Metadata-Version: 2.4
Name: bayaml
Version: 0.1.4
Summary: Baya – Modular ML Orchestration Framework for structured machine learning workflows.
Author: Aditya Sarode
License: MIT License
        
        Copyright (c) 2026 Aditya Sarode
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: GitHub, https://github.com/adityassarode
Project-URL: Repository, https://github.com/adityassarode/baya
Project-URL: Issues, https://github.com/adityassarode/baya/issues
Project-URL: Instagram, https://www.instagram.com/adityassarode
Project-URL: Buymeacoffee, https://www.buymeacoffee.com/adityassarode
Project-URL: Ko-fi, https://ko-fi.com/adityassarode
Keywords: machine-learning,data-science,mlops,orchestration,pipeline,framework,ai
Classifier: Development Status :: 3 - 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: pandas>=1.5
Requires-Dist: numpy>=1.22
Requires-Dist: scikit-learn>=1.2
Requires-Dist: scipy>=1.10
Requires-Dist: pyyaml>=6.0
Requires-Dist: matplotlib>=3.6
Requires-Dist: tabulate>=0.9.0
Requires-Dist: jinja2>=3.1.0
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Requires-Dist: mypy; extra == "dev"
Provides-Extra: viz
Requires-Dist: seaborn; extra == "viz"
Requires-Dist: plotly; extra == "viz"
Provides-Extra: deep
Requires-Dist: tensorflow; extra == "deep"
Requires-Dist: torch; extra == "deep"
Dynamic: license-file

# Baya
<p align="center">  
  
<a href="https://pypi.org/project/baya/">  
  <img src="https://img.shields.io/pypi/v/baya?style=for-the-badge&color=blue&label=PyPI" />  
</a>  
  
<a href="https://github.com/adityassarode">  
  <img src="https://img.shields.io/badge/GitHub-adityassarode-black?style=for-the-badge&logo=github&logoColor=white" />  
</a>  
  
<a href="https://www.instagram.com/adityassarode">  
  <img src="https://img.shields.io/badge/Instagram-@adityassarode-E4405F?style=for-the-badge&logo=instagram&logoColor=white" />  
</a>  
  
<a href="https://www.buymeacoffee.com/adityassarode">  
  <img src="https://img.shields.io/badge/Buy%20Me%20a%20Coffee-Support-yellow?style=for-the-badge&logo=buy-me-a-coffee&logoColor=black" />  
</a>  
  
<a href="https://ko-fi.com/adityassarode">  
  <img src="https://img.shields.io/badge/Ko--fi-Support-ff5e5b?style=for-the-badge&logo=ko-fi&logoColor=white" />  
</a>  
  
</p>  

---

🚀 Baya — Lightweight ML Orchestration & AutoML Framework

Baya is a structured Machine Learning orchestration framework for reproducible, automated, and production-ready ML workflows.

It combines:

AutoML

Natural language ML pipelines

Deterministic execution plans

Model deployment

Modular pipeline control


Designed for engineers, researchers, startups, and ML teams.


---

🔎 Why Baya?

Modern ML pipelines are often:

Hard to reproduce

Poorly structured

Over-engineered

Difficult to deploy


Baya provides a clean orchestration layer on top of scikit-learn to make ML:

Reproducible

Structured

Deterministic

Deployable


Without sacrificing flexibility.


---

🧠 Natural Language ML (.auto())

Train models using structured instructions:

```python
from baya import Project

p = Project()

result = p.auto(
    "use https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv "
    "treat tip as target "
    "train regression model using linear regression"
)

print(result)
```

What Happens Automatically

Dataset loading (CSV, URL, DataFrame)

Target detection

Task detection (classification/regression)

Automatic categorical encoding

Safe pipeline ordering

Train-test split

Model training

Evaluation

Deterministic plan hashing


This makes Baya a lightweight AutoML orchestration engine.


---

📊 Execution Plan Preview

Preview generated pipelines before execution:

```python
plan = p.auto(
    "use data.csv treat price as target train regression model",
    preview=True
)

for step in plan.steps:
    print(step.name)
```
Baya builds a deterministic execution plan with hashing for reproducibility.


---

⚙️ Core Capabilities

✔ Structured ML Orchestration

Layered architecture wrapping a deterministic execution engine.

✔ Auto Encoding

Categorical columns are automatically encoded before model training.

✔ Auto Task Detection

Classification vs regression inferred automatically.

✔ AutoML Engine

Model comparison, leaderboard tracking, cross-validation.

✔ Model Deployment

Export models for production use.


---

🚀 Deployment

### REST Deployment

```python
p.auto(
    "use data.csv treat target as target "
    "train classification model "
    "deploy as rest"
)
```

Generates a deployable REST bundle.


---

### ONNX Export (Edge / C++ Ready)

```python
p.auto(
    "use data.csv treat target as target "
    "train regression model "
    "deploy in c++"
)
```

Exports model as ONNX.


---

📦 Installation

```bash
pip install baya
```

Dependencies:

*   pandas
*   numpy
*   scikit-learn
*   matplotlib
*   pyyaml


---

🧩 API Layers

Baya supports three levels of abstraction.


---

### 1️⃣ Simple API

```python
from baya import quick_train

metrics = quick_train(
    data="data.csv",
    target="Target",
    model="linear_regression"
)
```

---

### 2️⃣ Fluent API

```python
from baya import Baya

metrics = (
    Baya("data.csv", target="Target")
    .train("logistic_regression")
    .evaluate()
)
```

---

### 3️⃣ Advanced Orchestration API

```python
from baya import Project

project = Project()
project.data.load("data.csv")
project.data.set_target("Target")
project.split.train_test()
project.model.create("linear_regression")
project.model.train()
project.evaluate.evaluate_regressor()
```
Full modular control.


