def OMP_cv(problem, **kwargs):
r"""High level description.
Requirements
------------
kwargs['choose'] must be a positive integer
kwargs['coef_tolerance'] must be a nonnegative float
Returns
-------
output : tuple
(optimum, maximum)
"""
data_list = [datum['data']['values'] for datum in problem.data]
data = numpy.array(data_list)
OMP = OrthogonalMatchingPursuitCV(max_iter=kwargs['choose'])
OMP.fit(data.T, problem.goal['data']['values'])
OMP_coefficients = OMP.coef_
optimum = [
problem.data[index] for index, element in enumerate(OMP_coefficients)
if abs(element) > kwargs['coef_tolerance']
]
maximum = OMP.score(data.T, problem.goal['data']['values'])
output = (optimum, maximum)
return output
print "\n**********测试OrthogonalMatchingPursuitCV类**********"
ompCV = OrthogonalMatchingPursuitCV(cv=5)
# 拟合训练集
ompCV.fit(train_X, train_Y.values.ravel())
# 打印最好的n_nonzero_coefs值
print "最好的n_nonzero_coefs值: ", ompCV.n_nonzero_coefs_
# 打印模型的系数
print "系数:", ompCV.coef_
print "截距:", ompCV.intercept_
print '训练集R2: ', r2_score(train_Y, ompCV.predict(train_X))
# 对于线性回归模型, 一般使用均方误差(Mean Squared Error,MSE)或者
# 均方根误差(Root Mean Squared Error,RMSE)在测试集上的表现来评该价模型的好坏.
test_Y_pred = ompCV.predict(test_X)
print "测试集得分:", ompCV.score(test_X, test_Y)
print "测试集MSE:", mean_squared_error(test_Y, test_Y_pred)
print "测试集RMSE:", np.sqrt(mean_squared_error(test_Y, test_Y_pred))
print "测试集R2:", r2_score(test_Y, test_Y_pred)
tss, rss, ess, r2 = xss(Y, ompCV.predict(X))
print "TSS(Total Sum of Squares): ", tss
print "RSS(Residual Sum of Squares): ", rss
print "ESS(Explained Sum of Squares): ", ess
print "R^2: ", r2
print "\n**********测试MultiTaskLasso类**********"
# 在初始化MultiTaskLasso类时, 指定参数alpha, 默认值是1.0.
multiTaskLasso = MultiTaskLasso(alpha=1.0)
# 拟合训练集
multiTaskLasso.fit(train_X, train_Y)
