Metadata-Version: 2.1
Name: rdt
Version: 0.1.2
Summary: A repository with reversible data transforms
Home-page: https://github.com/HDI-Project/RDT
Author: MIT Data To AI Lab
Author-email: dailabmit@gmail.com
License: MIT license
Description: <p align="left"> 
        <img width=15% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png" alt=“Copulas” />
          <i>An open source project from Data to AI Lab at MIT.</i>
        </p>
        
        [![][pypi-img]][pypi-url] [![][travis-img]][travis-url]
        
        # Reversible Data Transforms
        
        This a python library used to transform data for data science libraries and preserve the transformations in order to reverse them as needed.
        
        - Free software: MIT license
        - Documentation: https://HDI-Project.github.io/RDT
        
        [travis-img]: https://travis-ci.org/HDI-Project/RDT.svg?branch=master
        [travis-url]: https://travis-ci.org/HDI-Project/RDT
        [pypi-img]: https://img.shields.io/pypi/v/RDT.svg
        [pypi-url]: https://pypi.python.org/pypi/RDT
        
        
        
        ## Installation
        
        ### Install with pip
        
        The simplest and recommended way to install RDT is using `pip`:
        
        ```
        pip install rdt
        ```
        
        ### Install from sources
        
        You can also clone the repository and install it from sources
        
        ```
        git clone git@github.com:HDI-Project/RDT.git
        cd RDT
        pip install -e .
        ```
        
        ## Usage
        
        This library is used to apply desired transformations to individual tables or entire datasets
        all at once, with the goal of getting completely numeric tables as the output. The desired
        transformations can be specified at the column level, or dataset level. For example, you can
        apply a datetime transformation to only select columns, or you can specify that you want every
        datetime column in the dataset to go through that transformation.
        
        ### Transforming a column
        
        The base class of this library is the BaseTransformer class. This class provides method to fit
        a transformer to your data and transform it, a method to transform new data with an already
        fitted transformer and a method to reverse a transform and get data that looks like the original
        input. Each transformer class inherits from the BaseTransformer class, and thus has all
        these methods.
        
        Transformers take in a column and the meta data for that column as an input. Below we will
        demonstrate how to use a datetime transformer to transform and reverse transform a column.
        
        First we need to decompress the demo data included in the repository by running this
        command on a shell:
        
        ```
        tar -xvzf examples/data/airbnb.tar.gz -C examples/data/
        ```
        
        Afterwards, we can proceed to open a python interpreter and load the data
        
        ```python
        >>> from rdt.transfomers import get_col_info
        >>> demo_data = 'examples/data/airbnb/Airbnb_demo_meta.json'
        >>> column, column_metadata = get_col_info('users', 'date_account_created', demo_data)
        >>> column.head(5)
        0    2014-01-01
        1    2014-01-01
        2    2014-01-01
        3    2014-01-01
        4    2014-01-01
        Name: date_account_created, dtype: object
        
        >>> column_metadata
        {'name': 'date_account_created',
         'type': 'datetime',
         'format': '%Y-%m-%d',
         'uniques': 1634}
        
        ```
        
        Now we can transform the column.
        
        ```python
        >>> from rdt.transformers.DTTransformer import DTTransformer
        >>> transformer = DTTransformer()
        >>> transformed_data = transformer.fit_transform(column, column_metadata)
        >>> transformed_data.head(5)
        0                      1          1.388531e+18
        1                      1          1.388531e+18
        2                      1          1.388531e+18
        3                      1          1.388531e+18
        4                      1          1.388531e+18
        
        ```
        
        If you want to reverse the transformation and get the original data back, you can run the
        following command.
        
        ```python
        >>> reverse_transformed = transformer.reverse_transform(transformed_data, column_metadata)
        >>> reverse_transformed.head(5)
          date_account_created
          date_account_created
        0           2014-01-01
        1           2014-01-01
        2           2014-01-01
        3           2014-01-01
        4           2014-01-01
        ```
        
        ### Transforming a table
        
        You can also transform an entire table using the HyperTransformer class. Again, we can start by
        loading the data.
        
        ```python
        >>> from rdt.utils import get_table_dict
        >>> meta_file = 'examples/data/airbnb/Airbnb_demo_meta.json'
        >>> table_dict = get_table_dict(meta_file)
        >>> table, table_meta = table_dict['users']
        ```
        
        Now you can pass a list of the desired transformers into the `fit_transform_table` function to
        transform the whole table.
        
