machine-learning-algorithms

https://github.com/microsoft/nni/blob/8d5f643c64580bb26a7b10a3c4c9accf617f65b1/nni/compression/pytorch/speedup/jit_translate.py#L382

While trying to speedup my single shot detector, the following error comes up. Any way to fix this,

/usr/local/lib/python3.8/dist-packages/nni/compression/pytorch/speedup/jit_translate.py in forward(self, *args)
    363 
    364         def forward(self, *

Hello everyone,

First of all, I want to take a moment to thank all contributors and people who supported this project in any way ;) you are awesome!

If you like the project and have any interest in contributing/maintaining it, you can contact me here or send me a msg privately:

• Email: nidhalbacc@gmail.com

PS: You need to be familiar with python and machine learning

sklearn.utils are meant to be used internally within the scikit-learn package. They are not guaranteed to be stable between versions of scikit-learn. So depending on this submodule may limit cleanlab compatibility across sklearn versions.

Would not be too much work to replace the few cleanlab functions currently being

Describe the bug
If min_samples_split is a small float, then it may be equivalent to splitting on < 2 samples. This causes cuml to blow up:

RuntimeError: exception occured! file=../src/decisiontree/decisiontree.cu line=41: Invalid value for min_samples_split: 1. Should be >= 2.
Obtained 64 stack frames
#0 in /home/mboling/miniconda3/lib/python3.8/site-packages/cuml/common/../../..

Description

Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/

On MacOS, the tslearn.datasets does not work out-of-the-box.

In order to make it work, you need to apply the following steps:

1. Go to your finder
2. run "/Apps/Python/Install Certificates.command". This basically installs the certifi package with pip.

Perhaps we should add this to the documentation page of our datasets module?