
    J-jo                         d Z ddlZddlZddlZddlZddlZddlZddlZddl	Z	ddl
Z
	 	 ddZy# e$ r  ej                  d       Y yw xY w)zInterface wrappers for the `cma` module.

The `SkoptCMAoptimizer` wrapper interfaces an optimizer aligned with
`skopt.optimizer`.
    Nc	                    t        j                   t        j                         j                        t        j                         j                  j
                  d}	|rt        t        d |            }t        j                  j                  |      }
|#|
j                  |
j                               d   }n|
j                  |g      d   }t        j                         g g }}t        j                   |
j"                        j%                         j'                         d}fd}t)        j*                  |||      }||j,                  _        ddd}t(        j0                  j3                  | |j5                  ||            5 }t7        |      D ]  }|j9                         r n|j;                         }|
j=                  t        j                   |            } ||      }|j?                  ||       |j,                  jA                          |r|jC                          ||z  }||z  }t        jD                  jG                  ||      }|D ]
  } ||         ddd       t        jD                  jG                  |||
      }|j,                  jI                          ||_%        |j,                  |_&        |	|_'        |S # 1 sw Y   exY w)	a  
        Optmizer based on CMA-ES algorithm.
        This is essentially a wrapper fuction for the cma library function
        to align the interface with skopt library.

        Args:
            func (callable): function to optimize
            dimensions: list of tuples like ``4 * [(-1., 1.)]`` for defining the domain.
            n_calls: the number of samples.
            verbose: if this func should be verbose
            callback: the list of callback functions.
            n_jobs: number of cores to run different calls to `func` in parallel.
            x0: inital values
                if None, random point will be sampled
            sigma0: initial standard deviation relative to domain width
            normalize: whether optimization domain should be normalized

        Returns:
            `res` skopt.OptimizeResult object
            The optimization result returned as a dict object.
            Important attributes are:
            - `x` [list]: location of the minimum.
            - `fun` [float]: function value at the minimum.
            - `x_iters` [list of lists]: location of function evaluation for each
            iteration.
            - `func_vals` [array]: function value for each iteration.
            - `space` [skopt.space.Space]: the optimization space.

        Example::

            import cma.wrapper
            res = cma.wrapper.SkoptCMAoptimizer(lambda x: sum([xi**2 for xi in x]),
                                                2 * [(-1.,1.)], 55)
            res['cma_es'].logger.plot()

        )argsfunctionc                 B    t         j                  j                  | d      S )N	normalize)skoptspacecheck_dimension)xs    Y/Users/jameslopez/projects/TradingBot25/.venv/lib/python3.12/site-packages/cma/wrapper.py<lambda>z#SkoptCMAoptimizer.<locals>.<lambda>D   s    ekk6Q6QRSU`6a    Nr   )boundsverb_filenameprefixc                 0    t        j                         y )N)os
removedirs)selfr   kargstempdirs      r   delete_tempdirz)SkoptCMAoptimizer.<locals>.delete_tempdirP   s    MM'"r   )   )number_of_processes)(copyinspectcurrentframef_localsf_codeco_namelistmapr   r	   Space	transformrvstempfilemkdtempnparraytransformed_bounds	transposetolistcmaCMAEvolutionStrategylogger__del__optimization_toolsEvalParallel2getrangestopaskinverse_transformtelladddisputilscreate_resultloadcma_es
cma_loggerspecs)func
dimensionsn_callsverbosecallbackx0n_jobssigma0r   r@   r	   xiyioptionsr   modelswitchparallel_func_inew_xinew_xi_denormnew_yiresultsfr   s                           @r   SkoptCMAoptimizerrU      sG   R IIg224==>,,.55==

 4,acm(n#oj!!*-:EOOEIIK8;r??B4(+b""$RBhhu778BBDKKM#*

	 ((VW=- ##11$$*JJvv$> 2 @CPGn::< % 7 78H I&}5

66*  "EJJLm#f++33B;!A1W: %@$ ++++BE:"\\/@ @s   CK  K	zJinstall `skopt` ("pip install scikit-optimize") to use `SkoptCMAoptimizer`)F Nr   g      ?T)__doc__pdbr   r   r&   r   warningsnumpyr(   r-   r   rU   ImportErrorwarnrV   r   r   <module>r]      s_        	   

 PQ!Y  @MHMM #? @ @s   . AA