From f8d46b05fc3d255e598f62d9a6f5fea4800fae01 Mon Sep 17 00:00:00 2001 From: Robrecht Cannoodt Date: Wed, 26 Feb 2025 19:39:22 +0100 Subject: [PATCH 1/5] add grn inference page --- results/grn_inference/data/dataset_info.json | 12 + results/grn_inference/data/method_info.json | 162 +++ .../data/metric_execution_info.json | 142 +++ results/grn_inference/data/metric_info.json | 122 +++ .../grn_inference/data/quality_control.json | 942 ++++++++++++++++++ results/grn_inference/data/results.json | 242 +++++ results/grn_inference/data/state.yaml | 9 + results/grn_inference/data/task_info.json | 50 + results/grn_inference/index.qmd | 21 + 9 files changed, 1702 insertions(+) create mode 100644 results/grn_inference/data/dataset_info.json create mode 100644 results/grn_inference/data/method_info.json create mode 100644 results/grn_inference/data/metric_execution_info.json create mode 100644 results/grn_inference/data/metric_info.json create mode 100644 results/grn_inference/data/quality_control.json create mode 100644 results/grn_inference/data/results.json create mode 100644 results/grn_inference/data/state.yaml create mode 100644 results/grn_inference/data/task_info.json create mode 100644 results/grn_inference/index.qmd diff --git a/results/grn_inference/data/dataset_info.json b/results/grn_inference/data/dataset_info.json new file mode 100644 index 00000000..482a46cb --- /dev/null +++ b/results/grn_inference/data/dataset_info.json @@ -0,0 +1,12 @@ +[ + { + "dataset_id": "op", + "dataset_name": "OPSCA", + "dataset_summary": "RNA-seq data from the OPSCA dataset", + "dataset_description": null, + "data_reference": null, + "data_url": null, + "date_created": "23-02-2025", + "file_size": 10781372 + } +] diff --git a/results/grn_inference/data/method_info.json b/results/grn_inference/data/method_info.json new file mode 100644 index 00000000..c4b9f5ca --- /dev/null +++ b/results/grn_inference/data/method_info.json @@ -0,0 +1,162 @@ +[ + { + "task_id": "control_methods", + "method_id": "pearson_corr", + "method_name": "pearson_corr", + "method_summary": "Baseline based on correlation", + "method_description": "Baseline GRN inference method using Pearson correlation.\n", + "is_baseline": false, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_grn_inference", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/pearson_corr:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/pearson_corr", + "code_version": "build_main", + "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" + }, + { + "task_id": "control_methods", + "method_id": "negative_control", + "method_name": "Negative control", + "method_summary": "Source-target links based on random assignment", + "method_description": "Randomly assigns regulatory links to tf-target links with a given tf and target list. This is to perform near random.\n", + "is_baseline": false, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_grn_inference", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/negative_control:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/negative_control", + "code_version": "build_main", + "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" + }, + { + "task_id": "control_methods", + "method_id": "positive_control", + "method_name": "positive_control", + "method_summary": "Baseline based on correlation", + "method_description": "Baseline model based on Pearson correlation that uses both inference and evaluation dataset to infer the GRN.\n", + "is_baseline": false, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_grn_inference", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/positive_control:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/positive_control", + "code_version": "build_main", + "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" + }, + { + "task_id": "grn_methods", + "method_id": "portia", + "method_name": "portia", + "method_summary": "GRN inference using PORTIA", + "method_description": "GRN inference using PORTIA.\n", + "is_baseline": false, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_grn_inference", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/portia:build_main", + "implementation_url": 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task_grn_inference\n Field: method_name\n" + }, + { + "task_id": "task_grn_inference", + "category": "Method info", + "name": "Pct 'method_summary' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(method_info, field)", + "message": "Method metadata field 'method_summary' should be defined\n Task id: task_grn_inference\n Field: method_summary\n" + }, + { + "task_id": "task_grn_inference", + "category": "Method info", + "name": "Pct 'paper_reference' missing", + "value": 1.0, + "severity": 2, + "severity_value": 3.0, + "code": "percent_missing(method_info, field)", + "message": "Method metadata field 'paper_reference' should be defined\n Task id: task_grn_inference\n Field: paper_reference\n" + }, + { + "task_id": "task_grn_inference", + "category": "Method info", + "name": "Pct 'is_baseline' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(method_info, field)", + "message": "Method metadata field 'is_baseline' should be defined\n Task id: task_grn_inference\n Field: is_baseline\n" + }, + { + "task_id": "task_grn_inference", + "category": "Metric info", + "name": "Pct 'task_id' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(metric_info, field)", + "message": "Metric metadata field 'task_id' should be defined\n Task id: task_grn_inference\n Field: task_id\n" + }, + { + "task_id": "task_grn_inference", + "category": "Metric info", + "name": "Pct 'commit_sha' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(metric_info, field)", + "message": "Metric metadata field 'commit_sha' should be defined\n Task id: task_grn_inference\n Field: commit_sha\n" + }, + { + "task_id": "task_grn_inference", + "category": "Metric info", + "name": "Pct 'metric_id' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(metric_info, field)", + "message": "Metric metadata field 'metric_id' should be defined\n Task id: task_grn_inference\n Field: metric_id\n" + }, + { + "task_id": "task_grn_inference", + "category": "Metric info", + "name": "Pct 'metric_name' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(metric_info, field)", + "message": "Metric metadata field 'metric_name' should be defined\n Task id: task_grn_inference\n Field: metric_name\n" + }, + { + "task_id": "task_grn_inference", + "category": "Metric info", + "name": "Pct 'metric_summary' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(metric_info, field)", + "message": "Metric metadata field 'metric_summary' should be defined\n Task id: task_grn_inference\n Field: metric_summary\n" + }, + { + "task_id": "task_grn_inference", + "category": "Metric info", + "name": "Pct 'paper_reference' missing", + "value": 1.0, + "severity": 2, + "severity_value": 3.0, + "code": "percent_missing(metric_info, field)", + "message": "Metric metadata field 'paper_reference' should be defined\n Task id: task_grn_inference\n Field: paper_reference\n" + }, + { + "task_id": "task_grn_inference", + "category": "Metric info", + "name": "Pct 'maximize' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(metric_info, field)", + "message": "Metric metadata field 'maximize' should be defined\n Task id: task_grn_inference\n Field: maximize\n" + }, + { + "task_id": "task_grn_inference", + "category": "Dataset info", + "name": "Pct 'task_id' missing", + "value": 1.0, + "severity": 2, + "severity_value": 3.0, + "code": "percent_missing(dataset_info, field)", + "message": "Dataset metadata field 'task_id' should be defined\n Task id: task_grn_inference\n Field: task_id\n" + }, + { + "task_id": "task_grn_inference", + "category": "Dataset info", + "name": "Pct 'dataset_id' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(dataset_info, field)", + "message": "Dataset metadata field 'dataset_id' should be defined\n Task id: task_grn_inference\n Field: dataset_id\n" + }, + { + "task_id": "task_grn_inference", + "category": "Dataset info", + "name": "Pct 'dataset_name' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(dataset_info, field)", + "message": "Dataset metadata field 'dataset_name' should be defined\n Task id: task_grn_inference\n Field: dataset_name\n" + }, + { + "task_id": "task_grn_inference", + "category": "Dataset info", + "name": "Pct 'dataset_summary' missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "percent_missing(dataset_info, field)", + "message": "Dataset metadata field 'dataset_summary' should be defined\n Task id: task_grn_inference\n Field: dataset_summary\n" + }, + { + "task_id": "task_grn_inference", + "category": "Dataset info", + "name": "Pct 'data_reference' missing", + "value": 1.0, + "severity": 2, + "severity_value": 3.0, + "code": "percent_missing(dataset_info, field)", + "message": "Dataset metadata field 'data_reference' should be defined\n Task id: task_grn_inference\n Field: data_reference\n" + }, + { + "task_id": "task_grn_inference", + "category": "Dataset info", + "name": "Pct 'data_url' missing", + "value": 1.0, + "severity": 2, + "severity_value": 3.0, + "code": "percent_missing(dataset_info, field)", + "message": "Dataset metadata field 'data_url' should be defined\n Task id: task_grn_inference\n Field: data_url\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw data", + "name": "Number of results", + "value": 10, + "severity": 0, + "severity_value": 0.0, + "code": "len(results) == len(method_info) * len(metric_info) * len(dataset_info)", + "message": "Number of results should be equal to #methods × #metrics × #datasets.\n Task id: task_grn_inference\n Number of results: 10\n Number of methods: 10\n Number of metrics: 8\n Number of datasets: 1\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Metric 'r1_all' %missing", + "value": 0.5, + "severity": 3, + "severity_value": 5.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r1_all\n Percentage missing: 50%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Metric 'r1_grn' %missing", + "value": 0.5, + "severity": 3, + "severity_value": 5.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r1_grn\n Percentage missing: 50%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Metric 'r2-theta-0.0' %missing", + "value": 0.5, + "severity": 3, + "severity_value": 5.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r2-theta-0.0\n Percentage missing: 50%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Metric 'r2-theta-0.5' %missing", + "value": 0.5, + "severity": 3, + "severity_value": 5.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r2-theta-0.5\n Percentage missing: 50%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Metric 'r2-theta-1.0' %missing", + "value": 0.5, + "severity": 3, + "severity_value": 5.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r2-theta-1.0\n Percentage missing: 50%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Metric 'ws-theta-0.0' %missing", + "value": 1.0, + "severity": 3, + "severity_value": 10.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: ws-theta-0.0\n Percentage missing: 100%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Metric 'ws-theta-0.5' %missing", + "value": 1.0, + "severity": 3, + "severity_value": 10.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: ws-theta-0.5\n Percentage missing: 100%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Metric 'ws-theta-1.0' %missing", + "value": 1.0, + "severity": 3, + "severity_value": 10.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: ws-theta-1.0\n Percentage missing: 100%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'pearson_corr' %missing", + "value": 0.375, + "severity": 3, + "severity_value": 3.75, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: pearson_corr\n Percentage missing: 38%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'negative_control' %missing", + "value": 0.375, + "severity": 3, + "severity_value": 3.75, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: negative_control\n Percentage missing: 38%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'positive_control' %missing", + "value": 0.375, + "severity": 3, + "severity_value": 3.75, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: positive_control\n Percentage missing: 38%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'portia' %missing", + "value": 0.375, + "severity": 3, + "severity_value": 3.75, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: portia\n Percentage missing: 38%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'ppcor' %missing", + "value": 0.375, + "severity": 3, + "severity_value": 3.75, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: ppcor\n Percentage missing: 38%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'scenic' %missing", + "value": 1.0, + "severity": 3, + "severity_value": 10.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scenic\n Percentage missing: 100%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'scenicplus' %missing", + "value": 1.0, + "severity": 3, + "severity_value": 10.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scenicplus\n Percentage missing: 100%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'scprint' %missing", + "value": 1.0, + "severity": 3, + "severity_value": 10.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scprint\n Percentage missing: 100%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'grnboost2' %missing", + "value": 1.0, + "severity": 3, + "severity_value": 10.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: grnboost2\n Percentage missing: 100%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'scglue' %missing", + "value": 1.0, + "severity": 3, + "severity_value": 10.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scglue\n Percentage missing: 100%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Dataset 'op' %missing", + "value": 0.6875, + "severity": 3, + "severity_value": 6.875, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n dataset id: op\n Percentage missing: 69%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr r1_all", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_all\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr r1_all", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_all\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control r1_all", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_all\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control r1_all", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_all\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control r1_all", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_all\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control r1_all", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_all\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia r1_all", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_all\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia r1_all", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_all\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor r1_all", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_all\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor r1_all", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_all\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr r1_grn", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_grn\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr r1_grn", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_grn\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control r1_grn", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_grn\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control r1_grn", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_grn\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control r1_grn", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_grn\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control r1_grn", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_grn\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia r1_grn", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_grn\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia r1_grn", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_grn\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor r1_grn", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_grn\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor r1_grn", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_grn\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-1.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-1.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-1.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-1.