wmlba / datawig

Imputation of missing values in tables.

DataWig - Imputation for Tables

DataWig learns Machine Learning models to impute missing values in tables.

See our user-guide and extended documentation here.

Installation

CPU

pip3 install datawig

GPU

If you want to run DataWig on a GPU you need to make sure your version of Apache MXNet Incubating contains the GPU bindings. Depending on your version of CUDA, you can do this by running the following:

wget https://raw.githubusercontent.com/awslabs/datawig/master/requirements/requirements.gpu-cu${CUDA_VERSION}.txt
pip install datawig --no-deps -r requirements.gpu-cu${CUDA_VERSION}.txt
rm requirements.gpu-cu${CUDA_VERSION}.txt

where ${CUDA_VERSION} can be 75 (7.5), 80 (8.0), 90 (9.0), or 91 (9.1).

Running DataWig

The DataWig API expects your data as a pandas DataFrame. Here is an example of how the dataframe might look:

Product Type	Description	Size	Color
Shoe	Ideal for Running	12UK	Black
SDCards	Best SDCard ever ...	8GB	Blue
Dress	This yellow dress	M	?

For most use cases, the SimpleImputer class is the best starting point. DataWig expects you to provide the column name of the column you would like to impute values for (called output_column below) and some column names that contain values that you deem useful for imputation (called input_columns below).

Imputation of categorical columns

import datawig

df = datawig.utils.generate_df_string(num_samples=200, data_column_name='sentences', label_column_name='label')
df_train, df_test = datawig.utils.random_split(df)

#Initialize a SimpleImputer model
imputer = datawig.SimpleImputer(
    input_columns=['sentences'], # column(s) containing information about the column we want to impute
    output_column='label', # the column we'd like to impute values for
    output_path = 'imputer_model' # stores model data and metrics
    )

#Fit an imputer model on the train data
imputer.fit(train_df=df_train)

#Impute missing values and return original dataframe with predictions
imputed = imputer.predict(df_test)

Imputation of numerical columns

import datawig

df = datawig.utils.generate_df_numeric(num_samples=200, data_column_name='x', label_column_name='y')         
df_train, df_test = datawig.utils.random_split(df)

#Initialize a SimpleImputer model
imputer = datawig.SimpleImputer(
    input_columns=['x'], # column(s) containing information about the column we want to impute
    output_column='y', # the column we'd like to impute values for
    output_path = 'imputer_model' # stores model data and metrics
    )

#Fit an imputer model on the train data
imputer.fit(train_df=df_train, num_epochs=50)

#Impute missing values and return original dataframe with predictions
imputed = imputer.predict(df_test)
             

In order to have more control over the types of models and preprocessings, the Imputer class allows directly specifying all relevant model features and parameters.

For details on usage, refer to the provided examples.

Acknowledgments

Thanks to David Greenberg for the package name.

Building documentation

git clone [email protected]:awslabs/datawig.git
cd datawig/docs
make html
open _build/html/index.html

Executing Tests

Clone the repository from git and set up virtualenv in the root dir of the package:

python3 -m venv venv

Install the package from local sources:

./venv/bin/pip install -e .

Run tests:

./venv/bin/pip install -r requirements/requirements.dev.txt
./venv/bin/python -m pytest