def subsample_classes()

in datasets/oxford_pets.py [0:0]

• 32 lines of code
• 8 McCabe index (conditional complexity)

    def subsample_classes(*args, subsample="all"):
        """Divide classes into two groups. The first group
        represents base classes while the second group represents
        new classes.

        Args:
            args: a list of datasets, e.g. train, val and test.
            subsample (str): what classes to subsample.
        """
        assert subsample in ["all", "base", "new"]

        if subsample == "all":
            return args
        
        dataset = args[0]
        labels = set()
        for item in dataset:
            labels.add(item.label)
        labels = list(labels)
        labels.sort()
        n = len(labels)
        # Divide classes into two halves
        m = math.ceil(n / 2)

        print(f"SUBSAMPLE {subsample.upper()} CLASSES!")
        if subsample == "base":
            selected = labels[:m]  # take the first half
        else:
            selected = labels[m:]  # take the second half
        relabeler = {y: y_new for y_new, y in enumerate(selected)}
        
        output = []
        for dataset in args:
            dataset_new = []
            for item in dataset:
                if item.label not in selected:
                    continue
                item_new = Datum(
                    impath=item.impath,
                    label=relabeler[item.label],
                    classname=item.classname
                )
                dataset_new.append(item_new)
            output.append(dataset_new)
        
        return output