semvec/pypi-packages huggingface dataset:
txdps 0.0.1
Python package to deal with Texas DPS Appointments
Requires Python: >=3.7

# txdps.py
Tired of refreshing website for a TX DPS Appointment? Now do it programmatically!

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

## Features
* Check existing appointment
* Find available time slots at nearby DPS offices
* Get earlist time slot nearby
* Book an appointment programmatically

## Table of Contents

## Getting Started
ph-toolbox 0.1.1
A small example package
Requires Python: >=3.8

# Helper Functions Package

## Installation
```shell
 pipenv install
```

```shell
 export PYTHONPATH="${HOME}/Projects/pypi/ph_toolbox"
```

## Testing
``` shell
make test
```

```shell
 pipenv run pytest ./tests/
```

Or specific test:

```shell
 pipenv run pytest ./tests/ph_toolbox/test_config.py
```

## Running
csa-abc-lr 0.0.5
CSA-ABC-LR is a classification method that combines The CSA and The ABC algorithm with a logistic regression classification model

# CLONAL SELECTION ALGORITHM AND ARTIFICIAL BEE COLONY ALGORITHM WITH LOGISTIC REGRESSION (CSA-ABC-LR)

CSA-ABC-LR is a binary classification method in which the ABC algorithm is used instead of the Gradient Descent algorithm to train the weights in the Logistic Regression classification model. The purpose of the CSA-ABC algorithm in the CSA-ABC-LR method is to minimize the value of the cost function. The ABC algorithm is a very popular metaheuristic method that can search for solutions both locally and globally in the solution space. In addition, it has been shown in the study that the CSA-ABC-LR classification method achieves superior classification success compared to the LR classification method.

Although the ABC-LR classification method can handle complex and high-dimensional data, its runtime can be high. Therefore, CPU and GPU parallelized versions of the ABC-LR classification method are presented here, and significant improvements in runtime can be achieved.

ABC-LR is written in Python3 and continuously tested with Python 3.7 and 3.10.

## Installation

Install ABC-LR via PyPI:

```
pip install csa_abc_lr
```

Or alternatively, clone the environment:

```
git clone https://github.com/kagandedeturk/CSA-ABC-LR.git
```

## CPU Version Usage

```py

import numpy as np
parallelType = np
from abcLR import ABC_LR_Model
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
y = y.reshape(-1,1)

lb = -16
ub = 16
evaluationNumber = 80000
# FVS = trainData.shape[1]
limit = 50
P = 60
MR = 0.3
L2 = 0

model = ABC_LR_Model(lb=lb, ub=ub, evaluationNumber=evaluationNumber, limit=limit, P=P, MR=MR, L2=L2, parallelType=parallelType)
#start_time = dt.datetime.now()
model.fit(X, y)
#print(f"Run time: {dt.datetime.now()-start_time}")
print(f"Result: {model.score(X, y)}")

```

## GPU Version Usage

```py
import cupy as cp
parallelType = cp
from abcLR import ABC_LR_Model
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)

X = parallelType.array(X)
y = parallelType.array(y.reshape(-1,1))

lb = -16
ub = 16
evaluationNumber = 80000
# FVS = trainData.shape[1]
limit = 50
P = 60
MR = 0.3
L2 = 0

model = ABC_LR_Model(lb=lb, ub=ub, evaluationNumber=evaluationNumber, limit=limit, P=P, MR=MR, L2=L2, parallelType=parallelType)
#start_time = dt.datetime.now()
model.fit(X, y)
#print(f"Run time: {dt.datetime.now()-start_time}")
print(f"Result: {model.score(X, y)}")

```

## License

This program is free software: you can redistribute it and/or modify
it under the terms of the 3-clause BSD license (please see the LICENSE file).

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

You should have received a copy of the 3-clause BSD license
along with this program (see LICENSE file).
If not, see [here](https://opensource.org/licenses/BSD-3-Clause).

Copyright (c) 2022, Bilge Kagan Dedeturk (kagandedeturk@gmail.com)