bug fix: crash with dataset & epochs
This commit is contained in:
Steve Nyemba
parent
4398212caf
commit
0efd4b13bc
12
README.md
12
README.md
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@ -13,17 +13,19 @@ This package is designed to generate synthetic data from a dataset from an origi
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After installing the easiest way to get started is as follows (using pandas). The process is as follows:
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Read about [data-transport on github](https://github.com/lnyemba/data-transport) or on [healthcareio.the-phi.com/git/code/transport](https://healthcareio.the-phi.com/git/code/transport.git)
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**Train the GAN on the original/raw dataset**
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1. We define the data sources
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The sources will consists in source, target and logger20.
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import pandas as pd
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import data.maker
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import transport
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from transport import providers
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df = pd.read_csv('sample.csv')
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column = 'gender'
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id = 'id'
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context = 'demo'
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data.maker.train(context=context,data=df,column=column,id=id,logs='logs')
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The trainer will store the data on disk (for now) in a structured folder that will hold training models that will be used to generate the synthetic data.
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@ -3,3 +3,4 @@ from data.params import SYS_ARGS
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import transport
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from multiprocessing import Process, Queue
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from data.maker import prepare
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from data.maker import state
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91
data/gan.py
91
data/gan.py
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@ -100,6 +100,13 @@ class GNet :
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self.TOTAL_BATCHSIZE = self.BATCHSIZE_PER_GPU * self.NUM_GPUS
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self.STEPS_PER_EPOCH = 256 #int(np.load('ICD9/train.npy').shape[0] / 2000)
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self.MAX_EPOCHS = 10 if 'max_epochs' not in args else int(args['max_epochs'])
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CHECKPOINT_SKIPS = 10
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if self.MAX_EPOCHS < 2*CHECKPOINT_SKIPS :
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CHECKPOINT_SKIPS = 2
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self.CHECKPOINTS = np.repeat( np.divide(self.MAX_EPOCHS,CHECKPOINT_SKIPS),CHECKPOINT_SKIPS ).cumsum().astype(int).tolist()
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self.ROW_COUNT = args['real'].shape[0] if 'real' in args else 100
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self.CONTEXT = args['context']
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self.ATTRIBUTES = {"id":args['column_id'] if 'column_id' in args else None,"synthetic":args['column'] if 'column' in args else None}
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@ -120,14 +127,18 @@ class GNet :
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for key in ['train','output'] :
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self.mkdir(os.sep.join([self.log_dir,key]))
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self.mkdir (os.sep.join([self.log_dir,key,self.CONTEXT]))
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if 'partition' in args :
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self.mkdir (os.sep.join([self.log_dir,key,self.CONTEXT,str(args['partition'])]))
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# if 'partition' in args :
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# self.mkdir (os.sep.join([self.log_dir,key,self.CONTEXT,str(args['partition'])]))
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self.train_dir = os.sep.join([self.log_dir,'train',self.CONTEXT])
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self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
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if 'partition' in args :
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self.train_dir = os.sep.join([self.train_dir,str(args['partition'])])
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self.out_dir = os.sep.join([self.out_dir,str(args['partition'])])
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for checkpoint in self.CHECKPOINTS :
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self.mkdir (os.sep.join([self.train_dir,str(checkpoint)]))
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self.mkdir (os.sep.join([self.out_dir,str(checkpoint)]))
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# if self.logger :
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# We will clear the logs from the data-store
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@ -150,12 +161,13 @@ class GNet :
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attr = json.loads((open(_name)).read())
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for key in attr :
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value = attr[key]
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if not hasattr(self,key):
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setattr(self,key,value)
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self.train_dir = os.sep.join([self.log_dir,'train',self.CONTEXT])
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self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
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if 'partition' in args :
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self.train_dir = os.sep.join([self.train_dir,str(args['partition'])])
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self.out_dir = os.sep.join([self.out_dir,str(args['partition'])])
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# if 'partition' in args :
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# self.train_dir = os.sep.join([self.train_dir,str(args['partition'])])
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# self.out_dir = os.sep.join([self.out_dir,str(args['partition'])])
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def log_meta(self,**args) :
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@ -183,15 +195,24 @@ class GNet :
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suffix = self.CONTEXT #self.get.suffix()
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_name = os.sep.join([self.out_dir,'meta-'+suffix])
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f = open(_name+'.json','w')
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f.write(json.dumps(_object))
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# f = open(_name+'.json','w')
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# f.write(json.dumps(_object))
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# f.close()
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for _info in [{"name":os.sep.join([self.out_dir,'meta-'+suffix+'.json']),"data":_object},{"name":os.sep.join([self.out_dir,'epochs.json']),"data":self.logs['epochs'] if 'epochs' in self.logs else []}] :
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f = open(_info['name'],'w')
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f.write(json.dumps(_info['data']))
