mappers

Data transformer to apply a custom function and its inverse to a (sequence of) TimeSeries (similar to calling anomx.data.timeseries.TimeSeries.map on each series).

Parameters

fn

Either a function which takes a value and returns a value ie. f(x) = y Or a function which takes a value and its timestamp and returns a value ie. f(timestamp, x) = y.

inverse_fn

Similarly to fn, either a function which takes a value and returns a value ie. f(x) = y Or a function which takes a value and its timestamp and returns a value ie. f(timestamp, x) = y. inverse_fn should be such that inverse_fn(fn(x)) == x.

name

A specific name for the transformer.

n_jobs

The number of jobs to run in parallel. Parallel jobs are created only when a Sequence[TimeSeries] is passed as input to a method, parallelising operations regarding different TimeSeries. Defaults to 1 (sequential). Setting the parameter to -1 means using all the available processors. Note: for a small amount of data, the parallelisation overhead could end up increasing the total required amount of time.

verbose

Optionally, whether to print operations progress

Examples

>>> import numpy as np
>>> from anomx.data.timeseries import TimeSeries
>>> from anomx.data.processing.transformers import InvertibleMapper
>>> series = TimeSeries.from_values(np.array([1, 10, 100]))
>>> transformer = InvertibleMapper(np.log10, lambda x: 10 ** x)
>>> series_transformed = transformer.transform(series)
>>> print(series_transformed.values())
[[0.]
 [1.]
 [2.]]
 [3.]]
>>> series_restored = transformer.inverse_transform(series_transformed)
>>> print(series_restored.values())
[[  1.]
 [ 10.]
 [100.]]

Data transformer to apply a custom function to a (sequence of) TimeSeries (similar to calling anomx.data.timeseries.TimeSeries.map on each series).

The mapper takes care of parallelizing the operations on multiple series over multiple processors.

Parameters

fn

Either a function which takes a value and returns a value ie. f(x) = y Or a function which takes a value and its timestamp and returns a value ie. f(timestamp, x) = y.

name

A specific name for the transformer.

n_jobs

The number of jobs to run in parallel. Parallel jobs are created only when a Sequence[TimeSeries] is passed as input to a method, parallelising operations regarding different TimeSeries. Defaults to 1 (sequential). Setting the parameter to -1 means using all the available processors. Note: for a small amount of data, the parallelisation overhead could end up increasing the total required amount of time.

verbose

Optionally, whether to print operations progress

columns

Optionally, a string or list of strings specifying the names of the components (columns) to transform. If specified, only these components will be transformed, and the remaining components will be kept untouched. For more information refer to the BaseDataTransformer documentation. In case the transformer is applied on multiple TimeSeries, it is expected that all series have the same column order.

Examples

>>> import numpy as np
>>> from anomx.data.timeseries import TimeSeries
>>> from anomx.data.processing.transformers import Mapper
>>> series = TimeSeries.from_values(np.array([1, 10, 100]))
>>> transformer = Mapper(np.log10)
>>> series_transformed = transformer.transform(series)
>>> print(series_transformed.values())
[[0.]
 [1.]
 [2.]]