def check_vars_domain(self, lbs, ubs, names):
l_ubs = len(ubs)
l_lbs = len(lbs)
if l_lbs and l_ubs:
names = names or generate_constant(None, max(l_lbs, l_ubs))
for lb, ub, varname in izip(lbs, ubs, names):
self.check_var_domain(lb, ub, varname)
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 _make_expr_from_vars_coefs(cls, mdl, dvars, coefs, offset=0):
terms_dict = mdl._lfactory._new_term_dict()
for dv, k in izip(dvars, coefs):
if k:
terms_dict[dv] = k
return mdl._lfactory.linear_expr(arg=terms_dict, constant=offset, safe=True)
def _sparse_make_exprs(self, sp_mat, dvars, nb_exprs):
lfactory = self._linear_factory
exprs = [lfactory.linear_expr() for _ in range(nb_exprs)]
coo_mat = sp_mat.tocoo()
for coef, row, col in izip(coo_mat.data, coo_mat.row, coo_mat.col):
exprs[row]._add_term(dvars[col], coef)
return exprs
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 __add__(self, arg):
if isinstance(arg, PwlFunction._PwlAsBreaks):
all_x_coord = sorted({br[0] for br in self.breaksxy + arg.breaksxy})
all_breaks_left = self._get_all_breaks(all_x_coord)
all_breaks_right = arg._get_all_breaks(all_x_coord)
result_breaksxy = []
# Both lists have same size, with same x-coord for breaks ==> perform the addition on each break
for br_l, br_r in izip(all_breaks_left, all_breaks_right):
if isinstance(br_l, tuple) and isinstance(br_r, tuple):
result_breaksxy.append((br_l[0], br_l[1] + br_r[1]))
else:
if isinstance(br_l, tuple):
# br_r is a list containing 2 tuple pairs
result_breaksxy.append((br_l[0], br_l[1] + br_r[0][1]))
result_breaksxy.append((br_l[0], br_l[1] + br_r[1][1]))
elif isinstance(br_r, tuple):
# br_l is a list containing 2 tuple pairs
result_breaksxy.append((br_r[0], br_l[0][1] + br_r[1]))
result_breaksxy.append((br_r[0], br_l[1][1] + br_r[1]))
else:
# br_l and br_r are two lists, each containing 2 tuple pairs
result_breaksxy.append((br_l[0][0], br_l[0][1] + br_r[0][1]))
result_breaksxy.append((br_l[0][0], br_l[1][1] + br_r[1][1]))
result_preslope = self.preslope + arg.preslope
result_postslope = self.postslope + arg.postslope
return PwlFunction._PwlAsBreaks(*self._remove_useless_intermediate_breaks(
result_preslope, result_breaksxy, result_postslope))
elif is_number(arg):
return PwlFunction._PwlAsBreaks(
self.preslope, [(br[0], br[1] + arg) for br in self.breaksxy], self.postslope)
else:
raise DOcplexException("Invalid type for right hand side operand: {0!s}.".format(arg))
def notify_obj_indices(self, objs, indices):
# take the last one??
if indices:
idxmap = self._index_map
if idxmap is not None:
for obj, idx in izip(objs, indices):
idxmap[idx] = obj
def build_matrix_range_model_and_solve(cls, var_count, var_lbs, var_ubs,
var_types, var_names,
cts_mat, range_mins, range_maxs,
objsense, costs,
cast_to_float,
solution_maker=make_solution,
**transform_params):
cpx = cls.create_cplex()
# varlist = mdl.continuous_var_list(var_count, lb=var_lbs, ub=var_ubs, name=var_names)
cls.create_column_vars(cpx, var_count, var_lbs, var_ubs, var_types, var_names)
var_indices = list(range(var_count))
gen_rows = ModelAggregator.generate_rows(cts_mat)
cpx_rows = []
for row in gen_rows:
