76 lines
2.5 KiB
Python
76 lines
2.5 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
|
|
from data import 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']
|
|
logs = args['logs']
|
|
real = pd.get_dummies(df[column]).astype(np.float32).values
|
|
labels = pd.get_dummies(df[column_id]).astype(np.float32).values
|
|
|
|
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'])
|
|
|
|
else:
|
|
logger = None
|
|
|
|
trainer = gan.Train(context=context,max_epochs=max_epochs,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']
|
|
|
|
column = args['column']
|
|
column_id = args['id']
|
|
logs = args['logs']
|
|
context = args['context']
|
|
#
|
|
#@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
|
|
handler = gan.Predict (context=context,label=labels,values=values,column=column)
|
|
handler.load_meta(column)
|
|
r = handler.apply()
|
|
_df = df.copy()
|
|
_df[column] = r[column]
|
|
return _df
|