77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
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"""
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===================================================================
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Support Vector Regression (SVR) using linear and non-linear kernels
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===================================================================
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Toy example of 1D regression using linear, polynomial and RBF kernels.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.svm import SVR
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# %%
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# Generate sample data
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# --------------------
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X = np.sort(5 * np.random.rand(40, 1), axis=0)
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y = np.sin(X).ravel()
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# add noise to targets
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y[::5] += 3 * (0.5 - np.random.rand(8))
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# %%
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# Fit regression model
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# --------------------
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svr_rbf = SVR(kernel="rbf", C=100, gamma=0.1, epsilon=0.1)
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svr_lin = SVR(kernel="linear", C=100, gamma="auto")
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svr_poly = SVR(kernel="poly", C=100, gamma="auto", degree=3, epsilon=0.1, coef0=1)
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# %%
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# Look at the results
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# -------------------
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lw = 2
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svrs = [svr_rbf, svr_lin, svr_poly]
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kernel_label = ["RBF", "Linear", "Polynomial"]
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model_color = ["m", "c", "g"]
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fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)
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for ix, svr in enumerate(svrs):
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axes[ix].plot(
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X,
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svr.fit(X, y).predict(X),
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color=model_color[ix],
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lw=lw,
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label="{} model".format(kernel_label[ix]),
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)
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axes[ix].scatter(
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X[svr.support_],
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y[svr.support_],
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facecolor="none",
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edgecolor=model_color[ix],
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s=50,
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label="{} support vectors".format(kernel_label[ix]),
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)
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axes[ix].scatter(
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X[np.setdiff1d(np.arange(len(X)), svr.support_)],
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y[np.setdiff1d(np.arange(len(X)), svr.support_)],
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facecolor="none",
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edgecolor="k",
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s=50,
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label="other training data",
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)
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axes[ix].legend(
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loc="upper center",
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bbox_to_anchor=(0.5, 1.1),
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ncol=1,
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fancybox=True,
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shadow=True,
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)
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fig.text(0.5, 0.04, "data", ha="center", va="center")
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fig.text(0.06, 0.5, "target", ha="center", va="center", rotation="vertical")
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fig.suptitle("Support Vector Regression", fontsize=14)
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plt.show()
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