M/PhtdZddlZddlmZddlZGddZGddeZGdd eZ Gd d eZ dd Z ddZ ddZ ddZ ddZ ddZ ddZdS)zM Created on Fri Dec 19 11:29:18 2014 Author: Josef Perktold License: BSD-3 N)statscneZdZdZ ddZedZedZddZdd Z d d d Z dd Z dS)PredictionResultsBasez+Based class for get_prediction results Nc :||_||_||_||_||_||_|j|||dkrtj |_ d|_ dS|dkr tj |_ |jf|_ dS||_ d|_ dSNnormt) predictedvar_predfuncderivdf row_labels__dict__updaterrdist dist_argsr ) selfr r r rrrrkwdss f/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/base/_prediction_inference.py__init__zPredictionResultsBase.__init__s"    $ T""" <46>> DIDNNN S[[DI"gZDNNNDIDNNNc4tj|jSN)npsqrtr rs rsezPredictionResultsBase.se'swt}%%%rc |j|jz Sr)r rrs rtvalueszPredictionResultsBase.tvalues+s~''rr two-sidedc$|j|z |jz }|dvr.|jjt j|g|jRdz}nI|dvr|jj|g|jR}n,|dvr|jj|g|jR}ntd||fS)a0z- or t-test for hypothesis that mean is equal to value Parameters ---------- value : array_like value under the null hypothesis alternative : str 'two-sided', 'larger', 'smaller' Returns ------- stat : ndarray test statistic pvalue : ndarray p-value of the hypothesis test, the distribution is given by the attribute of the instance, specified in `__init__`. Default if not specified is the normal distribution. )r"z2-sided2s)largerl)smallerszinvalid alternative) r rrsfrabsrcdf ValueError)rvalue alternativestatpvalues rt_testzPredictionResultsBase.t_test/s*&$'1 8 8 8!TY\"&,,@@@@BFF O + +!TY\$8888FF , , ,"TY]49$.999FF233 3V|rc|d}|jjd|dz z g|R}|||zz }|||zz}tj||f}|Sz4internal function to avoid code duplication Nr @rppfr column_stack rcenterralpharqloweruppercis r_conf_int_genericz'PredictionResultsBase._conf_int_genericPf  I DIM!ebj. 59 5 5 5RR _eU^ , , r皙?r<c V||j|j||j}|S)aWConfidence interval for the predicted value. Parameters ---------- alpha : float, optional The significance level for the confidence interval. ie., The default `alpha` = .05 returns a 95% confidence interval. kwds : extra keyword arguments Ignored in base class, only for compatibility, consistent signature with subclasses Returns ------- ci : ndarray, (k_constraints, 2) The array has the lower and the upper limit of the confidence interval in the columns. r)rAr rr)rr<rr@s rconf_intzPredictionResultsBase.conf_int^s2( # #DNDGU.2n$>> rc||}i}|j|d<|j|d<|dddf|d<|dddf|d<||_t j||j| }|S) a\Summary frame Parameters ---------- alpha : float, optional The significance level for the confidence interval. ie., The default `alpha` = .05 returns a 95% confidence interval. Returns ------- pandas DataFrame with columns 'predicted', 'se', 'ci_lower', 'ci_upper' rDr rNrci_lowerr5ci_upperindexcolumns)rGr rtablepd DataFramerkeys)rr<r@ to_includeress r summary_framez#PredictionResultsBase.summary_framevs]]] ' ' "&. ;7 4!#AAAqD :!#AAAqD : l:T_#-??#4#4666 r)NNNNN)rr"rrC) __name__ __module__ __qualname____doc__rpropertyrr!r2rArGrTr rrrrs>B04    (&&X&((X(B    !%0rrc*eZdZ ddZddZd dZdS) PredictionResultsMonotonicNc "||_||_||_||_||_||_||_| |_||dkrtj |_ d|_ dS|dkr tj |_ |jf|_ dS||_ d|_ dSr) r r linpred linpred_ser rrrrrrrr ) rr r r^r_r rrrrs rrz#PredictionResultsMonotonic.__init__s#   $  $ <46>> DIDNNN S[[DI"gZDNNNDIDNNNrc|d}|jjd|dz z g|R}|||zz }|||zz}tj||f}|Sr4r7r:s rrAz,PredictionResultsMonotonic._conf_int_genericrBrendpointrCc ttjddd}|||k}|dkr@|s>||j|j||j}||}n0|dks|r(||j|j ||j}|S)UConfidence interval for the predicted value. This is currently only available for t and z tests. Parameters ---------- method : {"endpoint", "delta"} Method for confidence interval, "m If method is "endpoint", then the confidence interval of the linear predictor is transformed by the prediction function. If method is "delta", then the delta-method is used. The confidence interval in this case might reach outside the range of the prediction, for example probabilities larger