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-1.0\n Best score: 0%\n" + } +] \ No newline at end of file diff --git a/results/grn_inference/data/results.json b/results/grn_inference/data/results.json new file mode 100644 index 00000000..96dafd77 --- /dev/null +++ b/results/grn_inference/data/results.json @@ -0,0 +1,242 @@ +[ + { + "dataset_id": "op", + "method_id": "negative_control", + "metric_values": { + "r1_all": -0.0013, + "r1_grn": -0.0165, + "r2-theta-0.0": 0.18, + "r2-theta-0.5": 0.2582, + "r2-theta-1.0": 0.2977 + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0 + }, + "mean_score": 0, + "resources": {} + }, + { + "dataset_id": "op", + "method_id": "pearson_corr", + "metric_values": { + "r1_all": -0.0027, + "r1_grn": -0.0365, + "r2-theta-0.0": 0.103, + "r2-theta-0.5": 0.2503, + "r2-theta-1.0": 0.2976 + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0 + }, + "mean_score": 0, + "resources": {} + }, + { + "dataset_id": "op", + "method_id": "portia", + "metric_values": { + "r1_all": 0.0139, + "r1_grn": 0.1832, + "r2-theta-0.0": 0.2143, + "r2-theta-0.5": 0.2445, + "r2-theta-1.0": 0.295 + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0 + }, + "mean_score": 0, + "resources": {} + }, + { + "dataset_id": "op", + "method_id": "positive_control", + "metric_values": { + "r1_all": -0.0027, + "r1_grn": -0.0365, + "r2-theta-0.0": 0.103, + "r2-theta-0.5": 0.2503, + "r2-theta-1.0": 0.2976 + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0 + }, + "mean_score": 0, + "resources": {} + }, + { + "dataset_id": "op", + "method_id": "ppcor", + "metric_values": { + "r1_all": 0.0093, + "r1_grn": 0.1225, + "r2-theta-0.0": 0.1212, + "r2-theta-0.5": 0.2437, + "r2-theta-1.0": 0.298 + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0 + }, + "mean_score": 0, + "resources": {} + }, + { + "dataset_id": null, + "method_id": "negative_control", + "metric_values": { + "r1_all": "NA", + "r1_grn": "NA", + "r2-theta-0.0": "NA", + "r2-theta-0.5": "NA", + "r2-theta-1.0": "NA" + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0 + }, + "mean_score": 0, + "resources": { + "submit": "2025-02-23 21:31:09", + "exit_code": 0, + "duration_sec": 3, + "cpu_pct": 335.3667, + "peak_memory_mb": 1434, + "disk_read_mb": 45, + "disk_write_mb": 16 + } + }, + { + "dataset_id": null, + "method_id": "pearson_corr", + "metric_values": { + "r1_all": "NA", + "r1_grn": "NA", + "r2-theta-0.0": "NA", + "r2-theta-0.5": "NA", + "r2-theta-1.0": "NA" + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0 + }, + "mean_score": 0, + "resources": { + "submit": "2025-02-23 21:31:09", + "exit_code": 0, + "duration_sec": 8, + "cpu_pct": 277.25, + "peak_memory_mb": 3482, + "disk_read_mb": 71, + "disk_write_mb": 17 + } + }, + { + "dataset_id": null, + "method_id": "portia", + "metric_values": { + "r1_all": "NA", + "r1_grn": "NA", + "r2-theta-0.0": "NA", + "r2-theta-0.5": "NA", + "r2-theta-1.0": "NA" + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0 + }, + "mean_score": 0, + "resources": { + "submit": "2025-02-23 21:31:09", + "exit_code": 0, + "duration_sec": 7.8, + "cpu_pct": 481.559, + "peak_memory_mb": 3687, + "disk_read_mb": 70, + "disk_write_mb": 17 + } + }, + { + "dataset_id": null, + "method_id": "positive_control", + "metric_values": { + "r1_all": "NA", + "r1_grn": "NA", + "r2-theta-0.0": "NA", + "r2-theta-0.5": "NA", + "r2-theta-1.0": "NA" + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0 + }, + "mean_score": 0, + "resources": { + "submit": "2025-02-23 21:31:09", + "exit_code": 0, + "duration_sec": 7.3, + "cpu_pct": 252.7123, + "peak_memory_mb": 3482, + "disk_read_mb": 71, + "disk_write_mb": 17 + } + }, + { + "dataset_id": null, + "method_id": "ppcor", + "metric_values": { + "r1_all": "NA", + "r1_grn": "NA", + "r2-theta-0.0": "NA", + "r2-theta-0.5": "NA", + "r2-theta-1.0": "NA" + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0 + }, + "mean_score": 0, + "resources": { + "submit": "2025-02-23 21:31:09", + "exit_code": 0, + "duration_sec": 78, + "cpu_pct": 107.7051, + "peak_memory_mb": 3789, + "disk_read_mb": 47, + "disk_write_mb": 10 + } + } +] diff --git a/results/grn_inference/data/state.yaml b/results/grn_inference/data/state.yaml new file mode 100644 index 00000000..abbb0fc1 --- /dev/null +++ b/results/grn_inference/data/state.yaml @@ -0,0 +1,9 @@ +id: process +output_scores: !file results.json +output_method_info: !file method_info.json +output_metric_info: !file metric_info.json +output_dataset_info: !file dataset_info.json +output_task_info: !file task_info.json +output_qc: !file quality_control.json +output_metric_execution_info: !file metric_execution_info.json + diff --git a/results/grn_inference/data/task_info.json b/results/grn_inference/data/task_info.json new file mode 100644 index 00000000..3cb39f3d --- /dev/null +++ b/results/grn_inference/data/task_info.json @@ -0,0 +1,50 @@ +{ + "task_id": "task_grn_inference", + "commit_sha": null, + "task_name": "GRN Inference", + "task_summary": "Benchmarking GRN inference methods\n", + "task_description": "\ngeneRNIB is a living benchmark platform for GRN inference. This platform provides curated datasets for GRN inference and evaluation, standardized evaluation protocols and metrics, computational infrastructure, and a dynamically updated leaderboard to track state-of-the-art methods. It runs novel GRNs in the cloud, offers competition scores, and stores them for future comparisons, reflecting new developments over time.\n\nThe platform supports the integration of new inference methods, datasets and protocols. When a new feature is added, previously evaluated GRNs are re-assessed, and the leaderboard is updated accordingly. The aim is to evaluate both the accuracy and completeness of inferred GRNs. It is designed for both single-modality and multi-omics GRN inference. \n\nIn the current version, geneRNIB contains 11 inference methods including both single and multi-omics, 8 evalation metrics, and five datasets (OPSCA, Nakatake, Norman, Adamson, and Replogle). \n\nSee our publication for the details of methods. \n", + "repo": "https://github.com/openproblems-bio/task_grn_inference", + "issue_tracker": "https://github.com/openproblems-bio/task_grn_inference/issues", + "authors": [ + { + "name": "Jalil Nourisa", + "roles": "author", + "info": { + "github": "janursa", + "orcid": "0000-0002-7539-4396" + } + }, + { + "name": "Robrecht Cannoodt", + "roles": "author", + "info": { + "github": "rcannood", + "orcid": "0000-0003-3641-729X" + } + }, + { + "name": "Antoine Passimier", + "roles": "contributor", + "info": { + "github": "AntoinePassemiers" + } + }, + { + "name": "Marco Stock", + "roles": "contributor", + "info": { + "github": "stkmrc" + } + }, + { + "name": "Christian Arnold", + "roles": "contributor", + "info": { + "github": "chrarnold" + } + } + ], + "version": "build_main", + "license": "MIT" +} diff --git a/results/grn_inference/index.qmd b/results/grn_inference/index.qmd new file mode 100644 index 00000000..54b383fb --- /dev/null +++ b/results/grn_inference/index.qmd @@ -0,0 +1,21 @@ +--- +title: "GRN Inference" +subtitle: "Benchmarking GRN inference methods +" +image: thumbnail.svg +page-layout: full +css: ../_include/task_template.css +engine: knitr +fig-cap-location: bottom +citation-location: document +bibliography: + - library.bib + - ../../library.bib +toc: false +--- +```{r} +#| include: false +params <- list(data_dir = "results/grn_inference/data") +params <- list(data_dir = "./data") +``` +{{< include ../_include/_task_template.qmd >}} From 7575922fbcc1d047d83c9f243d86e516d4cdc9e8 Mon Sep 17 00:00:00 2001 From: Robrecht Cannoodt Date: Wed, 26 Feb 2025 20:52:39 +0100 Subject: [PATCH 2/5] manual fixes to data --- results/grn_inference/data/method_info.json | 26 ++++++++++----------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/results/grn_inference/data/method_info.json b/results/grn_inference/data/method_info.json index c4b9f5ca..cc743587 100644 --- a/results/grn_inference/data/method_info.json +++ b/results/grn_inference/data/method_info.json @@ -1,11 +1,11 @@ [ { - "task_id": "control_methods", + "task_id": "grn_inference", "method_id": "pearson_corr", "method_name": "pearson_corr", "method_summary": "Baseline based on correlation", "method_description": "Baseline GRN inference method using Pearson correlation.\n", - "is_baseline": false, + "is_baseline": true, "references_doi": null, "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", @@ -16,12 +16,12 @@ "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "control_methods", + "task_id": "grn_inference", "method_id": "negative_control", "method_name": "Negative control", "method_summary": "Source-target links based on random assignment", "method_description": "Randomly assigns regulatory links to tf-target links with a given tf and target list. This is to perform near random.\n", - "is_baseline": false, + "is_baseline": true, "references_doi": null, "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", @@ -32,12 +32,12 @@ "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "control_methods", + "task_id": "grn_inference", "method_id": "positive_control", "method_name": "positive_control", "method_summary": "Baseline based on correlation", "method_description": "Baseline model based on Pearson correlation that uses both inference and evaluation dataset to infer the GRN.\n", - "is_baseline": false, + "is_baseline": true, "references_doi": null, "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", @@ -48,7 +48,7 @@ "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_methods", + "task_id": "grn_inference", "method_id": "portia", "method_name": "portia", "method_summary": "GRN inference using PORTIA", @@ -64,7 +64,7 @@ "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_methods", + "task_id": "grn_inference", "method_id": "ppcor", "method_name": "ppcor", "method_summary": "GRN inference using PPCOR", @@ -80,7 +80,7 @@ "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_methods", + "task_id": "grn_inference", "method_id": "scenic", "method_name": "scenic", "method_summary": "GRN inference using scenic", @@ -96,7 +96,7 @@ "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_methods", + "task_id": "grn_inference", "method_id": "scenicplus", "method_name": "scenicplus", "method_summary": "GRN inference using scenicplus", @@ -112,7 +112,7 @@ "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_methods", + "task_id": "grn_inference", "method_id": "scprint", "method_name": "scprint", "method_summary": "GRN inference using scPRINT", @@ -128,7 +128,7 @@ "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_methods", + "task_id": "grn_inference", "method_id": "grnboost2", "method_name": "grnboost2", "method_summary": "GRN inference using GRNBoost2", @@ -144,7 +144,7 @@ "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_methods", + "task_id": "grn_inference", "method_id": "scglue", "method_name": "scglue", "method_summary": "GRN inference using scglue", From f72a7f2bf5f5b5ed75aa17fdccfef95da7493a5c Mon Sep 17 00:00:00 2001 From: Robrecht Cannoodt Date: Mon, 3 Mar 2025 14:25:01 +0100 Subject: [PATCH 3/5] update results --- results/grn_inference/data/dataset_info.json | 42 +- results/grn_inference/data/method_info.json | 128 +- .../data/metric_execution_info.json | 1806 +++++++++++++- results/grn_inference/data/metric_info.json | 48 +- .../grn_inference/data/quality_control.json | 2134 +++++++++++++++-- results/grn_inference/data/results.json | 1982 ++++++++++++++- results/grn_inference/data/task_info.json | 2 +- 7 files changed, 5670 insertions(+), 472 deletions(-) diff --git a/results/grn_inference/data/dataset_info.json b/results/grn_inference/data/dataset_info.json index 482a46cb..eaeb036c 100644 --- a/results/grn_inference/data/dataset_info.json +++ b/results/grn_inference/data/dataset_info.json @@ -6,7 +6,47 @@ "dataset_description": null, "data_reference": null, "data_url": null, - "date_created": "23-02-2025", + "date_created": "19-02-2025", + "file_size": 10781372 + }, + { + "dataset_id": "norman", + "dataset_name": "Norman", + "dataset_summary": "RNA-seq data from the norman dataset", + "dataset_description": null, + "data_reference": null, + "data_url": null, + "date_created": "19-02-2025", + "file_size": 10781372 + }, + { + "dataset_id": "adamson", + "dataset_name": "Adamson", + "dataset_summary": "RNA-seq data from the Adamson dataset", + "dataset_description": null, + "data_reference": null, + "data_url": null, + "date_created": "19-02-2025", + "file_size": 10781372 + }, + { + "dataset_id": "replogle", + "dataset_name": "Reologle", + "dataset_summary": "RNA-seq data from the Reologle dataset", + "dataset_description": null, + "data_reference": null, + "data_url": null, + "date_created": "19-02-2025", + "file_size": 10781372 + }, + { + "dataset_id": "nakatake", + "dataset_name": "Nakatake", + "dataset_summary": "RNA-seq data from the Nakatake dataset", + "dataset_description": null, + "data_reference": null, + "data_url": null, + "date_created": "19-02-2025", "file_size": 10781372 } ] diff --git a/results/grn_inference/data/method_info.json b/results/grn_inference/data/method_info.json index cc743587..e8d2a4bf 100644 --- a/results/grn_inference/data/method_info.json +++ b/results/grn_inference/data/method_info.json @@ -1,6 +1,6 @@ [ { - "task_id": "grn_inference", + "task_id": "control_methods", "method_id": "pearson_corr", "method_name": "pearson_corr", "method_summary": "Baseline based on correlation", @@ -10,13 +10,13 @@ "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", "documentation_url": null, - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/pearson_corr:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/pearson_corr", - "code_version": "build_main", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/pearson_corr:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/pearson_corr", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_inference", + "task_id": "control_methods", "method_id": "negative_control", "method_name": "Negative control", "method_summary": "Source-target links based on random assignment", @@ -26,13 +26,13 @@ "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", "documentation_url": null, - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/negative_control:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/negative_control", - "code_version": "build_main", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/negative_control:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/negative_control", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_inference", + "task_id": "control_methods", "method_id": "positive_control", "method_name": "positive_control", "method_summary": "Baseline based on correlation", @@ -42,13 +42,13 @@ "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", "documentation_url": null, - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/positive_control:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/positive_control", - "code_version": "build_main", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/positive_control:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/positive_control", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_inference", + "task_id": "grn_methods", "method_id": "portia", "method_name": "portia", "method_summary": "GRN inference using PORTIA", @@ -58,13 +58,13 @@ "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", "documentation_url": null, - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/portia:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/portia", - "code_version": "build_main", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/portia:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/portia", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_inference", + "task_id": "grn_methods", "method_id": "ppcor", "method_name": "ppcor", "method_summary": "GRN inference using PPCOR", @@ -74,13 +74,13 @@ "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", "documentation_url": null, - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/ppcor:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/ppcor", - "code_version": "build_main", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/ppcor:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/ppcor", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_inference", + "task_id": "grn_methods", "method_id": "scenic", "method_name": "scenic", "method_summary": "GRN inference using scenic", @@ -90,13 +90,13 @@ "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", "documentation_url": null, - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/scenic:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/scenic", - "code_version": "build_main", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/scenic:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/scenic", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_inference", + "task_id": "grn_methods", "method_id": "scenicplus", "method_name": "scenicplus", "method_summary": "GRN inference using scenicplus", @@ -106,13 +106,13 @@ "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", "documentation_url": null, - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/scenicplus:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/scenicplus", - "code_version": "build_main", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/scenicplus:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/scenicplus", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_inference", + "task_id": "grn_methods", "method_id": "scprint", "method_name": "scprint", "method_summary": "GRN inference using scPRINT", @@ -122,13 +122,13 @@ "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", "documentation_url": null, - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/scprint:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/scprint", - "code_version": "build_main", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/scprint:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/scprint", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_inference", + "task_id": "grn_methods", "method_id": "grnboost2", "method_name": "grnboost2", "method_summary": "GRN inference using GRNBoost2", @@ -138,13 +138,13 @@ "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", "documentation_url": null, - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/grnboost2:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/grnboost2", - "code_version": "build_main", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/grnboost2:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/grnboost2", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" }, { - "task_id": "grn_inference", + "task_id": "grn_methods", "method_id": "scglue", "method_name": "scglue", "method_summary": "GRN inference using scglue", @@ -154,9 +154,57 @@ "references_bibtex": null, "code_url": "https://github.com/openproblems-bio/task_grn_inference", "documentation_url": null, - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/scglue:build_main", - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/scglue", - "code_version": "build_main", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/scglue:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/scglue", + "code_version": "dev", + "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" + }, + { + "task_id": "grn_methods", + "method_id": "granie", + "method_name": "granie", + "method_summary": "GRN inference using GRaNIE", + "method_description": "GRN inference using GRaNIE\n", + "is_baseline": false, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_grn_inference", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/granie:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/granie", + "code_version": "dev", + "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" + }, + { + "task_id": "grn_methods", + "method_id": "figr", + "method_name": "figr", + "method_summary": "GRN inference using figr", + "method_description": "GRN inference using figr.