Metadata-Version: 2.1
Name: datawig
Version: 0.1.2
Summary: Imputation for tables with missing values
Home-page: https://github.com/awslabs/datawig
Author: datawig-dev
Author-email: datawig-dev@amazon.com
Maintainer-email: datawig-dev@amazon.com
License: Apache License 2.0
Description: DataWig - Imputation for Tables
        ================================
        
        [![PyPI version](https://badge.fury.io/py/datawig.svg)](https://badge.fury.io/py/datawig.svg)
        [![GitHub license](https://img.shields.io/github/license/awslabs/datawig.svg)](https://github.com/awslabs/datawig/blob/master/LICENSE)
        [![GitHub issues](https://img.shields.io/github/issues/awslabs/datawig.svg)](https://github.com/awslabs/datawig/issues)
        [![Build Status](https://travis-ci.org/awslabs/datawig.svg?branch=master)](https://travis-ci.org/awslabs/datawig)
        
        DataWig learns models to impute missing values in tables.
        
        For each to-be-imputed column, DataWig trains a supervised machine learning model
        to predict the observed values in that column using the data from other columns.
        
        ## Dependencies
        
        DataWig requires:
        - **Python3**
        - MXNet 1.3.0
        - numpy
        - pandas
        - scikit-learn
        
        ## Installation with pip
        ### CPU
        ```bash
        > 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:
        
        ```bash
        > 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](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html). Here is an example of how the dataframe might look:
        
        ![datawig dataframe example](https://s3.amazonaws.com/datawig/example_data/df_image_resize.png)
        
        
        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).
        
         ```python
            from datawig import SimpleImputer
            import pandas as pd
        
            df_train = pd.read_csv('/path/to/train/data.csv')
            df_test = pd.read_csv('/path/to/test/data.csv')
        
            #Initialize a SimpleImputer model
            imputer = SimpleImputer(
                input_columns=['item_name', 'description'], #columns containing information about the column we want to impute
                output_column='brand', #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)
         ```
        
        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](./examples).
        
        ## 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
        ```
        
        ### Acknowledgments
        Thanks to [David Greenberg](https://github.com/dgreenberg) for the package name.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3
Description-Content-Type: text/markdown
