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Flip dict levels produced by PrimalSolutionComponent.evaluate

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This commit is contained in:
Alinson S. Xavier
2020-04-15 13:11:34 -05:00
parent 10008d99b0
commit 4f39dc02d6
2 changed files with 16 additions and 16 deletions
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26
src/python/miplearn/components/primal.py
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@@ -18,6 +18,7 @@ class PrimalSolutionComponent(Component):
""" """
A component that predicts primal solutions. A component that predicts primal solutions.
""" """
def __init__(self, def __init__(self,
classifier=AdaptiveClassifier(), classifier=AdaptiveClassifier(),
mode="exact", mode="exact",
@@ -33,22 +34,22 @@ class PrimalSolutionComponent(Component):
self.classifiers = {} self.classifiers = {}
self.classifier_prototype = classifier self.classifier_prototype = classifier
self.dynamic_thresholds = dynamic_thresholds self.dynamic_thresholds = dynamic_thresholds
def before_solve(self, solver, instance, model): def before_solve(self, solver, instance, model):
solution = self.predict(instance) solution = self.predict(instance)
if self.mode == "heuristic": if self.mode == "heuristic":
solver.internal_solver.fix(solution) solver.internal_solver.fix(solution)
else: else:
solver.internal_solver.set_warm_start(solution) solver.internal_solver.set_warm_start(solution)
def after_solve(self, solver, instance, model, results): def after_solve(self, solver, instance, model, results):
pass pass
def fit(self, training_instances): def fit(self, training_instances):
logger.debug("Extracting features...") logger.debug("Extracting features...")
features = VariableFeaturesExtractor().extract(training_instances) features = VariableFeaturesExtractor().extract(training_instances)
solutions = SolutionExtractor().extract(training_instances) solutions = SolutionExtractor().extract(training_instances)
for category in tqdm(features.keys(), desc="Fit (Primal)"): for category in tqdm(features.keys(), desc="Fit (Primal)"):
x_train = features[category] x_train = features[category]
y_train = solutions[category] y_train = solutions[category]
@@ -69,11 +70,11 @@ class PrimalSolutionComponent(Component):
self.thresholds[category, label] = self.min_threshold[label] self.thresholds[category, label] = self.min_threshold[label]
logger.debug(" Setting threshold to %.4f" % self.min_threshold[label]) logger.debug(" Setting threshold to %.4f" % self.min_threshold[label])
continue continue
proba = pred.predict_proba(x_train) proba = pred.predict_proba(x_train)
assert isinstance(proba, np.ndarray), \ assert isinstance(proba, np.ndarray), \
"classifier should return numpy array" "classifier should return numpy array"
assert proba.shape == (x_train.shape[0], 2),\ assert proba.shape == (x_train.shape[0], 2), \
"classifier should return (%d,%d)-shaped array, not %s" % ( "classifier should return (%d,%d)-shaped array, not %s" % (
x_train.shape[0], 2, str(proba.shape)) x_train.shape[0], 2, str(proba.shape))
@@ -89,10 +90,10 @@ class PrimalSolutionComponent(Component):
if thresholds[k + 1] < self.min_threshold[label]: if thresholds[k + 1] < self.min_threshold[label]:
break break
k = k + 1 k = k + 1
logger.debug(" Setting threshold to %.4f (fpr=%.4f, tpr=%.4f)"% logger.debug(" Setting threshold to %.4f (fpr=%.4f, tpr=%.4f)" %
(thresholds[k], fpr[k], tpr[k])) (thresholds[k], fpr[k], tpr[k]))
self.thresholds[category, label] = thresholds[k] self.thresholds[category, label] = thresholds[k]
def predict(self, instance): def predict(self, instance):
x_test = VariableFeaturesExtractor().extract([instance]) x_test = VariableFeaturesExtractor().extract([instance])
solution = {} solution = {}
@@ -113,7 +114,8 @@ class PrimalSolutionComponent(Component):
return solution return solution
def evaluate(self, instances): def evaluate(self, instances):
ev = {} ev = {"Fix zero": {},
"Fix one": {}}
for instance_idx in tqdm(range(len(instances))): for instance_idx in tqdm(range(len(instances))):
instance = instances[instance_idx] instance = instances[instance_idx]
solution_actual = instance.solution solution_actual = instance.solution
@@ -146,8 +148,6 @@ class PrimalSolutionComponent(Component):
tn_one = len(pred_one_negative & vars_zero) tn_one = len(pred_one_negative & vars_zero)
fn_one = len(pred_one_negative & vars_one) fn_one = len(pred_one_negative & vars_one)
ev[instance_idx] = { ev["Fix zero"][instance_idx] = classifier_evaluation_dict(tp_zero, tn_zero, fp_zero, fn_zero)
"Fix zero": classifier_evaluation_dict(tp_zero, tn_zero, fp_zero, fn_zero), ev["Fix one"][instance_idx] = classifier_evaluation_dict(tp_one, tn_one, fp_one, fn_one)
"Fix one": classifier_evaluation_dict(tp_one, tn_one, fp_one, fn_one),
}
return ev return ev

6
src/python/miplearn/components/tests/test_primal.py
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@@ -50,7 +50,7 @@ def test_evaluate():
2: 1, 2: 1,
3: 1}} 3: 1}}
ev = comp.evaluate(instances[:1]) ev = comp.evaluate(instances[:1])
assert ev == {0: {'Fix one': {'Accuracy': 0.5, assert ev == {'Fix one': {0: {'Accuracy': 0.5,
'Condition negative': 1, 'Condition negative': 1,
'Condition negative (%)': 25.0, 'Condition negative (%)': 25.0,
'Condition positive': 3, 'Condition positive': 3,
@@ -69,8 +69,8 @@ def test_evaluate():
'True negative': 1, 'True negative': 1,
'True negative (%)': 25.0, 'True negative (%)': 25.0,
'True positive': 1, 'True positive': 1,
'True positive (%)': 25.0}, 'True positive (%)': 25.0}},
'Fix zero': {'Accuracy': 0.75, 'Fix zero': {0: {'Accuracy': 0.75,
'Condition negative': 3, 'Condition negative': 3,
'Condition negative (%)': 75.0, 'Condition negative (%)': 75.0,
'Condition positive': 1, 'Condition positive': 1,