From 24e9919fd1a3f41f20334a73cfbb3bd3cc92cf25 Mon Sep 17 00:00:00 2001 From: Robrecht Cannoodt Date: Fri, 10 Jan 2025 13:30:49 +0100 Subject: [PATCH] new results --- .../label_projection/data/dataset_info.json | 104 +- .../label_projection/data/method_info.json | 72 +- .../data/metric_execution_info.json | 3852 +++--------- .../label_projection/data/metric_info.json | 16 +- .../data/quality_control.json | 852 ++- results/label_projection/data/results.json | 5192 ++++------------- 6 files changed, 2356 insertions(+), 7732 deletions(-) diff --git a/results/label_projection/data/dataset_info.json b/results/label_projection/data/dataset_info.json index 156f0d4e..4e3fba59 100644 --- a/results/label_projection/data/dataset_info.json +++ b/results/label_projection/data/dataset_info.json @@ -6,28 +6,18 @@ "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": "08-01-2025", + "date_created": "09-01-2025", "file_size": 206108150 }, { - "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": "08-01-2025", - "file_size": 8339122 - }, - { - "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": "08-01-2025", - "file_size": 1727821930 + "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": "09-01-2025", + "file_size": 86763866 }, { "dataset_id": "cellxgene_census/hypomap", @@ -36,9 +26,19 @@ "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": "08-01-2025", + "date_created": "09-01-2025", "file_size": 23568346 }, + { + "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": "09-01-2025", + "file_size": 1727821930 + }, { "dataset_id": "cellxgene_census/immune_cell_atlas", "dataset_name": "Immune Cell Atlas", @@ -46,29 +46,9 @@ "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": "08-01-2025", + "date_created": "09-01-2025", "file_size": 341174505 }, - { - "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": "08-01-2025", - "file_size": 197407896 - }, - { - "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": "08-01-2025", - "file_size": 86763866 - }, { "dataset_id": "cellxgene_census/mouse_pancreas_atlas", "dataset_name": "Mouse Pancreatic Islet Atlas", @@ -76,47 +56,7 @@ "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": "08-01-2025", + "date_created": "09-01-2025", "file_size": 133936661 - }, - { - "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": "08-01-2025", - "file_size": 38683549 - }, - { - "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": "08-01-2025", - "file_size": 317012639 - }, - { - "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": "08-01-2025", - "file_size": 291760514 - }, - { - "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": "08-01-2025", - "file_size": 73130523 } ] diff --git a/results/label_projection/data/method_info.json b/results/label_projection/data/method_info.json index e3206f41..5339e067 100644 --- a/results/label_projection/data/method_info.json +++ b/results/label_projection/data/method_info.json @@ -11,9 +11,9 @@ "code_url": "https://github.com/openproblems-bio/task_label_projection", "documentation_url": null, "image": "https://ghcr.io/openproblems-bio/task_label_projection/control_methods/majority_vote:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/control_methods/majority_vote", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/control_methods/majority_vote", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "control_methods", @@ -27,9 +27,9 @@ "code_url": "https://github.com/openproblems-bio/task_label_projection", "documentation_url": null, "image": "https://ghcr.io/openproblems-bio/task_label_projection/control_methods/random_labels:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/control_methods/random_labels", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/control_methods/random_labels", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "control_methods", @@ -43,9 +43,9 @@ "code_url": "https://github.com/openproblems-bio/task_label_projection", "documentation_url": null, "image": "https://ghcr.io/openproblems-bio/task_label_projection/control_methods/true_labels:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/control_methods/true_labels", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/control_methods/true_labels", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -59,9 +59,9 @@ "code_url": "https://huggingface.co/ctheodoris/Geneformer", "documentation_url": "https://geneformer.readthedocs.io/en/latest/index.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/geneformer:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/geneformer", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/geneformer", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -75,9 +75,9 @@ "code_url": "https://github.com/scikit-learn/scikit-learn", "documentation_url": "https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/knn:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/knn", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/knn", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -91,9 +91,9 @@ "code_url": "https://github.com/scikit-learn/scikit-learn", "documentation_url": "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/logistic_regression:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/logistic_regression", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/logistic_regression", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -107,9 +107,9 @@ "code_url": "https://github.com/scikit-learn/scikit-learn", "documentation_url": "https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/mlp:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/mlp", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/mlp", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -123,9 +123,9 @@ "code_url": "https://github.com/scikit-learn/scikit-learn", "documentation_url": "https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/naive_bayes:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/naive_bayes", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/naive_bayes", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -139,9 +139,9 @@ "code_url": "https://github.com/scverse/scvi-tools", "documentation_url": "https://scarches.readthedocs.io/en/latest/scanvi_surgery_pipeline.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/scanvi:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/scanvi", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/scanvi", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -155,9 +155,9 @@ "code_url": "https://github.com/scverse/scvi-tools", "documentation_url": "https://docs.scvi-tools.org", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/scanvi_scarches:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/scanvi_scarches", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/scanvi_scarches", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -171,9 +171,9 @@ "code_url": "https://github.com/bowang-lab/scGPT", "documentation_url": "https://scgpt.readthedocs.io/en/latest/", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/scgpt_zero_shot:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/scgpt_zero_shot", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/scgpt_zero_shot", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -187,9 +187,9 @@ "code_url": "https://github.com/Genentech/scimilarity", "documentation_url": "https://genentech.github.io/scimilarity/index.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/scimilarity:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/scimilarity", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/scimilarity", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -203,9 +203,9 @@ "code_url": "https://github.com/Genentech/scimilarity", "documentation_url": "https://genentech.github.io/scimilarity/index.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/scimilarity_knn:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/scimilarity_knn", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/scimilarity_knn", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -219,9 +219,9 @@ "code_url": "https://github.com/cantinilab/scPRINT", "documentation_url": "https://cantinilab.github.io/scPRINT/", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/scprint:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/scprint", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/scprint", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -235,9 +235,9 @@ "code_url": "https://github.com/satijalab/seurat", "documentation_url": "https://satijalab.org/seurat/articles/integration_mapping.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/seurat_transferdata:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/seurat_transferdata", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/seurat_transferdata", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -251,9 +251,9 @@ "code_url": "https://www.bioconductor.org/packages/release/bioc/html/SingleR.html", "documentation_url": "https://www.bioconductor.org/packages/release/bioc/vignettes/SingleR/inst/doc/SingleR.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/singler:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/singler", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/singler", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -267,9 +267,9 @@ "code_url": "https://github.com/snap-stanford/UCE", "documentation_url": "https://github.com/snap-stanford/UCE/blob/main/README.md", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/uce:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/uce", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/uce", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" }, { "task_id": "methods", @@ -283,8 +283,8 @@ "code_url": "https://github.com/dmlc/xgboost", "documentation_url": "https://xgboost.readthedocs.io/en/stable/index.html", "image": "https://ghcr.io/openproblems-bio/task_label_projection/methods/xgboost:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/methods/xgboost", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/methods/xgboost", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d" + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf" } ] diff --git a/results/label_projection/data/metric_execution_info.json b/results/label_projection/data/metric_execution_info.json index d4cc5bea..81330991 100644 --- a/results/label_projection/data/metric_execution_info.json +++ b/results/label_projection/data/metric_execution_info.json @@ -1,321 +1,293 @@ [ { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "knn", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:22:05", + "submit": "2025-01-09 17:10:17", "exit_code": 0, - "duration_sec": 12.7, - "cpu_pct": 166.9, - "peak_memory_mb": 6964, - "disk_read_mb": 507, + "duration_sec": 4.5, + "cpu_pct": 242.1, + "peak_memory_mb": 5632, + "disk_read_mb": 111, "disk_write_mb": 1 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "knn", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:22:05", + "submit": "2025-01-09 17:10:16", "exit_code": 0, - "duration_sec": 61.2, - "cpu_pct": 110, - "peak_memory_mb": 6964, - "disk_read_mb": 1524, + "duration_sec": 18.6, + "cpu_pct": 132.8, + "peak_memory_mb": 2868, + "disk_read_mb": 336, "disk_write_mb": 3 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "logistic_regression", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:15:15", + "submit": "2025-01-09 17:12:26", "exit_code": 0, - "duration_sec": 14, - "cpu_pct": 155.7, - "peak_memory_mb": 6964, - "disk_read_mb": 507, + "duration_sec": 2.4, + "cpu_pct": 385.8, + "peak_memory_mb": 5632, + "disk_read_mb": 111, "disk_write_mb": 1 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "logistic_regression", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:15:15", + "submit": "2025-01-09 17:12:26", "exit_code": 0, - "duration_sec": 42, - "cpu_pct": 150.3, - "peak_memory_mb": 6964, - "disk_read_mb": 1524, + "duration_sec": 14.7, + "cpu_pct": 266.8, + "peak_memory_mb": 5530, + "disk_read_mb": 336, "disk_write_mb": 3 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "majority_vote", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:31:55", - "exit_code": 0, - "duration_sec": 12.5, - "cpu_pct": 163.4, - "peak_memory_mb": 6964, - "disk_read_mb": 507, - "disk_write_mb": 1 + "submit": "2025-01-09 17:08:37", + "exit_code": "NA", + "duration_sec": 9601, + "cpu_pct": "NA", + "peak_memory_mb": "NA", + "disk_read_mb": "NA", + "disk_write_mb": "NA" } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "majority_vote", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:31:55", + "submit": "2025-01-09 17:08:36", "exit_code": 0, - "duration_sec": 36.9, - "cpu_pct": 165.8, - "peak_memory_mb": 6964, - "disk_read_mb": 1524, + "duration_sec": 39.3, + "cpu_pct": 102.1, + "peak_memory_mb": 5530, + "disk_read_mb": 336, "disk_write_mb": 3 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "mlp", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:33:45", + "submit": "2025-01-09 17:12:56", "exit_code": 0, - "duration_sec": 19.2, - "cpu_pct": 123.5, - "peak_memory_mb": 6964, - "disk_read_mb": 507, + "duration_sec": 5.5, + "cpu_pct": 284.4, + "peak_memory_mb": 5735, + "disk_read_mb": 111, "disk_write_mb": 1 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "mlp", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:33:45", + "submit": "2025-01-09 17:12:56", "exit_code": 0, - "duration_sec": 48.3, - "cpu_pct": 138.6, - "peak_memory_mb": 7066, - "disk_read_mb": 1524, + "duration_sec": 17.1, + "cpu_pct": 248.6, + "peak_memory_mb": 5632, + "disk_read_mb": 336, "disk_write_mb": 3 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "naive_bayes", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:27:35", + "submit": "2025-01-09 17:10:16", "exit_code": 0, - "duration_sec": 19.9, - "cpu_pct": 115.6, - "peak_memory_mb": 6964, - "disk_read_mb": 507, + "duration_sec": 3.4, + "cpu_pct": 309.8, + "peak_memory_mb": 4301, + "disk_read_mb": 111, "disk_write_mb": 1 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "naive_bayes", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:27:35", + "submit": "2025-01-09 17:10:16", "exit_code": 0, - "duration_sec": 59.4, - "cpu_pct": 112.5, - "peak_memory_mb": 6964, - "disk_read_mb": 1524, + "duration_sec": 27, + "cpu_pct": 145.4, + "peak_memory_mb": 5632, + "disk_read_mb": 336, "disk_write_mb": 3 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "random_labels", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:28:05", + "submit": "2025-01-09 17:12:26", "exit_code": 0, - "duration_sec": 19, - "cpu_pct": 123.8, - "peak_memory_mb": 6964, - "disk_read_mb": 507, + "duration_sec": 5.7, + "cpu_pct": 240.6, + "peak_memory_mb": 5735, + "disk_read_mb": 111, "disk_write_mb": 1 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "random_labels", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:28:05", + "submit": "2025-01-09 17:12:26", "exit_code": 0, - "duration_sec": 37.5, - "cpu_pct": 165.4, - "peak_memory_mb": 6964, - "disk_read_mb": 1524, + "duration_sec": 35.7, + "cpu_pct": 135.1, + "peak_memory_mb": 5530, + "disk_read_mb": 336, "disk_write_mb": 3 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "scanvi", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:35:45", + "submit": "2025-01-09 17:16:26", "exit_code": 0, - "duration_sec": 8.4, - "cpu_pct": 165.5, - "peak_memory_mb": 6554, - "disk_read_mb": 339, + "duration_sec": 3.9, + "cpu_pct": 223.5, + "peak_memory_mb": 5530, + "disk_read_mb": 112, "disk_write_mb": 1 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "scanvi", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:35:45", + "submit": "2025-01-09 17:16:26", "exit_code": 0, - "duration_sec": 18.9, - "cpu_pct": 256.1, - "peak_memory_mb": 6452, - "disk_read_mb": 1017, + "duration_sec": 12, + "cpu_pct": 237.4, + "peak_memory_mb": 5530, + "disk_read_mb": 339, "disk_write_mb": 3 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "scanvi_scarches", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 09:48:05", + "submit": "2025-01-09 17:34:16", "exit_code": 0, - "duration_sec": 12.9, - "cpu_pct": 169.2, - "peak_memory_mb": 6964, - "disk_read_mb": 507, + "duration_sec": 2.9, + "cpu_pct": 438.2, + "peak_memory_mb": 5530, + "disk_read_mb": 