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Array-valued discrete distribution #1146

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Jun 1, 2022
Merged

Array-valued discrete distribution #1146

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ff335f1
Add tests that a matrix valued distribution should pass
Mv77 May 16, 2022
d2f038e
Let dim and draw support matrices
Mv77 May 16, 2022
4cf28a8
Implement calc_expectation
Mv77 May 16, 2022
4a52f23
Fix mvnorm test
Mv77 May 16, 2022
c811a18
Extra args in calc_expectations
Mv77 May 16, 2022
58e0247
Add support and test for dstn of function
Mv77 May 16, 2022
8ad4a34
Lint
Mv77 May 16, 2022
3ba9d11
Change DiscreteDistribution to assume its receiving an array
Mv77 May 20, 2022
d058028
Update combine_distributions and calc_expectation
Mv77 May 20, 2022
ddf7ba9
Update core tests
Mv77 May 20, 2022
e1f8a85
Update distr_of_function
Mv77 May 20, 2022
271c6c0
Change indexing to maintain sim tests
Mv77 May 20, 2022
6bd15d9
Revert the calc_expectations dimension upgrade
Mv77 May 20, 2022
390447b
Add behavior that old models expect
Mv77 May 20, 2022
de47745
Adapt models and tests to new dstn.X
Mv77 May 20, 2022
d863f45
Enforce dimension types in draw
Mv77 May 23, 2022
3cd5def
Fix failing examples
Mv77 May 24, 2022
e6d31b9
Update docstrings
Mv77 May 24, 2022
0dce71e
Update CHANGELOG.md
Mv77 May 24, 2022
73f2f78
Replace list(map()) with list comprehensions
Mv77 May 25, 2022
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1 change: 1 addition & 0 deletions Documentation/CHANGELOG.md
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Expand Up @@ -20,6 +20,7 @@ Release Date: TBD
* `FrameModel` and `FrameSet` classes introduced for more modular construction of framed models. `FrameAgentType` dedicated to simulation. [#1117](https://github.com/econ-ark/HARK/pull/1117)
* General control transitions based on decision rules in `FrameAgentType`. [#1117](https://github.com/econ-ark/HARK/pull/1117)
* Adds `distr_of_function` tool to calculate the distribution of a function of a discrete random variable. [#1144](https://github.com/econ-ark/HARK/pull/1144)
* Changes the `DiscreteDistribution` class to allow for arbitrary array-valued random variables. [#1146](https://github.com/econ-ark/HARK/pull/1146)


### Minor Changes
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2 changes: 1 addition & 1 deletion HARK/ConsumptionSaving/ConsIndShockModel.py
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Expand Up @@ -2800,7 +2800,7 @@ def __init__(self, sigma, n_approx, neutral_measure=False, seed=0):
)
# Change the pmf if necessary
if neutral_measure:
logn_approx.pmf = logn_approx.X * logn_approx.pmf
logn_approx.pmf = (logn_approx.X * logn_approx.pmf).flatten()

super().__init__(pmf=logn_approx.pmf, X=logn_approx.X, seed=seed)

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6 changes: 3 additions & 3 deletions HARK/ConsumptionSaving/ConsLaborModel.py
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Expand Up @@ -159,9 +159,9 @@ def solve_ConsLaborIntMarg(
# Unpack next period's solution and the productivity shock distribution, and define the inverse (marginal) utilty function
vPfunc_next = solution_next.vPfunc
TranShkPrbs = TranShkDstn.pmf
TranShkVals = TranShkDstn.X
TranShkVals = TranShkDstn.X.flatten()
PermShkPrbs = PermShkDstn.pmf
PermShkVals = PermShkDstn.X
PermShkVals = PermShkDstn.X.flatten()
TranShkCount = TranShkPrbs.size
PermShkCount = PermShkPrbs.size
uPinv = lambda X: CRRAutilityP_inv(X, gam=CRRA)
Expand Down Expand Up @@ -557,7 +557,7 @@ def update_TranShkGrid(self):
TranShkGrid = [] # Create an empty list for TranShkGrid that will be updated
for t in range(self.T_cycle):
TranShkGrid.append(
self.TranShkDstn[t].X
self.TranShkDstn[t].X.flatten()
) # Update/ Extend the list of TranShkGrid with the TranShkVals for each TranShkPrbs
self.TranShkGrid = TranShkGrid # Save that list in self (time-varying)
self.add_to_time_vary(
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4 changes: 2 additions & 2 deletions HARK/ConsumptionSaving/ConsMedModel.py
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Expand Up @@ -649,7 +649,7 @@ def update_solution_terminal(self):
"""
# Take last period data, whichever way time is flowing
MedPrice = self.MedPrice[-1]
MedShkVals = self.MedShkDstn[-1].X
MedShkVals = self.MedShkDstn[-1].X.flatten()
MedShkPrbs = self.MedShkDstn[-1].pmf

# Initialize grids of medical need shocks, market resources, and optimal consumption
Expand Down Expand Up @@ -993,7 +993,7 @@ def set_and_update_values(self, solution_next, IncShkDstn, LivPrb, DiscFac):

