667 lines
25 KiB
Python
667 lines
25 KiB
Python
"""
|
|
(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
|
|
from data.maker import prepare
|
|
from data.maker.state import State
|
|
import copy
|
|
import os
|
|
import nujson as json
|
|
from multiprocessing import Process, RLock
|
|
from datetime import datetime, timedelta
|
|
from multiprocessing import Queue
|
|
from data.maker.version import __version__
|
|
import time
|
|
|
|
|
|
|
|
class Learner(Process):
|
|
|
|
def __init__(self,**_args):
|
|
|
|
|
|
super(Learner, self).__init__()
|
|
self._arch = {'init':_args}
|
|
self.ndx = 0
|
|
self._queue = Queue()
|
|
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']
|
|
if 'context' not in self.info :
|
|
self.info['context'] = self.info['from']
|
|
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['logs'] if 'logs' 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','gpu':(self.gpu if self.gpu is not None else -1)}
|
|
self.log(**_log)
|
|
self.cache = []
|
|
# self.logpath= _args['logpath'] if 'logpath' in _args else 'logs'
|
|
# sel.max_epoc
|
|
self.logger = None
|
|
if 'logger' in self.store :
|
|
self.logger = transport.factory.instance(**self.store['logger'])
|
|
self.autopilot = False #-- to be set by caller
|
|
self._initStateSpace()
|
|
def _initStateSpace(self):
|
|
"""
|
|
Initializing state-space for the data-maker, The state-space functions are used as pre-post processing functions applied to the data accordingly i.e
|
|
- Trainer -> pre-processing
|
|
- Generation -> post processing
|
|
The specifications of a state space in the configuration file is as such
|
|
state:{pre:{path,pipeline:[]}, post:{path,pipeline:[]}}
|
|
"""
|
|
self._states = None
|
|
|
|
if 'state' in self.info :
|
|
try:
|
|
_config = self.info ['state']
|
|
self._states = State.instance(_config)
|
|
except Exception as e:
|
|
print (e)
|
|
pass
|
|
finally:
|
|
# __info = (pd.DataFrame(self._states)[['name','path','args']]).to_dict(orient='records')
|
|
if self._states :
|
|
__info = {}
|
|
# print (self._states)
|
|
for key in self._states :
|
|
_pipeline = self._states[key]
|
|
|
|
# __info[key] = ([{'name':_payload['name']} for _payload in _pipeline])
|
|
__info[key] = [{"name":_item['name'],"args":_item['args'],"path":_item['path']} for _item in self._states[key] if _item ]
|
|
self.log(object='state-space',action='load',input=__info)
|
|
|
|
|
|
|
|
def log(self,**_args):
|
|
|
|
try:
|
|
_context = self.info['context']
|
|
_label = self.info['info'] if 'info' in self.info else _context
|
|
# logger =
|
|
_args = dict({'ndx':self.ndx,'module':self.name,'table':self.info['from'],'context':_context,'info':_label,**_args})
|
|
if 'logger' in self.store :
|
|
logger = transport.factory.instance(**self.store['logger']) if 'logger' in self.store else transport.factory.instance(provider=transport.providers.CONSOLE,context='write',lock=True)
|
|
logger.write(_args)
|
|
self.ndx += 1
|
|
# if hasattr(logger,'close') :
|
|
# logger.close()
|
|
pass
|
|
except Exception as e:
|
|
# print ()
|
|
# print (_args)
|
|
# print (e)
|
|
pass
|
|
finally:
|
|
|
|
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'])
|
|
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)
|
|
#
|
|
# NOTE : PRE
|
|
# At this point we apply pre-processing of the data if there were ever a need for it
|
|
#
|
|
_log = {}
|
|
HAS_STATES = self._states is not None and 'pre' in self._states
|
|
NOT_GENERATING = self.name in ['Trainer','Shuffle']
|
|
IS_AUTOPILOT = self.autopilot
|
|
#
|
|
# allow calling pre-conditions if either of the conditions is true
|
