From 54b9dab048e2a318450b0bba87d542212ab2840b Mon Sep 17 00:00:00 2001
From: s444501 <krywas@st.amu.edu.pl>
Date: Sun, 1 May 2022 16:56:56 +0200
Subject: [PATCH] .

---
 eval.py                | 60 ++++++++++++++++++++++++++++++++++++++++++
 evaluation.Jenkinsfile | 42 +++++++++++++++++++++++++++++
 2 files changed, 102 insertions(+)
 create mode 100644 eval.py
 create mode 100644 evaluation.Jenkinsfile

diff --git a/eval.py b/eval.py
new file mode 100644
index 0000000..22fd9a9
--- /dev/null
+++ b/eval.py
@@ -0,0 +1,60 @@
+import torch
+import matplotlib.pyplot as plt
+import os
+import json
+from sklearn.metrics import accuracy_score, precision_score, recall_score
+import pandas as pd
+
+# Wczytywanie
+# Nie potrzebuje wczytywać modelu i danych testowych bo w jobie trenującym już stworzyłem csv z wynikami
+# model = torch.load('model.pkl')
+# test_set = pd.read_csv('d_test.csv', encoding='latin-1')
+test_results = pd.read_csv('neural_network_prediction_results.csv')
+
+
+# Ewaluacja
+y_true = test_results['Testing Y']
+y_predicted = test_results['Predicted Y']
+
+accuracy = accuracy_score(y_true, y_predicted)
+precision = precision_score(y_true, y_predicted, average='macro')
+recall = recall_score(y_true, y_predicted, average='macro')
+
+eval_results = {
+    'Accuracy': accuracy,
+    'Macro-Avg Precision': precision,
+    'Macro-Avg Recall': recall
+}
+
+
+filename = 'eval_results.json'
+if not os.path.exists(filename):
+    with open(filename, 'w') as file:
+        json.dump({'eval_results': []}, file, indent=2)
+
+with open(filename, 'r+') as file:
+    file_data = json.load(file)
+    file_data['eval_results'].append(eval_results)
+    file.seek(0)
+    json.dump(file_data, file, indent=2)
+
+with open(filename, 'r') as file:
+    results = json.load(file)['eval_results']
+    f_acc = []
+    f_prc = []
+    f_rec = []
+    for res in results:
+        f_acc.append(res['Accuracy'])
+        f_prc.append(res['Macro-Avg Precision'])
+        f_rec.append(res['Macro-Avg Recall'])
+
+build_axis = [i+1 for i in range(len(f_acc))]
+
+plt.xlabel('Build')
+plt.ylabel('Score')
+plt.plot(build_axis, f_acc, label='Accuracy')
+plt.plot(build_axis, f_prc, label='Macro-Avg Precision')
+plt.plot(build_axis, f_rec, label='Macro-Avg Recall')
+plt.legend()
+plt.show()
+plt.savefig('metrics.png')
diff --git a/evaluation.Jenkinsfile b/evaluation.Jenkinsfile
new file mode 100644
index 0000000..70773af
--- /dev/null
+++ b/evaluation.Jenkinsfile
@@ -0,0 +1,42 @@
+pipeline {
+    agent {
+	    docker {image 'zadanie'}
+	}
+	parameters {
+		buildSelector(
+			defaultSelector: lastSuccessful(),
+			description: 'Which build to use for copying artifacts',
+			name: 'BUILD_SELECTOR'
+		)
+		string(
+            defaultValue: '100',
+            description: 'number of epochs',
+            name: 'EPOCH'
+        )
+	}
+    stages {
+
+		stage('Copy artifacts') {
+			steps {
+                copyArtifacts fingerprintArtifacts: true, projectName: 's444501-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+			}
+		}
+        stage('Train model') {
+            steps {
+                withEnv(["EPOCH=${params.EPOCH}"]) {
+				    sh 'python biblioteki_ml.py $EPOCH'
+				}
+			}
+		}
+		stage('Archive model') {
+            steps {
+				archiveArtifacts artifacts: 'model.pkl, neural_network_prediction_results.csv'
+			}
+		}
+	}
+	post {
+        always {
+            emailext body: "${currentBuild.currentResult}", subject: 's444501-training', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+    }
+}
\ No newline at end of file