Refactoring machine learning code - namedtuple
This article covers:
Instead of using sometimes confusing indexing in your code, use a namedtuple instead. It’s backwards compatible, so you can still use the index, but you can make your code much more readable.
This is especially helpful when you transform between
numpy based code, where
PIL uses a column, row notation while
numpy uses a row, column notation.
Let’s consider this piece of code where we want to get the pixel locations of several points which are in the
def get_image_values(image_path, locations): img = Image.open(image_path).load() return [img[point, point] for point in locations] values = get_image_values('my_image.png', [[2, 3], [5, 3], [7, 9]])
To me this code is hard to read, because it’s not clear what these
point refer to and also why is
Let’s use a namedtuple to make this much easier to read:
from collections import namedtuple Point = namedtuple('Point', ['row', 'column']) def get_image_values(image_path, locations): img = Image.open(image_path).load() return [img[point.column, point.row] for point in locations] values = get_image_values('my_image.png', [ Point(row=2, column=3), Point(row=5, column=3), Point(row=7, column=9)] )
Note that you can still use
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