"""
Missing Values Filler
---------------------
"""
from typing import Any, Mapping, Union
from darts import TimeSeries
from darts.logging import get_logger, raise_if, raise_if_not
from darts.utils.missing_values import fill_missing_values
from .base_data_transformer import BaseDataTransformer
logger = get_logger(__name__)
[docs]class MissingValuesFiller(BaseDataTransformer):
def __init__(
self,
fill: Union[str, float] = "auto",
name: str = "MissingValuesFiller",
n_jobs: int = 1,
verbose: bool = False,
):
"""Data transformer to fill missing values from a (sequence of) deterministic ``TimeSeries``.
Parameters
----------
fill
The value used to replace the missing values.
If set to 'auto', will auto-fill missing values using the :func:`pd.Dataframe.interpolate()` method.
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 darts import TimeSeries
>>> from darts.dataprocessing.transformers import MissingValuesFiller
>>> values = np.arange(start=0, stop=1, step=0.1)
>>> values[5:8] = np.nan
>>> series = TimeSeries.from_values(values)
>>> transformer = MissingValuesFiller()
>>> series_filled = transformer.transform(series)
>>> print(series_filled)
<TimeSeries (DataArray) (time: 10, component: 1, sample: 1)>
array([[[0. ]],
[[0.1]],
[[0.2]],
[[0.3]],
[[0.4]],
[[0.5]],
[[0.6]],
[[0.7]],
[[0.8]],
[[0.9]]])
Coordinates:
* time (time) int64 0 1 2 3 4 5 6 7 8 9
* component (component) object '0'
Dimensions without coordinates: sample
"""
raise_if_not(
isinstance(fill, str) or isinstance(fill, float),
"`fill` should either be a string or a float",
logger,
)
raise_if(
isinstance(fill, str) and fill != "auto",
"invalid string for `fill`: can only be set to 'auto'",
logger,
)
# Define fixed params (i.e. attributes defined before calling `super().__init__`):
self._fill = fill
super().__init__(name=name, n_jobs=n_jobs, verbose=verbose)