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Generate metafeatures for a dataset in both .csv and .in format, used with the Moscato group's continued fraction memetic algorithm.

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FILE: gen-metafeatures.py

AUTHOR: Julia Sloan ([email protected])

DATE: July 2020, modified Jan. 2021, Sept. 2021

USAGE: run "python gen-metafeatures.py file mul div add sub" where "file" includes the filename and path to its directory if different from the directory of this file. The input file should be a .csv file containing the target values in the first column followed by all features, but no metafeatures. Include any combination of the four parameters "mul", "div", "add", "sub" depending on which metafeatures you wish to generate.

The output will titled "file-METAFEATURES.csv" in the same directory as the input file.

NOTE: "mul" includes squares of each of the original features as well as all products. "div" includes both f1/f2 and f2/f1, where f1 and f2 are original features. "sub" includes f1-f2, but not f2-f1. "add" includes f1+f2.

In the case of division by 0, the resulting cell is filled with Python's "sys.maxsize" constant divided by 10^8 to prevent overwhelming Excel (92233720368).

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Generate metafeatures for a dataset in both .csv and .in format, used with the Moscato group's continued fraction memetic algorithm.

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