diff --git a/CHANGELOG.md b/CHANGELOG.md index 3a57376f..55982db5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,8 @@ ## MAJOR CHANGES +* Update Dimensionality Reduction task to OpenProblems v2 results (PR #326). + * Migrated the result scaling from R to JavaScript to allow dynamically updating the results (PR #332). ## MINOR CHANGES diff --git a/results/dimensionality_reduction/data/dataset_info.json b/results/dimensionality_reduction/data/dataset_info.json index 52c65910..9d03d072 100644 --- a/results/dimensionality_reduction/data/dataset_info.json +++ b/results/dimensionality_reduction/data/dataset_info.json @@ -1,50 +1,162 @@ [ - { - "dataset_name": "Mouse hematopoietic stem cell differentiation", - "image": "openproblems", - "data_url": "https://ndownloader.figshare.com/files/36088649", - "data_reference": "nestorowa2016single", - "dataset_summary": "1.6k hematopoietic stem and progenitor cells from mouse bone marrow. Sequenced by Smart-seq2. 1920 cells x 43258 features with 3 cell type labels", - "task_id": "dimensionality_reduction", - "commit_sha": "ff1feaf0b741ec05b10084319a1175dfbf5e6faa", - "dataset_id": "mouse_hspc_nestorowa2016", - "source_dataset_id": "openproblems_v1/mouse_hspc_nestorowa2016", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/dimensionality_reduction/datasets/mouse_hspc_nestorowa2016.py" - }, - { - "dataset_name": "Mouse myeloid lineage differentiation", - "image": "openproblems", - "data_url": "https://figshare.com/ndownloader/files/36872214", - "data_reference": "olsson2016single", - "dataset_summary": "Myeloid lineage differentiation from mouse blood. Sequenced by SMARTseq in 2016 by Olsson et al. 660 cells x 112815 features with 4 cell type labels", - "task_id": "dimensionality_reduction", - "commit_sha": "ff1feaf0b741ec05b10084319a1175dfbf5e6faa", - "dataset_id": "olsson_2016_mouse_blood", - "source_dataset_id": "openproblems_v1/mouse_blood_olsson_labelled", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/dimensionality_reduction/datasets/mouse_blood_olsson_labelled.py" - }, - { - "dataset_name": "5k Peripheral blood mononuclear cells", - "image": "openproblems", - "data_url": "https://ndownloader.figshare.com/files/25555739", - "data_reference": "10x2019pbmc", - "dataset_summary": "5k Peripheral Blood Mononuclear Cells (PBMCs) from a healthy donor. Sequenced on 10X v3 chemistry in July 2019 by 10X Genomics. 5247 cells x 20822 features with no cell type labels", - "task_id": "dimensionality_reduction", - "commit_sha": "ff1feaf0b741ec05b10084319a1175dfbf5e6faa", - "dataset_id": "tenx_5k_pbmc", - "source_dataset_id": "openproblems_v1/tenx_5k_pbmc", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/dimensionality_reduction/datasets/tenx_5k_pbmc.py" - }, - { - "dataset_name": "Zebrafish", - "image": "openproblems", - "data_url": "https://ndownloader.figshare.com/files/24566651?private_link=e3921450ec1bd0587870", - "data_reference": "wagner2018single", - "dataset_summary": "90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084\u00b11156 cells per cell type).", - "task_id": "dimensionality_reduction", - "commit_sha": "ff1feaf0b741ec05b10084319a1175dfbf5e6faa", - "dataset_id": "zebrafish_labs", - "source_dataset_id": "openproblems_v1/zebrafish", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/dimensionality_reduction/datasets/zebrafish.py" - } -] \ No newline at end of file + { + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "dataset_name": "Mouse Pancreatic Islet Atlas", + "dataset_summary": "Mouse pancreatic islet scRNA-seq atlas across sexes, ages, and stress conditions including diabetes", + "dataset_description": "To better understand pancreatic β-cell heterogeneity we generated a mouse pancreatic islet atlas capturing a wide range of biological conditions. The atlas contains scRNA-seq datasets of over 300,000 mouse pancreatic islet cells, of which more than 100,000 are β-cells, from nine datasets with 56 samples, including two previously unpublished datasets. The samples vary in sex, age (ranging from embryonic to aged), chemical stress, and disease status (including T1D NOD model development and two T2D models, mSTZ and db/db) together with different diabetes treatments. Additional information about data fields is available in anndata uns field 'field_descriptions' and on https://github.com/theislab/mm_pancreas_atlas_rep/blob/main/resources/cellxgene.md.", + "data_reference": "hrovatin2023delineating", + "data_url": "https://cellxgene.cziscience.com/collections/296237e2-393d-4e31-b590-b03f74ac5070", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "openproblems_v1/cengen", + "dataset_name": "CeNGEN", + "dataset_summary": "Complete Gene Expression Map of an Entire Nervous System", + "dataset_description": "100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics.", + "data_reference": "hammarlund2018cengen", + "data_url": "https://www.cengen.org", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "openproblems_v1/immune_cells", + "dataset_name": "Human immune", + "dataset_summary": "Human immune cells dataset from the scIB benchmarks", + "dataset_description": "Human immune cells from peripheral blood and bone marrow taken from 5 datasets comprising 10 batches across technologies (10X, Smart-seq2).", + "data_reference": "luecken2022benchmarking", + "data_url": "https://theislab.github.io/scib-reproducibility/dataset_immune_cell_hum.html", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "cellxgene_census/gtex_v9", + "dataset_name": "GTEX v9", + "dataset_summary": "Single-nucleus cross-tissue molecular reference maps to decipher disease gene function", + "dataset_description": "Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.", + "data_reference": "eraslan2022singlenucleus", + "data_url": "https://cellxgene.cziscience.com/collections/a3ffde6c-7ad2-498a-903c-d58e732f7470", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "openproblems_v1/tnbc_wu2021", + "dataset_name": "Triple-Negative Breast Cancer", + "dataset_summary": "1535 cells from six fresh triple-negative breast cancer tumors.", + "dataset_description": "1535 cells from six TNBC donors by (Wu et al., 2021). This dataset includes cytokine activities, inferred using a multivariate linear model with cytokine-focused signatures, as assumed true cell-cell communication (Dimitrov et al., 2022).", + "data_reference": "wu2021single", + "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE118389", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "cellxgene_census/dkd", + "dataset_name": "Diabetic Kidney Disease", + "dataset_summary": "Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression", + "dataset_description": "Multimodal single cell sequencing is a powerful tool for interrogating cell-specific changes in transcription and chromatin accessibility. We performed single nucleus RNA (snRNA-seq) and assay for transposase accessible chromatin sequencing (snATAC-seq) on human kidney cortex from donors with and without diabetic kidney disease (DKD) to identify altered signaling pathways and transcription factors associated with DKD. Both snRNA-seq and snATAC-seq had an increased proportion of VCAM1+ injured proximal tubule cells (PT_VCAM1) in DKD samples. PT_VCAM1 has a pro-inflammatory expression signature and transcription factor motif enrichment implicated NFkB signaling. We used stratified linkage disequilibrium score regression to partition heritability of kidney-function-related traits using publicly-available GWAS summary statistics. Cell-specific PT_VCAM1 peaks were enriched for heritability of chronic kidney disease (CKD), suggesting that genetic background may regulate chromatin accessibility and DKD progression. snATAC-seq found cell-specific differentially accessible regions (DAR) throughout the nephron that change accessibility in DKD and these regions were enriched for glucocorticoid receptor (GR) motifs. Changes in chromatin accessibility were associated with decreased expression of insulin receptor, increased gluconeogenesis, and decreased expression of the GR cytosolic chaperone, FKBP5, in the diabetic proximal tubule. Cleavage under targets and release using nuclease (CUT&RUN) profiling of GR binding in bulk kidney cortex and an in vitro model of the proximal tubule (RPTEC) showed that DAR co-localize with GR binding sites. CRISPRi silencing of GR response elements (GRE) in the FKBP5 gene body reduced FKBP5 expression in RPTEC, suggesting that reduced FKBP5 chromatin accessibility in DKD may alter cellular response to GR. We developed an open-source tool for single cell allele specific analysis (SALSA) to model the effect of genetic background on gene expression. Heterozygous germline single nucleotide variants (SNV) in proximal tubule ATAC peaks were associated with allele-specific chromatin accessibility and differential expression of target genes within cis-coaccessibility networks. Partitioned heritability of proximal tubule ATAC peaks with a predicted allele-specific effect was enriched for eGFR, suggesting that genetic background may modify DKD progression in a cell-specific manner.", + "data_reference": "wilson2022multimodal", + "data_url": "https://cellxgene.cziscience.com/collections/b3e2c6e3-9b05-4da9-8f42-da38a664b45b", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "cellxgene_census/tabula_sapiens", + "dataset_name": "Tabula Sapiens", + "dataset_summary": "A multiple-organ, single-cell transcriptomic atlas of humans", + "dataset_description": "Tabula Sapiens is a benchmark, first-draft human cell atlas of nearly 500,000 cells from 24 organs of 15 normal human subjects. This work is the product of the Tabula Sapiens Consortium. Taking the organs from the same individual controls for genetic background, age, environment, and epigenetic effects and allows detailed analysis and comparison of cell types that are shared between tissues. Our work creates a detailed portrait of cell types as well as their distribution and variation in gene expression across tissues and within the endothelial, epithelial, stromal and immune compartments.", + "data_reference": "consortium2022tabula", + "data_url": "https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "openproblems_v1/pancreas", + "dataset_name": "Human pancreas", + "dataset_summary": "Human pancreas cells dataset from the scIB benchmarks", + "dataset_description": "Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq).", + "data_reference": "luecken2022benchmarking", + "data_url": "https://theislab.github.io/scib-reproducibility/dataset_pancreas.html", + "date_created": "19-12-2024", + "file_size": 1970090668 + }, + { + "dataset_id": "cellxgene_census/hypomap", + "dataset_name": "HypoMap", + "dataset_summary": "A unified single cell gene expression atlas of the murine hypothalamus", + "dataset_description": "The hypothalamus plays a key role in coordinating fundamental body functions. Despite recent progress in single-cell technologies, a unified catalogue and molecular characterization of the heterogeneous cell types and, specifically, neuronal subtypes in this brain region are still lacking. Here we present an integrated reference atlas “HypoMap” of the murine hypothalamus consisting of 384,925 cells, with the ability to incorporate new additional experiments. We validate HypoMap by comparing data collected from SmartSeq2 and bulk RNA sequencing of selected neuronal cell types with different degrees of cellular heterogeneity.", + "data_reference": "steuernagel2022hypomap", + "data_url": "https://cellxgene.cziscience.com/collections/d86517f0-fa7e-4266-b82e-a521350d6d36", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_name": "ABCA Mouse Brain scRNAseq", + "dataset_summary": "A high-resolution scRNAseq atlas of cell types in the whole mouse brain", + "dataset_description": "See dataset_reference for more information. Note that we only took the 10xv2 data from the dataset.", + "data_reference": "10.1038/s41586-023-06812-z", + "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE246717", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "openproblems_v1/zebrafish", + "dataset_name": "Zebrafish embryonic cells", + "dataset_summary": "Single-cell mRNA sequencing of zebrafish embryonic cells.", + "dataset_description": "90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene.", + "data_reference": "wagner2018single", + "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112294", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "openproblems_v1/allen_brain_atlas", + "dataset_name": "Mouse Brain Atlas", + "dataset_summary": "Adult mouse primary visual cortex", + "dataset_description": "A murine brain atlas with adjacent cell types as assumed benchmark truth, inferred from deconvolution proportion correlations using matching 10x Visium slides (see Dimitrov et al., 2022).", + "data_reference": "tasic2016adult", + "data_url": "http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71585", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "cellxgene_census/immune_cell_atlas", + "dataset_name": "Immune Cell Atlas", + "dataset_summary": "Cross-tissue immune cell analysis reveals tissue-specific features in humans", + "dataset_description": "Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis, and antigen receptor sequencing.", + "data_reference": "dominguez2022crosstissue", + "data_url": "https://cellxgene.cziscience.com/collections/62ef75e4-cbea-454e-a0ce-998ec40223d3", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "openproblems_v1/mouse_hspc_nestorowa2016", + "dataset_name": "Mouse HSPC", + "dataset_summary": "Haematopoeitic stem and progenitor cells from mouse bone marrow", + "dataset_description": "1656 hematopoietic stem and progenitor cells from mouse bone marrow. Sequenced by Smart-seq2.", + "data_reference": "nestorowa2016single", + "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE81682", + "date_created": "19-12-2024", + "file_size": 378345280 + }, + { + "dataset_id": "cellxgene_census/hcla", + "dataset_name": "Human Lung Cell Atlas", + "dataset_summary": "An integrated cell atlas of the human lung in health and disease (core)", + "dataset_description": "The integrated Human Lung Cell Atlas (HLCA) represents the first large-scale, integrated single-cell reference atlas of the human lung. It consists of over 2 million cells from the respiratory tract of 486 individuals, and includes 49 different datasets. It is split into the HLCA core, and the extended or full HLCA. The HLCA core includes data of healthy lung tissue from 107 individuals, and includes manual cell type annotations based on consensus across 6 independent experts, as well as demographic, biological and technical metadata.", + "data_reference": "sikkema2023integrated", + "data_url": "https://cellxgene.cziscience.com/collections/6f6d381a-7701-4781-935c-db10d30de293", + "date_created": "19-12-2024", + "file_size": "NA" + }, + { + "dataset_id": "openproblems_v1/mouse_blood_olsson_labelled", + "dataset_name": "Mouse myeloid", + "dataset_summary": "Myeloid lineage differentiation from mouse blood", + "dataset_description": "660 FACS-isolated myeloid cells from 9 experiments sequenced using C1 Fluidigm and SMARTseq in 2016 by Olsson et al.", + "data_reference": "olsson2016single", + "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70245", + "date_created": "19-12-2024", + "file_size": 66107536 + } +] diff --git a/results/dimensionality_reduction/data/method_info.json b/results/dimensionality_reduction/data/method_info.json index 240bbcd7..127e7bd1 100644 --- a/results/dimensionality_reduction/data/method_info.json +++ b/results/dimensionality_reduction/data/method_info.json @@ -1,392 +1,226 @@ [ { - "method_name": "densMAP (logCP10k)", - "method_summary": "densMAP is a modification of UMAP that adds an extra cost term in order to preserve information about the relative local density of the data. It is performed on the same inputs as UMAP.", - "paper_name": "Assessing single-cell transcriptomic variability through density-preserving data visualization", - "paper_reference": "narayan2021assessing", - "paper_year": 2021, - "code_url": "https://github.com/lmcinnes/umap/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "densmap_logCP10k", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/umap.py" - }, - { - "method_name": "densMAP (logCP10k, 1kHVG)", - "method_summary": "densMAP is a modification of UMAP that adds an extra cost term in order to preserve information about the relative local density of the data. It is performed on the same inputs as UMAP.", - "paper_name": "Assessing single-cell transcriptomic variability through density-preserving data visualization", - "paper_reference": "narayan2021assessing", - "paper_year": 2021, - "code_url": "https://github.com/lmcinnes/umap/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "densmap_logCP10k_1kHVG", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/umap.py" - }, - { - "method_name": "densMAP PCA (logCP10k)", - "method_summary": "densMAP is a modification of UMAP that adds an extra cost term in order to preserve information about the relative local density of the data. It is performed on the same inputs as UMAP.", - "paper_name": "Assessing single-cell transcriptomic variability through density-preserving data visualization", - "paper_reference": "narayan2021assessing", - "paper_year": 2021, - "code_url": "https://github.com/lmcinnes/umap/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "densmap_pca_logCP10k", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/umap.py" - }, - { - "method_name": "densMAP PCA (logCP10k, 1kHVG)", - "method_summary": "densMAP is a modification of UMAP that adds an extra cost term in order to preserve information about the relative local density of the data. It is performed on the same inputs as UMAP.", - "paper_name": "Assessing single-cell transcriptomic variability through density-preserving data visualization", - "paper_reference": "narayan2021assessing", - "paper_year": 2021, - "code_url": "https://github.com/lmcinnes/umap/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "densmap_pca_logCP10k_1kHVG", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/umap.py" - }, - { - "method_name": "Diffusion maps", - "method_summary": "Diffusion maps uses an affinity matrix to describe the similarity between data points, which is then transformed into a graph Laplacian. The eigenvalue-weighted eigenvectors of the graph Laplacian are then used to create the embedding. Diffusion maps is calculated on the logCPM expression matrix.", - "paper_name": "Diffusion maps", - "paper_reference": "coifman2006diffusion", - "paper_year": 2006, - "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "diffusion_map", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/diffusion_map.py" - }, - { - "method_name": "NeuralEE (CPU) (Default)", - "method_summary": "NeuralEE is a neural network implementation of elastic embedding. It is a non-linear method that preserves pairwise distances between data points. NeuralEE uses a neural network to optimize an objective function that measures the difference between pairwise distances in the original high-dimensional space and the two-dimensional space. It is computed on both the recommended input from the package authors of 500 HVGs selected from a logged expression matrix (without sequencing depth scaling) and the default logCPM matrix with 1000 HVGs.", - "paper_name": "NeuralEE: A GPU-Accelerated Elastic Embedding Dimensionality Reduction Method for Visualizing Large-Scale scRNA-Seq Data", - "paper_reference": "xiong2020neuralee", - "paper_year": 2020, - "code_url": "https://github.com/HiBearME/NeuralEE/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-pytorch", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "neuralee_default", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/neuralee.py" - }, - { - "method_name": "NeuralEE (CPU) (logCP10k, 1kHVG)", - "method_summary": "NeuralEE is a neural network implementation of elastic embedding. It is a non-linear method that preserves pairwise distances between data points. NeuralEE uses a neural network to optimize an objective function that measures the difference between pairwise distances in the original high-dimensional space and the two-dimensional space. It is computed on both the recommended input from the package authors of 500 HVGs selected from a logged expression matrix (without sequencing depth scaling) and the default logCPM matrix with 1000 HVGs.", - "paper_name": "NeuralEE: A GPU-Accelerated Elastic Embedding Dimensionality Reduction Method for Visualizing Large-Scale scRNA-Seq Data", - "paper_reference": "xiong2020neuralee", - "paper_year": 2020, - "code_url": "https://github.com/HiBearME/NeuralEE/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-pytorch", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "neuralee_logCP10k_1kHVG", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/neuralee.py" - }, - { - "method_name": "PCA (logCP10k)", - "method_summary": "PCA or \"Principal Component Analysis\" is a linear method that finds orthogonal directions in the data that capture the most variance. The first two principal components are chosen as the two-dimensional embedding. We select only the first two principal components as the two-dimensional embedding. PCA is calculated on the logCPM expression matrix with and without selecting 1000 HVGs.", - "paper_name": "On lines and planes of closest fit to systems of points in space", - "paper_reference": "pearson1901pca", - "paper_year": 1901, - "code_url": "https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "154ccb9fd99113f3d28d9c3f139194539a0290f9", - "method_id": "pca_logCP10k", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/pca.py" - }, - { - "method_name": "PCA (logCP10k, 1kHVG)", - "method_summary": "PCA or \"Principal Component Analysis\" is a linear method that finds orthogonal directions in the data that capture the most variance. The first two principal components are chosen as the two-dimensional embedding. We select only the first two principal components as the two-dimensional embedding. PCA is calculated on the logCPM expression matrix with and without selecting 1000 HVGs.", - "paper_name": "On lines and planes of closest fit to systems of points in space", - "paper_reference": "pearson1901pca", - "paper_year": 1901, - "code_url": "https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "154ccb9fd99113f3d28d9c3f139194539a0290f9", - "method_id": "pca_logCP10k_1kHVG", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/pca.py" - }, - { - "method_name": "PHATE (default)", - "method_summary": "PHATE or \u201cPotential of Heat - diffusion for Affinity - based Transition Embedding\u201d uses the potential of heat diffusion to preserve trajectories in a dataset via a diffusion process. It is an affinity - based method that creates an embedding by finding the dominant eigenvalues of a Markov transition matrix. We evaluate several variants including using the recommended square - root transformed CPM matrix as input, this input with the gamma parameter set to zero and the normal logCPM transformed matrix with and without HVG selection.", - "paper_name": "Visualizing Structure and Transitions in High-Dimensional Biological Data", - "paper_reference": "moon2019visualizing", - "paper_year": 2019, - "code_url": "https://github.com/KrishnaswamyLab/PHATE//tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "phate_default", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/phate.py" - }, - { - "method_name": "PHATE (logCP10k, 1kHVG)", - "method_summary": "PHATE or \u201cPotential of Heat - diffusion for Affinity - based Transition Embedding\u201d uses the potential of heat diffusion to preserve trajectories in a dataset via a diffusion process. It is an affinity - based method that creates an embedding by finding the dominant eigenvalues of a Markov transition matrix. We evaluate several variants including using the recommended square - root transformed CPM matrix as input, this input with the gamma parameter set to zero and the normal logCPM transformed matrix with and without HVG selection.", - "paper_name": "Visualizing Structure and Transitions in High-Dimensional Biological Data", - "paper_reference": "moon2019visualizing", - "paper_year": 2019, - "code_url": "https://github.com/KrishnaswamyLab/PHATE//tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "phate_logCP10k", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/phate.py" - }, - { - "method_name": "PHATE (logCP10k)", - "method_summary": "PHATE or \u201cPotential of Heat - diffusion for Affinity - based Transition Embedding\u201d uses the potential of heat diffusion to preserve trajectories in a dataset via a diffusion process. It is an affinity - based method that creates an embedding by finding the dominant eigenvalues of a Markov transition matrix. We evaluate several variants including using the recommended square - root transformed CPM matrix as input, this input with the gamma parameter set to zero and the normal logCPM transformed matrix with and without HVG selection.", - "paper_name": "Visualizing Structure and Transitions in High-Dimensional Biological Data", - "paper_reference": "moon2019visualizing", - "paper_year": 2019, - "code_url": "https://github.com/KrishnaswamyLab/PHATE//tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "phate_logCP10k_1kHVG", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/phate.py" - }, - { - "method_name": "PHATE (gamma=0)", - "method_summary": "PHATE or \u201cPotential of Heat - diffusion for Affinity - based Transition Embedding\u201d uses the potential of heat diffusion to preserve trajectories in a dataset via a diffusion process. It is an affinity - based method that creates an embedding by finding the dominant eigenvalues of a Markov transition matrix. We evaluate several variants including using the recommended square - root transformed CPM matrix as input, this input with the gamma parameter set to zero and the normal logCPM transformed matrix with and without HVG selection.", - "paper_name": "Visualizing Structure and Transitions in High-Dimensional Biological Data", - "paper_reference": "moon2019visualizing", - "paper_year": 2019, - "code_url": "https://github.com/KrishnaswamyLab/PHATE//tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "phate_sqrt", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/phate.py" - }, - { - "method_name": "PyMDE Preserve Distances (logCP10k)", - "method_summary": "PyMDE is a Python implementation of minimum-distortion embedding. It is a non-linear method that preserves distances between cells or neighborhoods in the high-dimensional space. It is computed with options to preserve distances between cells or neighbourhoods and with the logCPM matrix with and without HVG selection as input.", - "paper_name": "Minimum-Distortion Embedding", - "paper_reference": "agrawal2021mde", - "paper_year": 2021, - "code_url": "https://pymde.org//tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-pytorch", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "pymde_distances_log_cp10k", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/pymde.py" - }, - { - "method_name": "PyMDE Preserve Distances (logCP10k, 1kHVG)", - "method_summary": "PyMDE is a Python implementation of minimum-distortion embedding. It is a non-linear method that preserves distances between cells or neighborhoods in the high-dimensional space. It is computed with options to preserve distances between cells or neighbourhoods and with the logCPM matrix with and without HVG selection as input.", - "paper_name": "Minimum-Distortion Embedding", - "paper_reference": "agrawal2021mde", - "paper_year": 2021, - "code_url": "https://pymde.org//tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-pytorch", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "pymde_distances_log_cp10k_hvg", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/pymde.py" - }, - { - "method_name": "PyMDE Preserve Neighbors (logCP10k)", - "method_summary": "PyMDE is a Python implementation of minimum-distortion embedding. It is a non-linear method that preserves distances between cells or neighborhoods in the high-dimensional space. It is computed with options to preserve distances between cells or neighbourhoods and with the logCPM matrix with and without HVG selection as input.", - "paper_name": "Minimum-Distortion Embedding", - "paper_reference": "agrawal2021mde", - "paper_year": 2021, - "code_url": "https://pymde.org//tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-pytorch", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "pymde_neighbors_log_cp10k", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/pymde.py" - }, - { - "method_name": "PyMDE Preserve Neighbors (logCP10k, 1kHVG)", - "method_summary": "PyMDE is a Python implementation of minimum-distortion embedding. It is a non-linear method that preserves distances between cells or neighborhoods in the high-dimensional space. It is computed with options to preserve distances between cells or neighbourhoods and with the logCPM matrix with and without HVG selection as input.", - "paper_name": "Minimum-Distortion Embedding", - "paper_reference": "agrawal2021mde", - "paper_year": 2021, - "code_url": "https://pymde.org//tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-pytorch", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "pymde_neighbors_log_cp10k_hvg", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/pymde.py" - }, - { + "task_id": "control_methods", + "method_id": "random_features", "method_name": "Random Features", - "method_summary": "Randomly generated two-dimensional coordinates from a normal distribution.", - "paper_name": "Open Problems for Single Cell Analysis", - "paper_reference": "openproblems", - "paper_year": 2022, - "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", + "method_summary": "Negative control by randomly embedding into a 2D space.", + "method_description": "This method serves as a negative control, where the data is randomly embedded into a two-dimensional space, with no attempt to preserve the original structure.", "is_baseline": true, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "random_features", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/baseline.py" + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_dimensionality_reduction", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/control_methods/random_features:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/control_methods/random_features", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" }, { + "task_id": "control_methods", + "method_id": "spectral_features", "method_name": "Spectral Features", - "method_summary": "Use 1000-dimensional diffusions maps as an embedding", - "paper_name": "Open Problems for Single Cell Analysis", - "paper_reference": "openproblems", - "paper_year": 2022, - "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", + "method_summary": "Positive control by Use 1000-dimensional diffusions maps as an embedding.", + "method_description": "This serves as a positive control since it uses 1000-dimensional diffusions maps as an embedding", "is_baseline": true, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "spectral_features", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/baseline.py" + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_dimensionality_reduction", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/control_methods/spectral_features:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/control_methods/spectral_features", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" }, { + "task_id": "control_methods", + "method_id": "true_features", "method_name": "True Features", - "method_summary": "Use of the original feature inputs as the 'embedding'.", - "paper_name": "Open Problems for Single Cell Analysis", - "paper_reference": "openproblems", - "paper_year": 2022, - "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", + "method_summary": "Positive control by retaining the dimensionality without loss of information.", + "method_description": "This serves as a positive control since the original high-dimensional data is retained as is, without any loss of information", "is_baseline": true, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "true_features", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/baseline.py" - }, - { - "method_name": "t-SNE (logCP10k)", - "method_summary": "t-SNE or t-distributed Stochastic Neighbor Embedding converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data. We use the implementation in the scanpy package with the result of PCA on the logCPM expression matrix (with and without HVG selection).", - "paper_name": "Visualizing Data using t-SNE", - "paper_reference": "vandermaaten2008visualizing", - "paper_year": 2008, - "code_url": "https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "154ccb9fd99113f3d28d9c3f139194539a0290f9", - "method_id": "tsne_logCP10k", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/tsne.py" - }, - { - "method_name": "t-SNE (logCP10k, 1kHVG)", - "method_summary": "t-SNE or t-distributed Stochastic Neighbor Embedding converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data. We use the implementation in the scanpy package with the result of PCA on the logCPM expression matrix (with and without HVG selection).", - "paper_name": "Visualizing Data using t-SNE", - "paper_reference": "vandermaaten2008visualizing", - "paper_year": 2008, - "code_url": "https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "154ccb9fd99113f3d28d9c3f139194539a0290f9", - "method_id": "tsne_logCP10k_1kHVG", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/tsne.py" - }, - { - "method_name": "UMAP (logCP10k)", - "method_summary": "UMAP or Uniform Manifold Approximation and Projection is an algorithm for dimension reduction based on manifold learning techniques and ideas from topological data analysis. We perform UMAP on the logCPM expression matrix before and after HVG selection and with and without PCA as a pre-processing step.", - "paper_name": "UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction", - "paper_reference": "mcinnes2018umap", - "paper_year": 2018, - "code_url": "https://github.com/lmcinnes/umap/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "umap_logCP10k", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/umap.py" - }, - { - "method_name": "UMAP (logCP10k, 1kHVG)", - "method_summary": "UMAP or Uniform Manifold Approximation and Projection is an algorithm for dimension reduction based on manifold learning techniques and ideas from topological data analysis. We perform UMAP on the logCPM expression matrix before and after HVG selection and with and without PCA as a pre-processing step.", - "paper_name": "UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction", - "paper_reference": "mcinnes2018umap", - "paper_year": 2018, - "code_url": "https://github.com/lmcinnes/umap/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "umap_logCP10k_1kHVG", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/umap.py" - }, - { - "method_name": "UMAP PCA (logCP10k)", - "method_summary": "UMAP or Uniform Manifold Approximation and Projection is an algorithm for dimension reduction based on manifold learning techniques and ideas from topological data analysis. We perform UMAP on the logCPM expression matrix before and after HVG selection and with and without PCA as a pre-processing step.", - "paper_name": "UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction", - "paper_reference": "mcinnes2018umap", - "paper_year": 2018, - "code_url": "https://github.com/lmcinnes/umap/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "umap_pca_logCP10k", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/umap.py" - }, - { - "method_name": "UMAP PCA (logCP10k, 1kHVG)", - "method_summary": "UMAP or Uniform Manifold Approximation and Projection is an algorithm for dimension reduction based on manifold learning techniques and ideas from topological data analysis. We perform UMAP on the logCPM expression matrix before and after HVG selection and with and without PCA as a pre-processing step.", - "paper_name": "UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction", - "paper_reference": "mcinnes2018umap", - "paper_year": 2018, - "code_url": "https://github.com/lmcinnes/umap/tree/v1.0.0/openproblems/tasks", - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "is_baseline": false, - "code_version": "v1.0.0", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "method_id": "umap_pca_logCP10k_1kHVG", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/methods/umap.py" + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_dimensionality_reduction", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/control_methods/true_features:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/control_methods/true_features", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "densmap", + "method_name": "densMAP", + "method_summary": "Modified UMAP with preservation of local density information", + "method_description": "A modification of UMAP that adds an extra cost term in order to preserve information about the relative local density of the data. It is performed on the same inputs as UMAP.", + "is_baseline": false, + "references_doi": "10.1038/s41587-020-00801-7", + "references_bibtex": null, + "code_url": "https://github.com/lmcinnes/umap", + "documentation_url": "https://umap-learn.readthedocs.io/en/latest/densmap_demo.html", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/densmap:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/densmap", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "diffusion_map", + "method_name": "Diffusion Map", + "method_summary": "Finding meaningful geometric descriptions of datasets using diffusion maps.", + "method_description": "Implements diffusion map method of data parametrization, including creation and visualization of diffusion map, clustering with diffusion K-means and regression using adaptive regression model.", + "is_baseline": false, + "references_doi": "10.1016/j.acha.2006.04.006", + "references_bibtex": null, + "code_url": "https://github.com/theislab/destiny", + "documentation_url": "https://bioconductor.org/packages/release/bioc/html/destiny.html", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/diffusion_map:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/diffusion_map", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "ivis", + "method_name": "ivis", + "method_summary": "Structure-preserving dimensionality reduction using a siamese neural network trained on triplets.", + "method_description": "ivis is a machine learning library for reducing dimensionality of very large\ndatasets using Siamese Neural Networks. ivis preserves global data\nstructures in a low-dimensional space, adds new data points to existing\nembeddings using a parametric mapping function, and scales linearly to\nmillions of observations.\n", + "is_baseline": false, + "references_doi": "10.1038/s41598-019-45301-0", + "references_bibtex": null, + "code_url": "https://github.com/beringresearch/ivis", + "documentation_url": "https://beringresearch.github.io/ivis/", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/ivis:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/ivis", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "lmds", + "method_name": "LMDS", + "method_summary": "Landmark Multi-Dimensional Scaling", + "method_description": "Landmark Multi-Dimensional Scaling (LMDS) is a non-linear method for\ndimensionality reduction that is based on the concept of multi-dimensional\nscaling.\n", + "is_baseline": false, + "references_doi": "10.1038/s41587-019-0071-9", + "references_bibtex": null, + "code_url": "https://github.com/dynverse/lmds", + "documentation_url": "https://dynverse.org/lmds/", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/lmds:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/lmds", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "neuralee", + "method_name": "NeuralEE", + "method_summary": "Non-linear method that uses a neural network to preserve pairwise distances between data points in a high-dimensional space.", + "method_description": "A neural network implementation of elastic embedding. It is a\nnon-linear method that preserves pairwise distances between data points.\nNeuralEE uses a neural network to optimize an objective function that\nmeasures the difference between pairwise distances in the original\nhigh-dimensional space and the two-dimensional space. It is computed on both\nthe recommended input from the package authors of 500 HVGs selected from a\nlogged expression matrix (without sequencing depth scaling) and the default\nlogCPM matrix with 1000 HVGs.