Merge pull request scikit-learn#3778 from MechCoder/expose_positive

[MRG] Expose positive option in elasticnet and lasso path

Author: agramfort

doc/whats_new.rst

@@ -154,6 +154,10 @@ API changes summary
       ``precompute="auto"`` is now deprecated and will be removed in 0.18
       By `Manoj Kumar`_.
 
+    - Expose ``positive`` option in :func:`linear_model.enet_path` and
+      :func:`linear_model.enet_path` which constrains coefficients to be
+      positive. By `Manoj Kumar`_.
+
 .. _changes_0_15_2:
 
 0.15.2

sklearn/linear_model/coordinate_descent.py

@@ -107,7 +107,7 @@ def lasso_path(X, y, eps=1e-3, n_alphas=100, alphas=None,
                precompute='auto', Xy=None, fit_intercept=None,
                normalize=None, copy_X=True, coef_init=None,
                verbose=False, return_models=False, return_n_iter=False,
-               **params):
+               positive=False, **params):
     """Compute Lasso path with coordinate descent
 
     The Lasso optimization function varies for mono and multi-outputs.
@@ -182,6 +182,9 @@ def lasso_path(X, y, eps=1e-3, n_alphas=100, alphas=None,
     params : kwargs
         keyword arguments passed to the coordinate descent solver.
 
+    positive : bool, default False
+        If set to True, forces coefficients to be positive.
+
     Returns
     -------
     models : a list of models along the regularization path
@@ -266,14 +269,15 @@ def lasso_path(X, y, eps=1e-3, n_alphas=100, alphas=None,
                      alphas=alphas, precompute=precompute, Xy=Xy,
                      fit_intercept=fit_intercept, normalize=normalize,
                      copy_X=copy_X, coef_init=coef_init, verbose=verbose,
-                     return_models=return_models, **params)
+                     return_models=return_models, positive=positive,
+                     **params)
 
 
 def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
               precompute='auto', Xy=None, fit_intercept=True,
               normalize=False, copy_X=True, coef_init=None,
               verbose=False, return_models=False, return_n_iter=False,
-              **params):
+              positive=False, **params):
     """Compute elastic net path with coordinate descent
 
     The elastic net optimization function varies for mono and multi-outputs.
@@ -359,6 +363,9 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
     return_n_iter : bool
         whether to return the number of iterations or not.
 
+    positive : bool, default False
+        If set to True, forces coefficients to be positive.
+
     Returns
     -------
     models : a list of models along the regularization path
@@ -459,7 +466,6 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
 
     n_alphas = len(alphas)
     tol = params.get('tol', 1e-4)
-    positive = params.get('positive', False)
     max_iter = params.get('max_iter', 1000)
     dual_gaps = np.empty(n_alphas)
     n_iters = []

sklearn/linear_model/tests/test_coordinate_descent.py

@@ -21,7 +21,7 @@
 
 from sklearn.linear_model.coordinate_descent import Lasso, \
     LassoCV, ElasticNet, ElasticNetCV, MultiTaskLasso, MultiTaskElasticNet, \
-    MultiTaskElasticNetCV, MultiTaskLassoCV, lasso_path
+    MultiTaskElasticNetCV, MultiTaskLassoCV, lasso_path, enet_path
 from sklearn.linear_model import LassoLarsCV, lars_path
 
 
@@ -574,6 +574,17 @@ def test_deprection_precompute_enet():
     assert_warns(DeprecationWarning, clf.fit, X, y)
 
 
+def test_enet_path_positive():
+    """
+    Test that the coefs returned by positive=True in enet_path are positive
+    """
+
+    X, y, _, _ = build_dataset(n_samples=50, n_features=50)
+    for path in [enet_path, lasso_path]:
+        pos_path_coef = path(X, y, positive=True)[1]
+        assert_true(np.all(pos_path_coef >= 0))
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()