Move classifiers into their own module

src/python/miplearn/__init__.py

@@ -10,10 +10,11 @@ from .extractors import (SolutionExtractor,
 from .components.component import Component
 from .components.objective import ObjectiveValueComponent
 from .components.lazy import LazyConstraintsComponent
-from .components.primal import (PrimalSolutionComponent,
+from .components.primal import PrimalSolutionComponent
-                                AdaptivePredictor)
 from .components.branching import BranchPriorityComponent
 
+from .classifiers import AdaptiveClassifier
+
 from .benchmark import BenchmarkRunner
 
 from .instance import Instance

src/python/miplearn/classifiers/AdaptiveClassifier.py

@@ -0,0 +1,110 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  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 numpy as np
+
+from miplearn.classifiers import Classifier
+from sklearn.model_selection import cross_val_score
+
+logger = logging.getLogger(__name__)
+
+
+class AdaptiveClassifier(Classifier):
+    """
+    A classifier that automatically switches strategies based on the number of
+    samples and cross-validation scores.
+    """
+    def __init__(self,
+                 predictor=None,
+                 min_samples_predict=1,
+                 min_samples_cv=100,
+                 thr_fix=0.999,
+                 thr_alpha=0.50,
+                 thr_balance=0.95,
+                 ):
+        self.min_samples_predict = min_samples_predict
+        self.min_samples_cv = min_samples_cv
+        self.thr_fix = thr_fix
+        self.thr_alpha = thr_alpha
+        self.thr_balance = thr_balance
+        self.predictor_factory = predictor
+        self.predictor = None
+
+    def fit(self, x_train, y_train):
+        n_samples = x_train.shape[0]
+
+        # If number of samples is too small, don't predict anything.
+        if n_samples < self.min_samples_predict:
+            logger.debug("    Too few samples (%d); always predicting false" % n_samples)
+            self.predictor = 0
+            return
+
+        # If vast majority of observations are false, always return false.
+        y_train_avg = np.average(y_train)
+        if y_train_avg <= 1.0 - self.thr_fix:
+            logger.debug("    Most samples are negative (%.3f); always returning false" % y_train_avg)
+            self.predictor = 0
+            return
+
+        # If vast majority of observations are true, always return true.
+        if y_train_avg >= self.thr_fix:
+            logger.debug("    Most samples are positive (%.3f); always returning true" % y_train_avg)
+            self.predictor = 1
+            return
+
+        # If classes are too unbalanced, don't predict anything.
+        if y_train_avg < (1 - self.thr_balance) or y_train_avg > self.thr_balance:
+            logger.debug("    Classes are too unbalanced (%.3f); always returning false" % y_train_avg)
+            self.predictor = 0
+            return
+
+        # Select ML model if none is provided
+        if self.predictor_factory is None:
+            if n_samples < 30:
+                from sklearn.neighbors import KNeighborsClassifier
+                self.predictor_factory = KNeighborsClassifier(n_neighbors=n_samples)
+            else:
+                from sklearn.pipeline import make_pipeline
+                from sklearn.preprocessing import StandardScaler
+                from sklearn.linear_model import LogisticRegression
+                self.predictor_factory = make_pipeline(StandardScaler(), LogisticRegression())
+
+        # Create predictor
+        if callable(self.predictor_factory):
+            pred = self.predictor_factory()
+        else:
+            pred = deepcopy(self.predictor_factory)
+
+        # Skip cross-validation if number of samples is too small
+        if n_samples < self.min_samples_cv:
+            logger.debug("    Too few samples (%d); skipping cross validation" % n_samples)
+            self.predictor = pred
+            self.predictor.fit(x_train, y_train)
+            return
+
+        # Calculate cross-validation score
+        cv_score = np.mean(cross_val_score(pred, x_train, y_train, cv=5))
+        dummy_score = max(y_train_avg, 1 - y_train_avg)
+        cv_thr = 1. * self.thr_alpha + dummy_score * (1 - self.thr_alpha)
+
+        # If cross-validation score is too low, don't predict anything.
+        if cv_score < cv_thr:
+            logger.debug("    Score is too low (%.3f < %.3f); always returning false" % (cv_score, cv_thr))
+            self.predictor = 0
+        else:
+            logger.debug("    Score is acceptable (%.3f > %.3f); training classifier" % (cv_score, cv_thr))
+            self.predictor = pred
+            self.predictor.fit(x_train, y_train)
+
+    def predict_proba(self, x_test):
+        if isinstance(self.predictor, int):
+            y_pred = np.zeros((x_test.shape[0], 2))
+            y_pred[:, self.predictor] = 1.0
+            return y_pred
+        else:
+            return self.predictor.predict_proba(x_test)
+

