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Simplify PrimalSolutionComponent; remove dynamic thresholds

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Alinson S. Xavier
2020-04-15 17:56:22 -05:00
parent 6b50b707a9
commit a39159158e
2 changed files with 30 additions and 66 deletions
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92
src/python/miplearn/components/primal.py
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@@ -23,18 +23,13 @@ class PrimalSolutionComponent(Component):
def __init__(self,
classifier=AdaptiveClassifier(),
mode="exact",
max_fpr=[1e-3, 1e-3],
min_threshold=[0.75, 0.75],
dynamic_thresholds=True,
threshold=0.50,
):
self.mode = mode
self.is_warm_start_available = False
self.max_fpr = max_fpr
self.min_threshold = min_threshold
self.thresholds = {}
self.classifiers = {}
self.threshold = threshold
self.classifier_prototype = classifier
self.dynamic_thresholds = dynamic_thresholds
def before_solve(self, solver, instance, model):
solution = self.predict(instance)
@@ -51,75 +46,46 @@ class PrimalSolutionComponent(Component):
features = VariableFeaturesExtractor().extract(training_instances)
solutions = SolutionExtractor().extract(training_instances)
def _fit(args):
category, label = args[0], args[1]
for category in features.keys():
x_train = features[category]
y_train = solutions[category]
y = y_train[:, label].astype(int)
for label in [0, 1]:
y_train = solutions[category][:, label].astype(int)
if isinstance(self.classifier_prototype, list):
clf = deepcopy(self.classifier_prototype[label])
else:
clf = deepcopy(self.classifier_prototype)
clf.fit(x_train, y)
# If all samples are either positive or negative, make constant predictions
y_avg = np.average(y_train)
if y_avg < 0.001 or y_avg >= 0.999:
self.classifiers[category, label] = round(y_avg)
continue
y_avg = np.average(y)
if (not self.dynamic_thresholds) or y_avg <= 0.001 or y_avg >= 0.999:
return {"classifier": clf,
"threshold": self.min_threshold[label]}
# Create a copy of classifier prototype and train it
if isinstance(self.classifier_prototype, list):
clf = deepcopy(self.classifier_prototype[label])
else:
clf = deepcopy(self.classifier_prototype)
clf.fit(x_train, y_train)
proba = clf.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))
y_scores = proba[:, 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
self.thresholds[category, label] = thresholds[k]
return {"classifier": clf,
"threshold": thresholds[k]}
items = [(category, label)
for category in features.keys()
for label in [0, 1]]
if n_jobs == 1:
results = list(map(_fit, tqdm(items, desc="Fit (primal)")))
else:
results = p_map(_fit, items, num_cpus=n_jobs)
for (idx, (category, label)) in enumerate(items):
self.thresholds[category, label] = results[idx]["threshold"]
self.classifiers[category, label] = results[idx]["classifier"]
self.classifiers[category, label] = clf
def predict(self, instance):
x_test = VariableFeaturesExtractor().extract([instance])
solution = {}
x_test = VariableFeaturesExtractor().extract([instance])
var_split = Extractor.split_variables(instance)
for category in var_split.keys():
for (i, (var, index)) in enumerate(var_split[category]):
if var not in solution.keys():
solution[var] = {}
solution[var][index] = None
for label in [0, 1]:
if (category, label) not in self.classifiers.keys():
continue
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]:
for label in [0, 1]:
if (category, label) not in self.classifiers.keys():
continue
clf = self.classifiers[category, label]
if isinstance(clf, float):
ws = np.array([[1-clf, clf]
for _ in range(len(var_split[category]))])
else:
ws = clf.predict_proba(x_test[category])
for (i, (var, index)) in enumerate(var_split[category]):
if ws[i, 1] >= self.threshold:
solution[var][index] = label
return solution

4
src/python/miplearn/components/tests/test_primal.py
View File

@@ -38,8 +38,7 @@ def test_evaluate():
[0., 1.], # x[2] instances[0]
[1., 0.], # x[3] instances[0]
]))
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one],
dynamic_thresholds=False)
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one])
comp.fit(instances[:1])
assert comp.predict(instances[0]) == {"x": {0: 0,
1: 0,
@@ -97,4 +96,3 @@ def test_primal_parallel_fit():
comp = PrimalSolutionComponent()
comp.fit(instances, n_jobs=2)
assert len(comp.classifiers) == 2
assert len(comp.thresholds) == 2