Implement PrimalSolutionComponent.evaluate

src/python/miplearn/components/__init__.py

@@ -1,3 +1,32 @@
 #  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.
+
+
+def classifier_evaluation_dict(tp, tn, fp, fn):
+    p = tp + fn
+    n = fp + tn
+    d = {
+        "Predicted positive": fp + tp,
+        "Predicted negative": fn + tn,
+        "Condition positive": p,
+        "Condition negative": n,
+        "True positive": tp,
+        "True negative": tn,
+        "False positive": fp,
+        "False negative": fn,
+        "Accuracy": (tp + tn) / (p + n),
+        "F1 score": (2 * tp) / (2 * tp + fp + fn),
+        "Recall": tp / p,
+        "Precision": tp / (tp + fp),
+    }
+    t = (p + n) / 100.0
+    d["Predicted positive (%)"] = d["Predicted positive"] / t
+    d["Predicted negative (%)"] = d["Predicted negative"] / t
+    d["Condition positive (%)"] = d["Condition positive"] / t
+    d["Condition negative (%)"] = d["Condition negative"] / t
+    d["True positive (%)"] = d["True positive"] / t
+    d["True negative (%)"] = d["True negative"] / t
+    d["False positive (%)"] = d["False positive"] / t
+    d["False negative (%)"] = d["False negative"] / t
+    return d

src/python/miplearn/components/lazy.py

@@ -5,6 +5,7 @@
 from copy import deepcopy
 
 from miplearn.classifiers.counting import CountingClassifier
+from miplearn.components import classifier_evaluation_dict
 
 from .component import Component
 from ..extractors import *
@@ -67,54 +68,19 @@ class LazyConstraintsComponent(Component):
         return violations
 
     def evaluate(self, instances):
-        
-        def _classifier_evaluation_dict(tp, tn, fp, fn):
-            p = tp + fn
-            n = fp + tn
-            d = {
-                "Predicted positive": fp + tp,
-                "Predicted negative": fn + tn,
-                "Condition positive": p,
-                "Condition negative": n,
-                "True positive": tp,
-                "True negative": tn,
-                "False positive": fp,
-                "False negative": fn,
-            }
-            d["Accuracy"] = (tp + tn) / (p + n)
-            d["F1 score"] = (2 * tp) / (2 * tp + fp + fn)
-            d["Recall"] = tp / p
-            d["Precision"] = tp / (tp + fp)
-            T = (p + n) / 100.0
-            d["Predicted positive (%)"] = d["Predicted positive"] / T
-            d["Predicted negative (%)"] = d["Predicted negative"] / T
-            d["Condition positive (%)"] = d["Condition positive"] / T
-            d["Condition negative (%)"] = d["Condition negative"] / T
-            d["True positive (%)"] = d["True positive"] / T
-            d["True negative (%)"] = d["True negative"] / T
-            d["False positive (%)"] = d["False positive"] / T
-            d["False negative (%)"] = d["False negative"] / T
-            return d
-        
         results = {}
-        
         all_violations = set()
         for instance in instances:
             all_violations |= set(instance.found_violations)
-            
         for idx in tqdm(range(len(instances)), desc="Evaluate (lazy)"):
             instance = instances[idx]
             condition_positive = set(instance.found_violations)
             condition_negative = all_violations - condition_positive
             pred_positive = set(self.predict(instance)) & all_violations
             pred_negative = all_violations - pred_positive
-            
             tp = len(pred_positive & condition_positive)
             tn = len(pred_negative & condition_negative)
             fp = len(pred_positive & condition_negative)
             fn = len(pred_negative & condition_positive)
-            
+            results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
-            results[idx] = _classifier_evaluation_dict(tp, tn, fp, fn)
-            
-            
         return results

src/python/miplearn/components/primal.py

@@ -5,6 +5,7 @@
 from copy import deepcopy
 
 from miplearn.classifiers.adaptive import AdaptiveClassifier
+from miplearn.components import classifier_evaluation_dict
 from sklearn.metrics import roc_curve
 
