plot

Jenkinsfile

@@ -42,5 +42,17 @@ pipeline {
               }
             }
         }
+
+        stage('Plot Accuracy') {
+            steps {
+              script {
+               def customImage = docker.build("custom-image")
+                    customImage.inside {
+                        sh 'python3 ./plot.py'
+                        archiveArtifacts artifacts: 'accuracy.png', onlyIfSuccessful: true
+                    }
+              }
+            }
+        }
      }
 }

metrics.py

@@ -18,8 +18,7 @@ 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"Precision: {precision_micro}\n")
-    fp.write(f"Micro-average Recall: {recall_micro}\n")
+    fp.write(f"Recall: {recall_micro}\n")
-    fp.write(f"Micro-average F1-score: {f1_micro}\n")
+    fp.write(f"F1-score: {f1_micro}\n")
     fp.write(f"RMSE: {rmse}\n")
-    fp.write("--------------------\n")

plot.py

@@ -0,0 +1,21 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+accuracy = [] 
+
+f = open("metrics.txt", "r")
+for line in f:
+  parts = line.strip().split(' ')
+  if(parts[0] == 'Accuracy:'):
+    accuracy.append(float(parts[1]))
+
+y_values = np.arange(1, len(accuracy) + 1)
+
+plt.plot(y_values, accuracy, marker='o', linestyle='-', color='b')
+plt.xlabel('Build Number')
+plt.ylabel('Accuracy')
+plt.title('Accuracy Plot')
+plt.grid(True)
+plt.show()
+
+plt.savefig('accuracy.png')