diff --git a/sklearn/decomposition/dict_learning.py b/sklearn/decomposition/dict_learning.py
index b211c122e79c012ea6c0e13bfbc69e85509b1cd8..b8e74087b68daf0741486602856079e21a6ee73f 100644
--- a/sklearn/decomposition/dict_learning.py
+++ b/sklearn/decomposition/dict_learning.py
@@ -323,9 +323,8 @@ def _update_dict(dictionary, Y, code, verbose=False, return_r2=False,
def dict_learning(X, n_atoms, alpha, max_iter=100, tol=1e-8,
- method='lasso_lars', n_jobs=1, dict_init=None,
- code_init=None, callback=None, verbose=False,
- random_state=None):
+ method='lars', n_jobs=1, dict_init=None, code_init=None,
+ callback=None, verbose=False, random_state=None):
"""Solves a dictionary learning matrix factorization problem.
Finds the best dictionary and the corresponding sparse code for
@@ -354,10 +353,10 @@ def dict_learning(X, n_atoms, alpha, max_iter=100, tol=1e-8,
tol: float,
Tolerance for the stopping condition.
- method: {'lasso_lars', 'lasso_cd'}
- lasso_lars: uses the least angle regression method
+ method: {'lars', 'cd'}
+ lars: uses the least angle regression method to solve the lasso problem
(linear_model.lars_path)
- lasso_cd: uses the coordinate descent method to compute the
+ cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.
@@ -391,8 +390,10 @@ def dict_learning(X, n_atoms, alpha, max_iter=100, tol=1e-8,
Vector of errors at each iteration.
"""
- if method not in ('lasso_lars', 'lasso_cd'):
+ if method not in ('lars', 'cd'):
raise ValueError('Coding method not supported as a fit algorithm.')
+ method = 'lasso_' + method
+
t0 = time.time()
n_features = X.shape[1]
# Avoid integer division problems
@@ -474,8 +475,7 @@ def dict_learning(X, n_atoms, alpha, max_iter=100, tol=1e-8,
def dict_learning_online(X, n_atoms, alpha, n_iter=100, return_code=True,
dict_init=None, callback=None, chunk_size=3,
verbose=False, shuffle=True, n_jobs=1,
- method='lasso_lars', iter_offset=0,
- random_state=None):
+ method='lars', iter_offset=0, random_state=None):
"""Solves a dictionary learning matrix factorization problem online.
Finds the best dictionary and the corresponding sparse code for
@@ -524,10 +524,10 @@ def dict_learning_online(X, n_atoms, alpha, n_iter=100, return_code=True,
n_jobs: int,
number of parallel jobs to run, or -1 to autodetect.
- method: {'lasso_lars', 'lasso_cd'}
- lasso_lars: uses the least angle regression method
+ method: {'lars', 'cd'}
+ lars: uses the least angle regression method to solve the lasso problem
(linear_model.lars_path)
- lasso_cd: uses the coordinate descent method to compute the
+ cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.
@@ -546,8 +546,10 @@ def dict_learning_online(X, n_atoms, alpha, n_iter=100, return_code=True,
code: array of shape (n_samples, n_atoms),
the sparse code (only returned if `return_code=True`)
"""
- if method not in ('lasso_lars', 'lasso_cd'):
+ if method not in ('lars', 'cd'):
raise ValueError('Coding method not supported as a fit algorithm.')
+ method = 'lasso_' + method
+
t0 = time.time()
n_samples, n_features = X.shape
# Avoid integer division problems
@@ -715,10 +717,10 @@ class DictionaryLearning(BaseDictionaryLearning):
tol: float,
tolerance for numerical error
- fit_algorithm: {'lasso_lars', 'lasso_cd'}
- lasso_lars: uses the least angle regression method
+ fit_algorithm: {'lars', 'cd'}
+ lars: uses the least angle regression method to solve the lasso problem
(linear_model.lars_path)
- lasso_cd: uses the coordinate descent method to compute the
+ cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.
@@ -783,7 +785,7 @@ class DictionaryLearning(BaseDictionaryLearning):
"""
def __init__(self, n_atoms, alpha=1, max_iter=1000, tol=1e-8,
- fit_algorithm='lasso_lars', transform_algorithm='omp',
+ fit_algorithm='lars', transform_algorithm='omp',
transform_n_nonzero_coefs=None, transform_alpha=None,
n_jobs=1, code_init=None, dict_init=None, verbose=False,
split_sign=False, random_state=None):
@@ -851,7 +853,8 @@ class MiniBatchDictionaryLearning(BaseDictionaryLearning):
total number of iterations to perform
fit_algorithm: {'lars', 'cd'}
- lars: uses the least angle regression method (linear_model.lars_path)
+ lars: uses the least angle regression method to solve the lasso problem
+ (linear_model.lars_path)
cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.
@@ -875,7 +878,7 @@ class MiniBatchDictionaryLearning(BaseDictionaryLearning):
transform_alpha: float, 1. by default
If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the
penalty applied to the L1 norm.
- If `algorithm='threhold'`, `alpha` is the absolute value of the
+ If `algorithm='threshold'`, `alpha` is the absolute value of the
threshold below which coefficients will be squashed to zero.