than one or smaller than zero. alpha : float, optional The significance level for the confidence interval. ie., The default `alpha` = .05 returns a 95% confidence interval. kwds : extra keyword arguments currently ignored, only for compatibility, consistent signature Returns ------- ci : ndarray, (k_constraints, 2) The array has the lower and the upper limit of the confidence interval in the columns. rr5rarFdelta) rlinspacer allrAr^r_rr r)rmethodr<rtmp is_linear ci_linearr@s rrGz#PredictionResultsMonotonic.conf_ints6k!Q""YYs^^s*//11 Z   ..t|T_/49=/III9%%BB w  ) ''26.(BBB r)NNNNNNNrrarC)rVrWrXrrArGr rrr\r\sVEIGK    ,    ''''''rr\c"eZdZdZfdZxZS)PredictionResultsDeltaz-Prediction results based on delta method c |}|}tj||fi|dSr)r varsuperr)r results_deltarr r __class__s rrzPredictionResultsDelta.__init__sL!++--  $$&&H5555555r)rVrWrXrYr __classcell__)rss@rrnrnsB666666666rrncpeZdZdZ d dZedZedZedZd d Z d d Z dS)PredictionResultsMeanzPrediction results for GLM. This results class is used for backwards compatibility for `get_prediction` with GLM. The new PredictionResults classes dropped the `_mean` post fix in the attribute names. Nc ||_||_||_||_||_||_||_||dkrtj|_ d|_ dS|dkr tj |_ |jf|_ dS||_ d|_ dSr) r r r var_residrr^linkrrrrr ) rpredicted_mean var_pred_meanrxrrrr^rys rrzPredictionResultsMean.__init__s(% "$  <46>> DIDNNN S[[DI"gZDNNNDIDNNNrc|jSr)r rs rrzz$PredictionResultsMean.predicted_mean s ~rc|jSr)r rs rr{z#PredictionResultsMean.var_pred_means }rc|jSr)rrs rse_meanzPredictionResultsMean.se_means wrrarCc tjddd}|j||k}|dkr9|s7|j|d}|j|}n]|dks|rU|j}|jj d|dz z g|j R} |j | |zz } |j | |zz} tj | | f}|S) rcrr5rdraF)r<obsrer6) rrfryinversergr^rGrrr8rrzr9) rrhr<rrirjrkr@rr=r>r?s rrGzPredictionResultsMean.conf_ints6k!Q""Y&&s++s27799 Z    --Eu-EEI""9--BB w  ) B a%"*n>t~>>>A'!b&0E'!b&0E%00B rc||}i}|j|d<|j|d<|dddf|d<|dddf|d<||_t j||j| }|S) aoSummary frame Parameters ---------- alpha : float, optional The significance level for the confidence interval. ie., The default `alpha` = .05 returns a 95% confidence interval. Returns ------- pandas DataFrame with columns 'mean', 'mean_se', 'mean_ci_lower', 'mean_ci_upper'. rDmeanmean_seNr mean_ci_lowerr5 mean_ci_upperrK)rGrzrrNrOrPrrQ)rr<ci_meanrRrSs rrTz#PredictionResultsMean.summary_frameFs--e-,, !0 6 $ 9&-aaadm ?#&-aaadm ?# l:T_#-??#4#4666 r)NNNNNNrlrU) rVrWrXrYrrZrzr{rrGrTr rrrvrvsAEIM    *XXX))))VrrvTcX|rft|jdrQ|Oddlm}t |t jrt j|}||jjj |}||"t|dd}t|rd}tj |}|jdkr<|jjjdks|jjjddkr |dddf}tj|}n)|jj}|t|jjdd}||fS)a,Prepare or transform exog for prediction Parameters ---------- exog : array_like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you'd need to log the data first. row_labels : list of str or None If row_lables are provided, then they will replace the generated labels. Returns ------- exog : ndarray Prediction exog row_labels : list of str Labels or pandas index for rows of prediction formulaNr)dmatrixrLr5r)hasattrmodelpatsyr isinstancerOSeriesrPdata design_infogetattrcallablerasarrayndimexogshape atleast_2d)rr transformrrs r_get_exog_predictres66:WTZ33:8H!!!!!! dBI & & &<%%Dwtz2D99    w55J ## "! z$ 9>>tz3q88#z4Q71<<4=D}T""z   ,EEJ  rc t||||\}}|i}|jj|j|fi|}|}|jjj|j} | dz|tj ||j j z dz} |j } |jdkr |jd} ddg|j} t#|| | |j| ||| S) a Compute prediction results for GLM compatible models. Parameters ---------- exog : array_like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you'd need to log the data first. row_labels : list of str or None If row_lables are provided, then they will replace the generated labels. linpred : linear prediction instance Instance of linear prediction results used for confidence intervals based on endpoint transformation. link : instance of link function If no link function is provided, then the `model.family.link` is