from .core import LpSolver_CMD, LpSolver, subprocess, PulpSolverError, clock, log from .. import constants import os import warnings class CPLEX_CMD(LpSolver_CMD): """The CPLEX LP solver""" name = "CPLEX_CMD" def __init__( self, mip=True, msg=True, timeLimit=None, gapRel=None, gapAbs=None, options=None, warmStart=False, keepFiles=False, path=None, threads=None, logPath=None, maxMemory=None, maxNodes=None, ): """ :param bool mip: if False, assume LP even if integer variables :param bool msg: if False, no log is shown :param float timeLimit: maximum time for solver (in seconds) :param float gapRel: relative gap tolerance for the solver to stop (in fraction) :param float gapAbs: absolute gap tolerance for the solver to stop :param int threads: sets the maximum number of threads :param list options: list of additional options to pass to solver :param bool warmStart: if True, the solver will use the current value of variables as a start :param bool keepFiles: if True, files are saved in the current directory and not deleted after solving :param str path: path to the solver binary :param str logPath: path to the log file :param float maxMemory: max memory to use during the solving. Stops the solving when reached. :param int maxNodes: max number of nodes during branching. Stops the solving when reached. """ LpSolver_CMD.__init__( self, gapRel=gapRel, mip=mip, msg=msg, timeLimit=timeLimit, options=options, maxMemory=maxMemory, maxNodes=maxNodes, warmStart=warmStart, path=path, keepFiles=keepFiles, threads=threads, gapAbs=gapAbs, logPath=logPath, ) def defaultPath(self): return self.executableExtension("cplex") def available(self): """True if the solver is available""" return self.executable(self.path) def actualSolve(self, lp): """Solve a well formulated lp problem""" if not self.executable(self.path): raise PulpSolverError("PuLP: cannot execute " + self.path) tmpLp, tmpSol, tmpMst = self.create_tmp_files(lp.name, "lp", "sol", "mst") vs = lp.writeLP(tmpLp, writeSOS=1) try: os.remove(tmpSol) except: pass if not self.msg: cplex = subprocess.Popen( self.path, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, ) else: cplex = subprocess.Popen(self.path, stdin=subprocess.PIPE) cplex_cmds = "read " + tmpLp + "\n" if self.optionsDict.get("warmStart", False): self.writesol(filename=tmpMst, vs=vs) cplex_cmds += "read " + tmpMst + "\n" cplex_cmds += "set advance 1\n" if self.timeLimit is not None: cplex_cmds += "set timelimit " + str(self.timeLimit) + "\n" options = self.options + self.getOptions() for option in options: cplex_cmds += option + "\n" if lp.isMIP(): if self.mip: cplex_cmds += "mipopt\n" cplex_cmds += "change problem fixed\n" else: cplex_cmds += "change problem lp\n" cplex_cmds += "optimize\n" cplex_cmds += "write " + tmpSol + "\n" cplex_cmds += "quit\n" cplex_cmds = cplex_cmds.encode("UTF-8") cplex.communicate(cplex_cmds) if cplex.returncode != 0: raise PulpSolverError("PuLP: Error while trying to execute " + self.path) if not os.path.exists(tmpSol): status = constants.LpStatusInfeasible values = reducedCosts = shadowPrices = slacks = solStatus = None else: ( status, values, reducedCosts, shadowPrices, slacks, solStatus, ) = self.readsol(tmpSol) self.delete_tmp_files(tmpLp, tmpMst, tmpSol) if self.optionsDict.get("logPath") != "cplex.log": self.delete_tmp_files("cplex.log") if status != constants.LpStatusInfeasible: lp.assignVarsVals(values) lp.assignVarsDj(reducedCosts) lp.assignConsPi(shadowPrices) lp.assignConsSlack(slacks) lp.assignStatus(status, solStatus) return status def getOptions(self): # CPLEX parameters: https://www.ibm.com/support/knowledgecenter/en/SSSA5P_12.6.0/ilog.odms.cplex.help/CPLEX/GettingStarted/topics/tutorials/InteractiveOptimizer/settingParams.html # CPLEX status: https://www.ibm.com/support/knowledgecenter/en/SSSA5P_12.10.0/ilog.odms.cplex.help/refcallablelibrary/macros/Solution_status_codes.html params_eq = dict( logPath="set logFile {}", gapRel="set mip tolerances mipgap {}", gapAbs="set mip tolerances absmipgap {}", maxMemory="set mip limits treememory {}", threads="set threads {}", maxNodes="set mip limits nodes {}", ) return [ v.format(self.optionsDict[k]) for k, v in