def quad_matrix_sum(self, matrix, lvars, symmetric=False): # assume matrix is a NxN matrix # vars is a N-vector of variables dcc = self._quad_factory.term_dict_type qterms = dcc() gen_rows = self.generate_rows(matrix) for i, mrow in enumerate(gen_rows): vi = lvars[i] for j, k in enumerate(mrow): if k: vj = lvars[j] if i == j: qterms[VarPair(vi)] = k elif symmetric: if i < j: update_dict_from_item_value( qterms, VarPair(vi, vj), 2 * k) elif i > j: continue else: update_dict_from_item_value(qterms, VarPair(vi, vj), k) return self._to_expr(qcc=qterms)
def _sumsq(self, args): accumulated_ct = 0 number_validation_fn = self._checker.get_number_validation_fn() qcc = self._quad_factory.term_dict_type() lcc = self._linear_factory.term_dict_type() for item in args: if isinstance(item, Var): update_dict_from_item_value(qcc, VarPair(item, item), 1) elif isinstance(item, MonomialExpr): mcoef = item._coef # noinspection PyPep8 mvar = item._dvar update_dict_from_item_value(qcc, VarPair(mvar, mvar), mcoef**2) elif isinstance(item, LinearExpr): cst = item.get_constant() accumulated_ct += cst**2 for lv1, lk1 in item.iter_terms(): for lv2, lk2 in item.iter_terms(): if lv1 is lv2: update_dict_from_item_value( qcc, VarPair(lv1, lv1), lk1 * lk1) elif lv1._index < lv2._index: update_dict_from_item_value( qcc, VarPair(lv1, lv2), 2 * lk1 * lk2) else: pass if cst: update_dict_from_item_value(lcc, lv1, 2 * cst * lk1) elif isinstance(item, _IAdvancedExpr): fvar = item.functional_var update_dict_from_item_value(qcc, VarPair(fvar), 1) elif isinstance(item, ZeroExpr): pass elif is_number(item): safe_item = number_validation_fn( item) if number_validation_fn else item accumulated_ct += safe_item**2 else: self._model.fatal( "Model.sumsq() expects numbers/variables/linear expressions, got: {0!s}", item) return self._to_expr(qcc, lcc, constant=accumulated_ct)
def new_linexpr_product(self, linexpr, other): if isinstance(other, Var): return self.new_var_product(other, linexpr) elif isinstance(other, MonomialExpr): return self.new_monomial_product(other, linexpr) elif isinstance(other, LinearExpr): cst1 = linexpr.constant cst2 = other.constant fcc = self.term_dict_type() for lv1, lk1 in linexpr.iter_terms(): for lv2, lk2 in other.iter_terms(): update_dict_from_item_value(fcc, VarPair(lv1, lv2), lk1 * lk2) # this is quad qlinexpr = self.new_linear_expr() # add cst2 * linexp1 qlinexpr._add_expr_scaled(expr=linexpr, factor=cst2) # add cst1 * linexpr2 qlinexpr._add_expr_scaled(expr=other, factor=cst1) # and that's it # fix the constant qlinexpr.constant = cst1 * cst2 quad = QuadExpr(self._model, quads=fcc, linexpr=qlinexpr, safe=True) return quad else: self._unexpected_product_error(linexpr, other)
def _sumsq_vars_all_different(self, dvars): dcc = self._quad_factory.term_dict_type qcc = dcc() qcc_setitem = dcc.__setitem__ for t in dvars: qcc_setitem(qcc, VarPair(t), 1) return self._to_expr(qcc=qcc)
def _scal_prod_triple_vars(self, coefs, left_terms, right_terms): # INTERNAL # assuming all arguments are iterable. dcc = self.counter_type qcc = dcc() number_validation_fn = self._checker.get_number_validation_fn() if number_validation_fn: for coef, lterm, rterm in izip(coefs, left_terms, right_terms): safe_coef = number_validation_fn( coef) if number_validation_fn else coef update_dict_from_item_value(qcc, VarPair(lterm, rterm), safe_coef) else: for coef, lterm, rterm in izip(coefs, left_terms, right_terms): update_dict_from_item_value(qcc, VarPair(lterm, rterm), coef) return self._to_expr(qcc=qcc)