used. pred_kwds : dict Some models can take additional keyword arguments, such as offset or additional exog in multi-part models. See the predict method of the model for the details. Returns ------- prediction_results : generalized_linear_model.PredictionResults The prediction results instance contains prediction and prediction variance and can on demand calculate confidence intervals and summary tables for the prediction of the mean and of new observations. rrrNr%r5z fixed scalescalerr )rrrr^ry)rrpredictparams cov_paramsfamilyry inverse_derivrzrdotTsumrcov_typecov_kwdsuse_trvdf_resid) rrrrr^ry pred_kwdsrzcovb link_derivr{rxrs rget_prediction_glmrsN)     D* 'TZ' TGGYGGN ??  D"'55g6LMMJMTBF4,@,@,B%B$G$G$J$JJM I } %%M'* C= $D  y =twT ; ; ;;rct||||\}}|i}|jd}t|j|krt j|}nd}||}|t j||jjz d}| } d| d<|j j |j|fi| } ddg|j } t| ||j| | } | S) a Compute prediction results for linear prediction. Parameters ---------- exog : array_like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you'd need to log the data first. row_labels : list of str or None If row_lables are provided, then they will replace the generated labels. pred_kwargs : Some models can take additional keyword arguments, such as offset or additional exog in multi-part models. See the predict method of the model for the details. index : slice or array-index Is used to select rows and columns of cov_params, if the prediction function only depends on a subset of parameters. Returns ------- prediction_results : PredictionResults The prediction results instance contains prediction and prediction variance and can on demand calculate confidence intervals and summary tables for the prediction. rNr5columnlinearwhichrr )rrr)rrlenrrarangerrrrcopyrrrrr) rrrrrrLk1rr pred_kwds_linearr rrSs rget_prediction_linearrsH)     D* AB 4;  "  ??%? ( (DrvdDF++--22155H ~~'' (W" "4;II8HIII C= $D  8#'=t+5 " " "C Jrc Ft||||\}}|i}||jjj}|j}||}|t j||jjz d} | } d| d<|jj |j |fi| } |jj |j |fi|} || } | dz| z}dd g|j }t| ||j||| t j| |j| }|S) a Compute prediction results when endpoint transformation is valid. Parameters ---------- exog : array_like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you'd need to log the data first. row_labels : list of str or None If row_lables are provided, then they will replace the generated labels. link : instance of link function If no link function is provided, then the ``mmodel.family.link` is used. pred_kwargs : Some models can take additional keyword arguments, such as offset or additional exog in multi-part models. See the predict method of the model for the details. index : slice or array-index Is used to select rows and columns of cov_params, if the prediction function only depends on a subset of parameters. Returns ------- prediction_results : PredictionResults The prediction results instance contains prediction and prediction variance and can on demand calculate confidence intervals and summary tables for the prediction. rNrr5rrr%rr )rrrr^r_r r)rrrryrrrrrrrrrrr\rrr)rrrrryrrL func_derivr linpred_varrr^r rr rrSs rget_prediction_monotonicr!sZP)     D*  |z %#J ??%? ( (D"&tv..0055a88K ~~'' (W dj dGG6FGGG" "4;BB BBIG$$J1}{*H C= $D $Y(, D0:G02 0D0D*.,j  J J JC JrrFct||\}tjdfd}|} t | } | S)a compute prediction results Parameters ---------- exog : array_like, optional The values for which you want to predict. which : str The statistic that is prediction. Which statistics are available depends on the model.predict method. average : bool If average is True, then the mean prediction is computed, that is, predictions are computed for individual exog and then them mean over observation is used. If average is False, then the results are the predictions for all observations, i.e. same length as ``exog``. agg_weights : ndarray, optional Aggregation weights, only used if average is True. The weights are not normalized. