Add evaluation files

Dockerfile

@@ -10,3 +10,4 @@ RUN pip3 install numpy
 WORKDIR /app
 
 COPY ./training.py ./
+COPY ./evaluation.py ./

evaluation.Jenkinsfile

@@ -0,0 +1,27 @@
+pipeline {
+    agent {
+    	dockerfile true
+    }
+    	parameters {
+    			buildSelector(
+			defaultSelector: lastSuccessful(),
+			description: 'Which build to use for copying artifacts',
+			name: 'BUILD_SELECTOR'
+		)
+	}
+    stages {
+		stage('Check out from version control') {
+			steps {
+				checkout([$class: 'GitSCM', branches: [[name: '*/training_and_evaluation']], extensions: [], userRemoteConfigs: [[credentialsId: 's444421', url: 'https://git.wmi.amu.edu.pl/s444421/ium_444421.git']]])
+			}
+		}
+        stage('Script') {
+            steps {
+            	copyArtifacts filter: '*', projectName:'s444421-training/training_and_evaluation', selector: buildParameter('BUILD_SELECTOR')
+            	copyArtifacts filter: '*', projectName:'s444421-evaluation/training_and_evaluation', optional: true
+            	sh 'ipython ./evaluation.py'
+            	archiveArtifacts artifacts: 'build_accuracy.txt'
+            }
+        }
+    }
+}

evaluation.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+import numpy as np
+import pandas as pd
+from sklearn.metrics import accuracy_score
+import torch
+from torch import nn, optim
+import torch.nn.functional as F
+
+
+# In[ ]:
+
+
+class Net(nn.Module):
+    def __init__(self, n_features):
+        super(Net, self).__init__()
+        self.fc1 = nn.Linear(n_features, 5)
+        self.fc2 = nn.Linear(5, 3)
+        self.fc3 = nn.Linear(3, 1)
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return torch.sigmoid(self.fc3(x))
+
+
+# In[ ]:
+
+
+X_test = pd.read_csv('X_test.csv')
+y_test = pd.read_csv('y_test.csv')
+
+
+# In[ ]:
+
+
+X_test = torch.from_numpy(np.array(X_test)).float()
+y_test = torch.squeeze(torch.from_numpy(y_test.values).float())
+
+
+# In[ ]:
+
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+X_test = X_test.to(device)
+y_test = y_test.to(device)
+
+
+# In[ ]:
+
+
+net = torch.load('model.pth')
+
+
+# In[ ]:
+
+
+y_pred = net(X_test)
+y_pred = y_pred.ge(.5).view(-1).cpu()
+y_test = y_test.cpu()
+
+
+# In[ ]:
+
+
+accuracy = accuracy_score(y_test, y_pred)
+with open('build_accuracy.txt', 'a') as file:
+    file.write(str(accuracy))
+    file.write('\n')
+