Remove obsolete methods

miplearn/__init__.py

@@ -13,12 +13,6 @@ from .components.dynamic_user_cuts import UserCutsComponent
 from .components.objective import ObjectiveValueComponent
 from .components.primal import PrimalSolutionComponent
 from .components.static_lazy import StaticLazyConstraintsComponent
-from .features import (
-    Features,
-    TrainingSample,
-    Variable,
-    InstanceFeatures,
-)
 from .instance.base import Instance
 from .instance.picklegz import (
     PickleGzInstance,

miplearn/components/component.py

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

miplearn/components/dynamic_common.py

@@ -12,7 +12,7 @@ from miplearn.classifiers import Classifier
 from miplearn.classifiers.threshold import Threshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.component import Component
-from miplearn.features import TrainingSample, Sample
+from miplearn.features import Sample
 from miplearn.instance.base import Instance
 
 logger = logging.getLogger(__name__)
@@ -37,44 +37,6 @@ class DynamicConstraintsComponent(Component):
         self.known_cids: List[str] = []
         self.attr = attr
 
-    def sample_xy_with_cids_old(
-        self,
-        instance: Instance,
-        sample: TrainingSample,
-    ) -> Tuple[
-        Dict[Hashable, List[List[float]]],
-        Dict[Hashable, List[List[bool]]],
-        Dict[Hashable, List[str]],
-    ]:
-        x: Dict[Hashable, List[List[float]]] = {}
-        y: Dict[Hashable, List[List[bool]]] = {}
-        cids: Dict[Hashable, List[str]] = {}
-        for cid in self.known_cids:
-            category = instance.get_constraint_category(cid)
-            if category is None:
-                continue
-            if category not in x:
-                x[category] = []
-                y[category] = []
-                cids[category] = []
-            assert instance.features.instance is not None
-            assert instance.features.instance.user_features is not None
-            cfeatures = instance.get_constraint_features(cid)
-            assert cfeatures is not None
-            assert isinstance(cfeatures, list)
-            for ci in cfeatures:
-                assert isinstance(ci, float)
-            f = list(instance.features.instance.user_features)
-            f += cfeatures
-            x[category] += [f]
-            cids[category] += [cid]
-            if getattr(sample, self.attr) is not None:
-                if cid in getattr(sample, self.attr):
-                    y[category] += [[False, True]]
-                else:
-                    y[category] += [[True, False]]
-        return x, y, cids
-
     def sample_xy_with_cids(
         self,
         instance: Optional[Instance],
@@ -122,15 +84,6 @@ class DynamicConstraintsComponent(Component):
                         y[category] += [[True, False]]
         return x, y, cids
 
-    @overrides
-    def sample_xy_old(
-        self,
-        instance: Instance,
-        sample: TrainingSample,
-    ) -> Tuple[Dict, Dict]:
-        x, y, _ = self.sample_xy_with_cids_old(instance, sample)
-        return x, y
-
     @overrides
     def sample_xy(
         self,
@@ -140,29 +93,6 @@ class DynamicConstraintsComponent(Component):
         x, y, _ = self.sample_xy_with_cids(instance, sample)
         return x, y
 
-    def sample_predict_old(
-        self,
-        instance: Instance,
-        sample: TrainingSample,
-    ) -> List[Hashable]:
-        pred: List[Hashable] = []
-        if len(self.known_cids) == 0:
-            logger.info("Classifiers not fitted. Skipping.")
-            return pred
-        x, _, cids = self.sample_xy_with_cids_old(instance, sample)
-        for category in x.keys():
-            assert category in self.classifiers
-            assert category in self.thresholds
-            clf = self.classifiers[category]
-            thr = self.thresholds[category]
-            nx = np.array(x[category])
-            proba = clf.predict_proba(nx)
-            t = thr.predict(nx)
-            for i in range(proba.shape[0]):
-                if proba[i][1] > t[1]:
-                    pred += [cids[category][i]]
-        return pred
-
     def sample_predict(
         self,
         instance: Instance,
@@ -186,20 +116,6 @@ class DynamicConstraintsComponent(Component):
                     pred += [cids[category][i]]
         return pred
 
