41 lines
982 B
Python
41 lines
982 B
Python
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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def get_ranks(linear, rfr, f):
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ranks = dict()
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features = ["x%s" % i for i in range(1, 15)]
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ranks['Linear'] = rank_to_dict(linear.coef_, features)
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ranks['RFR'] = rank_to_dict(rfr.feature_importances_, features)
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ranks['f_reg'] = rank_to_dict(f, features)
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return ranks
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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(np.array(ranks).reshape(14, 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 mean_calc_and_sort(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 mean
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