params_eq.items() if k in self.optionsDict and self.optionsDict[k] is not None ] @staticmethod def readsol(filename): """Read a CPLEX solution file""" # CPLEX solution codes: http://www-eio.upc.es/lceio/manuals/cplex-11/html/overviewcplex/statuscodes.html try: import xml.etree.ElementTree as et except ImportError: import elementtree.ElementTree as et solutionXML = et.parse(filename).getroot() solutionheader = solutionXML.find("header") statusString = solutionheader.get("solutionStatusString") statusValue = solutionheader.get("solutionStatusValue") cplexStatus = { "1": constants.LpStatusOptimal, # optimal "101": constants.LpStatusOptimal, # mip optimal "102": constants.LpStatusOptimal, # mip optimal tolerance "104": constants.LpStatusOptimal, # max solution limit "105": constants.LpStatusOptimal, # node limit feasible "107": constants.LpStatusOptimal, # time lim feasible "109": constants.LpStatusOptimal, # fail but feasible "113": constants.LpStatusOptimal, # abort feasible } if statusValue not in cplexStatus: raise PulpSolverError( "Unknown status returned by CPLEX: \ncode: '{}', string: '{}'".format( statusValue, statusString ) ) status = cplexStatus[statusValue] # we check for integer feasible status to differentiate from optimal in solution status cplexSolStatus = { "104": constants.LpSolutionIntegerFeasible, # max solution limit "105": constants.LpSolutionIntegerFeasible, # node limit feasible "107": constants.LpSolutionIntegerFeasible, # time lim feasible "109": constants.LpSolutionIntegerFeasible, # fail but feasible "111": constants.LpSolutionIntegerFeasible, # memory limit feasible "113": constants.LpSolutionIntegerFeasible, # abort feasible } solStatus = cplexSolStatus.get(statusValue) shadowPrices = {} slacks = {} constraints = solutionXML.find("linearConstraints") for constraint in constraints: name = constraint.get("name") slack = constraint.get("slack") shadowPrice = constraint.get("dual") try: # See issue #508 shadowPrices[name] = float(shadowPrice) except TypeError: shadowPrices[name] = None slacks[name] = float(slack) values = {} reducedCosts = {} for variable in solutionXML.find("variables"): name = variable.get("name") value = variable.get("value") values[name] = float(value) reducedCost = variable.get("reducedCost") try: # See issue #508 reducedCosts[name] = float(reducedCost) except TypeError: reducedCosts[name] = None return status, values, reducedCosts, shadowPrices, slacks, solStatus def writesol(self, filename, vs): """Writes a CPLEX solution file""" try: import xml.etree.ElementTree as et except ImportError: import elementtree.ElementTree as et root = et.Element("CPLEXSolution", version="1.2") attrib_head = dict() attrib_quality = dict() et.SubElement(root, "header", attrib=attrib_head) et.SubElement(root, "header", attrib=attrib_quality) variables = et.SubElement(root, "variables") values = [(v.name, v.value()) for v in vs if v.value() is not None] for index, (name, value) in enumerate(values): attrib_vars = dict(name=name, value=str(value), index=str(index)) et.SubElement(variables, "variable", attrib=attrib_vars) mst = et.ElementTree(root) mst.write(filename, encoding="utf-8", xml_declaration=True) return True class CPLEX_PY(LpSolver): """ The CPLEX LP/MIP solver (via a Python Binding) This solver wraps the python api of cplex. It has been tested against cplex 12.3. For api functions that have not been wrapped in this solver please use the base cplex classes """ name = "CPLEX_PY" try: global cplex import cplex except Exception as e: err = e """The CPLEX LP/MIP solver from python. Something went wrong!!!!""" def available(self): """True if the solver is available""" return False def actualSolve(self, lp): """Solve a well formulated lp problem""" raise PulpSolverError(f"CPLEX_PY: Not Available:\n{self.err}") else: def __init__( self, mip=True, msg=True, timeLimit=None, gapRel=None, warmStart=False, logPath=None, threads=None, ): """ :param bool mip: if False, assume LP even if integer variables :param bool msg: if False, no log is shown :param float timeLimit: maximum time for solver (in seconds) :param float gapRel: relative gap tolerance for the solver to stop (in fraction) :param bool warmStart: if True, the solver will use the current value of variables as a start :param str logPath: path to the log file :param int threads: number of threads to be used by CPLEX to solve a problem (default None uses all available) """ LpSolver.