-    @overrides
-    def fit_old(self, training_instances: List[Instance]) -> None:
-        collected_cids = set()
-        for instance in training_instances:
-            instance.load()
-            for sample in instance.training_data:
-                if getattr(sample, self.attr) is None:
-                    continue
-                collected_cids |= getattr(sample, self.attr)
-            instance.free()
-        self.known_cids.clear()
-        self.known_cids.extend(sorted(collected_cids))
-        super().fit_old(training_instances)
-
     @overrides
     def fit(self, training_instances: List[Instance]) -> None:
         collected_cids = set()

miplearn/components/dynamic_lazy.py

@@ -8,13 +8,13 @@ from typing import Dict, List, TYPE_CHECKING, Hashable, Tuple, Any, Optional, Se
 import numpy as np
 from overrides import overrides
 
-from miplearn.instance.base import Instance
 from miplearn.classifiers import Classifier
 from miplearn.classifiers.counting import CountingClassifier
 from miplearn.classifiers.threshold import MinProbabilityThreshold, Threshold
 from miplearn.components.component import Component
 from miplearn.components.dynamic_common import DynamicConstraintsComponent
-from miplearn.features import TrainingSample, Features, Sample
+from miplearn.features import Sample
+from miplearn.instance.base import Instance
 from miplearn.types import LearningSolveStats
 
 logger = logging.getLogger(__name__)

miplearn/components/dynamic_user_cuts.py

@@ -3,18 +3,18 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import logging
-from typing import Any, TYPE_CHECKING, Hashable, Set, Tuple, Dict, List, Optional
+from typing import Any, TYPE_CHECKING, Hashable, Set, Tuple, Dict, List
 
 import numpy as np
 from overrides import overrides
 
-from miplearn.instance.base import Instance
 from miplearn.classifiers import Classifier
 from miplearn.classifiers.counting import CountingClassifier
 from miplearn.classifiers.threshold import Threshold, MinProbabilityThreshold
 from miplearn.components.component import Component
 from miplearn.components.dynamic_common import DynamicConstraintsComponent
-from miplearn.features import Features, TrainingSample, Sample
+from miplearn.features import Sample
+from miplearn.instance.base import Instance
 from miplearn.types import LearningSolveStats
 
 logger = logging.getLogger(__name__)

miplearn/components/objective.py

@@ -12,7 +12,7 @@ from sklearn.linear_model import LinearRegression
 from miplearn.classifiers import Regressor
 from miplearn.classifiers.sklearn import ScikitLearnRegressor
 from miplearn.components.component import Component
-from miplearn.features import TrainingSample, Features, Sample
+from miplearn.features import Sample
 from miplearn.instance.base import Instance
 from miplearn.types import LearningSolveStats
 

miplearn/components/primal.py

@@ -21,7 +21,7 @@ from miplearn.classifiers.adaptive import AdaptiveClassifier
 from miplearn.classifiers.threshold import MinPrecisionThreshold, Threshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.component import Component
-from miplearn.features import TrainingSample, Features, Sample
+from miplearn.features import Sample
 from miplearn.instance.base import Instance
 from miplearn.types import (
     LearningSolveStats,

miplearn/components/static_lazy.py

@@ -8,12 +8,12 @@ from typing import Dict, Tuple, List, Hashable, Any, TYPE_CHECKING, Set, Optiona
 import numpy as np
 from overrides import overrides
 
-from miplearn.instance.base import Instance
 from miplearn.classifiers import Classifier
 from miplearn.classifiers.counting import CountingClassifier
 from miplearn.classifiers.threshold import MinProbabilityThreshold, Threshold
 from miplearn.components.component import Component
-from miplearn.features import TrainingSample, Features, Constraint, Sample
+from miplearn.features import Constraint, Sample
+from miplearn.instance.base import Instance
 from miplearn.types import LearningSolveStats
 
 logger = logging.getLogger(__name__)

miplearn/features.py

@@ -6,31 +6,17 @@ import collections
 import numbers
 from dataclasses import dataclass
 from math import log, isfinite
-from typing import TYPE_CHECKING, Dict, Optional, Set, List, Hashable
+from typing import TYPE_CHECKING, Dict, Optional, List, Hashable
 
 import numpy as np
 
-from miplearn.types import Solution, Category
+from miplearn.types import Category
 
 if TYPE_CHECKING:
     from miplearn.solvers.internal import InternalSolver, LPSolveStats, MIPSolveStats
     from miplearn.instance.base import Instance
 
