aou/data-maker
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data-maker
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Steve Nyemba 2019-12-31 23:38:52 -06:00
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README.md
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@ -31,14 +31,14 @@ The trainer will store the data on disk (for now) in a structured folder that wi
**Generate a candidate dataset from the learned features** **Generate a candidate dataset from the learned features**
import pandas as pd import pandas as pd
import data.maker import data.maker
df = pd.read_csv('sample.csv') df = pd.read_csv('sample.csv')
id = 'id' id = 'id'
column = 'gender' column = 'gender'
context = 'demo' context = 'demo'
data.maker.generate(data=df,id=id,column=column,logs='logs') data.maker.generate(data=df,id=id,column=column,logs='logs')
## Limitations ## Limitations
@ -49,7 +49,7 @@ GANS will generate data assuming the original data has all the value space neede
Assuming we have a dataset with an gender attribute with values [M,F]. 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] The synthetic data will not be able to generate genders outside [M,F]
- Not advised on continuous values - Not advised on continuous values
GANS work well on discrete values and thus are not advised to be used. GANS work well on discrete values and thus are not advised to be used.