Morten Hjorth-Jensen
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.model_selection import KFold
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from sklearn.linear_model import Ridge
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from sklearn.model_selection import cross_val_score
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from sklearn.preprocessing import PolynomialFeatures
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# A seed just to ensure that the random numbers are the same for every run.
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np.random.seed(3155)
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# Generate the data.
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n = 100
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x = np.linspace(-3, 3, n).reshape(-1, 1)
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y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
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# Decide degree on polynomial to fit
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#poly = PolynomialFeatures(degree = 10)
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# Decide which values of lambda to use
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nlambdas = 500
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lambdas = np.logspace(-3, 5, nlambdas)
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# Initialize a KFold instance
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k = 5
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kfold = KFold(n_splits = k)
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estimated_mse_sklearn = np.zeros(nlambdas)
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i = 0
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for lmb in lambdas:
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ridge = Ridge(alpha = lmb)
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estimated_mse_folds = cross_val_score(ridge, x, y, scoring='neg_mean_squared_error', cv=kfold)
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estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)
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i += 1
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plt.figure()
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plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')
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plt.xlabel('log10(lambda)')
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plt.ylabel('MSE')
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plt.legend()
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plt.show()
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