evaluation

Jenkinsfile

@@ -1,37 +1,62 @@
 pipeline {
-    agent any
-
-    parameters {
-        string(name: 'CUTOFF', defaultValue: '100', description: 'Ilość wierszy do odcięcia')
-	    string(name: 'KAGGLE_USERNAME', defaultValue: '', description: 'Kaggle username')
-        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
+   agent {
+    dockerfile true
     }
 
+   triggers {
+     upstream(upstreamProjects: 's464937-training/training', threshold: hudson.model.Result.SUCCESS)
+   }
+
+   parameters {
+     buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
+     gitParameter branchFilter: 'origin/(.*)', defaultValue: 'training', name: 'BRANCH', type: 'PT_BRANCH'
+   }
+
     stages {
-        stage('Clone repo') {
+        stage('Clone Repository') {
             steps {
-                git branch: "main", url: "https://git.wmi.amu.edu.pl/s464937/ium_464937"
+                git branch: 'evaluation', url: "https://git.wmi.amu.edu.pl/s464937/ium_464937"
             }
         }
+        stage('Copy Dataset Artifacts') {
+            steps {
+                 copyArtifacts filter: 'data/dev.csv,data/test.csv,data/train.csv', projectName: 'z-s464937-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+            }
+        }
+        stage('Copy Training Artifacts') {
+          steps {
+            copyArtifacts filter: 'powerlifting_model.h5', projectName: 's464937-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
+          }
+        }
+        stage('Copy Evaluation Artifacts') {
+          steps {
+            copyArtifacts filter: 'metrics.txt', projectName: '_s464937-evaluation/evaluation', selector: buildParameter('BUILD_SELECTOR'), optional: true
+          }
+        }
+        stage("Run predictions") {
+            steps {
+                sh "chmod +x ./predict.py"
+                sh "python3 ./predict.py"
+                archiveArtifacts artifacts: 'powerlifting_test_predictions.csv', onlyIfSuccessful: true
+            }
+        }
+        stage('Run metrics') {
+          steps {
+            sh 'chmod +x ./metrics.py'
+            sh "python3 ./metrics.py ${currentBuild.number}"
+          }
+        }
 
-        stage('Download and preprocess') {
-            environment {
-                    KAGGLE_USERNAME = "szymonbartanowicz"
-                    KAGGLE_KEY = "4692239eb65f20ec79f9a59ef30e67eb"
-                }
-            steps {
-                withEnv([
-                    "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
-                    "KAGGLE_KEY=${env.KAGGLE_KEY}"
-                ]) {
-                    sh "bash ./script1.sh ${params.CUTOFF}"
-                }
-            }
+        stage('Run plot') {
+          steps {
+            sh 'chmod +x ./plot.py'
+            sh 'python3 ./plot.py'
+          }
         }
-        stage('Archive') {
-            steps {
-                archiveArtifacts artifacts: 'data/*', onlyIfSuccessful: true
-            }
+         stage('Archive Artifacts') {
+          steps {
+            archiveArtifacts artifacts: '*', onlyIfSuccessful: true
+          }
         }
     }
-}
+}

metrics.py

@@ -0,0 +1,24 @@
+# import pandas as pd
+# from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
+# from math import sqrt
+# import sys
+#
+# data = pd.read_csv('powerlifting_test_predictions.csv')
+# y_pred = data['Predictions']
+# y_test = data['Actual']
+# y_test_binary = (y_test >= 3).astype(int)
+#
+# build_number = sys.argv[1]
+#
+# accuracy = accuracy_score(y_test_binary, y_pred.round())
+# precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro')
+# rmse = sqrt(mean_squared_error(y_test, y_pred))
+#
+# print(f'Accuracy: {accuracy}')
+# print(f'Micro-avg Precision: {precision}')
+# print(f'Micro-avg Recall: {recall}')
+# print(f'F1 Score: {f1}')
+# print(f'RMSE: {rmse}')
+
+with open(r"metrics.txt", "a") as f:
+    f.write(f"{123},{1}\n")

plot.py

@@ -0,0 +1,22 @@
+import matplotlib.pyplot as plt
+
+def main():
+    accuracy = []
+    build_numbers = []
+
+    with open("maetrics.txt") as f:
+        for line in f:
+            accuracy.append(float(line.split(",")[0]))
+            build_numbers.append(int(line.split(",")[1]))
+
+    plt.plot(build_numbers, accuracy)
+    plt.xlabel("Build Number")
+    plt.ylabel("Accuracy")
+    plt.title("Accuracy of the model over time")
+    plt.xticks(range(min(build_numbers), max(build_numbers) + 1))
+    plt.show()
+
+    plt.savefig("plot.png")
+
+if __name__ == "__main__":
+    main()