creating checkpointdir in fit
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@ -49,7 +49,6 @@ class QuaNetTrainer(BaseQuantifier):
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}
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self.patience = patience
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os.makedirs(checkpointdir, exist_ok=True)
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if checkpointname is None:
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local_random = random.Random()
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random_code = '-'.join(str(local_random.randint(0, 1000000)) for _ in range(5))
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@ -74,6 +73,7 @@ class QuaNetTrainer(BaseQuantifier):
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print('Classifier data: ', len(classifier_data))
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print('Q-Training data: ', len(train_data))
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print('Q-Valid data: ', len(valid_data))
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os.makedirs(self.checkpointdir, exist_ok=True)
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# estimate the hard and soft stats tpr and fpr of the classifier
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self.tr_prev = data.prevalence()
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