68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.preprocessing import MinMaxScaler
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from RandomizedLasso import RandomizedLasso
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from sklearn.linear_model import Ridge
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from matplotlib import pyplot as plt
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import numpy as np
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np.random.seed(0)
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size = 1000
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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 + 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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ridge = Ridge(alpha=1.0)
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ridge.fit(X, Y)
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lasso = RandomizedLasso(alpha=0.007)
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lasso.fit(X, Y)
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randForestRegression = RandomForestRegressor(max_depth=4, min_samples_leaf=1, min_impurity_decrease=0, ccp_alpha=0)
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randForestRegression.fit(X, Y)
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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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ranks = {'Ridge': {}, 'RandomizedLasso': {}, 'RandomForestRegressor': {}}
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names = ["x%s" % i for i in range(1, 15)]
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ranks["Ridge"] = rank_to_dict(ridge.coef_, names)
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ranks["RandomizedLasso"] = rank_to_dict(lasso.coef_, names)
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ranks["RandomForestRegressor"] = rank_to_dict(randForestRegression.feature_importances_, names)
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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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print('VALUES')
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for r in ranks.items():
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print(r)
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print('MEAN')
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print(mean)
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for i, (model_name, features) in enumerate(ranks.items()):
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subplot = plt.subplot(2, 2, i + 1)
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subplot.set_title(model_name)
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subplot.bar(list(features.keys()), list(features.values()))
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subplot = plt.subplot(2, 2, 4)
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subplot.set_title('Mean')
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subplot.bar(list(mean.keys()), list(mean.values()))
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plt.show()
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