103 lines
3.7 KiB
Python
103 lines
3.7 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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from data.bridge import Binary
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import threading as thread
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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'] if (isinstance(args['column'],list)) else [args['column']]
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# column_id = args['id']
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df = args['data'] if not isinstance(args['data'],str) else pd.read_csv(args['data'])
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df.columns = [name.lower() for name in df.columns]
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#
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# If we have several columns we will proceed one at a time (it could be done in separate threads)
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# @TODO : Consider performing this task on several threads/GPUs simulataneously
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#
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handler = Binary()
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# args['label'] = pd.get_dummies(df[column_id]).astype(np.float32).values
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# args['label'] = handler.Export(df[[column_id]])
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# args['label'] = np.ones(df.shape[0]).reshape(df.shape[0],1)
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for col in column :
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args['real'] = pd.get_dummies(df[col].fillna('')).astype(np.float32).values
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# args['real'] = handler.Export(df[[col]])
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args['column'] = col
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args['context'] = col
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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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args['logger'] = logger
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else:
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logger = None
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trainer = gan.Train(**args)
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trainer.apply()
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def post(**args):
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"""
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This uploads the tensorflow checkpoint to a data-store (mongodb, biguqery, s3)
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"""
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pass
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def get(**args):
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"""
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This function will restore a checkpoint from a persistant storage on to disk
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"""
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pass
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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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df = args['data'] if not isinstance(args['data'],str) else pd.read_csv(args['data'])
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column = args['column'] if (isinstance(args['column'],list)) else [args['column']]
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# column_id = args['id']
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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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_df = df.copy()
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for col in column :
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args['context'] = col
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args['column'] = col
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values = df[col].unique().tolist()
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args['values'] = values
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args['row_count'] = df.shape[0]
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#
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# we can determine the cardinalities here so we know what to allow or disallow
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handler = gan.Predict (**args)
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handler.load_meta(col)
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# handler.ROW_COUNT = df[col].shape[0]
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r = handler.apply()
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# print (r)
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#
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print ([_df.shape,len(r[col])])
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_df[col] = r[col]
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# break
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return _df |