semvec/pypi-packages huggingface dataset:
kondo-ml 0.0.10
Python package for instance selection algorithms
Requires Python: >=3.10

# kondo-ML

kondo-ML is a package containing various instance selection algorithms
usable with regression models. The implementations are compatible with sklearn and follow
its outlier detection interface.

This is still a work in progress and some documentation is missing. Please refer to the source code
for each algorithm in the instance_selection folder.
## Install

The package can be installed via pip <br>
`pip install kondo_ml`

## Overview of algorithms

| Algorithm          | Goal                        |
|--------------------|-----------------------------|
| RegCNN             | Size reduction              |
| RegENN             | Noise filter                |
| RegENNTime         | Noise filter/drift handling |
| DROP-RX            | Noise filter/size reduction |
| Shapley            | Utility assignment          |
| FISH               | Drift Handling              |
| SELCON             | Size reduction              |
| Mutual Information | Noise filter                |
|                    |                             |

## Algorithm sources
RegCNN & RegENN: https://link.springer.com/chapter/10.1007/978-3-642-33266-1_33  <br>
DROP-RX: https://www.sciencedirect.com/science/article/abs/pii/S0925231216301953 <br>
Shapley: https://proceedings.mlr.press/v97/ghorbani19c/ghorbani19c.pdf  <br>
FISH: http://eprints.bournemouth.ac.uk/18567/1/FISH_journal_preprint.pdf <br>
SELCON: https://arxiv.org/abs/2106.12491 <br>
Mutual Information: https://research.cs.aalto.fi//aml/Publications/Publication167.pdf <br>

The SELCON implementation is taken from the author's github with minor changes: https://github.com/abir-de/SELCOn

## Example

```
# import instance selection algorithm of your choice
from kondo_ml.instace_selection import RegENNSelector
# initialize selector 
reg_enn = RegEnnSelector(alpha=1,nr_of_neighbors=3)
# predict labels (1 to use that instance, -1 to ignore)
labels = reg_enn.fit_predict(X,y)
# transform -1/1 labels into boolean 0/1 labels
from kondo_ml.utils import transform_selector_output_into_mask
boolean_labels = transform_selector_output_into_mask(labels)
# use selected instances for model training (any model, LR here as an example)
from sklearn.linear_model import LinearRegression
model = LinearRegression().fit(X[boolean_labels],y[boolean_labels])
```

More examples can be found in the notebooks of the examples folder

## Contribution

Please feel free to contribute documentation, tests or new algorithms to this package.
And let me know if you find any mistakes in the implementations
amm 0.0.1
AI Model Manager
Requires Python: >=3.6

# AMM

The AI model manager.

# Use it

```sh

# Initialize a new project
amm init

# Probe an existing directory to build a map file
amm probe

# Install a model from Civitai
amm install https://civitai.com/models/7240/meinamix

# Install a model to a specific place
amm install https://civitai.com/models/7240/meinamix ./models/meinamix

# Install a Lora from Civitai, and automatically pair it with previews
amm install https://civitai.com/models/13213

# Install a model from Hugging Face
amm install https://huggingface.co/THUDM/chatglm-6b/blob/main/pytorch_model-00001-of-00008.bin

# Download a model by name
amm install bloom

# Install from an existing amm.json
amm install
amm install -r non-default-named.amm.json

```

# License
MIT