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Use np.ndarray for var_types, basis_status

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master
Alinson S. Xavier 4 years ago
parent 7d55d6f34c
commit 45667ac2e4
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GPG Key ID: DCA0DAD4D2F58624
6 changed files with 31 additions and 23 deletions
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4
miplearn/features/extractor.py
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@ -36,7 +36,7 @@ class FeaturesExtractor:
sample.put_array("static_var_lower_bounds", variables.lower_bounds) sample.put_array("static_var_lower_bounds", variables.lower_bounds)
sample.put_array("static_var_names", variables.names) sample.put_array("static_var_names", variables.names)
sample.put_array("static_var_obj_coeffs", variables.obj_coeffs) sample.put_array("static_var_obj_coeffs", variables.obj_coeffs)
sample.put_vector("static_var_types", variables.types) sample.put_array("static_var_types", variables.types)
sample.put_array("static_var_upper_bounds", variables.upper_bounds) sample.put_array("static_var_upper_bounds", variables.upper_bounds)
sample.put_vector("static_constr_names", constraints.names) sample.put_vector("static_constr_names", constraints.names)
# sample.put("static_constr_lhs", constraints.lhs) # sample.put("static_constr_lhs", constraints.lhs)
@ -69,7 +69,7 @@ class FeaturesExtractor:
) -> None: ) -> None:
variables = solver.get_variables(with_static=False, with_sa=self.with_sa) variables = solver.get_variables(with_static=False, with_sa=self.with_sa)
constraints = solver.get_constraints(with_static=False, with_sa=self.with_sa) constraints = solver.get_constraints(with_static=False, with_sa=self.with_sa)
sample.put_vector("lp_var_basis_status", variables.basis_status) sample.put_array("lp_var_basis_status", variables.basis_status)
sample.put_array("lp_var_reduced_costs", variables.reduced_costs) sample.put_array("lp_var_reduced_costs", variables.reduced_costs)
sample.put_array("lp_var_sa_lb_down", variables.sa_lb_down) sample.put_array("lp_var_sa_lb_down", variables.sa_lb_down)
sample.put_array("lp_var_sa_lb_up", variables.sa_lb_up) sample.put_array("lp_var_sa_lb_up", variables.sa_lb_up)