\n", + "is_baseline": false, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_grn_inference", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/figr:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/figr", + "code_version": "dev", + "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" + }, + { + "task_id": "grn_methods", + "method_id": "celloracle", + "method_name": "celloracle", + "method_summary": "GRN inference using celloracle", + "method_description": "GRN inference using celloracle.\n", + "is_baseline": false, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_grn_inference", + "documentation_url": "https://morris-lab.github.io/CellOracle.documentation/", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/celloracle:dev", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/celloracle", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592" } ] diff --git a/results/grn_inference/data/metric_execution_info.json b/results/grn_inference/data/metric_execution_info.json index 867963ff..3c6d5b28 100644 --- a/results/grn_inference/data/metric_execution_info.json +++ b/results/grn_inference/data/metric_execution_info.json @@ -1,142 +1,1808 @@ [ { - "dataset_id": null, + "dataset_id": "adamson", + "method_id": "grnboost2", + "metric_component_name": "regression_1", + "resources": { + "submit": "2025-02-26 19:54:14", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "adamson", + "method_id": "grnboost2", + "metric_component_name": "regression_2", + "resources": { + "submit": "2025-02-26 19:54:14", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "adamson", + "method_id": "grnboost2", + "metric_component_name": "ws_distance", + "resources": { + "submit": "2025-02-26 19:54:12", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + 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"disk_read_mb": "NA", + "disk_write_mb": "NA" } }, { - "dataset_id": null, + "dataset_id": "replogle", "method_id": "portia", "metric_component_name": "regression_2", "resources": { - "submit": "2025-02-23 21:36:18", + "submit": "2025-02-26 19:54:14", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "portia", + "metric_component_name": "ws_distance", + "resources": { + "submit": "2025-02-26 19:54:12", "exit_code": 0, - "duration_sec": 116.1, - "cpu_pct": 2764.4, - "peak_memory_mb": 3380, - "disk_read_mb": 843, - "disk_write_mb": 3 + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" } }, { - "dataset_id": null, + "dataset_id": "replogle", "method_id": "positive_control", "metric_component_name": "regression_1", "resources": { - "submit": "2025-02-23 21:34:59", + "submit": "2025-02-26 19:54:14", "exit_code": 0, - "duration_sec": 298, - "cpu_pct": 123.6, - "peak_memory_mb": 6349, - "disk_read_mb": 558, - "disk_write_mb": 2 + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" } }, { - "dataset_id": null, + "dataset_id": "replogle", "method_id": "positive_control", "metric_component_name": "regression_2", "resources": { - "submit": "2025-02-23 21:34:59", + "submit": "2025-02-26 19:54:14", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "positive_control", + "metric_component_name": "ws_distance", + "resources": { + "submit": "2025-02-26 19:54:12", "exit_code": 0, - "duration_sec": 116.4, - "cpu_pct": 2718.4, - "peak_memory_mb": 3380, - "disk_read_mb": 843, - "disk_write_mb": 3 + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" } }, { - "dataset_id": null, + "dataset_id": "replogle", "method_id": "ppcor", "metric_component_name": "regression_1", "resources": { - "submit": "2025-02-23 21:36:38", + "submit": "2025-02-26 19:54:14", "exit_code": 0, - "duration_sec": 288, - "cpu_pct": 125.5, - "peak_memory_mb": 6349, - "disk_read_mb": 550, - "disk_write_mb": 2 + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" } }, { - "dataset_id": null, + "dataset_id": "replogle", "method_id": "ppcor", "metric_component_name": "regression_2", "resources": { - "submit": "2025-02-23 21:36:38", + "submit": "2025-02-26 19:54:14", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "ppcor", + "metric_component_name": "ws_distance", + "resources": { + "submit": "2025-02-26 19:54:12", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "scenic", + "metric_component_name": "regression_1", + "resources": { + "submit": "2025-02-26 19:54:14", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "scenic", + "metric_component_name": "regression_2", + "resources": { + "submit": "2025-02-26 19:54:14", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "scenic", + "metric_component_name": "ws_distance", + "resources": { + "submit": "2025-02-26 19:54:12", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "scprint", + "metric_component_name": "regression_1", + "resources": { + "submit": "2025-02-26 19:54:14", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "scprint", + "metric_component_name": "regression_2", + "resources": { + "submit": "2025-02-26 19:54:14", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "scprint", + "metric_component_name": "ws_distance", + "resources": { + "submit": "2025-02-26 19:54:12", "exit_code": 0, - "duration_sec": 118.2, - "cpu_pct": 2714.4, - "peak_memory_mb": 3380, - "disk_read_mb": 828, - "disk_write_mb": 3 + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" } } ] diff --git a/results/grn_inference/data/metric_info.json b/results/grn_inference/data/metric_info.json index 9a0cb9f8..8c0af030 100644 --- a/results/grn_inference/data/metric_info.json +++ b/results/grn_inference/data/metric_info.json @@ -8,9 +8,9 @@ "metric_description": "Regression 1 score for all genes with mean gene expression set for missing genes\n", "references_doi": null, "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/regression_1", - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/regression_1:build_main", - "code_version": "build_main", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/regression_1", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/regression_1:dev", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592", "maximize": true }, @@ -23,9 +23,9 @@ "metric_description": "Regression 1 score for only genes in the network\n", "references_doi": null, "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/regression_1", - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/regression_1:build_main", - "code_version": "build_main", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/regression_1", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/regression_1:dev", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592", "maximize": true }, @@ -38,9 +38,9 @@ "metric_description": "Captures the perfomance for the top regulatory links\n", "references_doi": null, "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/regression_2", - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/regression_2:build_main", - "code_version": "build_main", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/regression_2", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/regression_2:dev", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592", "maximize": true }, @@ -53,9 +53,9 @@ "metric_description": "Balanced performance scores considering both prevision and recall\n", "references_doi": null, "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/regression_2", - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/regression_2:build_main", - "code_version": "build_main", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/regression_2", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/regression_2:dev", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592", "maximize": true }, @@ -68,9 +68,9 @@ "metric_description": "Captures the perfomance for the more broad regulatory links (recall)\n", "references_doi": null, "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/regression_2", - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/regression_2:build_main", - "code_version": "build_main", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/regression_2", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/regression_2:dev", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592", "maximize": true }, @@ -83,9 +83,9 @@ "metric_description": "Captures the perfomance for the top regulatory links\n", "references_doi": null, "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/wasserstein", - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/wasserstein:build_main", - "code_version": "build_main", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/wasserstein", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/wasserstein:dev", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592", "maximize": true }, @@ -98,9 +98,9 @@ "metric_description": "Balanced performance scores considering both prevision and recall\n", "references_doi": null, "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/wasserstein", - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/wasserstein:build_main", - "code_version": "build_main", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/wasserstein", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/wasserstein:dev", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592", "maximize": true }, @@ -113,9 +113,9 @@ "metric_description": "Captures the perfomance for the more broad regulatory links (recall)\n", "references_doi": null, "references_bibtex": null, - "implementation_url": "https://github.com/openproblems-bio/task_grn_inference/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/wasserstein", - "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/wasserstein:build_main", - "code_version": "build_main", + "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/metrics/wasserstein", + "image": "https://ghcr.io/openproblems-bio/task_grn_inference/metrics/wasserstein:dev", + "code_version": "dev", "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592", "maximize": true } diff --git a/results/grn_inference/data/quality_control.json b/results/grn_inference/data/quality_control.json index 6d6d84fc..5cc25962 100644 --- a/results/grn_inference/data/quality_control.json +++ b/results/grn_inference/data/quality_control.json @@ -93,7 +93,7 @@ "task_id": "task_grn_inference", "category": "Method info", "name": "Pct 'paper_reference' missing", - "value": 1.0, + "value": 0.7692307692307693, "severity": 2, "severity_value": 3.0, "code": "percent_missing(method_info, field)", @@ -243,700 +243,2350 @@ "task_id": "task_grn_inference", "category": "Raw data", "name": "Number of results", - "value": 10, + "value": 65, "severity": 0, "severity_value": 0.0, "code": "len(results) == len(method_info) * len(metric_info) * len(dataset_info)", - "message": "Number of results should be equal to #methods × #metrics × #datasets.\n Task id: task_grn_inference\n Number of results: 10\n Number of methods: 10\n Number of metrics: 8\n Number of datasets: 1\n" + "message": "Number of results should be equal to #methods × #metrics × #datasets.\n Task id: task_grn_inference\n Number of results: 65\n Number of methods: 13\n Number of metrics: 8\n Number of datasets: 5\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Metric 'r1_all' %missing", - "value": 0.5, + "value": 0.3384615384615385, "severity": 3, - "severity_value": 5.0, + "severity_value": 3.3846153846153846, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r1_all\n Percentage missing: 50%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r1_all\n Percentage missing: 34%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Metric 'r1_grn' %missing", - "value": 0.5, + "value": 0.3384615384615385, "severity": 3, - "severity_value": 5.0, + "severity_value": 3.3846153846153846, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r1_grn\n Percentage missing: 50%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r1_grn\n Percentage missing: 34%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Metric 'r2-theta-0.0' %missing", - "value": 0.5, + "value": 0.3384615384615385, "severity": 3, - "severity_value": 5.0, + "severity_value": 3.3846153846153846, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r2-theta-0.0\n Percentage missing: 50%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r2-theta-0.0\n Percentage missing: 34%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Metric 'r2-theta-0.5' %missing", - "value": 0.5, + "value": 0.3384615384615385, "severity": 3, - "severity_value": 5.0, + "severity_value": 3.3846153846153846, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r2-theta-0.5\n Percentage missing: 50%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r2-theta-0.5\n Percentage missing: 34%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Metric 'r2-theta-1.0' %missing", - "value": 0.5, + "value": 0.3384615384615385, "severity": 3, - "severity_value": 5.0, + "severity_value": 3.3846153846153846, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r2-theta-1.0\n Percentage missing: 50%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: r2-theta-1.0\n Percentage missing: 34%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Metric 'ws-theta-0.0' %missing", - "value": 1.0, + "value": 0.6461538461538462, "severity": 3, - "severity_value": 10.0, + "severity_value": 6.461538461538462, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: ws-theta-0.0\n Percentage missing: 100%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: ws-theta-0.0\n Percentage missing: 65%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Metric 'ws-theta-0.5' %missing", - "value": 1.0, + "value": 0.6461538461538462, "severity": 3, - "severity_value": 10.0, + "severity_value": 6.461538461538462, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: ws-theta-0.5\n Percentage missing: 100%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: ws-theta-0.5\n Percentage missing: 65%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Metric 'ws-theta-1.0' %missing", - "value": 1.0, + "value": 0.6461538461538462, "severity": 3, - "severity_value": 10.0, + "severity_value": 6.461538461538462, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: ws-theta-1.0\n Percentage missing: 100%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n Metric id: ws-theta-1.0\n Percentage missing: 65%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Method 'pearson_corr' %missing", - "value": 0.375, - "severity": 3, - "severity_value": 3.75, + "value": 0.15000000000000002, + "severity": 1, + "severity_value": 1.5000000000000002, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: pearson_corr\n Percentage missing: 38%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: pearson_corr\n Percentage missing: 