bug fix and enhancement
This commit is contained in:
Steve Nyemba
parent
31c158149f
commit
b1796de6fc
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"cells": [],
|
||||
"metadata": {},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
from ubuntu
|
||||
RUN ["apt-get","update"]
|
||||
RUN ["apt-get","upgrade","-y"]
|
||||
RUN ["apt-get","install","-y","git", "python3-dev","tmux","locales","python3-pip","python3-numpy","python3-pandas","locales"]
|
||||
RUN ["pip3","install","pandas-gbq","tensorflow"]
|
||||
RUN ["mkdir","-p","/usr/apps"]
|
||||
WORKDIR /usr/apps
|
||||
RUN ["git","clone","https://hiplab.mc.vanderbilt.edu/git/gan.git","aou-gan"]
|
||||
File diff suppressed because one or more lines are too long
17
WGAN.py
17
WGAN.py
|
|
@ -3,7 +3,7 @@ from tensorflow.contrib.layers import l2_regularizer
|
|||
import numpy as np
|
||||
import time
|
||||
import os
|
||||
|
||||
import pandas as pd
|
||||
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
|
||||
|
||||
#### id of gpu to use
|
||||
|
|
@ -13,7 +13,7 @@ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
|
|||
|
||||
#### training data
|
||||
#### shape=(n_sample, n_code=854)
|
||||
REAL = np.load('')
|
||||
REAL = None #np.load('') #--diagnosis codes (binary)
|
||||
|
||||
#### demographic for training data
|
||||
#### shape=(n_sample, 6)
|
||||
|
|
@ -22,16 +22,16 @@ REAL = np.load('')
|
|||
#### elif sample_x's is within 18-44, then LABEL[x,3]=1
|
||||
#### elif sample_x's is within 45-64, then LABEL[x,4]=1
|
||||
#### elif sample_x's is within 64-, then LABEL[x,5]=1
|
||||
LABEL = np.load('')
|
||||
LABEL = None #np.load('') #-- demographics 0,5 set it to 1,0,0,0,0,0
|
||||
|
||||
#### training parameters
|
||||
NUM_GPUS = 1
|
||||
BATCHSIZE_PER_GPU = 2000
|
||||
TOTAL_BATCHSIZE = BATCHSIZE_PER_GPU * NUM_GPUS
|
||||
STEPS_PER_EPOCH = int(np.load('ICD9/train.npy').shape[0] / 2000)
|
||||
STEPS_PER_EPOCH = 256 #int(np.load('ICD9/train.npy').shape[0] / 2000)
|
||||
|
||||
g_structure = [128, 128]
|
||||
d_structure = [854, 256, 128]
|
||||
d_structure = [854, 256, 128] #-- change 854 to number of diagnosis
|
||||
z_dim = 128
|
||||
|
||||
def _variable_on_cpu(name, shape, initializer=None):
|
||||
|
|
@ -277,6 +277,13 @@ def generate(model_dir, synthetic_dir, demo):
|
|||
if __name__ == '__main__':
|
||||
#### args_1: number of training epochs
|
||||
#### args_2: dir to save the trained model
|
||||
from bridge import Binary
|
||||
df = pd.read_csv('exports/observation.csv')
|
||||
cols = 'observation_source_value'
|
||||
_map,_df = (Binary()).Export(df)
|
||||
i = np.arange(_map[cols]['start'],_map[cols]['end'])
|
||||
REAL = _df[:,i]
|
||||
LABEL = np.arange(0,_df.shape[0])
|
||||
train(500, '')
|
||||
|
||||
#### args_1: dir of trained model
|
||||
|
|
|
|||
41
bridge.py
41
bridge.py
|
|
@ -23,13 +23,12 @@ if len(sys.argv) > 1:
|
|||
value = None
|
||||
if sys.argv[i].startswith('--'):
|
||||
key = sys.argv[i].replace('-','')
|
||||
|
||||
SYS_ARGS[key] = 1
|
||||
if i + 1 < N:
|
||||
value = sys.argv[i + 1] = sys.argv[i+1].strip()
|
||||
if key and value:
|
||||
SYS_ARGS[key] = value
|
||||
if key == 'context':
|
||||
SYS_ARGS[key] = ('/'+value).replace('//','/')
|
||||
|
||||
|
||||
i += 2
|
||||
|
||||
|
|
@ -107,7 +106,7 @@ class pseudonym :
|
|||
# print (df.head()[:5])
|
||||
# sys.stdout.flush()
|
||||
TABLE_NAME = ".".join([args['dataset']+DATASET_SUFFIX,PSEUDO_TABLENAME])
|
||||
df.to_gbq(TABLE_NAME,credentials=credentials,if_exists='append')
|
||||
df.to_gbq(TABLE_NAME,credentials=credentials,if_exists='append',chunksize=10000)
|
||||
# df.to_gbq(TABLE_NAME.replace('.','_pseudo.'),credentials=credentials,if_exists='append')
|
||||
|
||||
class Builder :
|
||||
|
|
@ -159,18 +158,29 @@ class Binary :
|
|||
This function will convert a column into a binary matrix with the value-space representing each column of the resulting matrix
|
||||
:column a column vector i.e every item is a row
|
||||
"""
|
||||
values = np.unique(column)
|
||||
values.sort()
|
||||
# values = np.unique(column)
|
||||
|
||||
values = column.dropna().unique()
|
||||
values.sort()
|
||||
#
|
||||
# Let's treat the case of missing values i.e nulls
|
||||
#
|
||||
row_count,col_count = column.size,values.size
|
||||
|
||||
matrix = [ np.zeros(col_count) for i in np.arange(row_count)]
|
||||
#
|
||||
# let's create a binary matrix of the feature that was passed in
|
||||
# The indices of the matrix are inspired by classical x,y axis
|
||||
for yi in np.arange(row_count) :
|
||||
value = column[yi]
|
||||
xi = np.where(values == value)[0][0] #-- column index
|
||||
matrix[yi][xi] = 1
|
||||
|
||||
if col_count > 0 and values.size > 1:
|
||||
|
||||
for yi in np.arange(row_count) :
|
||||
value = column[yi]
|
||||
if value not in values :
|
||||
continue
|
||||
xi = np.where(values == value)
|
||||
xi = xi[0][0] #-- column index
|
||||
matrix[yi][xi] = 1
|
||||
|
||||
return matrix
|
||||
def Export(self,df) :
|
||||
|
|
@ -180,7 +190,9 @@ class Binary :
|
|||
"""
|
||||
#
|
||||
# This will give us a map of how each column was mapped to a bitstream
|
||||
_map = df.apply(lambda column: self.__stream(column.values),axis=0)
|
||||
|
||||
_map = df.fillna(np.nan).apply(lambda column: self.__stream(column),axis=0)
|
||||
|
||||
#
|
||||
# We will merge this to have a healthy matrix
|
||||
_matrix = _map.apply(lambda row: list(list(itertools.chain(*row.values.tolist()))),axis=1)
|
||||
|
|
@ -198,7 +210,7 @@ class Binary :
|
|||
_m[name] = {"start":beg,"end":end}
|
||||
beg = end
|
||||
|
||||
return _m,_matrix
|
||||
return _m,_matrix.astype(np.float32)
|
||||
|
||||
def Import(self,df,values,_map):
|
||||
"""
|
||||
|
|
@ -216,8 +228,8 @@ class Binary :
|
|||
|
||||
# has_basic = 'dataset' in SYS_ARGS.keys() and 'table' in SYS_ARGS.keys() and 'key' in SYS_ARGS.keys()
|
||||
# has_action= 'export' in SYS_ARGS.keys() or 'pseudo' in SYS_ARGS.keys()
|
||||
df = pd.DataFrame({"fname":['james','james','steve','kevin','kevin'],"lname":["bond","dean","nyemba",'james','johnson']})
|
||||
df['age'] = (np.random.sample(df.shape[0]) * 100).astype(np.int32)
|
||||
# df = pd.DataFrame({"fname":['james','james','steve','kevin','kevin'],"lname":["bond","dean","nyemba",'james','johnson']})
|
||||
# df['age'] = (np.random.sample(df.shape[0]) * 100).astype(np.int32)
|
||||
if __name__ == '__main__' :
|
||||
"""
|
||||
Run the program from the command line passing the following mandatory arguments
|
||||
|
|
@ -253,6 +265,7 @@ if __name__ == '__main__' :
|
|||
builder.process(**SYS_ARGS)
|
||||
else:
|
||||
print ("")
|
||||
print (SYS_ARGS.keys())
|
||||
print ("has basic ",has_basic)
|
||||
print ("has action ",has_action)
|
||||
# pseudonym.apply(table='person',dataset='wgan_original',key='./curation-test-2.json')
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -0,0 +1,12 @@
|
|||
{
|
||||
"type": "service_account",
|
||||
"project_id": "aou-res-curation-prod",
|
||||
"private_key_id": "ecbf77975c5b7b1f4d4b1680bf67a5e0fd11dfaf",
|
||||
"private_key": "-----BEGIN PRIVATE KEY-----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\nmb0kWAwnL39cUQyXJ5zBGd3K\n-----END PRIVATE KEY-----\n",
|
||||
"client_email": "aou-res-curation-prod@appspot.gserviceaccount.com",
|
||||
"client_id": "",
|
||||
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
|
||||
"token_uri": "https://oauth2.googleapis.com/token",
|
||||
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
|
||||
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/aou-res-curation-prod%40appspot.gserviceaccount.com"
|
||||
}
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
{
|
||||
"type": "service_account",
|
||||
"project_id": "aou-res-curation-test",
|
||||
"private_key_id": "be9cb7427212dea882379d125530f5339ba854a7",
|
||||
"private_key": "-----BEGIN PRIVATE KEY-----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\nRY2vrlcCx36QEcQdwFR72Q==\n-----END PRIVATE KEY-----\n",
|
||||
"client_email": "aou-res-curation-test@appspot.gserviceaccount.com",
|
||||
"client_id": "",
|
||||
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
|
||||
"token_uri": "https://oauth2.googleapis.com/token",
|
||||
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
|
||||
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/aou-res-curation-test%40appspot.gserviceaccount.com"
|
||||
}
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
{
|
||||
"type": "service_account",
|
||||
"project_id": "aou-res-curation-test",
|
||||
"private_key_id": "1ed8d298e4b5572e7556b2f079133ea04568396a",
|
||||
"private_key": "-----BEGIN PRIVATE KEY-----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\nXdLo0jPwQXGSTqOdPvNqBi0=\n-----END PRIVATE KEY-----\n",
|
||||
"client_email": "aou-res-curation-test@appspot.gserviceaccount.com",
|
||||
"client_id": "",
|
||||
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
|
||||
"token_uri": "https://oauth2.googleapis.com/token",
|
||||
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
|
||||
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/aou-res-curation-test%40appspot.gserviceaccount.com"
|
||||
}
|
||||
|
|
@ -0,0 +1,511 @@
|
|||
observation_id,person_id,observation_concept_id,observation_date,observation_datetime,observation_type_concept_id,value_as_number,value_as_string,value_as_concept_id,qualifier_concept_id,unit_concept_id,provider_id,visit_occurrence_id,observation_source_value,observation_source_concept_id,unit_source_value,qualifier_source_value,value_source_concept_id,value_source_value,questionnaire_response_id
|
||||
118208,5557,9425,3823,10549,1331,,16669.0,,,,,8936.0,11839,6242,1849.0,5535.0,,,
|
||||
112221,5557,1268,8176,4688,1331,,16669.0,,,,,9908.0,7436,6037,1849.0,5535.0,,,
|
||||
92924,5557,1268,3823,13501,1331,,16669.0,,,,,8043.0,7436,6037,1849.0,5535.0,,,
|
||||
87525,5557,562,6238,5732,1331,,16669.0,,,,,2539.0,3555,914,1849.0,5535.0,,,
|
||||
88732,5557,1268,3823,10549,1331,,16669.0,,,,,8936.0,7436,6037,1849.0,5535.0,,,
|
||||
127541,5230,255,4070,17510,1331,,,5490.0,,,,8823.0,10678,7672,,,,,
|
||||
143650,665,5987,6705,10269,1331,,,5490.0,,,,6992.0,11454,8747,,,,,
|
||||
69801,665,8220,11554,17750,1331,,,5490.0,,,,6910.0,4645,7332,,,,,
|
||||
102810,665,3637,1222,15887,1331,,,5490.0,,,,5139.0,5309,5692,,,,,
|
||||
143746,665,8499,3363,666,1331,,,5490.0,,,,7948.0,5963,2112,,,,,
|
||||
70261,665,7654,11258,18677,1331,,,5490.0,,,,12665.0,6282,333,,,,,
|
||||
40451,665,3637,1330,6520,1331,,,5490.0,,,,5793.0,6548,7716,,,,,
|
||||
20543,665,6866,9228,15133,1331,,,5490.0,,,,9252.0,11880,1768,,,,,
|
||||
100742,665,5987,11806,11363,1331,,,5490.0,,,,,4102,192,,,,,
|
||||
128493,665,3637,9520,13609,1331,,,5490.0,,,,8390.0,5309,5692,,,,,
|
||||
118347,665,4084,11258,18677,1331,,,5490.0,,,,12665.0,1637,10082,,,,,
|
||||
70737,665,9675,6995,21994,1331,,,5490.0,,,,8156.0,11592,3413,,,,,
|
||||
16780,9034,2273,8988,12680,1331,,,5490.0,,,,11630.0,7739,5531,,,,,
|
||||
48409,9034,2273,8530,10717,1331,,,5490.0,,,,2681.0,7739,5531,,,,,
|
||||
95301,665,2123,3877,22319,1331,,,5490.0,,,,12932.0,4701,11099,,,,,
|
||||
109187,665,8499,586,20186,1331,,,5490.0,,,,13320.0,5963,2112,,,,,
|
||||
131936,665,3637,3019,1916,1331,,,5490.0,,,,326.0,5309,5692,,,,,
|
||||
11545,665,9675,11258,18677,1331,,,5490.0,,,,12665.0,229,9044,,,,,
|
||||
45240,665,9675,5659,7261,1331,,,5490.0,,,,3579.0,3906,3214,,,,,
|
||||
94641,9034,2123,8988,12680,1331,,,5490.0,,,,11630.0,9951,1400,,,,,
|
||||
61317,665,8499,12397,21443,1331,,,5490.0,,,,7050.0,5963,2112,,,,,
|
||||
96121,665,5987,11258,18677,1331,,,5490.0,,,,12665.0,271,2597,,,,,
|
||||
141495,9034,2273,4972,4053,1331,,,5490.0,,,,10150.0,7739,5531,,,,,
|
||||
46013,665,7230,4975,8765,1331,,,5490.0,,,,8632.0,7140,9976,,,,,
|
||||
27580,665,6866,10230,22399,1331,,,5490.0,,,,11228.0,11880,1768,,,,,
|
||||