31
miplearn/solvers/gurobi.py
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@ -79,7 +79,7 @@ class GurobiSolver(InternalSolver):
self._gp_constrs: List["gurobipy.Constr"] = [] self._gp_constrs: List["gurobipy.Constr"] = []
self._var_names: np.ndarray = np.empty(0) self._var_names: np.ndarray = np.empty(0)
self._constr_names: List[str] = [] self._constr_names: List[str] = []
self._var_types: List[str] = [] self._var_types: np.ndarray = np.empty(0)
self._var_lbs: np.ndarray = np.empty(0) self._var_lbs: np.ndarray = np.empty(0)
self._var_ubs: np.ndarray = np.empty(0) self._var_ubs: np.ndarray = np.empty(0)
self._var_obj_coeffs: np.ndarray = np.empty(0) self._var_obj_coeffs: np.ndarray = np.empty(0)
@ -322,8 +322,9 @@ class GurobiSolver(InternalSolver):
else: else:
raise Exception(f"unknown vbasis: {basis_status}") raise Exception(f"unknown vbasis: {basis_status}")
basis_status: Optional[np.ndarray] = None
upper_bounds, lower_bounds, types, values = None, None, None, None upper_bounds, lower_bounds, types, values = None, None, None, None
obj_coeffs, reduced_costs, basis_status = None, None, None obj_coeffs, reduced_costs = None, None
sa_obj_up, sa_ub_up, sa_lb_up = None, None, None sa_obj_up, sa_ub_up, sa_lb_up = None, None, None
sa_obj_down, sa_ub_down, sa_lb_down = None, None, None sa_obj_down, sa_ub_down, sa_lb_down = None, None, None
@ -335,11 +336,12 @@ class GurobiSolver(InternalSolver):
if self._has_lp_solution: if self._has_lp_solution:
reduced_costs = np.array(model.getAttr("rc", self._gp_vars), dtype=float) reduced_costs = np.array(model.getAttr("rc", self._gp_vars), dtype=float)
basis_status = list( basis_status = np.array(
map( [
_parse_gurobi_vbasis, _parse_gurobi_vbasis(b)
model.getAttr("vbasis", self._gp_vars), for b in model.getAttr("vbasis", self._gp_vars)
) ],
dtype="S",
) )
if with_sa: if with_sa:
@ -513,7 +515,7 @@ class GurobiSolver(InternalSolver):
self._apply_params(streams) self._apply_params(streams)
assert self.model is not None assert self.model is not None
for (i, var) in enumerate(self._gp_vars): for (i, var) in enumerate(self._gp_vars):
if self._var_types[i] == "B": if self._var_types[i] == b"B":
var.vtype = self.gp.GRB.CONTINUOUS var.vtype = self.gp.GRB.CONTINUOUS
var.lb = 0.0 var.lb = 0.0
var.ub = 1.0 var.ub = 1.0
@ -521,7 +523,7 @@ class GurobiSolver(InternalSolver):
self.model.optimize() self.model.optimize()
self._dirty = False self._dirty = False
for (i, var) in enumerate(self._gp_vars): for (i, var) in enumerate(self._gp_vars):
if self._var_types[i] == "B": if self._var_types[i] == b"B":
var.vtype = self.gp.GRB.BINARY var.vtype = self.gp.GRB.BINARY
log = streams[0].getvalue() log = streams[0].getvalue()
self._has_lp_solution = self.model.solCount > 0 self._has_lp_solution = self.model.solCount > 0
@ -590,7 +592,10 @@ class GurobiSolver(InternalSolver):
self.model.getAttr("varName", gp_vars), self.model.getAttr("varName", gp_vars),
dtype="S", dtype="S",
) )
var_types: List[str] = self.model.getAttr("vtype", gp_vars) var_types: np.ndarray = np.array(
self.model.getAttr("vtype", gp_vars),
dtype="S",
)
var_ubs: np.ndarray = np.array( var_ubs: np.ndarray = np.array(
self.model.getAttr("ub", gp_vars), self.model.getAttr("ub", gp_vars),
dtype=float, dtype=float,
@ -611,7 +616,7 @@ class GurobiSolver(InternalSolver):
f"Duplicated variable name detected: {var_names[i]}. " f"Duplicated variable name detected: {var_names[i]}. "
f"Unique variable names are currently required." f"Unique variable names are currently required."
) )
if var_types[i] == "I": if var_types[i] == b"I":
assert var_ubs[i] == 1.0, ( assert var_ubs[i] == 1.0, (
"Only binary and continuous variables are currently supported. " "Only binary and continuous variables are currently supported. "
f"Integer variable {var_names[i]} has upper bound {var_ubs[i]}." f"Integer variable {var_names[i]} has upper bound {var_ubs[i]}."
@ -620,8 +625,8 @@ class GurobiSolver(InternalSolver):
"Only binary and continuous variables are currently supported. " "Only binary and continuous variables are currently supported. "
f"Integer variable {var_names[i]} has lower bound {var_ubs[i]}." f"Integer variable {var_names[i]} has lower bound {var_ubs[i]}."
) )
var_types[i] = "B" var_types[i] = b"B"
assert var_types[i] in ["B", "C"], ( assert var_types[i] in [b"B", b"C"], (
"Only binary and continuous variables are currently supported. " "Only binary and continuous variables are currently supported. "
f"Variable {var_names[i]} has type {var_types[i]}." f"Variable {var_names[i]} has type {var_types[i]}."
) )

4
miplearn/solvers/internal.py
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@ -51,7 +51,7 @@ class MIPSolveStats:
@dataclass @dataclass
class Variables: class Variables:
names: Optional[np.ndarray] = None names: Optional[np.ndarray] = None
basis_status: Optional[List[str]] = None basis_status: Optional[np.ndarray] = None
lower_bounds: Optional[np.ndarray] = None lower_bounds: Optional[np.ndarray] = None
obj_coeffs: Optional[np.ndarray] = None obj_coeffs: Optional[np.ndarray] = None
reduced_costs: Optional[np.ndarray] = None reduced_costs: Optional[np.ndarray] = None
@ -61,7 +61,7 @@ class Variables:
sa_obj_up: Optional[np.ndarray] = None sa_obj_up: Optional[np.ndarray] = None
sa_ub_down: Optional[np.ndarray] = None sa_ub_down: Optional[np.ndarray] = None
sa_ub_up: Optional[np.ndarray] = None sa_ub_up: Optional[np.ndarray] = None
types: Optional[List[str]] = None types: Optional[np.ndarray] = None
upper_bounds: Optional[np.ndarray] = None upper_bounds: Optional[np.ndarray] = None
values: Optional[np.ndarray] = None values: Optional[np.ndarray] = None