        ```python
        >>> from rdt.hyper_transformer import HyperTransformer
        >>> ht = HyperTransformer(meta_file)
        >>> tl = ['DTTransformer', 'NumberTransformer', 'CatTransformer']
        >>> transformed = ht.fit_transform_table(table, table_meta, transformer_list=tl)
        >>> transformed.head(3).T
                                             0             1             2
        ?date_account_created     1.000000e+00  1.000000e+00  1.000000e+00
        date_account_created      1.388531e+18  1.388531e+18  1.388531e+18
        ?timestamp_first_active   1.000000e+00  1.000000e+00  1.000000e+00
        timestamp_first_active    1.654000e+13  1.654000e+13  1.654000e+13
        ?date_first_booking       1.000000e+00  0.000000e+00  0.000000e+00
        date_first_booking        1.388790e+18  0.000000e+00  0.000000e+00
        ?gender                   1.000000e+00  1.000000e+00  1.000000e+00
        gender                    8.522112e-01  3.412078e-01  1.408864e-01
        ?age                      1.000000e+00  0.000000e+00  0.000000e+00
        age                       6.200000e+01  3.700000e+01  3.700000e+01
        ?signup_method            1.000000e+00  1.000000e+00  1.000000e+00
        signup_method             3.282037e-01  3.500181e-01  4.183867e-01
        ?signup_flow              1.000000e+00  1.000000e+00  1.000000e+00
        signup_flow               4.453093e-01  3.716032e-01  3.906801e-01
        ?language                 1.000000e+00  1.000000e+00  1.000000e+00
        language                  2.927157e-01  5.682538e-01  6.622744e-01
        ?affiliate_channel        1.000000e+00  1.000000e+00  1.000000e+00
        affiliate_channel         9.266169e-01  5.640470e-01  8.044208e-01
        ?affiliate_provider       1.000000e+00  1.000000e+00  1.000000e+00
        affiliate_provider        7.717574e-01  2.539509e-01  7.288847e-01
        ?first_affiliate_tracked  1.000000e+00  1.000000e+00  1.000000e+00
        first_affiliate_tracked   3.861429e-01  8.600605e-01  4.029200e-01
        ?signup_app               1.000000e+00  1.000000e+00  1.000000e+00
        signup_app                6.915504e-01  6.373492e-01  5.798949e-01
        ?first_device_type        1.000000e+00  1.000000e+00  1.000000e+00
        first_device_type         6.271052e-01  2.611754e-01  6.828802e-01
        ?first_browser            1.000000e+00  1.000000e+00  1.000000e+00
        first_browser             2.481743e-01  5.087636e-01  5.023412e-01
        
        ```
        
        You can then reverse transform the output to get a table in the original format, but it will
        only contain the columns corresponding to those that were transformed (ie. numeric columns).
        
        ```python
        >>> reverse_transformed = ht.reverse_transform_table(transformed, table_meta)
        >>> reverse_transformed.head(3).T
                                               0               1                2
        date_account_created          2014-01-01      2014-01-01       2014-01-01
        timestamp_first_active    19700101053540  19700101053540   19700101053540
        date_first_booking            2014-01-04             NaN              NaN
        gender                              MALE       -unknown-        -unknown-
        age                                   62             NaN              NaN
        signup_method                      basic           basic            basic
        signup_flow                            0               0                0
        language                              en              en               en
        affiliate_channel          sem-non-brand          direct        sem-brand
        affiliate_provider                google          direct           google
        first_affiliate_tracked              omg       untracked              omg
        signup_app                           Web             Web              Web
        first_device_type        Windows Desktop     Mac Desktop  Windows Desktop
        first_browser                     Chrome         Firefox          Firefox
        
        ```
        
        ### Transforming a dataset
        
        The hyper transformer is also capable of transforming all of the tables specified in your
        meta.json at once.
        
