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Add cell to download XGBoost data automatically #1717

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Mar 23, 2023
Merged

Add cell to download XGBoost data automatically #1717

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56058e3
Add cell to download XGBoost data automatically
charlesbvll Mar 17, 2023
1d3f0e3
Add new line at the end of gitignore
charlesbvll Mar 17, 2023
bb59298
Merge branch 'main' into automate-xgboost-download
charlesbvll Mar 19, 2023
771ab52
Merge branch 'main' into automate-xgboost-download
charlesbvll Mar 22, 2023
017544f
Remove useless FedXGBoost file
charlesbvll Mar 23, 2023
f40b121
Merge main and resolve conflicts
charlesbvll Mar 23, 2023
b242c4e
Revert formatting of pyproject.toml
charlesbvll Mar 23, 2023
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2 changes: 2 additions & 0 deletions examples/quickstart_xgboost_horizontal/.gitignore
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dataset

18 changes: 2 additions & 16 deletions examples/quickstart_xgboost_horizontal/README.md
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Expand Up @@ -11,22 +11,8 @@ This example demonstrates a federated XGBoost using Flower with PyTorch. This is

## Project Setup

This implementation can be easily run in Google Colab with the following file structure in Google Drive, * denotes folder:

```shell
—————————————————————————————————————————————————————————————————————
My Drive
XGBoost*
|----- code.ipynb
dataset*
binary_classifications*
|----- dataset file 1
|----- dataset file 2
regression*
|----- dataset file 1
|----- dataset file 2
—————————————————————————————————————————————————————————————————————
```
This implementation can be easily run in Google Colab with the button at the top of the README or as a standalone Jupyter notebook,
it will automatically download and extract the example data inside a `dataset` folder and `binary_classification` and `regression` sub-folders.

## Datasets

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64 changes: 42 additions & 22 deletions examples/quickstart_xgboost_horizontal/code_horizontal.ipynb
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Expand Up @@ -29,32 +29,52 @@
},
"outputs": [],
"source": [
"# File structure to run this implementation in Google Colab, * denotes folder:\n",
"#—————————————————————————————————————————————————————————————————————\n",
"# My Drive\n",
"# XGBoost*\n",
"# |----- code.ipynb (this file)\n",
"# dataset*\n",
"# binary_classifications*\n",
"# |----- dataset file 1\n",
"# |----- dataset file 2\n",
"# regression*\n",
"# |----- dataset file 1\n",
"# |----- dataset file 2 \n",
"#—————————————————————————————————————————————————————————————————————\n",
"\n",
"from google.colab import drive\n",
"drive.mount(\"/content/drive\")\n",
"\n",
"import os\n",
"import sys\n",
"GOOGLE_DRIVE_PATH_AFTER_MYDRIVE = \"FedXGBoost\"\n",
"GOOGLE_DRIVE_PATH = os.path.join(\"drive\", \"My Drive\", GOOGLE_DRIVE_PATH_AFTER_MYDRIVE)\n",
"print(os.listdir(GOOGLE_DRIVE_PATH))\n",
"import urllib.request\n",
"import bz2\n",
"import shutil\n",
"\n",
"CLASSIFICATION_PATH = os.path.join(\"dataset\", \"binary_classification\")\n",
"REGRESSION_PATH = os.path.join(\"dataset\", \"regression\")\n",
"\n",
"if not os.path.exists(CLASSIFICATION_PATH):\n",
" os.makedirs(CLASSIFICATION_PATH)\n",
" urllib.request.urlretrieve(\"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/cod-rna\",\n",
" f\"{os.path.join(CLASSIFICATION_PATH, 'cod-rna')}\")\n",
" urllib.request.urlretrieve(\"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/cod-rna.t\",\n",
" f\"{os.path.join(CLASSIFICATION_PATH, 'cod-rna.t')}\")\n",
" urllib.request.urlretrieve(\"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/cod-rna.r\",\n",
" f\"{os.path.join(CLASSIFICATION_PATH, 'cod-rna.r')}\")\n",
" urllib.request.urlretrieve(\"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/ijcnn1.t.bz2\",\n",
" f\"{os.path.join(CLASSIFICATION_PATH, 'ijcnn1.t.bz2')}\")\n",
" urllib.request.urlretrieve(\"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/ijcnn1.tr.bz2\",\n",
" f\"{os.path.join(CLASSIFICATION_PATH, 'ijcnn1.tr.bz2')}\")\n",
" for filepath in os.listdir(CLASSIFICATION_PATH):\n",
" if filepath[-3:] == \"bz2\":\n",
" abs_filepath = os.path.join(CLASSIFICATION_PATH, filepath)\n",
" with bz2.BZ2File(abs_filepath) as fr, open(abs_filepath[:-4],\"wb\") as fw:\n",
" shutil.copyfileobj(fr,fw)\n",
"\n",
"if not os.path.exists(REGRESSION_PATH):\n",
" os.makedirs(REGRESSION_PATH)\n",
" urllib.request.urlretrieve(\"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/eunite2001\",\n",
" f\"{os.path.join(REGRESSION_PATH, 'eunite2001')}\")\n",
" urllib.request.urlretrieve(\"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/eunite2001.t\",\n",
" f\"{os.path.join(REGRESSION_PATH, 'eunite2001.t')}\")\n",
" urllib.request.urlretrieve(\"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/YearPredictionMSD.bz2\",\n",
" f\"{os.path.join(REGRESSION_PATH, 'YearPredictionMSD.bz2')}\")\n",
" urllib.request.urlretrieve(\"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/YearPredictionMSD.t.bz2\",\n",
" f\"{os.path.join(REGRESSION_PATH, 'YearPredictionMSD.t.bz2')}\")\n",
" for filepath in os.listdir(REGRESSION_PATH):\n",
" if filepath[-3:] == \"bz2\":\n",
" abs_filepath = os.path.join(REGRESSION_PATH, filepath)\n",
" with bz2.BZ2File(abs_filepath) as fr, open(abs_filepath[:-4],\"wb\") as fw:\n",
" shutil.copyfileobj(fr,fw)\n",
"\n",
"\n",
"# Add to sys so we can import .py files.\n",
"sys.path.append(GOOGLE_DRIVE_PATH)\n",
"!nvidia-smi\n",
"!pip install uvicorn uvicorn fastapi requests starlette\n",
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"!pip install xgboost==1.7.2\n",
"!pip install torchmetrics\n",
"!pip install torchsummary\n",
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