CausalDA/experiments.py at master · MLforHealth/CausalDA · GitHub

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import numpy as np
from itertools import product

def combinations(grid):
    return list(dict(zip(grid.keys(), values)) for values in product(*grid.values()))

def get_hparams(experiment):
    if experiment not in globals():
        raise NotImplementedError
    return globals()[experiment].hparams()


def get_script_name(experiment):
    if experiment not in globals():
        raise NotImplementedError
    return globals()[experiment].fname


#### write experiments here
class Camelyon:
    fname = 'train.py'

    @staticmethod
    def hparams():
        grid = {
           'type': ['back', 'front', 'back_front', 'label_flip'],
           'data': ['camelyon'],
           'data_type': ['Conf', 'Deconf', 'DA', 'IF'],
           'domains': ((2, 3),),
           'corr-coff': list(np.linspace(0.65, 0.95, 4)),
           'seed': list(range(5)),
        }

        return combinations(grid)

class CXR:
    fname = 'train.py'

    @staticmethod
    def hparams():
        grid = {
           'type': ['back', 'front', 'back_front', 'label_flip'],
           'data': ['CXR'],
           'data_type': ['Conf', 'Deconf', 'DA', 'IF'],
           'corr-coff': list(np.linspace(0.65, 0.95, 4)),
           'seed': list(range(5)),
           'samples': [6500],
           'use_pretrained': [True],
           'cache_cxr': [True]
        }

        return combinations(grid)


class Poverty:
    fname = 'train.py'

    @staticmethod
    def hparams():
        grid = {
           'type': ['back', 'front', 'back_front', 'label_flip'],
           'data': ['poverty'],
           'data_type': ['Conf', 'Deconf', 'DA', 'IF'],
           'corr-coff': list(np.linspace(0.65, 0.95, 4)),
           'seed': list(range(5)),
           'samples': [300],
           'domains': (('malawi', 'kenya', 'tanzania', 'nigeria'),),
        }

        return combinations(grid)

class Synthetic:
    fname = 'train_synthetic.py'
    @staticmethod
    def hparams():
        hps = []
        for corr_coff in list(np.linspace(0.65, 0.95, 4)):
            hps += combinations({
                'type': ['back', 'front', 'back_front', 'par_back_front', 'label_flip'],
                'corr-coff': [corr_coff],
                'test-corr': [corr_coff]
            })

        return hps

class EnvClf:
    fname = 'train_env.py'

    @staticmethod
    def hparams():
        grid = {
           'data': ['CXR', 'camelyon', 'poverty', 'NIH', 'MNIST', 'CelebA'],
           'seed': list(range(5)),
            'use_pretrained': [True]
        }

        return combinations(grid)