PyMongo: how to query a series and find the closest match - python-3.x

Question

This is a simplified example of how my data is stored in MongoDB of a single athlete:
{ "_id" : ObjectId('5bd6eab25f74b70e5abb3326'),
"Result" : 12,
"Race" : [0.170, 4.234, 9.170]
"Painscore" : 68,
}
Now when this athlete has performed a race I want to search for the race that was MOST similar to the current one, and hence I want to compare both painscores.
IOT get the best 'match' I tried this:
query = [0.165, 4.031, 9.234]
closestBelow = db[athlete].find({'Race' : {"$lte": query}}, {"_id": 1, "Race": 1}).sort("Race", -1).limit(2)
for i in closestBelow:
print(i)
closestAbove = db[athlete].find({'Race' : {"$gte": query}}, {"_id": 1, "Race": 1}).sort("Race", 1).limit(2)
for i in closestAbove:
print(i)
This does not seem to work.
Question1: How can I give the mentioned query IOT find the race in Mongo that matches the best/closes?.. When taken in account that a race is almost never exactly the same.
Question2: How can i see a percentage of match per document so that an athlete knows how 'serious' he must interpreted the pain score?
Thank you.

Answer 1

Thanks to this website I found a solution: http://dataaspirant.com/2015/04/11/five-most-popular-similarity-measures-implementation-in-python/
Step 1: find your query;
Step 2: make a first selection based on query and append the results into a list (for example average);
Step 3: use a for loop to compare every item in the list with your query. Use Euclidean distance for this;
Step 4: when you have your matching processed, define the best match into a variable.
from pymongo import MongoClient
client = MongoClient('mongodb://localhost:27017/')
Database = 'Firstclass'
def newSearch(Athlete):
# STEP 1
db = client[Database]
lastDoc = [i for i in db[Athlete].find({},{ '_id': 1, 'Race': 1, 'Avarage': 1}).sort('_id', -1).limit(1)]
query = { '$and': [ { 'Average' : {'$gte': lastDoc[0].get('Average')*0.9} }, { 'Average' : {'$lte': lastDoc[0].get('Average')*1.1} } ] }
funnel = [x for x in db[Athlete].find(query, {'_id': 1, 'Race': 1}).sort('_id', -1).limit(15)]
#STEP 2
compareListID = []
compareListRace = []
for x in funnel:
if lastDoc[0].get('_id') != x.get('_id'):
compareListID.append(x.get('_id'))
compareListRace.append(x.get('Race'))
#STEP 3
for y in compareListRace:
ED = euclidean_distance(lastDoc[0].get('Race'),y)
ESlist.append(ED)
#STEP 4
matchObjID = compareListID[numpy.argmax(ESlist)]
matchRace = compareListRace[numpy.argmax(ESlist)]
newSearch('Jim')