Merge pull request #1239 from alanlujan91/distance_metric

Update MetricObject

HARK/ConsumptionSaving/ConsAggShockModel.py

@@ -9,7 +9,7 @@
 import numpy as np
 import scipy.stats as stats
 
-from HARK import AgentType, Market, MetricObject
+from HARK import AgentType, Market
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     ConsumerSolution,
     IndShockConsumerType,
@@ -34,6 +34,7 @@
     UpperEnvelope,
     VariableLowerBoundFunc2D,
 )
+from HARK.metric import MetricObject
 from HARK.rewards import (
     CRRAutility,
     CRRAutility_inv,

HARK/ConsumptionSaving/ConsGenIncProcessModel.py

@@ -6,7 +6,7 @@
 """
 import numpy as np
 
-from HARK import AgentType, MetricObject, make_one_period_oo_solver
+from HARK import AgentType, make_one_period_oo_solver
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     ConsIndShockSetup,
     ConsumerSolution,
@@ -26,6 +26,7 @@
     ValueFuncCRRA,
     VariableLowerBoundFunc2D,
 )
+from HARK.metric import MetricObject
 from HARK.rewards import (
     CRRAutility,
     CRRAutility_inv,

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -20,7 +20,6 @@
 
 from HARK import (
     AgentType,
-    MetricObject,
     NullFunc,
     _log,
     make_one_period_oo_solver,
@@ -53,6 +52,7 @@
     MargValueFuncCRRA,
     ValueFuncCRRA,
 )
+from HARK.metric import MetricObject
 from HARK.rewards import (
     CRRAutility,
     CRRAutility_inv,
@@ -447,9 +447,8 @@ def add_mNrmTrg(self, solution):
 
         # mNrmTrg solves Rcalbar*(m - c(m)) + E[inc_next] = m. Define a
         # rearranged version.
-        Ex_m_tp1_minus_m_t = (
-            lambda m: Ex_RNrmFac * (m - solution.cFunc(m)) + self.Ex_IncNext - m
-        )
+        def Ex_m_tp1_minus_m_t(m):
+            return Ex_RNrmFac * (m - solution.cFunc(m)) + self.Ex_IncNext - m
 
         # Minimum market resources plus next income is okay starting guess
         m_init_guess = self.mNrmMinNow + self.Ex_IncNext
@@ -507,9 +506,8 @@ def add_mNrmStE(self, solution):
                     solution.mNrmTrg = float("inf")
                 return solution
 
-        Ex_PermShk_tp1_times_m_tp1_minus_m_t = (
-            lambda mStE: PF_RNrm * (mStE - solution.cFunc(mStE)) + 1.0 - mStE
-        )
+        def Ex_PermShk_tp1_times_m_tp1_minus_m_t(mStE):
+            return PF_RNrm * (mStE - solution.cFunc(mStE)) + 1.0 - mStE
 
         # Minimum market resources plus next income is okay starting guess
         m_init_guess = self.mNrmMinNow + self.Ex_IncNext
@@ -1755,7 +1753,7 @@ def sim_birth(self, which_agents):
         ):  # If PerfMITShk not specified, let it be False
             self.PerfMITShk = False
         if (
-            self.PerfMITShk == False
+            self.PerfMITShk is False
         ):  # If True, Newborns inherit t_cycle of agent they replaced (i.e. t_cycles are not reset).
             self.t_cycle[
                 which_agents
@@ -2018,10 +2016,6 @@ def test(agent):
             False: "The given type violates the Finite Human Wealth Condition; the Finite Human wealth factor value is {0.FHWF}",
         }
 
-        verbose_messages = {
-            True: "  Therefore, the limiting consumption function is not c(m)=Infinity\nand human wealth normalized by permanent income is {0.hNrm}\nand the PDV of future consumption growth is {0.cNrmPDV}",
-            False: "  Therefore, the limiting consumption function is c(m)=Infinity for all m unless the RIC is also violated.  If both FHWC and RIC fail and the consumer faces a liquidity constraint, the limiting consumption function is nondegenerate but has a limiting slope of 0.  (https://econ-ark.github.io/BufferStockTheory#PFGICRawHoldsFHWCFailsRICFailsDiscuss)",
-        }
         verbose = self.verbose if verbose is None else verbose
         self.check_condition(name, test, messages, verbose)
 
