@ -12,32 +12,33 @@ This package is designed to generate synthetic data from a dataset from an origi
## Usage
After installing the easiest way to get started is as follows (using pandas). The process is as follows:
1. Train the GAN on the original/raw dataset
**Train the GAN on the original/raw dataset**
import pandas as pd
import data.maker
df = pd.read_csv('sample.csv')
column = 'gender'
id = 'id'
context = 'demo'
data.maker.train(context=context,data=df,column=column,id=id,logs='logs')
import pandas as pd
import data.maker
df = pd.read_csv('sample.csv')
column = 'gender'
id = 'id'
context = 'demo'
data.maker.train(context=context,data=df,column=column,id=id,logs='logs')
The trainer will store the data on disk (for now) in a structured folder that will hold training models that will be used to generate the synthetic data.
2. Generate a candidate dataset from the learnt features
**Generate a candidate dataset from the learned features**
import pandas as pd
import data.maker
import pandas as pd
import data.maker
df = pd.read_csv('sample.csv')
id = 'id'
column = 'gender'
context = 'demo'
data.maker.generate(data=df,id=id,column=column,logs='logs')
df = pd.read_csv('sample.csv')
id = 'id'
column = 'gender'
context = 'demo'
data.maker.generate(data=df,id=id,column=column,logs='logs')
## Limitations
@ -46,11 +47,14 @@ GANS will generate data assuming the original data has all the value space neede
- No new data will be created
Assuming we have a dataset with an gender attribute with values [M,F].
The synthetic data will not be able to generate genders outside [M,F]
- Not advised on continuous values
GANS work well on discrete values and thus are not advised to be used.
e.g:measurements (height, blood pressure, ...)
- For now will only perform on a single feature.
## Credits :