 from .component import Component
@@ -18,19 +19,19 @@ class PrimalSolutionComponent(Component):
     A component that predicts primal solutions.
     """
     def __init__(self,
-                 predictor=AdaptiveClassifier(),
+                 classifier=AdaptiveClassifier(),
                  mode="exact",
                  max_fpr=[1e-3, 1e-3],
                  min_threshold=[0.75, 0.75],
                  dynamic_thresholds=True,
                  ):
         self.mode = mode
-        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.classifiers = {}
+        self.classifier_prototype = classifier
         self.dynamic_thresholds = dynamic_thresholds
     
     def before_solve(self, solver, instance, model):
@@ -52,15 +53,15 @@ class PrimalSolutionComponent(Component):
             x_train = features[category]
             y_train = solutions[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)
+
+                logger.debug("Fitting predictors[%s, %s]:" % (category, label))
+                if isinstance(self.classifier_prototype, list):
+                    pred = deepcopy(self.classifier_prototype[label])
+                else:
+                    pred = deepcopy(self.classifier_prototype)
                 pred.fit(x_train, y)
+                self.classifiers[category, label] = pred
 
                 # If y is either always one or always zero, set fixed threshold
                 y_avg = np.average(y)
@@ -69,8 +70,15 @@ class PrimalSolutionComponent(Component):
                     logger.debug("    Setting threshold to %.4f" % self.min_threshold[label])
                     continue
                 
+                proba = pred.predict_proba(x_train)
+                assert isinstance(proba, np.ndarray), \
+                    "classifier should return numpy array"
+                assert proba.shape == (x_train.shape[0], 2),\
+                    "classifier should return (%d,%d)-shaped array, not %s" % (
+                        x_train.shape[0], 2, str(proba.shape))
+
                 # Calculate threshold dynamically using ROC curve
-                y_scores = pred.predict_proba(x_train)[:, 1]
+                y_scores = proba[:, 1]
                 fpr, tpr, thresholds = roc_curve(y, y_scores)
                 k = 0
                 while True:
@@ -95,11 +103,50 @@ class PrimalSolutionComponent(Component):
                     solution[var] = {}
                 solution[var][index] = None
                 for label in [0, 1]:
-                    if (category, label) not in self.predictors.keys():
+                    if (category, label) not in self.classifiers.keys():
                         continue
-                    ws = self.predictors[category, label].predict_proba(x_test[category])
+                    ws = self.classifiers[category, label].predict_proba(x_test[category])
                     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]:
                         solution[var][index] = label
         return solution
+
+    def evaluate(self, instances):
+        ev = {}
+        for (instance_idx, instance) in enumerate(instances):
+            solution_actual = instance.solution
+            solution_pred = self.predict(instance)
+
+            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():
+                for (idx, value) in var_dict.items():
+                    vars_all.add((varname, idx))
+                    if value > 0.5:
+                        vars_one.add((varname, idx))
+                    else:
+                        vars_zero.add((varname, idx))
+                    if solution_pred[varname][idx] is not None:
+                        if solution_pred[varname][idx] > 0.5:
+                            pred_one_positive.add((varname, idx))
+                        else:
+                            pred_zero_positive.add((varname, idx))
+            pred_one_negative = vars_all - pred_one_positive
+            pred_zero_negative = vars_all - pred_zero_positive
+
+            tp_zero = len(pred_zero_positive & vars_zero)
+            fp_zero = len(pred_zero_positive & vars_one)
+            tn_zero = len(pred_zero_negative & vars_one)
+            fn_zero = len(pred_zero_negative & vars_zero)
+
+            tp_one = len(pred_one_positive & vars_one)
+            fp_one = len(pred_one_positive & vars_zero)
+            tn_one = len(pred_one_negative & vars_zero)
+            fn_one = len(pred_one_negative & vars_one)
+
+            ev[instance_idx] = {
+                "Fix zero": classifier_evaluation_dict(tp_zero, tn_zero, fp_zero, fn_zero),
+                "Fix one": classifier_evaluation_dict(tp_one, tn_one, fp_one, fn_one),
+            }
+        return ev

