Exemplos de MeanSquaredError.evaluate em Python

Exemplo n.º 1

def evaluation_meansquarederror_modular (ground_truth, predicted):
	from modshogun import RegressionLabels
	from modshogun import MeanSquaredError

	ground_truth_labels = RegressionLabels(ground_truth)
	predicted_labels = RegressionLabels(predicted)

	evaluator = MeanSquaredError()
	mse = evaluator.evaluate(predicted_labels,ground_truth_labels)

	return mse

Exemplo n.º 2

Arquivo: regression_lars.py Projeto: JackieXie168/shogun

    lsr.set_labels(RegressionLabels(y))
    lsr.train(RealFeatures(X.T))

# gather LASSO path
path = np.zeros((p, LeastAngleRegression.get_path_size()))
for i in xrange(path.shape[1]):
    path[:, i] = LeastAngleRegression.get_w(i)

evaluator = MeanSquaredError()

# apply on training data
mse_train = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_train.shape[0]):
    LeastAngleRegression.switch_w(i)
    ypred = LeastAngleRegression.apply(RealFeatures(X.T))
    mse_train[i] = evaluator.evaluate(ypred, RegressionLabels(y))
ypred = lsr.apply(RealFeatures(X.T))
mse_train_lsr = evaluator.evaluate(ypred, RegressionLabels(y))

# apply on test data
mse_test = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_test.shape[0]):
    LeastAngleRegression.switch_w(i)
    ypred = LeastAngleRegression.apply(RealFeatures(Xtest.T))
    mse_test[i] = evaluator.evaluate(ypred, RegressionLabels(y))
ypred = lsr.apply(RealFeatures(Xtest.T))
mse_test_lsr = evaluator.evaluate(ypred, RegressionLabels(y))

fig = plt.figure()
ax_path = fig.add_subplot(1, 2, 1)
plt.plot(xrange(path.shape[1]), path.T, ".-")

Exemplo n.º 3

    lsr.set_labels(RegressionLabels(y))
    lsr.train(RealFeatures(X.T))

# gather LASSO path
path = np.zeros((p, LeastAngleRegression.get_path_size()))
for i in xrange(path.shape[1]):
    path[:, i] = LeastAngleRegression.get_w(i)

evaluator = MeanSquaredError()

# apply on training data
mse_train = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_train.shape[0]):
    LeastAngleRegression.switch_w(i)
    ypred = LeastAngleRegression.apply(RealFeatures(X.T))
    mse_train[i] = evaluator.evaluate(ypred, RegressionLabels(y))
ypred = lsr.apply(RealFeatures(X.T))
mse_train_lsr = evaluator.evaluate(ypred, RegressionLabels(y))

# apply on test data
mse_test = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_test.shape[0]):
    LeastAngleRegression.switch_w(i)
    ypred = LeastAngleRegression.apply(RealFeatures(Xtest.T))
    mse_test[i] = evaluator.evaluate(ypred, RegressionLabels(y))
ypred = lsr.apply(RealFeatures(Xtest.T))
mse_test_lsr = evaluator.evaluate(ypred, RegressionLabels(y))

fig = plt.figure()
ax_path = fig.add_subplot(1, 2, 1)
plt.plot(xrange(path.shape[1]), path.T, '.-')