├── .gitattributes ├── data ├── asas │ └── full.pkl ├── linear │ └── full.pkl └── macho │ └── full.pkl ├── keras_logs ├── linear │ └── n200 │ │ └── gru_096_x2_1m03_drop25_emb64_bidir │ │ └── weights.h5 ├── period │ └── uneven │ │ └── noise0.5 │ │ ├── gru_032_x1_5m04_drop25_bidir │ │ └── weights.h5 │ │ ├── gru_032_x2_5m04_drop25_bidir │ │ ├── weights.h5 │ │ └── training.csv │ │ ├── gru_032_x3_5m04_drop25_bidir │ │ └── weights.h5 │ │ ├── gru_064_x1_5m04_drop25_bidir │ │ └── weights.h5 │ │ ├── gru_064_x2_5m04_drop25_bidir │ │ └── weights.h5 │ │ ├── gru_064_x3_5m04_drop25_bidir │ │ ├── weights.h5 │ │ └── training.csv │ │ ├── gru_096_x1_5m04_drop25_bidir │ │ └── weights.h5 │ │ ├── gru_096_x2_5m04_drop25_bidir │ │ └── weights.h5 │ │ ├── gru_096_x3_5m04_drop25_bidir │ │ └── weights.h5 │ │ ├── gru_128_x2_5m04_drop25_bidir │ │ └── weights.h5 │ │ ├── lstm_064_x1_5m04_drop25_bidir │ │ └── weights.h5 │ │ ├── lstm_064_x2_5m04_drop25_bidir │ │ ├── weights.h5 │ │ └── training.csv │ │ └── lstm_064_x3_5m04_drop25_bidir │ │ ├── weights.h5 │ │ ├── training.csv │ │ ├── events.out.tfevents.1487280664.xs-0017 │ │ ├── events.out.tfevents.1500487444.benten.dlab.berkeley.edu │ │ └── param_log.json ├── asas_fold │ └── n200_ss0.7 │ │ └── gru_096_x2_5m04_drop25_emb64_bidir │ │ ├── weights.h5 │ │ ├── events.out.tfevents.1494390814.ucb-jupyter.calit2.optiputer.net │ │ └── param_log.json ├── asas_full │ └── n200_ss0.7 │ │ ├── gru_064_x1_1m03_drop25_emb64_bidir │ │ └── weights.h5 │ │ ├── gru_096_x2_5m04_drop25_emb16_bidir │ │ ├── weights.h5 │ │ ├── events.out.tfevents.1500421372.benten.dlab.berkeley.edu │ │ ├── param_log.json │ │ └── training.csv │ │ ├── gru_096_x2_5m04_drop25_emb32_bidir │ │ ├── weights.h5 │ │ ├── events.out.tfevents.1500421370.benten.dlab.berkeley.edu │ │ ├── param_log.json │ │ └── training.csv │ │ ├── gru_096_x2_5m04_drop25_emb64_bidir │ │ ├── weights.h5 │ │ ├── events.out.tfevents.1494391921.benten.dlab.berkeley.edu │ │ ├── events.out.tfevents.1500421374.benten.dlab.berkeley.edu │ │ ├── param_log.json │ │ └── training.csv │ │ └── gru_096_x2_5m04_drop25_emb8_bidir │ │ ├── weights.h5 │ │ ├── events.out.tfevents.1500421375.benten.dlab.berkeley.edu │ │ ├── param_log.json │ │ └── training.csv ├── macho_fold │ └── n200_ss0.7 │ │ └── gru_096_x2_5m04_drop25_emb64_bidir │ │ └── weights.h5 ├── autoencoder │ └── uneven │ │ └── noise0.5 │ │ ├── gru_032_x1_5m04_drop25_emb8_bidir │ │ └── weights.h5 │ │ ├── gru_032_x2_5m04_drop25_emb8_bidir │ │ └── weights.h5 │ │ ├── gru_032_x3_5m04_drop25_emb8_bidir │ │ └── weights.h5 │ │ ├── gru_064_x1_5m04_drop25_emb8_bidir │ │ └── weights.h5 │ │ ├── gru_064_x2_5m04_drop25_emb4_bidir │ │ ├── weights.h5 │ │ └── training.csv │ │ ├── gru_064_x2_5m04_drop25_emb8_bidir │ │ └── weights.h5 │ │ ├── gru_064_x3_5m04_drop25_emb8_bidir │ │ └── weights.h5 │ │ ├── gru_096_x1_5m04_drop25_emb8_bidir │ │ └── weights.h5 │ │ ├── gru_096_x2_5m04_drop25_emb4_bidir │ │ └── weights.h5 │ │ ├── gru_096_x2_5m04_drop25_emb8_bidir │ │ └── weights.h5 │ │ ├── gru_096_x3_5m04_drop25_emb8_bidir │ │ └── weights.h5 │ │ ├── gru_032_x1_5m04_drop25_emb16_bidir │ │ └── weights.h5 │ │ ├── gru_032_x1_5m04_drop25_emb32_bidir │ │ └── weights.h5 │ │ ├── gru_032_x1_5m04_drop25_emb64_bidir │ │ └── weights.h5 │ │ ├── gru_032_x2_5m04_drop25_emb16_bidir │ │ └── weights.h5 │ │ ├── gru_032_x2_5m04_drop25_emb32_bidir │ │ └── weights.h5 │ │ ├── gru_032_x2_5m04_drop25_emb64_bidir │ │ └── weights.h5 │ │ ├── gru_032_x3_5m04_drop25_emb16_bidir │ │ └── weights.h5 │ │ ├── gru_032_x3_5m04_drop25_emb32_bidir │ │ └── weights.h5 │ │ ├── gru_032_x3_5m04_drop25_emb64_bidir │ │ └── weights.h5 │ │ ├── gru_064_x1_5m04_drop25_emb16_bidir │ │ └── weights.h5 │ │ ├── gru_064_x1_5m04_drop25_emb32_bidir │ │ └── weights.h5 │ │ ├── gru_064_x1_5m04_drop25_emb64_bidir │ │ └── weights.h5 │ │ ├── gru_064_x2_5m04_drop25_emb16_bidir │ │ └── weights.h5 │ │ ├── gru_064_x2_5m04_drop25_emb32_bidir │ │ └── weights.h5 │ │ ├── gru_064_x2_5m04_drop25_emb64_bidir │ │ ├── weights.h5 │ │ └── training.csv │ │ ├── gru_064_x3_5m04_drop25_emb16_bidir │ │ └── weights.h5 │ │ ├── gru_064_x3_5m04_drop25_emb32_bidir │ │ └── weights.h5 │ │ ├── gru_064_x3_5m04_drop25_emb64_bidir │ │ └── weights.h5 │ │ ├── gru_096_x1_5m04_drop25_emb16_bidir │ │ └── weights.h5 │ │ ├── gru_096_x1_5m04_drop25_emb32_bidir │ │ └── weights.h5 │ │ ├── gru_096_x1_5m04_drop25_emb64_bidir │ │ └── weights.h5 │ │ ├── gru_096_x2_5m04_drop25_emb16_bidir │ │ └── weights.h5 │ │ ├── gru_096_x2_5m04_drop25_emb32_bidir │ │ └── weights.h5 │ │ ├── gru_096_x2_5m04_drop25_emb64_bidir │ │ └── weights.h5 │ │ ├── gru_096_x3_5m04_drop25_emb16_bidir │ │ └── weights.h5 │ │ ├── gru_096_x3_5m04_drop25_emb32_bidir │ │ └── weights.h5 │ │ └── gru_096_x3_5m04_drop25_emb64_bidir │ │ └── weights.h5 └── asas_linear_fold │ └── n200_ss0.7 │ └── gru_096_x2_5m04_drop25_emb64_bidir │ ├── weights.h5 │ ├── events.out.tfevents.1493343726.ucb-jupyter.calit2.optiputer.net │ ├── param_log.json │ └── training.csv ├── requirements.txt ├── README.md ├── LICENSE ├── .gitignore ├── period.py ├── sample_data.py ├── survey_autoencoder.py ├── survey_classifier.py ├── autoencoder.py ├── light_curve.py └── keras_util.py /.gitattributes: -------------------------------------------------------------------------------- 1 | *.pkl filter=lfs diff=lfs merge=lfs -text 2 | -------------------------------------------------------------------------------- /data/asas/full.pkl: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:d638076807600901ba136dbd0d250dd9111a3bb6a0772a3f5548aeb33647f524 3 | size 241282239 4 | -------------------------------------------------------------------------------- /data/linear/full.pkl: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid 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-------------------------------------------------------------------------------- /keras_logs/asas_linear_fold/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir/events.out.tfevents.1493343726.ucb-jupyter.calit2.optiputer.net: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bnaul/IrregularTimeSeriesAutoencoderPaper/HEAD/keras_logs/asas_linear_fold/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir/events.out.tfevents.1493343726.ucb-jupyter.calit2.optiputer.net -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Neural network autoencoders for unevenly sampled time series 2 | [![DOI](https://zenodo.org/badge/90776775.svg)](https://zenodo.org/badge/latestdoi/90776775) 3 | 4 | Code accompanying "A recurrent neural network for classification of unevenly sampled variable stars". 5 | 6 | - Code for scores/figures is found in `figures.ipynb` 7 | - Autoencoder network architecture is defined in `autoencoder.py` 8 | - Experiments for simulated data are found in `autoencoder.main` 9 | - Experiments for light curve data are found in `survey_autoencoder.main` 10 | - Light curve data is in `./data` 11 | - Model weights are saved in `./keras_logs` 12 | 13 | See mirror at https://gitlab.com/cesium-ml/IrregularTimeSeriesAutoencoderPaper to download the full set of input data. 14 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Brett Naul 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /keras_logs/period/uneven/noise0.5/lstm_064_x3_5m04_drop25_bidir/param_log.json: -------------------------------------------------------------------------------- 1 | { 2 | "N_test": 1000, 3 | "N_train": 50000, 4 | "batch_norm": false, 5 | "batch_size": 500, 6 | "bidirectional": true, 7 | "data_type": "sinusoid", 8 | "decode_layers": null, 9 | "decode_type": null, 10 | "drop_frac": 0.25, 11 | "embedding": null, 12 | "even": false, 13 | "filter_length": null, 14 | "finetune_rate": null, 15 | "first_N": null, 16 | "gpu_frac": null, 17 | "gpu_id": null, 18 | "loaded": false, 19 | "log_dir": "/home/bnaul/timeflow/keras_logs/period/uneven/noise0.5/lstm_064_x3_5m04_drop25_bidir", 20 | "lomb_score": null, 21 | "loss": "mse", 22 | "loss_weights": null, 23 | "lr": 0.0005, 24 | "m_max": 20.0, 25 | "metrics": [], 26 | "model_type": "lstm", 27 | "n_max": 200, 28 | "n_min": 200, 29 | "nb_epoch": 250, 30 | "no_train": false, 31 | "noisify": false, 32 | "num_layers": 3, 33 | "patience": 20, 34 | "period_fold": false, 35 | "pool": null, 36 | "pretrain": null, 37 | "pretrain_weights": null, 38 | "run": "lstm_064_x3_5m04_drop25_bidir", 39 | "sigma": 0.5, 40 | "sim_type": "period/uneven/noise0.5", 41 | "size": 64, 42 | "ss_resid": null, 43 | "survey_files": null, 44 | "validation_split": 0.2, 45 | "weights_path": "/home/bnaul/timeflow/keras_logs/period/uneven/noise0.5/lstm_064_x3_5m04_drop25_bidir/weights.h5" 46 | } -------------------------------------------------------------------------------- /keras_logs/asas_fold/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir/param_log.json: -------------------------------------------------------------------------------- 