gh-policyengine-policyengine-claude-country-models/skills/policyengine-vectorization-skill/SKILL.md

name, description

name	description
policyengine-vectorization	PolicyEngine vectorization patterns - NumPy operations, where/select usage, avoiding scalar logic with arrays

PolicyEngine Vectorization Patterns

Critical patterns for vectorized operations in PolicyEngine. Scalar logic with arrays will crash the microsimulation.

The Golden Rule

PolicyEngine processes multiple households simultaneously using NumPy arrays. NEVER use if-elif-else with entity data.

---

1. Critical: What Will Crash

❌ NEVER: if-elif-else with Arrays

# THIS WILL CRASH - household data is an array
def formula(household, period, parameters):
    income = household("income", period)
    if income > 1000:  # ❌ CRASH: "truth value of array is ambiguous"
        return 500
    else:
        return 100

✅ ALWAYS: Vectorized Operations

# CORRECT - works with arrays
def formula(household, period, parameters):
    income = household("income", period)
    return where(income > 1000, 500, 100)  # ✅ Vectorized

---

2. Common Vectorization Patterns

Pattern 1: Simple Conditions → where()

# Instead of if-else
❌ if age >= 65:
    amount = senior_amount
else:
    amount = regular_amount

✅ amount = where(age >= 65, senior_amount, regular_amount)

Pattern 2: Multiple Conditions → select()

# Instead of if-elif-else
❌ if age < 18:
    benefit = child_amount
elif age >= 65:
    benefit = senior_amount
else:
    benefit = adult_amount

✅ benefit = select(
    [age < 18, age >= 65],
    [child_amount, senior_amount],
    default=adult_amount
)

Pattern 3: Boolean Operations

# Combining conditions
eligible = (age >= 18) & (income < threshold)  # Use & not 'and'
eligible = (is_disabled | is_elderly)          # Use | not 'or'
eligible = ~is_excluded                        # Use ~ not 'not'

Pattern 4: Clipping Values

# Instead of if for bounds checking
❌ if amount < 0:
    amount = 0
elif amount > maximum:
    amount = maximum

✅ amount = clip(amount, 0, maximum)
# Or: amount = max_(0, min_(amount, maximum))

---

3. When if-else IS Acceptable

✅ OK: Parameter-Only Conditions

# OK - parameters are scalars, not arrays
def formula(entity, period, parameters):
    p = parameters(period).gov.program

    # This is fine - p.enabled is a scalar boolean
    if p.enabled:
        base = p.base_amount
    else:
        base = 0

    # But must vectorize when using entity data
    income = entity("income", period)
    return where(income < p.threshold, base, 0)

✅ OK: Control Flow (Not Data)

# OK - controlling which calculation to use
def formula(entity, period, parameters):
    year = period.start.year

    if year >= 2024:
        # Use new formula (still vectorized)
        return entity("new_calculation", period)
    else:
        # Use old formula (still vectorized)
        return entity("old_calculation", period)

---

4. Common Vectorization Mistakes

Mistake 1: Scalar Comparison with Array

❌ WRONG:
if household("income", period) > 1000:
    # Error: truth value of array is ambiguous

✅ CORRECT:
income = household("income", period)
high_income = income > 1000  # Boolean array
benefit = where(high_income, low_benefit, high_benefit)

Mistake 2: Using Python's and/or/not

❌ WRONG:
eligible = is_elderly or is_disabled  # Python's 'or'

✅ CORRECT:
eligible = is_elderly | is_disabled   # NumPy's '|'

Mistake 3: Nested if Statements

❌ WRONG:
if eligible:
    if income < threshold:
        return full_benefit
    else:
        return partial_benefit
else:
    return 0

✅ CORRECT:
return where(
    eligible,
    where(income < threshold, full_benefit, partial_benefit),
    0
)

---

5. Advanced Patterns

Pattern: Vectorized Lookup Tables

# Instead of if-elif for ranges
❌ if size == 1:
    amount = 100
elif size == 2:
    amount = 150
elif size == 3:
    amount = 190

✅ # Using parameter brackets
amount = p.benefit_schedule.calc(size)

✅ # Or using select
amounts = [100, 150, 190, 220, 250]
amount = select(
    [size == i for i in range(1, 6)],
    amounts[:5],
    default=amounts[-1]  # 5+ people
)

Pattern: Accumulating Conditions

# Building complex eligibility
income_eligible = income < p.income_threshold
resource_eligible = resources < p.resource_limit
demographic_eligible = (age < 18) | is_pregnant

# Combine with & (not 'and')
eligible = income_eligible & resource_eligible & demographic_eligible

Pattern: Conditional Accumulation

# Sum only for eligible members
person = household.members
is_eligible = person("is_eligible", period)
person_income = person("income", period)

# Only count income of eligible members
eligible_income = where(is_eligible, person_income, 0)
total = household.sum(eligible_income)

---

6. Performance Implications

Why Vectorization Matters

• Scalar logic: Processes 1 household at a time → SLOW
• Vectorized: Processes 1000s of households simultaneously → FAST

# Performance comparison
❌ SLOW (if it worked):
for household in households:
    if household.income > 1000:
        household.benefit = 500

✅ FAST:
benefits = where(incomes > 1000, 500, 100)  # All at once!

---

7. Testing for Vectorization Issues

Signs Your Code Isn't Vectorized

Error messages:

• "The truth value of an array is ambiguous"
• "ValueError: The truth value of an array with more than one element"

Performance:

• Tests run slowly
• Microsimulation times out

How to Test

# Your formula should work with arrays
def test_vectorization():
    # Create array inputs
    incomes = np.array([500, 1500, 3000])

    # Should return array output
    benefits = formula_with_arrays(incomes)
    assert len(benefits) == 3

---

Quick Reference Card

Operation	Scalar (WRONG)	Vectorized (CORRECT)
Simple condition	if x > 5:	where(x > 5, ...)
Multiple conditions	if-elif-else	select([...], [...])
Boolean AND	and	&
Boolean OR	or	|
Boolean NOT	not	~
Bounds checking	if x < 0: x = 0	max_(0, x)
Complex logic	Nested if	Nested where/select

---

For Agents

When implementing formulas:

1. Never use if-elif-else with entity data
2. Always use where() for simple conditions
3. Use select() for multiple conditions
4. Use NumPy operators (&, |, ~) not Python (and, or, not)
5. Test with arrays to ensure vectorization
6. Parameter conditions can use if-else (scalars)
7. Entity data must use vectorized operations