First evaluation test

Jenkinsfile_create_dataset

@@ -1,7 +1,5 @@
 pipeline {
-   agent { 
+   agent {docker { image 'snowycocoon/ium_434788:3'}}  
-      dockerfile true 
-   }  
    //Definijuemy parametry, które będzie można podać podczas wywoływania zadania
    parameters {
      string (

Jenkinsfile_dataset_stats

@@ -1,6 +1,6 @@
 pipeline {
     agent {
-        docker { image 'snowycocoon/ium_434788:2' }
+        docker { image 'snowycocoon/ium_434788:3' }
     }
     stages {
     stage('Test') {

Jenkinsfile_evaluation

@@ -1,10 +1,20 @@
 pipeline {
-    agent {docker { image 'adnovac/ium_s434760:1.0' }}
+    agent {docker { image 'snowycocoon/ium_434788:3'}}
 	parameters{
+         buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying data artifacts',
+            name: 'WHICH_BUILD_DATA'
+        )
         buildSelector(
             defaultSelector: lastSuccessful(),
-            description: 'Which build to use for copying artifacts',
+            description: 'Which build to use for copying train artifacts',
-            name: 'WHICH_BUILD'
+            name: 'WHICH_BUILD_TRAIN'
+        )
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying current project artifacts',
+            name: 'WHICH_BUILD_THIS'
         )
     }
 
@@ -14,6 +24,22 @@ pipeline {
                 steps
                 {
                     copyArtifacts(fingerprintArtifacts: true, projectName: 's434788-create-dataset', selector: buildParameter('WHICH_BUILD'))
+                    copyArtifacts(fingerprintArtifacts: true, projectName: 's434788-training/master', selector: buildParameter('WHICH_BUILD_TRAIN'))
+                    copyArtifacts(fingerprintArtifacts: true, optional: true, projectName: 's434788-evaluation/master', selector: buildParameter('WHICH_BUILD_THIS'))
+                }
+            }
+            stage('evaluate')
+            {
+                steps
+                {
+                    catchError {
+                        sh 'python3.8 Zadanie_06_evaluate.py'
+                    }
+                }
+            }
+            stage('archive artifacts') {
+                steps {
+                    archiveArtifacts 'results.txt,evaluation.png'
                 }
             }
 	}

Zadanie_06_evaluate.py

@@ -1 +1,36 @@
-print('evaluating')
+import pandas as pd
+import numpy as np
+from os import path
+from tensorflow import keras
+import sys
+import matplotlib.pyplot as plt
+from sklearn.metrics import accuracy_score, classification_report
+
+model = keras.models.load_model('saved_model.pb')
+print('evaluating')
+
+test_df =pd.read_csv('test.csv')
+y_test = test_df.quality
+x_test = test_df.drop(['quality'], axis= 1)
+
+y_pred = model.predict(x_test)
+
+y_pred = np.around(y_pred, decimals=0)
+
+results = accuracy_score(y_test,y_pred)
+with open('results.txt', 'a+', encoding="UTF-8") as f:
+    f.write(str(results) +"\n")
+
+with open('results.txt', 'r', encoding="UTF-8") as f:
+    lines = f.readlines()
+
+fig = plt.figure(figsize=(10,10))
+
+chart = fig.add_subplot()
+chart.set_ylabel("Accuracy")
+chart.set_xlabel("Number of build")
+x = np.arange(0, len(lines), 1)
+y = [float(x) for x in lines]
+print(y)
+plt.plot(x,y,"ro")
+plt.savefig("evaluation.png")