class_docstr: Implementation of Scikit-learn interface for xlearn models.
  
  :param model_type: one of 'lr', 'fm', 'ffm'
  :param task: 'binary' for classification or 'reg' for regression
  :param metric: 'acc', 'prec', 'recall', 'f1', 'auc' for classification,
      and 'mae', 'mape', 'rmsd (rmse)' for regression.
  :param log: location of log
  :param lr: learning rate
  :param k: latent factor for factorization
  :param reg_lambda: alias for lambda
  :param init: initial value
  :param fold: number of fold used in cross validation
  :param epoch: number of training epoch
  :param stop_window: window size for early stopping
  :param opt: optimizer option, one of 'sgd', 'adagrad', 'ftrl'
  :param nthread: number of threads (Deprecated, please use n_jobs)
  :param n_jobs: number of threads used to run xlearn.
  :param block_size: block size for on-disk training.
  :param alpha: alpha for FTRL
  :param beta: beta for FTRL
  :param lambda_1: lambda_1 for FTRL
  :param lambda_2: lambda_2 for FTRL
  :param kwargs: extra input arguments
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docstr: Get xlearn model parameters
  
  :return: model parameters used for training
docstr_tok: ["Get","xlearn","model","parameters",":","return",":","model","parameters","used","for","training"]
code: def get_xlearn_params(self):
      """Get xlearn model parameters
  
      :return: model parameters used for training
      """
      xlearn_param = self.get_params()
  
      # rename reg_lambda as lambda, and remove model_type
      xlearn_param["lambda"] = xlearn_param.pop("reg_lambda")
      xlearn_param.pop("model_type")
  
      # rename n_jobs to nthread for _XLearnModel
      n_jobs = xlearn_param.pop("n_jobs")
      if (
          "nthread" in xlearn_param
          and xlearn_param["nthread"] is not None
      ):
          warnings.warn(
              "The nthread parameter has been deprecated. Please use n_jobs instead.",
              DeprecationWarning,
          )
      else:
          xlearn_param["nthread"] = n_jobs
  
      return xlearn_param
  
imports: import os
  import shutil
  import tempfile
  import warnings
  import numpy
  from .xlearn import create_linear, create_fm, create_ffm
  from .data import DMatrix
  from sklearn.base import BaseEstimator
  from sklearn.utils import check_array, check_X_y
label_desc: Use image node_id 4 for calling the BaseXLearnModel obj's underlying member method code with example usage: obj.get_xlearn_params() and returns: xlearn_param