U md- @s2ddlZddlmZddlmZGdddZdS)N)mad)minimize_scalarc@sReZdZdZdddZdddZdd d Zd d ddifddZddifddZdS)BoxCoxz< Mixin class to allow for a Box-Cox transformation. NguerrerocKsrt|}t|dkr td|dkr>|j|fd|i|}t|drVt|}nt||d|}||fS)a Performs a Box-Cox transformation on the data array x. If lmbda is None, the indicated method is used to estimate a suitable lambda parameter. Parameters ---------- x : array_like lmbda : float The lambda parameter for the Box-Cox transform. If None, a value will be estimated by means of the specified method. method : {'guerrero', 'loglik'} The method to estimate the lambda parameter. Will only be used if lmbda is None, and defaults to 'guerrero', detailed in Guerrero (1993). 'loglik' maximizes the profile likelihood. **kwargs Options for the specified method. * For 'guerrero', this entails window_length, the grouping parameter, scale, the dispersion measure, and options, to be passed to the optimizer. * For 'loglik': options, to be passed to the optimizer. Returns ------- y : array_like The transformed series. lmbda : float The lmbda parameter used to transform the series. References ---------- Guerrero, Victor M. 1993. "Time-series analysis supported by power transformations". `Journal of Forecasting`. 12 (1): 37-48. Guerrero, Victor M. and Perera, Rafael. 2004. "Variance Stabilizing Power Transformation for Time Series," `Journal of Modern Applied Statistical Methods`. 3 (2): 357-369. Box, G. E. P., and D. R. Cox. 1964. "An Analysis of Transformations". `Journal of the Royal Statistical Society`. 26 (2): 211-252. rzNon-positive x.Nmethod?)npasarrayany ValueError _est_lambdaiscloselogpower)selfxlmbdarkwargsyrS/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/statsmodels/base/transform.pytransform_boxcox s)    zBoxCox.transform_boxcoxnaivecCs^|}t|}|dkrLt|dr2t|}qZt||dd|}ntd||S)a Back-transforms the Box-Cox transformed data array, by means of the indicated method. The provided argument lmbda should be the lambda parameter that was used to initially transform the data. Parameters ---------- x : array_like The transformed series. lmbda : float The lambda parameter that was used to transform the series. method : {'naive'} Indicates the method to be used in the untransformation. Defaults to 'naive', which reverses the transformation. NOTE: 'naive' is implemented natively, while other methods may be available in subclasses! Returns ------- y : array_like The untransformed series. rrrMethod '{0}' not understood.)lowerr r rexprr format)rrrrrrrruntransform_boxcoxFs   zBoxCox.untransform_boxcoxcKs|}t|dkr(tdt|n|d|dkr@td|dkr`|j|fd|i|}n.|dkr|j|fd|i|}ntd ||S) a Computes an estimate for the lambda parameter in the Box-Cox transformation using method. Parameters ---------- x : array_like The untransformed data. bounds : tuple Numeric 2-tuple, that indicate the solution space for the lambda parameter. Default (-1, 2). method : {'guerrero', 'loglik'} The method by which to estimate lambda. Defaults to 'guerrero', but the profile likelihood ('loglik') is also available. **kwargs Options for the specified method. * For 'guerrero': window_length (int), the seasonality/grouping parameter. Scale ({'mad', 'sd'}), the dispersion measure. Options (dict), to be passed to the optimizer. * For 'loglik': Options (dict), to be passed to the optimizer. Returns ------- lmbda : float The lambda parameter. r"z$Bounds of length {0} not understood.rrz Lower bound exceeds upper bound.rboundsZloglikr)rlenr r _guerrero_cv_loglik_boxcox)rrr#rrrrrrr ks zBoxCox._est_lambdasdmaxiterc st|}t||}t|||||||f}t|d|}|dkrbtj|dddn$|dkrxt|ddntd |fdd} t | |d |d } | j S) a Computes lambda using guerrero's coefficient of variation. If no seasonality is present in the data, window_length is set to 4 (as per Guerrero and Perera, (2004)). NOTE: Seasonality-specific auxiliaries *should* provide their own seasonality parameter. Parameters ---------- x : array_like bounds : tuple Numeric 2-tuple, that indicate the solution space for the lambda parameter. window_length : int Seasonality/grouping parameter. Default 4, as per Guerrero and Perera (2004). NOTE: this indicates the length of the individual groups, not the total number of groups! scale : {'sd', 'mad'} The dispersion measure to be used. 'sd' indicates the sample standard deviation, but the more robust 'mad' is also available. options : dict The options (as a dict) to be passed to the optimizer. rr(Zddofr)ZaxiszScale '{0}' not understood.cs0ttd|}tj|ddt|S)Nrr+)r dividerstdmean)rratZ dispersionr.rroptimsz"BoxCox._guerrero_cv..optimboundedr#roptions) r$intr Zreshaper.rr-rr rrr) rrr#Z window_lengthscaler4nobsgroupsZ grouped_datar1resrr0rr%s&  zBoxCox._guerrero_cvcs@tttfdd}t||d|d}|jS)a~ Taken from the Stata manual on Box-Cox regressions, where this is the special case of 'lhs only'. As an estimator for the variance, the sample variance is used, by means of the well-known formula. Parameters ---------- x : array_like options : dict The options (as a dict) to be passed to the optimizer. cs4|\}}d|dtt|S)Nrg@)rr rvar)rrr7rZsum_xrrrr1sz$BoxCox._loglik_boxcox..optimr2r3)r sumrr$rr)rrr#r4r1r9rr;rr&s zBoxCox._loglik_boxcox)Nr)r)r r) __name__ __module__ __qualname____doc__rrr r%r&rrrrrs ; % , 4r)numpyr Zstatsmodels.robustrZscipy.optimizerrrrrrs