def meta_setup(self):
req_cols = {
'x': 'float64',
'y': 'float64'
}
required = {
'points_df_in': req_cols,
}
input_meta = self.get_input_meta()
output_cols = ({
'distance_df': {
'distance_cudf': 'float64',
'x': 'float64',
'y': 'float64'
},
'distance_abs_df': {
'distance_abs_cudf': 'float64',
'x': 'float64',
'y': 'float64'
}
})
if 'points_df_in' in input_meta:
col_from_inport = input_meta['points_df_in']
# additional ports
output_cols['distance_df'].update(col_from_inport)
output_cols['distance_abs_df'].update(col_from_inport)
return MetaData(inports=required, outports=output_cols)
def meta_setup(self):
input_meta = self.get_input_meta()
if issubclass(self.instanceClass, TrainableNM):
input_meta = self.get_input_meta()
if self.INPUT_NM in input_meta:
if (share_weight in self.conf and
self.conf[share_weight] == 'Reuse'):
self.conf = input_meta[self.INPUT_NM]
if self.instance is not None:
inports = self.instance.input_ports
outports = self.instance.output_ports
else:
try:
p_inports = self.instanceClass.input_ports
p_outports = self.instanceClass.output_ports
feeder = FeedProperty(self.conf)
inports = p_inports.fget(feeder)
outports = p_outports.fget(feeder)
except Exception:
inports = None
outports = None
required = {}
out_meta = {}
if inports is not None:
for k in inports.keys():
required[k] = serialize_type(inports[k])
if outports is not None:
for k in outports.keys():
out_meta[k] = serialize_type(outports[k])
if self.instance is not None:
out_meta[self.OUTPUT_NM] = self.conf
metadata = MetaData(inports=required, outports=out_meta)
return metadata
def meta_setup(self):
columns_out = {
'points_df_out': {
'x': 'float64',
'y': 'float64'
},
}
return MetaData(inports={}, outports=columns_out)
def meta_setup(self):
required = {}
output = {}
output['axes'] = []
output['element'] = {}
output['element']['types'] = ['VoidType']
output['element']['fields'] = 'None'
output['element']['parameters'] = '{}'
required = self.get_input_meta()
required['input_tensor'] = copy.deepcopy(output)
metadata = MetaData(inports=required,
outports={self.OUTPUT_PORT_NAME: {}})
return metadata
def meta_setup(self, ):
cols_required = {'x': 'float64', 'y': 'float64'}
required = {
'points_df_in': cols_required,
'points_ddf_out': cols_required
}
input_meta = self.get_input_meta()
output_cols = ({'points_ddf_out': {'x': 'float64', 'y': 'float64'}})
if 'points_df_in' in input_meta:
col_from_inport = input_meta['points_df_in']
# additional ports
output_cols['points_ddf_out'].update(col_from_inport)
return MetaData(inports=required, outports=output_cols)
def meta_setup(self):
colsopt = self.conf['columns_option']
cols = {
'listnums': {
'list': 'numbers'
},
'mylistnums': {
'list': 'numbers'
},
'rangenums': {
'range': 'numbers'
},
'listnotnums': {
'list': 'notnumbers'
},
}.get(colsopt)
return MetaData(inports={}, outports={'numlist': cols})
def meta_setup(self):
df_out_10 = {
'date': 'date',
'AAA': 'float64',
'BBB': 'float64',
'CCC': 'float64',
'DDD': 'float64',
'EEE': 'float64',
'FFF': 'float64',
'GGG': 'float64',
'HHH': 'float64',
'III': 'float64',
'JJJ': 'float64',
}
df_out_17 = {
'date': 'date',
'BZA Index (Equities)': 'float64',
'CLA Comdty (Commodities)': 'float64',
'CNA Comdty (Fixed Income)': 'float64',
'ESA Index (Equities)': 'float64',
'G A Comdty (Fixed Income)': 'float64',
'GCA Comdty (Commodities)': 'float64',
'HIA Index (Equities)': 'float64',
'NKA Index (Equities)': 'float64',
'NQA Index (Equities)': 'float64',
'RXA Comdty (Fixed Income)': 'float64',
'SIA Comdty (Commodities)': 'float64',
'SMA Index (Equities)': 'float64',
'TYA Comdty (Fixed Income)': 'float64',
'VGA Index (Equities)': 'float64',
'XMA Comdty (Fixed Income)': 'float64',
'XPA Index (Equities)': 'float64',
'Z A Index (Equities)': 'float64',
}
assets_17 = self.conf.get('17assets', False)
columns_out = {}
columns_out['df_out'] = df_out_17 if assets_17 else df_out_10
return MetaData(inports={}, outports=columns_out)
def meta_setup(self):
required ={
"df1": {},
"df2": {}
}
return MetaData(inports=required, outports={'max_diff': {}})
def meta_setup(self):
required = {'inlist': {'list': 'numbers'}}
columns_out = {'sum': {'element': 'number'}}
return MetaData(inports=required, outports=columns_out)