 
-@dataclass
-class TrainingSample:
-    lp_log: Optional[str] = None
-    lp_solution: Optional[Solution] = None
-    lp_value: Optional[float] = None
-    lazy_enforced: Optional[Set[Hashable]] = None
-    lower_bound: Optional[float] = None
-    mip_log: Optional[str] = None
-    solution: Optional[Solution] = None
-    upper_bound: Optional[float] = None
-    slacks: Optional[Dict[str, float]] = None
-    user_cuts_enforced: Optional[Set[Hashable]] = None
-
-
 @dataclass
 class InstanceFeatures:
     user_features: Optional[List[float]] = None

miplearn/instance/base.py

@@ -8,7 +8,7 @@ from typing import Any, List, Optional, Hashable, TYPE_CHECKING
 
 from overrides import EnforceOverrides
 
-from miplearn.features import TrainingSample, Features, Sample
+from miplearn.features import Sample
 from miplearn.types import VariableName, Category
 
 logger = logging.getLogger(__name__)
@@ -31,8 +31,6 @@ class Instance(ABC, EnforceOverrides):
     """
 
     def __init__(self) -> None:
-        self.training_data: List[TrainingSample] = []
-        self.features: Features = Features()
         self.samples: List[Sample] = []
 
     @abstractmethod

miplearn/instance/picklegz.py

@@ -121,15 +121,11 @@ class PickleGzInstance(Instance):
             obj = read_pickle_gz(self.filename)
             assert isinstance(obj, Instance)
             self.instance = obj
-            self.features = self.instance.features
-            self.training_data = self.instance.training_data
             self.samples = self.instance.samples
 
     @overrides
     def free(self) -> None:
         self.instance = None  # type: ignore
-        self.features = None  # type: ignore
-        self.training_data = None  # type: ignore
         gc.collect()
 
     @overrides

miplearn/problems/tsp.py

@@ -11,7 +11,8 @@ from scipy.spatial.distance import pdist, squareform
 from scipy.stats import uniform, randint
 from scipy.stats.distributions import rv_frozen
 
-from miplearn import InternalSolver, BasePyomoSolver
+from miplearn.solvers.learning import InternalSolver
+from miplearn.solvers.pyomo.base import BasePyomoSolver
 from miplearn.instance.base import Instance
 from miplearn.types import VariableName, Category
 

miplearn/solvers/gurobi.py

@@ -136,15 +136,6 @@ class GurobiSolver(InternalSolver):
             var.lb = value
             var.ub = value
 
-    @overrides
-    def get_dual(self, cid: str) -> float:
-        assert self.model is not None
-        c = self.model.getConstrByName(cid)
-        if self.is_infeasible():
-            return c.farkasDual
-        else:
-            return c.pi
-
     @overrides
     def get_constraint_attrs(self) -> List[str]:
         return [
@@ -175,14 +166,6 @@ class GurobiSolver(InternalSolver):
             constraints[c.constrName] = constr
         return constraints
 
-    @overrides
-    def get_sense(self) -> str:
-        assert self.model is not None
-        if self.model.modelSense == 1:
-            return "min"
-        else:
-            return "max"
-
     @overrides
     def get_solution(self) -> Optional[Solution]:
         assert self.model is not None
@@ -224,12 +207,6 @@ class GurobiSolver(InternalSolver):
             "value",
         ]
 
-    @overrides
-    def get_variable_names(self) -> List[VariableName]:
-        self._raise_if_callback()
-        assert self.model is not None
-        return [v.varName for v in self.model.getVars()]
-
     @overrides
     def get_variables(self) -> Dict[str, Variable]:
         assert self.model is not None

miplearn/solvers/internal.py

@@ -124,10 +124,8 @@ class InternalSolver(ABC, EnforceOverrides):
         """
         Sets the warm start to be used by the solver.
 