|
# 1. states and not generating
|
|
# 2. IS_GENERATING and states and not autopilot
|
|
_ALLOW_PRE_CALL = (HAS_STATES and NOT_GENERATING) or (NOT_GENERATING is False and HAS_STATES and IS_AUTOPILOT is False)
|
|
if _ALLOW_PRE_CALL :
|
|
# if HAS_STATES and NOT_GENERATING or (HAS_STATES and IS_AUTOPILOT is False and NOT_GENERATING is False):
|
|
_logs = {'action':'status','input':{'pre':self._states['pre']}}
|
|
_beg = list(self._df.shape)
|
|
self._df = State.apply(self._df,self._states['pre'])
|
|
_end = list(self._df.shape)
|
|
_logs['input']['size'] = _beg,_end
|
|
self.log(**_log)
|
|
|
|
#
|
|
#
|
|
|
|
columns = self.columns if self.columns else self._df.columns
|
|
#
|
|
# Below is a source of inefficiency, unfortunately python's type inference doesn't work well in certain cases
|
|
# - The code below tries to address the issue (Perhaps better suited for the reading components)
|
|
|
|
for name in columns :
|
|
#
|
|
# randomly sampling 5 elements to make sense of data-types
|
|
|
|
if self._df[name].size < 5 :
|
|
continue
|
|
_index = np.random.choice(np.arange(self._df[name].shape[0]),5,False)
|
|
no_value = [type(value) in [int,float,np.int64,np.int32,np.float32,np.float64] for value in self._df[name].values[_index] if value is not None]
|
|
no_value = 0 if np.sum(no_value) > 0 else ''
|
|
try:
|
|
self._df[name] = self._df[name].fillna(no_value)
|
|
except Exception as e:
|
|
print (['.... skipping ',name,no_value])
|
|
finally:
|
|
pass
|
|
|
|
# _log[name] = self._df[name].dtypes.name
|
|
# _log[name] = reader.meta()
|
|
# _log = {'action':'structure','input':_log}
|
|
# self.log(**_log)
|
|
#
|
|
# convert the data to binary here ...
|
|
_schema = self.get_schema()
|
|
_args = {"schema":_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)
|
|
def get(self):
|
|
|
|
if self.cache :
|
|
return self.cache if len(self.cache) > 0 else(self.cache if not self.cache else self.cache[0])
|
|
else:
|
|
return self._queue.get() if self._queue.qsize() > 0 else []
|
|
|
|
def listen(self):
|
|
while True :
|
|
_info = self._queue.get()
|
|
self.cache.append(_info)
|
|
self._queue.task_done()
|
|
def publish(self,caller):
|
|
if hasattr(caller,'_queue') :
|
|
_queue = caller._queue
|
|
_queue.put(self.cache)
|
|
|
|
# _queue.join()
|
|
pass
|
|
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
|
|
self.checkpoint_skips = _args['checkpoint_skips'] if 'checkpoint_skips' in _args else None
|
|
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
|
|
if 'logger' in self.store :
|
|
_args['logger'] = transport.factory.instance(**self.store['logger'])
|
|
if self.checkpoint_skips :
|
|
_args['checkpoint_skips'] = self.checkpoint_skips
|
|
#
|
|
# 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=transport.providers.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}
|
|
_args['logs'] = self.network_args['logs']
|
|
_args['autopilot'] = self.autopilot
|
|
if self.gpu :
|
|
_args['gpu'] = self.gpu
|
|
|
|
#
|
|
# Let us find the smallest, the item is sorted by loss on disk
|
|
#
|
|
_epochs = [_e for _e in gTrain.logs['epochs'] if _e['path'] != '']
|
|
_epochs.sort(key=lambda _item: _item['loss'],reverse=False)
|
|
|
|
_args['network_args']['max_epochs'] = _epochs[0]['epochs']
|
|
self.log(action='autopilot',input={'epoch':_epochs[0]})
|
|
|
|
# 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)
|
|
|
|
if self.autopilot :
|
|
|
|
# g = Generator(**_args)
|
|
|
|
g = Generator(**self._arch['init'])
|
|
self._g = g
|
|
self._g.run()
|
|
#
|
|
#@TODO Find a way to have the data in the object ....
