Remove unused extractors

miplearn/__init__.py

@@ -19,12 +19,7 @@ from .components.primal import PrimalSolutionComponent
 from .components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
 from .components.steps.drop_redundant import DropRedundantInequalitiesStep
 from .components.steps.relax_integrality import RelaxIntegralityStep
-from .extractors import (
+from .extractors import InstanceFeaturesExtractor
-    SolutionExtractor,
-    InstanceFeaturesExtractor,
-    ObjectiveValueExtractor,
-    VariableFeaturesExtractor,
-)
 from .instance import Instance
 from .log import setup_logger
 from .solvers.gurobi import GurobiSolver

miplearn/extractors.py

@@ -67,61 +67,6 @@ class Extractor(ABC):
         return result
 
 
-class VariableFeaturesExtractor(Extractor):
-    def extract(self, instances):
-        result = {}
-        for instance in tqdm(
-            InstanceIterator(instances),
-            desc="Extract (vars)",
-            disable=len(instances) < 5,
-        ):
-            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] = []
-                for (var_name, index) in var_index_pairs:
-                    result[category] += [
-                        instance_features.tolist()
-                        + instance.get_variable_features(var_name, index).tolist()
-                        + [lp_solution[var_name][index]]
-                    ]
-        for category in result:
-            result[category] = np.array(result[category])
-        return result
-
-
-class SolutionExtractor(Extractor):
-    def __init__(self, relaxation=False):
-        self.relaxation = relaxation
-
-    def extract(self, instances):
-        result = {}
-        for instance in tqdm(
-            InstanceIterator(instances),
-            desc="Extract (solution)",
-            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:
-                    v = solution[var_name][index]
-                    if v is None:
-                        result[category] += [[0, 0]]
-                    else:
-                        result[category] += [[1 - v, v]]
-        for category in result:
-            result[category] = np.array(result[category])
-        return result
-
-
 class InstanceFeaturesExtractor(Extractor):
     def extract(self, instances):
         return np.vstack(
@@ -135,32 +80,3 @@ class InstanceFeaturesExtractor(Extractor):
                 for instance in InstanceIterator(instances)
             ]
         )
-
-
-class ObjectiveValueExtractor(Extractor):
-    def __init__(self, kind="lp"):
-        assert kind in ["lower bound", "upper bound", "lp"]
-        self.kind = kind
-
-    def extract(self, instances):
-        if self.kind == "lower bound":
-            return np.array(
-                [
-                    [instance.training_data[0]["Lower bound"]]
-                    for instance in InstanceIterator(instances)
-                ]
-            )
-        if self.kind == "upper bound":
-            return np.array(
-                [
-                    [instance.training_data[0]["Upper bound"]]
-                    for instance in InstanceIterator(instances)
-                ]
-            )
-        if self.kind == "lp":
-            return np.array(
-                [
-                    [instance.training_data[0]["LP value"]]
-                    for instance in InstanceIterator(instances)
-                ]
-            )

tests/test_extractors.py

@@ -3,11 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 import numpy as np
 
-from miplearn.extractors import (
+from miplearn.extractors import InstanceFeaturesExtractor
-    SolutionExtractor,
-    InstanceFeaturesExtractor,
-    VariableFeaturesExtractor,
-)
 from miplearn.problems.knapsack import KnapsackInstance
 from miplearn.solvers.learning import LearningSolver
 
@@ -32,38 +28,7 @@ def _get_instances():
     return instances, models
 
 
-def test_solution_extractor():
-    instances, models = _get_instances()
-    features = SolutionExtractor().extract(instances)
-    assert isinstance(features, dict)
-    assert "default" in features.keys()
-    assert isinstance(features["default"], np.ndarray)
-    assert features["default"].shape == (6, 2)
-    assert features["default"].ravel().tolist() == [
-        1.0,
-        0.0,
-        0.0,
-        1.0,
-        1.0,
-        0.0,
-        1.0,
-        0.0,
-        0.0,
-        1.0,
-        1.0,
-        0.0,
-    ]
-
-
 def test_instance_features_extractor():
     instances, models = _get_instances()
     features = InstanceFeaturesExtractor().extract(instances)
     assert features.shape == (2, 3)
-
-
-def test_variable_features_extractor():
-    instances, models = _get_instances()
-    features = VariableFeaturesExtractor().extract(instances)
-    assert isinstance(features, dict)
-    assert "default" in features
-    assert features["default"].shape == (6, 5)