def test_SVR():
"""
Test Support Vector Regression
"""
diabetes = datasets.load_diabetes()
for clf in (svm.NuSVR(kernel='linear', nu=.4),
svm.SVR(kernel='linear', C=10.),
svm.sparse.NuSVR(kernel='linear', nu=.4),
svm.sparse.SVR(kernel='linear', C=10.)):
clf.fit(diabetes.data, diabetes.target)
assert clf.score(diabetes.data, diabetes.target) > 0.02
def test_bayesian_on_diabetes():
"""
Test BayesianRidge on diabetes
"""
diabetes = datasets.load_diabetes()
X, y = diabetes.data, diabetes.target
clf = BayesianRidge(compute_score=True)
# Test with more samples than features
clf.fit(X, y)
# Test that scores are increasing at each iteration
assert_array_equal(np.diff(clf.all_score_) > 0, True)
# Test with more features than samples
X = X[:5, :]
y = y[:5]
clf.fit(X, y)
# Test that scores are increasing at each iteration
assert_array_equal(np.diff(clf.all_score_) > 0, True)
def test_bayesian_on_diabetes():
"""
Test BayesianRidge on diabetes
"""
raise nose.SkipTest("XFailed Test")
diabetes = datasets.load_diabetes()
X, y = diabetes.data, diabetes.target
clf = BayesianRidge(compute_score=True)
# Test with more samples than features
clf.fit(X, y)
# Test that scores are increasing at each iteration
assert_array_equal(np.diff(clf.scores_) > 0, True)
# Test with more features than samples
X = X[:5, :]
y = y[:5]
clf.fit(X, y)
# Test that scores are increasing at each iteration
assert_array_equal(np.diff(clf.scores_) > 0, True)
import numpy as np
from numpy.testing import (assert_array_almost_equal,
assert_almost_equal)
from nose.tools import assert_equal, assert_true
from ..lars import lars_path, LassoLARS, LARS
from ..coordinate_descent import Lasso
from scikits.learn import datasets
n, m = 10, 10
np.random.seed (0)
diabetes = datasets.load_diabetes()
X, y = diabetes.data, diabetes.target
def test_simple():
"""
Principle of LARS is to keep covariances tied and decreasing
"""
max_pred = 10
alphas_, active, coef_path_ = lars_path(diabetes.data, diabetes.target, max_iter=max_pred, method="lar")
for (i, coef_) in enumerate(coef_path_.T):
res = y - np.dot(X, coef_)
cov = np.dot(X.T, res)
C = np.max(abs(cov))
eps = 1e-3
ocur = len(cov[ C - eps < abs(cov)])
if i < max_pred:
assert ocur == i+1
determination (R2) without reperforming MLE, using the set of correlation
parameters found on the whole dataset.
"""
print __doc__
# Author: Vincent Dubourg <*****@*****.**>
# License: BSD style
import numpy as np
from scikits.learn import datasets
from scikits.learn.gaussian_process import GaussianProcess
from scikits.learn.cross_val import cross_val_score, KFold
from scikits.learn.metrics import r2_score
# Load the dataset from scikits' data sets
diabetes = datasets.load_diabetes()
X, y = diabetes.data, diabetes.target
# Instanciate a GP model
gp = GaussianProcess(regr='constant',
corr='absolute_exponential',
theta0=[1e-4] * 10,
thetaL=[1e-12] * 10,
thetaU=[1e-2] * 10,
nugget=1e-2,
optimizer='Welch')
# Fit the GP model to the data performing maximum likelihood estimation
gp.fit(X, y)
# Deactivate maximum likelihood estimation for the cross-validation loop
"On the degrees of freedom of the lasso" Hui Zou, Trevor Hastie, and Robert Tibshirani Ann. Statist. Volume 35, Number 5 (2007), 2173-2192. """ print __doc__ # Author: Alexandre Gramfort <*****@*****.**> # License: BSD Style. from math import log import numpy as np from scikits.learn.linear_model import lars_path from scikits.learn.datasets import load_diabetes diabetes = load_diabetes() X, y = diabetes.data, diabetes.target # add garbage features rng = np.random.RandomState(42) X = np.c_[X, rng.randn(X.shape[0], 10)] n_samples = X.shape[0] # Standardize the data to avoid intercept problems y -= np.mean(y) X -= np.mean(X, axis=0) X /= np.std(X, axis=0) print "Computing regularization path using the LARS ..." alphas, features, coefs = lars_path(X, y, method='lasso', verbose=True)
