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Tools for healthcare machine learning

Project description

# healthcareai

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The aim of **healthcareai** is to streamline machine learning in healthcare. The package has two main goals:

- Allow one to easily create models based on tabular data, and deploy a best model that pushes predictions to a database such as MSSQL, MySQL, SQLite or csv flat file.
- Provide tools related to data cleaning, manipulation, and imputation.

## Installation

### Windows

- If you haven't, install 64-bit Python 3.5 via [the Anaconda distribution](
- **Important** When prompted for the **Installation Type**, select **Just Me (recommended)**. This makes permissions later in the process much simpler.
- Open the terminal (i.e., CMD or PowerShell, if using Windows)
- Run `conda install pyodbc`
- Upgrade to latest scipy (note that upgrade command took forever)
- Run `conda remove scipy`
- Run `conda install scipy`
- Run `conda install scikit-learn`
- Install healthcareai using **one and only one** of these three methods (ordered from easiest to hardest).
1. **Recommended:** Install the latest release with conda by running `conda install -c catalyst healthcareai`
2. Install the latest release with pip run `pip install healthcareai`
3. If you know what you're doing, and instead want the bleeding-edge version direct from our github repo, run `pip install`

#### Why Anaconda?

We recommend using the Anaconda python distribution when working on Windows. There are a number of reasons:
- When running anaconda and installing packages using the `conda` command, you don't need to worry about [dependency hell](, particularly because packages aren't compiled on your machine; `conda` installs pre-compiled binaries.
- A great example of the pain the using `conda` saves you is with the python package **scipy**, which, by [their own admission]( *"is difficult"*.

### Linux

You may need to install the following dependencies:
- `sudo apt-get install python-tk`
- `sudo pip install pyodbc`
- Note you'll might run into trouble with the `pyodbc` dependency. You may first need to run `sudo apt-get install
unixodbc-dev` then retry `sudo pip install pyodbc`. Credit [stackoverflow](

Once you have the dependencies satisfied run `pip install healthcareai` or `sudo pip install healthcareai`

### macOS

- `pip install healthcareai` or `sudo pip install healthcareai`

### Linux and macOS (via docker)

- Install [docker](
- Clone this repo (look for the green button on the repo main page)
- cd into the cloned directory
- run `docker build -t healthcareai .`
- run the docker instance with `docker run -p 8888:8888 healthcareai`
- You should then have a jupyter notebook available on `http://localhost:8888`.

### Verify Installation

To verify that *healthcareai* installed correctly, open a terminal and run `python`. This opens an interactive python
console (also known as a [REPL]( Then enter this
command: `from healthcareai import SupervisedModelTrainer` and hit enter. If no error is thrown, you are ready to rock.

If you did get an error, or run into other installation issues, please [let us know](
or better yet post on [Stack Overflow]( (with the healthcare-ai
tag) so we can help others along this process.

## Getting started

- Visit []( to read the docs and find examples.
* Including this [notebook](notebooks/Example1.ipynb)
- Open Sphinx (which installed with Anaconda) and copy the examples into a new file
- Modify the queries and parameters to match your data
- If you plan on deploying a model (ie, pushing predictions to SQL Server), run this in SSMS beforehand:
CREATE TABLE [SAM].[dbo].[HCAIClassificationBASE] (
[BindingID] [int] ,
[BindingNM] [varchar] (255),
[LastLoadDTS] [datetime2] (7),
[PatientEncounterID] [decimal] (38, 0), --< change to your grain col
[PredictedProbNBR] [decimal] (38, 2),
[Factor1TXT] [varchar] (255),
[Factor2TXT] [varchar] (255),
[Factor3TXT] [varchar] (255))

CREATE TABLE [SAM].[dbo].[HCAIPredictionRegressionBASE] (
[BindingID] [int],
[BindingNM] [varchar] (255),
[LastLoadDTS] [datetime2] (7),
[PatientEncounterID] [decimal] (38, 0), --< change to your grain col
[PredictedValueNBR] [decimal] (38, 2),
[Factor1TXT] [varchar] (255),
[Factor2TXT] [varchar] (255),
[Factor3TXT] [varchar] (255))

## For Issues

- Double check that the code follows the examples [here](
- If you're still seeing an error, create a post in [Stack Overflow]( (with the healthcare-ai tag) that contains
* Details on your environment (OS, database type, R vs Py)
* Goals (ie, what are you trying to accomplish)
* Crystal clear steps for reproducing the error
- You can also log a new issue in the GitHub repo by clicking [here](

## PyPI Package Creation and Updating

**Note these instructions are for maintainers only.**

First, read this [Packaging and Distributing Projects]( guide.