src/python/miplearn/components/tests/test_primal.py

@@ -2,32 +2,91 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from miplearn import LearningSolver, PrimalSolutionComponent
+from unittest.mock import Mock
-from miplearn.problems.knapsack import KnapsackInstance
+
 import numpy as np
-import tempfile
+from miplearn import PrimalSolutionComponent
-
+from miplearn.classifiers import Classifier
-
+from miplearn.tests import get_training_instances_and_models
-def _get_instances():
-    instances = [
-        KnapsackInstance(
-            weights=[23., 26., 20., 18.],
-            prices=[505., 352., 458., 220.],
-            capacity=67.,
-        ),
-    ] * 5
-    models = [inst.to_model() for inst in instances]
-    solver = LearningSolver()
-    for i in range(len(instances)):
-        solver.solve(instances[i], models[i])
-    return instances, models
 
 
 def test_predict():
-    instances, models = _get_instances()
+    instances, models = get_training_instances_and_models()
     comp = PrimalSolutionComponent()
     comp.fit(instances)
     solution = comp.predict(instances[0])
     assert "x" in solution
-    for idx in range(4):
+    assert 0 in solution["x"]
-        assert idx in solution["x"]
+    assert 1 in solution["x"]
+    assert 2 in solution["x"]
+    assert 3 in solution["x"]
+
+
+def test_evaluate():
+    instances, models = get_training_instances_and_models()
+    clf_zero = Mock(spec=Classifier)
+    clf_zero.predict_proba = Mock(return_value=np.array([
+        [0., 1.],  # x[0]
+        [0., 1.],  # x[1]
+        [1., 0.],  # x[2]
+        [1., 0.],  # x[3]
+    ]))
+    clf_one = Mock(spec=Classifier)
+    clf_one.predict_proba = Mock(return_value=np.array([
+        [1., 0.],  # x[0] instances[0]
+        [1., 0.],  # x[1] instances[0]
+        [0., 1.],  # x[2] instances[0]
+        [1., 0.],  # x[3] instances[0]
+    ]))
+    comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one],
+                                   dynamic_thresholds=False)
+    comp.fit(instances[:1])
+    assert comp.predict(instances[0]) == {"x": {0: 0,
+                                                1: 0,
+                                                2: 1,
+                                                3: None}}
+    assert instances[0].solution == {"x": {0: 1,
+                                           1: 0,
+                                           2: 1,
+                                           3: 1}}
+    ev = comp.evaluate(instances[:1])
+    assert ev == {0: {'Fix one': {'Accuracy': 0.5,
+                                  'Condition negative': 1,
+                                  'Condition negative (%)': 25.0,
+                                  'Condition positive': 3,
+                                  'Condition positive (%)': 75.0,
+                                  'F1 score': 0.5,
+                                  'False negative': 2,
+                                  'False negative (%)': 50.0,
+                                  'False positive': 0,
+                                  'False positive (%)': 0.0,
+                                  'Precision': 1.0,
+                                  'Predicted negative': 3,
+                                  'Predicted negative (%)': 75.0,
+                                  'Predicted positive': 1,
+                                  'Predicted positive (%)': 25.0,
+                                  'Recall': 0.3333333333333333,
+                                  'True negative': 1,
+                                  'True negative (%)': 25.0,
+                                  'True positive': 1,
+                                  'True positive (%)': 25.0},
+                      'Fix zero': {'Accuracy': 0.75,
+                                   'Condition negative': 3,
+                                   'Condition negative (%)': 75.0,
+                                   'Condition positive': 1,
+                                   'Condition positive (%)': 25.0,
+                                   'F1 score': 0.6666666666666666,
+                                   'False negative': 0,
+                                   'False negative (%)': 0.0,
+                                   'False positive': 1,
+                                   'False positive (%)': 25.0,
+                                   'Precision': 0.5,
+                                   'Predicted negative': 2,
+                                   'Predicted negative (%)': 50.0,
+                                   'Predicted positive': 2,
+                                   'Predicted positive (%)': 50.0,
+                                   'Recall': 1.0,
+                                   'True negative': 2,
+                                   'True negative (%)': 50.0,
+                                   'True positive': 1,
+                                   'True positive (%)': 25.0}}}