43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
import config
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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from operator import itemgetter
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def rank_to_dict(ranks, names):
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ranks = np.abs(ranks)
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minmax = MinMaxScaler()
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ranks = minmax.fit_transform(
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np.array(ranks).reshape(config.FEATURES_AMOUNT, 1)).ravel()
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ranks = map(lambda x: round(x, 2), ranks)
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return dict(zip(names, ranks))
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def flip_array(arr):
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return-1 * arr + np.max(arr)
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def get_ranks(lasso, rfe, f):
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ranks = dict()
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names = ["x%s" % i for i in range(1, config.FEATURES_AMOUNT+1)]
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ranks[config.LASSO_TITLE] = rank_to_dict(lasso.coef_, names)
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ranks[config.RFE_TITLE] = rank_to_dict(flip_array(rfe.ranking_), names)
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ranks[config.F_REGRESSION_TITLE] = rank_to_dict(f, names)
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return ranks
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def calc_mean(ranks):
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mean = {}
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for key, value in ranks.items():
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print(key, value)
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for item in value.items():
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if item[0] not in mean:
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mean[item[0]] = 0
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mean[item[0]] += item[1]
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for key, value in mean.items():
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res = value/len(ranks)
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mean[key] = round(res, 2)
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return sorted(mean.items(), key=itemgetter(1), reverse=True)
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