dlpy.model.DataSpec¶
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class
dlpy.model.
DataSpec
(type_, layer, data=None, data_layer=None, nominals=None, numeric_nominal_parms=None, loss_scale_factor=None)¶ Bases: dlpy.utils.DLPyDict
Data spec parameters.
Parameters: - type_ : string
Specifies the type of the input data in the data spec.
Valid Values: NUMERICNOMINAL, NUMNOM, TEXT, IMAGE, OBJECTDETECTION- layer : string
Specifies the name of the layer to data spec.
- data : list, optional
Specifies the name of the columns/variables as the data, this might be input or output based on layer type.
- data_layer : string, optional
Specifies the name of the input layer that binds to the output layer.
- nominals : list, optional
Specifies the nominal input variables to use in the analysis.
- numeric_nominal_parms : DataSpecNumNomOpts, optional
Specifies the parameters for the numeric nominal data spec inputs.
- loss_scale_factor : double, optional
Specifies the value to scale the loss for a given task layer. This option only affects the task layers.
Returns: - DataSpec
A dictionary of data spec parameters.
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__init__
(type_, layer, data=None, data_layer=None, nominals=None, numeric_nominal_parms=None, loss_scale_factor=None)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__(type_, layer[, data, data_layer, …]) Initialize self. clear() get(k[,d]) items() keys() pop(k[,d]) If key is not found, d is returned if given, otherwise KeyError is raised. popitem() as a 2-tuple; but raise KeyError if D is empty. setdefault(k[,d]) update([E, ]**F) If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v values()