1 | { 2 | "N_test": 1000, 3 | "N_train": 50000, 4 | "batch_norm": false, 5 | "batch_size": 500, 6 | "bidirectional": true, 7 | "data_type": "sinusoid", 8 | "decode_layers": null, 9 | "decode_type": null, 10 | "drop_frac": 0.25, 11 | "embedding": 64, 12 | "even": false, 13 | "filter_length": null, 14 | "finetune_rate": null, 15 | "first_N": null, 16 | "gpu_frac": 0.4, 17 | "gpu_id": null, 18 | "loaded": false, 19 | "log_dir": "/home/bnaul/timeflow/keras_logs/asas_fold/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir", 20 | "lomb_score": null, 21 | "loss": "mse", 22 | "loss_weights": null, 23 | "lr": 0.0005, 24 | "m_max": 20.0, 25 | "metrics": [], 26 | "model_type": "gru", 27 | "n_max": 200, 28 | "n_min": 200, 29 | "nb_epoch": 500, 30 | "no_train": false, 31 | "noisify": false, 32 | "num_layers": 2, 33 | "period_fold": true, 34 | "pool": null, 35 | "pretrain": null, 36 | "pretrain_weights": null, 37 | "run": "gru_096_x2_5m04_drop25_emb64_bidir", 38 | "sigma": 2e-09, 39 | "sim_type": "asas_fold/n200_ss0.7", 40 | "size": 96, 41 | "ss_resid": 0.7, 42 | "survey_files": [ 43 | "data/asas/full.pkl" 44 | ], 45 | "validation_split": 0.0, 46 | "weights_path": "/home/bnaul/timeflow/keras_logs/asas_fold/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir/weights.h5" 47 | } -------------------------------------------------------------------------------- /keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb8_bidir/param_log.json: -------------------------------------------------------------------------------- 1 | { 2 | "N_test": 1000, 3 | "N_train": 50000, 4 | "batch_norm": false, 5 | "batch_size": 500, 6 | "bidirectional": true, 7 | "data_type": "sinusoid", 8 | "decode_layers": null, 9 | "decode_type": null, 10 | "drop_frac": 0.25, 11 | "embedding": 8, 12 | "even": false, 13 | "filter_length": null, 14 | "finetune_rate": null, 15 | "first_N": null, 16 | "gpu_frac": null, 17 | "gpu_id": null, 18 | "loaded": false, 19 | "log_dir": "/home/bnaul/timeflow/keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb8_bidir", 20 | "lomb_score": null, 21 | "loss": "mse", 22 | "loss_weights": null, 23 | "lr": 0.0005, 24 | "m_max": 20.0, 25 | "metrics": [], 26 | "model_type": "gru", 27 | "n_max": 200, 28 | "n_min": 200, 29 | "nb_epoch": 250, 30 | "no_train": false, 31 | "noisify": false, 32 | "num_layers": 2, 33 | "patience": 20, 34 | "period_fold": true, 35 | "pool": null, 36 | "pretrain": null, 37 | "pretrain_weights": null, 38 | "run": "gru_096_x2_5m04_drop25_emb8_bidir", 39 | "sigma": 2e-09, 40 | "sim_type": "asas_full/n200_ss0.7", 41 | "size": 96, 42 | "ss_resid": 0.7, 43 | "survey_files": [ 44 | "data/asas/full.pkl" 45 | ], 46 | "validation_split": 0.2, 47 | "weights_path": "/home/bnaul/timeflow/keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb8_bidir/weights.h5" 48 | } -------------------------------------------------------------------------------- /keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb16_bidir/param_log.json: -------------------------------------------------------------------------------- 1 | { 2 | "N_test": 1000, 3 | "N_train": 50000, 4 | "batch_norm": false, 5 | "batch_size": 500, 6 | "bidirectional": true, 7 | "data_type": "sinusoid", 8 | "decode_layers": null, 9 | "decode_type": null, 10 | "drop_frac": 0.25, 11 | "embedding": 16, 12 | "even": false, 13 | "filter_length": null, 14 | "finetune_rate": null, 15 | "first_N": null, 16 | "gpu_frac": null, 17 | "gpu_id": null, 18 | "loaded": false, 19 | "log_dir": "/home/bnaul/timeflow/keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb16_bidir", 20 | "lomb_score": null, 21 | "loss": "mse", 22 | "loss_weights": null, 23 | "lr": 0.0005, 24 | "m_max": 20.0, 25 | "metrics": [], 26 | "model_type": "gru", 27 | "n_max": 200, 28 | "n_min": 200, 29 | "nb_epoch": 250, 30 | "no_train": false, 31 | "noisify": false, 32 | "num_layers": 2, 33 | "patience": 20, 34 | "period_fold": true, 35 | "pool": null, 36 | "pretrain": null, 37 | "pretrain_weights": null, 38 | "run": "gru_096_x2_5m04_drop25_emb16_bidir", 39 | "sigma": 2e-09, 40 | "sim_type": "asas_full/n200_ss0.7", 41 | "size": 96, 42 | "ss_resid": 0.7, 43 | "survey_files": [ 44 | "data/asas/full.pkl" 45 | ], 46 | "validation_split": 0.2, 47 | "weights_path": "/home/bnaul/timeflow/keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb16_bidir/weights.h5" 48 | } -------------------------------------------------------------------------------- /keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb32_bidir/param_log.json: -------------------------------------------------------------------------------- 1 | { 2 | "N_test": 1000, 3 | "N_train": 50000, 4 | "batch_norm": false, 5 | "batch_size": 500, 6 | "bidirectional": true, 7 | "data_type": "sinusoid", 8 | "decode_layers": null, 9 | "decode_type": null, 10 | "drop_frac": 0.25, 11 | "embedding": 32, 12 | "even": false, 13 | "filter_length": null, 14 | "finetune_rate": null, 15 | "first_N": null, 16 | "gpu_frac": null, 17 | "gpu_id": null, 18 | "loaded": false, 19 | "log_dir": "/home/bnaul/timeflow/keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb32_bidir", 20 | "lomb_score": null, 21 | "loss": "mse", 22 | "loss_weights": null, 23 | "lr": 0.0005, 24 | "m_max": 20.0, 25 | "metrics": [], 26 | "model_type": "gru", 27 | "n_max": 200, 28 | "n_min": 200, 29 | "nb_epoch": 250, 30 | "no_train": false, 31 | "noisify": false, 32 | "num_layers": 2, 33 | "patience": 20, 34 | "period_fold": true, 35 | "pool": null, 36 | "pretrain": null, 37 | "pretrain_weights": null, 38 | "run": "gru_096_x2_5m04_drop25_emb32_bidir", 39 | "sigma": 2e-09, 40 | "sim_type": "asas_full/n200_ss0.7", 41 | "size": 96, 42 | "ss_resid": 0.7, 43 | "survey_files": [ 44 | "data/asas/full.pkl" 45 | ], 46 | "validation_split": 0.2, 47 | "weights_path": "/home/bnaul/timeflow/keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb32_bidir/weights.h5" 48 | } -------------------------------------------------------------------------------- /keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir/param_log.json: -------------------------------------------------------------------------------- 1 | { 2 | "N_test": 1000, 3 | "N_train": 50000, 4 | "batch_norm": false, 5 | "batch_size": 500, 6 | "bidirectional": true, 7 | "data_type": "sinusoid", 8 | "decode_layers": null, 9 | "decode_type": null, 10 | "drop_frac": 0.25, 11 | "embedding": 64, 12 | "even": false, 13 | "filter_length": null, 14 | "finetune_rate": null, 15 | "first_N": null, 16 | "gpu_frac": null, 17 | "gpu_id": null, 18 | "loaded": false, 19 | "log_dir": "/home/bnaul/timeflow/keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir", 20 | "lomb_score": null, 21 | "loss": "mse", 22 | "loss_weights": null, 23 | "lr": 0.0005, 24 | "m_max": 20.0, 25 | "metrics": [], 26 | "model_type": "gru", 27 | "n_max": 200, 28 | "n_min": 200, 29 | "nb_epoch": 250, 30 | "no_train": false, 31 | "noisify": false, 32 | "num_layers": 2, 33 | "patience": 20, 34 | "period_fold": true, 35 | "pool": null, 36 | "pretrain": null, 37 | "pretrain_weights": null, 38 | "run": "gru_096_x2_5m04_drop25_emb64_bidir", 39 | "sigma": 2e-09, 40 | "sim_type": "asas_full/n200_ss0.7", 41 | "size": 96, 42 | "ss_resid": 0.7, 43 | "survey_files": [ 44 | "data/asas/full.pkl" 45 | ], 46 | "validation_split": 0.2, 47 | "weights_path": "/home/bnaul/timeflow/keras_logs/asas_full/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir/weights.h5" 48 | } -------------------------------------------------------------------------------- /keras_logs/asas_linear_fold/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir/param_log.json: -------------------------------------------------------------------------------- 1 | { 2 | "N_test": 1000, 3 | "N_train": 50000, 4 | "batch_norm": false, 5 | "batch_size": 500, 6 | "bidirectional": true, 7 | "data_type": "sinusoid", 8 | "decode_layers": null, 9 | "decode_type": null, 10 | "drop_frac": 0.25, 11 | "embedding": 64, 12 | "even": false, 13 | "filter_length": null, 14 | "finetune_rate": null, 15 | "first_N": null, 16 | "gpu_frac": 0.4, 17 | "gpu_id": null, 18 | "loaded": false, 19 | "log_dir": "/home/bnaul/timeflow/keras_logs/asas_linear_fold/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir", 20 | "lomb_score": null, 21 | "loss": "mse", 22 | "loss_weights": null, 23 | "lr": 0.0005, 24 | "m_max": 20.0, 25 | "metrics": [], 26 | "model_type": "gru", 27 | "n_max": 200, 28 | "n_min": 200, 29 | "nb_epoch": 250, 30 | "no_train": false, 31 | "noisify": false, 32 | "num_layers": 2, 33 | "patience": 250, 34 | "period_fold": true, 35 | "pool": null, 36 | "pretrain": null, 37 | "pretrain_weights": null, 38 | "run": "gru_096_x2_5m04_drop25_emb64_bidir", 39 | "sigma": 2e-09, 40 | "sim_type": "asas_linear_fold/n200_ss0.7", 41 | "size": 96, 42 | "ss_resid": 0.7, 43 | "survey_files": [ 44 | "data/linear/full.pkl", 45 | "data/asas/full.pkl" 46 | ], 47 | "weights_path": "/home/bnaul/timeflow/keras_logs/asas_linear_fold/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir/weights.h5" 48 | } -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | 91 | *.swp 92 | *DS_Store 93 | -------------------------------------------------------------------------------- /period.