31663,665,1004,6701,19159,1331,,,5490.0,,,,12712.0,6220,7308,,,,,
|
||||
53487,665,9425,9168,23137,1331,,,5490.0,,,,3789.0,954,6242,,,,,
|
||||
96920,665,8499,7684,24162,1331,,,5490.0,,,,2125.0,6421,2112,,,,,
|
||||
140655,665,566,11289,23244,1331,,,5490.0,,,,8937.0,496,7067,,,,,
|
||||
22671,665,3637,7814,14867,1331,,,5490.0,,,,3367.0,5309,5692,,,,,
|
||||
73299,665,5987,11801,10623,1331,,,5490.0,,,,9541.0,8239,8155,,,,,
|
||||
33464,9034,5772,8988,12680,1331,,,5490.0,,,,11630.0,5293,3180,,,,,
|
||||
116430,665,8499,9520,13609,1331,,,5490.0,,,,8390.0,6421,2112,,,,,
|
||||
42612,665,566,11258,18677,1331,,,5490.0,,,,12665.0,496,7067,,,,,
|
||||
151800,665,2123,6995,21994,1331,,,5490.0,,,,8156.0,8751,11099,,,,,
|
||||
66963,665,5987,757,20626,1331,,,5490.0,,,,2315.0,11454,8747,,,,,
|
||||
20955,665,9675,757,20626,1331,,,5490.0,,,,2315.0,229,9044,,,,,
|
||||
29389,665,6866,9591,9107,1331,,,5490.0,,,,8111.0,11880,1768,,,,,
|
||||
47723,665,8881,3877,22319,1331,,,5490.0,,,,12932.0,9490,836,,,,,
|
||||
145483,665,8220,11289,23244,1331,,,5490.0,,,,8937.0,4645,7332,,,,,
|
||||
148716,665,5987,1387,17146,1331,,,5490.0,,,,1679.0,4102,192,,,,,
|
||||
60966,665,6866,6217,3474,1331,,,5490.0,,,,7540.0,11880,1768,,,,,
|
||||
74964,665,8220,6705,10269,1331,,,5490.0,,,,6992.0,4645,7332,,,,,
|
||||
111786,665,566,757,20626,1331,,,5490.0,,,,2315.0,496,7067,,,,,
|
||||
41338,665,9675,10799,349,1331,,,5490.0,,,,3164.0,5538,9044,,,,,
|
||||
27522,665,9675,6705,10269,1331,,,5490.0,,,,6992.0,229,9044,,,,,
|
||||
64564,665,3637,8188,3756,1331,,,5490.0,,,,3488.0,4974,7184,,,,,
|
||||
69978,665,7565,6701,19159,1331,,,5490.0,,,,12712.0,293,5494,,,,,
|
||||
34968,665,3637,7684,24162,1331,,,5490.0,,,,2125.0,5309,5692,,,,,
|
||||
23218,665,3637,245,13206,1331,,,5490.0,,,,,5309,5692,,,,,
|
||||
71119,665,8881,2149,3205,1331,,,5490.0,,,,6073.0,9490,836,,,,,
|
||||
66535,665,566,6705,10269,1331,,,5490.0,,,,6992.0,496,7067,,,,,
|
||||
65054,665,11121,11258,18677,1331,,,5490.0,,,,12665.0,4843,5773,,,,,
|
||||
74272,665,566,11554,17750,1331,,,5490.0,,,,6910.0,496,7067,,,,,
|
||||
110821,665,9675,2960,7987,1331,,,5490.0,,,,3465.0,3906,3214,,,,,
|
||||
146780,665,3637,12397,21443,1331,,,5490.0,,,,7050.0,6289,5692,,,,,
|
||||
53661,665,5987,11289,23244,1331,,,5490.0,,,,8937.0,6804,10990,,,,,
|
||||
115297,9034,3637,10182,380,1331,,,5490.0,,,,9044.0,11463,7184,,,,,
|
||||
88141,665,8220,11258,18677,1331,,,5490.0,,,,12665.0,4645,7332,,,,,
|
||||
108101,665,5987,6705,10269,1331,,,5490.0,,,,6992.0,271,2597,,,,,
|
||||
152503,665,5987,11554,17750,1331,,,5490.0,,,,6910.0,271,2597,,,,,
|
||||
88754,665,5987,2960,7987,1331,,,5490.0,,,,3465.0,271,2597,,,,,
|
||||
17476,9034,2273,12445,8047,1331,,,5490.0,,,,13143.0,7739,5531,,,,,
|
||||
1955,665,3637,3363,666,1331,,,5490.0,,,,7948.0,6289,5692,,,,,
|
||||
118939,665,3637,10248,5700,1331,,,5490.0,,,,5350.0,6289,5692,,,,,
|
||||
115691,665,5987,7754,13286,1331,,,5490.0,,,,13355.0,4102,192,,,,,
|
||||
111900,665,5987,11554,17750,1331,,,5490.0,,,,6910.0,6804,10990,,,,,
|
||||
99133,665,4351,11554,17750,1331,,,5490.0,,,,6910.0,3158,3294,,,,,
|
||||
33439,665,4351,10248,5700,1331,,,5490.0,,,,5350.0,10401,2940,,,,,
|
||||
76750,665,4351,11258,18677,1331,,,5490.0,,,,12665.0,3955,4266,,,,,
|
||||
101677,665,4351,11554,17750,1331,,,5490.0,,,,6910.0,3955,4266,,,,,
|
||||
7658,665,4351,757,20626,1331,,,5490.0,,,,2315.0,7938,758,,,,,
|
||||
8349,665,4351,11289,23244,1331,,,5490.0,,,,8937.0,3955,4266,,,,,
|
||||
83441,665,4351,2960,7987,1331,,,5490.0,,,,3465.0,3955,4266,,,,,
|
||||
31987,665,4351,757,20626,1331,,,5490.0,,,,2315.0,3955,4266,,,,,
|
||||
149415,7552,10002,9293,1136,1331,,,5490.0,,,,13107.0,9301,4502,,,,,
|
||||
109005,889,3080,2677,11631,1331,,,5490.0,,,,,12081,1503,,,,,
|
||||
51503,889,255,1957,19548,1331,,,5490.0,,,,,10678,7672,,,,,
|
||||
92084,584,8881,933,23961,1331,,,5490.0,,,,5399.0,1933,836,,,,,
|
||||
20482,584,8220,286,19980,1331,,,5490.0,,,,9158.0,2464,7332,,,,,
|
||||
142288,584,3637,6366,21753,1331,,,5490.0,,,,,6548,7716,,,,,
|
||||
26495,584,8220,7954,2585,1331,,,5490.0,,,,9965.0,2464,7332,,,,,
|
||||
44120,889,5772,12734,19047,1331,,,5490.0,,,,,4378,10734,,,,,
|
||||
25470,584,8220,6297,10755,1331,,,5490.0,,,,9721.0,2464,7332,,,,,
|
||||
55155,584,8220,12707,5062,1331,,,5490.0,,,,10641.0,2464,7332,,,,,
|
||||
59236,584,3228,8590,11654,1331,,,5490.0,,,,11500.0,6818,10843,,,,,
|
||||
51768,584,7973,913,21672,1331,,,5490.0,,,,3441.0,7416,7132,,,,,
|
||||
46767,889,5987,1789,4216,1331,,,5490.0,,,,,4102,192,,,,,
|
||||
98773,584,8220,8901,6846,1331,,,5490.0,,,,1663.0,2464,7332,,,,,
|
||||
102018,889,1815,995,12664,1331,,,5490.0,,,,4139.0,7236,6845,,,,,
|
||||
16147,889,9425,3510,1739,1331,,,5490.0,,,,3252.0,2685,6242,,,,,
|
||||
37396,889,4351,4335,5449,1331,,,5490.0,,,,11376.0,2443,7969,,,,,
|
||||
110428,889,3637,8343,20660,1331,,,5490.0,,,,3918.0,5309,5692,,,,,
|
||||
11390,889,8609,2960,7987,1331,,,5490.0,,,,,8379,1288,,,,,
|
||||
18726,584,2123,2873,3314,1331,,,5490.0,,,,7270.0,9951,1400,,,,,
|
||||
8677,889,5987,2836,20839,1331,,,5490.0,,,,11801.0,4102,192,,,,,
|
||||
31211,889,5904,1957,19548,1331,,,5490.0,,,,,2790,3686,,,,,
|
||||
123923,584,8220,693,17394,1331,,,5490.0,,,,,2464,7332,,,,,
|
||||
88350,584,8220,1675,17892,1331,,,5490.0,,,,1614.0,2464,7332,,,,,
|
||||
130425,584,8220,11192,21322,1331,,,5490.0,,,,11314.0,4645,7332,,,,,
|
||||
17957,584,8220,913,21672,1331,,,5490.0,,,,3441.0,2464,7332,,,,,
|
||||
148169,889,4351,4162,20465,1331,,,5490.0,,,,8503.0,4435,7969,,,,,
|
||||
46432,889,9425,6584,13541,1331,,,5490.0,,,,6965.0,2685,6242,,,,,
|
||||
117012,889,4351,765,5507,1331,,,5490.0,,,,12148.0,1376,374,,,,,
|
||||
127289,889,1815,9018,16763,1331,,,5490.0,,,,9990.0,3899,6845,,,,,
|
||||
129839,889,8499,7312,24175,1331,,,5490.0,,,,9424.0,6421,2112,,,,,
|
||||
137161,889,3637,6416,9237,1331,,,5490.0,,,,1274.0,5309,5692,,,,,
|
||||
86281,889,5987,564,14434,1331,,,5490.0,,,,,4102,192,,,,,
|
||||
121552,889,9425,12206,19851,1331,,,5490.0,,,,10580.0,2685,6242,,,,,
|
||||
5523,584,8220,8187,8921,1331,,,5490.0,,,,5043.0,2464,7332,,,,,
|
||||
131839,584,8220,3520,5888,1331,,,5490.0,,,,10633.0,2464,7332,,,,,
|
||||
75866,584,2123,6883,20216,1331,,,5490.0,,,,6350.0,8751,11099,,,,,
|
||||
71809,889,5987,6911,21649,1331,,,5490.0,,,,1570.0,342,192,,,,,
|
||||
134463,889,10181,3070,14517,1331,,,5490.0,,,,2327.0,10757,6956,,,,,
|
||||
39004,584,8220,9583,4888,1331,,,5490.0,,,,5606.0,2464,7332,,,,,
|
||||
89639,889,3637,12206,19851,1331,,,5490.0,,,,10580.0,5309,5692,,,,,
|
||||
73506,8266,2704,2555,12270,1331,,,5490.0,,,,9705.0,9842,670,,,,,
|
||||
139715,2487,10289,8577,23785,1331,,,5490.0,,,,2909.0,11400,5264,,,,,
|
||||
92298,2487,10289,2836,20839,1331,,,5490.0,,,,12203.0,11400,5264,,,,,
|
||||
112860,2487,10289,5502,18949,1331,,,5490.0,,,,5401.0,11400,5264,,,,,
|
||||
94341,2487,10289,10960,23341,1331,,,5490.0,,,,12230.0,11400,5264,,,,,
|
||||
84661,2487,5987,8577,23785,1331,,,5490.0,,,,2909.0,11655,8155,,,,,
|
||||
127815,8266,2704,8418,22417,1331,,,5490.0,,,,5987.0,9842,670,,,,,
|
||||
114637,2487,9284,11406,21765,1331,,,5490.0,,,,8760.0,2051,9025,,,,,
|
||||
120662,8266,2734,2555,12270,1331,,,5490.0,,,,3356.0,6992,3891,,,,,
|
||||
77654,2487,10289,10543,11301,1331,,,5490.0,,,,5797.0,11400,5264,,,,,
|
||||
52859,2487,1941,865,18965,1331,,,5490.0,,,,1317.0,10167,10122,,,,,
|
||||
99667,2487,5987,10402,21256,1331,,,5490.0,,,,4198.0,11655,8155,,,,,
|
||||
39458,2487,10289,10402,21256,1331,,,5490.0,,,,4198.0,11400,5264,,,,,
|
||||
137598,2487,2123,7653,5315,1331,,,5490.0,,,,12212.0,8088,8147,,,,,
|
||||
42479,2487,5987,5502,18949,1331,,,5490.0,,,,5401.0,11655,8155,,,,,
|
||||
47641,2487,562,7653,5315,1331,,,5490.0,,,,12212.0,9598,914,,,,,
|
||||
138942,2487,6587,10960,23341,1331,,,5490.0,,,,12230.0,10651,6830,,,,,
|
||||
3496,2487,10289,12854,1260,1331,,,5490.0,,,,3740.0,11400,5264,,,,,
|
||||
66663,2487,3637,3502,7058,1331,,,5490.0,,,,7985.0,5309,5692,,,,,
|
||||
144416,2487,10289,6526,13869,1331,,,5490.0,,,,4204.0,11400,5264,,,,,
|
||||
150343,229,5248,8255,21747,1331,,,5490.0,,,,5302.0,507,1962,,,,,
|
||||
52099,229,5987,4310,21923,1331,,,5490.0,,,,7701.0,4102,192,,,,,
|
||||
121789,229,566,8255,21747,1331,,,5490.0,,,,5302.0,496,7067,,,,,
|
||||
52153,229,1004,4824,16715,1331,,,5490.0,,,,8138.0,10961,10789,,,,,
|
||||
95103,229,5987,8913,18400,1331,,,5490.0,,,,9117.0,4102,192,,,,,
|
||||
102328,229,6847,7047,20430,1331,,,5490.0,,,,3158.0,5605,8226,,,,,
|
||||
126774,229,7565,4824,16715,1331,,,5490.0,,,,8138.0,776,3975,,,,,
|
||||
127133,229,4351,8255,21747,1331,,,5490.0,,,,5302.0,9451,8766,,,,,
|
||||
69764,229,7565,7636,3074,1331,,,5490.0,,,,10449.0,293,5494,,,,,
|
||||
93154,5895,5987,8255,21747,1331,,,5490.0,,,,,4102,192,,,,,
|
||||
45928,229,5987,8255,21747,1331,,,5490.0,,,,5302.0,11454,8747,,,,,
|
||||
9543,229,5987,2633,5260,1331,,,5490.0,,,,7738.0,4102,192,,,,,
|
||||
41640,229,6810,8396,4809,1331,,,5490.0,,,,7303.0,471,4817,,,,,
|
||||
10654,229,5987,4014,8773,1331,,,5490.0,,,,8388.0,4102,192,,,,,
|
||||
16687,229,6810,5258,10596,1331,,,5490.0,,,,4544.0,471,4817,,,,,
|
||||
25069,229,6810,5534,7595,1331,,,5490.0,,,,7577.0,471,4817,,,,,
|
||||
137225,229,8598,8255,21747,1331,,,5490.0,,,,5302.0,1179,10074,,,,,
|
||||
88948,229,4351,8255,21747,1331,,,5490.0,,,,5302.0,7108,6713,,,,,
|
||||
39653,229,1004,8255,21747,1331,,,5490.0,,,,5302.0,854,4938,,,,,
|
||||
137353,229,5248,4824,16715,1331,,,5490.0,,,,8138.0,3288,5861,,,,,
|
||||
32231,229,9305,8255,21747,1331,,,5490.0,,,,5302.0,12050,8552,,,,,
|
||||
48748,229,6810,4619,11842,1331,,,5490.0,,,,,471,4817,,,,,
|
||||
24139,229,1004,4824,16715,1331,,,5490.0,,,,8138.0,854,4938,,,,,
|
||||
119203,229,6810,4824,16715,1331,,,5490.0,,,,8138.0,7162,4817,,,,,
|
||||
37449,229,5044,4824,16715,1331,,,5490.0,,,,8138.0,8452,1985,,,,,
|
||||
98305,229,5987,8255,21747,1331,,,5490.0,,,,5302.0,271,2597,,,,,
|
||||
106283,229,5987,564,14434,1331,,,5490.0,,,,316.0,4102,192,,,,,
|
||||
83001,229,1004,8255,21747,1331,,,5490.0,,,,5302.0,10961,10789,,,,,
|
||||
42771,229,5987,9913,1154,1331,,,5490.0,,,,4659.0,4102,192,,,,,
|
||||
139650,229,9305,7636,3074,1331,,,5490.0,,,,10449.0,6002,5633,,,,,
|
||||
105281,229,6810,12756,8578,1331,,,5490.0,,,,6332.0,471,4817,,,,,
|
||||
140570,229,3087,11118,13079,1331,,,5490.0,,,,9512.0,9062,3458,,,,,
|
||||
151531,5895,5987,11997,21650,1331,,,5490.0,,,,11501.0,4102,192,,,,,
|
||||
58777,229,1004,7636,3074,1331,,,5490.0,,,,10449.0,6220,7308,,,,,
|
||||
84286,229,1941,7636,3074,1331,,,5490.0,,,,10449.0,10167,10122,,,,,
|
||||
51431,7508,183,12097,4178,1331,,16669.0,,,,,2792.0,7396,3649,1849.0,5535.0,,,
|
||||
6859,7508,3575,5313,16218,1331,,16669.0,,,,,2048.0,8641,4813,1849.0,5535.0,,,
|
||||
122615,7508,9051,5313,16218,1331,,16669.0,,,,,2048.0,5459,8021,1849.0,5535.0,,,
|
||||
29681,7508,8812,8043,16726,1331,,16669.0,,,,,6360.0,11218,1352,1849.0,5535.0,,,
|
||||
81739,7508,8006,8043,16726,1331,,16669.0,,,,,6360.0,11570,2035,1849.0,5535.0,,,
|
||||
75396,7508,1264,12103,21026,1331,,16669.0,,,,,2319.0,294,6737,1849.0,5535.0,,,
|
||||
1615,7508,5987,5313,16218,1331,,16669.0,,,,,2048.0,971,192,1849.0,5535.0,,,
|
||||
81559,7508,4351,7788,1933,1331,,16669.0,,,,,9700.0,6346,2294,1849.0,5535.0,,,
|
||||
142462,7508,4351,12103,21026,1331,,16669.0,,,,,2319.0,6346,2294,1849.0,5535.0,,,
|
||||
54948,7508,4351,1317,8742,1331,,16669.0,,,,,4893.0,9550,4725,1849.0,5535.0,,,
|
||||
91359,7508,4351,5313,16218,1331,,16669.0,,,,,2048.0,6346,2294,1849.0,5535.0,,,
|
||||
102634,2253,5987,11649,9206,1331,,,5490.0,,,,12897.0,342,192,,,,,
|
||||
57404,2253,5987,12081,20227,1331,,,5490.0,,,,3837.0,4102,192,,,,,
|
||||
59301,2253,7127,6349,18224,1331,,,5490.0,,,,342.0,5537,6041,,,,,
|
||||
46314,2253,8644,7278,22139,1331,,,5490.0,,,,5122.0,2352,1040,,,,,
|
||||
99875,2253,8881,9178,10472,1331,,,5490.0,,,,2495.0,9490,836,,,,,
