Source code for darts.dataprocessing.transformers.invertible_data_transformer
"""
Invertible Data Transformer Base Class
--------------------------------------
"""
from abc import abstractmethod
from typing import Any, List, Mapping, Optional, Sequence, Union
import numpy as np
from darts import TimeSeries
from darts.logging import get_logger, raise_log
from darts.utils import _build_tqdm_iterator, _parallel_apply
from .base_data_transformer import BaseDataTransformer
logger = get_logger(__name__)
[docs]class InvertibleDataTransformer(BaseDataTransformer):
def __init__(
self,
name: str = "InvertibleDataTransformer",
n_jobs: int = 1,
verbose: bool = False,
parallel_params: Union[bool, Sequence[str]] = False,
mask_components: bool = True,
):
"""Abstract class for invertible transformers.
All the deriving classes have to implement the static methods :func:`ts_transform()` and
:func:`ts_inverse_transform()`. For information on how to implement the :func:`ts_transform` method,
please refer to the :class:`.BaseDataTransformer` documentation.
The :func:`ts_inverse_transform()` method should be implemented in a virtually identical way to the
:func:`ts_transform()` method: it should accept a ``TimeSeries`` as a first argument, and a dictionary
of fixed parameters (as well as fitted parameters if the transformation also inherits from
`FittableDataTransformer`) as a second argument. Additionally, :func:`ts_inverse_transform()` should
also accept `*args` and `**kwargs` if additional positional/keyword arguments are expected to be
passed. The only difference between :func:`ts_inverse_transform()` and :func:`ts_transform()`
is that the former should 'undo' the transformation made to a `TimeSeries` by the latter. Please
refer to the :func:`ts_inverse_transform()` documentation for further information.
This class takes care of parallelizing the transformation on multiple ``TimeSeries`` when possible.
Parameters
----------
name
The data transformer's name
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
parallel_params
Optionally, specifies which fixed parameters (i.e. the attributes initialized in the child-most
class's `__init__`) take on different values for different parallel jobs. Fixed parameters specified
by `parallel_params` are assumed to be a `Sequence` of values that should be used for that parameter
in each parallel job; the length of this `Sequence` should equal the number of parallel jobs. If
`parallel_params=True`, every fixed parameter will take on a different value for each
parallel job. If `parallel_params=False`, every fixed parameter will take on the same value for
each parallel job. If `parallel_params` is a `Sequence` of fixed attribute names, only those
attribute names specified will take on different values between different parallel jobs.
mask_components
Optionally, whether to automatically apply any provided `component_mask`s to the
`TimeSeries` inputs passed to `transform`, `fit`, `inverse_transform`, or `fit_transform`.
If `True`, any specified `component_mask` will be applied to each input timeseries
before passing them to the called method; the masked components will also be automatically
'unmasked' in the returned `TimeSeries`. If `False`, then `component_mask` (if provided) will
be passed as a keyword argument, but won't automatically be applied to the input timeseries.
See `apply_component_mask` method of `BaseDataTransformer` for further details.
Notes
-----
Note: the :func:`ts_transform()` and :func:`ts_inverse_transform()` methods are designed to be
static methods instead of instance methods to allow an efficient parallelisation also when the
scaler instance is storing a non-negligible amount of data. Using instance methods would imply
copying the instance's data through multiple processes, which can easily introduce a bottleneck
and nullify parallelisation benefits.
Example
--------
>>> from darts.dataprocessing.transformers import InvertibleDataTransformer
>>> from darts.utils.timeseries_generation import linear_timeseries
>>>
>>> class SimpleTransform(InvertibleDataTransformer):
>>>
>>> def __init__(self, a):
>>> self._a = a
>>> super().__init__()
>>>
>>> @staticmethod
>>> def ts_transform(series, params, **kwargs):
>>> a = params['fixed']['_a']
>>> b = kwargs.pop('b')
>>> return a*series + b
>>>
>>> @staticmethod
>>> def ts_inverse_transform(series, params, **kwargs):
>>> a = params['fixed']['_a']
>>> b = kwargs.pop('b')
>>> return (series - b) / a
>>>
>>> series = linear_timeseries(length=5)
>>> print(series)
<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
array([[[0. ]],
[[0.25]],
[[0.5 ]],
[[0.75]],
[[1. ]]])
Coordinates:
* time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05
* component (component) object 'linear'
Dimensions without coordinates: sample
Attributes:
static_covariates: None
hierarchy: None
>>> transform = SimpleTransform(a=2)
>>> series = transform.transform(series, b=3)
>>> print(series)
<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
array([[[3. ]],
[[3.5]],
[[4. ]],
[[4.5]],
[[5. ]]])
Coordinates:
* time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05
* component (component) object 'linear'
Dimensions without coordinates: sample
Attributes:
static_covariates: None
hierarchy: None
>>> series = transform.inverse_transform(series, b=3)
>>> print(series)
<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
array([[[0. ]],
[[0.25]],
[[0.5 ]],
[[0.75]],
[[1. ]]])
Coordinates:
* time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05
* component (component) object 'linear'
Dimensions without coordinates: sample
Attributes:
static_covariates: None
hierarchy: None
"""
super().__init__(
name=name,
n_jobs=n_jobs,
verbose=verbose,
parallel_params=parallel_params,
mask_components=mask_components,
)
[docs] @staticmethod
@abstractmethod
def ts_inverse_transform(
series: TimeSeries, params: Mapping[str, Any]
) -> TimeSeries:
"""The function that will be applied to each series when :func:`inverse_transform` is called.
