class_docstr: .. include:: /global.rst
  
  This solver implements the Tree-structured Parzen Estimator, as described in [TPE2011]_.
  This solver uses Hyperopt in the back-end and exposes the TPE estimator with uniform priors.
  
  Please refer to |tpe| for details about this algorithm.
  
  .. [TPE2011] Bergstra, James S., et al. "Algorithms for hyper-parameter optimization." Advances in Neural Information Processing Systems. 2011
docstr: Initialize the TPE solver.
  
  :param num_evals: number of permitted function evaluations
  :type num_evals: int
  :param seed: the random seed to be used
  :type seed: double
  :param kwargs: box constraints for each hyperparameter
  :type kwargs: {'name': [lb, ub], ...}
code: def __init__(self, num_evals=100, seed=None, **kwargs):
      """
  
      Initialize the TPE solver.
  
      :param num_evals: number of permitted function evaluations
      :type num_evals: int
      :param seed: the random seed to be used
      :type seed: double
      :param kwargs: box constraints for each hyperparameter
      :type kwargs: {'name': [lb, ub], ...}
  
  
      """
      if not _hyperopt_available:
          raise ImportError(
              "This solver requires Hyperopt but it is missing."
          )
      if not _numpy_available:
          raise ImportError(
              "This solver requires NumPy but it is missing."
          )
  
      self._seed = seed
      self._bounds = kwargs
      self._num_evals = num_evals
  
file_path: optunity/wrappers/R/inst/optunity/solvers/TPE.py
imports: from .solver_registry import register_solver
  from .util import Solver, _copydoc
  import functools
  import random
label_desc: Use image node_id 1 to create a new TPE object from inherited base classes: Solver with example: obj = TPE(num_evals, seed)