---

🤖 AutoML

```python
from baya import automl

result = automl(
    data="data.csv",
    target="Target"
)

print(result["best_model"])
print(result["best_score"])
```

Includes:

*   Cross-validation
*   Model comparison
*   Leaderboard generation
*   Best model selection
*   Run tracking


---

📤 Export System

Export metrics, predictions, and reports:

*   CSV
*   JSON
*   Excel (XLSX)
*   PDF
*   DOCX
*   PNG / JPG


---

🏗 Architecture

Core Engine
↓
Execution Plan Builder
↓
Deterministic Plan Hashing
↓
Project API
↓
AutoML / CLI / Simple API

All APIs wrap the same deterministic engine.

No duplicated logic.


---

🔐 Reproducibility

Every `.auto()` run generates:

*   Dataset hash
*   Plan hash
*   Ordered execution steps

Ensuring reproducible ML workflows.


---

👨‍💻 Developer Setup

```bash
git clone https://github.com/adityassarode/baya
cd baya
pip install -e .[dev]
pytest
```

---

📈 Positioning

Baya is ideal for:

*   ML engineers building reproducible pipelines
*   Startups needing fast ML deployment
*   Data scientists wanting structured automation
*   Teams needing deterministic ML workflows


---

📄 License

MIT License


---

👤 Author

Baya is built and maintained by Aditya Sarode, focused on scalable AI systems, ML architecture, and production-ready engineering.
It combines:

AutoML

Natural language ML pipelines

Deterministic execution plans

Model deployment

Modular pipeline control


Designed for engineers, researchers, startups, and ML teams.


---

🔎 Why Baya?

Modern ML pipelines are often:

Hard to reproduce

Poorly structured

Over-engineered

Difficult to deploy


Baya provides a clean orchestration layer on top of scikit-learn to make ML:

Reproducible

Structured

Deterministic

Deployable


Without sacrificing flexibility.


---

🧠 Natural Language ML (.auto())

Train models using structured instructions:

from baya import Project

p = Project()

result = p.auto(
    "use https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv "
    "treat tip as target "
    "train regression model using linear regression"
)

print(result)

What Happens Automatically

Dataset loading (CSV, URL, DataFrame)

Target detection

Task detection (classification/regression)

Automatic categorical encoding

Safe pipeline ordering

Train-test split

Model training

Evaluation

Deterministic plan hashing


This makes Baya a lightweight AutoML orchestration engine.


---

📊 Execution Plan Preview

Preview generated pipelines before execution:

plan = p.auto(
    "use data.csv treat price as target train regression model",
    preview=True
)

for step in plan.steps:
    print(step.name)

Baya builds a deterministic execution plan with hashing for reproducibility.


---

⚙️ Core Capabilities

✔ Structured ML Orchestration

Layered architecture wrapping a deterministic execution engine.

✔ Auto Encoding

Categorical columns are automatically encoded before model training.

✔ Auto Task Detection

Classification vs regression inferred automatically.

✔ AutoML Engine

Model comparison, leaderboard tracking, cross-validation.

✔ Model Deployment

Export models for production use.


---

🚀 Deployment

REST Deployment

p.auto(
    "use data.csv treat target as target "
    "train classification model "
    "deploy as rest"
)

Generates a deployable REST bundle.


---

ONNX Export (Edge / C++ Ready)

p.auto(
    "use data.csv treat target as target "
    "train regression model "
    "deploy in c++"
)

Exports model as ONNX.


---

📦 Installation

pip install baya

Dependencies:

pandas

numpy

scikit-learn

matplotlib

pyyaml



---

🧩 API Layers

Baya supports three levels of abstraction.


---

1️⃣ Simple API

from baya import quick_train

metrics = quick_train(
    data="data.csv",
    target="Target",
    model="linear_regression"
)


---

2️⃣ Fluent API

from baya import Baya

metrics = (
    Baya("data.csv", target="Target")
    .train("logistic_regression")
    .evaluate()
)


---

3️⃣ Advanced Orchestration API

from baya import Project

project = Project()
project.data.load("data.csv")
project.data.set_target("Target")
project.split.train_test()
project.model.create("linear_regression")
project.model.train()
project.evaluate.evaluate_regressor()

Full modular control.


---

🤖 AutoML

from baya import automl

result = automl(
    data="data.csv",
    target="Target"
)

print(result["best_model"])
print(result["best_score"])

Includes:

Cross-validation

Model comparison

Leaderboard generation

Best model selection

Run tracking



---

📤 Export System

Export metrics, predictions, and reports:

CSV

JSON

Excel (XLSX)

PDF

DOCX

PNG / JPG



---

🏗 Architecture

Core Engine
↓
Execution Plan Builder
↓
Deterministic Plan Hashing
↓
Project API
↓
AutoML / CLI / Simple API

All APIs wrap the same deterministic engine.

No duplicated logic.


---

🔐 Reproducibility

Every .auto() run generates:

Dataset hash

Plan hash

Ordered execution steps


Ensuring reproducible ML workflows.


---

👨‍💻 Developer Setup

git clone https://github.com/adityassarode/baya
cd baya
pip install -e .[dev]
pytest


---

📈 Positioning

Baya is ideal for:

ML engineers building reproducible pipelines

Startups needing fast ML deployment

Data scientists wanting structured automation

Teams needing deterministic ML workflows



---

📄 License

MIT License


---

👤 Author

Baya is built and maintained by Aditya Sarode, focused on scalable AI systems, ML architecture, and production-ready engineering.
