Source code for darts.dataprocessing.transformers.missing_values_filler
"""
Missing Values Filler
---------------------
"""
from collections.abc import Mapping
from typing import Any
from darts import TimeSeries
from darts.dataprocessing.transformers.base_data_transformer import BaseDataTransformer
from darts.logging import get_logger, raise_if, raise_if_not
from darts.utils.missing_values import fill_missing_values
logger = get_logger(__name__)
[docs]
class MissingValuesFiller(BaseDataTransformer):
def __init__(
self,
fill: str | float = "auto",
name: str = "MissingValuesFiller",
n_jobs: int = 1,
verbose: bool = False,
columns: str | list[str] | None = None,
):
"""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
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.
Examples
--------
>>> import numpy as np
>>> from darts import TimeSeries
>>> from darts.dataprocessing.transformers import MissingValuesFiller
>>> values = np.arange(start=0, stop=1, step=0.25)
>>> values[1:3] = np.nan
>>> series = TimeSeries.from_values(values)
>>> transformer = MissingValuesFiller()
>>> series_filled = transformer.transform(series)
>>> print(series_filled.values())
[[0. ]
[0.25]
[0.5 ]
[0.75]]
"""
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, columns=columns)
