semvec/pypi-packages huggingface dataset:
mpypip 0.0.15
Install and manage pyproject.toml with pip commands.
Requires Python: >=3.8

# pyproject-pip (WIP)

[![PyPI - Version](https://img.shields.io/pypi/v/pyproject-pip.svg)](https://pypi.org/project/pyproject-pip)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyproject-pip.svg)](https://pypi.org/project/pyproject-pip)

-----

Install and manage pyproject.toml with pip commands.

See usage:

```
mpypip --help
```


## Table of Contents

- [pyproject-pip (WIP)](#pyproject-pip-wip)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [Usage](#usage)
  - [License](#license)

## Installation

```console
pip install pyproject-pip
```

## Usage

```console
pypip --help
```

```
Usage: pypip [OPTIONS] COMMAND [ARGS]...

Options:
  -v, --hatch-env TEXT  Specify the Hatch environment to use
  --help                Show this message and exit.

Commands:
  find       Find a package on PyPI and optionally sort the results.
  info       Get information about a package from PyPI.
  install    Install packages and update requirements.txt and...
  show       Show the dependencies from the pyproject.toml file.
  uninstall  Uninstall packages and update requirements.txt and...
```

## License

`pyproject-pip` is distributed under the terms of the [apache-2.0](https://spdx.org/licenses/apache-2.0.html) license.
metalm-tools 0.1.2
MetaLM工具集
Requires Python: <4.0,>=3.10

# MetaLM-tools

metalm的工具集，当前只有代码解释器这一个工具。


案例：
```
from metalm_tools import MetaLMInterpreterTool
import time 
import json

packages=['numpy','pandas']
#画图
c = """
import matplotlib.pyplot as plt
import time

# 数据
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

# 创建折线图
plt.plot(x, y, marker='o')

# 添加标题和轴标签
plt.title("Simple Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")

# 显示图形
plt.grid(True)  # 显示网格线
plt.show()
plt.savefig('test1.png')
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

# 创建折线图
plt.plot(x, y, marker='o')

# 添加标题和轴标签
plt.title("Simple Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")

# 显示图形
plt.grid(True)  # 显示网格线
plt.show()
plt.savefig('test2.png')

for i in x:
    print(i)
    time.sleep(1)
"""
#操作csv
c2 = """
import pandas as pd

# 读取CSV文件
df = pd.read_csv('/codebox/1.csv')

# 获取前5行
df_first_five = df.head(5)

# 保存前5行到新的CSV文件
df_first_five.to_csv('output_file.csv', index=False)

print("前5行已保存到 'output_file.csv'")

"""

tool = MetaLMInterpreterTool(<服务url>,'empty')
print(tool.session.session_id)
tool.install_python_packages(packages)
res1 = tool.upload_files([<上传文件>],['这是要分析的数据'])
res2 = tool.upload_files([<上传图片>],['这是图片'])
print('path:',res1,res2)
rsp = tool._run(c2)
rsp = json.loads(rsp)
print(rsp)
tbyte = tool.download_files(['output_file.csv'])
print(tbyte)

rsp = tool._run(c)
rsp = json.loads(rsp)
print(rsp)

tool.close()
```
autoqrels 0.0.1
a tool for automatically inferring query relevance assessments (qrels)
Requires Python: >=3.6

# autoqrels

`autoqrels` is a tool for automatically inferring query relevance assessments (qrels).

Currently, it supports the one-shot labeling approach (1SL) presented in *[MacAvaney and
Soldaini, One-Shot Labeling for Automatic Relevance Estimation, SIGIR 2023](https://arxiv.org/pdf/2302.11266.pdf)*.

This package adheres to the [`ir-measures`](https://ir-measur.es/) API, which means it can
be directly used by various tools, such as [PyTerrier](https://pyterrier.readthedocs.io/).

