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Add _gurobipy suffix to all build_model functions

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This commit is contained in:
Alinson S. Xavier
2024-02-06 16:08:24 -06:00
parent fb3f219ea8
commit b55554d410
28 changed files with 99 additions and 102 deletions
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2
miplearn/classifiers/minprob.py
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@@ -54,7 +54,7 @@ class MinProbabilityClassifier(BaseEstimator):
y_pred = []
for sample_idx in range(n_samples):
yi = float("nan")
for (class_idx, class_val) in enumerate(self.classes_):
for class_idx, class_val in enumerate(self.classes_):
if y_proba[sample_idx, class_idx] >= self.thresholds[class_idx]:
yi = class_val
y_pred.append(yi)

2
miplearn/components/primal/actions.py
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@@ -71,7 +71,7 @@ class EnforceProximity(PrimalComponentAction):
constr_lhs = []
constr_vars = []
constr_rhs = 0.0
for (i, var_name) in enumerate(var_names):
for i, var_name in enumerate(var_names):
if np.isnan(var_values[i]):
continue
constr_lhs.append(1.0 if var_values[i] < 0.5 else -1.0)

4
miplearn/components/primal/indep.py
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@@ -91,7 +91,7 @@ class IndependentVarsPrimalComponent:
logger.info(f"Training {n_bin_vars} classifiers...")
self.clf_ = {}
for (var_idx, var_name) in enumerate(self.bin_var_names_):
for var_idx, var_name in enumerate(self.bin_var_names_):
self.clf_[var_name] = self.clone_fn(self.base_clf)
self.clf_[var_name].fit(
x_np[var_idx::n_bin_vars, :], y_np[var_idx::n_bin_vars]
@@ -117,7 +117,7 @@ class IndependentVarsPrimalComponent:
# Predict optimal solution
logger.info("Predicting warm starts...")
y_pred = []
for (var_idx, var_name) in enumerate(self.bin_var_names_):
for var_idx, var_name in enumerate(self.bin_var_names_):
x_var = x_sample[var_idx, :].reshape(1, -1)
y_var = self.clf_[var_name].predict(x_var)
assert y_var.shape == (1,)

2
miplearn/components/priority.py
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@@ -25,7 +25,7 @@ class ExpertBranchPriorityComponent:
assert var_priority is not None
assert var_names is not None
for (var_idx, var_name) in enumerate(var_names):
for var_idx, var_name in enumerate(var_names):
if np.isfinite(var_priority[var_idx]):
var = model.getVarByName(var_name.decode())
var.branchPriority = int(log(1 + var_priority[var_idx]))

4
miplearn/problems/__init__.py
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@@ -11,7 +11,7 @@ from pyomo import environ as pe
def _gurobipy_set_params(model: gp.Model, params: Optional[dict[str, Any]]) -> None:
assert isinstance(model, gp.Model)
if params is not None:
for (param_name, param_value) in params.items():
for param_name, param_value in params.items():
setattr(model.params, param_name, param_value)
@@ -24,5 +24,5 @@ def _pyomo_set_params(
solver == "gurobi_persistent"
), "setting parameters is only supported with gurobi_persistent"
if solver == "gurobi_persistent" and params is not None:
for (param_name, param_value) in params.items():
for param_name, param_value in params.items():
model.solver.set_gurobi_param(param_name, param_value)

2
miplearn/problems/binpack.py
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@@ -109,7 +109,7 @@ class BinPackGenerator:
return [_sample() for n in range(n_samples)]
def build_binpack_model(data: Union[str, BinPackData]) -> GurobiModel:
def build_binpack_model_gurobipy(data: Union[str, BinPackData]) -> GurobiModel:
"""Converts bin packing problem data into a concrete Gurobipy model."""
if isinstance(data, str):
data = read_pkl_gz(data)

4
miplearn/problems/multiknapsack.py
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@@ -174,7 +174,9 @@ class MultiKnapsackGenerator:
return [_sample() for _ in range(n_samples)]
def build_multiknapsack_model(data: Union[str, MultiKnapsackData]) -> GurobiModel:
def build_multiknapsack_model_gurobipy(
data: Union[str, MultiKnapsackData]
) -> GurobiModel:
"""Converts multi-knapsack problem data into a concrete Gurobipy model."""
if isinstance(data, str):
data = read_pkl_gz(data)

