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Module 8: Generate Architecture Diagram
System architecture visualization.
"""
import os, sys
sys.path.insert(0, '/app/fraud_detection')
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.patches import FancyBboxPatch, FancyArrowPatch
import numpy as np
from config import FIGURES_DIR, FIG_DPI, FIG_BG
def draw_architecture():
"""Draw the system architecture diagram."""
fig, ax = plt.subplots(1, 1, figsize=(16, 10), facecolor=FIG_BG)
ax.set_xlim(0, 16)
ax.set_ylim(0, 10)
ax.axis('off')
# Colors
c_input = '#3498db'
c_process = '#2ecc71'
c_model = '#e74c3c'
c_output = '#f39c12'
c_storage = '#9b59b6'
def box(x, y, w, h, text, color, fontsize=9):
rect = FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.1",
facecolor=color, edgecolor='black', linewidth=1.5, alpha=0.85)
ax.add_patch(rect)
ax.text(x + w/2, y + h/2, text, ha='center', va='center',
fontsize=fontsize, fontweight='bold', color='white',
multialignment='center')
def arrow(x1, y1, x2, y2):
ax.annotate('', xy=(x2, y2), xytext=(x1, y1),
arrowprops=dict(arrowstyle='->', color='black', lw=2))
# Title
ax.text(8, 9.5, 'Fraud Detection System Architecture', ha='center',
fontsize=16, fontweight='bold', color='#2c3e50')
# Layer 1: Data Input
box(0.5, 7.5, 3, 1, 'Transaction\nStream', c_input, 10)
box(4, 7.5, 3, 1, 'Feature\nEngineering\n(12 features)', c_process, 9)
box(7.5, 7.5, 3, 1, 'RobustScaler\n(Fit on Train)', c_process, 9)
arrow(3.5, 8, 4, 8)
arrow(7, 8, 7.5, 8)
# Layer 2: Models
box(0.5, 5, 2.2, 1.2, 'Logistic\nRegression', c_model, 8)
box(3, 5, 2.2, 1.2, 'Random\nForest', c_model, 8)
box(5.5, 5, 2.2, 1.2, 'XGBoost\n(Best)', c_model, 8)
box(8, 5, 2.2, 1.2, 'LightGBM', c_model, 8)
box(10.5, 5, 2.2, 1.2, 'MLP\nNeural Net', c_model, 8)
box(13, 5, 2.5, 1.2, 'Autoencoder\n(Anomaly)', c_model, 8)
# Arrows from preprocessing to models
for x in [1.6, 4.1, 6.6, 9.1, 11.6, 14.25]:
arrow(9, 7.5, x, 6.2)
# Layer 3: Ensemble & Optimization
box(3, 2.8, 4, 1.2, 'Voting Ensemble\n(XGB + LGBM + RF)', c_output, 10)
box(8, 2.8, 4, 1.2, 'Optuna Tuning\n(Hyperparameter Opt)', c_storage, 9)
arrow(5, 5, 5, 4)
arrow(10, 5, 10, 4)
arrow(8, 3.4, 7, 3.4)
# Layer 4: Output
box(3, 0.5, 4, 1.5, 'FastAPI\nPOST /predict\n< 10ms latency', c_input, 9)
box(8, 0.5, 4, 1.5, 'Decision\nFraud Prob + Risk Level\n+ Top Risk Factors', c_output, 9)
arrow(5, 2.8, 5, 2)
arrow(7, 1.25, 8, 1.25)
# Monitoring box
box(12.5, 7.5, 3, 1, 'Monitoring\nDrift Detection\nRetraining', c_storage, 9)
arrow(10.5, 8, 12.5, 8)
# SHAP/LIME
box(12.5, 2.8, 3, 1.2, 'Explainability\nSHAP + LIME', c_process, 9)
arrow(12, 5, 14, 4)
plt.tight_layout()
plt.savefig(os.path.join(FIGURES_DIR, "architecture_diagram.png"), dpi=FIG_DPI, bbox_inches='tight', facecolor=FIG_BG)
plt.savefig(os.path.join(FIGURES_DIR, "architecture_diagram.pdf"), bbox_inches='tight', facecolor=FIG_BG)
plt.close()
print("Saved: architecture_diagram.png/pdf")
if __name__ == "__main__":
draw_architecture()
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