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f.close()
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return _object
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def mkdir (self,path):
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if not os.path.exists(path) :
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if os.sep in path :
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pass
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root = []
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for loc in path.split(os.sep) :
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for loc in path.strip().split(os.sep) :
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if loc == '' :
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root.append(os.sep)
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root.append(loc)
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if not os.path.exists(os.sep.join(root)) :
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os.mkdir(os.sep.join(root))
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@ -278,8 +299,10 @@ class Generator (GNet):
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tf.compat.v1.add_to_collection('glosses', loss)
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return loss, loss
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def load_meta(self, **args):
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super().load_meta(**args)
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# super().load_meta(**args)
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self.discriminator.load_meta(**args)
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def network(self,**args) :
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"""
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This function will build the network that will generate the synthetic candidates
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@ -381,6 +404,7 @@ class Train (GNet):
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self.logger.write({"module":"gan-train","action":"start","input":{"partition":self.PARTITION,"meta":self.meta} } )
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# self.log (real_shape=list(self._REAL.shape),label_shape = self._LABEL.shape,meta_data=self.meta)
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def load_meta(self, column):
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"""
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@ -445,7 +469,7 @@ class Train (GNet):
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else :
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dataset = tf.data.Dataset.from_tensor_slices(features_placeholder)
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# labels_placeholder = None
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dataset = dataset.repeat(10000)
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dataset = dataset.repeat(20000)
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dataset = dataset.batch(batch_size=self.BATCHSIZE_PER_GPU)
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dataset = dataset.prefetch(1)
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@ -472,9 +496,11 @@ class Train (GNet):
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if self._LABEL is not None :
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(real, label) = iterator.get_next()
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else:
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real = iterator.get_next()
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label= None
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loss, w = self.loss(scope=scope, stage=stage, real=real, label=label)
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#tf.get_variable_scope().reuse_variables()
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tf.compat.v1.get_variable_scope().reuse_variables()
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#vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
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@ -507,6 +533,7 @@ class Train (GNet):
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# init = tf.global_variables_initializer()
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init = tf.compat.v1.global_variables_initializer()
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logs = []
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self.logs['epochs'] = []
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#with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
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with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
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@ -536,25 +563,41 @@ class Train (GNet):
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logs.append({"epoch": int(epoch),"distance":float(-w_sum/(self.STEPS_PER_EPOCH*2)) })
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# if epoch % self.MAX_EPOCHS == 0:
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if epoch in [5,10,20,50,75, self.MAX_EPOCHS] :
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# if epoch in [5,10,20,50,75, self.MAX_EPOCHS] :
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if epoch in self.CHECKPOINTS or int(epoch) == 1:
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# suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
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suffix = self.CONTEXT #self.get.suffix()
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_name = os.sep.join([self.train_dir,suffix])
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_name = os.sep.join([self.train_dir,str(epoch),suffix])
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# saver.save(sess, self.train_dir, write_meta_graph=False, global_step=epoch)
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saver.save(sess, _name, write_meta_graph=False, global_step=epoch)
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#
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#
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if self.logger :
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row = {"module":"gan-train","action":"logs","input":{"partition":self.PARTITION,"logs":logs}} #,"model":pickle.dump(sess)}
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self.logger.write(row)
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#
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#
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logs = [{"path":_name,"epochs":int(epoch),"loss":float(-w_sum/(self.STEPS_PER_EPOCH*2))}]
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if self.logger :
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# row = {"module":"gan-train","action":"epochs","input":{"logs":logs}} #,"model":pickle.dump(sess)}
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# self.logger.write(row)
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self.logs['epochs'] += logs
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#
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# @TODO:
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# We should upload the files in the checkpoint
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# This would allow the learnt model to be portable to another system
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#
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tf.compat.v1.reset_default_graph()
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#
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# let's sort the epochs we've logged thus far (if any)
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#
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self.logs['epochs'].sort(key=lambda _item: _item['loss'])
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if self.logger :
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_log = {'module':'gan-train','action':'epochs','input':self.logs['epochs']}