111, "disk_write_mb": 1 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", + "dataset_id": "cellxgene_census/dkd", "method_id": "scanvi_scarches", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 09:48:05", - "exit_code": 0, - "duration_sec": 56.1, - "cpu_pct": 127.8, - "peak_memory_mb": 4301, - "disk_read_mb": 1524, - "disk_write_mb": 3 - } - }, - { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "singler", - "metric_component_name": "accuracy", - "resources": { - "submit": "2025-01-08 09:16:35", - "exit_code": 0, - "duration_sec": 8.6, - "cpu_pct": 206.4, - "peak_memory_mb": 6554, - "disk_read_mb": 337, - "disk_write_mb": 1 - } - }, - { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "singler", - "metric_component_name": "f1", - "resources": { - "submit": "2025-01-08 09:16:35", + "submit": "2025-01-09 17:34:16", "exit_code": 0, - "duration_sec": 19.5, - "cpu_pct": 243.7, - "peak_memory_mb": 6452, - "disk_read_mb": 1014, + "duration_sec": 33, + "cpu_pct": 115.2, + "peak_memory_mb": 5632, + "disk_read_mb": 336, "disk_write_mb": 3 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "true_labels", + "dataset_id": "cellxgene_census/dkd", + "method_id": "scimilarity", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:30:06", + "submit": "2025-01-09 17:46:40", "exit_code": 0, - "duration_sec": 22.3, - "cpu_pct": 99.6, - "peak_memory_mb": 7066, - "disk_read_mb": 507, + "duration_sec": 3.7, + "cpu_pct": 318.3, + "peak_memory_mb": 5530, + "disk_read_mb": 111, "disk_write_mb": 1 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "true_labels", + "dataset_id": "cellxgene_census/dkd", + "method_id": "scimilarity", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:30:05", + "submit": "2025-01-09 17:46:40", "exit_code": 0, - "duration_sec": 40.5, - "cpu_pct": 164.3, - "peak_memory_mb": 6964, - "disk_read_mb": 1524, + "duration_sec": 9, + "cpu_pct": 324.1, + "peak_memory_mb": 5530, + "disk_read_mb": 336, "disk_write_mb": 3 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "xgboost", + "dataset_id": "cellxgene_census/dkd", + "method_id": "scimilarity_knn", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 09:37:55", + "submit": "2025-01-09 17:45:40", "exit_code": 0, - "duration_sec": 21.5, - "cpu_pct": 112.8, - "peak_memory_mb": 4404, - "disk_read_mb": 507, + "duration_sec": 3.4, + "cpu_pct": 419.3, + "peak_memory_mb": 5735, + "disk_read_mb": 111, "disk_write_mb": 1 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "xgboost", + "dataset_id": "cellxgene_census/dkd", + "method_id": "scimilarity_knn", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 09:37:55", + "submit": "2025-01-09 17:45:40", "exit_code": 0, - "duration_sec": 66.3, - "cpu_pct": 110.7, - "peak_memory_mb": 4404, - "disk_read_mb": 1524, + "duration_sec": 11.4, + "cpu_pct": 308, + "peak_memory_mb": 5530, + "disk_read_mb": 336, "disk_write_mb": 3 } }, { "dataset_id": "cellxgene_census/dkd", - "method_id": "knn", + "method_id": "seurat_transferdata", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:07:05", + "submit": "2025-01-09 17:10:06", "exit_code": 0, - "duration_sec": 9.4, - "cpu_pct": 133.1, + "duration_sec": 3.3, + "cpu_pct": 408.7, "peak_memory_mb": 5530, "disk_read_mb": 111, "disk_write_mb": 1 @@ -323,27 +295,27 @@ }, { "dataset_id": "cellxgene_census/dkd", - "method_id": "knn", + "method_id": "seurat_transferdata", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:07:05", + "submit": "2025-01-09 17:10:06", "exit_code": 0, - "duration_sec": 7.2, - "cpu_pct": 419.2, - "peak_memory_mb": 5632, + "duration_sec": 39.3, + "cpu_pct": 95.6, + "peak_memory_mb": 5530, "disk_read_mb": 336, "disk_write_mb": 3 } }, { "dataset_id": "cellxgene_census/dkd", - "method_id": "logistic_regression", + "method_id": "singler", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:04:45", + "submit": "2025-01-09 17:17:26", "exit_code": 0, - "duration_sec": 2.5, - "cpu_pct": 459.4, + "duration_sec": 3.9, + "cpu_pct": 224.3, "peak_memory_mb": 5530, "disk_read_mb": 111, "disk_write_mb": 1 @@ -351,13 +323,13 @@ }, { "dataset_id": "cellxgene_census/dkd", - "method_id": "logistic_regression", + "method_id": "singler", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:04:45", + "submit": "2025-01-09 17:17:26", "exit_code": 0, - "duration_sec": 7.2, - "cpu_pct": 484.1, + "duration_sec": 13.8, + "cpu_pct": 200.5, "peak_memory_mb": 5632, "disk_read_mb": 336, "disk_write_mb": 3 @@ -365,13 +337,13 @@ }, { "dataset_id": "cellxgene_census/dkd", - "method_id": "majority_vote", + "method_id": "true_labels", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:06:55", + "submit": "2025-01-09 17:12:26", "exit_code": 0, "duration_sec": 2.5, - "cpu_pct": 433.3, + "cpu_pct": 365.7, "peak_memory_mb": 5530, "disk_read_mb": 111, "disk_write_mb": 1 @@ -379,13 +351,13 @@ }, { "dataset_id": "cellxgene_census/dkd", - "method_id": "majority_vote", + "method_id": "true_labels", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:06:55", + "submit": "2025-01-09 17:12:26", "exit_code": 0, - "duration_sec": 7.2, - "cpu_pct": 465.6, + "duration_sec": 17.1, + "cpu_pct": 284.6, "peak_memory_mb": 5632, "disk_read_mb": 336, "disk_write_mb": 3 @@ -393,13 +365,13 @@ }, { "dataset_id": "cellxgene_census/dkd", - "method_id": "mlp", + "method_id": "xgboost", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:07:15", + "submit": "2025-01-09 17:14:26", "exit_code": 0, - "duration_sec": 3.9, - "cpu_pct": 328.9, + "duration_sec": 11.9, + "cpu_pct": 124.5, "peak_memory_mb": 5530, "disk_read_mb": 111, "disk_write_mb": 1 @@ -407,377 +379,237 @@ }, { "dataset_id": "cellxgene_census/dkd", - "method_id": "mlp", + "method_id": "xgboost", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:07:15", + "submit": "2025-01-09 17:14:26", "exit_code": 0, - "duration_sec": 6.9, - "cpu_pct": 443.8, + "duration_sec": 26.7, + "cpu_pct": 149.7, "peak_memory_mb": 5632, "disk_read_mb": 336, "disk_write_mb": 3 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "naive_bayes", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "knn", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:04:35", + "submit": "2025-01-09 17:21:56", "exit_code": 0, - "duration_sec": 59.7, - "cpu_pct": 19.6, + "duration_sec": 4.4, + "cpu_pct": 266.2, "peak_memory_mb": 5632, - "disk_read_mb": 111, + "disk_read_mb": 225, "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "naive_bayes", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "knn", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:04:36", + "submit": "2025-01-09 17:21:56", "exit_code": 0, "duration_sec": 7.2, - "cpu_pct": 452.8, + "cpu_pct": 487.3, "peak_memory_mb": 5632, - "disk_read_mb": 336, + "disk_read_mb": 678, "disk_write_mb": 3 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "random_labels", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "logistic_regression", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:06:55", + "submit": "2025-01-09 17:13:47", "exit_code": 0, - "duration_sec": 6.9, - "cpu_pct": 132.9, - "peak_memory_mb": 2868, - "disk_read_mb": 111, + "duration_sec": 9.7, + "cpu_pct": 81.3, + "peak_memory_mb": 3072, + "disk_read_mb": 225, "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "random_labels", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "logistic_regression", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:06:55", + "submit": "2025-01-09 17:13:47", "exit_code": 0, - "duration_sec": 7.8, - "cpu_pct": 353.4, - "peak_memory_mb": 5530, - "disk_read_mb": 336, + "duration_sec": 10.5, + "cpu_pct": 372.7, + "peak_memory_mb": 5837, + "disk_read_mb": 678, "disk_write_mb": 3 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "scanvi", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "majority_vote", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:16:45", + "submit": "2025-01-09 17:11:36", "exit_code": 0, - "duration_sec": 2.4, - "cpu_pct": 482.2, + "duration_sec": 4.7, + "cpu_pct": 298.1, "peak_memory_mb": 5632, - "disk_read_mb": 112, + "disk_read_mb": 225, "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "scanvi", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "majority_vote", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:16:45", + "submit": "2025-01-09 17:11:36", "exit_code": 0, - "duration_sec": 7.2, - "cpu_pct": 426.2, - "peak_memory_mb": 5632, - "disk_read_mb": 339, + "duration_sec": 36.6, + "cpu_pct": 96, + "peak_memory_mb": 5530, + "disk_read_mb": 678, "disk_write_mb": 3 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "scanvi_scarches", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "mlp", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:33:25", + "submit": "2025-01-09 17:18:36", "exit_code": 0, - "duration_sec": 3, - "cpu_pct": 293.9, - "peak_memory_mb": 2868, - "disk_read_mb": 111, + "duration_sec": 4.7, + "cpu_pct": 227.7, + "peak_memory_mb": 5632, + "disk_read_mb": 225, "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "scanvi_scarches", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "mlp", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:33:25", + "submit": "2025-01-09 17:18:36", "exit_code": 0, - "duration_sec": 7.8, - "cpu_pct": 464.7, - "peak_memory_mb": 5530, - "disk_read_mb": 336, + "duration_sec": 10.2, + "cpu_pct": 429.4, + "peak_memory_mb": 5837, + "disk_read_mb": 678, "disk_write_mb": 3 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "scimilarity", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "naive_bayes", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:31:35", + "submit": "2025-01-09 17:10:36", "exit_code": 0, - "duration_sec": 7.7, - "cpu_pct": 170.5, - "peak_memory_mb": 5530, - "disk_read_mb": 111, + "duration_sec": 4.5, + "cpu_pct": 297.3, + "peak_memory_mb": 5632, + "disk_read_mb": 225, "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "scimilarity", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "naive_bayes", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:31:35", + "submit": "2025-01-09 17:10:36", "exit_code": 0, - "duration_sec": 32.7, - "cpu_pct": 121.6, + "duration_sec": 24.3, + "cpu_pct": 150.1, "peak_memory_mb": 5632, - "disk_read_mb": 336, + "disk_read_mb": 678, "disk_write_mb": 3 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "scimilarity_knn", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "random_labels", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:33:35", + "submit": "2025-01-09 17:11:37", "exit_code": 0, - "duration_sec": 2.6, - "cpu_pct": 394.3, + "duration_sec": 4.9, + "cpu_pct": 255.1, "peak_memory_mb": 5530, - "disk_read_mb": 111, + "disk_read_mb": 225, "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "scimilarity_knn", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "random_labels", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:33:35", + "submit": "2025-01-09 17:11:36", "exit_code": 0, - "duration_sec": 27.9, - "cpu_pct": 134.3, + "duration_sec": 7.5, + "cpu_pct": 329.3, "peak_memory_mb": 5632, - "disk_read_mb": 336, + "disk_read_mb": 678, "disk_write_mb": 3 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "seurat_transferdata", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "scanvi", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:07:35", + "submit": "2025-01-09 17:26:26", "exit_code": 0, - "duration_sec": 9.4, - "cpu_pct": 141, + "duration_sec": 4, + "cpu_pct": 226.9, "peak_memory_mb": 5530, - "disk_read_mb": 111, + "disk_read_mb": 226, "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "seurat_transferdata", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "scanvi", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:07:35", + "submit": "2025-01-09 17:26:26", "exit_code": 0, - "duration_sec": 28.5, - "cpu_pct": 147.3, + "duration_sec": 12.3, + "cpu_pct": 293.8, "peak_memory_mb": 5530, - "disk_read_mb": 336, + "disk_read_mb": 681, "disk_write_mb": 3 } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "singler", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "scanvi_scarches", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:12:05", + "submit": "2025-01-09 17:40:40", "exit_code": 0, - "duration_sec": 4.4, - "cpu_pct": 196.1, - "peak_memory_mb": 2970, - "disk_read_mb": 111, - "disk_write_mb": 1 - } - }, - { - "dataset_id": "cellxgene_census/dkd", - "method_id": "singler", - "metric_component_name": "f1", - "resources": { - "submit": "2025-01-08 08:12:05", - "exit_code": 0, - "duration_sec": 9, - "cpu_pct": 307.2, - "peak_memory_mb": 2868, - "disk_read_mb": 336, - "disk_write_mb": 3 - } - }, - { - "dataset_id": "cellxgene_census/dkd", - "method_id": "true_labels", - "metric_component_name": "accuracy", - "resources": { - "submit": "2025-01-08 08:07:15", - "exit_code": 0, - "duration_sec": 3.9, - "cpu_pct": 302, - "peak_memory_mb": 5530, - "disk_read_mb": 111, - "disk_write_mb": 1 - } - }, - { - "dataset_id": "cellxgene_census/dkd", - "method_id": "true_labels", - "metric_component_name": "f1", - "resources": { - "submit": "2025-01-08 08:07:15", - "exit_code": 0, - "duration_sec": 13.8, - "cpu_pct": 279.8, - "peak_memory_mb": 5632, - "disk_read_mb": 336, - "disk_write_mb": 3 - } - }, - { - "dataset_id": "cellxgene_census/dkd", - "method_id": "uce", - "metric_component_name": "accuracy", - "resources": { - "submit": "2025-01-08 09:34:15", - "exit_code": 0, - "duration_sec": 8, - "cpu_pct": 128.6, - "peak_memory_mb": 2970, - "disk_read_mb": 111, - "disk_write_mb": 1 - } - }, - { - "dataset_id": "cellxgene_census/dkd", - "method_id": "uce", - "metric_component_name": "f1", - "resources": { - "submit": "2025-01-08 09:34:15", - "exit_code": 0, - "duration_sec": 9.6, - "cpu_pct": 272.5, - "peak_memory_mb": 2970, - "disk_read_mb": 336, - "disk_write_mb": 3 - } - }, - { - "dataset_id": "cellxgene_census/dkd", - "method_id": "xgboost", - "metric_component_name": "accuracy", - "resources": { - "submit": "2025-01-08 08:08:05", - "exit_code": 0, - "duration_sec": 4.5, - "cpu_pct": 263.5, - "peak_memory_mb": 5632, - "disk_read_mb": 111, - "disk_write_mb": 1 - } - }, - { - "dataset_id": "cellxgene_census/dkd", - "method_id": "xgboost", - "metric_component_name": "f1", - "resources": { - "submit": "2025-01-08 08:08:05", - "exit_code": 0, - "duration_sec": 11.7, - "cpu_pct": 317.3, - "peak_memory_mb": 5530, - "disk_read_mb": 336, - "disk_write_mb": 3 - } - }, - { - "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "knn", - "metric_component_name": "accuracy", - "resources": { - "submit": "2025-01-08 08:19:55", - "exit_code": 0, - "duration_sec": 11.2, - "cpu_pct": 111.6, - "peak_memory_mb": 5530, - "disk_read_mb": 225, - "disk_write_mb": 1 - } - }, - { - "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "knn", - "metric_component_name": "f1", - "resources": { - "submit": "2025-01-08 08:19:55", - "exit_code": 0, - "duration_sec": 34.5, - "cpu_pct": 112.4, - "peak_memory_mb": 5530, - "disk_read_mb": 678, - "disk_write_mb": 3 - } - }, - { - "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "logistic_regression", - "metric_component_name": "accuracy", - "resources": { - "submit": "2025-01-08 08:10:15", - "exit_code": 0, - "duration_sec": 3.4, - "cpu_pct": 251.6, - "peak_memory_mb": 5632, - "disk_read_mb": 225, + "duration_sec": 3.4, + "cpu_pct": 348.8, + "peak_memory_mb": 5837, + "disk_read_mb": 225, "disk_write_mb": 1 } }, { "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "logistic_regression", + "method_id": "scanvi_scarches", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:10:15", + "submit": "2025-01-09 17:40:40", "exit_code": 0, - "duration_sec": 10.8, - "cpu_pct": 160.7, + "duration_sec": 7.5, + "cpu_pct": 454.4, "peak_memory_mb": 5632, "disk_read_mb": 678, "disk_write_mb": 3 @@ -785,13 +617,13 @@ }, { "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "majority_vote", + "method_id": "scimilarity", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:20:15", + "submit": "2025-01-09 17:52:50", "exit_code": 0, - "duration_sec": 4.7, - "cpu_pct": 271.2, + "duration_sec": 30.9, + "cpu_pct": 37, "peak_memory_mb": 5530, "disk_read_mb": 225, "disk_write_mb": 1 @@ -799,13 +631,13 @@ }, { "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "majority_vote", + "method_id": "scimilarity", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:20:15", + "submit": "2025-01-09 