chemicalchecker.tool.adanet.adanet_wrap.AdaNetWrapper

class AdaNetWrapper(traintest_file, **kwargs)

Bases: object

Wrapper class adapted from scripted examples on AdaNet’s github.

https://github.com/tensorflow/adanet/blob/master/adanet/ examples/tutorials/adanet_objective.ipynb

Methods

architecture	Extract the ensemble architecture from evaluation results.
get_trainable_variables	Return the weigths of the trained neural network.
input_fn	Generate an input function for the Estimator.
predict	Load model and return predictions.
predict_fn	Load model and return the predict function.
predict_online	Predict on given testset without killing the memory.
print_model_architechture	Print out the NAS network architechture structure.
save_model	Print out the NAS network architechture structure.
save_performances	Save stats and make plots.
train_and_evaluate	Train and evaluate AdaNet.

architecture()

Extract the ensemble architecture from evaluation results.

static get_trainable_variables(model_dir)

Return the weigths of the trained neural network.

Parameters:

model_dir (str) – path where of the saved model.

input_fn(split, training, augmentation=False)

Generate an input function for the Estimator.

Parameters:

• split (str) – the split to use within the traintest file.

• training (bool) – whether we are training or evaluating.

• augmentation (func) – a function to aument data, False if no aumentation is desired.

static predict(features, predict_fn=None, mask_fn=None, probs=False, samples=10, model_dir=None, consensus=False)

Load model and return predictions.

Parameters:

• model_dir (str) – path where to save the model.

• features (matrix) – a numpy matrix of Xs.

• predict_fn (func) – the predict function returned by predict_fn.

• probs (bool) – if this is a classifier return the probabilities.

• consensus (bool) – return also a sampling for consensus calculation. (regression only).

static predict_fn(model_dir)

Load model and return the predict function.

Parameters:

model_dir (str) – path where to save the model.

static predict_online(h5_file, split, predict_fn=None, mask_fn=None, batch_size=1000, limit=None, probs=False, n_classes=None, model_dir=None)

Predict on given testset without killing the memory.

Parameters:

• model_dir (str) – path where to save the model.

• h5_file (str) – path to h5 file compatible with Traintest.

• split (str) – the split to use within the h5_file.

• predict_fn (func) – the predict function returned by predict_fn.

• mask_fn (func) – a function masking part of the input.

• batch_size (int) – batch size for Traintest file.

• limit (int) – maximum number of predictions.

• probs (bool) – if this is a classifier return the probabilities.

static print_model_architechture(model_dir)

Print out the NAS network architechture structure.

Parameters:

model_dir (str) – path where of the saved model.

save_model(model_dir)

Print out the NAS network architechture structure.

Parameters:

model_dir (str) – path where to save the model.

save_performances(output_dir, plot, suffix=None, extra_predictors=None, do_plot=True)

Save stats and make plots.

train_and_evaluate(evaluate=True)

Train and evaluate AdaNet.