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bug fix: uploading data

This commit is contained in:
Steve Nyemba 2022-05-17 18:04:05 -05:00
parent 1dae4ffba8
commit 377e84daea
1 changed files with 45 additions and 27 deletions
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72
data/maker/__init__.py
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@ -96,14 +96,17 @@ class Learner(Process):
# #
# Below is a source of inefficiency, unfortunately python's type inference doesn't work well in certain cases # Below is a source of inefficiency, unfortunately python's type inference doesn't work well in certain cases
# - The code below tries to address the issue (Perhaps better suited for the reading components) # - The code below tries to address the issue (Perhaps better suited for the reading components)
_log = {}
for name in columns : for name in columns :
_index = np.random.choice(np.arange(self._df[name].size),5,False) _index = np.random.choice(np.arange(self._df[name].size),5,False)
no_value = [type(value) in [int,float,np.int64,np.int32,np.float32,np.float64] for value in self._df[name].values[_index]] no_value = [type(value) in [int,float,np.int64,np.int32,np.float32,np.float64] for value in self._df[name].values[_index]]
no_value = 0 if np.sum(no_value) > 0 else '' no_value = 0 if np.sum(no_value) > 0 else ''
self._df[name] = self._df[name].fillna(no_value) self._df[name] = self._df[name].fillna(no_value)
_log[name] = self._df[name].dtypes.name
_log = {'action':'structure','input':_log}
self.log(**_log)
# #
# convert the data to binary here ... # convert the data to binary here ...
_schema = self.get_schema() _schema = self.get_schema()
@ -293,46 +296,52 @@ class Generator (Learner):
name = _item['name'] name = _item['name']
if _item['type'].upper() in ['DATE','DATETIME','TIMESTAMP'] : if _item['type'].upper() in ['DATE','DATETIME','TIMESTAMP'] :
FORMAT = '%Y-%m-%d' FORMAT = '%m-%d-%Y'
try: # try:
# # #
#-- Sometimes data isn't all it's meant to be # #-- Sometimes data isn't all it's meant to be
SIZE = -1 # SIZE = -1
if 'format' in self.info and name in self.info['format'] : # if 'format' in self.info and name in self.info['format'] :
FORMAT = self.info['format'][name] # FORMAT = self.info['format'][name]
SIZE = 10 # SIZE = 10
elif _item['type'] in ['DATETIME','TIMESTAMP'] : # elif _item['type'] in ['DATETIME','TIMESTAMP'] :
FORMAT = '%Y-%m-%d %H:%M:%S' # FORMAT = '%m-%d-%Y %H:%M:%S'
SIZE = 19 # SIZE = 19
if SIZE > 0 : # if SIZE > 0 :
# values = pd.to_datetime(_df[name], format=FORMAT).astype(str)
# _df[name] = [_date[:SIZE].strip() for _date in values]
values = pd.to_datetime(_df[name], format=FORMAT).astype(str)
_df[name] = [_date[:SIZE] for _date in values]
# # _df[name] = _df[name].astype(str)
# r[name] = FORMAT
# # _df[name] = pd.to_datetime(_df[name], format=FORMAT) #.astype('datetime64[ns]')
# if _item['type'] in ['DATETIME','TIMESTAMP']:
# pass #;_df[name] = _df[name].fillna('').astype('datetime64[ns]')
r[name] = FORMAT # except Exception as e:
# _df[name] = pd.to_datetime(_df[name], format=FORMAT) #.astype('datetime64[ns]') # pass
if _item['type'] in ['DATETIME','TIMESTAMP']: # finally:
pass #;_df[name] = _df[name].fillna('').astype('datetime64[ns]') # pass
except Exception as e:
pass
finally:
pass
else: else:
# #
# Because types are inferred on the basis of the sample being processed they can sometimes be wrong # Because types are inferred on the basis of the sample being processed they can sometimes be wrong
# To help disambiguate we add the schema information # To help disambiguate we add the schema information
_type = None _type = None
if 'int' in _df[name].dtypes.name or 'int' in _item['type'].lower(): if 'int' in _df[name].dtypes.name or 'int' in _item['type'].lower():
_type = np.int _type = np.int
elif 'float' in _df[name].dtypes.name or 'float' in _item['type'].lower(): elif 'float' in _df[name].dtypes.name or 'float' in _item['type'].lower():
_type = np.float _type = np.float
if _type : if _type :
_df[name] = _df[name].fillna(0).replace('',0).astype(_type)
_df[name] = _df[name].fillna(0).replace('',0).replace('NA',0).replace('nan',0).astype(_type)
# else:
# _df[name] = _df[name].astype(str)
# _df = _df.replace('NaT','').replace('NA','') # _df = _df.replace('NaT','').replace('NA','')
if r : if r :
@ -373,10 +382,19 @@ class Generator (Learner):
_schema = self.get_schema() _schema = self.get_schema()
_schema = [{'name':_item.name,'type':_item.field_type} for _item in _schema] _schema = [{'name':_item.name,'type':_item.field_type} for _item in _schema]
_df = self.format(_df,_schema) _df = self.format(_df,_schema)
_log = [{"name":_schema[i]['name'],"dataframe":_df[_df.columns[i]].dtypes.name,"schema":_schema[i]['type']} for i in np.arange(len(_schema)) ]
self.log(**{"action":"consolidate","input":_log})
# w = transport.factory.instance(doc='observation',provider='mongodb',context='write',db='IOV01_LOGS',auth_file='/home/steve/dev/transport/mongo.json')
# w.write(_df)
# print (_df[cols])
writer = transport.factory.instance(**_store) writer = transport.factory.instance(**_store)
writer.write(_df,schema=_schema) writer.write(_df,schema=_schema)
# _df.to_csv('foo.csv')
self.log(**{'action':'write','input':{'rows':N,'candidates':len(_candidates)}}) self.log(**{'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
class Shuffle(Generator): class Shuffle(Generator):