Merge pull request scikit-learn#3248 from MechCoder/remove_precompute_multi

ogrisel · ogrisel · commit 5c46c0c5f401 · 2014-06-05T13:34:29.000+02:00
[MRG] Remove unused param precompute from MultiTask models
diff --git a/sklearn/linear_model/coordinate_descent.py b/sklearn/linear_model/coordinate_descent.py
@@ -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,
@@ -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
 
@@ -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]]
@@ -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
@@ -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
@@ -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
 
@@ -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)
