Metadata-Version: 1.0
Name: sherlockml-boltzmannclean
Version: 0.1.2
Summary: Fills missing values in a pandas DataFrame using a Restricted Boltzmann Machine.
Home-page: https://sherlockml.com
Author: ASI Data Science
Author-email: engineering@asidatascience.com
License: Apache 2.0
Description-Content-Type: UNKNOWN
Description: sherlockml-boltzmannclean
        =========================
        
        Fill missing values in a pandas DataFrame using a Restricted Boltzmann Machine.
        
        Provides a class implementing the scikit-learn transformer interface for creating and training a Restricted Boltzmann Machine. This can then be sampled from to fill in missing values in training data or new data of the same format. Utility functions for applying the transformations to a pandas DataFrame are provided, with the option to treat columns as either continuous numerical or categorical features.
        
        Installation
        ------------
        
        .. code-block:: bash
        
            pip install sherlockml-boltzmannclean
        
        
        Usage
        -----
        
        To fill in missing values from a DataFrame with the minimum of fuss, a cleaning function is provided.
        
        .. code-block:: python
        
            import boltzmannclean
        
            my_clean_dataframe = boltzmannclean.clean(
                dataframe=my_dataframe,
                numerical_columns=['Height', 'Weight'],
                categorical_columns=['Colour', 'Shape'],
                tune_rbm=True  # tune RBM hyperparameters for my data
            )
        
        To create and use the underlying scikit-learn transformer.
        
        .. code-block:: python
        
            my_rbm = boltzmannclean.RestrictedBoltzmannMachine(
                n_hidden=100, learn_rate=0.01,
                batchsize=10, dropout_fraction=0.5, max_epochs=1,
                adagrad=True
            )
        
            my_rbm.fit_transform(a_numpy_array)
        
        Here the default RBM hyperparameters are those listed above, and the numpy array operated on is expected to be composed entirely of numbers in the range [0,1] or np.nan/None. The hyperparameters are:
        
        - *n_hidden*: the size of the hidden layer
        - *learn_rate*: learning rate for stochastic gradient descent
        - *batchsize*: batchsize for stochastic gradient descent
        - *dropout_fraction*: fraction of hidden nodes to be dropped out on each backward pass during training
        - *max_epochs*: maximum number of passes over the training data
        - *adagrad*: whether to use the Adagrad update rules for stochastic gradient descent
        
        Example
        -------
        
        .. code-block:: python
        
            import boltzmannclean
            import numpy as np
            import pandas as pd
            from sklearn import datasets
        
            iris = datasets.load_iris()
        
            df_iris = pd.DataFrame(iris.data,columns=iris.feature_names)
            df_iris['target'] = pd.Series(iris.target, dtype=str)
        
            df_iris.head()
        
        =   =================   ================    =================   ================    ======
        _   sepal length (cm)   sepal width (cm)    petal length (cm)   petal width (cm)    target
        =   =================   ================    =================   ================    ======
        0   5.1                  3.5                  1.4                  0.2                  0
        1   4.9                  3.0                  1.4                  0.2                  0
        2   4.7                  3.2                  1.3                  0.2                  0
        3   4.6                  3.1                  1.5                  0.2                  0
        4   5.0                  3.6                  1.4                  0.2                  0
        =   =================   ================    =================   ================    ======
        
        
        Add some noise:
        
        .. code-block:: python
        
            noise = [(0,1),(2,0),(0,4)]
        
            for noisy in noise:
                df_iris.iloc[noisy] = None
        
            df_iris.head()
        
        =   =================   ================    =================   ================    ======
        _   sepal length (cm)   sepal width (cm)    petal length (cm)   petal width (cm)    target
        =   =================   ================    =================   ================    ======
        0   5.1                  NaN                  1.4                  0.2               None
        1   4.9                  3.0                  1.4                  0.2                  0
        2   NaN                  3.2                  1.3                  0.2                  0
        3   4.6                  3.1                  1.5                  0.2                  0
        4   5.0                  3.6                  1.4                  0.2                  0
        =   =================   ================    =================   ================    ======
        
        Clean the DataFrame:
        
        .. code-block:: python
        
            df_iris_cleaned = boltzmannclean.clean(
                dataframe=df_iris,
                numerical_columns=[
                    'sepal length (cm)', 'sepal width (cm)',
                    'petal length (cm)', 'petal width (cm)'
                ],
                categorical_columns=['target'],
                tune_rbm=True
            )
        
            df_iris_cleaned.round(1).head()
        
        
        =   =================   ================    =================   ================    ======
        _   sepal length (cm)   sepal width (cm)    petal length (cm)   petal width (cm)    target
        =   =================   ================    =================   ================    ======
        0   5.1                  3.3                  1.4                  0.2                  0
        1   4.9                  3.0                  1.4                  0.2                  0
        2   6.3                  3.2                  1.3                  0.2                  0
        3   4.6                  3.1                  1.5                  0.2                  0
        4   5.0                  3.6                  1.4                  0.2                  0
        =   =================   ================    =================   ================    ======
        
        The larger and more correlated the dataset is, the better the imputed values will be.
Platform: UNKNOWN
