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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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14
src/python/miplearn/components/primal.py
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@@ -18,6 +18,7 @@ class PrimalSolutionComponent(Component):
"""
A component that predicts primal solutions.
"""
def __init__(self,
classifier=AdaptiveClassifier(),
mode="exact",
@@ -73,7 +74,7 @@ class PrimalSolutionComponent(Component):
proba = pred.predict_proba(x_train)
assert isinstance(proba, np.ndarray), \
"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" % (
x_train.shape[0], 2, str(proba.shape))
@@ -89,7 +90,7 @@ class PrimalSolutionComponent(Component):
if thresholds[k + 1] < self.min_threshold[label]:
break
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]))
self.thresholds[category, label] = thresholds[k]
@@ -113,7 +114,8 @@ class PrimalSolutionComponent(Component):
return solution
def evaluate(self, instances):
ev = {}
ev = {"Fix zero": {},
"Fix one": {}}
for instance_idx in tqdm(range(len(instances))):
instance = instances[instance_idx]
solution_actual = instance.solution
@@ -146,8 +148,6 @@ class PrimalSolutionComponent(Component):
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),
}
ev["Fix zero"][instance_idx] = 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)
return ev

6
src/python/miplearn/components/tests/test_primal.py
View File

@@ -50,7 +50,7 @@ def test_evaluate():
2: 1,
3: 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 (%)': 25.0,
'Condition positive': 3,
@@ -69,8 +69,8 @@ def test_evaluate():
'True negative': 1,
'True negative (%)': 25.0,
'True positive': 1,
'True positive (%)': 25.0},
'Fix zero': {'Accuracy': 0.75,
'True positive (%)': 25.0}},
'Fix zero': {0: {'Accuracy': 0.75,
'Condition negative': 3,
'Condition negative (%)': 75.0,
'Condition positive': 1,