def test_csr_sparse_center_data():
# Test output format of sparse_center_data, when input is csr
X, y = make_regression()
X[X < 2.5] = 0.0
csr = sparse.csr_matrix(X)
csr_, y, _, _, _ = sparse_center_data(csr, y, True)
assert_equal(csr_.getformat(), 'csr')
def test_csr_sparse_center_data():
# Test output format of sparse_center_data, when input is csr
X, y = make_regression()
X[X < 2.5] = 0.0
csr = sparse.csr_matrix(X)
csr_, y, _, _, _ = sparse_center_data(csr, y, True)
assert_equal(csr_.getformat(), 'csr')
def _preprocess_data(X,
y,
fit_intercept,
normalize,
copy=True,
return_mean=False):
if not return_mean:
return center_data(X, y, fit_intercept, normalize, copy=False)
else:
return sparse_center_data(X, y, fit_intercept, normalize)
def test_sparse_center_data():
n_samples = 200
n_features = 2
rng = check_random_state(0)
# random_state not supported yet in sparse.rand
X = sparse.rand(n_samples, n_features, density=.5) # , random_state=rng
X = X.tolil()
y = rng.rand(n_samples)
XA = X.toarray()
# XXX: currently scaled to variance=n_samples
expected_X_std = np.std(XA, axis=0) * np.sqrt(X.shape[0])
Xt, yt, X_mean, y_mean, X_std = sparse_center_data(X,
y,
fit_intercept=False,
normalize=False)
assert_array_almost_equal(X_mean, np.zeros(n_features))
assert_array_almost_equal(y_mean, 0)
assert_array_almost_equal(X_std, np.ones(n_features))
assert_array_almost_equal(Xt.A, XA)
assert_array_almost_equal(yt, y)
Xt, yt, X_mean, y_mean, X_std = sparse_center_data(X,
y,
fit_intercept=True,
normalize=False)
assert_array_almost_equal(X_mean, np.mean(XA, axis=0))
assert_array_almost_equal(y_mean, np.mean(y, axis=0))
assert_array_almost_equal(X_std, np.ones(n_features))
assert_array_almost_equal(Xt.A, XA)
assert_array_almost_equal(yt, y - np.mean(y, axis=0))
Xt, yt, X_mean, y_mean, X_std = sparse_center_data(X,
y,
fit_intercept=True,
normalize=True)
assert_array_almost_equal(X_mean, np.mean(XA, axis=0))
assert_array_almost_equal(y_mean, np.mean(y, axis=0))
assert_array_almost_equal(X_std, expected_X_std)
assert_array_almost_equal(Xt.A, XA / expected_X_std)
assert_array_almost_equal(yt, y - np.mean(y, axis=0))
def test_sparse_center_data():
n_samples = 200
n_features = 2
rng = check_random_state(0)
# random_state not supported yet in sparse.rand
X = sparse.rand(n_samples, n_features, density=.5) # , random_state=rng
X = X.tolil()
y = rng.rand(n_samples)
XA = X.toarray()
# XXX: currently scaled to variance=n_samples
expected_X_std = np.std(XA, axis=0) * np.sqrt(X.shape[0])
Xt, yt, X_mean, y_mean, X_std = sparse_center_data(X, y,
fit_intercept=False,
normalize=False)
assert_array_almost_equal(X_mean, np.zeros(n_features))
assert_array_almost_equal(y_mean, 0)
assert_array_almost_equal(X_std, np.ones(n_features))
assert_array_almost_equal(Xt.A, XA)
assert_array_almost_equal(yt, y)
Xt, yt, X_mean, y_mean, X_std = sparse_center_data(X, y,
fit_intercept=True,
normalize=False)
assert_array_almost_equal(X_mean, np.mean(XA, axis=0))
assert_array_almost_equal(y_mean, np.mean(y, axis=0))
assert_array_almost_equal(X_std, np.ones(n_features))
assert_array_almost_equal(Xt.A, XA)
assert_array_almost_equal(yt, y - np.mean(y, axis=0))
Xt, yt, X_mean, y_mean, X_std = sparse_center_data(X, y,
fit_intercept=True,
normalize=True)
assert_array_almost_equal(X_mean, np.mean(XA, axis=0))
assert_array_almost_equal(y_mean, np.mean(y, axis=0))
assert_array_almost_equal(X_std, expected_X_std)
assert_array_almost_equal(Xt.A, XA / expected_X_std)
assert_array_almost_equal(yt, y - np.mean(y, axis=0))
def _sparse_fit(self, X, y, Xy=None, coef_init=None):
if X.shape[0] != y.shape[0]:
raise ValueError("X and y have incompatible shapes.\n" +
"Note: Sparse matrices cannot be indexed w/" +
"boolean masks (use `indices=True` in CV).")
