semvec/pypi-packages huggingface dataset:
kodu_optim 0.1.2
A distributed optimization package
Requires Python: >=3.13

# Kodu-Optim

Kodu-Optim is a distributed system designed to leverage [Optuna](https://optuna.org/) for hyperparameter optimization across multiple compute nodes. It enables efficient and scalable optimization for machine learning models and other computational tasks.

## Features

- Distributed hyperparameter optimization using Optuna.
- Scalable architecture for large-scale experiments.
- Easy integration with existing machine learning workflows.

## Installation

1. Clone the repository:
   ```bash
   git clone https://github.com/yourusername/kodu-optim.git
   cd kodu-optim
   ```

2. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

## Usage

1. Start the distributed system:
   ```bash
   python start_distributed.py
   ```

2. Define your Optuna study and objective function in your script:
   ```python
   import optuna

   def objective(trial):
       x = trial.suggest_float("x", -10, 10)
       return x ** 2

   study = optuna.create_study(direction="minimize")
   study.optimize(objective, n_trials=100)
   ```

3. Run your optimization script across the distributed nodes.

## Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

## License

This project is licensed under the MIT License. See the `LICENSE` file for details.
interception 0.6.0
interception cffi bindings
Requires Python: >=3.9

interception-cffi
=================
[![PyPI - Version](https://img.shields.io/pypi/v/interception)](https://pypi.org/project/interception/)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/interception)](https://pypi.org/project/interception/)
[![PyPI - Implementation](https://img.shields.io/pypi/implementation/interception)](https://pypi.org/project/interception/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/interception)](https://pypi.org/project/interception/)
[![PyPI - Wheel](https://img.shields.io/pypi/wheel/interception)](https://pypi.org/project/interception/)

[![Actions status](https://github.com/vpoverennov/interception-cffi/workflows/CI/badge.svg)](https://github.com/vpoverennov/interception-cffi/actions)
[![codecov](https://codecov.io/github/vpoverennov/interception-cffi/graph/badge.svg?token=UX0CP8IXON)](https://codecov.io/github/vpoverennov/interception-cffi)


Overview
--------

Python interception module is a low-level binding for [Interception][interception] library.
To use it you still need to install interception driver.
For installation guide refer to interception readme located in the [interception github repo][interception]

Installation
------------

`python -m pip install interception`


Usage
-----

Original samples are ported to python and can be found in [samples directory][samples]

You can also execute them as scripts using
`python -m interception.samples.x2y`

or list using
`python -m interception.samples`


[interception]: https://github.com/oblitum/interception/
[samples]: https://github.com/vpoverennov/interception-cffi/tree/master/samples
xlwings-utils-python 0.0.0
xlwings_utils
Requires Python: >=3.9

# xlwings_utils

## Introduction

 Badges

![PyPI](https://img.shields.io/pypi/v/xlwings_utils) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/xlwings_utils) ![PyPI - Implementation](https://img.shields.io/pypi/implementation/xlwings_utils)
![PyPI - License](https://img.shields.io/pypi/l/xlwings_utils) ![ruff](https://img.shields.io/badge/style-ruff-41B5BE?style=flat) 
![GitHub last commit](https://img.shields.io/github/last-commit/salabim/peek)
zmp-openapi-mcp-server 0.2.0
This is a mcp server generated automatically using the configured openapi specification
Requires Python: <4.0,>=3.11

# MCP Inspector CLI for lowlevel server
```bash
$ npx @modelcontextprotocol/inspector uv --directory /Users/kks/IdeaProjects/aiops/zmp-openapi-mcp-server run zmp-openapi-mcp-server --transport stdio --endpoint https://api.ags.cloudzcp.net --access-key xxx --spec-path /Users/kks/IdeaProjects/aiops/zmp-openapi-mcp-server/openapi/zmp_mixed_api_spec.json
```

# fastmcp cli command
```bash
$ mcp dev src/zmp_openapi_mcp_server/fastmcp/zcp_alert_manager.py
$ mcp run src/zmp_openapi_mcp_server/fastmcp/zcp_alert_manager.py -t sse
$ mcp install src/zmp_openapi_mcp_server/fastmcp/zcp_alert_manager.py --name alert-manager --with zmp-openapi-toolkit,pyyaml
```

# How to run the lowlevel.server
## sse
```bash
zmp-openapi-mcp-server --port 9999 --transport sse -e https://api.ags.cloudzcp.net -s xxx -p /Users/kks/IdeaProjects/aiops/zmp-openapi-mcp-server/openapi/zmp_mixed_api_spec.json
```

