Metadata-Version: 2.4
Name: deesseia
Version: 1.0.0
Summary: Deesseia - Goddess of Data Science
Author-email: Jean Decian <jeandecian@hotmail.fr>
License: MIT
Project-URL: Homepage, https://github.com/jeandecian/deesseia
Project-URL: Repository, https://github.com/jeandecian/deesseia
Project-URL: Documentation, https://deesseia.readthedocs.io
Project-URL: Issues, https://github.com/jeandecian/deesseia/issues
Keywords: data-science,machine-learning,deep-learning,nlp,statistics,visualization
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24.0
Requires-Dist: pandas>=1.5.0
Requires-Dist: pyarrow>=10.0.0
Requires-Dist: sqlalchemy>=2.0.0
Provides-Extra: dev
Requires-Dist: mypy>=1.0.0; extra == "dev"
Requires-Dist: pandas-stubs>=2.0.0; extra == "dev"
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: pytest-cov>=4.0.0; extra == "dev"
Dynamic: license-file

# Deesseia - Goddess of Data Science

> **Deesseia** from the French *déesse* (goddess), pronounced like the letters **D-S**.

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Vision

Deesseia simplifies data science workflows. It's an open-source, lightweight Python toolkit that provides a unified, intuitive interface from prototype to production, removing the friction of juggling multiple libraries so you can focus on solving problems, not managing dependencies.

## Library Structure

```text
deesseia (ds)
│
├── core          # Foundations: loading, cleaning, feature engineering
├── eda           # Exploratory Data Analysis & statistics
├── viz           # Visualization & geospatial
├── preprocess    # Scaling, encoding, imputation, splitting
├── ml            # Core ML: regression, classification, ensembles
├── dl            # Deep Learning: CNNs, RNNs, Transformers
├── nlp           # Natural Language Processing
├── llm           # Large Language Models & RAG
├── search        # Retrieval & vector search
├── cv            # Computer Vision
├── ts            # Time Series
├── graph         # Graph ML & Knowledge Graphs
├── rl            # Reinforcement Learning
├── metrics       # Evaluation metrics
├── evaluate      # Cross-validation, ROC, calibration
├── explain       # SHAP, LIME, feature importance
└── utils         # Logging, config, reproducibility
```