# NOTE: we are explicitly not centering the data the naive way to
# avoid breaking the sparsity of X
X_data, y, X_mean, y_mean, X_std = sparse_center_data(
X, y, self.fit_intercept, self.normalize)
if y.ndim == 1:
y = y[:, np.newaxis]
n_samples, n_features = X.shape[0], X.shape[1]
n_targets = y.shape[1]
coef_ = self._init_coef(coef_init, n_features, n_targets)
dual_gap_ = np.empty(n_targets)
eps_ = np.empty(n_targets)
l1_reg = self.alpha * self.l1_ratio* n_samples
l2_reg =0.0 #self.alpha * (1.0 - self.l1_ratio) * n_samples
for k in xrange(n_targets):
coef_[k, :], dual_gap_[k], eps_[k] = \
cd_fast.sparse_enet_coordinate_descent(
coef_[k, :], l1_reg, l2_reg, X_data, X.indices,
X.indptr, y[:, k], X_mean / X_std,
self.max_iter, self.tol, True)
if dual_gap_[k] > eps_[k]:
warnings.warn('Objective did not converge for ' +
'target %d, you might want' % k +
' to increase the number of iterations')
self.coef_, self.dual_gap_, self.eps_ = (np.squeeze(a) for a in
(coef_, dual_gap_, eps_))
self._set_intercept(X_mean, y_mean, X_std)
# return self for chaining fit and predict calls
return self
def test_deprecation_center_data():
n_samples = 200
n_features = 2
w = 1.0 + rng.rand(n_samples)
X = rng.rand(n_samples, n_features)
y = rng.rand(n_samples)
param_grid = product([True, False], [True, False], [True, False],
[None, w])
for (fit_intercept, normalize, copy, sample_weight) in param_grid:
XX = X.copy() # such that we can try copy=False as well
X1, y1, X1_mean, X1_var, y1_mean = \
center_data(XX, y, fit_intercept=fit_intercept,
normalize=normalize, copy=copy,
sample_weight=sample_weight)
XX = X.copy()
X2, y2, X2_mean, X2_var, y2_mean = \
_preprocess_data(XX, y, fit_intercept=fit_intercept,
normalize=normalize, copy=copy,
sample_weight=sample_weight)
assert_array_almost_equal(X1, X2)
assert_array_almost_equal(y1, y2)
assert_array_almost_equal(X1_mean, X2_mean)
assert_array_almost_equal(X1_var, X2_var)
assert_array_almost_equal(y1_mean, y2_mean)
# Sparse cases
X = sparse.csr_matrix(X)
for (fit_intercept, normalize, copy, sample_weight) in param_grid:
X1, y1, X1_mean, X1_var, y1_mean = \
center_data(X, y, fit_intercept=fit_intercept, normalize=normalize,
copy=copy, sample_weight=sample_weight)
X2, y2, X2_mean, X2_var, y2_mean = \
_preprocess_data(X, y, fit_intercept=fit_intercept,
normalize=normalize, copy=copy,
sample_weight=sample_weight, return_mean=False)
assert_array_almost_equal(X1.toarray(), X2.toarray())
assert_array_almost_equal(y1, y2)
assert_array_almost_equal(X1_mean, X2_mean)
assert_array_almost_equal(X1_var, X2_var)
assert_array_almost_equal(y1_mean, y2_mean)
for (fit_intercept, normalize) in product([True, False], [True, False]):
X1, y1, X1_mean, X1_var, y1_mean = \
sparse_center_data(X, y, fit_intercept=fit_intercept,
normalize=normalize)
X2, y2, X2_mean, X2_var, y2_mean = \
_preprocess_data(X, y, fit_intercept=fit_intercept,
normalize=normalize, return_mean=True)
assert_array_almost_equal(X1.toarray(), X2.toarray())
assert_array_almost_equal(y1, y2)
assert_array_almost_equal(X1_mean, X2_mean)
assert_array_almost_equal(X1_var, X2_var)
assert_array_almost_equal(y1_mean, y2_mean)
def test_deprecation_center_data():
n_samples = 200
n_features = 2
w = 1.0 + rng.rand(n_samples)
X = rng.rand(n_samples, n_features)
y = rng.rand(n_samples)
param_grid = product([True, False], [True, False], [True, False],
[None, w])
for (fit_intercept, normalize, copy, sample_weight) in param_grid:
XX = X.copy() # such that we can try copy=False as well
X1, y1, X1_mean, X1_var, y1_mean = \
center_data(XX, y, fit_intercept=fit_intercept,
normalize=normalize, copy=copy,
sample_weight=sample_weight)
XX = X.copy()
X2, y2, X2_mean, X2_var, y2_mean = \
_preprocess_data(XX, y, fit_intercept=fit_intercept,
normalize=normalize, copy=copy,
sample_weight=sample_weight)
assert_array_almost_equal(X1, X2)
assert_array_almost_equal(y1, y2)
assert_array_almost_equal(X1_mean, X2_mean)
assert_array_almost_equal(X1_var, X2_var)
assert_array_almost_equal(y1_mean, y2_mean)
# Sparse cases
X = sparse.csr_matrix(X)
for (fit_intercept, normalize, copy, sample_weight) in param_grid:
X1, y1, X1_mean, X1_var, y1_mean = \
center_data(X, y, fit_intercept=fit_intercept, normalize=normalize,
copy=copy, sample_weight=sample_weight)
X2, y2, X2_mean, X2_var, y2_mean = \
_preprocess_data(X, y, fit_intercept=fit_intercept,
normalize=normalize, copy=copy,
sample_weight=sample_weight, return_mean=False)
assert_array_almost_equal(X1.toarray(), X2.toarray())
assert_array_almost_equal(y1, y2)
assert_array_almost_equal(X1_mean, X2_mean)
assert_array_almost_equal(X1_var, X2_var)
assert_array_almost_equal(y1_mean, y2_mean)
for (fit_intercept, normalize) in product([True, False], [True, False]):
X1, y1, X1_mean, X1_var, y1_mean = \
sparse_center_data(X, y, fit_intercept=fit_intercept,
normalize=normalize)
X2, y2, X2_mean, X2_var, y2_mean = \
_preprocess_data(X, y, fit_intercept=fit_intercept,
normalize=normalize, return_mean=True)
assert_array_almost_equal(X1.toarray(), X2.toarray())
assert_array_almost_equal(y1, y2)
assert_array_almost_equal(X1_mean, X2_mean)
assert_array_almost_equal(X1_var, X2_var)
assert_array_almost_equal(y1_mean, y2_mean)
