Wi|ddlZddlmZddlmZmZmZddlm Z ddl Z ddl m Z ddl mZmZddlZddlZddlmZ ddlZ ddlZdd lmZd Z ddlZddlmZddlmcmZddlZddl Zddl!Z!GddeZ"dZ# d$dZ$d%dZ% d&dZ& d'dZ' d'dZ(dZ)d(dZ*d)dZ+d)dZ,GddejZj\Z/dZ0Gdd ejbZ2erGd!d"ejfZ4d#Z5yy#e$reded dwxYw#e$red ed dwxYw#e$r eddZYwxYw)*N)UMAP)warncatch_warningsfilterwarnings) TypingError)spectral_layout)check_random_state check_array)KDTreeaThe umap.parametric_umap package requires Tensorflow > 2.0 to be installed. You can install Tensorflow at https://www.tensorflow.org/install or you can install the CPU version of Tensorflow using pip install umap-learn[parametric_umap] z/umap.parametric_umap requires Tensorflow >= 2.0)opszEThe umap.parametric_umap package requires Keras >= 3 to be installed.z#umap.parametric_umap requires KerasTz7Torch and ONNX required for exporting to those formats.Fc eZdZddddddddddddif fd Zdfd Z dfd ZddZfd Zd Zdd Z d Z dd Z ddZ dZ dZxZS)ParametricUMAPNF?rc t|d i|||_||_||_||_||_||_||_||_ d|_ | |_ | |_ | |_ d|_d|_| |_| |_d|_t'|j(t*r)t,j.j1|j(d|_t,j4j7dd|_|ji|j:dj<d|j>k7r?tAd jC|j:dj<d|j>yy) af Parametric UMAP subclassing UMAP-learn, based on keras/tensorflow. There is also a non-parametric implementation contained within to compare with the base non-parametric implementation. Parameters ---------- batch_size : int, optional size of batch used for batch training, by default None dims : tuple, optional dimensionality of data, if not flat (e.g. (32x32x3 images for ConvNet), by default None encoder : keras.Sequential, optional The encoder Keras network decoder : keras.Sequential, optional the decoder Keras network parametric_reconstruction : bool, optional Whether the decoder is parametric or non-parametric, by default False parametric_reconstruction_loss_fcn : bool, optional What loss function to use for parametric reconstruction, by default keras.losses.BinaryCrossentropy parametric_reconstruction_loss_weight : float, optional How to weight the parametric reconstruction loss relative to umap loss, by default 1.0 autoencoder_loss : bool, optional [description], by default False reconstruction_validation : array, optional validation X data for reconstruction loss, by default None global_correlation_loss_weight : float, optional Whether to additionally train on correlation of global pairwise relationships (>0), by default 0 landmark_loss_fn : callable, optional The function to use for landmark loss, by default the euclidean distance landmark_loss_weight : float, optional How to weight the landmark loss relative to umap loss, by default 1.0 keras_fit_kwargs : dict, optional additional arguments for model.fit (like callbacks), by default {} NMbP?