foundation_PLR/configs/mlflow_registry/experiments.yaml at main · petteriTeikari/foundation_PLR · GitHub

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# MLflow Experiments Registry
# ============================
# AIDEV-NOTE: This is a STATIC registry of MLflow experiments.
# Generated from /home/petteri/mlruns on 2026-01-22.
# If new experiments are run, regenerate with: scripts/update_mlflow_registry.py
#
# Purpose: Provide human-readable documentation of available experiments
# without requiring MLflow directory scanning.

version: "1.0.0"
generated: "2026-01-22"
mlflow_tracking_uri: "/home/petteri/mlruns"

experiments:
  PLR_OutlierDetection:
    id: "996740926475477194"
    runs: 31
    description: "Evaluate outlier detection methods for PLR signal preprocessing"
    purpose: "Compare foundation models vs traditional methods for artifact detection"
    metrics_file: "metrics/outlier_detection.yaml"
    parameters_file: "parameters/outlier_detection.yaml"

  PLR_Imputation:
    id: "940304421003085572"
    runs: 138
    description: "Evaluate imputation methods for missing PLR signal reconstruction"
    purpose: "Compare foundation models vs deep learning for signal imputation"
    metrics_file: "metrics/imputation.yaml"
    parameters_file: "parameters/imputation.yaml"

  PLR_Featurization:
    id: "143964216992376241"
    runs: 162
    description: "Compare featurization approaches for PLR classification"
    purpose: "Handcrafted amplitude bins vs foundation model embeddings"
    metrics_file: "metrics/classification.yaml"
    parameters_file: "parameters/classification.yaml"

  PLR_Classification:
    id: "253031330985650090"
    runs: 410
    description: "End-to-end pipeline evaluation for glaucoma screening"
    purpose: "Evaluate how preprocessing choices affect downstream classification"
    metrics_file: "metrics/classification.yaml"
    parameters_file: "parameters/classification.yaml"

# Summary statistics
summary:
  total_runs: 741
  total_experiments: 4
  research_question: |
    How do preprocessing choices (outlier detection → imputation) affect
    downstream classification performance when using handcrafted features?