semvec/pypi-packages huggingface dataset:
cinder-ml-dashboard 0.1.0
A machine learning model analysis dashboard with AI assistant
Requires Python: >=3.8

# Cinder ML

A machine learning model analysis dashboard with Bit AI assistant for improving models.

## Features

- Interactive dashboard to analyze ML model performance
- Model improvement suggestions with code generation
- Supports PyTorch, TensorFlow, and scikit-learn models
- Visualization of error analysis, confidence distributions, and more
- Bit AI assistant for generating code improvements

## Installation

```bash
# Basic installation
pip install cinder-ml

# With code generation capabilities
pip install cinder-ml[code-generation]
GeeseTools 0.2.1
Modular and extensible data preprocessing library
Requires Python: <3.12,>=3.9

# 🪿🪿GeeseTools
- Fast & Flexible Data Analysis Toolkit

Welcome to **GeeseTools** – a lightweight and modular toolkit designed for quick data preprocessing, model building, evaluation, and visualizations. Perfect for quick experiments and rapid prototyping in machine learning workflows!

---

## Features

- Clean and preprocess your datasets effortlessly with `datapreprocessor`
- Quickly train and evaluate models with `utils`
- Auto-generate plots for insights and performance metrics
- Minimal setup, beginner-friendly, and fully extensible

---

## Module Structure

```
📦 GeeseTools/
├── 📂 data/                            
│   └──📄 heart.csv             # Default Dataset            
│    
├── 📁 DataPreProcessor       
│   └──📜 DataPreProcessor.py   # Main Script
│
└── 📁 utils                    
    ├──📜 train_models.py       # Model training,
    ├──📜 evaluation.py         # Model evaluation
    └──📜 plot.py               # Evaluation visualization


```

##  Installation


```bash
pip install GeeseTools
```

---

## 📚 How to Use

### 1. Import the modules

```python
from datapreprocessor import datapreprocessor as dpp
from utils import train_model as tm
from utils import evaluate_model as eval
from utils import plot
```

---

### 2. Preprocess your data

```python
# Creating object for DataPreProcessor Class
obj = dpp(pd.read_csv("heart.csv"), target="diagnosis")
```

---

### 3. Train a model

```python
model, task_type, history = tm.train_model(X_train, y_train)
```

---

### 4. Evaluate the model

```python
metric, y_pred = eval.evaluate_model(model, X_test, y_test, task_type)
```

---

### 5. Plot results

