o 0Gfw@s@dZddlZddlmZddlZGdddZ  d ddZdS) zM Created on Fri Dec 19 11:29:18 2014 Author: Josef Perktold License: BSD-3 N)statsc@sleZdZdZ dddZeddZeddZed d Zed d Z ed dZ dddZ dddZ dS)PredictionResultsaE Results class for predictions. Parameters ---------- predicted_mean : ndarray The array containing the prediction means. var_pred_mean : ndarray The array of the variance of the prediction means. var_resid : ndarray The array of residual variances. df : int The degree of freedom used if dist is 't'. dist : {'norm', 't', object} Either a string for the normal or t distribution or another object that exposes a `ppf` method. row_labels : list[str] Row labels used in summary frame. NcCsn||_||_||_||_||_|dus|dkr tj|_d|_dS|dkr/tj |_|jf|_dS||_d|_dS)Nnormt) predictedvar_preddf var_resid row_labelsrrdist dist_argsr)selfpredicted_mean var_pred_meanr r r r rrBlib/python3.10/site-packages/statsmodels/regression/_prediction.py__init__$s  zPredictionResults.__init__cCst|j|jSN)npsqrtrr rrrrse_obs6szPredictionResults.se_obscC|jSr)serrrrse_mean:szPredictionResults.se_meancCrr)rrrrrr>z PredictionResults.predicted_meancCrr)rrrrrrCrzPredictionResults.var_pred_meancCs t|jSr)rrrrrrrrHs zPredictionResults.seF皙?cCsX|r|jn|j}|jjd|dg|jR}|j||}|j||}t||fS)a. Returns the confidence interval of the value, `effect` of the constraint. This is currently only available for t and z tests. Parameters ---------- alpha : float, optional The significance level for the confidence interval. ie., The default `alpha` = .05 returns a 95% confidence interval. Returns ------- ci : ndarray, (k_constraints, 2) The array has the lower and the upper limit of the confidence interval in the columns. g@)rrr Zppfr rrZ column_stack)robsalpharqlowerupperrrrconf_intMs zPredictionResults.conf_intcCs|j|dd}|j|dd}i}|j|d<|j|d<|dddf|d<|dddf|d <|dddf|d <|dddf|d <||_tj||j|d }|S) NT)rrFZmeanZmean_serZ mean_ci_lowerrZ mean_ci_upperZ obs_ci_lowerZ obs_ci_upper)indexcolumns)r#rrtablepd DataFramer keys)rrZci_obsZci_meanZ to_includeresrrr summary_framehs   zPredictionResults.summary_frame)NNN)Fr)r) __name__ __module__ __qualname____doc__rpropertyrrrrrr#r+rrrrrs        rTc Cs|r%t|jdr%|dur%ddlm}t|tjrt|}||jjj |}|durb|dur9t |dd}t |r9d}t |}|jdkr\|jjddkrT|dddf}n|dddf}t |}n|jj}|durqt |jdd}|dur}t |jjdd}|durt |}|jdkr|jdks|jd|jdkrtd |duri}|jj|j|fi|}|}|t ||jjd} |j} |jd kr|jd } |dur| |} d d g|j} t|| | |j| |dS)a Compute prediction results. 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. weights : array_like, optional Weights interpreted as in WLS, used for the variance of the predicted residual. row_labels : list A list of row labels to use. If not provided, read `exog` is available. **kwargs Some models can take additional keyword arguments, see the predict method of the model for the details. Returns ------- 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. ZformulaNr)dmatrixr$rweightsr zweights has wrong shapez fixed scalescalerr)r r r ) hasattrZmodelZpatsyr1 isinstancer'ZSeriesr(dataZ design_infogetattrcallablerZasarrayndimparamsshapeZ atleast_2dexogsize ValueErrorZpredictZ cov_paramsdotTsumr3Zcov_typeZcov_kwdsZuse_trZdf_resid) rr<Z transformr2r Z pred_kwdsr1rZcovbrr r rrrget_prediction}sR#           rB)NTNNN) r/ZnumpyrZscipyrZpandasr'rrBrrrrs n