generalised unpack function after a model is solved (#784)

* generalised unpack function after a model is solved * fix examples with changes to unpackcFunc * stripout notebook output * add deprecate warning to unpackcFunc * Update CHANGELOG.md Co-authored-by: Sebastian Benthall <[email protected]>
econ-ark · Aug 7, 2020 · 5a3ab2a · 5a3ab2a
1 parent f79568c
commit 5a3ab2a
Show file tree

Hide file tree

Showing 18 changed files with 1,005 additions and 535 deletions.
diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
@@ -10,7 +10,7 @@ For more information on HARK, see [our Github organization](https://github.com/e
 
 ### 0.10.7
 
-Release Date: XX-XX-2020
+Release Date: 08-08-2020
 
 #### Major Changes
 
@@ -21,6 +21,7 @@ Release Date: XX-XX-2020
 
 #### Minor Changes 
 
+- unpackcFunc is deprecated, use unpack(parameter) to unpack a parameter after solving the model [#784](https://github.com/econ-ark/HARK/pull/784)
 - Remove deprecated Solution Class, use HARKObject across the codebase [#772](https://github.com/econ-ark/HARK/pull/772)
 - Add option to find crossing points in the envelope step of DCEGM algorithm [#758](https://github.com/econ-ark/HARK/pull/758)
 - Fix reset bug in the behaviour of AgentType.resetRNG(), implemented individual resetRNG methods for AgentTypes [#757](https://github.com/econ-ark/HARK/pull/757)

diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -1531,25 +1531,23 @@ def updateSolutionTerminal(self):
         self.solution_terminal.vPPfunc = MargMargValueFunc(self.cFunc_terminal_,self.CRRA)
 
     def unpackcFunc(self):
-        '''
+        ''' DEPRECATED: Use solution.unpack('cFunc') instead.
         "Unpacks" the consumption functions into their own field for easier access.
         After the model has been solved, the consumption functions reside in the
         attribute cFunc of each element of ConsumerType.solution.  This method
         creates a (time varying) attribute cFunc that contains a list of consumption
         functions.
-
         Parameters
         ----------
         none
-
         Returns
         -------
         none
         '''
-        self.cFunc = []
-        for solution_t in self.solution:
-            self.cFunc.append(solution_t.cFunc)
-        self.addToTimeVary('cFunc')
+        _log.critical("unpackcFunc is deprecated and it will soon be removed, " \
+            "please use unpack('cFunc') instead.")
+        self.unpack('cFunc')
+
 
     def initializeSim(self):
         self.PlvlAggNow = 1.0

diff --git a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
@@ -137,7 +137,7 @@ def test_baseEx(self):
         baseEx.cycles = 100   # Make this type have a finite horizon (Set T = 100)
 
         baseEx.solve()
-        baseEx.unpackcFunc()
+        baseEx.unpack('cFunc')
 
         m = np.linspace(0,9.5,1000)
 
@@ -163,7 +163,7 @@ def test_GICFails(self):
             **GIC_fail_dictionary)
 
         GICFailExample.solve()
-        GICFailExample.unpackcFunc()
+        GICFailExample.unpack('cFunc')
         m = np.linspace(0,5,1000)
         c_m = GICFailExample.cFunc[0](m)
 
@@ -177,7 +177,7 @@ def test_infinite_horizon(self):
                                           **self.base_params)
 
         baseEx_inf.solve()
-        baseEx_inf.unpackcFunc()
+        baseEx_inf.unpack('cFunc')
 
         m1 = np.linspace(1,baseEx_inf.solution[0].mNrmSS,50) # m1 defines the plot range on the left of target m value (e.g. m <= target m)
         c_m1 = baseEx_inf.cFunc[0](m1)

diff --git a/HARK/ConsumptionSaving/tests/test_modelcomparisons.py b/HARK/ConsumptionSaving/tests/test_modelcomparisons.py
@@ -49,14 +49,14 @@ def setUp(self):
 
         InfiniteType.updateIncomeProcess()
         InfiniteType.solve()
-        InfiniteType.unpackcFunc()
+        InfiniteType.unpack('cFunc')
 
         # Make and solve a perfect foresight consumer type with the same parameters
         PerfectForesightType = PerfForesightConsumerType(**test_dictionary)
         PerfectForesightType.cycles = 0
 
         PerfectForesightType.solve()
-        PerfectForesightType.unpackcFunc()
+        PerfectForesightType.unpack('cFunc')
 
         self.InfiniteType = InfiniteType
         self.PerfectForesightType = PerfectForesightType
@@ -139,7 +139,7 @@ def setUp(self):
                                   unemployed_income_dist]]
 
         MarkovType.solve()
-        MarkovType.unpackcFunc()
+        MarkovType.unpack('cFunc')
 
         self.TBSType = TBSType
         self.MarkovType = MarkovType

diff --git a/HARK/core.py b/HARK/core.py
@@ -283,6 +283,29 @@ def delFromTimeInv(self, *params):
             if param in self.time_inv:
                 self.time_inv.remove(param)
 
+    def unpack(self, parameter):
+        '''
+        Unpacks a parameter from a solution object for easier access.
+        After the model has been solved, the parameters (like consumption function)
+        reside in the attributes of each element of `ConsumerType.solution`
+        (e.g. `cFunc`).  This method creates a (time varying) attribute of the given
+        parameter name that contains a list of functions accessible by `ConsumerType.parameter`.
+
+        Parameters
+        ----------
+        parameter: str
+            Name of the function to unpack from the solution
+
+        Returns
+        -------
+        none
+        '''
+        setattr(self, parameter, list())
+        for solution_t in self.solution:
+            self.__dict__[parameter].append(solution_t.__dict__[parameter])
+        self.addToTimeVary(parameter)
+
+
     def solve(self, verbose=False):
         '''
         Solve the model for this instance of an agent type by backward induction.

