From 0fc24e86296c613f5aa24015518a9f187a2cdbb6 Mon Sep 17 00:00:00 2001 From: Andrew Grangaard Date: Thu, 28 Oct 2021 22:21:16 -0700 Subject: [PATCH] [mypy] Annotates other/scoring_algorithm (#5621) * scoring_algorithm: Moves doctest into function docstring so it will be run * [mypy] annotates other/scoring_algorithm * [mypy] renames temp var to unique value to work around mypy issue in other/scoring_algorithm reusing loop variables with the same name and different types gives this very confusing mypy error response. pyright correctly infers the types without issue. ``` scoring_algorithm.py:58: error: Incompatible types in assignment (expression has type "float", variable has type "List[float]") scoring_algorithm.py:60: error: Unsupported operand types for - ("List[float]" and "float") scoring_algorithm.py:65: error: Incompatible types in assignment (expression has type "float", variable has type "List[float]") scoring_algorithm.py:67: error: Unsupported operand types for - ("List[float]" and "float") Found 4 errors in 1 file (checked 1 source file) ``` * scoring_algorithm: uses enumeration instead of manual indexing on loop var * scoring_algorithm: sometimes we look before we leap. * clean-up: runs `black` to fix formatting --- other/scoring_algorithm.py | 29 ++++++++++++++--------------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/other/scoring_algorithm.py b/other/scoring_algorithm.py index 77e614e26..cc1744012 100644 --- a/other/scoring_algorithm.py +++ b/other/scoring_algorithm.py @@ -20,39 +20,38 @@ 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] - ->>> 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]] """ -def procentual_proximity(source_data: list, weights: list) -> list: +def procentual_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 = [] - for item in source_data: - for i in range(len(item)): - try: - data_lists[i].append(float(item[i])) - except IndexError: - # generate corresponding number of lists + data_lists: list[list[float]] = [] + for data in source_data: + for i, el in enumerate(data): + if len(data_lists) < i + 1: data_lists.append([]) - data_lists[i].append(float(item[i])) + data_lists[i].append(float(el)) - score_lists = [] + score_lists: list[list[float]] = [] # calculating each score for dlist, weight in zip(data_lists, weights): mind = min(dlist) maxd = max(dlist) - score = [] + score: list[float] = [] # for weight 0 score is 1 - actual score if weight == 0: for item in dlist: @@ -75,7 +74,7 @@ def procentual_proximity(source_data: list, weights: list) -> list: score_lists.append(score) # initialize final scores - final_scores = [0 for i in range(len(score_lists[0]))] + final_scores: list[float] = [0 for i in range(len(score_lists[0]))] # generate final scores for i, slist in enumerate(score_lists):