__init__( self, gapRel=gapRel, mip=mip, msg=msg, timeLimit=timeLimit, warmStart=warmStart, logPath=logPath, threads=threads, ) def available(self): """True if the solver is available""" return True def actualSolve(self, lp, callback=None): """ Solve a well formulated lp problem creates a cplex model, variables and constraints and attaches them to the lp model which it then solves """ self.buildSolverModel(lp) # set the initial solution log.debug("Solve the Model using cplex") self.callSolver(lp) # get the solution information solutionStatus = self.findSolutionValues(lp) for var in lp._variables: var.modified = False for constraint in lp.constraints.values(): constraint.modified = False return solutionStatus def buildSolverModel(self, lp): """ Takes the pulp lp model and translates it into a cplex model """ model_variables = lp.variables() self.n2v = {var.name: var for var in model_variables} if len(self.n2v) != len(model_variables): raise PulpSolverError( "Variables must have unique names for cplex solver" ) log.debug("create the cplex model") self.solverModel = lp.solverModel = cplex.Cplex() log.debug("set the name of the problem") if not self.mip: self.solverModel.set_problem_name(lp.name) log.debug("set the sense of the problem") if lp.sense == constants.LpMaximize: lp.solverModel.objective.set_sense( lp.solverModel.objective.sense.maximize ) if lp.objective is None: raise PulpSolverError("No objective set") obj = [float(lp.objective.get(var, 0.0)) for var in model_variables] def cplex_var_lb(var): if var.lowBound is not None: return float(var.lowBound) else: return -cplex.infinity lb = [cplex_var_lb(var) for var in model_variables] def cplex_var_ub(var): if var.upBound is not None: return float(var.upBound) else: return cplex.infinity ub = [cplex_var_ub(var) for var in model_variables] colnames = [var.name for var in model_variables] def cplex_var_types(var): if var.cat == constants.LpInteger: return "I" else: return "C" ctype = [cplex_var_types(var) for var in model_variables] ctype = "".join(ctype) lp.solverModel.variables.add( obj=obj, lb=lb, ub=ub, types=ctype, names=colnames ) rows = [] senses = [] rhs = [] rownames = list(lp.constraints.keys()) for constraint in lp.constraints.values(): # build the expression if len(constraint) == 0: # if the constraint is empty rows.append(([], [])) else: expr1 = [var.name for var in constraint.keys()] expr2 = [float(coeff) for coeff in constraint.values()] rows.append((expr1, expr2)) if constraint.sense == constants.LpConstraintLE: senses.append("L") elif constraint.sense == constants.LpConstraintGE: senses.append("G") elif constraint.sense == constants.LpConstraintEQ: senses.append("E") else: raise PulpSolverError("Detected an invalid constraint type") rhs.append(float(-constraint.constant)) lp.solverModel.linear_constraints.add( lin_expr=rows, senses=senses, rhs=rhs, names=rownames ) log.debug("set the type of the problem") if not self.mip: self.solverModel.set_problem_type(cplex.Cplex.problem_type.LP) log.debug("set the logging") if not self.msg: self.setlogfile(None) logPath = self.optionsDict.get("logPath") if logPath is not None: if self.msg: warnings.warn( "`logPath` argument replaces `msg=1`. The output will be redirected to the log file." ) self.setlogfile(open(logPath, "w")) gapRel = self.optionsDict.get("gapRel") if gapRel is not None: self.changeEpgap(gapRel) if self.timeLimit is not None: self.setTimeLimit(self.timeLimit) self.setThreads(self.optionsDict.get("threads", None)) if self.optionsDict.get("warmStart", False): # We assume "auto" for the effort_level effort = self.solverModel.MIP_starts.effort_level.auto start = [ (k, v.value()) for k, v in self.n2v.items() if v.value() is not None ] if not start: warnings.warn("No variable with value found: mipStart aborted") return ind, val = zip(*start) self.solverModel.MIP_starts.add( cplex.SparsePair(ind=ind, val=val), effort, "1" ) def setlogfile(self, fileobj): """ sets the logfile for cplex output """ self.solverModel.set_error_stream(fileobj) self.solverModel.set_log_stream(fileobj) self.solverModel.set_warning_stream(fileobj) self.solverModel.set_results_stream(fileobj) def setThreads(self, threads=None): """ Change cplex thread count used (None is default which uses all available resources) """ self.solverModel.parameters.threads.set(threads or 0) def changeEpgap(self, epgap=10**-4): """ Change cplex solver integer bound gap tolerence """ self.solverModel.parameters.mip.tolerances.mipgap.set(epgap) def setTimeLimit(self, timeLimit=0.0): """ Make cplex limit the time it takes --added CBM 8/28/09 """ self.solverModel.parameters.timelimit.set(timeLimit) def callSolver(self, isMIP): """Solves the problem with cplex""" # solve the problem self.solveTime = -clock() self.solverModel.solve() self.solveTime += clock() def findSolutionValues(self, lp): CplexLpStatus = { lp.solverModel.solution.status.MIP_optimal: constants.LpStatusOptimal, lp.solverModel.solution.status.optimal: constants.LpStatusOptimal, lp.solverModel.solution.status.optimal_tolerance: constants.LpStatusOptimal, lp.solverModel.solution.status.infeasible: constants.LpStatusInfeasible, lp.solverModel.solution.status.infeasible_or_unbounded: constants.LpStatusInfeasible, lp.solverModel.solution.status.MIP_infeasible: constants.LpStatusInfeasible, lp.solverModel.solution.status.MIP_infeasible_or_unbounded: constants.LpStatusInfeasible, lp.solverModel.solution.status.unbounded: constants.LpStatusUnbounded, lp.solverModel.solution.status.MIP_unbounded: constants.LpStatusUnbounded, lp.solverModel.solution.status.abort_dual_obj_limit: constants.LpStatusNotSolved, lp.solverModel.solution.status.abort_iteration_limit: constants.LpStatusNotSolved, lp.solverModel.solution.status.abort_obj_limit: constants.LpStatusNotSolved, lp.solverModel.solution.status.abort_relaxed: constants.LpStatusNotSolved, lp.solverModel.solution.status.abort_time_limit: constants.LpStatusNotSolved, lp.solverModel.solution.status.abort_user: constants.LpStatusNotSolved, lp.solverModel.solution.status.MIP_abort_feasible: constants.LpStatusOptimal, lp.solverModel.solution.status.MIP_time_limit_feasible: constants.LpStatusOptimal, lp.solverModel.solution.status.MIP_time_limit_infeasible: constants.LpStatusInfeasible, } lp.cplex_status = lp.solverModel.solution.get_status() status = CplexLpStatus.get(lp.cplex_status, constants.LpStatusUndefined) CplexSolStatus = { lp.solverModel.solution.status.MIP_time_limit_feasible: constants.LpSolutionIntegerFeasible, lp.solverModel.solution.status.MIP_abort_feasible: constants.LpSolutionIntegerFeasible, lp.solverModel.solution.status.MIP_feasible: constants.LpSolutionIntegerFeasible, } # TODO: I did not find the following status: CPXMIP_NODE_LIM_FEAS, CPXMIP_MEM_LIM_FEAS sol_status = CplexSolStatus.get(lp.cplex_status) lp.assignStatus(status, sol_status) var_names = [var.name for var in lp._variables] con_names = [con for con in lp.constraints] try: objectiveValue = lp.solverModel.solution.get_objective_value() variablevalues = dict( zip(var_names, lp.solverModel.solution.get_values(var_names)) ) lp.assignVarsVals(variablevalues) constraintslackvalues = dict( zip(con_names, lp.solverModel.solution.get_linear_slacks(con_names)) ) lp.assignConsSlack(constraintslackvalues) if lp.solverModel.get_problem_type() == cplex.Cplex.problem_type.LP: variabledjvalues = dict( zip( var_names, lp.solverModel.solution.get_reduced_costs(var_names), ) ) lp.assignVarsDj(variabledjvalues) constraintpivalues = dict( zip( con_names, lp.solverModel.solution.get_dual_values(con_names), ) ) lp.assignConsPi(constraintpivalues) except cplex.exceptions.CplexSolverError: # raises this error when there is no solution pass # put pi and slack variables against the constraints # TODO: clear up the name of self.n2c if self.msg: print("Cplex status=", lp.cplex_status) lp.resolveOK = True for var in lp._variables: var.isModified = False return status def actualResolve(self, lp, **kwargs): """ looks at which variables have been modified and changes them """ raise NotImplementedError("Resolves in CPLEX_PY not yet implemented") CPLEX = CPLEX_CMD