class_docstr: Same as sklearn `BaseSearchCV` but with distributed
  training using spark
  
  Args:
      estimator (estimator object):
          This is assumed to implement the scikit-learn estimator interface.
          Either estimator needs to provide a ``score`` function,
          or ``scoring`` must be passed.
      sc (sparkContext): Spark context for spark broadcasting and rdd operations.
      partitions (int or 'auto'): default 'auto'
          Number of partitions to use for parallelization of parameter
          search space. Integer values or None will be used directly for `numSlices`,
          while 'auto' will set `numSlices` to the number required fits.
      preds (bool): keep predictions as attribute
class_docstr_tok: ["Same","as","sklearn","`","BaseSearchCV","`","but","with","distributed","training","using","spark","Args",":","estimator","(","estimator","object",")",":","This","is","assumed","to","implement","the","scikit-learn","estimator","interface",".","Either","estimator","needs","to","provide","a","``","score","``","function",",","or","``","scoring","``","must","be","passed",".","sc","(","sparkContext",")",":","Spark","context","for","spark","broadcasting","and","rdd","operations",".","partitions","(","int","or","'auto","'",")",":","default","'auto'","Number","of","partitions","to","use","for","parallelization","of","parameter","search","space",".","Integer","values","or","None","will","be","used","directly","for","`","numSlices","`",",","while","'auto","'","will","set","`","numSlices","`","to","the","number","required","fits",".","preds","(","bool",")",":","keep","predictions","as","attribute"]
docstr: Get CV predictions 
docstr_tok: ["Get","CV","predictions"]
code: def get_preds(self):
      """Get CV predictions"""
      if hasattr(self, "preds_"):
          return self.preds_
  
imports: import time
  import numbers
  import random
  import numpy
  import pandas
  from copy import copy
  from abc import ABCMeta
  from joblib import Parallel, delayed
  from sklearn.model_selection import ParameterGrid, GridSearchCV, RandomizedSearchCV, ParameterSampler, check_cv
  from sklearn.metrics import check_scoring
  from sklearn.base import BaseEstimator, is_classifier
  from sklearn.utils.metaestimators import if_delegate_has_method
  from sklearn.utils.validation import indexable
  from functools import partial
  from numpy.ma import MaskedArray
  from scipy.stats import rankdata
  from scipy.sparse import issparse
  from itertools import product
  from collections import defaultdict
  from .validation import _check_estimator, _check_base_estimator, _validate_params, _validate_models, _validate_names, _validate_estimators, _check_n_iter, _is_arraylike, _num_samples, _safe_indexing, _check_is_fitted
  from .utils import _multimetric_score, _num_samples, _aggregate_score_dicts, _score, _check_multimetric_scoring, _safe_split, _dict_slice_remove
  from .base import _clone, _get_value, _parse_partitions
label_desc: Use image node_id 3 for calling the DistBaseSearchCV obj's underlying member method code with example usage: obj.get_preds() and returns: self