def _scal_prod_triple(self, coefs, left_terms, right_terms): # INTERNAL accumulated_ct = 0 qcc = self.counter_type() lcc = self.counter_type() number_validation_fn = self._checker.get_number_validation_fn() for coef, lterm, rterm in izip(coefs, left_terms, right_terms): if coef: safe_coef = number_validation_fn( coef) if number_validation_fn else coef lcst = lterm.get_constant() rcst = rterm.get_constant() accumulated_ct += safe_coef * lcst * rcst for lv, lk in lterm.iter_terms(): for rv, rk in rterm.iter_terms(): coef3 = safe_coef * lk * rk update_dict_from_item_value(qcc, VarPair(lv, rv), coef3) if rcst: for lv, lk in lterm.iter_terms(): update_dict_from_item_value(lcc, lv, safe_coef * lk * rcst) if lcst: for rv, rk in rterm.iter_terms(): update_dict_from_item_value(lcc, rv, safe_coef * rk * lcst) return self._to_expr(qcc, lcc, constant=accumulated_ct)
def _sparse_quad_matrix_sum(self, sp_coef_mat, lvars, symmetric=False): # assume matrix is a NxN matrix # vars is a N-vector of variables dcc = self._quad_factory.term_dict_type qterms = dcc() for e in range(sp_coef_mat.nnz): k = sp_coef_mat.data[e] if k: row = sp_coef_mat.row[e] col = sp_coef_mat.col[e] vi = lvars[row] vj = lvars[col] update_dict_from_item_value(qterms, VarPair(vi, vj), k) return self._to_expr(qcc=qterms)
def read(cls, filename, model_name=None, verbose=False, model_class=None, **kwargs): """ Reads a model from a CPLEX export file. Accepts all formats exported by CPLEX: LP, SAV, MPS. If an error occurs while reading the file, the message of the exception is printed and the function returns None. Args: filename: The file to read. model_name: An optional name for the newly created model. If None, the model name will be the path basename. verbose: An optional flag to print informative messages, default is False. model_class: An optional class type; must be a subclass of Model. The returned model is built using this model_class and the keyword arguments kwargs, if any. By default, the model is class is `Model` (see kwargs: A dict of keyword-based arguments that are used when creating the model instance. Example: `m = read_model("c:/temp/foo.mps", model_name="docplex_foo", solver_agent="docloud", output_level=100)` Returns: An instance of Model, or None if an exception is raised. See Also: :class:`docplex.mp.model.Model` """ if not os.path.exists(filename): raise IOError("* file not found: {0}".format(filename)) # extract basename if model_name: name_to_use = model_name else: basename = os.path.basename(filename) if '.' not in filename: raise RuntimeError( 'ModelReader.read_model(): path has no extension: {}'. format(filename)) dotpos = basename.find(".") if dotpos > 0: name_to_use = basename[:dotpos] else: # pragma: no cover name_to_use = basename model_class = model_class or Model if 0 == os.stat(filename).st_size: print("* file is empty: {0} - exiting".format(filename)) return model_class(name=name_to_use, **kwargs) if verbose: print("-> CPLEX starts reading file: {0}".format(filename)) cpx_adapter = cls._read_cplex(filename) cpx = cpx_adapter.cpx if verbose: print("<- CPLEX finished reading file: {0}".format(filename)) if not cpx: # pragma: no cover return None final_output_level = kwargs.get("output_level", "info") debug_read = kwargs.get("debug", False) try: # force no tck if 'checker' in kwargs: final_checker = kwargs['checker'] else: final_checker = 'default' # build the model with no checker, then restore final_checker in the end. kwargs['checker'] = 'off' ignore_names = kwargs.get('ignore_names', False) # ------------- mdl = model_class(name=name_to_use, **kwargs) lfactory = mdl._lfactory qfactory = mdl._qfactory mdl.set_quiet() # output level set to ERROR vartype_cont = mdl.continuous_vartype vartype_map = { 'B': mdl.binary_vartype, 'I': mdl.integer_vartype, 'C': mdl.continuous_vartype, 'S': mdl.semicontinuous_vartype } # 1 upload variables cpx_nb_vars = cpx.variables.get_num() def make_constant_expr(k): if k: return lfactory._new_safe_constant_expr(k) else: return lfactory.new_zero_expr() if