62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
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from sklearn.ensemble import RandomForestRegressor
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from RandomizedLasso import RandomizedLasso
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from sklearn.feature_selection import f_regression
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from sklearn.preprocessing import MinMaxScaler
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import numpy as np
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names = ["x%s" % i for i in range(1, 15)]
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def main():
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x,y = generation_data()
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# Сокращение признаков cлучайными деревьями (Random Forest Regressor)
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rfr = RandomForestRegressor()
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rfr.fit(x, y)
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# Модель линейной корреляции
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f, _ = f_regression(x, y, center=False)
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# Случайное Лассо
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randomized_lasso = RandomizedLasso(alpha=.01)
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randomized_lasso.fit(x, y)
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ranks = {"Random Forest Regressor": rank_to_dict(rfr.feature_importances_), 'f-Regression': rank_to_dict(f), "Randomize Lasso": rank_to_dict(randomized_lasso.coef_)}
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get_estimation(ranks)
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print_sorted_data(ranks)
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def generation_data():
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np.random.seed(0)
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size = 750
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X = np.random.uniform(0, 1, (size, 14))
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Y = (10 * np.sin(np.pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - .5) ** 2 +
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10 * X[:, 3] + 5 * X[:, 4] ** 5 + np.random.normal(0, 1))
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X[:, 10:] = X[:, :4] + np.random.normal(0, .025, (size, 4))
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return X, Y
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def rank_to_dict(ranks):
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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 get_estimation(ranks: {}):
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mean = {}
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for key, value in ranks.items():
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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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mean_sorted = sorted(mean.items(), key=lambda item: item[1], reverse=True)
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print("Средние значения")
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print(mean_sorted)
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print("4 самых важных признака по среднему значению")
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for item in mean_sorted[:4]:
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print('{0} - {1}'.format(item[0], item[1]))
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def print_sorted_data(ranks: {}):
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print()
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for key, value in ranks.items():
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ranks[key] = sorted(value.items(), key=lambda item: item[1], reverse=True)
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for key, value in ranks.items():
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print(key)
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print(value)
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main()
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