## Getting started

You can install `autoqrels` using pip:

```bash
pip install autoqrels
```

You can also work with the repository locally:

```bash
git clone https://github.com/seanmacavaney/autoqrels.git
cd autoqrels
python setup.py develop
```

## API

The primary interface in `autoqrels` is `autoqrels.Labeler`. A `Labeler` exposes a
method, `infer_qrels(run, qrels)`, which returns a new set of qrels that covers the
provided run:

 - `run` is a Pandas DataFrame with the columns `query_id` (str), `doc_id` (str), and `score` (float)
 - `qrels` is a Pandas DataFrame with the columns `query_id` (str), `doc_id` (str), and `relevance` (int)
 - The return value is a Pandas DataFrame with the columns `query_id` (str), `doc_id` (str), and `relevance` (float)

`Labeler`s also expose several measure definitions compatible with `ir_measures`:
[`labeler.SDCG@k`](https://ir-measur.es/en/latest/measures.html#sdcg),
[`labeler.RBP(p=persistence)`](https://ir-measur.es/en/latest/measures.html#rbp),
[`labeler.P@k`](https://ir-measur.es/en/latest/measures.html#p).
These measures can be used to calculate the corresponding effectivness, with the
addition of the labeler's inferred qrels. See the [ir-measures documentation](https://ir-measur.es/)
for more details.

We'll now explore the available `Labeler` implementations.

### `autoqrels.oneshot`: 1SL (One-shot Labeling)

**Reproduction: See repro instructions in [`repro/oneshot`](repro/oneshot).**

One-shot labelers work over a single known relevant document per query. An error
is raised if multiple relevant documents are provided.

Example:

```python
import autoqrels
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2019')
duot5 = autoqrels.oneshot.DuoT5(dataset=dataset, cache_path='data/duot5.cache.json.gz')
# measures:
duot5.SDCG@10
duot5.P@10
duot5.RBP
```

## Citation

If you use this work, please cite:

```bibtex
@inproceedings{autoqrels,
  author = {MacAvaney, Sean and Soldaini, Luca},
  title = {One-Shot Labeling for Automatic Relevance Estimation},
  booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year = {2023},
  url = {https://arxiv.org/abs/2302.11266}
}
```
ml-algo-automation 0.1.5
A library for comparing regression and classification models using scikit-learn.
Requires Python: >=3.6

# ML Automation

`ml_algo_automation` is a Python library that allows you to compare multiple machine learning models for both regression and classification tasks, using scikit-learn. It evaluates models based on various metrics and selects the best one according to the specified criterion.

## Features

- Compare multiple regression models (Linear Regression, Decision Tree, Random Forest, etc.)
- Compare multiple classification models (Logistic Regression, SVM, Random Forest, etc.)
- Select the best model based on metrics like R² Score, Accuracy, Precision, Recall, etc.

## Installation

You can install the package via pip:

```bash
pip install ml-algo-automation
```


## Usage

###Regression Example

```python
from ml_algo_automation.ml_model_comparator import a_b_testing_regression, get_best_model, get_model_results
from sklearn.datasets import load_diabetes

# Load the diabetes dataset
data = load_diabetes()
X, y = data.data, data.target

# Run the regression testing function
models, results = a_b_testing_regression(X, y)

# Find the best model based on R² Score
best_model_name = get_best_model(results, metric="R² Score")
print(f"\nThe best model based on R² Score is: {best_model_name} with an R² Score of {results[best_model_name]['R² Score']:.4f}\n")

# Print all model results
get_model_results(results)
```

###Classification Example

```python
from ml_algo_automation.ml_model_comparator import a_b_testing_classification, get_best_model, get_model_results
from sklearn.datasets import load_wine

# Load a sample dataset
data = load_wine()
X, y = data.data, data.target

# Run the classification testing function
models, results = a_b_testing_classification(X, y)

# Find the best model based on Precision
best_model_name = get_best_model(results, metric="Precision")
print(f"\nThe best model based on Precision is: {best_model_name} with a Precision of {results[best_model_name]['Precision']:.4f}\n")

# Print all model results
get_model_results(results)

```