bug fix: misc. improvements
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Steve Nyemba
parent
157df9334c
commit
f99af3655d
19
data/gan.py
19
data/gan.py
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@ -20,7 +20,9 @@ EMBEDDED IN CODE :
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"""
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import tensorflow as tf
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from tensorflow.contrib.layers import l2_regularizer
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# from tensorflow.contrib.layers import l2_regularizer
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from tensorflow.keras import layers
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from tensorflow.keras.regularizers import L2 as l2_regularizer
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import numpy as np
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import pandas as pd
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import time
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@ -34,7 +36,7 @@ import pickle
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ['CUDA_VISIBLE_DEVICES'] = "0"
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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tf.compat.v1.disable_eager_execution()
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# STEPS_PER_EPOCH = int(SYS_ARGS['epoch']) if 'epoch' in SYS_ARGS else 256
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# NUM_GPUS = 1 if 'num_gpu' not in SYS_ARGS else int(SYS_ARGS['num_gpu'])
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# BATCHSIZE_PER_GPU = 2000
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@ -211,13 +213,14 @@ class GNet :
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labels = None if 'labels' not in args else args['labels']
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n_labels= None if 'n_labels' not in args else args['n_labels']
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shift = [0] if self.__class__.__name__.lower() == 'generator' else [1] #-- not sure what this is doing
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mean, var = tf.nn.moments(inputs, shift, keep_dims=True)
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shape = inputs.shape[1].value
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# mean, var = tf.nn.moments(inputs, shift, keep_dims=True)
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mean, var = tf.nn.moments(inputs, shift,keepdims=True)
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# shape = inputs.shape[1].value
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shape = inputs.shape[1]
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if labels is not None:
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offset_m = self.get.variables(shape=[1,shape], name='offset'+name,
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initializer=tf.zeros_initializer)
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scale_m = self.get.variables(shape=[n_labels,shape], name='scale'+name,
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initializer=tf.ones_initializer)
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offset_m = self.get.variables(shape=[1,shape], name='offset'+name,initializer=tf.zeros_initializer)
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scale_m = self.get.variables(shape=[n_labels,shape], name='scale'+name,initializer=tf.ones_initializer)
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offset = tf.nn.embedding_lookup(offset_m, labels)
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scale = tf.nn.embedding_lookup(scale_m, labels)
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@ -106,6 +106,8 @@ def train (**_args):
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values = _inputhandler._map[key]['values'].tolist()
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_map[key] = {"beg":beg,"end":end,"values":np.array(values).astype(str).tolist()}
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info = {"rows":_matrix.shape[0],"cols":_matrix.shape[1],"map":_map}
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print()
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# print ([_args['context'],_inputhandler._io])
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logger.write({"module":"gan-train","action":"data-prep","context":_args['context'],"input":_inputhandler._io})
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args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
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@ -142,9 +144,10 @@ def generate(**_args):
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:param context
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:param logs
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"""
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_args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
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partition = _args['partition'] if 'partition' in _args else None
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if not partition :
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MAP_FLDER = os.sep.join([_args['logs'],'output',_args['context']])
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MAP_FOLDER = os.sep.join([_args['logs'],'output',_args['context']])
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# f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']))
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else:
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MAP_FOLDER = os.sep.join([_args['logs'],'output',_args['context'],str(partition)])
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18
pipeline.py
18
pipeline.py
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@ -151,6 +151,7 @@ class Components :
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if df.shape[0] and df.shape[0] :
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#
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# We have a full blown matrix to be processed
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print ('-- Training --')
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data.maker.train(**_args)
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else:
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print ("... skipping training !!")
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@ -260,15 +261,22 @@ class Components :
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#_df[name] = _df[name].dt.date
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# _df[name] = pd.to_datetime(_df[name].fillna(''),errors='coerce')
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else:
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pass
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_df[name] = pd.to_datetime(_df[name])
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else:
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value = 0
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if _item['type'] == 'INTEGER' :
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_type = np.int64
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elif _item['type'] in ['FLOAT','NUMERIC']:
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_type = np.float64
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else:
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_value = ''
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_df[name] = _df[name].fillna(_value).astype(_type)
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_df[name] = _df[name].fillna(_value) #.astype(_type)
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columns.append(name)
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writer.write(_df,schema=_schema,table=args['from'])
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print ()
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print (_df)
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writer.write(_df.astype(object),schema=_schema,table=args['from'])
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else:
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writer.write(_df,table=args['from'])
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@ -350,7 +358,7 @@ class Components :
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for _item in schema :
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dtype = str
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name = _item['name']
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novalue = -1
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novalue = 0
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if _item['type'] in ['INTEGER','NUMERIC']:
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dtype = np.int64
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@ -550,7 +558,7 @@ if __name__ == '__main__' :
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index = f[0] if f else 0
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#
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print ("..::: ",PIPELINE[index]['context'])
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print ("..::: ",PIPELINE[index]['context'],':::..')
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args = (PIPELINE[index])
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for key in _config :
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if key == 'pipeline' or key in args:
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@ -567,6 +575,7 @@ if __name__ == '__main__' :
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args['batch_size'] = 2000 #if 'batch_size' not in args else int(args['batch_size'])
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if 'dataset' not in args :
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args['dataset'] = 'combined20191004v2_deid'
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args['logs'] = args['logs'] if 'logs' in args else 'logs'
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PART_SIZE = int(args['part_size']) if 'part_size' in args else 8
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#
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# @TODO:
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@ -599,6 +608,7 @@ if __name__ == '__main__' :
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jobs.append(job)
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pass
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else:
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generator = Components()
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generator.generate(args)
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elif 'shuffle' in SYS_ARGS :
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