\n", + "is_baseline": false, + "references_doi": "10.3389/fgene.2020.00786", + "references_bibtex": null, + "code_url": "https://github.com/HiBearME/NeuralEE", + "documentation_url": "https://github.com/HiBearME/NeuralEE#readme", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/neuralee:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/neuralee", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "pca", + "method_name": "PCA", + "method_summary": "A linear method that finds orthogonal directions to compute the two-dimensional embedding.", + "method_description": "Principal Component Analysis is a linear method that finds orthogonal\ndirections in the data that capture the most variance. The first two\nprincipal components are chosen as the two-dimensional embedding. We select\nonly the first two principal components as the two-dimensional embedding. PCA\nis calculated on the logCPM expression matrix with and without selecting 1000\nHVGs.\n", + "is_baseline": false, + "references_doi": "10.1080/14786440109462720", + "references_bibtex": null, + "code_url": "https://github.com/scverse/scanpy", + "documentation_url": "https://scanpy.readthedocs.io/en/stable/generated/scanpy.pp.pca.html", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/pca:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/pca", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "phate", + "method_name": "PHATE", + "method_summary": "Preservating trajectories in a dataset by using heat diffusion potential.", + "method_description": "PHATE or \"Potential of Heat - diffusion for Affinity - based Transition\nEmbedding\" uses the potential of heat diffusion to preserve trajectories in\na dataset via a diffusion process. It is an affinity - based method that\ncreates an embedding by finding the dominant eigenvalues of a Markov\ntransition matrix. We evaluate several variants including using the\nrecommended square - root transformed CPM matrix as input, this input with\nthe gamma parameter set to zero and the normal logCPM transformed matrix\nwith and without HVG selection.\n", + "is_baseline": false, + "references_doi": "10.1038/s41587-019-0336-3", + "references_bibtex": null, + "code_url": "https://github.com/KrishnaswamyLab/PHATE", + "documentation_url": "https://phate.readthedocs.io/en/stable/", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/phate:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/phate", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "pymde", + "method_name": "PyMDE", + "method_summary": "A Python implementation of Minimum-Distortion Embedding", + "method_description": "PyMDE is a Python implementation of Minimum-Distortion Embedding. It is a\nnon-linear method that preserves distances between cells or neighbourhoods\nin the original space.\n", + "is_baseline": false, + "references_doi": "10.1561/2200000090", + "references_bibtex": null, + "code_url": "https://github.com/cvxgrp/pymde", + "documentation_url": "https://pymde.org", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/pymde:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/pymde", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "simlr", + "method_name": "SIMLR", + "method_summary": "Multikernel-based learning of distance metrics from gene expression data for dimension reduction, clustering and visulaization.", + "method_description": "Single-cell Interpretation via Multikernel LeaRning (SIMLR) learns\ncell-to-cell similarity measures from single-cell RNA-seq data in using\nGaussian kernels with various hyperparameters in order to perform dimension\nreduction, clustering and visualization. SIMLR assumes that if C separable\npopulations exist among the N cells, then the similarity matrix should have\nan approximate block-diagonal structure with C blocks whereby cells have\nlarger similarities to other cells within the same subpopulations. Learned\nsimilarity between two cells should be small if the Euclidean distance\nbetween them is large. The cell-to-cell similarity is computed using an\noptimization framework over an N x N similarity matrix, a low-dimensional\nauxilary matrix enforcing low rank constraint on the similarity matrix, and\nthe kernel weights. Dimension reduction is achieved by the stochastic\nneighbor embedding methodology with the learned similarities as input.\n", + "is_baseline": false, + "references_doi": "10.1038/nmeth.4207", + "references_bibtex": null, + "code_url": "https://github.com/BatzoglouLabSU/SIMLR", + "documentation_url": "https://bioconductor.org/packages/SIMLR/", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/simlr:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/simlr", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "tsne", + "method_name": "t-SNE", + "method_summary": "Minimizing Kullback-Leibler divergence by converting similarities into joint probabilities between data points and the low/high dimensional embedding.", + "method_description": "t-distributed Stochastic Neighbor Embedding converts similarities between\ndata points to joint probabilities and tries to minimize the\nKullback-Leibler divergence between the joint probabilities of the\nlow-dimensional embedding and the high-dimensional data. We use the\nimplementation in the scanpy package with the MulticoreTSNE backend taking\nthe result of PCA on the logCPM expression matrix (with and without HVG\nselection) as input.\n", + "is_baseline": false, + "references_doi": null, + "references_bibtex": "@article{JMLR:v9:vandermaaten08a,\n author = {Laurens van der Maaten and Geoffrey Hinton},\n title = {Visualizing Data using t-SNE},\n journal = {Journal of Machine Learning Research},\n year = {2008},\n volume = {9},\n number = {86},\n pages = {2579--2605},\n url = {http://jmlr.org/papers/v9/vandermaaten08a.html}\n}\n", + "code_url": "https://github.com/scverse/scanpy", + "documentation_url": "https://scanpy.readthedocs.io/en/stable/generated/scanpy.tl.tsne.html", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/tsne:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/tsne", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" + }, + { + "task_id": "methods", + "method_id": "umap", + "method_name": "UMAP", + "method_summary": "A manifold learning algorithm that utilizes topological data analysis for dimension reduction.", + "method_description": "Uniform Manifold Approximation and Projection is an algorithm for\ndimension reduction based on manifold learning techniques and ideas from\ntopological data analysis. We perform UMAP on the logCPM expression matrix\nbefore and after HVG selection and with and without PCA as a pre-processing\nstep.\n", + "is_baseline": false, + "references_doi": "10.48550/arxiv.1802.03426", + "references_bibtex": null, + "code_url": "https://github.com/lmcinnes/umap", + "documentation_url": "https://umap-learn.readthedocs.io/en/latest/", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/methods/umap:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/methods/umap", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a" } -] \ No newline at end of file +] diff --git a/results/dimensionality_reduction/data/metric_execution_info.json b/results/dimensionality_reduction/data/metric_execution_info.json new file mode 100644 index 00000000..3cd4cb17 --- /dev/null +++ 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"duration_sec": 3401, + "cpu_pct": "NA", + "peak_memory_mb": "NA", + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + } +] diff --git a/results/dimensionality_reduction/data/metric_info.json b/results/dimensionality_reduction/data/metric_info.json index 2298375d..5bd9b894 100644 --- a/results/dimensionality_reduction/data/metric_info.json +++ b/results/dimensionality_reduction/data/metric_info.json @@ -1,122 +1,212 @@ [ { - "metric_name": "continuity", - "metric_summary": "Continuity measures error of hard extrusions based on nearest neighbor coranking", - "paper_reference": "zhang2021pydrmetrics", - "maximize": true, - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "task_id": "dimensionality_reduction", - "commit_sha": "ff1feaf0b741ec05b10084319a1175dfbf5e6faa", - "metric_id": "continuity", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/metrics/nn_ranking.py", - "code_version": "v1.0.0" + "task_id": "metrics", + "component_name": "clustering_performance", + "metric_id": "normalized_mutual_information", + "metric_name": "NMI", + "metric_summary": "Normalized Mutual Information (NMI) is a measure of the concordance between clustering obtained from the reduced-dimensional embeddings and the cell labels.", + "metric_description": "The Normalized Mutual Information (NMI) is a measure of the similarity between cluster labels obtained from the clustering of dimensionality reduction embeddings and the true cell labels. It is a normalization of the Mutual Information (MI) score to scale the results between 0 (no mutual information) and 1 (perfect correlation).\nMutual Information quantifies the \"amount of information\" obtained about one random variable by observing the other random variable. Assuming two label assignments X and Y, it is given by:\n $MI(X,Y) = \\sum_{x=1}^{X}\\sum_{y=1}^{Y}p(x,y)log(\\frac{P(x,y)}{P(x)P'(y)})$,\nwhere P(x,y) is the joint probability mass function of X and Y, and P(x), P'(y) are the marginal probability mass functions of X and Y respectively. The mutual information is normalized by some generalized mean of H(X) and H(Y). Therefore, Normalized Mutual Information can be defined as:\n $NMI(X,Y) = \\frac{MI(X,Y)}{mean(H(X),H(Y))}$,\nwhere H(X) and H(Y) are the entropies of X and Y respectively. Higher NMI score suggests that the method is effective in preserving relevant information.\n", + "references_doi": "10.1371/journal.pone.0159161", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/clustering_performance", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/clustering_performance:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true }, { - "metric_name": "Density preservation", - "metric_summary": "Similarity between local densities in the high-dimensional data and the reduced data.", - "paper_reference": "narayan2021assessing", - "maximize": true, - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "metric_id": "density_preservation", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/metrics/density.py", - "code_version": "v1.0.0" + "task_id": "metrics", + "component_name": "clustering_performance", + "metric_id": "adjusted_rand_index", + "metric_name": "ARI", + "metric_summary": "Adjusted Rand Index (ARI) is a measure of the similarities between two cluster assignments of the reduced-dimensional embeddings and the true cell types.", + "metric_description": "Adjusted Rand Index (ARI) is a measure of similarity between two clusterings by considering all pairs of samples and counting pairs that are assigned in the same or different clusters in the predicted (from the reduced dimensional embeddings) and true clusterings (cell type labels). It is the Rand Index (RI) adjusted for chance.\nAssuming the C as the cell type labels and K as the clustering of the reduced dimensional embedding, Rand Index can be defined as:\n $RI = \\frac{a + b}{{C}_{2}^{n_{samples}}}$,\nwhere 'a' is the number of pairs of elements that are in the same set in C and in the same set in K, 'b' is the number of pairs of elements that are in different sets in C and in different sets in K, and ${C}_{2}^{n_{samples}}$ is the total number of possible pairs in the dataset. Random label assignments can be discounted as follows:\n $ARI = \\frac{RI - E[RI]}{max(RI) - E[RI]}$,\nwhere E[RI] is the expected RI of random labellings.\n", + "references_doi": null, + "references_bibtex": "@InProceedings{santos2009on,\n author = {Santos, Jorge M. and Embrechts, Mark\"},\n editor = {Alippi, Cesare and Polycarpou, Marios and Panayiotou, Christos and Ellinas, Georgios},\n title = {On the Use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification},\n booktitle = {Artificial Neural Networks -- ICANN 2009},\n year = {2009},\n publisher = {Springer Berlin Heidelberg},\n address = {Berlin, Heidelberg},\n pages = {175--184},\n isbn = {978-3-642-04277-5},\n doi = {10.1007/978-3-642-04277-5_18},\n url = {https://doi.org/10.1007/978-3-642-04277-5_18}\n}\n", + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/clustering_performance", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/clustering_performance:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true }, { - "metric_name": "Distance correlation", - "metric_summary": "Spearman correlation between all pairwise Euclidean distances in the original and dimension-reduced data", - "paper_reference": "schober2018correlation", - "maximize": true, - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "metric_id": "distance_correlation", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/metrics/distance_correlation.py", - "code_version": "v1.0.0" + "task_id": "metrics", + "component_name": "coranking", + "metric_id": "continuity_at_k30", + "metric_name": "Continuity at k=30", + "metric_summary": "The continuity metric at k=30 computed on the co-ranking matrix between expression matrix and embedding.", + "metric_description": "The continuity metric at k=30 computed on the co-ranking matrix between expression matrix and embedding.", + "references_doi": "10.1016/j.neunet.2006.05.014", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/coranking", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/coranking:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true }, { - "metric_name": "Distance correlation (spectral)", - "metric_summary": "Spearman correlation between all pairwise diffusion distances in the original and dimension-reduced data", - "paper_reference": "coifman2006diffusion", - "maximize": true, - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "metric_id": "distance_correlation_spectral", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/metrics/distance_correlation.py", - "code_version": "v1.0.0" + "task_id": "metrics", + "component_name": "coranking", + "metric_id": "trustworthiness_at_k30", + "metric_name": "Trustworthiness at k=30", + "metric_summary": "The trustworthiness metric at k=30 computed on the co-ranking matrix between expression matrix and embedding.", + "metric_description": "The trustworthiness metric at k=30 computed on the co-ranking matrix between expression matrix and embedding.", + "references_doi": "10.1016/j.neunet.2006.05.014", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/coranking", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/coranking:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true }, { - "metric_name": "local continuity meta criterion", - "metric_summary": "The local continuity meta criterion is the co-KNN size with baseline removal which favors locality", - "paper_reference": "zhang2021pydrmetrics", - "maximize": true, - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "task_id": "dimensionality_reduction", - "commit_sha": "ff1feaf0b741ec05b10084319a1175dfbf5e6faa", - "metric_id": "lcmc", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/metrics/nn_ranking.py", - "code_version": "v1.0.0" + "task_id": "metrics", + "component_name": "coranking", + "metric_id": "qnx_at_k30", + "metric_name": "The value for QNX at k=30", + "metric_summary": "The QNX metric at k=30 computed on the co-ranking matrix between expression matrix and embedding.", + "metric_description": "The QNX metric at k=30 computed on the co-ranking matrix between expression matrix and embedding.", + "references_doi": "10.1016/j.neucom.2008.12.017", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/coranking", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/coranking:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true }, { - "metric_name": "global property", - "metric_summary": "The global property metric is a summary of the global co-KNN", - "paper_reference": "zhang2021pydrmetrics", - "maximize": true, - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "task_id": "dimensionality_reduction", - "commit_sha": "ff1feaf0b741ec05b10084319a1175dfbf5e6faa", - "metric_id": "qglobal", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/metrics/nn_ranking.py", - "code_version": "v1.0.0" + "task_id": "metrics", + "component_name": "coranking", + "metric_id": "lcmc_at_k30", + "metric_name": "The value for LCMC at k=30", + "metric_summary": "The LCMC metric at k=30 computed on the co-ranking matrix between expression matrix and embedding.", + "metric_description": "The LCMC metric at k=30 computed on the co-ranking matrix between expression matrix and embedding.", + "references_doi": "10.1198/jasa.2009.0111", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/coranking", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/coranking:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true }, { - "metric_name": "local property", - "metric_summary": "The local property metric is a summary of the local co-KNN", - "paper_reference": "zhang2021pydrmetrics", - "maximize": true, - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "task_id": "dimensionality_reduction", - "commit_sha": "ff1feaf0b741ec05b10084319a1175dfbf5e6faa", + "task_id": "metrics", + "component_name": "coranking", + "metric_id": "qnx_auc", + "metric_name": "Area under the QNX curve", + "metric_summary": "The AU-QNX metric at k=30 computed on the co-ranking matrix between expression matrix and embedding.", + "metric_description": "The AU-QNX metric at k=30 computed on the co-ranking matrix between expression matrix and embedding.", + "references_doi": "10.48550/ARXIV.1110.3917", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/coranking", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/coranking:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true + }, + { + "task_id": "metrics", + "component_name": "coranking", "metric_id": "qlocal", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/metrics/nn_ranking.py", - "code_version": "v1.0.0" + "metric_name": "Local quality measure", + "metric_summary": "The local quality metric computed on the co-ranking matrix between expression matrix and embedding.", + "metric_description": "The local quality metric computed on the co-ranking matrix between expression matrix and embedding.", + "references_doi": "10.48550/ARXIV.1110.3917", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/coranking", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/coranking:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true + }, + { + "task_id": "metrics", + "component_name": "coranking", + "metric_id": "qglobal", + "metric_name": "Global quality measure", + "metric_summary": "The Global quality metric computed on the co-ranking matrix between expression matrix and embedding.", + "metric_description": "The Global quality metric computed on the co-ranking matrix between expression matrix and embedding.", + "references_doi": "10.48550/ARXIV.1110.3917", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/coranking", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/coranking:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true + }, + { + "task_id": "metrics", + "component_name": "density_preservation", + "metric_id": "density_preservation", + "metric_name": "Density preservation", + "metric_summary": "Similarity between local densities in the high-dimensional data and the reduced data.", + "metric_description": "\"Similarity between local densities in the high-dimensional data and the reduced data.\nThis is computed as the pearson correlation of local radii with the local radii in the original data space.\"\n", + "references_doi": "10.1038/s41587-020-00801-7", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/density_preservation", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/density_preservation:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true + }, + { + "task_id": "metrics", + "component_name": "distance_correlation", + "metric_id": "waypoint_distance_correlation", + "metric_name": "Waypoint Distance Correlation", + "metric_summary": "Calculates the distance correlation by computing Spearman correlations between distances between waypoint cells.", + "metric_description": "Calculates the distance correlation by computing Spearman correlations\nbetween distances between waypoint cells on the full (or processed) data\nmatrix and the dimensionally-reduced matrix. Also known as the\ncellstruct global single-cell (GS) score when using Pearson correlation.\n", + "references_doi": ["10.1101/2023.11.13.566337", "10.1038/s42003-022-03628-x"], + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/distance_correlation", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/distance_correlation:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true }, { - "metric_name": "co-KNN size", - "metric_summary": "co-KNN size counts how many points are in both k-nearest neighbors before and after the dimensionality reduction", - "paper_reference": "zhang2021pydrmetrics", - "maximize": true, - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "task_id": "dimensionality_reduction", - "commit_sha": "ff1feaf0b741ec05b10084319a1175dfbf5e6faa", - "metric_id": "qnn", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/metrics/nn_ranking.py", - "code_version": "v1.0.0" + "task_id": "metrics", + "component_name": "distance_correlation", + "metric_id": "centroid_distance_correlation", + "metric_name": "Centroid Distance Correlation", + "metric_summary": "Calculates the distance correlation by computing Spearman correlations between distances to label centroids.", + "metric_description": "Calculates the distance correlation by computing Spearman correlations\nbetween distances from waypoint cells to label centroids on the full\n(or processed) data matrix and the dimensionally-reduced matrix. Also\nknown as Point-Cluster Distance (PCD) correlation.\n", + "references_doi": "10.1038/s41467-023-37478-w", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/distance_correlation", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/distance_correlation:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true }, { - "metric_name": "co-KNN AUC", - "metric_summary": "co-KNN AUC is area under the co-KNN curve", - "paper_reference": "zhang2021pydrmetrics", - "maximize": true, - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "task_id": "dimensionality_reduction", - "commit_sha": "ff1feaf0b741ec05b10084319a1175dfbf5e6faa", - "metric_id": "qnn_auc", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/metrics/nn_ranking.py", - "code_version": "v1.0.0" + "task_id": "metrics", + "component_name": "distance_correlation", + "metric_id": "label_distance_correlation", + "metric_name": "Label Distance Correlation", + "metric_summary": "Calculates the distance correlation by computing Spearman correlations between distances between label centroids.", + "metric_description": "Calculates the distance correlation by computing Spearman correlations\nbetween distances between label centroids on the full (or processed)\ndata matrix and the dimensionally-reduced matrix. Also known as the\ncellstruct global cluster (GC) score when using Pearson correlation.\n", + "references_doi": ["10.1101/2023.11.13.566337", "10.1038/s42003-022-03628-x"], + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/distance_correlation", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/distance_correlation:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true }, { - "metric_name": "trustworthiness", - "metric_summary": "a measurement of similarity between the rank of each point's nearest neighbors in the high-dimensional data and the reduced data.", - "paper_reference": "venna2001neighborhood", - "maximize": true, - "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems", - "task_id": "dimensionality_reduction", - "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32", - "metric_id": "trustworthiness", - "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/dimensionality_reduction/metrics/trustworthiness.py", - "code_version": "v1.0.0" + "task_id": "metrics", + "component_name": "spectral_distance_correlation", + "metric_id": "spectral_distance_correlation", + "metric_name": "Spectral Distance Correlation", + "metric_summary": "Spearman correlation between all pairwise diffusion distances in the original and dimension-reduced data.", + "metric_description": "Spearman correlation between all pairwise diffusion distances in the\noriginal and dimension-reduced data.\n", + "references_doi": "10.1016/j.acha.2006.04.006", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_dimensionality_reduction/blob/7adb5a856f2374eda43ee7f133b183e65081635a/src/metrics/spectral_distance_correlation", + "image": "https://ghcr.io/openproblems-bio/task_dimensionality_reduction/metrics/spectral_distance_correlation:build_main", + "code_version": "build_main", + "commit_sha": "7adb5a856f2374eda43ee7f133b183e65081635a", + "maximize": true } -] \ No newline at end of file +] diff --git a/results/dimensionality_reduction/data/quality_control.json b/results/dimensionality_reduction/data/quality_control.json index c924f33b..5f2ee917 100644 --- a/results/dimensionality_reduction/data/quality_control.json +++ b/results/dimensionality_reduction/data/quality_control.json @@ -1,5862 +1,4612 @@ [ { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Task info", "name": "Pct 'task_id' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing([task_info], field)", - "message": "Task metadata field 'task_id' should be defined\n Task id: dimensionality_reduction\n Field: task_id\n" + "message": "Task metadata field 'task_id' should be 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"task_dimensionality_reduction", "category": "Task info", "name": "Pct 'task_summary' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing([task_info], field)", - "message": "Task metadata field 'task_summary' should be defined\n Task id: dimensionality_reduction\n Field: task_summary\n" + "message": "Task metadata field 'task_summary' should be defined\n Task id: task_dimensionality_reduction\n Field: task_summary\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Task info", "name": "Pct 'task_description' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing([task_info], field)", - "message": "Task metadata field 'task_description' should be defined\n Task id: dimensionality_reduction\n Field: task_description\n" + "message": "Task metadata field 'task_description' should be defined\n Task id: task_dimensionality_reduction\n Field: task_description\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Method info", "name": "Pct 'task_id' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(method_info, field)", - "message": "Method metadata field 'task_id' should be defined\n Task id: dimensionality_reduction\n Field: task_id\n" + "message": "Method metadata field 'task_id' should be defined\n Task id: task_dimensionality_reduction\n Field: task_id\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Method info", "name": "Pct 'commit_sha' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(method_info, field)", - "message": "Method metadata field 'commit_sha' should be defined\n Task id: dimensionality_reduction\n Field: commit_sha\n" + "message": "Method metadata field 'commit_sha' should be defined\n Task id: task_dimensionality_reduction\n Field: commit_sha\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Method info", "name": "Pct 'method_id' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(method_info, field)", - "message": "Method metadata field 'method_id' should be defined\n Task id: dimensionality_reduction\n Field: method_id\n" + "message": "Method metadata field 'method_id' should be defined\n Task id: task_dimensionality_reduction\n Field: method_id\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Method info", "name": "Pct 'method_name' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(method_info, field)", - "message": "Method metadata field 'method_name' should be defined\n Task id: dimensionality_reduction\n Field: method_name\n" + "message": "Method metadata field 'method_name' should be defined\n Task id: task_dimensionality_reduction\n Field: method_name\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Method info", "name": "Pct 'method_summary' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(method_info, field)", - "message": "Method metadata field 'method_summary' should be defined\n Task id: dimensionality_reduction\n Field: method_summary\n" + "message": "Method metadata field 'method_summary' should be defined\n Task id: task_dimensionality_reduction\n Field: method_summary\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Method info", "name": "Pct 'paper_reference' missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "value": 0.7857142857142857, + "severity": 2, + "severity_value": 3.0, "code": "percent_missing(method_info, field)", - "message": "Method metadata field 'paper_reference' should be defined\n Task id: dimensionality_reduction\n Field: paper_reference\n" + "message": "Method metadata field 'paper_reference' should be defined\n Task id: task_dimensionality_reduction\n Field: paper_reference\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Method info", "name": "Pct 'is_baseline' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(method_info, field)", - "message": "Method metadata field 'is_baseline' should be defined\n Task id: dimensionality_reduction\n Field: is_baseline\n" + "message": "Method metadata field 'is_baseline' should be defined\n Task id: task_dimensionality_reduction\n Field: is_baseline\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Metric info", "name": "Pct 'task_id' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(metric_info, field)", - "message": "Metric metadata field 'task_id' should be defined\n Task id: dimensionality_reduction\n Field: task_id\n" + "message": "Metric metadata field 'task_id' should be defined\n Task id: task_dimensionality_reduction\n Field: task_id\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Metric info", "name": "Pct 'commit_sha' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(metric_info, field)", - "message": "Metric metadata field 'commit_sha' should be defined\n Task id: dimensionality_reduction\n Field: commit_sha\n" + "message": "Metric metadata field 'commit_sha' should be defined\n Task id: task_dimensionality_reduction\n Field: commit_sha\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Metric info", "name": "Pct 'metric_id' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(metric_info, field)", - "message": "Metric metadata field 'metric_id' should be defined\n Task id: dimensionality_reduction\n Field: metric_id\n" + "message": "Metric metadata field 'metric_id' should be defined\n Task id: task_dimensionality_reduction\n Field: metric_id\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Metric info", "name": "Pct 'metric_name' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(metric_info, field)", - "message": "Metric metadata field 'metric_name' should be defined\n Task id: dimensionality_reduction\n Field: metric_name\n" + "message": "Metric metadata field 'metric_name' should be defined\n Task id: task_dimensionality_reduction\n Field: metric_name\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Metric info", "name": "Pct 'metric_summary' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(metric_info, field)", - "message": "Metric metadata field 'metric_summary' should be defined\n Task id: dimensionality_reduction\n Field: metric_summary\n" + "message": "Metric metadata field 'metric_summary' should be defined\n Task id: task_dimensionality_reduction\n Field: metric_summary\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Metric info", "name": "Pct 'paper_reference' missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "value": 1.0, + "severity": 2, + "severity_value": 3.0, "code": "percent_missing(metric_info, field)", - "message": "Metric metadata field 'paper_reference' should be defined\n Task id: dimensionality_reduction\n Field: paper_reference\n" + "message": "Metric metadata field 'paper_reference' should be defined\n Task id: task_dimensionality_reduction\n Field: paper_reference\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Metric info", "name": "Pct 'maximize' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(metric_info, field)", - "message": "Metric metadata field 'maximize' should be defined\n Task id: dimensionality_reduction\n Field: maximize\n" + "message": "Metric metadata field 'maximize' should be defined\n Task id: task_dimensionality_reduction\n Field: maximize\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Dataset info", "name": "Pct 'task_id' missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "value": 1.0, + "severity": 2, + "severity_value": 3.0, "code": "percent_missing(dataset_info, field)", - "message": "Dataset metadata field 'task_id' should be defined\n Task id: dimensionality_reduction\n Field: task_id\n" + "message": "Dataset metadata field 'task_id' should be defined\n Task id: task_dimensionality_reduction\n Field: task_id\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Dataset info", - "name": "Pct 'commit_sha' missing", + "name": "Pct 'dataset_id' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(dataset_info, field)", - "message": "Dataset metadata field 'commit_sha' should be defined\n Task id: dimensionality_reduction\n Field: commit_sha\n" + "message": "Dataset metadata field 'dataset_id' should be defined\n Task id: task_dimensionality_reduction\n Field: dataset_id\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Dataset info", - "name": "Pct 'dataset_id' missing", + "name": "Pct 'dataset_name' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(dataset_info, field)", - "message": "Dataset metadata field 'dataset_id' should be defined\n Task id: dimensionality_reduction\n Field: dataset_id\n" + "message": "Dataset metadata field 'dataset_name' should be defined\n Task id: task_dimensionality_reduction\n Field: dataset_name\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Dataset info", - "name": "Pct 'dataset_name' missing", + "name": "Pct 'dataset_summary' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(dataset_info, field)", - "message": "Dataset metadata field 'dataset_name' should be defined\n Task id: dimensionality_reduction\n Field: dataset_name\n" + "message": "Dataset metadata field 'dataset_summary' should be defined\n Task id: task_dimensionality_reduction\n Field: dataset_summary\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Dataset info", - "name": "Pct 'dataset_summary' missing", + "name": "Pct 'data_reference' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(dataset_info, field)", - "message": "Dataset metadata field 'dataset_summary' should be defined\n Task id: dimensionality_reduction\n Field: dataset_summary\n" + "message": "Dataset metadata field 'data_reference' should be defined\n Task id: task_dimensionality_reduction\n Field: data_reference\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Dataset info", - "name": "Pct 'data_reference' missing", + "name": "Pct 'data_url' missing", "value": 0.0, "severity": 0, "severity_value": 0.0, "code": "percent_missing(dataset_info, field)", - "message": "Dataset metadata field 'data_reference' should be defined\n Task id: dimensionality_reduction\n Field: data_reference\n" + "message": "Dataset metadata field 'data_url' should be defined\n Task id: task_dimensionality_reduction\n Field: data_url\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw data", "name": "Number of results", - "value": 104, + "value": 224, "severity": 0, "severity_value": 0.0, "code": "len(results) == len(method_info) * len(metric_info) * len(dataset_info)", - "message": "Number of results should be equal to #methods × #metrics × #datasets.\n Task id: dimensionality_reduction\n Number of results: 104\n Number of methods: 26\n Number of metrics: 10\n Number of datasets: 4\n" + "message": "Number of results should be equal to #methods × #metrics × #datasets.