Metadata-Version: 2.1
Name: batching
Version: 1.0.8
Summary: Batching is a set of tools to format data for training sequence models
Home-page: https://github.com/cirick/batching
Author: Charles Irick
Author-email: cirick@gmail.com
License: MIT
Download-URL: https://github.com/cirick/batching/archive/v1.0.8.tar.gz
Description: # Batching
        
        Batching is a set of tools to format data for training sequence models.
        
        [![Build Status](https://travis-ci.org/cirick/batching.svg?branch=master)](https://travis-ci.org/cirick/batching)
        [![Coverage Status](https://coveralls.io/repos/github/cirick/batching/badge.svg?branch=master)](https://coveralls.io/github/cirick/batching?branch=master)
        
        ## Installation
        ```shell
        $ pip install batching
        ```
        
        ## Example usage
        Example script exists in sample.py
        ```python
        # Metadata for batch info - including batch IDs and mappings to storage resouces like filenames
        storage_meta = StorageMeta(validation_split=0.2)
        
        # Storage for batch data - Memory, Files, S3
        storage = BatchStorageMemory(storage_meta)
        
        # Create batches - configuration contains feature names, windowing config, timeseries spacing
        batch_generator = Builder(storage, 
                                  feature_set, 
                                  look_back, 
                                  look_forward, 
                                  batch_seconds, 
                                  batch_size=128)
        batch_generator.generate_and_save_batches(list_of_dataframes)
        
        # Generator for feeding batches to training - tf.keras.model.fit_generator
        train_generator = BatchGenerator(storage)
        validation_generator = BatchGenerator(storage, is_validation=True)
        
        model = tf.keras.Sequential()
        model.add(tf.keras.layers.Dense(1, activation='sigmoid')
        model.compile(loss=tf.keras.losses.binary_crossentropy, 
                      optimizer=tf.keras.optimizers.Adam(), 
                      metrics=['accuracy'])
        model.fit_generator(train_generator,
                            validation_data=validation_generator,
                            epochs=epochs)
        ```
        
        ## License
        
        [![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://badges.mit-license.org)
        
        - **[MIT license](http://opensource.org/licenses/mit-license.php)**
        - Copyright 2015 © <a href="http://fvcproductions.com" target="_blank">FVCproductions</a>.
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