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | np.random.seed(0) 3 | from sklearn.preprocessing import StandardScaler 4 | from keras.layers import Input, LSTM, GRU, SimpleRNN 5 | from keras.models import Model, Sequential 6 | 7 | from autoencoder import encoder 8 | import sample_data 9 | import keras_util as ku 10 | 11 | 12 | def main(args=None): 13 | args = ku.parse_model_args(args) 14 | 15 | N = args.N_train + args.N_test 16 | train = np.arange(args.N_train); test = np.arange(args.N_test) + args.N_train 17 | X, Y, X_raw = sample_data.periodic(N, args.n_min, args.n_max, 18 | even=args.even, noise_sigma=args.sigma, 19 | kind=args.data_type) 20 | 21 | if args.even: 22 | X = X[:, :, 1:2] 23 | else: 24 | X[:, :, 0] = ku.times_to_lags(X_raw[:, :, 0]) 25 | X[np.isnan(X)] = -1. 26 | X_raw[np.isnan(X_raw)] = -1. 27 | 28 | Y = sample_data.phase_to_sin_cos(Y) 29 | scaler = StandardScaler(copy=False, with_mean=True, with_std=True) 30 | scaler.fit_transform(Y) 31 | if args.loss_weights: # so far, only used to zero out some columns 32 | Y *= args.loss_weights 33 | 34 | model_type_dict = {'gru': GRU, 'lstm': LSTM, 'vanilla': SimpleRNN} 35 | 36 | model_input = Input(shape=(X.shape[1], X.shape[-1]), name='main_input') 37 | encode = encoder(model_input, layer=model_type_dict[args.model_type], 38 | output_size=Y.shape[-1], **vars(args)) 39 | model = Model(model_input, encode) 40 | 41 | run = ku.get_run_id(**vars(args)) 42 | 43 | history = ku.train_and_log(X[train], Y[train], run, model, **vars(args)) 44 | return X, Y, X_raw, scaler, model, args 45 | 46 | 47 | if __name__ == '__main__': 48 | X, Y, X_raw, scaler, model, args = main() 49 | -------------------------------------------------------------------------------- /sample_data.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from keras.preprocessing.sequence import pad_sequences 3 | 4 | 5 | def phase_to_sin_cos(Y): 6 | """Reparametrize sinusoid parameters: 7 | w, A, phi, b --> p, A_cos, A_sin, b 8 | 9 | Estimating these parameters seems to be easier in practice. 10 | """ 11 | w, A, phi, b = Y.T 12 | 13 | A_cos = A * np.sin(phi) 14 | A_sin = A * np.cos(phi) 15 | p = w ** -1 16 | 17 | return np.c_[p, A_cos, A_sin, b] 18 | 19 | 20 | def _random_times(N, even=True, t_max=4 * np.pi, n_min=None, n_max=None, t_shape=2, t_scale=0.05): 21 | if n_min is None and n_max is None: 22 | raise ValueError("Either n_min or n_max is required.") 23 | elif n_min is None: 24 | n_min = n_max 25 | elif n_max is None: 26 | n_max = n_min 27 | 28 | if even: 29 | return np.tile(np.linspace(0., t_max, n_max), (N, 1)) 30 | else: 31 | lags = [t_scale * np.random.pareto(t_shape, size=np.random.randint(n_min, n_max + 1)) 32 | for i in range(N)] 33 | return [np.r_[0, np.cumsum(lags_i)] for lags_i in lags] 34 | 35 | 36 | def _periodic_params(N, A_min, A_max, w_min, w_max): 37 | w = 1. / np.random.uniform(1. / w_max, 1. / w_min, size=N) 38 | A = np.random.uniform(A_min, A_max, size=N) 39 | phi = 2 * np.pi * np.random.random(size=N) 40 | b = np.random.normal(scale=1, size=N) 41 | 42 | return w, A, phi, b 43 | 44 | 45 | def _sinusoid(w, A, phi, b): 46 | return lambda t: A * np.sin(2 * np.pi * w * t + phi) + b 47 | 48 | 49 | def periodic(N, n_min, n_max, t_max=4 * np.pi, even=True, A_min=0.5, A_max=2.0, 50 | noise_sigma=0., w_min=0.1, w_max=1., t_shape=2, t_scale=0.05, 51 | kind='sinusoid'): 52 | """Returns periodic data (values, (freq, amplitude, phase, offset))""" 53 | t = _random_times(N, even, t_max, n_min, n_max, t_shape, t_scale) 54 | w, A, phi, b = _periodic_params(N, A_min, A_max, w_min, w_max) 55 | 56 | X_list = [np.c_[t[i], _sinusoid(w[i], A[i], phi[i], b[i])(t[i])] for i in range(N)] 57 | X_raw = pad_sequences(X_list, maxlen=n_max, value=np.nan, dtype='float', padding='post') 58 | X = X_raw.copy() 59 | X[:, :, 1] = X_raw[:, :, 1] + np.random.normal(scale=noise_sigma + 1e-9, size=(N, n_max)) 60 | Y = np.c_[w, A, phi, b] 61 | 62 | return X, Y, X_raw 63 | -------------------------------------------------------------------------------- /survey_autoencoder.