext.stateRewardPredictorFactory = StateRewardPredictorWithTorchModelsSupportFactoryImpl()
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}
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}

‎utbot-analytics-torch/src/main/kotlin/org/utbot/predictors/StateRewardPredictorFactory.kt

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package org.utbot.predictors
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import org.utbot.analytics.StateRewardPredictorFactory
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import org.utbot.framework.UtSettings
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/**
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* Creates [StateRewardPredictor], by checking the [UtSettings] configuration.
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*/
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class StateRewardPredictorWithTorchModelsSupportFactoryImpl : StateRewardPredictorFactory {
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override operator fun invoke() = StateRewardPredictorTorch()
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}

‎utbot-analytics/src/main/kotlin/org/utbot/predictors/StateRewardPredictorTorch.kt renamed to ‎utbot-analytics-torch/src/main/kotlin/org/utbot/predictors/StateRewardPredictorTorch.kt

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@@ -12,10 +12,11 @@ import java.io.Closeable
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import java.nio.file.Paths
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class StateRewardPredictorTorch : StateRewardPredictor, Closeable {
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val model: Model = Model.newInstance("model")
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val model: Model
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init {
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model.load(Paths.get(UtSettings.rewardModelPath, "model.pt1"))
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model = Model.newInstance("model")
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model.load(Paths.get(UtSettings.modelPath, "model.pt1"))
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}
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private val predictor: Predictor<List<Float>, Float> = model.newPredictor(object : Translator<List<Float>, Float> {

‎utbot-analytics-torch/src/test/kotlin/org/utbot/predictors/NNStateRewardPredictorTest.kt

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package org.utbot.predictors
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import org.junit.jupiter.api.Assertions.assertEquals
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import org.junit.jupiter.api.Disabled
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import org.junit.jupiter.api.Test
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import org.utbot.analytics.StateRewardPredictor
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import org.utbot.testcheckers.withModelPath
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import kotlin.system.measureNanoTime
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class NNStateRewardPredictorTest {
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@Test
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@Disabled("Just to see the performance of predictors")
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fun simpleTest() {
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withModelPath("src/test/resources") {
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val pred = StateRewardPredictorTorch()
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val features = listOf(0.0, 0.0)
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assertEquals(5.0, pred.predict(features))
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}
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}
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@Disabled("Just to see the performance of predictors")
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@Test
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fun performanceTest() {
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val features = (1..13).map { 1.0 }.toList()
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withModelPath("models") {
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val averageTime = calcAverageTimeForModelPredict(::StateRewardPredictorTorch, 100, features)
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println(averageTime)
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}
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}
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private fun calcAverageTimeForModelPredict(
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model: () -> StateRewardPredictor,
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iterations: Int,
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features: List<Double>
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): Double {
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val pred = model()
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(1..iterations).map {
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pred.predict(features)
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}
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return (1..iterations)
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.map { measureNanoTime { pred.predict(features) } }
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.average()
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}
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}

‎utbot-analytics-torch/src/test/resources/log4j2.xml

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<?xml version="1.0" encoding="UTF-8"?>
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<Configuration>
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<Appenders>
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<RollingFile name="FrameworkAppender"
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fileName="logs/utbot.log"
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filePattern="logs/utbot-%d{MM-dd-yyyy-HH-mm-ss}.log.gz"
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ignoreExceptions="false">
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<PatternLayout pattern="%d{HH:mm:ss.SSS} | %-5level | %c{1} | %msg%n"/>
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<Policies>
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<OnStartupTriggeringPolicy/>
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<SizeBasedTriggeringPolicy size="100 MB"/>
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</Policies>
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</RollingFile>
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<Console name="Console" target="SYSTEM_OUT">
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<PatternLayout pattern="%d{HH:mm:ss.SSS} | %-5level | %msg%n"/>
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</Console>
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</Appenders>
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<Loggers>
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<Logger name="smile" level="trace">
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<AppenderRef ref="Console"/>
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</Logger>
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<Logger name="org.utbot.models" level="trace"/>
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<Logger name="org.utbot" level="debug">
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<AppenderRef ref="FrameworkAppender"/>
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</Logger>
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<!-- uncomment to log solver check -->
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<!-- <Logger name="org.utbot.engine.pc" level="trace">-->
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<!-- <AppenderRef ref="Console"/>-->
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<!-- </Logger>-->
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<Root level="info">
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<!-- <AppenderRef ref="Console"/>-->
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</Root>
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</Loggers>
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</Configuration>