|
||||
101072,2253,6743,10798,14751,1331,,,5490.0,,,,9281.0,5140,4357,,,,,
|
||||
54214,2253,8644,12081,20227,1331,,,5490.0,,,,4620.0,2352,1040,,,,,
|
||||
29372,2253,5987,7278,22139,1331,,,5490.0,,,,7573.0,4102,192,,,,,
|
||||
16606,2253,5987,757,20626,1331,,,5490.0,,,,11146.0,4102,192,,,,,
|
||||
36083,2253,5987,5502,18949,1331,,,5490.0,,,,9128.0,342,192,,,,,
|
||||
90263,2253,1042,457,8629,1331,,,5490.0,,,,10905.0,10444,3856,,,,,
|
||||
141677,2253,5622,6349,18224,1331,,,5490.0,,,,342.0,559,1123,,,,,
|
||||
112438,2253,7973,1690,15078,1331,,,5490.0,,,,9394.0,11760,10316,,,,,
|
||||
40469,2253,1941,10102,2413,1331,,,5490.0,,,,8328.0,10167,10122,,,,,
|
||||
61710,2253,5987,9456,2238,1331,,,5490.0,,,,7941.0,4102,192,,,,,
|
||||
146689,2253,4555,9643,3647,1331,,,5490.0,,,,11276.0,5174,241,,,,,
|
||||
123770,2253,8644,6203,22276,1331,,,5490.0,,,,4005.0,2352,1040,,,,,
|
||||
145101,2253,1941,5436,3310,1331,,,5490.0,,,,2108.0,898,10122,,,,,
|
||||
16732,2253,1941,10245,6616,1331,,,5490.0,,,,11097.0,10167,10122,,,,,
|
||||
141264,2253,5987,6203,22276,1331,,,5490.0,,,,4005.0,11454,8747,,,,,
|
||||
33788,2253,2734,457,8629,1331,,,5490.0,,,,10905.0,6992,3891,,,,,
|
||||
98588,2253,2734,8880,6505,1331,,,5490.0,,,,12136.0,84,6098,,,,,
|
||||
55856,2253,4351,6349,18224,1331,,,5490.0,,,,342.0,1557,1323,,,,,
|
||||
90501,2253,4351,1690,15078,1331,,,5490.0,,,,9394.0,1557,1323,,,,,
|
||||
17854,2253,4351,6349,18224,1331,,,5490.0,,,,342.0,463,7738,,,,,
|
||||
93949,2253,4351,1690,15078,1331,,,5490.0,,,,9394.0,463,7738,,,,,
|
||||
83743,2253,4351,12597,22984,1331,,,5490.0,,,,8497.0,463,7738,,,,,
|
||||
122179,2253,4351,1464,7793,1331,,,5490.0,,,,4455.0,463,7738,,,,,
|
||||
72134,2253,4351,9643,3647,1331,,,5490.0,,,,11276.0,463,7738,,,,,
|
||||
82182,2253,4351,4589,9396,1331,,,5490.0,,,,11234.0,463,7738,,,,,
|
||||
98134,2253,4351,6355,22424,1331,,,5490.0,,,,5801.0,463,7738,,,,,
|
||||
105276,2253,4351,457,8629,1331,,,5490.0,,,,10905.0,1272,2294,,,,,
|
||||
80892,2253,4351,1983,18523,1331,,,5490.0,,,,13679.0,463,7738,,,,,
|
||||
138861,2253,4351,3131,13151,1331,,,5490.0,,,,9955.0,463,7738,,,,,
|
||||
125680,2253,4351,1464,7793,1331,,,5490.0,,,,4455.0,10344,10492,,,,,
|
||||
62465,2253,4351,9808,2635,1331,,,5490.0,,,,5887.0,463,7738,,,,,
|
||||
43261,2253,4351,7015,21437,1331,,,5490.0,,,,8370.0,7216,5174,,,,,
|
||||
65994,2253,4351,8428,6179,1331,,,5490.0,,,,1370.0,463,7738,,,,,
|
||||
44338,2253,4351,8579,8565,1331,,,5490.0,,,,5190.0,1557,1323,,,,,
|
||||
97523,2253,4351,6203,22276,1331,,,5490.0,,,,4005.0,10401,2940,,,,,
|
||||
88274,2253,4351,1716,23720,1331,,,5490.0,,,,1242.0,7216,5174,,,,,
|
||||
84947,2253,4351,10804,16846,1331,,,5490.0,,,,7350.0,463,7738,,,,,
|
||||
78897,2253,4351,2276,12297,1331,,,5490.0,,,,4111.0,463,7738,,,,,
|
||||
19460,2253,4351,8801,10295,1331,,,5490.0,,,,10979.0,463,7738,,,,,
|
||||
45054,2253,4351,12840,4293,1331,,,5490.0,,,,2696.0,7457,1323,,,,,
|
||||
3724,2253,4351,8579,8565,1331,,,5490.0,,,,5190.0,6544,2324,,,,,
|
||||
88025,2253,4351,9643,3647,1331,,,5490.0,,,,11276.0,4132,10267,,,,,
|
||||
20599,6920,5987,12639,11106,1331,,,5490.0,,,,3916.0,342,192,,,,,
|
||||
149124,4834,3962,5830,3432,1331,,,5490.0,,,,7824.0,1636,4542,,,,,
|
||||
63370,4834,3962,11863,11571,1331,,,5490.0,,,,9856.0,9269,2063,,,,,
|
||||
60927,4834,4880,8645,5004,1331,,,5490.0,,,,7989.0,8711,5316,,,,,
|
||||
52404,4834,3962,1463,13009,1331,,,5490.0,,,,12139.0,4275,2063,,,,,
|
||||
18949,6920,10181,1276,22405,1331,,,5490.0,,,,2705.0,2732,566,,,,,
|
||||
67625,4834,3962,963,4234,1331,,,5490.0,,,,13601.0,9269,2063,,,,,
|
||||
51784,4834,3962,1282,18682,1331,,,5490.0,,,,8989.0,4275,2063,,,,,
|
||||
128512,4834,1004,8645,5004,1331,,,5490.0,,,,7989.0,10961,10789,,,,,
|
||||
131302,6920,3087,5659,7261,1331,,,5490.0,,,,5010.0,6673,9982,,,,,
|
||||
24005,4834,1004,8645,5004,1331,,,5490.0,,,,7989.0,854,4938,,,,,
|
||||
52479,4834,7565,8645,5004,1331,,,5490.0,,,,7989.0,293,5494,,,,,
|
||||
5760,4834,3080,7073,23818,1331,,,5490.0,,,,1898.0,8631,7975,,,,,
|
||||
147602,4834,10181,1073,18068,1331,,,5490.0,,,,2651.0,7220,8765,,,,,
|
||||
10640,4834,10181,9811,22231,1331,,,5490.0,,,,589.0,9884,8765,,,,,
|
||||
93595,4834,3080,1073,18068,1331,,,5490.0,,,,2651.0,5484,1813,,,,,
|
||||
16943,6920,2734,12639,11106,1331,,,5490.0,,,,3916.0,1045,5056,,,,,
|
||||
127327,4834,3962,7853,12266,1331,,,5490.0,,,,11693.0,4275,2063,,,,,
|
||||
54714,4834,3962,5830,3432,1331,,,5490.0,,,,7824.0,8342,6187,,,,,
|
||||
60188,4834,225,798,18716,1331,,,5490.0,,,,10891.0,9529,6714,,,,,
|
||||
113924,4834,3962,11750,20208,1331,,,5490.0,,,,13110.0,1636,4542,,,,,
|
||||
24149,6920,7565,924,21423,1331,,,5490.0,,,,3325.0,293,5494,,,,,
|
||||
120692,4834,3962,6041,2660,1331,,,5490.0,,,,4909.0,4275,2063,,,,,
|
||||
123053,6920,5987,924,21423,1331,,,5490.0,,,,3325.0,342,192,,,,,
|
||||
78517,4834,3962,9735,13036,1331,,,5490.0,,,,2852.0,4275,2063,,,,,
|
||||
136540,4834,225,12816,2955,1331,,,5490.0,,,,13282.0,9529,6714,,,,,
|
||||
56770,4834,5248,8645,5004,1331,,,5490.0,,,,7989.0,7110,9680,,,,,
|
||||
115892,4834,2495,8645,5004,1331,,,5490.0,,,,7989.0,10328,5186,,,,,
|
||||
149330,9633,7230,8490,11530,1331,,16669.0,,,,,13068.0,11185,10988,1849.0,5535.0,,,
|
||||
120230,9633,7230,1947,19476,1331,,16669.0,,,,,6918.0,11185,10988,1849.0,5535.0,,,
|
||||
118473,9633,4351,3273,21867,1331,,16669.0,,,,,6912.0,5611,3167,1849.0,5535.0,,,
|
||||
41457,9633,7230,4110,20602,1331,,16669.0,,,,,9771.0,11185,10988,1849.0,5535.0,,,
|
||||
55276,9633,7230,1947,12291,1331,,16669.0,,,,,4856.0,11185,10988,1849.0,5535.0,,,
|
||||
42791,7659,4351,11141,10173,1331,,16669.0,,,,,10857.0,6346,2294,1849.0,5535.0,,,
|
||||
54119,7659,4351,5193,15553,1331,,16669.0,,,,,13571.0,8007,118,1849.0,5535.0,,,
|
||||
51285,7659,9675,4517,14075,1331,,16669.0,,,,,6033.0,1041,3214,1849.0,5535.0,,,
|
||||
55583,7659,4351,4517,14075,1331,,16669.0,,,,,6033.0,6346,2294,1849.0,5535.0,,,
|
||||
152488,10034,10574,12103,14043,1331,,,5490.0,,,,10617.0,7922,1613,,,,,
|
||||
98325,7659,3600,5193,15553,1331,,16669.0,,,,,13571.0,7600,1586,1849.0,5535.0,,,
|
||||
136758,2700,255,2965,23642,1331,,,5490.0,,,,966.0,827,10862,,,,,
|
||||
60119,7659,4351,3795,16087,1331,,16669.0,,,,,12477.0,6346,2294,1849.0,5535.0,,,
|
||||
11703,2700,8644,3887,183,1331,,,5490.0,,,,6827.0,12129,45,,,,,
|
||||
95659,2700,10181,2045,18201,1331,,,5490.0,,,,6978.0,2434,9641,,,,,
|
||||
86997,7659,5987,1264,10453,1331,,16669.0,,,,,7937.0,692,2597,1849.0,5535.0,,,
|
||||
134716,2700,4351,7671,19255,1331,,,5490.0,,,,10761.0,2498,9470,,,,,
|
||||
34136,2700,4351,6769,23457,1331,,,5490.0,,,,216.0,2498,9470,,,,,
|
||||
70662,2700,4351,7938,8240,1331,,,5490.0,,,,5022.0,3870,3127,,,,,
|
||||
123762,7659,255,3795,16087,1331,,16669.0,,,,,12477.0,10889,7672,1849.0,5535.0,,,
|
||||
58274,2700,2734,9260,19762,1331,,,5490.0,,,,1426.0,4526,9165,,,,,
|
||||
148981,2700,3575,3887,183,1331,,,5490.0,,,,6827.0,5361,4813,,,,,
|
||||
16736,2700,1042,3887,183,1331,,,5490.0,,,,6827.0,10444,3856,,,,,
|
||||
95110,2700,4351,4122,20791,1331,,,5490.0,,,,1735.0,5236,7272,,,,,
|
||||
43316,2700,4351,3887,183,1331,,,5490.0,,,,6827.0,7500,2632,,,,,
|
||||
94283,2700,9433,3887,183,1331,,,5490.0,,,,6827.0,3548,6892,,,,,
|
||||
72347,2700,4351,11289,23244,1331,,,5490.0,,,,7034.0,3870,3127,,,,,
|
||||
94264,2700,8644,11697,12815,1331,,,5490.0,,,,7172.0,7817,11228,,,,,
|
||||
70625,2700,4351,11289,23244,1331,,,5490.0,,,,7034.0,5236,7272,,,,,
|
||||
118474,2700,4351,11289,23244,1331,,,5490.0,,,,7034.0,7500,2632,,,,,
|
||||
92735,2700,2734,1463,13009,1331,,,5490.0,,,,8045.0,5513,11371,,,,,
|
||||
93664,2700,4351,1904,14127,1331,,,5490.0,,,,5893.0,1272,2294,,,,,
|
||||
126651,2700,4351,5478,3145,1331,,,5490.0,,,,11028.0,5236,7272,,,,,
|
||||
2601,2700,1296,3887,183,1331,,,5490.0,,,,6827.0,6175,266,,,,,
|
||||
121941,2700,4351,1904,14127,1331,,,5490.0,,,,5893.0,5236,7272,,,,,
|
||||
131680,2700,566,1904,14127,1331,,,5490.0,,,,5893.0,496,7067,,,,,
|
||||
64594,2700,4351,11697,12815,1331,,,5490.0,,,,7172.0,3870,3127,,,,,
|
||||
140307,2700,4351,2769,16084,1331,,,5490.0,,,,3265.0,1272,2294,,,,,
|
||||
119028,2700,2734,2965,23642,1331,,,5490.0,,,,4475.0,84,6098,,,,,
|
||||
32014,2700,4351,5487,9554,1331,,,5490.0,,,,6103.0,2498,9470,,,,,
|
||||
15371,2700,5987,1904,14127,1331,,,5490.0,,,,5893.0,271,2597,,,,,
|
||||
42228,2700,2734,10486,18241,1331,,,5490.0,,,,885.0,4526,9165,,,,,
|
||||
49765,2700,5987,12229,19094,1331,,,5490.0,,,,11759.0,271,2597,,,,,
|
||||
95955,2700,2734,4392,9298,1331,,,5490.0,,,,13378.0,5513,11371,,,,,
|
||||
86113,2700,10347,8406,628,1331,,,5490.0,,,,9199.0,9013,2475,,,,,
|
||||
146880,2700,4351,10496,17766,1331,,,5490.0,,,,264.0,2498,9470,,,,,
|
||||
31815,2700,4351,2769,16084,1331,,,5490.0,,,,3265.0,10663,9470,,,,,
|
||||
146126,2700,4351,10496,17766,1331,,,5490.0,,,,264.0,187,7272,,,,,
|
||||
117743,2700,3493,3887,183,1331,,,5490.0,,,,6827.0,5926,2051,,,,,
|
||||
50121,2700,10181,11697,12815,1331,,,5490.0,,,,7172.0,514,1799,,,,,
|
||||
2371,2700,4351,12229,19094,1331,,,5490.0,,,,11759.0,5236,7272,,,,,
|
||||
127277,2700,566,11289,23244,1331,,,5490.0,,,,7034.0,496,7067,,,,,
|
||||
33457,2700,4351,12499,4680,1331,,,5490.0,,,,10434.0,9042,2097,,,,,
|
||||
102325,2700,4351,7671,19255,1331,,,5490.0,,,,10761.0,9042,2097,,,,,
|
||||
72241,2700,4351,11289,23244,1331,,,5490.0,,,,7034.0,1272,2294,,,,,
|
||||
105384,2700,5987,10719,8159,1331,,,5490.0,,,,3212.0,342,192,,,,,
|
||||
15937,2700,4351,10207,21944,1331,,,5490.0,,,,9818.0,9042,2097,,,,,
|
||||
132531,2700,9675,2769,16084,1331,,,5490.0,,,,3265.0,420,2969,,,,,
|
||||
62830,2700,4351,2769,16084,1331,,,5490.0,,,,3265.0,11562,2097,,,,,
|
||||
20912,2700,7699,4122,20791,1331,,,5490.0,,,,1735.0,6864,3621,,,,,
|
||||
88682,2700,4351,12499,4680,1331,,,5490.0,,,,10434.0,2498,9470,,,,,
|
||||
77519,2700,4351,10207,21944,1331,,,5490.0,,,,9818.0,2498,9470,,,,,
|
||||
17620,2700,4351,11697,12815,1331,,,5490.0,,,,7172.0,7500,2632,,,,,
|
||||
35366,2700,4351,4122,20791,1331,,,5490.0,,,,1735.0,1272,2294,,,,,
|
||||
18793,2700,10574,11697,12815,1331,,,5490.0,,,,7172.0,5388,1613,,,,,
|
||||
9423,2700,4351,8447,11940,1331,,,5490.0,,,,7421.0,9042,2097,,,,,
|
||||
133036,2700,255,1018,20367,1331,,,5490.0,,,,8237.0,827,10862,,,,,
|
||||
44732,2700,2431,2045,18201,1331,,,5490.0,,,,6978.0,4152,6170,,,,,
|
||||
107150,2700,4351,7938,8240,1331,,,5490.0,,,,5022.0,5236,7272,,,,,
|
||||
117279,2700,5987,10496,17766,1331,,,5490.0,,,,264.0,4102,192,,,,,
|
||||
125689,2700,4351,7938,8240,1331,,,5490.0,,,,5022.0,10663,9470,,,,,
|
||||
76836,2700,8600,11289,23244,1331,,,5490.0,,,,7034.0,8747,7989,,,,,
|
||||
7573,2700,566,12229,19094,1331,,,5490.0,,,,11759.0,496,7067,,,,,
|
||||
94634,2700,9675,12229,19094,1331,,,5490.0,,,,11759.0,420,2969,,,,,
|
||||
32886,2700,4351,12229,19094,1331,,,5490.0,,,,11759.0,10663,9470,,,,,
|
||||
104258,2700,4351,9465,481,1331,,,5490.0,,,,7767.0,2498,9470,,,,,
|
||||
30325,2700,5987,7938,8240,1331,,,5490.0,,,,5022.0,271,2597,,,,,
|
||||
75482,2700,5987,12206,19851,1331,,,5490.0,,,,11913.0,342,192,,,,,
|
||||
75245,2700,4351,4122,20791,1331,,,5490.0,,,,1735.0,3870,3127,,,,,
|
||||
4022,2700,4351,12748,18235,1331,,,5490.0,,,,149.0,9042,2097,,,,,
|
||||
71386,2700,4351,1994,9287,1331,,,5490.0,,,,12875.0,5236,7272,,,,,
|
||||
139341,2700,4351,6769,23457,1331,,,5490.0,,,,216.0,9042,2097,,,,,
|
||||
132605,7659,4351,8374,14572,1331,,16669.0,,,,,12842.0,527,8825,1849.0,5535.0,,,
|
||||
96863,2700,4351,8447,11940,1331,,,5490.0,,,,7421.0,2498,9470,,,,,
|
||||
93150,2700,5987,11289,23244,1331,,,5490.0,,,,7034.0,271,2597,,,,,
|
||||
29482,2700,10347,12229,19094,1331,,,5490.0,,,,2544.0,2339,2475,,,,,
|
||||
116558,2700,4351,7938,8240,1331,,,5490.0,,,,5022.0,1272,2294,,,,,
|
||||
4684,2700,10181,3887,183,1331,,,5490.0,,,,6827.0,514,1799,,,,,
|
||||
73909,2700,4351,12748,18235,1331,,,5490.0,,,,149.0,2498,9470,,,,,
