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Make sample_ method accept instance

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This commit is contained in:
Alinson S. Xavier
2021-04-06 06:48:47 -05:00
parent bb91c83187
commit c6aee4f90d
8 changed files with 91 additions and 72 deletions
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15
miplearn/components/component.py
View File

@@ -108,14 +108,13 @@ class Component:
@staticmethod
def sample_xy(
features: Features,
instance: Instance,
sample: TrainingSample,
) -> Tuple[Dict, Dict]:
"""
Given a set of features and a training sample, returns a pair of x and y
dictionaries containing, respectively, the matrices of ML features and the
labels for the sample. If the training sample does not include label
information, returns (x, {}).
Returns a pair of x and y dictionaries containing, respectively, the matrices
of ML features and the labels for the sample. If the training sample does not
include label information, returns (x, {}).
"""
pass
@@ -128,7 +127,7 @@ class Component:
for instance in instances:
assert isinstance(instance, Instance)
for sample in instance.training_data:
xy = self.sample_xy(instance.features, sample)
xy = self.sample_xy(instance, sample)
if xy is None:
continue
x_sample, y_sample = xy
@@ -203,12 +202,12 @@ class Component:
ev = []
for instance in instances:
for sample in instance.training_data:
ev += [self.sample_evaluate(instance.features, sample)]
ev += [self.sample_evaluate(instance, sample)]
return ev
def sample_evaluate(
self,
features: Features,
instance: Instance,
sample: TrainingSample,
) -> Dict[Hashable, Dict[str, float]]:
return {}

5
miplearn/components/lazy_dynamic.py
View File

@@ -4,7 +4,7 @@
import logging
import sys
from typing import Any, Dict, List, TYPE_CHECKING, Set, Hashable
from typing import Any, Dict, List, TYPE_CHECKING, Hashable
import numpy as np
from tqdm.auto import tqdm
@@ -14,12 +14,11 @@ from miplearn.classifiers.counting import CountingClassifier
from miplearn.components import classifier_evaluation_dict
from miplearn.components.component import Component
from miplearn.extractors import InstanceFeaturesExtractor
from miplearn.features import TrainingSample
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from miplearn.solvers.learning import LearningSolver, Instance
from miplearn.solvers.learning import Instance
class DynamicLazyConstraintsComponent(Component):

16
miplearn/components/lazy_static.py
View File

@@ -66,7 +66,7 @@ class StaticLazyConstraintsComponent(Component):
if not features.instance.lazy_constraint_count == 0:
logger.info("Instance does not have static lazy constraints. Skipping.")
logger.info("Predicting required lazy constraints...")
self.enforced_cids = set(self.sample_predict(features, training_data))
self.enforced_cids = set(self.sample_predict(instance, training_data))
logger.info("Moving lazy constraints to the pool...")
self.pool = {}
for (cid, cdict) in features.constraints.items():
@@ -144,14 +144,14 @@ class StaticLazyConstraintsComponent(Component):
def sample_predict(
self,
features: Features,
instance: "Instance",
sample: TrainingSample,
) -> List[str]:
assert features.constraints is not None
assert instance.features.constraints is not None
x, y = self.sample_xy(features, sample)
x, y = self.sample_xy(instance, sample)
category_to_cids: Dict[Hashable, List[str]] = {}
for (cid, cfeatures) in features.constraints.items():
for (cid, cfeatures) in instance.features.constraints.items():
if cfeatures.category is None:
continue
category = cfeatures.category
@@ -173,13 +173,13 @@ class StaticLazyConstraintsComponent(Component):
@staticmethod
def sample_xy(
features: Features,
instance: "Instance",
sample: TrainingSample,
) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
assert features.constraints is not None
assert instance.features.constraints is not None
x: Dict = {}
y: Dict = {}
for (cid, cfeatures) in features.constraints.items():
for (cid, cfeatures) in instance.features.constraints.items():
if not cfeatures.lazy:
continue
category = cfeatures.category

