def test_normalize_unnormalize_without_dataset(self, stats):
result_normalized, _ = MainTransformer.normalize(None, {},
inplace=False)
result_unnormalize = MainTransformer.unnormalize(None, {},
inplace=False)
assert result_normalized is None and result_unnormalize is None
def test_normalize_and_unnormalize(self, stats):
copied_dataset = pandas.DataFrame({
'TestColumn': [1.0, 2.0, 3.0],
'TestColumn2': [5.0, 6.0, 7.0]
})
unnormalized_dataset = copied_dataset.copy()
normalized, statistics = MainTransformer.normalize(copied_dataset,
inplace=False,
stats=stats)
if stats is None:
mean_one = np.mean(copied_dataset['TestColumn'])
std_one = np.std(copied_dataset['TestColumn'])
mean_two = np.mean(copied_dataset['TestColumn2'])
std_two = np.std(copied_dataset['TestColumn2'])
else:
mean_one = stats['TestColumn']['mean']
std_one = stats['TestColumn']['std']
mean_two = stats['TestColumn2']['mean']
std_two = stats['TestColumn2']['std']
copied_dataset['TestColumn'] -= mean_one
copied_dataset['TestColumn2'] -= mean_two
copied_dataset['TestColumn'] /= std_one
copied_dataset['TestColumn2'] /= std_two
manual_stats = {
'TestColumn': {
'mean': mean_one,
'std': std_one
},
'TestColumn2': {
'mean': mean_two,
'std': std_two
}
}
assert copied_dataset.equals(normalized)
assert statistics == manual_stats
MainTransformer.unnormalize(normalized, statistics, inplace=True)
print(unnormalized_dataset)
print(normalized)
assert unnormalized_dataset.equals(normalized)