semvec/pypi-packages huggingface dataset:
portalcomponentscg 1.1.0
Components para o portal de clientes CG.
License: MIT

# Portal Components

## Getting Started
#### Dependencies
You need Python 3.7 or later to use **portalcomponents**.
```
pip install setuptools
pip install wheel
pip install twine
```
#### Installation
Clone this repo to your local machine using:
```
git clone https://github.com/ramirooliveiracg/cgcomponents.git
```
## Features
- File structure for PyPI packages
- Setup with package informations
- License example
pyFFM 0.0.7
Python implementation of Factorization Machines (+ Field Aware)
License: MIT

```
        _________  _.
.__    |__ |__ | \/ |
|__\__/|   |   |    |
|   /       
```

A python implementation of Factorization Machines / Field-aware Factorization Machines with a simple interface.

(FFM fully functioning/tested, FM still not fully tested)

Supports classification and regression.

#### Project Structure
```
pyffm
 ├── engine
 │   ├── model
 │   │   ├── __init__.py
 │   │   ├── base_model.py
 │   │   ├── ffm_model.py
 │   │   └── fm_model.py
 │   ├── __init__.py
 │   ├── base_engine.py
 │   ├── ffm_engine.py
 │   └── fm_engine.py
 ├── test
 │   ├── data
 │   │   ├── __init__.py
 │   │   ├── bigdata.te.txt
 │   │   ├── bigdata.tr.txt
 │   │   └── small_sample_train.csv
 │   ├── __init__.py
 │   ├── test_ctrengine.py
 │   ├── test_pyffm.py
 │   └── test_utils.py
 ├── __init__.py
 ├── pyffm.py
 └── util.py
LICENSE
README.md
setup.py
```


#### Installation:
```shell script
pip install pyffm
``` 

#### Basic example:
```python
import pandas as pd
from pyffm import PyFFM

training_params = {'epochs': 2, 'reg_lambda': 0.002}
pyffm = PyFFM(model='ffm', training_params=training_params)

from pyffm.test.data import sample_df  # Small training data sample 

# Make sure your file has a label column, default name is 'click' but you can either rename it or pass in label=label_column_name

# Balance the dataset so we get some non-zero predictions (very small training sample)
balanced_df = sample_df[sample_df['click'] == 1].append(sample_df[sample_df['click'] == 0].sample(n=1000)).sample(frac=1)

train_data = balanced_df.sample(frac=0.9)
predict_data = balanced_df.drop(train_data.index)

pyffm.train(train_data)
preds = pyffm.predict(predict_data.drop(columns='click'))


```

Sample data from:  
https://github.com/ycjuan/libffm  
and:  
https://www.kaggle.com/c/criteo-display-ad-challenge

Created using the algorithm described in the original paper:  
https://www.csie.ntu.edu.tw/~cjlin/libffm/
vids-db-server 1.0.0
Server running vids_db
License: MIT
Requires Python: >=3.6.0

# vids_db_server

Server for storing video information.

## Platform Unit Tests

[![Actions Status](https://github.com/zackees/vids-db-server/workflows/MacOS_Tests/badge.svg)](https://github.com/zackees/vids-db-server/actions/workflows/test_macos.yml)
[![Actions Status](https://github.com/zackees/vids-db-server/workflows/Win_Tests/badge.svg)](https://github.com/zackees/vids-db-server/actions/workflows/test_win.yml)
[![Actions Status](https://github.com/zackees/vids-db-server/workflows/Ubuntu_Tests/badge.svg)](https://github.com/zackees/vids-db-server/actions/workflows/test_ubuntu.yml)


# Demo

  * `pip install vids-db-server`
  * `vids_db_server --port 1234`
  * Now open up `http://127.0.0.1:1234` in a browser.

# Demo from github

  * `git clone https://github.com/zackees/vids-db-server`
  * `cd vids_db_server`
  * `pip install -e .`
  * `run_dev.sh` (Browser will open up automatically)

# Docker Production test

  * `git clone https://github.com/zackees/vids-db-server`
  * `cd vids_db_server`
  * `docker-compose up`
  * Now open up `http://127.0.0.1:80/`

# Full Tests + linting

  * `git clone https://github.com/zackees/vids-db-server`
  * `cd vids_db_server`
  * `tox`