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        "description": "DataWig - Imputation for Tables\n================================\n\n[![PyPI version](https://badge.fury.io/py/datawig.svg)](https://badge.fury.io/py/datawig.svg)\n[![GitHub license](https://img.shields.io/github/license/awslabs/datawig.svg)](https://github.com/awslabs/datawig/blob/master/LICENSE)\n[![GitHub issues](https://img.shields.io/github/issues/awslabs/datawig.svg)](https://github.com/awslabs/datawig/issues)\n[![Build Status](https://travis-ci.org/awslabs/datawig.svg?branch=master)](https://travis-ci.org/awslabs/datawig)\n\nDataWig learns Machine Learning models to impute missing values in tables.\n\nSee our user-guide and extended documentation [here](https://datawig.readthedocs.io/en/latest).\n\n## Installation\n\n### CPU\n```bash\npip3 install datawig\n```\n\n### GPU\nIf you want to run DataWig on a GPU you need to make sure your version of Apache MXNet Incubating contains the GPU bindings.\nDepending on your version of CUDA, you can do this by running the following:\n\n```bash\nwget https://raw.githubusercontent.com/awslabs/datawig/master/requirements/requirements.gpu-cu${CUDA_VERSION}.txt\npip install datawig --no-deps -r requirements.gpu-cu${CUDA_VERSION}.txt\nrm requirements.gpu-cu${CUDA_VERSION}.txt\n```\nwhere `${CUDA_VERSION}` can be `75` (7.5), `80` (8.0), `90` (9.0), or `91` (9.1).\n\n## Running DataWig\nThe DataWig API expects your data as a [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html). Here is an example of how the dataframe might look:\n\n|Product Type | Description           | Size | Color |\n|-------------|-----------------------|------|-------|\n|   Shoe      | Ideal for Running     | 12UK | Black |\n| SDCards     | Best SDCard ever ...  | 8GB  | Blue  |\n| Dress       | This **yellow** dress | M    | **?** |\n\nFor 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).\n\n### Imputation of categorical columns\n\n```python\nimport datawig\n\ndf = datawig.utils.generate_df_string(num_samples=200, data_column_name='sentences', label_column_name='label')\ndf_train, df_test = datawig.utils.random_split(df)\n\n#Initialize a SimpleImputer model\nimputer = datawig.SimpleImputer(\n    input_columns=['sentences'], # column(s) containing information about the column we want to impute\n    output_column='label', # the column we'd like to impute values for\n    output_path = 'imputer_model' # stores model data and metrics\n    )\n\n#Fit an imputer model on the train data\nimputer.fit(train_df=df_train)\n\n#Impute missing values and return original dataframe with predictions\nimputed = imputer.predict(df_test)\n```\n\n### Imputation of numerical columns\n\n```python\nimport datawig\n\ndf = datawig.utils.generate_df_numeric(num_samples=200, data_column_name='x', label_column_name='y')         \ndf_train, df_test = datawig.utils.random_split(df)\n\n#Initialize a SimpleImputer model\nimputer = datawig.SimpleImputer(\n    input_columns=['x'], # column(s) containing information about the column we want to impute\n    output_column='y', # the column we'd like to impute values for\n    output_path = 'imputer_model' # stores model data and metrics\n    )\n\n#Fit an imputer model on the train data\nimputer.fit(train_df=df_train, num_epochs=50)\n\n#Impute missing values and return original dataframe with predictions\nimputed = imputer.predict(df_test)\n\n```\n\nIn order to have more control over the types of models and preprocessings, the `Imputer` class allows directly specifying all relevant model features and parameters. \n\nFor details on usage, refer to the provided [examples](./examples).\n\n### Acknowledgments\nThanks to [David Greenberg](https://github.com/dgreenberg) for the package name.\n\n### Building documentation\n\n```bash\ngit clone git@github.com:awslabs/datawig.git\ncd datawig/docs\nmake html\nopen _build/html/index.html\n```\n\n\n### Executing Tests\n\nClone the repository from git and set up virtualenv in the root dir of the package:\n\n```\npython3 -m venv venv\n```\n\nInstall the package from local sources:\n\n```\n./venv/bin/pip install -e .\n```\n\nRun tests:\n\n```\n./venv/bin/pip install -r requirements/requirements.dev.txt\n./venv/bin/python -m pytest\n```\n\n",
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