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120 lines
3.6 KiB
Python
120 lines
3.6 KiB
Python
"""
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| developed by: markmelnic
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| original repo: https://github.com/markmelnic/Scoring-Algorithm
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Analyse data using a range based percentual proximity algorithm
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and calculate the linear maximum likelihood estimation.
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The basic principle is that all values supplied will be broken
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down to a range from ``0`` to ``1`` and each column's score will be added
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up to get the total score.
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Example for data of vehicles
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::
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price|mileage|registration_year
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20k |60k |2012
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22k |50k |2011
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23k |90k |2015
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16k |210k |2010
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We want the vehicle with the lowest price,
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lowest mileage but newest registration year.
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Thus the weights for each column are as follows:
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``[0, 0, 1]``
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"""
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def get_data(source_data: list[list[float]]) -> list[list[float]]:
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"""
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>>> get_data([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]])
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[[20.0, 23.0, 22.0], [60.0, 90.0, 50.0], [2012.0, 2015.0, 2011.0]]
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"""
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data_lists: list[list[float]] = []
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for data in source_data:
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for i, el in enumerate(data):
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if len(data_lists) < i + 1:
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data_lists.append([])
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data_lists[i].append(float(el))
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return data_lists
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def calculate_each_score(
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data_lists: list[list[float]], weights: list[int]
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) -> list[list[float]]:
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"""
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>>> calculate_each_score([[20, 23, 22], [60, 90, 50], [2012, 2015, 2011]],
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... [0, 0, 1])
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[[1.0, 0.0, 0.33333333333333337], [0.75, 0.0, 1.0], [0.25, 1.0, 0.0]]
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"""
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score_lists: list[list[float]] = []
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for dlist, weight in zip(data_lists, weights):
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mind = min(dlist)
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maxd = max(dlist)
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score: list[float] = []
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# for weight 0 score is 1 - actual score
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if weight == 0:
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for item in dlist:
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try:
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score.append(1 - ((item - mind) / (maxd - mind)))
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except ZeroDivisionError:
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score.append(1)
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elif weight == 1:
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for item in dlist:
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try:
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score.append((item - mind) / (maxd - mind))
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except ZeroDivisionError:
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score.append(0)
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# weight not 0 or 1
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else:
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msg = f"Invalid weight of {weight:f} provided"
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raise ValueError(msg)
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score_lists.append(score)
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return score_lists
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def generate_final_scores(score_lists: list[list[float]]) -> list[float]:
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"""
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>>> generate_final_scores([[1.0, 0.0, 0.33333333333333337],
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... [0.75, 0.0, 1.0],
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... [0.25, 1.0, 0.0]])
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[2.0, 1.0, 1.3333333333333335]
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"""
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# initialize final scores
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final_scores: list[float] = [0 for i in range(len(score_lists[0]))]
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for slist in score_lists:
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for j, ele in enumerate(slist):
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final_scores[j] = final_scores[j] + ele
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return final_scores
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def procentual_proximity(
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source_data: list[list[float]], weights: list[int]
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) -> list[list[float]]:
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"""
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| `weights` - ``int`` list
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| possible values - ``0`` / ``1``
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* ``0`` if lower values have higher weight in the data set
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* ``1`` if higher values have higher weight in the data set
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>>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
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[[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
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"""
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data_lists = get_data(source_data)
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score_lists = calculate_each_score(data_lists, weights)
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final_scores = generate_final_scores(score_lists)
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# append scores to source data
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for i, ele in enumerate(final_scores):
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source_data[i].append(ele)
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return source_data
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