mhjensen
45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.linear_model import LinearRegression, Ridge, Lasso
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.model_selection import train_test_split
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from sklearn.pipeline import make_pipeline
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from sklearn.utils import resample
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from sklearn import preprocessing
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np.random.seed(2018)
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n = 40
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n_boostraps = 100
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maxdegree = 14
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# Make data set.
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x = np.linspace(-3, 3, n).reshape(-1, 1)
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x = preprocessing.scale(x)
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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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error = np.zeros(maxdegree)
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bias = np.zeros(maxdegree)
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variance = np.zeros(maxdegree)
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polydegree = np.zeros(maxdegree)
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
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for degree in range(maxdegree):
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model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))
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y_pred = np.empty((y_test.shape[0], n_boostraps))
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for i in range(n_boostraps):
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x_, y_ = resample(x_train, y_train)
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y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()
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polydegree[degree] = degree
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error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
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bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
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variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
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plt.plot(polydegree, error, label='Error')
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plt.plot(polydegree, bias, label='bias')
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plt.plot(polydegree, variance, label='Variance')
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plt.legend()
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plt.show()
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