IUM_07

Dockerfile

@@ -10,7 +10,7 @@ ENV TZ=Etc/UTC
 RUN apt update && \
     apt install -y python3 python3-pip unzip
 
-RUN pip install kaggle pandas seaborn scikit-learn tensorflow
+RUN pip install kaggle pandas seaborn scikit-learn tensorflow sacred pymongo --break-system-packages
 
 WORKDIR /app
 

Jenkinsfile

@@ -48,5 +48,19 @@ pipeline {
                 archiveArtifacts artifacts: 'beer_review_sentiment_model.h5,beer_review_sentiment_predictions.csv', onlyIfSuccessful: true
             }
         }
+
+        stage('Sacred') {
+              steps {
+                sh 'chmod +x sacred/sacred_training_model.py'
+                sh 'python3 sacred/sacred_training_model.py'
+              }
+            }
+
+            stage('Archive Artifacts from Experiments') {
+              steps {
+                archiveArtifacts artifacts: 'sacred_runs/**/*.*', onlyIfSuccessful: true
+                }
+              }
+        }
     }
 }

sacred/sacred_training_model.py

@@ -0,0 +1,84 @@
+import pandas as pd
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Embedding, GlobalAveragePooling1D, Dense
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
+from sacred import Experiment
+from sacred.observers import MongoObserver, FileStorageObserver
+from math import sqrt
+
+ex = Experiment('464979')
+ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017'))
+ex.observers.append(FileStorageObserver('sacred_runs'))
+
+@ex.config
+def my_config():
+    epochs = 10
+    batch_size = 32
+
+@ex.automain
+def run_experiment(epochs, batch_size, _run):
+    train_data = pd.read_csv('beer_reviews_train.csv')
+    X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
+    y_train = train_data['review_overall']
+
+    tokenizer = Tokenizer(num_words=10000)
+    tokenizer.fit_on_texts(X_train)
+    X_train_seq = tokenizer.texts_to_sequences(X_train)
+
+    X_train_pad = pad_sequences(X_train_seq, maxlen=100)
+
+    model = Sequential([
+        Embedding(input_dim=10000, output_dim=16, input_length=100),
+        GlobalAveragePooling1D(),
+        Dense(16, activation='relu'),
+        Dense(1, activation='sigmoid')
+    ])
+
+    model.compile(optimizer='adam',
+                  loss='binary_crossentropy',
+                  metrics=['accuracy'])
+
+    model.fit(X_train_pad, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1)
+
+    model.save('beer_review_sentiment_model.keras')
+    _run.add_artifact('beer_review_model.h5')
+
+    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']
+
+    tokenizer = Tokenizer(num_words=10000)
+    tokenizer.fit_on_texts(X_test)
+
+    X_test_text = X_test.astype(str).agg(' '.join, axis=1)
+    X_test_seq = tokenizer.texts_to_sequences(X_test_text)
+    X_test_pad = pad_sequences(X_test_seq, maxlen=100)
+
+    predictions = model.predict(X_test_pad)
+
+    if len(predictions.shape) > 1:
+        predictions = predictions[:, 0]
+
+    results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
+    results.to_csv('beer_review_sentiment_predictions.csv', index=False)
+
+    y_pred = results['Predictions']
+    y_test = results['Actual']
+    y_test_binary = (y_test >= 3).astype(int)
+
+    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}')
+
+    _run.add_resource('./beer_reviews_train.csv')
+    _run.add_resource('./beer_reviews_test.csv')
+
+    return accuracy