plot

Jenkinsfile-evaluation

@@ -28,24 +28,29 @@ pipeline {
                     steps
                     {
                         copyArtifacts(fingerprintArtifacts: true, projectName: 's434742-create-dataset', selector: buildParameter('BUILD_SELECTOR_DATASET'))
-                        copyArtifacts(fingerprintArtifacts: true, projectName: 's434742-training', selector: buildParameter('BUILD_SELECTOR_TRAINING'))
-                        copyArtifacts(fingerprintArtifacts: true, projectName: 's434742-evaluation', selector: buildParameter('BUILD_SELECTOR_EVALUATION'))
+                        copyArtifacts(fingerprintArtifacts: true, projectName: 's434742-training/master', selector: buildParameter('BUILD_SELECTOR_TRAINING'))
+                        copyArtifacts(fingerprintArtifacts: true, projectName: 's434742-evaluation/master', selector: buildParameter('BUILD_SELECTOR_EVALUATION', optional=true))
                     }
                 }
 
-        stage('docker-training') {
+        stage('evaluation') {
 			steps {
                 script {
-                    def img = docker.build('patlaz/ium:1.0')
-                    img.inside {
-                        sh 'chmod +x avocado-evaluation.py'
-                        sh 'python3 avocado-evaluation.py'
-                 
-                    }
+             
+                    sh 'chmod +x avocado-evaluation.py'
+                    sh 'python3 avocado-evaluation.py'
+                
                 }
 			}
 		}
 
+        	stage('archiveArtifacts') {
+			steps{
+				archiveArtifacts 'eval_results.txt'
+                archiveArtifacts 'eval_plot.png'
+			}
+		}
+
 
         stage('sendMail') {
             steps{

avocado-evaluation.py

@@ -3,23 +3,7 @@ import numpy as np
 from tensorflow import keras
 import matplotlib.pyplot as plt
 from keras import backend as K
-
-def recall_m(y_true, y_pred):
-    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
-    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
-    recall = true_positives / (possible_positives + K.epsilon())
-    return recall
-
-def precision_m(y_true, y_pred):
-    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
-    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
-    precision = true_positives / (predicted_positives + K.epsilon())
-    return precision
-
-def f1_m(y_true, y_pred):
-    precision = precision_m(y_true, y_pred)
-    recall = recall_m(y_true, y_pred)
-    return 2*((precision*recall)/(precision+recall+K.epsilon()))
+from sklearn.metrics import mean_squared_error
 
 
 # zaladowanie modelu
@@ -37,6 +21,27 @@ y_train = avocado_train[['type']]
 X_test = avocado_test[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
 y_test = avocado_test[['type']]
 
+# prediction
+predictions = model.predict(X_test)
+#pd.DataFrame(predictions).to_csv('prediction_results.csv')
+
 # ewaluacja
-loss, accuracy, f1_score, precision, recall = model.evaluate(X_test, y_test, verbose=0)
-# with open('')
+error = mean_squared_error(y_test, predictions)
+print('Error: ', error)
+
+with open('eval_results.txt', 'a') as f:
+    f.write(str(error) + "\n")
+
+# wykres
+with open('results.txt', 'r') as f:
+    lines = f.readlines()
+
+
+fig = plt.figure(figsize=(5,5))
+chart = fig.add_subplot()
+chart.set_ylabel("RMSE")
+chart.set_xlabel("Build")
+x = np.arange(0, len(lines), 1)
+y = [float(x) for x in lines]
+plt.plot(x, y, "ro")
+plt.savefig("eval_plot.png")

avocado-training.py

@@ -43,11 +43,3 @@ model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_dat
 
 # zapisanie modelu
 model.save('avocado_model.h5')
-
-# predict
-predictions = model.predict(X_test)
-pd.DataFrame(predictions).to_csv('prediction_results.csv')
-
-# ewaluacja
-# error = mean_squared_error(y_test, predictions)
-# print('Error: ', error)