3 [h: @sDdZddlmZddlmZddlZddlZddljZ ddl Z ddl Z ddl m ZddlmZddlmZmZd d lmZydd lmZWnek reZYnXeejZee jZeed krdd l mZn dd lmZddZddZ ddZ!Gddde j"j#Z$edGdddeZddZ%ddZ&GdddZ'dS)zCompatibility fixes for older version of python, numpy and scipy If you add content to this file, please give the version of the package at which the fixe is no longer needed. )update_wrapper) LooseVersionN)lsqr) MaskedArray)config_context get_config) deprecated) parse_versionz1.4)lobpcgcCs||kS)N)Xr r ;/tmp/pip-build-zwgx3nbq/scikit-learn/sklearn/utils/fixes.py_object_dtype_isnan/srcCs(ttdkstj| r ddiSiSdS)zReturns the copy=False parameter for {ndarray, csr_matrix, csc_matrix}.astype when possible, otherwise don't specify z1.1copyFN) sp_versionr spissparse)rr r r_astype_copy_false4srcKsddl}t|jtdkr|St|jjddh}|rNtdt||jfi}d|kr|d}|dkrvtd |d d dd ||d <d|kr|d}|dkrtd||dkrd |d <|S)aSet joblib.Parallel arguments in a compatible way for 0.11 and 0.12+ For joblib 0.11 this maps both ``prefer`` and ``require`` parameters to a specific ``backend``. Parameters ---------- prefer : str in {'processes', 'threads'} or None Soft hint to choose the default backend if no specific backend was selected with the parallel_backend context manager. require : 'sharedmem' or None Hard condstraint to select the backend. If set to 'sharedmem', the selected backend will be single-host and thread-based even if the user asked for a non-thread based backend with parallel_backend. See joblib.Parallel documentation for more details rNz0.12preferrequirez%unhandled arguments %s with joblib %sthreads processeszprefer=%s is not supported threadingmultiprocessing)rrNbackend sharedmemzrequire=%s is not supported)rrN)Nr) joblibr __version__setkeys differenceNotImplementedErrorlist ValueError)kwargsr extra_argsargsrrr r r_joblib_parallel_args?s,  r)c@seZdZdZdS) loguniformawA class supporting log-uniform random variables. Parameters ---------- low : float The minimum value high : float The maximum value Methods ------- rvs(self, size=None, random_state=None) Generate log-uniform random variables The most useful method for Scikit-learn usage is highlighted here. For a full list, see `scipy.stats.reciprocal `_. This list includes all functions of ``scipy.stats`` continuous distributions such as ``pdf``. Notes ----- This class generates values between ``low`` and ``high`` or low <= loguniform(low, high).rvs() <= high The logarithmic probability density function (PDF) is uniform. When ``x`` is a uniformly distributed random variable between 0 and 1, ``10**x`` are random variables that are equally likely to be returned. This class is an alias to ``scipy.stats.reciprocal``, which uses the reciprocal distribution: https://en.wikipedia.org/wiki/Reciprocal_distribution Examples -------- >>> from sklearn.utils.fixes import loguniform >>> rv = loguniform(1e-3, 1e1) >>> rvs = rv.rvs(random_state=42, size=1000) >>> rvs.min() # doctest: +SKIP 0.0010435856341129003 >>> rvs.max() # doctest: +SKIP 9.97403052786026 N)__name__ __module__ __qualname____doc__r r r rr*os.r*zMaskedArray is deprecated in version 0.23 and will be removed in version 1.0 (renaming of 0.25). Use numpy.ma.MaskedArray instead.c@s eZdZdS)rN)r+r,r-r r r rrsrc Csttdkrtj|||dS|dkr,|j}tj|jtjsDtd|j |j krXt dd|j }t t |dgt t |d|j }g}xbt ||jD]R\}}|dkr|j|q|d|d ||dd}|jtj|j|qWt|}||SdS) zQImplements a simplified version of np.take_along_axis if numpy version < 1.15z1.15)arrindicesaxisNz"`indices` must be an integer arrayz;`indices` and `arr` must have the same number of dimensionsr )r )r2) np_versionr npZtake_along_axisflattenZ issubdtypeZdtypeZintp IndexErrorndimr%r$rangezipshapeappendZarangeZreshapetuple) r/r0r1Z shape_onesZ dest_dimsZ fancy_indexZdimnZ ind_shaper r r_take_along_axiss(     r>cstjfdd}|S)z6Decorator used to capture the arguments of a function.cst||fS)N) _FuncWrapper)r(r&)functionr rdelayed_functionsz!delayed..delayed_function) functoolswraps)r@rAr )r@rdelayedsrDc@s eZdZdZddZddZdS)r?z;"Load the global configuration before calling the function.cCs||_t|_t||jdS)N)r@rconfigr)selfr@r r r__init__sz_FuncWrapper.__init__c Os$tf|j|j||SQRXdS)N)rrEr@)rFr(r&r r r__call__sz_FuncWrapper.__call__N)r+r,r-r.rGrHr r r rr?sr?)(r.rBrZdistutils.versionrZnumpyr4Z scipy.sparsesparserZscipyZ scipy.statsZscipy.sparse.linalgrZ sparse_lsqrZnumpy.marZ _MaskedArrayZ_configrr deprecationr pkg_resourcesr ImportErrorrr3rr Zexternals._lobpcgrrr)statsZ reciprocalr*r>rDr?r r r rs<            01%