Make sample_ method accept instance

miplearn/components/component.py

@@ -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 {}

miplearn/components/lazy_dynamic.py

@@ -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):

miplearn/components/lazy_static.py

@@ -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

miplearn/components/objective.py

@@ -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:

miplearn/components/primal.py

@@ -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():

tests/components/test_lazy_static.py

@@ -23,6 +23,14 @@ from miplearn.features import (
 )
 
 
+@pytest.fixture
+def instance(features: Features) -> Instance:
+    instance = Mock(spec=Instance)
+    instance.features = features
+    instance.has_static_lazy_constraints = Mock(return_value=True)
+    return instance
+
+
 @pytest.fixture
 def sample() -> TrainingSample:
     return TrainingSample(
@@ -67,7 +75,7 @@ def features() -> Features:
     )
 
 
-def test_usage_with_solver(features: Features) -> None:
+def test_usage_with_solver(instance: Instance) -> None:
     solver = Mock(spec=LearningSolver)
     solver.use_lazy_cb = False
     solver.gap_tolerance = 1e-4
@@ -76,9 +84,6 @@ def test_usage_with_solver(features: Features) -> None:
     internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
     internal.is_constraint_satisfied = Mock(return_value=False)
 
-    instance = Mock(spec=Instance)
-    instance.has_static_lazy_constraints = Mock(return_value=True)
-
     component = StaticLazyConstraintsComponent(violation_tolerance=1.0)
     component.thresholds["type-a"] = MinProbabilityThreshold([0.5, 0.5])
     component.thresholds["type-b"] = MinProbabilityThreshold([0.5, 0.5])
@@ -112,7 +117,7 @@ def test_usage_with_solver(features: Features) -> None:
         instance=instance,
         model=None,
         stats=stats,
-        features=features,
+        features=instance.features,
         training_data=sample,
     )
 
@@ -149,7 +154,7 @@ def test_usage_with_solver(features: Features) -> None:
         instance=instance,
         model=None,
         stats=stats,
-        features=features,
+        features=instance.features,
         training_data=sample,
     )
 
@@ -164,7 +169,7 @@ def test_usage_with_solver(features: Features) -> None:
 
 
 def test_sample_predict(
-    features: Features,
+    instance: Instance,
     sample: TrainingSample,
 ) -> None:
     comp = StaticLazyConstraintsComponent()
@@ -184,7 +189,7 @@ def test_sample_predict(
             [0.0, 1.0],  # c4
         ]
     )
-    pred = comp.sample_predict(features, sample)
+    pred = comp.sample_predict(instance, sample)
     assert pred == ["c1", "c2", "c4"]
 
 
@@ -229,7 +234,7 @@ def test_fit_xy() -> None:
 
 
 def test_sample_xy(
-    features: Features,
+    instance: Instance,
     sample: TrainingSample,
 ) -> None:
     x_expected = {
@@ -240,7 +245,7 @@ def test_sample_xy(
         "type-a": [[False, True], [False, True], [True, False]],
         "type-b": [[False, True]],
     }
-    xy = StaticLazyConstraintsComponent.sample_xy(features, sample)
+    xy = StaticLazyConstraintsComponent.sample_xy(instance, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected

tests/components/test_objective.py

@@ -7,7 +7,7 @@ from unittest.mock import Mock
 import pytest
 from numpy.testing import assert_array_equal
 
-from miplearn import GurobiPyomoSolver, LearningSolver, Regressor
+from miplearn import GurobiPyomoSolver, LearningSolver, Regressor, Instance
 from miplearn.components.objective import ObjectiveValueComponent
 from miplearn.features import TrainingSample, InstanceFeatures, Features
 from tests.fixtures.knapsack import get_knapsack_instance
@@ -15,6 +15,13 @@ from tests.fixtures.knapsack import get_knapsack_instance
 import numpy as np
 
 
+@pytest.fixture
+def instance(features: Features) -> Instance:
+    instance = Mock(spec=Instance)
+    instance.features = features
+    return instance
+
+
 @pytest.fixture
 def features() -> Features:
     return Features(
@@ -50,7 +57,7 @@ def sample_without_ub() -> TrainingSample:
 
 
 def test_sample_xy(
-    features: Features,
+    instance: Instance,
     sample: TrainingSample,
 ) -> None:
     x_expected = {
@@ -61,7 +68,7 @@ def test_sample_xy(
         "Lower bound": [[1.0]],
         "Upper bound": [[2.0]],
     }
-    xy = ObjectiveValueComponent.sample_xy(features, sample)
+    xy = ObjectiveValueComponent.sample_xy(instance, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -69,7 +76,7 @@ def test_sample_xy(
 
 
 def test_sample_xy_without_lp(
-    features: Features,
+    instance: Instance,
     sample_without_lp: TrainingSample,
 ) -> None:
     x_expected = {
@@ -80,7 +87,7 @@ def test_sample_xy_without_lp(
         "Lower bound": [[1.0]],
         "Upper bound": [[2.0]],
     }
-    xy = ObjectiveValueComponent.sample_xy(features, sample_without_lp)
+    xy = ObjectiveValueComponent.sample_xy(instance, sample_without_lp)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -88,7 +95,7 @@ def test_sample_xy_without_lp(
 
 
 def test_sample_xy_without_ub(
-    features: Features,
+    instance: Instance,
     sample_without_ub: TrainingSample,
 ) -> None:
     x_expected = {
@@ -96,7 +103,7 @@ def test_sample_xy_without_ub(
         "Upper bound": [[1.0, 2.0, 3.0]],
     }
     y_expected = {"Lower bound": [[1.0]]}
-    xy = ObjectiveValueComponent.sample_xy(features, sample_without_ub)
+    xy = ObjectiveValueComponent.sample_xy(instance, sample_without_ub)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -170,10 +177,10 @@ def test_fit_xy_without_ub() -> None:
 