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self.logger.write(_log)
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#
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# @TODO:
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# Make another copy of this on disk to be able to load it should we not have a logger setup
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#
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self.log_meta()
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class Predict(GNet):
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"""
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This class uses synthetic data given a learned model
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@ -565,6 +608,7 @@ class Predict(GNet):
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self.values = args['values']
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self.ROW_COUNT = args['row_count']
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self.oROW_COUNT = self.ROW_COUNT
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# self.MISSING_VALUES = np.nan_to_num(np.nan)
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# if 'no_value' in args and args['no_value'] not in ['na','','NA'] :
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# self.MISSING_VALUES = args['no_value']
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@ -577,9 +621,20 @@ class Predict(GNet):
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super().load_meta(**args)
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self.generator.load_meta(**args)
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self.ROW_COUNT = self.oROW_COUNT
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#
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# updating the input/output for the generator, so it points properly
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#
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for object in [self,self.generator] :
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_train_dir = os.sep.join([self.log_dir,'train',self.CONTEXT,str(self.MAX_EPOCHS)])
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_out_dir= os.sep.join([self.log_dir,'output',self.CONTEXT,str(self.MAX_EPOCHS)])
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setattr(object,'train_dir',_train_dir)
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setattr(object,'out_dir',_out_dir)
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def apply(self,**args):
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suffix = self.CONTEXT #self.get.suffix()
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model_dir = os.sep.join([self.train_dir,suffix+'-'+str(self.MAX_EPOCHS)])
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# model_dir = os.sep.join([self.train_dir,str(self.MAX_EPOCHS)])
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demo = self._LABEL #np.zeros([self.ROW_COUNT,self.NUM_LABELS]) #args['de"shape":{"LABEL":list(self._LABEL.shape)} mo']
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#
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# setup computational graph
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@ -15,6 +15,7 @@ import transport
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# from data.bridge import Binary
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import threading
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from data.maker import prepare
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from data.maker.state import State
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import copy
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import os
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import nujson as json
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@ -25,6 +26,7 @@ from multiprocessing import Queue
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import time
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class Learner(Process):
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def __init__(self,**_args):
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@ -48,7 +50,7 @@ class Learner(Process):
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if 'network_args' not in _args :
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self.network_args ={
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'context':self.info['context'] ,
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'logs':_args['logpath'] if 'logpath' in _args else 'logs',
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'logs':_args['logs'] if 'logs' in _args else 'logs',
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'max_epochs':int(_args['epochs']) if 'epochs' in _args else 2,
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'batch_size':int (_args['batch']) if 'batch' in _args else 2000
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}
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@ -72,6 +74,36 @@ class Learner(Process):
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self.logger = None
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if 'logger' in self.store :
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self.logger = transport.factory.instance(**self.store['logger'])
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self.autopilot = False #-- to be set by caller
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self._initStateSpace()
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def _initStateSpace(self):
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"""
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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
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- Trainer -> pre-processing
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- Generation -> post processing
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The specifications of a state space in the configuration file is as such
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state:{pre:{path,pipeline:[]}, post:{path,pipeline:[]}}
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"""
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self._states = None
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if 'state' in self.info :
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try:
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_config = self.info ['state']
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self._states = State.instance(_config)
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except Exception as e:
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print (e)
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pass
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finally:
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# __info = (pd.DataFrame(self._states)[['name','path','args']]).to_dict(orient='records')
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if self._states :
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__info = {}
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for key in self._states :
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__info[key] = [{"name":_item['name'],"args":_item['args'],"path":_item['path']} for _item in self._states[key]]
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self.log(object='state-space',action='load',input=__info)
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def log(self,**_args):
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try:
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@ -108,11 +140,36 @@ class Learner(Process):
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_read_args= self.info
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if self._df is None :
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self._df = reader.read(**_read_args)
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#
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# NOTE : PRE