verbose: print("-- uploading {0} variables...".format(cpx_nb_vars)) cpx_var_names = [] if ignore_names else cls._safe_call_get_names( cpx_adapter, cpx.variables.get_names) if cpx._is_MIP(): cpx_vartypes = [ vartype_map.get(cpxt, vartype_cont) for cpxt in cpx.variables.get_types() ] else: cpx_vartypes = [vartype_cont] * cpx_nb_vars cpx_var_lbs = cpx.variables.get_lower_bounds() cpx_var_ubs = cpx.variables.get_upper_bounds() # map from cplex variable indices to docplex's # use to skip range vars # cplex : [x, Rg1, y] -> {0:0, 2: 1} if cpx_var_names: model_varnames = cpx_var_names else: model_varnames = [None] * cpx_nb_vars model_lbs = cpx_var_lbs model_ubs = cpx_var_ubs model_types = cpx_vartypes # vars model_vars = lfactory.new_multitype_var_list( cpx_nb_vars, model_types, model_lbs, model_ubs, model_varnames) # inverse map from indices to docplex vars cpx_var_index_to_docplex = { v: model_vars[v] for v in range(cpx_nb_vars) } # 2. upload linear constraints and ranges (mixed in cplex) cpx_linearcts = cpx.linear_constraints nb_linear_cts = cpx_linearcts.get_num() # all_rows1 = cpx_linearcts.get_rows() all_rows = cpx_adapter.fast_get_rows(cpx) all_rhs = cpx_linearcts.get_rhs() all_senses = cpx_linearcts.get_senses() all_range_values = cpx_linearcts.get_range_values() cpx_ctnames = [] if ignore_names else cls._safe_call_get_names( cpx_adapter, cpx_linearcts.get_names) deferred_cts = [] if verbose: print("-- uploading {0} linear constraints...".format( nb_linear_cts)) for c in range(nb_linear_cts): row = all_rows[c] sense = all_senses[c] rhs = all_rhs[c] ctname = cpx_ctnames[c] if cpx_ctnames else None range_val = all_range_values[c] indices, coefs = row expr = cls._make_expr_from_varmap_coefs( lfactory, cpx_var_index_to_docplex, indices, coefs) if sense == 'R': # rangeval can be negative !!! issue 52 if range_val >= 0: range_lb = rhs range_ub = rhs + range_val else: range_ub = rhs range_lb = rhs + range_val rgct = mdl.range_constraint(lb=range_lb, ub=range_ub, expr=expr, rng_name=ctname) deferred_cts.append(rgct) else: op = cls.parse_sense(sense) rhs_expr = make_constant_expr(rhs) ct = LinearConstraint(mdl, expr, op, rhs_expr, ctname) deferred_cts.append(ct) if deferred_cts: # add constraint as a block lfactory._post_constraint_block(posted_cts=deferred_cts) # 3. upload Quadratic constraints cpx_quadraticcts = cpx.quadratic_constraints nb_quadratic_cts = cpx_quadraticcts.get_num() if nb_quadratic_cts: all_rhs = cpx_quadraticcts.get_rhs() all_linear_nb_non_zeros = cpx_quadraticcts.get_linear_num_nonzeros( ) all_linear_components = cpx_quadraticcts.get_linear_components( ) all_quadratic_nb_non_zeros = cpx_quadraticcts.get_quad_num_nonzeros( ) all_quadratic_components = cpx_quadraticcts.get_quadratic_components( ) all_senses = cpx_quadraticcts.get_senses() cpx_ctnames = [] if ignore_names else cls._safe_call_get_names( cpx_adapter, cpx_quadraticcts.get_names) for c in range(nb_quadratic_cts): rhs = all_rhs[c] linear_nb_non_zeros = all_linear_nb_non_zeros[c] linear_component = all_linear_components[c] quadratic_nb_non_zeros = all_quadratic_nb_non_zeros[c] quadratic_component = all_quadratic_components[c] sense = all_senses[c] ctname = cpx_ctnames[c] if cpx_ctnames else None if linear_nb_non_zeros > 0: indices, coefs = linear_component.unpack() # linexpr = mdl._aggregator._scal_prod((cpx_var_index_to_docplex[idx] for idx in indices), coefs) linexpr = cls._make_expr_from_varmap_coefs( lfactory, cpx_var_index_to_docplex, indices, coefs) else: linexpr = None if quadratic_nb_non_zeros > 0: qfactory = mdl._qfactory ind1, ind2, coefs = quadratic_component.unpack() quads = qfactory.term_dict_type() for idx1, idx2, coef in izip(ind1, ind2, coefs): quads[VarPair( cpx_var_index_to_docplex[idx1], cpx_var_index_to_docplex[idx2])] = coef