rapper API¶
A class and some utilities to wrap PySP. In particular to enable programmatic access to some of the functionality in runef and runph for ConcreteModels Author: David L. Woodruff, started February 2017
-
class
pysp.util.rapper.StochSolver(fsfile, fsfct=None, tree_model=None, phopts=None)[source]¶ Bases:
objectA class for solving stochastic versions of concrete models and abstract models. Inspired by the IDAES use case and by daps ability to create tree models. Author: David L. Woodruff, February 2017
- Parameters
fsfile (str) – is a path to the file that contains the scenario callback for concrete or the reference model for abstract.
fsfct (str, or fct, or None) –
str: callback function name in the filefct: callback function (fsfile is ignored)None: it is a AbstractModeltree_model (concrete model, or networkx tree, or path) – gives the tree as a concrete model (which could be a fct) or a valid networkx scenario tree or path to AMPL data file.
phopts – dictionary of ph options; needed during construction if there is bundling.
-
scenario_tree¶ scenario tree object (that includes data)
-
make_ef(verbose=False, generate_weighted_cvar=False, cvar_weight=None, risk_alpha=None, cc_indicator_var_name=None, cc_alpha=0.0)[source]¶ Make an ef object (used by solve_ef); all Args are optional.
- Parameters
verbose (boolean) – indicates verbosity to PySP for construction
generate_weighted_cvar (boolean) – indicates we want weighted CVar
cvar_weight (float) – weight for the cvar term
risk_alpha (float) – alpha value for cvar
cc_indicator_var_name (string) – name of the Var used for chance constraint
cc_alpha (float) – alpha for chance constraint
- Returns
the ef object
- Return type
ef_instance
-
root_E_obj()[source]¶ post solve Expected cost of the solution in the scenario tree (xbar)
- Returns
the expected costs of the solution in the tree (xbar)
- Return type
float
-
root_Var_solution()[source]¶ Generator to loop over x-bar
- Yields
name, value pair for root node solution values
-
solve_ef(subsolver, sopts=None, tee=False, need_gap=False, verbose=False, generate_weighted_cvar=False, cvar_weight=None, risk_alpha=None, cc_indicator_var_name=None, cc_alpha=0.0)[source]¶ Solve the stochastic program directly using the extensive form. All Args other than subsolver are optional.
- Parameters
subsolver (str) – the solver to call (e.g., ‘ipopt’)
sopts (dict) – solver options
tee (bool) – indicates dynamic solver output to terminal.
need_gap (bool) – indicates the need for the optimality gap
verbose (boolean) – indicates verbosity to PySP for construction
generate_weighted_cvar (boolean) – indicates we want weighted CVar
cvar_weight (float) – weight for the cvar term
risk_alpha (float) – alpha value for cvar
cc_indicator_var_name (string) – name of the Var used for chance constraint
cc_alpha (float) – alpha for chance constraint
Returns: (Pyomo solver result, float)
solve_result is the solver return value.
absgap is the absolute optimality gap (might not be valid); only if requested
Note
Also update the scenario tree, populated with the solution. Also attach the full ef instance to the object. So you might want obj = pyo.value(stsolver.ef_instance.MASTER) This needs more work to deal with solver failure (dlw, March, 2018)
-
solve_ph(subsolver, default_rho, phopts=None, sopts=None)[source]¶ Solve the stochastic program given by this.scenario_tree using ph
- Parameters
subsolver (str) – the solver to call (e.g., ‘ipopt’)
default_rho (float) – the rho value to use by default
phopts – dictionary of ph options (optional)
sopts – dictionary of subsolver options (optional)
Returns: the ph object
Note
Updates the scenario tree, populated with the xbar values; however, you probably want to do obj, xhat = ph.compute_and_report_inner_bound_using_xhat() where ph is the return value.