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# At this point we apply pre-processing of the data if there were ever a need for it
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#
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_log = {}
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HAS_STATES = self._states is not None and 'pre' in self._states
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NOT_GENERATING = self.name in ['Trainer','Shuffle']
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IS_AUTOPILOT = self.autopilot
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#
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# allow calling pre-conditions if either of the conditions is true
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# 1. states and not generating
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# 2. IS_GENERATING and states and not autopilot
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_ALLOW_PRE_CALL = (HAS_STATES and NOT_GENERATING) or (NOT_GENERATING is False and HAS_STATES and IS_AUTOPILOT is False)
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if _ALLOW_PRE_CALL :
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# if HAS_STATES and NOT_GENERATING or (HAS_STATES and IS_AUTOPILOT is False and NOT_GENERATING is False):
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_logs = {'action':'status','input':{'pre':self._states['pre']}}
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_beg = list(self._df.shape)
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self._df = State.apply(self._df,self._states['pre'])
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_end = list(self._df.shape)
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_logs['input']['size'] = _beg,_end
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self.log(**_log)
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#
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#
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columns = self.columns if self.columns else self._df.columns
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#
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# Below is a source of inefficiency, unfortunately python's type inference doesn't work well in certain cases
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# - The code below tries to address the issue (Perhaps better suited for the reading components)
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_log = {}
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for name in columns :
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#
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# randomly sampling 5 elements to make sense of data-types
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@ -201,8 +258,14 @@ class Trainer(Learner):
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# @TODO: At this point we need to generate another some other objects
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#
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_args = {"network_args":self.network_args,"store":self.store,"info":self.info,"candidates":self.candidates,"data":self._df}
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_args['logs'] = self.network_args['logs']
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_args['autopilot'] = self.autopilot
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if self.gpu :
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_args['gpu'] = self.gpu
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#
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# Let us find the smallest, the item is sorted by loss ...
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_args['epochs'] = gTrain.logs['epochs'][0]['epochs']
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g = Generator(**_args)
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# g.run()
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@ -239,6 +302,7 @@ class Generator (Learner):
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file.close()
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else:
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self._map = {}
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self.autopilot = False if 'autopilot' not in _args else _args['autopilot']
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def run(self):
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self.initalize()
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if self._encoder is None :
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@ -416,33 +480,32 @@ class Generator (Learner):
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_df = self._df.copy()
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_df[self.columns] = _iodf[self.columns]
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N += _df.shape[0]
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#
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#@TODO:
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# Improve formatting with better post-processing pipeline
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if 'approximate' in self.info :
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_df = self.approximate(_df)
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if 'make_date' in self.info :
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for name in self.info['make_date'] :
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# iname = self.info['make_date']['init_field']
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iname = self.info['make_date'][name]
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if self._states :
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_df = State.apply(_df,self._states['post'])
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# #
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# #@TODO:
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# # Improve formatting with better post-processing pipeline
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# if 'approximate' in self.info :
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# _df = self.approximate(_df)
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# if 'make_date' in self.info :
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# for name in self.info['make_date'] :
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# # iname = self.info['make_date']['init_field']
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# iname = self.info['make_date'][name]
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years = _df[iname]
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_dates = [self.make_date(year=_year,field=name) for _year in years]
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if _dates :
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_df[name] = _dates
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# years = _df[iname]
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# _dates = [self.make_date(year=_year,field=name) for _year in years]
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# if _dates :
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# _df[name] = _dates
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||||
|
||||
|
||||
|
||||
_schema = self.get_schema()
|
||||
# _schema = [{'name':_item.name,'type':_item.field_type} for _item in _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":_log})
|
||||
|
||||
# w = transport.factory.instance(doc='observation',provider='mongodb',context='write',db='IOV01_LOGS',auth_file='/home/steve/dev/transport/mongo.json')
|
||||
# w.write(_df)
|
||||
# cols = [name for name in _df.columns if name.endswith('datetime')]
|
||||
# print (_df[cols])
|
||||
|
||||
if _store :
|
||||
writer = transport.factory.instance(**_store)
|
||||
if _store['provider'] == 'bigquery':
|
||||
|
|
@ -507,8 +570,10 @@ class factory :
|
|||
: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 :
|
||||
|
|
|
|||
Loading…
Reference in New Issue