Source code for darts.dataprocessing.transformers.boxcox

Box-Cox Transformer

from typing import Any, Mapping, Optional, Sequence, Union

    from typing import Literal
except ImportError:
    from typing_extensions import Literal

import numpy as np
import pandas as pd
from scipy.special import inv_boxcox
from scipy.stats import boxcox, boxcox_normmax

from darts.logging import get_logger, raise_if
from darts.timeseries import TimeSeries

from .fittable_data_transformer import FittableDataTransformer
from .invertible_data_transformer import InvertibleDataTransformer

logger = get_logger(__name__)

[docs]class BoxCox(FittableDataTransformer, InvertibleDataTransformer): def __init__( self, name: str = "BoxCox", lmbda: Optional[ Union[float, Sequence[float], Sequence[Sequence[float]]] ] = None, optim_method: Literal["mle", "pearsonr"] = "mle", global_fit: bool = False, n_jobs: int = 1, verbose: bool = False, ): """Box-Cox data transformer. See [1]_ for more information about Box-Cox transforms. The transformation is applied independently for each dimension (component) of the time series. For stochastic series, it is done jointly over all samples, effectively merging all samples of a component in order to compute the transform. Notes ----- The scaler will not scale the series' static covariates. This has to be done either before constructing the series, or later on by extracting the covariates, transforming the values and then reapplying them to the series. For this, see TimeSeries properties `TimeSeries.static_covariates` and method `TimeSeries.with_static_covariates()` Parameters ---------- name A specific name for the transformer lmbda The parameter :math:`\\lambda` of the Box-Cox transform. If a single float is given, the same :math:`\\lambda` value will be used for all dimensions of the series, for all the series. If a sequence is given, there is one value per component in the series. If a sequence of sequence is given, there is one value per component for all series. If `None` given, will automatically find an optimal value of :math:`\\lambda` (for each dimension of the time series, for each time series) using :func:`scipy.stats.boxcox_normmax` with ``method=optim_method``. optim_method Specifies which method to use to find an optimal value for the lmbda parameter. Either 'mle' or 'pearsonr'. Ignored if `lmbda` is not `None`. global_fit Optionally, whether all `TimeSeries` passed to the `fit()` method should be used to fit a *single* set of parameters, or if a different set of parameters should be independently fitted to each provided `TimeSeries`. If `True`, then a `Sequence[TimeSeries]` is passed to `ts_fit` and a single set of parameters is fitted using all provided `TimeSeries`. If `False`, then each `TimeSeries` is individually passed to `ts_fit`, and a different set of fitted parameters if yielded for each of these fitting operations. See `FittableDataTransformer` documentation for further details. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, 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 Whether to print operations progress Examples -------- >>> from darts.datasets import AirPassengersDataset >>> from darts.dataprocessing.transformers import BoxCox >>> series = AirPassengersDataset().load() >>> transformer = BoxCox(lmbda=0.2) >>> series_transformed = transformer.fit_transform(series) >>> print(series_transformed.head()) <TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)> array([[[7.84735157]], [[7.98214351]], [[8.2765364 ]], [[8.21563229]], [[8.04749318]]]) Coordinates: * Month (Month) datetime64[ns] 1949-01-01 1949-02-01 ... 1949-05-01 * component (component) object '#Passengers' Dimensions without coordinates: sample References ---------- .. [1] """ raise_if( not isinstance(optim_method, str) or optim_method not in ["mle", "pearsonr"], "optim_method parameter must be either 'mle' or 'pearsonr'", logger, ) # Define fixed params (i.e. attributes defined before calling `super().__init__`): self._lmbda = lmbda self._optim_method = optim_method if isinstance(lmbda, Sequence) and isinstance(lmbda[0], Sequence): parallel_params = ("_lmbda",) else: parallel_params = False super().__init__( name=name, n_jobs=n_jobs, verbose=verbose, parallel_params=parallel_params, mask_components=True, global_fit=global_fit, )
[docs] @staticmethod def ts_fit( series: Union[TimeSeries, Sequence[TimeSeries]], params: Mapping[str, Any], *args, **kwargs ) -> Union[Sequence[float], pd.Series]: lmbda, method = params["fixed"]["_lmbda"], params["fixed"]["_optim_method"] # If `global_fit` is `True`, then `series` will be ` Sequence[TimeSeries]`; # otherwise, `series` is a single `TimeSeries`: if isinstance(series, TimeSeries): series = [series] if lmbda is None: # Compute optimal lmbda for each dimension of the time series. In this case, the return type is # an ndarray and not a Sequence vals = np.concatenate([BoxCox.stack_samples(ts) for ts in series], axis=0) lmbda = np.apply_along_axis(boxcox_normmax, axis=0, arr=vals, method=method) elif isinstance(lmbda, Sequence): raise_if( len(lmbda) != series[0].width, "lmbda should have one value per dimension (ie. column or variable) of the time series", logger, ) else: # Replicate lmbda to match dimensions of the time series lmbda = [lmbda] * series[0].width return lmbda
[docs] @staticmethod def ts_transform( series: TimeSeries, params: Mapping[str, Any], **kwargs ) -> TimeSeries: lmbda = params["fitted"] vals = BoxCox.stack_samples(series) transformed_vals = np.stack( [boxcox(vals[:, i], lmbda=lmbda[i]) for i in range(series.width)], axis=1 ) transformed_vals = BoxCox.unstack_samples(transformed_vals, series=series) return series.with_values(transformed_vals)
[docs] @staticmethod def ts_inverse_transform( series: TimeSeries, params: Mapping[str, Any], **kwargs ) -> TimeSeries: lmbda = params["fitted"] vals = BoxCox.stack_samples(series) inv_transformed_vals = np.stack( [inv_boxcox(vals[:, i], lmbda[i]) for i in range(series.width)], axis=1 ) inv_transformed_vals = BoxCox.unstack_samples( inv_transformed_vals, series=series ) return series.with_values(inv_transformed_vals)