@ clipvaluerzNDimensionality of embedder network output ({}) doesnot match n_components ({}))"super__init__dimsencoderdecoderparametric_reconstruction%parametric_reconstruction_loss_weight"parametric_reconstruction_loss_fcnautoencoder_loss batch_sizeloss_report_frequencyglobal_correlation_loss_weightlandmark_loss_fnlandmark_loss_weight prev_epoch_X window_valsreconstruction_validationkeras_fit_kwargsparametric_model isinstance random_stateintkerasutilsset_random_seedn_training_epochs optimizersAdam optimizeroutputsshape n_components ValueErrorformat)selfr"rrrrr rr!r)r$r%r&r*kwargs __class__s `/home/sietch6/trending-topics-pipeline/venv/lib/python3.12/site-packages/umap/parametric_umap.pyrzParametricUMAP.__init__0snh "6"   )B& 1 23U/ 0$%'".L+ 0$8!  & &!1 $ d'' - KK ' '(9(9 :"#))..ts.C << #q!''+t/@/@@ 66rOzParametricUMAP.fits:8   T ).@D.H I!#266266*+,qwwqz9t~~d&7&789:" 4#4#456A  )FE-.H  $UG,j 9; ;;- '$, % DG7;%q=O  7;q!8J;K Kr?cP|jdu|duzrtjtjtjtjgg|j dzt |j|jz}tj||jf}|-t|}t|}||k7rtd|d|d|jdk(r&| td||_ t|9|||St|9|||SrA)r'rErFrGrHr7rIrJrKrLr9rMrNr fit_transformrPs r>rWzParametricUMAP.fit_transforms?<   T ).@D.H I!#266266*+,qwwqz9t~~d&7&789:" 4#4#456A  )FE-.H  $UG,j 9; ;;- '$, % DG7(%q=O)  7(ABT(U Ur?c|r|n |j}|jjtj|||j S)aTransform X into the existing embedded space and return that transformed output. Parameters ---------- X : array, shape (n_samples, n_features) New data to be transformed. batch_size : int, optional Batch size for inference, defaults to the self.batch_size used in training. Returns ------- X_new : array, shape (n_samples, n_components) Embedding of the new data in low-dimensional space. r"verbose)r"rpredictrE asanyarrayrZ)r;rQr"s r>rJzParametricUMAP.transformsA $.Z4?? ||## MM! T\\$  r?c|jrE|jjtj||j |j St|!|S)aTransform X in the existing embedded space back into the input data space and return that transformed output. Parameters ---------- X : array, shape (n_samples, n_components) New points to be inverse transformed. Returns ------- X_new : array, shape (n_samples, n_features) Generated data points new data in data space. rY) rrr[rEr\r"rZrinverse_transform)r;rQr=s r>r^z ParametricUMAP.inverse_transform,sT  ) )<<'' a T__dll( 7,Q/ /r?cF|j}t|j|j|j|j |j |j|j||j|j|j|j|j |_y)zDefine the model in keras) negative_sample_raterr!parametric_reconstruction_loss_fnrrr$r!r%r&r5N)r UMAPModel_a_br`rrr rr$r!r%r&r5r+)r;prlws r> _define_modelzParametricUMAP._define_model@s99 ) GG GG!%!:!:LLLL.2.U.U&*&D&D26+/+N+N!22!22!%!:!:nn! r?c |jdk(r |j}|jtj|dg|_nNt |jdkDr6tj |t |gt|jz}|jr;tj|dkDstj|dkr td|t|tjd}t||j|j |j"|j|j$| \}|_}}} |_t)j*t)j,|j/tj0d |_t)j*t)j,| j/tj0d |_|j6md} t |} t9|j:|j<|j>|j| |j| \|_|_|jAtC||j"z |jDz } |jr|jFt |jdkDrOtj |jFt |jFgt|jz|_#|jFt)jH|jFfd |jFif} nd} |j6jJ|f|jD|jLz| | d |jN}tQ|d s|jR|_*nC|jRjWD]&}|jT|xx|jR|z cc<(|j:jY||jZ}|ifS)NrBrrrzMData should be scaled to the range 0-1 for cross-entropy reconstruction loss.z