noether.data.pipeline.sample_processors.moment_normalization¶

Classes¶

MomentNormalizationSampleProcessor

Normalizes a value with its mean and standard deviation (i.e., its moments).

Module Contents¶

class noether.data.pipeline.sample_processors.moment_normalization.MomentNormalizationSampleProcessor(item, mean=None, std=None, logmean=None, logstd=None, logscale=False)¶

Bases: noether.data.pipeline.sample_processor.SampleProcessor

Normalizes a value with its mean and standard deviation (i.e., its moments).

# dummy example
processor = MomentNormalizationSampleProcessor(
    item="measurement",
    mean=[10.0],
    std=[2.0],
    logscale=False,
)
input_sample = {
    "measurement": torch.tensor([[12.0], [14.0], [8.0]]),
    "other_item": torch.tensor([[1.0], [2.0], [3.0]]),
}
output_sample = processor(input_sample)
# output_sample['measurement'] will be tensor([[1.0], [2.0], [-1.0]])
# output_sample['other_item'] will be unchanged.

Parameters:

item (str) – The item (i.e., key in the input sample dictionary) to normalize.
mean (collections.abc.Sequence[float] | None) – The mean of the value. Mandatory if logscale=False.
std (collections.abc.Sequence[float] | None) – The standard deviation of the value. Mandatory if logscale=False.
logmean (collections.abc.Sequence[float] | None) – The mean of the value in logscale. Mandatory if logscale=True.
logstd (collections.abc.Sequence[float] | None) – The standard deviation of the value in logscale. Mandatory if logscale=True.
logscale (bool) – Whether to convert the value to logscale before normalization.

item¶

mean_tensor = None¶

std_tensor = None¶

logmean_tensor = None¶

logstd_tensor = None¶

logscale = False¶

inverse(key, value)¶

Inverts the normalization from the __call__ method of a single item in the batch.

Parameters:

key (str) – The name of the item.
value (torch.Tensor) – The value of the item.

Returns:

The same name and the denormalized value.

Return type:

(key, value)