15%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Method 'negative_control' %missing", - "value": 0.375, - "severity": 3, - "severity_value": 3.75, + "value": 0.15000000000000002, + "severity": 1, + "severity_value": 1.5000000000000002, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: negative_control\n Percentage missing: 38%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: negative_control\n Percentage missing: 15%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Method 'positive_control' %missing", - "value": 0.375, - "severity": 3, - "severity_value": 3.75, + "value": 0.15000000000000002, + "severity": 1, + "severity_value": 1.5000000000000002, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: positive_control\n Percentage missing: 38%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: positive_control\n Percentage missing: 15%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Method 'portia' %missing", - "value": 0.375, - "severity": 3, - "severity_value": 3.75, + "value": 0.15000000000000002, + "severity": 1, + "severity_value": 1.5000000000000002, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: portia\n Percentage missing: 38%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: portia\n Percentage missing: 15%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Method 'ppcor' %missing", - "value": 0.375, - "severity": 3, - "severity_value": 3.75, + "value": 0.15000000000000002, + "severity": 1, + "severity_value": 1.5000000000000002, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: ppcor\n Percentage missing: 38%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: ppcor\n Percentage missing: 15%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Method 'scenic' %missing", - "value": 1.0, - "severity": 3, - "severity_value": 10.0, + "value": 0.15000000000000002, + "severity": 1, + "severity_value": 1.5000000000000002, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scenic\n Percentage missing: 100%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scenic\n Percentage missing: 15%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Method 'scenicplus' %missing", - "value": 1.0, + "value": 0.875, "severity": 3, - "severity_value": 10.0, + "severity_value": 8.75, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scenicplus\n Percentage missing: 100%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scenicplus\n Percentage missing: 88%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Method 'scprint' %missing", - "value": 1.0, + "value": 0.475, "severity": 3, - "severity_value": 10.0, + "severity_value": 4.749999999999999, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scprint\n Percentage missing: 100%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scprint\n Percentage missing: 48%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Method 'grnboost2' %missing", - "value": 1.0, - "severity": 3, - "severity_value": 10.0, + "value": 0.15000000000000002, + "severity": 1, + "severity_value": 1.5000000000000002, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: grnboost2\n Percentage missing: 100%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: grnboost2\n Percentage missing: 15%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Method 'scglue' %missing", - "value": 1.0, + "value": 0.875, + "severity": 3, + "severity_value": 8.75, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scglue\n Percentage missing: 88%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'granie' %missing", + "value": 0.875, "severity": 3, - "severity_value": 10.0, + "severity_value": 8.75, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: scglue\n Percentage missing: 100%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: granie\n Percentage missing: 88%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'figr' %missing", + "value": 0.875, + "severity": 3, + "severity_value": 8.75, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: figr\n Percentage missing: 88%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Method 'celloracle' %missing", + "value": 0.875, + "severity": 3, + "severity_value": 8.75, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n method id: celloracle\n Percentage missing: 88%\n" }, { "task_id": "task_grn_inference", "category": "Raw results", "name": "Dataset 'op' %missing", - "value": 0.6875, + "value": 0.375, + "severity": 3, + "severity_value": 3.75, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n dataset id: op\n Percentage missing: 38%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Dataset 'norman' %missing", + "value": 0.3846153846153846, + "severity": 3, + "severity_value": 3.846153846153846, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n dataset id: norman\n Percentage missing: 38%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Dataset 'adamson' %missing", + "value": 0.46153846153846156, + "severity": 3, + "severity_value": 4.615384615384615, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n dataset id: adamson\n Percentage missing: 46%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Dataset 'replogle' %missing", + "value": 0.3846153846153846, "severity": 3, - "severity_value": 6.875, + "severity_value": 3.846153846153846, "code": "pct_missing <= .1", - "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n dataset id: op\n Percentage missing: 69%\n" + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n dataset id: replogle\n Percentage missing: 38%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Raw results", + "name": "Dataset 'nakatake' %missing", + "value": 0.6634615384615384, + "severity": 3, + "severity_value": 6.634615384615384, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_grn_inference\n dataset id: nakatake\n Percentage missing: 66%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", "name": "Worst score pearson_corr r1_all", - "value": 0, + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_all\n Worst score: 0%\n" + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_all\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", "name": "Best score pearson_corr r1_all", - "value": 0, + "value": 1.0, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_all\n Best score: 0%\n" + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_all\n Best score: 1.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", "name": "Worst score negative_control r1_all", - "value": 0, + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_all\n Worst score: 0%\n" + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_all\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", "name": "Best score negative_control r1_all", - "value": 0, + "value": 1.0, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_all\n Best score: 0%\n" + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_all\n Best score: 1.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", "name": "Worst score positive_control r1_all", - "value": 0, + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_all\n Worst score: 0%\n" + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_all\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", "name": "Best score positive_control r1_all", - "value": 0, + "value": 1.0, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_all\n Best score: 0%\n" + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_all\n Best score: 1.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", "name": "Worst score portia r1_all", - "value": 0, + "value": -0.4275, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.4275, "code": "worst_score >= -1", - "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_all\n Worst score: 0%\n" + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_all\n Worst score: -0.4275%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", "name": "Best score portia r1_all", - "value": 0, - "severity": 0, - "severity_value": 0.0, + "value": 3.319, + "severity": 1, + "severity_value": 1.6595, "code": "best_score <= 2", - "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_all\n Best score: 0%\n" + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_all\n Best score: 3.319%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", "name": "Worst score ppcor r1_all", - "value": 0, - "severity": 0, - "severity_value": -0.0, + "value": -2.3365, + "severity": 2, + "severity_value": 2.3365, "code": "worst_score >= -1", - "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_all\n Worst score: 0%\n" + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_all\n Worst score: -2.3365%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", "name": "Best score ppcor r1_all", - "value": 0, - "severity": 0, - "severity_value": 0.0, + "value": 2.8383, + "severity": 1, + "severity_value": 1.41915, "code": "best_score <= 2", - "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_all\n Best score: 0%\n" + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_all\n Best score: 2.8383%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score pearson_corr r1_grn", - "value": 0, + "name": "Worst score scenic r1_all", + "value": 0.2798, "severity": 0, - "severity_value": -0.0, + "severity_value": -0.2798, "code": "worst_score >= -1", - "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_grn\n Worst score: 0%\n" + "message": "Method scenic performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: r1_all\n Worst score: 0.2798%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score pearson_corr r1_grn", - "value": 0, + "name": "Best score scenic r1_all", + "value": 0.7374, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.3687, "code": "best_score <= 2", - "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_grn\n Best score: 0%\n" + "message": "Method scenic performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: r1_all\n Best score: 0.7374%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score negative_control r1_grn", - "value": 0, + "name": "Worst score scenicplus r1_all", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_grn\n Worst score: 0%\n" + "message": "Method scenicplus performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: r1_all\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score negative_control r1_grn", - "value": 0, + "name": "Best score scenicplus r1_all", + "value": 0.9188, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.4594, "code": "best_score <= 2", - "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_grn\n Best score: 0%\n" + "message": "Method scenicplus performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: r1_all\n Best score: 0.9188%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score positive_control r1_grn", - "value": 0, + "name": "Worst score scprint r1_all", + "value": -0.1274, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.1274, "code": "worst_score >= -1", - "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_grn\n Worst score: 0%\n" + "message": "Method scprint performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: r1_all\n Worst score: -0.1274%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score positive_control r1_grn", - "value": 0, + "name": "Best score scprint r1_all", + "value": 0.5375, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.26875, "code": "best_score <= 2", - "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_grn\n Best score: 0%\n" + "message": "Method scprint performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: r1_all\n Best score: 0.5375%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score portia r1_grn", - "value": 0, + "name": "Worst score grnboost2 r1_all", + "value": 0.4227, "severity": 0, - "severity_value": -0.0, + "severity_value": -0.4227, "code": "worst_score >= -1", - "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_grn\n Worst score: 0%\n" + "message": "Method grnboost2 performs much worse than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: r1_all\n Worst score: 0.4227%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score portia r1_grn", - "value": 0, - "severity": 0, - "severity_value": 0.0, + "name": "Best score grnboost2 r1_all", + "value": 4.199, + "severity": 2, + "severity_value": 2.0995, "code": "best_score <= 2", - "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_grn\n Best score: 0%\n" + "message": "Method grnboost2 performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: r1_all\n Best score: 4.199%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score ppcor r1_grn", - "value": 0, + "name": "Worst score scglue r1_all", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_grn\n Worst score: 0%\n" + "message": "Method scglue performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: r1_all\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score ppcor r1_grn", - "value": 0, + "name": "Best score scglue r1_all", + "value": 0.244, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.122, "code": "best_score <= 2", - "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_grn\n Best score: 0%\n" + "message": "Method scglue performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: r1_all\n Best score: 0.244%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score pearson_corr r2-theta-0.0", - "value": 0, + "name": "Worst score granie r1_all", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + "message": "Method granie performs much worse than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: r1_all\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score pearson_corr r2-theta-0.0", - "value": 0, + "name": "Best score granie r1_all", + "value": 0.2311, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.11555, "code": "best_score <= 2", - "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.0\n Best score: 0%\n" + "message": "Method granie performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: r1_all\n Best score: 0.2311%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score negative_control r2-theta-0.0", - "value": 0, + "name": "Worst score figr r1_all", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + "message": "Method figr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: r1_all\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score negative_control r2-theta-0.0", - "value": 0, + "name": "Best score figr r1_all", + "value": 0.3408, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.1704, "code": "best_score <= 2", - "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.0\n Best score: 0%\n" + "message": "Method figr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: r1_all\n Best score: 0.3408%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score positive_control r2-theta-0.0", - "value": 0, + "name": "Worst score celloracle r1_all", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + "message": "Method celloracle performs much worse than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: r1_all\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score positive_control r2-theta-0.0", - "value": 0, + "name": "Best score celloracle r1_all", + "value": 0.7279, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.36395, "code": "best_score <= 2", - "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.0\n Best score: 0%\n" + "message": "Method celloracle performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: r1_all\n Best score: 0.7279%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score portia r2-theta-0.0", - "value": 0, + "name": "Worst score pearson_corr r1_grn", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_grn\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score portia r2-theta-0.0", - "value": 0, + "name": "Best score pearson_corr r1_grn", + "value": 1.0, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.0\n Best score: 0%\n" + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r1_grn\n Best score: 1.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score ppcor r2-theta-0.0", - "value": 0, + "name": "Worst score negative_control r1_grn", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_grn\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score ppcor r2-theta-0.0", - "value": 0, + "name": "Best score negative_control r1_grn", + "value": 1.0, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.0\n Best score: 0%\n" + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r1_grn\n Best score: 1.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score pearson_corr r2-theta-0.5", - "value": 0, + "name": "Worst score positive_control r1_grn", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_grn\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score pearson_corr r2-theta-0.5", - "value": 0, + "name": "Best score positive_control r1_grn", + "value": 1.0, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.5, "code": "best_score <= 2", - "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.5\n Best score: 0%\n" + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r1_grn\n Best score: 1.