add plot

evaluate/Jenkinsfile

@@ -17,7 +17,7 @@ pipeline {
             steps {
                 copyArtifacts fingerprintArtifacts: true, projectName: 'z-s464980-create-dataset', selector: buildParameter('BUILD_SELECTOR')
                 copyArtifacts fingerprintArtifacts: true, projectName: 's464980-training/master', selector: buildParameter('BUILD_SELECTOR')
-                copyArtifacts fingerprintArtifacts: true, projectName: 's464980-evaluation/master', selector: buildParameter('BUILD_SELECTOR'), optional: true
+                // copyArtifacts fingerprintArtifacts: true, projectName: 's464980-evaluation/master', selector: buildParameter('BUILD_SELECTOR'), optional: true
             }
         }
         stage("Prediction") {
@@ -31,7 +31,7 @@ pipeline {
             steps {
                 sh "chmod +x ./predict.py"
                 sh "python ./predict.py"
-                archiveArtifacts artifacts: 'predictions.txt,accuracy.txt', onlyIfSuccessful: true
+                archiveArtifacts artifacts: 'predictions.txt,accuracy.txt,rmse.jpg', onlyIfSuccessful: true
             }
         }
     }

predict.py

@@ -2,6 +2,8 @@ import pandas as pd
 from tensorflow import keras
 import numpy as np
 from sklearn.metrics import root_mean_squared_error
+import matplotlib.pyplot as plt
+
 np.set_printoptions(threshold=np.inf)
 
 data = pd.read_csv("df_test.csv")
@@ -17,4 +19,15 @@ with open("predictions.txt", "w") as f:
 
 accuracy = root_mean_squared_error(y_test, predictions)
 with open("rmse.txt", 'a') as file:
-    file.write(str(accuracy))
+    file.write(str(accuracy)+"\n")
+
+with open("rmse.txt", 'r') as file:
+    lines = file.readlines()
+    num_lines = len(lines)
+    lines = [float(line.replace("\n", "")) for line in lines]
+
+plt.plot(range(1, num_lines+1), lines)
+plt.xlabel("Build number")
+plt.ylabel("RMSE value")
+plt.title("RMSE")
+plt.savefig("rmse.jpg")

requirements.txt

@@ -2,3 +2,4 @@ pandas
 scikit-learn
 tensorflow
 numpy
+matplotlib