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import torch
import json
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix, classification_report
from torch_geometric.data import Data
from gnn_aml import GAT, prepare_graph
from graph_aml import detect_pattern
# Load Model
print("🔍 Loading Trained Model...")
model = GAT(num_node_features=3, hidden_dim=16, output_dim=2)
model.load_state_dict(torch.load("trained_model.pth"))
model.eval()
# Load New Test Data
print("📥 Loading New Test Transactions...")
with open("test_transactions.json", "r") as f:
test_transactions = json.load(f)
# Prepare Graph Data
print("🔄 Preparing Test Graph Data...")
test_graph, _ = prepare_graph()
# Run Model Predictions
print("🧠 Running Predictions...")
with torch.no_grad():
output = model(test_graph)
probs = torch.softmax(output, dim=1) # Convert logits to probabilities
predictions = (probs[:, 1] > 0.75).long() # 1 = AML, 0 = Normal
# Store predictions
test_results = []
y_true = [] # True labels
y_pred = [] # Predicted labels
for txn, prediction in zip(test_transactions, predictions):
risk_score = txn["RiskScore"]
true_label = 1 if txn["AML_Flag"] == 1 else 0 # True AML label
predicted_label = prediction.item()
# Update labels for confusion matrix
y_true.append(true_label)
y_pred.append(predicted_label)
if risk_score < 0.5:
predicted_pattern = "None"
elif predicted_label == 1:
predicted_pattern = detect_pattern(test_graph)
else:
predicted_pattern = "None"
test_results.append({
"TransactionID": txn["TransactionID"],
"TrueLabel": true_label,
"PredictedLabel": predicted_label,
"PredictedPattern": predicted_pattern,
"RiskScore": risk_score
})
# Save results to file
with open("new_test_results_v2.json", "w") as f:
json.dump(test_results, f, indent=4)
# **✅ Compute Accuracy Metrics**
print("\n📊 **Final Test Results:**")
cm = confusion_matrix(y_true, y_pred)
report = classification_report(y_true, y_pred, target_names=[
"Normal", "AML"], digits=4)
print("\n🔢 **Confusion Matrix:**\n", cm)
print("\n📄 **Classification Report:**\n", report)
# **✅ Plot Confusion Matrix**
plt.figure(figsize=(6, 5))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", xticklabels=[
"Normal", "AML"], yticklabels=["Normal", "AML"])
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix")
plt.show()
# **✅ Plot Prediction Distribution**
labels, counts = np.unique(y_pred, return_counts=True)
plt.figure(figsize=(6, 5))
plt.bar(["Normal", "AML"], counts, color=["green", "red"])
plt.xlabel("Transaction Classification")
plt.ylabel("Number of Transactions")
plt.title("AML vs. Normal Transactions Detected")
plt.show()
print("✅ Accuracy analysis complete! Check charts & logs.")
# import torch
# import json
# from torch_geometric.data import Data
# from gnn_aml import GAT, prepare_graph
# from graph_aml import detect_pattern
# # Load Model
# print("🔍 Loading Trained Model...")
# model = GAT(num_node_features=3, hidden_dim=16, output_dim=2)
# model.load_state_dict(torch.load("trained_model.pth"))
# model.eval()
# # Load New Test Data
# print("📥 Loading New Test Transactions...")
# with open("test_transactions.json", "r") as f:
# test_transactions = json.load(f)
# # Prepare Graph Data
# print("🔄 Preparing Test Graph Data...")
# test_graph, _ = prepare_graph()
# # Run Model Predictions
# print("🧠 Running Predictions...")
# with torch.no_grad():
# output = model(test_graph)
# probs = torch.softmax(output, dim=1) # Convert logits to probabilities
# predictions = (probs[:, 1] > 0.75).long() # 1 = AML, 0 = Normal
# # Store predictions
# test_results = []
# aml_count = 0
# normal_count = 0
# for txn, prediction in zip(test_transactions, predictions):
# risk_score = txn["RiskScore"]
# predicted_label = prediction.item()
# if risk_score < 0.5:
# predicted_pattern = "None" # ✅ Mark as safe
# normal_count += 1 # ✅ Count normal transactions
# elif predicted_label == 1:
# predicted_pattern = detect_pattern(
# test_graph) # ✅ Detect actual pattern
# aml_count += 1 # ✅ Count AML transactions
# else:
# predicted_pattern = "None"
# normal_count += 1 # ✅ Count normal transactions
# test_results.append({
# "TransactionID": txn["TransactionID"],
# "PredictedPattern": predicted_pattern,
# "RiskScore": risk_score
# })
# # **✅ Move logging here, after results are fully analyzed**
# print("\n📊 **Final Test Results:**")
# print(f"🔴 AML Detected: {aml_count}")
# print(f"🟢 Normal Transactions: {normal_count}")
# # Save results to file
# with open("new_test_results_v2.json", "w") as f:
# json.dump(test_results, f, indent=4)
# print("✅ Test results saved to `new_test_results_v2.json`")
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