data-maker/data/maker/__init__.py

"""
(c) 2019 Data Maker, hiplab.mc.vanderbilt.edu
version 1.0.0

This package serves as a proxy to the overall usage of the framework.
This package is designed to generate synthetic data from a dataset from an original dataset using deep learning techniques

@TODO:
    - Make configurable GPU, EPOCHS
"""
import pandas as pd
import numpy as np
import data.gan as gan
import transport
# from data.bridge import Binary
import threading as thread
from data.maker import prepare
import copy
import os
import json
from multiprocessing import Process, RLock
from datetime import datetime, timedelta

class ContinuousToDiscrete :
    ROUND_UP = 2
    @staticmethod
    def binary(X,n=4) :
        """
        This function will convert a continous stream of information into a variety a bit stream of bins
        """
        values = np.array(X).astype(np.float32)
        BOUNDS = ContinuousToDiscrete.bounds(values,n)
        matrix = np.repeat(np.zeros(n),len(X)).reshape(len(X),n)

    
    @staticmethod
    def bounds(x,n):
        # return np.array_split(x,n)
        values = np.round(x,ContinuousToDiscrete.ROUND_UP)
        return list(pd.cut(values,n).categories)
        

        
    @staticmethod
    def continuous(X,BIN_SIZE=4) :
        """
        This function will approximate a binary vector given boundary information
        :X  binary matrix
        :BIN_SIZE
        """
        BOUNDS = ContinuousToDiscrete.bounds(X,BIN_SIZE)
        
        values = []
        # _BINARY= ContinuousToDiscrete.binary(X,BIN_SIZE)
        # # # print (BOUNDS)
        l = {}
        for i in np.arange(len(X)): #value in X :
            
            value = X[i]
            
            for item in BOUNDS :
                if value >= item.left and value <= item.right :
                    values += [np.round(np.random.uniform(item.left,item.right),ContinuousToDiscrete.ROUND_UP)]
                    break
            # 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 ]
            
    
        # # values = []
        # for row in _BINARY :
        #     # ubound = BOUNDS[row.index(1)]
        #     index = np.where(row == 1)[0][0]
            
        #     ubound = BOUNDS[ index ].right
        #     lbound = BOUNDS[ index ].left
            
        #     x_ = np.round(np.random.uniform(lbound,ubound),ContinuousToDiscrete.ROUND_UP).astype(float)            
        #     values.append(x_)
            
        #     lbound = ubound

        # values = [np.random.uniform() for item in BOUNDS]
        
        return values
            

def train (**_args):
    """
    :params sql
    :params store
    """
    
    _inputhandler = prepare.Input(**_args)
    values,_matrix = _inputhandler.convert()
    args  = {"real":_matrix,"context":_args['context']}
    _map = {}
    if 'store' in _args :
        #
        # This 
        
        args['store'] = copy.deepcopy(_args['store']['logs'])
        if 'args' in _args['store']:
            args['store']['args']['doc'] = _args['context']
        else:
            
            args['store']['doc'] = _args['context']
        logger = transport.factory.instance(**args['store'])
        args['logger'] = logger
        
        for key in _inputhandler._map :
            beg = _inputhandler._map[key]['beg']
            end = _inputhandler._map[key]['end']
            values = _inputhandler._map[key]['values'].tolist()
            _map[key] = {"beg":beg,"end":end,"values":np.array(values).astype(str).tolist()}
        info = {"rows":_matrix.shape[0],"cols":_matrix.shape[1],"map":_map}
        print()
        # print ([_args['context'],_inputhandler._io])
        logger.write({"module":"gan-train","action":"data-prep","context":_args['context'],"input":_inputhandler._io})
    
    args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
    args ['max_epochs'] = _args['max_epochs']
    args['matrix_size'] = _matrix.shape[0]
    args['batch_size'] = 2000
    if 'partition' in _args :
        args['partition'] = _args['partition']
    if 'gpu' in _args :
        args['gpu'] = _args['gpu']
    # os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) if 'gpu' in args else '0'
    
    trainer = gan.Train(**args)   
    #
    # @TODO: Write the map.json in the output directory for the logs
    # 
    # f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']),'w')
    f = open(os.sep.join([trainer.out_dir,'map.json']),'w')
    f.write(json.dumps(_map))
    f.close()