diff --git a/examples/ConsIndShockModel/IndShockConsumerType.ipynb b/examples/ConsIndShockModel/IndShockConsumerType.ipynb
diff --git a/examples/ConsIndShockModel/IndShockConsumerType.py b/examples/ConsIndShockModel/IndShockConsumerType.py
@@ -3,15 +3,26 @@
 #   jupytext:
 #     cell_metadata_filter: collapsed,code_folding,heading_collapsed,hidden
 #     formats: ipynb,py:percent
+#     notebook_metadata_filter: all
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.2'
-#       jupytext_version: 1.2.4
+#       jupytext_version: 1.2.3
 #   kernelspec:
 #     display_name: Python 3
 #     language: python
 #     name: python3
+#   language_info:
+#     codemirror_mode:
+#       name: ipython
+#       version: 3
+#     file_extension: .py
+#     mimetype: text/x-python
+#     name: python
+#     nbconvert_exporter: python
+#     pygments_lexer: ipython3
+#     version: 3.7.6
 # ---
 
 # %% [markdown]
@@ -21,8 +32,6 @@
 # %% {"code_folding": [0]}
 # Initial imports and notebook setup, click arrow to show
 from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType
-import HARK.logger as logger
-logger.verbose()
 from HARK.utilities import plotFuncsDer, plotFuncs
 from time import clock
 import matplotlib.pyplot as plt
@@ -347,7 +356,7 @@
 # %%
 print('Consumption functions across the lifecycle:')
 mMin = np.min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)])
-LifecycleExample.unpackcFunc() # This makes all of the cFuncs accessible in the attribute cFunc
+LifecycleExample.unpack('cFunc') # This makes all of the cFuncs accessible in the attribute cFunc
 plotFuncs(LifecycleExample.cFunc,mMin,5)
 
 # %% [markdown]
@@ -412,12 +421,10 @@
 CyclicalExample.cycles = 0 # Make this consumer type have an infinite horizon
 CyclicalExample.solve()
 
-CyclicalExample.unpackcFunc()
+CyclicalExample.unpack('cFunc')
 print('Quarterly consumption functions:')
 mMin = min([X.mNrmMin for X in CyclicalExample.solution])
 plotFuncs(CyclicalExample.cFunc,mMin,5)
 
 # %% [markdown]
 # The very low green consumption function corresponds to the quarter in which the ski instructors make most of their income.  They know that they are about to experience a 70% drop in "permanent" income, so they do not consume much *relative to their income this quarter*.  In the other three quarters, *normalized* consumption is much higher, as current "permanent" income is low relative to future expectations.  In *level*, the consumption chosen in each quarter is much more similar
-
-# %%
diff --git a/examples/ConsumptionSaving/example_ConsAggShockModel.ipynb b/examples/ConsumptionSaving/example_ConsAggShockModel.ipynb
@@ -91,7 +91,7 @@
     "        \"Consumption function at each aggregate market resources-to-labor ratio gridpoint:\"\n",
     "    )\n",
     "    m_grid = np.linspace(0, 10, 200)\n",
-    "    AggShockExample.unpackcFunc()\n",
+    "    AggShockExample.unpack('cFunc')\n",
     "    for M in AggShockExample.Mgrid.tolist():\n",
     "        mMin = AggShockExample.solution[0].mNrmMin(M)\n",
     "        c_at_this_M = AggShockExample.cFunc[0](m_grid + mMin, M * np.ones_like(m_grid))\n",
@@ -123,9 +123,9 @@
     "    print(\n",
     "        \"Consumption function at each aggregate market resources gridpoint (in general equilibrium):\"\n",
     "    )\n",
-    "    AggShockExample.unpackcFunc()\n",
+    "    AggShockExample.unpack('cFunc')\n",
     "    m_grid = np.linspace(0, 10, 200)\n",
-    "    AggShockExample.unpackcFunc()\n",
+    "    AggShockExample.unpack('cFunc')\n",
     "    for M in AggShockExample.Mgrid.tolist():\n",
     "        mMin = AggShockExample.solution[0].mNrmMin(M)\n",
     "        c_at_this_M = AggShockExample.cFunc[0](m_grid + mMin, M * np.ones_like(m_grid))\n",
@@ -181,7 +181,7 @@
     "\n",
     "    print(\"Consumption function at each aggregate market resources-to-labor ratio gridpoint (for each macro state):\")\n",
     "    m_grid = np.linspace(0, 10, 200)\n",
-    "    AggShockMrkvExample.unpackcFunc()\n",
+    "    AggShockMrkvExample.unpack('cFunc')\n",
     "    for i in range(2):\n",
     "        for M in AggShockMrkvExample.Mgrid.tolist():\n",
     "            mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M)\n",
@@ -214,7 +214,7 @@
     "\n",
     "    print(\"Consumption function at each aggregate market resources-to-labor ratio gridpoint (for each macro state):\")\n",
     "    m_grid = np.linspace(0, 10, 200)\n",
-    "    AggShockMrkvExample.unpackcFunc()\n",
+    "    AggShockMrkvExample.unpack('cFunc')\n",
     "    for i in range(2):\n",
     "        for M in AggShockMrkvExample.Mgrid.tolist():\n",
     "            mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M)\n",
@@ -229,9 +229,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "if solve_krusell_smith:\n",
@@ -352,8 +350,57 @@
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
+   "formats": "ipynb,py",
    "main_language": "python",
    "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,