biXddlmZddlZddlZddlZddlmZddlm Z ddl m Z ddZ ddZ dZd Zd Zd Zd Zd ZdZdZy)) annotationsN)partial)islice)BagcJt|||}|jt|S)aChooses k unique random elements from a bag. Returns a new bag containing elements from the population while leaving the original population unchanged. Parameters ---------- population: Bag Elements to sample. k: integer, optional Number of elements to sample. split_every: int (optional) Group partitions into groups of this size while performing reduction. Defaults to 8. Examples -------- >>> import dask.bag as db >>> from dask.bag import random >>> b = db.from_sequence(range(5), npartitions=2) >>> list(random.sample(b, 3).compute()) # doctest: +SKIP [1, 3, 5]  populationk split_every)_samplemap_partitions_finalize_sampler r r ress J/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/dask/bag/random.pysampler s&0 Z1+ FC   . 22cJt|||}|jt|S)a7 Return a k sized list of elements chosen with replacement. Parameters ---------- population: Bag Elements to sample. k: integer, optional Number of elements to sample. split_every: int (optional) Group partitions into groups of this size while performing reduction. Defaults to 8. Examples -------- >>> import dask.bag as db >>> from dask.bag import random >>> b = db.from_sequence(range(5), npartitions=2) >>> list(random.choices(b, 3).compute()) # doctest: +SKIP [1, 1, 5] r)_sample_with_replacementr rrs rchoicesr(s&, #jA; WC   . 22rc4g}g}d}|D];}|\}}|j|||z }t|} |j|| f=||kDr|s||fSg} |D]\}} | dkDs || |zz } | | g| zz } |rtjnt } | || ||fS)aq Reduce function used on the sample and choice functions. Parameters ---------- reduce_iter : iterable Each element is a tuple coming generated by the _sample_map_partitions function. replace: bool If True, sample with replacement. If False, sample without replacement. Returns a sequence of uniformly distributed samples; r)r weightsr )extendlenappendrndr&_weighted_sampling_without_replacement) reduce_iterr replacens_kssnis_in_ik_ipp_i sample_funcs r_sample_reducer*Bs E A A ! c   S#h c3Z ! 1uW!t  AS 7q/C # A ")#++.TK !Q! 4a 77rctt|Dcgc]1}tjt j ||z |f3}}t j||Dcgc] }||d c}Scc}wcc}w)zk Source: Weighted random sampling with a reservoir, Pavlos S. Efraimidis, Paul G. Spirakis )rangermathlogrrandomheapqnlargest)r rr r#eltxs rrrhsl >C3w<=P QDHHSZZ\ "WQZ / 3 QC Q&+nnQ&< =Jqt  == R =s 6A9'A>c|dkr td|jtt|tt|dt |S)Nrz(Cannot take a negative number of samplesr Fr rout_typer ) ValueError reductionr_sample_map_partitionsr*rrs rr r qsJ1uCDD   &!,!U3  rcB|d}t||kr td|S)NrzSample larger than population)rr:)rr rs rrr|s' ^F 6{Q899 Mrcgd}}t|}t||D]}|j||dz }tjtj t j|z }|dz t|z}t||D]u\}}||k(rf||t j|<|tjtj t j|z z}|t|z }|dz }w||fS)z Reservoir sampling strategy based on the L algorithm See https://en.wikipedia.org/wiki/Reservoir_sampling#An_optimal_algorithm rr,) iterrrr.expr/rr0 _geometric enumerate randrange) r r reservoir stream_lengthstreamewnxtr#s rr<r<s "1}I * F FA   #**,'!+,A q5JqM !C&!$1 8*+IcmmA& ' $((3::<0145 5A :a= C  m ##rcr|jtt|tt|dt|S)Nr6Tr7r8)r;r'_sample_with_replacement_map_partitionsr*rrs rrrs7   71=!T2  rc "t|}t|}t|Dcgc]}|c}d}}t|Dcgc]}tj}}|Dcgc] }t |} }t | } t|dD]p\} }| | k(rat| D]H\} } | | k(s ||| <|| xxtjzcc<| | xxt || z cc<Jt | } |dz }r||fScc}wcc}wcc}w)z Reservoir sampling with replacement, the main idea is to use k reservoirs of size 1 See Section Applications in http://utopia.duth.gr/~pefraimi/research/data/2007EncOfAlg.pdf r,)r?nextr-rr0rAminrB)r r rFrG_rDrErHwirImin_nxtr#jr"s rrKrKs  * F V A+084a4a}I$Qx(!(A($% &b:b> &C &#hG&!$ 1 <!# /1<#$IaLaDCJJL(DFj1..F  / #hG   m ### 5( &s DD D cttjtjddtjd|z z dzS)Nrr,)intr.r/runiform)r's rrArAs7 txx Aq)*TXXa!e_< = AAr)N)r,N) __future__rr1r.r0r functoolsr itertoolsr dask.bag.corerrrr*rr rr<rrKrArrr[sN" 3834#8L>$2$8Br