-        The solution should be a dictionary following the same format as the
-        one produced by `get_solution`. Only one warm start is supported.
-        Calling this function when a warm start already exists will
-        remove the previous warm start.
+        Only one warm start is supported. Calling this function when a warm start
+        already exists will remove the previous warm start.
         """
         pass
 
@@ -154,11 +152,8 @@ class InternalSolver(ABC, EnforceOverrides):
     @abstractmethod
     def fix(self, solution: Solution) -> None:
         """
-        Fixes the values of a subset of decision variables.
-
-        The values should be provided in the dictionary format generated by
-        `get_solution`. Missing values in the solution indicate variables
-        that should be left free.
+        Fixes the values of a subset of decision variables. Missing values in the
+        solution indicate variables that should be left free.
         """
         pass
 
@@ -170,9 +165,7 @@ class InternalSolver(ABC, EnforceOverrides):
         with higher priority are picked first, given that they are fractional.
         Ties are solved arbitrarily. By default, all variables have priority zero.
 
-        The priorities should be provided in the dictionary format generated by
-        `get_solution`. Missing values indicate variables whose priorities
-        should not be modified.
+        Missing values indicate variables whose priorities should not be modified.
         """
         raise NotImplementedError()
 
@@ -216,34 +209,6 @@ class InternalSolver(ABC, EnforceOverrides):
         """
         pass
 
-    @abstractmethod
-    def get_dual(self, cid: str) -> float:
-        """
-        If the model is feasible and has been solved to optimality, returns the
-        optimal value of the dual variable associated with this constraint. If the
-        model is infeasible, returns a portion of the infeasibility certificate
-        corresponding to the given constraint.
-
-        Only available for relaxed problems. Must be called after solve.
-        """
-        pass
-
-    @abstractmethod
-    def get_sense(self) -> str:
-        """
-        Returns the sense of the problem (either "min" or "max").
-        """
-        pass
-
-    @abstractmethod
-    def get_variable_names(self) -> List[VariableName]:
-        """
-        Returns a list containing the names of all variables in the model. This
-        method is used by the ML components to query what variables are there in the
-        model before a solution is available.
-        """
-        pass
-
     @abstractmethod
     def clone(self) -> "InternalSolver":
         """

miplearn/solvers/learning.py

@@ -4,7 +4,7 @@
 
 import logging
 import traceback
-from typing import Optional, List, Any, cast, Callable, Dict, Tuple
+from typing import Optional, List, Any, cast, Dict, Tuple
 
 from p_tqdm import p_map
 
@@ -13,7 +13,7 @@ from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
 from miplearn.components.dynamic_user_cuts import UserCutsComponent
 from miplearn.components.objective import ObjectiveValueComponent
 from miplearn.components.primal import PrimalSolutionComponent
-from miplearn.features import FeaturesExtractor, TrainingSample, Sample
+from miplearn.features import FeaturesExtractor, Sample
 from miplearn.instance.base import Instance
 from miplearn.instance.picklegz import PickleGzInstance
 from miplearn.solvers import _RedirectOutput
@@ -138,9 +138,7 @@ class LearningSolver:
 
         # Initialize training sample
         # -------------------------------------------------------
-        training_sample = TrainingSample()
         sample = Sample()
-        instance.training_data.append(training_sample)
         instance.samples.append(sample)
 
         # Initialize stats
@@ -160,7 +158,6 @@ class LearningSolver:
         logger.info("Extracting features (after-load)...")
         features = FeaturesExtractor(self.internal_solver).extract(instance)
         features.extra = {}
-        instance.features.__dict__ = features.__dict__
         sample.after_load = features
 
         callback_args = (
@@ -171,15 +168,6 @@ class LearningSolver:
             sample,
         )
 