    trainer.apply()
    pass    

def get(**args):
    """
    This function will restore a checkpoint from a persistant storage on to disk
    """
    pass
def generate(**_args):
    """
    This function will generate a set of records, before we must load the parameters needed
    :param data
    :param context
    :param logs
    """
    _args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
    partition = _args['partition'] if 'partition' in _args else None
    if not partition :
        MAP_FOLDER = os.sep.join([_args['logs'],'output',_args['context']])
        # f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']))
    else:
        MAP_FOLDER = os.sep.join([_args['logs'],'output',_args['context'],str(partition)])
        # f = open(os.sep.join([_args['logs'],'output',_args['context'],str(partition),'map.json']))
    f = open(os.sep.join([MAP_FOLDER,'map.json']))
    _map = json.loads(f.read())
    f.close()
    #
    # 
    # if 'file' in _args :
    #     df = pd.read_csv(_args['file'])
    # else:
    #     df = _args['data'] if not isinstance(_args['data'],str) else pd.read_csv(_args['data'])
    args = {"context":_args['context'],"max_epochs":_args['max_epochs'],"candidates":_args['candidates']}
    args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
    args ['max_epochs'] = _args['max_epochs']
    # args['matrix_size'] = _matrix.shape[0]
    args['batch_size'] = 2000
    args['partition'] = 0 if 'partition' not in _args else _args['partition']
    args['row_count'] = _args['data'].shape[0]
    #
    # @TODO: perhaps get the space of values here ... (not sure it's a good idea)
    #
    _args['map']  = _map
    _inputhandler = prepare.Input(**_args)
    values,_matrix = _inputhandler.convert()    
    args['values'] = np.array(values)
    if 'gpu' in _args :
        args['gpu'] = _args['gpu']
       
    handler     = gan.Predict (**args)
    lparams = {'columns':None}
    if partition :
        lparams['partition'] = partition
    handler.load_meta(**lparams)
    #
    # Let us now format the matrices by reverting them to a data-frame with values
    #

    candidates = handler.apply(candidates=args['candidates'])       
    return [_inputhandler.revert(matrix=_matrix) for _matrix in candidates]

class Learner(Process):
    def __init__(self,**_args):
        
        
        super(Learner, self).__init__() 
        if 'gpu' in _args :
            print (_args['gpu'])
            os.environ['CUDA_VISIBLE_DEVICES'] = str(_args['gpu'])
            self.gpu = int(_args['gpu'])
        else:
            self.gpu = None
        
        self.info = _args['info']
        self.columns    = self.info['columns'] if 'columns' in self.info else None
        self.store      = _args['store']
        self.logger = transport.factory.instance(_args['logger']) if 'logger' in self.store else transport.factory.instance(provider='console',context='write',lock=True)
        if 'network_args' not in _args :
            self.network_args ={
                'context':self.info['context'] ,
                'logs':_args['logpath'] if 'logpath' in _args else 'logs',
                'max_epochs':int(_args['epochs']) if 'epochs' in _args else 2,
                'batch_size':int (_args['batch']) if 'batch' in _args else 2000
            }
        else:
            self.network_args = _args['network_args']
        self._encoder = None
        self._map = None
        self._df = _args['data'] if 'data' in _args else None
        #
        # @TODO: allow for verbose mode so we have a sens of what is going on within the newtork
        #

        # self.logpath= _args['logpath'] if 'logpath' in _args else 'logs'
        # sel.max_epoc
    def get_schema(self):
        if self.store['source']['provider'] != 'bigquery' :
            return [{'name':self._df.dtypes.index.tolist()[i],'type':self._df.dtypes.astype(str).tolist()[i]}for i in range(self._df.dtypes.shape[0])]
        else:
            reader  = transport.factory.instance(**self.store['source'])
            return reader.meta(table=self.info['from'])
    def initalize(self):
        reader  = transport.factory.instance(**self.store['source'])
        _read_args= self.info
        if self._df is None :
            self._df     = reader.read(**_read_args)
        columns = self.columns if self.columns else self._df.columns
        #
        # convert the data to binary here ...
        
        _args = {"schema":self.get_schema(),"data":self._df,"columns":columns}
        if self._map :
            _args['map'] = self._map
        self._encoder = prepare.Input(**_args)  if self._df.shape[0] > 0 else None     
class Trainer(Learner):
    """
    This will perform training using a GAN
    """
    def __init__(self,**_args):
        super().__init__(**_args)
        # self.info   = _args['info']
        self.limit  = int(_args['limit']) if 'limit' in _args else None
        self.name   =  _args['name']
        self.autopilot = _args['autopilot'] if 'autopilot' in _args else False
        self.generate = None
        self.candidates = int(_args['candidates']) if 'candidates' in _args else 1
    def run(self):
        self.initalize()
        if self._encoder is None :
            #
            # @TODO Log that the dataset was empty or not statistically relevant
            return 
        _space,_matrix = self._encoder.convert()
        
        _args   = self.network_args
        if self.gpu :
            _args['gpu'] = self.gpu
        _args['real'] = _matrix
        _args['candidates'] = self.candidates
        #
        # At this point we have the binary matrix, we can initiate training
        #

        gTrain = gan.Train(**_args)
        gTrain.apply()
       
        writer = transport.factory.instance(provider='file',context='write',path=os.sep.join([gTrain.out_dir,'map.json']))
        writer.write(self._encoder._map,overwrite=True)
        writer.close()

