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Use numba and fast math for CRRA utilities #1128

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Use numba and fast math for CRRA utilities #1128

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8feb5f5
use numba and fast math
alanlujan91 Mar 25, 2022
c6578e7
fix error
alanlujan91 Mar 25, 2022
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2 changes: 1 addition & 1 deletion HARK/ConsumptionSaving/ConsIndShockModelFast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@
MargValueFuncCRRA,
MargMargValueFuncCRRA,
)
from HARK.numba import (
from HARK.utilities import (
CRRAutility,
CRRAutilityP,
CRRAutilityPP,
Expand Down
36 changes: 24 additions & 12 deletions HARK/utilities.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
# import sys
# exception_type, value, traceback = sys.exc_info()
# raise ImportError('HARK must be used in a graphical environment.', exception_type, value, traceback)
from numba import njit
from scipy.interpolate import interp1d
import warnings

Expand Down Expand Up @@ -108,6 +109,7 @@ def distance(self, other):
# ==============================================================================
# ============== Define utility functions ===============================
# ==============================================================================
@njit(fastmath=True, cache=True)
def CRRAutility(c, gam):
"""
Evaluates constant relative risk aversion (CRRA) utility of consumption c
Expand All @@ -132,12 +134,13 @@ def CRRAutility(c, gam):
>>> utility(c=c, gam=gamma)
-1.0
"""

if gam == 1:
return np.log(c)
else:
return c ** (1.0 - gam) / (1.0 - gam)


def uFunc_CRRA_stone_geary(c, CRRA, stone_geary):
"""
Evaluates Stone-Geary version of a constant relative risk aversion (CRRA)
Expand Down Expand Up @@ -165,9 +168,10 @@ def uFunc_CRRA_stone_geary(c, CRRA, stone_geary):
-1.0
"""
if CRRA == 1:
return np.log( stone_geary + c)
return np.log(stone_geary + c)
else:
return ( stone_geary + c ) ** (1.0 - CRRA) / (1.0 - CRRA)
return (stone_geary + c) ** (1.0 - CRRA) / (1.0 - CRRA)


def uPFunc_CRRA_stone_geary(c, CRRA, stone_geary):
"""
Expand All @@ -189,7 +193,8 @@ def uPFunc_CRRA_stone_geary(c, CRRA, stone_geary):
marginal utility

"""
return ( stone_geary + c ) ** (- CRRA)
return (stone_geary + c) ** (-CRRA)


def uPPFunc_CRRA_stone_geary(c, CRRA, stone_geary):
"""
Expand All @@ -210,9 +215,10 @@ def uPPFunc_CRRA_stone_geary(c, CRRA, stone_geary):
marginal utility

"""
return (- CRRA)*( stone_geary + c ) ** (- CRRA - 1)
return (-CRRA) * (stone_geary + c) ** (-CRRA - 1)


@njit(fastmath=True, cache=True)
def CRRAutilityP(c, gam):
"""
Evaluates constant relative risk aversion (CRRA) marginal utility of consumption
Expand All @@ -232,11 +238,12 @@ def CRRAutilityP(c, gam):
"""

if gam == 1:
return 1/c
return 1 / c

return c ** -gam


@njit(fastmath=True, cache=True)
def CRRAutilityPP(c, gam):
"""
Evaluates constant relative risk aversion (CRRA) marginal marginal utility of
Expand All @@ -254,10 +261,11 @@ def CRRAutilityPP(c, gam):
(unnamed) : float
Marginal marginal utility
"""

return -gam * c ** (-gam - 1.0)


@njit(fastmath=True, cache=True)
def CRRAutilityPPP(c, gam):
"""
Evaluates constant relative risk aversion (CRRA) marginal marginal marginal
Expand All @@ -275,10 +283,11 @@ def CRRAutilityPPP(c, gam):
(unnamed) : float
Marginal marginal marginal utility
"""

return (gam + 1.0) * gam * c ** (-gam - 2.0)


@njit(fastmath=True, cache=True)
def CRRAutilityPPPP(c, gam):
"""
Evaluates constant relative risk aversion (CRRA) marginal marginal marginal
Expand All @@ -296,10 +305,11 @@ def CRRAutilityPPPP(c, gam):
(unnamed) : float
Marginal marginal marginal marginal utility
"""

return -(gam + 2.0) * (gam + 1.0) * gam * c ** (-gam - 3.0)


@njit(fastmath=True, cache=True)
def CRRAutility_inv(u, gam):
"""
Evaluates the inverse of the CRRA utility function (with risk aversion para-
Expand All @@ -323,6 +333,7 @@ def CRRAutility_inv(u, gam):
return ((1.0 - gam) * u) ** (1 / (1.0 - gam))


@njit(fastmath=True, cache=True)
def CRRAutilityP_inv(uP, gam):
"""
Evaluates the inverse of the CRRA marginal utility function (with risk aversion
Expand All @@ -343,6 +354,7 @@ def CRRAutilityP_inv(uP, gam):
return uP ** (-1.0 / gam)


@njit(fastmath=True, cache=True)
def CRRAutility_invP(u, gam):
"""
Evaluates the derivative of the inverse of the CRRA utility function (with
Expand All @@ -366,6 +378,7 @@ def CRRAutility_invP(u, gam):
return ((1.0 - gam) * u) ** (gam / (1.0 - gam))


@njit(fastmath=True, cache=True)
def CRRAutilityP_invP(uP, gam):
"""
Evaluates the derivative of the inverse of the CRRA marginal utility function
Expand Down Expand Up @@ -1009,7 +1022,6 @@ def setup_latex_env_notebook(pf, latexExists):
output of determine_platform()
"""
import os
from matplotlib import rc
import matplotlib.pyplot as plt

plt.rc("font", family="serif")
Expand All @@ -1032,7 +1044,7 @@ def setup_latex_env_notebook(pf, latexExists):
)
# Latex expects paths to be separated by /. \ might result in pieces
# being interpreted as commands.
latexdefs_path = os.getcwd().replace(os.path.sep, '/') + "/latexdefs.tex"
latexdefs_path = os.getcwd().replace(os.path.sep, "/") + "/latexdefs.tex"
if os.path.isfile(latexdefs_path):
latex_preamble = latex_preamble + r"\input{" + latexdefs_path + r"}"
else: # the required latex_envs package needs this file to exist even if it is empty
Expand Down