content: # -*- coding: utf-8 -*-
  """
  Created on Fri Dec 19 11:29:18 2014
  
  Author: Josef Perktold
  License: BSD-3
  
  """
  
  import numpy as np
  from scipy import stats
  
  # this is similar to ContrastResults after t_test, partially copied and adjusted
  class PredictionResults(object):
  
      def __init__(self, predicted_mean, var_pred_mean, var_resid,
                   df=None, dist=None, row_labels=None):
          self.predicted_mean = predicted_mean
          self.var_pred_mean = var_pred_mean
          self.df = df
          self.var_resid = var_resid
          self.row_labels = row_labels
  
          if dist is None or dist == 'norm':
              self.dist = stats.norm
              self.dist_args = ()
          elif dist == 't':
              self.dist = stats.t
              self.dist_args = (self.df,)
          else:
              self.dist = dist
              self.dist_args = ()
  
      @property
      def se_obs(self):
          return np.sqrt(self.var_pred_mean + self.var_resid)
  
      @property
      def se_mean(self):
          return np.sqrt(self.var_pred_mean)
  
      def conf_int(self, obs=False, alpha=0.05):
          """
          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.
  
          """
  
          se = self.se_obs if obs else self.se_mean
  
          q = self.dist.ppf(1 - alpha / 2., *self.dist_args)
          lower = self.predicted_mean - q * se
          upper = self.predicted_mean + q * se
          return np.column_stack((lower, upper))
  
  
      def summary_frame(self, what='all', alpha=0.05):
          # TODO: finish and cleanup
          import pandas as pd
          from statsmodels.compat.collections import OrderedDict
          ci_obs = self.conf_int(alpha=alpha, obs=True) # need to split
          ci_mean = self.conf_int(alpha=alpha, obs=False)
          to_include = OrderedDict()
          to_include['mean'] = self.predicted_mean
          to_include['mean_se'] = self.se_mean
          to_include['mean_ci_lower'] = ci_mean[:, 0]
          to_include['mean_ci_upper'] = ci_mean[:, 1]
          to_include['obs_ci_lower'] = ci_obs[:, 0]
          to_include['obs_ci_upper'] = ci_obs[:, 1]
  
          self.table = to_include
          #OrderedDict doesn't work to preserve sequence
          # pandas dict doesn't handle 2d_array
          #data = np.column_stack(list(to_include.values()))
          #names = ....
          res = pd.DataFrame(to_include, index=self.row_labels,
                             columns=to_include.keys())
          return res
  
  
  def get_prediction(self, exog=None, transform=True, weights=None,
                     row_labels=None, pred_kwds=None):
      """
      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.
      args, kwargs :
          Some models can take additional arguments or keywords, see the
          predict method of the model for the details.
  
      Returns
      -------
      prediction_results : instance
          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.
  
      """
  
      ### prepare exog and row_labels, based on base Results.predict
      if transform and hasattr(self.model, 'formula') and exog is not None:
          from patsy import dmatrix
          exog = dmatrix(self.model.data.design_info.builder,
                         exog)
  
      if exog is not None:
          if row_labels is None:
              if hasattr(exog, 'index'):
                  row_labels = exog.index
              else:
                  row_labels = None
  
          exog = np.asarray(exog)
          if exog.ndim == 1 and (self.model.exog.ndim == 1 or
                                 self.model.exog.shape[1] == 1):
              exog = exog[:, None]
          exog = np.atleast_2d(exog)  # needed in count model shape[1]
      else:
          exog = self.model.exog
          if weights is None:
              weights = getattr(self.model, 'weights', None)
  
          if row_labels is None:
              row_labels = getattr(self.model.data, 'row_labels', None)
  
      # need to handle other arrays, TODO: is delegating to model possible ?
      if weights is not None:
          weights = np.asarray(weights)
          if (weights.size > 1 and
             (weights.ndim != 1 or weights.shape[0] == exog.shape[1])):
              raise ValueError('weights has wrong shape')
  
      ### end
  
      if pred_kwds is None:
          pred_kwds = {}
      predicted_mean = self.model.predict(self.params, exog, **pred_kwds)
  
      covb = self.cov_params()
      var_pred_mean = (exog * np.dot(covb, exog.T).T).sum(1)
  
      # TODO: check that we have correct scale, Refactor scale #???
      var_resid = self.scale / weights # self.mse_resid / weights
      # special case for now:
      if self.cov_type == 'fixed scale':
          var_resid = self.cov_kwds['scale'] / weights
  
      dist = ['norm', 't'][self.use_t]
      return PredictionResults(predicted_mean, var_pred_mean, var_resid,
                               df=self.df_resid, dist=dist,
                               row_labels=row_labels)
  