Add type annotations to components

miplearn/components/component.py

@@ -2,8 +2,16 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from typing import Any, List, Union, TYPE_CHECKING
+
+from miplearn.instance import Instance
+from miplearn.types import MIPSolveStats, TrainingSample
+
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import LearningSolver
 
 
 class Component(ABC):
@@ -15,18 +23,35 @@ class Component(ABC):
     strategy.
     """
 
-    def before_solve(self, solver, instance, model):
+    def before_solve(
+        self,
+        solver: LearningSolver,
+        instance: Instance,
+        model: Any,
+    ) -> None:
+        """
+        Method called by LearningSolver before the problem is solved.
+
+        Parameters
+        ----------
+        solver
+            The solver calling this method.
+        instance
+            The instance being solved.
+        model
+            The concrete optimization model being solved.
+        """
         return
 
     @abstractmethod
     def after_solve(
         self,
-        solver,
-        instance,
-        model,
-        stats,
-        training_data,
-    ):
+        solver: LearningSolver,
+        instance: Instance,
+        model: Any,
+        stats: MIPSolveStats,
+        training_data: TrainingSample,
+    ) -> None:
         """
         Method called by LearningSolver after the problem is solved to optimality.
 
@@ -40,19 +65,23 @@ class Component(ABC):
             The concrete optimization model being solved.
         stats: dict
             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.
+            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.
         training_data: dict
-            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.
+            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.
         """
         pass
 
-    def fit(self, training_instances):
+    def fit(
+        self,
+        training_instances: Union[List[str], List[Instance]],
+    ) -> None:
         return
 
     def iteration_cb(self, solver, instance, model):

miplearn/components/cuts.py

@@ -5,10 +5,12 @@
 import logging
 import sys
 from copy import deepcopy
+from typing import Any, Dict
 
 import numpy as np
 from tqdm.auto import tqdm
 
+from miplearn.classifiers import Classifier
 from miplearn.classifiers.counting import CountingClassifier
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.component import Component
@@ -24,15 +26,12 @@ class UserCutsComponent(Component):
 
     def __init__(
         self,
-        classifier=CountingClassifier(),
-        threshold=0.05,
+        classifier: Classifier = CountingClassifier(),
+        threshold: float = 0.05,
     ):
-        self.violations = set()
-        self.count = {}
-        self.n_samples = 0
-        self.threshold = threshold
-        self.classifier_prototype = classifier
-        self.classifiers = {}
+        self.threshold: float = threshold
+        self.classifier_prototype: Classifier = classifier
+        self.classifiers: Dict[Any, Classifier] = {}
 
     def before_solve(self, solver, instance, model):
         instance.found_violated_user_cuts = []

miplearn/components/lazy_dynamic.py

@@ -5,10 +5,12 @@
 import logging
 import sys
 from copy import deepcopy
+from typing import Any, Dict
 
 import numpy as np
 from tqdm.auto import tqdm
 
+from miplearn.classifiers import Classifier
 from miplearn.classifiers.counting import CountingClassifier
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.component import Component
@@ -24,15 +26,12 @@ class DynamicLazyConstraintsComponent(Component):
 
     def __init__(
         self,
-        classifier=CountingClassifier(),
-        threshold=0.05,
+        classifier: Classifier = CountingClassifier(),
+        threshold: float = 0.05,
     ):
-        self.violations = set()
-        self.count = {}
-        self.n_samples = 0
-        self.threshold = threshold
-        self.classifier_prototype = classifier
-        self.classifiers = {}
+        self.threshold: float = threshold
+        self.classifier_prototype: Classifier = classifier
+        self.classifiers: Dict[Any, Classifier] = {}
 
     def before_solve(self, solver, instance, model):
         instance.found_violated_lazy_constraints = []

miplearn/components/objective.py

@@ -15,6 +15,7 @@ from sklearn.metrics import (
     r2_score,
 )
 
+from miplearn.classifiers import Regressor
 from miplearn.components.component import Component
 from miplearn.extractors import InstanceFeaturesExtractor, ObjectiveValueExtractor
 
@@ -26,7 +27,10 @@ class ObjectiveValueComponent(Component):
     A Component which predicts the optimal objective value of the problem.
     """
 
-    def __init__(self, regressor=LinearRegression()):
+    def __init__(
+        self,
+        regressor: Regressor = LinearRegression(),
+    ) -> None:
         self.ub_regressor = None
         self.lb_regressor = None
         self.regressor_prototype = regressor

miplearn/components/primal.py

@@ -2,14 +2,19 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+import logging
 from copy import deepcopy
-import sys
+from typing import Union, Dict, Any
 
-from .component import Component
-from ..classifiers.adaptive import AdaptiveClassifier
-from ..classifiers.threshold import MinPrecisionThreshold, DynamicThreshold
-from ..components import classifier_evaluation_dict
-from ..extractors import *
+import numpy as np
+from tqdm.auto import tqdm
+
+from miplearn.classifiers import Classifier
+from miplearn.classifiers.adaptive import AdaptiveClassifier
+from miplearn.classifiers.threshold import MinPrecisionThreshold, DynamicThreshold
+from miplearn.components import classifier_evaluation_dict
+from miplearn.components.component import Component
+from miplearn.extractors import VariableFeaturesExtractor, SolutionExtractor, Extractor
 
 logger = logging.getLogger(__name__)
 
@@ -21,13 +26,13 @@ class PrimalSolutionComponent(Component):
 
     def __init__(
         self,
-        classifier=AdaptiveClassifier(),
-        mode="exact",
-        threshold=MinPrecisionThreshold(0.98),
-    ):
+        classifier: Classifier = AdaptiveClassifier(),
+        mode: str = "exact",
+        threshold: Union[float, DynamicThreshold] = MinPrecisionThreshold(0.98),
+    ) -> None:
         self.mode = mode
-        self.classifiers = {}
-        self.thresholds = {}
+        self.classifiers: Dict[Any, Classifier] = {}
+        self.thresholds: Dict[Any, Union[float, DynamicThreshold]] = {}
         self.threshold_prototype = threshold
         self.classifier_prototype = classifier