The function must take as first argument a ``TimeSeries`` object and, as a second argument, a
dictionary containing the fixed and/or fitted parameters of the transformation; this function
should then return an inverse transformed ``TimeSeries`` object (i.e. `ts_inverse_transform` should
'undo' the transformation performed by `ts_transform`).
The `params` dictionary *can* contain up to two keys:
1. `params['fixed']` stores the fixed parameters of the transformation (i.e. attributed
defined in the `__init__` method of the child-most class *before* `super().__init__` is called);
`params['fixed']` is a dictionary itself, whose keys are the names of the fixed parameter
attributes. For example, if `_my_fixed_param` is defined as an attribute in the child-most
class, then this fixed parameter value can be accessed through `params['fixed']['_my_fixed_param']`.
2. If the transform inherits from the :class:`.FittableDataTransformer` class, then `params['fitted']`
will store the fitted parameters of the transformation; the fitted parameters are simply the output(s)
returned by the `ts_fit` function, whatever those output(s) may be. See :class:`.FittableDataTransformer`
for further details about fitted parameters.
Any positional/keyword argument supplied to the `transform` method are passed as positional/keyword arguments
to `ts_inverse_transform`; hence, `ts_inverse_transform` should also accept `*args` and/or `**kwargs` if
positional/keyword arguments are passed to `transform`. Note that if the `mask_components` attribute of
`InvertibleDataTransformer` is set to `False`, then the `component_mask` provided to `transform` will be passed
as an additional keyword argument to `ts_inverse_transform`.
The `BaseDataTransformer` class, from which `InvertibleDataTransformer` inherits, includes some helper methods
which may prove useful when implementing a `ts_inverse_transform` function:
1. The `apply_component_mask` and `unapply_component_mask` methods, which apply and 'unapply'
`component_mask`s to a `TimeSeries` respectively; these methods are automatically called in `transform` if
the `mask_component` attribute of `InvertibleDataTransformer` is set to `True`, but you may want to manually
call them if you set `mask_components` to `False` and wish to manually specify how `component_mask`s are
applied to a `TimeSeries`.
2. The `stack_samples` method, which stacks all the samples in a `TimeSeries` along
the component axis, so that the `TimeSeries` goes from shape `(n_timesteps, n_components, n_samples)` to
shape `(n_timesteps, n_components * n_samples)`. This stacking is useful if a pointwise inverse transform
is being implemented (i.e. transforming the value at time `t` depends only on the value of the series at
that time `t`). Once transformed, the stacked `TimeSeries` can be 'unstacked' using the `unstack_samples`
method.
This method is not implemented in the base class and must be implemented in the deriving classes.
Parameters
----------
series
series to be transformed.
params
Dictionary containing the parameters of the transformation function. Fixed parameters
(i.e. attributes defined in the child-most class of the transformation prior to
calling `super.__init__()`) are stored under the `'fixed'` key. If the transformation
inherits from the `FittableDataTransformer` class, then the fitted parameters of the
transformation (i.e. the values returned by `ts_fit`) are stored under the
`'fitted'` key.
args
Any additional keyword arguments provided to `inverse_transform`.
kwargs
Any additional keyword arguments provided to `inverse_transform`. Note that if the `mask_component`
attribute of `InvertibleDataTransformer` is set to `False`, then `component_mask` will
be passed as a keyword argument.
Notes
-----
This method is designed to be a static method instead of instance methods to allow an efficient
parallelisation also when the scaler instance is storing a non-negligible amount of data. Using instance
methods would imply copying the instance's data through multiple processes, which can easily introduce a
bottleneck and nullify parallelisation benefits.
