U md>@sdZddlmZmZddlmZddlmZddl m Z ddl m Z ddl mZdeeeeee deeeeeedfeeed ddZdS)z)Embed high-dimensional data using TriMap )OptionalUnion)AnnDataN)settings)Literal)logging  euclidean@@@@F)angularr ZhammingZ manhattan) adata n_components n_inliers n_outliersn_randommetric weight_adjlrn_itersverbosecopyreturnc  Cs&zddlm} Wntk r,tdYnX| r:|n|}td} | rT|n|}| dkrftjn| } t| t rx| n| dk} d|j kr|j dj d}|j ddddt |df}n"|j }t|rtd td | ||||||||| d |}||j d <tjd | dd| r"|SdS)ah TriMap: Large-scale Dimensionality Reduction Using Triplets [Amid19]_. TriMap is a dimensionality reduction method that uses triplet constraints to form a low-dimensional embedding of a set of points. The triplet constraints are of the form "point i is closer to point j than point k". The triplets are sampled from the high-dimensional representation of the points and a weighting scheme is used to reflect the importance of each triplet. TriMap provides a significantly better global view of the data than the other dimensionality reduction methods such t-SNE, LargeVis, and UMAP. The global structure includes relative distances of the clusters, multiple scales in the data, and the existence of possible outliers. We define a global score to quantify the quality of an embedding in reflecting the global structure of the data. Parameters ---------- adata Annotated data matrix. n_components Number of dimensions of the embedding. n_inliers Number of inlier points for triplet constraints. n_outliers Number of outlier points for triplet constraints. n_random Number of random triplet constraints per point. metric Distance measure: 'angular', 'euclidean', 'hamming', 'manhattan'. weight_adj Adjusting the weights using a non-linear transformation. lr Learning rate. n_iters Number of iterations. verbose If `True`, print the progress report. If `None`, `sc.settings.verbosity` is used. copy Return a copy instead of writing to `adata`. Returns ------- Depending on `copy`, returns or updates `adata` with the following fields. **X_trimap** : :class:`~numpy.ndarray`, (:attr:`~anndata.AnnData.obsm`, shape=(n_samples, n_components), dtype `float`) TriMap coordinates of data. Example ------- >>> import scanpy as sc >>> import scanpy.external as sce >>> pbmc = sc.datasets.pbmc68k_reduced() >>> pbmc = sce.tl.trimap(pbmc, copy=True) >>> sce.pl.trimap(pbmc, color=['bulk_labels'], s=10) r)TRIMAPz2 please install trimap: sudo pip install trimapzcomputing TriMapNZX_pcadz_trimap currently does not support sparse matrices. Pleaseuse a dense matrix or apply pca first.z5`X_pca` not found. Run `sc.pp.pca` first for speedup.) Zn_dimsrrrrZdistancerrrX_trimapz finishedz5added 'X_trimap', TriMap coordinates (adata.obsm))timedeep)trimapr ImportErrorrlogginfor verbosity isinstanceboolZobsmshapeminXscpissparse ValueErrorwarningZ fit_transform)rrrrrrrrrrrrstartr'Z n_dim_pcar,r r2S/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/scanpy/external/tl/_trimap.pyr#sLI  "    r#) r r r r r r rrNF)__doc__typingrrZanndatarZ scipy.sparsesparser-Z _settingsrZ_compatrrr%intfloatr)r#r2r2r2r3s<