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 Learner(Process):

    def __init__(self,**_args):
        
        
        super(Learner, self).__init__() 
        self.ndx = 0
        self.lock = RLock()
        if 'gpu' in _args :
            
            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']
        
        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
        self.name   =  self.__class__.__name__
        
        #
        # @TODO: allow for verbose mode so we have a sens of what is going on within the newtork
        #
        
        _log = {'action':'init','context':self.info['context'],'gpu':(self.gpu if self.gpu is not None else -1)}
        self.log(**_log)

        # self.logpath= _args['logpath'] if 'logpath' in _args else 'logs'
        # sel.max_epoc
    def log(self,**_args):
        # self.lock.acquire()
        try:
            logger = transport.factory.instance(**self.store['logger']) if 'logger' in self.store else transport.factory.instance(provider='console',context='write',lock=True)
            _args = dict({'ndx':self.ndx,'module':self.name,'table':self.info['from'],'info':self.info['context'],**_args})
            logger.write(_args)
            self.ndx += 1
            if hasattr(logger,'close') :
                logger.close()
        except Exception as e:
            print ()
            print (_args)
            print (e)
            pass
        finally:
            # self.lock.release()
            pass
    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             
        _log = {'action':'data-prep','input':{'rows':int(self._df.shape[0]),'cols':int(self._df.shape[1]) } }
        self.log(**_log)
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.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
        #
        beg = datetime.now() #.strftime('%Y-%m-%d %H:%M:%S')
        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() 
        
        end = datetime.now() #.strftime('%Y-%m-%d %H:%M:%S')
        _min = float((end-beg).seconds/ 60)
        _logs = {'action':'train','input':{'start':beg.strftime('%Y-%m-%d %H:%M:%S'),'minutes':_min,"unique_counts":self._encoder._io[0]}}
        self.log(**_logs)
        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'])
        self.log(**{'action':'init-map','input':{'filename':filename,'exists':os.path.exists(filename)}})
        if os.path.exists(filename):
            file = open(filename)
            self._map = json.loads(file.read())
            file.close()
        else:
            self._map = {}
    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]
        
        _size = np.sum([len(_item) for _item in _iomatrix])
        _log = {'action':'io-data','input':{'candidates':len(_candidates),'rows':int(_size)}}
        self.log(**_log)
        self.post(_candidates)
    def approximate(self,_df):
        _columns = self.info['approximate']
 
        for name in _columns :
            if _df[name].size > 100 :
                BATCH_SIZE = 10
                
            else:
                BATCH_SIZE = 1
            batches = np.array_split(_df[name].fillna(np.nan).values,BATCH_SIZE)
            _type = np.int64 if 'int' in self.info['approximate'][name]else np.float64
            x = []
            _log = {'action':'approximate','input':{'batch':BATCH_SIZE,'col':name}}
            for values in batches :
               
                index = [ _x not in ['',None,np.nan] for _x in 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 :   
                _log['input']['identical_percentage'] = 100 * (1 - np.divide( (_df[name].dropna() == x).sum(),_df[name].dropna().size))
                _df[name] = x  #np.array(x,dtype=np.int64) if 'int' in _type  else np.arry(x,dtype=np.float64)
                
                self.log(**_log)
        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,minute=0,hour=0,second=0)
        FORMAT =  '%Y-%m-%d'
        _name = _args['field'] if 'field' in _args else None
        if 'format' in self.info and _name in self.info['format']:
            # _name = _args['field']
            FORMAT = self.info['format'][_name]
        

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

        pass
    def format(self,_df,_schema):
        r = {}
        for _item in _schema :
            name = _item['name']
            
            if _item['type'].upper() in ['DATE','DATETIME','TIMESTAMP'] :
                FORMAT = '%Y-%m-%d'
                
                if 'format' in self.info and name in self.info['format'] :
                    FORMAT = self.info['format'][name]
                elif _item['type'] in ['DATETIME','TIMESTAMP'] :
                        FORMAT = '%Y-%m-%d %H:%M:%S'
                
                r[name] = FORMAT
                _df[name] = pd.to_datetime(_df[name], format=FORMAT) #.astype('datetime64[ns]')
                if _item['type'] in ['DATETIME','TIMESTAMP']:
                    _df[name] = _df[name].astype('datetime64[ns]')
                else:
                    _df[name] = _df[name].astype(str)
                _df[name] = _df[name].fillna('')
        if r :
            self.log(**{'action':'format','input':r})
        return _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)
        N = 0
        for _iodf in _candidates :
            _df = self._df.copy()
            _df[self.columns] = _iodf[self.columns]
            N += _df.shape[0]
            #
            #@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]
            _df = self.format(_df,_schema)
            
            writer.write(_df,schema=_schema)
        
        self.log(**{'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
class Shuffle(Generator):    
    """
    This is a method that will yield data with low utility
    """
    def __init__(self,**_args):
        super().__init__(**_args)
    def run(self):
        

        self.initalize()
        _index = np.arange(self._df.shape[0])
        np.random.shuffle(_index)
        _iocolumns = self.info['columns']
        _ocolumns = list(set(self._df.columns) - set(_iocolumns) )
        # _iodf = pd.DataFrame(self._df[_ocolumns],self._df.loc[_index][_iocolumns],index=np.arange(_index.size))
        _iodf = pd.DataFrame(self._df[_iocolumns],index = np.arange(_index.size))
        self._df = self._df[_ocolumns].join(_iodf)
       
        
        _log = {'action':'io-data','input':{'candidates':1,'rows':int(self._df.shape[0])}}
        self.log(**_log)
        try:
            self.post([self._df])
            self.log(**{'action':'completed','input':{'candidates':1,'rows':int(self._df.shape[0])}})
        except Exception as e :
            # print (e)
            self.log(**{'action':'failed','input':{'msg':e,'info':self.info}})
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
        """
        
        if _args['apply'] == 'shuffle' :
            return Shuffle(**_args)
        elif _args['apply'] == 'generate' :
            return Generator(**_args)
        else:
            return Trainer(**_args)