Source code for darts.dataprocessing.transformers.missing_values_filler

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)
[docs] @staticmethod def ts_transform( series: TimeSeries, params: Mapping[str, Any], **kwargs ) -> TimeSeries: return fill_missing_values(series, params["fixed"]["_fill"], **kwargs)