else: # pragma: no cover # should not happen, but who knows quads = None quad_expr = mdl._aggregator._quad_factory.new_quad( quads=quads, linexpr=linexpr, safe=True) op = ComparisonType.cplex_ctsense_to_python_op(sense) ct = op(quad_expr, rhs) mdl.add_constraint(ct, ctname) # 4. upload indicators cpx_indicators = cpx.indicator_constraints nb_indicators = cpx_indicators.get_num() if nb_indicators: all_ind_names = [] if ignore_names else cls._safe_call_get_names( cpx_adapter, cpx_indicators.get_names) all_ind_bvars = cpx_indicators.get_indicator_variables() all_ind_rhs = cpx_indicators.get_rhs() all_ind_linearcts = cpx_indicators.get_linear_components() all_ind_senses = cpx_indicators.get_senses() all_ind_complemented = cpx_indicators.get_complemented() all_ind_types = cpx_indicators.get_types() ind_equiv_type = 3 for i in range(nb_indicators): ind_bvar = all_ind_bvars[i] ind_name = all_ind_names[i] if all_ind_names else None ind_rhs = all_ind_rhs[i] ind_linear = all_ind_linearcts[i] # SparsePair(ind, val) ind_sense = all_ind_senses[i] ind_complemented = all_ind_complemented[i] ind_type = all_ind_types[i] # 1 . check the bvar is ok ind_bvar = cpx_var_index_to_docplex[ind_bvar] # each var appears once ind_linexpr = cls._build_linear_expr_from_sparse_pair( lfactory, cpx_var_index_to_docplex, ind_linear) op = ComparisonType.cplex_ctsense_to_python_op(ind_sense) ind_lct = op(ind_linexpr, ind_rhs) if ind_type == ind_equiv_type: logct = lfactory.new_equivalence_constraint( ind_bvar, ind_lct, true_value=1 - ind_complemented, name=ind_name) else: logct = lfactory.new_indicator_constraint( ind_bvar, ind_lct, true_value=1 - ind_complemented, name=ind_name) mdl.add(logct) # 5. upload Piecewise linear constraints try: cpx_pwl = cpx.pwl_constraints cpx_pwl_defs = cpx_pwl.get_definitions() pwl_fallback_names = [""] * cpx_pwl.get_num() cpx_pwl_names = pwl_fallback_names if ignore_names else cls._safe_call_get_names( cpx_adapter, cpx_pwl.get_names, pwl_fallback_names) for (vary_idx, varx_idx, preslope, postslope, breakx, breaky), pwl_name in izip(cpx_pwl_defs, cpx_pwl_names): varx = cpx_var_index_to_docplex.get(varx_idx, None) vary = cpx_var_index_to_docplex.get(vary_idx, None) breakxy = [(brkx, brky) for brkx, brky in zip(breakx, breaky)] pwl_func = mdl.piecewise(preslope, breakxy, postslope, name=pwl_name) pwl_expr = mdl._lfactory.new_pwl_expr( pwl_func, varx, 0, add_counter_suffix=False, resolve=False) pwl_expr._f_var = vary pwl_expr._ensure_resolved() except AttributeError: # pragma: no cover pass # Do not check for PWLs if Cplex version does not support them # 6. upload objective # noinspection PyPep8 try: cpx_multiobj = cpx.multiobj except AttributeError: # pragma: no cover # pre-12.9 version cpx_multiobj = None if cpx_multiobj is None or cpx_multiobj.get_num() <= 1: cpx_obj = cpx.objective cpx_sense = cpx_obj.get_sense() cpx_all_lin_obj_coeffs = cpx_obj.get_linear() all_obj_vars = [] all_obj_coefs = [] for v in range(cpx_nb_vars): if v in cpx_var_index_to_docplex: obj_coeff = cpx_all_lin_obj_coeffs[v] all_obj_coefs.append(obj_coeff) all_obj_vars.append(cpx_var_index_to_docplex[v]) # obj_expr = mdl._aggregator._scal_prod(all_obj_vars, all_obj_coefs) obj_expr = cls._make_expr_from_vars_coefs( mdl, all_obj_vars, all_obj_coefs) if cpx_obj.get_num_quadratic_variables() > 0: cpx_all_quad_cols_coeffs = cpx_obj.get_quadratic() quads = qfactory.term_dict_type() for v, col_coefs in izip(cpx_var_index_to_docplex, cpx_all_quad_cols_coeffs): var1 = cpx_var_index_to_docplex[v] indices, coefs = col_coefs.unpack() for idx, coef in izip(indices, coefs): vp = VarPair(var1, cpx_var_index_to_docplex[idx]) quads[vp] = quads.get(vp, 0) + coef / 2 obj_expr += qfactory.new_quad(quads=quads, linexpr=None) obj_expr += cpx.objective.get_offset() is_maximize = cpx_sense == cpx_adapter.cplex_module._internal._subinterfaces.ObjSense.maximize if is_maximize: mdl.maximize(obj_expr) else: mdl.minimize(obj_expr) else: # we have multiple objective nb_multiobjs = cpx_multiobj.get_num() exprs = [0] * nb_multiobjs priorities = [1] * nb_multiobjs weights = [1] * nb_multiobjs abstols = [0] * nb_multiobjs reltols = [0] * nb_multiobjs names = cpx_multiobj.get_names() for m in range(nb_multiobjs): (obj_coeffs, obj_offset, weight, prio, abstol, reltol) = cpx_multiobj.get_definition(m) obj_expr = cls._make_expr_from_coef_vector( mdl, cpx_var_index_to_docplex, obj_coeffs, obj_offset) exprs[m] = obj_expr priorities[m] = prio weights[m] = weight abstols[m] = abstol reltols[m] = reltol sense = cpx_multiobj.get_sense() mdl.set_multi_objective(sense, exprs, priorities, weights, abstols, reltols, names) # upload sos cpx_sos = cpx.SOS cpx_sos_num = cpx_sos.get_num() if cpx_sos_num > 0: cpx_sos_types = cpx_sos.get_types() cpx_sos_indices = cpx_sos.get_sets() cpx_sos_names = cpx_sos.get_names() if not cpx_sos_names: cpx_sos_names = [None] * cpx_sos_num for sostype, sos_sparse, sos_name in izip( cpx_sos_types, cpx_sos_indices, cpx_sos_names): sos_var_indices = sos_sparse.ind sos_weights = sos_sparse.val isostype = int(sostype) sos_vars = [ cpx_var_index_to_docplex[var_ix] for var_ix in sos_var_indices ] mdl.add_sos(dvars=sos_vars, sos_arg=isostype, name=sos_name, weights=sos_weights) # upload lazy constraints cpx_linear_advanced = cpx.linear_constraints.advanced cpx_lazyct_num = cpx_linear_advanced.get_num_lazy_constraints() if cpx_lazyct_num: print( "WARNING: found {0} lazy constraints that cannot be uploaded to DOcplex" .format(cpx_lazyct_num)) mdl.output_level = final_output_level if final_checker: # need to restore checker mdl.set_checker(final_checker) except cpx_adapter.CplexError as cpx_e: # pragma: no cover print("* CPLEX error: {0!s} reading file {1}".format( cpx_e, filename)) mdl = None if debug_read: raise except ModelReaderError as mre: # pragma: no cover print("! Model reader error: {0!s} while reading file {1}".format( mre, filename)) mdl = None if debug_read: raise except DOcplexException as doe: # pragma: no cover print("! Internal DOcplex error: {0!s} while reading file {1}". format(doe, filename)) mdl = None if debug_read: raise # except Exception as any_e: # pragma: no cover # print("Internal exception raised: {0} msg={1!s} while reading file '{2}'".format(type(any_e), any_e, filename)) # mdl = None # if debug_read: # raise finally: # clean up CPLEX instance... cpx.end() return mdl
def read_model(self, filename, model_name=None, verbose=False, model_class=None, **kwargs): """ Reads a model from a CPLEX export file. Accepts all formats exported by CPLEX: LP, SAV, MPS. If an error occurs while reading the file, the message of the exception is printed and the function returns None. Args: filename: The file to read. model_name: An optional name for the newly created model. If None, the model name will be the path basename. verbose: An optional flag to print informative messages, default is False. model_class: An optional class type; must be a subclass of Model. The returned model is built using this model_class and the keyword arguments kwargs, if any. By default, the model is class is `Model` (see kwargs: A dict of keyword-based arguments that are used when creating the model instance. Example: `m = read_model("c:/temp/foo.mps", model_name="docplex_foo", solver_agent="docloud", output_level=100)` Returns: An instance of Model, or None if an exception is raised. See Also: :class:`docplex.mp.model.Model` """ if not Cplex: # pragma: no cover raise RuntimeError( "ModelReader.read_model() requires CPLEX runtime.") if not os.path.exists(filename): raise