# need this step as cplex may crash with np types.
frow = [float(k) for k in row] if cast_to_float else row
cpx_rows.append([var_indices, frow])
nb_rows = len(cpx_rows)
if nb_rows:
#ctsense = ComparisonType.parse(transform_params.get('sense', 'le'))
cpx_senses = 'R' * nb_rows
cpx_ranges = [float(rmin - rmax) for rmin, rmax in izip(range_mins, range_maxs)]
# rhs is UB
cpx_rhss = [float(rmax) for rmax in range_maxs]
add_linear(cpx, cpx_rows, cpx_senses, rhs=cpx_rhss, names=[], ranges=cpx_ranges)
if costs is not None:
# set linear objective for all variables.
fcosts = [float(k) for k in costs]
static_fast_set_linear_obj(cpx, var_indices, fcosts)
cpx.objective.set_sense(objsense.cplex_coef)
# here we go to solve...
return cls._solve(cpx, var_names, solution_maker=solution_maker, **transform_params)
def notify_obj_indices(self, objs, indices):
# take the last one??
if indices:
_AutomaticSymbolGenerator.notify_new_index(self, max(indices))
idxmap = self._index_map
if idxmap is not None:
for obj, idx in izip(objs, indices):
idxmap[idx] = obj
def check_vars_domain(self, lbs, ubs, names):
l_ubs = len(ubs)
l_lbs = len(lbs)
if l_lbs and l_ubs:
names = names or generate_constant(None, max(l_lbs, l_ubs))
# noinspection PyArgumentList,PyArgumentList
for lb, ub, varname in izip(lbs, ubs, names):
self.check_var_domain(lb, ub, varname)
def _make_expr_from_varmap_coefs(cls, lfactory, varmap, dvarxs, coefs, offset=0):
terms_dict = lfactory._new_term_dict()
for dvx, k in izip(dvarxs, coefs):
dv = varmap.get(dvx)
if dv is not None and k:
terms_dict[dv] = k
return lfactory.linear_expr(arg=terms_dict, constant=offset, safe=True)
def vector_compare(self, left_exprs, right_exprs, sense):
lfactory = self._linear_factory
assert len(left_exprs) == len(right_exprs)
cts = [
lfactory._new_binary_constraint(left, sense, right)
for left, right in izip(left_exprs, right_exprs)
]
return cts
def _make_expr_from_varmap_coefs(cls,
lfactory,
varmap,
var_indices,
coefs,
offset=0):
if Environment.env_is_python36:
terms_dict = {
varmap.get(dvx): k
for dvx, k in izip(var_indices, coefs)
}
else:
terms_dict = lfactory._new_term_dict()
for dvx, k in izip(var_indices, coefs):
dv = varmap.get(dvx)
if dv is not None:
terms_dict[dv] = k
return lfactory.linear_expr(arg=terms_dict, constant=offset, safe=True)
def make_solution_from_vars(self, dvars):
# build a solution object from array of solution values
# noinspection PyUnresolvedReferences
if dvars:
indices = [v._index for v in dvars]
var_values = super(ModelCallbackMixin, self).get_values(indices)
var_value_dict = {v: val for v, val in izip(dvars, var_values)}
else:
var_value_dict = {}
return self.model.new_solution(var_value_dict)
def _matrix_ranges(self, coef_mat, svars, lbs, ubs):
row_gen = self.generate_rows(coef_mat)
lfactory = self._linear_factory
return [
lfactory.new_range_constraint(expr=self._scal_prod(svars, row),
lb=lb,
ub=ub)
for row, lb, ub in izip(row_gen, lbs, ubs)
]
def _scal_prod_vars_all_different(self, terms, coefs):
checker = self._checker
if not is_iterable(coefs, accept_string=False):
checker.typecheck_num(coefs)
return coefs * self._sum_vars_all_different(terms)
else:
# coefs is iterable
lcc_type = self.counter_type
lcc = lcc_type()
lcc_setitem = lcc_type.__setitem__
number_validation_fn = checker.get_number_validation_fn()
if number_validation_fn:
for dvar, coef in izip(terms, coefs):
