.. _jlp-py:


######
jlp.py
######

**********************
class JLP(Optimizer):
**********************

Interface between SIMO optimizer and JLP linear programming library.

Parameters:

- ...


def __init__(self, logger, logname, optlogger, taskdef, simdbin, opdbin, simdbout, opdbout, \**keywords):
=========================================================================================================

Initialize optimizer::

    >>> epsilon = 0.00001
    >>> from simo.optimization.jlp import JLP
    >>> execfile('optimization/test/mocks4optimizer.py')
    >>> chart_path = 'optimization/test'

    >>> jlp = JLP(logger, logname, taskdef, simdbin, simdbout, False, False,
    ...           False, chart_path, True, 'test', True, timeout=30)
    Called Logger.log_message(
        'optimization-test',
        'ERROR',
        "Parameter 'maxobjects' missing")
    Called Logger.log_message(
        'optimization-test',
        'ERROR',
        "Parameter 'jfolder' missing")
    Called Logger.log_message(
        'optimization-test',
        'ERROR',
        "Parameter 'prefix' missing")

    >>> jlp = JLP(logger, logname, taskdef, simdbin, simdbout, False, False,
    ...           False, chart_path, True, 'test', True,
    ...           maxobjects=1000, jfolder='', prefix='test', timeout=30,
    ...           do_split=False, skip_weight_splits=None)

    >>> jlp2 = JLP(logger, logname, taskdef2, simdbin, simdbout, False, False,
    ...           False, chart_path, True, 'test', True,
    ...           maxobjects=1000, jfolder='', prefix='test', timeout=30,
    ...           do_split=False, skip_weight_splits=None)


def optimize(self):
===================

Construct J input files, run J program as a subprocess and process J output


def _process_branch(self, unitsol, weights):
============================================

Process unit split by the JLP algorithm


def _process_infeasible(self):
==============================

Process JLP output when the problem is infeasible


def _copy_result_file(self, respath, iteration):
================================================

Parameters:

respath -- result file path, str
iteration -- iteration indice, int

Copy JLP result file to result folder


def _read_solution(self, iteration):
====================================

Parameters:

iteration -- iteration indice, int

Read and process solution from JLP output files


def _remove_j_files(self):
==========================

Remove J input and output files


def _update_stack(self, stack):
===============================

Parameters:

stack -- postfix stack, list

Update infix stack::

    >>> stack = [['a'], ['b']]
    >>> jlp._update_stack(stack)
    >>> print stack
    [['a', 'b']]


def _process_const_expr(self, num_of_expr, expressions):
========================================================

Parameters:

num_of_expr -- number of expressions as integer
expressions -- SIMO postfix expressions in a list

Process invalid constraint postfix expression::

    >>> jlp._init_ok = True
    >>> jlp._process_const_expr(invalid_taskdef.constraints)
    Called Logger.log_message(
        'optimization-test',
        'ERROR',
        "Invalid operator for J '/'")
    Called Logger.log_message(
        'optimization-test',
        'ERROR',
        'JLP condition right hand side must be a numerical value')
    >>> jlp.j_consts.infix # doctest: +NORMALIZE_WHITESPACE
    ['c0 / c1 > 1.05', 'c2 > c3']
    >>> jlp.j_consts.names
    ['c0', 'c1', 'c2']
    >>> jlp.j_consts.order
    [0, 1, 2]
    >>> print jlp._init_ok
    False

Process constraint postfix expression back to infix form and try to figure out if the expression is good for JLP::

    >>> jlp._process_const_expr(taskdef.constraints)
    >>> jlp.j_consts.infix # doctest: +NORMALIZE_WHITESPACE
    ['c0 - c1 > 0.00', 'c2 - c3 > 0.00', 'tr1 - tr2 > 0.00']
    >>> jlp.j_consts.names # doctest: +NORMALIZE_WHITESPACE
    ['c0', 'c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7']
    >>> jlp.j_consts.order
    [0, 1, 2, 3, 4, 5, 6, 7]
    >>> jlp.transformations
    ['tr1 = c4 * c5', 'tr2 = c6 * c7']


def _process_gp_vars(self, no):
===============================

Parameters::

no -- number of subobjectives, int

Construct slack and surplus variables needed in JLP goal programming tasks ::

    >>> jlp2._process_gp_vars(4)


def _write_j_par(self, partype):
================================

Parameters:

partype -- parameter type, either 'init' or 'close'

Write J parameter file (j.par)::

    >>> jlp._write_j_par('init')
    >>> fp = open('j.par', 'r')
    >>> contents = fp.read()
    >>> print contents
    !1000
    ;incl('test_problem.txt')
    <BLANKLINE>
    >>> fp.close()


def _write_j_problem(self):
===========================

Write the optimisation problem definition to a file for J::

