============================
Weak Supervision Baselines
============================

This section provides comprehensive documentation for weak supervision baseline algorithms implemented in the framework. Each algorithm includes detailed pseudocode, implementation notes, and evaluation results.

.. note::
   All algorithms follow the universal baseline comparison framework structure with standardized training, evaluation, and hyperparameter tuning capabilities.

Current Algorithms
==================

.. grid:: 1 2 2 2
    :gutter: 4
    :margin: 0
    :padding: 3 4 0 0

    .. grid-item-card::
        :class-card: intro-card
        :shadow: md
        :link: lol
        :link-type: doc

        LoL (Losses over Labels)
        ^^^^^^^^^^^^^^^^^^^^^^^^

        A weak supervision method that learns from noisy labels by modeling label noise
        through a losses-over-labels approach.

        **Key Features:**
        
        * Label noise modeling
        * Gradient-based optimization
        * Multiple method variants (LoL, LoL_simple)

        +++

        :bdg-primary:`Algorithm` :bdg-secondary:`Pseudocode` :bdg-info:`Results`

    .. grid-item-card::
        :class-card: intro-card
        :shadow: md
        :link: lpws
        :link-type: doc

        LPWS (Label Propagation with Weak Supervision)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

        Graph-based label propagation enhanced with weak supervision signals
        for semi-supervised learning tasks.

        **Key Features:**
        
        * Graph-based propagation
        * Weak labeler integration
        * Multiple propagation variants

        +++

        :bdg-primary:`Algorithm` :bdg-secondary:`Pseudocode` :bdg-info:`Results`

Usage Examples
==============

All algorithms can be executed using the standard command-line interface:

.. code-block:: bash

   # Evaluate an algorithm
   python bin/lol.py --data youtube --mode eval --output results/lol_youtube
   
   # Hyperparameter tuning
   python bin/lol.py --data youtube --mode tune --output results/lol_tune --n-trials 50
   
   # Using custom configuration
   python bin/lol.py --data youtube --config config/lol_custom.toml --mode eval

.. toctree::
   :maxdepth: 1
   :caption: Algorithm Documentation:
   :hidden:

   lol
   lpws


Algorithm Comparison
======================

.. csv-table:: Algorithm Comparison Overview
   :header: "Algorithm", "Type", "Key Approach",  "Complexity"
   :widths: 25, 25, 25, 25

   "LoL", "Noise Modeling", "Losses over labels", "$O(nke)$ where n=samples, k=classes, e=epochs"
   "LPWS", "Graph Propagation", "Label propagation + weak supervision", 


Misc.
======

.. dropdown:: Algorithm Documentation Structure
   :icon: file-directory

   Each algorithm documentation follows a standardized academic structure:

   1. **Overview/Introduction** - Algorithm motivation and high-level description
   2. **Pseudocode** - Detailed algorithmic steps in standard format
   3. **Implementation Details** - Framework-specific implementation notes
   4. **Evaluation Results** - Performance metrics and experimental analysis
   5. **Presentation Slides** - Beamer presentation about this work
   6. **References** - Academic papers citation and link



.. dropdown:: Contributing New Algorithms
   :icon: plus-circle

   To add a new algorithm to the baseline collection:

   1. **Create Algorithm Module** in ``src/[algorithm_name]/``
   2. **Implement BaseTrainer Interface** following the framework pattern
   3. **Add Executable Script** in ``bin/[algorithm_name].py``
   4. **Create Documentation** using the template structure
   5. **Update This Index** to include the new algorithm