DataWig learns Machine Learning models to impute missing values in tables.
See our user-guide and extended documentation here.
pip3 install datawig
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).
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).
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)
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.
Thanks to David Greenberg for the package name.
git clone git@github.com:awslabs/datawig.git
cd datawig/docs
make html
open _build/html/index.html
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