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performance_analysis.py
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performance_analysis.py
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
def performance_analysis(actual_results, predicted_results, times):
accuracy = accuracy_score(actual_results, predicted_results)
precision = precision_score(actual_results, predicted_results, average='macro')
recall = recall_score(actual_results, predicted_results, average='macro')
f1 = f1_score(actual_results, predicted_results, average='macro')
avg_time = sum(times) / len(times)
cm = confusion_matrix(actual_results, predicted_results)
plt.figure(figsize=(8, 6))
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.colorbar()
classes = ['Scheherazade New', 'Marhey', 'Lemonada', 'IBM Plex Sans Arabic']
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
plt.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.xlabel('Predicted label')
plt.ylabel('True label')
plt.show()
return accuracy, precision, recall, f1, avg_time