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bug fixes and optimizations

This commit is contained in:
Steve Nyemba 2020-04-01 00:21:51 -05:00
parent 459afa2890
commit 4c297679dc
3 changed files with 29 additions and 21 deletions
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46
data/maker/__init__.py
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@ -27,22 +27,25 @@ class ContinuousToDiscrete :
values = np.array(X).astype(np.float32) values = np.array(X).astype(np.float32)
BOUNDS = ContinuousToDiscrete.bounds(values,n) BOUNDS = ContinuousToDiscrete.bounds(values,n)
# _map = [{"index":BOUNDS.index(i),"ubound":i} for i in BOUNDS] # _map = [{"index":BOUNDS.index(i),"ubound":i} for i in BOUNDS]
_matrix = [] # _matrix = []
m = [] # m = []
for value in X : # for value in X :
x_ = np.zeros(n) # x_ = np.zeros(n)
for row in BOUNDS : # for row in BOUNDS :
# if value>= row.left and value <= row.right :
# index = BOUNDS.index(row)
# x_[index] = 1
# break
# _matrix += x_.tolist()
# #
# # for items in BOUNDS :
# # index = BOUNDS.index(items)
# return np.array(_matrix).reshape(len(X),n)
matrix = np.repeat(np.zeros(n),len(X)).reshape(len(X),n)
if value>= row.left and value <= row.right :
index = BOUNDS.index(row)
x_[index] = 1
break
_matrix += x_.tolist()
#
# for items in BOUNDS :
# index = BOUNDS.index(items)
return np.array(_matrix).reshape(len(X),n)
@staticmethod @staticmethod
def bounds(x,n): def bounds(x,n):
@ -65,9 +68,15 @@ class ContinuousToDiscrete :
# _BINARY= ContinuousToDiscrete.binary(X,BIN_SIZE) # _BINARY= ContinuousToDiscrete.binary(X,BIN_SIZE)
# # # print (BOUNDS) # # # print (BOUNDS)
l = {} l = {}
for value in X : for i in np.arange(len(X)): #value in X :
values += [ np.round(np.random.uniform(item.left,item.right),ContinuousToDiscrete.ROUND_UP) for item in BOUNDS if value >= item.left and value <= item.right ]
value = X[i]
for item in BOUNDS :
if value >= item.left and value <= item.right :
values += [np.round(np.random.uniform(item.left,item.right),ContinuousToDiscrete.ROUND_UP)]
break
# values += [ np.round(np.random.uniform(item.left,item.right),ContinuousToDiscrete.ROUND_UP) for item in BOUNDS if value >= item.left and value <= item.right ]
# # values = [] # # values = []
@ -223,11 +232,10 @@ def generate(**args):
i = np.where (i == False)[0] i = np.where (i == False)[0]
else: else:
i = np.where( r[col] != None)[0] i = np.where( r[col] != None)[0]
_approx = ContinuousToDiscrete.continuous(r[col][i],BIN_SIZE) _approx = ContinuousToDiscrete.continuous(r[col][i],BIN_SIZE) #-- approximating based on arbitrary bins
r[col][i] = _approx r[col][i] = _approx
_df[col] = r[col] #ContinuousToDiscrete.continuous(r[col],BIN_SIZE) if col in CONTINUOUS else r[col] _df[col] = r[col]
# _df[col] = r[col]
# #
# @TODO: log basic stats about the synthetic attribute # @TODO: log basic stats about the synthetic attribute
# #

2
pipeline.py
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@ -47,7 +47,7 @@ class Components :
logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']}) logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
logger.write({"module":"bigquery","action":"read","input":{"sql":SQL}}) logger.write({"module":"bigquery","action":"read","input":{"sql":SQL}})
credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json') credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
df = pd.read_gbq(SQL,credentials=credentials,dialect='standard').astype(object) df = pd.read_gbq(SQL,credentials=credentials,dialect='standard')
return df return df
# return lambda: pd.read_gbq(SQL,credentials=credentials,dialect='standard')[args['columns']].dropna() # return lambda: pd.read_gbq(SQL,credentials=credentials,dialect='standard')[args['columns']].dropna()

2
setup.py
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@ -4,7 +4,7 @@ import sys
def read(fname): def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read() return open(os.path.join(os.path.dirname(__file__), fname)).read()
args = {"name":"data-maker","version":"1.2.5","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT", args = {"name":"data-maker","version":"1.2.6","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
"packages":find_packages(),"keywords":["healthcare","data","transport","protocol"]} "packages":find_packages(),"keywords":["healthcare","data","transport","protocol"]}
args["install_requires"] = ['data-transport@git+https://dev.the-phi.com/git/steve/data-transport.git','tensorflow==1.15','pandas','pandas-gbq','pymongo'] args["install_requires"] = ['data-transport@git+https://dev.the-phi.com/git/steve/data-transport.git','tensorflow==1.15','pandas','pandas-gbq','pymongo']
args['url'] = 'https://hiplab.mc.vanderbilt.edu/git/aou/data-maker.git' args['url'] = 'https://hiplab.mc.vanderbilt.edu/git/aou/data-maker.git'