semvec/pypi-packages huggingface dataset:
easy-recommender 0.1.3
A simple recommendation system using LightFM
License: MIT
Requires Python: >=3.11
Keywords: recommendation, machine learning, collaborative filtering

# Easy Recommender

**English** | [日本語](README.ja.md)

⚠️ **This package is currently broken and under repair. Please do not use.** ⚠️

**This project only works with Python 3.11.11 because LightFM requires this specific version. Other versions are not supported.**

A simple and efficient recommendation system library using LightFM.

## Features

- Simple API for building recommendation systems
- Support for both implicit and explicit feedback
- Built on top of proven library (LightFM)
- Easy data preprocessing utilities

## Installation

```bash
pip install easy-recommender
```

## Quick Start

```python
from easy_recommender import recommend, process_df, build_feature_data
import pandas as pd

# Load your data
df = pd.read_csv('your_ratings.csv')

user_features = ['gender', 'age']
item_features = ['price', 'genre']

# Get recommendations
recommendations = recommend(
    processed_df, 
    user_features, 
    item_features,
)

print(recommendations)
```

Higher values indicate items that are more recommended.

If you want to recommend users for items instead, please swap the user and item columns.

## Data Requirements
This library takes user behavior log CSV as an argument.
Behavior logs are records of users purchasing/viewing/liking item A.

Specifically, to use this library, you need CSV data in the following format:

- **CSV with headers**: Must include column names
- **Required columns**: `user_id` and `item_id` must be present
- **Feature columns**: 
  - User-related column names should be specified as `user_features` (list of strings)
  - Item-related column names should be specified as `item_features` (list of strings)
  - If you have columns that don't belong to either users or items, include them in `user_features`
- **Data quality**: Columns with many null or undefined values may affect recommendation accuracy. Please remove such columns in advance.

### Data Example
For example, the following data format is expected. Note that case sensitivity matters.
Also, there is no automatic conversion between numeric and string types. If you write rankings as "1", it's treated as a string. If categories are handled as numbers like 2,3, they are treated as numeric values, which may reduce recommendation accuracy.

```csv
user_id,item_id,gender,age,price,genre
1,101,M,25,1200,Action
1,102,M,25,800,Comedy
2,101,F,30,1200,Action
```

## Requirements

- Python =3.11.11

## License

MIT License

## References

This implementation is based on the approach described in:
https://zenn.dev/genda_jp/articles/2c2a1b5d185741
hk1980 0.2.3
A forked project from hk80. This project also convert the coordinates between Hong Kong 1980 Grid System and World Geodetic System 1984 with pyproj, with a more update version of pyproj and providing better performance.
License: MIT
Requires Python: <4.0,>=3.10
Keywords: hk80, hk1980, wgs1984, wgs84, grid, coordinate, hk

# hk1980
A forked project from hk80. This project also convert the coordinates between Hong Kong 1980 Grid System and World Geodetic System 1984 with pyproj, with a more update version of `pyproj` and providing better performance. 
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) or [poetry](https://python-poetry.org/) to install foobar.

```bash
pip install hk1980
```
or
```bash
poetry add hk1980
```

## Usage

```python
from hk1980 import LatLon, HK80

hku = LatLon(22.284034, 114.137814).to_hk80()
print(hku.northing, hku.easting) # 836303.204 818195.94
print(hku.x, hku.y) # 818195.94 836303.204

hku = HK80(northing=816128, easting=832243).to_wgs84()
print(hku.latitude, hku.longitude) # 22.42944514 113.98124272
print(hku.x, hku.y) # 113.98124272 22.42944514
```
## Performance

hk1980 is roughly well performant than hk80 than 238 times.

```python
#hk80
import timeit
stmt = "LatLon(22.284034, 114.137814).to_hk80()"
_time_ms = timeit.timeit(stmt=stmt, globals=globals(), number=100)*1000
print(f"time: {_time_ms} ms") # 238.1038 ms

#hk1980
import timeit
stmt = "LatLon(22.284034, 114.137814).to_hk80()"
_time_ms = timeit.timeit(stmt=stmt, globals=globals(), number=100)*1000
print(f"time: {_time_ms} ms") # 1.0652 ms
```
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
eng1014 0.0.1
ENG1014 Numerical Methods Module for Monash students
License: MIT
Requires Python: >=3.8

# ENG1014

ENG1014 Numerical Methods Module for Monash students.  
If this package needs to be updated for any sem, feel free to email me at: Luca@Lowndes.net

## Installation
```bash
pip install eng1014
```
numline 0.0.0
Pure NumPy machine-learning toolkit (name reservation)
License: MIT
Requires Python: >=3.8

```markdown
# Numline

*Pure NumPy Machine Learning – coming soon.*

---

🚧 **This is a placeholder release**  
The package currently reserves the name on PyPI. Real code will land in `v0.1.0`.

Stay tuned for a lightweight, dependency-free ML toolkit built entirely on NumPy + SciPy.

---

### Installation (placeholder)

```bash
pip install numline
```

License

MIT © liujunyi

```