IUM_06

IUM_05-predict.py

@@ -1,18 +1,18 @@
 import pandas as pd
-import numpy as np
 import tensorflow as tf
 
 test_data = pd.read_csv('./beer_reviews_test.csv')
 X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
+y_test = test_data['review_overall']
 
 model = tf.keras.models.load_model('beer_review_sentiment_model.h5')
 
-tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
+predictions = model.predict(X_test)
 
+print(f'Predictions shape: {predictions.shape}')
 
-X_test_seq = tokenizer.texts_to_sequences(X_test)
+if len(predictions.shape) > 1:
-X_test_pad = tf.keras.preprocessing.sequence.pad_sequences(X_test_seq, maxlen=100)
+    predictions = predictions[:, 0]
 
-predictions = model.predict(X_test_pad)
+results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
-
+results.to_csv('beer_review_sentiment_predictions.csv', index=False)
-np.savetxt('beer_review_sentiment_predictions.csv', predictions, delimiter=',', fmt='%.10f')

IUM_06-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('beer_review_sentiment_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"beer_metrics.txt", "a") as f:
+    f.write(f"{accuracy},{build_number}\n")

IUM_06-plot.py

@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+
+
+def main():
+    accuracy = []
+    build_numbers = []
+
+    with open("beer_metrics.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("acc.png")
+
+
+if __name__ == "__main__":
+    main()

Jenkinsfile

@@ -1,52 +1,62 @@
 pipeline {
-    agent any
+   agent {
-
+    dockerfile true
-    parameters {
-        string(name: 'CUTOFF', defaultValue: '10000', description: 'Liczba wierszy do obcięcia ze zbioru danych')
-	    string(name: 'KAGGLE_USERNAME', defaultValue: '', description: 'Kaggle username')
-        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
     }
 
+   triggers {
+     upstream(upstreamProjects: 's464979-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 Repository') {
             steps {
-                git url: "https://git.wmi.amu.edu.pl/s464979/ium_464979"
+                git branch: 'evaluation', url: "https://git.wmi.amu.edu.pl/s464979/ium_464979"
             }
         }
-        stage('Download dataset') {
+        stage('Copy Dataset Artifacts') {
             steps {
-                 withEnv(["KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", "KAGGLE_KEY=${env.KAGGLE_KEY}"]) {
+                 copyArtifacts filter: 'beer_reviews.csv,beer_reviews_train.csv,beer_reviews_test.csv', projectName: 'z-s464979-create-dataset', selector: buildParameter('BUILD_SELECTOR')
-                    sh "kaggle datasets download -d thedevastator/1-5-million-beer-reviews-from-beer-advocate --unzip"
-                }
             }
         }
-        stage('Process and Split Dataset') {
+        stage('Copy Training Artifacts') {
-            agent {
+          steps {
-                dockerfile {
+            copyArtifacts filter: 'beer_review_sentiment_model.h5', projectName: 's464979-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
-                    filename 'Dockerfile'
+          }
-                    reuseNode true
-                }
-            }
-            steps {
-                sh "chmod +x ./IUM_05-split.py"
-                sh "python3 ./IUM_05-split.py"
-                archiveArtifacts artifacts: 'beer_reviews.csv,beer_reviews_train.csv,beer_reviews_test.csv', onlyIfSuccessful: true
-            }
         }
-        stage("Run") {
+        stage('Copy Evaluation Artifacts') {
-            agent {
+          steps {
-                dockerfile {
+            copyArtifacts filter: 'beer_metrics.txt', projectName: '_s464979-evaluation/evaluation', selector: buildParameter('BUILD_SELECTOR'), optional: true
-                    filename 'Dockerfile'
+          }
-                    reuseNode true
+        }
-                }
+        stage("Run predictions") {
-            }
             steps {
-                sh "chmod +x ./IUM_05-model.py"
                 sh "chmod +x ./IUM_05-predict.py"
-                sh "python3 ./IUM_05-model.py"
                 sh "python3 ./IUM_05-predict.py"
-                archiveArtifacts artifacts: 'beer_review_sentiment_model.h5,beer_review_sentiment_predictions.csv', onlyIfSuccessful: true
+                archiveArtifacts artifacts: 'beer_review_sentiment_predictions.csv', onlyIfSuccessful: true
             }
         }
+        stage('Run metrics') {
+          steps {
+            sh 'chmod +x ./IUM_06-metrics.py'
+            sh "python3 ./IUM_06-metrics.py ${currentBuild.number}"
+          }
+        }
+
+        stage('Run plot') {
+          steps {
+            sh 'chmod +x ./IUM_06-plot.py'
+            sh 'python3 ./IUM_06-plot.py'
+          }
+        }
+         stage('Archive Artifacts') {
+          steps {
+            archiveArtifacts artifacts: '*', onlyIfSuccessful: true
+          }
+        }
     }
 }

Test.txt

@@ -1 +0,0 @@
-asdasd