## stdio
first install the zmp-openapi-mcp-server
```bash
pip install zmp-openapi-mcp-server
```

then configure the mcp server into the mcp host like claude desktop or cursor
```json
{
  "mcpServers": {
    "zmp-openapi-mcp-server": {
      "command": "python3",
      "args": [
        "-m",
        "zmp_openapi_mcp_server",
        "--transport",
        "stdio",
        "--endpoint",
        "https://api.ags.cloudzcp.net",
        "--access-key",
        "xxxxx",
        "--spec-path",
        "/Users/kks/IdeaProjects/aiops/zmp-openapi-mcp-server/openapi/zmp_mixed_api_spec.json"
      ]
    }
  }
}
```


# backup the claude desktop mcp conf
```json
{
  "mcpServers": {
    "zmp-openapi-mcp-server": {
      "command": "python3",
      "args": [
        "-m",
        "zmp_openapi_mcp_server",
        "--transport",
        "stdio",
        "--endpoint",
        "https://api.ags.cloudzcp.net",
        "--access-key",
        "xxxx",
        "--spec-path",
        "/Users/kks/IdeaProjects/aiops/zmp-openapi-mcp-server/openapi/zmp_mixed_api_spec.json"
      ]
    },
    "tavily-mcp": {
      "command": "npx",
      "args": ["-y", "tavily-mcp@0.1.4"],
      "env": {
        "TAVILY_API_KEY": "xxxxx"
      }
    },
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/kks/Desktop"
      ]
    }
  }
}
 ```
ClassifAIer 0.1.6
A library for text classification using LangChain embeddings and scikit-learn classifiers.
Requires Python: >=3.7

# ClassifAIer

![coverage](https://img.shields.io/badge/coverage-95.83%25-green)
![tests](https://img.shields.io/badge/tests-7-blue)
![test_ratio](https://img.shields.io/badge/test_ratio-%25-yellow)

ClassifAIer is a Python library that combines **scikit-learn** classifiers with **LangChain** embedding libraries, enabling seamless text classification using embeddings from large language models. This library offers a user-friendly interface, allowing you to classify text data in a human-like manner.

## Features

- **Embedding Support**: Ability to work with embeddings from large language models like OpenAI and compatible with embeddings supported by LangChain.
- **Parametric Classifiers**: Compatibility with a wide range of classifiers from `scikit-learn` (e.g., `RandomForestClassifier`, `KNeighborsClassifier`, etc.).
- **Easy to Use**: Simplifies text classification tasks with a user-friendly API.
- **Save and Load**: Allows you to save and reload trained models for reuse.

## Requirements

To use this library, you need Python 3.7 or higher. The required packages will be automatically installed when you install this library.

- scikit-learn
- langchain-core
- langchain
- numpy

## Installation

You can install the required libraries using the following command:

```bash
pip install ClassifAIer
```

## Usage

```python
from classifaier import ClassifAIer
from langchain.embeddings import OpenAIEmbeddings
from sklearn.ensemble import RandomForestClassifier

# Initialize the embedding provider
embedding_provider = OpenAIEmbeddings(api_key='YOUR_API_KEY')

random_forest_classifier_params = {
    "n_estimators": 100,
    "max_depth": None,
    "min_samples_split": 2,
    "min_samples_leaf": 1,
    "random_state": 42
}

random_forest_classifier = RandomForestClassifier(**random_forest_classifier_params)

# Create a classifier instance
classifier = ClassifAIer(embedding_provider=embedding_provider, classifier=random_forest_classifier)

# Prepare your data
texts = ["İspanya Birinci Futbol Ligi (La Liga) ekibi Athletic Bilbao, golcü oyuncusu Aritz Aduriz'in sözleşmesini bir yıllığına uzattı.", "Piyasalar ABD'nin enflasyon verilerine odaklandı."]
labels = ["spor", "ekonomi"]

# Train the model
classifier.fit(texts, labels)

# Make predictions
predictions = classifier.predict_all(["Fildişi Sahili Milli Takımı'nın Belçikalı teknik direktörü Marc Wilmots görevinden ayrıldı.", "Borsa, günü yükselişle tamamladı"])
print(predictions)  # Output: ['spor', 'ekonomi']

# Save the model
classifier.save("my_classifier.pkl")

# Load the model
loaded_classifier = ClassifAIer.load("my_classifier.pkl", embedding_provider)
```

## Contributing

Contributions are welcome! If you have suggestions or improvements, please create a pull request or open an issue.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.