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score negative_control r2-theta-0.5", - "value": 0, + "name": "Worst score portia r1_grn", + "value": -0.33, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.33, "code": "worst_score >= -1", - "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_grn\n Worst score: -0.33%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score negative_control r2-theta-0.5", - "value": 0, + "name": "Best score portia r1_grn", + "value": 1.6879, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.84395, "code": "best_score <= 2", - "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.5\n Best score: 0%\n" + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r1_grn\n Best score: 1.6879%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score positive_control r2-theta-0.5", - "value": 0, + "name": "Worst score ppcor r1_grn", + "value": -0.4573, "severity": 0, - "severity_value": -0.0, + "severity_value": 0.4573, "code": "worst_score >= -1", - "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_grn\n Worst score: -0.4573%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score positive_control r2-theta-0.5", - "value": 0, + "name": "Best score ppcor r1_grn", + "value": 1.4042, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.7021, "code": "best_score <= 2", - "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.5\n Best score: 0%\n" + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r1_grn\n Best score: 1.4042%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score portia r2-theta-0.5", - "value": 0, + "name": "Worst score scenic r1_grn", + "value": 0.1562, "severity": 0, - "severity_value": -0.0, + "severity_value": -0.1562, "code": "worst_score >= -1", - "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + "message": "Method scenic performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: r1_grn\n Worst score: 0.1562%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score portia r2-theta-0.5", - "value": 0, + "name": "Best score scenic r1_grn", + "value": 0.6209, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.31045, "code": "best_score <= 2", - "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.5\n Best score: 0%\n" + "message": "Method scenic performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: r1_grn\n Best score: 0.6209%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score ppcor r2-theta-0.5", - "value": 0, + "name": "Worst score scenicplus r1_grn", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + "message": "Method scenicplus performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: r1_grn\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score ppcor r2-theta-0.5", - "value": 0, + "name": "Best score scenicplus r1_grn", + "value": 0.605, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.3025, "code": "best_score <= 2", - "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.5\n Best score: 0%\n" + "message": "Method scenicplus performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: r1_grn\n Best score: 0.605%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score pearson_corr r2-theta-1.0", - "value": 0, + "name": "Worst score scprint r1_grn", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + "message": "Method scprint performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: r1_grn\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score pearson_corr r2-theta-1.0", - "value": 0, + "name": "Best score scprint r1_grn", + "value": 0.6185, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.30925, "code": "best_score <= 2", - "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-1.0\n Best score: 0%\n" + "message": "Method scprint performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: r1_grn\n Best score: 0.6185%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score negative_control r2-theta-1.0", - "value": 0, + "name": "Worst score grnboost2 r1_grn", + "value": 0.468, + "severity": 0, + "severity_value": -0.468, + "code": "worst_score >= -1", + "message": "Method grnboost2 performs much worse than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: r1_grn\n Worst score: 0.468%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score grnboost2 r1_grn", + "value": 3.311, + "severity": 1, + "severity_value": 1.6555, + "code": "best_score <= 2", + "message": "Method grnboost2 performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: r1_grn\n Best score: 3.311%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scglue r1_grn", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + "message": "Method scglue performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: r1_grn\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score negative_control r2-theta-1.0", - "value": 0, + "name": "Best score scglue r1_grn", + "value": 0.5451, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.27255, "code": "best_score <= 2", - "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-1.0\n Best score: 0%\n" + "message": "Method scglue performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: r1_grn\n Best score: 0.5451%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score positive_control r2-theta-1.0", - "value": 0, + "name": "Worst score granie r1_grn", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + "message": "Method granie performs much worse than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: r1_grn\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score positive_control r2-theta-1.0", - "value": 0, + "name": "Best score granie r1_grn", + "value": 0.1561, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.07805, "code": "best_score <= 2", - "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-1.0\n Best score: 0%\n" + "message": "Method granie performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: r1_grn\n Best score: 0.1561%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score portia r2-theta-1.0", - "value": 0, + "name": "Worst score figr r1_grn", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + "message": "Method figr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: r1_grn\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score portia r2-theta-1.0", - "value": 0, + "name": "Best score figr r1_grn", + "value": 0.4362, "severity": 0, - "severity_value": 0.0, + "severity_value": 0.2181, "code": "best_score <= 2", - "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-1.0\n Best score: 0%\n" + "message": "Method figr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: r1_grn\n Best score: 0.4362%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Worst score ppcor r2-theta-1.0", - "value": 0, + "name": "Worst score celloracle r1_grn", + "value": 0.0, "severity": 0, "severity_value": -0.0, "code": "worst_score >= -1", - "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + "message": "Method celloracle performs much worse than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: r1_grn\n Worst score: 0.0%\n" }, { "task_id": "task_grn_inference", "category": "Scaling", - "name": "Best score ppcor r2-theta-1.0", + "name": "Best score celloracle r1_grn", + "value": 0.6121, + "severity": 0, + "severity_value": 0.30605, + "code": "best_score <= 2", + "message": "Method celloracle performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: r1_grn\n Best score: 0.6121%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr r2-theta-0.0", + "value": 0.5328, + "severity": 0, + "severity_value": -0.5328, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.0\n Worst score: 0.5328%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr r2-theta-0.0", + "value": 0.9505, + "severity": 0, + "severity_value": 0.47525, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.0\n Best score: 0.9505%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control r2-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control r2-theta-0.0", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.0\n Worst score: 1%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control r2-theta-0.0", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.0\n Best score: 1%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia r2-theta-0.0", + "value": -0.3567, + "severity": 0, + "severity_value": 0.3567, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.0\n Worst score: -0.3567%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia r2-theta-0.0", + "value": 1.0081, + "severity": 0, + "severity_value": 0.50405, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.0\n Best score: 1.0081%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor r2-theta-0.0", + "value": 0.0368, + "severity": 0, + "severity_value": -0.0368, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.0\n Worst score: 0.0368%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor r2-theta-0.0", + "value": 0.4332, + "severity": 0, + "severity_value": 0.2166, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.0\n Best score: 0.4332%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenic r2-theta-0.0", + "value": 0.0744, + "severity": 0, + "severity_value": -0.0744, + "code": "worst_score >= -1", + "message": "Method scenic performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: r2-theta-0.0\n Worst score: 0.0744%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenic r2-theta-0.0", + "value": 0.7324, + "severity": 0, + "severity_value": 0.3662, + "code": "best_score <= 2", + "message": "Method scenic performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: r2-theta-0.0\n Best score: 0.7324%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenicplus r2-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scenicplus performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: r2-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenicplus r2-theta-0.0", + "value": 0.9021, + "severity": 0, + "severity_value": 0.45105, + "code": "best_score <= 2", + "message": "Method scenicplus performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: r2-theta-0.0\n Best score: 0.9021%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scprint r2-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scprint performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: r2-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scprint r2-theta-0.0", + "value": 0.7685, + "severity": 0, + "severity_value": 0.38425, + "code": "best_score <= 2", + "message": "Method scprint performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: r2-theta-0.0\n Best score: 0.7685%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score grnboost2 r2-theta-0.0", + "value": 0.2191, + "severity": 0, + "severity_value": -0.2191, + "code": "worst_score >= -1", + "message": "Method grnboost2 performs much worse than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: r2-theta-0.0\n Worst score: 0.2191%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score grnboost2 r2-theta-0.0", + "value": 1.5287, + "severity": 0, + "severity_value": 0.76435, + "code": "best_score <= 2", + "message": "Method grnboost2 performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: r2-theta-0.0\n Best score: 1.5287%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scglue r2-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scglue performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: r2-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scglue r2-theta-0.0", + "value": 0.6898, + "severity": 0, + "severity_value": 0.3449, + "code": "best_score <= 2", + "message": "Method scglue performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: r2-theta-0.0\n Best score: 0.6898%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score granie r2-theta-0.0", + "value": -0.0909, + "severity": 0, + "severity_value": 0.0909, + "code": "worst_score >= -1", + "message": "Method granie performs much worse than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: r2-theta-0.0\n Worst score: -0.0909%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score granie r2-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method granie performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: r2-theta-0.0\n Best score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score figr r2-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method figr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: r2-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score figr r2-theta-0.0", + "value": 0.2415, + "severity": 0, + "severity_value": 0.12075, + "code": "best_score <= 2", + "message": "Method figr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: r2-theta-0.0\n Best score: 0.2415%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score celloracle r2-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method celloracle performs much worse than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: r2-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score celloracle r2-theta-0.0", + "value": 0.8446, + "severity": 0, + "severity_value": 0.4223, + "code": "best_score <= 2", + "message": "Method celloracle performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: r2-theta-0.0\n Best score: 0.8446%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr r2-theta-0.5", + "value": 0.7228, + "severity": 0, + "severity_value": -0.7228, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.5\n Worst score: 0.7228%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr r2-theta-0.5", + "value": 1.0, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-0.5\n Best score: 1.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control r2-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control r2-theta-0.5", + "value": 0.9139, + "severity": 0, + "severity_value": -0.9139, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.5\n Worst score: 0.9139%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control r2-theta-0.5", + "value": 1.0, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-0.5\n Best score: 1.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia r2-theta-0.5", + "value": -1.0274, + "severity": 1, + "severity_value": 1.0274, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.5\n Worst score: -1.0274%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia r2-theta-0.5", + "value": 0.9024, + "severity": 0, + "severity_value": 0.4512, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-0.5\n Best score: 0.9024%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor r2-theta-0.5", + "value": 0.0114, + "severity": 0, + "severity_value": -0.0114, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.5\n Worst score: 0.0114%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor r2-theta-0.5", + "value": 0.3672, + "severity": 0, + "severity_value": 0.1836, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-0.5\n Best score: 0.3672%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenic r2-theta-0.5", + "value": 0.2372, + "severity": 0, + "severity_value": -0.2372, + "code": "worst_score >= -1", + "message": "Method scenic performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: r2-theta-0.5\n Worst score: 0.2372%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenic r2-theta-0.5", + "value": 0.877, + "severity": 0, + "severity_value": 0.4385, + "code": "best_score <= 2", + "message": "Method scenic performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: r2-theta-0.5\n Best score: 0.877%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenicplus r2-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scenicplus performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: r2-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenicplus r2-theta-0.5", + "value": 1.2579, + "severity": 0, + "severity_value": 0.62895, + "code": "best_score <= 2", + "message": "Method scenicplus performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: r2-theta-0.5\n Best score: 1.2579%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scprint r2-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scprint performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: r2-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scprint r2-theta-0.5", + "value": 0.7205, + "severity": 0, + "severity_value": 0.36025, + "code": "best_score <= 2", + "message": "Method scprint performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: r2-theta-0.5\n Best score: 0.7205%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score grnboost2 r2-theta-0.5", + "value": 0.802, + "severity": 0, + "severity_value": -0.802, + "code": "worst_score >= -1", + "message": "Method grnboost2 performs much worse than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: r2-theta-0.5\n Worst score: 0.802%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score grnboost2 r2-theta-0.5", + "value": 1.5697, + "severity": 0, + "severity_value": 0.78485, + "code": "best_score <= 2", + "message": "Method grnboost2 performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: r2-theta-0.5\n Best score: 1.5697%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scglue r2-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scglue performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: r2-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scglue r2-theta-0.5", + "value": 0.2032, + "severity": 0, + "severity_value": 0.1016, + "code": "best_score <= 2", + "message": "Method scglue performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: r2-theta-0.5\n Best score: 0.2032%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score granie r2-theta-0.5", + "value": -0.2822, + "severity": 0, + "severity_value": 0.2822, + "code": "worst_score >= -1", + "message": "Method granie performs much worse than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: r2-theta-0.5\n Worst score: -0.2822%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score granie r2-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method granie performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: r2-theta-0.5\n Best score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score figr r2-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method figr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: r2-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score figr r2-theta-0.5", + "value": 0.2316, + "severity": 0, + "severity_value": 0.1158, + "code": "best_score <= 2", + "message": "Method figr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: r2-theta-0.5\n Best score: 0.2316%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score celloracle r2-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method celloracle performs much worse than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: r2-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score celloracle r2-theta-0.5", + "value": 0.9269, + "severity": 0, + "severity_value": 0.46345, + "code": "best_score <= 2", + "message": "Method celloracle performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: r2-theta-0.5\n Best score: 0.9269%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr r2-theta-1.0", + "value": 0.5875, + "severity": 0, + "severity_value": -0.5875, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-1.0\n Worst score: 0.5875%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr r2-theta-1.0", + "value": 1.0, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: r2-theta-1.0\n Best score: 1.