semvec/pypi-packages huggingface dataset:
classWeightLearn 1.0.0
Optuna Class Weight Cost-Sensitive Learning

### Cost Sensitive Learning
This is an Extended research from CTGAN-ENN, on algorithm-level approach by fine tuning class-weight using optuna, the supported model are SVM, Random Forest and LightBGM (other models come soon), the classifier model will run with the best class_weight parameter<br>

### Instalation
```bash
pip install classWeightLearn
```
### Example Usage
```bash
from classWeightLearn import find_best_class_weight

best_weight_svc = find_best_class_weight(X, y, model_name='svc', n_trials=1)
classifier = SVC(class_weight=best_weight_svc)
```

### Variables
- X : all features of your dataset
- y : target label of your dataset
- model_name : supported models are (svc, lightgbm and random_forest)
- n_trials : the number of trial that optuna will run (default:1)
computenest-cli 1.9.6
A command line interface for running the compute nest project

# ComputeNest-CLI

`computenest-cli` is a command-line tool that integrates the creation, updating, and deployment of artifacts, as well as
the creation and updating of services within the ComputeNest framework. It allows users to manage their services,
construct artifacts, and handle custom operations, such as custom image creation.

Documentation for operations can be found in the Project Links or [computenest-cli-docs](https://github.com/aliyun-computenest/computenest-cli-docs).
ctganenn 1.0.11
CTGAN-ENN : Tabular GAN-based Hybrid sampling method and cost-sensitive learning

# CTGAN-ENN

CTGAN-ENN : Tabular GAN-based Hybrid sampling method.
- A sampling method that combine CTGAN (Conditional Tabular GAN) and ENN(Edited Nearest Neighboor)
- CTGAN is a powerfull oversampling method based on GAN for tabular data
- ENN is an efficient undersampling method to remove overlapped data 

## Installation

Install CTGAN-ENN using pip:

```bash
pip install ctganenn
```

## Usage

### Variables

- minClass: the minority class in the dataset (dataframe).
- majClass: the majority class in the dataset (dataframe).
- genData: how much data that you want generate from minorty class.
- targetLabel: what is your target label name in dataset.

### Example Usage
```bash
from ctganenn import CTGANENN
```

### use the CTGANENN function with 4 variables
```bash
X, y = CTGANENN(minClass,majClass,genData,targetLabel)
```
### Output
the output of method are X and y :
- X : all features of your dataset
- y : target label of your dataset

### Classification process
you can process the X and y variable to the next step for classification stage. For example using Decision Tree Classifier:

```bash
model = tree.DecisionTreeClassifier()
classification = model.fit(X, y)
```

### Cost Sensitive Learning
This version support cost-sensitive learning by fine tuning class-weight using optuna, the supported model are SVM, Random Forest and LightBGM (other models come soon), the classifier model will run with the best class_weight parameter<br>
Example Usage:
```bash
best_weight_svc = find_best_class_weight(X, y, model_name='svc', n_trials=1)
classifier = SVC(class_weight=best_weight_svc)
```

### Variables
- X : all features of your dataset
- y : target label of your dataset
- model_name : supported models are (svc, lightgbm and random_forest)
- n_trials : the number of trial that optuna will run (default:1)

## Limitation
CTGAN-ENN on this version only works for binary classification