lab06_02

Jenkinsfile_evaluation

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+pipeline {
+   agent {
+      dockerfile true
+   }
+   parameters{
+      buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying artifacts',
+            name: 'WHICH_BUILD_DATA'
+        )
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying artifacts',
+            name: 'WHICH_BUILD_TRAIN'
+        )
+   }
+   stages {
+      stage('checkout') {
+            steps {
+                copyArtifacts fingerprintArtifacts: true, projectName: 's430705-create-dataset', selector: buildParameter('WHICH_BUILD_DATA')
+            }
+        }
+      stage('Docker'){
+         steps{
+            copyArtifacts fingerprintArtifacts: true, projectName: 's430705-training/master', selector: buildParameter('WHICH_BUILD_TRAIN')
+            sh 'python3 "./lab06-eval.py" >> eval.txt'
+         }
+      }
+      stage('archiveArtifacts') {
+         steps {
+            archiveArtifacts 'eval.txt'
+         }
+      }
+       stage('sendMail') {
+            steps{
+                emailext body: currentBuild.result ?: 'SUCCESS EVALUATION',
+                subject: 's430705 evaluation',
+                to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+            }
+        }
+   }
+}

lab06-eval.py

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+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.callbacks import EarlyStopping
+from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score
+from tensorflow.keras.models import load_model
+import pandas as pd
+
+test_df = pd.read_csv('test.csv')
+test_df.drop(test_df.columns[0], axis=1, inplace=True)
+x_test = test_df.drop("rating", axis=1)
+y_test = test_df["rating"]
+
+model = Sequential()
+model = load_model('model_movies')
+
+y_pred = model.predict(x_test.values)
+
+rmse = mean_squared_error(y_test, y_pred)
+
+print(f"RMSE: {rmse}")