|
||||
97855,7659,4351,1264,10453,1331,,16669.0,,,,,7937.0,6346,2294,1849.0,5535.0,,,
|
||||
109491,7659,9675,3795,16087,1331,,16669.0,,,,,12477.0,1041,3214,1849.0,5535.0,,,
|
||||
146722,2700,566,3887,183,1331,,,5490.0,,,,6827.0,496,7067,,,,,
|
||||
91197,2700,4351,12229,19094,1331,,,5490.0,,,,11759.0,1272,2294,,,,,
|
||||
38628,2700,4351,4046,3456,1331,,,5490.0,,,,2833.0,5236,7272,,,,,
|
||||
79700,2700,4351,10207,21944,1331,,,5490.0,,,,9818.0,187,7272,,,,,
|
||||
143477,2700,1042,4122,20791,1331,,,5490.0,,,,1735.0,10444,3856,,,,,
|
||||
92361,2700,4351,12590,4092,1331,,,5490.0,,,,11086.0,10663,9470,,,,,
|
||||
14997,2700,4351,11697,12815,1331,,,5490.0,,,,7172.0,1272,2294,,,,,
|
||||
63342,2700,4351,1496,21359,1331,,,5490.0,,,,472.0,2498,9470,,,,,
|
||||
120528,2700,4351,3281,19634,1331,,,5490.0,,,,9852.0,2498,9470,,,,,
|
||||
79324,8152,6810,900,10177,1331,,,5490.0,,,,6652.0,5996,11201,,,,,
|
||||
136801,2307,2734,12004,5958,1331,,,5490.0,,,,282.0,11653,5056,,,,,
|
||||
73243,116,4351,11283,14033,1331,,,5490.0,,,,1557.0,8883,1419,,,,,
|
||||
138438,2307,2734,1923,18654,1331,,,5490.0,,,,7800.0,10721,10970,,,,,
|
||||
66709,2307,2734,5753,4626,1331,,,5490.0,,,,11576.0,11653,5056,,,,,
|
||||
62643,8152,4351,4122,20791,1331,,,5490.0,,,,8213.0,3354,3113,,,,,
|
||||
146223,8152,4351,6549,14351,1331,,,5490.0,,,,8146.0,3354,3113,,,,,
|
||||
79298,2307,2734,1923,18654,1331,,,5490.0,,,,7800.0,1045,5056,,,,,
|
||||
79119,8152,10002,1986,23382,1331,,,5490.0,,,,12798.0,9301,4502,,,,,
|
||||
60837,8152,10181,924,21423,1331,,,5490.0,,,,1106.0,5222,4027,,,,,
|
||||
91118,116,4351,9499,19378,1331,,,5490.0,,,,,7583,7509,,,,,
|
||||
82859,8152,3080,5506,13264,1331,,,5490.0,,,,11994.0,769,9646,,,,,
|
||||
17002,2307,2734,2555,12270,1331,,,5490.0,,,,8508.0,1045,5056,,,,,
|
||||
139557,8152,3228,5006,4393,1331,,,5490.0,,,,667.0,6818,10843,,,,,
|
||||
82902,8152,255,10201,21330,1331,,,5490.0,,,,,10678,7672,,,,,
|
||||
72158,8152,10181,1923,18654,1331,,,5490.0,,,,4652.0,5222,4027,,,,,
|
||||
39205,8152,10181,6549,14351,1331,,,5490.0,,,,8146.0,5222,4027,,,,,
|
||||
108802,8152,4351,7408,12272,1331,,,5490.0,,,,10591.0,3354,3113,,,,,
|
||||
44813,8152,7973,9063,10243,1331,,,5490.0,,,,9785.0,11760,10316,,,,,
|
||||
146404,8152,10181,7502,17073,1331,,,5490.0,,,,2532.0,10609,4027,,,,,
|
||||
138344,8152,10181,8523,6872,1331,,,5490.0,,,,7972.0,5222,4027,,,,,
|
||||
93483,8152,2123,715,1969,1331,,,5490.0,,,,5980.0,4701,11099,,,,,
|
||||
60977,2307,2734,12004,5958,1331,,,5490.0,,,,282.0,8450,11159,,,,,
|
||||
128515,8152,10181,8987,8977,1331,,,5490.0,,,,13574.0,5222,4027,,,,,
|
||||
9145,8152,5091,1276,22405,1331,,,5490.0,,,,10326.0,1476,1616,,,,,
|
||||
46575,8152,1296,10201,21330,1331,,,5490.0,,,,,8029,266,,,,,
|
||||
146423,8152,4351,5506,13264,1331,,,5490.0,,,,11994.0,3354,3113,,,,,
|
||||
64620,8152,10181,7408,12272,1331,,,5490.0,,,,10591.0,5222,4027,,,,,
|
||||
51828,550,10802,5455,5898,1331,,,5490.0,,,,13379.0,2737,1719,,,,,
|
||||
56304,10728,566,1893,1726,1331,,,5490.0,,,,4674.0,496,7067,,,,,
|
||||
108541,10728,4351,2393,7359,1331,,,5490.0,,,,2765.0,5566,6758,,,,,
|
||||
49558,2750,2734,4217,20407,1331,,,5490.0,,,,2827.0,7698,1035,,,,,
|
||||
85216,10133,4233,12824,4546,1331,,,5490.0,,,,6321.0,3016,6002,,,,,
|
||||
91572,5111,4493,3763,10600,1331,,,5490.0,,,,4480.0,6075,3507,,,,,
|
||||
73956,10728,1435,6300,5515,1331,,,5490.0,,,,916.0,7993,7839,,,,,
|
||||
48984,10275,3637,10121,19454,1331,,,5490.0,,,,8210.0,11463,7184,,,,,
|
||||
142573,10728,566,219,23469,1331,,,5490.0,,,,11746.0,496,7067,,,,,
|
||||
64314,10728,955,9090,13934,1331,,,5490.0,,,,1623.0,9552,3593,,,,,
|
||||
29365,550,1004,11019,22714,1331,,,5490.0,,,,1069.0,10961,10789,,,,,
|
||||
49651,10728,5987,12068,15316,1331,,,5490.0,,,,11605.0,342,192,,,,,
|
||||
29895,10728,1004,219,23469,1331,,,5490.0,,,,11746.0,10961,10789,,,,,
|
||||
129088,10133,7565,3166,23012,1331,,,5490.0,,,,3341.0,776,3975,,,,,
|
||||
133995,10728,1941,11365,1678,1331,,,5490.0,,,,12444.0,10167,10122,,,,,
|
||||
72054,10728,7583,566,5478,1331,,,5490.0,,,,3541.0,4980,3722,,,,,
|
||||
118335,5111,5987,2894,21368,1331,,,5490.0,,,,13287.0,4102,192,,,,,
|
||||
11262,2750,2734,7502,17073,1331,,,5490.0,,,,10124.0,2539,1131,,,,,
|
||||
36486,550,10802,4217,20407,1331,,,5490.0,,,,3988.0,2737,1719,,,,,
|
||||
47382,10728,4351,9090,13934,1331,,,5490.0,,,,6252.0,8212,637,,,,,
|
||||
84012,905,4351,8139,3884,1331,,,5490.0,,,,10668.0,7216,5174,,,,,
|
||||
87336,550,6923,4149,20375,1331,,,5490.0,,,,3288.0,12145,5318,,,,,
|
||||
17728,2750,6810,6337,1859,1331,,,5490.0,,,,2816.0,12107,8083,,,,,
|
||||
40296,10133,3962,4401,13613,1331,,,5490.0,,,,452.0,9648,8086,,,,,
|
||||
73394,10728,8841,3781,594,1331,,,5490.0,,,,7968.0,993,4939,,,,,
|
||||
118410,10728,1004,1893,1726,1331,,,5490.0,,,,4674.0,6220,7308,,,,,
|
||||
16806,905,4448,9667,19409,1331,,,5490.0,,,,11331.0,8695,7688,,,,,
|
||||
66479,10133,3962,6053,20022,1331,,,5490.0,,,,,12005,4247,,,,,
|
||||
90328,10728,4448,2393,7359,1331,,,5490.0,,,,2765.0,8695,7688,,,,,
|
||||
46784,10133,3080,7412,12094,1331,,,5490.0,,,,183.0,4263,2367,,,,,
|
||||
82216,10728,562,219,23469,1331,,,5490.0,,,,11746.0,1203,6643,,,,,
|
||||
412,10728,566,12068,15316,1331,,,5490.0,,,,11605.0,496,7067,,,,,
|
||||
5045,10728,3087,3398,8402,1331,,,5490.0,,,,3757.0,9062,3458,,,,,
|
||||
22956,550,2734,9500,16635,1331,,,5490.0,,,,2304.0,5574,169,,,,,
|
||||
142801,2750,6810,12753,13301,1331,,,5490.0,,,,1862.0,10079,4473,,,,,
|
||||
151340,10728,7665,6300,5515,1331,,,5490.0,,,,916.0,11748,4356,,,,,
|
||||
67296,10728,7583,696,9377,1331,,,5490.0,,,,6107.0,6030,5989,,,,,
|
||||
41923,10728,8600,6300,5515,1331,,,5490.0,,,,916.0,8747,7989,,,,,
|
||||
117410,10728,8127,11242,9728,1331,,,5490.0,,,,11470.0,9838,570,,,,,
|
||||
139897,10728,4448,924,21423,1331,,,5490.0,,,,10542.0,12228,8718,,,,,
|
||||
6300,2750,2734,8041,6798,1331,,,5490.0,,,,711.0,7698,1035,,,,,
|
||||
71404,10728,4351,3781,594,1331,,,5490.0,,,,7968.0,4928,8818,,,,,
|
||||
77825,905,1004,5863,1194,1331,,,5490.0,,,,6118.0,6220,7308,,,,,
|
||||
94778,10728,4448,10522,4738,1331,,,5490.0,,,,9127.0,8695,7688,,,,,
|
||||
91779,10728,566,9293,1136,1331,,,5490.0,,,,13617.0,496,7067,,,,,
|
||||
40277,550,6648,11019,22714,1331,,,5490.0,,,,1069.0,5281,1610,,,,,
|
||||
22792,10728,1941,1876,19568,1331,,,5490.0,,,,3734.0,10167,10122,,,,,
|
||||
136217,10728,7565,2393,7359,1331,,,5490.0,,,,2765.0,293,5494,,,,,
|
||||
125663,10728,7230,11242,9728,1331,,,5490.0,,,,11470.0,11277,10988,,,,,
|
||||
66229,10133,1004,3166,23012,1331,,,5490.0,,,,3341.0,10961,10789,,,,,
|
||||
9095,905,6805,7302,15180,1331,,,5490.0,,,,8712.0,9060,7178,,,,,
|
||||
73836,10728,11044,3731,16960,1331,,,5490.0,,,,9412.0,3423,10109,,,,,
|
||||
116018,10728,7230,10095,3395,1331,,,5490.0,,,,3630.0,11277,10988,,,,,
|
||||
121817,10728,4448,1893,1726,1331,,,5490.0,,,,4674.0,8695,7688,,,,,
|
||||
63363,550,2734,10891,5037,1331,,,5490.0,,,,13689.0,84,6098,,,,,
|
||||
136254,10728,5205,3731,16960,1331,,,5490.0,,,,9412.0,6478,844,,,,,
|
||||
148707,10728,1435,11806,11363,1331,,,5490.0,,,,3869.0,7993,7839,,,,,
|
||||
127694,10728,5987,1893,1726,1331,,,5490.0,,,,4674.0,11454,8747,,,,,
|
||||
78192,10728,566,12498,1026,1331,,,5490.0,,,,6554.0,496,7067,,,,,
|
||||
52646,550,7709,4631,16086,1331,,,5490.0,,,,2249.0,3948,2570,,,,,
|
||||
129120,550,1004,4149,20375,1331,,,5490.0,,,,3288.0,10961,10789,,,,,
|
||||
118757,10728,2123,4717,2168,1331,,,5490.0,,,,1820.0,8751,11099,,,,,
|
||||
33593,10728,1004,424,1544,1331,,,5490.0,,,,8084.0,6220,7308,,,,,
|
||||
135746,10728,4351,2393,7359,1331,,,5490.0,,,,2765.0,12161,4725,,,,,
|
||||
8324,10728,4351,4419,19766,1331,,,5490.0,,,,10053.0,8212,637,,,,,
|
||||
44090,550,4448,3893,7064,1331,,,5490.0,,,,4949.0,12228,8718,,,,,
|
||||
1094,10728,1004,2393,7359,1331,,,5490.0,,,,2765.0,6220,7308,,,,,
|
||||
79187,10699,3080,9293,1136,1331,,,5490.0,,,,7844.0,8879,11361,,,,,
|
||||
99692,10133,225,7412,12094,1331,,,5490.0,,,,183.0,9529,6714,,,,,
|
||||
7697,10133,2870,3166,23012,1331,,,5490.0,,,,3341.0,4289,4768,,,,,
|
||||
5405,10728,4351,11242,9728,1331,,,5490.0,,,,11470.0,1759,10739,,,,,
|
||||
152451,10728,2142,9293,1136,1331,,,5490.0,,,,13617.0,9372,9736,,,,,
|
||||
135625,10728,7565,424,1544,1331,,,5490.0,,,,8084.0,293,5494,,,,,
|
||||
19823,550,5193,2070,12178,1331,,,5490.0,,,,12437.0,10497,7014,,,,,
|
||||
62394,5111,1296,7887,21946,1331,,,5490.0,,,,4969.0,8029,266,,,,,
|
||||
81412,10728,6597,6300,5515,1331,,,5490.0,,,,916.0,3346,10111,,,,,
|
||||
101501,550,1004,3893,7064,1331,,,5490.0,,,,4949.0,6220,7308,,,,,
|
||||
128554,905,4448,5863,1194,1331,,,5490.0,,,,6118.0,8695,7688,,,,,
|
||||
27047,550,2734,9990,10656,1331,,,5490.0,,,,12360.0,10721,10970,,,,,
|
||||
112501,550,10802,8847,13219,1331,,,5490.0,,,,1657.0,8133,1719,,,,,
|
||||
100620,550,5987,9990,10656,1331,,,5490.0,,,,12360.0,8239,8155,,,,,
|
||||
137771,905,4351,11886,22117,1331,,,5490.0,,,,1362.0,5029,1419,,,,,
|
||||
99485,905,7565,5863,1194,1331,,,5490.0,,,,6118.0,293,5494,,,,,
|
||||
86584,10728,7565,3072,22322,1331,,,5490.0,,,,11466.0,293,5494,,,,,
|
||||
95473,10728,566,11365,1678,1331,,,5490.0,,,,12444.0,496,7067,,,,,
|
||||
19487,550,1941,7932,18329,1331,,,5490.0,,,,11598.0,10167,10122,,,,,
|
||||
35822,550,4351,12054,18743,1331,,,5490.0,,,,3604.0,463,7738,,,,,
|
||||
8875,10728,1004,10522,4738,1331,,,5490.0,,,,9127.0,6220,7308,,,,,
|
||||
45253,10728,8841,10095,3395,1331,,,5490.0,,,,3630.0,993,4939,,,,,
|
||||
105563,550,6847,4769,1790,1331,,,5490.0,,,,2074.0,5605,8226,,,,,
|
||||
44031,550,7565,4149,20375,1331,,,5490.0,,,,3288.0,293,5494,,,,,
|
||||
137146,10728,9284,3072,22322,1331,,,5490.0,,,,11466.0,2691,5807,,,,,
|
||||
64163,5111,3160,7887,21946,1331,,,5490.0,,,,4969.0,468,9518,,,,,
|
||||
22025,905,4351,5863,1194,1331,,,5490.0,,,,6118.0,8607,3368,,,,,
|
||||
10329,550,7565,11019,22714,1331,,,5490.0,,,,1069.0,6921,6205,,,,,
|
||||
63434,2750,6810,4667,15577,1331,,,5490.0,,,,10048.0,12107,8083,,,,,
|
||||
114290,10728,4351,2393,7359,1331,,,5490.0,,,,2765.0,8607,3368,,,,,
|
||||
129485,10728,4351,2393,7359,1331,,,5490.0,,,,2765.0,2443,7969,,,,,
|
||||
22412,10728,4351,6300,5515,1331,,,5490.0,,,,916.0,4928,8818,,,,,
|
||||
53813,10728,3575,12068,15316,1331,,,5490.0,,,,11605.0,5361,4813,,,,,
|
||||
133268,10699,2734,11063,13550,1331,,,5490.0,,,,537.0,6391,7789,,,,,
|
||||
70729,10728,4448,424,1544,1331,,,5490.0,,,,8084.0,8695,7688,,,,,
|
||||
37223,10728,566,6300,5515,1331,,,5490.0,,,,916.0,496,7067,,,,,
|
||||
16349,10728,7565,10522,4738,1331,,,5490.0,,,,9127.0,293,5494,,,,,
|
||||
8537,10699,4493,2622,15821,1331,,,5490.0,,,,10456.0,6634,2711,,,,,
|
||||
109704,10728,4351,8923,17405,1331,,,5490.0,,,,13047.0,5695,3655,,,,,
|
||||
55354,10728,1941,11242,9728,1331,,,5490.0,,,,11470.0,10167,10122,,,,,
|
||||
67069,10728,7565,1893,1726,1331,,,5490.0,,,,4674.0,293,5494,,,,,
|
||||
100888,2750,4277,4217,20407,1331,,,5490.0,,,,2827.0,1092,10993,,,,,
|
||||
100249,10728,5987,6300,5515,1331,,,5490.0,,,,916.0,11454,8747,,,,,
|
||||
2822,10728,1004,3072,22322,1331,,,5490.0,,,,11466.0,6220,7308,,,,,
|
||||
56020,905,7565,9667,19409,1331,,,5490.0,,,,11331.0,293,5494,,,,,
|
||||
16263,10728,7565,219,23469,1331,,,5490.0,,,,11746.0,293,5494,,,,,
|
||||
12783,10699,4351,11563,8635,1331,,,5490.0,,,,1997.0,7144,8490,,,,,
|
||||
99876,550,7565,3893,7064,1331,,,5490.0,,,,4949.0,293,5494,,,,,
|
||||
48203,905,1004,9667,19409,1331,,,5490.0,,,,11331.0,6220,7308,,,,,
|
||||
49641,10133,3962,10655,9580,1331,,,5490.0,,,,6815.0,12005,4247,,,,,
|
||||
129655,10728,6910,6300,5515,1331,,,5490.0,,,,916.0,11748,4356,,,,,
|
||||
128043,2750,4277,8041,6798,1331,,,5490.0,,,,711.0,1092,10993,,,,,
|
||||
64265,10728,2142,6300,5515,1331,,,5490.0,,,,916.0,9372,9736,,,,,
|
||||
|
|
|
@ -0,0 +1,24 @@
|
|||
SELECT
