docs models and algos done

Bilkent-CYBORG · Nov 4, 2024 · 5bff7d2 · 5bff7d2
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diff --git a/docs/source/algorithms.rst b/docs/source/algorithms.rst
@@ -15,10 +15,64 @@ Algorithms for Vector Optimization
 ~~~~~~~~~~~~~~~~~
 
 .. autoclass:: NaiveElimination
-   :members:
+    :members:
 
 :hidden:`PaVeBa`
 ~~~~~~~~~~~~~~~~~
 
 .. autoclass:: PaVeBa
-   :members:
+    :members:
+
+:hidden:`PaVeBaGP`
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: PaVeBaGP
+    :members:
+
+:hidden:`VOGP`
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: VOGP
+    :members:
+
+
+Algorithms for Vector Optimization in Continuous Domains
+-----------------------------
+
+:hidden:`VOGP_AD`
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: VOGP_AD
+    :members:
+
+
+Algorithms for Vector Optimization with Decoupled Objectives
+-----------------------------
+
+:hidden:`PaVeBaPartialGP`
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: PaVeBaPartialGP
+    :members:
+
+:hidden:`DecoupledGP`
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: DecoupledGP
+    :members:
+
+
+Algorithms for Multi-Objective Optimization
+-----------------------------
+
+:hidden:`Auer`
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: Auer
+    :members:
+
+:hidden:`EpsilonPAL`
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: EpsilonPAL
+    :members:
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -5,11 +5,6 @@
 
 import os
 import sys
-import ast
-import pkgutil
-import importlib
-from pathlib import Path
-from types import ModuleType
 
 sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..", "..")))
 autodoc_mock_imports = [

diff --git a/docs/source/models.rst b/docs/source/models.rst
@@ -1,12 +1,22 @@
 .. role:: hidden
     :class: hidden-section
 
+
 vectoptal.models
 ===================================
 
 .. automodule:: vectoptal.models
 .. currentmodule:: vectoptal.models
 
+.. autoclass:: Model
+    :members:
+
+.. autoclass:: ModelList
+
+
+.. autoclass:: GPModel
+    :members:
+
 
 Empirical Models
 -----------------------------
@@ -15,4 +25,41 @@ Empirical Models
 ~~~~~~~~~~~~~~~~~
 
 .. autoclass:: EmpiricalMeanVarModel
-   :members:
+    :members:
+
+
+Gaussian Process Based Models
+-----------------------------
+
+.. autoclass:: GPyTorchModel
+    :members:
+
+.. autoclass:: SingleTaskGP
+    :members:
+
+.. autoclass:: MultitaskExactGPModel
+    :members:
+
+.. autoclass:: BatchIndependentExactGPModel
+    :members:
+
+.. autoclass:: GPyTorchMultioutputExactModel
+    :members:
+
+:hidden:`CorrelatedExactGPyTorchModel`
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: CorrelatedExactGPyTorchModel
+    :members:
+
+:hidden:`IndependentExactGPyTorchModel`
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: IndependentExactGPyTorchModel
+    :members:
+
+:hidden:`GPyTorchModelListExactModel`
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: GPyTorchModelListExactModel
+    :members:
diff --git a/vectoptal/algorithms/paveba.py b/vectoptal/algorithms/paveba.py
@@ -54,8 +54,7 @@ class PaVeBa(PALAlgorithm):
         >>> pareto_indices = algorithm.P
 
     Reference:
-        "Learning the Pareto Set Under Incomplete Preferences:
-            Pure Exploration in Vector Bandits",
+        "Learning the Pareto Set Under Incomplete Preferences: Pure Exploration in Vector Bandits",
         Karagözlü, Yıldırım, Ararat, Tekin, AISTATS, '24
         https://proceedings.mlr.press/v238/karagozlu24a.html
     """

diff --git a/vectoptal/algorithms/paveba_gp.py b/vectoptal/algorithms/paveba_gp.py
@@ -64,8 +64,7 @@ class PaVeBaGP(PALAlgorithm):
         >>> pareto_indices = algorithm.P
 
     Reference:
-        "Learning the Pareto Set Under Incomplete Preferences:
-            Pure Exploration in Vector Bandits",
+        "Learning the Pareto Set Under Incomplete Preferences: Pure Exploration in Vector Bandits",
         Karagözlü, Yıldırım, Ararat, Tekin, AISTATS, '24
         https://proceedings.mlr.press/v238/karagozlu24a.html
     """

diff --git a/vectoptal/models/empirical_mean_var.py b/vectoptal/models/empirical_mean_var.py
@@ -6,12 +6,32 @@
 
 
 class EmpiricalMeanVarModel(Model):
+    """
+    Implements a model that tracks empirical means and variances for each design.
+
+    This class tracks the empirical means and variances for each design in the input space,
+    controlled by flags `track_means` and `track_variances`.
+
+    :param input_dim: The dimension of the input space.
+    :type input_dim: int
+    :param output_dim: The dimension of the output space.
+    :type output_dim: int
+    :param noise_var: The variance of the noise in the output space.
+    :type noise_var: float
+    :param design_count: The number of designs to track.
+    :type design_count: int
+    :param track_means: A flag to enable/disable tracking of means, defaults to True.
+    :type track_means: bool
+    :param track_variances: A flag to enable/disable tracking of variances, defaults to True.
+    :type track_variances: bool
+    """
+
     def __init__(
         self,
-        input_dim,
-        output_dim,
-        noise_var,
-        design_count,
+        input_dim: int,
+        output_dim: int,
+        noise_var: float,
+        design_count: int,
         track_means: bool = True,
         track_variances: bool = True,
     ):