72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
from sklearn.linear_model import Lasso
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from sklearn.ensemble import RandomForestRegressor
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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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# Генерация исходных данных
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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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# Лассо
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lasso = Lasso(alpha=0.05)
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lasso.fit(X, Y)
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# Случайные деревья
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rf = RandomForestRegressor(n_estimators=100, random_state=0)
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rf.fit(X, Y)
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# Линейная корреляция (f_regression)
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correlation_coeffs, _ = f_regression(X, Y)
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# Ранжирование с использованием MinMaxScaler
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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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# Ранжирование для каждой модели
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ranks = {}
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names = ["x%s" % i for i in range(1, 15)]
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ranks["Lasso"] = rank_to_dict(lasso.coef_, names)
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ranks["Random Forest"] = rank_to_dict(rf.feature_importances_, names)
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ranks["Correlation"] = rank_to_dict(correlation_coeffs, names)
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# Создание пустого словаря для данных
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mean = {}
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# Обработка словаря ranks
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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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# Нахождение среднего по каждому признаку
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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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# Сортировка и вывод списка средних значений
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mean_dict = dict(mean)
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print("MEAN")
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print(mean_dict)
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# Вывод результатов ранжирования для каждой модели
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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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# Вывод топ-4 признаков с их значениями
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top_features = sorted(mean.items(), key=lambda x: x[1], reverse=True)[:4]
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print("Top 4 features with values:")
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for feature, value in top_features:
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print(f"{feature}: {value}")
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