2
miplearn/problems/pmedian.py
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@@ -141,7 +141,7 @@ class PMedianGenerator:
return [_sample() for _ in range(n_samples)]
def build_pmedian_model(data: Union[str, PMedianData]) -> GurobiModel:
def build_pmedian_model_gurobipy(data: Union[str, PMedianData]) -> GurobiModel:
"""Converts capacitated p-median data into a concrete Gurobipy model."""
if isinstance(data, str):
data = read_pkl_gz(data)

2
miplearn/problems/setpack.py
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@@ -53,7 +53,7 @@ class SetPackGenerator:
]
def build_setpack_model(data: Union[str, SetPackData]) -> GurobiModel:
def build_setpack_model_gurobipy(data: Union[str, SetPackData]) -> GurobiModel:
if isinstance(data, str):
data = read_pkl_gz(data)
assert isinstance(data, SetPackData)

4
miplearn/problems/stab.py
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@@ -101,7 +101,7 @@ def build_stab_model_gurobipy(
model.setObjective(quicksum(-data.weights[i] * x[i] for i in nodes))
# Edge inequalities
for (i1, i2) in data.graph.edges:
for i1, i2 in data.graph.edges:
model.addConstr(x[i1] + x[i2] <= 1)
def cuts_separate(m: GurobiModel) -> List[Hashable]:
@@ -137,7 +137,7 @@ def build_stab_model_pyomo(
# Edge inequalities
model.edge_eqs = pe.ConstraintList()
for (i1, i2) in data.graph.edges:
for i1, i2 in data.graph.edges:
model.edge_eqs.add(model.x[i1] + model.x[i2] <= 1)
# Clique inequalities

2
miplearn/problems/tsp.py
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@@ -119,7 +119,6 @@ def build_tsp_model_gurobipy(
data: Union[str, TravelingSalesmanData],
params: Optional[dict[str, Any]] = None,
) -> GurobiModel:
model = gp.Model()
_gurobipy_set_params(model, params)
@@ -173,7 +172,6 @@ def build_tsp_model_pyomo(
solver: str = "gurobi_persistent",
params: Optional[dict[str, Any]] = None,
) -> PyomoModel:
model = pe.ConcreteModel()
data = _tsp_read(data)

2
miplearn/problems/uc.py
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@@ -112,7 +112,7 @@ class UnitCommitmentGenerator:
return [_sample() for _ in range(n_samples)]
def build_uc_model(data: Union[str, UnitCommitmentData]) -> GurobiModel:
def build_uc_model_gurobipy(data: Union[str, UnitCommitmentData]) -> GurobiModel:
"""
Models the unit commitment problem according to equations (1)-(5) of:

6
miplearn/problems/vertexcover.py
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@@ -40,7 +40,9 @@ class MinWeightVertexCoverGenerator:
]
def build_vertexcover_model(data: Union[str, MinWeightVertexCoverData]) -> GurobiModel:
def build_vertexcover_model_gurobipy(
data: Union[str, MinWeightVertexCoverData]
) -> GurobiModel:
if isinstance(data, str):
data = read_pkl_gz(data)
assert isinstance(data, MinWeightVertexCoverData)
@@ -48,7 +50,7 @@ def build_vertexcover_model(data: Union[str, MinWeightVertexCoverData]) -> Gurob
nodes = list(data.graph.nodes)
x = model.addVars(nodes, vtype=GRB.BINARY, name="x")
model.setObjective(quicksum(data.weights[i] * x[i] for i in nodes))
for (v1, v2) in data.graph.edges:
for v1, v2 in data.graph.edges:
model.addConstr(x[v1] + x[v2] >= 1)
model.update()
return GurobiModel(model)

