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https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-09 10:58:51 -06:00
Remove obsolete methods
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@@ -7,7 +7,7 @@ from typing import Any, List, TYPE_CHECKING, Tuple, Dict, Hashable, Optional
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import numpy as np
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from overrides import EnforceOverrides
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from miplearn.features import TrainingSample, Features, Sample
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from miplearn.features import Sample
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from miplearn.instance.base import Instance
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from miplearn.types import LearningSolveStats
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@@ -39,21 +39,6 @@ class Component(EnforceOverrides):
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"""
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return
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def after_solve_lp_old(
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self,
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solver: "LearningSolver",
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instance: Instance,
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model: Any,
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stats: LearningSolveStats,
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features: Features,
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training_data: TrainingSample,
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) -> None:
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"""
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Method called by LearningSolver after the root LP relaxation is solved.
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See before_solve_lp for a description of the parameters.
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"""
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return
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def after_solve_mip(
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self,
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solver: "LearningSolver",
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@@ -68,21 +53,6 @@ class Component(EnforceOverrides):
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"""
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return
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def after_solve_mip_old(
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self,
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solver: "LearningSolver",
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instance: Instance,
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model: Any,
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stats: LearningSolveStats,
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features: Features,
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training_data: TrainingSample,
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) -> None:
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"""
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Method called by LearningSolver after the MIP is solved.
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See before_solve_lp for a description of the parameters.
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"""
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return
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def before_solve_lp(
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self,
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solver: "LearningSolver",
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@@ -115,43 +85,6 @@ class Component(EnforceOverrides):
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"""
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return
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def before_solve_lp_old(
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self,
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solver: "LearningSolver",
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instance: Instance,
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model: Any,
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stats: LearningSolveStats,
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features: Features,
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training_data: TrainingSample,
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) -> None:
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"""
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Method called by LearningSolver before the root LP relaxation is solved.
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Parameters
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----------
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solver: LearningSolver
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The solver calling this method.
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instance: Instance
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The instance being solved.
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model
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The concrete optimization model being solved.
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stats: LearningSolveStats
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A dictionary containing statistics about the solution process, such as
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number of nodes explored and running time. Components are free to add
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their own statistics here. For example, PrimalSolutionComponent adds
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statistics regarding the number of predicted variables. All statistics in
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this dictionary are exported to the benchmark CSV file.
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features: miplearn.features.Features
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Features describing the model.
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training_data: TrainingSample
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A dictionary containing data that may be useful for training machine
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learning models and accelerating the solution process. Components are
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free to add their own training data here. For example,
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PrimalSolutionComponent adds the current primal solution. The data must
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be pickable.
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"""
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return
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def before_solve_mip(
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self,
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solver: "LearningSolver",
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@@ -166,30 +99,6 @@ class Component(EnforceOverrides):
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"""
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return
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def before_solve_mip_old(
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self,
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solver: "LearningSolver",
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instance: Instance,
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model: Any,
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stats: LearningSolveStats,
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features: Features,
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training_data: TrainingSample,
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) -> None:
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"""
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Method called by LearningSolver before the MIP is solved.
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See before_solve_lp for a description of the parameters.
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"""
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return
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def evaluate_old(self, instances: List[Instance]) -> List:
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ev = []
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for instance in instances:
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instance.load()
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for sample in instance.training_data:
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ev += [self.sample_evaluate_old(instance, sample)]
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instance.free()
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return ev
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def fit(
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self,
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training_instances: List[Instance],
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@@ -200,16 +109,6 @@ class Component(EnforceOverrides):
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y[cat] = np.array(y[cat])
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self.fit_xy(x, y)
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def fit_old(
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self,
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training_instances: List[Instance],
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) -> None:
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x, y = self.xy_instances_old(training_instances)
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for cat in x.keys():
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x[cat] = np.array(x[cat])
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y[cat] = np.array(y[cat])
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self.fit_xy(x, y)
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def fit_xy(
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self,
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x: Dict[Hashable, np.ndarray],
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@@ -259,13 +158,6 @@ class Component(EnforceOverrides):
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) -> None:
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return
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def sample_evaluate_old(
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self,
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instance: Instance,
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sample: TrainingSample,
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) -> Dict[Hashable, Dict[str, float]]:
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return {}
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def sample_evaluate(
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self,
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instance: Optional[Instance],
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@@ -285,18 +177,6 @@ class Component(EnforceOverrides):
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"""
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pass
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def sample_xy_old(
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self,
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instance: Instance,
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sample: TrainingSample,
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) -> Tuple[Dict, Dict]:
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"""
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Returns a pair of x and y dictionaries containing, respectively, the matrices
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of ML features and the labels for the sample. If the training sample does not
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include label information, returns (x, {}).
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"""
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pass
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def user_cut_cb(
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self,
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solver: "LearningSolver",
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@@ -323,25 +203,3 @@ class Component(EnforceOverrides):
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y_combined[cat] += y_sample[cat]
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instance.free()
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return x_combined, y_combined
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def xy_instances_old(
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self,
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instances: List[Instance],
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) -> Tuple[Dict, Dict]:
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x_combined: Dict = {}
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y_combined: Dict = {}
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for instance in instances:
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instance.load()
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for sample in instance.training_data:
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xy = self.sample_xy_old(instance, sample)
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if xy is None:
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continue
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x_sample, y_sample = xy
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for cat in x_sample.keys():
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if cat not in x_combined:
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x_combined[cat] = []
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y_combined[cat] = []
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x_combined[cat] += x_sample[cat]
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y_combined[cat] += y_sample[cat]
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instance.free()
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return x_combined, y_combined
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