## Data Science Domains

| Domain                             | Subdomains                                                                                                                                                                                                                                                                                |
| ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Core Foundations**               | Data Loading, Data Cleaning, Data Preprocessing, Feature Engineering, Data Validation                                                                                                                                                                                                     |
| **Exploratory Data Analysis**      | Descriptive Statistics, Inferential Statistics, Hypothesis Testing, Probability Distributions, Correlation Analysis, Missing Value Analysis                                                                                                                                               |
| **Visualization**                  | Statistical Plots, Interactive Dashboards, Geospatial Mapping, Data Storytelling                                                                                                                                                                                                          |
| **Preprocessing**                  | Scaling, Encoding, Imputation, Splitting                                                                                                                                                                                                                                                  |
| **Machine Learning**               | Regression (Linear, Ridge/Lasso, Polynomial, SVR, Tree-based), Classification (Logistic, Naive Bayes, KNN, SVM, Decision Trees, Random Forest, XGBoost/LightGBM/CatBoost), Unsupervised (Clustering: K-Means, Hierarchical, DBSCAN, GMM; Dimensionality Reduction: PCA, t-SNE, UMAP, LDA) |
| **Deep Learning**                  | Neural Networks (MLP, CNNs, RNNs/LSTMs, Transformers), Advanced Architectures (Autoencoders, VAEs, GANs, Diffusion Models), Transfer Learning (Fine-tuning, Feature Extraction)                                                                                                           |
| **Natural Language Processing**    | Text Preprocessing (Tokenization, Stemming, Lemmatization), Text Representation (Bag-of-Words, TF-IDF, Word Embeddings, Sentence Embeddings), Core Tasks (NER, POS Tagging, Dependency Parsing, Text Classification, Sentiment Analysis, Topic Modeling, Summarization)                   |
| **Large Language Models & Agents** | Language Models (GPT, LLaMA, Mistral, BERT), Prompt Engineering (Zero/Few-shot, Chain-of-Thought), Fine-tuning (SFT, RLHF, PEFT/LoRA), RAG (Retrieval, Generation, GraphRAG), AI Agents (Tool Calling, ReAct, Multi-agent Systems)                                                        |
| **Search & Retrieval**             | Sparse Retrieval (BM25, TF-IDF), Dense Retrieval (Embeddings, Vector Search), Hybrid (RRF, Reranking)                                                                                                                                                                                     |
| **Computer Vision**                | Image Processing, Object Detection (YOLO, R-CNN, SSD), Segmentation (Semantic, Instance, Panoptic), OCR (Tesseract, PaddleOCR)                                                                                                                                                            |
| **Time Series**                    | Decomposition (Trend, Seasonality), Forecasting (ARIMA, SARIMA, Prophet, LSTM-TS), Anomaly Detection                                                                                                                                                                                      |
| **Graph & Geometric ML**           | Graph Analytics (Centrality, Community Detection), GNNs (GCN, GAT, GraphSAGE), Knowledge Graphs (GraphRAG, Ontologies)                                                                                                                                                                    |
| **Reinforcement Learning**         | Value-based (Q-Learning, DQN), Policy-based (PPO, A2C, DDPG), MDP                                                                                                                                                                                                                         |
| **Evaluation Metrics**             | Classification Metrics (Accuracy, Precision, Recall, F1, ROC-AUC, PR-AUC), Regression Metrics (MAE, MSE, RMSE, R², MAPE, SMAPE, MASE), Ranking Metrics (nDCG, MRR, Recall@k), Clustering Metrics (Silhouette, Davies-Bouldin, Calinski-Harabasz)                                          |
| **Model Evaluation**               | Cross-Validation, Model Calibration, Drift Detection, Model Monitoring, Experiment Tracking                                                                                                                                                                                               |
| **Explainability**                 | SHAP, LIME, Feature Importance, Model Interpretability                                                                                                                                                                                                                                    |
| **Utilities**                      | Logging, Configuration Management, Reproducibility, Model Deployment                                                                                                                                                                                                                      |

## Roadmap

A detailed roadmap with all versions, phases, and key features is available in [ROADMAP.md](ROADMAP.md).

**High-level overview:**

| Major Version | Phase       | Focus                                                      | Status         |
| ------------- | ----------- | ---------------------------------------------------------- | -------------- |
| **v1.x.x**    | Foundations | Data loading, cleaning, EDA, visualization, preprocessing  | In Development |
| **v2.x.x**    | Core ML     | Regression, classification, unsupervised, model evaluation | Planned        |
| **v3.x.x**    | Advanced ML | Deep learning, NLP, LLMs, computer vision, audio           | Planned        |
| **v4.x.x**    | Specialized | Time series, graph ML, reinforcement learning, Bayesian    | Planned        |
| **v5.x.x+**   | Evolution   | Continuous improvement, community-driven                   | Future         |

## Installation

> Coming soon - will be available via `pip install deesseia` after the v0.1.0 release.

With optional dependencies:

> Coming soon

For development:

```bash
# 1. Create virtual environment
python -m venv .venv

# 2. Activate it
# On macOS/Linux:
source .venv/bin/activate

# On Windows:
.venv\Scripts\activate

# 3. Install the package in development mode with dev dependencies
pip install -e ".[dev]"
```

## Quick Start

Coming soon

## Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on commit conventions, SOLID principles, and code quality.

## Documentation

Comprehensive documentation is under active development and will be available at [deesseia.readthedocs.io](https://deesseia.readthedocs.io) **with the v0.1.0 release**.

## License

See [LICENSE](LICENSE) for details.

## Changelog

All notable changes are documented in [CHANGELOG.md](CHANGELOG.md) following [Keep a Changelog](https://keepachangelog.com/).

## Contact

- **Maintainer**: [Jean Decian](https://github.com/jeandecian)