        #
        # @TODO: At this point we need to generate another some other objects
        #
        _args = {"network_args":self.network_args,"store":self.store,"info":self.info,"candidates":self.candidates,"data":self._df}
        if self.gpu :
            _args['gpu'] = self.gpu
        g = Generator(**_args)
        # g.run() 
        self.generate = g
        if self.autopilot :
            self.generate.run()
    def generate (self):
        if self.autopilot :
            print( "Autopilot is set ... No need to call this function")
        else:
            raise Exception( "Autopilot has not been, Wait till training is finished. Use is_alive function on process object")

class Generator (Learner):
    def __init__(self,**_args):
        super().__init__(**_args)
        #
        # We need to load the mapping information for the space we are working with ...
        #
        self.network_args['candidates'] = int(_args['candidates']) if 'candidates' in _args else 1
        filename = os.sep.join([self.network_args['logs'],'output',self.network_args['context'],'map.json'])
        file = open(filename)
        self._map = json.loads(file.read())
        file.close()
    def run(self):
        self.initalize()
        if self._encoder is None :
            #
            # @TODO Log that the dataset was empty or not statistically relevant
            return 
        
        #
        # The values will be returned because we have provided _map information from the constructor
        #

        values,_matrix = self._encoder.convert()
        _args = self.network_args
        _args['map'] = self._map
        _args['values'] = np.array(values)
        _args['row_count'] = self._df.shape[0]
        if self.gpu :
            _args['gpu'] = self.gpu
        gHandler = gan.Predict(**_args)
        gHandler.load_meta(columns=None)
        _iomatrix = gHandler.apply()
        _candidates= [ self._encoder.revert(matrix=_item) for _item in _iomatrix]
        self.post(_candidates)
    def approximate(self,_df):
        _columns = self.info['approximate']
        # _schema = {}
        # for _info in self.get_schema() :
        #     _schema[_info['name']] = _info['type'] 

        
        for name in _columns :
            batches = np.array_split(_df[name].fillna(np.nan).values,2)
            _type = np.int64 if 'int' in self.info['approximate'][name]else np.float64
            x = []
            for values in batches :
                
                index = np.where(values != '')
                _values = np.random.dirichlet(values[index].astype(_type))                                
                values[index] = list(values[index] + _values )if np.random.randint(0,2) else list(values[index] - _values)
                values[index] = values[index].astype(_type)
                x += values.tolist()
            if x :                
                _df[name] = x  #np.array(x,dtype=np.int64) if 'int' in _type  else np.arry(x,dtype=np.float64)
        return _df
    def make_date(self,**_args) :
        """
        :param year  initial value        
        """
        if _args['year'] in ['',None,np.nan] :
            return None
        year = int(_args['year'])
        offset = _args['offset'] if 'offset' in _args else 0
        month   = np.random.randint(1,13)
        if month == 2:
            _end = 28 if year % 4 != 0 else 29
        else:
            _end = 31 if month in [1,3,5,7,8,10,12] else 30
        day = np.random.randint(1,_end)

        #-- synthetic date
        _date = datetime(year=year,month=month,day=day)
        FORMAT =  '%Y-%m-%d'
        if 'format' in self.info and 'field' in _args and _args['field'] in self.info['format']:
            _name = _args['field']
            FORMAT = self.info['format'][_name]


        
        r = []
        if offset :
            r = [_date.strftime(FORMAT)]
            for _delta in offset :
                _date = _date + timedelta(_delta)
                r.append(_date.strftime(FORMAT))
            return r
        else:
            return _date.strftime(FORMAT)

        pass
    def format(self,_df):
        pass
    def post(self,_candidates):
        
        _store = self.store['target'] if 'target' in self.store else {'provider':'console'}
        _store['lock'] = True
        _store['context'] = 'write' #-- Just in case
        if 'table' not in _store :
            _store['table'] = self.info['from']
        writer = transport.factory.instance(**_store)
       
        for _iodf in _candidates :
            _df = self._df.copy()
            _df[self.columns] = _iodf[self.columns]
            #
            #@TODO:
            # Improve formatting with better post-processing pipeline
            if 'approximate' in self.info :                
                _df = self.approximate(_df)
            if 'make_date' in self.info :
                for name in self.info['make_date'] :
                    # iname = self.info['make_date']['init_field']
                    iname = self.info['make_date'][name]

                    years = _df[iname]
                    _dates = [self.make_date(year=year,field=name) for year in years]
                    if _dates :
                        _df[name] = _dates
            _schema = self.get_schema()
            _schema = [{'name':_item.name,'type':_item.field_type} for _item in _schema]
            writer.write(_df[self.columns],schema=_schema)
        pass
class factory :
    _infocache = {}
    @staticmethod
    def instance(**_args):
        """
        An instance of an object that trains and generates candidate datasets
        :param gpu (optional)         index of the gpu to be used if using one
        :param store                  {source,target} if no target is provided console will be output
        :param epochs (default 2)     number of epochs to train
        :param candidates(default 1)  number of candidates to generate
        :param info             {columns,sql,from}
        :param autopilot        will generate output automatically
        :param batch (default 2k)           size of the batch
        """
        return Trainer(**_args)