src/python/miplearn/classifiers/__init__.py

@@ -0,0 +1,25 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from abc import ABC, abstractmethod
+
+
+class Classifier(ABC):
+    @abstractmethod
+    def fit(self, x_train, y_train):
+        pass
+
+    @abstractmethod
+    def predict_proba(self, x_test):
+        pass
+
+
+class Regressor(ABC):
+    @abstractmethod
+    def fit(self, x_train, y_train):
+        pass
+
+    @abstractmethod
+    def predict(self):
+        pass

src/python/miplearn/components/__init__.py

@@ -1,4 +1,3 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
-

src/python/miplearn/components/primal.py

@@ -4,12 +4,8 @@
 
 from copy import deepcopy
 
-from sklearn.linear_model import LogisticRegression
+from miplearn.classifiers.AdaptiveClassifier import AdaptiveClassifier
 from sklearn.metrics import roc_curve
-from sklearn.model_selection import cross_val_score
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.pipeline import make_pipeline
-from sklearn.preprocessing import StandardScaler
 
 from .component import Component
 from ..extractors import *
@@ -17,100 +13,12 @@ from ..extractors import *
 logger = logging.getLogger(__name__)
 
 
-class AdaptivePredictor:
-    def __init__(self,
-                 predictor=None,
-                 min_samples_predict=1,
-                 min_samples_cv=100,
-                 thr_fix=0.999,
-                 thr_alpha=0.50,
-                 thr_balance=0.95,
-                ):
-        self.min_samples_predict = min_samples_predict
-        self.min_samples_cv = min_samples_cv
-        self.thr_fix = thr_fix
-        self.thr_alpha = thr_alpha
-        self.thr_balance = thr_balance
-        self.predictor_factory = predictor
-        
-    def fit(self, x_train, y_train):
-        n_samples = x_train.shape[0]
-        
-        # If number of samples is too small, don't predict anything.
-        if n_samples < self.min_samples_predict:
-            logger.debug("    Too few samples (%d); always predicting false" % n_samples)
-            self.predictor = 0
-            return
-        
-        # If vast majority of observations are false, always return false.
-        y_train_avg = np.average(y_train)
-        if y_train_avg <= 1.0 - self.thr_fix:
-            logger.debug("    Most samples are negative (%.3f); always returning false" % y_train_avg)
-            self.predictor = 0
-            return
-        
-        # If vast majority of observations are true, always return true.
-        if y_train_avg >= self.thr_fix:
-            logger.debug("    Most samples are positive (%.3f); always returning true" % y_train_avg)
-            self.predictor = 1
-            return
-        
-        # If classes are too unbalanced, don't predict anything.
-        if y_train_avg < (1 - self.thr_balance) or y_train_avg > self.thr_balance:
-            logger.debug("    Classes are too unbalanced (%.3f); always returning false" % y_train_avg)
-            self.predictor = 0
-            return
-
-        # Select ML model if none is provided
-        if self.predictor_factory is None:
-            if n_samples < 30:
-                self.predictor_factory = KNeighborsClassifier(n_neighbors=n_samples)
-            else:
-                self.predictor_factory = make_pipeline(StandardScaler(), LogisticRegression())
-        
-        # Create predictor
-        if callable(self.predictor_factory):
-            pred = self.predictor_factory()
-        else:
-            pred = deepcopy(self.predictor_factory)
-        
-        # Skip cross-validation if number of samples is too small
-        if n_samples < self.min_samples_cv:
-            logger.debug("    Too few samples (%d); skipping cross validation" % n_samples)
-            self.predictor = pred
-            self.predictor.fit(x_train, y_train)
-            return
-        
-        # Calculate cross-validation score
-        cv_score = np.mean(cross_val_score(pred, x_train, y_train, cv=5))
-        dummy_score = max(y_train_avg, 1 - y_train_avg)
-        cv_thr = 1. * self.thr_alpha + dummy_score * (1 - self.thr_alpha)
-
-        # If cross-validation score is too low, don't predict anything.
-        if cv_score < cv_thr:
-            logger.debug("    Score is too low (%.3f < %.3f); always returning false" % (cv_score, cv_thr))
-            self.predictor = 0
-        else:
-            logger.debug("    Score is acceptable (%.3f > %.3f); training classifier" % (cv_score, cv_thr))
-            self.predictor = pred
-            self.predictor.fit(x_train, y_train)
-        
-    def predict_proba(self, x_test):
-        if isinstance(self.predictor, int):
-            y_pred = np.zeros((x_test.shape[0], 2))
-            y_pred[:, self.predictor] = 1.0
-            return y_pred
-        else:
-            return self.predictor.predict_proba(x_test)
-
-        
 class PrimalSolutionComponent(Component):
     """
     A component that predicts primal solutions.
     """
-    
     def __init__(self,
-                 predictor=AdaptivePredictor(),
+                 predictor=AdaptiveClassifier(),
                  mode="exact",
                  max_fpr=[1e-3, 1e-3],
                  min_threshold=[0.75, 0.75],