allow-nan)dtypeensure_all_finiterCrreconstruction)epochssteps_per_epochvalidation_data_history)rZ).rMrNrrEr7rLreshaperIrmaxminrr float32construct_edge_datasetgraph_n_epochsr"r$ edge_weightr rG expand_dimsastypeint64headtailr+prepare_networksrrr8rfr.r#r) zeros_likerOr2r*hasattrhistoryrokeysr[rZ)r;rQrvinitr-rD edge_datasetn_edgesr{r|init_embeddingn_datarmrnrkey embeddings r>_fit_embed_datazParametricUMAP._fit_embed_dataSsS ;;- 'A 99 !R)DI499~!JJq3q6(T$))_"<=  ) )rvvay3"&&)c/ _   )!,"jj"-"  # KK MM OO  * *  / /1   O     IIcoodkk"((.CQGH IIcoodkk"((.CQGH  (!NVF)9  !! ..* &DL$,    g7$:T:TTU  * *..:499~!13227789DOK2.22NN4#A#AB"4#A#AB O#O,$''++  --0F0FF++  ##  tZ(#OODM++- ; c"gooc&::" ;LL((DLL(A "}r?cVtd|jjDS)Nc3JK|]\}}t||r |dvr||fyw))r5rrr+N) should_pickle).0kvs r> z.ParametricUMAP.__getstate__..s3 AQ"PPF s!#)dict__dict__itemsr;s r> __getstate__zParametricUMAP.__getstate__s) ----/   r?c|jWtjj|d}|jj ||rt dj ||jWtjj|d}|jj ||rt dj ||jWtjj|d}|jj ||rt dj |i}|rdt|dr|j|d<|` t|d r;t|jdr%|jj|d <|j` t5td tjj|d }t|d 5} tj || tj"ddd|rt dj |ddd|r(d|vr |d|_ d |vr|d |j_ yyy#1swYXxYw#1swY@xYw)N encoder.keraszKeras encoder model saved to {} decoder.keraszKeras decoder model saved to {}zparametric_model.keraszKeras full model saved to {} _raw_datarootknn_search_indexknnignore model.pklwbz*Pickle of ParametricUMAP model saved to {})rospathjoinsaveprintr:rr+rrrrropenpickledumpHIGHEST_PROTOCOL) r; save_locationrZexclude_raw_dataencoder_outputdecoder_outputparametric_model_outputraw_data model_outputoutputs r>rzParametricUMAP.saves << #WW\\-IN LL  n -7>>~NO << #WW\\-IN LL  n -7>>~NO  ,&(ggll7' #  ! ! & &'> ?4;;> Nt/0W%%{6#'"7"7"A"A))3  Y 8 $77<< {CLlD) CV D&&*A*AB CBII,WX  Y !!)&!1 2:5/%%/!   C C Y Ys$8I&I:$II II&c ||_||_||_|jdk(rtttj j t|jdt|jd|zd|_ ||j|_ y|jdk(r)| td||_ ||j|_ ytd)aAdd some points from a dataset X as "landmarks." Parameters ---------- X : array, shape (n_samples, n_features) Old data to be retained. sample_pct : float, optional Percentage of old data to use as landmarks. sample_mode : str, optional Method for sampling points. Allows "uniform" and "predefined." landmark_loss_weight : float, optional Multiplier for landmark loss function. uniformrF)replace predeterminedNz'Choice of sample_mode is not supported.) sample_pct sample_moder&rIrErandomchoiceranger7r.prev_epoch_idxr'r9)r;rQrrr&idxs r> add_landmarkszParametricUMAP.add_landmarkss,%&$8!   y ("&   !''!*%s1771: +B'CU!#D  !"$"5"5 6D     0{ !JKK&)#$%d&9&9$:!FG Gr?cd|_yN)r'rs r>remove_landmarkszParametricUMAP.remove_landmarks+s  r?c|j}t|jd|j}t ||}t j d|jd}t jj|||S)z(Exports trained parametric UMAP as ONNX.rr) rPumapNetrr8 weight_copiertorchrandnonnxexport)r;rkmpm dummy_inputs r>to_ONNXzParametricUMAP.to_ONNX.sf\\ diilD$5$5 6 2r "kk!TYYq\2 zz  [-@@r?)NNNr)TF){Gz?rrN)__name__ __module__ __qualname__rrOrWrJr^rfrrrrrr __classcell__r=s@r>rr/s "'+/.1"&'( eN;L|IM@VD ,0( &l\ 4Br!  )HV! Ar?rc|j}|j|jd}||jddkrd}nd}d|j|j|jj t |z k<|j ||jz}|j}|j}|j}||||||fS)a< gets elements of graphs, weights, and number of epochs per edge Parameters ---------- graph_ : scipy.sparse.csr.csr_matrix umap graph of probabilities n_epochs : int maximum number of epochs per edge Returns ------- graph scipy.sparse.csr.csr_matrix umap graph epochs_per_sample np.array number of epochs to train each sample for head np.array edge head tail np.array edge tail weight np.array edge weight n_vertices int number of vertices in graph rr'irh) tocoosum_duplicatesr7datarqfloateliminate_zerosrowcol)rurvgraph n_verticesepochs_per_sampler{r|weights r>get_graph_elementsr<s: LLNE QJ ;;q>U "HHDGEJJuzzUZZ^^-h?@A  5::- 99D 99D ZZF #T4 CCr?c| td}t|trG|dk(rB|jdd|jd|fj t j}|St|tr|dk(rt||||||}dt j|jz } || zj t j|jd |jd|g j t jz}|St j|} t| jd k(rt j| d jd| jdkrt| } | j!| d \} } t j"| dddf}| |jd|z| j j t jz}|S| }S)a*Initialize embedding using graph. This is for direct embeddings. Parameters ---------- init : str, optional Type of initialization to use. Either random, or spectral, by default "spectral" Returns ------- embedding : np.array the initialized embedding Nrg$g$@r)lowhighsizespectral)rM metric_kwds-C6?)scaleraxis)rrr)r r,strrr7ryrErsrabsrqnormalrGrLuniquer querymean)rrr8r-rM _metric_kwdsrrinitialisation expansion init_datatreedistindnndists r>init_embedding_from_graphrrs)$/ $!1 ((D A 'E) &  H C D# 4:#5)    $  266.15577 #i/77 JJ    A =  & JJ  * HHTN y 1 $yy+11!4yq7IIi( JJyAJ6 cad,% (;(;&.y)<)&$% & r?c@tj||d|zzz S)a convert distance representation into log probability, as a function of a, b params Parameters ---------- distances : array euclidean distance between two points in embedding a : float, optional parameter based on min_dist, by default 1.0 b : float, optional parameter based on min_dist, by default 1.0 Returns ------- float log probability in embedding space r)r log1p) distancesabs r>#convert_distance_to_log_probabilityrs$& IIa)A.. / //r?c| tj|z}d|z tj||z z|z}||z}|||fS)a Compute cross entropy between low and high probability Parameters ---------- probabilities_graph : array high dimensional probabilities log_probabilities_distance : array low dimensional log probabilities EPS : float, optional offset to ensure log is taken of a positive number, by default 1e-4 repulsion_strength : float, optional strength of repulsion between negative samples, by default 1.0 Returns ------- attraction_term: float attraction term for cross entropy loss repellant_term: float repellent term for cross entropy loss cross_entropy: float cross entropy umap loss r)r log_sigmoid)probabilities_graphlog_probabilities_distanceEPSrepulsion_strengthattraction_termrepellant_termCEs