‎utbot-analytics/build.gradle

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compileTestJava.dependsOn tasks.getByPath(':utbot-framework:testClasses')
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1414
dependencies {
15-
implementation(project(":utbot-api"))
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implementation(project(":utbot-core"))
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implementation(project(":utbot-summary"))
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implementation(project(":utbot-framework-api"))
19-
implementation(project(":utbot-fuzzers"))
20-
implementation(project(":utbot-instrumentation"))
21-
implementation(project(":utbot-framework"))
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api(project(":utbot-api"))
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api(project(":utbot-core"))
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api(project(":utbot-summary"))
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api(project(":utbot-framework-api"))
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api(project(":utbot-fuzzers"))
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api(project(":utbot-instrumentation"))
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api(project(":utbot-framework"))
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testImplementation project(':utbot-sample')
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testImplementation group: 'junit', name: 'junit', version: junit4_version
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implementation group: 'tech.tablesaw', name: 'tablesaw-jsplot', version: '0.38.2'
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implementation group: 'org.apache.commons', name: 'commons-text', version: '1.9'
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implementation group: 'com.github.javaparser', name: 'javaparser-core', version: '3.22.1'
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44-
implementation group: 'org.jsoup', name: 'jsoup', version: jsoup_version
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implementation "ai.djl:api:$djl_api_version"
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implementation "ai.djl.pytorch:pytorch-engine:$djl_api_version"
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implementation "ai.djl.pytorch:pytorch-native-auto:$pytorch_native_version"
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testImplementation project(':utbot-framework').sourceSets.test.output
5144
}
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5346
test {
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minHeapSize = "128m"
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maxHeapSize = "3072m"
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‎utbot-analytics/src/main/kotlin/org/utbot/predictors/LinearStateRewardPredictor.kt

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* Last weight is bias
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*/
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private fun loadWeights(path: String): Matrix {
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val weightsFile = File("${UtSettings.rewardModelPath}/${path}")
22+
val weightsFile = File("${UtSettings.modelPath}/${path}")
2323
lateinit var weightsArray: DoubleArray
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2525
try {

‎utbot-analytics/src/main/kotlin/org/utbot/predictors/NNJson.kt

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}
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3535
internal fun loadModel(path: String): NNJson {
36-
val modelFile = Paths.get(UtSettings.rewardModelPath, path).toFile()
36+
val modelFile = Paths.get(UtSettings.modelPath, path).toFile()
3737
lateinit var nnJson: NNJson
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3939
try {

‎utbot-analytics/src/main/kotlin/org/utbot/predictors/StateRewardPredictorFactory.kt

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class StateRewardPredictorFactoryImpl : StateRewardPredictorFactory {
1111
override operator fun invoke() = when (UtSettings.stateRewardPredictorType) {
1212
StateRewardPredictorType.BASE -> NNStateRewardPredictorBase()
13-
StateRewardPredictorType.TORCH -> StateRewardPredictorTorch()
1413
StateRewardPredictorType.LINEAR -> LinearStateRewardPredictor()
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}
1615
}

‎utbot-analytics/src/main/kotlin/org/utbot/predictors/scalers.kt

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internal fun loadScaler(path: String): StandardScaler =
1515
try {
16-
Paths.get(UtSettings.rewardModelPath, path).toFile().bufferedReader().use {
16+
Paths.get(UtSettings.modelPath, path).toFile().bufferedReader().use {
1717
val mean = it.readLine()?.splitByCommaIntoDoubleArray() ?: error("There is not mean in $path")
1818
val variance = it.readLine()?.splitByCommaIntoDoubleArray() ?: error("There is not variance in $path")
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StandardScaler(Matrix(mean), Matrix(variance))

‎utbot-analytics/src/test/kotlin/org/utbot/predictors/LinearStateRewardPredictorTest.kt

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55
import org.utbot.framework.PathSelectorType
66
import org.utbot.framework.UtSettings
77
import org.utbot.testcheckers.withPathSelectorType
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import org.utbot.testcheckers.withRewardModelPath
8+
import org.utbot.testcheckers.withModelPath
99

1010
class LinearStateRewardPredictorTest {
1111
@Test
1212
fun simpleTest() {
13-
withRewardModelPath("src/test/resources") {
13+
withModelPath("src/test/resources") {
1414
val pred = LinearStateRewardPredictor()
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1616
val features = listOf(
@@ -24,8 +24,8 @@ class LinearStateRewardPredictorTest {
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2525
@Test
2626
fun wrongFormatTest() {
27-
withRewardModelPath("src/test/resources") {
28-
withPathSelectorType(PathSelectorType.NN_REWARD_GUIDED_SELECTOR) {
27+
withModelPath("src/test/resources") {
28+
withPathSelectorType(PathSelectorType.ML_SELECTOR) {
2929
LinearStateRewardPredictor("wrong_format_linear.txt")
3030
assertEquals(PathSelectorType.INHERITORS_SELECTOR, UtSettings.pathSelectorType)
3131
}
@@ -34,7 +34,7 @@ class LinearStateRewardPredictorTest {
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3535
@Test
3636
fun simpleTestNotBatch() {
37-
withRewardModelPath("src/test/resources") {
37+
withModelPath("src/test/resources") {
3838
val pred = LinearStateRewardPredictor()
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val features = listOf(2.0, 3.0)

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