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255 lines
9.3 KiB
Python
255 lines
9.3 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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class ContinuousToDiscrete :
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ROUND_UP = 2
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@staticmethod
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def binary(X,n=4) :
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"""
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This function will convert a continous stream of information into a variety a bit stream of bins
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"""
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# BOUNDS = np.repeat(np.divide(X.max(),n),n).cumsum().tolist()
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# print ( X.values.astype(np.float32))
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# print ("___________________________")
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values = np.array(X).astype(np.float32)
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BOUNDS = ContinuousToDiscrete.bounds(values,n)
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# _map = [{"index":BOUNDS.index(i),"ubound":i} for i in BOUNDS]
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# _matrix = []
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# m = []
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# for value in X :
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# x_ = np.zeros(n)
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# for row in BOUNDS :
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# if value>= row.left and value <= row.right :
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# index = BOUNDS.index(row)
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# x_[index] = 1
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# break
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# _matrix += x_.tolist()
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# #
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# # for items in BOUNDS :
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# # index = BOUNDS.index(items)
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# return np.array(_matrix).reshape(len(X),n)
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matrix = np.repeat(np.zeros(n),len(X)).reshape(len(X),n)
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@staticmethod
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def bounds(x,n):
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# return np.array_split(x,n)
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values = np.round(x,ContinuousToDiscrete.ROUND_UP)
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return list(pd.cut(values,n).categories)
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@staticmethod
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def continuous(X,BIN_SIZE=4) :
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"""
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This function will approximate a binary vector given boundary information
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:X binary matrix
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:BIN_SIZE
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"""
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BOUNDS = ContinuousToDiscrete.bounds(X,BIN_SIZE)
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values = []
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# _BINARY= ContinuousToDiscrete.binary(X,BIN_SIZE)
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# # # print (BOUNDS)
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l = {}
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for i in np.arange(len(X)): #value in X :
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value = X[i]
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for item in BOUNDS :
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if value >= item.left and value <= item.right :
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values += [np.round(np.random.uniform(item.left,item.right),ContinuousToDiscrete.ROUND_UP)]
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break
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# values += [ np.round(np.random.uniform(item.left,item.right),ContinuousToDiscrete.ROUND_UP) for item in BOUNDS if value >= item.left and value <= item.right ]
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# # values = []
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# for row in _BINARY :
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# # ubound = BOUNDS[row.index(1)]
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# index = np.where(row == 1)[0][0]
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# ubound = BOUNDS[ index ].right
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# lbound = BOUNDS[ index ].left
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# x_ = np.round(np.random.uniform(lbound,ubound),ContinuousToDiscrete.ROUND_UP).astype(float)
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# values.append(x_)
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# lbound = ubound
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# values = [np.random.uniform() for item in BOUNDS]
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return values
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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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# CONTINUOUS = args['continuous'] if 'continuous' in args else []
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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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# @TODO:
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# Consider sequential training of sub population for extremely large datasets
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#
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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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for col in column :
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# args['real'] = pd.get_dummies(df[col].fillna('')).astype(np.float32).values
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# if 'float' not in df[col].dtypes.name :
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# args['real'] = pd.get_dummies(df[col].fillna('')).astype(np.float32).values
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# if col in CONTINUOUS:
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# BIN_SIZE = 4 if 'bin_size' not in args else int(args['bin_size'])
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# args['real'] = ContinuousToDiscrete.binary(df[col],BIN_SIZE).astype(np.float32)
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# # args['real'] = args['real'].reshape(df.shape[0],BIN_SIZE)
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# else:
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# df.to_csv('tmp-'+args['logs'].replace('/','_')+'-'+col+'.csv',index=False)
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# print (df[col].dtypes)
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# print (df[col].dropna/(axis=1).unique())
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# args['real'] = pd.get_dummies(df[col].dropna()).astype(np.float32).values
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msize = args['matrix_size'] if 'matrix_size' in args else -1
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args['real'] = (Binary()).apply(df[col],msize)
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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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info = {"rows":args['real'].shape[0],"cols":args['real'].shape[1],"name":col,"partition":args['partition']}
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logger.write({"module":"gan-train","action":"data-prep","input":info})
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else:
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logger = None
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args['column'] = col
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args['context'] = col
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#
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# If the s
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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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CONTINUOUS = args['continuous'] if 'continuous' in args else []
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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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BIN_SIZE = 4 if 'bin_size' not in args else int(args['bin_size'])
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NO_VALUE = dict(args['no_value']) if type(args['no_value']) == dict else args['no_value']
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bhandler = Binary()
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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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# if 'float' in df[col].dtypes.name or col in CONTINUOUS :
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# #
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# # We should create the bins for the values we are observing here
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# BIN_SIZE = 4 if 'bin_size' not in args else int(args['bin_size'])
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# values = ContinuousToDiscrete.continuous(df[col].values,BIN_SIZE)
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# # values = np.unique(values).tolist()
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# else:
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# if col in CONTINUOUS :
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# values = ContinuousToDiscrete.binary(df[col],BIN_SIZE).astype(np.float32).T
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# else:
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# values = df[col].dropna().unique().tolist()
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msize = args['matrix_size'] if 'matrix_size' in args else -1
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values = bhandler.get_column_values(df[col])
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args['values'] = values
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args['row_count'] = df.shape[0]
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if col in NO_VALUE :
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args['no_value'] = NO_VALUE[col]
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else:
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args['no_value'] = NO_VALUE
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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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r = handler.apply()
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if col in CONTINUOUS :
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r[col] = np.array(r[col])
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MISSING= np.nan if args['no_value'] in ['na','','NA'] else args['no_value']
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if np.isnan(MISSING):
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i = np.isnan(r[col])
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i = np.where (i == False)[0]
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else:
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i = np.where( r[col] != None)[0]
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_approx = ContinuousToDiscrete.continuous(r[col][i],BIN_SIZE) #-- approximating based on arbitrary bins
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r[col][i] = _approx
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_df[col] = r[col]
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#
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# Let's cast the type to the original type (it makes the data more usable)
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#
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otype = df[col].dtype
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_df[col] = _df[col].astype(otype)
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#
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# @TODO: log basic stats about the synthetic attribute
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#
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# print (r)s
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# break
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return _df |