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76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
"""
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(c) 2019 Data Maker, hiplab.mc.vanderbilt.edu
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version 1.0.0
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This package serves as a proxy to the overall usage of the framework.
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This package is designed to generate synthetic data from a dataset from an original dataset using deep learning techniques
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@TODO:
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- Make configurable GPU, EPOCHS
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"""
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import pandas as pd
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import numpy as np
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import data.gan as gan
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from transport import factory
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def train (**args) :
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"""
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This function is intended to train the GAN in order to learn about the distribution of the features
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:column columns that need to be synthesized (discrete)
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:logs where the output of the (location on disk)
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:id identifier of the dataset
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:data data-frame to be synthesized
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:context label of what we are synthesizing
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"""
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column = args['column']
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column_id = args['id']
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df = args['data']
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logs = args['logs']
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real = pd.get_dummies(df[column]).astype(np.float32).values
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labels = pd.get_dummies(df[column_id]).astype(np.float32).values
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max_epochs = 10 if 'max_epochs' not in args else args['max_epochs']
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context = args['context']
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if 'store' in args :
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args['store']['args']['doc'] = context
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logger = factory.instance(**args['store'])
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else:
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logger = None
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trainer = gan.Train(context=context,max_epochs=max_epochs,real=real,label=labels,column=column,column_id=column_id,logger = logger,logs=logs)
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return trainer.apply()
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def generate(**args):
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"""
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This function will generate a synthetic dataset on the basis of a model that has been learnt for the dataset
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@return pandas.DataFrame
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:data data-frame to be synthesized
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:column columns that need to be synthesized (discrete)
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:id column identifying an entity
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:logs location on disk where the learnt knowledge of the dataset is
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"""
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df = args['data']
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column = args['column']
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column_id = args['id']
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logs = args['logs']
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context = args['context']
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#
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#@TODO:
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# If the identifier is not present, we should fine a way to determine or make one
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#
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#ocolumns= list(set(df.columns.tolist())- set(columns))
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values = df[column].unique().tolist()
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values.sort()
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labels = pd.get_dummies(df[column_id]).astype(np.float32).values
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handler = gan.Predict (context=context,label=labels,values=values,column=column)
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handler.load_meta(column)
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r = handler.apply()
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_df = df.copy()
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_df[column] = r[column]
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return _df
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