Add LSTMED + parametrize all variants of DAGMM #83
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# Conflicts: # src/evaluation/evaluator.py
# Conflicts: # main.py # src/evaluation/evaluator.py
WGierke
reviewed
Jun 14, 2018
main.py
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| @@ -17,8 +17,8 @@ def main(): | |||
| def run_pipeline(): | |||
| if os.environ.get("CIRCLECI", False): | |||
| datasets = [SyntheticDataGenerator.extreme_1()] | |||
| detectors = [RecurrentEBM(num_epochs=2), LSTMAD(num_epochs=5), Donut(max_epoch=5), DAGMM(), | |||
| LSTM_Enc_Dec(epochs=2)] | |||
| detectors = [DAGMM(sequence_length=15, autoencoder_type=LSTMAutoEncoder), Donut(max_epoch=5), | |||
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Do you mind adding the three remaining sequence_length-autoencoder_type-combinations of DAGMM to the detectors as well?
maxifischer
reviewed
Jun 14, 2018
src/algorithms/autoencoder.py
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| enc = self._encoder(x) | ||
| dec = self._decoder(enc) | ||
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| return dec, enc |
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maybe swap the return values, might lead to confusion
| df_evaluation = pd.DataFrame( | ||
| columns=["dataset", "algorithm", "accuracy", "precision", "recall", "F1-score", "F0.1-score"]) | ||
| for _ in range(5): | ||
| evaluator.evaluate() | ||
| df = evaluator.benchmarks() | ||
| df_evaluation = df_evaluation.append(df) | ||
| print(df_evaluation.to_string()) |
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Delete print line or log instead
Chaoste
reviewed
Jun 14, 2018
src/algorithms/lstm_enc_dec_axl.py
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| self.lstmed.batch_size = prediction_batch_size # (!) | ||
| self.lstmed.eval() | ||
| errors = [np.nan]*(self.sequence_length-1) |
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Would have been usefulto see a comment here - was thinking about it's meaning but I've got it now ;)
Chaoste
reviewed
Jun 14, 2018
main.py
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| @@ -3,7 +3,7 @@ | |||
| import numpy as np | |||
| import pandas as pd | |||
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| from src.algorithms import DAGMM, Donut, RecurrentEBM, LSTMAD, LSTM_Enc_Dec | |||
| from src.algorithms import DAGMM, Donut, LSTM_Enc_Dec, LSTMAD, LSTMAutoEncoder, LSTMED, RecurrentEBM | |||
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I guess you know what I think about this ^^
Does it outperform the old LSTM in every way? If so we can drop it because optimizing it won't be worth it.
added 5 commits
June 14, 2018 18:26
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Please re-review |
# Conflicts: # src/algorithms/lstm_enc_dec_axl.py
… covariance matrices
…mble # Conflicts: # main.py
WGierke
approved these changes
Jun 20, 2018
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sequence_lengthparameterAutoEncoderclass which can be passed via paramE.g.
DAGMM(sequence_length=1, autoencoder=NNAutoEncoder)=> Original DAGMMDAGMM(sequence_length=15, autoencoder=NNAutoEncoder=> DAGMM w/ windowDAGMM(sequence_length=1, autoencoder=LSTMAutoEncoder)=> LSTM-DAGMM w/o windowDAGMM(sequence_length=15, autoencoder=LSTMAutoEncoder)=> LSTM-DAGMM w/ window