-        callback_args_old = (
-            self,
-            instance,
-            model,
-            stats,
-            instance.features,
-            training_sample,
-        )
-
         # Solve root LP relaxation
         # -------------------------------------------------------
         lp_stats = None
@@ -187,19 +175,14 @@ class LearningSolver:
             logger.debug("Running before_solve_lp callbacks...")
             for component in self.components.values():
                 component.before_solve_lp(*callback_args)
-                component.before_solve_lp_old(*callback_args_old)
 
             logger.info("Solving root LP relaxation...")
             lp_stats = self.internal_solver.solve_lp(tee=tee)
             stats.update(cast(LearningSolveStats, lp_stats.__dict__))
-            training_sample.lp_solution = self.internal_solver.get_solution()
-            training_sample.lp_value = lp_stats.lp_value
-            training_sample.lp_log = lp_stats.lp_log
 
             logger.debug("Running after_solve_lp callbacks...")
             for component in self.components.values():
                 component.after_solve_lp(*callback_args)
-                component.after_solve_lp_old(*callback_args_old)
 
             # Extract features (after-lp)
             # -------------------------------------------------------
@@ -245,7 +228,6 @@ class LearningSolver:
         logger.debug("Running before_solve_mip callbacks...")
         for component in self.components.values():
             component.before_solve_mip(*callback_args)
-            component.before_solve_mip_old(*callback_args_old)
 
         # Solve MIP
         # -------------------------------------------------------
@@ -272,19 +254,11 @@ class LearningSolver:
         features.extra = {}
         sample.after_mip = features
 
-        # Add some information to training_sample
-        # -------------------------------------------------------
-        training_sample.lower_bound = mip_stats.mip_lower_bound
-        training_sample.upper_bound = mip_stats.mip_upper_bound
-        training_sample.mip_log = mip_stats.mip_log
-        training_sample.solution = self.internal_solver.get_solution()
-
         # After-solve callbacks
         # -------------------------------------------------------
         logger.debug("Calling after_solve_mip callbacks...")
         for component in self.components.values():
             component.after_solve_mip(*callback_args)
-            component.after_solve_mip_old(*callback_args_old)
 
         # Flush
         # -------------------------------------------------------
@@ -414,12 +388,11 @@ class LearningSolver:
 
     def fit(self, training_instances: List[Instance]) -> None:
         if len(training_instances) == 0:
-            logger.warn("Empty list of training instances provided. Skipping.")
+            logger.warning("Empty list of training instances provided. Skipping.")
             return
         for component in self.components.values():
             logger.info(f"Fitting {component.__class__.__name__}...")
             component.fit(training_instances)
-            component.fit_old(training_instances)
 
     def _add_component(self, component: Component) -> None:
         name = component.__class__.__name__

miplearn/solvers/pyomo/base.py

@@ -155,11 +155,6 @@ class BasePyomoSolver(InternalSolver):
             "slack",
         ]
 
-    @overrides
-    def get_dual(self, cid: str) -> float:
-        constr = self._cname_to_constr[cid]
-        return self._pyomo_solver.dual[constr]
-
     @overrides
     def get_solution(self) -> Optional[Solution]:
         assert self.model is not None
@@ -173,21 +168,6 @@ class BasePyomoSolver(InternalSolver):
                 solution[f"{var}[{index}]"] = var[index].value
         return solution
 
-    @overrides
-    def get_variable_names(self) -> List[VariableName]:
-        assert self.model is not None
-        variables: List[VariableName] = []
-        for var in self.model.component_objects(Var):
-            for index in var:
-                if var[index].fixed:
-                    continue
-                variables += [f"{var}[{index}]"]
-        return variables
-
-    @overrides
-    def get_sense(self) -> str:
-        return self._obj_sense
-
     @overrides
     def get_variables(self) -> Dict[str, Variable]:
         assert self.model is not None

miplearn/types.py

@@ -2,7 +2,7 @@
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from typing import Optional, Dict, Callable, Any, Union, Tuple, TYPE_CHECKING, Hashable
+from typing import Optional, Dict, Callable, Any, Union, TYPE_CHECKING, Hashable
 
 from mypy_extensions import TypedDict
 

tests/components/test_component.py

@@ -9,8 +9,8 @@ from miplearn.features import Features
 from miplearn.instance.base import Instance
 
