103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
"""
|
|
(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
|
|
from data.bridge import Binary
|
|
import threading as thread
|
|
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'] if (isinstance(args['column'],list)) else [args['column']]
|
|
|
|
# column_id = args['id']
|
|
df = args['data'] if not isinstance(args['data'],str) else pd.read_csv(args['data'])
|
|
df.columns = [name.lower() for name in df.columns]
|
|
|
|
#
|
|
# If we have several columns we will proceed one at a time (it could be done in separate threads)
|
|
# @TODO : Consider performing this task on several threads/GPUs simulataneously
|
|
#
|
|
handler = Binary()
|
|
# args['label'] = pd.get_dummies(df[column_id]).astype(np.float32).values
|
|
# args['label'] = handler.Export(df[[column_id]])
|
|
# args['label'] = np.ones(df.shape[0]).reshape(df.shape[0],1)
|
|
for col in column :
|
|
# args['real'] = pd.get_dummies(df[col]).astype(np.float32).values
|
|
args['real'] = handler.Export(df[[col]])
|
|
args['column'] = col
|
|
args['context'] = col
|
|
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.apply()
|
|
def post(**args):
|
|
"""
|
|
This uploads the tensorflow checkpoint to a data-store (mongodb, biguqery, s3)
|
|
|
|
"""
|
|
pass
|
|
def get(**args):
|
|
"""
|
|
This function will restore a checkpoint from a persistant storage on to disk
|
|
"""
|
|
pass
|
|
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'] if (isinstance(args['column'],list)) else [args['column']]
|
|
column_id = args['id']
|
|
#
|
|
#@TODO:
|
|
# If the identifier is not present, we should fine a way to determine or make one
|
|
#
|
|
# args['label'] = pd.get_dummies(df[column_id]).astype(np.float32).values
|
|
bwrangler = Binary()
|
|
# args['label'] = bwrangler.Export(df[[column_id]])
|
|
_df = df.copy()
|
|
for col in column :
|
|
args['context'] = col
|
|
args['column'] = col
|
|
values = df[col].unique().tolist()
|
|
# values.sort()
|
|
args['values'] = values
|
|
#
|
|
# we can determine the cardinalities here so we know what to allow or disallow
|
|
handler = gan.Predict (**args)
|
|
handler.load_meta(col)
|
|
r = handler.apply()
|
|
# print (r)
|
|
_df[col] = r[col]
|
|
# break
|
|
return _df |