semvec/pypi-packages huggingface dataset:
acp-package 0.0.8
python implementation of Approximate full Conformal Prediction (ACP)
Requires Python: >=3.6

This repository contains the Python implementation of [Approximating Full Conformal Prediction at Scale via Influence Functions](https://arxiv.org/abs/2202.01315).

* [Overview](#overview)
* [Usage](#usage)
* [Reference](#reference)

## Overview

Approximate full Conformal Prediction (ACP) outputs a prediction set that provably contains the true label with at least a probability specified by the practicioner. In large datasets, ACP inherits the statistical power of full Conformal Prediction, yielding tight prediction sets with validity guarantees. The method works as a wrapper for any differentiable ML model.

## Usage

### Requirements

* python 3.6 or higher
* numpy
* torch
* tqdm
* pandas

### Installation

First of all, install the package:

```bash
pip install acp-package
```

### Constructing valid prediction sets

Include the following import in your file:

```bash
from acp.methods import ACP_D #Deleted scheme, import ACP_O for the ordinary scheme
```
Now you can use ACP in your own models. The framework is compatible with any PyTorch model with methods `.predict()` and `.fit()`. 

Once you instantiate your model, wrap ACP around it. ACP allows generating tight prediction sets with validity guarantees.

```bash
ACP = ACP_D(Xtrain, Ytrain, model, seed = SEED, verbose = True)
sets = ACP.predict(Xtest, epsilon, out_file = "results/test")
```

## Reference

Abad J., Bhatt U., Weller A. and Cherubin G. 
“Approximating Full Conformal Prediction at Scale via Influence Functions.” 2022.

 BiBTeX:

```
@inproceedings{Abad2022ApproximatingFC,
  title={Approximating Full Conformal Prediction at Scale via Influence Functions},
  author={Javier Abad and Umang Bhatt and Adrian Weller and Giovanni Cherubin},
  year={2022}
}
```
easy-icm-runner 2.3.0
A wrapper for IBM ICMs Scheduler API Calls'
Requires Python: >=3.6

# easy-icm-runner :rocket:
Simplified Job Execution for Varicent's ICM v10 using REST APIs

## Installation:
```
pip install easy-icm-runner
```

## Usage:
This project can be used as a module within your custom program, or standalone from the command line.  Below we demonstrate sample usage:

### Python

The snippet below demonstrates running a job _syncronously_ in python code.  Such a method will be desirable for incorporating an ICM job step into a more complex application, or such tasks as integrating your own secret and configuration management.
```python
from icm_runner import Runner

job_runner = Runner()

# get an authentication token to use in remaining operations
job_runner.get_token(username='icm username', password='icm password')
# start job
activity_id = job_runner.run_process_by_name(model_name='model name', process_name='process name', follow=True)
# poll for status until complete
job_runner.monitor_activity(model_name='model name', activity_id=activity_id, interval_mins=0.1)

```

### Command Line
For those of you who are not budding pythonistas, or just looking for a simple solution to job scheduling we also allow a command line entrypoint.   
```text
$ python -m icm_runner -u "icm username" -p "icm password" -m "model name" -j "process name"
```