perfspec-learning/about.md

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# PerfSPEC Learning Phase - ABOUT

Based in [PrefSPEC: Performance Profiling-based Proactive Security Policy Enforcement for Containers](https://ieeexplore.ieee.org/document/10577533) document presented in [1], this repository contains source files used to generate and process data.

For more info use: 

- [PrefSPEC main description](Readme.md) 
- [PrefSPEC introduction](intro.md) 
- [PrefSPEC reference document](PerfSPEC.pdf)
- [Presentación in Spanish](presentacion.pdf)
- [How to install](https://repo.jesusperez.pro/jesus/perfspec-learning/src/branch/main/install.md)
- [Autogenerated graph view of actions and events distribution](actions_distribution.pdf)

<div style="margin: auto">
 <a target="_blank" href="perfspec-learning/src/branch/main/presentacion.pdf"><img src="imgs/prefSPEC-learning.png" width="800"></a>
</div>

# What is done so far ? 

- [X] Good look and feel and interactions among processing, analisys and presentation layers
- [X] Use better software packages management like [uv](https://docs.astral.sh/uv/) to complement Python **pip**
- [X] Use an <u>>open</u> and compatible notebook like [Marimo](https://marimo.io/) to support alternative dataframes engines like [Polars](https://pola.rs/) rather than [Pandas](https://pandas.pydata.org/) 
- [X] Use settings and structures to play with different settinigs and options 
- [X] Implement one customized [LSTM](https://en.wikipedia.org/wiki/Long_short-term_memory) model within notebooks
- [X] Use of different metrics to apply to the training models with custumezable adjustments and checkpoints
- [X] Use notebooks as python scripts for command-line use cases like collect predictions or automatic train models
- [X] Use [Dry](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself) to reuse code and centralize common tasks like settings or loading resources. This is main use and existence of [lib_perfspec.py](learning/python/lib_perfspec.py)
- [X] Splitting basic tasks among several specific **notebooks**: 
    - **Preprocessing data** collection to generate clean and usefull (critical actions) info to train models and ranking [prepare_perfspec.py](learning/python/prepare_perfspec.py)
    - **Train models** to get predictions [train_perfspec.py](learning/python/train_perfspec.py)
    - **Get predictions** from existing models [run_perfspec.py](learning/python/run_perfspec.py)
    - **Review and analisys** trained models for better results [model_perfspec.py](learning/python/model_perfspec.py) 

# Plans 


# In Review