docs: def make_predictor(regressor=LassoLarsIC(fit_intercept=False),
                     Selector=GridSearchCV, fourier_degree=(2, 25),
                     selector_processes=1,
                     use_baart=False, scoring='r2', scoring_cv=3,
                     **kwargs):
      """make_predictor(regressor=LassoLarsIC(fit_intercept=False), Selector=GridSearchCV, fourier_degree=(2, 25), selector_processes=1, use_baart=False, scoring='r2', scoring_cv=3, **kwargs)
  
      Makes a predictor object for use in :func:`get_lightcurve`.
  
      **Parameters**
  
      regressor : object with "fit" and "transform" methods, optional
          Regression object used for solving Fourier matrix
          (default ``sklearn.linear_model.LassoLarsIC(fit_intercept=False)``).
      Selector : class with "fit" and "predict" methods, optional
          Model selection class used for finding the best fit
          (default :class:`sklearn.grid_search.GridSearchCV`).
      selector_processes : positive integer, optional
          Number of processes to use for *Selector* (default 1).
      use_baart : boolean, optional
          If True, ignores *Selector* and uses Baart's Criteria to find
          the Fourier degree, within the boundaries (default False).
      fourier_degree : 2-tuple, optional
          Tuple containing lower and upper bounds on Fourier degree, in that
          order (default (2, 25)).
      scoring : str, optional
          Scoring method to use for *Selector*. This parameter can be:
              * "r2", in which case use :math:`R^2` (the default)
              * "mse", in which case use mean square error
      scoring_cv : positive integer, optional
          Number of cross validation folds used in scoring (default 3).
  
      **Returns**
  
      out : object with "fit" and "predict" methods
          The created predictor object.
      """
      fourier = Fourier(degree_range=fourier_degree, regressor=regressor) \
                if use_baart else Fourier()
      pipeline = Pipeline([('Fourier', fourier), ('Regressor', regressor)])
      if use_baart:
          return pipeline
      else:
          params = {'Fourier__degree': list(range(fourier_degree[0],
                                                  fourier_degree[1]+1))}
          return Selector(pipeline, params, scoring=scoring, cv=scoring_cv,
                          n_jobs=selector_processes)