created evaluation

Jenkinsfile_train

@@ -26,14 +26,14 @@ pipeline {
 
 		stage('archiveArtifacts') {
             steps {
-				archiveArtifacts 'model/saved_model.pb'
+				archiveArtifacts 'model'
 			}
 		}
 	}
 	post {
 		success {
 			emailext body: 'Success train',	subject: 's437622 train', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
-		}   
+		}
 
 		failure {
 			emailext body: 'Failed train', subject: 's437622 train', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'

evaluation.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import numpy as np
+from tensorflow import keras
+import matplotlib.pyplot as plt
+from sklearn.metrics import accuracy_score, f1_score
+
+
+learned_model = 'model'
+model = keras.models.load_model(learned_model)
+
+train=pd.read_csv('train.csv', header=None, skiprows=1)
+indexNames = train[train[1] ==2].index
+train.drop(indexNames, inplace=True)
+cols=[0,2,3]
+X=train[cols].to_numpy()
+y=train[1].to_numpy()
+X=np.asarray(X).astype('float32')
+
+test=pd.read_csv('test.csv', header=None, skiprows=1)
+cols=[0,2,3]
+indexNames = test[test[1] ==2].index
+test.drop(indexNames, inplace=True)
+X_test=test[cols].to_numpy()
+y_test=test[1].to_numpy()
+X_test=np.asarray(X_test).astype('float32')
+
+predictions = model.predict(X_test)
+
+
+acc = accuracy_score(y_test, predictions)
+print('Accuracy: ', acc)
+
+f1=f1_score(y_test, predictions)
+print('F1: ', f1)
+
+with open('evaluation.txt', 'a') as f:
+    f.write(str(acc) + "\n")
+
+with open('evaluation.txt', 'r') as f:
+    lines = f.readlines()
+
+
+fig = plt.figure(figsize=(5,5))
+chart = fig.add_subplot()
+chart.set_ylabel("Accuracy")
+chart.set_xlabel("Build")
+x = np.arange(0, len(lines), 1)
+y = [float(x) for x in lines]
+plt.plot(x, y, "go")
+plt.savefig("evaluation.png")

evaluation.txt

@@ -0,0 +1,3 @@
+0.5406397482957525
+0.5406397482957525
+0.5406397482957525