The aim of healthcareai is to streamline machine learning in healthcare. The package has two main goals:
conda install pyodbc
conda remove scipy
conda install scipy
conda install scikit-learn
pip install healthcareai
pip install https://github.com/HealthCatalyst/healthcareai-py/zipball/master
We recommend using the Anaconda python distribution when working on Windows. There are a number of reasons:
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.conda
saves you is with the python package scipy, which, by their own admission "is difficult".You may need to install the following dependencies:
sudo apt-get install python-tk
sudo pip install pyodbc
pyodbc
dependency. You may first need to run sudo apt-get install unixodbc-dev
then retry sudo pip install pyodbc
. Credit stackoverflowOnce you have the dependencies satisfied run pip install healthcareai
or sudo pip install healthcareai
pip install healthcareai
or sudo pip install healthcareai
docker build -t healthcareai .
docker run -p 8888:8888 healthcareai
http://localhost:8888
.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.
Read through the Getting Started section of the healthcareai-py documentation.
Read through the example files to learn how to use the healthcareai-py API.
example_regression_1.py
or example_classification_1.py
using our sample diabetes dataset.example_regression_2.py
or example_classification_2.py
after running one of the first examples.example_advanced.py
.To train and evaluate your own model, modify the queries and parameters in either example_regression_1.py
or example_classification_1.py
to match your own data.
Decide what type of prediction output you want. See Choosing a Prediction Output Type for details.
Set up your database tables to match the schema of the output type you chose.
Congratulations! After running one of the example files with your own data, you should have a trained model. To use your model to make predictions, modify either example_regression_2.py
or example_classification_2.py
to use your new model. You can then run it to see the results.