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Remove sample.after_mip

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This commit is contained in:
Alinson S. Xavier
2021-07-01 11:45:19 -05:00
parent 7c4c301611
commit 4093ac62fd
8 changed files with 45 additions and 73 deletions
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20
miplearn/components/objective.py
View File

@@ -95,13 +95,12 @@ class ObjectiveValueComponent(Component):
# Labels
y: Dict[Hashable, List[List[float]]] = {}
if sample.after_mip is not None:
mip_stats = sample.after_mip.mip_solve
assert mip_stats is not None
if mip_stats.mip_lower_bound is not None:
y["Lower bound"] = [[mip_stats.mip_lower_bound]]
if mip_stats.mip_upper_bound is not None:
y["Upper bound"] = [[mip_stats.mip_upper_bound]]
mip_lower_bound = sample.get("mip_lower_bound")
mip_upper_bound = sample.get("mip_upper_bound")
if mip_lower_bound is not None:
y["Lower bound"] = [[mip_lower_bound]]
if mip_upper_bound is not None:
y["Upper bound"] = [[mip_upper_bound]]
return x, y
@@ -111,9 +110,6 @@ class ObjectiveValueComponent(Component):
instance: Instance,
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
assert sample.after_mip is not None
assert sample.after_mip.mip_solve is not None
def compare(y_pred: float, y_actual: float) -> Dict[str, float]:
err = np.round(abs(y_pred - y_actual), 8)
return {
@@ -125,8 +121,8 @@ class ObjectiveValueComponent(Component):
result: Dict[Hashable, Dict[str, float]] = {}
pred = self.sample_predict(sample)
actual_ub = sample.after_mip.mip_solve.mip_upper_bound
actual_lb = sample.after_mip.mip_solve.mip_lower_bound
actual_ub = sample.get("mip_upper_bound")
actual_lb = sample.get("mip_lower_bound")
if actual_ub is not None:
result["Upper bound"] = compare(pred["Upper bound"], actual_ub)
if actual_lb is not None:

18
miplearn/components/primal.py
View File

@@ -155,6 +155,7 @@ class PrimalSolutionComponent(Component):
assert sample.after_load.variables is not None
assert sample.after_load.variables.names is not None
assert sample.after_load.variables.categories is not None
mip_var_values = sample.get("mip_var_values")
for (i, var_name) in enumerate(sample.after_load.variables.names):
# Initialize categories
@@ -174,10 +175,8 @@ class PrimalSolutionComponent(Component):
x[category].append(features)
# Labels
if sample.after_mip is not None:
assert sample.after_mip.variables is not None
assert sample.after_mip.variables.values is not None
opt_value = sample.after_mip.variables.values[i]
if mip_var_values is not None:
opt_value = mip_var_values[i]
assert opt_value is not None
assert 0.0 - 1e-5 <= opt_value <= 1.0 + 1e-5, (
f"Variable {var_name} has non-binary value {opt_value} in the "
@@ -194,14 +193,13 @@ class PrimalSolutionComponent(Component):
_: Optional[Instance],
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
assert sample.after_mip is not None
assert sample.after_mip.variables is not None
assert sample.after_mip.variables.values is not None
assert sample.after_mip.variables.names is not None
mip_var_values = sample.get("mip_var_values")
var_names = sample.get("var_names")
assert mip_var_values is not None
assert var_names is not None
solution_actual = {
var_name: sample.after_mip.variables.values[i]
for (i, var_name) in enumerate(sample.after_mip.variables.names)
var_name: mip_var_values[i] for (i, var_name) in enumerate(var_names)
}
solution_pred = self.sample_predict(sample)
vars_all, vars_one, vars_zero = set(), set(), set()

2
miplearn/features.py
View File

@@ -148,7 +148,6 @@ class Sample:
self,
after_load: Optional[Features] = None,
after_lp: Optional[Features] = None,
after_mip: Optional[Features] = None,
data: Optional[Dict[str, Any]] = None,
) -> None:
if data is None:
@@ -156,7 +155,6 @@ class Sample:
self._data: Dict[str, Any] = data
self.after_load = after_load
self.after_lp = after_lp
self.after_mip = after_mip
def get(self, key: str) -> Optional[Any]:
if key in self._data:

13
miplearn/solvers/learning.py
View File

@@ -210,6 +210,7 @@ class LearningSolver:
# -------------------------------------------------------
logger.info("Extracting features (after-lp)...")
initial_time = time.time()
self.extractor.extract_after_lp_features(self.internal_solver, sample)
features = self.extractor.extract(
instance,
self.internal_solver,
@@ -219,6 +220,8 @@ class LearningSolver:
"Features (after-lp) extracted in %.2f seconds"
% (time.time() - initial_time)
)
for (k, v) in lp_stats.__dict__.items():
sample.put(k, v)
features.lp_solve = lp_stats
sample.after_lp = features
@@ -282,17 +285,13 @@ class LearningSolver:
# -------------------------------------------------------
logger.info("Extracting features (after-mip)...")
initial_time = time.time()
features = self.extractor.extract(
instance,
self.internal_solver,
with_static=False,
)
self.extractor.extract_after_mip_features(self.internal_solver, sample)
for (k, v) in mip_stats.__dict__.items():
sample.put(k, v)
logger.info(
"Features (after-mip) extracted in %.2f seconds"
% (time.time() - initial_time)
)
features.mip_solve = mip_stats
sample.after_mip = features
# After-solve callbacks
# -------------------------------------------------------