17:52:50", "exit_code": 0, - "duration_sec": 8.1, - "cpu_pct": 410.4, + "duration_sec": 8.7, + "cpu_pct": 404.2, "peak_memory_mb": 5530, "disk_read_mb": 678, "disk_write_mb": 3 @@ -813,13 +645,13 @@ }, { "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "mlp", + "method_id": "seurat_transferdata", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:25:25", + "submit": "2025-01-09 17:17:06", "exit_code": 0, "duration_sec": 2.6, - "cpu_pct": 405.2, + "cpu_pct": 360.7, "peak_memory_mb": 5530, "disk_read_mb": 225, "disk_write_mb": 1 @@ -827,111 +659,55 @@ }, { "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "mlp", + "method_id": "seurat_transferdata", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:25:25", + "submit": "2025-01-09 17:17:06", "exit_code": 0, - "duration_sec": 9.3, - "cpu_pct": 233.5, - "peak_memory_mb": 2868, + "duration_sec": 8.1, + "cpu_pct": 304.6, + "peak_memory_mb": 5530, "disk_read_mb": 678, "disk_write_mb": 3 } }, { "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "naive_bayes", + "method_id": "true_labels", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:20:25", + "submit": "2025-01-09 17:11:36", "exit_code": 0, - 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"duration_sec": 10.2, - "cpu_pct": 266.3, - "peak_memory_mb": 3175, - "disk_read_mb": 924, + "duration_sec": 33.9, + "cpu_pct": 139.5, + "peak_memory_mb": 7271, + "disk_read_mb": 4917, "disk_write_mb": 3 } }, { - "dataset_id": "openproblems_v1/zebrafish", + "dataset_id": "cellxgene_census/tabula_sapiens", "method_id": "true_labels", "metric_component_name": "accuracy", "resources": { - "submit": "2025-01-08 08:56:05", + "submit": "2025-01-09 17:36:06", "exit_code": 0, - "duration_sec": 2.7, - "cpu_pct": 314.4, - "peak_memory_mb": 5530, - "disk_read_mb": 308, + "duration_sec": 11, + "cpu_pct": 92.4, + "peak_memory_mb": 4608, + "disk_read_mb": 1639, "disk_write_mb": 1 } }, { - "dataset_id": "openproblems_v1/zebrafish", + "dataset_id": "cellxgene_census/tabula_sapiens", "method_id": "true_labels", "metric_component_name": "f1", "resources": { - "submit": "2025-01-08 08:56:05", - "exit_code": 0, - "duration_sec": 33.6, - "cpu_pct": 122.3, - "peak_memory_mb": 5632, - "disk_read_mb": 924, - "disk_write_mb": 3 - } - }, - { - "dataset_id": "openproblems_v1/zebrafish", - "method_id": "xgboost", - "metric_component_name": "accuracy", - "resources": { - "submit": "2025-01-08 08:59:25", - "exit_code": 0, - "duration_sec": 2.6, - "cpu_pct": 392.3, - "peak_memory_mb": 5632, - "disk_read_mb": 308, - "disk_write_mb": 1 - } - }, - { - "dataset_id": "openproblems_v1/zebrafish", - "method_id": "xgboost", - "metric_component_name": "f1", - "resources": { - "submit": "2025-01-08 08:59:25", + "submit": "2025-01-09 17:36:06", "exit_code": 0, - "duration_sec": 7.8, - "cpu_pct": 387.4, - "peak_memory_mb": 5632, - "disk_read_mb": 924, + "duration_sec": 26.4, + "cpu_pct": 146.3, + "peak_memory_mb": 7271, + "disk_read_mb": 4917, "disk_write_mb": 3 } } diff --git a/results/label_projection/data/metric_info.json b/results/label_projection/data/metric_info.json index b702b546..f9bdb7df 100644 --- a/results/label_projection/data/metric_info.json +++ b/results/label_projection/data/metric_info.json @@ -8,10 +8,10 @@ "metric_description": "The percentage of correctly predicted labels.", "references_doi": "10.48550/arxiv.2008.05756", "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/metrics/accuracy", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/metrics/accuracy", "image": "https://ghcr.io/openproblems-bio/task_label_projection/metrics/accuracy:build_main", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d", + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf", "maximize": true }, { @@ -23,10 +23,10 @@ "metric_description": "Calculates the F1 score for each label, and find their average weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall.", "references_doi": "10.48550/arxiv.2008.05756", "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/metrics/f1", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/metrics/f1", "image": "https://ghcr.io/openproblems-bio/task_label_projection/metrics/f1:build_main", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d", + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf", "maximize": true }, { @@ -38,10 +38,10 @@ "metric_description": "Calculates the F1 score for each label, and find their unweighted mean. This does not take label imbalance into account.", "references_doi": "10.48550/arxiv.2008.05756", "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/metrics/f1", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/metrics/f1", "image": "https://ghcr.io/openproblems-bio/task_label_projection/metrics/f1:build_main", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d", + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf", "maximize": true }, { @@ -53,10 +53,10 @@ "metric_description": "Calculates the F1 score globally by counting the total true positives, false negatives and false positives.", "references_doi": "10.48550/arxiv.2008.05756", "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/0def7c62cbda5cdf52c7406a06f91d9e36a5535d/src/metrics/f1", + "implementation_url": "https://github.com/openproblems-bio/task_label_projection/blob/411f6777a671ac671b6b4579ab0d369d8d12dedf/src/metrics/f1", "image": "https://ghcr.io/openproblems-bio/task_label_projection/metrics/f1:build_main", "code_version": "build_main", - "commit_sha": "0def7c62cbda5cdf52c7406a06f91d9e36a5535d", + "commit_sha": "411f6777a671ac671b6b4579ab0d369d8d12dedf", "maximize": true } ] diff --git a/results/label_projection/data/quality_control.json b/results/label_projection/data/quality_control.json index 69162bd2..10366010 100644 --- a/results/label_projection/data/quality_control.json +++ b/results/label_projection/data/quality_control.json @@ -243,61 +243,61 @@ "task_id": "task_label_projection", "category": "Raw data", "name": "Number of results", - "value": 216, + "value": 102, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.5555555555555558, "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: task_label_projection\n Number of results: 216\n Number of methods: 18\n Number of metrics: 4\n Number of datasets: 12\n" + "message": "Number of results should be equal to #methods × #metrics × #datasets.\n Task id: task_label_projection\n Number of results: 102\n Number of methods: 18\n Number of metrics: 4\n Number of datasets: 6\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Metric 'accuracy' %missing", - "value": 0.31481481481481477, + "value": 0.37037037037037035, "severity": 3, - "severity_value": 3.1481481481481475, + "severity_value": 3.7037037037037033, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n Metric id: accuracy\n Percentage missing: 31%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n Metric id: accuracy\n Percentage missing: 37%\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Metric 'f1_weighted' %missing", - "value": 0.31481481481481477, + "value": 0.36111111111111116, "severity": 3, - "severity_value": 3.1481481481481475, + "severity_value": 3.6111111111111116, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n Metric id: f1_weighted\n Percentage missing: 31%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n Metric id: f1_weighted\n Percentage missing: 36%\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Metric 'f1_macro' %missing", - "value": 0.31481481481481477, + "value": 0.36111111111111116, "severity": 3, - "severity_value": 3.1481481481481475, + "severity_value": 3.6111111111111116, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n Metric id: f1_macro\n Percentage missing: 31%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n Metric id: f1_macro\n Percentage missing: 36%\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Metric 'f1_micro' %missing", - "value": 0.31481481481481477, + "value": 0.36111111111111116, "severity": 3, - "severity_value": 3.1481481481481475, + "severity_value": 3.6111111111111116, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n Metric id: f1_micro\n Percentage missing: 31%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n Metric id: f1_micro\n Percentage missing: 36%\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Method 'majority_vote' %missing", - "value": 0.0, + "value": 0.04166666666666663, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.4166666666666663, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: majority_vote\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: majority_vote\n Percentage missing: 4%\n" }, { "task_id": "task_label_projection", @@ -313,11 +313,11 @@ "task_id": "task_label_projection", "category": "Raw results", "name": "Method 'true_labels' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "value": 0.16666666666666663, + "severity": 1, + "severity_value": 1.6666666666666663, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: true_labels\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: true_labels\n Percentage missing: 17%\n" }, { "task_id": "task_label_projection", @@ -363,11 +363,11 @@ "task_id": "task_label_projection", "category": "Raw results", "name": "Method 'naive_bayes' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "value": 0.16666666666666663, + "severity": 1, + "severity_value": 1.6666666666666663, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: naive_bayes\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: naive_bayes\n Percentage missing: 17%\n" }, { "task_id": "task_label_projection", @@ -383,11 +383,11 @@ "task_id": "task_label_projection", "category": "Raw results", "name": "Method 'scanvi_scarches' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "value": 0.16666666666666663, + "severity": 1, + "severity_value": 1.6666666666666663, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: scanvi_scarches\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: scanvi_scarches\n Percentage missing: 17%\n" }, { "task_id": "task_label_projection", @@ -403,21 +403,21 @@ "task_id": "task_label_projection", "category": "Raw results", "name": "Method 'scimilarity' %missing", - "value": 0.6666666666666667, + "value": 0.5, "severity": 3, - "severity_value": 6.666666666666667, + "severity_value": 5.0, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: scimilarity\n Percentage missing: 67%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: scimilarity\n Percentage missing: 50%\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Method 'scimilarity_knn' %missing", - "value": 0.6666666666666667, + "value": 0.5, "severity": 3, - "severity_value": 6.666666666666667, + "severity_value": 5.0, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: scimilarity_knn\n Percentage missing: 67%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: scimilarity_knn\n Percentage missing: 50%\n" }, { "task_id": "task_label_projection", @@ -433,111 +433,91 @@ "task_id": "task_label_projection", "category": "Raw results", "name": "Method 'seurat_transferdata' %missing", - "value": 0.16666666666666663, - "severity": 1, - "severity_value": 1.6666666666666663, + "value": 0.33333333333333337, + "severity": 3, + "severity_value": 3.3333333333333335, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: seurat_transferdata\n Percentage missing: 17%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: seurat_transferdata\n Percentage missing: 33%\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Method 'singler' %missing", - "value": 0.25, - "severity": 2, - "severity_value": 2.5, + "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: task_label_projection\n method id: singler\n Percentage missing: 25%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: singler\n Percentage missing: 50%\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Method 'uce' %missing", - "value": 0.9166666666666666, + "value": 0.8333333333333334, "severity": 3, - "severity_value": 9.166666666666666, + "severity_value": 8.333333333333334, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: uce\n Percentage missing: 92%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: uce\n Percentage missing: 83%\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Method 'xgboost' %missing", - "value": 0.0, - "severity": 0, - "severity_value": 0.0, + "value": 0.33333333333333337, + "severity": 3, + "severity_value": 3.3333333333333335, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: xgboost\n Percentage missing: 0%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n method id: xgboost\n Percentage missing: 33%\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Dataset 'cellxgene_census/gtex_v9' %missing", - "value": 0.2222222222222222, - "severity": 2, - "severity_value": 2.222222222222222, - "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/gtex_v9\n Percentage missing: 22%\n" - }, - { - "task_id": "task_label_projection", - "category": "Raw results", - "name": "Dataset 'openproblems_v1/cengen' %missing", "value": 0.33333333333333337, "severity": 3, "severity_value": 3.3333333333333335, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: openproblems_v1/cengen\n Percentage missing: 33%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/gtex_v9\n Percentage missing: 33%\n" }, { "task_id": "task_label_projection", "category": "Raw results", - "name": "Dataset 'cellxgene_census/tabula_sapiens' %missing", - "value": 0.2777777777777778, + "name": "Dataset 'cellxgene_census/dkd' %missing", + "value": 0.23611111111111116, "severity": 2, - "severity_value": 2.7777777777777777, + "severity_value": 2.3611111111111116, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/tabula_sapiens\n Percentage missing: 28%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/dkd\n Percentage missing: 24%\n" }, { "task_id": "task_label_projection", "category": "Raw results", "name": "Dataset 'cellxgene_census/hypomap' %missing", - "value": 0.33333333333333337, + "value": 0.5, "severity": 3, - "severity_value": 3.3333333333333335, + "severity_value": 5.0, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/hypomap\n Percentage missing: 33%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/hypomap\n Percentage missing: 50%\n" }, { "task_id": "task_label_projection", "category": "Raw results", - "name": "Dataset 'cellxgene_census/immune_cell_atlas' %missing", - "value": 0.2777777777777778, - "severity": 2, - "severity_value": 2.7777777777777777, - "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/immune_cell_atlas\n Percentage missing: 28%\n" - }, - { - "task_id": "task_label_projection", - "category": "Raw results", - "name": "Dataset 'cellxgene_census/hcla' %missing", - "value": 0.38888888888888884, + "name": "Dataset 'cellxgene_census/tabula_sapiens' %missing", + "value": 0.4444444444444444, "severity": 3, - "severity_value": 3.8888888888888884, + "severity_value": 4.444444444444444, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/hcla\n Percentage missing: 39%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/tabula_sapiens\n Percentage missing: 44%\n" }, { "task_id": "task_label_projection", "category": "Raw results", - "name": "Dataset 'cellxgene_census/dkd' %missing", - "value": 0.16666666666666663, - "severity": 1, - "severity_value": 1.6666666666666663, + "name": "Dataset 'cellxgene_census/immune_cell_atlas' %missing", + "value": 0.2777777777777778, + "severity": 2, + "severity_value": 2.7777777777777777, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/dkd\n Percentage missing: 17%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/immune_cell_atlas\n Percentage missing: 28%\n" }, { "task_id": "task_label_projection", @@ -549,46 +529,6 @@ "code": "pct_missing <= .1", "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: cellxgene_census/mouse_pancreas_atlas\n Percentage missing: 39%\n" }, - { - "task_id": "task_label_projection", - "category": "Raw results", - "name": "Dataset 'openproblems_v1/immune_cells' %missing", - "value": 0.33333333333333337, - "severity": 3, - "severity_value": 3.3333333333333335, - "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: openproblems_v1/immune_cells\n Percentage missing: 33%\n" - }, - { - "task_id": "task_label_projection", - "category": "Raw results", - "name": "Dataset 'allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2' %missing", - "value": 0.38888888888888884, - "severity": 3, - "severity_value": 3.8888888888888884, - "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2\n Percentage missing: 39%\n" - }, - { - "task_id": "task_label_projection", - "category": "Raw results", - "name": "Dataset 'openproblems_v1/zebrafish' %missing", - "value": 0.33333333333333337, - "severity": 3, - "severity_value": 3.3333333333333335, - "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: openproblems_v1/zebrafish\n Percentage missing: 33%\n" - }, - { - "task_id": "task_label_projection", - "category": "Raw results", - "name": "Dataset 'openproblems_v1/pancreas' %missing", - "value": 0.33333333333333337, - "severity": 3, - "severity_value": 3.3333333333333335, - "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_label_projection\n dataset id: openproblems_v1/pancreas\n Percentage missing: 33%\n" - }, { "task_id": "task_label_projection", "category": "Scaling", @@ -603,41 +543,41 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score majority_vote accuracy", - "value": 0.4546, + "value": 1.0, "severity": 0, - "severity_value": 0.2273, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method majority_vote performs a lot better than baselines.