jenkins evaluation

Jenkinsfile

@@ -6,7 +6,6 @@ pipeline {
                 description: 'Build for copying artifacts',
                 name: 'BUILD_SELECTOR'
             )
-            string(name: 'EPOCHS', defaultValue: '10', description: 'epochs')
         }
     stages {
         stage('Git Checkout') {
@@ -16,16 +15,27 @@ pipeline {
         }
      stage('Copy Artifacts') {
             steps {
-               copyArtifacts fingerprintArtifacts: true, projectName: 'z-s464914-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+               copyArtifacts filter: '*', projectName: 's464914-training/experiments/', selector: buildParameter('BUILD_SELECTOR')
             }
         }
-         stage('Train') {
+         stage('Prediction') {
             steps {
               script {
                def customImage = docker.build("custom-image")
                     customImage.inside {
-                        sh 'python3 ./model.py ' + params.EPOCHS
-                        archiveArtifacts artifacts: 'model.pth, predictions.txt', onlyIfSuccessful: true
+                        sh 'python3 ./prediction.py'
+                        archiveArtifacts artifacts: 'predictions.txt', onlyIfSuccessful: true
+                    }
+              }
+            }
+        }
+        stage('Metrics') {
+            steps {
+              script {
+               def customImage = docker.build("custom-image")
+                    customImage.inside {
+                        sh 'python3 ./metrics.py'
+                        archiveArtifacts artifacts: 'metrics.txt', onlyIfSuccessful: true
                     }
               }
             }

metrics.py

@@ -0,0 +1,25 @@
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error
+import numpy as np
+
+true_labels = []
+predicted_labels = []
+
+f = open("predictions.txt", "r")
+for line in f:
+  parts = line.strip().split(' ')
+  true_labels.append(int(parts[3]))
+  predicted_labels.append(int(parts[1]))
+
+accuracy = accuracy_score(true_labels, predicted_labels)
+precision_micro = precision_score(true_labels, predicted_labels, average='micro')
+recall_micro = recall_score(true_labels, predicted_labels, average='micro')
+f1_micro = f1_score(true_labels, predicted_labels, average='micro')
+rmse = np.sqrt(mean_squared_error(true_labels, predicted_labels))
+
+with open(r'metrics.txt', 'a') as fp:
+    fp.write(f"Accuracy: {accuracy}\n")
+    fp.write(f"Micro-average Precision: {precision_micro}\n")
+    fp.write(f"Micro-average Recall: {recall_micro}\n")
+    fp.write(f"Micro-average F1-score: {f1_micro}\n")
+    fp.write(f"RMSE: {rmse}\n")
+    fp.write("--------------------\n")

prediction.py

@@ -6,6 +6,8 @@ import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 import torch.nn.functional as F
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error
+import numpy as np
 
 device = (
     "cuda"
@@ -41,7 +43,6 @@ def predict(model, input_data):
     
     return predicted_class.item()  # Return the predicted class label
 
-
 def main():
     forest_test = pd.read_csv('forest_test.csv')
 
@@ -55,15 +56,23 @@ def main():
     load_model(model, model_path)
 
     predictions = []
-    for input_data in X_test:
-        predicted_class = predict(model, input_data)
-        predictions.append(predicted_class)
+    correct = 0
+    total = 0
+    with torch.no_grad():
+        for input_data, target in zip(X_test, y_test):
+            output = model(input_data)
+            _, predicted_class = torch.max(output, 0)
+            prediction_entry = f"predicted: {predicted_class.item()} true_label: {target}"
+            predictions.append(prediction_entry)
+            total += 1
+            if predicted_class.item() == target:
+                correct += 1
+
 
     with open(r'predictions.txt', 'w') as fp:
         for item in predictions:
             # write each item on a new line
             fp.write("%s\n" % item)
 
-
 if __name__ == "__main__":
     main()