Wiʆ VddlZddlZddlmZddlmZddlm Z ejdZ ejdddejjejjejjejjdd Zd Zd Zejedd Zejedd ZddefdZ ddZdZdddej0dd dd fdZdZdddej0dd dd fdZdZ ddZy)N)tqdm) tau_rand_intc|dkDry|dkry|S)zStandard clamping of a value into a fixed range (in this case -4.0 to 4.0) Parameters ---------- val: float The value to be clamped. Returns ------- The clamped value, now fixed to be in the range -4.0 to 4.0. g@g)vals X/home/sietch6/trending-topics-pipeline/venv/lib/python3.12/site-packages/umap/layouts.pyclipr s Sy t zf4(f4[::1],f4[::1])T)resultdiffdimi)fastmathcachelocalscnd}|jd}t|D]}||||z }|||zz }|S)zReduced Euclidean distance. Parameters ---------- x: array of shape (embedding_dim,) y: array of shape (embedding_dim,) Returns ------- The squared euclidean distance between x and y r)shaperange)xyr r rr s rrdistrsO.F ''!*C 3Ztad{$+ Mr c xtj|jdD]}|||ks ||}||}||}||}t||} |rdd|t | |zzz }!||zt | |dz zd|t | |zzz }"|!||z }#|!||z }$|#d|d|!z zz t j ||z |"zz}%|$d|d|!z zz t j ||z |"zz}&||z}'||||||z z|'z z }(||||||z z|'z z })||z|(|%z|)|&zzz|||zz |z }*| dkDr.d|z|zt | |dz z}+|+|t | |zdzz}+nd}+t| D]\},t|+||,||,z z}-|r|-td*z||,||,z zz }-||,xx|-| zz cc<| sL||,xx|- | zz cc<^||xx||z cc<t|||z | |z }.t|.D]}/t|||z}||}t||} | dkDr$d| z|z}+|+d| z|t | |zdzzz}+n||k(rTd}+t| D]1},|+dkDrt|+||,||,z z}-nd}-||,xx|-| zz cc<3||xx|.| |zz cc<y) Nr?r@MbP?) numbaprangerrpownpexprr intr)0head_embeddingtail_embeddingheadtail n_verticesepochs_per_sampleabrng_state_per_samplegammar move_otheralphaepochs_per_negative_sampleepoch_of_next_negative_sampleepoch_of_next_samplen densmap_flag dens_phi_sum dens_re_sum dens_re_cov dens_re_std dens_re_mean dens_lambdadens_Rdens_mu dens_mu_totrjkcurrentother dist_squaredphi dphi_termq_jkq_kjdrkdrj re_std_sqweight_kweight_jgrad_cor_coeff grad_coeffdgrad_d n_neg_samplesps0 r'_optimize_layout_euclidean_single_epochrT?s:\\+11!4 5^  "a 'QAQA$Q'G"1%E %0LS1s<';#;;<EC a!e44a#lTUBV>V8VW\!_,\!_,1C=(BFF;q>,BBYN1C=(BFF;q>,BBYN(+5 1I![^l%BCiOP 1I![^l%BCiOP  !"#~368qzK/1! !c!!AX\C a#g,FF a#lA"66<<  3Z 0jGAJq,ABCd1~#5eAh9N#OPPF fun, !H%/H 0 ! #'8'; ; #21559STU9VVM=) 1 !5a!89JF&q)$We4 #%!$uqJ5<#7C a0014#J!V!$Js1A!C'!%jGAJq4I&J!K!"AJ&5.0J 1# 10 *! , :1 ==  ,y^r c|jd|jdtj|jD]q}||} ||} || } || } t | | } dd|t | |zzz }|| xx|| zz cc<|| xx|| zz cc<|| xx|z cc<|| xx|z cc<sd}t |jD]&}tj|||||z z||<(y)Nrrg:0yE>) fillr r!sizerr"rr#log)r&r'r(r)r,r-re_sumphi_sumrr@rArBrCrDrEepsilons r-_optimize_layout_euclidean_densmap_epoch_initr\s KKN LLO \\$)) $  G G #q!We, S1s<3334q S<'' q S<''  c  c  G 6;; ?FF7fQi'!*&<=>q ?r Frparallelr^c|rtStS)N)3_nb_optimize_layout_euclidean_single_epoch_parallel*_nb_optimize_layout_euclidean_single_epoch)r^s r._get_optimize_layout_euclidean_single_epoch_fnrbsBB99r r@cF|jd}| }|| z }|j}|j}t| }|i}|i}|rtjt d| }t j|ddz }|d}|d}|d }t j|t j }t j|t j }|d } nd }d }t jdt j }t jdt j }t jdt j }t jdt j }d}!g}"t|tr |}!t|!}d |vr| |d <t j|jd t| f| t j |ddd fj!t j"j%t jj'ddz}#t)t+|fi|D],}$|xr$|dd kDxr|$dzt-|z d|dz kD}%|%rl||||||||t j.t j0| z}&t j2|}'t j4|||dz z }(nd }&d }'d }(||||||||||#| |||||||$|%|||(|&|'||||| dt-|$t-|z z z}|r#|$t7|dz zd k(rt9d|$d|d|!