 
-def test_xy_instance_old() -> None:
-    def _sample_xy_old(features: Features, sample: str) -> Tuple[Dict, Dict]:
+def test_xy_instance() -> None:
+    def _sample_xy(features: Features, sample: str) -> Tuple[Dict, Dict]:
         x = {
             "s1": {
                 "category_a": [
@@ -55,12 +55,10 @@ def test_xy_instance_old() -> None:
 
     comp = Component()
     instance_1 = Mock(spec=Instance)
-    instance_1.training_data = ["s1", "s2"]
-    instance_1.features = {}
+    instance_1.samples = ["s1", "s2"]
     instance_2 = Mock(spec=Instance)
-    instance_2.training_data = ["s3"]
-    instance_2.features = {}
-    comp.sample_xy_old = _sample_xy_old  # type: ignore
+    instance_2.samples = ["s3"]
+    comp.sample_xy = _sample_xy  # type: ignore
     x_expected = {
         "category_a": [
             [1, 2, 3],
@@ -96,6 +94,6 @@ def test_xy_instance_old() -> None:
             [11],
         ],
     }
-    x_actual, y_actual = comp.xy_instances_old([instance_1, instance_2])
+    x_actual, y_actual = comp.xy_instances([instance_1, instance_2])
     assert x_actual == x_expected
     assert y_actual == y_expected

tests/components/test_dynamic_lazy.py

@@ -13,7 +13,6 @@ from miplearn.classifiers.threshold import MinProbabilityThreshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
 from miplearn.features import (
-    TrainingSample,
     Features,
     InstanceFeatures,
     Sample,
@@ -24,60 +23,6 @@ from miplearn.solvers.tests import assert_equals
 E = 0.1
 
 
-@pytest.fixture
-def training_instances_old() -> List[Instance]:
-    instances = [cast(Instance, Mock(spec=Instance)) for _ in range(2)]
-    instances[0].features = Features(
-        instance=InstanceFeatures(
-            user_features=[50.0],
-        ),
-    )
-    instances[0].training_data = [
-        TrainingSample(lazy_enforced={"c1", "c2"}),
-        TrainingSample(lazy_enforced={"c2", "c3"}),
-    ]
-    instances[0].get_constraint_category = Mock(  # type: ignore
-        side_effect=lambda cid: {
-            "c1": "type-a",
-            "c2": "type-a",
-            "c3": "type-b",
-            "c4": "type-b",
-        }[cid]
-    )
-    instances[0].get_constraint_features = Mock(  # type: ignore
-        side_effect=lambda cid: {
-            "c1": [1.0, 2.0, 3.0],
-            "c2": [4.0, 5.0, 6.0],
-            "c3": [1.0, 2.0],
-            "c4": [3.0, 4.0],
-        }[cid]
-    )
-    instances[1].features = Features(
-        instance=InstanceFeatures(
-            user_features=[80.0],
-        ),
-    )
-    instances[1].training_data = [
-        TrainingSample(lazy_enforced={"c3", "c4"}),
-    ]
-    instances[1].get_constraint_category = Mock(  # type: ignore
-        side_effect=lambda cid: {
-            "c1": None,
-            "c2": "type-a",
-            "c3": "type-b",
-            "c4": "type-b",
-        }[cid]
-    )
-    instances[1].get_constraint_features = Mock(  # type: ignore
-        side_effect=lambda cid: {
-            "c2": [7.0, 8.0, 9.0],
-            "c3": [5.0, 6.0],
-            "c4": [7.0, 8.0],
-        }[cid]
-    )
-    return instances
-
-
 @pytest.fixture
 def training_instances() -> List[Instance]:
     instances = [cast(Instance, Mock(spec=Instance)) for _ in range(2)]

tests/components/test_dynamic_user_cuts.py

@@ -12,7 +12,7 @@ from gurobipy import GRB
 from networkx import Graph
 from overrides import overrides
 
