Remove PerVariableTransformer

miplearn/branching.py

@@ -3,13 +3,10 @@
 # Written by Alinson S. Xavier <axavier@anl.gov>
 
 from . import Component
-from .transformers import PerVariableTransformer
+from .extractors import Extractor
 from abc import ABC, abstractmethod
 from sklearn.neighbors import KNeighborsRegressor
 import numpy as np
-from p_tqdm import p_map
-
-
 from tqdm.auto import tqdm
 from joblib import Parallel, delayed
 import multiprocessing
@@ -18,7 +15,6 @@ class BranchPriorityComponent(Component):
     def __init__(self,
                  node_limit=1_000,
                 ):
-        self.transformer = PerVariableTransformer()
         self.pending_instances = []
         self.x_train = {}
         self.y_train = {}
@@ -28,7 +24,7 @@ class BranchPriorityComponent(Component):
     def before_solve(self, solver, instance, model):
         assert solver.is_persistent, "BranchPriorityComponent requires a persistent solver"
         from gurobipy import GRB
-        var_split = self.transformer.split_variables(instance, model)
+        var_split = Extractor.split_variables(instance, model)
         for category in var_split.keys():
             if category not in self.predictors.keys():
                 continue

miplearn/transformers.py

@@ -1,65 +0,0 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
-# Written by Alinson S. Xavier <axavier@anl.gov>
-
-import numpy as np
-from pyomo.core import Var
-
-
-class PerVariableTransformer:
-    """
-    Class that converts a miplearn.Instance into a matrix of features that is suitable
-    for training machine learning models that make one decision per decision variable.
-    """
-
-    def __init__(self):
-        pass
-
-    def transform_instance(self, instance, var_index_pairs):
-        instance_features = self._get_instance_features(instance)
-        variable_features = self._get_variable_features(instance, var_index_pairs)
-        return np.vstack([
-            np.hstack([instance_features, vf])
-            for vf in variable_features
-        ])
-
-    @staticmethod
-    def _get_instance_features(instance):
-        features = instance.get_instance_features()
-        assert isinstance(features, np.ndarray)
-        return features
-
-    @staticmethod
-    def _get_variable_features(instance, var_index_pairs):
-        features = []
-        expected_shape = None
-        for (var, index) in var_index_pairs:
-            vf = instance.get_variable_features(var, index)
-            assert isinstance(vf, np.ndarray)
-            if expected_shape is None:
-                assert len(vf.shape) == 1
-                expected_shape = vf.shape
-            else:
-                assert vf.shape == expected_shape
-            features += [vf]
-        return np.array(features)
-
-    @staticmethod
-    def transform_solution(var_index_pairs):
-        y = []
-        for (var, index) in var_index_pairs:
-            y += [[1 - var[index].value, var[index].value]]
-        return np.array(y)
-
-    @staticmethod
-    def split_variables(instance, model):
-        result = {}
-        for var in model.component_objects(Var):
-            for index in var:
-                category = instance.get_variable_category(var, index)
-                if category is None:
-                    continue
-                if category not in result.keys():
-                    result[category] = []
-                result[category] += [(var, index)]
-        return result

miplearn/warmstart.py

@@ -3,7 +3,6 @@
 # Written by Alinson S. Xavier <axavier@anl.gov>
 
 from . import Component
-from .transformers import PerVariableTransformer
 from .extractors import *
 
 from abc import ABC, abstractmethod
@@ -134,7 +133,6 @@ class WarmStartComponent(Component):
                  mode="exact",
                 ):
         self.mode = mode
-        self.transformer = PerVariableTransformer()
         self.x_train = {}
         self.y_train = {}
         self.predictors = {}