ó áp7]c@sFddlZddlmZddlmZdefd„ƒYZdS(iÿÿÿÿN(tmad(tminimize_scalartBoxCoxcBsdeZdZddd„Zdd„Zddd„Zdd id d 6d „Zid d 6d „ZRS(s< Mixin class to allow for a Box-Cox transformation. tguerrerocKs¢tj|ƒ}tj|dkƒr3tdƒ‚n|dkrZ|j|d||}ntj|dƒr~tj|ƒ}ntj||ƒd|}||fS(s„ 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. isNon-positive x.tmethodggð?N( tnptasarraytanyt ValueErrortNonet _est_lambdatisclosetlogtpower(tselftxtlmbdaRtkwargsty((s9lib/python2.7/site-packages/statsmodels/base/transform.pyttransform_boxcox s)   tnaivecCs…|jƒ}tj|ƒ}|dkrltj|dƒrKtj|ƒ}qtj||dd|ƒ}ntdj|ƒƒ‚|S(s 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. Rgigð?sMethod '{0}' not understood.(tlowerRRR texpR Rtformat(RRRRR((s9lib/python2.7/site-packages/statsmodels/base/transform.pytuntransform_boxcoxFs  !iÿÿÿÿicKsÆ|jƒ}t|ƒdkr<tdjt|ƒƒƒ‚n#|d|dkr_tdƒ‚n|dkr†|j|d||}n<|dkr­|j|d||}ntd j|ƒƒ‚|S( sÎ 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. is$Bounds of length {0} not understood.iis Lower bound exceeds upper bound.RtboundstlogliksMethod '{0}' not understood.(RtlenRRt _guerrero_cvt_loglik_boxcox(RRRRRR((s9lib/python2.7/site-packages/statsmodels/base/transform.pyR ks    itsditmaxiterc sõt|ƒ}t||ƒ}tj|||||!||fƒ}tj|dƒ‰|jƒ}|dkrˆtj|dddƒ‰n6|dkr©t|ddƒ‰ntdj |ƒƒ‚‡‡fd†} t | d|d d d |ƒ} | j S( sß Computes lambda using guerrero's coefficient of variation. If no seasonality is present in the data, window_length is set to 4 (as per Guerrero and Perera, (2004)). NOTE: Seasonality-specific auxiliaries *should* provide their own seasonality parameter. Parameters ---------- x : array_like bounds : tuple Numeric 2-tuple, that indicate the solution space for the lambda parameter. window_length : int Seasonality/grouping parameter. Default 4, as per Guerrero and Perera (2004). NOTE: this indicates the length of the individual groups, not the total number of groups! scale : {'sd', 'mad'} The dispersion measure to be used. 'sd' indicates the sample standard deviation, but the more robust 'mad' is also available. options : dict The options (as a dict) to be passed to the optimizer. iRtddofRtaxissScale '{0}' not understood.csBtjˆtjˆd|ƒƒ}tj|ddƒtj|ƒS(NiR (RtdivideR tstdtmean(Rtrat(t dispersionR$(s9lib/python2.7/site-packages/statsmodels/base/transform.pytoptimÁs"RRtboundedtoptions( RtintRtreshapeR$RR#RRRRR( RRRt window_lengthtscaleR)tnobstgroupst grouped_dataR'tres((R&R$s9lib/python2.7/site-packages/statsmodels/base/transform.pyR—s"      csatjtjˆƒƒ‰tˆƒ‰‡‡‡‡fd†}t|d|ddd|ƒ}|jS(s~ 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. csBˆjˆ|ƒ\}}d|ˆˆdtjtj|ƒƒS(Nig@(RRR tvar(RR(R.Rtsum_xR(s9lib/python2.7/site-packages/statsmodels/base/transform.pyR'ÚsRRR(R)(RtsumR RRR(RRRR)R'R1((R.RR3Rs9lib/python2.7/site-packages/statsmodels/base/transform.pyRËs    N(iÿÿÿÿi( t__name__t __module__t__doc__R RRR RR(((s9lib/python2.7/site-packages/statsmodels/base/transform.pyRs; %,3(tnumpyRtstatsmodels.robustRtscipy.optimizeRtobjectR(((s9lib/python2.7/site-packages/statsmodels/base/transform.pyts