        ```python
        >>> from rdt.hyper_transformer import HyperTransformer
        >>> meta_file = 'examples/data/airbnb/Airbnb_demo_meta.json'
        >>> ht = HyperTransformer(meta_file)
        >>> tl = ['DTTransformer', 'NumberTransformer', 'CatTransformer']
        >>> transformed = ht.fit_transform(transformer_list=tl)
        >>> transformed['users'].head(3).T
                                             0             1             2
        ?date_account_created     1.000000e+00  1.000000e+00  1.000000e+00
        date_account_created      1.388531e+18  1.388531e+18  1.388531e+18
        ?timestamp_first_active   1.000000e+00  1.000000e+00  1.000000e+00
        timestamp_first_active    1.654000e+13  1.654000e+13  1.654000e+13
        ?date_first_booking       1.000000e+00  0.000000e+00  0.000000e+00
        date_first_booking        1.388790e+18  0.000000e+00  0.000000e+00
        ?gender                   1.000000e+00  1.000000e+00  1.000000e+00
        gender                    9.061832e-01  1.729590e-01  4.287514e-02
        ?age                      1.000000e+00  0.000000e+00  0.000000e+00
        age                       6.200000e+01  3.700000e+01  3.700000e+01
        ?signup_method            1.000000e+00  1.000000e+00  1.000000e+00
        signup_method             5.306912e-01  4.082081e-01  3.028973e-01
        ?signup_flow              1.000000e+00  1.000000e+00  1.000000e+00
        signup_flow               4.597129e-01  4.751324e-01  5.495054e-01
        ?language                 1.000000e+00  1.000000e+00  1.000000e+00
        language                  2.947847e-01  4.170684e-01  5.057820e-01
        ?affiliate_channel        1.000000e+00  1.000000e+00  1.000000e+00
        affiliate_channel         9.213130e-01  4.712533e-01  8.231925e-01
        ?affiliate_provider       1.000000e+00  1.000000e+00  1.000000e+00
        affiliate_provider        7.649791e-01  2.028804e-01  7.174262e-01
        ?first_affiliate_tracked  1.000000e+00  1.000000e+00  1.000000e+00
        first_affiliate_tracked   3.716114e-01  6.723371e-01  3.710109e-01
        ?signup_app               1.000000e+00  1.000000e+00  1.000000e+00
        signup_app                3.583918e-01  2.627690e-01  4.544640e-01
        ?first_device_type        1.000000e+00  1.000000e+00  1.000000e+00
        first_device_type         6.621950e-01  3.078130e-01  7.152115e-01
        ?first_browser            1.000000e+00  1.000000e+00  1.000000e+00
        first_browser             2.410379e-01  4.766930e-01  4.865389e-01
        
        >>> transformed['sessions'].head(3).T
                                 0             1           2
        ?action           1.000000      1.000000    1.000000
        action            0.361382      0.597891    0.353806
        ?action_type      1.000000      1.000000    1.000000
        action_type       0.089913      0.560351    0.046400
        ?action_detail    1.000000      1.000000    1.000000
        action_detail     0.070212      0.852246    0.107477
        ?device_type      1.000000      1.000000    1.000000
        device_type       0.726447      0.711231    0.710298
        ?secs_elapsed     1.000000      1.000000    1.000000
        secs_elapsed    319.000000  67753.000000  301.000000
        
        >>> reverse_transformed = ht.reverse_transform(tables=transformed)
        >>> reverse_transformed['users'].head(3).T
                                               0               1                2
        date_account_created          2014-01-01      2014-01-01       2014-01-01
        timestamp_first_active    19700101053540  19700101053540   19700101053540
        date_first_booking            2014-01-04             NaN              NaN
        gender                              MALE       -unknown-        -unknown-
        age                                   62             NaN              NaN
        signup_method                      basic           basic            basic
        signup_flow                            0               0                0
        language                              en              en               en
        affiliate_channel          sem-non-brand          direct        sem-brand
        affiliate_provider                google          direct           google
        first_affiliate_tracked              omg       untracked              omg
        signup_app                           Web             Web              Web
        first_device_type        Windows Desktop     Mac Desktop  Windows Desktop
        first_browser                     Chrome         Firefox          Firefox
        
        >>> reverse_transformed['sessions'].head(3).T
                                     0                    1                2
        action                  lookup       search_results           lookup
        action_type               None                click             None
        action_detail             None  view_search_results             None
        device_type    Windows Desktop      Windows Desktop  Windows Desktop
        secs_elapsed               319                67753              301
        
        ```
        
        
        # History
        
        ## 0.1.2
        
        * Add a numerical transformer for positive numbers.
        * Add option to anonymize data on categorical transformer.
        * Move the `col_meta` argument from method-level to class-level.
        * Move the logic for missing values from the transformers into the `HyperTransformer`.
        * Removed unreacheble lines in `NullTransformer`.
        * `Numbertransfomer` to set default value to 0 when the column is null.
        * Add a CLA for collaborators.
        * Refactor performance-wise the transformers.
        
        ## 0.1.1
        
        * Improve handling of NaN in NumberTransformer and CatTransformer.
        * Add unittests for HyperTransformer.
        * Remove unused methods `get_types` and `impute_table` from HyperTransformer.
        * Make NumberTransformer enforce dtype int on integer data.
        * Make DTTransformer check data format before transforming.
        * Add minimal API Reference.
        * Merge `rdt.utils` into `HyperTransformer` class. 
        
        ## 0.1.0
        
        * First release on PyPI.
        
Keywords: rdt
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: test
