MM remarks

skglm/experimental/quantile_regression.py

@@ -11,12 +11,12 @@ class Pinball(BaseDatafit):
 
  quantile * max(y - Xw, 0) + (1 - quantile) * max(Xw - y, 0)
 
- such that quantile in ``[0, 1]``.
+ such that ``quantile`` in [0, 1].
 
  Parameters
  ----------
  quantile : float
- Quantile must be in ``[0, 1]``. When ``quantile=0.5``,
+ Quantile must be in [0, 1]. When ``quantile=0.5``,
  the datafit becomes a Least Absolute Deviation (LAD) datafit.
  """
 
@@ -35,12 +35,12 @@ def value(self, y, w, Xw):
  return np.sum(loss)
 
  def prox(self, w, step, y):
- """Prox of ||y - . || with step ``step``."""
+ """Prox of ``step * ||y - . ||``."""
  shift_cst = (self.quantile - 1/2) * step
  return y - ST_vec(y - w - shift_cst, step / 2)
 
  def prox_conjugate(self, z, step, y):
- """Prox of ||y - . ||^* with step ``step``."""
+ """Prox of ``step * ||y - . ||^*``."""
  # using Moreau decomposition
  inv_step = 1 / step
  return z - step * self.prox(inv_step * z, inv_step, y)