s

JenkinsfileEvaluate

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+pipeline {
+    agent {
+        docker {
+            image 'python:3.11'
+            args '-v /root/.cache:/root/.cache -u root'
+        }
+    }
+    parameters {
+        gitParameter name: 'BRANCH', type: 'PT_BRANCH'
+        buildSelector(name: 'BUILD_NUMBER', description: 'Wybierz numer buildu', defaultSelector: lastSuccessful())
+    }
+    stages {
+        stage('Preparation') {
+            steps {
+                sh 'pip install pandas tensorflow scikit-learn matplotlib'
+            }
+        }
+        stage('Pobierz dane') {
+            steps {
+                script {
+                    copyArtifacts(projectName: 's487187-create-dataset', fingerprintArtifacts: true)
+                }
+            }
+        }
+        stage('Pobierz model') {
+            steps {
+                script {
+                    copyArtifacts(projectName: 's487187-training', selector: specific("${params.BUILD_NUMBER}"), filter: 'model.h5', fingerprintArtifacts: true)
+                }
+            }
+        }
+        stage('Ewaluuj model') {
+            steps {
+                script {
+                    sh "python3 evaluate.py"
+                }
+            }
+        }
+        stage('Zarchiwizuj wyniki') {
+            steps {
+                archiveArtifacts artifacts: 'metrics.txt,plot.png', fingerprint: true
+            }
+        }
+    }
+}

evaluate.py

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+import tensorflow as tf
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import accuracy_score, f1_score, mean_squared_error
+import matplotlib.pyplot as plt
+import os
+
+model = tf.keras.models.load_model('model.h5')
+
+
+test_data = pd.read_csv('test_data.csv', sep=';')
+test_data = pd.get_dummies(test_data, columns=['Sex', 'Medal'])
+test_data = test_data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])
+
+scaler = MinMaxScaler()
+test_data = pd.DataFrame(scaler.fit_transform(test_data), columns=test_data.columns)
+
+X_test = test_data.filter(regex='Sex|Age')
+y_test = test_data.filter(regex='Medal')
+y_test = pd.get_dummies(y_test)
+
+X_test = X_test.fillna(0)
+y_test = y_test.fillna(0)
+
+y_pred = model.predict(X_test)
+
+top_1_accuracy = tf.keras.metrics.categorical_accuracy(y_test, y_pred)
+top_5_accuracy = tf.keras.metrics.top_k_categorical_accuracy(y_test, y_pred, k=5)
+
+metrics_file = 'metrics.txt'
+if os.path.exists(metrics_file):
+    metrics_df = pd.read_csv(metrics_file)
+else:
+    metrics_df = pd.DataFrame(columns=['top_1_accuracy', 'top_5_accuracy'])
+metrics_df = metrics_df.append({'top_1_accuracy': np.mean(top_1_accuracy), 'top_5_accuracy': np.mean(top_5_accuracy)}, ignore_index=True)
+metrics_df.to_csv(metrics_file, index=False)
+
+plt.figure(figsize=(10, 6))
+plt.plot(metrics_df['top_1_accuracy'], label='Top-1 Accuracy')
+plt.plot(metrics_df['top_5_accuracy'], label='Top-5 Accuracy')
+plt.legend()
+plt.savefig('plot.png')