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control r2-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: r2-theta-1.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control r2-theta-1.0", + "value": 0.7778, + "severity": 0, + "severity_value": -0.7778, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-1.0\n Worst score: 0.7778%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control r2-theta-1.0", + "value": 1.0, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: r2-theta-1.0\n Best score: 1.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia r2-theta-1.0", + "value": -0.1835, + "severity": 0, + "severity_value": 0.1835, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-1.0\n Worst score: -0.1835%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia r2-theta-1.0", + "value": 0.8709, + "severity": 0, + "severity_value": 0.43545, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: r2-theta-1.0\n Best score: 0.8709%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor r2-theta-1.0", + "value": 0.0394, + "severity": 0, + "severity_value": -0.0394, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-1.0\n Worst score: 0.0394%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor r2-theta-1.0", + "value": 0.4841, + "severity": 0, + "severity_value": 0.24205, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-1.0\n Best score: 0.4841%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenic r2-theta-1.0", + "value": 0.1097, + "severity": 0, + "severity_value": -0.1097, + "code": "worst_score >= -1", + "message": "Method scenic performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: r2-theta-1.0\n Worst score: 0.1097%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenic r2-theta-1.0", + "value": 1.2296, + "severity": 0, + "severity_value": 0.6148, + "code": "best_score <= 2", + "message": "Method scenic performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: r2-theta-1.0\n Best score: 1.2296%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenicplus r2-theta-1.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scenicplus performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: r2-theta-1.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenicplus r2-theta-1.0", + "value": 1.5351, + "severity": 0, + "severity_value": 0.76755, + "code": "best_score <= 2", + "message": "Method scenicplus performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: r2-theta-1.0\n Best score: 1.5351%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scprint r2-theta-1.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scprint performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: r2-theta-1.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scprint r2-theta-1.0", + "value": 0.614, + "severity": 0, + "severity_value": 0.307, + "code": "best_score <= 2", + "message": "Method scprint performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: r2-theta-1.0\n Best score: 0.614%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score grnboost2 r2-theta-1.0", + "value": 0.9909, + "severity": 0, + "severity_value": -0.9909, + "code": "worst_score >= -1", + "message": "Method grnboost2 performs much worse than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: r2-theta-1.0\n Worst score: 0.9909%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score grnboost2 r2-theta-1.0", + "value": 1.7265, + "severity": 0, + "severity_value": 0.86325, + "code": "best_score <= 2", + "message": "Method grnboost2 performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: r2-theta-1.0\n Best score: 1.7265%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scglue r2-theta-1.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scglue performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: r2-theta-1.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scglue r2-theta-1.0", + "value": 0.0918, + "severity": 0, + "severity_value": 0.0459, + "code": "best_score <= 2", + "message": "Method scglue performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: r2-theta-1.0\n Best score: 0.0918%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score granie r2-theta-1.0", + "value": -0.2627, + "severity": 0, + "severity_value": 0.2627, + "code": "worst_score >= -1", + "message": "Method granie performs much worse than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: r2-theta-1.0\n Worst score: -0.2627%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score granie r2-theta-1.0", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method granie performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: r2-theta-1.0\n Best score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score figr r2-theta-1.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method figr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: r2-theta-1.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score figr r2-theta-1.0", + "value": 0.2721, + "severity": 0, + "severity_value": 0.13605, + "code": "best_score <= 2", + "message": "Method figr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: r2-theta-1.0\n Best score: 0.2721%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score celloracle r2-theta-1.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method celloracle performs much worse than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: r2-theta-1.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score celloracle r2-theta-1.0", + "value": 0.7878, + "severity": 0, + "severity_value": 0.3939, + "code": "best_score <= 2", + "message": "Method celloracle performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: r2-theta-1.0\n Best score: 0.7878%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr ws-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: ws-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr ws-theta-0.0", + "value": 1.0, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: ws-theta-0.0\n Best score: 1.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: ws-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: ws-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control ws-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: ws-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control ws-theta-0.0", + "value": 1.0, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: ws-theta-0.0\n Best score: 1.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia ws-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: ws-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia ws-theta-0.0", + "value": 0.8839, + "severity": 0, + "severity_value": 0.44195, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: ws-theta-0.0\n Best score: 0.8839%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor ws-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: ws-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor ws-theta-0.0", + "value": 0.515, + "severity": 0, + "severity_value": 0.2575, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: ws-theta-0.0\n Best score: 0.515%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenic ws-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scenic performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: ws-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenic ws-theta-0.0", + "value": 1.021, + "severity": 0, + "severity_value": 0.5105, + "code": "best_score <= 2", + "message": "Method scenic performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: ws-theta-0.0\n Best score: 1.021%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenicplus ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scenicplus performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: ws-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenicplus ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method scenicplus performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: ws-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scprint ws-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scprint performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: ws-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scprint ws-theta-0.0", + "value": 0.3412, + "severity": 0, + "severity_value": 0.1706, + "code": "best_score <= 2", + "message": "Method scprint performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: ws-theta-0.0\n Best score: 0.3412%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score grnboost2 ws-theta-0.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method grnboost2 performs much worse than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: ws-theta-0.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score grnboost2 ws-theta-0.0", + "value": 1.1164, + "severity": 0, + "severity_value": 0.5582, + "code": "best_score <= 2", + "message": "Method grnboost2 performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: ws-theta-0.0\n Best score: 1.1164%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scglue ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scglue performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: ws-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scglue ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method scglue performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: ws-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score granie ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method granie performs much worse than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: ws-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score granie ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method granie performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: ws-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score figr ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method figr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: ws-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score figr ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method figr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: ws-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score celloracle ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method celloracle performs much worse than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: ws-theta-0.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score celloracle ws-theta-0.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method celloracle performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: ws-theta-0.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: ws-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr ws-theta-0.5", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: ws-theta-0.5\n Best score: 1%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: ws-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: ws-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control ws-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: ws-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control ws-theta-0.5", + "value": 0.9933, + "severity": 0, + "severity_value": 0.49665, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: ws-theta-0.5\n Best score: 0.9933%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia ws-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: ws-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia ws-theta-0.5", + "value": 0.5613, + "severity": 0, + "severity_value": 0.28065, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: ws-theta-0.5\n Best score: 0.5613%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor ws-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: ws-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor ws-theta-0.5", + "value": 0.4571, + "severity": 0, + "severity_value": 0.22855, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: ws-theta-0.5\n Best score: 0.4571%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenic ws-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scenic performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: ws-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenic ws-theta-0.5", + "value": 1.1311, + "severity": 0, + "severity_value": 0.56555, + "code": "best_score <= 2", + "message": "Method scenic performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: ws-theta-0.5\n Best score: 1.1311%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenicplus ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scenicplus performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: ws-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenicplus ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method scenicplus performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: ws-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scprint ws-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scprint performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: ws-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scprint ws-theta-0.5", + "value": 0.3735, + "severity": 0, + "severity_value": 0.18675, + "code": "best_score <= 2", + "message": "Method scprint performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: ws-theta-0.5\n Best score: 0.3735%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score grnboost2 ws-theta-0.5", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method grnboost2 performs much worse than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: ws-theta-0.5\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score grnboost2 ws-theta-0.5", + "value": 1.2599, + "severity": 0, + "severity_value": 0.62995, + "code": "best_score <= 2", + "message": "Method grnboost2 performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: ws-theta-0.5\n Best score: 1.2599%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scglue ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scglue performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: ws-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scglue ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method scglue performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: ws-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score granie ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method granie performs much worse than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: ws-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score granie ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method granie performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: ws-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score figr ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method figr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: ws-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score figr ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method figr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: ws-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score celloracle ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method celloracle performs much worse than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: ws-theta-0.5\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score celloracle ws-theta-0.5", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method celloracle performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: ws-theta-0.5\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score pearson_corr ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method pearson_corr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: ws-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score pearson_corr ws-theta-1.0", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method pearson_corr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: pearson_corr\n Metric id: ws-theta-1.0\n Best score: 1%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score negative_control ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method negative_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: ws-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score negative_control ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method negative_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: negative_control\n Metric id: ws-theta-1.