JenkinsfileEval

@@ -1,20 +0,0 @@
-node {
- stage('Preparation') {
-    checkout scm
-     copyArtifacts projectName: 's470618-metrics', filter: 'metrics.txt', fingerprintArtifacts: true, selector: lastSuccessful(), optional: true, target: './train-eval'
-     copyArtifacts projectName: 's470618-training', filter: '*.pt', fingerprintArtifacts: true, selector: lastSuccessful(), target: '.'
- 
- stage('Evaluate metrics') {
-            sh "pip install matplotlib"
-            sh "cd train-eval && ./eval.py"
-            sh "./plot_metrics.py"
-     }
- }
-
- stage('artifacts') {
-         echo 'saving artifacts'
-         archiveArtifacts 'metrics.txt', 'prediction.tsv', 'metrics.png'
-}
-}
-}
-

JenkinsfileTrain

@@ -1,6 +1,6 @@
 node {
     checkout scm
-    def dockerimage = docker.build("train-image", "dockerfile_train")
+    def dockerimage = docker.build("train-image", "./train-eval")
     dockerimage.inside {
  stage('Preparation') {
      properties([

train-eval/eval.py

@@ -45,7 +45,7 @@ print ("The accuracy is", acc)
 print ("The precission score is ", prec)
 print ("The recall score is ", recall)
 
-file = open('metrics.txt', 'a')
+file = open('metrics.txt', 'w')
 file.write(str(acc)  + '\t' + str(prec) + '\t' + str(recall))
 file.close()
 

train-eval/learning.py

@@ -3,7 +3,7 @@ import numpy as np
 import torch
 from torch import nn
 import pandas as pd
-# import subprocess
+import subprocess
 import sys
 
 from sklearn.model_selection import train_test_split
@@ -47,7 +47,7 @@ if __name__ == "__main__":
     Y = df[['Survived']]
     X.loc[:,('Sex')].replace(['female', 'male'], [0,1], inplace=True) #categorical data transformed to 
 
-    X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, shuffle=True) #split the date into train and test sets
+    X_train, X_test, Y_train, Y_test = train_test_split(X,Y, random_state=45, test_size=0.2, shuffle=True) #split the date into train and test sets
 
     testing_data = pd.concat([X_test, Y_test], axis=1)
     testing_data.to_csv('testing_data.csv', sep=',')

train-eval/plot_metrics.py

@@ -1,19 +0,0 @@
-#!/usr/bin/python3
-import matplotlib
-import matplotlib.pyplot as plt 
-
-data = []
-with open('metrics.txt', 'r') as metrics:
-    for line in metrics:
-        data.append(line.strip().split('\t'))
-
-
-# print(acc)
-labels = ['accuracy','precision','recall']
-builds = [x for x in range(len(data))]
-fig = plt.figure()
-ax = fig.add_subplot(1,1,1)
-for i in range(3):
-    ax.plot(builds, [data[x][i] for x in range(len(data))], label=labels[i])
-plt.legend()
-plt.savefig('metrics.png')