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_id' AND CAST(values AS STRING)=CAST(observation.observation_id AS STRING ) ) as observation_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'person_id' AND CAST(values AS STRING)=CAST(observation.person_id AS STRING ) ) as person_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_concept_id' AND CAST(values AS STRING)=CAST(observation.observation_concept_id AS STRING ) ) as observation_concept_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_date' AND CAST(values AS STRING)=CAST(observation.observation_date AS STRING ) ) as observation_date,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_datetime' AND CAST(values AS STRING)=CAST(observation.observation_datetime AS STRING ) ) as observation_datetime,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_type_concept_id' AND CAST(values AS STRING)=CAST(observation.observation_type_concept_id AS STRING ) ) as observation_type_concept_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'value_as_number' AND CAST(values AS STRING)=CAST(observation.value_as_number AS STRING ) ) as value_as_number,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'value_as_string' AND CAST(values AS STRING)=CAST(observation.value_as_string AS STRING ) ) as value_as_string,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'value_as_concept_id' AND CAST(values AS STRING)=CAST(observation.value_as_concept_id AS STRING ) ) as value_as_concept_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'qualifier_concept_id' AND CAST(values AS STRING)=CAST(observation.qualifier_concept_id AS STRING ) ) as qualifier_concept_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'unit_concept_id' AND CAST(values AS STRING)=CAST(observation.unit_concept_id AS STRING ) ) as unit_concept_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'provider_id' AND CAST(values AS STRING)=CAST(observation.provider_id AS STRING ) ) as provider_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'visit_occurrence_id' AND CAST(values AS STRING)=CAST(observation.visit_occurrence_id AS STRING ) ) as visit_occurrence_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_source_value' AND CAST(values AS STRING)=CAST(observation.observation_source_value AS STRING ) ) as observation_source_value,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_source_concept_id' AND CAST(values AS STRING)=CAST(observation.observation_source_concept_id AS STRING ) ) as observation_source_concept_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'unit_source_value' AND CAST(values AS STRING)=CAST(observation.unit_source_value AS STRING ) ) as unit_source_value,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'qualifier_source_value' AND CAST(values AS STRING)=CAST(observation.qualifier_source_value AS STRING ) ) as qualifier_source_value,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'value_source_concept_id' AND CAST(values AS STRING)=CAST(observation.value_source_concept_id AS STRING ) ) as value_source_concept_id,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'value_source_value' AND CAST(values AS STRING)=CAST(observation.value_source_value AS STRING ) ) as value_source_value,
|
||||
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'questionnaire_response_id' AND CAST(values AS STRING)=CAST(observation.questionnaire_response_id AS STRING ) ) as questionnaire_response_id
|
||||
FROM wgan_original.observation
|
||||
WHERE
|
||||
REGEXP_CONTAINS(UPPER(observation_source_value),'ICD')
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
id,first_name,last_name,age,gender
|
||||
100,steve,nyemba,40,m
|
||||
101,elon,nyemba,5,m
|
||||
200,steve,mqueen,80,m
|
||||
201,james,dean,80,m
|
||||
300,james,bond,50,m
|
||||
400,elon,musk,40,m
|
||||
401,kevin,james,50,m
|
||||
303,kevin,johnson,40,m
|
||||
103,Bari,nyemba,5,f
|
||||
|
|
|
@ -0,0 +1,546 @@
|
|||
"""
|
||||
usage :
|
||||
optional :
|
||||
--num_gpu number of gpus to use will default to 1
|
||||
--epoch steps per epoch default to 256
|
||||
"""
|
||||
import tensorflow as tf
|
||||
from tensorflow.contrib.layers import l2_regularizer
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import time
|
||||
import os
|
||||
import sys
|
||||
from params import SYS_ARGS
|
||||
from bridge import Binary
|
||||
import json
|
||||
|
||||
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
|
||||
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
|
||||
|
||||
# STEPS_PER_EPOCH = int(SYS_ARGS['epoch']) if 'epoch' in SYS_ARGS else 256
|
||||
# NUM_GPUS = 1 if 'num_gpu' not in SYS_ARGS else int(SYS_ARGS['num_gpu'])
|
||||
# BATCHSIZE_PER_GPU = 2000
|
||||
# TOTAL_BATCHSIZE = BATCHSIZE_PER_GPU * NUM_GPUS
|
||||
|
||||
class void :
|
||||
pass
|
||||
class GNet :
|
||||
"""
|
||||
This is the base class of a generative network functions, the details will be implemented in the subclasses.
|
||||
An instance of this class is accessed as follows
|
||||
object.layers.normalize applies batch normalization or otherwise
|
||||
obect.get.variables instanciate variables on cpu and return a reference (tensor)
|
||||
"""
|
||||
def __init__(self,**args):
|
||||
self.layers = void()
|
||||
self.layers.normalize = self.normalize
|
||||
|
||||
self.get = void()
|
||||
self.get.variables = self._variable_on_cpu
|
||||
|
||||
self.NUM_GPUS = 1
|
||||
|
||||
|
||||
self.X_SPACE_SIZE = args['real'].shape[1] if 'real' in args else 854
|
||||
self.G_STRUCTURE = [128,128] #[self.X_SPACE_SIZE, self.X_SPACE_SIZE]
|
||||
self.D_STRUCTURE = [self.X_SPACE_SIZE,256,128] #[self.X_SPACE_SIZE, self.X_SPACE_SIZE*2, self.X_SPACE_SIZE] #-- change 854 to number of diagnosis
|
||||
# self.NUM_LABELS = 8 if 'label' not in args elif len(args['label'].shape) args['label'].shape[1]
|
||||
if 'label' in args and len(args['label'].shape) == 2 :
|
||||
self.NUM_LABELS = args['label'].shape[1]
|
||||
elif 'label' in args and len(args['label']) == 1 :
|
||||
self.NUM_LABELS = args['label'].shape[0]
|
||||
else:
|
||||
self.NUM_LABELS = 8
|
||||
self.Z_DIM = 128 #self.X_SPACE_SIZE
|
||||
self.BATCHSIZE_PER_GPU = args['real'].shape[0] if 'real' in args else 256
|
||||
self.TOTAL_BATCHSIZE = self.BATCHSIZE_PER_GPU * self.NUM_GPUS
|
||||
self.STEPS_PER_EPOCH = 256 #int(np.load('ICD9/train.npy').shape[0] / 2000)
|
||||
self.MAX_EPOCHS = 10 if 'max_epochs' not in args else int(args['max_epochs'])
|
||||
self.ROW_COUNT = args['real'].shape[0] if 'real' in args else 100
|
||||
self.CONTEXT = args['context']
|
||||
self.ATTRIBUTES = {"id":args['column_id'] if 'column_id' in args else None,"synthetic":args['column'] if 'column' in args else None}
|
||||
self._REAL = args['real'] if 'real' in args else None
|
||||
self._LABEL = args['label'] if 'label' in args else None
|
||||
|
||||
self.init_logs(**args)
|
||||
|
||||
def init_logs(self,**args):
|
||||
self.log_dir = args['logs'] if 'logs' in args else 'logs'
|
||||
self.mkdir(self.log_dir)
|
||||
#
|
||||
#
|
||||
for key in ['train','output'] :
|
||||
self.mkdir(os.sep.join([self.log_dir,key]))
|
||||
self.mkdir (os.sep.join([self.log_dir,key,self.CONTEXT]))
|
||||
|
||||
self.train_dir = os.sep.join([self.log_dir,'train',self.CONTEXT])
|
||||
self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
|
||||
|
||||
def load_meta(self,column):
|
||||
"""
|
||||
This function is designed to accomodate the uses of the sub-classes outside of a strict dependency model.
|
||||
Because prediction and training can happen independently
|
||||
"""
|
||||
_name = os.sep.join([self.out_dir,'meta-'+column+'.json'])
|
||||
if os.path.exists(_name) :
|
||||
attr = json.loads((open(_name)).read())
|
||||
for key in attr :
|
||||
value = attr[key]
|
||||
setattr(self,key,value)
|
||||
self.train_dir = os.sep.join([self.log_dir,'train',self.CONTEXT])
|
||||
self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
|
||||
|
||||
|
||||
def log_meta(self,**args) :
|
||||
object = {
|
||||
'CONTEXT':self.CONTEXT,
|
||||
'ATTRIBUTES':self.ATTRIBUTES,
|
||||
'BATCHSIZE_PER_GPU':self.BATCHSIZE_PER_GPU,
|
||||
'Z_DIM':self.Z_DIM,
|
||||
"X_SPACE_SIZE":self.X_SPACE_SIZE,
|
||||
"D_STRUCTURE":self.D_STRUCTURE,
|
||||
"G_STRUCTURE":self.G_STRUCTURE,
|
||||
"NUM_GPUS":self.NUM_GPUS,
|
||||
"NUM_LABELS":self.NUM_LABELS,
|
||||
"MAX_EPOCHS":self.MAX_EPOCHS,
|
||||
"ROW_COUNT":self.ROW_COUNT
|
||||
}
|
||||
if args and 'key' in args and 'value' in args :
|
||||
key = args['key']
|
||||
value= args['value']
|
||||
object[key] = value
|
||||
_name = os.sep.join([self.out_dir,'meta-'+SYS_ARGS['column']])
|
||||
f = open(_name+'.json','w')
|
||||
f.write(json.dumps(object))
|
||||
def mkdir (self,path):
|
||||
if not os.path.exists(path) :
|
||||
os.mkdir(path)
|
||||
|
||||
|
||||
def normalize(self,**args):
|
||||
"""
|
||||
This function will perform a batch normalization on an network layer
|
||||
inputs input layer of the neural network
|
||||
name name of the scope the
|
||||
labels labels (attributes not synthesized) by default None
|
||||
n_labels number of labels default None
|
||||
"""
|
||||
inputs = args['inputs']
|
||||
name = args['name']
|
||||
labels = None if 'labels' not in args else args['labels']
|
||||
n_labels= None if 'n_labels' not in args else args['n_labels']
|
||||
shift = [0] if self.__class__.__name__.lower() == 'generator' else [1] #-- not sure what this is doing
|
||||
mean, var = tf.nn.moments(inputs, shift, keep_dims=True)
|
||||
shape = inputs.shape[1].value
|
||||
offset_m = self.get.variables(shape=[n_labels,shape], name='offset'+name,
|
||||
initializer=tf.zeros_initializer)
|
||||
scale_m = self.get.variables(shape=[n_labels,shape], name='scale'+name,
|
||||
initializer=tf.ones_initializer)
|
||||
|
||||
offset = tf.nn.embedding_lookup(offset_m, labels)
|
||||
scale = tf.nn.embedding_lookup(scale_m, labels)
|
||||
result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-8)
|
||||
return result
|
||||
|
||||
def _variable_on_cpu(self,**args):
|
||||
"""
|
||||
This function makes sure variables/tensors are not created on the GPU but rather on the CPU
|
||||
"""
|
||||
|
||||
name = args['name']
|
||||
shape = args['shape']
|
||||
initializer=None if 'initializer' not in args else args['initializer']
|
||||
with tf.device('/cpu:0') :
|
||||
cpu_var = tf.compat.v1.get_variable(name,shape,initializer= initializer)
|
||||
return cpu_var
|
||||
def average_gradients(self,tower_grads):
|
||||
average_grads = []
|
||||
for grad_and_vars in zip(*tower_grads):
|
||||
grads = []
|
||||
for g, _ in grad_and_vars:
|
||||
expanded_g = tf.expand_dims(g, 0)
|
||||
grads.append(expanded_g)
|
||||
|
||||
grad = tf.concat(axis=0, values=grads)
|
||||
grad = tf.reduce_mean(grad, 0)
|
||||
|
||||
v = grad_and_vars[0][1]
|
||||
grad_and_var = (grad, v)
|
||||
average_grads.append(grad_and_var)
|
||||
return average_grads
|
||||
|
||||
|
||||
class Generator (GNet):
|
||||
"""
|
||||
This class is designed to handle generation of candidate datasets for this it will aggregate a discriminator, this allows the generator not to be random
|
||||
|
||||
"""
|
||||
def __init__(self,**args):
|
||||
GNet.__init__(self,**args)
|
||||
self.discriminator = Discriminator(**args)
|
||||
def loss(self,**args):
|
||||
fake = args['fake']
|
||||
label = args['label']
|
||||
y_hat_fake = self.discriminator.network(inputs=fake, label=label)
|
||||
all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
|
||||
loss = -tf.reduce_mean(y_hat_fake) + sum(all_regs)
|
||||
tf.add_to_collection('glosses', loss)
|
||||
return loss, loss
|
||||
def load_meta(self, column):
|
||||
super().load_meta(column)
|
||||
self.discriminator.load_meta(column)
|
||||
def network(self,**args) :
|
||||
"""
|
||||
This function will build the network that will generate the synthetic candidates
|
||||
:inputs matrix of data that we need
|
||||
:dim dimensions of ...