lcc_setitem(lcc, dvar, number_validation_fn(coef))
else:
for dvar, coef in izip(terms, coefs):
lcc_setitem(lcc, dvar, coef)
return self._to_expr(qcc=None, lcc=lcc)
def _matrix_constraints(self, coef_mat, svars, srhs, op):
row_gen = self.generate_rows(coef_mat)
lfactory = self._linear_factory
return [
lfactory._new_binary_constraint(lhs=self._scal_prod(svars, row),
sense=op,
rhs=rhs)
for row, rhs in izip(row_gen, srhs)
]
def compile_naming_function(keys,
user_name,
dimension=1,
key_format=None,
_default_key_format='_%s',
stringifier=str_flatten_tuple):
# INTERNAL
# builds a naming rule from an input , a dimension, and an optional meta-format
# Makes sure the format string does contain the right number of format slots
assert user_name is not None
if is_string(user_name):
if key_format is None:
used_key_format = _default_key_format
elif is_string(key_format):
# -- make sure some %s is inside, otherwise add it
if '%s' in key_format:
used_key_format = key_format
else:
used_key_format = key_format + '%s'
else: # pragma: no cover
raise DOcplexException(
"key format expects string format or None, got: {0!r}".format(
key_format))
fixed_format_string = fix_format_string(user_name, dimension,
used_key_format)
if 1 == dimension:
return lambda k: fixed_format_string % stringifier(k)
else:
# here keys are tuples of size >= 2
return lambda key_tuple: fixed_format_string % key_tuple
elif is_function(user_name):
return user_name
elif is_iterable(user_name):
# check that the iterable has same len as keys,
# otherwise thereis no more default naming and None cannot appear in CPLEX name arrays
list_names = list(user_name)
if len(list_names) < len(keys):
raise DOcplexException(
"An array of names should have same len as keys, expecting: {0}, go: {1}"
.format(len(keys), len(list_names)))
key_to_names_dict = {k: nm for k, nm in izip(keys, list_names)}
# use a closure
return lambda k: key_to_names_dict[
k] # if k in key_to_names_dict else default_fn()
else:
raise DOcplexException(
'Cannot use this for naming variables: {0!r} - expecting string, function or iterable'
.format(user_name))
def make_solution_from_vars(self, dvars):
# build a solution object from array of solution values
# noinspection PyUnresolvedReferences
m = self.model
if dvars:
indices = [v._index for v in dvars]
# this calls the Cplex callback method get_values, which crashes if called with empty list
var_values = super(ModelCallbackMixin, self).get_values(indices)
var_value_dict = {v: val for v, val in izip(dvars, var_values)}
else:
var_value_dict = {}
return m.new_solution(var_value_dict)
def _print_sos_block(self, wrapper, mdl):
if mdl.number_of_sos > 0:
wrapper.write('SOS')
wrapper.flush(print_newline=True)
name_fn = self._var_print_name
for s, sos in enumerate(mdl.iter_sos(), start=1):
# always print a name string, s1, s2, ... if no name
sos_name = sos.get_name() or 's%d' % s
wrapper.write('%s:' % sos_name)
wrapper.write('S%d ::' % sos.sos_type.value) # 1 or 2
ranks = sos.weights
# noinspection PyArgumentList
for rank, sos_var in izip(ranks, sos._variables):
wrapper.write('%s : %d' % (name_fn(sos_var), rank))
wrapper.flush(print_newline=True)
def make_solution_from_vars(self, dvars):
""" Creates an intermediate solution from a list of variables.
:param dvars: a list of DOcplex variables.
:return: a :class:`docplex.mp.solution.SolveSolution` object.
"""
if dvars:
indices = [v._index for v in dvars]
# this calls the Cplex callback method get_values, which crashes if called with empty list
# noinspection PyUnresolvedReferences
var_values = super(ModelCallbackMixin, self).get_values(indices)
# noinspection PyArgumentList
var_value_dict = {v: val for v, val in izip(dvars, var_values)}
else: # pragma: no cover
var_value_dict = {}
return self.model.new_solution(var_value_dict)
def _scal_prod(self, terms, coefs):
# INTERNAL
checker = self._checker
total_num = 0
lcc = self.counter_type()
qcc = None
number_validation_fn = checker.get_number_validation_fn()
for item, coef in izip(terms, coefs):
if not coef:
continue
safe_coef = number_validation_fn(
coef) if number_validation_fn else coef
if isinstance(item, Var):
update_dict_from_item_value(lcc, item, safe_coef)
elif isinstance(item, AbstractLinearExpr):
total_num += safe_coef * item.get_constant()
for lv, lk in item.iter_terms():
update_dict_from_item_value(lcc, lv, lk * safe_coef)
elif isinstance(item, QuadExpr):
if qcc is None:
qcc = self.counter_type()
for qv, qk in item.iter_quads():
update_dict_from_item_value(qcc, qv, qk * safe_coef)
qlin = item.get_linear_part()
for v, k in qlin.iter_terms():
update_dict_from_item_value(lcc, v, k * safe_coef)
total_num += safe_coef * qlin.constant
# --- try conversion ---
else:
try:
e = item.to_linear_expr()
total_num += e.get_constant()
for dv, k, in e.iter_terms():
update_dict_from_item_value(lcc, dv, k * safe_coef)
except AttributeError:
self._model.fatal(
"scal_prod accepts variables, expressions, numbers, not: {0!s}",
item)
return self._to_expr(qcc, lcc, total_num)
def __add__(self, arg):
if isinstance(arg, PwlFunction._PwlAsSlopes):
all_x_coord = sorted({sbr[1] for sbr in self.slopebreaksx + arg.slopebreaksx})
all_slopebreaks_left = self._get_all_slopebreaks(all_x_coord)
all_slopebreaks_right = arg._get_all_slopebreaks(all_x_coord)
result_slopebreaksxy = []
# Both lists have same size, with same x-coord for slopebreaks
# ==> perform the addition of slopes on each break
for sbr_l, sbr_r in izip(all_slopebreaks_left, all_slopebreaks_right):
if isinstance(sbr_l, tuple) and isinstance(sbr_r, tuple):
result_slopebreaksxy.append((sbr_l[0] + sbr_r[0], sbr_l[1]))
else:
if isinstance(sbr_l, tuple):
# sbr_r is a list containing 2 tuple pairs
result_slopebreaksxy.append((sbr_l[0] + sbr_r[0][0], sbr_l[1]))
result_slopebreaksxy.append((sbr_r[1][0], sbr_l[1]))
elif isinstance(sbr_r, tuple):
# sbr_l is a list containing 2 tuple pairs
result_slopebreaksxy.append((sbr_l[0][0] + sbr_r[0], sbr_r[1]))
result_slopebreaksxy.append((sbr_l[1][0], sbr_r[1]))
else:
# sbr_l and sbr_r are two lists, each containing 2 tuple pairs
result_slopebreaksxy.append((sbr_l[0][0] + sbr_r[0][0], sbr_l[0][1]))
result_slopebreaksxy.append((sbr_l[1][0] + sbr_r[1][0], sbr_l[0][1]))
result_lastslope = self.lastslope + arg.lastslope
if self.anchor[0] == arg.anchor[0]:
result_anchor = (self.anchor[0], self.anchor[1] + arg.anchor[1])
else:
# Compute a new anchor based on the last x-coord in the slopebreakx list + anchor point
anchor_l = self._get_safe_xy_anchor()
anchor_r = arg._get_safe_xy_anchor()
delta = anchor_r[0] - anchor_l[0]
if anchor_l[0] < anchor_r[0]:
result_anchor = (anchor_r[0], anchor_l[1] + anchor_r[1] + delta * self.lastslope)
else:
result_anchor = (anchor_l[0], anchor_l[1] + anchor_r[1] - delta * arg.lastslope)
return PwlFunction._PwlAsSlopes(*self._remove_useless_intermediate_slopes(
result_slopebreaksxy, result_lastslope, result_anchor))
elif is_number(arg):