@@ -2393,7 +2387,7 @@ def define_distribution_grid(
         if not hasattr(self, "neutral_measure"):
             self.neutral_measure = False
 
-        if num_pointsM == None:
+        if num_pointsM is None:
             m_points = self.mCount
         else:
             m_points = num_pointsM
@@ -2446,7 +2440,7 @@ def define_distribution_grid(
                 self.dist_pGrid = dist_pGrid
 
             if (
-                self.neutral_measure == True
+                self.neutral_measure is True
             ):  # If true Use Harmenberg 2021's Neutral Measure. For more information, see https://econ-ark.org/materials/harmenberg-aggregation?launch
                 self.dist_pGrid = np.array([1])
 
@@ -2456,7 +2450,7 @@ def define_distribution_grid(
             )
 
         elif self.T_cycle != 0:
-            if num_pointsM == None:
+            if num_pointsM is None:
                 m_points = self.mCount
             else:
                 m_points = num_pointsM
@@ -2510,7 +2504,7 @@ def define_distribution_grid(
                 self.dist_pGrid = dist_pGrid
 
             if (
-                self.neutral_measure == True
+                self.neutral_measure is True
             ):  # If true Use Harmenberg 2021's Neutral Measure. For more information, see https://econ-ark.org/materials/harmenberg-aggregation?launch
                 self.dist_pGrid = self.T_cycle * [np.array([1])]
 

HARK/ConsumptionSaving/ConsIndShockModelFast.py

@@ -20,7 +20,7 @@
 from numba import njit
 from quantecon.optimize import newton_secant
 
-from HARK import MetricObject, make_one_period_oo_solver
+from HARK import make_one_period_oo_solver
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     ConsIndShockSolverBasic,
     ConsPerfForesightSolver,
@@ -36,6 +36,7 @@
     MargValueFuncCRRA,
     ValueFuncCRRA,
 )
+from HARK.metric import MetricObject
 from HARK.numba_tools import (
     CRRAutility,
     CRRAutility_inv,

HARK/ConsumptionSaving/ConsLabeledModel.py

@@ -20,8 +20,9 @@
     init_risky_asset,
     init_risky_share_fixed,
 )
-from HARK.core import MetricObject, make_one_period_oo_solver
+from HARK.core import make_one_period_oo_solver
 from HARK.distribution import DiscreteDistributionLabeled
+from HARK.metric import MetricObject
 from HARK.rewards import UtilityFuncCRRA
 
 

HARK/ConsumptionSaving/ConsLaborModel.py

@@ -18,7 +18,6 @@
     IndShockConsumerType,
     init_idiosyncratic_shocks,
 )
-from HARK.core import MetricObject
 from HARK.interpolation import (
     BilinearInterp,
     ConstantFunction,
@@ -28,6 +27,7 @@
     ValueFuncCRRA,
     VariableLowerBoundFunc2D,
 )
+from HARK.metric import MetricObject
 from HARK.rewards import CRRAutilityP, CRRAutilityP_inv
 
 

HARK/ConsumptionSaving/ConsMedModel.py

@@ -6,7 +6,7 @@
 import numpy as np
 from scipy.optimize import brentq
 
-from HARK import AgentType, MetricObject, make_one_period_oo_solver
+from HARK import AgentType, make_one_period_oo_solver
 from HARK.ConsumptionSaving.ConsGenIncProcessModel import (
     ConsGenIncProcessSolver,
     PersistentShockConsumerType,
@@ -29,6 +29,7 @@
     ValueFuncCRRA,
     VariableLowerBoundFunc3D,
 )
+from HARK.metric import MetricObject
 from HARK.rewards import (
     CRRAutility,
     CRRAutility_inv,
@@ -113,11 +114,12 @@ def __init__(
                 elif MedShk == 0:  # All consumption when MedShk = 0
                     cLvl = xLvl
                 else:
-                    optMedZeroFunc = (
-                        lambda c: (MedShk / MedPrice) ** (-1.0 / CRRAcon)
-                        * ((xLvl - c) / MedPrice) ** (CRRAmed / CRRAcon)
-                        - c
-                    )
+
+                    def optMedZeroFunc(c):
+                        return (MedShk / MedPrice) ** (-1.0 / CRRAcon) * (
+                            (xLvl - c) / MedPrice
+                        ) ** (CRRAmed / CRRAcon) - c
+
                     # Find solution to FOC
                     cLvl = brentq(optMedZeroFunc, 0.0, xLvl)
                 cLvlGrid[i, j] = cLvl

