semvec/pypi-packages huggingface dataset:
milesianpy 0.0.10
A math calculation package.
Requires Python: >=3.8

![alt text](docs/Logo.png)

Math right at your finger-tips.

## Usage

![screenshot](docs/screenshot.png)

**Parsers**:

- `basic_parser`: A basic parser that can parse basic math expressions.
- `number_parser`: A parser that can parse numbers.
- `operator_parser`: A parser that can parse operators.

## [Documentation](https://milesianpy-docs.com/)

## Installation

```bash
pip install milesianpy
```

## Development

### Setup

For first time setup:

```bash
make setup
```

> To build, publish, and clean the artifacts in one simple step, run:
> `make all`

### Building

To build the package, run:

```bash
make build
```

### Publishing

To publish the package to pypi, run:

```bash
make publish
```
sial-pkg 0.0.8
Statistical Inference After Learning
Requires Python: >=3.9

# SIAL: Statistical Inference After Learning


## Overview
 
`sial` is a python package for conducting statistical inference on feature importance in machine learning. It is written on the basis of [`scikit-learn`](https://scikit-learn.org/stable/index.html). Hence, in principle, any regressor/classifier learned by `scikit-learn` can be inferred by `sial`.

`sial` provides two types of `Inferer` for making inferences: `CIT` for conditional independence test and `RIT` for risk invariance test. `CIT` can implement the holdout randomization test (HRT; Tansey et al., 2022), residual permutation test (RPT; Huang, 2024), and conditional predictive impact (CPI; Watson & Wright, 2021). On the other hand, `RIT` can conduct leave-one-covariate-out (LOCO; Lei et al., 2018) and plug-in estimation (PIE; Williamson et al., 2023). In addition, `sial` also constructs a `Crosser` class. With the help of `Crosser`, cross-fitting and $p$-value combination can be easily implemented to improve the stability and even statistical power of the CITs and RITs.

## Installation
`sial` can be installed via `pip`:
```
pip install sial-pkg
```

## Tutorial

Three Jupyter notebooks are available to learn the use of `sial`:

1. [Conditional Independence Tests](https://github.com/aipsylab/sial/blob/main/tutorial/tutorial_1_cit.ipynb)
2. [Risk Invariance Tests](https://github.com/aipsylab/sial/blob/main/tutorial/tutorial_2_rit.ipynb)
3. [Cross-Fitting and $p$-Value Combination](https://github.com/aipsylab/sial/blob/main/tutorial/tutorial_3_crosser.ipynb)

These notebooks assume that readers have experiences in using `scikit-learn`.

## References

Huang, P.-H. (2024). Residual Permutation Tests for Feature Importance in Machine Learning. [Manuscript submitted for publication].

Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018). Distribution-free predictive inference for regression. Journal of the American Statistical Association, 113(523), 1094–1111. doi: 10.1080/01621459.2017.1307116

Tansey, W., Veitch, V., Zhang, H., Rabadan, R., & Blei, D. M. (2022). The holdout randomization test for feature selection in black box models. Journal of Computational and Graphical Statistics, 31(1), 151–162. doi: 10.1080/10618600.2021.1923520

Watson, D., & Wright, M. (2021). Testing conditional independence in supervised learning algorithms. Machine Learning, 110, 1-23. doi: 10.1007/s10994-021-06030-6

Williamson, B. D., Gilbert, P. B., Simon, N. R., & Carone, M. (2023). A general framework for inference on algorithm-agnostic variable importance. Journal of the American Statistical Association, 118(543), 1645–1658. doi: 10.1080/01621459.2021.2003200
apache-skywalking-py312 1.2.2
The Python Agent for Apache SkyWalking, which provides the native tracing/metrics/logging/profiling abilities for Python projects. this is support python3.12.
Requires Python: <4.0,>=3.12

SkyWalking Python Agent requires Apache SkyWalking 8.0+ and Python 3.7+.
This is support py3.12 .