14
miplearn/solvers/gurobi.py
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@@ -184,7 +184,7 @@ class GurobiModel(AbstractModel):
assert var_names.shape == var_values.shape
n_fixed = 0
for (var_idx, var_name) in enumerate(var_names):
for var_idx, var_name in enumerate(var_names):
var_val = var_values[var_idx]
if np.isfinite(var_val):
var = self.inner.getVarByName(var_name.decode())
@@ -229,7 +229,7 @@ class GurobiModel(AbstractModel):
self.inner.numStart = n_starts
for start_idx in range(n_starts):
self.inner.params.startNumber = start_idx
for (var_idx, var_name) in enumerate(var_names):
for var_idx, var_name in enumerate(var_names):
var_val = var_values[start_idx, var_idx]
if np.isfinite(var_val):
var = self.inner.getVarByName(var_name.decode())
@@ -243,14 +243,14 @@ class GurobiModel(AbstractModel):
def _extract_after_load_vars(self, h5: H5File) -> None:
gp_vars = self.inner.getVars()
for (h5_field, gp_field) in {
for h5_field, gp_field in {
"static_var_names": "varName",
"static_var_types": "vtype",
}.items():
h5.put_array(
h5_field, np.array(self.inner.getAttr(gp_field, gp_vars), dtype="S")
)
for (h5_field, gp_field) in {
for h5_field, gp_field in {
"static_var_upper_bounds": "ub",
"static_var_lower_bounds": "lb",
"static_var_obj_coeffs": "obj",
@@ -267,7 +267,7 @@ class GurobiModel(AbstractModel):
names = np.array(self.inner.getAttr("constrName", gp_constrs), dtype="S")
nrows, ncols = len(gp_constrs), len(gp_vars)
tmp = lil_matrix((nrows, ncols), dtype=float)
for (i, gp_constr) in enumerate(gp_constrs):
for i, gp_constr in enumerate(gp_constrs):
expr = self.inner.getRow(gp_constr)
for j in range(expr.size()):
tmp[i, expr.getVar(j).index] = expr.getCoeff(j)
@@ -302,7 +302,7 @@ class GurobiModel(AbstractModel):
dtype="S",
),
)
for (h5_field, gp_field) in {
for h5_field, gp_field in {
"lp_var_reduced_costs": "rc",
"lp_var_sa_obj_up": "saobjUp",
"lp_var_sa_obj_down": "saobjLow",
@@ -336,7 +336,7 @@ class GurobiModel(AbstractModel):
dtype="S",
),
)
for (h5_field, gp_field) in {
for h5_field, gp_field in {
"lp_constr_dual_values": "pi",
"lp_constr_sa_rhs_up": "saRhsUp",
"lp_constr_sa_rhs_down": "saRhsLow",

10
miplearn/solvers/pyomo.py
View File

@@ -141,7 +141,7 @@ class PyomoModel(AbstractModel):
stats: Optional[Dict] = None,
) -> None:
variables = self._var_names_to_vars(var_names)
for (var, val) in zip(variables, var_values):
for var, val in zip(variables, var_values):
if np.isfinite(val):
var.fix(val)
self.solver.update_var(var)
@@ -195,7 +195,7 @@ class PyomoModel(AbstractModel):
assert var_names.shape[0] == n_vars
assert n_starts == 1, "Pyomo does not support multiple warm starts"
variables = self._var_names_to_vars(var_names)
for (var, val) in zip(variables, var_values[0, :]):
for var, val in zip(variables, var_values[0, :]):
if np.isfinite(val):
var.value = val
self._is_warm_start_available = True
@@ -215,7 +215,7 @@ class PyomoModel(AbstractModel):
obj_count += 1
assert obj_count == 1, f"One objective function expected; found {obj_count}"
for (i, var) in enumerate(self.inner.component_objects(pyomo.core.Var)):
for i, var in enumerate(self.inner.component_objects(pyomo.core.Var)):
for idx in var:
v = var[idx]
@@ -316,9 +316,7 @@ class PyomoModel(AbstractModel):
raise Exception(f"Unknown expression type: {expr.__class__.__name__}")
curr_row = 0
for (i, constr) in enumerate(
self.inner.component_objects(pyomo.core.Constraint)
):
for i, constr in enumerate(self.inner.component_objects(pyomo.core.Constraint)):
if len(constr) > 1:
for idx in constr:
names.append(constr[idx].name)