\n Task id: task_dimensionality_reduction\n Number of results: 224\n Number of methods: 14\n Number of metrics: 14\n Number of datasets: 16\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Metric 'continuity' %missing", - "value": 0.25, - "severity": 2, - "severity_value": 2.5, + "name": "Metric 'normalized_mutual_information' %missing", + "value": 0.5178571428571428, + "severity": 3, + "severity_value": 5.178571428571428, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n Metric id: continuity\n Percentage missing: 25%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: normalized_mutual_information\n Percentage missing: 52%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Metric 'density_preservation' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "name": "Metric 'adjusted_rand_index' %missing", + "value": 0.5178571428571428, + "severity": 3, + "severity_value": 5.178571428571428, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n Metric id: density_preservation\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: adjusted_rand_index\n Percentage missing: 52%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Metric 'distance_correlation' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "name": "Metric 'continuity_at_k30' %missing", + "value": 0.6919642857142857, + "severity": 3, + "severity_value": 6.919642857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n Metric id: distance_correlation\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: continuity_at_k30\n Percentage missing: 69%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Metric 'distance_correlation_spectral' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "name": "Metric 'trustworthiness_at_k30' %missing", + "value": 0.6919642857142857, + "severity": 3, + "severity_value": 6.919642857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n Metric id: distance_correlation_spectral\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: trustworthiness_at_k30\n Percentage missing: 69%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Metric 'lcmc' %missing", - "value": 0.25, - "severity": 2, - "severity_value": 2.5, + "name": "Metric 'qnx_at_k30' %missing", + "value": 0.6919642857142857, + "severity": 3, + "severity_value": 6.919642857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n Metric id: lcmc\n Percentage missing: 25%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: qnx_at_k30\n Percentage missing: 69%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Metric 'qglobal' %missing", - "value": 0.25, - "severity": 2, - "severity_value": 2.5, + "name": "Metric 'lcmc_at_k30' %missing", + "value": 0.6919642857142857, + "severity": 3, + "severity_value": 6.919642857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n Metric id: qglobal\n Percentage missing: 25%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: lcmc_at_k30\n Percentage missing: 69%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Metric 'qlocal' %missing", - "value": 0.25, - "severity": 2, - "severity_value": 2.5, + "name": "Metric 'qnx_auc' %missing", + "value": 0.6919642857142857, + "severity": 3, + "severity_value": 6.919642857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n Metric id: qlocal\n Percentage missing: 25%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: qnx_auc\n Percentage missing: 69%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Metric 'qnn' %missing", - "value": 0.25, - "severity": 2, - "severity_value": 2.5, + "name": "Metric 'qlocal' %missing", + "value": 0.6919642857142857, + "severity": 3, + "severity_value": 6.919642857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n Metric id: qnn\n Percentage missing: 25%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: qlocal\n Percentage missing: 69%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Metric 'qnn_auc' %missing", - "value": 0.25, - "severity": 2, - "severity_value": 2.5, + "name": "Metric 'qglobal' %missing", + "value": 0.6919642857142857, + "severity": 3, + "severity_value": 6.919642857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n Metric id: qnn_auc\n Percentage missing: 25%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: qglobal\n Percentage missing: 69%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Metric 'trustworthiness' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "name": "Metric 'density_preservation' %missing", + "value": 0.5580357142857143, + "severity": 3, + "severity_value": 5.580357142857142, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n Metric id: trustworthiness\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: density_preservation\n Percentage missing: 56%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'densmap_logCP10k' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Metric 'waypoint_distance_correlation' %missing", + "value": 0.4866071428571429, + "severity": 3, + "severity_value": 4.866071428571429, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: densmap_logCP10k\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: waypoint_distance_correlation\n Percentage missing: 49%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'densmap_logCP10k_1kHVG' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Metric 'centroid_distance_correlation' %missing", + "value": 0.4866071428571429, + "severity": 3, + "severity_value": 4.866071428571429, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: densmap_logCP10k_1kHVG\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: centroid_distance_correlation\n Percentage missing: 49%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'densmap_pca_logCP10k' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Metric 'label_distance_correlation' %missing", + "value": 0.4866071428571429, + "severity": 3, + "severity_value": 4.866071428571429, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: densmap_pca_logCP10k\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: label_distance_correlation\n Percentage missing: 49%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'densmap_pca_logCP10k_1kHVG' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Metric 'spectral_distance_correlation' %missing", + "value": 0.6785714285714286, + "severity": 3, + "severity_value": 6.785714285714286, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: densmap_pca_logCP10k_1kHVG\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n Metric id: spectral_distance_correlation\n Percentage missing: 68%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'diffusion_map' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'random_features' %missing", + "value": 0.5357142857142857, + "severity": 3, + "severity_value": 5.357142857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: diffusion_map\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: random_features\n Percentage missing: 54%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'neuralee_default' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'spectral_features' %missing", + "value": 0.6294642857142857, + "severity": 3, + "severity_value": 6.294642857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: neuralee_default\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: spectral_features\n Percentage missing: 63%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'neuralee_logCP10k_1kHVG' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'true_features' %missing", + "value": 0.6428571428571428, + "severity": 3, + "severity_value": 6.428571428571428, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: neuralee_logCP10k_1kHVG\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: true_features\n Percentage missing: 64%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'pca_logCP10k' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'densmap' %missing", + "value": 0.6741071428571428, + "severity": 3, + "severity_value": 6.741071428571428, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: pca_logCP10k\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: densmap\n Percentage missing: 67%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'pca_logCP10k_1kHVG' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'diffusion_map' %missing", + "value": 0.8125, + "severity": 3, + "severity_value": 8.125, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: pca_logCP10k_1kHVG\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: diffusion_map\n Percentage missing: 81%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'phate_default' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'ivis' %missing", + "value": 0.5, + "severity": 3, + "severity_value": 5.0, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: phate_default\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: ivis\n Percentage missing: 50%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'phate_logCP10k' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'lmds' %missing", + "value": 0.5580357142857143, + "severity": 3, + "severity_value": 5.580357142857142, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: phate_logCP10k\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: lmds\n Percentage missing: 56%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'phate_logCP10k_1kHVG' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'neuralee' %missing", + "value": 0.5267857142857143, + "severity": 3, + "severity_value": 5.267857142857142, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: phate_logCP10k_1kHVG\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: neuralee\n Percentage missing: 53%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'phate_sqrt' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'pca' %missing", + "value": 0.5625, + "severity": 3, + "severity_value": 5.625, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: phate_sqrt\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: pca\n Percentage missing: 56%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'pymde_distances_log_cp10k' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'phate' %missing", + "value": 0.5044642857142857, + "severity": 3, + "severity_value": 5.044642857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: pymde_distances_log_cp10k\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: phate\n Percentage missing: 50%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'pymde_distances_log_cp10k_hvg' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'pymde' %missing", + "value": 0.5892857142857143, + "severity": 3, + "severity_value": 5.892857142857142, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: pymde_distances_log_cp10k_hvg\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: pymde\n Percentage missing: 59%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'pymde_neighbors_log_cp10k' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'simlr' %missing", + "value": 0.9464285714285714, + "severity": 3, + "severity_value": 9.464285714285714, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: pymde_neighbors_log_cp10k\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: simlr\n Percentage missing: 95%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'pymde_neighbors_log_cp10k_hvg' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'tsne' %missing", + "value": 0.5401785714285714, + "severity": 3, + "severity_value": 5.4017857142857135, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: pymde_neighbors_log_cp10k_hvg\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: tsne\n Percentage missing: 54%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'random_features' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Method 'umap' %missing", + "value": 0.5535714285714286, + "severity": 3, + "severity_value": 5.535714285714286, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: random_features\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n method id: umap\n Percentage missing: 55%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'spectral_features' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Dataset 'cellxgene_census/mouse_pancreas_atlas' %missing", + "value": 0.9030612244897959, + "severity": 3, + "severity_value": 9.030612244897958, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: spectral_features\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: cellxgene_census/mouse_pancreas_atlas\n Percentage missing: 90%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'true_features' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Dataset 'openproblems_v1/cengen' %missing", + "value": 0.8061224489795918, + "severity": 3, + "severity_value": 8.061224489795919, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: true_features\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: openproblems_v1/cengen\n Percentage missing: 81%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'tsne_logCP10k' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Dataset 'openproblems_v1/immune_cells' %missing", + "value": 0.4285714285714286, + "severity": 3, + "severity_value": 4.285714285714286, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: tsne_logCP10k\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: openproblems_v1/immune_cells\n Percentage missing: 43%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'tsne_logCP10k_1kHVG' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Dataset 'cellxgene_census/gtex_v9' %missing", + "value": 0.7959183673469388, + "severity": 3, + "severity_value": 7.959183673469387, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: tsne_logCP10k_1kHVG\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: cellxgene_census/gtex_v9\n Percentage missing: 80%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'umap_logCP10k' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Dataset 'openproblems_v1/tnbc_wu2021' %missing", + "value": 0.7857142857142857, + "severity": 3, + "severity_value": 7.857142857142857, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: umap_logCP10k\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: openproblems_v1/tnbc_wu2021\n Percentage missing: 79%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'umap_logCP10k_1kHVG' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Dataset 'cellxgene_census/dkd' %missing", + "value": 0.4744897959183674, + "severity": 3, + "severity_value": 4.744897959183674, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: umap_logCP10k_1kHVG\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: cellxgene_census/dkd\n Percentage missing: 47%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'umap_pca_logCP10k' %missing", - "value": 0.15000000000000002, - "severity": 1, - "severity_value": 1.5000000000000002, + "name": "Dataset 'cellxgene_census/tabula_sapiens' %missing", + "value": 1.0, + "severity": 3, + "severity_value": 10.0, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: umap_pca_logCP10k\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: cellxgene_census/tabula_sapiens\n Percentage missing: 100%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Method 'umap_pca_logCP10k_1kHVG' %missing", - "value": 0.15000000000000002, + "name": "Dataset 'openproblems_v1/pancreas' %missing", + "value": 0.18877551020408156, "severity": 1, - "severity_value": 1.5000000000000002, + "severity_value": 1.8877551020408156, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n method id: umap_pca_logCP10k_1kHVG\n Percentage missing: 15%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: openproblems_v1/pancreas\n Percentage missing: 19%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Dataset 'mouse_hspc_nestorowa2016' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "name": "Dataset 'cellxgene_census/hypomap' %missing", + "value": 0.8673469387755102, + "severity": 3, + "severity_value": 8.673469387755102, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n dataset id: mouse_hspc_nestorowa2016\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: cellxgene_census/hypomap\n Percentage missing: 87%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Dataset 'olsson_2016_mouse_blood' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "name": "Dataset 'allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2' %missing", + "value": 0.9438775510204082, + "severity": 3, + "severity_value": 9.438775510204081, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n dataset id: olsson_2016_mouse_blood\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2\n Percentage missing: 94%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Dataset 'tenx_5k_pbmc' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "name": "Dataset 'openproblems_v1/zebrafish' %missing", + "value": 0.33673469387755095, + "severity": 3, + "severity_value": 3.3673469387755093, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n dataset id: tenx_5k_pbmc\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: openproblems_v1/zebrafish\n Percentage missing: 34%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Raw results", - "name": "Dataset 'zebrafish_labs' %missing", - "value": 0.6, - "severity": 3, - "severity_value": 5.999999999999999, + "name": "Dataset 'openproblems_v1/allen_brain_atlas' %missing", + "value": 0.26530612244897955, + "severity": 2, + "severity_value": 2.6530612244897953, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: dimensionality_reduction\n dataset id: zebrafish_labs\n Percentage missing: 60%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_logCP10k continuity", - "value": 0.47526992417135194, - "severity": 0, - "severity_value": -0.47526992417135194, - "code": "worst_score >= -1", - "message": "Method densmap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: continuity\n Worst score: 0.47526992417135194%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_logCP10k continuity", - "value": 0.9665645852166399, - "severity": 0, - "severity_value": 0.48328229260831995, - "code": "best_score <= 2", - "message": "Method densmap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: continuity\n Best score: 0.9665645852166399%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_logCP10k_1kHVG continuity", - "value": 0.20564606558352608, - "severity": 0, - "severity_value": -0.20564606558352608, - "code": "worst_score >= -1", - "message": "Method densmap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: continuity\n Worst score: 0.20564606558352608%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_logCP10k_1kHVG continuity", - "value": 0.9271973334872036, - "severity": 0, - "severity_value": 0.4635986667436018, - "code": "best_score <= 2", - "message": "Method densmap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: continuity\n Best score: 0.9271973334872036%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k continuity", - "value": 0.25679102400619935, - "severity": 0, - "severity_value": -0.25679102400619935, - "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: continuity\n Worst score: 0.25679102400619935%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_pca_logCP10k continuity", - "value": 0.9589227949373869, - "severity": 0, - "severity_value": 0.47946139746869343, - "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: continuity\n Best score: 0.9589227949373869%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k_1kHVG continuity", - "value": 0.17309946791609446, - "severity": 0, - "severity_value": -0.17309946791609446, - "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: continuity\n Worst score: 0.17309946791609446%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_pca_logCP10k_1kHVG continuity", - "value": 0.94872433805558, - "severity": 0, - "severity_value": 0.47436216902779, - "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: continuity\n Best score: 0.94872433805558%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score diffusion_map continuity", - "value": -0.29286072344507724, - "severity": 0, - "severity_value": 0.29286072344507724, - "code": "worst_score >= -1", - "message": "Method diffusion_map performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: continuity\n Worst score: -0.29286072344507724%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score diffusion_map continuity", - "value": 0.6200256307785902, - "severity": 0, - "severity_value": 0.3100128153892951, - "code": "best_score <= 2", - "message": "Method diffusion_map performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: continuity\n Best score: 0.6200256307785902%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score neuralee_default continuity", - "value": 0.34462852597422344, - "severity": 0, - "severity_value": -0.34462852597422344, - "code": "worst_score >= -1", - "message": "Method neuralee_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: continuity\n Worst score: 0.34462852597422344%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score neuralee_default continuity", - "value": 0.9661948694276387, - "severity": 0, - "severity_value": 0.48309743471381933, - "code": "best_score <= 2", - "message": "Method neuralee_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: continuity\n Best score: 0.9661948694276387%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score neuralee_logCP10k_1kHVG continuity", - "value": 0.31909440490164465, - "severity": 0, - "severity_value": -0.31909440490164465, - "code": "worst_score >= -1", - "message": "Method neuralee_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: continuity\n Worst score: 0.31909440490164465%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score neuralee_logCP10k_1kHVG continuity", - "value": 0.9255818393324884, - "severity": 0, - "severity_value": 0.4627909196662442, - "code": "best_score <= 2", - "message": "Method neuralee_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: continuity\n Best score: 0.9255818393324884%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: openproblems_v1/allen_brain_atlas\n Percentage missing: 27%\n" }, { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pca_logCP10k continuity", - "value": 0.18857126448435055, - "severity": 0, - "severity_value": -0.18857126448435055, - "code": "worst_score >= -1", - "message": "Method pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: continuity\n Worst score: 0.18857126448435055%\n" + "task_id": "task_dimensionality_reduction", + "category": "Raw results", + "name": "Dataset 'cellxgene_census/immune_cell_atlas' %missing", + "value": 0.9081632653061225, + "severity": 3, + "severity_value": 9.081632653061224, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: cellxgene_census/immune_cell_atlas\n Percentage missing: 91%\n" }, { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pca_logCP10k continuity", - "value": 0.9306326271690527, + "task_id": "task_dimensionality_reduction", + "category": "Raw results", + "name": "Dataset 'openproblems_v1/mouse_hspc_nestorowa2016' %missing", + "value": 0.08163265306122447, "severity": 0, - "severity_value": 0.46531631358452635, - "code": "best_score <= 2", - "message": "Method pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: continuity\n Best score: 0.9306326271690527%\n" + "severity_value": 0.8163265306122447, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: openproblems_v1/mouse_hspc_nestorowa2016\n Percentage missing: 8%\n" }, { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pca_logCP10k_1kHVG continuity", - "value": 0.19055402665420088, - "severity": 0, - "severity_value": -0.19055402665420088, - "code": "worst_score >= -1", - "message": "Method pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: continuity\n Worst score: 0.19055402665420088%\n" + "task_id": "task_dimensionality_reduction", + "category": "Raw results", + "name": "Dataset 'cellxgene_census/hcla' %missing", + "value": 1.0, + "severity": 3, + "severity_value": 10.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: cellxgene_census/hcla\n Percentage missing: 100%\n" }, { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pca_logCP10k_1kHVG continuity", - "value": 0.935172081685106, + "task_id": "task_dimensionality_reduction", + "category": "Raw results", + "name": "Dataset 'openproblems_v1/mouse_blood_olsson_labelled' %missing", + "value": 0.015306122448979553, "severity": 0, - "severity_value": 0.467586040842553, - "code": "best_score <= 2", - "message": "Method pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: continuity\n Best score: 0.935172081685106%\n" + "severity_value": 0.15306122448979553, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_dimensionality_reduction\n dataset id: openproblems_v1/mouse_blood_olsson_labelled\n Percentage missing: 2%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_default continuity", - "value": 0.18421360172732457, + "name": "Worst score random_features normalized_mutual_information", + "value": 0, "severity": 0, - "severity_value": -0.18421360172732457, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: continuity\n Worst score: 0.18421360172732457%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: normalized_mutual_information\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_default continuity", - "value": 0.9438309821831182, + "name": "Best score random_features normalized_mutual_information", + "value": 0, "severity": 0, - "severity_value": 0.4719154910915591, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method phate_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: continuity\n Best score: 0.9438309821831182%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: normalized_mutual_information\n Best score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k continuity", - "value": 0.26491882868406935, + "name": "Worst score spectral_features normalized_mutual_information", + "value": 0.0, "severity": 0, - "severity_value": -0.26491882868406935, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: continuity\n Worst score: 0.26491882868406935%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k continuity", - "value": 0.942253100732949, + "name": "Best score spectral_features normalized_mutual_information", + "value": 1.0, "severity": 0, - "severity_value": 0.4711265503664745, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method phate_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: continuity\n Best score: 0.942253100732949%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: normalized_mutual_information\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k_1kHVG continuity", - "value": 0.2662548204755576, + "name": "Worst score true_features normalized_mutual_information", + "value": 0, "severity": 0, - "severity_value": -0.2662548204755576, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: continuity\n Worst score: 0.2662548204755576%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: normalized_mutual_information\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k_1kHVG continuity", - "value": 0.9460827579230663, + "name": "Best score true_features normalized_mutual_information", + "value": 1, "severity": 0, - "severity_value": 0.47304137896153314, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method phate_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: continuity\n Best score: 0.9460827579230663%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: normalized_mutual_information\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_sqrt continuity", - "value": 0.17224012681183978, + "name": "Worst score densmap normalized_mutual_information", + "value": 0.0, "severity": 0, - "severity_value": -0.17224012681183978, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_sqrt performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: continuity\n Worst score: 0.17224012681183978%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_sqrt continuity", - "value": 0.9465869676252919, + "name": "Best score densmap normalized_mutual_information", + "value": 0.9912, "severity": 0, - "severity_value": 0.47329348381264597, + "severity_value": 0.4956, "code": "best_score <= 2", - "message": "Method phate_sqrt performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: continuity\n Best score: 0.9465869676252919%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: normalized_mutual_information\n Best score: 0.9912%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k continuity", - "value": 0.3473104030427833, + "name": "Worst score diffusion_map normalized_mutual_information", + "value": 0.0, "severity": 0, - "severity_value": -0.3473104030427833, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: continuity\n Worst score: 0.3473104030427833%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k continuity", - "value": 0.7926188316734936, + "name": "Best score diffusion_map normalized_mutual_information", + "value": 0.7559, "severity": 0, - "severity_value": 0.3963094158367468, + "severity_value": 0.37795, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: continuity\n Best score: 0.7926188316734936%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: normalized_mutual_information\n Best score: 0.7559%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k_hvg continuity", - "value": 0.171041938466826, + "name": "Worst score ivis normalized_mutual_information", + "value": 0.0, "severity": 0, - "severity_value": -0.171041938466826, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: continuity\n Worst score: 0.171041938466826%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k_hvg continuity", - "value": 0.7477932670658687, + "name": "Best score ivis normalized_mutual_information", + "value": 1.2289, "severity": 0, - "severity_value": 0.37389663353293434, + "severity_value": 0.61445, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: continuity\n Best score: 0.7477932670658687%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: normalized_mutual_information\n Best score: 1.2289%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k continuity", - "value": 0.23586476136077475, + "name": "Worst score lmds normalized_mutual_information", + "value": 0.0, "severity": 0, - "severity_value": -0.23586476136077475, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: continuity\n Worst score: 0.23586476136077475%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k continuity", - "value": 0.9589437383751453, + "name": "Best score lmds normalized_mutual_information", + "value": 0.9089, "severity": 0, - "severity_value": 0.4794718691875727, + "severity_value": 0.45445, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: continuity\n Best score: 0.9589437383751453%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: normalized_mutual_information\n Best score: 0.9089%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k_hvg continuity", - "value": 0.27924092055523647, + "name": "Worst score neuralee normalized_mutual_information", + "value": 0.0, "severity": 0, - "severity_value": -0.27924092055523647, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: continuity\n Worst score: 0.27924092055523647%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k_hvg continuity", - "value": 0.9477082251433807, + "name": "Best score neuralee normalized_mutual_information", + "value": 1.3235, "severity": 0, - "severity_value": 0.47385411257169036, + "severity_value": 0.66175, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: continuity\n Best score: 0.9477082251433807%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: normalized_mutual_information\n Best score: 1.3235%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score random_features continuity", + "name": "Worst score pca normalized_mutual_information", "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method random_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: continuity\n Worst score: 0.0%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score random_features continuity", - "value": 0.44138763314714935, + "name": "Best score pca normalized_mutual_information", + "value": 0.8343, "severity": 0, - "severity_value": 0.22069381657357467, + "severity_value": 0.41715, "code": "best_score <= 2", - "message": "Method random_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: continuity\n Best score: 0.44138763314714935%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: normalized_mutual_information\n Best score: 0.8343%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score spectral_features continuity", + "name": "Worst score phate normalized_mutual_information", "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method spectral_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: continuity\n Worst score: 0.0%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score spectral_features continuity", - "value": 0.8262031434406542, + "name": "Best score phate normalized_mutual_information", + "value": 1.2364, "severity": 0, - "severity_value": 0.4131015717203271, + "severity_value": 0.6182, "code": "best_score <= 2", - "message": "Method spectral_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: continuity\n Best score: 0.8262031434406542%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: normalized_mutual_information\n Best score: 1.2364%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score true_features continuity", - "value": 1.0, + "name": "Worst score pymde normalized_mutual_information", + "value": 0.0, "severity": 0, - "severity_value": -1.0, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method true_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: continuity\n Worst score: 1.0%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score true_features continuity", - "value": 1.0, + "name": "Best score pymde normalized_mutual_information", + "value": 1.1965, "severity": 0, - "severity_value": 0.5, + "severity_value": 0.59825, "code": "best_score <= 2", - "message": "Method true_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: continuity\n Best score: 1.0%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: normalized_mutual_information\n Best score: 1.1965%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k continuity", - "value": 0.2820387908534595, + "name": "Worst score simlr normalized_mutual_information", + "value": 0, "severity": 0, - "severity_value": -0.2820387908534595, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: continuity\n Worst score: 0.2820387908534595%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: normalized_mutual_information\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k continuity", - "value": 0.9630030610187772, + "name": "Best score simlr normalized_mutual_information", + "value": 0, "severity": 0, - "severity_value": 0.4815015305093886, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method tsne_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: continuity\n Best score: 0.9630030610187772%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: normalized_mutual_information\n Best score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k_1kHVG continuity", - "value": 0.2877724833914735, + "name": "Worst score tsne normalized_mutual_information", + "value": 0.0, "severity": 0, - "severity_value": -0.2877724833914735, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: continuity\n Worst score: 0.2877724833914735%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k_1kHVG continuity", - "value": 0.9492442197180331, + "name": "Best score tsne normalized_mutual_information", + "value": 1.2267, "severity": 0, - "severity_value": 0.47462210985901654, + "severity_value": 0.61335, "code": "best_score <= 2", - "message": "Method tsne_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: continuity\n Best score: 0.9492442197180331%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: normalized_mutual_information\n Best score: 1.2267%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k continuity", - "value": 0.4664961287143587, - "severity": 0, - "severity_value": -0.4664961287143587, - "code": "worst_score >= -1", - "message": "Method umap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: continuity\n Worst score: 0.4664961287143587%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_logCP10k continuity", - "value": 0.9596400008263396, - "severity": 0, - "severity_value": 0.4798200004131698, - "code": "best_score <= 2", - "message": "Method umap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: continuity\n Best score: 0.9596400008263396%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_logCP10k_1kHVG continuity", - "value": 0.26823245956469327, - "severity": 0, - "severity_value": -0.26823245956469327, - "code": "worst_score >= -1", - "message": "Method umap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: continuity\n Worst score: 0.26823245956469327%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_logCP10k_1kHVG continuity", - "value": 0.9210597664450508, - "severity": 0, - "severity_value": 0.4605298832225254, - "code": "best_score <= 2", - "message": "Method umap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: continuity\n Best score: 0.9210597664450508%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_pca_logCP10k continuity", - "value": 0.12220810198880588, - "severity": 0, - "severity_value": -0.12220810198880588, - "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: continuity\n Worst score: 0.12220810198880588%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_pca_logCP10k continuity", - "value": 0.950890915320837, - "severity": 0, - "severity_value": 0.4754454576604185, - "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: continuity\n Best score: 0.950890915320837%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_pca_logCP10k_1kHVG continuity", - "value": 0.25657078722112103, - "severity": 0, - "severity_value": -0.25657078722112103, - "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: continuity\n Worst score: 0.25657078722112103%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_pca_logCP10k_1kHVG continuity", - "value": 0.9455635886224418, - "severity": 0, - "severity_value": 0.4727817943112209, - "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: continuity\n Best score: 0.9455635886224418%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_logCP10k density_preservation", - "value": 0.2955350778950185, - "severity": 0, - "severity_value": -0.2955350778950185, - "code": "worst_score >= -1", - "message": "Method densmap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: density_preservation\n Worst score: 0.2955350778950185%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_logCP10k density_preservation", - "value": 0.8613167348869383, - "severity": 0, - "severity_value": 0.43065836744346914, - "code": "best_score <= 2", - "message": "Method densmap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: density_preservation\n Best score: 0.8613167348869383%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_logCP10k_1kHVG density_preservation", - "value": 0.23991535824316948, - "severity": 0, - "severity_value": -0.23991535824316948, - "code": "worst_score >= -1", - "message": "Method densmap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: density_preservation\n Worst score: 0.23991535824316948%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_logCP10k_1kHVG density_preservation", - "value": 0.6596090030162456, - "severity": 0, - "severity_value": 0.3298045015081228, - "code": "best_score <= 2", - "message": "Method densmap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: density_preservation\n Best score: 0.6596090030162456%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k density_preservation", - "value": 0.10570710656816465, - "severity": 0, - "severity_value": -0.10570710656816465, - "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: density_preservation\n Worst score: 0.10570710656816465%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_pca_logCP10k density_preservation", - "value": 0.7150885137445556, - "severity": 0, - "severity_value": 0.3575442568722778, - "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: density_preservation\n Best score: 0.7150885137445556%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k_1kHVG density_preservation", - "value": 0.2552368695266385, - "severity": 0, - "severity_value": -0.2552368695266385, - "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: density_preservation\n Worst score: 0.2552368695266385%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_pca_logCP10k_1kHVG density_preservation", - "value": 0.5527788600621616, - "severity": 0, - "severity_value": 0.2763894300310808, - "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: density_preservation\n Best score: 0.5527788600621616%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score diffusion_map density_preservation", - "value": -0.4960720063510423, - "severity": 0, - "severity_value": 0.4960720063510423, - "code": "worst_score >= -1", - "message": "Method diffusion_map performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: density_preservation\n Worst score: -0.4960720063510423%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score diffusion_map density_preservation", - "value": 0.20689710779847084, - "severity": 0, - "severity_value": 0.10344855389923542, - "code": "best_score <= 2", - "message": "Method diffusion_map performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: density_preservation\n Best score: 0.20689710779847084%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score neuralee_default density_preservation", - "value": -0.19887548657996712, - "severity": 0, - "severity_value": 0.19887548657996712, - "code": "worst_score >= -1", - "message": "Method neuralee_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: density_preservation\n Worst score: -0.19887548657996712%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score neuralee_default density_preservation", - "value": 0.1469188183790711, - "severity": 0, - "severity_value": 0.07345940918953554, - "code": "best_score <= 2", - "message": "Method neuralee_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: density_preservation\n Best score: 0.1469188183790711%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score neuralee_logCP10k_1kHVG density_preservation", - "value": 0.07624106970733975, - "severity": 0, - "severity_value": -0.07624106970733975, - "code": "worst_score >= -1", - "message": "Method neuralee_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: density_preservation\n Worst score: 0.07624106970733975%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score neuralee_logCP10k_1kHVG density_preservation", - "value": 0.3468706881596839, - "severity": 0, - "severity_value": 0.17343534407984196, - "code": "best_score <= 2", - "message": "Method neuralee_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: density_preservation\n Best score: 0.3468706881596839%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pca_logCP10k density_preservation", - "value": -0.13765550123714906, - "severity": 0, - "severity_value": 0.13765550123714906, - "code": "worst_score >= -1", - "message": "Method pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: density_preservation\n Worst score: -0.13765550123714906%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pca_logCP10k density_preservation", - "value": 0.5052678995911704, - "severity": 0, - "severity_value": 0.2526339497955852, - "code": "best_score <= 2", - "message": "Method pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: density_preservation\n Best score: 0.5052678995911704%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pca_logCP10k_1kHVG density_preservation", - "value": 0.06598168528876719, - "severity": 0, - "severity_value": -0.06598168528876719, - "code": "worst_score >= -1", - "message": "Method pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: density_preservation\n Worst score: 0.06598168528876719%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pca_logCP10k_1kHVG density_preservation", - "value": 0.3584577631902049, - "severity": 0, - "severity_value": 0.17922888159510245, - "code": "best_score <= 2", - "message": "Method pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: density_preservation\n Best score: 0.3584577631902049%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score phate_default density_preservation", - "value": -0.3654036030088246, - "severity": 0, - "severity_value": 0.3654036030088246, - "code": "worst_score >= -1", - "message": "Method phate_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: density_preservation\n Worst score: -0.3654036030088246%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score phate_default density_preservation", - "value": 0.3163491344382979, - "severity": 0, - "severity_value": 0.15817456721914894, - "code": "best_score <= 2", - "message": "Method phate_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: density_preservation\n Best score: 0.3163491344382979%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score phate_logCP10k density_preservation", - "value": -0.2685649319509923, - "severity": 0, - "severity_value": 0.2685649319509923, - "code": "worst_score >= -1", - "message": "Method phate_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: density_preservation\n Worst score: -0.2685649319509923%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score phate_logCP10k density_preservation", - "value": 0.1732406423793636, - "severity": 0, - "severity_value": 0.0866203211896818, - "code": "best_score <= 2", - "message": "Method phate_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: density_preservation\n Best score: 0.1732406423793636%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score phate_logCP10k_1kHVG density_preservation", - "value": -0.4799646776309383, - "severity": 0, - "severity_value": 0.4799646776309383, - "code": "worst_score >= -1", - "message": "Method phate_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: density_preservation\n Worst score: -0.4799646776309383%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score phate_logCP10k_1kHVG density_preservation", - "value": 0.19414568527902007, - "severity": 0, - "severity_value": 0.09707284263951003, - "code": "best_score <= 2", - "message": "Method phate_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: density_preservation\n Best score: 0.19414568527902007%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score phate_sqrt density_preservation", - "value": -0.2114017960057374, - "severity": 0, - "severity_value": 0.2114017960057374, - "code": "worst_score >= -1", - "message": "Method phate_sqrt performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: density_preservation\n Worst score: -0.2114017960057374%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score