|
|
|
|
|
|
|
|
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'])
|
|
_suffix = self.network_args['context']
|
|
filename = os.sep.join([self.network_args['logs'],'output',self.network_args['context'],'meta-',_suffix+'.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 = {}
|
|
self.autopilot = False if 'autopilot' not in _args else _args['autopilot']
|
|
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
|
|
if 'logger' in self.store :
|
|
_args['logger'] = transport.factory.instance(**self.store['logger'])
|
|
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.cache = _candidates
|
|
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]
|
|
|
|
if np.sum(index) == 0:
|
|
#
|
|
# Sometimes messy data has unpleasant surprises
|
|
continue
|
|
|
|
_values = np.random.rand( len(values[index]))
|
|
_values += np.std(values[index]) / 4
|
|
|
|
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 * (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):
|
|
"""
|
|
:_df data-frame being processed
|
|
:_schema table schema with types
|
|
"""
|
|
|
|
|
|
_columns = [_item['name'] for _item in _schema if _item['name'] in _df.columns]
|
|
_map = {'INT64':np.int64,'FLOAT64':np.float64,'DATE':np.datetime64,'TIMESTAMP':(lambda field: pd.to_datetime(field).dt.tz_localize(None))}
|
|
|
|
# pd.to_datetime(_df.measurement_datetime).dt.tz_localize(None)
|
|
|
|
for _item in _schema :
|
|
_name = _item['name']
|
|
if _item['type'] not in _map :
|
|
continue
|
|
_pointer = _map[_item['type']]
|
|
try:
|
|
if type(_pointer).__name__ == 'type':
|
|
if _item['type'] in ['INT64','FLOAT64'] :
|
|
|
|
novalue = np.int64(0) if _item['type'] == 'INT64' else np.float64(0)
|
|
elif _item['type'] == 'STRING' :
|
|
novalue = ''
|
|
|
|
if _item['type'] in ['INT64','FLOAT64','STRING'] :
|
|
|
|
_df[_name] = _df[_name].fillna(novalue)
|
|
|
|
#
|
|
# This is not guaranteed to work but may help ...
|
|
_df[_name] = _df[_name].values.astype(_pointer)
|
|
|
|
|
|
|
|
else:
|
|
_df[_name] = _pointer(_df[_name])
|
|
pass
|
|
except Exception as e:
|
|
|
|
pass
|
|
# bqw = transport.factory.instance(**_store['target'])
|
|
# bqw.write(_df,schema=_schema)
|
|
|
|
return _df #[_columns]
|
|
def post(self,_candidates):
|
|
|
|
if 'target' in self.store :
|
|
_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']
|
|
else:
|
|
_store = None
|
|
N = 0
|
|
|
|
|
|
_haslist = np.sum([type(_item)==list for _item in self.columns]) > 0
|
|
_schema = self.get_schema()
|
|
#
|
|
# If the schema doesn't match the data we need to skip it
|
|
# This happens when the data comes from a query, the post processing needs to handle this
|
|
#
|
|
|
|
# _names = [_field['name'] for _field in _schema]
|
|
# _columns = _candidates[0].columns.tolist()
|
|
# _common = list( set(_columns) & set(_names) )
|
|
# if not (len(_common) == len(_columns) and len(_names) == len(_common)) :
|
|
# _schema = None
|
|
for _iodf in _candidates :
|
|
_df = self._df.copy()
|
|
|
|
if self.columns and _haslist is False:
|
|
_df[self.columns] = _iodf[self.columns]
|
|
else:
|
|
#
|
|
# In here we have the case of all attributes have undergone random permutations
|
|
#
|
|
_df = _iodf
|
|
|
|
|
|
N += _df.shape[0]
|
|
