FIX: parameter order in docstrings (#42)

* Fix docstrings

celer/dropin_sklearn.py

@@ -57,21 +57,21 @@ class Lasso(Lasso_sklearn):
     p0 : int
         First working set size.
 
+    verbose : bool or integer
+        Amount of verbosity.
+
     tol : float, optional
         The tolerance for the optimization: the solver runs until the duality
         gap is smaller than ``tol`` or the maximum number of iteration is
         reached.
 
-    verbose : bool or integer
-        Amount of verbosity.
-
     prune : 0 | 1, optional
         Whether or not to use pruning when growing working sets.
 
-    fit_intercept : bool, optional, default True
+    fit_intercept : bool, optional (default=True)
         Whether or not to fit an intercept.
 
-    normalize : bool, optional, default False
+    normalize : bool, optional (default=False)
         This parameter is ignored when ``fit_intercept`` is set to False.
         If True,  the regressors X will be normalized before regression by
         subtracting the mean and dividing by the l2-norm.
@@ -117,7 +117,7 @@ class Lasso(Lasso_sklearn):
 
     def __init__(self, alpha=1., max_iter=100, gap_freq=10,
                  max_epochs=50000, p0=10, verbose=0, tol=1e-4, prune=0,
-                 fit_intercept=True, normalize=True):
+                 fit_intercept=True, normalize=False):
         super(Lasso, self).__init__(
             alpha=alpha, tol=tol, max_iter=max_iter,
             fit_intercept=fit_intercept, normalize=normalize)
@@ -166,7 +166,7 @@ class LassoCV(LassoCV_sklearn):
         to false, no intercept will be used in calculations
         (e.g. data is expected to be already centered).
 
-    normalize : bool, optional, default False
+    normalize : bool, optional (default=False)
         This parameter is ignored when ``fit_intercept`` is set to False.
         If True,  the regressors X will be normalized before regression by
         subtracting the mean and dividing by the l2-norm.
@@ -191,6 +191,21 @@ class LassoCV(LassoCV_sklearn):
     verbose : bool or integer
         Amount of verbosity.
 
+    gap_freq : int, optional (default=10)
+        In the inner loop, the duality gap is computed every `gap_freq`
+        coordinate descent epochs.
+
+    max_epochs : int, optional (default=50000)
+        Maximum number of coordinate descent epochs when solving a subproblem.
+
+    p0 : int, optional (default=10)
+        Number of features in the first working set.
+
+    prune : bool, optional (default=False)
+        Whether to use pruning when growing the working sets.
+
+    precompute : ignored parameter, kept for sklearn compatibility.
+
     Attributes
     ----------
     alpha_ : float
@@ -223,10 +238,9 @@ class LassoCV(LassoCV_sklearn):
     """
 
     def __init__(self, eps=1e-3, n_alphas=100, alphas=None,
-                 fit_intercept=True, max_iter=100,
+                 fit_intercept=True, normalize=False, max_iter=100,
                  tol=1e-4, cv=None, verbose=0, gap_freq=10,
-                 max_epochs=50000, p0=10, prune=0,
-                 normalize=False, precompute='auto'):
+                 max_epochs=50000, p0=10, prune=0, precompute='auto'):
         super(LassoCV, self).__init__(
             eps=eps, n_alphas=n_alphas, alphas=alphas, max_iter=max_iter,
             tol=tol, cv=cv, fit_intercept=fit_intercept, normalize=normalize,

celer/homotopy.py

@@ -42,6 +42,9 @@ def celer_path(X, y, eps=1e-3, n_alphas=100, alphas=None,
         List of alphas where to compute the models.
         If ``None`` alphas are set automatically
 
+    coef_init : ndarray, shape (n_features,) | None, optional, (defualt=None)
+        Initial value of coefficients. If None, np.zeros(n_features) is used.
+
     max_iter : int, optional
         The maximum number of iterations (subproblem definitions)
 
@@ -72,6 +75,10 @@ def celer_path(X, y, eps=1e-3, n_alphas=100, alphas=None,
     return_thetas : bool, optional
         If True, dual variables along the path are returned.
 
+    monitor : bool, optional (default=False)
+        Whether to return timings and gaps for each alpha. Used only for single
+        alpha.
+
     X_offset : np.array, shape (n_features,), optional
         Used to center sparse X without breaking sparsity. Mean of each column.
         See sklearn.linear_model.base._preprocess_data().

celer/wrapper.py

@@ -36,13 +36,13 @@ def celer(X, y, alpha, w_init=None, max_iter=100, gap_freq=10,
     max_iter : int, optional
         Maximum number of outer loop (working set definition) iterations.
 
+    gap_freq : int, optional
+        Number of epochs between every gap computation in the inner solver.
+
     max_epochs : int, optional
         Maximum number of epochs for the coordinate descent solver called on
         the subproblems.
 
-    gap_freq : int, optional
-        Number of epochs between every gap computation in the inner solver.
-
     p0 : int, optional
         Size of the first working set.