Implement AdaptiveSolver; reorganize imports

benchmark/benchmark.py

@@ -12,13 +12,18 @@ Options:
 """
 from docopt import docopt
 import importlib, pathlib
-from miplearn import LearningSolver, BenchmarkRunner
+from miplearn import (LearningSolver,
-from miplearn.warmstart import WarmStartComponent
+                      BenchmarkRunner,
-from miplearn.branching import BranchPriorityComponent
+                      WarmStartComponent,
+                      BranchPriorityComponent,
+                     )
 from numpy import median
 import pyomo.environ as pe
 import pickle
 
+import logging
+logging.getLogger('pyomo.core').setLevel(logging.ERROR)
+
 args = docopt(__doc__)
 basepath = args["<challenge>"]
 pathlib.Path(basepath).mkdir(parents=True, exist_ok=True)
@@ -60,7 +65,7 @@ def train():
         internal_solver_factory=train_solver_factory,
         components={
             "warm-start": WarmStartComponent(),
-            "branch-priority": BranchPriorityComponent(),
+            #"branch-priority": BranchPriorityComponent(),
         },
     )
     solver.parallel_solve(train_instances, n_jobs=10)
@@ -89,7 +94,7 @@ def test_ml():
             internal_solver_factory=test_solver_factory,
             components={
                 "warm-start": WarmStartComponent(),
-                "branch-priority": BranchPriorityComponent(),
+                #"branch-priority": BranchPriorityComponent(),
             },
         ),
         "ml-heuristic": LearningSolver(
@@ -97,7 +102,7 @@ def test_ml():
             mode="heuristic",
             components={
                 "warm-start": WarmStartComponent(),
-                "branch-priority": BranchPriorityComponent(),
+                #"branch-priority": BranchPriorityComponent(),
             },
         ),
     }

miplearn/__init__.py

@@ -6,7 +6,9 @@
 from .components.component import Component
 from .components.warmstart import (WarmStartComponent,
                                    KnnWarmStartPredictor,
-                                   LogisticWarmStartPredictor)
+                                   LogisticWarmStartPredictor,
+                                   AdaptivePredictor,
+                                  )
 from .components.branching import BranchPriorityComponent
 from .extractors import UserFeaturesExtractor, SolutionExtractor
 from .benchmark import BenchmarkRunner

miplearn/components/tests/test_warmstart.py

@@ -26,12 +26,16 @@ def test_warm_start_save_load():
     comp = solver.components["warm-start"]
     assert comp.x_train["default"].shape == (8, 6)
     assert comp.y_train["default"].shape == (8, 2)
-    assert "default" in comp.predictors.keys()
+    assert ("default", 0) in comp.predictors.keys()
+    assert ("default", 1) in comp.predictors.keys()
     solver.save_state(state_file.name)
     
+    solver.solve(_get_instances()[0])
+    
     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
     solver.load_state(state_file.name)
     comp = solver.components["warm-start"]
     assert comp.x_train["default"].shape == (8, 6)
     assert comp.y_train["default"].shape == (8, 2)
-    assert "default" in comp.predictors.keys()
+    assert ("default", 0) in comp.predictors.keys()
+    assert ("default", 1) in comp.predictors.keys()