r>compute_cross_entropyr sh8+*S__=W-XXO # #$ ??5 69S S U   > )B NB ..r?c |tjtjj|tjj tjj ddtjj ddtjj ddtjj t |dg}||rtjtjj|ftjj ddtjj ddtjj ddtjj t tj|dd tjj|g}||fS) a` Generates a set of keras networks for the encoder and decoder if one has not already been predefined. Parameters ---------- encoder : keras.Sequential The encoder Keras network decoder : keras.Sequential the decoder Keras network n_components : int the dimensionality of the latent space dims : tuple of shape (dim1, dim2, dim3...) dimensionality of data n_data : number of elements in dataset # of elements in training dataset parametric_reconstruction : bool Whether the decoder is parametric or non-parametric init_embedding : array (optional, default None) The initial embedding, for nonparametric embeddings Returns ------- encoder: keras.Sequential encoder keras network decoder: keras.Sequential decoder keras network N)r7drelu)units activationz)r namerecon)r rr) r/ SequentiallayersInputFlattenDenser.rEprodReshape)rrr8rrrrs r>r}r}slL"" """. $$& """@ """@ """@ ""\):"E     $&&LL&&l_&=LL&&SV&DLL&&SV&DLL&&SV&DLL&&!"''$-0w4'LL((.  G G r?cTdjdzdkDrdndjdzdkDrdndfd}fd}t||\} } } } } } ttj|dgtj | | j d tj | | j d }}tjjtt|}||j tj}||j tj}tjjj||f}|j }|j!d }|j#d }|j%|tjj&j( }|j%|tjj&j( }|j+d }|t|| | | fS)a Construct a tf.data.Dataset of edges, sampled by edge weight. Parameters ---------- X : array, shape (n_samples, n_features) New data to be transformed. graph_ : scipy.sparse.csr.csr_matrix Generated UMAP graph n_epochs : int # of epochs to train each edge batch_size : int batch size parametric_reconstruction : bool Whether the decoder is parametric or non-parametric landmark_positions : array, shape (n_samples, n_components), optional The desired position in low-dimensional space of each sample in X. Points that are not landmarks should have nan coordinates. c ||Srr)tensorindexs r> gather_indexz,construct_edge_dataset..gather_indexNs e}r?g& .>g?TFcrWtj|gtjgd}tj|gtjgd}n,tj|}tj|}||||fS)Nr)tf py_functionrsgather)edge_to edge_from edge_to_batchedge_from_batchrQrgather_indices_in_pythons r>gather_Xz(construct_edge_dataset..gather_XYs #NN get_outputsz+construct_edge_dataset..get_outputses3::a45 )A -,9G( ) $)6G$ %  )0)+ #5w"?"**** & *,3Ew)O &/88r?ir.r)drop_remainder)num_parallel_callsr)nbytesrr.rErrr,ryr permutationrrLrzrrDatasetfrom_tensor_slicesshufflebatchmap experimentalAUTOTUNEprefetch)rQrurvr"rr$rDr'r._rr{r|rr edges_to_expedges_from_exp shuffle_maskrrr&r-s` ```` @@@r>rtrt1s: ()xx$'L#L188BN77??55|^6TUL&&(L''.L%%j%FL##RWW%9%9%B%B$L ##(<(<(E(E$L ((,L S%6dF JJr?c b tjtj|dj }tj tj|jd}y#tj tjjttjjtjjtttf$r%}t!dj#||Yd}~yd}~wt$$r}t!d|d|d|Yd}~yd}~wwxYw) a Checks if a dictionary item can be pickled Parameters ---------- key : try key for dictionary element val : None element of dictionary Returns ------- picklable: bool whether the dictionary item can be pickled base64zDid not pickle {}: {}NFzFailed at pickling :z due to T)codecsencoderdumpsdecodeloads PicklingErrorrerrorsInvalidArgumentError TypeError InternalError NotFoundError OverflowErrorrAttributeErrorrr:r9)rvalpickledr;es r>rrs -- S 18<CCE LLw~~'7B C   &&     $ + +C 34  "3%qXaS 9:s%A.A11A3D.