update doc

doc/_templates/autoapi/index.rst

@@ -0,0 +1,11 @@
+API Reference
+=============
+
+This page contains auto-generated API reference documentation [#f1]_.
+
+.. toctree::
+   :titlesonly:
+
+   /autoapi/rehline/index
+
+.. [#f1] Created with `sphinx-autoapi <https://github.com/readthedocs/sphinx-autoapi>`_

doc/source/api.rst

@@ -7,9 +7,11 @@ API Reference
 rehline.ReHLine
 ###############
 
-ReHLine
-*******
+.. toctree::
 
-.. autoclass:: rehline._class.ReHLine
-   :members:
-   :show-inheritance:
+    autoapi/rehline/index
+
+.. .. autoclass:: rehline._class.ReHLine
+..    :members:
+..    :show-inheritance:
+         

doc/source/conf.py

@@ -25,8 +25,10 @@
 # release = '0.10'
 
 import sys, os
-# sys.path.append('.')
-# sys.path.append('..')
+sys.path.append('.')
+sys.path.insert(0, os.path.abspath('../..'))
+sys.path.insert(0, os.path.abspath('../rehline'))
+# sys.path.append('../..')
 # sys.path.insert(0, os.path.abspath('.'))
 # sys.path.insert(0, os.path.abspath('../'))
 # sys.path.insert(0, os.path.abspath('../..'))
@@ -39,13 +41,15 @@
 master_doc = 'index'
 extensions = [
 	'sphinx.ext.autodoc',
+    'autoapi.extension',
     # "sphinx.ext.linkcode",
     # "sphinx.ext.intersphinx",
     "sphinx_autodoc_typehints",
     'sphinx.ext.autosummary',
 	'numpydoc',
 	'nbsphinx'
 	]
+autoapi_dirs = ['../../rehline']
 
 autosummary_generate = True
 numpydoc_show_class_members = False
@@ -102,3 +106,15 @@
 # html_css_files = [
 #     'css/custom.css',
 # ]
+
+def skip_submodules(app, what, name, obj, skip, options):
+    if what == "module":
+        skip = True
+    return skip
+
+
+def setup(sphinx):
+    sphinx.connect("autoapi-skip-member", skip_submodules)
+
+autoapi_template_dir = "_templates/autoapi"
+# autoapi_add_toctree_entry = False

doc/source/example.rst

@@ -0,0 +1,2 @@
+Example
+=======

doc/source/index.rst

@@ -63,7 +63,14 @@ See more details in `installation <./installation.rst>`_.
   \min_{\mathbf{\beta} \in \mathbb{R}^d} \sum_{i=1}^n \sum_{l=1}^L \text{ReLU}( u_{li} \mathbf{x}_i^\intercal \mathbf{\beta} + v_{li}) + \sum_{i=1}^n \sum_{h=1}^H {\text{ReHU}}_{\tau_{hi}}( s_{hi} \mathbf{x}_i^\intercal \mathbf{\beta} + t_{hi}) + \frac{1}{2} \| \mathbf{\beta} \|_2^2, \ \text{ s.t. } \mathbf{A} \mathbf{\beta} + \mathbf{b} \geq \mathbf{0},
 
 
-where :math:`\mathbf{U} = (u_{li}),\mathbf{V} = (v_{li}) \in \mathbb{R}^{L \times n}` and :math:`\mathbf{S} = (s_{hi}),\mathbf{T} = (t_{hi}),\mathbf{\tau} = (\tau_{hi}) \in \mathbb{R}^{H \times n}` are the ReLU-ReHU loss parameters, and $(\mathbf{A},\mathbf{b})$ are the constraint parameters. This formulation has a wide range of applications spanning various fields, including statistics, machine learning, computational biology, and social studies. Some popular examples include SVMs with fairness constraints (FairSVM), elastic net regularized quantile regression (ElasticQR), and ridge regularized Huber minimization (RidgeHuber).
+where :math:`\mathbf{U} = (u_{li}),\mathbf{V} = (v_{li}) \in \mathbb{R}^{L \times n}` 
+and :math:`\mathbf{S} = (s_{hi}),\mathbf{T} = (t_{hi}),\mathbf{\tau} = (\tau_{hi}) \in \mathbb{R}^{H \times n}` 
+are the ReLU-ReHU loss parameters, and :math:`(\mathbf{A},\mathbf{b})` are the constraint parameters. 
+This formulation has a wide range of applications spanning various fields, including statistics, 
+machine learning, computational biology, and social studies. 
+Some popular examples include SVMs with fairness constraints (FairSVM), 
+elastic net regularized quantile regression (ElasticQR), 
+and ridge regularized Huber minimization (RidgeHuber).
 
 .. image:: ./figs/tab.png
 
@@ -99,7 +106,6 @@ Reference
 
    installation
    example
-   api
 
 Indices and tables
 ==================

rehline/__init__.py

@@ -3,4 +3,8 @@
 
 from ._loss import ReHLoss, PQLoss
 from ._class import ReHLine, ReHLine_solver
-from ._base import make_fair_classification
+from ._base import relu, rehu, make_fair_classification
+
+__all__ = ("ReHLine",
+           "ReHLoss", 
+           "make_fair_classification", "relu", "rehu")

rehline/_base.py

@@ -12,21 +12,27 @@
 
 def relu(x):
     """
-    Evaluation of ReLU given a vector
+    Evaluation of ReLU given a vector.
 
     Parameters
     ----------
 
     x: {array-like} of shape (n_samples, )
     Training vector, where `n_samples` is the number of samples
 
+
+    Returns
+    -------
+    array of shape (n_samples, )
+        An array with ReLU applied, i.e., all negative values are replaced with 0.
+
     """
     return np.maximum(x, 0)
 
 
 def rehu(x, cut=1):
     """
-    Evaluation of ReHU given a vector
+    Evaluation of ReHU given a vector.
 
     Parameters
     ----------
@@ -37,6 +43,11 @@ def rehu(x, cut=1):
     cut: {array-like} of shape (n_samples, )
         Cutpoints of ReHU, where `n_samples` is the number of samples
 
+    Returns
+    -------
+    array of shape (n_samples, ) 
+        The result of the ReHU function.
+
     """
     n_samples = x.shape[0]
     cut = cut * np.ones_like(x)