|
||||
"""
|
||||
x = args['inputs']
|
||||
tmp_dim = self.Z_DIM if 'dim' not in args else args['dim']
|
||||
label = args['label']
|
||||
|
||||
with tf.compat.v1.variable_scope('G', reuse=tf.compat.v1.AUTO_REUSE , regularizer=l2_regularizer(0.00001)):
|
||||
for i, dim in enumerate(self.G_STRUCTURE[:-1]):
|
||||
kernel = self.get.variables(name='W_' + str(i), shape=[tmp_dim, dim])
|
||||
h1 = self.normalize(inputs=tf.matmul(x, kernel),shift=0, name='cbn' + str(i), labels=label, n_labels=self.NUM_LABELS)
|
||||
h2 = tf.nn.relu(h1)
|
||||
x = x + h2
|
||||
tmp_dim = dim
|
||||
i = len(self.G_STRUCTURE) - 1
|
||||
#
|
||||
# This seems to be an extra hidden layer:
|
||||
# It's goal is to map continuous values to discrete values (pre-trained to do this)
|
||||
kernel = self.get.variables(name='W_' + str(i), shape=[tmp_dim, self.G_STRUCTURE[-1]])
|
||||
h1 = self.normalize(inputs=tf.matmul(x, kernel), name='cbn' + str(i),
|
||||
labels=label, n_labels=self.NUM_LABELS)
|
||||
h2 = tf.nn.tanh(h1)
|
||||
x = x + h2
|
||||
# This seems to be the output layer
|
||||
#
|
||||
kernel = self.get.variables(name='W_' + str(i+1), shape=[self.Z_DIM, self.X_SPACE_SIZE])
|
||||
bias = self.get.variables(name='b_' + str(i+1), shape=[self.X_SPACE_SIZE])
|
||||
x = tf.nn.sigmoid(tf.add(tf.matmul(x, kernel), bias))
|
||||
return x
|
||||
|
||||
class Discriminator(GNet):
|
||||
def __init__(self,**args):
|
||||
GNet.__init__(self,**args)
|
||||
def network(self,**args):
|
||||
"""
|
||||
This function will apply a computational graph on a dataset passed in with the associated labels and the last layer must have a single output (neuron)
|
||||
:inputs
|
||||
:label
|
||||
"""
|
||||
x = args['inputs']
|
||||
print ()
|
||||
print (x[:3,:])
|
||||
print()
|
||||
label = args['label']
|
||||
with tf.compat.v1.variable_scope('D', reuse=tf.compat.v1.AUTO_REUSE , regularizer=l2_regularizer(0.00001)):
|
||||
for i, dim in enumerate(self.D_STRUCTURE[1:]):
|
||||
kernel = self.get.variables(name='W_' + str(i), shape=[self.D_STRUCTURE[i], dim])
|
||||
bias = self.get.variables(name='b_' + str(i), shape=[dim])
|
||||
print (["\t",bias,kernel])
|
||||
x = tf.nn.relu(tf.add(tf.matmul(x, kernel), bias))
|
||||
x = self.normalize(inputs=x, name='cln' + str(i), shift=1,labels=label, n_labels=self.NUM_LABELS)
|
||||
i = len(self.D_STRUCTURE)
|
||||
kernel = self.get.variables(name='W_' + str(i), shape=[self.D_STRUCTURE[-1], 1])
|
||||
bias = self.get.variables(name='b_' + str(i), shape=[1])
|
||||
y = tf.add(tf.matmul(x, kernel), bias)
|
||||
return y
|
||||
|
||||
def loss(self,**args) :
|
||||
"""
|
||||
This function compute the loss of
|
||||
:real
|
||||
:fake
|
||||
:label
|
||||
"""
|
||||
real = args['real']
|
||||
fake = args['fake']
|
||||
label = args['label']
|
||||
epsilon = tf.random.uniform(shape=[self.BATCHSIZE_PER_GPU,1],minval=0,maxval=1)
|
||||
|
||||
x_hat = real + epsilon * (fake - real)
|
||||
y_hat_fake = self.network(inputs=fake, label=label)
|
||||
|
||||
y_hat_real = self.network(inputs=real, label=label)
|
||||
y_hat = self.network(inputs=x_hat, label=label)
|
||||
|
||||
grad = tf.gradients(y_hat, [x_hat])[0]
|
||||
slopes = tf.sqrt(tf.reduce_sum(tf.square(grad), 1))
|
||||
gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
|
||||
all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
|
||||
w_distance = -tf.reduce_mean(y_hat_real) + tf.reduce_mean(y_hat_fake)
|
||||
loss = w_distance + 10 * gradient_penalty + sum(all_regs)
|
||||
tf.add_to_collection('dlosses', loss)
|
||||
|
||||
return w_distance, loss
|
||||
class Train (GNet):
|
||||
def __init__(self,**args):
|
||||
GNet.__init__(self,**args)
|
||||
self.generator = Generator(**args)
|
||||
self.discriminator = Discriminator(**args)
|
||||
self._REAL = args['real']
|
||||
self._LABEL= args['label']
|
||||
# print ([" *** ",self.BATCHSIZE_PER_GPU])
|
||||
self.log_meta()
|
||||
def load_meta(self, column):
|
||||
"""
|
||||
This function will delegate the calls to load meta data to it's dependents
|
||||
column name
|
||||
"""
|
||||
super().load_meta(column)
|
||||
self.generator.load_meta(column)
|
||||
self.discriminator.load_meta(column)
|
||||
def loss(self,**args):
|
||||
"""
|
||||
This function will compute a "tower" loss of the generated candidate against real data
|
||||
Training will consist in having both generator and discriminators
|
||||
:scope
|
||||
:stage
|
||||
:real
|
||||
:label
|
||||
"""
|
||||
|
||||
scope = args['scope']
|
||||
stage = args['stage']
|
||||
real = args['real']
|
||||
label = args['label']
|
||||
label = tf.cast(label, tf.int32)
|
||||
#
|
||||
# @TODO: Ziqi needs to explain what's going on here
|
||||
m = [[i] for i in np.arange(self._LABEL.shape[1]-2)]
|
||||
label = label[:, 1] * len(m) + tf.squeeze(
|
||||
tf.matmul(label[:, 2:], tf.constant(m, dtype=tf.int32))
|
||||
)
|
||||
# label = label[:,1] * 4 + tf.squeeze( label[:,2]*[[0],[1],[2],[3]] )
|
||||
z = tf.random.normal(shape=[self.BATCHSIZE_PER_GPU, self.Z_DIM])
|
||||
|
||||
fake = self.generator.network(inputs=z, label=label)
|
||||
if stage == 'D':
|
||||
w, loss = self.discriminator.loss(real=real, fake=fake, label=label)
|
||||
losses = tf.get_collection('dlosses', scope)
|
||||
else:
|
||||
w, loss = self.generator.loss(fake=fake, label=label)
|
||||
losses = tf.get_collection('glosses', scope)
|
||||
|
||||
total_loss = tf.add_n(losses, name='total_loss')
|
||||
|
||||
return total_loss, w
|
||||
def input_fn(self):
|
||||
"""
|
||||
This function seems to produce
|
||||
"""
|
||||
features_placeholder = tf.compat.v1.placeholder(shape=self._REAL.shape, dtype=tf.float32)
|
||||
labels_placeholder = tf.compat.v1.placeholder(shape=self._LABEL.shape, dtype=tf.float32)
|
||||
dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder))
|
||||
dataset = dataset.repeat(10000)
|
||||
dataset = dataset.batch(batch_size=self.BATCHSIZE_PER_GPU)
|
||||
dataset = dataset.prefetch(1)
|
||||
iterator = dataset.make_initializable_iterator()
|
||||
# next_element = iterator.get_next()
|
||||
# init_op = iterator.initializer
|
||||
return iterator, features_placeholder, labels_placeholder
|
||||
|
||||
def network(self,**args):
|
||||
# def graph(stage, opt):
|
||||
# global_step = tf.get_variable(stage+'_step', [], initializer=tf.constant_initializer(0), trainable=False)
|
||||
stage = args['stage']
|
||||
opt = args['opt']
|
||||
tower_grads = []
|
||||
per_gpu_w = []
|
||||
iterator, features_placeholder, labels_placeholder = self.input_fn()
|
||||
with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope()):
|
||||
for i in range(self.NUM_GPUS):
|
||||
with tf.device('/gpu:%d' % i):
|
||||
with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
|
||||
(real, label) = iterator.get_next()
|
||||
loss, w = self.loss(scope=scope, stage=stage, real=self._REAL, label=self._LABEL)
|
||||
tf.get_variable_scope().reuse_variables()
|
||||
vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
|
||||
grads = opt.compute_gradients(loss, vars_)
|
||||
tower_grads.append(grads)
|
||||
per_gpu_w.append(w)
|
||||
|
||||
grads = self.average_gradients(tower_grads)
|
||||
apply_gradient_op = opt.apply_gradients(grads)
|
||||
|
||||
mean_w = tf.reduce_mean(per_gpu_w)
|
||||
train_op = apply_gradient_op
|
||||
return train_op, mean_w, iterator, features_placeholder, labels_placeholder
|
||||
def apply(self,**args):
|
||||
# max_epochs = args['max_epochs'] if 'max_epochs' in args else 10
|
||||
REAL = self._REAL
|
||||
LABEL= self._LABEL
|
||||
with tf.device('/cpu:0'):
|
||||
opt_d = tf.compat.v1.train.AdamOptimizer(1e-4)
|
||||
opt_g = tf.compat.v1.train.AdamOptimizer(1e-4)
|
||||
|
||||
train_d, w_distance, iterator_d, features_placeholder_d, labels_placeholder_d = self.network(stage='D', opt=opt_d)
|
||||
train_g, _, iterator_g, features_placeholder_g, labels_placeholder_g = self.network(stage='G', opt=opt_g)
|
||||
# saver = tf.train.Saver()
|
||||
saver = tf.compat.v1.train.Saver()
|
||||
init = tf.global_variables_initializer()
|
||||
|
||||
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
|
||||
sess.run(init)
|
||||
sess.run(iterator_d.initializer,
|
||||
feed_dict={features_placeholder_d: REAL, labels_placeholder_d: LABEL})
|
||||
sess.run(iterator_g.initializer,
|
||||
feed_dict={features_placeholder_g: REAL, labels_placeholder_g: LABEL})
|
||||
|
||||
for epoch in range(1, self.MAX_EPOCHS + 1):
|
||||
start_time = time.time()
|
||||
w_sum = 0
|
||||
for i in range(self.STEPS_PER_EPOCH):
|
||||
for _ in range(2):
|
||||
_, w = sess.run([train_d, w_distance])
|
||||
w_sum += w
|
||||
sess.run(train_g)
|
||||
duration = time.time() - start_time
|
||||
|
||||
assert not np.isnan(w_sum), 'Model diverged with loss = NaN'
|
||||
|
||||
format_str = 'epoch: %d, w_distance = %f (%.1f)'
|
||||
print(format_str % (epoch, -w_sum/(self.STEPS_PER_EPOCH*2), duration))
|
||||
if epoch % self.MAX_EPOCHS == 0:
|
||||
|
||||
_name = os.sep.join([self.train_dir,self.ATTRIBUTES['synthetic']])
|
||||
# saver.save(sess, self.train_dir, write_meta_graph=False, global_step=epoch)
|
||||
saver.save(sess, _name, write_meta_graph=False, global_step=epoch)
|
||||
#
|
||||
#
|
||||
|
||||
class Predict(GNet):
|
||||
"""
|
||||
This class uses synthetic data given a learned model
|
||||
"""
|
||||
def __init__(self,**args):
|
||||
GNet.__init__(self,**args)
|
||||
self.generator = Generator(**args)
|
||||
self.values = values
|
||||
def load_meta(self, column):
|
||||
super().load_meta(column)
|
||||
self.generator.load_meta(column)
|
||||
def apply(self,**args):
|
||||
# print (self.train_dir)
|
||||
model_dir = os.sep.join([self.train_dir,self.ATTRIBUTES['synthetic']+'-'+str(self.MAX_EPOCHS)])
|
||||
demo = self._LABEL #np.zeros([self.ROW_COUNT,self.NUM_LABELS]) #args['de"shape":{"LABEL":list(self._LABEL.shape)} mo']
|
||||
tf.compat.v1.reset_default_graph()
|
||||
z = tf.random.normal(shape=[self.BATCHSIZE_PER_GPU, self.Z_DIM])
|
||||
y = tf.compat.v1.placeholder(shape=[self.BATCHSIZE_PER_GPU, self.NUM_LABELS], dtype=tf.int32)
|
||||
ma = [[i] for i in np.arange(self.NUM_LABELS - 2)]
|
||||
label = y[:, 1] * len(ma) + tf.squeeze(tf.matmul(y[:, 2:], tf.constant(ma, dtype=tf.int32)))
|
||||
|
||||
fake = self.generator.network(inputs=z, label=label)
|
||||
init = tf.compat.v1.global_variables_initializer()
|
||||
saver = tf.compat.v1.train.Saver()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
|
||||
# sess.run(init)
|
||||
saver.restore(sess, model_dir)
|
||||
labels = np.zeros((self.ROW_COUNT,self.NUM_LABELS) )
|
||||
|
||||
labels= demo
|
||||
f = sess.run(fake,feed_dict={y:labels})
|
||||
#
|
||||
# if we are dealing with numeric values only we can perform a simple marginal sum against the indexes
|
||||
#
|
||||
|
||||
df = ( pd.DataFrame(np.round(f).astype(np.int32),columns=values))
|
||||
# i = df.T.index.astype(np.int32) #-- These are numeric pseudonyms
|
||||
# df = (i * df).sum(axis=1)
|
||||
#
|
||||
# In case we are dealing with actual values like diagnosis codes we can perform
|
||||
#
|
||||
r = np.zeros((self.ROW_COUNT,1))
|
||||
for col in df :
|
||||
i = np.where(df[col])[0]
|
||||
r[i] = col
|
||||
df = pd.DataFrame(r,columns=[self.ATTRIBUTES['synthetic']])
|
||||
|
||||
return df.to_dict(orient='list')
|
||||
# count = str(len(os.listdir(self.out_dir)))
|
||||
# _name = os.sep.join([self.out_dir,self.CONTEXT+'-'+count+'.csv'])
|
||||
# df.to_csv(_name,index=False)
|
||||
|
||||
|
||||
# output.extend(np.round(f))
|
||||
|
||||
# for m in range(2):
|
||||
# for n in range(2, self.NUM_LABELS):
|
||||
# idx1 = (demo[:, m] == 1)
|
||||
# idx2 = (demo[:, n] == 1)
|
||||
# idx = [idx1[j] and idx2[j] for j in range(len(idx1))]
|
||||
# num = np.sum(idx)
|
||||
# print ("_____________________")
|
||||
# print (idx1)
|
||||
# print (idx2)
|
||||
# print (idx)
|
||||
# print (num)
|
||||
# print ("_____________________")
|
||||
# nbatch = int(np.ceil(num / self.BATCHSIZE_PER_GPU))
|
||||
# label_input = np.zeros((nbatch*self.BATCHSIZE_PER_GPU, self.NUM_LABELS))
|
||||
# label_input[:, n] = 1
|
||||
# label_input[:, m] = 1
|
||||
# output = []
|
||||
# for i in range(nbatch):
|
||||
# f = sess.run(fake,feed_dict={y: label_input[i* self.BATCHSIZE_PER_GPU:(i+1)* self.BATCHSIZE_PER_GPU]})
|
||||
# output.extend(np.round(f))
|
||||
# output = np.array(output)[:num]
|
||||
# print ([m,n,output])
|
||||
|
||||
# np.save(self.out_dir + str(m) + str(n), output)
|
||||
|
||||
|
||||
if __name__ == '__main__' :