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import joblib 3 | from keras.layers import Input, LSTM, GRU, SimpleRNN 4 | from keras.models import Model 5 | from keras.preprocessing.sequence import pad_sequences 6 | 7 | import keras_util as ku 8 | from autoencoder import encoder, decoder 9 | from light_curve import LightCurve 10 | 11 | 12 | def preprocess(X_raw, m_max=np.inf): 13 | X = X_raw.copy() 14 | 15 | wrong_units = np.all(np.isnan(X[:, :, 1])) | (np.nanmax(X[:, :, 1], axis=1) > m_max) 16 | X = X[~wrong_units, :, :] 17 | 18 | # Replace times w/ lags 19 | X[:, :, 0] = ku.times_to_lags(X[:, :, 0]) 20 | 21 | means = np.atleast_2d(np.nanmean(X[:, :, 1], axis=1)).T 22 | X[:, :, 1] -= means 23 | 24 | scales = np.atleast_2d(np.nanstd(X[:, :, 1], axis=1)).T 25 | X[:, :, 1] /= scales 26 | 27 | # Drop_errors from input; only used as weights 28 | X = X[:, :, :2] 29 | 30 | return X, means, scales, wrong_units 31 | 32 | 33 | def main(args=None): 34 | """Train an autoencoder model from `LightCurve` objects saved in 35 | `args.survey_files`. 36 | 37 | args: dict 38 | Dictionary of values to override default values in `keras_util.parse_model_args`; 39 | can also be passed via command line. See `parse_model_args` for full list of 40 | possible arguments. 41 | """ 42 | args = ku.parse_model_args(args) 43 | 44 | np.random.seed(0) 45 | 46 | if not args.survey_files: 47 | raise ValueError("No survey files given") 48 | lc_lists = [joblib.load(f) for f in args.survey_files] 49 | n_reps = [max(len(y) for y in lc_lists) // len(x) for x in lc_lists] 50 | combined = sum([x * i for x, i in zip(lc_lists, n_reps)], []) 51 | if args.lomb_score: 52 | combined = [lc for lc in combined if lc.best_score >= args.lomb_score] 53 | if args.ss_resid: 54 | combined = [lc for lc in combined if lc.ss_resid <= args.ss_resid] 55 | split = [el for lc in combined for el in lc.split(args.n_min, args.n_max)] 56 | if args.period_fold: 57 | for lc in split: 58 | lc.period_fold() 59 | X_list = [np.c_[lc.times, lc.measurements, lc.errors] for lc in split] 60 | 61 | X_raw = pad_sequences(X_list, value=np.nan, dtype='float', padding='post') 62 | if args.N_train is not None: 63 | X_raw = X_raw[:args.N_train] 64 | 65 | model_type_dict = {'gru': GRU, 'lstm': LSTM, 'vanilla': SimpleRNN} 66 | X, means, scales, wrong_units = preprocess(X_raw, args.m_max) 67 | main_input = Input(shape=(X.shape[1], X.shape[-1]), name='main_input') 68 | aux_input = Input(shape=(X.shape[1], X.shape[-1] - 1), name='aux_input') 69 | model_input = [main_input, aux_input] 70 | encode = encoder(main_input, layer=model_type_dict[args.model_type], 71 | output_size=args.embedding, **vars(args)) 72 | decode = decoder(encode, num_layers=args.decode_layers if args.decode_layers 73 | else args.num_layers, 74 | layer=model_type_dict[args.decode_type if args.decode_type 75 | else args.model_type], 76 | n_step=X.shape[1], aux_input=aux_input, 77 | **{k: v for k, v in vars(args).items() if k != 'num_layers'}) 78 | model = Model(model_input, decode) 79 | 80 | run = ku.get_run_id(**vars(args)) 81 | 82 | errors = X_raw[:, :, 2] / scales 83 | sample_weight = 1. / errors 84 | sample_weight[np.isnan(sample_weight)] = 0.0 85 | X[np.isnan(X)] = 0. 86 | 87 | history = ku.train_and_log({'main_input': X, 'aux_input': np.delete(X, 1, axis=2)}, 88 | X[:, :, [1]], run, model, sample_weight=sample_weight, 89 | errors=errors, validation_split=0.0, **vars(args)) 90 | 91 | return X, X_raw, model, means, scales, wrong_units, args 92 | 93 | 94 | if __name__ == '__main__': 95 | X, X_raw, model, means, scales, wrong_units, args = main() 96 | -------------------------------------------------------------------------------- /survey_classifier.py: -------------------------------------------------------------------------------- 1 | import glob 2 | import os 3 | import numpy as np 4 | import pandas as pd 5 | import joblib 6 | from sklearn.model_selection import StratifiedKFold 7 | from keras.layers import (Input, Dense, TimeDistributed, Activation, LSTM, GRU, 8 | Dropout, merge, Reshape, Flatten, RepeatVector, 9 | Conv1D, MaxPooling1D, SimpleRNN) 10 | import tensorflow as tf 11 | import keras.backend as K 12 | from keras.models import Model, Sequential 13 | from keras.utils.np_utils import to_categorical 14 | from keras.preprocessing.sequence import pad_sequences 15 | 16 | import keras_util as ku 17 | from autoencoder import encoder 18 | from survey_autoencoder import preprocess, main