\n Task id: task_label_projection\n Method id: majority_vote\n Metric id: accuracy\n Best score: 0.4546%\n" + "message": "Method majority_vote performs a lot better than baselines.\n Task id: task_label_projection\n Method id: majority_vote\n Metric id: accuracy\n Best score: 1.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score random_labels accuracy", - "value": 0.0, + "value": 0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method random_labels performs much worse than baselines.\n Task id: task_label_projection\n Method id: random_labels\n Metric id: accuracy\n Worst score: 0.0%\n" + "message": "Method random_labels performs much worse than baselines.\n Task id: task_label_projection\n Method id: random_labels\n Metric id: accuracy\n Worst score: 0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score random_labels accuracy", - "value": 0.0737, + "value": 0, "severity": 0, - "severity_value": 0.03685, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method random_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: random_labels\n Metric id: accuracy\n Best score: 0.0737%\n" + "message": "Method random_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: random_labels\n Metric id: accuracy\n Best score: 0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score true_labels accuracy", - "value": 1, + "value": 0, "severity": 0, - "severity_value": -1.0, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method true_labels performs much worse than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: accuracy\n Worst score: 1%\n" + "message": "Method true_labels performs much worse than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: accuracy\n Worst score: 0%\n" }, { "task_id": "task_label_projection", @@ -649,145 +589,125 @@ "code": "best_score <= 2", "message": "Method true_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: accuracy\n Best score: 1%\n" }, - { - "task_id": "task_label_projection", - "category": "Scaling", - "name": "Worst score geneformer accuracy", - "value": 0, - "severity": 0, - "severity_value": -0.0, - "code": "worst_score >= -1", - "message": "Method geneformer performs much worse than baselines.\n Task id: task_label_projection\n Method id: geneformer\n Metric id: accuracy\n Worst score: 0%\n" - }, - { - "task_id": "task_label_projection", - "category": "Scaling", - "name": "Best score geneformer accuracy", - "value": 0, - "severity": 0, - "severity_value": 0.0, - "code": "best_score <= 2", - "message": "Method geneformer performs a lot better than baselines.\n Task id: task_label_projection\n Method id: geneformer\n Metric id: accuracy\n Best score: 0%\n" - }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score knn accuracy", - "value": 0.2486, + "value": 0.7651, "severity": 0, - "severity_value": -0.2486, + "severity_value": -0.7651, "code": "worst_score >= -1", - "message": "Method knn performs much worse than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: accuracy\n Worst score: 0.2486%\n" + "message": "Method knn performs much worse than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: accuracy\n Worst score: 0.7651%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score knn accuracy", - "value": 0.9992, - "severity": 0, - "severity_value": 0.4996, + "value": 2.503, + "severity": 1, + "severity_value": 1.2515, "code": "best_score <= 2", - "message": "Method knn performs a lot better than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: accuracy\n Best score: 0.9992%\n" + "message": "Method knn performs a lot better than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: accuracy\n Best score: 2.503%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score logistic_regression accuracy", - "value": -0.0107, + "value": 0.77, "severity": 0, - "severity_value": 0.0107, + "severity_value": -0.77, "code": "worst_score >= -1", - "message": "Method logistic_regression performs much worse than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: accuracy\n Worst score: -0.0107%\n" + "message": "Method logistic_regression performs much worse than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: accuracy\n Worst score: 0.77%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score logistic_regression accuracy", - "value": 0.9992, - "severity": 0, - "severity_value": 0.4996, + "value": 2.509, + "severity": 1, + "severity_value": 1.2545, "code": "best_score <= 2", - "message": "Method logistic_regression performs a lot better than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: accuracy\n Best score: 0.9992%\n" + "message": "Method logistic_regression performs a lot better than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: accuracy\n Best score: 2.509%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score mlp accuracy", - "value": 0.2644, + "value": 0.7263, "severity": 0, - "severity_value": -0.2644, + "severity_value": -0.7263, "code": "worst_score >= -1", - "message": "Method mlp performs much worse than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: accuracy\n Worst score: 0.2644%\n" + "message": "Method mlp performs much worse than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: accuracy\n Worst score: 0.7263%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score mlp accuracy", - "value": 1.0, - "severity": 0, - "severity_value": 0.5, + "value": 2.515, + "severity": 1, + "severity_value": 1.2575, "code": "best_score <= 2", - "message": "Method mlp performs a lot better than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: accuracy\n Best score: 1.0%\n" + "message": "Method mlp performs a lot better than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: accuracy\n Best score: 2.515%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score naive_bayes accuracy", - "value": 0.189, + "value": 0.0, "severity": 0, - "severity_value": -0.189, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method naive_bayes performs much worse than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: accuracy\n Worst score: 0.189%\n" + "message": "Method naive_bayes performs much worse than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: accuracy\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score naive_bayes accuracy", - "value": 0.9851, + "value": 0.8717, "severity": 0, - "severity_value": 0.49255, + "severity_value": 0.43585, "code": "best_score <= 2", - "message": "Method naive_bayes performs a lot better than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: accuracy\n Best score: 0.9851%\n" + "message": "Method naive_bayes performs a lot better than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: accuracy\n Best score: 0.8717%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score scanvi accuracy", - "value": 0.2797, + "value": 0.8225, "severity": 0, - "severity_value": -0.2797, + "severity_value": -0.8225, "code": "worst_score >= -1", - "message": "Method scanvi performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: accuracy\n Worst score: 0.2797%\n" + "message": "Method scanvi performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: accuracy\n Worst score: 0.8225%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score scanvi accuracy", - "value": 0.9967, - "severity": 0, - "severity_value": 0.49835, + "value": 2.509, + "severity": 1, + "severity_value": 1.2545, "code": "best_score <= 2", - "message": "Method scanvi performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: accuracy\n Best score: 0.9967%\n" + "message": "Method scanvi performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: accuracy\n Best score: 2.509%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score scanvi_scarches accuracy", - "value": 0.28, + "value": 0.0, "severity": 0, - "severity_value": -0.28, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method scanvi_scarches performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: accuracy\n Worst score: 0.28%\n" + "message": "Method scanvi_scarches performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: accuracy\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score scanvi_scarches accuracy", - "value": 0.9983, - "severity": 0, - "severity_value": 0.49915, + "value": 2.503, + "severity": 1, + "severity_value": 1.2515, "code": "best_score <= 2", - "message": "Method scanvi_scarches performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: accuracy\n Best score: 0.9983%\n" + "message": "Method scanvi_scarches performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: accuracy\n Best score: 2.503%\n" }, { "task_id": "task_label_projection", @@ -823,11 +743,11 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score scimilarity accuracy", - "value": 0.873, + "value": 0.8703, "severity": 0, - "severity_value": 0.4365, + "severity_value": 0.43515, "code": "best_score <= 2", - "message": "Method scimilarity performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scimilarity\n Metric id: accuracy\n Best score: 0.873%\n" + "message": "Method scimilarity performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scimilarity\n Metric id: accuracy\n Best score: 0.8703%\n" }, { "task_id": "task_label_projection", @@ -843,11 +763,11 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score scimilarity_knn accuracy", - "value": 0.9188, + "value": 0.9171, "severity": 0, - "severity_value": 0.4594, + "severity_value": 0.45855, "code": "best_score <= 2", - "message": "Method scimilarity_knn performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scimilarity_knn\n Metric id: accuracy\n Best score: 0.9188%\n" + "message": "Method scimilarity_knn performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scimilarity_knn\n Metric id: accuracy\n Best score: 0.9171%\n" }, { "task_id": "task_label_projection", @@ -883,11 +803,11 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score seurat_transferdata accuracy", - "value": 0.9925, + "value": 0.9452, "severity": 0, - "severity_value": 0.49625, + "severity_value": 0.4726, "code": "best_score <= 2", - "message": "Method seurat_transferdata performs a lot better than baselines.\n Task id: task_label_projection\n Method id: seurat_transferdata\n Metric id: accuracy\n Best score: 0.9925%\n" + "message": "Method seurat_transferdata performs a lot better than baselines.\n Task id: task_label_projection\n Method id: seurat_transferdata\n Metric id: accuracy\n Best score: 0.9452%\n" }, { "task_id": "task_label_projection", @@ -903,51 +823,51 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score singler accuracy", - "value": 0.9892, - "severity": 0, - "severity_value": 0.4946, + "value": 2.485, + "severity": 1, + "severity_value": 1.2425, "code": "best_score <= 2", - "message": "Method singler performs a lot better than baselines.\n Task id: task_label_projection\n Method id: singler\n Metric id: accuracy\n Best score: 0.9892%\n" + "message": "Method singler performs a lot better than baselines.\n Task id: task_label_projection\n Method id: singler\n Metric id: accuracy\n Best score: 2.485%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score uce accuracy", - "value": 0.0, + "value": -0.0293, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.0293, "code": "worst_score >= -1", - "message": "Method uce performs much worse than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: accuracy\n Worst score: 0.0%\n" + "message": "Method uce performs much worse than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: accuracy\n Worst score: -0.0293%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score uce accuracy", - "value": 0.0234, + "value": 0.0, "severity": 0, - "severity_value": 0.0117, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method uce performs a lot better than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: accuracy\n Best score: 0.0234%\n" + "message": "Method uce performs a lot better than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: accuracy\n Best score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score xgboost accuracy", - "value": 0.2561, + "value": 0.0, "severity": 0, - "severity_value": -0.2561, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method xgboost performs much worse than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: accuracy\n Worst score: 0.2561%\n" + "message": "Method xgboost performs much worse than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: accuracy\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score xgboost accuracy", - "value": 0.995, - "severity": 0, - "severity_value": 0.4975, + "value": 2.509, + "severity": 1, + "severity_value": 1.2545, "code": "best_score <= 2", - "message": "Method xgboost performs a lot better than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: accuracy\n Best score: 0.995%\n" + "message": "Method xgboost performs a lot better than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: accuracy\n Best score: 2.509%\n" }, { "task_id": "task_label_projection", @@ -963,11 +883,11 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score majority_vote f1_weighted", - "value": 0.2066, + "value": 1.0, "severity": 0, - "severity_value": 0.1033, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method majority_vote performs a lot better than baselines.\n Task id: task_label_projection\n Method id: majority_vote\n Metric id: f1_weighted\n Best score: 0.2066%\n" + "message": "Method majority_vote performs a lot better than baselines.\n Task id: task_label_projection\n Method id: majority_vote\n Metric id: f1_weighted\n Best score: 1.0%\n" }, { "task_id": "task_label_projection", @@ -983,21 +903,21 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score random_labels f1_weighted", - "value": 0.1408, + "value": 0.1438, "severity": 0, - "severity_value": 0.0704, + "severity_value": 0.0719, "code": "best_score <= 2", - "message": "Method random_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: random_labels\n Metric id: f1_weighted\n Best score: 0.1408%\n" + "message": "Method random_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: random_labels\n Metric id: f1_weighted\n Best score: 0.1438%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score true_labels f1_weighted", - "value": 1, + "value": 0, "severity": 0, - "severity_value": -1.0, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method true_labels performs much worse than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: f1_weighted\n Worst score: 1%\n" + "message": "Method true_labels performs much worse than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: f1_weighted\n Worst score: 0%\n" }, { "task_id": "task_label_projection", @@ -1009,145 +929,125 @@ "code": "best_score <= 2", "message": "Method true_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: f1_weighted\n Best score: 1%\n" }, - { - "task_id": "task_label_projection", - "category": "Scaling", - "name": "Worst score geneformer f1_weighted", - "value": 0, - "severity": 0, - "severity_value": -0.0, - "code": "worst_score >= -1", - "message": "Method geneformer performs much worse than baselines.