|
||||
#
|
||||
# Now we get things done ...
|
||||
column = SYS_ARGS['column']
|
||||
column_id = SYS_ARGS['id'] if 'id' in SYS_ARGS else 'person_id'
|
||||
df = pd.read_csv(SYS_ARGS['raw-data'])
|
||||
LABEL = pd.get_dummies(df[column_id]).astype(np.float32).values
|
||||
|
||||
context = SYS_ARGS['raw-data'].split(os.sep)[-1:][0][:-4]
|
||||
if set(['train','learn']) & set(SYS_ARGS.keys()):
|
||||
|
||||
df = pd.read_csv(SYS_ARGS['raw-data'])
|
||||
|
||||
# cols = SYS_ARGS['column']
|
||||
# _map,_df = (Binary()).Export(df)
|
||||
# i = np.arange(_map[column]['start'],_map[column]['end'])
|
||||
max_epochs = np.int32(SYS_ARGS['max_epochs']) if 'max_epochs' in SYS_ARGS else 10
|
||||
# REAL = _df[:,i]
|
||||
REAL = pd.get_dummies(df[column]).astype(np.float32).values
|
||||
LABEL = pd.get_dummies(df[column_id]).astype(np.float32).values
|
||||
trainer = Train(context=context,max_epochs=max_epochs,real=REAL,label=LABEL,column=column,column_id=column_id)
|
||||
trainer.apply()
|
||||
|
||||
|
||||
|
||||
|
||||
#
|
||||
# We should train upon this data
|
||||
#
|
||||
# -- we need to convert the data-frame to binary matrix, given a column
|
||||
#
|
||||
pass
|
||||
elif 'generate' in SYS_ARGS:
|
||||
values = df[column].unique().tolist()
|
||||
values.sort()
|
||||
p = Predict(context=context,label=LABEL,values=values)
|
||||
p.load_meta(column)
|
||||
r = p.apply()
|
||||
print (df)
|
||||
print ()
|
||||
df[column] = r[column]
|
||||
print (df)
|
||||
|
||||
else:
|
||||
print (SYS_ARGS.keys())
|
||||
print (__doc__)
|
||||
pass
|
||||
|
||||
|
|
@ -0,0 +1,286 @@
|
|||
import tensorflow as tf
|
||||
from tensorflow.contrib.layers import l2_regularizer
|
||||
import numpy as np
|
||||
import time
|
||||
import os
|
||||
|
||||
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
|
||||
|
||||
#### id of gpu to use
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
|
||||
|
||||
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
|
||||
|
||||
#### training data
|
||||
#### shape=(n_sample, n_code=854)
|
||||
REAL = np.load('')
|
||||
|
||||
#### demographic for training data
|
||||
#### shape=(n_sample, 6)
|
||||
#### if sample_x is male, then LABEL[x,0]=1, else LABEL[x,1]=1
|
||||
#### if sample_x's is within 0-17, then LABEL[x,2]=1
|
||||
#### elif sample_x's is within 18-44, then LABEL[x,3]=1
|
||||
#### elif sample_x's is within 45-64, then LABEL[x,4]=1
|
||||
#### elif sample_x's is within 64-, then LABEL[x,5]=1
|
||||
LABEL = np.load('')
|
||||
|
||||
#### training parameters
|
||||
NUM_GPUS = 1
|
||||
BATCHSIZE_PER_GPU = 2000
|
||||
TOTAL_BATCHSIZE = BATCHSIZE_PER_GPU * NUM_GPUS
|
||||
STEPS_PER_EPOCH = int(np.load('ICD9/train.npy').shape[0] / 2000)
|
||||
|
||||
g_structure = [128, 128]
|
||||
d_structure = [854, 256, 128]
|
||||
z_dim = 128
|
||||
|
||||
def _variable_on_cpu(name, shape, initializer=None):
|
||||
with tf.device('/cpu:0'):
|
||||
var = tf.get_variable(name, shape, initializer=initializer)
|
||||
return var
|
||||
|
||||
|
||||
def batchnorm(inputs, name, labels=None, n_labels=None):
|
||||
mean, var = tf.nn.moments(inputs, [0], keep_dims=True)
|
||||
shape = mean.shape[1].value
|
||||
offset_m = _variable_on_cpu(shape=[n_labels,shape], name='offset'+name,
|
||||
initializer=tf.zeros_initializer)
|
||||
scale_m = _variable_on_cpu(shape=[n_labels,shape], name='scale'+name,
|
||||
initializer=tf.ones_initializer)
|
||||
offset = tf.nn.embedding_lookup(offset_m, labels)
|
||||
scale = tf.nn.embedding_lookup(scale_m, labels)
|
||||
result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-8)
|
||||
return result
|
||||
|
||||
|
||||
def layernorm(inputs, name, labels=None, n_labels=None):
|
||||
mean, var = tf.nn.moments(inputs, [1], keep_dims=True)
|
||||
shape = inputs.shape[1].value
|
||||
offset_m = _variable_on_cpu(shape=[n_labels,shape], name='offset'+name,
|
||||
initializer=tf.zeros_initializer)
|
||||
scale_m = _variable_on_cpu(shape=[n_labels,shape], name='scale'+name,
|
||||
initializer=tf.ones_initializer)
|
||||
offset = tf.nn.embedding_lookup(offset_m, labels)
|
||||
scale = tf.nn.embedding_lookup(scale_m, labels)
|
||||
result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-8)
|
||||
return result
|
||||
|
||||
|
||||
def input_fn():
|
||||
features_placeholder = tf.placeholder(shape=REAL.shape, dtype=tf.float32)
|
||||
labels_placeholder = tf.placeholder(shape=LABEL.shape, dtype=tf.float32)
|
||||
dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder))
|
||||
dataset = dataset.repeat(10000)
|
||||
dataset = dataset.batch(batch_size=BATCHSIZE_PER_GPU)
|
||||
dataset = dataset.prefetch(1)
|
||||
iterator = dataset.make_initializable_iterator()
|
||||
# next_element = iterator.get_next()
|
||||
# init_op = iterator.initializer
|
||||
return iterator, features_placeholder, labels_placeholder
|
||||
|
||||
|
||||
def generator(z, label):
|
||||
x = z
|
||||
tmp_dim = z_dim
|
||||
with tf.variable_scope('G', reuse=tf.AUTO_REUSE, regularizer=l2_regularizer(0.00001)):
|
||||
for i, dim in enumerate(g_structure[:-1]):
|
||||
kernel = _variable_on_cpu('W_' + str(i), shape=[tmp_dim, dim])
|
||||
h1 = batchnorm(tf.matmul(x, kernel), name='cbn' + str(i), labels=label, n_labels=8)
|
||||
h2 = tf.nn.relu(h1)
|
||||
x = x + h2
|
||||
tmp_dim = dim
|
||||
i = len(g_structure) - 1
|
||||
kernel = _variable_on_cpu('W_' + str(i), shape=[tmp_dim, g_structure[-1]])
|
||||
h1 = batchnorm(tf.matmul(x, kernel), name='cbn' + str(i),
|
||||
labels=label, n_labels=8)
|
||||
h2 = tf.nn.tanh(h1)
|
||||
x = x + h2
|
||||
|
||||
kernel = _variable_on_cpu('W_' + str(i+1), shape=[128, 854])
|
||||
bias = _variable_on_cpu('b_' + str(i+1), shape=[854])
|
||||
x = tf.nn.sigmoid(tf.add(tf.matmul(x, kernel), bias))
|
||||
return x
|
||||
|
||||
|
||||
def discriminator(x, label):
|
||||
with tf.variable_scope('D', reuse=tf.AUTO_REUSE, regularizer=l2_regularizer(0.00001)):
|
||||
for i, dim in enumerate(d_structure[1:]):
|
||||
kernel = _variable_on_cpu('W_' + str(i), shape=[d_structure[i], dim])
|
||||
bias = _variable_on_cpu('b_' + str(i), shape=[dim])
|
||||
x = tf.nn.relu(tf.add(tf.matmul(x, kernel), bias))
|
||||
x = layernorm(x, name='cln' + str(i), labels=label, n_labels=8)
|
||||
i = len(d_structure)
|
||||
kernel = _variable_on_cpu('W_' + str(i), shape=[d_structure[-1], 1])
|
||||
bias = _variable_on_cpu('b_' + str(i), shape=[1])
|
||||
y = tf.add(tf.matmul(x, kernel), bias)
|
||||
return y
|
||||
|
||||
|
||||
def compute_dloss(real, fake, label):
|
||||
epsilon = tf.random_uniform(
|
||||
shape=[BATCHSIZE_PER_GPU, 1],
|
||||
minval=0.,
|
||||
maxval=1.)
|
||||
x_hat = real + epsilon * (fake - real)
|
||||
y_hat_fake = discriminator(fake, label)
|
||||
y_hat_real = discriminator(real, label)
|
||||
y_hat = discriminator(x_hat, label)
|
||||
|
||||
grad = tf.gradients(y_hat, [x_hat])[0]
|
||||
slopes = tf.sqrt(tf.reduce_sum(tf.square(grad), 1))
|
||||
gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
|
||||
all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
|
||||
w_distance = -tf.reduce_mean(y_hat_real) + tf.reduce_mean(y_hat_fake)
|
||||
loss = w_distance + 10 * gradient_penalty + sum(all_regs)
|
||||
tf.add_to_collection('dlosses', loss)
|
||||
|
||||
return w_distance, loss
|
||||
|
||||
|
||||
def compute_gloss(fake, label):
|
||||
y_hat_fake = discriminator(fake, label)
|
||||
all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
|
||||
loss = -tf.reduce_mean(y_hat_fake) + sum(all_regs)
|
||||
tf.add_to_collection('glosses', loss)
|
||||
return loss, loss
|
||||
|
||||
|
||||
def tower_loss(scope, stage, real, label):
|
||||
label = tf.cast(label, tf.int32)
|
||||
label = label[:, 1] * 4 + tf.squeeze(
|
||||
tf.matmul(label[:, 2:], tf.constant([[0], [1], [2], [3]], dtype=tf.int32)))
|
||||
z = tf.random_normal(shape=[BATCHSIZE_PER_GPU, z_dim])
|
||||
fake = generator(z, label)
|
||||
if stage == 'D':
|
||||
w, loss = compute_dloss(real, fake, label)
|
||||
losses = tf.get_collection('dlosses', scope)
|
||||
else:
|
||||
w, loss = compute_gloss(fake, label)
|
||||
losses = tf.get_collection('glosses', scope)
|
||||
|
||||
total_loss = tf.add_n(losses, name='total_loss')
|
||||
|
||||
# loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
|
||||
# loss_averages_op = loss_averages.apply(losses + [total_loss])
|
||||
#
|
||||
# with tf.control_dependencies([loss_averages_op]):
|
||||
# total_loss = tf.identity(total_loss)
|
||||
|
||||
return total_loss, w
|
||||
|
||||
|
||||
def average_gradients(tower_grads):
|
||||
average_grads = []
|
||||
for grad_and_vars in zip(*tower_grads):
|
||||
grads = []
|
||||
for g, _ in grad_and_vars:
|
||||
expanded_g = tf.expand_dims(g, 0)
|
||||
grads.append(expanded_g)
|
||||
|
||||
grad = tf.concat(axis=0, values=grads)
|
||||
grad = tf.reduce_mean(grad, 0)
|
||||
|
||||
v = grad_and_vars[0][1]
|
||||
grad_and_var = (grad, v)
|
||||
average_grads.append(grad_and_var)
|
||||
return average_grads
|
||||
|
||||
|
||||
def graph(stage, opt):
|
||||
# global_step = tf.get_variable(stage+'_step', [], initializer=tf.constant_initializer(0), trainable=False)
|
||||
tower_grads = []
|
||||
per_gpu_w = []
|
||||
iterator, features_placeholder, labels_placeholder = input_fn()
|
||||
with tf.variable_scope(tf.get_variable_scope()):
|
||||
for i in range(NUM_GPUS):
|
||||
with tf.device('/gpu:%d' % i):
|
||||
with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
|
||||
(real, label) = iterator.get_next()
|
||||
loss, w = tower_loss(scope, stage, real, label)
|
||||
tf.get_variable_scope().reuse_variables()
|
||||
vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
|
||||
grads = opt.compute_gradients(loss, vars_)
|
||||
tower_grads.append(grads)
|
||||
per_gpu_w.append(w)
|
||||
|
||||
grads = average_gradients(tower_grads)
|
||||
apply_gradient_op = opt.apply_gradients(grads)
|
||||
|
||||
mean_w = tf.reduce_mean(per_gpu_w)
|
||||
train_op = apply_gradient_op
|
||||
return train_op, mean_w, iterator, features_placeholder, labels_placeholder
|
||||
|
||||
|
||||
def train(max_epochs, train_dir):
|
||||
with tf.device('/cpu:0'):
|
||||
opt_d = tf.train.AdamOptimizer(1e-4)
|
||||
opt_g = tf.train.AdamOptimizer(1e-4)
|
||||
train_d, w_distance, iterator_d, features_placeholder_d, labels_placeholder_d = graph('D', opt_d)
|
||||
train_g, _, iterator_g, features_placeholder_g, labels_placeholder_g = graph('G', opt_g)
|
||||
saver = tf.train.Saver()
|
||||
init = tf.global_variables_initializer()
|
||||
|
||||
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
|
||||
sess.run(init)
|
||||
sess.run(iterator_d.initializer,
|
||||
feed_dict={features_placeholder_d: REAL, labels_placeholder_d: LABEL})
|
||||
sess.run(iterator_g.initializer,
|
||||
feed_dict={features_placeholder_g: REAL, labels_placeholder_g: LABEL})
|
||||
|
||||
for epoch in range(1, max_epochs + 1):
|
||||
start_time = time.time()
|
||||
w_sum = 0
|
||||
for i in range(STEPS_PER_EPOCH):
|
||||
for _ in range(2):
|
||||
_, w = sess.run([train_d, w_distance])
|
||||
w_sum += w
|
||||
sess.run(train_g)
|
||||
duration = time.time() - start_time
|
||||
|
||||
assert not np.isnan(w_sum), 'Model diverged with loss = NaN'
|
||||
|
||||
format_str = 'epoch: %d, w_distance = %f (%.1f)'
|
||||
print(format_str % (epoch, -w_sum/(STEPS_PER_EPOCH*2), duration))
|
||||
if epoch % 500 == 0:
|
||||
# checkpoint_path = os.path.join(train_dir, 'multi')
|
||||
saver.save(sess, train_dir, write_meta_graph=False, global_step=epoch)
|
||||
# saver.save(sess, train_dir, global_step=epoch)
|
||||
|
||||
|
||||
def generate(model_dir, synthetic_dir, demo):
|
||||
tf.reset_default_graph()
|
||||
z = tf.random_normal(shape=[BATCHSIZE_PER_GPU, z_dim])
|
||||
y = tf.placeholder(shape=[BATCHSIZE_PER_GPU, 6], dtype=tf.int32)
|
||||
label = y[:, 1] * 4 + tf.squeeze(tf.matmul(y[:, 2:], tf.constant([[0], [1], [2], [3]], dtype=tf.int32)))
|
||||
fake = generator(z, label)
|
||||
saver = tf.train.Saver()
|
||||
with tf.Session() as sess:
|
||||
saver.restore(sess, model_dir)
|
||||
for m in range(2):
|
||||
for n in range(2, 6):
|
||||
idx1 = (demo[:, m] == 1)
|
||||
idx2 = (demo[:, n] == 1)
|
||||
idx = [idx1[j] and idx2[j] for j in range(len(idx1))]
|
||||
num = np.sum(idx)
|
||||
nbatch = int(np.ceil(num / BATCHSIZE_PER_GPU))
|
||||
label_input = np.zeros((nbatch*BATCHSIZE_PER_GPU, 6))
|
||||
label_input[:, n] = 1
|
||||
label_input[:, m] = 1
|
||||
output = []
|
||||
for i in range(nbatch):
|
||||
f = sess.run(fake,feed_dict={y: label_input[i*BATCHSIZE_PER_GPU:(i+1)*BATCHSIZE_PER_GPU]})
|
||||
output.extend(np.round(f))
|
||||
output = np.array(output)[:num]
|
||||
np.save(synthetic_dir + str(m) + str(n), output)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
#### args_1: number of training epochs
|
||||
#### args_2: dir to save the trained model
|
||||
train(500, '')
|
||||
|
||||
#### args_1: dir of trained model
|
||||
#### args_2: dir to save synthetic data
|
||||
#### args_3, label of data-to-be-generated
|
||||
generate('', '', demo=LABEL)
|
||||
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
import sys
|
||||
|
||||
SYS_ARGS = {'context':''}
|
||||
if len(sys.argv) > 1:
|
||||
|
||||
N = len(sys.argv)
|
||||
for i in range(1,N):
|
||||
value = None
|
||||
if sys.argv[i].startswith('--'):
|
||||
key = sys.argv[i][2:] #.replace('-','')
|
||||
SYS_ARGS[key] = 1
|
||||
if i + 1 < N:
|
||||
value = sys.argv[i + 1] = sys.argv[i+1].strip()
|
||||
if key and value:
|
||||
SYS_ARGS[key] = value
|
||||
|
||||
|
||||
i += 2
|
||||
Binary file not shown.