-from miplearn import InternalSolver
+from miplearn.solvers.learning import InternalSolver
 from miplearn.components.dynamic_user_cuts import UserCutsComponent
 from miplearn.instance.base import Instance
 from miplearn.solvers.gurobi import GurobiSolver

tests/problems/test_tsp.py

@@ -38,17 +38,21 @@ def test_instance() -> None:
     )
     instance = TravelingSalesmanInstance(n_cities, distances)
     solver = LearningSolver()
-    stats = solver.solve(instance)
-    solution = instance.training_data[0].solution
-    assert solution is not None
-    assert solution["x[(0, 1)]"] == 1.0
-    assert solution["x[(0, 2)]"] == 0.0
-    assert solution["x[(0, 3)]"] == 1.0
-    assert solution["x[(1, 2)]"] == 1.0
-    assert solution["x[(1, 3)]"] == 0.0
-    assert solution["x[(2, 3)]"] == 1.0
-    assert stats["mip_lower_bound"] == 4.0
-    assert stats["mip_upper_bound"] == 4.0
+    solver.solve(instance)
+    assert len(instance.samples) == 1
+    assert instance.samples[0].after_mip is not None
+    features = instance.samples[0].after_mip
+    assert features is not None
+    assert features.variables is not None
+    assert features.variables["x[(0, 1)]"].value == 1.0
+    assert features.variables["x[(0, 2)]"].value == 0.0
+    assert features.variables["x[(0, 3)]"].value == 1.0
+    assert features.variables["x[(1, 2)]"].value == 1.0
+    assert features.variables["x[(1, 3)]"].value == 0.0
+    assert features.variables["x[(2, 3)]"].value == 1.0
+    assert features.mip_solve is not None
+    assert features.mip_solve.mip_lower_bound == 4.0
+    assert features.mip_solve.mip_upper_bound == 4.0
 
 
 def test_subtour() -> None:
@@ -67,18 +71,20 @@ def test_subtour() -> None:
     instance = TravelingSalesmanInstance(n_cities, distances)
     solver = LearningSolver()
     solver.solve(instance)
+    assert len(instance.samples) == 1
     assert instance.samples[0].after_mip is not None
-    assert instance.samples[0].after_mip.extra is not None
-    lazy_enforced = instance.samples[0].after_mip.extra["lazy_enforced"]
+    features = instance.samples[0].after_mip
+    assert features.extra is not None
+    assert "lazy_enforced" in features.extra
+    lazy_enforced = features.extra["lazy_enforced"]
     assert lazy_enforced is not None
     assert len(lazy_enforced) > 0
-    solution = instance.training_data[0].solution
-    assert solution is not None
-    assert solution["x[(0, 1)]"] == 1.0
-    assert solution["x[(0, 4)]"] == 1.0
-    assert solution["x[(1, 2)]"] == 1.0
-    assert solution["x[(2, 3)]"] == 1.0
-    assert solution["x[(3, 5)]"] == 1.0
-    assert solution["x[(4, 5)]"] == 1.0
+    assert features.variables is not None
+    assert features.variables["x[(0, 1)]"].value == 1.0
+    assert features.variables["x[(0, 4)]"].value == 1.0
+    assert features.variables["x[(1, 2)]"].value == 1.0
+    assert features.variables["x[(2, 3)]"].value == 1.0
+    assert features.variables["x[(3, 5)]"].value == 1.0
+    assert features.variables["x[(4, 5)]"].value == 1.0
     solver.fit([instance])
     solver.solve(instance)

tests/solvers/test_learning_solver.py

@@ -34,29 +34,38 @@ def test_learning_solver(
             )
 