2
miplearn/solvers/pyomo/base.py
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@ -327,7 +327,7 @@ class BasePyomoSolver(InternalSolver):
return Variables( return Variables(
names=_none_if_empty(np.array(names, dtype="S")), names=_none_if_empty(np.array(names, dtype="S")),
types=_none_if_empty(types), types=_none_if_empty(np.array(types, dtype="S")),
upper_bounds=_none_if_empty(np.array(upper_bounds, dtype=float)), upper_bounds=_none_if_empty(np.array(upper_bounds, dtype=float)),
lower_bounds=_none_if_empty(np.array(lower_bounds, dtype=float)), lower_bounds=_none_if_empty(np.array(lower_bounds, dtype=float)),
obj_coeffs=_none_if_empty(np.array(obj_coeffs, dtype=float)), obj_coeffs=_none_if_empty(np.array(obj_coeffs, dtype=float)),

4
miplearn/solvers/tests/__init__.py
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@ -44,7 +44,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
names=np.array(["x[0]", "x[1]", "x[2]", "x[3]", "z"], dtype="S"), names=np.array(["x[0]", "x[1]", "x[2]", "x[3]", "z"], dtype="S"),
lower_bounds=np.array([0.0, 0.0, 0.0, 0.0, 0.0]), lower_bounds=np.array([0.0, 0.0, 0.0, 0.0, 0.0]),
upper_bounds=np.array([1.0, 1.0, 1.0, 1.0, 67.0]), upper_bounds=np.array([1.0, 1.0, 1.0, 1.0, 67.0]),
types=["B", "B", "B", "B", "C"], types=np.array(["B", "B", "B", "B", "C"], dtype="S"),
obj_coeffs=np.array([505.0, 352.0, 458.0, 220.0, 0.0]), obj_coeffs=np.array([505.0, 352.0, 458.0, 220.0, 0.0]),
), ),
) )
@ -85,7 +85,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
solver.get_variable_attrs(), solver.get_variable_attrs(),
Variables( Variables(
names=np.array(["x[0]", "x[1]", "x[2]", "x[3]", "z"], dtype="S"), names=np.array(["x[0]", "x[1]", "x[2]", "x[3]", "z"], dtype="S"),
basis_status=["U", "B", "U", "L", "U"], basis_status=np.array(["U", "B", "U", "L", "U"], dtype="S"),
reduced_costs=np.array( reduced_costs=np.array(
[193.615385, 0.0, 187.230769, -23.692308, 13.538462] [193.615385, 0.0, 187.230769, -23.692308, 13.538462]
), ),

9
tests/features/test_extractor.py
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@ -41,7 +41,10 @@ def test_knapsack() -> None:
assert_equals( assert_equals(
sample.get_vector("static_var_obj_coeffs"), [505.0, 352.0, 458.0, 220.0, 0.0] sample.get_vector("static_var_obj_coeffs"), [505.0, 352.0, 458.0, 220.0, 0.0]
) )
assert_equals(sample.get_vector("static_var_types"), ["B", "B", "B", "B", "C"]) assert_equals(
sample.get_array("static_var_types"),
np.array(["B", "B", "B", "B", "C"], dtype="S"),
)
assert_equals( assert_equals(
sample.get_vector("static_var_upper_bounds"), [1.0, 1.0, 1.0, 1.0, 67.0] sample.get_vector("static_var_upper_bounds"), [1.0, 1.0, 1.0, 1.0, 67.0]
) )
@ -76,8 +79,8 @@ def test_knapsack() -> None:
solver.solve_lp() solver.solve_lp()
extractor.extract_after_lp_features(solver, sample) extractor.extract_after_lp_features(solver, sample)
assert_equals( assert_equals(
sample.get_vector("lp_var_basis_status"), sample.get_array("lp_var_basis_status"),
["U", "B", "U", "L", "U"], np.array(["U", "B", "U", "L", "U"], dtype="S"),
) )
assert_equals( assert_equals(
sample.get_vector("lp_var_reduced_costs"), sample.get_vector("lp_var_reduced_costs"),

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