Domain Presets Reference

BehaviorSim provides 4 built-in domain presets accessible via Simulator.from_preset(name).

Registered Presets

1. Finance Preset (finance)

• Profile: balanced_investor
• States: Stable, Active, Volatile, Drawdown, Recovered, Closed
• Features: portfolio_value, daily_return, transaction_count, trade_volume, volatility, drawdown, risk_alert

2. Healthcare Preset (healthcare)

• Profile: stable_patient
• States: Baseline, Elevated, Critical, Recovery, Discharged
• Features: heart_rate_bpm, systolic_bp, spo2_pct, temperature_c, alert_triggered, mobility_score

3. Education Preset (education)

• Profile: average
• States: Optimal, Overload, Underload
• Features: difficulty, accuracy, nrt, retries, help_requested, confidence

4. Mobile App Preset (mobile_app)

• Profile: casual_browser
• States: Browsing, ActiveSession, CheckoutFlow, Idle, Churned
• Features: session_time_seconds, action_count, scroll_depth, button_clicks, notification_clicked, cart_value

Running Preset Simulations

You can execute each preset directly using the public Python API:

from behaviorsim import Simulator

# 1. Finance Preset
sim_fin = Simulator.from_preset("finance")
df_fin = sim_fin.generate(num_interactions=50, seed=42)
print("Finance rows:", len(df_fin), "Cols:", list(df_fin.columns[:4]))

# 2. Healthcare Preset (Synthetic statistical testbed)
sim_hc = Simulator.from_preset("healthcare")
df_hc = sim_hc.generate(num_interactions=50, seed=42)
print("Healthcare rows:", len(df_hc), "Cols:", list(df_hc.columns[:4]))

# 3. Education Preset
sim_edu = Simulator.from_preset("education")
df_edu = sim_edu.generate(num_interactions=50, seed=42)
print("Education rows:", len(df_edu), "Cols:", list(df_edu.columns[:4]))

# 4. Mobile App Preset
sim_mob = Simulator.from_preset("mobile_app")
df_mob = sim_mob.generate(num_interactions=50, seed=42)
print("Mobile App rows:", len(df_mob), "Cols:", list(df_mob.columns[:4]))

Important Scientific Disclaimers