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Collect features 3 times (after-load, after-lp, after-mip)

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Alinson S. Xavier
2021-04-11 08:03:46 -05:00
parent d85a63f869
commit 6afdf2ed55
3 changed files with 23 additions and 4 deletions
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3
miplearn/instance/base.py
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@@ -33,6 +33,9 @@ class Instance(ABC, EnforceOverrides):
def __init__(self) -> None:
self.training_data: List[TrainingSample] = []
self.features: Features = Features()
self.features_after_load: List[Features] = []
self.features_after_lp: List[Features] = []
self.features_after_mip: List[Features] = []
@abstractmethod
def to_model(self) -> Any:

3
miplearn/instance/picklegz.py
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@@ -123,6 +123,9 @@ class PickleGzInstance(Instance):
self.instance = obj
self.features = self.instance.features
self.training_data = self.instance.training_data
self.features_after_load = self.instance.features_after_load
self.features_after_lp = self.instance.features_after_lp
self.features_after_mip = self.instance.features_after_mip
@overrides
def free(self) -> None:

21
miplearn/solvers/learning.py
View File

@@ -153,11 +153,12 @@ class LearningSolver:
assert isinstance(self.internal_solver, InternalSolver)
self.internal_solver.set_instance(instance, model)
# Extract features
# Extract features (after-load)
# -------------------------------------------------------
if instance.features.instance is None:
logger.info("Extracting features...")
FeaturesExtractor(self.internal_solver).extract(instance)
logger.info("Extracting features (after-load)...")
features = FeaturesExtractor(self.internal_solver).extract(instance)
instance.features.__dict__ = features.__dict__
instance.features_after_load.append(features)
callback_args = (
self,
@@ -186,6 +187,12 @@ class LearningSolver:
for component in self.components.values():
component.after_solve_lp(*callback_args)
# Extract features (after-lp)
# -------------------------------------------------------
logger.info("Extracting features (after-lp)...")
features = FeaturesExtractor(self.internal_solver).extract(instance)
instance.features_after_lp.append(features)
# Callback wrappers
# -------------------------------------------------------
def iteration_cb_wrapper() -> bool:
@@ -242,6 +249,12 @@ class LearningSolver:
)
stats["Mode"] = self.mode
# Extract features (after-mip)
# -------------------------------------------------------
logger.info("Extracting features (after-mip)...")
features = FeaturesExtractor(self.internal_solver).extract(instance)
instance.features_after_mip.append(features)
# Add some information to training_sample
# -------------------------------------------------------
training_sample.lower_bound = stats["Lower bound"]