\n Task id: task_label_projection\n Method id: geneformer\n Metric id: f1_weighted\n Worst score: 0%\n" - }, - { - "task_id": "task_label_projection", - "category": "Scaling", - "name": "Best score geneformer f1_weighted", - "value": 0, - "severity": 0, - "severity_value": 0.0, - "code": "best_score <= 2", - "message": "Method geneformer performs a lot better than baselines.\n Task id: task_label_projection\n Method id: geneformer\n Metric id: f1_weighted\n Best score: 0%\n" - }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score knn f1_weighted", - "value": 0.2459, + "value": 0.7724, "severity": 0, - "severity_value": -0.2459, + "severity_value": -0.7724, "code": "worst_score >= -1", - "message": "Method knn performs much worse than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_weighted\n Worst score: 0.2459%\n" + "message": "Method knn performs much worse than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_weighted\n Worst score: 0.7724%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score knn f1_weighted", - "value": 0.9992, - "severity": 0, - "severity_value": 0.4996, + "value": 8.8465, + "severity": 3, + "severity_value": 4.42325, "code": "best_score <= 2", - "message": "Method knn performs a lot better than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_weighted\n Best score: 0.9992%\n" + "message": "Method knn performs a lot better than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_weighted\n Best score: 8.8465%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score logistic_regression f1_weighted", - "value": 0.1681, + "value": 0.7761, "severity": 0, - "severity_value": -0.1681, + "severity_value": -0.7761, "code": "worst_score >= -1", - "message": "Method logistic_regression performs much worse than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_weighted\n Worst score: 0.1681%\n" + "message": "Method logistic_regression performs much worse than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_weighted\n Worst score: 0.7761%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score logistic_regression f1_weighted", - "value": 0.9992, - "severity": 0, - "severity_value": 0.4996, + "value": 8.8727, + "severity": 3, + "severity_value": 4.43635, "code": "best_score <= 2", - "message": "Method logistic_regression performs a lot better than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_weighted\n Best score: 0.9992%\n" + "message": "Method logistic_regression performs a lot better than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_weighted\n Best score: 8.8727%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score mlp f1_weighted", - "value": 0.2594, + "value": 0.7202, "severity": 0, - "severity_value": -0.2594, + "severity_value": -0.7202, "code": "worst_score >= -1", - "message": "Method mlp performs much worse than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_weighted\n Worst score: 0.2594%\n" + "message": "Method mlp performs much worse than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_weighted\n Worst score: 0.7202%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score mlp f1_weighted", - "value": 1.0, - "severity": 0, - "severity_value": 0.5, + "value": 8.8839, + "severity": 3, + "severity_value": 4.44195, "code": "best_score <= 2", - "message": "Method mlp performs a lot better than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_weighted\n Best score: 1.0%\n" + "message": "Method mlp performs a lot better than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_weighted\n Best score: 8.8839%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score naive_bayes f1_weighted", - "value": 0.2154, + "value": 0.0, "severity": 0, - "severity_value": -0.2154, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method naive_bayes performs much worse than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_weighted\n Worst score: 0.2154%\n" + "message": "Method naive_bayes performs much worse than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_weighted\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score naive_bayes f1_weighted", - "value": 0.9858, + "value": 0.9102, "severity": 0, - "severity_value": 0.4929, + "severity_value": 0.4551, "code": "best_score <= 2", - "message": "Method naive_bayes performs a lot better than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_weighted\n Best score: 0.9858%\n" + "message": "Method naive_bayes performs a lot better than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_weighted\n Best score: 0.9102%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score scanvi f1_weighted", - "value": 0.2525, + "value": 0.822, "severity": 0, - "severity_value": -0.2525, + "severity_value": -0.822, "code": "worst_score >= -1", - "message": "Method scanvi performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_weighted\n Worst score: 0.2525%\n" + "message": "Method scanvi performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_weighted\n Worst score: 0.822%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score scanvi f1_weighted", - "value": 0.997, - "severity": 0, - "severity_value": 0.4985, + "value": 8.8614, + "severity": 3, + "severity_value": 4.4307, "code": "best_score <= 2", - "message": "Method scanvi performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_weighted\n Best score: 0.997%\n" + "message": "Method scanvi performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_weighted\n Best score: 8.8614%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score scanvi_scarches f1_weighted", - "value": 0.2639, + "value": 0.0, "severity": 0, - "severity_value": -0.2639, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method scanvi_scarches performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_weighted\n Worst score: 0.2639%\n" + "message": "Method scanvi_scarches performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_weighted\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score scanvi_scarches f1_weighted", - "value": 0.9985, - "severity": 0, - "severity_value": 0.49925, + "value": 8.8376, + "severity": 3, + "severity_value": 4.4188, "code": "best_score <= 2", - "message": "Method scanvi_scarches performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_weighted\n Best score: 0.9985%\n" + "message": "Method scanvi_scarches performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_weighted\n Best score: 8.8376%\n" }, { "task_id": "task_label_projection", @@ -1243,11 +1143,11 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score seurat_transferdata f1_weighted", - "value": 0.9933, + "value": 0.9465, "severity": 0, - "severity_value": 0.49665, + "severity_value": 0.47325, "code": "best_score <= 2", - "message": "Method seurat_transferdata performs a lot better than baselines.\n Task id: task_label_projection\n Method id: seurat_transferdata\n Metric id: f1_weighted\n Best score: 0.9933%\n" + "message": "Method seurat_transferdata performs a lot better than baselines.\n Task id: task_label_projection\n Method id: seurat_transferdata\n Metric id: f1_weighted\n Best score: 0.9465%\n" }, { "task_id": "task_label_projection", @@ -1263,51 +1163,51 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score singler f1_weighted", - "value": 0.9905, - "severity": 0, - "severity_value": 0.49525, + "value": 8.7829, + "severity": 3, + "severity_value": 4.39145, "code": "best_score <= 2", - "message": "Method singler performs a lot better than baselines.\n Task id: task_label_projection\n Method id: singler\n Metric id: f1_weighted\n Best score: 0.9905%\n" + "message": "Method singler performs a lot better than baselines.\n Task id: task_label_projection\n Method id: singler\n Metric id: f1_weighted\n Best score: 8.7829%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score uce f1_weighted", - "value": 0.0, + "value": -0.0034, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.0034, "code": "worst_score >= -1", - "message": "Method uce performs much worse than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_weighted\n Worst score: 0.0%\n" + "message": "Method uce performs much worse than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_weighted\n Worst score: -0.0034%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score uce f1_weighted", - "value": 0.1518, + "value": 0.0, "severity": 0, - "severity_value": 0.0759, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method uce performs a lot better than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_weighted\n Best score: 0.1518%\n" + "message": "Method uce performs a lot better than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_weighted\n Best score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score xgboost f1_weighted", - "value": 0.2498, + "value": 0.0, "severity": 0, - "severity_value": -0.2498, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method xgboost performs much worse than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_weighted\n Worst score: 0.2498%\n" + "message": "Method xgboost performs much worse than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_weighted\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score xgboost f1_weighted", - "value": 0.9955, - "severity": 0, - "severity_value": 0.49775, + "value": 8.8589, + "severity": 3, + "severity_value": 4.42945, "code": "best_score <= 2", - "message": "Method xgboost performs a lot better than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_weighted\n Best score: 0.9955%\n" + "message": "Method xgboost performs a lot better than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_weighted\n Best score: 8.8589%\n" }, { "task_id": "task_label_projection", @@ -1323,11 +1223,11 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score majority_vote f1_macro", - "value": 0.0155, + "value": 1.0, "severity": 0, - "severity_value": 0.00775, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method majority_vote performs a lot better than baselines.\n Task id: task_label_projection\n Method id: majority_vote\n Metric id: f1_macro\n Best score: 0.0155%\n" + "message": "Method majority_vote performs a lot better than baselines.\n Task id: task_label_projection\n Method id: majority_vote\n Metric id: f1_macro\n Best score: 1.0%\n" }, { "task_id": "task_label_projection", @@ -1343,21 +1243,21 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score random_labels f1_macro", - "value": 0.0482, + "value": 0.0596, "severity": 0, - "severity_value": 0.0241, + "severity_value": 0.0298, "code": "best_score <= 2", - "message": "Method random_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: random_labels\n Metric id: f1_macro\n Best score: 0.0482%\n" + "message": "Method random_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: random_labels\n Metric id: f1_macro\n Best score: 0.0596%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score true_labels f1_macro", - "value": 1, + "value": 0, "severity": 0, - "severity_value": -1.0, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method true_labels performs much worse than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: f1_macro\n Worst score: 1%\n" + "message": "Method true_labels performs much worse than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: f1_macro\n Worst score: 0%\n" }, { "task_id": "task_label_projection", @@ -1369,145 +1269,125 @@ "code": "best_score <= 2", "message": "Method true_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: f1_macro\n Best score: 1%\n" }, - { - "task_id": "task_label_projection", - "category": "Scaling", - "name": "Worst score geneformer f1_macro", - "value": 0, - "severity": 0, - "severity_value": -0.0, - "code": "worst_score >= -1", - "message": "Method geneformer performs much worse than baselines.\n Task id: task_label_projection\n Method id: geneformer\n Metric id: f1_macro\n Worst score: 0%\n" - }, - { - "task_id": "task_label_projection", - "category": "Scaling", - "name": "Best score geneformer f1_macro", - "value": 0, - "severity": 0, - "severity_value": 0.0, - "code": "best_score <= 2", - "message": "Method geneformer performs a lot better than baselines.\n Task id: task_label_projection\n Method id: geneformer\n Metric id: f1_macro\n Best score: 0%\n" - }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score knn f1_macro", - "value": 0.1876, + "value": 0.2675, "severity": 0, - "severity_value": -0.1876, + "severity_value": -0.2675, "code": "worst_score >= -1", - "message": "Method knn performs much worse than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_macro\n Worst score: 0.1876%\n" + "message": "Method knn performs much worse than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_macro\n Worst score: 0.2675%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score knn f1_macro", - "value": 0.9997, - "severity": 0, - "severity_value": 0.49985, + "value": 33.9136, + "severity": 3, + "severity_value": 16.9568, "code": "best_score <= 2", - "message": "Method knn performs a lot better than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_macro\n Best score: 0.9997%\n" + "message": "Method knn performs a lot better than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_macro\n Best score: 33.9136%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score logistic_regression f1_macro", - "value": 0.1708, + "value": 0.2957, "severity": 0, - "severity_value": -0.1708, + "severity_value": -0.2957, "code": "worst_score >= -1", - "message": "Method logistic_regression performs much worse than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_macro\n Worst score: 0.1708%\n" + "message": "Method logistic_regression performs much worse than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_macro\n Worst score: 0.2957%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score logistic_regression f1_macro", - "value": 0.9997, - "severity": 0, - "severity_value": 0.49985, + "value": 33.2817, + "severity": 3, + "severity_value": 16.64085, "code": "best_score <= 2", - "message": "Method logistic_regression performs a lot better than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_macro\n Best score: 0.9997%\n" + "message": "Method logistic_regression performs a lot better than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_macro\n Best score: 33.2817%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score mlp f1_macro", - "value": 0.2181, + "value": 0.24, "severity": 0, - "severity_value": -0.2181, + "severity_value": -0.24, "code": "worst_score >= -1", - "message": "Method mlp performs much worse than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_macro\n Worst score: 0.2181%\n" + "message": "Method mlp performs much worse than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_macro\n Worst score: 0.24%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score mlp f1_macro", - "value": 1.0, - "severity": 0, - "severity_value": 0.5, + "value": 39.0123, + "severity": 3, + "severity_value": 19.50615, "code": "best_score <= 2", - "message": "Method mlp performs a lot better than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_macro\n Best score: 1.0%\n" + "message": "Method mlp performs a lot better than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_macro\n Best score: 39.0123%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score naive_bayes f1_macro", - "value": 0.1763, + "value": 0.0, "severity": 0, - "severity_value": -0.1763, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method naive_bayes performs much worse than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_macro\n Worst score: 0.1763%\n" + "message": "Method naive_bayes performs much worse than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_macro\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score naive_bayes f1_macro", - "value": 0.8912, + "value": 0.8303, "severity": 0, - "severity_value": 0.4456, + "severity_value": 0.41515, "code": "best_score <= 2", - "message": "Method naive_bayes performs a lot better