kxytechnologies / kxy-python Goto Github PK

A toolkit to boost the productivity of machine learning engineers.

License: GNU General Public License v3.0

Python 64.16% Makefile 0.42% Dockerfile 0.19% Jupyter Notebook 35.23%

machine-learning machine-learning-library information-theory python feature-engineering feature-selection model-compression

kxy-python's Introduction

Boosting The Productivity of Machine Learning Engineers

Documentation

https://www.kxy.ai/reference/

Blog

https://blog.kxy.ai

Installation

From PyPi:

pip install kxy -U

From GitHub:

git clone https://github.com/kxytechnologies/kxy-python.git & cd ./kxy-python & pip install .

Authentication

All heavy-duty computations are run on our serverless infrastructure and require an API key. To configure the package with your API key, run

kxy configure

and follow the instructions. To get your own API key you need an account; you can sign up here. You'll then be automatically given an API key which you can find here.

Docker

The Docker image kxytechnologies/kxy has been built for your convenience, and comes with anaconda, auto-sklearn, and the kxy package.

To start a Jupyter Notebook server from a sandboxed Docker environment, run

docker run -i -t -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"

where you should replace <YOUR API KEY> with your API key and navigate to http://localhost:5555 in your browser. This docker environment comes with all examples available on the documentation website.

To start a Jupyter Notebook server from an existing directory of notebooks, run

docker run -i -t --mount src=</path/to/your/local/dir>,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"

where you should replace </path/to/your/local/dir> with the path to your local notebook folder and navigate to http://localhost:5555 in your browser.

You can also get the same Docker image from GitHub here.

Other Programming Language

We plan to release friendly API client in more programming language.

In the meantime, you can directly issue requests to our RESTFul API using your favorite programming language.

Pricing

All API keys are given a free quota (a few dozen backend tasks) that should be enough to try out the package and see if you love it. Beyond the free quota you will be billed a small fee per task.

KXY is free for academic use; simply signup with your university email.

KXY is also free for Kaggle competitions; sign up and email [email protected] to get a promotional code.

kxy-python's People

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pseongmin sanyam07 trendingtechnology mariianne tailtom the-moliver fudp straitrobot fsaforo1 ferxisbaret harel-coffee

kxy-python's Issues

generate_features Documentation?

Is there any documentation on how to use the generate_features function? It doesn't appear in the documentation and I can't find it in the github. e.g. how to use the entity column, how to format time-series data in advance for it, etc'.
Thanks!

Cannot find the implementation for paper MIND

Where is the implementation for the AISTATS paper MIND? I cannot find it in the repo and in the doc.

When dimension d>10, the variability of the estimator becomes very large.

It seems that the error is multiplicative wrt the true MI. So as number of variables increase, the estimator breaks down. Mind sharing how you stabilized it?
also, the loss function its the DV bound, not what's in the paper? Am I missing something?

I've always loved your work going back to your idea of learning portfolios directly from data using GPs (you were well ahead of the curve on this one). I'd love to use this project, but it seems it's been abandoned, I've resorted to doing my own implementation. Any hints would be much appreciated. :)

reopen posibility to get api key

Is this project closed? Would be interested in testing it out but it is not possible to register new accounts.

Is This Project Still Active

Hi, we're interested in testing out this platform, but it looks like neither the codebase nor the blog has been updated since May of last year. Is this project still under active development? Curious to take it for a spin.

error in import kxy

Hi,
After installing the kxy package and configuring the API key, the import kxy shows the error below:

.../python3.9/site-packages/kxy/pfs/pfs_selector.py in <module>
      6 import numpy as np
      7 
----> 8 import tensorflow as tf
      9 from tensorflow.keras.callbacks import EarlyStopping, TerminateOnNaN
     10 from tensorflow.keras.optimizers import Adam

ModuleNotFoundError: No module named 'tensorflow'

what version of tensorflow is needed for kxy to work?

error kxy.data_valuation

Hi,
After running chievable_performance_df = X_train_reduced.kxy.data_valuation(target_column='state', problem_type='classification', include_mutual_information=True, anonymize=True) I get the following error and the function does not return anything:
`During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/usr/lib/python3.9/asyncio/tasks.py", line 258, in __step
result = coro.throw(exc)
File "/home/lucy/Downloads/general/lib/python3.9/site-packages/tornado/websocket.py", line 1104, in wrapper
raise WebSocketClosedError()
tornado.websocket.WebSocketClosedError
Task exception was never retrieved
future: <Task finished name='Task-46004' coro=<WebSocketProtocol13.write_message..wrapper() done, defined at /home/lucy/Downloads/general/lib/python3.9/site-packages/tornado/websocket.py:1100> exception=WebSocketClosedError()>
Traceback (most recent call last):
File "/home/lucy/Downloads/general/lib/python3.9/site-packages/tornado/websocket.py", line 1102, in wrapper
await fut
File "/usr/lib/python3.9/asyncio/tasks.py", line 328, in __wakeup
future.result()
tornado.iostream.StreamClosedError: Stream is closed
`

multi-column prediction

I would like to try Kxy with multi-column prediction in autogluon; but it gave errors.
Does Kxy support the multi-column prediction in autogluon?