class_docstr: Abstract base class for time series panel tasks, e.g., classifiers, regressors.
  
  The class contains boilerplate for checks and data conversions,
  which are common to multiple descendants, most importantly,
  BaseClassifier and BaseRegressor.
docstr: Vectorized/iterated loop over methods of BaseClassifier, BaseRegressor.
  
  Stores one estimator per loop index.
docstr_tok: ["Vectorized\/iterated","loop","over","methods","of","BaseClassifier",",","BaseRegressor",".","Stores","one","estimator","per","loop","index","."]
code: def _vectorize(self, methodname, **kwargs):
      """Vectorized/iterated loop over methods of BaseClassifier, BaseRegressor.
  
      Stores one estimator per loop index.
      """
      # retrieve data arguments
      X = kwargs.pop("X", None)
      y = kwargs.pop("y", None)
  
      # add some common arguments to kwargs
      kwargs["rowname_default"] = self.EST_TYPE_PLURAL
      kwargs["colname_default"] = self.EST_TYPE_PLURAL
      kwargs["backend"] = self.get_config()["backend:parallel"]
      kwargs["backend_params"] = self.get_config()[
          "backend:parallel:params"
      ]
  
      if methodname == "fit":
          self._yvec = y
  
          ests_ = self._yvec.vectorize_est(
              self, method="clone", **kwargs
          )
          ests_fit = self._yvec.vectorize_est(
              ests_,
              method=methodname,
              args={"y": y},
              X=X,
              **kwargs,
          )
          setattr(self, self.VECTORIZATION_ATTR, ests_fit)
          return self
      else:  # methodname == "predict" or methodname == "predict_proba":
          ests_ = getattr(self, self.VECTORIZATION_ATTR)
          y_pred = self._yvec.vectorize_est(
              ests_,
              method=methodname,
              X=X,
              **kwargs,
          )
          y_pred = pd.DataFrame(
              {
                  str(i): y_pred[col].values[0]
                  for i, col in enumerate(y_pred.columns)
              }
          )
          return y_pred
  
file_path: sktime/sktime/base/_base_panel.py
imports: import numpy
  import pandas
  from sktime.base import BaseEstimator
  from sktime.datatypes import MTYPE_LIST_PANEL, MTYPE_LIST_TABLE, VectorizedDF, check_is_error_msg, check_is_scitype, convert
  from sktime.utils.warnings import warn
label_desc: Use image node_id 1 for calling the BasePanelMixin obj's underlying member method code with example usage: obj._vectorize(methodname) and returns: self, y_pred