synthetic_aia_mia.predictor package

Submodules

synthetic_aia_mia.predictor.adult module

Define structures to manage and interface a fully connected neural network for adult.

class synthetic_aia_mia.predictor.adult.AdultDataset(data)[source]

Bases: Dataset

Pytorch dataset to handle adult data.

class synthetic_aia_mia.predictor.adult.AdultNN[source]

Bases: object

Wrapper arround pytorch neural network. Interfare for hyper parameter optimisation using raytune.

fit(dadata)[source]

Train and tune hyper parameters.

Parameters:

data – Dataset the will be split for training and hyper parameter tuning. Dataset must contain a column called “PINCP” used as training label.

predict(dadata)[source]

Use a trained TabularNN to predict label of dataset.

Parameters:

dadata (fetch_data.Dataset) – Dataset to evaluate.

Returns:

Input dataset completed with hard labels, soft labels and loss.

Return type:

fetch_data.Dataset

class synthetic_aia_mia.predictor.adult.TabularNN(input_size, l1, l2, l3, l4, output_size)[source]

Bases: Module

Pytorch neural network for adult.

forward(x)[source]

Forward pass in the neural network.

Parameters:

x (torch.tensor) – Data points.

Returns:

Neural network function applied to x.

Return type:

torch.tensor

synthetic_aia_mia.predictor.utk module

Define structures to manage and interface a fully connected neural network for UTKfaces.

class synthetic_aia_mia.predictor.utk.CNN(input_size, c, l1, l2, output_size)[source]

Bases: Module

Pytorch convulutional neural network for UTKfaces.

forward(x)[source]

Forward pass in the neural network.

Parameters:

x (torch.tensor) – Data points.

Returns:

Neural network function applied to x.

Return type:

torch.tensor

class synthetic_aia_mia.predictor.utk.UtkDataset(data)[source]

Bases: Dataset

Pytorch dataset to handle adult data.

class synthetic_aia_mia.predictor.utk.UtkNN[source]

Bases: object

Wrapper arround pytorch neural network. Interfare for hyper parameter optimisation using raytune.

fit(data)[source]

Train and tune hyper parameters.

Parameters:

data (Dictionary of numpy.ndarray) – Dataset the will be split for training and hyper parameter tuning. Dataset must contain a column called “PINCP” used as training label.

predict(data)[source]

Use a trained CNN to predict label of dataset.

Parameters:

data (numpy.ndarray) – Dataset without label.

Returns:

Prediction.

Return type:

numpy.ndarray

Module contents