19
miplearn/components/objective.py
View File

@@ -44,7 +44,7 @@ class ObjectiveValueComponent(Component):
training_data: TrainingSample,
) -> None:
logger.info("Predicting optimal value...")
pred = self.sample_predict(features, training_data)
pred = self.sample_predict(instance, training_data)
for (c, v) in pred.items():
logger.info(f"Predicted {c.lower()}: %.6e" % v)
stats[f"Objective: Predicted {c.lower()}"] = v # type: ignore
@@ -61,11 +61,11 @@ class ObjectiveValueComponent(Component):
def sample_predict(
self,
features: Features,
instance: Instance,
sample: TrainingSample,
) -> Dict[str, float]:
pred: Dict[str, float] = {}
x, _ = self.sample_xy(features, sample)
x, _ = self.sample_xy(instance, sample)
for c in ["Upper bound", "Lower bound"]:
if c in self.regressors is not None:
pred[c] = self.regressors[c].predict(np.array(x[c]))[0, 0]
@@ -75,14 +75,15 @@ class ObjectiveValueComponent(Component):
@staticmethod
def sample_xy(
features: Features,
instance: Instance,
sample: TrainingSample,
) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
assert features.instance is not None
assert features.instance.user_features is not None
ifeatures = instance.features.instance
assert ifeatures is not None
assert ifeatures.user_features is not None
x: Dict[Hashable, List[List[float]]] = {}
y: Dict[Hashable, List[List[float]]] = {}
f = list(features.instance.user_features)
f = list(ifeatures.user_features)
if sample.lp_value is not None:
f += [sample.lp_value]
x["Upper bound"] = [f]
@@ -95,7 +96,7 @@ class ObjectiveValueComponent(Component):
def sample_evaluate(
self,
features: Features,
instance: Instance,
sample: TrainingSample,
) -> Dict[Hashable, Dict[str, float]]:
def compare(y_pred: float, y_actual: float) -> Dict[str, float]:
@@ -108,7 +109,7 @@ class ObjectiveValueComponent(Component):
}
result: Dict[Hashable, Dict[str, float]] = {}
pred = self.sample_predict(features, sample)
pred = self.sample_predict(instance, sample)
if sample.upper_bound is not None:
result["Upper bound"] = compare(pred["Upper bound"], sample.upper_bound)
if sample.lower_bound is not None:

22
miplearn/components/primal.py
View File

@@ -73,7 +73,7 @@ class PrimalSolutionComponent(Component):
# Predict solution and provide it to the solver
logger.info("Predicting MIP solution...")
solution = self.sample_predict(features, training_data)
solution = self.sample_predict(instance, training_data)
assert solver.internal_solver is not None
if self.mode == "heuristic":
solver.internal_solver.fix(solution)
@@ -101,20 +101,20 @@ class PrimalSolutionComponent(Component):
def sample_predict(
self,
features: Features,
instance: Instance,
sample: TrainingSample,
) -> Solution:
assert features.variables is not None
assert instance.features.variables is not None
# Initialize empty solution
solution: Solution = {}
for (var_name, var_dict) in features.variables.items():
for (var_name, var_dict) in instance.features.variables.items():
solution[var_name] = {}
for idx in var_dict.keys():
solution[var_name][idx] = None
# Compute y_pred
x, _ = self.sample_xy(features, sample)
x, _ = self.sample_xy(instance, sample)
y_pred = {}
for category in x.keys():
assert category in self.classifiers, (
@@ -133,7 +133,7 @@ class PrimalSolutionComponent(Component):
# Convert y_pred into solution
category_offset: Dict[Hashable, int] = {cat: 0 for cat in x.keys()}
for (var_name, var_dict) in features.variables.items():
for (var_name, var_dict) in instance.features.variables.items():
for (idx, var_features) in var_dict.items():
category = var_features.category
offset = category_offset[category]
@@ -147,16 +147,16 @@ class PrimalSolutionComponent(Component):
@staticmethod
def sample_xy(
features: Features,
instance: Instance,
sample: TrainingSample,
) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
assert features.variables is not None
assert instance.features.variables is not None
x: Dict = {}
y: Dict = {}
solution: Optional[Solution] = None
if sample.solution is not None:
solution = sample.solution
for (var_name, var_dict) in features.variables.items():
for (var_name, var_dict) in instance.features.variables.items():
for (idx, var_features) in var_dict.items():
category = var_features.category
if category is None:
@@ -186,12 +186,12 @@ class PrimalSolutionComponent(Component):
def sample_evaluate(
self,
features: Features,
instance: Instance,
sample: TrainingSample,
) -> Dict[Hashable, Dict[str, float]]:
solution_actual = sample.solution
assert solution_actual is not None
solution_pred = self.sample_predict(features, sample)
solution_pred = self.sample_predict(instance, sample)
vars_all, vars_one, vars_zero = set(), set(), set()
pred_one_positive, pred_zero_positive = set(), set()
for (varname, var_dict) in solution_actual.items():