8000 [MRG] Remove unused param precompute from MultiTask models by MechCoder · Pull Request #3248 · scikit-learn/scikit-learn · GitHub
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[MRG] Remove unused param precompute from MultiTask models #3248

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Merged
merged 1 commit into from
Jun 5, 2014
Merged

[MRG] Remove unused param precompute from MultiTask models #3248

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30 changes: 12 additions & 18 deletions sklearn/linear_model/coordinate_descent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -889,7 +889,13 @@ def _path_residuals(X, y, train, test, path, path_params, alphas=None,
y_test = y[test]
fit_intercept = path_params['fit_intercept']
normalize = path_params['normalize']
precompute = path_params['precompute']

if y.ndim == 1:
precompute = path_params['precompute']
else:
# No Gram variant of multi-task exists right now.
# Fall back to default enet_multitask
precompute = False

X_train, y_train, X_mean, y_mean, X_std, precompute, Xy = \
_pre_fit(X_train, y_train, None, precompute, normalize, fit_intercept,
Expand Down Expand Up @@ -1638,11 +1644,6 @@ class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin):
List of alphas where to compute the models.
If not provided, set automatically.

precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.

n_alphas : int, optional
Number of alphas along the regularization path

Expand Down Expand Up @@ -1716,8 +1717,7 @@ class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin):
... #doctest: +NORMALIZE_WHITESPACE
MultiTaskElasticNetCV(alphas=None, copy_X=True, cv=None, eps=0.001,
fit_intercept=True, l1_ratio=0.5, max_iter=1000, n_alphas=100,
n_jobs=1, normalize=False, precompute='auto', tol=0.0001,
verbose=0)
n_jobs=1, normalize=False, tol=0.0001, verbose=0)
>>> print(clf.coef_)
[[ 0.52875032 0.46958558]
[ 0.52875032 0.46958558]]
Expand All @@ -1740,7 +1740,7 @@ class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin):
path = staticmethod(enet_path)

def __init__(self, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
fit_intercept=True, normalize=False, precompute='auto',
fit_intercept=True, normalize=False,
max_iter=1000, tol=1e-4, cv=None, copy_X=True,
verbose=0, n_jobs=1):
self.l1_ratio = l1_ratio
Expand All @@ -1749,7 +1749,6 @@ def __init__(self, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
self.alphas = alphas
self.fit_intercept = fit_intercept
self.normalize = normalize
self.precompute = precompute
self.max_iter = max_iter
self.tol = tol
self.cv = cv
Expand Down Expand Up @@ -1781,11 +1780,6 @@ class MultiTaskLassoCV(LinearModelCV, RegressorMixin):
List of alphas where to compute the models.
If not provided, set automaticlly.

precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.

n_alphas : int, optional
Number of alphas along the regularization path

Expand Down Expand Up @@ -1856,10 +1850,10 @@ class MultiTaskLassoCV(LinearModelCV, RegressorMixin):
path = staticmethod(lasso_path)

def __init__(self, eps=1e-3, n_alphas=100, alphas=None, fit_intercept=True,
normalize=False, precompute='auto', max_iter=1000, tol=1e-4,
copy_X=True, cv=None, verbose=False, n_jobs=1):
normalize=False, max_iter=1000, tol=1e-4, copy_X=True,
cv=None, verbose=False, n_jobs=1):
super(MultiTaskLassoCV, self).__init__(
eps=eps, n_alphas=n_alphas, alphas=alphas,
fit_intercept=fit_intercept, normalize=normalize,
precompute=precompute, max_iter=max_iter, tol=tol, copy_X=copy_X,
max_iter=max_iter, tol=tol, copy_X=copy_X,
cv=cv, verbose=verbose, n_jobs=n_jobs)
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