EviGRN/conf/tf_eager_integrated_standard.yml at main · BioinfoMachineLearning/EviGRN · GitHub

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workflow:
  id: tf_eager_integrated_run
  seed: 42
  artifact_root: artifacts
  single_artifact_dir: true

acquisition:
  enabled: false
  reuse_if_exists: true
  expr: Data/sc-RNA-seq/Example/ExpressionData.csv
  species: mouse
  cell_type: example cell type
  cell_line: ""
  cell_context: ""
  dataset_id: example_dataset
  out_manifest: artifacts/tf_eager_integrated_run/acquisition/multimodal_manifest.json
  gold_network: ""
  genome: mm10
  strict: true
  min_promoter_coverage: 0.6
  coverage_denominator: rnaseq_expressed
  max_atac_candidates: 5
  skip_atac_search: false
  skip_motif: false

dataset:
  mode: beeline_csv
  dataset_id: example_dataset
  species: mouse
  expression_path: Data/sc-RNA-seq/Example/ExpressionData.csv
  tf_file: Data/sc-RNA-seq/Example/TFs.csv
  modalities:
    - scrna
    - atac

cell_context:
  use_scanpy: false
  cell_type: example cell type

candidates:
  mode: tf_centered_window
  expression_transform: arcsinh
  corr_threshold: 0.25
  negative_ratio: 5
  train_window_neighbors: 200
  train_subgraph_bootstraps: 5
  val_subgraph_bootstraps: 5
  test_subgraph_bootstraps: 5
  train_include_positives: true
  blind_ensure_coverage: true
  eval_ensure_coverage: true
  blind_exhaustive_all_pairs: false
  expr_weak_percentile: 0.3
  expr_probable_percentile: 0.6
  motif_score_threshold: 0.0
  accessibility_threshold: 0.0
  linkage_threshold: 0.0
  rescue_motif: true
  rescue_accessibility: true
  rescue_max_per_tf: 100

disable_priors: true
use_ortholog_lookup: false

scoring:
  device: cuda

build_device: cpu
prior_device: cpu

split:
  enabled: false
  gold_edges: ""
  out: artifacts/tf_eager_integrated_run/splits/split_manifest.csv
  fold_id: example_f1
  node_split_mode: expression
  target_gene_policy: audit
  train_ratio: 0.7
  val_ratio: 0.1
  test_ratio: 0.2

tf_eager:
  strategy: leave_one_tf_out
  fold_id: example_f1
  checkpoint: artifacts/tf_eager_integrated_run/tf_eager/tf_eager.pt
  train_windows_jsonl: artifacts/tf_eager_integrated_run/tf_eager/train_windows.jsonl
  test_windows_jsonl: artifacts/tf_eager_integrated_run/tf_eager/test_windows.jsonl
  window_size: 100
  prior_train_frac: 0.8
  prior_bootstrap: 8

build_train_windows:
  enabled: false
  subset: train
  reuse_if_exists: true
  tf_workers: 8

train_tf_eager:
  enabled: false
  reuse_if_exists: false
  epochs: 100
  lr: 0.001
  weight_decay: 0.05
  dropout: 0.2
  val_frac: 0.1
  device: cuda
  batch_size: 8
  num_workers: 0
  early_stopping_patience: 10
  early_stopping_min_delta: 0.0001
  checkpoint_metric: val_auprc
  seed: 42
  wandb: false
  wandb_project: grn-agent-tf-eager
  wandb_run_name: tf_eager_integrated_run_train

build_test_windows:
  enabled: true
  subset: test
  reuse_if_exists: true
  tf_workers: 8
  build_device: cpu

infer_tf_eager:
  enabled: true
  reuse_if_exists: false
  scored_csv: artifacts/tf_eager_integrated_run/tf_eager/test_scored_edges.csv
  network_csv: artifacts/tf_eager_integrated_run/tf_eager/test_network.csv
  evidence_jsonl: artifacts/tf_eager_integrated_run/tf_eager/test_flat_evidence.jsonl
  threshold: 0.5
  topk_per_tf: 100
  aggregation_vote_threshold: 0.3
  device: cuda

evaluation:
  enabled: true
  reuse_if_exists: false
  gold_edges: ""
  strategy: leave_one_tf_out
  fold_id: example_f1
  subset: test
  negative_ratios: "1,2,5,10"
  negative_repeats: 1
  k_values: "10,50,100"
  out_report: artifacts/tf_eager_integrated_run/evaluation/eval_test_by_ratio.json
  wandb: false
  wandb_project: grn-agent-tf-eager
  wandb_run_name: tf_eager_integrated_run_eval