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data-maker
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Handling of continous values

This commit is contained in:
Steve Nyemba 2020-02-28 21:37:26 -06:00
parent bd6fb03f8d
commit 3fbd68309f
3 changed files with 103 additions and 25 deletions
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8
data/gan.py
View File

@ -604,7 +604,7 @@ class Predict(GNet):
r = np.zeros(self.ROW_COUNT)
df.columns = self.values
if len(found):
print (len(found),NTH_VALID_CANDIDATE)
# print (len(found),NTH_VALID_CANDIDATE)
# x = df * self.values
#
# let's get the missing rows (if any) ...
@ -704,10 +704,10 @@ if __name__ == '__main__' :
p = Predict(context=context,label=LABEL,values=values,column=column)
p.load_meta(column)
r = p.apply()
print (df)
print ()
# print (df)
# print ()
df[column] = r[column]
print (df)
# print (df)
else:

100
data/maker/__init__.py
View File

@ -14,6 +14,68 @@ import data.gan as gan
from transport import factory
from data.bridge import Binary
import threading as thread
class ContinuousToDiscrete :
@staticmethod
def binary(X,n=4) :
"""
This function will convert a continous stream of information into a variety a bit stream of bins
"""
# BOUNDS = np.repeat(np.divide(X.max(),n),n).cumsum().tolist()
BOUNDS = ContinuousToDiscrete.bounds(X,n)
# _map = [{"index":BOUNDS.index(i),"ubound":i} for i in BOUNDS]
_matrix = []
m = []
for value in X :
x_ = np.zeros(n)
_matrix.append(x_)
for row in BOUNDS :
if value>= row.left and value <= row.right :
index = BOUNDS.index(row)
x_[index] = 1
break
return _matrix
@staticmethod
def bounds(x,n):
return list(pd.cut(np.array(x),n).categories)
@staticmethod
def continuous(X,BIN_SIZE=4) :
"""
This function will approximate a binary vector given boundary information
:X binary matrix
:BIN_SIZE
"""
BOUNDS = ContinuousToDiscrete.bounds(X,BIN_SIZE)
values = []
_BINARY= ContinuousToDiscrete.binary(X,BIN_SIZE)
# # print (BOUNDS)
# values = []
for row in _BINARY :
# ubound = BOUNDS[row.index(1)]
index = np.where(row == 1)[0][0]
ubound = BOUNDS[ index ].right
lbound = BOUNDS[ index ].left
x_ = np.round(np.random.uniform(lbound,ubound),3).astype(float)
values.append(x_)
lbound = ubound
return values
def train (**args) :
"""
This function is intended to train the GAN in order to learn about the distribution of the features
@ -24,22 +86,30 @@ def train (**args) :
:context label of what we are synthesizing
"""
column = args['column'] if (isinstance(args['column'],list)) else [args['column']]
CONTINUOUS = args['continuous'] if 'continuous' in args else []
# column_id = args['id']
df = args['data'] if not isinstance(args['data'],str) else pd.read_csv(args['data'])
df.columns = [name.lower() for name in df.columns]
#
# @TODO:
# Consider sequential training of sub population for extremely large datasets
#
#
# If we have several columns we will proceed one at a time (it could be done in separate threads)
# @TODO : Consider performing this task on several threads/GPUs simulataneously
#
handler = Binary()
# args['label'] = pd.get_dummies(df[column_id]).astype(np.float32).values
# args['label'] = handler.Export(df[[column_id]])
# args['label'] = np.ones(df.shape[0]).reshape(df.shape[0],1)
for col in column :
# args['real'] = pd.get_dummies(df[col].fillna('')).astype(np.float32).values
# if 'float' not in df[col].dtypes.name :
# args['real'] = pd.get_dummies(df[col].fillna('')).astype(np.float32).values
if 'float' in df[col].dtypes.name and col in CONTINUOUS:
BIN_SIZE = 10 if 'bin_size' not in args else int(args['bin_size'])
args['real'] = ContinuousToDiscrete.binary(df[col],BIN_SIZE).astype(np.float32)
else:
args['real'] = pd.get_dummies(df[col].fillna('')).astype(np.float32).values
# args['real'] = handler.Export(df[[col]])
args['column'] = col
args['context'] = col
context = args['context']
@ -75,7 +145,7 @@ def generate(**args):
"""
# df = args['data']
df = args['data'] if not isinstance(args['data'],str) else pd.read_csv(args['data'])
CONTINUOUS = args['continous'] if 'continuous' in args else []
column = args['column'] if (isinstance(args['column'],list)) else [args['column']]
# column_id = args['id']
#
@ -86,18 +156,26 @@ def generate(**args):
for col in column :
args['context'] = col
args['column'] = col
if 'float' in df[col].dtypes.name or col in CONTINUOUS :
#
# We should create the bins for the values we are observing here
BIN_SIZE = 4 if 'bin_size' not in args else int(args['bin_size'])
values = ContinuousToDiscrete.continuous(df[col].values,BIN_SIZE)
else:
values = df[col].unique().tolist()
args['values'] = values
args['row_count'] = df.shape[0]
#
# we can determine the cardinalities here so we know what to allow or disallow
handler = gan.Predict (**args)
handler.load_meta(col)
# handler.ROW_COUNT = df[col].shape[0]
r = handler.apply()
# print (r)
#
print ([_df.shape,len(r[col])])
_df[col] = r[col]
#
# @TODO: log basic stats about the synthetic attribute
#
# break
return _df

6
data/maker/__main__.py
View File

@ -17,9 +17,9 @@ if 'config' in SYS_ARGS :
odf = pd.read_csv (ARGS['data'])
odf.columns = [name.lower() for name in odf.columns]
column = ARGS['column'] if isinstance(ARGS['column'],list) else [ARGS['column']]
print (odf.head())
print (_df.head())
# print(pd.merge(odf,_df,rsuffix='_io'))
# print (odf.head())
# print (_df.head())
print(odf.join(_df[column],rsuffix='_io'))
# print (_df[column].risk.evaluate(flag='synth'))
# print (odf[column].risk.evaluate(flag='original'))
# _x = pd.get_dummies(_df[column]).values