|$|!vs|"j;|j/|!|"j;|j|!|S|"S)a^ Improve an embedding using stochastic gradient descent to minimize the fuzzy set cross entropy between the 1-skeletons of the high dimensional and low dimensional fuzzy simplicial sets. In practice this is done by sampling edges based on their membership strength (with the (1-p) terms coming from negative sampling similar to word2vec). Parameters ---------- head_embedding: array of shape (n_samples, n_components) The initial embedding to be improved by SGD. tail_embedding: array of shape (source_samples, n_components) The reference embedding of embedded points. If not embedding new previously unseen points with respect to an existing embedding this is simply the head_embedding (again); otherwise it provides the existing embedding to embed with respect to. head: array of shape (n_1_simplices) The indices of the heads of 1-simplices with non-zero membership. tail: array of shape (n_1_simplices) The indices of the tails of 1-simplices with non-zero membership. n_epochs: int, or list of int The number of training epochs to use in optimization, or a list of epochs at which to save the embedding. In case of a list, the optimization will use the maximum number of epochs in the list, and will return a list of embedding in the order of increasing epoch, regardless of the order in the epoch list. n_vertices: int The number of vertices (0-simplices) in the dataset. epochs_per_sample: array of shape (n_1_simplices) A float value of the number of epochs per 1-simplex. 1-simplices with weaker membership strength will have more epochs between being sampled. a: float Parameter of differentiable approximation of right adjoint functor b: float Parameter of differentiable approximation of right adjoint functor rng_state: array of int64, shape (3,) The internal state of the rng gamma: float (optional, default 1.0) Weight to apply to negative samples. initial_alpha: float (optional, default 1.0) Initial learning rate for the SGD. negative_sample_rate: int (optional, default 5) Number of negative samples to use per positive sample. parallel: bool (optional, default False) Whether to run the computation using numba parallel. Running in parallel is non-deterministic, and is not used if a random seed has been set, to ensure reproducibility. verbose: bool (optional, default False) Whether to report information on the current progress of the algorithm. densmap: bool (optional, default False) Whether to use the density-augmented densMAP objective densmap_kwds: dict (optional, default None) Auxiliary data for densMAP tqdm_kwds: dict (optional, default None) Keyword arguments for tqdm progress bar. move_other: bool (optional, default False) Whether to adjust tail_embedding alongside head_embedding Returns ------- embedding: array of shape (n_samples, n_components) The optimized embedding. rNTr]mu_sumrlambdaRmudtype var_shiftrdisablefracr z completed z / epochs)rcopyrbr njitr\r#sumzerosfloat32 isinstancelistmaxfulllenint64astypefloat64viewreshaperrfloatsqrtvarmeandotr%printappend))r&r'r(r)n_epochsr*r+r,r- rng_stater/ initial_alphanegative_sample_rater^verbosedensmap densmap_kwds tqdm_kwdsr0r r1r2r3r4 optimize_fn dens_init_fnr?r<r=r>r7r8dens_var_shift epochs_listembedding_listr.r5r6r:r;r9s) roptimize_layout_euclideanrsd   q !C E!25I!I$>$C$C$E!,113AJK  zz 9 ff\(34q8 "8, c"t$xx "**= hhz< %k2  !2::.((1BJJ/xx4 hhq 3 KN(D! {# !#*{ )77   a #i.19BHHq!t##BJJ/44RXX>FFr1MN%/ /Y /B9  Kh'!+ Kq5E(O+L4H0HI    ''"&&"5"FGK77;/L&&f5aHKKLK           & )          7 <a5?(B!CD q3x"}--2 .!UHh ?  "qK'7  ! !."5"5"7 8EB9Jn1134(0>DnDr c t|jdD]}||| ks ||}||}||}||}|||g|\}}|||g|\}}|dkDr td|t|d|zzzd}nd}d|z|dz z|dzz }t|D]G}t|||z}||xx|| zz cc<| s't|||z}||xx|| zz cc<I||xx||z cc<t | | |z | |z } t| D]}!t |||z}||}|||g|\}}|dkDr td|t|d|zzzd}n||k(rQd}|dz|z|z|dzz }t|D]#}t|||z}||xx|| zz cc<%| |xx| | |zz cc<|| fS)Nrrrrrmrư>)rrr"r r%r)"r+r4r(r)r&r' output_metricoutput_metric_kwdsr r1r0r5r3r2r.r*r,r-r/rr@rArBrC dist_outputgrad_dist_output_rev_grad_dist_outputw_lrOrPrQrRrSs" r%_optimize_layout_generic_single_epochrs*$**1- .9  "a 'QAQA$Q'G"1%E,9-!3- )K)'4E7&XEW&X #A#S 1q3{AE#:::R@Q#'*kD.@AJ3Z /j+;A+>>? fun, !*/CA/F"FGF!H.H  / ! #'8'; ; #21559STU9VVM=) 1 !5a!89JF&q)0=U1%71- -$q1s;A'>#>>DC!VC"QY]S0K$4FG s1A!*/?/B"BCFAJ&5.0J1% 1, *! , :1 ==  ,o9t !> >>r rc|jd}| }|| z }|j}|j}tjtd}|i}d|vr| |d<t j |jdt| f| t j|dddfjt jjt jjddz}tt|fi|D]9}|||||||| |||||||||||| | d t|t|z z z};|S) a Improve an embedding using stochastic gradient descent to minimize the fuzzy set cross entropy between the 1-skeletons of the high dimensional and low dimensional fuzzy simplicial sets. In practice this is done by sampling edges based on their membership strength (with the (1-p) terms coming from negative sampling similar to word2vec). Parameters ---------- head_embedding: array of shape (n_samples, n_components) The initial embedding to be improved by SGD. tail_embedding: array of shape (source_samples, n_components) The reference embedding of embedded points. If not embedding new previously unseen points with respect to an existing embedding this is simply the head_embedding (again); otherwise it provides the existing embedding to embed with respect to. head: array of shape (n_1_simplices) The indices of the heads of 1-simplices with non-zero membership. tail: array of shape (n_1_simplices) The indices of the tails of 1-simplices with non-zero membership. n_epochs: int The number of training epochs to use in optimization. n_vertices: int The number of vertices (0-simplices) in the dataset. epochs_per_sample: array of shape (n_1_simplices) A float value of the number of epochs per 1-simplex. 