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calculate precision, recall, and f1 score for suspected origins

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Youngjoon Lee 2024-06-10 19:11:40 +09:00
parent a29849c31d
commit c3c45039ed
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9
mixnet/v2/sim/analysis.py
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@ -118,6 +118,15 @@ class Analysis:
plt.tight_layout()
plt.show()
# Calculate precision, recall, and F1 score
truth = set(truth_df[truth_df[COL_MSG_CNT] > 0][COL_NODE_ID])
suspected = set(suspected_df[suspected_df[COL_MSG_CNT] > 0][COL_NODE_ID])
true_positives = truth.intersection(suspected)
precision = len(true_positives) / len(suspected) * 100.0 if len(suspected) > 0 else 0.0
recall = len(true_positives) / len(truth) * 100.0 if len(truth) > 0 else 0.0
f1_score = 2 * precision * recall / (precision + recall) if precision + recall > 0 else 0.0
print(f"Precision: {precision:.2f}%, Recall: {recall:.2f}%, F1 Score: {f1_score:.2f}%")
def messages_in_node_over_time(self):
dataframes = []
for window, msg_pools in enumerate(self.sim.p2p.adversary.msg_pools_per_window):