    >>> jlp._obj_vars = ['x0', 'x1']
    >>> jlp._write_j_problem()
    >>> fp = open('test_problem.txt', 'r')
    >>> contents = fp.read()
    >>> print contents  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    ! SUBOBJECTIVE VARIABLES
    !   x0 = 1.0 * (sum[1:5](operation:cashflow:discount))
    !   x1 = 1.0 * (sum[6:10](operation:cashflow:discount))
    <BLANKLINE>
    ! CONSTRAINT VARIABLES (see SIMO lexicon for details)
    !   c0 = V, steps 20-20
    !   c1 = V, steps 10-10
    !   c2 = Volume, steps 20-end
    !   c3 = Volume, steps 10-end
    !   c4 = V, steps 20-20
    !   c5 = AREA, steps 20-20
    !   c6 = V, steps 10-10
    !   c7 = AREA, steps 10-10
    <BLANKLINE>
    ! READ OBJECTIVE AND CONSTRAINT VARIABLE VALUES FROM FILE
    udat=data(read->(id,nbranch),in->'test_unitdata.txt')
    sdat=data(read->(x0,x1,c0,c1,c2,c3,c4,c5,c6,c7), in->'test_vardata.txt')
    linkdata(data->udat,subdata->sdat,nobsw->nbranch)
    <BLANKLINE>
    ! TRANSFORM ILLEGAL CONSTRAINTS
    strans=trans()
    tr1 = c4 * c5
    tr2 = c6 * c7
    /
    <BLANKLINE>
    ! OPTIMISATION PROBLEM DEFINITION
    pr=problem()
    <BLANKLINE>
    ! OBJECTIVE FUNCTION DEFINITION
    0.3*x0+0.7*x1==max
    <BLANKLINE>
    ! CONSTRAINT DEFINITIONS
    c0 - c1 > 0.00
    c2 - c3 > 0.00
    tr1 - tr2 > 0.00
    /
    jlp(problem->pr,data->udat,subtrans->strans,report->'...jlp_output.txt')
    branch_report=trans()
    do(i,1,weights())
    write('...bresult.txt',$,matrix%udat(unit(i),1),schedw(i),price%unit(unit(i)))
    enddo
    /
    weight_report=trans()
    do(i,1,partweights())
    write('...wresult.txt',$,partunit(i),partschedw(i),partweight(i))
    enddo
    /
    ;if(Feasible);then
    call(branch_report)
    call(weight_report)
    ;else
    write('...bresult.txt','(~infeasible problem~)')
    ;endif
    end
    <BLANKLINE>

    >>> fp.close()

    >>> jlp2._obj_vars = ['x0', 'x1', 'x2', 'x3']
    >>> jlp2._write_j_problem()
    >>> fp = open('test_problem.txt', 'r')
    >>> contents = fp.read()
    >>> print contents  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    ! SUBOBJECTIVE VARIABLES
    !   x0 = 0.25 * (sum[1:5](operation:cashflow))
    !   x1 = 0.25 * (sum[6:10](operation:cashflow))
    !   x2 = 0.25 * (sum[11:15](operation:cashflow))
    !   x3 = 0.25 * (sum[16:20](operation:cashflow))
    <BLANKLINE>
    ! READ OBJECTIVE AND CONSTRAINT VARIABLE VALUES FROM FILE
    udat=data(read->(id,nbranch),in->'test_unitdata.txt')
    sdat=data(read->(x0,x1,x2,x3), in->'test_vardata.txt')
    linkdata(data->udat,subdata->sdat,nobsw->nbranch)
    <BLANKLINE>
    ! OPTIMISATION PROBLEM DEFINITION
    pr=problem()
    <BLANKLINE>
    ! OBJECTIVE FUNCTION DEFINITION
    0.25*sp0+0.25*sl0+0.25*sp1+0.25*sl1+0.25*sp2+0.25*sl2+0.25*sp3+0.25*sl3==min
    0.25*x0-sp0+sl0=1000.0
    0.25*x1-sp1+sl1=2000.0
    0.25*x2-sp2+sl2=3000.0
    0.25*x3-sp3+sl3=4000.0
    /
    jlp(problem->pr,data->udat,z->,report->'...jlp_output.txt')
    branch_report=trans()
    do(i,1,weights())
    write('...bresult.txt',$,matrix%udat(unit(i),1),schedw(i),price%unit(unit(i)))
    enddo
    /
    weight_report=trans()
    do(i,1,partweights())
    write('...wresult.txt',$,partunit(i),partschedw(i),partweight(i))
    enddo
    /
    ;if(Feasible);then
    call(branch_report)
    call(weight_report)
    ;else
    write('...bresult.txt','(~infeasible problem~)')
    ;endif
    end
    <BLANKLINE>


def _write_j_data(self):
========================

Write the optimisation data to two data files for J. The unit data contains the simulation unit id and number of simulated alternatives for each unit. The variable contains the objective and constraint variable values for each alternative in each simulation unit::

   >>> omatrix2.task_def = taskdef
   >>> jlp._data = omatrix2
   >>> jlp._write_j_data()  # doctest: +ELLIPSIS
   Called OMatrix._evaluator.evaluate(...)

   >>> fp = open('test_unitdata.txt', 'r')
   >>> print fp.read()
   0,2
   1,5
   2,3
   3,4
   4,1
   <BLANKLINE>
   >>> fp.close()
   >>> fp = open('test_vardata.txt', 'r')
   >>> print fp.read()
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   1.2345,1.2345,4.69,4.69,4.69,4.69,4.69,4.69,4.69,4.69
   <BLANKLINE>
   >>> fp.close()