evaluation.py

from sklearn.metrics import accuracy_score, f1_score, classification_report
import pandas as pd

def evaluate_predictions(filename):
    data = pd.read_csv(filename)

    # Map sentiment classes to match between 'sentiment_score' and 'summary'
    sentiment_mapping = {"Neutral": "Neutral", "Positive sentiment": "Positive", "Negative sentiment": "Negative"}
    data['sentiment_score_mapped'] = data['sentiment_score'].map(sentiment_mapping)

    accuracy = accuracy_score(data['summary'], data['sentiment_score_mapped'])
    f1 = f1_score(data['summary'], data['sentiment_score_mapped'], average='weighted')
    class_report = classification_report(data['summary'], data['sentiment_score_mapped'])

    print(f"Accuracy: {accuracy}")
    print(f"F1 Score: {f1}")
    print("Classification Report:\n", class_report)

# Call the function with the path to your CSV file
# evaluate_predictions('predictions.csv')