semvec/pypi-packages huggingface dataset:
kagglerecipes 0.0.5
Useful code for different Kaggle competitions. Currently covering RSNA-MICCAI Brain Tumor Radiogenomic Classification Kaggle competition.
License: MIT License
Requires Python: >=3.6
Keywords: wandb kaggle-competition brain-tumor-classification

# Kaggle Recipes




![kaggle_wandb.png](https://github.com/ayulockin/kagglerecipes/blob/master/nbs/data/images/kaggle_wandb.png)

## What's New



#### Sept 2, 2021

- Release version 0.0.3

- Added support for multiprocessing to extract DICOM metadata for RSNA-MICCAI Brain Tumor Classification competition.

- Bug fixes. 



#### Aug 27, 2021

- Released the library on PyPI.

- Easily create voxel manipulated dataset for RSNA-MICCAI Brain Tumor Classification competition. 

- Extract dicom metadata. 

- Added utilities to log dataframe as tables and files/directory as artifacts. 

- Added utiities to log basic W&B charts (line, bar, and scatter).

## Kaggle Competitions

- [RSNA-MICCAI Brain Tumor Radiogenomic Classification](https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification)

## Code & Scripts



#### RSNA-MICCAI Brain Tumor Radiogenomic Classification

- LINK TO NOTEBOOK

## Install

`pip install kagglerecipes`

## Sample Datasets 



We have also logged smaller subsets of Kaggle commpeition datasets local development and fast prototyping. 



#### RSNA-MICCAI Brain Tumor Radiogenomic Classification

* Download it manually from [here](https://wandb.ai/wandb_fc/rsna-miccai-brain/artifacts/dataset/sample/0c38392ee79fd5f85e97/files).

* Or download it using this code snippet.

  ```

  import wandb

  run = wandb.init()

  artifact = run.use_artifact('wandb_fc/rsna-miccai-brain/sample:v0', type='dataset')

  artifact_dir = artifact.download()

  ```

## Credits



The code in this repository is a combination of many of the Kagglers' work that they have shared publicly as Kaggle kernels.



* [Connecting voxel spaces](https://www.kaggle.com/boojum/connecting-voxel-spaces) by [Michael Beregov](https://www.kaggle.com/boojum)

* [Normalized Voxels: Align Planes and Crop](https://www.kaggle.com/ren4yu/normalized-voxels-align-planes-and-crop) by [yu4u](https://www.kaggle.com/ren4yu)

* [DICOM to 2D Resized Axial PNGs 256x256 [x36]](https://www.kaggle.com/smoschou55/dicom-to-2d-resized-axial-pngs-256x256-x36) by [Sofia Moschou](https://www.kaggle.com/smoschou55)