10
tests/components/test_objective.py
View File

@@ -25,12 +25,10 @@ def sample() -> Sample:
after_lp=Features(
lp_solve=LPSolveStats(),
),
after_mip=Features(
mip_solve=MIPSolveStats(
mip_lower_bound=1.0,
mip_upper_bound=2.0,
)
),
data={
"mip_lower_bound": 1.0,
"mip_upper_bound": 2.0,
},
)
sample.after_load.instance.to_list = Mock(return_value=[1.0, 2.0]) # type: ignore
sample.after_lp.lp_solve.to_list = Mock(return_value=[3.0]) # type: ignore

10
tests/components/test_primal.py
View File

@@ -36,12 +36,10 @@ def sample() -> Sample:
after_lp=Features(
variables=VariableFeatures(),
),
after_mip=Features(
variables=VariableFeatures(
names=["x[0]", "x[1]", "x[2]", "x[3]"],
values=[0.0, 1.0, 1.0, 0.0],
)
),
data={
"var_names": ["x[0]", "x[1]", "x[2]", "x[3]"],
"mip_var_values": [0.0, 1.0, 1.0, 0.0],
},
)
sample.after_load.instance.to_list = Mock(return_value=[5.0]) # type: ignore
sample.after_load.variables.to_list = Mock( # type:ignore

16
tests/problems/test_tsp.py
View File

@@ -41,14 +41,9 @@ def test_instance() -> None:
solver.solve(instance)
assert len(instance.get_samples()) == 1
sample = instance.get_samples()[0]
assert sample.after_mip is not None
features = sample.after_mip
assert features is not None
assert features.variables is not None
assert features.variables.values == [1.0, 0.0, 1.0, 1.0, 0.0, 1.0]
assert features.mip_solve is not None
assert features.mip_solve.mip_lower_bound == 4.0
assert features.mip_solve.mip_upper_bound == 4.0
assert sample.get("mip_var_values") == [1.0, 0.0, 1.0, 1.0, 0.0, 1.0]
assert sample.get("mip_lower_bound") == 4.0
assert sample.get("mip_upper_bound") == 4.0
def test_subtour() -> None:
@@ -73,10 +68,7 @@ def test_subtour() -> None:
lazy_enforced = sample.get("lazy_enforced")
assert lazy_enforced is not None
assert len(lazy_enforced) > 0
assert sample.after_mip is not None
features = sample.after_mip
assert features.variables is not None
assert features.variables.values == [
assert sample.get("mip_var_values") == [
1.0,
0.0,
0.0,

29
tests/solvers/test_learning_solver.py
View File

@@ -38,25 +38,18 @@ def test_learning_solver(
assert len(instance.get_samples()) > 0
sample = instance.get_samples()[0]
after_mip = sample.after_mip
assert after_mip is not None
assert after_mip.variables is not None
assert after_mip.variables.values == [1.0, 0.0, 1.0, 1.0, 61.0]
assert after_mip.mip_solve is not None
assert after_mip.mip_solve.mip_lower_bound == 1183.0
assert after_mip.mip_solve.mip_upper_bound == 1183.0
assert after_mip.mip_solve.mip_log is not None
assert len(after_mip.mip_solve.mip_log) > 100
assert sample.get("mip_var_values") == [1.0, 0.0, 1.0, 1.0, 61.0]
assert sample.get("mip_lower_bound") == 1183.0
assert sample.get("mip_upper_bound") == 1183.0
mip_log = sample.get("mip_log")
assert mip_log is not None
assert len(mip_log) > 100
after_lp = sample.after_lp
assert after_lp is not None
assert after_lp.variables is not None
assert_equals(after_lp.variables.values, [1.0, 0.923077, 1.0, 0.0, 67.0])
assert after_lp.lp_solve is not None
assert after_lp.lp_solve.lp_value is not None
assert round(after_lp.lp_solve.lp_value, 3) == 1287.923
assert after_lp.lp_solve.lp_log is not None
assert len(after_lp.lp_solve.lp_log) > 100
assert_equals(sample.get("lp_var_values"), [1.0, 0.923077, 1.0, 0.0, 67.0])
assert_equals(sample.get("lp_value"), 1287.923077)
lp_log = sample.get("lp_log")
assert lp_log is not None
assert len(lp_log) > 100
solver.fit([instance], n_jobs=4)
solver.solve(instance)