evaluate toxic task head by mltsk pipe

toxic_task_callback.py

class AucCallback(Callback):
    def __init__(self, dataset, call_model=None, savepath=None, name="AUC"):

        self.call_model = call_model
        self.dataset = dataset
        self.best = 0
        self.name = name
        self.savepath = savepath
        super(AucCallback, self).__init__()

    def on_epoch_end(self, epoch, logs=None):
        
        if logs is None:
            logs = {}
            
        m_pr = self.call_model.predict(self.dataset[0], batch_size=64)
        coef = roc_auc_score(self.dataset[1], m_pr)
        if coef > self.best:
            self.best = coef
            print("*** New best: {} = {}".format(self.name, coef))
            if self.savepath:
                self.call_model.save_weights(self.savepath)
        else:
            print("{} = {}".format(self.name, coef))
            
        logs['val_'+self.name] = coef

    def on_train_begin(self, logs=None):
        self.on_epoch_end(None)