From f3f6ab7deed61b4dbd477e245e9665934b1cedae Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20=C5=81=C4=85czkowski?= <broketmpl@gmail.com>
Date: Fri, 19 Apr 2024 12:02:09 +0200
Subject: [PATCH] IUM_06 - update evaluation/Jenkinsfile, update predict.py -
 metrics plot

---
 evaluation/Jenkinsfile |  2 +-
 predict.py             | 17 ++++++++++++++++-
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/evaluation/Jenkinsfile b/evaluation/Jenkinsfile
index 17fa89b..bb165b8 100644
--- a/evaluation/Jenkinsfile
+++ b/evaluation/Jenkinsfile
@@ -34,7 +34,7 @@ pipeline {
             steps {
                 sh "chmod +x ./predict.py"
                 sh "python3 ./predict.py"
-                archiveArtifacts artifacts: 'predictions.csv, metrics.csv', onlyIfSuccessful: true
+                archiveArtifacts artifacts: 'predictions.csv, metrics.csv, metrics.png', onlyIfSuccessful: true
             }
         }
     }
diff --git a/predict.py b/predict.py
index b4193a9..9905bac 100644
--- a/predict.py
+++ b/predict.py
@@ -5,6 +5,9 @@ import numpy as np
 
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 
+import matplotlib.pyplot as plt
+import seaborn as sns
+
 from NeuralNetwork import NeuralNetwork
 
 # Load model if it exists
@@ -42,6 +45,18 @@ if os.path.exists('./models/model.pth'):
             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)
+            metrics = pd.read_csv('metrics.csv')
+            metrics = metrics._append({'Accuracy': accuracy, 'Precision': precision, 'Recall': recall, 'F1': f1}, ignore_index=True)
+            metrics.to_csv('metrics.csv', index=False, mode='a', header=False)
+
+            # Plot metrics line chart
+            sns.set(style='whitegrid')
+            plt.figure(figsize=(8, 6))
+            sns.lineplot(data=metrics)
+            plt.title('Metrics history')
+            plt.xlabel('History number')
+            plt.ylabel('Value')
+            plt.legend()
+            plt.savefig('metrics.png')
 else:
     raise FileNotFoundError('Model not found')