phate_sqrt density_preservation", - "value": 0.2938020907734585, - "severity": 0, - "severity_value": 0.14690104538672924, - "code": "best_score <= 2", - "message": "Method phate_sqrt performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: density_preservation\n Best score: 0.2938020907734585%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k density_preservation", - "value": 0.09920962584352336, - "severity": 0, - "severity_value": -0.09920962584352336, - "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: density_preservation\n Worst score: 0.09920962584352336%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k density_preservation", - "value": 0.5501213523818723, - "severity": 0, - "severity_value": 0.27506067619093616, - "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: density_preservation\n Best score: 0.5501213523818723%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k_hvg density_preservation", - "value": 0.09963920269152156, - "severity": 0, - "severity_value": -0.09963920269152156, - "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: density_preservation\n Worst score: 0.09963920269152156%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k_hvg density_preservation", - "value": 0.37570687108996553, - "severity": 0, - "severity_value": 0.18785343554498277, - "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: density_preservation\n Best score: 0.37570687108996553%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k density_preservation", - "value": -0.1227210790061811, - "severity": 0, - "severity_value": 0.1227210790061811, - "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: density_preservation\n Worst score: -0.1227210790061811%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k density_preservation", - "value": 0.20651999473024432, - "severity": 0, - "severity_value": 0.10325999736512216, - "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: density_preservation\n Best score: 0.20651999473024432%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k_hvg density_preservation", - "value": -0.22851357557300056, - "severity": 0, - "severity_value": 0.22851357557300056, - "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: density_preservation\n Worst score: -0.22851357557300056%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k_hvg density_preservation", - "value": 0.24780750761417839, - "severity": 0, - "severity_value": 0.12390375380708919, - "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: density_preservation\n Best score: 0.24780750761417839%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score random_features density_preservation", - "value": 0.0, - "severity": 0, - "severity_value": -0.0, - "code": "worst_score >= -1", - "message": "Method random_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: density_preservation\n Worst score: 0.0%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score random_features density_preservation", - "value": 0.0772305624804399, - "severity": 0, - "severity_value": 0.03861528124021995, - "code": "best_score <= 2", - "message": "Method random_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: density_preservation\n Best score: 0.0772305624804399%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score spectral_features density_preservation", - "value": 0.0, - "severity": 0, - "severity_value": -0.0, - "code": "worst_score >= -1", - "message": "Method spectral_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: density_preservation\n Worst score: 0.0%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score spectral_features density_preservation", - "value": 0.2992703304961135, - "severity": 0, - "severity_value": 0.14963516524805676, - "code": "best_score <= 2", - "message": "Method spectral_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: density_preservation\n Best score: 0.2992703304961135%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score true_features density_preservation", - "value": 1.0, - "severity": 0, - "severity_value": -1.0, - "code": "worst_score >= -1", - "message": "Method true_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: density_preservation\n Worst score: 1.0%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score true_features density_preservation", - "value": 1.0, - "severity": 0, - "severity_value": 0.5, - "code": "best_score <= 2", - "message": "Method true_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: density_preservation\n Best score: 1.0%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score tsne_logCP10k density_preservation", - "value": -0.02481524751011151, - "severity": 0, - "severity_value": 0.02481524751011151, - "code": "worst_score >= -1", - "message": "Method tsne_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: density_preservation\n Worst score: -0.02481524751011151%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score tsne_logCP10k density_preservation", - "value": 0.40935398601211004, - "severity": 0, - "severity_value": 0.20467699300605502, - "code": "best_score <= 2", - "message": "Method tsne_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: density_preservation\n Best score: 0.40935398601211004%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score tsne_logCP10k_1kHVG density_preservation", - "value": -0.1610314880081163, - "severity": 0, - "severity_value": 0.1610314880081163, - "code": "worst_score >= -1", - "message": "Method tsne_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: density_preservation\n Worst score: -0.1610314880081163%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score tsne_logCP10k_1kHVG density_preservation", - "value": 0.28922278467346957, - "severity": 0, - "severity_value": 0.14461139233673478, - "code": "best_score <= 2", - "message": "Method tsne_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: density_preservation\n Best score: 0.28922278467346957%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_logCP10k density_preservation", - "value": -0.06000165321719728, - "severity": 0, - "severity_value": 0.06000165321719728, - "code": "worst_score >= -1", - "message": "Method umap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: density_preservation\n Worst score: -0.06000165321719728%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_logCP10k density_preservation", - "value": 0.4073390952182907, - "severity": 0, - "severity_value": 0.20366954760914535, - "code": "best_score <= 2", - "message": "Method umap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: density_preservation\n Best score: 0.4073390952182907%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_logCP10k_1kHVG density_preservation", - "value": -0.212010964625805, - "severity": 0, - "severity_value": 0.212010964625805, - "code": "worst_score >= -1", - "message": "Method umap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: density_preservation\n Worst score: -0.212010964625805%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_logCP10k_1kHVG density_preservation", - "value": 0.16434303866816527, - "severity": 0, - "severity_value": 0.08217151933408263, - "code": "best_score <= 2", - "message": "Method umap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: density_preservation\n Best score: 0.16434303866816527%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_pca_logCP10k density_preservation", - "value": -0.06035317608819602, - "severity": 0, - "severity_value": 0.06035317608819602, - "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: density_preservation\n Worst score: -0.06035317608819602%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_pca_logCP10k density_preservation", - "value": 0.1723535160035282, - "severity": 0, - "severity_value": 0.0861767580017641, - "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: density_preservation\n Best score: 0.1723535160035282%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_pca_logCP10k_1kHVG density_preservation", - "value": -0.14079223895258283, - "severity": 0, - "severity_value": 0.14079223895258283, - "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: density_preservation\n Worst score: -0.14079223895258283%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_pca_logCP10k_1kHVG density_preservation", - "value": 0.157406024925527, - "severity": 0, - "severity_value": 0.0787030124627635, - "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: density_preservation\n Best score: 0.157406024925527%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_logCP10k distance_correlation", - "value": 0.3181150688178256, - "severity": 0, - "severity_value": -0.3181150688178256, - "code": "worst_score >= -1", - "message": "Method densmap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: distance_correlation\n Worst score: 0.3181150688178256%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_logCP10k distance_correlation", - "value": 0.7906704932995618, - "severity": 0, - "severity_value": 0.3953352466497809, - "code": "best_score <= 2", - "message": "Method densmap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: distance_correlation\n Best score: 0.7906704932995618%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_logCP10k_1kHVG distance_correlation", - "value": 0.029324627269438817, - "severity": 0, - "severity_value": -0.029324627269438817, - "code": "worst_score >= -1", - "message": "Method densmap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: distance_correlation\n Worst score: 0.029324627269438817%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_logCP10k_1kHVG distance_correlation", - "value": 0.6050161807526102, - "severity": 0, - "severity_value": 0.3025080903763051, - "code": "best_score <= 2", - "message": "Method densmap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: distance_correlation\n Best score: 0.6050161807526102%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k distance_correlation", - "value": 0.2219893131222716, - "severity": 0, - "severity_value": -0.2219893131222716, - "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: distance_correlation\n Worst score: 0.2219893131222716%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_pca_logCP10k distance_correlation", - "value": 0.5492636248119163, - "severity": 0, - "severity_value": 0.27463181240595813, - "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: distance_correlation\n Best score: 0.5492636248119163%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k_1kHVG distance_correlation", - "value": 0.008177772310870517, - "severity": 0, - "severity_value": -0.008177772310870517, - "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: distance_correlation\n Worst score: 0.008177772310870517%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score densmap_pca_logCP10k_1kHVG distance_correlation", - "value": 0.6636352448019011, - "severity": 0, - "severity_value": 0.33181762240095053, - "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: distance_correlation\n Best score: 0.6636352448019011%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score diffusion_map distance_correlation", - "value": -0.06865590690619464, - "severity": 0, - "severity_value": 0.06865590690619464, - "code": "worst_score >= -1", - "message": "Method diffusion_map performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: distance_correlation\n Worst score: -0.06865590690619464%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score diffusion_map distance_correlation", - "value": 0.7387861275003851, - "severity": 0, - "severity_value": 0.36939306375019254, - "code": "best_score <= 2", - "message": "Method diffusion_map performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: distance_correlation\n Best score: 0.7387861275003851%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score neuralee_default distance_correlation", - "value": 0.006027144785871717, - "severity": 0, - "severity_value": -0.006027144785871717, - "code": "worst_score >= -1", - "message": "Method neuralee_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: distance_correlation\n Worst score: 0.006027144785871717%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score neuralee_default distance_correlation", - "value": 0.5997196367335375, - "severity": 0, - "severity_value": 0.2998598183667687, - "code": "best_score <= 2", - "message": "Method neuralee_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: distance_correlation\n Best score: 0.5997196367335375%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score neuralee_logCP10k_1kHVG distance_correlation", - "value": 0.07587041486178933, - "severity": 0, - "severity_value": -0.07587041486178933, - "code": "worst_score >= -1", - "message": "Method neuralee_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: distance_correlation\n Worst score: 0.07587041486178933%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score neuralee_logCP10k_1kHVG distance_correlation", - "value": 0.5834253747061966, - "severity": 0, - "severity_value": 0.2917126873530983, - "code": "best_score <= 2", - "message": "Method neuralee_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: distance_correlation\n Best score: 0.5834253747061966%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pca_logCP10k distance_correlation", - "value": 0.07964916253602294, - "severity": 0, - "severity_value": -0.07964916253602294, - "code": "worst_score >= -1", - "message": "Method pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: distance_correlation\n Worst score: 0.07964916253602294%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pca_logCP10k distance_correlation", - "value": 0.8462625321131627, - "severity": 0, - "severity_value": 0.42313126605658136, - "code": "best_score <= 2", - "message": "Method pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: distance_correlation\n Best score: 0.8462625321131627%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pca_logCP10k_1kHVG distance_correlation", - "value": -0.03985506592302395, - "severity": 0, - "severity_value": 0.03985506592302395, - "code": "worst_score >= -1", - "message": "Method pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: distance_correlation\n Worst score: -0.03985506592302395%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pca_logCP10k_1kHVG distance_correlation", - "value": 0.5080833384282012, - "severity": 0, - "severity_value": 0.2540416692141006, - "code": "best_score <= 2", - "message": "Method pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: distance_correlation\n Best score: 0.5080833384282012%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score phate_default distance_correlation", - "value": -0.023984637336505382, - "severity": 0, - "severity_value": 0.023984637336505382, - "code": "worst_score >= -1", - "message": "Method phate_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: distance_correlation\n Worst score: -0.023984637336505382%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score phate_default distance_correlation", - "value": 0.5599921942524181, - "severity": 0, - "severity_value": 0.27999609712620904, - "code": "best_score <= 2", - "message": "Method phate_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: distance_correlation\n Best score: 0.5599921942524181%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score phate_logCP10k distance_correlation", - "value": -0.00828585814614641, - "severity": 0, - "severity_value": 0.00828585814614641, - "code": "worst_score >= -1", - "message": "Method phate_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: distance_correlation\n Worst score: -0.00828585814614641%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score phate_logCP10k distance_correlation", - "value": 0.6880245250363551, - "severity": 0, - "severity_value": 0.34401226251817757, - "code": "best_score <= 2", - "message": "Method phate_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: distance_correlation\n Best score: 0.6880245250363551%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score phate_logCP10k_1kHVG distance_correlation", - "value": -0.0035757510018645865, - "severity": 0, - "severity_value": 0.0035757510018645865, - "code": "worst_score >= -1", - "message": "Method phate_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: distance_correlation\n Worst score: -0.0035757510018645865%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score phate_logCP10k_1kHVG distance_correlation", - "value": 0.5342489229830019, - "severity": 0, - "severity_value": 0.26712446149150093, - "code": "best_score <= 2", - "message": "Method phate_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: distance_correlation\n Best score: 0.5342489229830019%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score phate_sqrt distance_correlation", - "value": -0.010607137642241157, - "severity": 0, - "severity_value": 0.010607137642241157, - "code": "worst_score >= -1", - "message": "Method phate_sqrt performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: distance_correlation\n Worst score: -0.010607137642241157%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score phate_sqrt distance_correlation", - "value": 0.5677488309722799, - "severity": 0, - "severity_value": 0.28387441548613995, - "code": "best_score <= 2", - "message": "Method phate_sqrt performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: distance_correlation\n Best score: 0.5677488309722799%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k distance_correlation", - "value": 0.27079306466624903, - "severity": 0, - "severity_value": -0.27079306466624903, - "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: distance_correlation\n Worst score: 0.27079306466624903%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k distance_correlation", - "value": 0.9604127541602097, - "severity": 0, - "severity_value": 0.48020637708010483, - "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: distance_correlation\n Best score: 0.9604127541602097%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k_hvg distance_correlation", - "value": 0.11958302475143105, - "severity": 0, - "severity_value": -0.11958302475143105, - "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: distance_correlation\n Worst score: 0.11958302475143105%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k_hvg distance_correlation", - "value": 0.5699814438000853, - "severity": 0, - "severity_value": 0.28499072190004265, - "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: distance_correlation\n Best score: 0.5699814438000853%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k distance_correlation", - "value": 0.13744468051918057, - "severity": 0, - "severity_value": -0.13744468051918057, - "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: distance_correlation\n Worst score: 0.13744468051918057%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k distance_correlation", - "value": 0.6443101014303435, - "severity": 0, - "severity_value": 0.32215505071517175, - "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: distance_correlation\n Best score: 0.6443101014303435%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k_hvg distance_correlation", - "value": 0.07071216535396146, - "severity": 0, - "severity_value": -0.07071216535396146, - "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: distance_correlation\n Worst score: 0.07071216535396146%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k_hvg distance_correlation", - "value": 0.6176440332645243, - "severity": 0, - "severity_value": 0.30882201663226216, - "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: distance_correlation\n Best score: 0.6176440332645243%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score random_features distance_correlation", - "value": 0.0, - "severity": 0, - "severity_value": -0.0, - "code": "worst_score >= -1", - "message": "Method random_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: distance_correlation\n Worst score: 0.0%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score random_features distance_correlation", - "value": 0.054656954323661015, - "severity": 0, - "severity_value": 0.027328477161830508, - "code": "best_score <= 2", - "message": "Method random_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: distance_correlation\n Best score: 0.054656954323661015%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score spectral_features distance_correlation", + "name": "Worst score umap normalized_mutual_information", "value": 0.0, "severity": 0, - "severity_value": -0.0, - "code": "worst_score >= -1", - "message": "Method spectral_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: distance_correlation\n Worst score: 0.0%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score spectral_features distance_correlation", - "value": 0.19760760861063076, - "severity": 0, - "severity_value": 0.09880380430531538, - "code": "best_score <= 2", - "message": "Method spectral_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: distance_correlation\n Best score: 0.19760760861063076%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score true_features distance_correlation", - "value": 1.0, - "severity": 0, - "severity_value": -1.0, - "code": "worst_score >= -1", - "message": "Method true_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: distance_correlation\n Worst score: 1.0%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score true_features distance_correlation", - "value": 1.0, - "severity": 0, - "severity_value": 0.5, - "code": "best_score <= 2", - "message": "Method true_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: distance_correlation\n Best score: 1.0%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score tsne_logCP10k distance_correlation", - "value": 0.19467487134734468, - "severity": 0, - "severity_value": -0.19467487134734468, - "code": "worst_score >= -1", - "message": "Method tsne_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: distance_correlation\n Worst score: 0.19467487134734468%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score tsne_logCP10k distance_correlation", - "value": 0.5955003955799931, - "severity": 0, - "severity_value": 0.29775019778999656, - "code": "best_score <= 2", - "message": "Method tsne_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: distance_correlation\n Best score: 0.5955003955799931%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score tsne_logCP10k_1kHVG distance_correlation", - "value": 0.08976071642969732, - "severity": 0, - "severity_value": -0.08976071642969732, - "code": "worst_score >= -1", - "message": "Method tsne_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: distance_correlation\n Worst score: 0.08976071642969732%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score tsne_logCP10k_1kHVG distance_correlation", - "value": 0.4950593727465205, - "severity": 0, - "severity_value": 0.24752968637326025, - "code": "best_score <= 2", - "message": "Method tsne_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: distance_correlation\n Best score: 0.4950593727465205%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_logCP10k distance_correlation", - "value": 0.07161358982875504, - "severity": 0, - "severity_value": -0.07161358982875504, - "code": "worst_score >= -1", - "message": "Method umap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: distance_correlation\n Worst score: 0.07161358982875504%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_logCP10k distance_correlation", - "value": 0.7776342713757153, - "severity": 0, - "severity_value": 0.38881713568785764, - "code": "best_score <= 2", - "message": "Method umap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: distance_correlation\n Best score: 0.7776342713757153%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_logCP10k_1kHVG distance_correlation", - "value": 0.008527516076888496, - "severity": 0, - "severity_value": -0.008527516076888496, - "code": "worst_score >= -1", - "message": "Method umap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: distance_correlation\n Worst score: 0.008527516076888496%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_logCP10k_1kHVG distance_correlation", - "value": 0.5684596761616335, - "severity": 0, - "severity_value": 0.28422983808081675, - "code": "best_score <= 2", - "message": "Method umap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: distance_correlation\n Best score: 0.5684596761616335%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_pca_logCP10k distance_correlation", - "value": 0.15341806261715984, - "severity": 0, - "severity_value": -0.15341806261715984, - "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: distance_correlation\n Worst score: 0.15341806261715984%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Best score umap_pca_logCP10k distance_correlation", - "value": 0.6153638835529462, - "severity": 0, - "severity_value": 0.3076819417764731, - "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: distance_correlation\n Best score: 0.6153638835529462%\n" - }, - { - "task_id": "dimensionality_reduction", - "category": "Scaling", - "name": "Worst score umap_pca_logCP10k_1kHVG distance_correlation", - "value": 0.03905586206630158, - "severity": 0, - "severity_value": -0.03905586206630158, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: distance_correlation\n Worst score: 0.03905586206630158%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: normalized_mutual_information\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k_1kHVG distance_correlation", - "value": 0.6038889716290405, + "name": "Best score umap normalized_mutual_information", + "value": 1.2753, "severity": 0, - "severity_value": 0.30194448581452027, + "severity_value": 0.63765, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: distance_correlation\n Best score: 0.6038889716290405%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: normalized_mutual_information\n Best score: 1.2753%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k distance_correlation_spectral", - "value": -0.20966785508529612, + "name": "Worst score random_features adjusted_rand_index", + "value": 0, "severity": 0, - "severity_value": 0.20966785508529612, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: distance_correlation_spectral\n Worst score: -0.20966785508529612%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: adjusted_rand_index\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k distance_correlation_spectral", - "value": 0.16859931173217121, + "name": "Best score random_features adjusted_rand_index", + "value": 0, "severity": 0, - "severity_value": 0.08429965586608561, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method densmap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: distance_correlation_spectral\n Best score: 0.16859931173217121%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: adjusted_rand_index\n Best score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k_1kHVG distance_correlation_spectral", - "value": -0.23202278751872368, + "name": "Worst score spectral_features adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.23202278751872368, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Worst score: -0.23202278751872368%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k_1kHVG distance_correlation_spectral", - "value": 0.3626254713858348, + "name": "Best score spectral_features adjusted_rand_index", + "value": 1.0, "severity": 0, - "severity_value": 0.1813127356929174, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method densmap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Best score: 0.3626254713858348%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: adjusted_rand_index\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k distance_correlation_spectral", - "value": -0.18631943798402603, + "name": "Worst score true_features adjusted_rand_index", + "value": 0, "severity": 0, - "severity_value": 0.18631943798402603, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: distance_correlation_spectral\n Worst score: -0.18631943798402603%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: adjusted_rand_index\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k distance_correlation_spectral", - "value": 0.33932938155998854, + "name": "Best score true_features adjusted_rand_index", + "value": 1, "severity": 0, - "severity_value": 0.16966469077999427, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: distance_correlation_spectral\n Best score: 0.33932938155998854%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: adjusted_rand_index\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k_1kHVG distance_correlation_spectral", - "value": -0.2443006070082448, + "name": "Worst score densmap adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.2443006070082448, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Worst score: -0.2443006070082448%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k_1kHVG distance_correlation_spectral", - "value": 0.3198531121358052, + "name": "Best score densmap adjusted_rand_index", + "value": 0.9907, "severity": 0, - "severity_value": 0.1599265560679026, + "severity_value": 0.49535, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Best score: 0.3198531121358052%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: adjusted_rand_index\n Best score: 0.9907%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score diffusion_map distance_correlation_spectral", - "value": -0.3714387384801409, + "name": "Worst score diffusion_map adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.3714387384801409, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method diffusion_map performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: distance_correlation_spectral\n Worst score: -0.3714387384801409%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score diffusion_map distance_correlation_spectral", - "value": 0.36438603241090245, + "name": "Best score diffusion_map adjusted_rand_index", + "value": 0.6004, "severity": 0, - "severity_value": 0.18219301620545122, + "severity_value": 0.3002, "code": "best_score <= 2", - "message": "Method diffusion_map performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: distance_correlation_spectral\n Best score: 0.36438603241090245%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: adjusted_rand_index\n Best score: 0.6004%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_default distance_correlation_spectral", - "value": -0.17133761030908973, + "name": "Worst score ivis adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.17133761030908973, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method neuralee_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: distance_correlation_spectral\n Worst score: -0.17133761030908973%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_default distance_correlation_spectral", - "value": 0.33184509773566706, + "name": "Best score ivis adjusted_rand_index", + "value": 0.988, "severity": 0, - "severity_value": 0.16592254886783353, + "severity_value": 0.494, "code": "best_score <= 2", - "message": "Method neuralee_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: distance_correlation_spectral\n Best score: 0.33184509773566706%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: adjusted_rand_index\n Best score: 0.988%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_logCP10k_1kHVG distance_correlation_spectral", - "value": -0.2053097517542226, + "name": "Worst score lmds adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.2053097517542226, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method neuralee_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Worst score: -0.2053097517542226%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_logCP10k_1kHVG distance_correlation_spectral", - "value": 0.3228362451013264, + "name": "Best score lmds adjusted_rand_index", + "value": 0.8557, "severity": 0, - "severity_value": 0.1614181225506632, + "severity_value": 0.42785, "code": "best_score <= 2", - "message": "Method neuralee_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Best score: 0.3228362451013264%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: adjusted_rand_index\n Best score: 0.8557%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k distance_correlation_spectral", - "value": -0.3175916323955235, + "name": "Worst score neuralee adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.3175916323955235, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: distance_correlation_spectral\n Worst score: -0.3175916323955235%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k distance_correlation_spectral", - "value": 0.1952388488601746, + "name": "Best score neuralee adjusted_rand_index", + "value": 1.4152, "severity": 0, - "severity_value": 0.0976194244300873, + "severity_value": 0.7076, "code": "best_score <= 2", - "message": "Method pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: distance_correlation_spectral\n Best score: 0.1952388488601746%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: adjusted_rand_index\n Best score: 1.4152%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k_1kHVG distance_correlation_spectral", - "value": -0.31729507068859525, + "name": "Worst score pca adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.31729507068859525, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Worst score: -0.31729507068859525%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k_1kHVG distance_correlation_spectral", - "value": 0.3863954998626982, + "name": "Best score pca adjusted_rand_index", + "value": 0.8029, "severity": 0, - "severity_value": 0.1931977499313491, + "severity_value": 0.40145, "code": "best_score <= 2", - "message": "Method pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Best score: 0.3863954998626982%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: adjusted_rand_index\n Best score: 0.8029%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_default distance_correlation_spectral", - "value": -0.36210284895553563, + "name": "Worst score phate adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.36210284895553563, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: distance_correlation_spectral\n Worst score: -0.36210284895553563%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_default distance_correlation_spectral", - "value": 0.2902525637488602, + "name": "Best score phate adjusted_rand_index", + "value": 1.006, "severity": 0, - "severity_value": 0.1451262818744301, + "severity_value": 0.503, "code": "best_score <= 2", - "message": "Method phate_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: distance_correlation_spectral\n Best score: 0.2902525637488602%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: adjusted_rand_index\n Best score: 1.006%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k distance_correlation_spectral", - "value": -0.19455396890537302, + "name": "Worst score pymde adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.19455396890537302, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: distance_correlation_spectral\n Worst score: -0.19455396890537302%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k distance_correlation_spectral", - "value": 0.32840571234734306, + "name": "Best score pymde adjusted_rand_index", + "value": 0.8382, "severity": 0, - "severity_value": 0.16420285617367153, + "severity_value": 0.4191, "code": "best_score <= 2", - "message": "Method phate_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: distance_correlation_spectral\n Best score: 0.32840571234734306%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: adjusted_rand_index\n Best score: 0.8382%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k_1kHVG distance_correlation_spectral", - "value": -0.30732176884748447, + "name": "Worst score simlr adjusted_rand_index", + "value": 0, "severity": 0, - "severity_value": 0.30732176884748447, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Worst score: -0.30732176884748447%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: adjusted_rand_index\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k_1kHVG distance_correlation_spectral", - "value": 0.2899874119770617, + "name": "Best score simlr adjusted_rand_index", + "value": 0, "severity": 0, - "severity_value": 0.14499370598853084, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method phate_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Best score: 0.2899874119770617%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: adjusted_rand_index\n Best score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_sqrt distance_correlation_spectral", - "value": -0.36345696335890065, + "name": "Worst score tsne adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.36345696335890065, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_sqrt performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: distance_correlation_spectral\n Worst score: -0.36345696335890065%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_sqrt distance_correlation_spectral", - "value": 0.327083474732928, + "name": "Best score tsne adjusted_rand_index", + "value": 1.0, "severity": 0, - "severity_value": 0.163541737366464, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method phate_sqrt performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: distance_correlation_spectral\n Best score: 0.327083474732928%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: adjusted_rand_index\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k distance_correlation_spectral", - "value": -0.16335477176353116, + "name": "Worst score umap adjusted_rand_index", + "value": 0.0, "severity": 0, - "severity_value": 0.16335477176353116, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: distance_correlation_spectral\n Worst score: -0.16335477176353116%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: adjusted_rand_index\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k distance_correlation_spectral", - "value": 0.2346029524324164, + "name": "Best score umap adjusted_rand_index", + "value": 1.125, "severity": 0, - "severity_value": 0.1173014762162082, + "severity_value": 0.5625, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: distance_correlation_spectral\n Best score: 0.2346029524324164%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: adjusted_rand_index\n Best score: 1.125%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k_hvg distance_correlation_spectral", - "value": -0.282515243048863, + "name": "Worst score random_features continuity_at_k30", + "value": 0.0, "severity": 0, - "severity_value": 0.282515243048863, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: distance_correlation_spectral\n Worst score: -0.282515243048863%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k_hvg distance_correlation_spectral", - "value": 0.4334728312767784, + "name": "Best score random_features continuity_at_k30", + "value": 1.0, "severity": 0, - "severity_value": 0.2167364156383892, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: distance_correlation_spectral\n Best score: 0.4334728312767784%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: continuity_at_k30\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k distance_correlation_spectral", - "value": -0.26031307882034477, + "name": "Worst score spectral_features continuity_at_k30", + "value": 0.0, "severity": 0, - "severity_value": 0.26031307882034477, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: distance_correlation_spectral\n Worst score: -0.26031307882034477%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k distance_correlation_spectral", - "value": 0.35149307870473795, + "name": "Best score spectral_features continuity_at_k30", + "value": 0.826, "severity": 0, - "severity_value": 0.17574653935236897, + "severity_value": 0.413, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: distance_correlation_spectral\n Best score: 0.35149307870473795%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: continuity_at_k30\n Best score: 0.826%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k_hvg distance_correlation_spectral", - "value": -0.18139135747095803, + "name": "Worst score true_features continuity_at_k30", + "value": 0, "severity": 0, - "severity_value": 0.18139135747095803, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: distance_correlation_spectral\n Worst score: -0.18139135747095803%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: continuity_at_k30\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k_hvg distance_correlation_spectral", - "value": 0.31567672531173563, + "name": "Best score true_features continuity_at_k30", + "value": 1, "severity": 0, - "severity_value": 0.15783836265586781, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: distance_correlation_spectral\n Best score: 0.31567672531173563%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: continuity_at_k30\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score random_features distance_correlation_spectral", + "name": "Worst score densmap continuity_at_k30", "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method