"""
pass
[docs] def inverse_transform(
self,
series: Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]],
*args,
component_mask: Optional[np.array] = None,
**kwargs,
) -> Union[TimeSeries, List[TimeSeries], List[List[TimeSeries]]]:
"""Inverse transforms a (sequence of) series by calling the user-implemented `ts_inverse_transform` method.
In case a sequence or list of lists is passed as input data, this function takes care of parallelising the
transformation of multiple series in the sequence at the same time. Additionally,
if the `mask_components` attribute was set to `True` when instantiating `InvertibleDataTransformer`,
then any provided `component_mask`s will be automatically applied to each input `TimeSeries`;
please refer to 'Notes' for further details on component masking.
Any additionally specified `*args` and `**kwargs` are automatically passed to `ts_inverse_transform`.
Parameters
----------
series
The series to inverse-transform.
If a single `TimeSeries`, returns a single series.
If a sequence of `TimeSeries`, returns a list of series. The series should be in the same order as the
sequence used to fit the transformer.
If a list of lists of `TimeSeries`, returns a list of lists of series. This can for example be the output
of `ForecastingModel.historical_forecasts()` when using multiple series. Each inner list should contain
`TimeSeries` related to the same series. The order of inner lists should be the same as the sequence used
to fit the transformer.
args
Additional positional arguments for the :func:`ts_inverse_transform()` method
component_mask : Optional[np.ndarray] = None
Optionally, a 1-D boolean np.ndarray of length ``series.n_components`` that specifies
which components of the underlying `series` the inverse transform should consider.
kwargs
Additional keyword arguments for the :func:`ts_inverse_transform()` method
Returns
-------
Union[TimeSeries, List[TimeSeries], List[List[TimeSeries]]]
Inverse transformed data.
Notes
-----
If the `mask_components` attribute was set to `True` when instantiating `InvertibleDataTransformer`,
then any provided `component_mask`s will be automatically applied to each `TimeSeries` input to
transform; `component_mask`s are simply boolean arrays of shape `(series.n_components,)` that
specify which components of each `series` should be transformed using `ts_inverse_transform` and which
components should not. If `component_mask[i]` is `True`, then the `i`th component of each
`series` will be transformed by `ts_inverse_transform`. Conversely, if `component_mask[i]` is `False`,
the `i`th component will be removed from each `series` before being passed to `ts_inverse_transform`;
after transforming this masked series, the untransformed `i`th component will be 'added back'
to the output. Note that automatic `component_mask`ing can only be performed if the `ts_inverse_transform`
does *not* change the number of timesteps in each series; if this were to happen, then the transformed
and untransformed components are unable to be concatenated back together along the component axis.
If `mask_components` was set to `False` when instantiating `InvertibleDataTransformer`, then any provided
`component_masks` will be passed as a keyword argument `ts_inverse_transform`; the user can then manually
specify how the `component_mask` should be applied to each series.
"""
if hasattr(self, "_fit_called") and not self._fit_called:
raise_log(
ValueError("fit() must have been called before inverse_transform()"),
logger=logger,
)
desc = f"Inverse ({self._name})"
# Take note of original input for unmasking purposes:
called_with_single_series = False
called_with_sequence_series = False
if isinstance(series, TimeSeries):
input_series = [series]
data = [series]
transformer_selector = [0]
called_with_single_series = True
elif isinstance(series[0], TimeSeries): # Sequence[TimeSeries]
input_series = series
data = series
transformer_selector = range(len(series))
called_with_sequence_series = True
else: # Sequence[Sequence[TimeSeries]]
input_series = []
data = []
transformer_selector = []
for idx, series_list in enumerate(series):
input_series.extend(series_list)
data.extend(series_list)
transformer_selector += [idx] * len(series_list)
if self._mask_components:
data = [
self.apply_component_mask(ts, component_mask, return_ts=True)
for ts in data
]
else:
kwargs["component_mask"] = component_mask
input_iterator = _build_tqdm_iterator(
zip(data, self._get_params(transformer_selector=transformer_selector)),
verbose=self._verbose,
desc=desc,
total=len(transformer_selector),
)
transformed_data = _parallel_apply(
input_iterator,
self.__class__.ts_inverse_transform,
self._n_jobs,
args,
kwargs,
)
if self._mask_components:
unmasked = []
for ts, transformed_ts in zip(input_series, transformed_data):
unmasked.append(
self.unapply_component_mask(ts, transformed_ts, component_mask)
)
transformed_data = unmasked
if called_with_single_series:
return transformed_data[0]
elif called_with_sequence_series:
return transformed_data
else:
cum_len = np.cumsum([0] + [len(s_) for s_ in series])
return [
transformed_data[cum_len[i] : cum_len[i + 1]]
for i in range(len(cum_len) - 1)
]