miplearn/components/warmstart.py

@@ -12,12 +12,250 @@ from sklearn.pipeline import make_pipeline
 from sklearn.linear_model import LogisticRegression
 from sklearn.preprocessing import StandardScaler
 from sklearn.model_selection import cross_val_score
+from sklearn.metrics import roc_curve
 from sklearn.neighbors import KNeighborsClassifier
 from tqdm.auto import tqdm
 import pyomo.environ as pe
 import logging
 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=1.0,
+                ):
+        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 WarmStartComponent(Component):
+    def __init__(self,
+                 predictor=AdaptivePredictor(),
+                 mode="exact",
+                 max_fpr=[0.01, 0.01],
+                 min_threshold=[0.75, 0.75],
+                 dynamic_thresholds=False,
+                ):
+        self.mode = mode
+        self.x_train = {}
+        self.y_train = {}
+        self.predictors = {}
+        self.is_warm_start_available = False
+        self.max_fpr = max_fpr
+        self.min_threshold = min_threshold
+        self.thresholds = {}
+        self.predictor_factory = predictor
+        self.dynamic_thresholds = dynamic_thresholds
+        
+    
+    def before_solve(self, solver, instance, model):
+#         # Solve linear relaxation
+#         lr_solver = pe.SolverFactory("gurobi")
+#         lr_solver.options["threads"] = 4
+#         lr_solver.options["relax_integrality"] = 1
+#         lr_solver.solve(model, tee=solver.tee)
+        
+        # Build x_test
+        x_test = CombinedExtractor([UserFeaturesExtractor(),
+                                    SolutionExtractor(),
+                                   ]).extract([instance], [model])
+        
+        # Update self.x_train
+        self.x_train = Extractor.merge([self.x_train, x_test],
+                                       vertical=True)
+        
+        # Predict solutions
+        count_total, count_fixed = 0, 0
+        var_split = Extractor.split_variables(instance, model)
+        for category in var_split.keys():
+            var_index_pairs = var_split[category]
+            
+            # Clear current values
+            for i in range(len(var_index_pairs)):
+                var, index = var_index_pairs[i]
+                var[index].value = None
+            
+            # Make predictions
+            for label in [0,1]:
+                if (category, label) not in self.predictors.keys():
+                    continue
+                ws = self.predictors[category, label].predict_proba(x_test[category])
+                assert ws.shape == (len(var_index_pairs), 2)
+                for i in range(len(var_index_pairs)):
+                    count_total += 1
+                    var, index = var_index_pairs[i]
+                    logger.debug("%s[%s] ws=%.6f threshold=%.6f" % (var, index, ws[i, 1], self.thresholds[category, label]))
+                    if ws[i, 1] > self.thresholds[category, label]:
+                        logger.debug("Setting %s[%s] to %d" % (var, index, label))
+                        count_fixed += 1
+                        if self.mode == "heuristic":
+                            var[index].fix(label)
+                            if solver.is_persistent:
+                                solver.internal_solver.update_var(var[index])
+                        else:
+                            var[index].value = label
+                            self.is_warm_start_available = True
+                            
+            # Clear current values
+            for i in range(len(var_index_pairs)):
+                var, index = var_index_pairs[i]
+                if var[index].value is None:
+                    logger.debug("Variable %s[%s] not set" % (var, index))
+                else:
+                    logger.debug("Varible %s[%s] set to %.2f" % (var, index, var[index].value))
+
+                
+        logger.info("Setting values for %d variables (out of %d)" % (count_fixed, count_total // 2))
+
+
+    def after_solve(self, solver, instance, model):
+        y_test = SolutionExtractor().extract([instance], [model])
+        self.y_train = Extractor.merge([self.y_train, y_test], vertical=True)        
+                
+    def fit(self, solver, n_jobs=1):
+        for category in tqdm(self.x_train.keys(), desc="Fit (warm start)"):
+            x_train = self.x_train[category]
+            y_train = self.y_train[category]
+            for label in [0, 1]:
+                logger.debug("Fitting predictors[%s, %s]:" % (category, label))
+                
+                if callable(self.predictor_factory):
+                    pred = self.predictor_factory(category, label)
+                else:
+                    pred = deepcopy(self.predictor_factory)
+                self.predictors[category, label] = pred
+                y = y_train[:, label].astype(int)
+                pred.fit(x_train, y)
+
+                # If y is either always one or always zero, set fixed threshold
+                y_avg = np.average(y)
+                if (not self.dynamic_thresholds) or y_avg <= 0.001 or y_avg >= 0.999:
+                    self.thresholds[category, label] = self.min_threshold[label]
+                    logger.debug("    Setting threshold to %.4f" % self.min_threshold[label])
+                    continue
+                
+                # Calculate threshold dynamically using ROC curve
+                y_scores = pred.predict_proba(x_train)[:, 1]
+                fpr, tpr, thresholds = roc_curve(y, y_scores)
+                k = 0
+                while True:
+                    if (k + 1) > len(fpr):
+                        break
+                    if fpr[k + 1] > self.max_fpr[label]:
+                        break
+                    if thresholds[k + 1] < self.min_threshold[label]:
+                        break
+                    k = k + 1
+                logger.debug("    Setting threshold to %.4f (fpr=%.4f, tpr=%.4f)" % (thresholds[k], fpr[k], tpr[k]))
+                self.thresholds[category, label] = thresholds[k]
+                
+
+    def merge(self, other_components):
+        # Merge x_train and y_train
+        keys = set(self.x_train.keys())
+        for comp in other_components:
+            keys = keys.union(set(comp.x_train.keys()))
+        for key in keys:
+            x_train_submatrices = [comp.x_train[key]
+                                   for comp in other_components
+                                   if key in comp.x_train.keys()]
+            y_train_submatrices = [comp.y_train[key]
+                                   for comp in other_components
+                                   if key in comp.y_train.keys()]
+            if key in self.x_train.keys():
+                x_train_submatrices += [self.x_train[key]]
+                y_train_submatrices += [self.y_train[key]]
+            self.x_train[key] = np.vstack(x_train_submatrices)
+            self.y_train[key] = np.vstack(y_train_submatrices)
+
+        # Merge trained predictors
+        for comp in other_components:
+            for key in comp.predictors.keys():
+                if key not in self.predictors.keys():
+                    self.predictors[key] = comp.predictors[key]
+                    self.thresholds[key] = comp.thresholds[key]
+
+                    
+# Deprecated               
 class WarmStartPredictor(ABC):
     def __init__(self, thr_clip=[0.50, 0.50]):
         self.models = [None, None]
@@ -49,7 +287,8 @@ class WarmStartPredictor(ABC):
     def _fit(self, x_train, y_train, label):
         pass
 