as survey_autoencoder 19 | from light_curve import LightCurve 20 | 21 | 22 | def main(args=None): 23 | args = ku.parse_model_args(args) 24 | 25 | args.loss = 'categorical_crossentropy' 26 | 27 | np.random.seed(0) 28 | 29 | if not args.survey_files: 30 | raise ValueError("No survey files given") 31 | classes = ['RR_Lyrae_FM', 'W_Ursae_Maj', 'Classical_Cepheid', 32 | 'Beta_Persei', 'Semireg_PV'] 33 | lc_lists = [joblib.load(f) for f in args.survey_files] 34 | combined = [lc for lc_list in lc_lists for lc in lc_list] 35 | combined = [lc for lc in combined if lc.label in classes] 36 | if args.lomb_score: 37 | combined = [lc for lc in combined if lc.best_score >= args.lomb_score] 38 | split = [el for lc in combined for el in lc.split(args.n_min, args.n_max)] 39 | if args.period_fold: 40 | for lc in split: 41 | lc.period_fold() 42 | X_list = [np.c_[lc.times, lc.measurements, lc.errors] for lc in split] 43 | 44 | classnames, y_inds = np.unique([lc.label for lc in split], return_inverse=True) 45 | Y = to_categorical(y_inds, len(classnames)) 46 | 47 | X_raw = pad_sequences(X_list, value=np.nan, dtype='float', padding='post') 48 | X, means, scales, wrong_units = preprocess(X_raw, args.m_max) 49 | Y = Y[~wrong_units] 50 | 51 | # Remove errors 52 | X = X[:, :, :2] 53 | 54 | if args.even: 55 | X = X[:, :, 1:] 56 | 57 | # shuffled_inds = np.random.permutation(np.arange(len(X))) 58 | # train = np.sort(shuffled_inds[:args.N_train]) 59 | # valid = np.sort(shuffled_inds[args.N_train:]) 60 | train, valid = list(StratifiedKFold(n_splits=5, shuffle=True, random_state=0).split(X_list, y_inds))[0] 61 | 62 | model_type_dict = {'gru': GRU, 'lstm': LSTM, 'vanilla': SimpleRNN, 63 | 'conv': Conv1D}#, 'atrous': AtrousConv1D, 'phased': PhasedLSTM} 64 | 65 | # if args.pretrain: 66 | # auto_args = {k: v for k, v in args.__dict__.items() if k != 'pretrain'} 67 | # auto_args['sim_type'] = args.pretrain 68 | ## auto_args['no_train'] = True 69 | # auto_args['epochs'] = 1; auto_args['loss'] = 'mse'; auto_args['batch_size'] = 32; auto_args['sim_type'] = 'test' 70 | # _, _, auto_model, _ = survey_autoencoder(auto_args) 71 | # for layer in auto_model.layers: 72 | # layer.trainable = False 73 | # model_input = auto_model.input[0] 74 | # encode = auto_model.get_layer('encoding').output 75 | # else: 76 | # model_input = Input(shape=(X.shape[1], X.shape[-1]), name='main_input') 77 | # encode = encoder(model_input, layer=model_type_dict[args.model_type], 78 | # output_size=args.embedding, **vars(args)) 79 | model_input = Input(shape=(X.shape[1], X.shape[-1]), name='main_input') 80 | encode = encoder(model_input, layer=model_type_dict[args.model_type], 81 | output_size=args.embedding, **vars(args)) 82 | 83 | scale_param_input = Input(shape=(2,), name='scale_params') 84 | merged = merge([encode, scale_param_input], mode='concat') 85 | 86 | out = Dense(args.size + 2, activation='relu')(merged) 87 | out = Dense(Y.shape[-1], activation='softmax')(out) 88 | model = Model([model_input, scale_param_input], out) 89 | 90 | run = ku.get_run_id(**vars(args)) 91 | if args.pretrain: 92 | for layer in model.layers: 93 | layer.trainable = False 94 | pretrain_weights = os.path.join('keras_logs', args.pretrain, run, 'weights.h5') 95 | else: 96 | pretrain_weights = None 97 | 98 | history = ku.train_and_log([X[train], np.c_[means, scales][train]], Y[train], 99 | run, model, metrics=['accuracy'], 100 | validation_data=([X[valid], np.c_[means, scales][valid]], Y[valid]), 101 | pretrain_weights=pretrain_weights, **vars(args)) 102 | return X, X_raw, Y, model, args 103 | 104 | 105 | if __name__ == '__main__': 106 | X, X_raw, Y, model, args = main() 107 | -------------------------------------------------------------------------------- /autoencoder.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | np.random.seed(0) 3 | from keras.layers import (Input, Dense, TimeDistributed, LSTM, GRU, Dropout, merge, 4 | Flatten, RepeatVector, Recurrent, Bidirectional, SimpleRNN) 5 | from keras.models import Model 6 | 7 | import sample_data 8 | import keras_util as ku 9 | 10 | 11 | def encoder(model_input, layer, size, num_layers, drop_frac=0.0, output_size=None, 12 | bidirectional=False, **parsed_args): 13 | """Encoder module of autoencoder architecture. 14 | 15 | Can be used either as the encoding component of an autoencoder or as a standalone 16 | encoder, which takes (possibly irregularly-sampled) time series as inputs and produces 17 | a fixed-length vector as output. 