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: Verify that we can effectively make a solver from box.
  
  >>> s = TPE.suggest_from_box(30, x=[0, 1], y=[-1, 0], z=[-1, 1])
  >>> solver = TPE(**s) #doctest:+SKIP
code: def suggest_from_box(num_evals, **kwargs):
      """
      Verify that we can effectively make a solver from box.
  
      >>> s = TPE.suggest_from_box(30, x=[0, 1], y=[-1, 0], z=[-1, 1])
      >>> solver = TPE(**s) #doctest:+SKIP
  
      """
      d = dict(kwargs)
      d["num_evals"] = num_evals
      return d
  
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 2 for calling the TPE obj's underlying member method code with example usage: obj.suggest_from_box(num_evals) and returns: d