# Also unpack the medical shock distribution
self.MedShkPrbs = self.MedShkDstn.pmf
self.MedShkVals = self.MedShkDstn.X
self.MedShkVals = self.MedShkDstn.X.flatten()

def def_utility_funcs(self):
"""
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4 changes: 2 additions & 2 deletions HARK/ConsumptionSaving/ConsPrefShockModel.py
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Expand Up @@ -369,7 +369,7 @@ def __init__(
CubicBool,
)
self.PrefShkPrbs = PrefShkDstn.pmf
self.PrefShkVals = PrefShkDstn.X
self.PrefShkVals = PrefShkDstn.X.flatten()

def get_points_for_interpolation(self, EndOfPrdvP, aNrmNow):
"""
Expand Down Expand Up @@ -685,4 +685,4 @@ def __init__(
CubicBool,
)
self.PrefShkPrbs = PrefShkDstn.pmf
self.PrefShkVals = PrefShkDstn.X
self.PrefShkVals = PrefShkDstn.X.flatten()
2 changes: 1 addition & 1 deletion HARK/ConsumptionSaving/ConsRiskyContribModel.py
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Expand Up @@ -1206,7 +1206,7 @@ def post_return_derivs(inc_shocks, b_aux, g_aux, s):

# Define grids
b_aux_grid = np.concatenate([np.array([0.0]), Rfree * aXtraGrid])
g_aux_grid = np.concatenate([np.array([0.0]), max(RiskyDstn.X) * nNrmGrid])
g_aux_grid = np.concatenate([np.array([0.0]), max(RiskyDstn.X.flatten()) * nNrmGrid])
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It seems like a lot of extra code is now needed to flip vertical vectors/arrays into horizontal ones.
Would it be possible to have the arrays oriented horizontal in the DiscreteDistribution object?

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Possible yes, but I still thought this would be the path of least resistance.

I think other parts of HARK that deal with multivariate distributions use things like dstn.X[n] to find all the possible draws of the nth dimension. This seems like an intuitive and compact code pattern that I would like to keep. It was easier for me to conceptually think that nature was always the last dimension.

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"X" is truly a terrible name for an object property that is doing so much work. See #1051

I'm not suggesting you replace "X" with something else in this PR, but I wonder what mathematical object X is now.

It's possible that we could have multiple ways of accessing this data within a distribution, so it requires less manipulation before entering and after pulling it from the object.


# Create tiled arrays with conforming dimensions.
b_aux_tiled, g_aux_tiled, Share_tiled = np.meshgrid(
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4 changes: 2 additions & 2 deletions HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py
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Expand Up @@ -62,10 +62,10 @@ def setUp(self):

# Replace the default (lognormal) income distribution with a custom one
employed_income_dist = DiscreteDistribution(
np.ones(1), [np.ones(1), np.ones(1)]
np.ones(1), np.array([[1.0],[1.0]])
) # Definitely get income
unemployed_income_dist = DiscreteDistribution(
np.ones(1), [np.ones(1), np.zeros(1)]
np.ones(1), np.array([[1.0],[0.0]])
) # Definitely don't
self.model.IncShkDstn = [
[
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4 changes: 2 additions & 2 deletions HARK/ConsumptionSaving/tests/test_ConsPrefShockModel.py
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Expand Up @@ -18,7 +18,7 @@ def test_solution(self):
self.assertEqual(self.agent.solution[0].mNrmMin, 0)
m = np.linspace(self.agent.solution[0].mNrmMin, 5, 200)

self.assertAlmostEqual(self.agent.PrefShkDstn[0].X[5], 0.69046812)
self.assertAlmostEqual(self.agent.PrefShkDstn[0].X[0,5], 0.69046812)

self.assertAlmostEqual(
self.agent.solution[0].cFunc(m, np.ones_like(m))[35], 0.8123891603954809
Expand Down Expand Up @@ -58,7 +58,7 @@ def test_solution(self):

m = np.linspace(self.agent.solution[0].mNrmMin, 5, 200)

self.assertAlmostEqual(self.agent.PrefShkDstn[0].X[5], 0.6904681186891202)
self.assertAlmostEqual(self.agent.PrefShkDstn[0].X[0,5], 0.6904681186891202)

c = self.agent.solution[0].cFunc(m, np.ones_like(m))
self.assertAlmostEqual(c[5], 0.13237946)
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4 changes: 2 additions & 2 deletions HARK/ConsumptionSaving/tests/test_modelcomparisons.py
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Expand Up @@ -159,10 +159,10 @@ def setUp(self):
MarkovType = MarkovConsumerType(**Markov_primitives)
MarkovType.cycles = 0
employed_income_dist = DiscreteDistribution(
np.ones(1), [np.ones(1), np.ones(1)]
np.ones(1), np.array([[1.0],[1.0]])
)
unemployed_income_dist = DiscreteDistribution(
np.ones(1), [np.ones(1), np.zeros(1)]
np.ones(1), np.array([[1.0],[0.0]])
)
MarkovType.IncShkDstn = [[employed_income_dist, unemployed_income_dist]]

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4 changes: 2 additions & 2 deletions HARK/core.py
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Expand Up @@ -1549,7 +1549,7 @@ def distribute_params(agent, param_name, param_count, distribution):
param_count : int
Number of different values the parameter will take on.
distribution : Distribution
A distribution.
A 1-D distribution.

Returns
-------
Expand All @@ -1567,7 +1567,7 @@ def distribute_params(agent, param_name, param_count, distribution):
agent_set[j].assign_parameters(**{'AgentCount': int(agent.AgentCount * param_dist.pmf[j])})
# agent_set[j].__dict__[param_name] = param_dist.X[j]

agent_set[j].assign_parameters(**{param_name: param_dist.X[j]})
agent_set[j].assign_parameters(**{param_name: param_dist.X[0,j]})



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