             solver.solve(instance)
-            assert hasattr(instance, "features")
-
-            sample = instance.training_data[0]
-            assert sample.solution is not None
-            assert sample.solution["x[0]"] == 1.0
-            assert sample.solution["x[1]"] == 0.0
-            assert sample.solution["x[2]"] == 1.0
-            assert sample.solution["x[3]"] == 1.0
-            assert sample.lower_bound == 1183.0
-            assert sample.upper_bound == 1183.0
-            assert sample.lp_solution is not None
-            assert sample.lp_solution["x[0]"] is not None
-            assert sample.lp_solution["x[1]"] is not None
-            assert sample.lp_solution["x[2]"] is not None
-            assert sample.lp_solution["x[3]"] is not None
-            assert round(sample.lp_solution["x[0]"], 3) == 1.000
-            assert round(sample.lp_solution["x[1]"], 3) == 0.923
-            assert round(sample.lp_solution["x[2]"], 3) == 1.000
-            assert round(sample.lp_solution["x[3]"], 3) == 0.000
-            assert sample.lp_value is not None
-            assert round(sample.lp_value, 3) == 1287.923
-            assert sample.mip_log is not None
-            assert len(sample.mip_log) > 100
+            assert len(instance.samples) > 0
+            sample = instance.samples[0]
+
+            after_mip = sample.after_mip
+            assert after_mip is not None
+            assert after_mip.variables is not None
+            assert after_mip.mip_solve is not None
+            assert after_mip.variables["x[0]"].value == 1.0
+            assert after_mip.variables["x[1]"].value == 0.0
+            assert after_mip.variables["x[2]"].value == 1.0
+            assert after_mip.variables["x[3]"].value == 1.0
+            assert after_mip.mip_solve.mip_lower_bound == 1183.0
+            assert after_mip.mip_solve.mip_upper_bound == 1183.0
+            assert after_mip.mip_solve.mip_log is not None
+            assert len(after_mip.mip_solve.mip_log) > 100
+
+            after_lp = sample.after_lp
+            assert after_lp is not None
+            assert after_lp.variables is not None
+            assert after_lp.lp_solve is not None
+            assert after_lp.variables["x[0]"].value is not None
+            assert after_lp.variables["x[1]"].value is not None
+            assert after_lp.variables["x[2]"].value is not None
+            assert after_lp.variables["x[3]"].value is not None
+            assert round(after_lp.variables["x[0]"].value, 3) == 1.000
+            assert round(after_lp.variables["x[1]"].value, 3) == 0.923
+            assert round(after_lp.variables["x[2]"].value, 3) == 1.000
+            assert round(after_lp.variables["x[3]"].value, 3) == 0.000
+            assert after_lp.lp_solve.lp_value is not None
+            assert round(after_lp.lp_solve.lp_value, 3) == 1287.923
+            assert after_lp.lp_solve.lp_log is not None
+            assert len(after_lp.lp_solve.lp_log) > 100
 
             solver.fit([instance])
             solver.solve(instance)
@@ -90,9 +99,7 @@ def test_parallel_solve(
         results = solver.parallel_solve(instances, n_jobs=3)
         assert len(results) == 10
         for instance in instances:
-            data = instance.training_data[0]
-            assert data.solution is not None
-            assert len(data.solution.keys()) == 5
+            assert len(instance.samples) == 1
 
 
 def test_solve_fit_from_disk(
@@ -111,19 +118,13 @@ def test_solve_fit_from_disk(
         solver = LearningSolver(solver=internal_solver)
         solver.solve(instances[0])
         instance_loaded = read_pickle_gz(cast(PickleGzInstance, instances[0]).filename)
-        assert len(instance_loaded.training_data) > 0
-        assert instance_loaded.features.instance is not None
-        assert instance_loaded.features.variables is not None
-        assert instance_loaded.features.constraints is not None
+        assert len(instance_loaded.samples) > 0
 
         # Test: parallel_solve
         solver.parallel_solve(instances)
         for instance in instances:
             instance_loaded = read_pickle_gz(cast(PickleGzInstance, instance).filename)
-            assert len(instance_loaded.training_data) > 0
-            assert instance_loaded.features.instance is not None
-            assert instance_loaded.features.variables is not None
-            assert instance_loaded.features.constraints is not None
+            assert len(instance_loaded.samples) > 0
 
         # Delete temporary files
         for instance in instances: