svm_diabetes.py

import numpy as np
from sklearn.svm import SVR
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
import matplotlib.pyplot as plt

# #############################################################################
diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)

# Use only one feature
diabetes_X = diabetes_X[:, np.newaxis, 0]

# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]

# Split the targets into training/testing sets
diabetes_y_train = diabetes_y[:-20]
diabetes_y_test = diabetes_y[-20:]


# #############################################################################
# Fit regression model
svr_rbf = SVR(kernel="rbf", C=100, gamma=0.1, epsilon=0.1)
svr_lin = SVR(kernel="linear", C=100, gamma="auto")
svr_poly = SVR(kernel="poly", C=100, gamma="auto", degree=3, epsilon=0.1, coef0=1)

# #############################################################################
# Look at the results
lw = 2

svrs = [svr_rbf, svr_lin, svr_poly]
kernel_label = ["RBF", "Linear", "Polynomial"]
model_color = ["m", "c", "g"]

for svr in svrs:
    diabetes_y_pred = svr.predict(diabetes_X_test)
    print("Mean squared error: %.2f" % mean_squared_error(diabetes_y_test, diabetes_y_pred))
    # The coefficient of determination: 1 is perfect prediction
    print("Coefficient of determination: %.2f" % r2_score(diabetes_y_test, diabetes_y_pred))