diff --git a/miplearn/components/component.py b/miplearn/components/component.py index e7451f0..474b284 100644 --- a/miplearn/components/component.py +++ b/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 {} diff --git a/miplearn/components/lazy_dynamic.py b/miplearn/components/lazy_dynamic.py index 70a7920..5334ef5 100644 --- a/miplearn/components/lazy_dynamic.py +++ b/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): diff --git a/miplearn/components/lazy_static.py b/miplearn/components/lazy_static.py index d67ba41..e56d5aa 100644 --- a/miplearn/components/lazy_static.py +++ b/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 diff --git a/miplearn/components/objective.py b/miplearn/components/objective.py index ddf59ec..522ea2b 100644 --- a/miplearn/components/objective.py +++ b/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: diff --git a/miplearn/components/primal.py b/miplearn/components/primal.py index 2229a31..6ab2c7f 100644 --- a/miplearn/components/primal.py +++ b/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(): diff --git a/tests/components/test_lazy_static.py b/tests/components/test_lazy_static.py index a26c5c5..61d7751 100644 --- a/tests/components/test_lazy_static.py +++ b/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 diff --git a/tests/components/test_objective.py b/tests/components/test_objective.py index a3b8a4f..442de2e 100644 --- a/tests/components/test_objective.py +++ b/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, diff --git a/tests/components/test_primal.py b/tests/components/test_primal.py index 17d7c25..1279135 100644 --- a/tests/components/test_primal.py +++ b/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),