config.vsh.yaml

__merge__: ../../api/comp_method.yaml
name: guanlab_dengkw_pm
label: Guanlab-dengkw
summary: A kernel ridge regression method with RBF kernel. 
description: |
  This is a solution developed by Team Guanlab - dengkw in the Neurips 2021 competition to predict one modality
  from another using kernel ridge regression (KRR) with RBF kernel. Truncated SVD is applied on the combined
  training and test data from modality 1 followed by row-wise z-score normalization on the reduced matrix. The
  truncated SVD of modality 2 is predicted by training a KRR model on the normalized training matrix of modality 1.
  Predictions on the normalized test matrix are then re-mapped to the modality 2 feature space via the right
  singular vectors.
references:
  doi: 10.1101/2022.04.11.487796
info:
  preferred_normalization: log_cp10k
  documentation_url: https://github.com/openproblems-bio/neurips2021_multimodal_topmethods/tree/main/src/predict_modality/methods/Guanlab-dengkw
  repository_url: https://github.com/openproblems-bio/neurips2021_multimodal_topmethods/tree/main/src/predict_modality/methods/Guanlab-dengkw
  competition_submission_id: 170636
arguments:
  - name: "--distance_method"
    type: "string"
    default: "minkowski"
    description: The distance metric to use. Possible values include `euclidean` and `minkowski`.
    choices: [euclidean, minkowski]
  - name: "--n_pcs"
    type: "integer"
    default: 50
    description: Number of components to use for dimensionality reduction.
resources:
  - type: python_script
    path: script.py
engines:
  - type: docker
    image: openproblems/base_python:1
    setup:
      - type: python
        packages: 
          - scikit-learn
          - pandas
          - numpy
runners:
  - type: executable
  - type: nextflow
    directives:
      label: [hightime, highmem, highcpu]