Dj 8ddlZddlmZddlmZGddZy)N)mad)minimize_scalarcHeZdZdZd dZd dZddZdddd ifd Zdd ifd Zy)BoxCoxz< Mixin class to allow for a Box-Cox transformation. Nc >tj|}tj|dkr td||j|fd|i|}tj |drtj |}||fStj||dz |z }||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.method?)npasarrayany ValueError _est_lambdaiscloselogpower)selfxlmbdarkwargsys ^C:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\statsmodels/base/transform.pytransform_boxcoxzBoxCox.transform_boxcox sR JJqM 66!q&>./ / =$D$$Q/,2/'-/E ::eR q A%x!U#b(E1A%xc|j}tj|}|dk(rNtj|drtj|}|Stj ||zdzd|z }|St d|d)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. naiver r Method '' not understood.)lowerr r rexprr)rrrrrs runtransform_boxcoxzBoxCox.untransform_boxcoxFs0 JJqM W zz%$FF1I  HHUQY]BJ7xx/@AB Brc >|j}t|dk7r#tdjt||d|dk\r td|dk(r|j|fd|i|}|S|dk(r|j |fd|i|}|Std |d ) a Computes an estimate for the lambda parameter in the Box-Cox transformation using method. Parameters ---------- x : array_like The untransformed data. bounds : tuple Numeric 2-tuple, that indicate the solution space for the lambda parameter. Default (-1, 2). method : {'guerrero', 'loglik'} The method by which to estimate lambda. Defaults to 'guerrero', but the profile likelihood ('loglik') is also available. **kwargs Options for the specified method. * For 'guerrero': window_length (int), the seasonality/grouping parameter. Scale ({'mad', 'sd'}), the dispersion measure. Options (dict), to be passed to the optimizer. * For 'loglik': Options (dict), to be passed to the optimizer. Returns ------- lmbda : float The lambda parameter. z#Bounds of length {} not understood.rrz Lower bound exceeds upper bound.guerreroboundsloglikrr)r lenrformat _guerrero_cv_loglik_boxcox)rrr&rrrs rrzBoxCox._est_lambdaks6 v;! B$fS[13 3 AY&) #?@ @ Z %D%%aAA&AE  x 'D''C&CFCE xx/@AB Brsdmaxiterc t|}t||z }tj||||zz |||f}tj|d |j }|dk(rtj |dd n"|dk(rt|d ntd|d fd} t| |d | } | jS) 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-ddofr)axiszScale 'rctjtjd|z }tj|dtj|z S)Nrr1)r dividerstdmean)rrat dispersionr7s roptimz"BoxCox._guerrero_cv..optims@))Jq5y(ABC66#A&5 5rboundedr&roptions) r(intr reshaper7r r6rrrr) rrr& window_lengthscaler=nobsgroups grouped_datar:resr9r7s @@rr*zBoxCox._guerrero_cvs41vTM)*zz!DF],B$CT"J#)="9; ww|Q'  D= aa8J e^\2Jwug->?@ @ 6e%+%.&-/uu rctjtjtfd}t ||d|}|j S)a~ Taken from the Stata manual on Box-Cox regressions, where this is the special case of 'lhs only'. As an estimator for the variance, the sample variance is used, by means of the well-known formula. Parameters ---------- x : array_like options : dict The options (as a dict) to be passed to the optimizer. cj|\}}d|z zdz tjtj|zzS)Nrg@)rr rvar)rrrBrsum_xrs rr:z$BoxCox._loglik_boxcox..optimsG,,Q6HAuI&$)rvvbffQi7H)HH Hrr;r<)r sumrr(rr)rrr&r=r:rErBrIs`` @@rr+zBoxCox._loglik_boxcoxsLrvvay!1v Ie%+%.&-/uu r)Nr%)r))r$r%) __name__ __module__ __qualname____doc__rr"rr*r+rrrrs;9v#J*X56T'_2h2;Brr)numpyr statsmodels.robustrscipy.optimizerrrPrrrTs"*\\r