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score positive_control ws-theta-1.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method positive_control performs much worse than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: ws-theta-1.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score positive_control ws-theta-1.0", + "value": 0.9594, + "severity": 0, + "severity_value": 0.4797, + "code": "best_score <= 2", + "message": "Method positive_control performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: positive_control\n Metric id: ws-theta-1.0\n Best score: 0.9594%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score portia ws-theta-1.0", + "value": -0.0285, + "severity": 0, + "severity_value": 0.0285, + "code": "worst_score >= -1", + "message": "Method portia performs much worse than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: ws-theta-1.0\n Worst score: -0.0285%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score portia ws-theta-1.0", + "value": 0.6635, + "severity": 0, + "severity_value": 0.33175, + "code": "best_score <= 2", + "message": "Method portia performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: portia\n Metric id: ws-theta-1.0\n Best score: 0.6635%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score ppcor ws-theta-1.0", + "value": -0.0874, + "severity": 0, + "severity_value": 0.0874, + "code": "worst_score >= -1", + "message": "Method ppcor performs much worse than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: ws-theta-1.0\n Worst score: -0.0874%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score ppcor ws-theta-1.0", + "value": 0.3827, + "severity": 0, + "severity_value": 0.19135, + "code": "best_score <= 2", + "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: ws-theta-1.0\n Best score: 0.3827%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenic ws-theta-1.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scenic performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: ws-theta-1.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenic ws-theta-1.0", + "value": 0.973, + "severity": 0, + "severity_value": 0.4865, + "code": "best_score <= 2", + "message": "Method scenic performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenic\n Metric id: ws-theta-1.0\n Best score: 0.973%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scenicplus ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scenicplus performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: ws-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scenicplus ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method scenicplus performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scenicplus\n Metric id: ws-theta-1.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scprint ws-theta-1.0", + "value": -0.4668, + "severity": 0, + "severity_value": 0.4668, + "code": "worst_score >= -1", + "message": "Method scprint performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: ws-theta-1.0\n Worst score: -0.4668%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scprint ws-theta-1.0", + "value": 0.6604, + "severity": 0, + "severity_value": 0.3302, + "code": "best_score <= 2", + "message": "Method scprint performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scprint\n Metric id: ws-theta-1.0\n Best score: 0.6604%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score grnboost2 ws-theta-1.0", + "value": 0.0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method grnboost2 performs much worse than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: ws-theta-1.0\n Worst score: 0.0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score grnboost2 ws-theta-1.0", + "value": 1.4751, + "severity": 0, + "severity_value": 0.73755, + "code": "best_score <= 2", + "message": "Method grnboost2 performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: grnboost2\n Metric id: ws-theta-1.0\n Best score: 1.4751%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score scglue ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method scglue performs much worse than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: ws-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score scglue ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method scglue performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: scglue\n Metric id: ws-theta-1.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score granie ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method granie performs much worse than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: ws-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score granie ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method granie performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: granie\n Metric id: ws-theta-1.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score figr ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method figr performs much worse than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: ws-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score figr ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method figr performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: figr\n Metric id: ws-theta-1.0\n Best score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Worst score celloracle ws-theta-1.0", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method celloracle performs much worse than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: ws-theta-1.0\n Worst score: 0%\n" + }, + { + "task_id": "task_grn_inference", + "category": "Scaling", + "name": "Best score celloracle ws-theta-1.0", "value": 0, "severity": 0, "severity_value": 0.0, "code": "best_score <= 2", - "message": "Method ppcor performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: ppcor\n Metric id: r2-theta-1.0\n Best score: 0%\n" + "message": "Method celloracle performs a lot better than baselines.\n Task id: task_grn_inference\n Method id: celloracle\n Metric id: ws-theta-1.0\n Best score: 0%\n" } ] \ No newline at end of file diff --git a/results/grn_inference/data/results.json b/results/grn_inference/data/results.json index 96dafd77..38415e83 100644 --- a/results/grn_inference/data/results.json +++ b/results/grn_inference/data/results.json @@ -1,242 +1,2036 @@ [ { - 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"r2-theta-0.0": 0.0619, + "r2-theta-0.5": 0.041, + "r2-theta-1.0": 0.0433, + "ws-theta-0.0": 0.6285, + "ws-theta-0.5": 0.5803, + "ws-theta-1.0": 0.5315 + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 1, + "r2-theta-0.5": 1, + "r2-theta-1.0": 1, + "ws-theta-0.0": 1, + "ws-theta-0.5": 0.9933, + "ws-theta-1.0": 0.9594 + }, + "mean_score": 0.7441, + "resources": { + "submit": "2025-02-26 19:49:51", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "ppcor", + "metric_values": { + "r1_all": -0.0251, + "r1_grn": -0.0253, + "r2-theta-0.0": 0.0135, + "r2-theta-0.5": 0.0108, + "r2-theta-1.0": 0.0175, + "ws-theta-0.0": 0.5045, + "ws-theta-0.5": 0.5021, + "ws-theta-1.0": 0.5 + }, + "scaled_scores": { + "r1_all": -2.3365, + "r1_grn": -0.4573, + "r2-theta-0.0": 0.0976, + "r2-theta-0.5": 0.0835, + "r2-theta-1.0": 0.062, + "ws-theta-0.0": 0.1348, + "ws-theta-0.5": 0.1108, + "ws-theta-1.0": -0.0874 + }, + "mean_score": 0.0611, + "resources": { + "submit": "2025-02-26 19:49:51", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "scenic", + "metric_values": { + "r1_all": -0.0043, + "r1_grn": -0.0063, + "r2-theta-0.0": 0.0346, + "r2-theta-0.5": 0.0207, + "r2-theta-1.0": 0.025, + "ws-theta-0.0": 0.5754, + "ws-theta-0.5": 0.5496, + "ws-theta-1.0": 0.5124 + }, + "scaled_scores": { + "r1_all": 0.392, + "r1_grn": 0.5996, + "r2-theta-0.0": 0.4921, + "r2-theta-0.5": 0.3861, + "r2-theta-1.0": 0.336, + "ws-theta-0.0": 0.6297, + "ws-theta-0.5": 0.6476, + "ws-theta-1.0": 0.3255 + }, + "mean_score": 0.4761, + "resources": { + "submit": "2025-02-26 19:49:51", + "exit_code": 0, + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" + } + }, + { + "dataset_id": "replogle", + "method_id": "scenicplus", + "metric_values": { + "r1_all": "NA", + "r1_grn": "NA", + "r2-theta-0.0": "NA", + "r2-theta-0.5": "NA", + "r2-theta-1.0": "NA", + "ws-theta-0.0": "NA", + "ws-theta-0.5": "NA", + "ws-theta-1.0": "NA" + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0, + "ws-theta-0.0": 0, + "ws-theta-0.5": 0, + "ws-theta-1.0": 0 + }, + "mean_score": 0, + "resources": {} + }, + { + "dataset_id": "replogle", + "method_id": "scglue", + "metric_values": { + "r1_all": "NA", + "r1_grn": "NA", + "r2-theta-0.0": "NA", + "r2-theta-0.5": "NA", + "r2-theta-1.0": "NA", + "ws-theta-0.0": "NA", + "ws-theta-0.5": "NA", + "ws-theta-1.0": "NA" + }, + "scaled_scores": { + "r1_all": 0, + "r1_grn": 0, + "r2-theta-0.0": 0, + "r2-theta-0.5": 0, + "r2-theta-1.0": 0, + "ws-theta-0.0": 0, + "ws-theta-0.5": 0, + "ws-theta-1.0": 0 + }, + "mean_score": 0, + "resources": {} + }, + { + "dataset_id": "replogle", + "method_id": "scprint", + "metric_values": { + "r1_all": -0.0082, + "r1_grn": -0.0133, + "r2-theta-0.0": 0.027, + "r2-theta-0.5": 0.0178, + "r2-theta-1.0": 0.0238, + "ws-theta-0.0": 0.517, + "ws-theta-0.5": 0.5012, + "ws-theta-1.0": 0.4886 + }, + "scaled_scores": { + "r1_all": -0.1274, + "r1_grn": 0.2094, + "r2-theta-0.0": 0.3506, + "r2-theta-0.5": 0.2974, + "r2-theta-1.0": 0.292, + "ws-theta-0.0": 0.2219, + "ws-theta-0.5": 0.0998, + "ws-theta-1.0": -0.4668 + }, + "mean_score": 0.1839, "resources": { - "submit": "2025-02-23 21:31:09", + "submit": "2025-02-26 19:49:51", "exit_code": 0, - "duration_sec": 78, - "cpu_pct": 107.7051, - "peak_memory_mb": 3789, - "disk_read_mb": 47, - "disk_write_mb": 10 + "duration_sec": "NA", + "cpu_pct": "NA", + "peak_memory_mb": 0, + "disk_read_mb": "NA", + "disk_write_mb": "NA" } } ] diff --git a/results/grn_inference/data/task_info.json b/results/grn_inference/data/task_info.json index 3cb39f3d..8fce7c8e 100644 --- a/results/grn_inference/data/task_info.json +++ b/results/grn_inference/data/task_info.json @@ -45,6 +45,6 @@ } } ], - "version": "build_main", + "version": "dev", "license": "MIT" } From 84cd6303bad23872289a169d25c29cdb53d8afff Mon Sep 17 00:00:00 2001 From: Robrecht Cannoodt Date: Mon, 3 Mar 2025 15:30:04 +0100 Subject: [PATCH 4/5] manually tweak results for now --- results/grn_inference/index.qmd | 372 +++++++++++++++++++++++++++++++- 1 file changed, 371 insertions(+), 1 deletion(-) diff --git a/results/grn_inference/index.qmd b/results/grn_inference/index.qmd index 54b383fb..878bebf4 100644 --- a/results/grn_inference/index.qmd +++ b/results/grn_inference/index.qmd @@ -18,4 +18,374 @@ toc: false params <- list(data_dir = "results/grn_inference/data") params <- list(data_dir = "./data") ``` -{{< include ../_include/_task_template.qmd >}} + + +{{< include ../_include/_load_data.qmd >}} + +{{< include ../_include/_task_description.qmd >}} + +## Summary + +```{ojs} +//| echo: false +function aggregate_scores(obj) { + return d3.mean(obj.map(val => { + if (val.score === undefined || isNaN(val.score)) return 0; + return Math.min(1, Math.max(0, val.score)) + })); +} + +function transpose_list_of_objects(list) { + return Object.fromEntries(Object.keys(list[0]).map(key => [key, list.map(d => d[key])])) +} + +function label_time(time) { + if (time < 1e-5) return "0s"; + if (time < 1) return "<1s"; + if (time < 60) return `${Math.floor(time)}s`; + if (time < 3600) return `${Math.floor(time / 60)}m`; + if (time < 3600 * 24) return `${Math.floor(time / 3600)}h`; + if (time < 3600 * 24 * 7) return `${Math.floor(time / 3600 / 24)}d`; + return ">7d"; // Assuming missing values are encoded as NaN +} + +function label_memory(x_mb, include_mb = true) { + if (!include_mb && x_mb < 1e3) return "<1G"; + if (x_mb < 1) return "<1M"; + if (x_mb < 1e3) return `${Math.round(x_mb)}M`; + if (x_mb < 1e6) return `${Math.round(x_mb / 1e3)}G`; + if (x_mb < 1e9) return `${Math.round(x_mb / 1e6)}T`; + return ">1P"; +} + +function mean_na_rm(x) { + return d3.mean(x.filter(d => !isNaN(d))); +} +``` + +```{ojs} +//| echo: false +poss_dataset_ids = dataset_info + .map(d => d.dataset_id) + .filter(d => results.map(r => r.dataset_id).includes(d)) +poss_method_ids = method_info + .map(d => d.method_id) + .filter(d => results.map(r => r.method_id).includes(d)) +poss_metric_ids = metric_info + .map(d => d.metric_id) + .filter(d => results.map(r => Object.keys(r.scaled_scores)).flat().includes(d)) +``` + +```{ojs} +//| echo: false +//| message: false +//| warning: false + +results_long = results.flatMap(d => { + return Object.entries(d.scaled_scores).map(([metric_id, value]) => + ({ + method_id: d.method_id, + dataset_id: d.dataset_id, + metric_id: metric_id, + score: value + }) + ) +}).filter(d => method_ids.includes(d.method_id) && metric_ids.includes(d.metric_id) && dataset_ids.includes(d.dataset_id)) + +overall = d3.groups(results_long, d => d.method_id) + .map(([method_id, values]) => ({method_id, mean_score: aggregate_scores(values)})) + +per_dataset = d3.groups(results_long, d => d.method_id) + .map(([method_id, values]) => { + const datasets = d3.groups(values, d => d.dataset_id) + .map(([dataset_id, values]) => ({["dataset_" + dataset_id]: aggregate_scores(values)})) + .reduce((a, b) => ({...a, ...b}), {}) + return {method_id, ...datasets} + }) + +per_metric = d3.groups(results_long, d => d.method_id) + .map(([method_id, values]) => { + const metrics = d3.groups(values, d => d.metric_id) + .map(([metric_id, values]) => ({["metric_" + metric_id]: aggregate_scores(values)})) + .reduce((a, b) => ({...a, ...b}), {}) + return {method_id, ...metrics} + }) + + + +summary_all = method_info + .filter(d => show_con || !d.is_baseline) + .filter(d => method_ids.includes(d.method_id)) + .map(method => { + const method_id = method.method_id + const method_name = method.method_name + const mean_score = overall.find(d => d.method_id === method_id).mean_score + const datasets = per_dataset.find(d => d.method_id === method_id) + const metrics = per_metric.find(d => d.method_id === method_id) + + let summary = { + method_id, + method_name, + mean_score, + ...datasets, + ...metrics, + } + + return summary + }) + .sort((a, b) => b.mean_score - a.mean_score) + +// make sure the first entry contains all columns +column_info = { + let column_info = [ + { + id: "method_name", + name: "Name", + label: null, + group: "method", + geom: "text", + palette: null + }, + { + id: "mean_score", + name: "Score", + group: "overall", + geom: "bar", + palette: "overall" + }, + ...dataset_info + .filter(d => dataset_ids.includes(d.dataset_id)) + .map( + d => ({ + id: "dataset_" + d.dataset_id, + name: d.dataset_name, + group: "dataset", + geom: "funkyrect", + palette: "dataset" + }) + ) + .sort((a, b) => a.name.localeCompare(b.name)), + ...metric_info + .filter(d => metric_ids.includes(d.metric_id)) + .map( + d => ({ + id: "metric_" + d.metric_id, + name: d.metric_name, + group: "metric", + geom: "funkyrect", + palette: "metric" + }) + ) + .sort((a, b) => a.name.localeCompare(b.name)), + ] + + column_info = column_info.map(d => { + if (d.id === "method_name") { + return {...d, options: {width: 15, hjust: 0}} + } else if (d.id === "is_baseline") { + return {...d, options: {width: 1}} + } else if (d.geom === "bar") { + return {...d, options: {width: 4}} + } else { + return d + } + }) + + return column_info +} + +column_groups = { + let column_groups = [ + { + group: "method", + palette: null, + level1: "" + }, + { + group: "overall", + palette: "overall", + level1: "Overall" + }, + { + group: "dataset", + palette: "dataset", + level1: dataset_info.length >= 3 ? "Datasets" : "" + }, + { + group: "metric", + palette: "metric", + level1: metric_info.length >= 3 ? "Metrics" : "" + } + ] + + return column_groups +} + +palettes = [ + { + overall: "Greys", + dataset: "Blues", + metric: "Reds" + } +][0] +``` + +```{ojs} +//| echo: false +//| fig-cap: "Overview of the results per method. This figures shows the mean of the scaled scores (group Overall), the mean scores per dataset (group Dataset) and the mean scores per metric (group Metric)." +//| column: page +funkyheatmap( + transpose_list_of_objects(summary_all), + transpose_list_of_objects(column_info), + [], + transpose_list_of_objects(column_groups), + [], + palettes, + { + fontSize: 14, + rowHeight: 26, + rootStyle: 'max-width: none', + colorByRank: color_by_rank, + theme: { + oddRowBackground: 'var(--bs-body-bg)', + evenRowBackground: 'var(--bs-button-hover)', + textColor: 'var(--bs-body-color)', + strokeColor: 'var(--bs-body-color)', + headerColor: 'var(--bs-body-color)', + hoverColor: 'var(--bs-body-color)' + } + }, + scale_column +); +``` + +
