data-maker/data/maker/__init__.py

"""
(c) 2019 Data Maker, hiplab.mc.vanderbilt.edu
version 1.0.0

This package serves as a proxy to the overall usage of the framework.
This package is designed to generate synthetic data from a dataset from an original dataset using deep learning techniques

@TODO:
    - Make configurable GPU, EPOCHS
"""
import pandas as pd
import numpy as np
import data.gan as gan
from transport import factory
def train (**args) :
    """
    This function is intended to train the GAN in order to learn about the distribution of the features
    :column     columns that need to be synthesized (discrete)
    :logs       where the output of the (location on disk)
    :id         identifier of the dataset
    :data       data-frame to be synthesized
    :context    label of what we are synthesizing
    """
    column      = args['column']
    
    column_id   = args['id']
    df          = args['data'] if not isinstance(args['data'],str) else pd.read_csv(args['data'])
    # logs        = args['logs']
    # real        = pd.get_dummies(df[column]).astype(np.float32).values
    # labels      = pd.get_dummies(df[column_id]).astype(np.float32).values
    args['real']        = pd.get_dummies(df[column]).astype(np.float32).values
    args['label']      = pd.get_dummies(df[column_id]).astype(np.float32).values
    # num_gpu     = 1 if 'num_gpu' not in args else args['num_gpu']
    # max_epochs  = 10 if 'max_epochs' not in args else args['max_epochs']
    context     = args['context']

    if 'store' in args :
        args['store']['args']['doc'] = context
        logger = factory.instance(**args['store'])
        args['logger'] = logger
        
    else:
        logger = None
    trainer = gan.Train(**args)    
    # trainer     = gan.Train(context=context,max_epochs=max_epochs,num_gpu=num_gpu,real=real,label=labels,column=column,column_id=column_id,logger = logger,logs=logs)
    return trainer.apply()

def generate(**args):
    """
    This function will generate a synthetic dataset on the basis of a model that has been learnt for the dataset
    @return pandas.DataFrame

    :data   data-frame to be synthesized
    :column   columns that need to be synthesized (discrete)
    :id     column identifying an entity
    :logs   location on disk where the learnt knowledge of the dataset is
    """
    # df      = args['data']
    df      = args['data'] if not isinstance(args['data'],str) else pd.read_csv(args['data'])
    column      = args['column'] 
    column_id   = args['id']
    # logs        = args['logs']
    # context = args['context']
    # num_gpu     = 1 if 'num_gpu' not in args else args['num_gpu']
    # max_epochs  = 10 if 'max_epochs' not in args else args['max_epochs']

    #
    #@TODO:
    #   If the identifier is not present, we should fine a way to determine or make one
    #
    #ocolumns= list(set(df.columns.tolist())- set(columns))
    
    values = df[column].unique().tolist()
    values.sort()

    # labels = pd.get_dummies(df[column_id]).astype(np.float32).values
    args['label']  = pd.get_dummies(df[column_id]).astype(np.float32).values
    args['values']  = values
    # handler = gan.Predict (context=context,label=labels,max_epochs=max_epochs,num_gpu=num_gpu,values=values,column=column,logs=logs)
    handler = gan.Predict (**args)
    handler.load_meta(column)
    r =  handler.apply()
    _df = df.copy()
    _df[column] = r[column]
    return _df