Metadata-Version: 1.0
Name: MultiAug
Version: 0.1.15
Summary: Multi-modal data augmentation library for machine learning
Home-page: http://pypi.python.org/pypi/MultiAug/
Author: Devin Taylor
Author-email: dev.t03@gmail.com
License: LICENSE.txt
Description: # MultiAug
        
        MultiAug is a multi-modal data augmentation library for use in machine learning. The library aims to provide the following functionality:
        
        * For datasets where there are multiple modalities describing the same sample point (i.e. tabular data and image data), generate new data points by augmenting corresponding samples in the different modalities
        * Augmentation for 3D images
        * Augmentation for tabular data
        
        Functionally, the library presents a similar API to [imgaug](https://github.com/aleju/imgaug) python library
        
        ## Current Features
        
        3D image augmentation
        
        * Random rotation
        
        Tabular data augmentation
        
        * Featurewise Gaussian noise
        
        ## API
        
        Operators:
        
        * The `OneOf()` method with apply one of the transformations provided in the list to the corresponding modality
        
            * `augment` can either be a fraction of the dataset to augment or a predetermined list of indices in the dataset that you want to augment
            * `image3d_transforms` list of possible augmentations to apply to 3D images
            * `tabular_transforms` list of possible augmentations to apply to tabular data
        
        ## Examples
        
        Randomly augment 50% of the data by rotating 3D images about the x, y, z axes by `angle` degrees
        
        ```python
        import multiaug.augmenters as aug
        a = aug.OneOf(augment=0.5, image3d_transforms=[aug.image3d_augmenters.Rotate3d(angle=5)])
        data, labels = load_data() # must return (B x H x W x D, [int]) where [int] is categorical integers
        new_data, new_labels = a.apply_image3d(data, labels)
        ```
        
        Randomly augment 50% of the data by applying featurewise Gaussian noise as 10% of the variance of each feature
        
        
        ```python
        import multiaug.augmenters as aug
        a = aug.OneOf(augment=0.5, tabular_transforms=[aug.tabular_augmenters.GaussianPerturbation(method='variance', fraction=0.1)])
        data, labels = load_data() # must return (B x Feats, [int]) where [int] is categorical integers
        new_data, new_labels = a.apply_tabular(data, labels)
        ```
        
        Randomly augment 50% of the data by applying rotation to 3D images and featurewise Guassian noise to the corresponding tabular data
        
        ```python
        import multiaug.augmenters as aug
        a = aug.OneOf(augment=0.5, image3d_transforms=[aug.image3d_augmenters.Rotate3d(angle=5)], tabular_transforms=[aug.tabular_augmenters.GaussianPerturbation(method='variance', fraction=0.1)])
        image_data, labels = load_data() # must return (B x H x W x D, [int]) where [int] is categorical integers
        tabular_data, _ = load_data() # must return (B x Feats, [int]) where [int] is categorical integers
        new_image_data, new_labels = a.apply_image3d(image_data, labels)
        new_tabular_data, _ = a.apply_tabular(tabular_data, labels)
        ```
        
Platform: UNKNOWN
