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Move collected data to instance.training_data

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Alinson S. Xavier
2021-01-21 08:21:40 -06:00
parent 23dd311d75
commit 06402516e6
11 changed files with 97 additions and 89 deletions
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40
miplearn/extractors.py
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@@ -2,14 +2,13 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
import gzip
import logging
import pickle
import gzip
from abc import ABC, abstractmethod
import numpy as np
from tqdm.auto import tqdm
from abc import ABC, abstractmethod
logger = logging.getLogger(__name__)
@@ -48,10 +47,10 @@ class Extractor(ABC):
@staticmethod
def split_variables(instance):
assert hasattr(instance, "lp_solution")
result = {}
for var_name in instance.lp_solution:
for index in instance.lp_solution[var_name]:
lp_solution = instance.training_data[0]["LP solution"]
for var_name in lp_solution:
for index in lp_solution[var_name]:
category = instance.get_variable_category(var_name, index)
if category is None:
continue
@@ -71,6 +70,7 @@ class VariableFeaturesExtractor(Extractor):
):
instance_features = instance.get_instance_features()
var_split = self.split_variables(instance)
lp_solution = instance.training_data[0]["LP solution"]
for (category, var_index_pairs) in var_split.items():
if category not in result:
result[category] = []
@@ -78,7 +78,7 @@ class VariableFeaturesExtractor(Extractor):
result[category] += [
instance_features.tolist()
+ instance.get_variable_features(var_name, index).tolist()
+ [instance.lp_solution[var_name][index]]
+ [lp_solution[var_name][index]]
]
for category in result:
result[category] = np.array(result[category])
@@ -97,14 +97,15 @@ class SolutionExtractor(Extractor):
disable=len(instances) < 5,
):
var_split = self.split_variables(instance)
if self.relaxation:
solution = instance.training_data[0]["LP solution"]
else:
solution = instance.training_data[0]["Solution"]
for (category, var_index_pairs) in var_split.items():
if category not in result:
result[category] = []
for (var_name, index) in var_index_pairs:
if self.relaxation:
v = instance.lp_solution[var_name][index]
else:
v = instance.solution[var_name][index]
v = solution[var_name][index]
if v is None:
result[category] += [[0, 0]]
else:
@@ -121,7 +122,7 @@ class InstanceFeaturesExtractor(Extractor):
np.hstack(
[
instance.get_instance_features(),
instance.lp_value,
instance.training_data[0]["LP value"],
]
)
for instance in InstanceIterator(instances)
@@ -137,13 +138,22 @@ class ObjectiveValueExtractor(Extractor):
def extract(self, instances):
if self.kind == "lower bound":
return np.array(
[[instance.lower_bound] for instance in InstanceIterator(instances)]
[
[instance.training_data[0]["Lower bound"]]
for instance in InstanceIterator(instances)
]
)
if self.kind == "upper bound":
return np.array(
[[instance.upper_bound] for instance in InstanceIterator(instances)]
[
[instance.training_data[0]["Upper bound"]]
for instance in InstanceIterator(instances)
]
)
if self.kind == "lp":
return np.array(
[[instance.lp_value] for instance in InstanceIterator(instances)]
[
[instance.training_data[0]["LP value"]]
for instance in InstanceIterator(instances)
]
)