```python
plot.plot_model_outputs(y_test, y_pred) # for Classification problem
or
plot.plot_model_outputs(history) # for Regression problem

```

---

##  Example Notebook

Check out [DataAnalysis.ipynb](https://github.com/Abhijeet-Real/DataPreProcessor/blob/main/DataAnalysis.ipynb)  for a full example pipeline from preprocessing to visualization.

---

##  Dependencies

- `scipy` `pandas` `ipython`
- `seaborn` `openpyxl`
- `matplotlib` `scikit-learn`
- `imbalanced-learn`

---

## Contributing

Feel free to fork and improve! PRs are welcome for new features, improvements, or bug fixes.

---

## Contact

Made with ❤️ by Abhijeet  
[LinkedIn](https://www.linkedin.com/in/abhijeet-099670300/) | [GitHub](https://github.com/Abhijeet-Real/)

---

## License
MIT © Abhijeet
You're free to use, modify, and distribute this project with proper attribution.

---
pythonic-fp.credits 1.3.1
Pythonic Functional Programming - Credits
Requires Python: >=3.12

# PyPI Pythonic FP Namespace Projects

Collection of Python packages to aid in general software development.
These Python developer libraries consist of Python PyPI namespace
packages all under the **pythonic-fp** "namespace."

These tools tend to prefer a functional programming approach yet
still endeavor to remain Pythonic.

| Python Package | Projects (PyPI) | Source Code (GitHub) |
|:-------------- |:--------------- |:-------------------- |
| pythonic-fp.credits | [pythonic-fp.credits][10] | [pythonic-fp][20] |
| pythonic-fp.circulararray | [pythonic-fp.circulararray][11] | [pythonic-fp-circulararray][21] |
| pythonic-fp.containers | [pythonic-fp.containers][12] | [pythonic-fp-containers][22] |
| pythonic-fp.fp | [pythonic-fp.fp][13] | [pythonic-fp-fp][23] |
| pythonic-fp.iterables | [pythonic-fp.iterables][14] | [pythonic-fp-iterables][24] |
| pythonic-fp.queues | [pythonic-fp.queues][15] | [pythonic-fp-queues][25] |
| pythonic-fp.splitends | [pythonic-fp.splitends][16] | [pythonic-fp-splitends][26] |

The main purpose of the `grscheele/pythonic-fp` GitHut repo is to host
the documentation and serve as a homepage for all the Pythonic FP
namespace projects. It also maintains pythonic-fp-credits namespace
project.

The PyPI `pythonic-fp` project, located under the `name_claim/`
directory, is a stub PyPI project whose sole purpose is to reserve the
`pythonic-fp` name on PyPI. It is **NOT TO BE INSTALLED**. It is there
to prevent others claiming the name and causing confusion. Installing it
will break the all of the namespace projects.

### Detailed Documentation

Detailed pdoc generated [documentation][1] for 
all [PyPI pythonic-fp namespace nrojects][2].

### License Information

This repo itself, as well as all other Pythonic FP namespace projects,
are licensed under the Apache License Version 2.0, January 2004.

See [CHANGELOG.md](./CHANGELOG.md) for ongoing changes.

[1]: https://grscheller.github.io/pythonic-fp/
[2]: https://github.com/grscheller/pythonic-fp/blob/main/README.md
[10]: https://pypi.org/project/pythonic-fp.credits/
[11]: https://pypi.org/project/pythonic-fp.circulararray/
[12]: https://pypi.org/project/pythonic-fp.containers/
[13]: https://pypi.org/project/pythonic-fp.fp/
[14]: https://pypi.org/project/pythonic-fp.iterables/
[15]: https://pypi.org/project/pythonic-fp.queues/
[16]: https://pypi.org/project/pythonic-fp.splitends/
[20]: https://github.com/grscheller/pythonic-fp/
[21]: https://github.com/grscheller/pythonic-fp-circulararray/
[22]: https://github.com/grscheller/pythonic-fp-containers/
[23]: https://github.com/grscheller/pythonic-fp-fp/
[24]: https://github.com/grscheller/pythonic-fp-iterables/
[25]: https://github.com/grscheller/pythonic-fp-queues/
[26]: https://github.com/grscheller/pythonic-fp-splitends/
engression-pytorch 0.1.3
The engression loss (energy score) proposed by Shen et al. for distributional regression with a few convenient wrappers, in Pytorch.
Requires Python: >=3.9

## Engression - Pytorch

The engression loss (energy score) proposed by [Shen et al.](https://arxiv.org/abs/2307.00835) for [distributional regression](https://doi.org/10.1016/j.ecosta.2021.07.006) with a few convenient wrappers, in Pytorch.

The paper's original code by Xinwei Shen is available [here](https://github.com/xwshen51/engression). 

### Install
```
pip install engression-pytorch
```

### Usage
```python
import torch
from engression_pytorch import EnergyScoreLoss, gConcat

batch_size, input_dim, out_dim = 32, 1, 1
noise_dim = 100

x = torch.randn(batch_size, input_dim)
y = torch.randn(batch_size, out_dim)

model = nn.Linear(input_dim + noise_dim, out_dim)

g = gSampler(
    model = model,
    noise_dim = noise_dim,
    noise_type = noise_type,
    noise_scale = 1.0,
    merge_mode = 'concat', # 'add', or lambda x, eps: ...
    m_train = 2, 
    m_eval = 512,
)

g.train() # change m to m_train
preds = g(x) # (batch_size, m_train, output_dim)

# loss = energy_score(y, preds, beta = 1.0, p = 2)
loss = EnergyScoreLoss(beta = 1.0, p = 2)(y, preds)
loss.backward()

g.eval() # changes m to m_eval
sample = g(x) # (batch_size, m_eval, output_dim)
```

### Citations
```bibtex
@misc{shen2024engressionextrapolationlensdistributional,
      title={Engression: Extrapolation through the Lens of Distributional Regression}, 
      author={Xinwei Shen and Nicolai Meinshausen},
      year={2024},
      eprint={2307.00835},
      archivePrefix={arXiv},
      primaryClass={stat.ME},
      url={https://arxiv.org/abs/2307.00835}, 
}
```
```bibtex
@article{KNEIB202399,
title = {Rage Against the Mean – A Review of Distributional Regression Approaches},
journal = {Econometrics and Statistics},
volume = {26},
pages = {99-123},
year = {2023},
issn = {2452-3062},
doi = {https://doi.org/10.1016/j.ecosta.2021.07.006},
url = {https://www.sciencedirect.com/science/article/pii/S2452306221000824},
author = {Thomas Kneib and Alexander Silbersdorff and Benjamin Säfken},
}
```