than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_macro\n Best score: 0.8912%\n" + "message": "Method naive_bayes performs a lot better than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_macro\n Best score: 0.8303%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score scanvi f1_macro", - "value": 0.2035, + "value": 0.2971, "severity": 0, - "severity_value": -0.2035, + "severity_value": -0.2971, "code": "worst_score >= -1", - "message": "Method scanvi performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_macro\n Worst score: 0.2035%\n" + "message": "Method scanvi performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_macro\n Worst score: 0.2971%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score scanvi f1_macro", - "value": 0.999, - "severity": 0, - "severity_value": 0.4995, + "value": 37.8264, + "severity": 3, + "severity_value": 18.9132, "code": "best_score <= 2", - "message": "Method scanvi performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_macro\n Best score: 0.999%\n" + "message": "Method scanvi performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_macro\n Best score: 37.8264%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score scanvi_scarches f1_macro", - "value": 0.2324, + "value": 0.0, "severity": 0, - "severity_value": -0.2324, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method scanvi_scarches performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_macro\n Worst score: 0.2324%\n" + "message": "Method scanvi_scarches performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_macro\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score scanvi_scarches f1_macro", - "value": 0.9956, - "severity": 0, - "severity_value": 0.4978, + "value": 37.4777, + "severity": 3, + "severity_value": 18.73885, "code": "best_score <= 2", - "message": "Method scanvi_scarches performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_macro\n Best score: 0.9956%\n" + "message": "Method scanvi_scarches performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_macro\n Best score: 37.4777%\n" }, { "task_id": "task_label_projection", @@ -1603,11 +1483,11 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score seurat_transferdata f1_macro", - "value": 0.9013, + "value": 0.8661, "severity": 0, - "severity_value": 0.45065, + "severity_value": 0.43305, "code": "best_score <= 2", - "message": "Method seurat_transferdata performs a lot better than baselines.\n Task id: task_label_projection\n Method id: seurat_transferdata\n Metric id: f1_macro\n Best score: 0.9013%\n" + "message": "Method seurat_transferdata performs a lot better than baselines.\n Task id: task_label_projection\n Method id: seurat_transferdata\n Metric id: f1_macro\n Best score: 0.8661%\n" }, { "task_id": "task_label_projection", @@ -1623,51 +1503,51 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score singler f1_macro", - "value": 0.8229, - "severity": 0, - "severity_value": 0.41145, + "value": 31.8083, + "severity": 3, + "severity_value": 15.90415, "code": "best_score <= 2", - "message": "Method singler performs a lot better than baselines.\n Task id: task_label_projection\n Method id: singler\n Metric id: f1_macro\n Best score: 0.8229%\n" + "message": "Method singler performs a lot better than baselines.\n Task id: task_label_projection\n Method id: singler\n Metric id: f1_macro\n Best score: 31.8083%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score uce f1_macro", - "value": 0.0, + "value": -0.004, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.004, "code": "worst_score >= -1", - "message": "Method uce performs much worse than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_macro\n Worst score: 0.0%\n" + "message": "Method uce performs much worse than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_macro\n Worst score: -0.004%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score uce f1_macro", - "value": 0.0501, + "value": 0.0, "severity": 0, - "severity_value": 0.02505, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method uce performs a lot better than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_macro\n Best score: 0.0501%\n" + "message": "Method uce performs a lot better than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_macro\n Best score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score xgboost f1_macro", - "value": 0.2179, + "value": 0.0, "severity": 0, - "severity_value": -0.2179, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method xgboost performs much worse than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_macro\n Worst score: 0.2179%\n" + "message": "Method xgboost performs much worse than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_macro\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score xgboost f1_macro", - "value": 0.9985, - "severity": 0, - "severity_value": 0.49925, + "value": 38.7267, + "severity": 3, + "severity_value": 19.36335, "code": "best_score <= 2", - "message": "Method xgboost performs a lot better than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_macro\n Best score: 0.9985%\n" + "message": "Method xgboost performs a lot better than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_macro\n Best score: 38.7267%\n" }, { "task_id": "task_label_projection", @@ -1683,11 +1563,11 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score majority_vote f1_micro", - "value": 0.4546, + "value": 1.0, "severity": 0, - "severity_value": 0.2273, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method majority_vote performs a lot better than baselines.\n Task id: task_label_projection\n Method id: majority_vote\n Metric id: f1_micro\n Best score: 0.4546%\n" + "message": "Method majority_vote performs a lot better than baselines.\n Task id: task_label_projection\n Method id: majority_vote\n Metric id: f1_micro\n Best score: 1.0%\n" }, { "task_id": "task_label_projection", @@ -1703,21 +1583,21 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score random_labels f1_micro", - "value": 0.0737, + "value": 0.0211, "severity": 0, - "severity_value": 0.03685, + "severity_value": 0.01055, "code": "best_score <= 2", - "message": "Method random_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: random_labels\n Metric id: f1_micro\n Best score: 0.0737%\n" + "message": "Method random_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: random_labels\n Metric id: f1_micro\n Best score: 0.0211%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score true_labels f1_micro", - "value": 1, + "value": 0, "severity": 0, - "severity_value": -1.0, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method true_labels performs much worse than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: f1_micro\n Worst score: 1%\n" + "message": "Method true_labels performs much worse than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: f1_micro\n Worst score: 0%\n" }, { "task_id": "task_label_projection", @@ -1729,145 +1609,125 @@ "code": "best_score <= 2", "message": "Method true_labels performs a lot better than baselines.\n Task id: task_label_projection\n Method id: true_labels\n Metric id: f1_micro\n Best score: 1%\n" }, - { - "task_id": "task_label_projection", - "category": "Scaling", - "name": "Worst score geneformer f1_micro", - "value": 0, - "severity": 0, - "severity_value": -0.0, - "code": "worst_score >= -1", - "message": "Method geneformer performs much worse than baselines.\n Task id: task_label_projection\n Method id: geneformer\n Metric id: f1_micro\n Worst score: 0%\n" - }, - { - "task_id": "task_label_projection", - "category": "Scaling", - "name": "Best score geneformer f1_micro", - "value": 0, - "severity": 0, - "severity_value": 0.0, - "code": "best_score <= 2", - "message": "Method geneformer performs a lot better than baselines.\n Task id: task_label_projection\n Method id: geneformer\n Metric id: f1_micro\n Best score: 0%\n" - }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score knn f1_micro", - "value": 0.2486, + "value": 0.7651, "severity": 0, - "severity_value": -0.2486, + "severity_value": -0.7651, "code": "worst_score >= -1", - "message": "Method knn performs much worse than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_micro\n Worst score: 0.2486%\n" + "message": "Method knn performs much worse than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_micro\n Worst score: 0.7651%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score knn f1_micro", - "value": 0.9992, - "severity": 0, - "severity_value": 0.4996, + "value": 2.503, + "severity": 1, + "severity_value": 1.2515, "code": "best_score <= 2", - "message": "Method knn performs a lot better than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_micro\n Best score: 0.9992%\n" + "message": "Method knn performs a lot better than baselines.\n Task id: task_label_projection\n Method id: knn\n Metric id: f1_micro\n Best score: 2.503%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score logistic_regression f1_micro", - "value": -0.0107, + "value": 0.77, "severity": 0, - "severity_value": 0.0107, + "severity_value": -0.77, "code": "worst_score >= -1", - "message": "Method logistic_regression performs much worse than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_micro\n Worst score: -0.0107%\n" + "message": "Method logistic_regression performs much worse than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_micro\n Worst score: 0.77%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score logistic_regression f1_micro", - "value": 0.9992, - "severity": 0, - "severity_value": 0.4996, + "value": 2.509, + "severity": 1, + "severity_value": 1.2545, "code": "best_score <= 2", - "message": "Method logistic_regression performs a lot better than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_micro\n Best score: 0.9992%\n" + "message": "Method logistic_regression performs a lot better than baselines.\n Task id: task_label_projection\n Method id: logistic_regression\n Metric id: f1_micro\n Best score: 2.509%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score mlp f1_micro", - "value": 0.2644, + "value": 0.7263, "severity": 0, - "severity_value": -0.2644, + "severity_value": -0.7263, "code": "worst_score >= -1", - "message": "Method mlp performs much worse than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_micro\n Worst score: 0.2644%\n" + "message": "Method mlp performs much worse than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_micro\n Worst score: 0.7263%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score mlp f1_micro", - "value": 1.0, - "severity": 0, - "severity_value": 0.5, + "value": 2.515, + "severity": 1, + "severity_value": 1.2575, "code": "best_score <= 2", - "message": "Method mlp performs a lot better than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_micro\n Best score: 1.0%\n" + "message": "Method mlp performs a lot better than baselines.\n Task id: task_label_projection\n Method id: mlp\n Metric id: f1_micro\n Best score: 2.515%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score naive_bayes f1_micro", - "value": 0.189, + "value": 0.0, "severity": 0, - "severity_value": -0.189, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method naive_bayes performs much worse than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_micro\n Worst score: 0.189%\n" + "message": "Method naive_bayes performs much worse than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_micro\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score naive_bayes f1_micro", - "value": 0.9851, + "value": 0.8744, "severity": 0, - "severity_value": 0.49255, + "severity_value": 0.4372, "code": "best_score <= 2", - "message": "Method naive_bayes performs a lot better than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_micro\n Best score: 0.9851%\n" + "message": "Method naive_bayes performs a lot better than baselines.\n Task id: task_label_projection\n Method id: naive_bayes\n Metric id: f1_micro\n Best score: 0.8744%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score scanvi f1_micro", - "value": 0.2797, + "value": 0.8225, "severity": 0, - "severity_value": -0.2797, + "severity_value": -0.8225, "code": "worst_score >= -1", - "message": "Method scanvi performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_micro\n Worst score: 0.2797%\n" + "message": "Method scanvi performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_micro\n Worst score: 0.8225%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score scanvi f1_micro", - "value": 0.9967, - "severity": 0, - "severity_value": 0.49835, + "value": 2.509, + "severity": 1, + "severity_value": 1.2545, "code": "best_score <= 2", - "message": "Method scanvi performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_micro\n Best score: 0.9967%\n" + "message": "Method scanvi performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi\n Metric id: f1_micro\n Best score: 2.509%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score scanvi_scarches f1_micro", - "value": 0.28, + "value": 0.0, "severity": 0, - "severity_value": -0.28, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method scanvi_scarches performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_micro\n Worst score: 0.28%\n" + "message": "Method scanvi_scarches performs much worse than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_micro\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score scanvi_scarches f1_micro", - "value": 0.9983, - "severity": 0, - "severity_value": 0.49915, + "value": 2.503, + "severity": 1, + "severity_value": 1.2515, "code": "best_score <= 2", - "message": "Method scanvi_scarches performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_micro\n Best score: 0.9983%\n" + "message": "Method scanvi_scarches performs a lot better than baselines.\n Task id: task_label_projection\n Method id: scanvi_scarches\n Metric id: f1_micro\n Best score: 2.503%\n" }, { "task_id": "task_label_projection", @@ -1963,11 +1823,11 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score seurat_transferdata f1_micro", - "value": 0.9925, + "value": 0.9464, "severity": 0, - "severity_value": 0.49625, + "severity_value": 0.4732, "code": "best_score <= 2", - "message": "Method seurat_transferdata performs a lot better than baselines.\n Task id: task_label_projection\n Method id: seurat_transferdata\n Metric id: f1_micro\n Best score: 0.9925%\n" + "message": "Method seurat_transferdata performs a lot better than baselines.\n Task id: task_label_projection\n Method id: seurat_transferdata\n Metric id: f1_micro\n Best score: 0.9464%\n" }, { "task_id": "task_label_projection", @@ -1983,50 +1843,50 @@ "task_id": "task_label_projection", "category": "Scaling", "name": "Best score singler f1_micro", - "value": 0.9892, - "severity": 0, - "severity_value": 0.4946, + "value": 2.485, + "severity": 1, + "severity_value": 1.2425, "code": "best_score <= 2", - "message": "Method singler performs a lot better than baselines.\n Task id: task_label_projection\n Method id: singler\n Metric id: f1_micro\n Best score: 0.9892%\n" + "message": "Method singler performs a lot better than baselines.\n Task id: task_label_projection\n Method id: singler\n Metric id: f1_micro\n Best score: 2.485%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score uce f1_micro", - "value": 0.0, + "value": -0.0293, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.0293, "code": "worst_score >= -1", - "message": "Method uce performs much worse than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_micro\n Worst score: 0.0%\n" + "message": "Method uce performs much worse than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_micro\n Worst score: -0.0293%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score uce f1_micro", - "value": 0.0234, + "value": 0.0, "severity": 0, - "severity_value": 0.0117, + "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method uce performs a lot better than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_micro\n Best score: 0.0234%\n" + "message": "Method uce performs a lot better than baselines.