-
+    
+# Deprecated               
 class LogisticWarmStartPredictor(WarmStartPredictor):
     def __init__(self,
                  min_samples=100,
@@ -91,6 +330,7 @@ class LogisticWarmStartPredictor(WarmStartPredictor):
         return reg
     
     
+# Deprecated
 class KnnWarmStartPredictor(WarmStartPredictor):
     def __init__(self,
                  k=50,
@@ -128,102 +368,5 @@ class KnnWarmStartPredictor(WarmStartPredictor):
         return knn
     
     
-class WarmStartComponent(Component):
-    def __init__(self,
-                 predictor_prototype=KnnWarmStartPredictor(),
-                 mode="exact",
-                ):
-        self.mode = mode
-        self.x_train = {}
-        self.y_train = {}
-        self.predictors = {}
-        self.predictor_prototype = predictor_prototype
-        self.is_warm_start_available = False
     
-    def before_solve(self, solver, instance, model):
-        # Solve linear relaxation
-        lr_solver = pe.SolverFactory("gurobi")
-        lr_solver.options["threads"] = 4
-        lr_solver.options["relax_integrality"] = 1
-        lr_solver.solve(model, tee=solver.tee)
-        
-        # Build x_test
-        x_test = CombinedExtractor([UserFeaturesExtractor(),
-                                    SolutionExtractor(),
-                                   ]).extract([instance], [model])
-        
-        # Update self.x_train
-        self.x_train = Extractor.merge([self.x_train, x_test],
-                                       vertical=True)
-        
-        # Predict solutions
-        count_total, count_fixed = 0, 0
-        var_split = Extractor.split_variables(instance, model)
-        for category in var_split.keys():
-            var_index_pairs = var_split[category]
-            if category not in self.predictors.keys():
-                continue
-            ws = self.predictors[category].predict(x_test[category])
-            assert ws.shape == (len(var_index_pairs), 2)
-            for i in range(len(var_index_pairs)):
-                var, index = var_index_pairs[i]
-                count_total += 1
-                if self.mode == "heuristic":
-                    if ws[i,0] > 0.5:
-                        var[index].fix(0)
-                        count_fixed += 1
-                        if solver.is_persistent:
-                            solver.internal_solver.update_var(var[index])
-                    elif ws[i,1] > 0.5:
-                        var[index].fix(1)
-                        count_fixed += 1
-                        if solver.is_persistent:
-                            solver.internal_solver.update_var(var[index])
-                else:
-                    var[index].value = None
-                    if ws[i,0] > 0.5:
-                        count_fixed += 1
-                        var[index].value = 0
-                        self.is_warm_start_available = True
-                    elif ws[i,1] > 0.5:
-                        count_fixed += 1
-                        var[index].value = 1
-                        self.is_warm_start_available = True
-        logger.info("Setting values for %d variables (out of %d)" % (count_fixed, count_total))
-
-
-    def after_solve(self, solver, instance, model):
-        y_test = SolutionExtractor().extract([instance], [model])
-        self.y_train = Extractor.merge([self.y_train, y_test], vertical=True)        
-                
-    def fit(self, solver, n_jobs=1):
-        for category in tqdm(self.x_train.keys(), desc="Warm start"):
-            x_train = self.x_train[category]
-            y_train = self.y_train[category]
-            self.predictors[category] = deepcopy(self.predictor_prototype)
-            self.predictors[category].fit(x_train, y_train)
-
-    def merge(self, other_components):
-        # Merge x_train and y_train
-        keys = set(self.x_train.keys())
-        for comp in other_components:
-            keys = keys.union(set(comp.x_train.keys()))
-        for key in keys:
-            x_train_submatrices = [comp.x_train[key]
-                                   for comp in other_components
-                                   if key in comp.x_train.keys()]
-            y_train_submatrices = [comp.y_train[key]
-                                   for comp in other_components
-                                   if key in comp.y_train.keys()]
-            if key in self.x_train.keys():
-                x_train_submatrices += [self.x_train[key]]
-                y_train_submatrices += [self.y_train[key]]
-            self.x_train[key] = np.vstack(x_train_submatrices)
-            self.y_train[key] = np.vstack(y_train_submatrices)
-
-        # Merge trained predictors
-        for comp in other_components:
-            for key in comp.predictors.keys():
-                if key not in self.predictors.keys():
-                    self.predictors[key] = comp.predictors[key]