IOError("* file not found: {0}".format(filename)) # extract basename if model_name: name_to_use = model_name else: basename = os.path.basename(filename) dotpos = basename.find(".") if dotpos > 0: name_to_use = basename[:dotpos] else: name_to_use = basename model_class = model_class or Model if 0 == os.stat(filename).st_size: print("* file is empty: {0} - exiting".format(filename)) return model_class(name=name_to_use, **kwargs) # print("-> start reading file: {0}".format(filename)) cpx = self._cplex_read(filename, verbose=verbose) if not cpx: # pragma: no cover return None range_map = {} final_output_level = kwargs.get("output_level", "info") debug_read = kwargs.get("debug", False) try: # force no tck if 'checker' in kwargs: final_checker = kwargs['checker'] else: final_checker = 'default' # build the model with no checker, then restore final_checker in the end. kwargs['checker'] = 'off' # ------------- mdl = model_class(name=name_to_use, **kwargs) lfactory = mdl._lfactory qfactory = mdl._qfactory mdl.set_quiet() # output level set to ERROR vartype_cont = mdl.continuous_vartype vartype_map = { 'B': mdl.binary_vartype, 'I': mdl.integer_vartype, 'C': mdl.continuous_vartype, 'S': mdl.semicontinuous_vartype } # 1 upload variables cpx_nb_vars = cpx.variables.get_num() cpx_var_names = self._safe_call_get_names(cpx.variables) if cpx._is_MIP(): cpx_vartypes = [ vartype_map.get(cpxt, vartype_cont) for cpxt in cpx.variables.get_types() ] else: cpx_vartypes = [vartype_cont] * cpx_nb_vars cpx_var_lbs = cpx.variables.get_lower_bounds() cpx_var_ubs = cpx.variables.get_upper_bounds() # map from cplex variable indices to docplex's # use to skip range vars # cplex : [x, Rg1, y] -> {0:0, 2: 1} var_index_map = {} d = 0 model_varnames = [] model_lbs = [] model_ubs = [] model_types = [] for v in range(cpx_nb_vars): varname = cpx_var_names[v] if cpx_var_names else None if varname and varname.startswith("Rg"): # generated var for ranges range_map[v] = self._RangeData(var_index=v, var_name=varname, ub=cpx_var_ubs[v]) else: # docplex_var = lfactory.new_var(vartype, lb, ub, varname) var_index_map[v] = d model_varnames.append(varname) model_types.append(cpx_vartypes[v]) model_lbs.append(cpx_var_lbs[v]) model_ubs.append(cpx_var_ubs[v]) d += 1 # vars model_vars = lfactory.new_multitype_var_list( d, model_types, model_lbs, model_ubs, model_varnames) cpx_var_index_to_docplex = { v: model_vars[var_index_map[v]] for v in var_index_map.keys() } # 2. upload linear constraints and ranges (mixed in cplex) cpx_linearcts = cpx.linear_constraints nb_linear_cts = cpx_linearcts.get_num() all_rows = cpx_linearcts.get_rows() all_rhs = cpx_linearcts.get_rhs() all_senses = cpx_linearcts.get_senses() all_range_values = cpx_linearcts.get_range_values() cpx_ctnames = self._safe_call_get_names(cpx_linearcts) has_range = range_map or any(s == "R" for s in all_senses) deferred_cts = [] for c in range(nb_linear_cts): row = all_rows[c] sense = all_senses[c] rhs = all_rhs[c] ctname = cpx_ctnames[c] if cpx_ctnames else None range_val = all_range_values[c] indices = row.ind coefs = row.val range_data = None if not has_range: expr = mdl._aggregator._scal_prod( (cpx_var_index_to_docplex[idx] for idx in indices), coefs) op = ComparisonType.parse(sense) ct = lfactory._new_binary_constraint(lhs=expr, rhs=rhs, sense=op) ct.name = ctname deferred_cts.append(ct) else: expr = lfactory.linear_expr() rcoef = 1 for idx, koef in izip(indices, coefs): var = cpx_var_index_to_docplex.get(idx, None) if var: expr._add_term(var, koef) elif idx in range_map: # this is a range: coeff must be 1 or -1 abscoef = koef if koef >= 0 else -koef rcoef = koef assert abscoef == 1, "range var has coef different from 1: {}".format( koef) assert range_data is None, "range_data is not None: {0!s}".format( range_data) # cannot use two range vars range_data = range_map[idx] else: # pragma: no cover # this is an internal error. raise ModelReaderError( "ERROR: index not in var map or range map: {0}" .format(idx)) if range_data: label = ctname or 'c#%d' % (c + 1) if sense not in "EL": # pragma: no cover raise ModelReaderError( "{0} range sense is not E: {1!s}".format( label, sense)) if rcoef < 0: # -1 actually rng_lb = rhs rng_ub = rhs + range_data.ub elif rcoef > 0: # koef is 1 here rng_lb = rhs - range_data.ub rng_ub = rhs else: # pragma: no cover raise ModelReaderError( "unexpected range coef: {}".format(rcoef)) mdl.add_range(lb=rng_lb, expr=expr, ub=rng_ub, rng_name=ctname) else: if sense == 'R': # range min is rangeval range_lb = rhs range_ub = rhs + range_val mdl.add_range(lb=range_lb, ub=range_ub, expr=expr, rng_name=ctname) else: op = ComparisonType.cplex_ctsense_to_python_op( sense) ct = op(expr, rhs) mdl.add_constraint(ct, ctname) if deferred_cts: # add constraint as a block lfactory._post_constraint_block(posted_cts=deferred_cts) # 3. upload Quadratic constraints cpx_quadraticcts = cpx.quadratic_constraints nb_quadratic_cts = cpx_quadraticcts.get_num() all_rhs = cpx_quadraticcts.get_rhs() all_linear_nb_non_zeros = cpx_quadraticcts.get_linear_num_nonzeros( ) all_linear_components = cpx_quadraticcts.get_linear_components() all_quadratic_nb_non_zeros = cpx_quadraticcts.get_quad_num_nonzeros( ) all_quadratic_components = cpx_quadraticcts.get_quadratic_components( ) all_senses = cpx_quadraticcts.get_senses() cpx_ctnames = self._safe_call_get_names(cpx_quadraticcts) for c in range(nb_quadratic_cts): rhs = all_rhs[c] linear_nb_non_zeros = all_linear_nb_non_zeros[c] linear_component = all_linear_components[c] quadratic_nb_non_zeros = all_quadratic_nb_non_zeros[c] quadratic_component = all_quadratic_components[c] sense = all_senses[c] ctname = cpx_ctnames[c] if cpx_ctnames else None if linear_nb_non_zeros > 0: indices, coefs = linear_component.unpack() linexpr = mdl._aggregator._scal_prod( (cpx_var_index_to_docplex[idx] for idx in indices), coefs) else: linexpr = None if quadratic_nb_non_zeros > 0: qfactory = mdl._qfactory ind1, ind2, coefs = quadratic_component.unpack() quads = qfactory.term_dict_type() for idx1, idx2, coef in izip(ind1, ind2, coefs): quads[VarPair(cpx_var_index_to_docplex[idx1], cpx_var_index_to_docplex[idx2])] = coef else: # pragma: no cover # should not happen, but who knows quads = None quad_expr = mdl._aggregator._quad_factory.new_quad( quads=quads, linexpr=linexpr, safe=True) op = ComparisonType.cplex_ctsense_to_python_op(sense) ct = op(quad_expr, rhs) mdl.add_constraint(ct, ctname) # 4. upload indicators cpx_indicators = cpx.indicator_constraints nb_indicators = cpx_indicators.get_num() all_ind_names = self._safe_call_get_names(cpx_indicators) all_ind_bvars = cpx_indicators.get_indicator_variables() all_ind_rhs = cpx_indicators.get_rhs() all_ind_linearcts = cpx_indicators.get_linear_components() all_ind_senses = cpx_indicators.get_senses() all_ind_complemented = cpx_indicators.get_complemented() lfactory = mdl._lfactory for i in range(nb_indicators): ind_bvar = all_ind_bvars[i] ind_name = all_ind_names[i] if all_ind_names else None ind_rhs = all_ind_rhs[i] ind_linear = all_ind_linearcts[i] # SparsePair(ind, val) ind_sense = all_ind_senses[i] ind_complemented = all_ind_complemented[i] # 1 . check the bvar is ok ind_bvar = cpx_var_index_to_docplex[ind_bvar] # each var appears once ind_linexpr = self._build_linear_expr_from_sparse_pair( lfactory, cpx_var_index_to_docplex, ind_linear) op = ComparisonType.cplex_ctsense_to_python_op(ind_sense) ind_ct = op(ind_linexpr, ind_rhs) indct = lfactory.new_indicator_constraint(ind_bvar, ind_ct, active_value=1 - ind_complemented, name=ind_name) mdl.add(indct) # 5. upload Piecewise linear constraints try: cpx_pwl = cpx.pwl_constraints cpx_pwl_defs = cpx_pwl.get_definitions() pwl_fallback_names = [""] * cpx_pwl.get_num() cpx_pwl_names = self._safe_call_get_names( cpx_pwl, pwl_fallback_names) for (vary_idx, varx_idx, preslope, postslope, breakx, breaky), pwl_name in izip(cpx_pwl_defs, cpx_pwl_names): varx = cpx_var_index_to_docplex.get(varx_idx, None) vary = cpx_var_index_to_docplex.get(vary_idx, None) breakxy = [(brkx, brky) for brkx, brky in zip(breakx, breaky)] pwl_func = mdl.piecewise(preslope, breakxy, postslope, name=pwl_name) pwl_expr = mdl._lfactory.new_pwl_expr( pwl_func, varx, 0, add_counter_suffix=False, resolve=False) pwl_expr._f_var = vary pwl_expr._ensure_resolved() except AttributeError: # pragma: no cover pass # Do not check for PWLs if Cplex version does not support them # 6. upload objective cpx_obj = cpx.objective cpx_sense = cpx_obj.get_sense() cpx_all_lin_obj_coeffs = cpx_obj.get_linear() # noinspection PyPep8 all_obj_vars = [] all_obj_coefs = [] for v in range(cpx_nb_vars): if v in cpx_var_index_to_docplex: obj_coeff = cpx_all_lin_obj_coeffs[v] all_obj_coefs.append(obj_coeff) all_obj_vars.append(cpx_var_index_to_docplex[v]) # obj_expr._add_term(idx_to_var_map[v], cpx_all_obj_coeffs[v]) obj_expr = mdl._aggregator._scal_prod(all_obj_vars, all_obj_coefs) if cpx_obj.get_num_quadratic_variables() > 0: cpx_all_quad_cols_coeffs = cpx_obj.get_quadratic() quads = qfactory.term_dict_type() for v, col_coefs in izip(cpx_var_index_to_docplex, cpx_all_quad_cols_coeffs): var1 = cpx_var_index_to_docplex[v] indices, coefs = col_coefs.unpack() for idx, coef in izip(indices, coefs): vp = VarPair(var1, cpx_var_index_to_docplex[idx]) quads[vp] = quads.get(vp, 0) + coef / 2 obj_expr += qfactory.new_quad(quads=quads, linexpr=None) obj_expr += cpx.objective.get_offset() is_maximize = cpx_sense == ObjSense.maximize if is_maximize: mdl.maximize(obj_expr) else: mdl.minimize(obj_expr) # upload sos cpx_sos = cpx.SOS cpx_sos_num = cpx_sos.get_num() if cpx_sos_num > 0: cpx_sos_types = cpx_sos.get_types() cpx_sos_indices = cpx_sos.get_sets() cpx_sos_names = cpx_sos.get_names() if not cpx_sos_names: cpx_sos_names = [None] * cpx_sos_num for sostype, sos_sparse, sos_name in izip( cpx_sos_types, cpx_sos_indices, cpx_sos_names): sos_var_indices = sos_sparse.ind isostype = int(sostype) sos_vars = [ cpx_var_index_to_docplex[var_ix] for var_ix in sos_var_indices ] mdl.add_sos(dvars=sos_vars, sos_arg=isostype, name=sos_name) # upload lazy constraints cpx_linear_advanced = cpx.linear_constraints.advanced cpx_lazyct_num = cpx_linear_advanced.get_num_lazy_constraints() if cpx_lazyct_num: print( "WARNING: found {0} lazy constraints that cannot be uploaded to DOcplex" .format(cpx_lazyct_num)) mdl.output_level = final_output_level if final_checker: # need to restore checker mdl.set_checker(final_checker) except CplexError as cpx_e: # pragma: no cover print("* CPLEX error: {0!s} reading file {1}".format( cpx_e, filename)) mdl = None if debug_read: raise except ModelReaderError as mre: # pragma: no cover print("! Model reader error: {0!s} while reading file {1}".format( mre, filename)) mdl = None if debug_read: raise except DOcplexException as doe: # pragma: no cover print("! Internal DOcplex error: {0!s} while reading file {1}". format(doe, filename)) mdl = None if debug_read: raise except Exception as any_e: # pragma: no cover print("Internal exception raised: {0!s} while reading file {1}". format(any_e, filename)) mdl = None if debug_read: raise finally: # clean up CPLEX instance... del cpx return mdl
def _sumsq_vars(self, dvars): qcc = self._quad_factory.term_dict_type() for v in dvars: update_dict_from_item_value(qcc, VarPair(v), 1) return self._to_expr(qcc=qcc)