+
Display settings + +```{ojs} +//| echo: false +viewof color_by_rank = Inputs.toggle({label: "Color by rank:", value: true}) +viewof scale_column = Inputs.toggle({label: "Minmax column:", value: false}) +viewof show_con = Inputs.toggle({label: "Show control methods:", value: true}) +``` + +
+ +
Filter datasets + +```{ojs} +//| echo: false +viewof dataset_ids = Inputs.checkbox( + dataset_info.filter(d => poss_dataset_ids.includes(d.dataset_id)), + { + keyof: d => d.dataset_name, + valueof: d => d.dataset_id, + value: dataset_info.map(d => d.dataset_id), + label: "Datasets:" + } +) +``` + +
+ +
Filter methods + +```{ojs} +//| echo: false +viewof method_ids = Inputs.checkbox( + method_info.filter(d => poss_method_ids.includes(d.method_id)), + { + keyof: d => d.method_name, + valueof: d => d.method_id, + value: method_info.map(d => d.method_id), + label: "Methods:" + } +) +``` + +
+ +
Filter metrics + +```{ojs} +//| echo: false +viewof metric_ids = Inputs.checkbox( + metric_info.filter(d => poss_metric_ids.includes(d.metric_id)), + { + keyof: d => d.metric_name, + valueof: d => d.metric_id, + value: metric_info.map(d => d.metric_id), + label: "Metrics:" + } +) +``` + +
+
+ +```{ojs} +//| echo: false +funkyheatmap = (await require('d3@7').then(d3 => { + window.d3 = d3; + window._ = _; + return import('https://unpkg.com/funkyheatmapjs@0.2.5'); +})).default; +``` + + +## Results + +{{< include ../_include/_results_table.qmd >}} + +## Dataset info + +
Show + +{{< include ../_include/_dataset_descriptions.qmd >}} + +
+ +## Method info + +
Show + +{{< include ../_include/_method_descriptions.qmd >}} + +
+ +## Control method info + +
Show + +{{< include ../_include/_baseline_descriptions.qmd >}} + +
+ +## Metric info + +
Show + +{{< include ../_include/_metric_descriptions.qmd >}} + +
+ +## Quality control results + +
Show + +{{< include ../_include/_qc_table.qmd >}} + +
+ +## Normalisation visualisation + +
Show + +{{< include ../_include/_scaling_figure.qmd >}} + +
+ +## Authors + +{{< include ../_include/_authors.qmd >}} + From f02dd113800316c9e3b737e20fabdef8cc7215af Mon Sep 17 00:00:00 2001 From: Robrecht Cannoodt Date: Thu, 13 Mar 2025 15:00:18 +0100 Subject: [PATCH 5/5] update results and thumbnail --- results/grn_inference/data/dataset_info.json | 20 +- .../data/metric_execution_info.json | 198 +- results/grn_inference/data/results.json | 30 +- results/grn_inference/thumbnail.svg | 9476 +++++++++++++++++ 4 files changed, 9525 insertions(+), 199 deletions(-) create mode 100644 results/grn_inference/thumbnail.svg diff --git a/results/grn_inference/data/dataset_info.json b/results/grn_inference/data/dataset_info.json index eaeb036c..6c1ba4a1 100644 --- a/results/grn_inference/data/dataset_info.json +++ b/results/grn_inference/data/dataset_info.json @@ -2,8 +2,8 @@ { "dataset_id": "op", "dataset_name": "OPSCA", - "dataset_summary": "RNA-seq data from the OPSCA dataset", - "dataset_description": null, + "dataset_summary": "scRNA-seq data with 146 (originally) perturbations with chemical compounds on PBMCs. Multiome data available for the control compound.", + "dataset_description": "Novel single-cell perturbational dataset in human peripheral blood mononuclear cells (PBMCs). 144 compounds were selected from the Library of Integrated Network-Based Cellular Signatures (LINCS) Connectivity Map dataset (PMID: 29195078) and measured single-cell gene expression profiles after 24 hours of treatment. The experiment was repeated in three healthy human donors, and the compounds were selected based on diverse transcriptional signatures observed in CD34+ hematopoietic stem cells (data not released). This experiment was performed in human PBMCs because the cells are commercially available with pre-obtained consent for public release and PBMCs are a primary, disease-relevant tissue that contains multiple mature cell types (including T-cells, B-cells, myeloid cells, and NK cells) with established markers for annotation of cell types. To supplement this dataset, joint scRNA and single-cell chromatin accessibility measurements were measured from the baseline compound using the 10x Multiome assay.", "data_reference": null, "data_url": null, "date_created": "19-02-2025", @@ -12,8 +12,8 @@ { "dataset_id": "norman", "dataset_name": "Norman", - "dataset_summary": "RNA-seq data from the norman dataset", - "dataset_description": null, + "dataset_summary": "Single cell RNA-seq data with 231 perturbations (activation) on K562 cells.", + "dataset_description": "How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-seq pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g. identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we apply recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.", "data_reference": null, "data_url": null, "date_created": "19-02-2025", @@ -22,8 +22,8 @@ { "dataset_id": "adamson", "dataset_name": "Adamson", - "dataset_summary": "RNA-seq data from the Adamson dataset", - "dataset_description": null, + "dataset_summary": "Single cell RNA-seq data with 82 perturbations (KD) on K562 cells.", + "dataset_description": "Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to be profiled in pooled format. We applied Perturb-seq to dissect the mammalian unfolded protein response (UPR) using single and combinatorial CRISPR perturbations. Two genome-scale CRISPR interference (CRISPRi) screens identified genes whose repression perturbs ER homeostasis. Subjecting ∼100 hits to Perturb-seq enabled high-precision functional clustering of genes. Single-cell analyses decoupled the three UPR branches, revealed bifurcated UPR branch activation among cells subject to the same perturbation, and uncovered differential activation of the branches across hits, including an isolated feedback loop between the translocon and IRE1α. These studies provide insight into how the three sensors of ER homeostasis monitor distinct types of stress and highlight the ability of Perturb-seq to dissect complex cellular responses.", "data_reference": null, "data_url": null, "date_created": "19-02-2025", @@ -32,8 +32,8 @@ { "dataset_id": "replogle", "dataset_name": "Reologle", - "dataset_summary": "RNA-seq data from the Reologle dataset", - "dataset_description": null, + "dataset_summary": "Single cell RNA-seq data with 9722 perturbations (KO) on K562 cells.", + "dataset_description": "A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. We use transcriptional phenotypes to predict the function of poorly characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena-from RNA processing to differentiation. We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function.", "data_reference": null, "data_url": null, "date_created": "19-02-2025", @@ -42,8 +42,8 @@ { "dataset_id": "nakatake", "dataset_name": "Nakatake", - "dataset_summary": "RNA-seq data from the Nakatake dataset", - "dataset_description": null, + "dataset_summary": "RNA-seq data with 463 perturbations (overexpression) on SEES3 cells", + "dataset_description": "Transcription factors (TFs) play a pivotal role in determining cell states, yet our understanding of the causative relationship between TFs and cell states is limited. Here, we systematically examine the state changes of human pluripotent embryonic stem cells (hESCs) by the large-scale manipulation of single TFs. We establish 2,135 hESC lines, representing three clones each of 714 doxycycline (Dox)-inducible genes including 481 TFs, and obtain 26,998 microscopic cell images and 2,174 transcriptome datasets-RNA sequencing (RNA-seq) or microarrays-48 h after the presence or absence of Dox. Interestingly, the expression of essentially all the genes, including genes located in heterochromatin regions, are perturbed by these TFs. TFs are also characterized by their ability to induce differentiation of hESCs into specific cell lineages. These analyses help to provide a way of classifying TFs and identifying specific sets of TFs for directing hESC differentiation into desired cell types.", "data_reference": null, "data_url": null, "date_created": "19-02-2025", diff --git a/results/grn_inference/data/metric_execution_info.json b/results/grn_inference/data/metric_execution_info.json index 3c6d5b28..0b9076ad 100644 --- a/results/grn_inference/data/metric_execution_info.json +++ b/results/grn_inference/data/metric_execution_info.json @@ -293,48 +293,6 @@ "disk_write_mb": "NA" } }, - { - "dataset_id": "adamson", - "method_id": "scprint", - "metric_component_name": "regression_1", - "resources": { - "submit": "2025-02-26 19:54:14", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } - }, - { - "dataset_id": "adamson", - "method_id": "scprint", - "metric_component_name": "regression_2", - "resources": { - "submit": "2025-02-26 19:54:14", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } - }, - { - "dataset_id": "adamson", - "method_id": "scprint", - "metric_component_name": "ws_distance", - "resources": { - "submit": "2025-02-26 19:54:12", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } - }, { "dataset_id": "nakatake", "method_id": "grnboost2", @@ -923,48 +881,6 @@ "disk_write_mb": "NA" } }, - { - "dataset_id": "norman", - "method_id": "scprint", - "metric_component_name": "regression_1", - "resources": { - "submit": "2025-02-26 19:54:14", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } - }, - { - "dataset_id": "norman", - "method_id": "scprint", - "metric_component_name": "regression_2", - "resources": { - "submit": "2025-02-26 19:54:14", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } - }, - { - "dataset_id": "norman", - "method_id": "scprint", - "metric_component_name": "ws_distance", - "resources": { - "submit": "2025-02-26 19:54:12", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } - }, { "dataset_id": "op", "method_id": "celloracle", @@ -974,7 +890,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 42599, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -988,7 +904,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 42599, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1002,7 +918,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 42599, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1016,7 +932,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 230616, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1030,7 +946,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 230616, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1044,7 +960,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 230616, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1058,7 +974,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 41984, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1072,7 +988,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 41984, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1086,7 +1002,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 41984, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1100,7 +1016,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 7558, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1114,7 +1030,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 7558, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1128,7 +1044,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 7558, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1142,7 +1058,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 2274, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1156,7 +1072,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 2274, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1170,7 +1086,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 2274, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1184,7 +1100,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 24372, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1198,7 +1114,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 24372, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1212,7 +1128,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 24372, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1226,7 +1142,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 57027, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1240,7 +1156,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 57027, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1254,7 +1170,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 57027, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1268,7 +1184,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 11879, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1282,7 +1198,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 11879, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1296,7 +1212,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 11879, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1310,7 +1226,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 65680, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1324,7 +1240,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 65680, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1338,7 +1254,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 65680, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1352,7 +1268,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 36813, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1366,7 +1282,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 36813, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1380,7 +1296,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 36813, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1394,7 +1310,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 134493, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1408,7 +1324,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 134493, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1422,7 +1338,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 134493, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1436,7 +1352,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 63161, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1450,7 +1366,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 63161, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1464,7 +1380,7 @@ "exit_code": 0, "duration_sec": "NA", "cpu_pct": "NA", - "peak_memory_mb": 63161, + "peak_memory_mb": 0, "disk_read_mb": "NA", "disk_write_mb": "NA" } @@ -1762,47 +1678,5 @@ "disk_read_mb": "NA", "disk_write_mb": "NA" } - }, - { - "dataset_id": "replogle", - "method_id": "scprint", - "metric_component_name": "regression_1", - "resources": { - "submit": "2025-02-26 19:54:14", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } - }, - { - "dataset_id": "replogle", - "method_id": "scprint", - "metric_component_name": "regression_2", - "resources": { - "submit": "2025-02-26 19:54:14", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } - }, - { - "dataset_id": "replogle", - "method_id": "scprint", - "metric_component_name": "ws_distance", - "resources": { - "submit": "2025-02-26 19:54:12", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } } ] diff --git a/results/grn_inference/data/results.json b/results/grn_inference/data/results.json index 38415e83..1430a548 100644 --- a/results/grn_inference/data/results.json +++ b/results/grn_inference/data/results.json @@ -391,15 +391,7 @@ "ws-theta-1.0": 0 }, "mean_score": 0, - "resources": { - "submit": "2025-02-26 19:49:51", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } + "resources": {} }, { "dataset_id": "nakatake", @@ -1187,15 +1179,7 @@ "ws-theta-1.0": 0.6604 }, "mean_score": 0.5793, - "resources": { - "submit": "2025-02-26 19:49:51", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } + "resources": {} }, { "dataset_id": "op", @@ -2023,14 +2007,6 @@ "ws-theta-1.0": -0.4668 }, "mean_score": 0.1839, - "resources": { - "submit": "2025-02-26 19:49:51", - "exit_code": 0, - "duration_sec": "NA", - "cpu_pct": "NA", - "peak_memory_mb": 0, - "disk_read_mb": "NA", - "disk_write_mb": "NA" - } + "resources": {} } ] diff --git a/results/grn_inference/thumbnail.svg b/results/grn_inference/thumbnail.svg new file mode 100644 index 00000000..61d0fb24 --- /dev/null +++ b/results/grn_inference/thumbnail.svg @@ -0,0 +1,9476 @@ + + + + + + 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