mhjensen
48 lines
1.6 KiB
Plaintext
48 lines
1.6 KiB
Plaintext
import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.model_selection import train_test_split
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import xgboost as xgb
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from sklearn.preprocessing import StandardScaler
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import scikitplot as skplt
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from sklearn.metrics import mean_squared_error
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n = 500
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n_boostraps = 100
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maxdegree = 8
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# Make data set.
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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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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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scaler = StandardScaler()
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scaler.fit(X_train)
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X_train_scaled = scaler.transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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for degree in range(maxdegree):
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model = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
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max_depth = maxdegree, alpha = 10, n_estimators = 10)
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model.fit(X_train_scaled,y_train)
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y_pred = model.predict(X_test_scaled)
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polydegree[degree] = degree
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error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
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bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
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variance[degree] = np.mean( np.var(y_pred) )
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print('Polynomial degree:', degree)
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print('Error:', error[degree])
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print('Bias^2:', bias[degree])
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print('Var:', variance[degree])
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print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
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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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