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AdaptiveClassifier: improve logging

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Alinson S. Xavier
2020-04-16 16:34:18 -05:00
parent 4c152d60f7
commit 07aabd6897
1 changed files with 4 additions and 4 deletions
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8
src/python/miplearn/classifiers/adaptive.py
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@@ -49,17 +49,17 @@ class AdaptiveClassifier(Classifier):
self.classifier = None self.classifier = None
def fit(self, x_train, y_train): def fit(self, x_train, y_train):
best_clf = None best_name, best_clf, best_score = None, None, -float("inf")
best_score = -float("inf")
n_samples = x_train.shape[0] n_samples = x_train.shape[0]
for clf_dict in self.candidates.values(): for (name, clf_dict) in self.candidates.items():
if n_samples < clf_dict["min samples"]: if n_samples < clf_dict["min samples"]:
continue continue
clf = deepcopy(clf_dict["classifier"]) clf = deepcopy(clf_dict["classifier"])
clf.fit(x_train, y_train) clf.fit(x_train, y_train)
score = self.evaluator.evaluate(clf, x_train, y_train) score = self.evaluator.evaluate(clf, x_train, y_train)
if score > best_score: if score > best_score:
best_clf, best_score = clf, score best_name, best_clf, best_score = name, clf, score
logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
self.classifier = best_clf self.classifier = best_clf
def predict_proba(self, x_test): def predict_proba(self, x_test):