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you'd need to log the data first. row_labels : list of str or None If row_lables are provided, then they will replace the generated labels. pred_kwargs : Some models can take additional keyword arguments, such as offset or additional exog in multi-part models. See the predict method of the model for the details. Returns ------- prediction_results : generalized_linear_model.PredictionResults The prediction results instance contains prediction and prediction variance and can on demand calculate confidence intervals and summary tables for the prediction of the mean and of new observations. rNg?cjj|fdi}r'|jjzdj}|S)z2Prediction function as function of params r)rrrr)ppred agg_weightsaveragerrrrs rf_predz$get_prediction_delta..f_predsT"tz!!TDDD)DD  7F[]*00446D r)rrarray_get_wald_nonlinearrn) rrrrrrrrrnlpmrSs ````` ` rget_prediction_deltarlsj)     D* hrll   # #F + +D  & &C Jrc nt|jdd}|dkrt|||||} n|dkr|dur|dur|jjjd} t |j| krtj| } nd} ||d <t|jd d} | 0t|jd rt|jj d d} | 3d dl } | d d dl m}|} t!||||| || } nt#||||||} | S)a8 Compute prediction results when endpoint transformation is valid. Parameters ---------- exog : array_like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you'd need to log the data first. which : str Which statistic is to be predicted. Default is "mean". The available statistics and options depend on the model. see the model.predict docstring linear : bool Linear has been replaced by the `which` keyword and will be deprecated. If linear is True, then `which` is ignored and the linear prediction is returned. row_labels : list of str or None If row_lables are provided, then they will replace the generated labels. average : bool If average is True, then the mean prediction is computed, that is, predictions are computed for individual exog and then the average over observation is used. If average is False, then the results are the predictions for all observations, i.e. same length as ``exog``. agg_weights : ndarray, optional Aggregation weights, only used if average is True. The weights are not normalized. **kwargs : Some models can take additional keyword arguments, such as offset, exposure or additional exog in multi-part models like zero inflated models. See the predict method of the model for the details. Returns ------- prediction_results : PredictionResults The prediction results instance contains prediction and prediction variance and can on demand calculate confidence intervals and summary dataframe for the prediction. Notes ----- Status: new in 0.14, experimental _use_endpointTr)rrrrrFr5Nrryrrz(using default log-link in get_predictionlinks)rrrryrrL)rrrrr)rrrrrrrrrrrwarningswarnstatsmodels.genmod.familiesrLogrr)rrrrrrrr use_endpointrSrrLryrrs rget_predictionrsn4:==L # !  6//|t33'U:J:J Z_ "1 % t{R  IbMMEEE" 'tz6400 <tz8,, @tz0&$?? < OOO MMD E E E 9 9 9 9 9 999;;D& !# #  Jrcddlm}|||}|t|dr|j}t j|}||}||}|dzt j |z}tj } t|t j || || } t||| || |} | S)aI results for univariate, nonlinear, monotonicaly transformed parameters This provides transformed values, standard errors and confidence interval for transformations of parameters, for example in calculating rates with `exp(params)` in the case of Poisson or other models with exponential mean function. rrNrLr%)rrry)rrr^ry)rrrrrLrrrrdiagrrrvIdentity) rrryrrrrzrr{rr^rSs rparams_transform_univariater)s211111 | )yy{{gfg66\ Z  F\\&))N##F++JMBGJ$7$77M :D$ ##$ENN$4$4666G  DwT ; ; ;C Jr)NTN)NTNNNN)NTNNN)NrFNTNN)NTrNFNN)NNN)rYnumpyrscipyrpandasrOrr\rnrvrrrrrrrr rrrsAAAAAAAAHMMMMM!6MMM` 6 6 6 6 62 6 6 6sssss1sssl1111h37;?!%B;B;B;B;J6:AE@@@@F9=3737HHHHZJJJJZ;A?C!mmmm`JN+/$$$$$$r