Package: wids-datathon-2020
wids_datathon_2020The challenge is to create a model that uses data from the first 24 hours of intensive care to predict patient survival. (Kaggle Proj)https://www.kaggle.com/c/widsdatathon2020/overviewHow-to Perform InferenceThis project provides a publicly-accessible and straight forward way to perform batch or realtime inference based on WiDS Datathon 2020 data.$pipinstallwids-datathon-2020

$mkdir-pdata/externaldata/rawdata/interimdata/processeddata/predictionsmodels/
$wget-Odata/external/widsdatathon2020.ziphttps://github.com/iainwo/kaggle/blob/master/wids-datathon-2020/data/external/widsdatathon2020.zip

$python-mwids_datathon_2020.data.unzip_dataset
$python-mwids_datathon_2020.learndata/raw/training_v2.csv
$python-mwids_datathon_2020.inferencedata/raw/unlabeled.csv

$headdata/predictions/unlabeled.csvHow-to Develop$echo'setup development environment'$gitclonehttps://github.com/iainwo/kaggle.git
$cdwids-datathon-2020/
$makecreate_environment
$condaactivatewids_datathon_2020
$makerequirements

$echo'make some changes to the wids-datathon-2020 python module'$vimmy-file.py

$echo'use the module'$makedata
$makemodel
$makepredictionsOther Commands(wids_datathon_2020)talisman-2:wids-datathon-2020iainwong$make
Availablerules:

cleanDeleteallcompiledPythonfilescreate_environmentSetuppythoninterpreterenvironmentdataMakeDatasetdata_finalMakeDatasetforKaggleSubmissionedaGeneratevisualsforfeatureEDAlintLintusingflake8modelMakeModelpredictionsMakePredictionsrequirementsInstallPythonDependenciesrequirements_devInstallDevelopmentDepssync_data_from_s3DownloadDatafromS3sync_data_to_s3UploadDatatoS3testRununitteststest_environmentTestpythonenvironmentissetupcorrectlyProject Organization├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.orgProject based on thekaggle-data-scienceproject template.