If `algorithm='omp'`, `alpha` is the tolerance parameter: the value of
the reconstruction error targeted. In this case, it overrides
@@ -917,7 +920,7 @@ class MiniBatchDictionaryLearning(BaseDictionaryLearning):
"""
def __init__(self, n_atoms, alpha=1, n_iter=1000,
- fit_algorithm='lasso_lars', n_jobs=1, chunk_size=3,
+ fit_algorithm='lars', n_jobs=1, chunk_size=3,
shuffle=True, dict_init=None, transform_algorithm='omp',
transform_n_nonzero_coefs=None, transform_alpha=None,
verbose=False, split_sign=False, random_state=None):
diff --git a/sklearn/decomposition/sparse_pca.py b/sklearn/decomposition/sparse_pca.py
index 7bcf2b729b34aa853c3c1cdc636e2ea34b966a13..018a891f14c99fe5b4b848d2200a20246495ec4a 100644
--- a/sklearn/decomposition/sparse_pca.py
+++ b/sklearn/decomposition/sparse_pca.py
@@ -36,10 +36,10 @@ class SparsePCA(BaseEstimator, TransformerMixin):
tol: float,
Tolerance for the stopping condition.
- method: {'lasso_lars', 'lasso_cd'}
- lasso_lars: uses the least angle regression method
+ method: {'lars', 'cd'}
+ lars: uses the least angle regression method to solve the lasso problem
(linear_model.lars_path)
- lasso_cd: uses the coordinate descent method to compute the
+ cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.
@@ -72,7 +72,7 @@ class SparsePCA(BaseEstimator, TransformerMixin):
"""
def __init__(self, n_components, alpha=1, ridge_alpha=0.01, max_iter=1000,
- tol=1e-8, method='lasso_lars', n_jobs=1, U_init=None,
+ tol=1e-8, method='lars', n_jobs=1, U_init=None,
V_init=None, verbose=False, random_state=None):
self.n_components = n_components
self.alpha = alpha
@@ -185,10 +185,10 @@ class MiniBatchSparsePCA(SparsePCA):
n_jobs: int,
number of parallel jobs to run, or -1 to autodetect.
- method: {'lasso_lars', 'lasso_cd'}
- lasso_lars: uses the least angle regression method
+ method: {'lars', 'cd'}
+ lars: uses the least angle regression method to solve the lasso problem
(linear_model.lars_path)
- lasso_cd: uses the coordinate descent method to compute the
+ cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.
@@ -198,7 +198,7 @@ class MiniBatchSparsePCA(SparsePCA):
"""
def __init__(self, n_components, alpha=1, ridge_alpha=0.01, n_iter=100,
callback=None, chunk_size=3, verbose=False, shuffle=True,
- n_jobs=1, method='lasso_lars', random_state=None):
+ n_jobs=1, method='lars', random_state=None):
self.n_components = n_components
self.alpha = alpha
self.ridge_alpha = ridge_alpha
diff --git a/sklearn/decomposition/tests/test_sparse_pca.py b/sklearn/decomposition/tests/test_sparse_pca.py
index e8e3cda667d0117a8c2b9e74f5dbdfa613c984b0..82c57e55c975b6ad873185e1ddc2122b72aaad27 100644
--- a/sklearn/decomposition/tests/test_sparse_pca.py
+++ b/sklearn/decomposition/tests/test_sparse_pca.py
@@ -52,7 +52,7 @@ def test_correct_shapes():
def test_fit_transform():
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
- spca_lars = SparsePCA(n_components=3, method='lasso_lars',
+ spca_lars = SparsePCA(n_components=3, method='lars',
random_state=rng)
spca_lars.fit(Y)
U1 = spca_lars.transform(Y)
@@ -71,7 +71,7 @@ def test_fit_transform():
U2 = spca.transform(Y)
assert_array_almost_equal(U1, U2)
# Test that CD gives similar results
- spca_lasso = SparsePCA(n_components=3, method='lasso_cd', random_state=rng)
+ spca_lasso = SparsePCA(n_components=3, method='cd', random_state=rng)
spca_lasso.fit(Y)
assert_array_almost_equal(spca_lasso.components_, spca_lars.components_)
@@ -79,10 +79,10 @@ def test_fit_transform():
def test_fit_transform_tall():
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 65, (8, 8), random_state=rng) # tall array
- spca_lars = SparsePCA(n_components=3, method='lasso_lars',
+ spca_lars = SparsePCA(n_components=3, method='lars',
random_state=rng)
U1 = spca_lars.fit_transform(Y)
- spca_lasso = SparsePCA(n_components=3, method='lasso_cd', random_state=rng)
+ spca_lasso = SparsePCA(n_components=3, method='cd', random_state=rng)
U2 = spca_lasso.fit(Y).transform(Y)
assert_array_almost_equal(U1, U2)
@@ -131,6 +131,6 @@ def test_mini_batch_fit_transform():
random_state=rng).fit(Y).transform(Y)
assert_array_almost_equal(U1, U2)
# Test that CD gives similar results
- spca_lasso = MiniBatchSparsePCA(n_components=3, method='lasso_cd',
+ spca_lasso = MiniBatchSparsePCA(n_components=3, method='cd',
random_state=rng).fit(Y)
assert_array_almost_equal(spca_lasso.components_, spca_lars.components_)