From d1e34dda5b5c9df05569a0feed4eb14c87a5de0b Mon Sep 17 00:00:00 2001
From: Gael Varoquaux <gael.varoquaux@normalesup.org>
Date: Sun, 6 May 2012 22:42:38 +0200
Subject: [PATCH] CLEAN sparse_encode: remove unused arguments
And use the ones that should
---
sklearn/decomposition/dict_learning.py | 33 ++++++++++++--------------
1 file changed, 15 insertions(+), 18 deletions(-)
diff --git a/sklearn/decomposition/dict_learning.py b/sklearn/decomposition/dict_learning.py
index 9b79571fab..0186fcc44d 100644
--- a/sklearn/decomposition/dict_learning.py
+++ b/sklearn/decomposition/dict_learning.py
@@ -22,8 +22,8 @@ from ..linear_model import Lasso, orthogonal_mp_gram, lars_path
def _sparse_encode(X, dictionary, gram=None, cov=None, algorithm='lasso_lars',
- n_nonzero_coefs=None, alpha=None, copy_gram=True,
- copy_cov=True, init=None, max_iter=1000):
+ n_nonzero_coefs=None, alpha=None, copy_cov=True,
+ init=None, max_iter=1000):
"""Generic sparse coding
Each column of the result is the solution to a Lasso problem.
@@ -74,10 +74,6 @@ def _sparse_encode(X, dictionary, gram=None, cov=None, algorithm='lasso_lars',
max_iter: int, 1000 by default
Maximum number of iterations to perform if `algorithm='lasso_cd'`.
- copy_gram: boolean, optional
- Whether to copy the precomputed Gram matrix; if False, it may be
- overwritten.
-
copy_cov: boolean, optional
Whether to copy the precomputed covariance matrix; if False, it may be
overwritten.
@@ -135,7 +131,7 @@ def _sparse_encode(X, dictionary, gram=None, cov=None, algorithm='lasso_lars',
alpha /= n_features # account for scaling
new_code = np.empty((n_samples, n_atoms))
clf = Lasso(alpha=alpha, fit_intercept=False, precompute=gram,
- max_iter=1000)
+ max_iter=max_iter)
for k in xrange(n_samples):
# A huge amount of time is spent in this loop. It needs to be
# tight
@@ -180,7 +176,7 @@ def _sparse_encode(X, dictionary, gram=None, cov=None, algorithm='lasso_lars',
def sparse_encode(X, dictionary, gram=None, cov=None, algorithm='lasso_lars',
- n_nonzero_coefs=None, alpha=None, copy_gram=True,
+ n_nonzero_coefs=None, alpha=None, copy_gram=None,
copy_cov=True, init=None, max_iter=1000, n_jobs=1):
"""Sparse coding
@@ -236,10 +232,6 @@ def sparse_encode(X, dictionary, gram=None, cov=None, algorithm='lasso_lars',
max_iter: int, 1000 by default
Maximum number of iterations to perform if `algorithm='lasso_cd'`.
- copy_gram: boolean, optional
- Whether to copy the precomputed Gram matrix; if False, it may be
- overwritten.
-
copy_cov: boolean, optional
Whether to copy the precomputed covariance matrix; if False, it may be
overwritten.
@@ -262,29 +254,34 @@ def sparse_encode(X, dictionary, gram=None, cov=None, algorithm='lasso_lars',
warnings.warn("Please note: the interface of sparse_encode has changed: "
"It now follows the dictionary learning API and it also "
"handles parallelization. Please read the docstring for "
- "more information.")
+ "more information.", stacklevel=2)
+ if copy_gram is not None:
+ warnings.warn("copy_gram in sparse_encode is deprecated: it"
+ "lead to errors.", DeprecationWarning, stacklevel=2)
dictionary = np.asarray(dictionary)
X = np.asarray(X)
n_samples, n_features = X.shape
n_atoms = dictionary.shape[0]
if gram is None:
- copy_gram = False
gram = np.dot(dictionary, dictionary.T)
if cov is None and algorithm != 'lasso_cd':
copy_cov = False
cov = np.dot(dictionary, X.T)
if n_jobs == 1 or algorithm == 'threshold':
- return _sparse_encode(X, dictionary, gram, cov, algorithm,
- n_nonzero_coefs, alpha, copy_gram, copy_cov, init)
+ return _sparse_encode(X, dictionary, gram=gram, cov=cov,
+ algorithm=algorithm, n_nonzero_coefs=n_nonzero_coefs,
+ alpha=alpha, copy_cov=copy_cov,
+ init=init, max_iter=max_iter)
code = np.empty((n_samples, n_atoms))
slices = list(gen_even_slices(n_samples, n_jobs))
code_views = Parallel(n_jobs=n_jobs)(
delayed(sparse_encode)(X[this_slice], dictionary, gram,
cov[:, this_slice], algorithm,
n_nonzero_coefs, alpha,
- copy_gram, copy_cov,
+ copy_cov=copy_cov,
init=init[this_slice] if init is not
- None else None)
+ None else None,
+ max_iter=max_iter)
for this_slice in slices)
for this_slice, this_view in zip(slices, code_views):
code[this_slice] = this_view
--
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