task 2 first half

Jenkinsfile_eval

@@ -0,0 +1,37 @@
+pipeline {
+    agent { 
+        dockerfile  true
+    }
+    parameters {
+        string(
+            defaultValue: '200',
+            description: 'number of epochs',
+            name: 'EPOCH'
+             )
+    }
+
+    stages {
+        stage('Stage 1') {
+            steps {
+                echo 'Hello world!' 
+            }
+        }
+
+        stage('Copy from different Pipeline') {
+			steps {
+                copyArtifacts fingerprintArtifacts: true, projectName: 's444517-create-dataset', selector: lastSuccessful(),
+                copyArtifacts fingerprintArtifacts: true, projectName: '444517-training/master', selector: lastSuccessful()
+                copyArtifacts fingerprintArtifacts: true, projectName: 's444517-evaluation/master', selector: lastSuccessful(), optional: true
+			}
+		}
+
+        stage('Get data save artifacts') {
+            steps {
+                    sh 'python3 ./nn_train_eval.py'
+                    archiveArtifacts artifacts: 'my_model/saved_model.pb, metrics.txt'
+                
+            }
+        }
+    }
+}
+

Jenkinsfile_train

@@ -31,10 +31,10 @@ pipeline {
             }
         }
     }
-    post {
-        always {
-            emailext body: "${currentBuild.currentResult}", subject: 's444517_build_status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
-        }
-    }
+    //post {
+    //    always {
+    //        emailext body: "${currentBuild.currentResult}", subject: 's444517_build_status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+    //    }
+    //}
 }
 

nn_train_eval.py

@@ -0,0 +1,42 @@
+
+
+from sklearn.metrics import accuracy_score, recall_score
+import matplotlib.pyplot as plt
+
+# reading data
+def read_data(file_name):
+    y_pred = []
+    y_true = []
+    with open(file_name, encoding="utf-8") as file:
+            for line in file.readlines():
+                y_pred.append(line.split(",")[0])
+                y_true.append(line.split(",")[1][:-1])
+    return y_pred, y_true
+
+# saving new values
+def new_metrics():
+    y_pred, y_true = read_data("results.txt")
+    acc = accuracy_score(y_true, y_pred)
+    recc = recall_score(y_true, y_pred, average='macro')
+
+    with open("metrics.txt", 'a') as f:
+        f.write(f"{acc},{recc}\n")
+    f.close()
+
+# drawing a plot
+def draw_plt():
+    acc, recc = read_data("metrics.txt")
+    no_of_entries = list(range(1, len(acc)+1))
+    print(acc)
+    print(recc)
+
+    plt.plot(no_of_entries, acc, color='green', lw=2, label='Accuracy')
+    plt.plot(no_of_entries, recc, color='blue', lw=2, label='Recall')
+    plt.xlabel('Number of builds')
+    plt.ylabel('Metrics')
+    plt.legend()
+    plt.savefig("output.jpg")
+
+
+new_metrics()
+draw_plt()