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ggml-ot 0.9.92 documentation - Home ggml-ot 0.9.92 documentation - Home
  • Installation
  • Tutorials
    • Quickstart
    • Interpreting Latent Gene Space
    • Hyperparameter Tuning
    • GPU Training with Sinkhorn
    • Computational Speed-ups
    • GGML Method Overview
  • API
    • Core functions: ggml_ot
      • ggml_ot.train
      • ggml_ot.train_emd2
      • ggml_ot.train_sinkhorn
      • ggml_ot.tune
      • ggml_ot.test
      • ggml_ot.train_test
      • ggml_ot.from_anndata
      • ggml_ot.from_numpy
    • Dataset classes: .data
      • ggml_ot.data.AnnData_TripletDataset
      • ggml_ot.data.TripletDataset
      • ggml_ot.data.load_cellxgene
      • ggml_ot.data.from_synth
      • ggml_ot.data.from_synth_gmm
    • Plotting: .pl
      • ggml_ot.pl.clustermap
      • ggml_ot.pl.embedding
      • ggml_ot.pl.clustermap_embedding
      • ggml_ot.pl.table
      • ggml_ot.pl.confusion_matrix
      • ggml_ot.pl.contour_hyperparams
      • ggml_ot.pl.scatter_subspace
      • ggml_ot.pl.scatter_3d
      • ggml_ot.pl.ellipse_overlay
      • ggml_ot.pl.panel_subspaces
      • ggml_ot.pl.plot_gmm_panel
      • ggml_ot.pl.panel_synth_dataset
    • Analysis tools: .gene
      • ggml_ot.gene.ranking
      • ggml_ot.gene.enrichment
      • ggml_ot.gene.top_ranked
    • Settings: .settings
      • ggml_ot.settings
    • GMM API: .gmm
      • ggml_ot.train_gmm
      • ggml_ot.gmm.fit_gmm
  • Contributor’s Guide
    • Issues and Planning
    • Development Setup
    • Documentation
    • Testing
    • Pull Requests & Releases
  • References
  • .md

Tutorials

Contents

  • Supervised Optimal Transport
  • Tuning & Performance
  • Method & Theory

Tutorials#

In the following tutorials, we provide guidance on all relevant functions of the ggml-ot package.

Supervised Optimal Transport#

Quickstart
Interpreting Latent Gene Space

Tuning & Performance#

Hyperparameter Tuning
GPU Training with Sinkhorn
Computational Speed-ups
Cross-validation

Method & Theory#

GGML Method Overview
../_images/cross_validation.png

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Installation

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Quickstart

Contents
  • Supervised Optimal Transport
  • Tuning & Performance
  • Method & Theory

By Damin Kuehn