IUM_06

IUM_05-predict.py

@@ -1,25 +1,18 @@
 import pandas as pd
-import numpy as np
 import tensorflow as tf
 
-# Load the test data
 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']
 
-# Load the model
 model = tf.keras.models.load_model('beer_review_sentiment_model.h5')
 
-# Make predictions
 predictions = model.predict(X_test)
 
-# Check the shape of the predictions
 print(f'Predictions shape: {predictions.shape}')
 
-# If predictions have more than one dimension, select only one dimension
 if len(predictions.shape) > 1:
     predictions = predictions[:, 0]
 
-# Save predictions and actual data to a CSV file
 results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
 results.to_csv('beer_review_sentiment_predictions.csv', index=False)

IUM_06-metrics.py

@@ -3,7 +3,6 @@ from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mea
 from math import sqrt
 import sys
 
-# Load the predictions data
 data = pd.read_csv('beer_review_sentiment_predictions.csv')
 y_pred = data['Predictions']
 y_test = data['Actual']
@@ -11,7 +10,6 @@ y_test_binary = (y_test >= 3).astype(int)
 
 build_number = sys.argv[1]
 
-# Calculate metrics
 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))