\n Task id: task_label_projection\n Method id: uce\n Metric id: f1_micro\n Best score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Worst score xgboost f1_micro", - "value": 0.2561, + "value": 0.0, "severity": 0, - "severity_value": -0.2561, + "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method xgboost performs much worse than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_micro\n Worst score: 0.2561%\n" + "message": "Method xgboost performs much worse than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_micro\n Worst score: 0.0%\n" }, { "task_id": "task_label_projection", "category": "Scaling", "name": "Best score xgboost f1_micro", - "value": 0.995, - "severity": 0, - "severity_value": 0.4975, + "value": 2.509, + "severity": 1, + "severity_value": 1.2545, "code": "best_score <= 2", - "message": "Method xgboost performs a lot better than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_micro\n Best score: 0.995%\n" + "message": "Method xgboost performs a lot better than baselines.\n Task id: task_label_projection\n Method id: xgboost\n Metric id: f1_micro\n Best score: 2.509%\n" } ] \ No newline at end of file diff --git a/results/label_projection/data/results.json b/results/label_projection/data/results.json index 1b62ea6b..4354bc5f 100644 --- a/results/label_projection/data/results.json +++ b/results/label_projection/data/results.json @@ -1,394 +1,4 @@ [ - { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "knn", - "metric_values": { - "accuracy": 0.9993, - "f1_macro": 0.9997, - "f1_micro": 0.9993, - "f1_weighted": 0.9993 - }, - "scaled_scores": { - "accuracy": 0.9992, - "f1_macro": 0.9997, - "f1_micro": 0.9992, - "f1_weighted": 0.9992 - }, - "mean_score": 0.9993, - "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 0, - "duration_sec": 19.1, - "cpu_pct": 155.1, - "peak_memory_mb": 24064, - "disk_read_mb": 17613, - "disk_write_mb": 171 - } - }, - { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "logistic_regression", - "metric_values": { - "accuracy": 0.9993, - "f1_macro": 0.9997, - "f1_micro": 0.9993, - "f1_weighted": 0.9993 - }, - "scaled_scores": { - "accuracy": 0.9992, - "f1_macro": 0.9997, - "f1_micro": 0.9992, - "f1_weighted": 0.9992 - }, - "mean_score": 0.9993, - "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 0, - "duration_sec": 72, - "cpu_pct": 1120.6, - "peak_memory_mb": 24167, - "disk_read_mb": 17613, - "disk_write_mb": 171 - } - }, - { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "majority_vote", - "metric_values": { - "accuracy": 0.175, - "f1_macro": 0.0298, - "f1_micro": 0.175, - "f1_weighted": 0.0521 - }, - "scaled_scores": { - "accuracy": 0.0472, - "f1_macro": 0.0071, - "f1_micro": 0.0472, - "f1_weighted": 0 - }, - "mean_score": 0.0254, - "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 0, - "duration_sec": 130, - "cpu_pct": 24.7, - "peak_memory_mb": 23962, - "disk_read_mb": 17613, - "disk_write_mb": 171 - } - }, - { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "mlp", - "metric_values": { - "accuracy": 1, - "f1_macro": 1, - "f1_micro": 1, - "f1_weighted": 1 - }, - "scaled_scores": { - "accuracy": 1, - "f1_macro": 1, - "f1_micro": 1, - "f1_weighted": 1 - }, - "mean_score": 1, - "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 0, - "duration_sec": 403, - "cpu_pct": 628.5, - "peak_memory_mb": 21402, - "disk_read_mb": 17613, - "disk_write_mb": 171 - } - }, - { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - 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"accuracy": 0.9409, - "f1_macro": 0.8881, - "f1_micro": 0.9409, - "f1_weighted": 0.9428 + "accuracy": 0.9443, + "f1_macro": 0.8883, + "f1_micro": 0.9443, + "f1_weighted": 0.9465 }, "scaled_scores": { - "accuracy": 0.9298, - "f1_macro": 0.8857, - "f1_micro": 0.9298, - "f1_weighted": 0.9402 + "accuracy": 0.9326, + "f1_macro": 0.8859, + "f1_micro": 0.934, + "f1_weighted": 0.9441 }, - "mean_score": 0.9214, + "mean_score": 0.9241, "resources": { - "submit": "2025-01-08 08:03:18", + "submit": "2025-01-09 17:04:57", "exit_code": 0, - "duration_sec": 1200, - "cpu_pct": 105.2, + "duration_sec": 1169, + "cpu_pct": 107, "peak_memory_mb": 22631, "disk_read_mb": 1332, "disk_write_mb": 1 @@ -607,18 +217,18 @@ "f1_weighted": 0.8883 }, "scaled_scores": { - "accuracy": 0.873, + "accuracy": 0.8703, "f1_macro": 0.7031, "f1_micro": 0.873, "f1_weighted": 0.8833 }, - "mean_score": 0.8331, + "mean_score": 0.8324, "resources": { - "submit": "2025-01-08 08:03:16", + "submit": "2025-01-09 17:04:57", "exit_code": 0, - 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"submit": "2025-01-08 08:03:17", + "submit": "2025-01-09 17:04:57", "exit_code": 0, - "duration_sec": 131, - "cpu_pct": 4215.5, - "peak_memory_mb": 25293, + "duration_sec": 229, + "cpu_pct": 1861, + "peak_memory_mb": 18330, "disk_read_mb": 1332, "disk_write_mb": 1 } @@ -789,18 +399,18 @@ "f1_weighted": 0.8582 }, "scaled_scores": { - "accuracy": 0.8476, - "f1_macro": 0.3871, - "f1_micro": 0.8476, + "accuracy": 0.8471, + "f1_macro": 0.3865, + "f1_micro": 0.8471, "f1_weighted": 0.8565 }, - "mean_score": 0.7347, + "mean_score": 0.7343, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:27", "exit_code": 0, - "duration_sec": 16.7, - "cpu_pct": 264.9, - "peak_memory_mb": 5632, + "duration_sec": 21.5, + "cpu_pct": 448.6, + "peak_memory_mb": 11981, "disk_read_mb": 2970, "disk_write_mb": 1 } @@ -815,18 +425,18 @@ "f1_weighted": 0.8851 }, "scaled_scores": { - "accuracy": 0.8791, - "f1_macro": 0.4398, - "f1_micro": 0.8791, + "accuracy": 0.8788, + "f1_macro": 0.4393, + "f1_micro": 0.8788, "f1_weighted": 0.8837 }, - "mean_score": 0.7704, + "mean_score": 0.7701, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:27", "exit_code": 0, - "duration_sec": 34.4, - "cpu_pct": 2301.9, - "peak_memory_mb": 8909, + "duration_sec": 70, + "cpu_pct": 912.7, + "peak_memory_mb": 6247, "disk_read_mb": 2970, "disk_write_mb": 1 } @@ -841,18 +451,18 @@ "f1_weighted": 0.0118 }, "scaled_scores": { - "accuracy": 0.0507, - "f1_macro": 0.0013, - "f1_micro": 0.0507, + "accuracy": 0.0477, + "f1_macro": 0.0004, + "f1_micro": 0.0477, "f1_weighted": 0 }, - "mean_score": 0.0257, + "mean_score": 0.0239, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:27", "exit_code": 0, - "duration_sec": 8.7, - "cpu_pct": 77, - "peak_memory_mb": 4506, + "duration_sec": 9.9, + "cpu_pct": 104.8, + "peak_memory_mb": 5837, "disk_read_mb": 2970, "disk_write_mb": 1 } @@ -861,24 +471,24 @@ "dataset_id": "cellxgene_census/gtex_v9", "method_id": "mlp", "metric_values": { - "accuracy": 0.8714, - "f1_macro": 0.3353, - "f1_micro": 0.8714, - "f1_weighted": 0.8748 + "accuracy": 0.8634, + "f1_macro": 0.3083, + "f1_micro": 0.8634, + "f1_weighted": 0.8705 }, "scaled_scores": { - "accuracy": 0.8673, - "f1_macro": 0.3307, - "f1_micro": 0.8673, - "f1_weighted": 0.8733 + "accuracy": 0.8586, + "f1_macro": 0.3029, + "f1_micro": 0.8586, + "f1_weighted": 0.869 }, - "mean_score": 0.7346, + "mean_score": 0.7223, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:27", "exit_code": 0, - "duration_sec": 323, - "cpu_pct": 673.3, - "peak_memory_mb": 4711, + "duration_sec": 485, + "cpu_pct": 689.9, + "peak_memory_mb": 6042, "disk_read_mb": 2970, "disk_write_mb": 1 } @@ -893,18 +503,18 @@ "f1_weighted": 0.7937 }, "scaled_scores": { - "accuracy": 0.7523, - "f1_macro": 0.3316, - "f1_micro": 0.7523, + "accuracy": 0.7515, + "f1_macro": 0.331, + "f1_micro": 0.7515, "f1_weighted": 0.7913 }, - "mean_score": 0.6569, + "mean_score": 0.6563, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:27", "exit_code": 0, - 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"accuracy": 0.8854, - "f1_macro": 0.4032, - "f1_micro": 0.8854, - "f1_weighted": 0.8967 + "accuracy": 0.8921, + "f1_macro": 0.3775, + "f1_micro": 0.8921, + "f1_weighted": 0.8993 }, "scaled_scores": { - "accuracy": 0.8817, - "f1_macro": 0.399, - "f1_micro": 0.8817, - "f1_weighted": 0.8955 + "accuracy": 0.8883, + "f1_macro": 0.3726, + "f1_micro": 0.8883, + "f1_weighted": 0.8981 }, - "mean_score": 0.7645, + "mean_score": 0.7618, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:27", "exit_code": 0, - "duration_sec": 579, - "cpu_pct": 100.5, - "peak_memory_mb": 41677, + "duration_sec": 538, + "cpu_pct": 100.6, + "peak_memory_mb": 41882, "disk_read_mb": 3072, "disk_write_mb": 2 } @@ -965,23 +575,23 @@ "dataset_id": "cellxgene_census/gtex_v9", "method_id": "scanvi_scarches", "metric_values": { - "accuracy": 0.8962, - "f1_macro": 0.4291, - "f1_micro": 0.8962, - "f1_weighted": 0.9054 + "accuracy": 0.8942, + "f1_macro": 0.4411, + "f1_micro": 0.8942, + "f1_weighted": 0.9033 }, "scaled_scores": { - "accuracy": 0.8929, - "f1_macro": 0.4251, - "f1_micro": 0.8929, - "f1_weighted": 0.9043 + "accuracy": 0.8904, + "f1_macro": 0.4367, + "f1_micro": 0.8904, + "f1_weighted": 0.9021 }, - "mean_score": 0.7788, + "mean_score": 0.7799, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:27", "exit_code": 0, - "duration_sec": 1536, - "cpu_pct": 101.5, + "duration_sec": 1543, + "cpu_pct": 100.9, "peak_memory_mb": 41984, "disk_read_mb": 3072, "disk_write_mb": 1 @@ -997,17 +607,17 @@ "f1_weighted": 0.6504 }, "scaled_scores": { - "accuracy": 0.6559, - "f1_macro": 0.3207, - "f1_micro": 0.6559, + "accuracy": 0.6548, + "f1_macro": 0.3201, + "f1_micro": 0.6548, "f1_weighted": 0.6462 }, - "mean_score": 0.5697, + "mean_score": 0.569, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:27", "exit_code": 0, - "duration_sec": 740, - "cpu_pct": 117.3, + "duration_sec": 1476, + "cpu_pct": 112.8, "peak_memory_mb": 34714, "disk_read_mb": 41882, "disk_write_mb": 41882 @@ -1017,26 +627,26 @@ "dataset_id": "cellxgene_census/gtex_v9", "method_id": "scimilarity_knn", "metric_values": { - "accuracy": 0.8253, - "f1_macro": 0.4563, - "f1_micro": 0.8253, - "f1_weighted": 0.8386 + "accuracy": "NA", + "f1_macro": "NA", + "f1_micro": "NA", + "f1_weighted": "NA" }, "scaled_scores": { - "accuracy": 0.8197, - "f1_macro": 0.4525, - "f1_micro": 0.8197, - "f1_weighted": 0.8367 + "accuracy": 0, + "f1_macro": 0, + "f1_micro": 0, + "f1_weighted": 0 }, - "mean_score": 0.7321, + "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 0, - "duration_sec": 508, - "cpu_pct": 128.1, - "peak_memory_mb": 33485, - "disk_read_mb": 41882, - "disk_write_mb": 41882 + "submit": "2025-01-09 17:05:27", + "exit_code": "NA", + "duration_sec": 10321, + "cpu_pct": "NA", + "peak_memory_mb": "NA", + "disk_read_mb": "NA", + "disk_write_mb": "NA" } }, { @@ -1049,18 +659,18 @@ "f1_weighted": 0.8457 }, "scaled_scores": { - "accuracy": 0.8359, - "f1_macro": 0.4146, - "f1_micro": 0.8359, + "accuracy": 0.8354, + "f1_macro": 0.414, + "f1_micro": 0.8354, "f1_weighted": 0.8438 }, - "mean_score": 0.7326, + "mean_score": 0.7322, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:27", "exit_code": 0, - "duration_sec": 230, - "cpu_pct": 104.4, - "peak_memory_mb": 23552, + "duration_sec": 379, + "cpu_pct": 102.1, + "peak_memory_mb": 20890, "disk_read_mb": 3072, "disk_write_mb": 1 } @@ -1069,26 +679,26 @@ "dataset_id": "cellxgene_census/gtex_v9", "method_id": "singler", "metric_values": { - "accuracy": 0.7903, - "f1_macro": 0.3753, - "f1_micro": 0.7903, - "f1_weighted": 0.823 + "accuracy": "NA", + "f1_macro": "NA", + "f1_micro": "NA", + "f1_weighted": "NA" }, "scaled_scores": { - "accuracy": 0.7836, - "f1_macro": 0.3709, - "f1_micro": 0.7836, - "f1_weighted": 0.8209 + "accuracy": 0, + "f1_macro": 0, + "f1_micro": 0, + "f1_weighted": 0 }, - "mean_score": 0.6898, + "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 0, - "duration_sec": 3735, - "cpu_pct": 100.6, - "peak_memory_mb": 19149, - "disk_read_mb": 3072, - "disk_write_mb": 1 + "submit": "2025-01-09 17:05:27", + "exit_code": "NA", + "duration_sec": 450, + "cpu_pct": "NA", + "peak_memory_mb": "NA", + "disk_read_mb": "NA", + "disk_write_mb": "NA" } }, { @@ -1108,11 +718,11 @@ }, "mean_score": 1, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:27", "exit_code": 0, - "duration_sec": 5.9, - "cpu_pct": 225.6, - "peak_memory_mb": 5530, + "duration_sec": 8.1, + "cpu_pct": 108.5, + "peak_memory_mb": 3072, "disk_read_mb": 411, "disk_write_mb": 1 } @@ -1120,6 +730,32 @@ { "dataset_id": "cellxgene_census/gtex_v9", "method_id": "uce", + "metric_values": { + "accuracy": 0.0056, + "f1_macro": 0.0039, + "f1_micro": 0.0056, + "f1_weighted": 0.0085 + }, + "scaled_scores": { + "accuracy": -0.0293, + "f1_macro": -0.004, + "f1_micro": -0.0293, + "f1_weighted": -0.0034 + }, + "mean_score": 0, + "resources": { + "submit": "2025-01-09 17:05:27", + "exit_code": 0, + "duration_sec": 24299, + "cpu_pct": 281.8, + "peak_memory_mb": 524084, + "disk_read_mb": 49562, + "disk_write_mb": 30618 + } + }, + { + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "xgboost", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -1134,9 +770,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:21:45", - 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"method_id": "scgpt_zero_shot", + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "method_id": "scanvi_scarches", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.9649, + "f1_macro": 0.8824, + "f1_micro": 0.9649, + "f1_weighted": 0.9642 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 0.9548, + "f1_macro": 0.875, + "f1_micro": 0.9548, + "f1_weighted": 0.9508 }, - "mean_score": 0, + "mean_score": 0.9339, "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 1, - "duration_sec": 170, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:05:57", + "exit_code": 0, + "duration_sec": 1731, + "cpu_pct": 100.7, + "peak_memory_mb": 59597, + "disk_read_mb": 13312, + "disk_write_mb": 1 } }, { - "dataset_id": "allen_brain_cell_atlas/2023_yao_mouse_brain_scrnaseq_10xv2", - "method_id": "scprint", + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "method_id": "scimilarity", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -4748,9 +1784,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:26", + "submit": "2025-01-09 17:05:57", "exit_code": 1, - "duration_sec": 170, + "duration_sec": 1024, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -4758,8 +1794,8 @@ } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "geneformer", + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "method_id": "scimilarity_knn", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -4774,9 +1810,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:19", + "submit": "2025-01-09 17:05:57", "exit_code": 1, - "duration_sec": 530, + "duration_sec": 1024, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -4784,8 +1820,8 @@ } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "scgpt_zero_shot", + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "method_id": "seurat_transferdata", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -4800,9 +1836,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:18", - "exit_code": 1, - "duration_sec": 240, + "submit": "2025-01-09 17:05:57", + "exit_code": "NA", + "duration_sec": 9421, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -4810,60 +1846,60 @@ } }, { - "dataset_id": "cellxgene_census/dkd", - "method_id": "scprint", + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "method_id": "singler", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.8104, + "f1_macro": 0.7019, + "f1_micro": 0.8104, + "f1_weighted": 0.8509 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 0.7561, + "f1_macro": 0.6832, + "f1_micro": 0.7561, + "f1_weighted": 0.7951 }, - "mean_score": 0, + "mean_score": 0.7477, "resources": { - "submit": "2025-01-08 08:03:19", - "exit_code": 1, - "duration_sec": 60, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:05:57", + "exit_code": 0, + "duration_sec": 5449, + "cpu_pct": 100.3, + "peak_memory_mb": 48128, + "disk_read_mb": 13312, + "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "geneformer", + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "method_id": "true_labels", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 1, + "f1_macro": 1, + "f1_micro": 1, + "f1_weighted": 1 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 1, + "f1_macro": 1, + "f1_micro": 1, + "f1_weighted": 1 }, - "mean_score": 0, + "mean_score": 1, "resources": { - "submit": "2025-01-08 08:03:25", - "exit_code": 1, - "duration_sec": 1380, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:05:57", + "exit_code": 0, + "duration_sec": 1.8, + "cpu_pct": 685, + "peak_memory_mb": 