synthetic.predictor package¶
Submodules¶
synthetic.predictor.adult module¶
Define structures to manage and interface a fully connected neural network for adult.
- class synthetic.predictor.adult.AdultDataset(data)[source]¶
Bases:
Dataset
Pytorch dataset to handle adult data.
- __annotations__ = {}¶
- __init__(data)[source]¶
Make data conversion for pytorch integration.
- Parameters:
data (pandas.dataframe) – dataset to convert.
- __module__ = 'synthetic.predictor.adult'¶
- __parameters__ = ()¶
- class synthetic.predictor.adult.AdultNN[source]¶
Bases:
object
Wrapper arround pytorch neural network. Interfare for hyper parameter optimisation using raytune.
- __dict__ = mappingproxy({'__module__': 'synthetic.predictor.adult', '__doc__': 'Wrapper arround pytorch neural network. Interfare for hyper parameter optimisation using raytune.', '__init__': <function AdultNN.__init__>, 'fit': <function AdultNN.fit>, 'predict': <function AdultNN.predict>, '__dict__': <attribute '__dict__' of 'AdultNN' objects>, '__weakref__': <attribute '__weakref__' of 'AdultNN' objects>, '__annotations__': {}})¶
- __module__ = 'synthetic.predictor.adult'¶
- __weakref__¶
list of weak references to the object (if defined)
- class synthetic.predictor.adult.TabularNN(input_size, l1, l2, output_size)[source]¶
Bases:
Module
Pytorch neural network for adult.
- __annotations__ = {}¶
- __init__(input_size, l1, l2, output_size)[source]¶
Sets layers for a neural network.
- Parameters:
input_size (int) – Number of features.
hidden_size (int) – Number of neurons/hidden layer.
l1 (int) – Size of the first layer.
l2 (int) – Size of the second layer.
output_size (int) – Number classes in the labels.
- __module__ = 'synthetic.predictor.adult'¶