1-simplices with weaker membership strength will have more epochs between being sampled. a: float Parameter of differentiable approximation of right adjoint functor b: float Parameter of differentiable approximation of right adjoint functor rng_state: array of int64, shape (3,) The internal state of the rng gamma: float (optional, default 1.0) Weight to apply to negative samples. initial_alpha: float (optional, default 1.0) Initial learning rate for the SGD. negative_sample_rate: int (optional, default 5) Number of negative samples to use per positive sample. verbose: bool (optional, default False) Whether to report information on the current progress of the algorithm. tqdm_kwds: dict (optional, default None) Keyword arguments for tqdm progress bar. move_other: bool (optional, default False) Whether to adjust tail_embedding alongside head_embedding Returns ------- embedding: array of shape (n_samples, n_components) The optimized embedding. rTrNrlrrirmr)rrqr rrrr#ryrzr{r|r}r~rrrr)r&r'r(r)rr*r+r,r-rr/rrrrrrr0r r1r2r3r4rr.r5s roptimize_layout_genericrsll   q !C E!25I!I$>$C$C$E!,113**-K   !#*{ )77   a #i.19BHHq!t##BJJ/44RXX>FFr1MN%/ /Y /E           ) &  ' *a5?(B!CD-E0 r c  t|jdD]h}||| ks ||}||}||}||}|||g|\}}||}d|| |zdzz }t| D]7}t|||z}||xx|| zz cc<| s'||xx| | zz cc<9||xx||z cc<t| ||z ||z }t|D]} t ||z}||}|||g|\}}t j t|||z d | |dzz }!| d|!z d|!z | |zdzz z}t| D]#}t|||z}||xx|| zz cc<%||xx|||zz cc<ky)Nrrr)rrr r%rr#r$rx)"r+r4r(r)r&r'rrweightsigmasr r1r0r5r3r2rr*rhosr/rr@rArBrCrrrrOrPrQrRrSw_hs" r%_optimize_layout_inverse_single_epochrs%,$**1- ./  "a 'QAQA$Q'G"1%E,9-!3- )K))CfQi$ 678J3Z 0j+;A+>>? fun, !H%/H  0 ! #'8'; ; #21559STU9VVM=) 1 +j8&q)0=U1%71- - ffc+Q"7>>&)dBRST#VCQWq 4ID4P'QR s1A!*/?/B"BCFAJ&5.0J1 1" *! , :1 ==  ,[/r ch|jd}|}| |z }|j}| j}tjtd}|i}d|vr| |d<t t |fi|D]:}|| |||||||||||||||| ||| |dt|t|z z z}<|S)a Improve an embedding using stochastic gradient descent to minimize the fuzzy set cross entropy between the 1-skeletons of the high dimensional and low dimensional fuzzy simplicial sets. In practice this is done by sampling edges based on their membership strength (with the (1-p) terms coming from negative sampling similar to word2vec). Parameters ---------- head_embedding: array of shape (n_samples, n_components) The initial embedding to be improved by SGD. tail_embedding: array of shape (source_samples, n_components) The reference embedding of embedded points. If not embedding new previously unseen points with respect to an existing embedding this is simply the head_embedding (again); otherwise it provides the existing embedding to embed with respect to. head: array of shape (n_1_simplices) The indices of the heads of 1-simplices with non-zero membership. tail: array of shape (n_1_simplices) The indices of the tails of 1-simplices with non-zero membership. weight: array of shape (n_1_simplices) The membership weights of the 1-simplices. sigmas: rhos: n_epochs: int The number of training epochs to use in optimization. n_vertices: int The number of vertices (0-simplices) in the dataset. epochs_per_sample: array of shape (n_1_simplices) A float value of the number of epochs per 1-simplex. 