random_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: distance_correlation_spectral\n Worst score: 0.0%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score random_features distance_correlation_spectral", - "value": 0.19544464154335522, + "name": "Best score densmap continuity_at_k30", + "value": 1.6611, "severity": 0, - "severity_value": 0.09772232077167761, + "severity_value": 0.83055, "code": "best_score <= 2", - "message": "Method random_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: distance_correlation_spectral\n Best score: 0.19544464154335522%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: continuity_at_k30\n Best score: 1.6611%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score spectral_features distance_correlation_spectral", - "value": 1.0, + "name": "Worst score diffusion_map continuity_at_k30", + "value": -0.0912, "severity": 0, - "severity_value": -1.0, + "severity_value": 0.0912, "code": "worst_score >= -1", - "message": "Method spectral_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: distance_correlation_spectral\n Worst score: 1.0%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: continuity_at_k30\n Worst score: -0.0912%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score spectral_features distance_correlation_spectral", - "value": 1.0, + "name": "Best score diffusion_map continuity_at_k30", + "value": 0.9005, "severity": 0, - "severity_value": 0.5, + "severity_value": 0.45025, "code": "best_score <= 2", - "message": "Method spectral_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: distance_correlation_spectral\n Best score: 1.0%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: continuity_at_k30\n Best score: 0.9005%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score true_features distance_correlation_spectral", + "name": "Worst score ivis continuity_at_k30", "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method true_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: distance_correlation_spectral\n Worst score: 0.0%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score true_features distance_correlation_spectral", - "value": 0.4106328817274773, - "severity": 0, - "severity_value": 0.20531644086373865, + "name": "Best score ivis continuity_at_k30", + "value": 2.3603, + "severity": 1, + "severity_value": 1.18015, "code": "best_score <= 2", - "message": "Method true_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: distance_correlation_spectral\n Best score: 0.4106328817274773%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: continuity_at_k30\n Best score: 2.3603%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k distance_correlation_spectral", - "value": -0.25052609771357115, + "name": "Worst score lmds continuity_at_k30", + "value": 0.0, "severity": 0, - "severity_value": 0.25052609771357115, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: distance_correlation_spectral\n Worst score: -0.25052609771357115%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k distance_correlation_spectral", - "value": 0.28862647346201026, - "severity": 0, - "severity_value": 0.14431323673100513, + "name": "Best score lmds continuity_at_k30", + "value": 2.0962, + "severity": 1, + "severity_value": 1.0481, "code": "best_score <= 2", - "message": "Method tsne_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: distance_correlation_spectral\n Best score: 0.28862647346201026%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: continuity_at_k30\n Best score: 2.0962%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k_1kHVG distance_correlation_spectral", - "value": -0.10395514317858019, + "name": "Worst score neuralee continuity_at_k30", + "value": 0.0, "severity": 0, - "severity_value": 0.10395514317858019, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Worst score: -0.10395514317858019%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k_1kHVG distance_correlation_spectral", - "value": 0.3262446415140944, - "severity": 0, - "severity_value": 0.1631223207570472, + "name": "Best score neuralee continuity_at_k30", + "value": 2.3749, + "severity": 1, + "severity_value": 1.18745, "code": "best_score <= 2", - "message": "Method tsne_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Best score: 0.3262446415140944%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: continuity_at_k30\n Best score: 2.3749%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k distance_correlation_spectral", - "value": -0.21467600490561717, + "name": "Worst score pca continuity_at_k30", + "value": 0.0, "severity": 0, - "severity_value": 0.21467600490561717, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: distance_correlation_spectral\n Worst score: -0.21467600490561717%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k distance_correlation_spectral", - "value": 0.13558659136055504, - "severity": 0, - "severity_value": 0.06779329568027752, + "name": "Best score pca continuity_at_k30", + "value": 2.1583, + "severity": 1, + "severity_value": 1.07915, "code": "best_score <= 2", - "message": "Method umap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: distance_correlation_spectral\n Best score: 0.13558659136055504%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: continuity_at_k30\n Best score: 2.1583%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k_1kHVG distance_correlation_spectral", - "value": -0.16458288553955802, + "name": "Worst score phate continuity_at_k30", + "value": 0.0, "severity": 0, - "severity_value": 0.16458288553955802, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Worst score: -0.16458288553955802%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k_1kHVG distance_correlation_spectral", - "value": 0.2963634093263127, - "severity": 0, - "severity_value": 0.14818170466315636, + "name": "Best score phate continuity_at_k30", + "value": 2.3821, + "severity": 1, + "severity_value": 1.19105, "code": "best_score <= 2", - "message": "Method umap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Best score: 0.2963634093263127%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: continuity_at_k30\n Best score: 2.3821%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k distance_correlation_spectral", - "value": -0.29032190853260054, + "name": "Worst score pymde continuity_at_k30", + "value": 0.0, "severity": 0, - "severity_value": 0.29032190853260054, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: distance_correlation_spectral\n Worst score: -0.29032190853260054%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k distance_correlation_spectral", - "value": 0.25830929270663866, - "severity": 0, - "severity_value": 0.12915464635331933, + "name": "Best score pymde continuity_at_k30", + "value": 2.3297, + "severity": 1, + "severity_value": 1.16485, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: distance_correlation_spectral\n Best score: 0.25830929270663866%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: continuity_at_k30\n Best score: 2.3297%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k_1kHVG distance_correlation_spectral", - "value": -0.1564569336922395, + "name": "Worst score simlr continuity_at_k30", + "value": 0.0, "severity": 0, - "severity_value": 0.1564569336922395, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Worst score: -0.1564569336922395%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k_1kHVG distance_correlation_spectral", - "value": 0.3069937174531186, + "name": "Best score simlr continuity_at_k30", + "value": 0.9378, "severity": 0, - "severity_value": 0.1534968587265593, + "severity_value": 0.4689, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: distance_correlation_spectral\n Best score: 0.3069937174531186%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: continuity_at_k30\n Best score: 0.9378%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k lcmc", - "value": 0.026440189087412868, + "name": "Worst score tsne continuity_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.026440189087412868, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: lcmc\n Worst score: 0.026440189087412868%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k lcmc", - "value": 0.5314470244502538, - "severity": 0, - "severity_value": 0.2657235122251269, + "name": "Best score tsne continuity_at_k30", + "value": 2.3402, + "severity": 1, + "severity_value": 1.1701, "code": "best_score <= 2", - "message": "Method densmap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: lcmc\n Best score: 0.5314470244502538%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: continuity_at_k30\n Best score: 2.3402%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k_1kHVG lcmc", - "value": 0.041952999328209645, + "name": "Worst score umap continuity_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.041952999328209645, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: lcmc\n Worst score: 0.041952999328209645%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: continuity_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k_1kHVG lcmc", - "value": 0.4245220154800349, - "severity": 0, - "severity_value": 0.21226100774001744, + "name": "Best score umap continuity_at_k30", + "value": 2.1088, + "severity": 1, + "severity_value": 1.0544, "code": "best_score <= 2", - "message": "Method densmap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: lcmc\n Best score: 0.4245220154800349%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: continuity_at_k30\n Best score: 2.1088%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k lcmc", - "value": 0.048331926066081984, + "name": "Worst score random_features trustworthiness_at_k30", + "value": 0, "severity": 0, - "severity_value": -0.048331926066081984, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: lcmc\n Worst score: 0.048331926066081984%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: trustworthiness_at_k30\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k lcmc", - "value": 0.4997180788354093, + "name": "Best score random_features trustworthiness_at_k30", + "value": 0, "severity": 0, - "severity_value": 0.24985903941770465, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: lcmc\n Best score: 0.4997180788354093%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: trustworthiness_at_k30\n Best score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k_1kHVG lcmc", - "value": 0.04202079468484404, + "name": "Worst score spectral_features trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.04202079468484404, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: lcmc\n Worst score: 0.04202079468484404%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k_1kHVG lcmc", - "value": 0.4544056589266492, + "name": "Best score spectral_features trustworthiness_at_k30", + "value": 1.0, "severity": 0, - "severity_value": 0.2272028294633246, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: lcmc\n Best score: 0.4544056589266492%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: trustworthiness_at_k30\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score diffusion_map lcmc", - "value": 0.00861617350680727, + "name": "Worst score true_features trustworthiness_at_k30", + "value": 0, "severity": 0, - "severity_value": -0.00861617350680727, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method diffusion_map performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: lcmc\n Worst score: 0.00861617350680727%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: trustworthiness_at_k30\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score diffusion_map lcmc", - "value": 0.22148751858116766, + "name": "Best score true_features trustworthiness_at_k30", + "value": 1, "severity": 0, - "severity_value": 0.11074375929058383, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method diffusion_map performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: lcmc\n Best score: 0.22148751858116766%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: trustworthiness_at_k30\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_default lcmc", - "value": 0.024375512317183656, + "name": "Worst score densmap trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.024375512317183656, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method neuralee_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: lcmc\n Worst score: 0.024375512317183656%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_default lcmc", - "value": 0.4977702598800554, + "name": "Best score densmap trustworthiness_at_k30", + "value": 0.9594, "severity": 0, - "severity_value": 0.2488851299400277, + "severity_value": 0.4797, "code": "best_score <= 2", - "message": "Method neuralee_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: lcmc\n Best score: 0.4977702598800554%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: trustworthiness_at_k30\n Best score: 0.9594%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_logCP10k_1kHVG lcmc", - "value": 0.03550627723370292, + "name": "Worst score diffusion_map trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.03550627723370292, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method neuralee_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: lcmc\n Worst score: 0.03550627723370292%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_logCP10k_1kHVG lcmc", - "value": 0.43226203290788867, + "name": "Best score diffusion_map trustworthiness_at_k30", + "value": 0.8217, "severity": 0, - "severity_value": 0.21613101645394434, + "severity_value": 0.41085, "code": "best_score <= 2", - "message": "Method neuralee_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: lcmc\n Best score: 0.43226203290788867%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: trustworthiness_at_k30\n Best score: 0.8217%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k lcmc", - "value": 0.026803818727542787, + "name": "Worst score ivis trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.026803818727542787, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: lcmc\n Worst score: 0.026803818727542787%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k lcmc", - "value": 0.3854118611922702, - "severity": 0, - "severity_value": 0.1927059305961351, + "name": "Best score ivis trustworthiness_at_k30", + "value": 9.3931, + "severity": 3, + "severity_value": 4.69655, "code": "best_score <= 2", - "message": "Method pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: lcmc\n Best score: 0.3854118611922702%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: trustworthiness_at_k30\n Best score: 9.3931%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k_1kHVG lcmc", - "value": 0.026674391228513495, + "name": "Worst score lmds trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.026674391228513495, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: lcmc\n Worst score: 0.026674391228513495%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k_1kHVG lcmc", - "value": 0.44056589266492385, - "severity": 0, - "severity_value": 0.22028294633246193, + "name": "Best score lmds trustworthiness_at_k30", + "value": 6.8527, + "severity": 3, + "severity_value": 3.42635, "code": "best_score <= 2", - "message": "Method pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: lcmc\n Best score: 0.44056589266492385%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: trustworthiness_at_k30\n Best score: 6.8527%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_default lcmc", - "value": 0.03959248827448491, + "name": "Worst score neuralee trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.03959248827448491, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: lcmc\n Worst score: 0.03959248827448491%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_default lcmc", - "value": 0.39202419396176125, - "severity": 0, - "severity_value": 0.19601209698088062, + "name": "Best score neuralee trustworthiness_at_k30", + "value": 9.5086, + "severity": 3, + "severity_value": 4.7543, "code": "best_score <= 2", - "message": "Method phate_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: lcmc\n Best score: 0.39202419396176125%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: trustworthiness_at_k30\n Best score: 9.5086%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k lcmc", - "value": 0.03615341472884939, + "name": "Worst score pca trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.03615341472884939, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: lcmc\n Worst score: 0.03615341472884939%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k lcmc", - "value": 0.40796555435952636, - "severity": 0, - "severity_value": 0.20398277717976318, + "name": "Best score pca trustworthiness_at_k30", + "value": 7.0793, + "severity": 3, + "severity_value": 3.53965, "code": "best_score <= 2", - "message": "Method phate_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: lcmc\n Best score: 0.40796555435952636%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: trustworthiness_at_k30\n Best score: 7.0793%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k_1kHVG lcmc", - "value": 0.04304388824859941, + "name": "Worst score phate trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.04304388824859941, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: lcmc\n Worst score: 0.04304388824859941%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k_1kHVG lcmc", - "value": 0.40781177917884054, - "severity": 0, - "severity_value": 0.20390588958942027, + "name": "Best score phate trustworthiness_at_k30", + "value": 10.5926, + "severity": 3, + "severity_value": 5.2963, "code": "best_score <= 2", - "message": "Method phate_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: lcmc\n Best score: 0.40781177917884054%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: trustworthiness_at_k30\n Best score: 10.5926%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_sqrt lcmc", - "value": 0.03927816434827091, + "name": "Worst score pymde trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.03927816434827091, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_sqrt performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: lcmc\n Worst score: 0.03927816434827091%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_sqrt lcmc", - "value": 0.3971500333179558, - "severity": 0, - "severity_value": 0.1985750166589779, + "name": "Best score pymde trustworthiness_at_k30", + "value": 10.8827, + "severity": 3, + "severity_value": 5.44135, "code": "best_score <= 2", - "message": "Method phate_sqrt performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: lcmc\n Best score: 0.3971500333179558%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: trustworthiness_at_k30\n Best score: 10.8827%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k lcmc", - "value": 0.03089619298256427, + "name": "Worst score simlr trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.03089619298256427, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: lcmc\n Worst score: 0.03089619298256427%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k lcmc", - "value": 0.25147367881490595, + "name": "Best score simlr trustworthiness_at_k30", + "value": 0.9277, "severity": 0, - "severity_value": 0.12573683940745298, + "severity_value": 0.46385, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: lcmc\n Best score: 0.25147367881490595%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: trustworthiness_at_k30\n Best score: 0.9277%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k_hvg lcmc", - "value": 0.026384720159257455, + "name": "Worst score tsne trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.026384720159257455, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: lcmc\n Worst score: 0.026384720159257455%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k_hvg lcmc", - "value": 0.2432723358449946, - "severity": 0, - "severity_value": 0.1216361679224973, + "name": "Best score tsne trustworthiness_at_k30", + "value": 11.2382, + "severity": 3, + "severity_value": 5.6191, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: lcmc\n Best score: 0.2432723358449946%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: trustworthiness_at_k30\n Best score: 11.2382%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k lcmc", - "value": 0.04629806536705022, + "name": "Worst score umap trustworthiness_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.04629806536705022, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: lcmc\n Worst score: 0.04629806536705022%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: trustworthiness_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k lcmc", - "value": 0.48264903377928137, - "severity": 0, - "severity_value": 0.24132451688964068, + "name": "Best score umap trustworthiness_at_k30", + "value": 10.2908, + "severity": 3, + "severity_value": 5.1454, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: lcmc\n Best score: 0.48264903377928137%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: trustworthiness_at_k30\n Best score: 10.2908%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k_hvg lcmc", - "value": 0.04251385182400325, + "name": "Worst score random_features qnx_at_k30", + "value": 0, "severity": 0, - "severity_value": -0.04251385182400325, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: lcmc\n Worst score: 0.04251385182400325%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: qnx_at_k30\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k_hvg lcmc", - "value": 0.43954072479368494, + "name": "Best score random_features qnx_at_k30", + "value": 0, "severity": 0, - "severity_value": 0.21977036239684247, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: lcmc\n Best score: 0.43954072479368494%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: qnx_at_k30\n Best score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score random_features lcmc", + "name": "Worst score spectral_features qnx_at_k30", "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method random_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: lcmc\n Worst score: 0.0%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score random_features lcmc", - "value": 0.0035068689022699114, + "name": "Best score spectral_features qnx_at_k30", + "value": 1.0, "severity": 0, - "severity_value": 0.0017534344511349557, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method random_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: lcmc\n Best score: 0.0035068689022699114%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: qnx_at_k30\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score spectral_features lcmc", - "value": 0.0, + "name": "Worst score true_features qnx_at_k30", + "value": 0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method spectral_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: lcmc\n Worst score: 0.0%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: qnx_at_k30\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score spectral_features lcmc", - "value": 0.042185657901481356, + "name": "Best score true_features qnx_at_k30", + "value": 1, "severity": 0, - "severity_value": 0.021092828950740678, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method spectral_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: lcmc\n Best score: 0.042185657901481356%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: qnx_at_k30\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score true_features lcmc", - "value": 1.0, + "name": "Worst score densmap qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -1.0, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method true_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: lcmc\n Worst score: 1.0%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score true_features lcmc", - "value": 1.0, - "severity": 0, - "severity_value": 0.5, + "name": "Best score densmap qnx_at_k30", + "value": 4.6967, + "severity": 2, + "severity_value": 2.34835, "code": "best_score <= 2", - "message": "Method true_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: lcmc\n Best score: 1.0%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: qnx_at_k30\n Best score: 4.6967%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k lcmc", - "value": 0.05071092676252519, + "name": "Worst score diffusion_map qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.05071092676252519, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: lcmc\n Worst score: 0.05071092676252519%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k lcmc", - "value": 0.516992157465785, + "name": "Best score diffusion_map qnx_at_k30", + "value": 0.3856, "severity": 0, - "severity_value": 0.2584960787328925, + "severity_value": 0.1928, "code": "best_score <= 2", - "message": "Method tsne_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: lcmc\n Best score: 0.516992157465785%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: qnx_at_k30\n Best score: 0.3856%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k_1kHVG lcmc", - "value": 0.04563860144342478, + "name": "Worst score ivis qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.04563860144342478, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: lcmc\n Worst score: 0.04563860144342478%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k_1kHVG lcmc", - "value": 0.4614280588446358, - "severity": 0, - "severity_value": 0.2307140294223179, + "name": "Best score ivis qnx_at_k30", + "value": 4.2844, + "severity": 2, + "severity_value": 2.1422, "code": "best_score <= 2", - "message": "Method tsne_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: lcmc\n Best score: 0.4614280588446358%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: qnx_at_k30\n Best score: 4.2844%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k lcmc", - "value": 0.02856649800003698, + "name": "Worst score lmds qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.02856649800003698, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: lcmc\n Worst score: 0.02856649800003698%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k lcmc", - "value": 0.48090624839817514, - "severity": 0, - "severity_value": 0.24045312419908757, + "name": "Best score lmds qnx_at_k30", + "value": 3.1645, + "severity": 1, + "severity_value": 1.58225, "code": "best_score <= 2", - "message": "Method umap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: lcmc\n Best score: 0.48090624839817514%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: qnx_at_k30\n Best score: 3.1645%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k_1kHVG lcmc", - "value": 0.039968444343093816, + "name": "Worst score neuralee qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.039968444343093816, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: lcmc\n Worst score: 0.039968444343093816%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k_1kHVG lcmc", - "value": 0.41893485058178276, + "name": "Best score neuralee qnx_at_k30", + "value": 1.8322, "severity": 0, - "severity_value": 0.20946742529089138, + "severity_value": 0.9161, "code": "best_score <= 2", - "message": "Method umap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: lcmc\n Best score: 0.41893485058178276%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: qnx_at_k30\n Best score: 1.8322%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k lcmc", - "value": 0.04649528822271391, + "name": "Worst score pca qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.04649528822271391, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: lcmc\n Worst score: 0.04649528822271391%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k lcmc", - "value": 0.4727049054282638, + "name": "Best score pca qnx_at_k30", + "value": 0.7525, "severity": 0, - "severity_value": 0.2363524527141319, + "severity_value": 0.37625, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: lcmc\n Best score: 0.4727049054282638%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: qnx_at_k30\n Best score: 0.7525%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k_1kHVG lcmc", - "value": 0.04090525290749632, + "name": "Worst score phate qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.04090525290749632, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: lcmc\n Worst score: 0.04090525290749632%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k_1kHVG lcmc", - "value": 0.4178071659234199, - "severity": 0, - "severity_value": 0.20890358296170994, + "name": "Best score phate qnx_at_k30", + "value": 13.4841, + "severity": 3, + "severity_value": 6.74205, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: lcmc\n Best score: 0.4178071659234199%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: qnx_at_k30\n Best score: 13.4841%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k qglobal", - "value": 0.18155717676094182, + "name": "Worst score pymde qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.18155717676094182, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: qglobal\n Worst score: 0.18155717676094182%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k qglobal", - "value": 0.6005484950734467, - "severity": 0, - "severity_value": 0.3002742475367233, + "name": "Best score pymde qnx_at_k30", + "value": 3.5836, + "severity": 1, + "severity_value": 1.7918, "code": "best_score <= 2", - "message": "Method densmap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: qglobal\n Best score: 0.6005484950734467%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: qnx_at_k30\n Best score: 3.5836%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k_1kHVG qglobal", - "value": 0.11140957969429321, + "name": "Worst score simlr qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.11140957969429321, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: qglobal\n Worst score: 0.11140957969429321%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k_1kHVG qglobal", - "value": 0.49157668182275666, + "name": "Best score simlr qnx_at_k30", + "value": 0.4573, "severity": 0, - "severity_value": 0.24578834091137833, + "severity_value": 0.22865, "code": "best_score <= 2", - "message": "Method densmap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: qglobal\n Best score: 0.49157668182275666%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: qnx_at_k30\n Best score: 0.4573%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k qglobal", - "value": 0.16039581192584737, + "name": "Worst score tsne qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.16039581192584737, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: qglobal\n Worst score: 0.16039581192584737%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k qglobal", - "value": 0.5264889469565174, - "severity": 0, - "severity_value": 0.2632444734782587, + "name": "Best score tsne qnx_at_k30", + "value": 4.2739, + "severity": 2, + "severity_value": 2.13695, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: qglobal\n Best score: 0.5264889469565174%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: qnx_at_k30\n Best score: 4.2739%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k_1kHVG qglobal", - "value": 0.13709918071399985, + "name": "Worst score umap qnx_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.13709918071399985, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: qglobal\n Worst score: 0.13709918071399985%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: qnx_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k_1kHVG qglobal", - "value": 0.4630662282296097, - "severity": 0, - "severity_value": 0.23153311411480484, + "name": "Best score umap qnx_at_k30", + "value": 6.5191, + "severity": 3, + "severity_value": 3.25955, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: qglobal\n Best score: 0.4630662282296097%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: qnx_at_k30\n Best score: 6.5191%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score diffusion_map qglobal", - "value": 0.08130609571431195, + "name": "Worst score random_features lcmc_at_k30", + "value": 0, "severity": 0, - "severity_value": -0.08130609571431195, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method diffusion_map performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: qglobal\n Worst score: 0.08130609571431195%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: lcmc_at_k30\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score diffusion_map qglobal", - "value": 0.6097122953949146, + "name": "Best score random_features lcmc_at_k30", + "value": 0, "severity": 0, - "severity_value": 0.3048561476974573, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method diffusion_map performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: qglobal\n Best score: 0.6097122953949146%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: lcmc_at_k30\n Best score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_default qglobal", - "value": 0.1266686041419728, + "name": "Worst score spectral_features lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.1266686041419728, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method neuralee_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: qglobal\n Worst score: 0.1266686041419728%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_default qglobal", - "value": 0.5049265813129604, + "name": "Best score spectral_features lcmc_at_k30", + "value": 1.0, "severity": 0, - "severity_value": 0.2524632906564802, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method neuralee_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: qglobal\n Best score: 0.5049265813129604%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: lcmc_at_k30\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_logCP10k_1kHVG qglobal", - "value": 0.1646098178803601, + "name": "Worst score true_features lcmc_at_k30", + "value": 0, "severity": 0, - "severity_value": -0.1646098178803601, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method neuralee_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: qglobal\n Worst score: 0.1646098178803601%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: lcmc_at_k30\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_logCP10k_1kHVG qglobal", - "value": 0.5205925384411647, + "name": "Best score true_features lcmc_at_k30", + "value": 1, "severity": 0, - "severity_value": 0.26029626922058235, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method neuralee_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: qglobal\n Best score: 0.5205925384411647%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: lcmc_at_k30\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k qglobal", - "value": 0.08901507463949256, + "name": "Worst score densmap lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.08901507463949256, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: qglobal\n Worst score: 0.08901507463949256%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k qglobal", - "value": 0.6041896174722956, - "severity": 0, - "severity_value": 0.3020948087361478, + "name": "Best score densmap lcmc_at_k30", + "value": 4.6967, + "severity": 2, + "severity_value": 2.34835, "code": "best_score <= 2", - "message": "Method pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: qglobal\n Best score: 0.6041896174722956%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: lcmc_at_k30\n Best score: 4.6967%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k_1kHVG qglobal", - "value": 0.12365983059498456, + "name": "Worst score diffusion_map lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.12365983059498456, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: qglobal\n Worst score: 0.12365983059498456%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k_1kHVG qglobal", - "value": 0.4961062352043671, + "name": "Best score diffusion_map lcmc_at_k30", + "value": 0.3856, "severity": 0, - "severity_value": 0.24805311760218354, + "severity_value": 0.1928, "code": "best_score <= 2", - "message": "Method pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: qglobal\n Best score: 0.4961062352043671%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: lcmc_at_k30\n Best score: 0.3856%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_default qglobal", - "value": 0.09443795928499116, + "name": "Worst score ivis lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.09443795928499116, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: qglobal\n Worst score: 0.09443795928499116%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_default qglobal", - "value": 0.42420958528575725, - "severity": 0, - "severity_value": 0.21210479264287863, + "name": "Best score ivis lcmc_at_k30", + "value": 4.2844, + "severity": 2, + "severity_value": 2.1422, "code": "best_score <= 2", - "message": "Method phate_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: qglobal\n Best score: 0.42420958528575725%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: lcmc_at_k30\n Best score: 4.2844%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k qglobal", - "value": 0.1637560928116802, + "name": "Worst score lmds lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.1637560928116802, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: qglobal\n Worst score: 0.1637560928116802%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k qglobal", - "value": 0.5208015213093337, - "severity": 0, - "severity_value": 0.26040076065466683, + "name": "Best score lmds lcmc_at_k30", + "value": 3.1645, + "severity": 1, + "severity_value": 1.58225, "code": "best_score <= 2", - "message": "Method phate_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: qglobal\n Best score: 0.5208015213093337%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: lcmc_at_k30\n Best score: 3.1645%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k_1kHVG qglobal", - "value": 0.11741183810218496, + "name": "Worst score neuralee lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.11741183810218496, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: qglobal\n Worst score: 0.11741183810218496%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k_1kHVG qglobal", - "value": 0.5140730841859757, + "name": "Best score neuralee lcmc_at_k30", + "value": 1.8322, "severity": 0, - "severity_value": 0.2570365420929879, + "severity_value": 0.9161, "code": "best_score <= 2", - "message": "Method phate_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: qglobal\n Best score: 0.5140730841859757%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: lcmc_at_k30\n Best score: 1.8322%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_sqrt qglobal", - "value": 0.10282588005826147, + "name": "Worst score pca lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.10282588005826147, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_sqrt performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: qglobal\n Worst score: 0.10282588005826147%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_sqrt qglobal", - "value": 0.5016544141772978, + "name": "Best score pca lcmc_at_k30", + "value": 0.7525, "severity": 0, - "severity_value": 0.2508272070886489, + "severity_value": 0.37625, "code": "best_score <= 2", - "message": "Method phate_sqrt performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: qglobal\n Best score: 0.5016544141772978%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: lcmc_at_k30\n Best score: 0.7525%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k qglobal", - "value": 0.08568942574828445, + "name": "Worst score phate lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.08568942574828445, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: qglobal\n Worst score: 0.08568942574828445%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k qglobal", - "value": 0.6281071109984805, - "severity": 0, - "severity_value": 0.31405355549924024, + "name": "Best score phate lcmc_at_k30", + "value": 13.4841, + "severity": 3, + "severity_value": 6.74205, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: qglobal\n Best score: 0.6281071109984805%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: lcmc_at_k30\n Best score: 13.4841%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k_hvg qglobal", - "value": 0.1834070482684052, + "name": "Worst score pymde lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.1834070482684052, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: qglobal\n Worst score: 0.1834070482684052%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k_hvg qglobal", - "value": 0.5195844166756017, - "severity": 0, - "severity_value": 0.25979220833780087, + "name": "Best score pymde lcmc_at_k30", + "value": 3.5836, + "severity": 1, + "severity_value": 1.7918, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: qglobal\n Best score: 0.5195844166756017%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: lcmc_at_k30\n Best score: 3.5836%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k qglobal", - "value": 0.15375241018229951, + "name": "Worst score simlr lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.15375241018229951, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: qglobal\n Worst score: 0.15375241018229951%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k qglobal", - "value": 