 
 def test_sample_predict(
-    features: Features,
+    instance: Instance,
     sample: TrainingSample,
 ) -> None:
-    x, y = ObjectiveValueComponent.sample_xy(features, sample)
+    x, y = ObjectiveValueComponent.sample_xy(instance, sample)
     comp = ObjectiveValueComponent()
     comp.regressors["Lower bound"] = Mock(spec=Regressor)
     comp.regressors["Upper bound"] = Mock(spec=Regressor)
@@ -183,7 +190,7 @@ def test_sample_predict(
     comp.regressors["Upper bound"].predict = Mock(  # type: ignore
         side_effect=lambda _: np.array([[60.0]])
     )
-    pred = comp.sample_predict(features, sample)
+    pred = comp.sample_predict(instance, sample)
     assert pred == {
         "Lower bound": 50.0,
         "Upper bound": 60.0,
@@ -199,16 +206,16 @@ def test_sample_predict(
 
 
 def test_sample_predict_without_ub(
-    features: Features,
+    instance: Instance,
     sample_without_ub: TrainingSample,
 ) -> None:
-    x, y = ObjectiveValueComponent.sample_xy(features, sample_without_ub)
+    x, y = ObjectiveValueComponent.sample_xy(instance, sample_without_ub)
     comp = ObjectiveValueComponent()
     comp.regressors["Lower bound"] = Mock(spec=Regressor)
     comp.regressors["Lower bound"].predict = Mock(  # type: ignore
         side_effect=lambda _: np.array([[50.0]])
     )
-    pred = comp.sample_predict(features, sample_without_ub)
+    pred = comp.sample_predict(instance, sample_without_ub)
     assert pred == {
         "Lower bound": 50.0,
     }
@@ -218,13 +225,13 @@ def test_sample_predict_without_ub(
     )
 
 
-def test_sample_evaluate(features: Features, sample: TrainingSample) -> None:
+def test_sample_evaluate(instance: Instance, sample: TrainingSample) -> None:
     comp = ObjectiveValueComponent()
     comp.regressors["Lower bound"] = Mock(spec=Regressor)
     comp.regressors["Lower bound"].predict = lambda _: np.array([[1.05]])  # type: ignore
     comp.regressors["Upper bound"] = Mock(spec=Regressor)
     comp.regressors["Upper bound"].predict = lambda _: np.array([[2.50]])  # type: ignore
-    ev = comp.sample_evaluate(features, sample)
+    ev = comp.sample_evaluate(instance, sample)
     assert ev == {
         "Lower bound": {
             "Actual value": 1.0,

tests/components/test_primal.py

@@ -8,7 +8,7 @@ import numpy as np
 from numpy.testing import assert_array_equal
 from scipy.stats import randint
 
-from miplearn import Classifier, LearningSolver
+from miplearn import Classifier, LearningSolver, Instance
 from miplearn.classifiers.threshold import Threshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.primal import PrimalSolutionComponent
@@ -38,6 +38,8 @@ def test_xy() -> None:
             }
         }
     )
+    instance = Mock(spec=Instance)
+    instance.features = features
     sample = TrainingSample(
         solution={
             "x": {
@@ -70,7 +72,7 @@ def test_xy() -> None:
             [True, False],
         ]
     }
-    xy = PrimalSolutionComponent.sample_xy(features, sample)
+    xy = PrimalSolutionComponent.sample_xy(instance, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -99,6 +101,8 @@ def test_xy_without_lp_solution() -> None:
             }
         }
     )
+    instance = Mock(spec=Instance)
+    instance.features = features
     sample = TrainingSample(
         solution={
             "x": {
@@ -123,7 +127,7 @@ def test_xy_without_lp_solution() -> None:
             [True, False],
         ]
     }
-    xy = PrimalSolutionComponent.sample_xy(features, sample)
+    xy = PrimalSolutionComponent.sample_xy(instance, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -161,6 +165,8 @@ def test_predict() -> None:
             }
         }
     )
+    instance = Mock(spec=Instance)
+    instance.features = features
     sample = TrainingSample(
         lp_solution={
             "x": {
@@ -170,11 +176,11 @@ def test_predict() -> None:
             }
         }
     )
-    x, _ = PrimalSolutionComponent.sample_xy(features, sample)
+    x, _ = PrimalSolutionComponent.sample_xy(instance, sample)
     comp = PrimalSolutionComponent()
     comp.classifiers = {"default": clf}
     comp.thresholds = {"default": thr}
-    solution_actual = comp.sample_predict(features, sample)
+    solution_actual = comp.sample_predict(instance, sample)
     clf.predict_proba.assert_called_once()
     assert_array_equal(x["default"], clf.predict_proba.call_args[0][0])
     thr.predict.assert_called_once()
@@ -243,7 +249,7 @@ def test_evaluate() -> None:
             4: 1.0,
         }
     }
-    features = Features(
+    features: Features = Features(
         variables={
             "x": {
                 0: VariableFeatures(),
@@ -254,7 +260,9 @@ def test_evaluate() -> None:
             }
         }
     )
-    sample = TrainingSample(
+    instance = Mock(spec=Instance)
+    instance.features = features
+    sample: TrainingSample = TrainingSample(
         solution={
             "x": {
                 0: 1.0,
@@ -265,7 +273,7 @@ def test_evaluate() -> None:
             }
         }
     )
-    ev = comp.sample_evaluate(features, sample)
+    ev = comp.sample_evaluate(instance, sample)
     assert ev == {
         0: classifier_evaluation_dict(tp=1, fp=1, tn=3, fn=0),
         1: classifier_evaluation_dict(tp=2, fp=0, tn=1, fn=2),