if self._states and 'post' in self._states:
|
|
_df = State.apply(_df,self._states['post'])
|
|
|
|
#
|
|
# Let us format the data frame so as to be able to minimize write errors
|
|
#
|
|
if _schema :
|
|
_df = self._format(_df,_schema)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# _df = self.format(_df,_schema)
|
|
# _log = [{"name":_schema[i]['name'],"dataframe":_df[_df.columns[i]].dtypes.name,"schema":_schema[i]['type']} for i in np.arange(len(_schema)) ]
|
|
self.log(**{"action":"consolidate","input":{"rows":N,"candidate":_candidates.index(_iodf)}})
|
|
|
|
|
|
if _store :
|
|
_log = {'action':'write','input':{'table':self.info['from'],'schema':[],'rows':_df.shape[0]}}
|
|
|
|
writer = transport.factory.instance(**_store)
|
|
|
|
if _store['provider'] == 'bigquery' and _schema:
|
|
try:
|
|
_log['schema'] = _schema
|
|
writer.write(_df,schema=_schema)
|
|
except Exception as e:
|
|
print (e)
|
|
writer.write(_df)
|
|
else:
|
|
writer.write(_df)
|
|
self.log(**_log)
|
|
else:
|
|
self.cache.append(_df)
|
|
|
|
|
|
|
|
|
|
|
|
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)
|
|
if 'data' not in _args :
|
|
reader = transport.factory.instance(**self.store['source'])
|
|
self._df = reader.read(sql=self.info['sql'])
|
|
|
|
def run(self):
|
|
|
|
|
|
|
|
self.initalize()
|
|
#
|
|
# If we are given lists of columns instead of a list-of-list
|
|
# unpack the list
|
|
_invColumns = []
|
|
_colNames = []
|
|
_ucolNames= []
|
|
_rmColumns = []
|
|
for _item in self.info['columns'] :
|
|
if type(_item) == list :
|
|
_invColumns.append(_item)
|
|
_rmColumns += _item
|
|
|
|
elif _item in self._df.columns.tolist():
|
|
_colNames.append(_item)
|
|
#
|
|
# At this point we build the matrix of elements we are interested in considering the any unspecified column
|
|
#
|
|
|
|
if _colNames :
|
|
_invColumns.append(_colNames)
|
|
_ucolNames = list(set(self._df.columns) - set(_colNames) - set(_rmColumns))
|
|
if _ucolNames :
|
|
_invColumns += [ [_name] for _name in _ucolNames]
|
|
|
|
_xdf = pd.DataFrame()
|
|
_xdf = pd.DataFrame()
|
|
_index = np.arange(self._df.shape[0])
|
|
|
|
for _columns in _invColumns :
|
|
|
|
_tmpdf = self._df[_columns].copy()[_columns]
|
|
np.random.seed(1)
|
|
np.random.shuffle(_index)
|
|
|
|
# _values = _tmpdf.values[_index]
|
|
#_tmpdf = _tmpdf.iloc[_index]
|
|
_tmpdf = pd.DataFrame(_tmpdf.values[_index],columns=_columns)
|
|
if _xdf.shape[0] == 0 :
|
|
_xdf = _tmpdf
|
|
else:
|
|
_xdf = _xdf.join(_tmpdf)
|
|
|
|
_xdf = _xdf[self._df.columns]
|
|
self._df = _xdf
|
|
_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 apply :
|
|
TRAIN,GENERATE,RANDOM = 'train','generate','random'
|
|
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'] in [apply.RANDOM] :
|
|
pthread = Shuffle(**_args)
|
|
elif _args['apply'] == apply.GENERATE :
|
|
pthread = Generator(**_args)
|
|
else:
|
|
pthread= Trainer(**_args)
|
|
if 'start' in _args and _args['start'] == True :
|
|
pthread.start()
|
|
return pthread
|
|
|
|
class plugins:
|
|
@staticmethod
|
|
def load(_config):
|
|
"""
|
|
This function attempts to load the plugins to insure they are valid
|
|
_config configuration for plugin specifications {pre:{pipeline,path},post:{pipeline,path}}
|
|
"""
|
|
|
|
|