2765, + "disk_read_mb": 273, + "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "scgpt_zero_shot", + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "method_id": "uce", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -4878,9 +1914,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 1, - "duration_sec": 270, + "submit": "2025-01-09 17:38:10", + "exit_code": "NA", + "duration_sec": 5881, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -4888,216 +1924,216 @@ } }, { - "dataset_id": "cellxgene_census/gtex_v9", - "method_id": "scprint", + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "method_id": "xgboost", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.9618, + "f1_macro": 0.866, + "f1_micro": 0.9618, + "f1_weighted": 0.9604 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 0.9509, + "f1_macro": 0.8576, + "f1_micro": 0.9509, + "f1_weighted": 0.9456 }, - "mean_score": 0, + "mean_score": 0.9262, "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 1, - "duration_sec": 60, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:27:46", + "exit_code": 0, + "duration_sec": 292, + "cpu_pct": 4783.2, + "peak_memory_mb": 66765, + "disk_read_mb": 13312, + "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/hcla", - "method_id": "geneformer", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "knn", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.7721, + "f1_macro": 0.2696, + "f1_micro": 0.7721, + "f1_weighted": 0.7757 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 0.7651, + "f1_macro": 0.2675, + "f1_micro": 0.7651, + "f1_weighted": 0.7724 }, - "mean_score": 0, + "mean_score": 0.6425, "resources": { - "submit": "2025-01-08 08:03:27", - "exit_code": 1, - "duration_sec": 160, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:06:47", + "exit_code": 0, + "duration_sec": 14.9, + "cpu_pct": 774.7, + "peak_memory_mb": 25396, + "disk_read_mb": 19456, + "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/hcla", - "method_id": "scgpt_zero_shot", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "logistic_regression", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.7768, + "f1_macro": 0.2977, + "f1_micro": 0.7768, + "f1_weighted": 0.7793 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 0.77, + "f1_macro": 0.2957, + "f1_micro": 0.77, + "f1_weighted": 0.7761 }, - "mean_score": 0, + "mean_score": 0.653, "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 1, - "duration_sec": 160, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:06:47", + "exit_code": 0, + "duration_sec": 237, + "cpu_pct": 995.6, + "peak_memory_mb": 26522, + "disk_read_mb": 19456, + "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/hcla", - "method_id": "scprint", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "majority_vote", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.0892, + "f1_macro": 0.0029, + "f1_micro": 0.0892, + "f1_weighted": 0.0146 }, "scaled_scores": { - "accuracy": 0, + "accuracy": 0.0615, "f1_macro": 0, - "f1_micro": 0, + "f1_micro": 0.0615, "f1_weighted": 0 }, - "mean_score": 0, + "mean_score": 0.0307, "resources": { - "submit": "2025-01-08 08:03:27", - "exit_code": 1, - "duration_sec": 180, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:06:46", + "exit_code": 0, + "duration_sec": 137, + "cpu_pct": 25.1, + "peak_memory_mb": 22324, + "disk_read_mb": 19456, + "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/hypomap", - "method_id": "geneformer", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "mlp", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.7344, + "f1_macro": 0.2422, + "f1_micro": 0.7344, + "f1_weighted": 0.7243 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 0.7263, + "f1_macro": 0.24, + "f1_micro": 0.7263, + "f1_weighted": 0.7202 }, - "mean_score": 0, + "mean_score": 0.6032, "resources": { - "submit": "2025-01-08 08:03:20", - "exit_code": 1, - "duration_sec": 120, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:06:47", + "exit_code": 0, + "duration_sec": 799, + "cpu_pct": 1183.3, + "peak_memory_mb": 25805, + "disk_read_mb": 19456, + "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/hypomap", - "method_id": "scgpt_zero_shot", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "naive_bayes", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.7496, + "f1_macro": 0.2954, + "f1_micro": 0.7496, + "f1_weighted": 0.7566 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 0.742, + "f1_macro": 0.2934, + "f1_micro": 0.742, + "f1_weighted": 0.753 }, - "mean_score": 0, + "mean_score": 0.6326, "resources": { - "submit": "2025-01-08 08:03:19", - "exit_code": 1, - "duration_sec": 120, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:06:47", + "exit_code": 0, + "duration_sec": 20.2, + "cpu_pct": 122.3, + "peak_memory_mb": 22631, + "disk_read_mb": 19456, + "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/hypomap", - "method_id": "scprint", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "random_labels", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.0296, + "f1_macro": 0.0032, + "f1_micro": 0.0296, + "f1_weighted": 0.0426 }, "scaled_scores": { "accuracy": 0, - "f1_macro": 0, + "f1_macro": 0.0003, "f1_micro": 0, - "f1_weighted": 0 + "f1_weighted": 0.0284 }, - "mean_score": 0, + "mean_score": 0.0072, "resources": { - "submit": "2025-01-08 08:03:18", - "exit_code": 1, - "duration_sec": 130, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:06:47", + "exit_code": 0, + "duration_sec": 110, + "cpu_pct": 24.7, + "peak_memory_mb": 25088, + "disk_read_mb": 19456, + "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/immune_cell_atlas", - "method_id": "geneformer", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "scanvi", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.8277, + "f1_macro": 0.2992, + "f1_micro": 0.8277, + "f1_weighted": 0.8246 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 0.8225, + "f1_macro": 0.2971, + "f1_micro": 0.8225, + "f1_weighted": 0.822 }, - "mean_score": 0, + "mean_score": 0.691, "resources": { - "submit": "2025-01-08 08:03:26", - "exit_code": 1, - "duration_sec": 1730, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 18:01:20", + "exit_code": 0, + "duration_sec": 1421, + "cpu_pct": 99.9, + "peak_memory_mb": 127693, + "disk_read_mb": 19559, + "disk_write_mb": 3 } }, { - "dataset_id": "cellxgene_census/immune_cell_atlas", - "method_id": "scgpt_zero_shot", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "scanvi_scarches", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5112,9 +2148,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:27", - "exit_code": 1, - "duration_sec": 160, + "submit": "2025-01-09 18:57:30", + "exit_code": "NA", + "duration_sec": 1710, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5122,8 +2158,8 @@ } }, { - "dataset_id": "cellxgene_census/immune_cell_atlas", - "method_id": "scprint", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "scimilarity", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5138,9 +2174,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:27", - "exit_code": 1, - "duration_sec": 110, + "submit": "2025-01-09 17:55:20", + "exit_code": "NA", + "duration_sec": 2301, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5148,60 +2184,60 @@ } }, { - "dataset_id": "cellxgene_census/mouse_pancreas_atlas", - "method_id": "geneformer", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "scimilarity_knn", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.8138, + "f1_macro": 0.3342, + "f1_micro": 0.8138, + "f1_weighted": 0.8184 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 0.8081, + "f1_macro": 0.3323, + "f1_micro": 0.8081, + "f1_weighted": 0.8157 }, - "mean_score": 0, + "mean_score": 0.691, "resources": { - "submit": "2025-01-08 08:03:46", - "exit_code": 1, - "duration_sec": 130, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 18:02:40", + "exit_code": 0, + "duration_sec": 502, + "cpu_pct": 296.9, + "peak_memory_mb": 83456, + "disk_read_mb": 58368, + "disk_write_mb": 41882 } }, { - "dataset_id": "cellxgene_census/mouse_pancreas_atlas", - "method_id": "scgpt_zero_shot", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "seurat_transferdata", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 0.8534, + "f1_macro": 0.4562, + "f1_micro": 0.8534, + "f1_weighted": 0.8527 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 0.8489, + "f1_macro": 0.4546, + "f1_micro": 0.8489, + "f1_weighted": 0.8505 }, - "mean_score": 0, + "mean_score": 0.7507, "resources": { - "submit": "2025-01-08 08:03:46", - "exit_code": 1, - "duration_sec": 130, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:06:47", + "exit_code": 0, + "duration_sec": 684, + "cpu_pct": 102.2, + "peak_memory_mb": 96564, + "disk_read_mb": 19559, + "disk_write_mb": 1 } }, { - "dataset_id": "cellxgene_census/mouse_pancreas_atlas", - "method_id": "scprint", + "dataset_id": "cellxgene_census/tabula_sapiens", + "method_id": "singler", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5216,9 +2252,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:46", - "exit_code": 1, - "duration_sec": 140, + "submit": "2025-01-09 17:06:47", + "exit_code": "NA", + "duration_sec": 8985, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5227,33 +2263,33 @@ }, { "dataset_id": "cellxgene_census/tabula_sapiens", - "method_id": "geneformer", + "method_id": "true_labels", "metric_values": { - "accuracy": "NA", - "f1_macro": "NA", - "f1_micro": "NA", - "f1_weighted": "NA" + "accuracy": 1, + "f1_macro": 1, + "f1_micro": 1, + "f1_weighted": 1 }, "scaled_scores": { - "accuracy": 0, - "f1_macro": 0, - "f1_micro": 0, - "f1_weighted": 0 + "accuracy": 1, + "f1_macro": 1, + "f1_micro": 1, + "f1_weighted": 1 }, - "mean_score": 0, + "mean_score": 1, "resources": { - "submit": "2025-01-08 09:18:25", - "exit_code": 143, - "duration_sec": 14412, - "cpu_pct": "NA", - "peak_memory_mb": "NA", - "disk_read_mb": "NA", - "disk_write_mb": "NA" + "submit": "2025-01-09 17:06:46", + "exit_code": 0, + "duration_sec": 19.3, + "cpu_pct": 70.6, + "peak_memory_mb": 8807, + "disk_read_mb": 3277, + "disk_write_mb": 1 } }, { "dataset_id": "cellxgene_census/tabula_sapiens", - "method_id": "scgpt_zero_shot", + "method_id": "uce", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5268,9 +2304,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 09:08:45", - "exit_code": 1, - "duration_sec": 3650, + "submit": "2025-01-09 17:49:50", + "exit_code": 143, + "duration_sec": 28811, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5279,7 +2315,7 @@ }, { "dataset_id": "cellxgene_census/tabula_sapiens", - "method_id": "scprint", + "method_id": "xgboost", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5294,9 +2330,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:56", - "exit_code": 1, - "duration_sec": 180, + "submit": "2025-01-09 17:38:50", + "exit_code": "NA", + "duration_sec": 1600, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5304,8 +2340,8 @@ } }, { - "dataset_id": "openproblems_v1/cengen", - "method_id": "geneformer", + "dataset_id": "cellxgene_census/dkd", + "method_id": "scgpt_zero_shot", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5320,9 +2356,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:56", + "submit": "2025-01-09 17:04:57", "exit_code": 1, - "duration_sec": 30.1, + "duration_sec": 140, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5330,8 +2366,8 @@ } }, { - "dataset_id": "openproblems_v1/cengen", - "method_id": "scgpt_zero_shot", + "dataset_id": "cellxgene_census/dkd", + "method_id": "scprint", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5346,9 +2382,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:56", + "submit": "2025-01-09 17:04:57", "exit_code": 1, - "duration_sec": 30, + "duration_sec": 50.2, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5356,8 +2392,8 @@ } }, { - "dataset_id": "openproblems_v1/cengen", - "method_id": "scprint", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "scgpt_zero_shot", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5372,9 +2408,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:56", + "submit": "2025-01-09 17:05:27", "exit_code": 1, - "duration_sec": 40.6, + "duration_sec": 670, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5382,8 +2418,8 @@ } }, { - "dataset_id": "openproblems_v1/immune_cells", - "method_id": "geneformer", + "dataset_id": "cellxgene_census/gtex_v9", + "method_id": "scprint", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5398,9 +2434,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:46", + "submit": "2025-01-09 17:05:27", "exit_code": 1, - "duration_sec": 30.5, + "duration_sec": 50.1, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5408,7 +2444,7 @@ } }, { - "dataset_id": "openproblems_v1/immune_cells", + "dataset_id": "cellxgene_census/hypomap", "method_id": "scgpt_zero_shot", "metric_values": { "accuracy": "NA", @@ -5424,9 +2460,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:47", + "submit": "2025-01-09 17:05:57", "exit_code": 1, - "duration_sec": 20.2, + "duration_sec": 130, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5434,7 +2470,7 @@ } }, { - "dataset_id": "openproblems_v1/immune_cells", + "dataset_id": "cellxgene_census/hypomap", "method_id": "scprint", "metric_values": { "accuracy": "NA", @@ -5450,9 +2486,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:47", + "submit": "2025-01-09 17:05:57", "exit_code": 1, - "duration_sec": 40, + "duration_sec": 140, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5460,8 +2496,8 @@ } }, { - "dataset_id": "openproblems_v1/pancreas", - "method_id": "geneformer", + "dataset_id": "cellxgene_census/immune_cell_atlas", + "method_id": "scgpt_zero_shot", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5476,9 +2512,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:56", + "submit": "2025-01-09 17:04:48", "exit_code": 1, - "duration_sec": 30.2, + "duration_sec": 420, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5486,8 +2522,8 @@ } }, { - "dataset_id": "openproblems_v1/pancreas", - "method_id": "scgpt_zero_shot", + "dataset_id": "cellxgene_census/immune_cell_atlas", + "method_id": "scprint", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5502,9 +2538,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:57", + "submit": "2025-01-09 17:04:48", "exit_code": 1, - "duration_sec": 29.9, + "duration_sec": 190, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5512,8 +2548,8 @@ } }, { - "dataset_id": "openproblems_v1/pancreas", - "method_id": "scprint", + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "method_id": "scgpt_zero_shot", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5528,9 +2564,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:03:56", + "submit": "2025-01-09 17:05:57", "exit_code": 1, - "duration_sec": 40.1, + "duration_sec": 130, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5538,8 +2574,8 @@ } }, { - "dataset_id": "openproblems_v1/zebrafish", - "method_id": "geneformer", + "dataset_id": "cellxgene_census/mouse_pancreas_atlas", + "method_id": "scprint", "metric_values": { "accuracy": "NA", "f1_macro": "NA", @@ -5554,9 +2590,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:05:36", - "exit_code": 1, - "duration_sec": 30.1, + "submit": "2025-01-09 17:05:57", + "exit_code": "NA", + "duration_sec": 10021, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5564,7 +2600,7 @@ } }, { - "dataset_id": "openproblems_v1/zebrafish", + "dataset_id": "cellxgene_census/tabula_sapiens", "method_id": "scgpt_zero_shot", "metric_values": { "accuracy": "NA", @@ -5580,9 +2616,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:05:36", + "submit": "2025-01-09 17:51:10", "exit_code": 1, - "duration_sec": 30.1, + "duration_sec": 6331, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA", @@ -5590,7 +2626,7 @@ } }, { - "dataset_id": "openproblems_v1/zebrafish", + "dataset_id": "cellxgene_census/tabula_sapiens", "method_id": "scprint", "metric_values": { "accuracy": "NA", @@ -5606,9 +2642,9 @@ }, "mean_score": 0, "resources": { - "submit": "2025-01-08 08:05:36", + "submit": "2025-01-09 17:06:46", "exit_code": 1, - "duration_sec": 40.1, + "duration_sec": 190, "cpu_pct": "NA", "peak_memory_mb": "NA", "disk_read_mb": "NA",