git clone
the repo locally
Install Anaconda
Install UMAP:
conda install umap-learn -c conda-forge
Verify that the environment defaults to python 3
npm install
sh setup-develop.sh
sh update-develop.sh
Note: We recommend using Jupyter in full screen mode when running VAINE. Widget may appear truncated otherwise.
Open Jupyter Notebook:
jupyter notebook
Navigate to notebooks/VAINE demo
This file should run without errors
Load dataset from local filepath or url:
import pandas as pd
df = pd.read_json(url)
df = pd.read_csv(url)
If necessary, create an index column. Note that indices need to be unique:
df = df.set_index('variable')
Define treatment, outcome and ignore variables. VAINE currently only handles numerical variables, categorical variables should be ignored.
treatments = df.columns[df.columns.str.startswith('Treatment')].tolist()
outcomes = df.columns[df.columns.str.startswith('Outcome')].tolist()
ignore = ['variable 1', 'variable 2']
Run VAINE:
vaineWidget(df, treatments, outcomes, ignore)