$D D.D))D.ctjj|d}tjt |d}|rt dj|tjj|d}tjj|r@tjj||_ |rt dj|tjj|d}tjj|r>tjj||_ t dj|tjj|d}tjj|r>tjj||_t d j||S) a Load a parametric UMAP model consisting of a umap-learn UMAP object and corresponding keras models. Parameters ---------- save_location : str the folder that the model was saved in verbose : bool, optional Whether to print the loading steps, by default True Returns ------- parametric_umap.ParametricUMAP Parametric UMAP objects rrbz-Pickle of ParametricUMAP model loaded from {}rz"Keras encoder model loaded from {}rz"Keras decoder model loaded from {}r+zKeras full model loaded from {})rrrrloadrrr:existsr/models load_modelrrr+)rrZrmodelrrrs r>load_ParametricUMAPrYsB(77<< {;L KKlD1 3E =DD\RSWW\\-AN ww~~n% //?  6==nM NWW\\-AN ww~~n% //?  299.IJ!ggll=:LM ww~~-.!&!8!89P!Q /667NOP Lr?ctj|}|tj|ddz }|1|}tj|tj|zd|}nUtj||j}|tj|ddz }t |j dz g}dg}tj|d}tj|d}tj|}tj|}tj |d}tj |d}tj|||f} tj|||f} tj|||f} tj|||f} tj| tj| tj||jz } tj| |dzdf} tj| } tj| tj | dd||fz} |stj| d } | S) zAdapted from TF Probability.rTrkeepdimsNrirint32rr) r convert_to_tensorrconjrirLr7cast transposerpmatmulsqueeze) xrRr\ event_axis sample_axisx_permedy_permedn_events n_samples x_permed_flat y_permed_flatcovs r> covarianceros a A CHHQQ ..Ay XXa#((1+oAI  ! !!177 3 T2 2!''lQ&' #K*G4J((;g6K}}QH}}QHyy"1%H (#A&IKK8Y*?@MKK8Y*?@MKK8Y*?@MKK8Y*?@M **]CMM-$@ ACHH177E C ++ 1a C -- C ++  #rh11 C kk#A& Jr?c|tj|ddz }||tj|ddz }t|||S)NrTr[rerRr\)r stdrorqs r> correlationrs.sD CGGAA --A} D1 1 Q 22r?c$eZdZdZfdZxZS) StopGradientc,tj|Sr)r stop_gradientr;res r>callzStopGradient.call6s  ##r?c t|Sr)r get_config)r;r=s r>r{zStopGradient.get_config9sw!##r?)rrrryr{rrs@r>ruru5s$$$r?ructjjtjtj ||z dS)Nrr)r/ activationsr r rnorm)rRy_preds r>_default_landmark_lossr=s3    ! !#((388FQJQ+G"H IIr?cZeZdZ dfd ZdZd dZd dZdZdZdZ xZ S) rbcBt||||_||_||_| |_| |_||_||_||_ | |_ | |_ | |_ |xs!tjjdd}|j!|tj"j%|_tj(j+|_|&tj.j1d|_n||_| t4|_ y| |_ y)N)rrrr)r5T) from_logits)rrrrrr$rr` umap_loss_a umap_loss_br!r%r&r/r3r4compilerrflattenr SeedGeneratorseed_generatorlossesBinaryCrossentropyrar)r;rrr`rrr5rarrr$r!r%r&rr=s r>rzUMAPModel.__init__Ds" d#  )B&.L+ 1 2%9!&& 0 0$8!K!1!1!6!6ts!6!K  y )||++- #ll88: , 45:\\5T5T 6U6D 26WD 2  #$:D !$4D !r?c|\}}|j|}|j|}||d}|jrG|jr|j|}n$|jt j |}||d<|S)N) embedding_toembedding_fromrk)rrr!rr rw)r;inputsto_xfrom_xrrrembedding_to_recons r>ryzUMAPModel.callvs f||D) f-),   ) )$$%)\\,%?"&*\\#2C2CL2Q%R"'9F# $ r?c g}|jD]D}|jtj|tj j F|j|j||jdkDr!