Implementation of BETA BCO Ranking systems

[HadleyKing]

Implement the ideas from #328 into the BCO Scoring function:

bco_api/biocompute/services.py

Lines 599 to 621 in 456d002

	def bco_score(bco_instance: Bco) -> Bco:
	"""BCO Score
	Process and score BioCompute Objects (BCOs).
	"""
	contents = bco_instance.contents
	if "usability_domain" not in contents:
	bco_instance.score = 0
	return bco_instance
	try:
	usability_domain_length = sum(len(s) for s in contents['usability_domain'])
	score = {"usability_domain_length": usability_domain_length}
	except TypeError:
	score = {"usability_domain_length": 0}
	usability_domain_length = 0
	bco_instance.score = usability_domain_length
	return bco_instance

[Kirans0615]

BCO Scoring System New.zip

[seankim658]

Spoke with Hadley and we had some ideas for the representation of the scores in the data model. I've been implementing scores in the biomarker project for scoring "trustworthy" biomarkers and a few things that we've done that have made things easier to track are:

1. Have some sort of internal versioning for the scores. The scoring is an iterative process that changes over time and as it changes having some sort of way to delineate which scores come from which version formula is very helpful.
2. When calculating the scores, create an object with the formula breakdown. This can be used both internally when investigating scores and on the frontend to show users how the score was calculated/where the weights are coming from. The biomarker project has that information in our data schema and returns it on API requests. It looks like this:

{
  "score": 3.4,
    "score_info": {
      "contributions": [
        {
          "c": "first_pmid",
          "w": 1,
          "f": 1
        },
        {
          "c": "other_pmid",
          "w": 0.2,
          "f": 7
        },
        {
          "c": "first_source",
          "w": 1,
          "f": 1
        },
        {
          "c": "other_source",
          "w": 0.1,
          "f": 0
        },
        {
          "c": "generic_condition_pen",
          "w": -4,
          "f": 0
        },
        {
          "c": "loinc",
          "w": 1,
          "f": 0
        }
      ],
      "formula": "sum(w*f)",
      "variables": {
        "w": "weight",
        "c": "condition",
        "f": "frequency"
      }
    }
}

This shows that the score was calculated by the sum of the weights times the frequencies. For example, having one PMID associated with the biomarker is a weight of 1. Additional PMIDs get a weight of 0.2, and so on. So the calculation for this score was 1(1) + 0.2(7) + 1(1).

[tiwa1154]

Write a FAQ on how the ranking system works, criteria, etc?

[tiwa1154]

FAQ created in #446