Dockerfile

Jenkinsfile

@@ -1,10 +1,5 @@
 pipeline {
-  agent { 
+  agent any
-    dockerfile {
-      filename 'Dockerfile'
-      args '-u root'
-    }
-  }
   
   parameters {
     string (
@@ -20,16 +15,39 @@ pipeline {
     )
   }
 
-  environment {
+  stages {
-    KAGGLE_USERNAME = "${params.KAGGLE_USERNAME}"
+    stage('Clone Repository') {
-    KAGGLE_KEY = "${params.KAGGLE_KEY}"
+      steps {
+        git branch: 'main', url: 'https://git.wmi.amu.edu.pl/s464913/ium_464913.git'
+      }
+    }
+
+    stage('Download dataset') {
+      steps {
+        withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}", "KAGGLE_KEY=${params.KAGGLE_KEY}"]) {
+          sh 'kaggle datasets download -d mlg-ulb/creditcardfraud'
+          sh 'unzip -o creditcardfraud.zip'
+          sh 'rm creditcardfraud.zip'
+        }
+      }
     }
 
-  stages {
     stage('Run create-dataset script') {
+        agent {
+          dockerfile {
+            reuseNode true
+        }
+      }
+
       steps {
         sh 'chmod +x create-dataset.py'
-        sh 'python3 ./create-dataset.py $KAGGLE_USERNAME'
+        sh 'python3 ./create-dataset.py'
+      }
+    }
+
+    stage('Archive Artifacts') {
+      steps {
+        archiveArtifacts artifacts: 'data/*', onlyIfSuccessful: true
       }
     }
   }

create-dataset.py

@@ -91,10 +91,25 @@ def main():
     # download_kaggle_dataset()
     os.makedirs("data", exist_ok=True)
 
-    os.system("pwd")
+    os.system
-    os.system("rm -rf /var/lib/jenkins/workspace/z-s464913-create-dataset-1/data/")
 
-    os.system("ls -l /var/lib/jenkins/workspace/z-s464913-create-dataset-1/")
+    df = load_data("creditcard.csv")
+    df = normalize_data(df)
+
+    undersample_data, X_undersample, y_undersample = create_undersample_data(df)
+    X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = (
+        split_undersample_data(X_undersample, y_undersample)
+    )
+    save_undersample_data(
+        undersample_data,
+        X_train_undersample,
+        X_test_undersample,
+        y_train_undersample,
+        y_test_undersample,
+    )
+
+    X_train, X_test, y_train, y_test = split_whole_data(df)
+    save_whole_data(df, X_train, X_test, y_train, y_test)
 
 
 if __name__ == "__main__":