It's also worth noting that while this *should* be done on the [pypi test site](, I've
run into a great deal of trouble with conflicting guides authenticating to the test site. So be smart about this.

1. **Build a source distribution**: from python3 (ran in windows anaconda python 3) run `python sdist`
2. **Register the package** by using the[form on pypi](
Upload your `PKG-INFO` that was generated inside the `.egg` file.
3. **Upload the package** using [twine](
- `twine upload dist/healthcareai-<version>.tar.gz`
- **NOTE** You can only ever upload a file name **once**. To get around this I was adding a *rc* number to the
version in ``. However, this **will break the appveyor build**, so you'll need to remove the `.rc` before
you push to github.
4. Verify install on all three platforms (linux, macOS, windows) by:
1. `pip uninstall healthcareai`
2. `pip install healthcareai`
3. From a python console, type `from healthcareai import SupervisedModelTrainer`

### Release process (Including Read The Docs)

1. update all version numbers
- ``
2. update CHANGELOG
- Move all items under **unreleased** to a new release number
- Leave the template under **unreleased**
3. merge in the PR
4. create release on github releases (making sure this matches the release number in ``)
5. Create and upload the new pypi release (see above)
6. update readthedocs settings
- **Admin** > **Versions**
- Ensure that the new release number is checked for **public**
7. Manually build new read the docs
- **Builds** > **Build version <new release>**
8. verify the new version builds and is viewable at the public url

### Conda Packaging and Distribution

Creating a conda package is much easier if you have already built the PyPI package.

1. Install prerequisites (only needed once)
+ Install conda build `conda install conda-build`
+ Install anaconda cli `conda install anaconda-client`
+ Login to with `anaconda login`
2. Configure conda
+ `conda config --set always_yes true`
+ `conda config --set anaconda_upload no`
3. Create the skeleton conda recipe from the existing PyPI package
+ `conda skeleton pypi healthcareai`
4. Build the conda package for the main python versions
+ `conda build --python 2.7 healthcareai`
+ `conda build --python 3.4 healthcareai`
+ `conda build --python 3.5 healthcareai`
+ `conda build --python 3.6 healthcareai`
5. Convert the existing builds to work on all platforms (win32, win64, osx62, linux32, linux64). Note this can take a while.
+ `conda convert --platform all win-64/healthcareai-*-py*.tar.bz2 -o <PATH_TO_BUILD_DIRECTORY>`
6. Upload to anaconda using the anaconda cli
+ Note that you'll have to keep track of where the builds are put!
+ `anaconda upload <PATH_TO_BUILD_DIRECTORY>/**/healthcareai*.tar.bz2`
7. Clean up the mess
+ `conda build purge`

##### Helpful Resources

- Conda [Building Packages](
- [ dashboard](
- Taken from the excellent [ docs](
- Also, some taken from this [Travis CI build](

## Sphinx Progress

Ideally, this project will have a user guide, (currently in the form of the docs folder) and method level documentation generated by sphinx.

1. Install sphinx
2. install

From the `dox/_build` (you may need to create it if it doesn't exist) directory, run`sphinx-apidoc.exe -f -o ../ ../../healthcareai && sphinx-build.exe -b html ../ ./ && python -m http.server 8888 --bind`

### Sphinx resources

- [An idiot’s guide to Python documentation with Sphinx and ReadTheDocs](
- [First Steps with Sphinx](
- [Napoleon - Marching toward legible docstrings](
- [napoleon configuration](

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