0.49338171562950583, + "name": "Best score simlr lcmc_at_k30", + "value": 0.4573, "severity": 0, - "severity_value": 0.24669085781475292, + "severity_value": 0.22865, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: qglobal\n Best score: 0.49338171562950583%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: lcmc_at_k30\n Best score: 0.4573%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k_hvg qglobal", - "value": 0.1496503802716244, + "name": "Worst score tsne lcmc_at_k30", + "value": 0.0, "severity": 0, - "severity_value": -0.1496503802716244, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: qglobal\n Worst score: 0.1496503802716244%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k_hvg qglobal", - "value": 0.5249785699182626, - "severity": 0, - "severity_value": 0.2624892849591313, + "name": "Best score tsne lcmc_at_k30", + "value": 4.2739, + "severity": 2, + "severity_value": 2.13695, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: qglobal\n Best score: 0.5249785699182626%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: lcmc_at_k30\n Best score: 4.2739%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score random_features qglobal", + "name": "Worst score umap lcmc_at_k30", "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method random_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: qglobal\n Worst score: 0.0%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: lcmc_at_k30\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score random_features qglobal", - "value": 0.5333657087060505, - "severity": 0, - "severity_value": 0.26668285435302524, + "name": "Best score umap lcmc_at_k30", + "value": 6.5191, + "severity": 3, + "severity_value": 3.25955, "code": "best_score <= 2", - "message": "Method random_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: qglobal\n Best score: 0.5333657087060505%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: lcmc_at_k30\n Best score: 6.5191%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score spectral_features qglobal", - "value": 0.0, + "name": "Worst score random_features qnx_auc", + "value": 0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method spectral_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: qglobal\n Worst score: 0.0%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: qnx_auc\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score spectral_features qglobal", - "value": 0.3596008677513624, + "name": "Best score random_features qnx_auc", + "value": 1, "severity": 0, - "severity_value": 0.1798004338756812, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method spectral_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: qglobal\n Best score: 0.3596008677513624%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: qnx_auc\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score true_features qglobal", - "value": 1.0, + "name": "Worst score spectral_features qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -1.0, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method true_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: qglobal\n Worst score: 1.0%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score true_features qglobal", + "name": "Best score spectral_features qnx_auc", "value": 1.0, "severity": 0, "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method true_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: qglobal\n Best score: 1.0%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: qnx_auc\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k qglobal", - "value": 0.16782597754645734, + "name": "Worst score true_features qnx_auc", + "value": 0, "severity": 0, - "severity_value": -0.16782597754645734, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: qglobal\n Worst score: 0.16782597754645734%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: qnx_auc\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k qglobal", - "value": 0.5708453075425906, + "name": "Best score true_features qnx_auc", + "value": 1, "severity": 0, - "severity_value": 0.2854226537712953, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method tsne_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: qglobal\n Best score: 0.5708453075425906%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: qnx_auc\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k_1kHVG qglobal", - "value": 0.17339777659652558, + "name": "Worst score densmap qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -0.17339777659652558, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: qglobal\n Worst score: 0.17339777659652558%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k_1kHVG qglobal", - "value": 0.42508569170132765, - "severity": 0, - "severity_value": 0.21254284585066383, + "name": "Best score densmap qnx_auc", + "value": 3.7203, + "severity": 1, + "severity_value": 1.86015, "code": "best_score <= 2", - "message": "Method tsne_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: qglobal\n Best score: 0.42508569170132765%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: qnx_auc\n Best score: 3.7203%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k qglobal", - "value": 0.11829302519157324, + "name": "Worst score diffusion_map qnx_auc", + "value": -0.0151, "severity": 0, - "severity_value": -0.11829302519157324, + "severity_value": 0.0151, "code": "worst_score >= -1", - "message": "Method umap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: qglobal\n Worst score: 0.11829302519157324%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: qnx_auc\n Worst score: -0.0151%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k qglobal", - "value": 0.6294113381763855, + "name": "Best score diffusion_map qnx_auc", + "value": 0.1863, "severity": 0, - "severity_value": 0.31470566908819275, + "severity_value": 0.09315, "code": "best_score <= 2", - "message": "Method umap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: qglobal\n Best score: 0.6294113381763855%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: qnx_auc\n Best score: 0.1863%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k_1kHVG qglobal", - "value": 0.11588066888999285, + "name": "Worst score ivis qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -0.11588066888999285, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: qglobal\n Worst score: 0.11588066888999285%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k_1kHVG qglobal", - "value": 0.5276627099014327, - "severity": 0, - "severity_value": 0.26383135495071636, + "name": "Best score ivis qnx_auc", + "value": 3.8202, + "severity": 1, + "severity_value": 1.9101, "code": "best_score <= 2", - "message": "Method umap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: qglobal\n Best score: 0.5276627099014327%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: qnx_auc\n Best score: 3.8202%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k qglobal", - "value": 0.1719573771849933, + "name": "Worst score lmds qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -0.1719573771849933, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: qglobal\n Worst score: 0.1719573771849933%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k qglobal", - "value": 0.5491020003049403, - "severity": 0, - "severity_value": 0.27455100015247014, + "name": "Best score lmds qnx_auc", + "value": 4.0983, + "severity": 2, + "severity_value": 2.04915, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: qglobal\n Best score: 0.5491020003049403%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: qnx_auc\n Best score: 4.0983%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k_1kHVG qglobal", - "value": 0.13823926952857032, + "name": "Worst score neuralee qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -0.13823926952857032, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: qglobal\n Worst score: 0.13823926952857032%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k_1kHVG qglobal", - "value": 0.4454384975354424, - "severity": 0, - "severity_value": 0.2227192487677212, + "name": "Best score neuralee qnx_auc", + "value": 4.0378, + "severity": 2, + "severity_value": 2.0189, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: qglobal\n Best score: 0.4454384975354424%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: qnx_auc\n Best score: 4.0378%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k qlocal", - "value": 0.17097569462595794, + "name": "Worst score pca qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -0.17097569462595794, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: qlocal\n Worst score: 0.17097569462595794%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k qlocal", - "value": 0.46882170983442223, - "severity": 0, - "severity_value": 0.23441085491721111, + "name": "Best score pca qnx_auc", + "value": 4.1541, + "severity": 2, + "severity_value": 2.07705, "code": "best_score <= 2", - "message": "Method densmap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: qlocal\n Best score: 0.46882170983442223%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: qnx_auc\n Best score: 4.1541%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k_1kHVG qlocal", - "value": 0.1579674797537078, + "name": "Worst score phate qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -0.1579674797537078, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: qlocal\n Worst score: 0.1579674797537078%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k_1kHVG qlocal", - "value": 0.3790171042170763, - "severity": 0, - "severity_value": 0.18950855210853815, + "name": "Best score phate qnx_auc", + "value": 3.88, + "severity": 1, + "severity_value": 1.94, "code": "best_score <= 2", - "message": "Method densmap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: qlocal\n Best score: 0.3790171042170763%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: qnx_auc\n Best score: 3.88%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k qlocal", - "value": 0.18999823248443845, + "name": "Worst score pymde qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -0.18999823248443845, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: qlocal\n Worst score: 0.18999823248443845%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k qlocal", - "value": 0.44744328462348065, - "severity": 0, - "severity_value": 0.22372164231174033, + "name": "Best score pymde qnx_auc", + "value": 3.1983, + "severity": 1, + "severity_value": 1.59915, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: qlocal\n Best score: 0.44744328462348065%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: qnx_auc\n Best score: 3.1983%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k_1kHVG qlocal", - "value": 0.16160655626107367, + "name": "Worst score simlr qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -0.16160655626107367, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: qlocal\n Worst score: 0.16160655626107367%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k_1kHVG qlocal", - "value": 0.43108340266190265, + "name": "Best score simlr qnx_auc", + "value": 0.3989, "severity": 0, - "severity_value": 0.21554170133095132, + "severity_value": 0.19945, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: qlocal\n Best score: 0.43108340266190265%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: qnx_auc\n Best score: 0.3989%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score diffusion_map qlocal", - "value": 0.12624402492909334, + "name": "Worst score tsne qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -0.12624402492909334, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method diffusion_map performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: qlocal\n Worst score: 0.12624402492909334%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score diffusion_map qlocal", - "value": 0.35537869427970886, - "severity": 0, - "severity_value": 0.17768934713985443, + "name": "Best score tsne qnx_auc", + "value": 3.4711, + "severity": 1, + "severity_value": 1.73555, "code": "best_score <= 2", - "message": "Method diffusion_map performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: qlocal\n Best score: 0.35537869427970886%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: qnx_auc\n Best score: 3.4711%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_default qlocal", - "value": 0.1470187453451742, + "name": "Worst score umap qnx_auc", + "value": 0.0, "severity": 0, - "severity_value": -0.1470187453451742, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method neuralee_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: qlocal\n Worst score: 0.1470187453451742%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: qnx_auc\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_default qlocal", - "value": 0.41669566563433585, - "severity": 0, - "severity_value": 0.20834783281716793, + "name": "Best score umap qnx_auc", + "value": 2.9941, + "severity": 1, + "severity_value": 1.49705, "code": "best_score <= 2", - "message": "Method neuralee_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: qlocal\n Best score: 0.41669566563433585%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: qnx_auc\n Best score: 2.9941%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_logCP10k_1kHVG qlocal", - "value": 0.15613428617690037, + "name": "Worst score random_features qlocal", + "value": 0, "severity": 0, - "severity_value": -0.15613428617690037, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method neuralee_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: qlocal\n Worst score: 0.15613428617690037%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: qlocal\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_logCP10k_1kHVG qlocal", - "value": 0.39507318661519897, + "name": "Best score random_features qlocal", + "value": 1, "severity": 0, - "severity_value": 0.19753659330759948, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method neuralee_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: qlocal\n Best score: 0.39507318661519897%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: qlocal\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k qlocal", - "value": 0.14465610088960526, + "name": "Worst score spectral_features qlocal", + "value": 0.0, "severity": 0, - "severity_value": -0.14465610088960526, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: qlocal\n Worst score: 0.14465610088960526%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: qlocal\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k qlocal", - "value": 0.38895943385477716, + "name": "Best score spectral_features qlocal", + "value": 1.0, "severity": 0, - "severity_value": 0.19447971692738858, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: qlocal\n Best score: 0.38895943385477716%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: qlocal\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k_1kHVG qlocal", - "value": 0.15175855485207163, + "name": "Worst score true_features qlocal", + "value": 0, "severity": 0, - "severity_value": -0.15175855485207163, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: qlocal\n Worst score: 0.15175855485207163%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: qlocal\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k_1kHVG qlocal", - "value": 0.41748888715791144, + "name": "Best score true_features qlocal", + "value": 1, "severity": 0, - "severity_value": 0.20874444357895572, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: qlocal\n Best score: 0.41748888715791144%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: qlocal\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_default qlocal", - "value": 0.14281452908008235, + "name": "Worst score densmap qlocal", + "value": -0.1031, "severity": 0, - "severity_value": -0.14281452908008235, + "severity_value": 0.1031, "code": "worst_score >= -1", - "message": "Method phate_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: qlocal\n Worst score: 0.14281452908008235%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: qlocal\n Worst score: -0.1031%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_default qlocal", - "value": 0.40049931770113123, - "severity": 0, - "severity_value": 0.20024965885056561, + "name": "Best score densmap qlocal", + "value": 2.5299, + "severity": 1, + "severity_value": 1.26495, "code": "best_score <= 2", - "message": "Method phate_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: qlocal\n Best score: 0.40049931770113123%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: qlocal\n Best score: 2.5299%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k qlocal", - "value": 0.16788431414472899, + "name": "Worst score diffusion_map qlocal", + "value": -0.1886, "severity": 0, - "severity_value": -0.16788431414472899, + "severity_value": 0.1886, "code": "worst_score >= -1", - "message": "Method phate_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: qlocal\n Worst score: 0.16788431414472899%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: qlocal\n Worst score: -0.1886%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k qlocal", - "value": 0.4127883799521322, + "name": "Best score diffusion_map qlocal", + "value": 0.4048, "severity": 0, - "severity_value": 0.2063941899760661, + "severity_value": 0.2024, "code": "best_score <= 2", - "message": "Method phate_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: qlocal\n Best score: 0.4127883799521322%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: qlocal\n Best score: 0.4048%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k_1kHVG qlocal", - "value": 0.16054115464048352, + "name": "Worst score ivis qlocal", + "value": -0.1327, "severity": 0, - "severity_value": -0.16054115464048352, + "severity_value": 0.1327, "code": "worst_score >= -1", - "message": "Method phate_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: qlocal\n Worst score: 0.16054115464048352%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: qlocal\n Worst score: -0.1327%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k_1kHVG qlocal", - "value": 0.41023757113371156, - "severity": 0, - "severity_value": 0.20511878556685578, + "name": "Best score ivis qlocal", + "value": 3.2761, + "severity": 1, + "severity_value": 1.63805, "code": "best_score <= 2", - "message": "Method phate_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: qlocal\n Best score: 0.41023757113371156%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: qlocal\n Best score: 3.2761%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_sqrt qlocal", - "value": 0.14671344217804969, + "name": "Worst score lmds qlocal", + "value": -0.1487, "severity": 0, - "severity_value": -0.14671344217804969, + "severity_value": 0.1487, "code": "worst_score >= -1", - "message": "Method phate_sqrt performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: qlocal\n Worst score: 0.14671344217804969%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: qlocal\n Worst score: -0.1487%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_sqrt qlocal", - "value": 0.40462715618236234, - "severity": 0, - "severity_value": 0.20231357809118117, + "name": "Best score lmds qlocal", + "value": 3.0514, + "severity": 1, + "severity_value": 1.5257, "code": "best_score <= 2", - "message": "Method phate_sqrt performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: qlocal\n Best score: 0.40462715618236234%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: qlocal\n Best score: 3.0514%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k qlocal", - "value": 0.1470535656873212, + "name": "Worst score neuralee qlocal", + "value": -0.1151, "severity": 0, - "severity_value": -0.1470535656873212, + "severity_value": 0.1151, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: qlocal\n Worst score: 0.1470535656873212%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: qlocal\n Worst score: -0.1151%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k qlocal", - "value": 0.3490141952585424, - "severity": 0, - "severity_value": 0.1745070976292712, + "name": "Best score neuralee qlocal", + "value": 2.0619, + "severity": 1, + "severity_value": 1.03095, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: qlocal\n Best score: 0.3490141952585424%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: qlocal\n Best score: 2.0619%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k_hvg qlocal", - "value": 0.16511633758015515, + "name": "Worst score pca qlocal", + "value": -0.1385, "severity": 0, - "severity_value": -0.16511633758015515, + "severity_value": 0.1385, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: qlocal\n Worst score: 0.16511633758015515%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: qlocal\n Worst score: -0.1385%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k_hvg qlocal", - "value": 0.32706575603129306, - "severity": 0, - "severity_value": 0.16353287801564653, + "name": "Best score pca qlocal", + "value": 2.3297, + "severity": 1, + "severity_value": 1.16485, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: qlocal\n Best score: 0.32706575603129306%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: qlocal\n Best score: 2.3297%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k qlocal", - "value": 0.16939153694767903, + "name": "Worst score phate qlocal", + "value": -0.0745, "severity": 0, - "severity_value": -0.16939153694767903, + "severity_value": 0.0745, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: qlocal\n Worst score: 0.16939153694767903%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: qlocal\n Worst score: -0.0745%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k qlocal", - "value": 0.44480000673881215, - "severity": 0, - "severity_value": 0.22240000336940607, + "name": "Best score phate qlocal", + "value": 7.9226, + "severity": 3, + "severity_value": 3.9613, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: qlocal\n Best score: 0.44480000673881215%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: qlocal\n Best score: 7.9226%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k_hvg qlocal", - "value": 0.1636215804389685, + "name": "Worst score pymde qlocal", + "value": -0.1006, "severity": 0, - "severity_value": -0.1636215804389685, + "severity_value": 0.1006, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: qlocal\n Worst score: 0.1636215804389685%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: qlocal\n Worst score: -0.1006%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k_hvg qlocal", - "value": 0.4224726414842883, + "name": "Best score pymde qlocal", + "value": 1.9615, "severity": 0, - "severity_value": 0.21123632074214416, + "severity_value": 0.98075, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: qlocal\n Best score: 0.4224726414842883%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: qlocal\n Best score: 1.9615%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score random_features qlocal", + "name": "Worst score simlr qlocal", "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method random_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: qlocal\n Worst score: 0.0%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: qlocal\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score random_features qlocal", - "value": 0.2603575864448636, + "name": "Best score simlr qlocal", + "value": 0.4473, "severity": 0, - "severity_value": 0.1301787932224318, + "severity_value": 0.22365, "code": "best_score <= 2", - "message": "Method random_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: qlocal\n Best score: 0.2603575864448636%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: qlocal\n Best score: 0.4473%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score spectral_features qlocal", - "value": 0.0, + "name": "Worst score tsne qlocal", + "value": -0.1038, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.1038, "code": "worst_score >= -1", - "message": "Method spectral_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: qlocal\n Worst score: 0.0%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: qlocal\n Worst score: -0.1038%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score spectral_features qlocal", - "value": 0.2642299173228899, + "name": "Best score tsne qlocal", + "value": 1.6432, "severity": 0, - "severity_value": 0.13211495866144496, + "severity_value": 0.8216, "code": "best_score <= 2", - "message": "Method spectral_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: qlocal\n Best score: 0.2642299173228899%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: qlocal\n Best score: 1.6432%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score true_features qlocal", - "value": 1.0, + "name": "Worst score umap qlocal", + "value": -0.113, "severity": 0, - "severity_value": -1.0, + "severity_value": 0.113, "code": "worst_score >= -1", - "message": "Method true_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: qlocal\n Worst score: 1.0%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: qlocal\n Worst score: -0.113%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score true_features qlocal", - "value": 1.0, - "severity": 0, - "severity_value": 0.5, + "name": "Best score umap qlocal", + "value": 3.3753, + "severity": 1, + "severity_value": 1.68765, "code": "best_score <= 2", - "message": "Method true_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: qlocal\n Best score: 1.0%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: qlocal\n Best score: 3.3753%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k qlocal", - "value": 0.1723742647386066, + "name": "Worst score random_features qglobal", + "value": 0.0, "severity": 0, - "severity_value": -0.1723742647386066, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: qlocal\n Worst score: 0.1723742647386066%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: qglobal\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k qlocal", - "value": 0.4683100717474159, + "name": "Best score random_features qglobal", + "value": 1.0, "severity": 0, - "severity_value": 0.23415503587370795, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method tsne_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: qlocal\n Best score: 0.4683100717474159%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: qglobal\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k_1kHVG qlocal", - "value": 0.1646063432270821, + "name": "Worst score spectral_features qglobal", + "value": 0.0, "severity": 0, - "severity_value": -0.1646063432270821, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: qlocal\n Worst score: 0.1646063432270821%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: qglobal\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k_1kHVG qlocal", - "value": 0.43842906833197376, + "name": "Best score spectral_features qglobal", + "value": 1.0, "severity": 0, - "severity_value": 0.21921453416598688, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method tsne_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: qlocal\n Best score: 0.43842906833197376%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: qglobal\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k qlocal", - "value": 0.1722487485552314, + "name": "Worst score true_features qglobal", + "value": 0, "severity": 0, - "severity_value": -0.1722487485552314, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: qlocal\n Worst score: 0.1722487485552314%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: qglobal\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k qlocal", - "value": 0.4615553646026806, + "name": "Best score true_features qglobal", + "value": 1, "severity": 0, - "severity_value": 0.2307776823013403, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method umap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: qlocal\n Best score: 0.4615553646026806%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: qglobal\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k_1kHVG qlocal", - "value": 0.16059793490989033, + "name": "Worst score densmap qglobal", + "value": -0.3211, "severity": 0, - "severity_value": -0.16059793490989033, + "severity_value": 0.3211, "code": "worst_score >= -1", - "message": "Method umap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: qlocal\n Worst score: 0.16059793490989033%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: qglobal\n Worst score: -0.3211%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k_1kHVG qlocal", - "value": 0.35550679326366635, - "severity": 0, - "severity_value": 0.17775339663183318, + "name": "Best score densmap qglobal", + "value": 7.3477, + "severity": 3, + "severity_value": 3.67385, "code": "best_score <= 2", - "message": "Method umap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: qlocal\n Best score: 0.35550679326366635%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: qglobal\n Best score: 7.3477%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k qlocal", - "value": 0.17800818879062918, - "severity": 0, - "severity_value": -0.17800818879062918, + "name": "Worst score diffusion_map qglobal", + "value": -1.0157, + "severity": 1, + "severity_value": 1.0157, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: qlocal\n Worst score: 0.17800818879062918%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: qglobal\n Worst score: -1.0157%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k qlocal", - "value": 0.4440996831766928, + "name": "Best score diffusion_map qglobal", + "value": 0.0, "severity": 0, - "severity_value": 0.2220498415883464, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: qlocal\n Best score: 0.4440996831766928%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: qglobal\n Best score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k_1kHVG qlocal", - "value": 0.1663600378931109, - "severity": 0, - "severity_value": -0.1663600378931109, + "name": "Worst score ivis qglobal", + "value": -3.1868, + "severity": 3, + "severity_value": 3.1868, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: qlocal\n Worst score: 0.1663600378931109%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: qglobal\n Worst score: -3.1868%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k_1kHVG qlocal", - "value": 0.4220857527729769, + "name": "Best score ivis qglobal", + "value": 0.9543, "severity": 0, - "severity_value": 0.21104287638648844, + "severity_value": 0.47715, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: qlocal\n Best score: 0.4220857527729769%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: qglobal\n Best score: 0.9543%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k qnn", - "value": 0.026445241705810554, + "name": "Worst score lmds qglobal", + "value": -0.6323, "severity": 0, - "severity_value": -0.026445241705810554, + "severity_value": 0.6323, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: qnn\n Worst score: 0.026445241705810554%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: qglobal\n Worst score: -0.6323%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k qnn", - "value": 0.5322928432077216, - "severity": 0, - "severity_value": 0.2661464216038608, + "name": "Best score lmds qglobal", + "value": 4.4777, + "severity": 2, + "severity_value": 2.23885, "code": "best_score <= 2", - "message": "Method densmap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: qnn\n Best score: 0.5322928432077216%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: qglobal\n Best score: 4.4777%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k_1kHVG qnn", - "value": 0.041961016384953954, + "name": "Worst score neuralee qglobal", + "value": -0.8475, "severity": 0, - "severity_value": -0.041961016384953954, + "severity_value": 0.8475, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: qnn\n Worst score: 0.041961016384953954%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: qglobal\n Worst score: -0.8475%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k_1kHVG qnn", - "value": 0.4251976588972174, + "name": "Best score neuralee qglobal", + "value": 1.0985, "severity": 0, - "severity_value": 0.2125988294486087, + "severity_value": 0.54925, "code": "best_score <= 2", - "message": "Method densmap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: qnn\n Best score: 0.4251976588972174%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: qglobal\n Best score: 1.0985%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k qnn", - "value": 0.048341162111180974, + "name": "Worst score pca qglobal", + "value": -0.6007, "severity": 0, - "severity_value": -0.048341162111180974, + "severity_value": 0.6007, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: qnn\n Worst score: 0.048341162111180974%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: qglobal\n Worst score: -0.6007%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k qnn", - "value": 0.5005133997330321, + "name": "Best score pca qglobal", + "value": 1.4645, "severity": 0, - "severity_value": 0.25025669986651605, + "severity_value": 0.73225, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: qnn\n Best score: 0.5005133997330321%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: qglobal\n Best score: 1.4645%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k_1kHVG qnn", - "value": 0.042028824697020135, + "name": "Worst score phate qglobal", + "value": -0.5368, "severity": 0, - "severity_value": -0.042028824697020135, + "severity_value": 0.5368, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: qnn\n Worst score: 0.042028824697020135%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: qglobal\n Worst score: -0.5368%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k_1kHVG qnn", - "value": 0.4551288633329911, - "severity": 0, - "severity_value": 0.22756443166649554, + "name": "Best score phate qglobal", + "value": 3.8276, + "severity": 1, + "severity_value": 1.9138, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: qnn\n Best score: 0.4551288633329911%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: qglobal\n Best score: 3.8276%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score diffusion_map qnn", - "value": 0.008617820024410992, + "name": "Worst score pymde qglobal", + "value": -0.3054, "severity": 0, - "severity_value": -0.008617820024410992, + "severity_value": 0.3054, "code": "worst_score >= -1", - "message": "Method diffusion_map performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: qnn\n Worst score: 0.008617820024410992%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: qglobal\n Worst score: -0.3054%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score diffusion_map qnn", - "value": 0.2218400246431872, + "name": "Best score pymde qglobal", + "value": 0.8172, "severity": 0, - "severity_value": 0.1109200123215936, + "severity_value": 0.4086, "code": "best_score <= 2", - "message": "Method diffusion_map performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: qnn\n Best score: 0.2218400246431872%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: qglobal\n Best score: 0.8172%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_default qnn", - "value": 0.024380170383795044, + "name": "Worst score simlr qglobal", + "value": 0.0, "severity": 0, - "severity_value": -0.024380170383795044, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method neuralee_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: qnn\n Worst score: 0.024380170383795044%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: qglobal\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_default qnn", - "value": 0.49856248074751003, + "name": "Best score simlr qglobal", + "value": 0.2747, "severity": 0, - "severity_value": 0.24928124037375501, + "severity_value": 0.13735, "code": "best_score <= 2", - "message": "Method neuralee_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: qnn\n Best score: 0.49856248074751003%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: qglobal\n Best score: 0.2747%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_logCP10k_1kHVG qnn", - "value": 0.03551306234666075, + "name": "Worst score tsne qglobal", + "value": -0.8937, "severity": 0, - "severity_value": -0.03551306234666075, + "severity_value": 0.8937, "code": "worst_score >= -1", - "message": "Method neuralee_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: qnn\n Worst score: 0.03551306234666075%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: qglobal\n Worst score: -0.8937%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_logCP10k_1kHVG qnn", - "value": 0.43294999486600266, + "name": "Best score tsne qglobal", + "value": 0.8225, "severity": 0, - "severity_value": 0.21647499743300133, + "severity_value": 0.41125, "code": "best_score <= 2", - "message": "Method neuralee_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: qnn\n Best score: 0.43294999486600266%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: qglobal\n Best score: 0.8225%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k qnn", - "value": 0.026808940834165525, - "severity": 0, - "severity_value": -0.026808940834165525, + "name": "Worst score umap qglobal", + "value": -2.4134, + "severity": 2, + "severity_value": 2.4134, "code": "worst_score >= -1", - "message": "Method pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: qnn\n Worst score: 0.026808940834165525%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: qglobal\n Worst score: -2.4134%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k qnn", - "value": 0.3860252592668652, + "name": "Best score umap qglobal", + "value": 0.6305, "severity": 0, - "severity_value": 0.1930126296334326, + "severity_value": 0.31525, "code": "best_score <= 2", - "message": "Method pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: qnn\n Best score: 0.3860252592668652%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: qglobal\n Best score: 0.6305%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k_1kHVG qnn", - "value": 0.026679488602039184, + "name": "Worst score random_features density_preservation", + "value": 0.0, "severity": 0, - "severity_value": -0.026679488602039184, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: qnn\n Worst score: 0.026679488602039184%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: density_preservation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k_1kHVG qnn", - "value": 0.4412670705411233, + "name": "Best score random_features density_preservation", + "value": 1.0, "severity": 0, - "severity_value": 0.22063353527056165, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: qnn\n Best score: 0.4412670705411233%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: density_preservation\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_default qnn", - "value": 0.039600054246649655, + "name": "Worst score spectral_features density_preservation", + "value": 0.0, "severity": 0, - "severity_value": -0.039600054246649655, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: qnn\n Worst score: 0.039600054246649655%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: density_preservation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_default qnn", - "value": 0.39264811582297976, + "name": "Best score spectral_features density_preservation", + "value": 0.2628, "severity": 0, - "severity_value": 0.19632405791148988, + "severity_value": 0.1314, "code": "best_score <= 2", - "message": "Method phate_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: qnn\n Best score: 0.39264811582297976%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: density_preservation\n Best score: 0.2628%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k qnn", - "value": 0.03616032350729248, + "name": "Worst score true_features density_preservation", + "value": 0, "severity": 0, - "severity_value": -0.03616032350729248, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: qnn\n Worst score: 0.03616032350729248%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: density_preservation\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k qnn", - "value": 0.40861484752027927, + "name": "Best score true_features density_preservation", + "value": 1, "severity": 0, - "severity_value": 0.20430742376013963, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method phate_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: qnn\n Best score: 0.40861484752027927%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: density_preservation\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k_1kHVG qnn", - "value": 0.043052113770018864, + "name": "Worst score densmap density_preservation", + "value": 0.0, "severity": 0, - "severity_value": -0.043052113770018864, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: qnn\n Worst score: 0.043052113770018864%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: density_preservation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k_1kHVG qnn", - "value": 0.40846082760036967, - "severity": 0, - "severity_value": 0.20423041380018483, + "name": "Best score densmap density_preservation", + "value": 2.2993, + "severity": 1, + "severity_value": 1.14965, "code": "best_score <= 2", - "message": "Method phate_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: qnn\n Best score: 0.40846082760036967%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: density_preservation\n Best score: 2.2993%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_sqrt qnn", - "value": 0.03928567025434281, + "name": "Worst score diffusion_map density_preservation", + "value": -0.3786, "severity": 0, - "severity_value": -0.03928567025434281, + "severity_value": 0.3786, "code": "worst_score >= -1", - "message": "Method phate_sqrt performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: qnn\n Worst score: 0.03928567025434281%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: density_preservation\n Worst score: -0.3786%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_sqrt qnn", - "value": 0.3977821131533012, + "name": "Best score diffusion_map density_preservation", + "value": 0.3794, "severity": 0, - "severity_value": 0.1988910565766506, + "severity_value": 0.1897, "code": "best_score <= 2", - "message": "Method phate_sqrt performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: qnn\n Best score: 0.3977821131533012%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: density_preservation\n Best score: 0.3794%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k qnn", - "value": 0.03090209712616045, + "name": "Worst score ivis density_preservation", + "value": 0.0, "severity": 0, - "severity_value": -0.03090209712616045, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: qnn\n Worst score: 0.03090209712616045%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: density_preservation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k qnn", - "value": 0.25187390902556733, + "name": "Best score ivis density_preservation", + "value": 1.8656, "severity": 0, - "severity_value": 0.12593695451278367, + "severity_value": 0.9328, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: qnn\n Best score: 0.25187390902556733%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: density_preservation\n Best score: 1.8656%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k_hvg qnn", - "value": 0.026389762177756407, - "severity": 0, - "severity_value": -0.026389762177756407, + "name": "Worst score lmds density_preservation", + "value": -2.6199, + "severity": 2, + "severity_value": 2.6199, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: qnn\n Worst score: 0.026389762177756407%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: density_preservation\n Worst score: -2.6199%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k_hvg qnn", - "value": 0.24365951329705307, + "name": "Best score lmds density_preservation", + "value": 0.7219, "severity": 0, - "severity_value": 0.12182975664852654, + "severity_value": 0.36095, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: qnn\n Best score: 0.24365951329705307%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: density_preservation\n Best score: 0.7219%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k qnn", - "value": 0.046306912749195545, + "name": "Worst score neuralee density_preservation", + "value": 0.0, "severity": 0, - "severity_value": -0.046306912749195545, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: qnn\n Worst score: 0.046306912749195545%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: density_preservation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k qnn", - "value": 0.48341718862306193, - "severity": 0, - "severity_value": 0.24170859431153097, + "name": "Best score neuralee density_preservation", + "value": 2.0767, + "severity": 1, + "severity_value": 1.03835, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: qnn\n Best score: 0.48341718862306193%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: density_preservation\n Best score: 2.0767%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k_hvg qnn", - "value": 0.042521976057501444, - "severity": 0, - "severity_value": -0.042521976057501444, + "name": "Worst score pca density_preservation", + "value": -2.3768, + "severity": 2, + "severity_value": 2.3768, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: qnn\n Worst score: 0.042521976057501444%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: density_preservation\n Worst score: -2.3768%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k_hvg qnn", - "value": 0.4402402710750591, + "name": "Best score pca density_preservation", + "value": 0.6411, "severity": 0, - "severity_value": 0.22012013553752954, + "severity_value": 0.32055, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: qnn\n Best score: 0.4402402710750591%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: density_preservation\n Best score: 0.6411%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score random_features qnn", - "value": 0.0, + "name": "Worst score phate density_preservation", + "value": -0.6427, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.6427, "code": "worst_score >= -1", - "message": "Method random_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: qnn\n Worst score: 0.0%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: density_preservation\n Worst score: -0.6427%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score random_features qnn", - "value": 0.003507539051423358, + "name": "Best score phate density_preservation", + "value": 0.338, "severity": 0, - "severity_value": 0.001753769525711679, + "severity_value": 0.169, "code": "best_score <= 2", - "message": "Method random_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: qnn\n Best score: 0.003507539051423358%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: density_preservation\n Best score: 0.338%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score spectral_features qnn", - "value": 0.0, + "name": "Worst score pymde density_preservation", + "value": -0.0203, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.0203, "code": "worst_score >= -1", - "message": "Method spectral_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: qnn\n Worst score: 0.0%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: density_preservation\n Worst score: -0.0203%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score spectral_features qnn", - "value": 0.042252798028545015, + "name": "Best score pymde density_preservation", + "value": 1.4838, "severity": 0, - "severity_value": 0.021126399014272507, + "severity_value": 0.7419, "code": "best_score <= 2", - "message": "Method spectral_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: qnn\n Best score: 0.042252798028545015%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: density_preservation\n Best score: 1.4838%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score true_features qnn", - "value": 1.0, + "name": "Worst score simlr density_preservation", + "value": 0.0, "severity": 0, - "severity_value": -1.0, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method true_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: qnn\n Worst score: 1.0%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: density_preservation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score true_features qnn", - "value": 1.0, + "name": "Best score simlr density_preservation", + "value": 0.7732, "severity": 0, - "severity_value": 0.5, + "severity_value": 0.3866, "code": "best_score <= 2", - "message": "Method true_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: qnn\n Best score: 1.0%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: density_preservation\n Best score: 0.7732%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k qnn", - "value": 0.05072061742550332, + "name": "Worst score tsne density_preservation", + "value": 0.0, "severity": 0, - "severity_value": -0.05072061742550332, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: qnn\n Worst score: 0.05072061742550332%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: density_preservation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k qnn", - "value": 0.5178149707362152, + "name": "Best score tsne density_preservation", + "value": 1.5848, "severity": 0, - "severity_value": 0.2589074853681076, + "severity_value": 0.7924, "code": "best_score <= 2", - "message": "Method tsne_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: qnn\n Best score: 0.5178149707362152%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: density_preservation\n Best score: 1.5848%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k_1kHVG qnn", - "value": 0.04564732280455179, + "name": "Worst score umap density_preservation", + "value": -0.0166, "severity": 0, - "severity_value": -0.04564732280455179, + "severity_value": 0.0166, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: qnn\n Worst score: 0.04564732280455179%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: density_preservation\n Worst score: -0.0166%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k_1kHVG qnn", - "value": 0.4621624396755314, + "name": "Best score umap density_preservation", + "value": 0.2727, "severity": 0, - "severity_value": 0.2310812198377657, + "severity_value": 0.13635, "code": "best_score <= 2", - "message": "Method tsne_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: qnn\n Best score: 0.4621624396755314%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: density_preservation\n Best score: 0.2727%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k qnn", - "value": 0.02857195694788623, + "name": "Worst score random_features waypoint_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.02857195694788623, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: qnn\n Worst score: 0.02857195694788623%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: waypoint_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k qnn", - "value": 0.4816716295307526, + "name": "Best score random_features waypoint_distance_correlation", + "value": 1.0, "severity": 0, - "severity_value": 0.2408358147653763, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method umap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: qnn\n Best score: 0.4816716295307526%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: waypoint_distance_correlation\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k_1kHVG qnn", - "value": 0.03997608215901666, + "name": "Worst score spectral_features waypoint_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.03997608215901666, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: qnn\n Worst score: 0.03997608215901666%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: waypoint_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k_1kHVG qnn", - "value": 0.4196016018071671, + "name": "Best score spectral_features waypoint_distance_correlation", + "value": 1.0, "severity": 0, - "severity_value": 0.20980080090358355, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method umap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: qnn\n Best score: 0.4196016018071671%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: waypoint_distance_correlation\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k qnn", - "value": 0.046504173293388074, + "name": "Worst score true_features waypoint_distance_correlation", + "value": 0, "severity": 0, - "severity_value": -0.046504173293388074, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: qnn\n Worst score: 0.046504173293388074%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: waypoint_distance_correlation\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k qnn", - "value": 0.4734572338022384, + "name": "Best score true_features waypoint_distance_correlation", + "value": 1, "severity": 0, - "severity_value": 0.2367286169011192, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: qnn\n Best score: 0.4734572338022384%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: waypoint_distance_correlation\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k_1kHVG qnn", - "value": 0.040913069743931156, + "name": "Worst score densmap waypoint_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.040913069743931156, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: qnn\n Worst score: 0.040913069743931156%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: waypoint_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k_1kHVG qnn", - "value": 0.4184721223944963, - "severity": 0, - "severity_value": 0.20923606119724816, + "name": "Best score densmap waypoint_distance_correlation", + "value": 16.3823, + "severity": 3, + "severity_value": 8.19115, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: qnn\n Best score: 0.4184721223944963%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: waypoint_distance_correlation\n Best score: 16.3823%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k qnn_auc", - "value": 0.10386591966176861, + "name": "Worst score diffusion_map waypoint_distance_correlation", + "value": -0.2362, "severity": 0, - "severity_value": -0.10386591966176861, + "severity_value": 0.2362, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: qnn_auc\n Worst score: 0.10386591966176861%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: waypoint_distance_correlation\n Worst score: -0.2362%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k qnn_auc", - "value": 0.42403180418336084, + "name": "Best score diffusion_map waypoint_distance_correlation", + "value": 0.0813, "severity": 0, - "severity_value": 0.21201590209168042, + "severity_value": 0.04065, "code": "best_score <= 2", - "message": "Method densmap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: qnn_auc\n Best score: 0.42403180418336084%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: waypoint_distance_correlation\n Best score: 0.0813%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k_1kHVG qnn_auc", - "value": 0.04774813953795953, - "severity": 0, - "severity_value": -0.04774813953795953, + "name": "Worst score ivis waypoint_distance_correlation", + "value": -1.6269, + "severity": 1, + "severity_value": 1.6269, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: qnn_auc\n Worst score: 0.04774813953795953%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: waypoint_distance_correlation\n Worst score: -1.6269%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k_1kHVG qnn_auc", - "value": 0.29371195406088657, - "severity": 0, - "severity_value": 0.14685597703044329, + "name": "Best score ivis waypoint_distance_correlation", + "value": 9.274, + "severity": 3, + "severity_value": 4.637, "code": "best_score <= 2", - "message": "Method densmap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: qnn_auc\n Best score: 0.29371195406088657%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: waypoint_distance_correlation\n Best score: 9.274%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k qnn_auc", - "value": 0.12857193175995874, + "name": "Worst score lmds waypoint_distance_correlation", + "value": -0.0238, "severity": 0, - "severity_value": -0.12857193175995874, + "severity_value": 0.0238, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: qnn_auc\n Worst score: 0.12857193175995874%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: waypoint_distance_correlation\n Worst score: -0.0238%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k qnn_auc", - "value": 0.33530015664871127, - "severity": 0, - "severity_value": 0.16765007832435563, + "name": "Best score lmds waypoint_distance_correlation", + "value": 12.2345, + "severity": 3, + "severity_value": 6.11725, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: qnn_auc\n Best score: 0.33530015664871127%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: waypoint_distance_correlation\n Best score: 12.2345%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k_1kHVG qnn_auc", - "value": 0.04233076617519793, + "name": "Worst score neuralee waypoint_distance_correlation", + "value": -0.4424, "severity": 0, - "severity_value": -0.04233076617519793, + "severity_value": 0.4424, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: qnn_auc\n Worst score: 0.04233076617519793%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: waypoint_distance_correlation\n Worst score: -0.4424%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k_1kHVG qnn_auc", - "value": 0.3207949154625169, - "severity": 0, - "severity_value": 0.16039745773125846, + "name": "Best score neuralee waypoint_distance_correlation", + "value": 16.6811, + "severity": 3, + "severity_value": 8.34055, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: qnn_auc\n Best score: 0.3207949154625169%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: waypoint_distance_correlation\n Best score: 16.6811%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score diffusion_map qnn_auc", - "value": 0.0, + "name": "Worst score pca waypoint_distance_correlation", + "value": -0.0699, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.0699, "code": "worst_score >= -1", - "message": "Method diffusion_map performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: qnn_auc\n Worst score: 0.0%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: waypoint_distance_correlation\n Worst score: -0.0699%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score diffusion_map qnn_auc", - "value": 0.3658330916929651, - "severity": 0, - "severity_value": 0.18291654584648254, + "name": "Best score pca waypoint_distance_correlation", + "value": 15.3011, + "severity": 3, + "severity_value": 7.65055, "code": "best_score <= 2", - "message": "Method diffusion_map performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: qnn_auc\n Best score: 0.3658330916929651%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: waypoint_distance_correlation\n Best score: 15.3011%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_default qnn_auc", - "value": 0.028851809700012332, + "name": "Worst score phate waypoint_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.028851809700012332, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method neuralee_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: qnn_auc\n Worst score: 0.028851809700012332%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: waypoint_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_default qnn_auc", - "value": 0.38005321670319747, - "severity": 0, - "severity_value": 0.19002660835159874, + "name": "Best score phate waypoint_distance_correlation", + "value": 8.0966, + "severity": 3, + "severity_value": 4.0483, "code": "best_score <= 2", - "message": "Method neuralee_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: qnn_auc\n Best score: 0.38005321670319747%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: waypoint_distance_correlation\n Best score: 8.0966%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_logCP10k_1kHVG qnn_auc", - "value": 0.06719819498702062, + "name": "Worst score pymde waypoint_distance_correlation", + "value": -0.0445, "severity": 0, - "severity_value": -0.06719819498702062, + "severity_value": 0.0445, "code": "worst_score >= -1", - "message": "Method neuralee_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: qnn_auc\n Worst score: 0.06719819498702062%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: waypoint_distance_correlation\n Worst score: -0.0445%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_logCP10k_1kHVG qnn_auc", - "value": 0.398382401902053, - "severity": 0, - "severity_value": 0.1991912009510265, + "name": "Best score pymde waypoint_distance_correlation", + "value": 2.703, + "severity": 1, + "severity_value": 1.3515, "code": "best_score <= 2", - "message": "Method neuralee_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: qnn_auc\n Best score: 0.398382401902053%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: waypoint_distance_correlation\n Best score: 2.703%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k qnn_auc", - "value": 0.048609193171115894, + "name": "Worst score simlr waypoint_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.048609193171115894, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: qnn_auc\n Worst score: 0.048609193171115894%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: waypoint_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k qnn_auc", - "value": 0.3783829166989352, + "name": "Best score simlr waypoint_distance_correlation", + "value": 0.4971, "severity": 0, - "severity_value": 0.1891914583494676, + "severity_value": 0.24855, "code": "best_score <= 2", - "message": "Method pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: qnn_auc\n Best score: 0.3783829166989352%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: waypoint_distance_correlation\n Best score: 0.4971%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k_1kHVG qnn_auc", - "value": 0.008228977362711776, + "name": "Worst score tsne waypoint_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.008228977362711776, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: qnn_auc\n Worst score: 0.008228977362711776%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: waypoint_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k_1kHVG qnn_auc", - "value": 0.3542359601581635, - "severity": 0, - "severity_value": 0.17711798007908175, + "name": "Best score tsne waypoint_distance_correlation", + "value": 2.6704, + "severity": 1, + "severity_value": 1.3352, "code": "best_score <= 2", - "message": "Method pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: qnn_auc\n Best score: 0.3542359601581635%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: waypoint_distance_correlation\n Best score: 2.6704%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_default qnn_auc", - "value": 0.029901727909329878, - "severity": 0, - "severity_value": -0.029901727909329878, + "name": "Worst score umap waypoint_distance_correlation", + "value": -2.6565, + "severity": 2, + "severity_value": 2.6565, "code": "worst_score >= -1", - "message": "Method phate_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: qnn_auc\n Worst score: 0.029901727909329878%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: waypoint_distance_correlation\n Worst score: -2.6565%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_default qnn_auc", - "value": 0.3343517201188912, - "severity": 0, - "severity_value": 0.1671758600594456, + "name": "Best score umap waypoint_distance_correlation", + "value": 10.2115, + "severity": 3, + "severity_value": 5.10575, "code": "best_score <= 2", - "message": "Method phate_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: qnn_auc\n Best score: 0.3343517201188912%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: waypoint_distance_correlation\n Best score: 10.2115%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k qnn_auc", - "value": 0.04941823871238116, + "name": "Worst score random_features centroid_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.04941823871238116, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: qnn_auc\n Worst score: 0.04941823871238116%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: centroid_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k qnn_auc", - "value": 0.3294860015142558, + "name": "Best score random_features centroid_distance_correlation", + "value": 0.1856, "severity": 0, - "severity_value": 0.1647430007571279, + "severity_value": 0.0928, "code": "best_score <= 2", - "message": "Method phate_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: qnn_auc\n Best score: 0.3294860015142558%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: centroid_distance_correlation\n Best score: 0.1856%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k_1kHVG qnn_auc", - "value": 0.04360795684151797, + "name": "Worst score spectral_features centroid_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.04360795684151797, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: qnn_auc\n Worst score: 0.04360795684151797%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: centroid_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k_1kHVG qnn_auc", - "value": 0.3183068417672998, + "name": "Best score spectral_features centroid_distance_correlation", + "value": 1.0, "severity": 0, - "severity_value": 0.1591534208836499, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method phate_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: qnn_auc\n Best score: 0.3183068417672998%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: centroid_distance_correlation\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_sqrt qnn_auc", - "value": 0.03908790034061904, + "name": "Worst score true_features centroid_distance_correlation", + "value": 0, "severity": 0, - "severity_value": -0.03908790034061904, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_sqrt performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: qnn_auc\n Worst score: 0.03908790034061904%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: centroid_distance_correlation\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_sqrt qnn_auc", - "value": 0.35744633666189196, + "name": "Best score true_features centroid_distance_correlation", + "value": 1, "severity": 0, - "severity_value": 0.17872316833094598, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method phate_sqrt performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: qnn_auc\n Best score: 0.35744633666189196%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: centroid_distance_correlation\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k qnn_auc", - "value": 0.16071352813339979, + "name": "Worst score densmap centroid_distance_correlation", + "value": -0.1894, "severity": 0, - "severity_value": -0.16071352813339979, + "severity_value": 0.1894, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: qnn_auc\n Worst score: 0.16071352813339979%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: centroid_distance_correlation\n Worst score: -0.1894%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k qnn_auc", - "value": 0.4082884570733085, - "severity": 0, - "severity_value": 0.20414422853665426, + "name": "Best score densmap centroid_distance_correlation", + "value": 31.2864, + "severity": 3, + "severity_value": 15.6432, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: qnn_auc\n Best score: 0.4082884570733085%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: centroid_distance_correlation\n Best score: 31.2864%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k_hvg qnn_auc", - "value": 0.0648420725148049, + "name": "Worst score diffusion_map centroid_distance_correlation", + "value": -0.3053, "severity": 0, - "severity_value": -0.0648420725148049, + "severity_value": 0.3053, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: qnn_auc\n Worst score: 0.0648420725148049%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: centroid_distance_correlation\n Worst score: -0.3053%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k_hvg qnn_auc", - "value": 0.28726102228894645, + "name": "Best score diffusion_map centroid_distance_correlation", + "value": 0.1132, "severity": 0, - "severity_value": 0.14363051114447323, + "severity_value": 0.0566, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: qnn_auc\n Best score: 0.28726102228894645%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: centroid_distance_correlation\n Best score: 0.1132%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k qnn_auc", - "value": 0.08545205761591257, + "name": "Worst score ivis centroid_distance_correlation", + "value": -0.1221, "severity": 0, - "severity_value": -0.08545205761591257, + "severity_value": 0.1221, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: qnn_auc\n Worst score: 0.08545205761591257%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: centroid_distance_correlation\n Worst score: -0.1221%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k qnn_auc", - "value": 0.38044674850096266, - "severity": 0, - "severity_value": 0.19022337425048133, + "name": "Best score ivis centroid_distance_correlation", + "value": 25.9972, + "severity": 3, + "severity_value": 12.9986, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: qnn_auc\n Best score: 0.38044674850096266%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: centroid_distance_correlation\n Best score: 25.9972%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k_hvg qnn_auc", - "value": 0.05435069995050923, + "name": "Worst score lmds centroid_distance_correlation", + "value": -0.1574, "severity": 0, - "severity_value": -0.05435069995050923, + "severity_value": 0.1574, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: qnn_auc\n Worst score: 0.05435069995050923%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: centroid_distance_correlation\n Worst score: -0.1574%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k_hvg qnn_auc", - "value": 0.3606151860826848, - "severity": 0, - "severity_value": 0.1803075930413424, + "name": "Best score lmds centroid_distance_correlation", + "value": 29.7886, + "severity": 3, + "severity_value": 14.8943, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: qnn_auc\n Best score: 0.3606151860826848%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: centroid_distance_correlation\n Best score: 29.7886%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score random_features qnn_auc", - "value": 0.0, + "name": "Worst score neuralee centroid_distance_correlation", + "value": -0.1911, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.1911, "code": "worst_score >= -1", - "message": "Method random_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: qnn_auc\n Worst score: 0.0%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: centroid_distance_correlation\n Worst score: -0.1911%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score random_features qnn_auc", - "value": 0.0010685900298674778, - "severity": 0, - "severity_value": 0.0005342950149337389, + "name": "Best score neuralee centroid_distance_correlation", + "value": 34.1907, + "severity": 3, + "severity_value": 17.09535, "code": "best_score <= 2", - "message": "Method random_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: qnn_auc\n Best score: 0.0010685900298674778%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: centroid_distance_correlation\n Best score: 34.1907%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score spectral_features qnn_auc", - "value": 0.0, + "name": "Worst score pca centroid_distance_correlation", + "value": -0.2755, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.2755, "code": "worst_score >= -1", - "message": "Method spectral_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: qnn_auc\n Worst score: 0.0%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: centroid_distance_correlation\n Worst score: -0.2755%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score spectral_features qnn_auc", - "value": 0.22651557913735587, - "severity": 0, - "severity_value": 0.11325778956867794, + "name": "Best score pca centroid_distance_correlation", + "value": 28.5627, + "severity": 3, + "severity_value": 14.28135, "code": "best_score <= 2", - "message": "Method spectral_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: qnn_auc\n Best score: 0.22651557913735587%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: centroid_distance_correlation\n Best score: 28.5627%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score true_features qnn_auc", - "value": 1.0, + "name": "Worst score phate centroid_distance_correlation", + "value": -0.1235, "severity": 0, - "severity_value": -1.0, + "severity_value": 0.1235, "code": "worst_score >= -1", - "message": "Method true_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: qnn_auc\n Worst score: 1.0%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: centroid_distance_correlation\n Worst score: -0.1235%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score true_features qnn_auc", - "value": 1.0, - "severity": 0, - "severity_value": 0.5, + "name": "Best score phate centroid_distance_correlation", + "value": 16.8833, + "severity": 3, + "severity_value": 8.44165, "code": "best_score <= 2", - "message": "Method true_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: qnn_auc\n Best score: 1.0%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: centroid_distance_correlation\n Best score: 16.8833%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k qnn_auc", - "value": 0.10058139409005618, + "name": "Worst score pymde centroid_distance_correlation", + "value": -0.1693, "severity": 0, - "severity_value": -0.10058139409005618, + "severity_value": 0.1693, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: qnn_auc\n Worst score: 0.10058139409005618%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: centroid_distance_correlation\n Worst score: -0.1693%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k qnn_auc", - "value": 0.3581249108484952, + "name": "Best score pymde centroid_distance_correlation", + "value": 1.5467, "severity": 0, - "severity_value": 0.1790624554242476, + "severity_value": 0.77335, "code": "best_score <= 2", - "message": "Method tsne_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: qnn_auc\n Best score: 0.3581249108484952%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: centroid_distance_correlation\n Best score: 1.5467%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k_1kHVG qnn_auc", - "value": 0.07779989036073176, + "name": "Worst score simlr centroid_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.07779989036073176, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: qnn_auc\n Worst score: 0.07779989036073176%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: centroid_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k_1kHVG qnn_auc", - "value": 0.38241415297088094, + "name": "Best score simlr centroid_distance_correlation", + "value": 0.5485, "severity": 0, - "severity_value": 0.19120707648544047, + "severity_value": 0.27425, "code": "best_score <= 2", - "message": "Method tsne_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: qnn_auc\n Best score: 0.38241415297088094%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: centroid_distance_correlation\n Best score: 0.5485%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k qnn_auc", - "value": 0.03135711909651273, + "name": "Worst score tsne centroid_distance_correlation", + "value": -0.1163, "severity": 0, - "severity_value": -0.03135711909651273, + "severity_value": 0.1163, "code": "worst_score >= -1", - "message": "Method umap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: qnn_auc\n Worst score: 0.03135711909651273%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: centroid_distance_correlation\n Worst score: -0.1163%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k qnn_auc", - "value": 0.3730188446525014, + "name": "Best score tsne centroid_distance_correlation", + "value": 1.2605, "severity": 0, - "severity_value": 0.1865094223262507, + "severity_value": 0.63025, "code": "best_score <= 2", - "message": "Method umap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: qnn_auc\n Best score: 0.3730188446525014%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: centroid_distance_correlation\n Best score: 1.2605%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k_1kHVG qnn_auc", - "value": 0.04005272682254235, + "name": "Worst score umap centroid_distance_correlation", + "value": -0.9478, "severity": 0, - "severity_value": -0.04005272682254235, + "severity_value": 0.9478, "code": "worst_score >= -1", - "message": "Method umap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: qnn_auc\n Worst score: 0.04005272682254235%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: centroid_distance_correlation\n Worst score: -0.9478%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k_1kHVG qnn_auc", - "value": 0.30648736137034427, - "severity": 0, - "severity_value": 0.15324368068517213, + "name": "Best score umap centroid_distance_correlation", + "value": 23.6242, + "severity": 3, + "severity_value": 11.8121, "code": "best_score <= 2", - "message": "Method umap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: qnn_auc\n Best score: 0.30648736137034427%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: centroid_distance_correlation\n Best score: 23.6242%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k qnn_auc", - "value": 0.0981236679828561, + "name": "Worst score random_features label_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.0981236679828561, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: qnn_auc\n Worst score: 0.0981236679828561%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: label_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k qnn_auc", - "value": 0.3351471254470795, + "name": "Best score random_features label_distance_correlation", + "value": 1.0, "severity": 0, - "severity_value": 0.16757356272353974, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: qnn_auc\n Best score: 0.3351471254470795%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: label_distance_correlation\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k_1kHVG qnn_auc", - "value": 0.044447812500770345, + "name": "Worst score spectral_features label_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.044447812500770345, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: qnn_auc\n Worst score: 0.044447812500770345%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: label_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k_1kHVG qnn_auc", - "value": 0.35422341204956365, + "name": "Best score spectral_features label_distance_correlation", + "value": 1.0, "severity": 0, - "severity_value": 0.17711170602478182, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: qnn_auc\n Best score: 0.35422341204956365%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: label_distance_correlation\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k trustworthiness", - "value": 0.22617992879049034, + "name": "Worst score true_features label_distance_correlation", + "value": 0, "severity": 0, - "severity_value": -0.22617992879049034, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: trustworthiness\n Worst score: 0.22617992879049034%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: label_distance_correlation\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k trustworthiness", - "value": 0.9286172159714817, + "name": "Best score true_features label_distance_correlation", + "value": 1, "severity": 0, - "severity_value": 0.46430860798574086, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method densmap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k\n Metric id: trustworthiness\n Best score: 0.9286172159714817%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: label_distance_correlation\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_logCP10k_1kHVG trustworthiness", - "value": 0.2708232003812969, + "name": "Worst score densmap label_distance_correlation", + "value": -0.6228, "severity": 0, - "severity_value": -0.2708232003812969, + "severity_value": 0.6228, "code": "worst_score >= -1", - "message": "Method densmap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: trustworthiness\n Worst score: 0.2708232003812969%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: label_distance_correlation\n Worst score: -0.6228%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_logCP10k_1kHVG trustworthiness", - "value": 0.8894501647118734, - "severity": 0, - "severity_value": 0.4447250823559367, + "name": "Best score densmap label_distance_correlation", + "value": 4.2203, + "severity": 2, + "severity_value": 2.11015, "code": "best_score <= 2", - "message": "Method densmap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_logCP10k_1kHVG\n Metric id: trustworthiness\n Best score: 0.8894501647118734%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: label_distance_correlation\n Best score: 4.2203%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k trustworthiness", - "value": 0.3744211076226745, - "severity": 0, - "severity_value": -0.3744211076226745, + "name": "Worst score diffusion_map label_distance_correlation", + "value": -2.8527, + "severity": 2, + "severity_value": 2.8527, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: trustworthiness\n Worst score: 0.3744211076226745%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: label_distance_correlation\n Worst score: -2.8527%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k trustworthiness", - "value": 0.920101214252749, + "name": "Best score diffusion_map label_distance_correlation", + "value": 0.6466, "severity": 0, - "severity_value": 0.4600506071263745, + "severity_value": 0.3233, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k\n Metric id: trustworthiness\n Best score: 0.920101214252749%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: label_distance_correlation\n Best score: 0.6466%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score densmap_pca_logCP10k_1kHVG trustworthiness", - "value": 0.26970043107252134, + "name": "Worst score ivis label_distance_correlation", + "value": -0.6202, "severity": 0, - "severity_value": -0.26970043107252134, + "severity_value": 0.6202, "code": "worst_score >= -1", - "message": "Method densmap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: trustworthiness\n