add png plot

Jenkinsfile_evaluation

@@ -23,7 +23,7 @@ pipeline {
     }
     post {
         success {
-            archiveArtifacts artifacts: 'metrics.csv', followSymlinks: false
+            archiveArtifacts artifacts: 'metrics.*', followSymlinks: false
         }
         always {
             emailext body: "${currentBuild.currentResult}", subject: 's444507-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'

lab06_evaluation.py

@@ -11,6 +11,8 @@ from csv import DictWriter
 import torch.nn.functional as F
 import sys
 import os
+import matplotlib.pyplot as plt
+import json
 
 class Model(nn.Module):
     def __init__(self, input_dim):
@@ -66,6 +68,18 @@ def print_metrics(test_labels, predictions):
         print(e)
 
 
+def draw_plot():
+    metrics = pd.read_csv('metrics_new.csv', delimiter=',', header=None)
+    build_axis = metrics[0][:]
+    plt.xlabel('Build')
+    plt.ylabel('Score')
+    plt.plot(build_axis, metrics[2][:], label='Accuracy')
+    plt.plot(build_axis, metrics[1][:], label='F1 Score')
+    plt.legend()
+    plt.show()
+    plt.savefig('metrics.png')
+
+
 model = torch.load("CarPrices_pytorch_model.pkl")
 cars_dev = pd.read_csv('./Car_Prices_Poland_Kaggle_dev.csv', usecols=[1, 4, 5, 6, 10], sep=',', names=[str(i) for i in range(5)])
 cars_dev = cars_dev.loc[(cars_dev['0'] == 'audi') | (cars_dev['0'] == 'bmw') | (cars_dev['0'] == 'ford') | (cars_dev['0'] == 'opel') | (cars_dev['0'] == 'volkswagen')]
@@ -76,5 +90,7 @@ pred = model(x_test)
 pred = pred.detach().numpy()
 print_metrics(labels_test, pred)
 
+draw_plot()
+