feat: add jenkins for evaluate pipeline, create saving accuracy in file

Jenkinsfile-eval

@@ -0,0 +1,84 @@
+node {
+    stage('Preparation') { 
+        properties([
+            pipelineTriggers([
+                // TODO change auto run after train
+                upstream(
+                    threshold: hudson.model.Result.SUCCESS,
+                    upstreamProjects: 's424714-create-dataset'
+                    )
+            ]),
+            copyArtifactPermission('*'),
+            parameters([
+                buildSelector(
+                    defaultSelector: lastSuccessful(),
+                    description: 'Which build to use for copying artifacts',
+                    name: 'BUILD_SELECTOR'
+                ),
+
+            ])
+        ])
+    }
+
+    stage('Git clone') {
+        //cloning git repo
+        checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: 's424714', url: 'https://git.wmi.amu.edu.pl/s424714/ium_424714']]])
+    }
+
+    stage('Dockerfile build') {
+
+
+            sh "chmod +x -R ${env.WORKSPACE}"
+                copyArtifacts fingerprintArtifacts: true, projectName: 's424714-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+                //TODO from train
+                // copyArtifacts fingerprintArtifacts: true, projectName: 's424714-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+
+            def dockerImage = docker.build("s424714-model")
+
+            dockerImage.inside {
+            withEnv(["TRANSFORMERS_CACHE=./.cache"]) {
+                stage("Docker: cloning artifacts"){
+                    sh 'mkdir -p ./data/dataset'
+                    sh 'mv -t ./data/dataset train.csv test.csv val.csv'
+                    sh 'mv -t ./data True.csv Fake.csv'
+                    sh 'mv model.pt ./results/model.pt'
+                }
+                stage("Docker: Running training model")
+                {
+                    sh 'mkdir -p ./.cache'
+                    // sh ""
+                    sh 'python ./src/main.py --test '
+                    sh "cp ./results/*.csv ${WORKSPACE}" 
+                }
+
+                }
+            }
+
+    }
+
+    stage('Saving artefacts') {
+        echo 'Goodbye!'
+        sh 'ls'
+        archiveArtifacts artifacts: '*.csv' 
+    }
+    stage('Drawing plot') {
+        plot csvFileName: 'plot-accuracy.csv', 
+        csvSeries: [[
+                            file: 'acc.csv',
+                            exclusionValues: '',
+                            displayTableFlag: false,
+                            inclusionFlag: 'OFF',
+                            url: '']],
+        group: 'Plot Group',
+        title: 'Accuracy',
+        style: 'line',
+        exclZero: false,
+        keepRecords: false,
+        logarithmic: false,
+        numBuilds: '',
+        useDescr: false,
+        yaxis: '',
+        yaxisMaximum: '',
+        yaxisMinimum: ''
+    }
+}

src/evaluate.py

@@ -44,5 +44,6 @@ def evaluate(
             results.extend(output.argmax(dim=1).tolist())
             total_acc_test += acc
 
-    print(f"Test Accuracy: {total_acc_test / len(test_data): .3f}")
-    return results
+    accuracy = round(total_acc_test / len(test_data), 3)
+    print(f"Test Accuracy: {accuracy: .3f}")
+    return results, accuracy

src/main.py

@@ -75,9 +75,10 @@ if __name__ == "__main__":
     # evaluating model
     if args.test:
         model = utils.load_model(model=BertClassifier(), model_path=args.model_path)  # loading model from model.pt file
-        results = evaluate(
+        results, accuracy = evaluate(
             model=model,
             test_data=test_data,
             batch_size=BATCH_SIZE,
         )
         utils.save_results(labels=test_data["label"], results=results, file_path=args.results_path)
+        utils.save_data_to_csv("./results/acc.csv", accuracy)

src/models/utils.py

@@ -32,3 +32,15 @@ class utils:
 
         df = pd.DataFrame({"labels": labels, "results": results})
         df.to_csv(file_path, index=False)
+
+    @staticmethod
+    def save_data_to_csv(file_path: str, data: float) -> None:
+        file_path = Path(file_path)
+
+        df = pd.DataFrame([data])
+        df.to_csv(file_path, mode="a", header=False, index=False)
+
+
+if __name__ == "__main__":
+    x = 1.2
+    utils.save_data_to_csv("./results/eval.csv", x)