scoring_algorithm.py

"""
developed by: markmelnic
original repo: https://github.com/markmelnic/Scoring-Algorithm

Analyse data using a range based percentual proximity algorithm
and calculate the linear maximum likelihood estimation.
The basic principle is that all values supplied will be broken
down to a range from 0 to 1 and each column's score will be added
up to get the total score.

==========
Example for data of vehicles
price|mileage|registration_year
20k |60k |2012
22k |50k |2011
23k |90k |2015
16k |210k |2010

We want the vehicle with the lowest price,
lowest mileage but newest registration year.
Thus the weights for each column are as follows:
[0, 0, 1]
"""


defprocentual_proximity(
source_data: list[list[float]], weights: list[int]
) ->list[list[float]]:

"""
 weights - int list
 possible values - 0 / 1
 0 if lower values have higher weight in the data set
 1 if higher values have higher weight in the data set

 >>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
 [[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
 """

# getting data
data_lists: list[list[float]] = []
fordatainsource_data:
fori, elinenumerate(data):
iflen(data_lists) <i+1:
data_lists.append([])
data_lists[i].append(float(el))

score_lists: list[list[float]] = []
# calculating each score
fordlist, weightinzip(data_lists, weights):
mind=min(dlist)
maxd=max(dlist)

score: list[float] = []
# for weight 0 score is 1 - actual score
ifweight==0:
foritemindlist:
try:
score.append(1- ((item-mind) / (maxd-mind)))
exceptZeroDivisionError:
score.append(1)

elifweight==1:
foritemindlist:
try:
score.append((item-mind) / (maxd-mind))
exceptZeroDivisionError:
score.append(0)

# weight not 0 or 1
else:
raiseValueError(f"Invalid weight of {weight:f} provided")

score_lists.append(score)

# initialize final scores
final_scores: list[float] = [0foriinrange(len(score_lists[0]))]

# generate final scores
forslistinscore_lists:
forj, eleinenumerate(slist):
final_scores[j] =final_scores[j] +ele

# append scores to source data
fori, eleinenumerate(final_scores):
source_data[i].append(ele)

returnsource_data