HARK/ConsumptionSaving/ConsPortfolioModel.py

@@ -7,46 +7,28 @@
 
 import numpy as np
 
-from HARK import (  # Basic HARK features
-    AgentType,
-    MetricObject,
-    NullFunc,
-    make_one_period_oo_solver,
-)
-from HARK.ConsumptionSaving.ConsIndShockModel import (
-    IndShockConsumerType,  # PortfolioConsumerType inherits from it
-)
-from HARK.ConsumptionSaving.ConsIndShockModel import (
-    init_idiosyncratic_shocks,  # Baseline dictionary to build on
-)
-from HARK.ConsumptionSaving.ConsIndShockModel import utility  # CRRA utility function
-from HARK.ConsumptionSaving.ConsIndShockModel import (
-    utility_inv,  # Inverse CRRA utility function
-)
+from HARK import AgentType, NullFunc, make_one_period_oo_solver
 from HARK.ConsumptionSaving.ConsIndShockModel import (
-    utility_invP,  # Derivative of inverse CRRA utility function
-)
-from HARK.ConsumptionSaving.ConsIndShockModel import (
-    utilityP,  # CRRA marginal utility function
-)
-from HARK.ConsumptionSaving.ConsIndShockModel import (
-    utilityP_inv,  # Inverse CRRA marginal utility function
+    IndShockConsumerType,
+    init_idiosyncratic_shocks,
+    utility,
+    utility_inv,
+    utility_invP,
+    utilityP,
+    utilityP_inv,
 )
 from HARK.ConsumptionSaving.ConsRiskyAssetModel import RiskyAssetConsumerType
-from HARK.distribution import calc_expectation
-from HARK.interpolation import BilinearInterp  # 2D interpolator
-from HARK.interpolation import (
-    ConstantFunction,  # Interpolator-like class that returns constant value
-)
-from HARK.interpolation import CubicInterp  # Piecewise cubic interpolation
-from HARK.interpolation import (
-    IdentityFunction,  # Interpolator-like class that returns one of its arguments
-)
-from HARK.interpolation import LinearInterp  # Piecewise linear interpolation
 from HARK.interpolation import (
-    LinearInterpOnInterp1D,  # Interpolator over 1D interpolations
+    BilinearInterp,
+    ConstantFunction,
+    CubicInterp,
+    IdentityFunction,
+    LinearInterp,
+    LinearInterpOnInterp1D,
+    MargValueFuncCRRA,
+    ValueFuncCRRA,
 )
-from HARK.interpolation import MargValueFuncCRRA, ValueFuncCRRA
+from HARK.metric import MetricObject
 
 
 # Define a class to represent the single period solution of the portfolio choice problem

HARK/ConsumptionSaving/ConsRiskyContribModel.py

@@ -25,7 +25,7 @@
 
 import numpy as np
 
-from HARK import MetricObject, NullFunc  # Basic HARK features
+from HARK import NullFunc  # Basic HARK features
 from HARK.ConsumptionSaving.ConsIndShockModel import utility  # CRRA utility function
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     utility_inv,  # Inverse CRRA utility function
@@ -53,6 +53,7 @@
 from HARK.interpolation import LinearInterp  # Piecewise linear interpolation
 from HARK.interpolation import TrilinearInterp  # 3D interpolator
 from HARK.interpolation import DiscreteInterp, MargValueFuncCRRA, ValueFuncCRRA
+from HARK.metric import MetricObject
 from HARK.utilities import make_grid_exp_mult