1-simplices with weaker membership strength will have more epochs between being sampled. a: float Parameter of differentiable approximation of right adjoint functor b: float Parameter of differentiable approximation of right adjoint functor rng_state: array of int64, shape (3,) The internal state of the rng gamma: float (optional, default 1.0) Weight to apply to negative samples. initial_alpha: float (optional, default 1.0) Initial learning rate for the SGD. negative_sample_rate: int (optional, default 5) Number of negative samples to use per positive sample. verbose: bool (optional, default False) Whether to report information on the current progress of the algorithm. tqdm_kwds: dict (optional, default None) Keyword arguments for tqdm progress bar. move_other: bool (optional, default False) Whether to adjust tail_embedding alongside head_embedding Returns ------- embedding: array of shape (n_samples, n_components) The optimized embedding. rTrrlr)rrqr rrrrrr)r&r'r(r)rrrrr*r+r,r-rr/rrrrrrr0r r1r2r3r4rr5s roptimize_layout_inversers@   q !C E!25I!I$>$C$C$E!,113**-K   !#*{ ) %/ /Y /E             ) &    ) ,a5?(B!CD/E2 r cd t|}|jddz dz}d}|D]$}|jd|k\s|jd}&tj|j tj }tj jt| dtj j|t|D]}|D]}|||jdks||||ks&|||}|||}|||}|||}t||}|dkDr.d|z|zt||dz z}||t||zdzz}nd}t| D]} t||| || z z}!t| |D]}"||"z}#||#cxkDr dcxk\r|"k7sn|||"|z|f}$|$dk\s.|!t| tjtj|"dz z|||"|z|fz|| ||#|$| fz zz}!|| xxt|!|zz cc<| st||| || z z}%t| |D]}"||"z}#||#cxkDr dcxk\r|"k7sn|||"|z|f}$|$dk\s.|%t| tjtj|"dz z|||"|z|fz|| ||#|$| fz zz}%|| xxt|%|zz cc<|||xx|||z cc<|||dkDrt||||z |||z }&nd}&t|&D]C}'t!| ||jdz}|||}t||}|dkDr$d| z|z}|d|z|t||zdzzz}n||k(red}t| D]} |dkDrt||| || z z}!nd}!t| |D]}"||"z}#||#cxkDr dcxk\r|"k7sn|||"|z|f}$|$dk\s.|!t| tjtj|"dz z|||"|z|fz|| ||#|$| fz zz}!|| xxt|!|zz cc<F|||xx|&|||zz cc<y) Nrrrrrrrr)rzrr#aranger|int32randomseedabsshufflerrr"r r$r%r)(head_embeddingstail_embeddingsheadstailsr+r,r-regularisation_weights relationsrr/lambda_r r0r1r2r3r4r5 n_embeddings window_size max_n_edgese_p_sembedding_orderrmr@rArBrCrDrOrPrQoffset neighbor_midentified_index other_grad_drRrSs( r/_optimize_layout_aligned_euclidean_single_epochrms*u:L??1%)a/KK") ;;q>[ (++a.K)ii -44RXX>OIINN3y|$%IIo& ; s r A'*00338LQ8OPQ8RVW8W!HQK!HQK)!,Q/'*1-$We4 #%!%AL!c'0J!JJ!c,&:":S"@@J!$Js)?A!* U1X0E"FGF"' k"B"%&Z '*CCVC/8F[>S)?V%Q'*.?.B1.EE*-a03a7$':1=a@@4Q7:;%M %&M}-);A$Y//!2D2J2J12MMA+A.q1E#(%#8L#c)%(5[1_ "u|';L! 44q8' a %( "3Z;%+%)* U1X8M*N%OF%(F&+[L+&F&F)*VJ+jGAGG3q$A!$DD3ar sr c,|djd}| }tjjj tj j ddd}tjjj tj j ddd}tjjj tj j ddd}tt|D]}|j||jtj |z |j||jtj |j||jtj tjtd|}|i}d|vr| |d<tt|fi|D]9}||||||| | |||| | |||||||| dt|t|z z z};|S)NrrTr]rlr)rr typedList empty_listtypesrurrzrr|r#rrrrr)rrrrrr+rrrr,r-r/rrrr^rrr0r r1r2r3r4rrr5s r!optimize_layout_aligned_euclideanrs* !  " "1 %C E!&!1!1!rs # *   ++%% ##{{ [[      *{|?B.8UZZ+dU.*7Aejj+dT73 :T:$    'ME`O?z .. %DNEl .. +K\TB   'Lr