params.yaml

setupEnv:
  name: rayTune

downloadSpeciesData:
  output_dir: data/downloaded

isolate3UTRs:
  output_dir: data/3UTRs
  transcriptSection: three_prime_utr,CDS,five_prime_utr

preprocessFlyUTRs:
  output_dir: output/data/flyUTRs
  kmer: 4
  oma_groups: data/oma-groups.txt.gz
  oma_ncbi_map: data/oma-ncbi.txt.gz
  #ncbi_flybase_map: "data/id_validation_table_ncbi_flybase.txt"
  ncbi_geneID_map: data/gene2refseq_Dmel.txt
  geneID_flybase_map: data/GeneID_to_FlybaseID.txt
  #maxSeqLen: null

trainToFlyUTRs:
  test_run: false
  do_train: true
  do_eval: false
  do_predict: false
  do_visualize: false
  output_dir: output/flyTrained
  kmer: 4
  model_type: 3utrfly
  should_continue: false
  model_name_or_path: data/4-new-12w-0
  tokenizer_name: rna4
  evaluate_during_training: true
  do_visualize_during_training: false
  numEpochsBeforeEarlyStopping: 20
  patience: 10
  per_gpu_train_batch_size: 48
  num_train_epochs: 100
  learning_rate: 5e-6
  beta1: 0.9
  beta2: 0.98
  adam_epsilon: 1e-6
  weight_decay: 0.01
  warmup_percent: 0.1
  seed: 6
  eval_all_checkpoints: false
  no_cuda: false
  logging_steps: 100
  save_steps: 1000
  save_total_limit: 2
  overwrite_output_dir: true
  neptune_token:
  neptune_project:
  overwrite_cache: false
  do_lower_case: false
  curriculumLearning: false

mergeUTRsAndDecayRates:
  decay_rates: "data/decay/parameters_estimates_extended_zygotic_tr_18022025_filtered.csv" #"data/results_zygotic_tr_extended_filtered.csv" #"data/maternal-halflifes_modified_filtered.csv"
  output_file: "output/data/utrDecayRates.fasta"

isolateFullTranscripts:
  output_file: output/data/full_transcripts/Dmela-full_transcripts.fa

getDecayResiduals:
  codon_output: output/data/codons/codon_frequencies.pkl
  residuals: output/data/codons/decayResiduals.fasta
  extraFeatures: output/data/codons/extraFeatures.csv

runViennaRNA:
  output_csv: output/data/codons/vienna_features.csv
  condaPath: /mnt/mr01-home01/m65338lb/.local/bin/micromamba
  maxWorkers: 8
  # RNAfold_path: "/mnt/mr01-home01/m65338lb/.local/share/mamba/envs/inseq/bin/RNAfold"

preprocessData:
  output_dir: output/data/decay
  kmer: 4

fineTuneModel:
  output_dir: output/ftModel
  do_train: true
  do_eval: false
  do_predict: false
  do_visualize: false

predict:
  output_dir: output/predict
  do_train: false
  do_eval: false
  do_predict: true
  do_visualize: false

ablate_features:
  output_dir: output/data/ablations

modelParams:
  model_type: 3utrfly
  task_name: rnadecay
  should_continue: false
  evaluate_during_training: true
  do_visualize_during_training: false
  patience: 5
  per_gpu_train_batch_size: 48
  per_gpu_eval_batch_size: 48
  num_train_epochs: 50
  warmup_percent: 0.1
  seed: 6
  eval_all_checkpoints: false
  no_cuda: false
  logging_steps: 100
  save_steps: 100000
  save_total_limit: 10
  overwrite_output_dir: true
  neptune_token:
  neptune_project:
  overwrite_cache: false
  do_lower_case: false

rayTune:
  ray_tune_samples: 100
  ray_tune_max_epochs: 50
  ray_tune_initial_points: 5
  ray_tune_grace_period: 4
  ray_tune_reduction_factor: 2
  ray_tune_cpu_per_trial: 2
  ray_tune_gpu_per_trial: 1.0
  ray_tune_local_dir: output/ray_results

importanceAnalysis:
  output_dir: output/importance
  pickle_file: shap.pkl
  debug: false

visualizeImportance:
  scoresOnly: shapScores.npy
  save_path: visualizeImportance.html
  save_tokenized_path: visualizeImportance_tokenized.html

findMotifs:
  #p_adjust: "fdr_bh"
  output_dir: output/motifs
  control_file: control_seqs.fasta
  motif_file: motif_seqs.fasta
  control_positions: control_positions.txt
  motif_positions: motif_positions.txt
  interest_file: interest_seqs.fasta

motifEnrichment:
  output_dir: output/motifEnrichment

motifEnrichment_highLowDecay:
  output_dir: output/motifEnrichment_highLow

prepRandomizeSeqsAndExtraFeatures:
  sequence_dir: output/data/sanityCheck
  seq_file: dev

randomizeSeqsAndExtraFeatures:
  per_gpu_pred_batch_size: 128

splitHighLowDecay:
  output: output/splitDecay
  high: highDecay.fasta
  low: lowDecay.fasta

prepForMAST:
  output_dir: output/data/MEME_suite
  output_fasta: TEseqs_split_by_utr.fasta

getMASTBackgroundFile:
  output_file: nucFrequencies.txt

runMAST:
  output_dir: output/mast_out
  output_file: mast_results.txt
  motif_file: /opt/meme/share/meme-5.5.7/db/motif_databases/RNA/Ray2013_rbp_Drosophila_melanogaster.meme
  mast_pvalue_threshold: 0.05

codonOnlyClassificationMLP:
  output_dir: output/codonOnlyClassificationMLP
  label: codonOnly
  patience: 7
  per_device_batch_size: 48
  num_train_epochs: 100

allExtraFeaturesClassificationMLP:
  label: allExtraFeatures

extraFeaturesAndMotifCountsClassificationMLP:
  label: extraFeaturesAndMotifCounts

rayTuneClassificationMLP:
  # Ray Tune specific parameters
  ray_tune_samples: 300           # Number of hyperparameter combinations to try
  ray_tune_max_epochs: 100        # Maximum epochs per trial
  ray_tune_initial_points: 30        # Maximum epochs per trial
  ray_tune_grace_period: 5       # Minimum epochs before early stopping
  ray_tune_reduction_factor: 2   # ASHA scheduler reduction factor
  ray_tune_cpu_per_trial: 1      # CPUs allocated per trial
  ray_tune_gpu_per_trial: 0    # GPUs allocated per trial
  ray_tune_local_dir: "output/ray_results_MLP"  # Directory for Ray Tune results
  train_final_model: true        # Whether to train final model with best hyperparameters