return PwlFunction._PwlAsSlopes(copy.deepcopy(self.slopebreaksx),
self.lastslope, (self.anchor[0], self.anchor[1] + arg))
else:
raise DOcplexException("Invalid type for right hand side operand: {0!s}.".format(arg))
def equals(self,
other,
check_models=False,
obj_precision=1e-3,
var_precision=1e-6):
from itertools import dropwhile
if check_models and (self.model != other.model):
return False
if is_iterable(self.objective_value) and is_iterable(
other.objective_value):
if len(self.objective_value) == len(other.objective_value):
for self_obj_val, other_obj_val in zip(self.objective_value,
other.objective_value):
if abs(self_obj_val - other_obj_val) >= obj_precision:
return False
else: # Different number of objectives
return False
elif not is_iterable(self.objective_value) and not is_iterable(
other.objective_value):
if abs(self.objective_value -
other.objective_value) >= obj_precision:
return False
else: # One solution is for multi-objective, and not the other
return False
# noinspection PyPep8
this_triplets = [(dv.index, dv.name, svalue)
for dv, svalue in dropwhile(lambda dvv: not dvv[1],
self.iter_var_values())]
other_triplets = [(dv.index, dv.name, svalue)
for dv, svalue in dropwhile(lambda dvv: not dvv[1],
other.iter_var_values())]
# noinspection PyArgumentList
res = True
for this_triple, other_triple in izip(this_triplets, other_triplets):
this_index, this_name, this_val = this_triple
other_index, other_name, other_val = other_triple
if other_index != this_index or this_name != other_name or \
abs(this_val - other_val) >= var_precision:
res = False
break
return res
def new_range_block(self, lbs, exprs, ubs, names):
try:
n_exprs = len(exprs)
if n_exprs != len(lbs): # pragma: no cover
self.fatal(
'incorrect number of expressions: expecting {0}, got: {1}'.
format(len(lbs), n_exprs))
except TypeError:
pass # no len available.
if not names:
names = generate_constant(None, None)
ranges = [
self.new_range_constraint(lb, exp, ub, name)
for lb, exp, ub, name in izip(lbs, exprs, ubs, names)
]
# else:
# ranges = [self.new_range_constraint(lb, exp, ub) for lb, exp, ub in izip(lbs, exprs, ubs)]
self._post_constraint_block(ranges)
return ranges
def new_var_df(self,
keys1,
keys2,
vartype,
lb=None,
ub=None,
name=None): # pragma: no cover
try:
from pandas import DataFrame
except ImportError:
DataFrame = None
self.fatal('make_var_df() requires pandas module - not found')
_, row_keys = self.make_key_seq(keys1, name)
_, col_keys = self.make_key_seq(keys2, name)
matrix_keys = [(k1, k2) for k1 in row_keys for k2 in col_keys]
ctn = self._new_var_container(vartype,
key_list=matrix_keys,
lb=lb,
ub=ub,
name=name)
lvars = self.new_var_list(ctn,
matrix_keys,
vartype,
lb,
ub,
name,
dimension=2,
key_format=None)
# TODO: see how to do without this temp dict
dd = dict(izip(matrix_keys, lvars))
# row-oriented dict
rowd = {
row_k: [dd[row_k, col_k] for col_k in col_keys]
for row_k in row_keys
}
vdtf = DataFrame.from_dict(rowd, orient='index', dtype='object')
# convert to string or not?
vdtf.columns = col_keys
return vdtf
def new_batch_indicator_constraints(self, bvars, linear_cts, active_values,
names):
return [
self.new_indicator_constraint(bv, lct, active, name) for bv, lct,
active, name in izip(bvars, linear_cts, active_values, names)
]
def _build_linear_expr_from_sparse_pair(lfactory, var_map, cpx_sparsepair):
expr = lfactory.linear_expr(arg=0, safe=True)
for ix, k in izip(cpx_sparsepair.ind, cpx_sparsepair.val):
dv = var_map[ix]
expr._add_term(dv, k)
return expr
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