Merge pull request #1465 from sbenthall/i1373

YAML configuration for consumer portfolio problem, with parser and working tests.

Documentation/CHANGELOG.md

@@ -18,6 +18,7 @@ Release Date: TBD
 - Allows structural equations in model files to be provided in string form [#1427](https://github.com/econ-ark/HARK/pull/1427)
 - Introduces `HARK.parser' module for parsing configuration files into models [#1427](https://github.com/econ-ark/HARK/pull/1427)
 - Allows construction of shocks with arguments based on mathematical expressions [#1464](https://github.com/econ-ark/HARK/pull/1464)
+- YAML configuration file for the normalized consumption and portolio choice [#1465](https://github.com/econ-ark/HARK/pull/1465)
 
 #### Minor Changes
 

HARK/model.py

@@ -257,6 +257,10 @@ def __post_init__(self):
             if isinstance(self.dynamics[v], str):
                 self.dynamics[v] = math_text_to_lambda(self.dynamics[v])
 
+        for r in self.reward:
+            if isinstance(self.reward[r], str):
+                self.reward[r] = math_text_to_lambda(self.reward[r])
+
     def get_shocks(self):
         return self.shocks
 

HARK/models/consumer.yaml

@@ -0,0 +1,43 @@
+#
+# A YAML configuration file for the consumption portfolio problem blocks.
+#
+
+calibration:
+    DiscFac: 0.96
+    CRRA: 2.0
+    R: 1.03 # can be overriden by portfolio dynamics
+    Rfree: 1.03
+    EqP: 0.02
+    LivPrb: 0.98
+    PermGroFac: 1.01
+    BoroCnstArt: None
+    TranShkStd: 0.1
+    RiskyStd: 0.1
+
+blocks:
+    - &cons_normalized
+        name: consumption normalized
+        shocks:
+            live: !Bernoulli
+                p: LivPrb
+            theta: !MeanOneLogNormal
+                sigma: TranShkStd
+        dynamics:
+            b: k * R / PermGroFac
+            m: b + theta
+            c: !Control
+                info: m
+            a: m - c
+
+        reward:
+            u: c ** (1 - CRRA) / (1 - CRRA)
+    - &portfolio_choice
+        name: portfolio choice
+        shocks:
+            risky_return: !Lognormal
+                mean: Rfree + EqP
+                std: RiskyStd
+            dynamics:
+                stigma: !Control
+                    info: a
+                R: Rfree + (risky_return - Rfree) * stigma

HARK/parser.py

@@ -1,5 +1,16 @@
+from HARK.distribution import Bernoulli, Lognormal, MeanOneLogNormal
 from sympy.utilities.lambdify import lambdify
 from sympy.parsing.sympy_parser import parse_expr
+import yaml
+
+
+class ControlToken:
+    """
+    Represents a parsed Control variable.
+    """
+
+    def __init__(self, args):
+        pass
 
 
 class Expression:
@@ -17,10 +28,40 @@ def func(self):
         return lambdify(list(self.expr.free_symbols), self.expr, "numpy")
 
 
+def tuple_constructor_from_class(cls):
+    def constructor(loader, node):
+        value = loader.construct_mapping(node)
+        return (cls, value)
+
+    return constructor
+
+
 def math_text_to_lambda(text):
     """
     Returns a function represented by the given mathematical text.
     """
     expr = parse_expr(text)
     func = lambdify(list(expr.free_symbols), expr, "numpy")
     return func
+
+
+def harklang_loader():
+    """
+    A PyYAML loader that supports tags for HARKLang,
+    such as random variables and model tags.
+    """
+    loader = yaml.SafeLoader
+    yaml.SafeLoader.add_constructor(
+        "!Bernoulli", tuple_constructor_from_class(Bernoulli)
+    )
+    yaml.SafeLoader.add_constructor(
+        "!MeanOneLogNormal", tuple_constructor_from_class(MeanOneLogNormal)
+    )
+    yaml.SafeLoader.add_constructor(
+        "!Lognormal", tuple_constructor_from_class(Lognormal)
+    )
+    yaml.SafeLoader.add_constructor(
+        "!Control", tuple_constructor_from_class(ControlToken)
+    )
+
+    return loader

HARK/tests/test_parser.py

@@ -0,0 +1,33 @@
+import os
+import unittest
+
+import HARK.model as model
+import HARK.parser as parser
+import yaml
+
+
+class test_consumption_parsing(unittest.TestCase):
+    def setUp(self):
+        this_file_path = os.path.dirname(__file__)
+        consumer_yaml_path = os.path.join(this_file_path, "../models/consumer.yaml")
+
+        self.consumer_yaml_file = open(consumer_yaml_path, "r")
+
+    def test_parse(self):
+        self.consumer_yaml_file
+
+        config = yaml.load(self.consumer_yaml_file, Loader=parser.harklang_loader())
+
+        self.assertEqual(config["calibration"]["DiscFac"], 0.96)
+        self.assertEqual(config["blocks"][0]["name"], "consumption normalized")
+
+        ## construct and test the consumption block
+        cons_norm_block = model.DBlock(**config["blocks"][0])
+        cons_norm_block.construct_shocks(config["calibration"])
+        cons_norm_block.discretize({"theta": {"N": 5}})
+        self.assertEqual(cons_norm_block.calc_reward({"c": 1, "CRRA": 2})["u"], -1.0)
+
+        ## construct and test the portfolio block
+        portfolio_block = model.DBlock(**config["blocks"][1])
+        portfolio_block.construct_shocks(config["calibration"])
+        portfolio_block.discretize({"risky_return": {"N": 5}})

requirements/base.txt

@@ -5,6 +5,7 @@ networkx>=3
 numba<0.60.0
 numpy>=1.23
 pandas>=1.5
+pyyaml>=6.0
 quantecon
 scipy>=1.10
 seaborn>=0.12