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