Stochastic transitions

CHANGELOG.md

@@ -2,6 +2,10 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+## [1.28.5] - 2023-06-28
+
+- Enable automated calibration of transfers and updated documentation to cover this feature
+
 ## [1.28.4] - 2023-05-27
 
 - Added an option to save initial compartment sizes inside a `ParameterSet`. Importantly, this saved representation allows setting the initial _subcompartment_ sizes for a `TimedCompartment`. It therefore offers the possibility of initializing the model in a steady state computed from a previous simulation run, that would not be possible to initialize conventionally because standard initialization uniformly distributes people into the subcompartments of a timed compartment.  

atomica/calibration.py

@@ -43,7 +43,6 @@ def _update_parset(parset, y_factors, pars_to_adjust):
             tokens = par_name.split("_from_")
             par = parset.transfers[tokens[0]][tokens[1]]
             par.y_factor[pop_name] = y_factors[i]
-            raise NotImplementedError
 
 
 def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, project):
@@ -123,7 +122,10 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
     :param pars_to_adjust: list of tuples, (par_name,pop_name,lower_limit,upper_limit)
                            the pop name can be None, which will be expanded to all populations
                            relevant to the parameter independently, or 'all' which will instead operate
-                           on the meta y factor.
+                           on the meta y factor. To calibrate a transfer, the parameter name should be set to
+                           ``'<tranfer_code_name>_from_<from_pop>'`` and then the destination population can be specified
+                           as the ``pop_name``. For example, to automatically calibrate an aging transfer 'age' from 5-14
+                           to 15-64, the tuple would contain ``pars_to_adjust=[('age_from_5-14','15-64',...)]``
     :param output_quantities: list of tuples, (var_label,pop_name,weight,metric), for use in the objective
                               function. pop_name=None will expand to all pops. pop_name='all' is not supported
     :param max_time: If using ASD, the maximum run time
@@ -243,7 +245,6 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
             tokens = par_name.split("_from_")
             par = args["parset"].transfers[tokens[0]][tokens[1]]
             logger.debug("parset.transfers['{}']['{}'].y_factor['{}']={:.2f}".format(tokens[0], tokens[1], pop_name, par.y_factor[pop_name]))
-            raise NotImplementedError  # Transfers might be handled differently in Atomica
 
     args["parset"].name = "calibrated_" + args["parset"].name
 

atomica/demos.py

@@ -1,4 +1,4 @@
 """
 Defines some defaults for Atomica projects
 
 Version: 2018sep24
@@ -11,10 +11,30 @@
 from .scenarios import BudgetScenario
 from .utils import TimeSeries
 
-__all__ = ["demo", "make_demo_scenarios"]
-
-
-def demo(which: str = None, do_run: bool = True, addprogs: bool = True) -> Project:
+__all__ = ["demo", "demo_projects", "make_demo_scenarios"]
+
+
+demo_projects = [
+    "udt",
+    "udt_dyn",
+    "usdt",
+    "cervicalcancer",
+    "sir",
+    "diabetes",
+    "combined",
+    "service",
+    "hypertension",
+    "hypertension_dyn",
+    "hiv",
+    "hiv_dyn",
+    "tb_simple",
+    "tb_simple_dyn",
+    "tb",
+    "dt",
+]
+
+
+def demo(which: str = "sir", do_run: bool = True, addprogs: bool = True) -> Project:
     """
     Return a demo project
 
@@ -25,30 +45,11 @@
 
     """
 
-    options = [
-        "udt",
-        "udt_dyn",
-        "usdt",
-        "cervicalcancer",
-        "sir",
-        "diabetes",
-        "combined",
-        # 'service',
-        "hypertension",
-        "hypertension_dyn",
-        "hiv",
-        "hiv_dyn",
-        "tb_simple",
-        "tb_simple_dyn",
-        "environment",
-        "tb",
-    ]
-
-    dtdict = sc.odict.fromkeys(options, 1.0)
+    dtdict = sc.odict.fromkeys(demo_projects, 1.0)
     dtdict["tb"] = 0.5
 
-    if which is None or which not in options:
-        raise Exception("Supported project types are:\n%s" % ("\n".join(options)))
+    if which not in demo_projects:
+        raise Exception("Supported demonstration projects are:\n%s" % ("\n".join(demos)))
 
     framework = LIBRARY_PATH / f"{which}_framework.xlsx"
     databook = LIBRARY_PATH / f"{which}_databook.xlsx"
@@ -61,7 +62,7 @@
     if do_run:
         P.run_sim(store_results=True)
 
-    if addprogs:
+    if addprogs and progbook.exists():
         logger.debug("Loading progbook")
         P.load_progbook(progbook)
 

atomica/excel.py

@@ -1,4 +1,4 @@
 # -*- coding: utf-8 -*-
 """
 Miscellaneous utility functions for Excel files
 
@@ -424,7 +424,8 @@
             raise Exception('Unknown TimeDependentConnections type - must be "transfer" or "interaction"')
 
     def __repr__(self):
-        return '<TDC %s "%s">' % (self.type.title(), self.code_name)
+        # return '<TDC %s "%s">' % (self.type.title(), self.code_name)
+        return sc.prepr(self)
 
     @classmethod
     def from_tables(cls, tables: list, interaction_type):
@@ -834,7 +835,7 @@
             from_idx = self.from_pops.index(from_pop)
             to_idx = self.to_pops.index(to_pop)
             if boolean_choice:
-                value = "Y" if value else "N"
+                value = "Y" if (value.vals or value.assumption) else "N"
             content[from_idx, to_idx] = value
 
         # Write the content

atomica/migration.py

@@ -807,9 +807,8 @@ def _convert_framework_columns(framework):
     framework._validate()
     return framework
 
+
 @migration("ParameterSet", "1.28.3", "1.28.4", "Add initialization atttribute")
 def _parset_add_initialization(parset):
     parset.initialization = None
     return parset
-
-