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Remove obsolete methods

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This commit is contained in:
Alinson S. Xavier
2021-04-13 09:42:25 -05:00
parent c26b852c67
commit c4a6665825
22 changed files with 93 additions and 499 deletions
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23
miplearn/solvers/gurobi.py
View File

@@ -136,15 +136,6 @@ class GurobiSolver(InternalSolver):
var.lb = value
var.ub = value
@overrides
def get_dual(self, cid: str) -> float:
assert self.model is not None
c = self.model.getConstrByName(cid)
if self.is_infeasible():
return c.farkasDual
else:
return c.pi
@overrides
def get_constraint_attrs(self) -> List[str]:
return [
@@ -175,14 +166,6 @@ class GurobiSolver(InternalSolver):
constraints[c.constrName] = constr
return constraints
@overrides
def get_sense(self) -> str:
assert self.model is not None
if self.model.modelSense == 1:
return "min"
else:
return "max"
@overrides
def get_solution(self) -> Optional[Solution]:
assert self.model is not None
@@ -224,12 +207,6 @@ class GurobiSolver(InternalSolver):
"value",
]
@overrides
def get_variable_names(self) -> List[VariableName]:
self._raise_if_callback()
assert self.model is not None
return [v.varName for v in self.model.getVars()]
@overrides
def get_variables(self) -> Dict[str, Variable]:
assert self.model is not None

45
miplearn/solvers/internal.py
View File

@@ -124,10 +124,8 @@ class InternalSolver(ABC, EnforceOverrides):
"""
Sets the warm start to be used by the solver.
The solution should be a dictionary following the same format as the
one produced by `get_solution`. Only one warm start is supported.
Calling this function when a warm start already exists will
remove the previous warm start.
Only one warm start is supported. Calling this function when a warm start
already exists will remove the previous warm start.
"""
pass
@@ -154,11 +152,8 @@ class InternalSolver(ABC, EnforceOverrides):
@abstractmethod
def fix(self, solution: Solution) -> None:
"""
Fixes the values of a subset of decision variables.
The values should be provided in the dictionary format generated by
`get_solution`. Missing values in the solution indicate variables
that should be left free.
Fixes the values of a subset of decision variables. Missing values in the
solution indicate variables that should be left free.
"""
pass
@@ -170,9 +165,7 @@ class InternalSolver(ABC, EnforceOverrides):
with higher priority are picked first, given that they are fractional.
Ties are solved arbitrarily. By default, all variables have priority zero.
The priorities should be provided in the dictionary format generated by
`get_solution`. Missing values indicate variables whose priorities
should not be modified.
Missing values indicate variables whose priorities should not be modified.
"""
raise NotImplementedError()
@@ -216,34 +209,6 @@ class InternalSolver(ABC, EnforceOverrides):
"""
pass
@abstractmethod
def get_dual(self, cid: str) -> float:
"""
If the model is feasible and has been solved to optimality, returns the
optimal value of the dual variable associated with this constraint. If the
model is infeasible, returns a portion of the infeasibility certificate
corresponding to the given constraint.
Only available for relaxed problems. Must be called after solve.
"""
pass
@abstractmethod
def get_sense(self) -> str:
"""
Returns the sense of the problem (either "min" or "max").
"""
pass
@abstractmethod
def get_variable_names(self) -> List[VariableName]:
"""
Returns a list containing the names of all variables in the model. This
method is used by the ML components to query what variables are there in the
model before a solution is available.
"""
pass
@abstractmethod
def clone(self) -> "InternalSolver":
"""