Worst score: 0.26970043107252134%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: label_distance_correlation\n Worst score: -0.6202%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score densmap_pca_logCP10k_1kHVG trustworthiness", - "value": 0.9119525120549994, - "severity": 0, - "severity_value": 0.4559762560274997, + "name": "Best score ivis label_distance_correlation", + "value": 4.3802, + "severity": 2, + "severity_value": 2.1901, "code": "best_score <= 2", - "message": "Method densmap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: densmap_pca_logCP10k_1kHVG\n Metric id: trustworthiness\n Best score: 0.9119525120549994%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: label_distance_correlation\n Best score: 4.3802%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score diffusion_map trustworthiness", - "value": 0.16187179198612492, + "name": "Worst score lmds label_distance_correlation", + "value": -0.124, "severity": 0, - "severity_value": -0.16187179198612492, + "severity_value": 0.124, "code": "worst_score >= -1", - "message": "Method diffusion_map performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: trustworthiness\n Worst score: 0.16187179198612492%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: label_distance_correlation\n Worst score: -0.124%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score diffusion_map trustworthiness", - "value": 0.804129414198641, - "severity": 0, - "severity_value": 0.4020647070993205, + "name": "Best score lmds label_distance_correlation", + "value": 5.8214, + "severity": 2, + "severity_value": 2.9107, "code": "best_score <= 2", - "message": "Method diffusion_map performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: diffusion_map\n Metric id: trustworthiness\n Best score: 0.804129414198641%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: label_distance_correlation\n Best score: 5.8214%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_default trustworthiness", - "value": 0.1994166428551849, + "name": "Worst score neuralee label_distance_correlation", + "value": -0.1611, "severity": 0, - "severity_value": -0.1994166428551849, + "severity_value": 0.1611, "code": "worst_score >= -1", - "message": "Method neuralee_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: trustworthiness\n Worst score: 0.1994166428551849%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: label_distance_correlation\n Worst score: -0.1611%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_default trustworthiness", - "value": 0.9132628865159064, - "severity": 0, - "severity_value": 0.4566314432579532, + "name": "Best score neuralee label_distance_correlation", + "value": 4.3047, + "severity": 2, + "severity_value": 2.15235, "code": "best_score <= 2", - "message": "Method neuralee_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_default\n Metric id: trustworthiness\n Best score: 0.9132628865159064%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: label_distance_correlation\n Best score: 4.3047%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score neuralee_logCP10k_1kHVG trustworthiness", - "value": 0.25344111840416617, + "name": "Worst score pca label_distance_correlation", + "value": -0.6202, "severity": 0, - "severity_value": -0.25344111840416617, + "severity_value": 0.6202, "code": "worst_score >= -1", - "message": "Method neuralee_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: trustworthiness\n Worst score: 0.25344111840416617%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: label_distance_correlation\n Worst score: -0.6202%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score neuralee_logCP10k_1kHVG trustworthiness", - "value": 0.897177777424129, - "severity": 0, - "severity_value": 0.4485888887120645, + "name": "Best score pca label_distance_correlation", + "value": 5.7965, + "severity": 2, + "severity_value": 2.89825, "code": "best_score <= 2", - "message": "Method neuralee_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: neuralee_logCP10k_1kHVG\n Metric id: trustworthiness\n Best score: 0.897177777424129%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: label_distance_correlation\n Best score: 5.7965%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k trustworthiness", - "value": 0.2374906975601547, + "name": "Worst score phate label_distance_correlation", + "value": -0.8728, "severity": 0, - "severity_value": -0.2374906975601547, + "severity_value": 0.8728, "code": "worst_score >= -1", - "message": "Method pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: trustworthiness\n Worst score: 0.2374906975601547%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: label_distance_correlation\n Worst score: -0.8728%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k trustworthiness", - "value": 0.7978413991756448, - "severity": 0, - "severity_value": 0.3989206995878224, + "name": "Best score phate label_distance_correlation", + "value": 3.2205, + "severity": 1, + "severity_value": 1.61025, "code": "best_score <= 2", - "message": "Method pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k\n Metric id: trustworthiness\n Best score: 0.7978413991756448%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: label_distance_correlation\n Best score: 3.2205%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pca_logCP10k_1kHVG trustworthiness", - "value": 0.19779681383477243, - "severity": 0, - "severity_value": -0.19779681383477243, + "name": "Worst score pymde label_distance_correlation", + "value": -1.0344, + "severity": 1, + "severity_value": 1.0344, "code": "worst_score >= -1", - "message": "Method pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: trustworthiness\n Worst score: 0.19779681383477243%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: label_distance_correlation\n Worst score: -1.0344%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pca_logCP10k_1kHVG trustworthiness", - "value": 0.893187453252065, + "name": "Best score pymde label_distance_correlation", + "value": 0.9699, "severity": 0, - "severity_value": 0.4465937266260325, + "severity_value": 0.48495, "code": "best_score <= 2", - "message": "Method pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pca_logCP10k_1kHVG\n Metric id: trustworthiness\n Best score: 0.893187453252065%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: label_distance_correlation\n Best score: 0.9699%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_default trustworthiness", - "value": 0.3173330328322507, + "name": "Worst score simlr label_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.3173330328322507, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_default performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: trustworthiness\n Worst score: 0.3173330328322507%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: label_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_default trustworthiness", - "value": 0.9057800340563681, + "name": "Best score simlr label_distance_correlation", + "value": 0.1067, "severity": 0, - "severity_value": 0.45289001702818404, + "severity_value": 0.05335, "code": "best_score <= 2", - "message": "Method phate_default performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_default\n Metric id: trustworthiness\n Best score: 0.9057800340563681%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: label_distance_correlation\n Best score: 0.1067%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k trustworthiness", - "value": 0.24735912718281464, + "name": "Worst score tsne label_distance_correlation", + "value": -0.9923, "severity": 0, - "severity_value": -0.24735912718281464, + "severity_value": 0.9923, "code": "worst_score >= -1", - "message": "Method phate_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: trustworthiness\n Worst score: 0.24735912718281464%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: label_distance_correlation\n Worst score: -0.9923%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k trustworthiness", - "value": 0.9047462482295463, + "name": "Best score tsne label_distance_correlation", + "value": 0.8605, "severity": 0, - "severity_value": 0.45237312411477315, + "severity_value": 0.43025, "code": "best_score <= 2", - "message": "Method phate_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k\n Metric id: trustworthiness\n Best score: 0.9047462482295463%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: label_distance_correlation\n Best score: 0.8605%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_logCP10k_1kHVG trustworthiness", - "value": 0.32266598523080237, - "severity": 0, - "severity_value": -0.32266598523080237, + "name": "Worst score umap label_distance_correlation", + "value": -3.2248, + "severity": 3, + "severity_value": 3.2248, "code": "worst_score >= -1", - "message": "Method phate_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: trustworthiness\n Worst score: 0.32266598523080237%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: label_distance_correlation\n Worst score: -3.2248%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_logCP10k_1kHVG trustworthiness", - "value": 0.908724159332877, - "severity": 0, - "severity_value": 0.4543620796664385, + "name": "Best score umap label_distance_correlation", + "value": 3.9434, + "severity": 1, + "severity_value": 1.9717, "code": "best_score <= 2", - "message": "Method phate_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_logCP10k_1kHVG\n Metric id: trustworthiness\n Best score: 0.908724159332877%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: label_distance_correlation\n Best score: 3.9434%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score phate_sqrt trustworthiness", - "value": 0.3202521669854965, + "name": "Worst score random_features spectral_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.3202521669854965, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method phate_sqrt performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: trustworthiness\n Worst score: 0.3202521669854965%\n" + "message": "Method random_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: spectral_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score phate_sqrt trustworthiness", - "value": 0.9070162483886881, + "name": "Best score random_features spectral_distance_correlation", + "value": 0.374, "severity": 0, - "severity_value": 0.45350812419434405, + "severity_value": 0.187, "code": "best_score <= 2", - "message": "Method phate_sqrt performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: phate_sqrt\n Metric id: trustworthiness\n Best score: 0.9070162483886881%\n" + "message": "Method random_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: random_features\n Metric id: spectral_distance_correlation\n Best score: 0.374%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k trustworthiness", - "value": 0.22693660000809465, + "name": "Worst score spectral_features spectral_distance_correlation", + "value": 0, "severity": 0, - "severity_value": -0.22693660000809465, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: trustworthiness\n Worst score: 0.22693660000809465%\n" + "message": "Method spectral_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: spectral_distance_correlation\n Worst score: 0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k trustworthiness", - "value": 0.6393821173581329, + "name": "Best score spectral_features spectral_distance_correlation", + "value": 1, "severity": 0, - "severity_value": 0.31969105867906644, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k\n Metric id: trustworthiness\n Best score: 0.6393821173581329%\n" + "message": "Method spectral_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: spectral_features\n Metric id: spectral_distance_correlation\n Best score: 1%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_distances_log_cp10k_hvg trustworthiness", - "value": 0.2358241201215652, + "name": "Worst score true_features spectral_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.2358241201215652, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pymde_distances_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: trustworthiness\n Worst score: 0.2358241201215652%\n" + "message": "Method true_features performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: spectral_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_distances_log_cp10k_hvg trustworthiness", - "value": 0.6779814440536627, + "name": "Best score true_features spectral_distance_correlation", + "value": 1.0, "severity": 0, - "severity_value": 0.33899072202683134, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method pymde_distances_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_distances_log_cp10k_hvg\n Metric id: trustworthiness\n Best score: 0.6779814440536627%\n" + "message": "Method true_features performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: true_features\n Metric id: spectral_distance_correlation\n Best score: 1.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k trustworthiness", - "value": 0.3694751690125947, + "name": "Worst score densmap spectral_distance_correlation", + "value": -0.2802, "severity": 0, - "severity_value": -0.3694751690125947, + "severity_value": 0.2802, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: trustworthiness\n Worst score: 0.3694751690125947%\n" + "message": "Method densmap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: spectral_distance_correlation\n Worst score: -0.2802%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k trustworthiness", - "value": 0.923740471378328, - "severity": 0, - "severity_value": 0.461870235689164, + "name": "Best score densmap spectral_distance_correlation", + "value": 3.9116, + "severity": 1, + "severity_value": 1.9558, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k\n Metric id: trustworthiness\n Best score: 0.923740471378328%\n" + "message": "Method densmap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: densmap\n Metric id: spectral_distance_correlation\n Best score: 3.9116%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score pymde_neighbors_log_cp10k_hvg trustworthiness", - "value": 0.2810298404987273, + "name": "Worst score diffusion_map spectral_distance_correlation", + "value": -0.3021, "severity": 0, - "severity_value": -0.2810298404987273, + "severity_value": 0.3021, "code": "worst_score >= -1", - "message": "Method pymde_neighbors_log_cp10k_hvg performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: trustworthiness\n Worst score: 0.2810298404987273%\n" + "message": "Method diffusion_map performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: spectral_distance_correlation\n Worst score: -0.3021%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score pymde_neighbors_log_cp10k_hvg trustworthiness", - "value": 0.9080990499228161, - "severity": 0, - "severity_value": 0.45404952496140805, + "name": "Best score diffusion_map spectral_distance_correlation", + "value": 2.1859, + "severity": 1, + "severity_value": 1.09295, "code": "best_score <= 2", - "message": "Method pymde_neighbors_log_cp10k_hvg performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: pymde_neighbors_log_cp10k_hvg\n Metric id: trustworthiness\n Best score: 0.9080990499228161%\n" + "message": "Method diffusion_map performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: diffusion_map\n Metric id: spectral_distance_correlation\n Best score: 2.1859%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score random_features trustworthiness", + "name": "Worst score ivis spectral_distance_correlation", "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method random_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: trustworthiness\n Worst score: 0.0%\n" + "message": "Method ivis performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: spectral_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score random_features trustworthiness", - "value": 0.00203388643855179, + "name": "Best score ivis spectral_distance_correlation", + "value": 0.2139, "severity": 0, - "severity_value": 0.001016943219275895, + "severity_value": 0.10695, "code": "best_score <= 2", - "message": "Method random_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: random_features\n Metric id: trustworthiness\n Best score: 0.00203388643855179%\n" + "message": "Method ivis performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: ivis\n Metric id: spectral_distance_correlation\n Best score: 0.2139%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score spectral_features trustworthiness", - "value": 0.0, + "name": "Worst score lmds spectral_distance_correlation", + "value": -0.3215, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.3215, "code": "worst_score >= -1", - "message": "Method spectral_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: trustworthiness\n Worst score: 0.0%\n" + "message": "Method lmds performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: spectral_distance_correlation\n Worst score: -0.3215%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score spectral_features trustworthiness", - "value": 0.7303749944001828, + "name": "Best score lmds spectral_distance_correlation", + "value": 1.2668, "severity": 0, - "severity_value": 0.3651874972000914, + "severity_value": 0.6334, "code": "best_score <= 2", - "message": "Method spectral_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: spectral_features\n Metric id: trustworthiness\n Best score: 0.7303749944001828%\n" + "message": "Method lmds performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: lmds\n Metric id: spectral_distance_correlation\n Best score: 1.2668%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score true_features trustworthiness", - "value": 1.0, + "name": "Worst score neuralee spectral_distance_correlation", + "value": -0.2549, "severity": 0, - "severity_value": -1.0, + "severity_value": 0.2549, "code": "worst_score >= -1", - "message": "Method true_features performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: trustworthiness\n Worst score: 1.0%\n" + "message": "Method neuralee performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: spectral_distance_correlation\n Worst score: -0.2549%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score true_features trustworthiness", - "value": 1.0, + "name": "Best score neuralee spectral_distance_correlation", + "value": 1.112, "severity": 0, - "severity_value": 0.5, + "severity_value": 0.556, "code": "best_score <= 2", - "message": "Method true_features performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: true_features\n Metric id: trustworthiness\n Best score: 1.0%\n" + "message": "Method neuralee performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: neuralee\n Metric id: spectral_distance_correlation\n Best score: 1.112%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k trustworthiness", - "value": 0.40450628331398153, + "name": "Worst score pca spectral_distance_correlation", + "value": -0.2494, "severity": 0, - "severity_value": -0.40450628331398153, + "severity_value": 0.2494, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: trustworthiness\n Worst score: 0.40450628331398153%\n" + "message": "Method pca performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: spectral_distance_correlation\n Worst score: -0.2494%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k trustworthiness", - "value": 0.9316138580772474, - "severity": 0, - "severity_value": 0.4658069290386237, + "name": "Best score pca spectral_distance_correlation", + "value": 3.527, + "severity": 1, + "severity_value": 1.7635, "code": "best_score <= 2", - "message": "Method tsne_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k\n Metric id: trustworthiness\n Best score: 0.9316138580772474%\n" + "message": "Method pca performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pca\n Metric id: spectral_distance_correlation\n Best score: 3.527%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score tsne_logCP10k_1kHVG trustworthiness", - "value": 0.29627415474071345, + "name": "Worst score phate spectral_distance_correlation", + "value": -0.0076, "severity": 0, - "severity_value": -0.29627415474071345, + "severity_value": 0.0076, "code": "worst_score >= -1", - "message": "Method tsne_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: trustworthiness\n Worst score: 0.29627415474071345%\n" + "message": "Method phate performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: spectral_distance_correlation\n Worst score: -0.0076%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score tsne_logCP10k_1kHVG trustworthiness", - "value": 0.91241656985534, - "severity": 0, - "severity_value": 0.45620828492767, + "name": "Best score phate spectral_distance_correlation", + "value": 2.351, + "severity": 1, + "severity_value": 1.1755, "code": "best_score <= 2", - "message": "Method tsne_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: tsne_logCP10k_1kHVG\n Metric id: trustworthiness\n Best score: 0.91241656985534%\n" + "message": "Method phate performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: phate\n Metric id: spectral_distance_correlation\n Best score: 2.351%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k trustworthiness", - "value": 0.20176991566093655, + "name": "Worst score pymde spectral_distance_correlation", + "value": -0.2547, "severity": 0, - "severity_value": -0.20176991566093655, + "severity_value": 0.2547, "code": "worst_score >= -1", - "message": "Method umap_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: trustworthiness\n Worst score: 0.20176991566093655%\n" + "message": "Method pymde performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: spectral_distance_correlation\n Worst score: -0.2547%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k trustworthiness", - "value": 0.9219128857201967, + "name": "Best score pymde spectral_distance_correlation", + "value": 1.7228, "severity": 0, - "severity_value": 0.46095644286009835, + "severity_value": 0.8614, "code": "best_score <= 2", - "message": "Method umap_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k\n Metric id: trustworthiness\n Best score: 0.9219128857201967%\n" + "message": "Method pymde performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: pymde\n Metric id: spectral_distance_correlation\n Best score: 1.7228%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_logCP10k_1kHVG trustworthiness", - "value": 0.2839675419201029, + "name": "Worst score simlr spectral_distance_correlation", + "value": 0.0, "severity": 0, - "severity_value": -0.2839675419201029, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method umap_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: trustworthiness\n Worst score: 0.2839675419201029%\n" + "message": "Method simlr performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: spectral_distance_correlation\n Worst score: 0.0%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_logCP10k_1kHVG trustworthiness", - "value": 0.8861677037414261, - "severity": 0, - "severity_value": 0.44308385187071303, + "name": "Best score simlr spectral_distance_correlation", + "value": 2.347, + "severity": 1, + "severity_value": 1.1735, "code": "best_score <= 2", - "message": "Method umap_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_logCP10k_1kHVG\n Metric id: trustworthiness\n Best score: 0.8861677037414261%\n" + "message": "Method simlr performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: simlr\n Metric id: spectral_distance_correlation\n Best score: 2.347%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k trustworthiness", - "value": 0.3843107098020755, + "name": "Worst score tsne spectral_distance_correlation", + "value": -0.1946, "severity": 0, - "severity_value": -0.3843107098020755, + "severity_value": 0.1946, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: trustworthiness\n Worst score: 0.3843107098020755%\n" + "message": "Method tsne performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: spectral_distance_correlation\n Worst score: -0.1946%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k trustworthiness", - "value": 0.9239693174403617, - "severity": 0, - "severity_value": 0.46198465872018085, + "name": "Best score tsne spectral_distance_correlation", + "value": 2.5529, + "severity": 1, + "severity_value": 1.27645, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k\n Metric id: trustworthiness\n Best score: 0.9239693174403617%\n" + "message": "Method tsne performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: tsne\n Metric id: spectral_distance_correlation\n Best score: 2.5529%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Worst score umap_pca_logCP10k_1kHVG trustworthiness", - "value": 0.2839656338214019, + "name": "Worst score umap spectral_distance_correlation", + "value": -0.1622, "severity": 0, - "severity_value": -0.2839656338214019, + "severity_value": 0.1622, "code": "worst_score >= -1", - "message": "Method umap_pca_logCP10k_1kHVG performs much worse than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: trustworthiness\n Worst score: 0.2839656338214019%\n" + "message": "Method umap performs much worse than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: spectral_distance_correlation\n Worst score: -0.1622%\n" }, { - "task_id": "dimensionality_reduction", + "task_id": "task_dimensionality_reduction", "category": "Scaling", - "name": "Best score umap_pca_logCP10k_1kHVG trustworthiness", - "value": 0.9051441029966422, + "name": "Best score umap spectral_distance_correlation", + "value": 1.0188, "severity": 0, - "severity_value": 0.4525720514983211, + "severity_value": 0.5094, "code": "best_score <= 2", - "message": "Method umap_pca_logCP10k_1kHVG performs a lot better than baselines.\n Task id: dimensionality_reduction\n Method id: umap_pca_logCP10k_1kHVG\n Metric id: trustworthiness\n Best score: 0.9051441029966422%\n" + "message": "Method umap performs a lot better than baselines.\n Task id: task_dimensionality_reduction\n Method id: umap\n Metric id: spectral_distance_correlation\n Best score: 1.0188%\n" } ] \ No newline at end of file diff --git a/results/dimensionality_reduction/data/results.json b/results/dimensionality_reduction/data/results.json index 05ff23ce..d1ea4a4f 100644 --- a/results/dimensionality_reduction/data/results.json +++ b/results/dimensionality_reduction/data/results.json @@ -1,3850 +1,10298 @@ [ - { - "task_id": "dimensionality_reduction", - "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", - "method_id": "pca_logCP10k", - "dataset_id": "zebrafish_labs", - "submission_time": "2023-02-21 17:59:03.360", - "code_version": "1.1.3", - "resources": { - "duration_sec": 280.0, - "cpu_pct": 118.9, - "peak_memory_mb": 1700.0, - "disk_read_mb": 1200.0, - "disk_write_mb": 1100.0 - }, - "metric_values": { - "density_preservation": -0.03645033758101916, - "distance_correlation": 0.7266117806252675, - "distance_correlation_spectral": -0.015858191645029596, - "trustworthiness": 0.7912630138100286 - }, - "scaled_scores": { - "density_preservation": -0.016067405057167836, - "distance_correlation": 0.8462625321131627, - "distance_correlation_spectral": 0.0004836016713153229, - "trustworthiness": 0.5830249299528386 - }, - "mean_score": 0.3534259146700372 - }, - { - "task_id": "dimensionality_reduction", - "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", - "method_id": "pca_logCP10k_1kHVG", - "dataset_id": "zebrafish_labs", - "submission_time": "2023-02-21 17:59:03.727", - "code_version": "1.1.3", - "resources": { - "duration_sec": 348.0, - "cpu_pct": 37.8, - "peak_memory_mb": 1400.0, - "disk_read_mb": 1200.0, - "disk_write_mb": 1500.0 - }, - "metric_values": { - "density_preservation": 0.06707118145615036, - "distance_correlation": 0.4386363102597822, - "distance_correlation_spectral": 0.1710424467369666, - "trustworthiness": 0.7042859471900272 - }, - "scaled_scores": { - "density_preservation": 0.08541824978005755, - "distance_correlation": 0.5080833384282012, - "distance_correlation_spectral": 0.1924307692285588, - "trustworthiness": 0.40927868062563555 - }, - "mean_score": 0.2988027595156133 - }, - { - "task_id": "dimensionality_reduction", - "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", - "method_id": "umap_pca_logCP10k", - "dataset_id": "zebrafish_labs", - "submission_time": "2023-02-21 17:59:02.609", - "code_version": "0.5.3", - "resources": { - "duration_sec": 360.0, - "cpu_pct": 165.4, - "peak_memory_mb": 1800.0, - "disk_read_mb": 1200.0, - "disk_write_mb": 1100.0 - }, - "metric_values": { - "density_preservation": -0.08162450821841145, - "distance_correlation": 0.5299907342464462, - "distance_correlation_spectral": -0.02121837414768316, - "trustworthiness": 0.9056031398553867 - }, - "scaled_scores": { - "density_preservation": -0.06035317608819602, - "distance_correlation": 0.6153638835529462, - "distance_correlation_spectral": -0.005021311837173567, - "trustworthiness": 0.8114318976742833 - }, - "mean_score": 0.340355323325465 - }, - { - "task_id": "dimensionality_reduction", - "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", - "method_id": "true_features", - "dataset_id": "zebrafish_labs", - "submission_time": "2023-02-21 17:59:02.707", - "code_version": "0.7.0", - "resources": { - "duration_sec": 399.0, - "cpu_pct": 13.2, - "peak_memory_mb": 3400.0, - "disk_read_mb": 1200.0, - "disk_write_mb": 3600.0 - }, - "metric_values": { - "density_preservation": 1.0, - "distance_correlation": 0.8575264567917553, - "distance_correlation_spectral": -0.016329078828423323, - "trustworthiness": 1.0 - }, - "scaled_scores": { - "density_preservation": 1.0, - "distance_correlation": 1.0, - "distance_correlation_spectral": 0.0, - "trustworthiness": 1.0 - }, - "mean_score": 0.75 - }, - { - "task_id": "dimensionality_reduction", - "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", - "method_id": "random_features", - "dataset_id": "zebrafish_labs", - "submission_time": "2023-02-21 17:59:02.620", - "code_version": "0.7.0", - "resources": { - "duration_sec": 409.0, - "cpu_pct": 8.6, - "peak_memory_mb": 1400.0, - "disk_read_mb": 1200.0, - "disk_write_mb": 1100.0 - }, - "metric_values": { - "density_preservation": -0.00227465148146574, - "distance_correlation": 0.0059794789083215455, - "distance_correlation_spectral": 0.004993022064087217, - "trustworthiness": 0.4994017599988353 - }, - "scaled_scores": { - "density_preservation": 0.01743617869607411, - "distance_correlation": 0.0, - "distance_correlation_spectral": 0.02189782179514169, - "trustworthiness": 0.0 - }, - "mean_score": 0.00983350012280395 - }, - { - "task_id": "dimensionality_reduction", - "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", - "method_id": "phate_logCP10k", - "dataset_id": "zebrafish_labs", - "submission_time": "2023-02-21 17:59:03.664", - "code_version": "1.0.10", - "resources": { - "duration_sec": 498.0, - "cpu_pct": 248.3, - "peak_memory_mb": 1500.0, - "disk_read_mb": 1200.0, - "disk_write_mb": 1500.0 - }, - "metric_values": { - "density_preservation": 0.03219668832383617, - "distance_correlation": 0.5918646839127146, - "distance_correlation_spectral": 0.16867876098415066, - "trustworthiness": 0.8427375774501069 - }, - "scaled_scores": { - "density_preservation": 0.05122960180070893, - "distance_correlation": 0.6880245250363551, - "distance_correlation_spectral": 0.190003261280078, - "trustworthiness": 0.6858510278631279 - }, - "mean_score": 0.4037771039950675 - }, - { - "task_id": "dimensionality_reduction", - "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", - "method_id": "umap_logCP10k_1kHVG", - "dataset_id": "zebrafish_labs", - "submission_time": "2023-02-21 17:59:12.065", - "code_version": "0.5.3", - "resources": { - "duration_sec": 510.0, - "cpu_pct": 174.4, - "peak_memory_mb": 1500.0, - "disk_read_mb": 1200.0, - "disk_write_mb": 1500.0 - }, - "metric_values": { - "density_preservation": -0.020176128354979086, - "distance_correlation": 0.49004959819235605, - "distance_correlation_spectral": 0.11990823769617477, - "trustworthiness": 0.8657855187682948 - }, - "scaled_scores": { - "density_preservation": -0.00011324600285871615, - "distance_correlation": 0.5684596761616335, - "distance_correlation_spectral": 0.1399158785592209, - "trustworthiness": 0.7318918236081035 - }, - "mean_score": 0.3600385330815248 - }, - { - "task_id": "dimensionality_reduction", - "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", - "method_id": "densmap_pca_logCP10k_1kHVG", - "dataset_id": "zebrafish_labs", - "submission_time": "2023-02-21 17:59:02.916", - "code_version": "0.5.3", - "resources": { - "duration_sec": 579.0, - "cpu_pct": 112.7, - "peak_memory_mb": 1400.0, - "disk_read_mb": 1200.0, - "disk_write_mb": 1500.0 - }, - "metric_values": { - "density_preservation": 0.25263552803355854, - "distance_correlation": 0.5710960660363131, - "distance_correlation_spectral": 0.16472875691271904, - "trustworthiness": 0.853650829626246 - }, - "scaled_scores": { - "density_preservation": 0.2673332699807215, - "distance_correlation": 0.6636352448019011, - "distance_correlation_spectral": 0.18594660261954818, - "trustworthiness": 0.7076514484481339 - }, - "mean_score": 0.4561416414625762 - }, - { - "task_id": "dimensionality_reduction", - "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", - "method_id": "umap_pca_logCP10k_1kHVG", - "dataset_id": "zebrafish_labs", - "submission_time": "2023-02-21 17:59:02.569", - "code_version": "0.5.3", - "resources": { - "duration_sec": 609.0, - "cpu_pct": 128.6, - "peak_memory_mb": 1400.0, - "disk_read_mb": 1200.0, - "disk_write_mb": 1500.0 - }, - "metric_values": { - "density_preservation": -0.06258027096228788, - "distance_correlation": 0.5202193076761656, - "distance_correlation_spectral": 0.13291561708718708, - "trustworthiness": 0.8870589293981929 - }, - "scaled_scores": { - "density_preservation": -0.041683464642798464, - "distance_correlation": 0.6038889716290405, - "distance_correlation_spectral": 0.15327447194371405, - "trustworthiness": 0.7743877992828253 - }, - "mean_score": 0.37246694455319534 - }, - { - "task_id": "dimensionality_reduction", - "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", - "method_id": "phate_default", - "dataset_id": "zebrafish_labs", - "submission_time": "2023-02-21 17:59:03.566", - "code_version": "1.0.10", - "resources": { - "duration_sec": 648.0, - "cpu_pct": 178.2, - "peak_memory_mb": 1900.0, - "disk_read_mb": 1200.0, - "disk_write_mb": 1500.0 - }, - "metric_values": { - "density_preservation": 0.06264228756143621, - "distance_correlation": 0.48283913956228103, - "distance_correlation_spectral": 0.008965405531047806, - "trustworthiness": 0.8806154110834628 - }, - "scaled_scores": { - 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"density_preservation": 0, + "spectral_distance_correlation": 0, + "waypoint_distance_correlation": 0, + "centroid_distance_correlation": 0, + "label_distance_correlation": 0, + "continuity_at_k30": 0, + "trustworthiness_at_k30": 0, + "qnx_at_k30": 0, + "lcmc_at_k30": 0, + "qnx_auc": 0, + "qlocal": 0, + "qglobal": 0 + }, + "mean_score": 0, + "resources": { + "submit": "2024-12-19 14:55:43", + "exit_code": "NA", + "duration_sec": 6880, + "cpu_pct": "NA", + "peak_memory_mb": "NA", + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + } +] diff --git a/results/dimensionality_reduction/data/state.yaml b/results/dimensionality_reduction/data/state.yaml new file mode 100644 index 00000000..abbb0fc1 --- /dev/null +++ b/results/dimensionality_reduction/data/state.yaml @@ -0,0 +1,9 @@ +id: process +output_scores: !file results.json +output_method_info: !file method_info.json +output_metric_info: !file metric_info.json +output_dataset_info: !file dataset_info.json +output_task_info: !file task_info.json +output_qc: !file quality_control.json +output_metric_execution_info: !file metric_execution_info.json + diff --git a/results/dimensionality_reduction/data/task_info.json b/results/dimensionality_reduction/data/task_info.json index 802fc777..f9c8fe84 100644 --- a/results/dimensionality_reduction/data/task_info.json +++ b/results/dimensionality_reduction/data/task_info.json @@ -1,10 +1,11 @@ { - "task_id": "dimensionality_reduction", - "commit_sha": "0a0e902bd1482e35418f7816fc91e9bc31a33126", - "task_name": "Dimensionality reduction for visualisation", - "task_summary": "Reduction of high-dimensional datasets to 2D for visualization & interpretation", - "task_description": "\nDimensionality reduction is one of the key challenges in single-cell data\nrepresentation. Routine single-cell RNA sequencing (scRNA-seq) experiments measure cells\nin roughly 20,000-30,000 dimensions (i.e., features - mostly gene transcripts but also\nother functional elements encoded in mRNA such as lncRNAs). Since its inception,\nscRNA-seq experiments have been growing in terms of the number of cells measured.\nOriginally, cutting-edge SmartSeq experiments would yield a few hundred cells, at best.\nNow, it is not uncommon to see experiments that yield over [100,000\ncells](https://openproblems.bio/bibliography#tabula2018single) or even [> 1 million\ncells.](https://openproblems.bio/bibliography#cao2020human)\n\nEach *feature* in a dataset functions as a single dimension. While each of the ~30,000\ndimensions measured in each cell contribute to an underlying data structure, the overall\nstructure of the data is challenging to display in few dimensions due to data sparsity\nand the [*\"curse of\ndimensionality\"*](https://en.wikipedia.org/wiki/Curse_of_dimensionality) (distances in\nhigh dimensional data don’t distinguish data points well). Thus, we need to find a way\nto [dimensionally reduce](https://en.wikipedia.org/wiki/Dimensionality_reduction) the\ndata for visualization and interpretation.\n\n", - "repo": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks/dimensionality_reduction", + "task_id": "task_dimensionality_reduction", + "commit_sha": null, + "task_name": "Dimensionality Reduction for Visualization", + "task_summary": "Reduction of high-dimensional datasets to 2D for visualization & interpretation.", + "task_description": "Data visualisation is an important part of all stages of single-cell analysis, from\ninitial quality control to interpretation and presentation of final results. For bulk RNA-seq\nstudies, linear dimensionality reduction techniques such as PCA and MDS are commonly used\nto visualise the variation between samples. While these methods are highly effective they\ncan only be used to show the first few components of variation which cannot fully represent\nthe increased complexity and number of observations in single-cell datasets. For this reason\nnon-linear techniques (most notably t-SNE and UMAP) have become the standard for visualising\nsingle-cell studies. These methods attempt to compress a dataset into a two-dimensional space\nwhile attempting to capture as much of the variance between observations as possible. Many\nmethods for solving this problem now exist. In general these methods try to preserve distances,\nwhile some additionally consider aspects such as density within the embedded space or conservation\nof continuous trajectories. Despite almost every single-cell study using one of these visualisations\nthere has been debate as to whether they can effectively capture the variation in single-cell\ndatasets [@chari2023speciousart].\n\nThe dimensionality reduction task attempts to quantify the ability of methods to embed the\ninformation present in complex single-cell studies into a two-dimensional space. Thus, this task\nis specifically designed for dimensionality reduction for visualisation and does not consider other\nuses of dimensionality reduction in standard single-cell workflows such as improving the\nsignal-to-noise ratio (and in fact several of the methods use PCA as a pre-processing step for this\nreason). Unlike most tasks, methods for the dimensionality reduction task must accept a matrix\ncontaining expression values normalised to 10,000 counts per cell and log transformed (log-10k) and\nproduce a two-dimensional coordinate for each cell. Pre-normalised matrices are required to\nenforce consistency between the metric evaluation (which generally requires normalised data) and\nthe method runs. When these are not consistent, methods that use the same normalisation as used in\nthe metric tend to score more highly. For some methods we also evaluate the pre-processing\nrecommended by the method.\n", + "repo": "https://github.com/openproblems-bio/task_dimensionality_reduction", + "issue_tracker": "https://github.com/openproblems-bio/task_dimensionality_reduction/issues", "authors": [ { "name": "Luke Zappia", @@ -76,6 +77,6 @@ } } ], - "version": "v1.0.0", + "version": "build_main", "license": "MIT" } diff --git a/results/dimensionality_reduction/index.qmd b/results/dimensionality_reduction/index.qmd index 0e3da35d..5e66cf36 100644 --- a/results/dimensionality_reduction/index.qmd +++ b/results/dimensionality_reduction/index.qmd @@ -1,5 +1,5 @@ --- -title: "Dimensionality reduction for visualisation" +title: "Dimensionality reduction for visualization" subtitle: "Reduction of high-dimensional datasets to 2D for visualization & interpretation" image: thumbnail.svg page-layout: full