#43 #75 #81 Adapt Donut to Missing Values #82
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Chaoste
previously requested changes
Jun 14, 2018
|
I adapted the two that could not handle NaN's until now so they behave like if I would have not removed that use_zero flag. |
danthe96
approved these changes
Jun 14, 2018
main.py
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| @@ -36,7 +38,7 @@ def run_pipeline(): | |||
| SyntheticDataGenerator.extreme_1_polluted(0.5), | |||
| SyntheticDataGenerator.extreme_1_polluted(1) | |||
| ] | |||
| detectors = [RecurrentEBM(num_epochs=15), LSTMAD(), Donut(), DAGMM(), LSTM_Enc_Dec(epochs=200)] | |||
| detectors = [RecurrentEBM(num_epochs=15), LSTMAD(), Donut(), DAGMM(), LSTM_Enc_Dec(num_epochs=200)] | |||
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While you're at it, can you change num_epochs for LSTM_Enc_Dec to something low like 5-15 or so? It makes the benchmark run forever, and I just can't imagine that many epochs bringing any benefit.
src/evaluation/experiments.py
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| @@ -13,7 +13,7 @@ def run_pollution_experiment(outlier_type='extreme_1', output_dir=None, steps=5) | |||
| datasets = [ | |||
| SyntheticDataGenerator.get(f'{outlier_type}_polluted', pollution) for pollution in np.linspace(0, 1, steps) | |||
| ] | |||
| detectors = [LSTM_Enc_Dec(epochs=200), DAGMM(), Donut(), RecurrentEBM(), LSTMAD()] | |||
| detectors = [LSTM_Enc_Dec(num_epochs=200), DAGMM(), Donut(), RecurrentEBM(), LSTMAD()] | |||
This was referenced Jun 15, 2018
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num_epochsThis addresses Training Donut raises "Tensor had NaN values" #43, Adapt Donut to Missing Data #75 and Training Donut on Missing Values Outlier (100%) throws ValueError #81 .