|
|
@ -0,0 +1,287 @@
|
|||
import tensorflow as tf
|
||||
from tensorflow.contrib.layers import l2_regularizer
|
||||
import numpy as np
|
||||
import time
|
||||
import os
|
||||
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
|
||||
# os.environ['CUDA_VISIBLE_DEVICES'] = "4,5"
|
||||
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
|
||||
|
||||
|
||||
FLAGS = tf.app.flags.FLAGS
|
||||
|
||||
tf.app.flags.DEFINE_string('train_dir', 'google_cloud_test/',
|
||||
"""Directory where to store checkpoint. """)
|
||||
tf.app.flags.DEFINE_string('save_dir', 'google_cloud_test/',
|
||||
"""Directory where to save generated data. """)
|
||||
tf.app.flags.DEFINE_integer('max_steps', 100,
|
||||
"""Number of batches to run in each epoch.""")
|
||||
tf.app.flags.DEFINE_integer('max_epochs', 100,
|
||||
"""Number of epochs to run.""")
|
||||
tf.app.flags.DEFINE_integer('batchsize', 10,
|
||||
"""Batchsize.""")
|
||||
tf.app.flags.DEFINE_integer('z_dim', 10,
|
||||
"""Dimensionality of random input.""")
|
||||
tf.app.flags.DEFINE_integer('data_dim', 30,
|
||||
"""Dimensionality of data.""")
|
||||
tf.app.flags.DEFINE_integer('demo_dim', 8,
|
||||
"""Dimensionality of demographics.""")
|
||||
tf.app.flags.DEFINE_float('reg', 0.0001,
|
||||
"""L2 regularization.""")
|
||||
|
||||
g_structure = [FLAGS.z_dim, FLAGS.z_dim]
|
||||
d_structure = [FLAGS.data_dim, int(FLAGS.data_dim/2), FLAGS.z_dim]
|
||||
|
||||
|
||||
def _variable_on_cpu(name, shape, initializer=None):
|
||||
with tf.device('/cpu:0'):
|
||||
var = tf.get_variable(name, shape, initializer=initializer)
|
||||
return var
|
||||
|
||||
|
||||
def batchnorm(inputs, name, labels=None, n_labels=None):
|
||||
mean, var = tf.nn.moments(inputs, [0], keep_dims=True)
|
||||
shape = mean.shape[1].value
|
||||
offset_m = _variable_on_cpu(shape=[n_labels,shape], name='offset'+name,
|
||||
initializer=tf.zeros_initializer)
|
||||
scale_m = _variable_on_cpu(shape=[n_labels,shape], name='scale'+name,
|
||||
initializer=tf.ones_initializer)
|
||||
offset = tf.nn.embedding_lookup(offset_m, labels)
|
||||
scale = tf.nn.embedding_lookup(scale_m, labels)
|
||||
result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-8)
|
||||
return result
|
||||
|
||||
|
||||
def layernorm(inputs, name, labels=None, n_labels=None):
|
||||
mean, var = tf.nn.moments(inputs, [1], keep_dims=True)
|
||||
shape = inputs.shape[1].value
|
||||
offset_m = _variable_on_cpu(shape=[n_labels,shape], name='offset'+name,
|
||||
initializer=tf.zeros_initializer)
|
||||
scale_m = _variable_on_cpu(shape=[n_labels,shape], name='scale'+name,
|
||||
initializer=tf.ones_initializer)
|
||||
offset = tf.nn.embedding_lookup(offset_m, labels)
|
||||
scale = tf.nn.embedding_lookup(scale_m, labels)
|
||||
result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-8)
|
||||
return result
|
||||
|
||||
|
||||
def input_fn():
|
||||
features_placeholder = tf.placeholder(shape=[None, FLAGS.data_dim], dtype=tf.float32)
|
||||
labels_placeholder = tf.placeholder(shape=[None, 6], dtype=tf.float32)
|
||||
dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder))
|
||||
dataset = dataset.repeat(10000)
|
||||
dataset = dataset.batch(batch_size=FLAGS.batchsize)
|
||||
dataset = dataset.prefetch(1)
|
||||
iterator = dataset.make_initializable_iterator()
|
||||
return iterator, features_placeholder, labels_placeholder
|
||||
|
||||
|
||||
def generator(z, label):
|
||||
x = z
|
||||
tmp_dim = FLAGS.z_dim
|
||||
with tf.variable_scope('G', reuse=tf.AUTO_REUSE, regularizer=l2_regularizer(FLAGS.reg)):
|
||||
for i, dim in enumerate(g_structure[:-1]):
|
||||
kernel = _variable_on_cpu('W_' + str(i), shape=[tmp_dim, dim])
|
||||
h1 = batchnorm(tf.matmul(x, kernel), name='cbn' + str(i), labels=label, n_labels=FLAGS.demo_dim)
|
||||
h2 = tf.nn.relu(h1)
|
||||
x = x + h2
|
||||
tmp_dim = dim
|
||||
i = len(g_structure) - 1
|
||||
kernel = _variable_on_cpu('W_' + str(i), shape=[tmp_dim, g_structure[-1]])
|
||||
h1 = batchnorm(tf.matmul(x, kernel), name='cbn' + str(i),
|
||||
labels=label, n_labels=FLAGS.demo_dim)
|
||||
h2 = tf.nn.tanh(h1)
|
||||
x = x + h2
|
||||
|
||||
kernel = _variable_on_cpu('W_' + str(i+1), shape=[FLAGS.z_dim, FLAGS.data_dim])
|
||||
bias = _variable_on_cpu('b_' + str(i+1), shape=[FLAGS.data_dim])
|
||||
x = tf.nn.sigmoid(tf.add(tf.matmul(x, kernel), bias))
|
||||
return x
|
||||
|
||||
|
||||
def discriminator(x, label):
|
||||
with tf.variable_scope('D', reuse=tf.AUTO_REUSE, regularizer=l2_regularizer(FLAGS.reg)):
|
||||
for i, dim in enumerate(d_structure[1:]):
|
||||
kernel = _variable_on_cpu('W_' + str(i), shape=[d_structure[i], dim])
|
||||
bias = _variable_on_cpu('b_' + str(i), shape=[dim])
|
||||
x = tf.nn.relu(tf.add(tf.matmul(x, kernel), bias))
|
||||
x = layernorm(x, name='cln' + str(i), labels=label, n_labels=FLAGS.demo_dim)
|
||||
i = len(d_structure)
|
||||
kernel = _variable_on_cpu('W_' + str(i), shape=[d_structure[-1], 1])
|
||||
bias = _variable_on_cpu('b_' + str(i), shape=[1])
|
||||
y = tf.add(tf.matmul(x, kernel), bias)
|
||||
return y
|
||||
|
||||
|
||||
def compute_dloss(real, fake, label):
|
||||
epsilon = tf.random_uniform(
|
||||
shape=[FLAGS.batchsize, 1],
|
||||
minval=0.,
|
||||
maxval=1.)
|
||||
x_hat = real + epsilon * (fake - real)
|
||||
y_hat_fake = discriminator(fake, label)
|
||||
y_hat_real = discriminator(real, label)
|
||||
y_hat = discriminator(x_hat, label)
|
||||
|
||||
grad = tf.gradients(y_hat, [x_hat])[0]
|
||||
slopes = tf.sqrt(tf.reduce_sum(tf.square(grad), 1))
|
||||
gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
|
||||
all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
|
||||
w_distance = -tf.reduce_mean(y_hat_real) + tf.reduce_mean(y_hat_fake)+sum(all_regs)
|
||||
loss = w_distance + 10 * gradient_penalty
|
||||
tf.add_to_collection('dlosses', loss)
|
||||
|
||||
return w_distance, loss
|
||||
|
||||
|
||||
def compute_gloss(fake, label):
|
||||
y_hat_fake = discriminator(fake, label)
|
||||
all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
|
||||
loss = -tf.reduce_mean(y_hat_fake)+sum(all_regs)
|
||||
tf.add_to_collection('glosses', loss)
|
||||
return loss, loss
|
||||
|
||||
|
||||
def tower_loss(scope, stage, real, label):
|
||||
label = tf.cast(label, tf.int32)
|
||||
print ([stage,label.shape])
|
||||
label = label[:, 1] * 4 + tf.squeeze(
|
||||
tf.matmul(label[:, 2:], tf.constant([[0], [1], [2], [3]], dtype=tf.int32)))
|
||||
z = tf.random_normal(shape=[FLAGS.batchsize, FLAGS.z_dim])
|
||||
fake = generator(z, label)
|
||||
if stage == 'D':
|
||||
w, loss = compute_dloss(real, fake, label)
|
||||
losses = tf.get_collection('dlosses', scope)
|
||||
else:
|
||||
w, loss = compute_gloss(fake, label)
|
||||
losses = tf.get_collection('glosses', scope)
|
||||
|
||||
total_loss = tf.add_n(losses, name='total_loss')
|
||||
return total_loss, w
|
||||
|
||||
|
||||
def average_gradients(tower_grads):
|
||||
average_grads = []
|
||||
for grad_and_vars in zip(*tower_grads):
|
||||
grads = []
|
||||
for g, _ in grad_and_vars:
|
||||
expanded_g = tf.expand_dims(g, 0)
|
||||
grads.append(expanded_g)
|
||||
|
||||
grad = tf.concat(axis=0, values=grads)
|
||||
grad = tf.reduce_mean(grad, 0)
|
||||
|
||||
v = grad_and_vars[0][1]
|
||||
grad_and_var = (grad, v)
|
||||
average_grads.append(grad_and_var)
|
||||
return average_grads
|
||||
|
||||
|
||||
def graph(stage, opt):
|
||||
tower_grads = []
|
||||
per_gpu_w = []
|
||||
iterator, features_placeholder, labels_placeholder = input_fn()
|
||||
with tf.variable_scope(tf.get_variable_scope()):
|
||||
for i in range(1):
|
||||
with tf.device('/cpu:0'):
|
||||
with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
|
||||
(real, label) = iterator.get_next()
|
||||
|
||||
loss, w = tower_loss(scope, stage, real, label)
|
||||
tf.get_variable_scope().reuse_variables()
|
||||
vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
|
||||
grads = opt.compute_gradients(loss, vars_)
|
||||
tower_grads.append(grads)
|
||||
per_gpu_w.append(w)
|
||||
|
||||
grads = average_gradients(tower_grads)
|
||||
apply_gradient_op = opt.apply_gradients(grads)
|
||||
|
||||
mean_w = tf.reduce_mean(per_gpu_w)
|
||||
train_op = apply_gradient_op
|
||||
return train_op, mean_w, iterator, features_placeholder, labels_placeholder
|
||||
|
||||
|
||||
def train(data, demo):
|
||||
with tf.device('/cpu:0'):
|
||||
opt_d = tf.train.AdamOptimizer(1e-4)
|
||||
opt_g = tf.train.AdamOptimizer(1e-4)
|
||||
train_d, w_distance, iterator_d, features_placeholder_d, labels_placeholder_d = graph('D', opt_d)
|
||||
train_g, _, iterator_g, features_placeholder_g, labels_placeholder_g = graph('G', opt_g)
|
||||
saver = tf.train.Saver()
|
||||
init = tf.global_variables_initializer()
|
||||
|
||||
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
|
||||
sess.run(init)
|
||||
sess.run(iterator_d.initializer,
|
||||
feed_dict={features_placeholder_d: data,
|
||||
labels_placeholder_d: demo})
|
||||
sess.run(iterator_g.initializer,
|
||||
feed_dict={features_placeholder_g: data,
|
||||
labels_placeholder_g: demo})
|
||||
|
||||
for epoch in range(1, FLAGS.max_epochs + 1):
|
||||
start_time = time.time()
|
||||
w_sum = 0
|
||||
for i in range(FLAGS.max_steps):
|
||||
for _ in range(2):
|
||||
_, w = sess.run([train_d, w_distance])
|
||||
w_sum += w
|
||||
sess.run(train_g)
|
||||
duration = time.time() - start_time
|
||||
|
||||
assert not np.isnan(w_sum), 'Model diverged with loss = NaN'
|
||||
|
||||
format_str = 'epoch: %d, w_distance = %f (%.1f)'
|
||||
print(format_str % (epoch, -w_sum/(FLAGS.max_steps*2), duration))
|
||||
if epoch % FLAGS.max_epochs == 0:
|
||||
# checkpoint_path = os.path.join(train_dir, 'multi')
|
||||
saver.save(sess, FLAGS.train_dir + 'emr_wgan', write_meta_graph=False, global_step=epoch)
|
||||
# saver.save(sess, train_dir, global_step=epoch)
|
||||
|
||||
|
||||
def generate(demo):
|
||||
z = tf.random_normal(shape=[FLAGS.batchsize, FLAGS.z_dim])
|
||||
y = tf.placeholder(shape=[FLAGS.batchsize, 6], dtype=tf.int32)
|
||||
label = y[:, 1] * 4 + tf.squeeze(tf.matmul(y[:, 2:], tf.constant([[0], [1], [2], [3]], dtype=tf.int32)))
|
||||
fake = generator(z, label)
|
||||
saver = tf.train.Saver()
|
||||
with tf.Session() as sess:
|
||||
saver.restore(sess, FLAGS.train_dir + 'emr_wgan-' + str(FLAGS.max_epochs))
|
||||
for m in range(2):
|
||||
for n in range(2, 6):
|
||||
idx1 = (demo[:, m] == 1)
|
||||
idx2 = (demo[:, n] == 1)
|
||||
idx = [idx1[j] and idx2[j] for j in range(len(idx1))]
|
||||
num = np.sum(idx)
|
||||
nbatch = int(np.ceil(num / FLAGS.batchsize))
|
||||
label_input = np.zeros((nbatch*FLAGS.batchsize, 6))
|
||||
label_input[:, n] = 1
|
||||
label_input[:, m] = 1
|
||||
output = []
|
||||
for i in range(nbatch):
|
||||
f = sess.run(fake,feed_dict={y: label_input[i*FLAGS.batchsize:(i+1)*FLAGS.batchsize]})
|
||||
output.extend(np.round(f))
|
||||
output = np.array(output)[:num]
|
||||
np.save(FLAGS.save_dir + 'synthetic_' + str(m) + str(n), output)
|
||||
|
||||
|
||||
def load_data():
|
||||
data = np.zeros(3000)
|
||||
idx = np.random.choice(np.arange(3000),size=900)
|
||||
data[idx] = 1
|
||||
data = np.reshape(data, (100,30))
|
||||
idx = np.random.randint(2,6,size=100)
|
||||
idx2 = np.random.randint(2,size=100)
|
||||
demo = np.zeros((100,6))
|
||||
demo[np.arange(100), idx] = 1
|
||||
demo[np.arange(100), idx2] = 1
|
||||
return data, demo
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
data, demo = load_data()
|
||||
print ([data.shape,demo.shape])
|
||||
train(data, demo)
|
||||
# generate(demo)
|
||||
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
{
|
||||
"type": "service_account",
|
||||
"project_id": "aou-res-deid-vumc-test",
|
||||
"private_key_id": "8b7acef9a1f1137799011cf13cf0906e331c472e",
|
||||
"private_key": "-----BEGIN PRIVATE KEY-----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\n530zPFObyt2kI9+E75+G/w==\n-----END PRIVATE KEY-----\n",
|
||||
"client_email": "dev-deid-600@aou-res-deid-vumc-test.iam.gserviceaccount.com",
|
||||
"client_id": "104228831510203920964",
|
||||
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
|
||||
"token_uri": "https://oauth2.googleapis.com/token",
|
||||
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
|
||||
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/dev-deid-600%40aou-res-deid-vumc-test.iam.gserviceaccount.com"
|
||||
}
|
||||
Loading…
Reference in New Issue