33
miplearn/solvers/learning.py
View File

@@ -4,7 +4,7 @@
import logging
import traceback
from typing import Optional, List, Any, cast, Callable, Dict, Tuple
from typing import Optional, List, Any, cast, Dict, Tuple
from p_tqdm import p_map
@@ -13,7 +13,7 @@ from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
from miplearn.components.dynamic_user_cuts import UserCutsComponent
from miplearn.components.objective import ObjectiveValueComponent
from miplearn.components.primal import PrimalSolutionComponent
from miplearn.features import FeaturesExtractor, TrainingSample, Sample
from miplearn.features import FeaturesExtractor, Sample
from miplearn.instance.base import Instance
from miplearn.instance.picklegz import PickleGzInstance
from miplearn.solvers import _RedirectOutput
@@ -138,9 +138,7 @@ class LearningSolver:
# Initialize training sample
# -------------------------------------------------------
training_sample = TrainingSample()
sample = Sample()
instance.training_data.append(training_sample)
instance.samples.append(sample)
# Initialize stats
@@ -160,7 +158,6 @@ class LearningSolver:
logger.info("Extracting features (after-load)...")
features = FeaturesExtractor(self.internal_solver).extract(instance)
features.extra = {}
instance.features.__dict__ = features.__dict__
sample.after_load = features
callback_args = (
@@ -171,15 +168,6 @@ class LearningSolver:
sample,
)
callback_args_old = (
self,
instance,
model,
stats,
instance.features,
training_sample,
)
# Solve root LP relaxation
# -------------------------------------------------------
lp_stats = None
@@ -187,19 +175,14 @@ class LearningSolver:
logger.debug("Running before_solve_lp callbacks...")
for component in self.components.values():
component.before_solve_lp(*callback_args)
component.before_solve_lp_old(*callback_args_old)
logger.info("Solving root LP relaxation...")
lp_stats = self.internal_solver.solve_lp(tee=tee)
stats.update(cast(LearningSolveStats, lp_stats.__dict__))
training_sample.lp_solution = self.internal_solver.get_solution()
training_sample.lp_value = lp_stats.lp_value
training_sample.lp_log = lp_stats.lp_log
logger.debug("Running after_solve_lp callbacks...")
for component in self.components.values():
component.after_solve_lp(*callback_args)
component.after_solve_lp_old(*callback_args_old)
# Extract features (after-lp)
# -------------------------------------------------------
@@ -245,7 +228,6 @@ class LearningSolver:
logger.debug("Running before_solve_mip callbacks...")
for component in self.components.values():
component.before_solve_mip(*callback_args)
component.before_solve_mip_old(*callback_args_old)
# Solve MIP
# -------------------------------------------------------
@@ -272,19 +254,11 @@ class LearningSolver:
features.extra = {}
sample.after_mip = features
# Add some information to training_sample
# -------------------------------------------------------
training_sample.lower_bound = mip_stats.mip_lower_bound
training_sample.upper_bound = mip_stats.mip_upper_bound
training_sample.mip_log = mip_stats.mip_log
training_sample.solution = self.internal_solver.get_solution()
# After-solve callbacks
# -------------------------------------------------------
logger.debug("Calling after_solve_mip callbacks...")
for component in self.components.values():
component.after_solve_mip(*callback_args)
component.after_solve_mip_old(*callback_args_old)
# Flush
# -------------------------------------------------------
@@ -414,12 +388,11 @@ class LearningSolver:
def fit(self, training_instances: List[Instance]) -> None:
if len(training_instances) == 0:
logger.warn("Empty list of training instances provided. Skipping.")
logger.warning("Empty list of training instances provided. Skipping.")
return
for component in self.components.values():
logger.info(f"Fitting {component.__class__.__name__}...")
component.fit(training_instances)
component.fit_old(training_instances)
def _add_component(self, component: Component) -> None:
name = component.__class__.__name__

20
miplearn/solvers/pyomo/base.py
View File

@@ -155,11 +155,6 @@ class BasePyomoSolver(InternalSolver):
"slack",
]
@overrides
def get_dual(self, cid: str) -> float:
constr = self._cname_to_constr[cid]
return self._pyomo_solver.dual[constr]
@overrides
def get_solution(self) -> Optional[Solution]:
assert self.model is not None
@@ -173,21 +168,6 @@ class BasePyomoSolver(InternalSolver):
solution[f"{var}[{index}]"] = var[index].value
return solution
@overrides
def get_variable_names(self) -> List[VariableName]:
assert self.model is not None
variables: List[VariableName] = []
for var in self.model.component_objects(Var):
for index in var:
if var[index].fixed:
continue
variables += [f"{var}[{index}]"]
return variables
@overrides
def get_sense(self) -> str:
return self._obj_sense
@overrides
def get_variables(self) -> Dict[str, Variable]:
assert self.model is not None