|j|j|||jr!|j|j||d|vr!|j|j||tj|S)Nr]rr+)rappendr rar/backendfloatx _umap_lossr$_global_correlation_lossr_parametric_reconstruction_loss_landmark_losssum)r;rerRr sample_weightr<rlosss r> compute_losszUMAPModel.compute_losssKK HD MM#((4u}}/C/C/EF G H  doof-.  . . 2 MM$776B C  ) ) MM$>>q&I J A  MM$--a8 9wwvr?c|d}|d}tj||jd}tj||jd}tj|d}tj j tj||j}tjjdk(rtj||} n||} tjtj||z dtj|| z dgd} t| |j |j"} tj|d} tjtj$| ftj&| |jzfgd} t)| | |\}}}tj*|S) Nrrrrseed tensorflowr)r)r r,r`r7r/rr5arangerconfigrrr!rKr~rrroneszerosr r)r;rrrrembedding_neg_to repeat_negrepeat_neg_batch_dimshuffled_indicesembedding_neg_fromdistance_embeddingrr"rattraction_lossrepellant_lossce_losss r>rzUMAPModel._umap_losssn-  01::lD4M4MTUVZZ0I0IPQR "yy4Q7 <<// JJ+ ,43F3F0  <<   !\ 1!#:7G!H !+,>QG   &I  0 0$2B2B& " YY|,Q/ !oo*' :(A(AACD   6K  &16 2.' xx  r?c z|j|d}|j|d}d}||}||}tj|dd}tj|dd}tj|dd|ddz d}tj|dd|ddz d}|tj j |j|j d zz}tjttj|dtj|d }| |jzS) Nr*rc^|tj|z tj|z Sr)r rrr)res r>z_scorez3UMAPModel._global_correlation_loss..z_scores! Oswwqz1 1r?irrrrrg|=)rerR) rr clipr~r/rrr7rrdrsrxr$) r;rRrrez_xrdxdzcorr_ds r>rz"UMAPModel._global_correlation_losss LL/0 1ll6.12 2 AJcl HHQR hhsC$ XXaeafn1 - XXc!"gCR(q 1%,,&&rxxd6I6I&JUR R #//"b1S__R5L M w<<<rz)UMAPModel._parametric_reconstruction_losss655  (8!9 d@@@@r?cN|d}tjtj|tj|d|d}tjtj|tj||}|j |||j zS)Nr+r)x1x2)r whereisnanr~r%r&)r;rRry_toclean_y_pred_to clean_y_tos r>rzUMAPModel._landmark_losss )) IIdO~~f^45n%  YYsyy3>>$3GDQ   ! !*o >'' ( r?) NNFrrhFNr umap_model)NNNN)r) rrrrryrrrrrrrs@r>rbrbCsF*."'.1'* 05d&.2!h=8A  r?rbc$eZdZfdZdZxZS)rctt| tj|d|_tjdd|_tjdd|_tjd||_y)Nr ) rrrnnLineardense1dense2dense3dense4)r;indimoutdimr=s r>rzPumapNet.__init__s] (D * ,))E3/DK))C-DK))C-DK))C0DK r?c6|j|}tj|}|j|}tj|}|j |}tj|}|j |}tj|}|Sr)rFr rrrrxs r>forwardzPumapNet.forward%so AAq A AAq A AAq A AAq AHr?)rrrrrrrs@r>rrs  ( r?rcP|j}tt|dz }|jj Dcgc]}|}}t dDcgc]}|d|zj dd}}|j}t |D]:}|d|zdz|||dz<tj|d|z|||dz<<|j D]} tj|| || <|j||Scc}wcc}w)a4Copies weights from a parametric UMAP encoder to pytorch. Parameters ---------- km : encoder extracted from parametric UMAP. pm: a PumapNet object. Will be overwritten. Returns ------- pm : PumapNet Object. Net with copied weights. r.rrz.biasz.weight) get_weightsr.rL state_dictrrsplitrErbr from_numpyload_state_dict) rrkweightsn_layersreall_keysipm_namespyt_state_dictrs r>rr4s0>>#s8}q() "}}3356!A66;@8DaHQUO))#.q1DDx TA4#3F"GN3  H >* 7Ds  D !D#)r)rr)rrr)T)NF)6numpyrEr)rwarningsrrrnumbarr umap.spectralr sklearn.utilsr r rBrsklearn.neighborsr rr ImportErrorr/r torch_importedrtorch.nnrtorch.nn.functional functionalr torch.onnx torchvisionrrrrr r}rtrrYrorsrLayerrurModelrbModulerrrr?r>rs99 )9$SG  ## JATJAZ3DnNX7t0.SV(/dAVhKV%P,^8v3$5<<%%$J C  C T299JB O* S   G HdR S G TU ; <$FG FGNs)C5 DD)5D D&)D;:D;