IUM_06 - add evaluation Jenkinsfile, update Dockerfile, update models/Jenkinsfile, update predict.py script

Dockerfile

@@ -20,6 +20,7 @@ WORKDIR /app
 # Python scripts
 COPY download_dataset.py ./
 COPY get_stats.py ./
+COPY NeuralNetwork.py ./
 COPY create_model.py ./
 COPY predict.py ./
 

evaluation/Jenkinsfile

@@ -0,0 +1,40 @@
+pipeline {
+    agent any
+
+    stages {
+        stage('Clone repository') {
+            steps {
+                checkout scm
+            }
+        }
+
+        stage('Copy artifacts') {
+            agent {
+                dockerfile {
+                    filename 'Dockerfile'
+                    reuseNode true
+                }
+            }
+
+            steps {
+                copyArtifacts(projectName: 's464863-training/main', filter: 'models/model.pth, datasets/test.csv', selector: lastSuccessful())
+                copyArtifacts(projectName: 's464863-evaluation/main', filter: 'metrics.csv', selector: lastSuccessful(), optional: true)
+            }
+        }
+
+        stage('Evaluate model') {
+            agent {
+                dockerfile {
+                    filename 'Dockerfile'
+                    reuseNode true
+                }
+            }
+
+            steps {
+                sh "chmod +x ./predict.py"
+                sh "python3 ./predict.py"
+                archiveArtifacts artifacts: 'predictions.csv, metrics.csv', onlyIfSuccessful: true
+            }
+        }
+    }
+}

models/Jenkinsfile

@@ -51,6 +51,7 @@ pipeline {
                 sh "chmod +x ./create_model.py"
                 sh "python3 ./create_model.py"
                 archiveArtifacts artifacts: 'models/model.pth, datasets/*', onlyIfSuccessful: true
+                build job: 's464863-evaluation/main', wait: false
             }
         }
     }

predict.py

@@ -3,8 +3,9 @@ import os
 import pandas as pd
 import numpy as np
 
-from NeuralNetwork import NeuralNetwork
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 
+from NeuralNetwork import NeuralNetwork
 
 # Load model if it exists
 if os.path.exists('./models/model.pth'):
@@ -29,5 +30,18 @@ if os.path.exists('./models/model.pth'):
 
         # Save predictions to CSV
         pd.DataFrame(y_pred, columns=['Prediction']).to_csv('predictions.csv', index=False)
+
+        # Calculate metrics
+        accuracy = accuracy_score(y_test, y_pred)
+        precision = precision_score(y_test, y_pred)
+        recall = recall_score(y_test, y_pred)
+        f1 = f1_score(y_test, y_pred)
+
+        # Save metrics to CSV (append mode, if file exists, if not, create it)
+        if not os.path.exists('metrics.csv'):
+            pd.DataFrame([[accuracy, precision, recall, f1]], columns=['Accuracy', 'Precision', 'Recall', 'F1']).to_csv('metrics.csv', index=False)
+        else:
+            # without header
+            pd.DataFrame([[accuracy, precision, recall, f1]], columns=['Accuracy', 'Precision', 'Recall', 'F1']).to_csv('metrics.csv', index=False, mode='a', header=False)
 else:
     raise FileNotFoundError('Model not found')