class_docstr: k-fold cross-validation for GAM
  
  Warning: The API of this class is preliminary and will change.
  
  Parameters
  ----------
  smoother : additive smoother instance
  alphas : list of iteratables
      list of alpha for smooths. The product space will be used as alpha
      grid for cross-validation
  gam : model class
      model class for creating a model with k-fole training data
  cost : function
      cost function for the prediction error
  endog : ndarray
      dependent (response) variable of the model
  cv_iterator : instance of cross-validation iterator
class_docstr_tok: ["k-fold","cross-validation","for","GAM","Warning",":","The","API","of","this","class","is","preliminary","and","will","change",".","Parameters","--","--","--","--","--","smoother",":","additive","smoother","instance","alphas",":","list","of","iteratables","list","of","alpha","for","smooths",".","The","product","space","will","be","used","as","alpha","grid","for","cross-validation","gam",":","model","class","model","class","for","creating","a","model","with","k-fole","training","data","cost",":","function","cost","function","for","the","prediction","error","endog",":","ndarray","dependent","(","response",")","variable","of","the","model","cv_iterator",":","instance","of","cross-validation","iterator"]
code: def __init__(
      self, smoother, alphas, gam, cost, endog, exog, cv_iterator
  ):
      self.cost = cost
      self.smoother = smoother
      self.gam = gam
      self.alphas = alphas
      self.alphas_grid = list(itertools.product(*self.alphas))
      self.endog = endog
      self.exog = exog
      self.cv_iterator = cv_iterator
      self.cv_error = np.zeros(
          shape=(
              len(
                  self.alphas_grid,
              )
          )
      )
      self.cv_std = np.zeros(
          shape=(
              len(
                  self.alphas_grid,
              )
          )
      )
      self.alpha_cv = None
  
imports: from abc import ABCMeta, abstractmethod
  from statsmodels.compat.python import with_metaclass
  import itertools
  import numpy
  from statsmodels.gam.smooth_basis import GenericSmoothers, UnivariateGenericSmoother
label_desc: Use image node_id 1 to create a new MultivariateGAMCVPath object  with example: obj = MultivariateGAMCVPath(smoother, alphas, gam, cost, endog, exog, cv_iterator)