18 | 19 | model_input: `keras.layers.Input` 20 | Input layer containing (y) or (dt, y) values 21 | layer: `keras.layers.Recurrent` 22 | Desired `keras` recurrent layer class 23 | size: int 24 | Number of units within each hidden layer 25 | num_layers: int 26 | Number of hidden layers 27 | drop_frac: float 28 | Dropout rate 29 | output_size: int, optional 30 | Size of encoding layer; defaults to `size` 31 | bidirectional: bool, optional 32 | Whether the bidirectional version of `layer` should be used; defaults to `False` 33 | """ 34 | 35 | if output_size is None: 36 | output_size = size 37 | encode = model_input 38 | for i in range(num_layers): 39 | wrapper = Bidirectional if bidirectional else lambda x: x 40 | encode = wrapper(layer(size, name='encode_{}'.format(i), 41 | return_sequences=(i < num_layers - 1)))(encode) 42 | if drop_frac > 0.0: 43 | encode = Dropout(drop_frac, name='drop_encode_{}'.format(i))(encode) 44 | encode = Dense(output_size, activation='linear', name='encoding')(encode) 45 | return encode 46 | 47 | 48 | def decoder(encode, layer, n_step, size, num_layers, drop_frac=0.0, aux_input=None, 49 | bidirectional=False, **parsed_args): 50 | """Decoder module of autoencoder architecture. 51 | 52 | Can be used either as the decoding component of an autoencoder or as a standalone 53 | decoder, which takes a fixed-length input vector and generates a length-`n_step` 54 | time series as output. 55 | 56 | layer: `keras.layers.Recurrent` 57 | Desired `keras` recurrent layer class 58 | n_step: int 59 | Length of output time series 60 | size: int 61 | Number of units within each hidden layer 62 | num_layers: int 63 | Number of hidden layers 64 | drop_frac: float 65 | Dropout rate 66 | aux_input: `keras.layers.Input`, optional 67 | Input layer containing `dt` values; if `None` then the sequence is assumed to be 68 | evenly-sampled 69 | bidirectional: bool, optional 70 | Whether the bidirectional version of `layer` should be used; defaults to `False` 71 | """ 72 | decode = RepeatVector(n_step, name='repeat')(encode) 73 | if aux_input is not None: 74 | decode = merge([aux_input, decode], mode='concat') 75 | 76 | for i in range(num_layers): 77 | if drop_frac > 0.0 and i > 0: # skip these for first layer for symmetry 78 | decode = Dropout(drop_frac, name='drop_decode_{}'.format(i))(decode) 79 | wrapper = Bidirectional if bidirectional else lambda x: x 80 | decode = wrapper(layer(size, name='decode_{}'.format(i), 81 | return_sequences=True))(decode) 82 | 83 | decode = TimeDistributed(Dense(1, activation='linear'), name='time_dist')(decode) 84 | return decode 85 | 86 | 87 | def main(args=None): 88 | """Generate random periodic time series and train an autoencoder model. 89 | 90 | args: dict 91 | Dictionary of values to override default values in `keras_util.parse_model_args`; 92 | can also be passed via command line. See `parse_model_args` for full list of 93 | possible arguments. 94 | """ 95 | args = ku.parse_model_args(args) 96 | 97 | train = np.arange(args.N_train); test = np.arange(args.N_test) + args.N_train 98 | X, Y, X_raw = sample_data.periodic(args.N_train + args.N_test, args.n_min, args.n_max, 99 | even=args.even, noise_sigma=args.sigma, 100 | kind=args.data_type) 101 | 102 | if args.even: 103 | X = X[:, :, 1:2] 104 | X_raw = X_raw[:, :, 1:2] 105 | else: 106 | X[:, :, 0] = ku.times_to_lags(X_raw[:, :, 0]) 107 | X[np.isnan(X)] = -1. 108 | X_raw[np.isnan(X_raw)] = -1. 109 | 110 | model_type_dict = {'gru': GRU, 'lstm': LSTM, 'vanilla': SimpleRNN} 111 | 112 | main_input = Input(shape=(X.shape[1], X.shape[-1]), name='main_input') 113 | if args.even: 114 | model_input = main_input 115 | aux_input = None 116 | else: 117 | aux_input = Input(shape=(X.shape[1], X.shape[-1] - 1), name='aux_input') 118 | model_input = [main_input, aux_input] 119 | 120 | encode = encoder(main_input, layer=model_type_dict[args.model_type], 121 | output_size=args.embedding, **vars(args)) 122 | decode = decoder(encode, num_layers=args.decode_layers if args.decode_layers 123 | else args.num_layers, 124 | layer=model_type_dict[args.decode_type if args.decode_type 125 | else args.model_type], 126 | n_step=X.shape[1], aux_input=aux_input, 127 | **{k: v for k, v in vars(args).items() if k != 'num_layers'}) 128 | model = Model(model_input, decode) 129 | 130 | run = ku.get_run_id(**vars(args)) 131 | 132 | if args.even: 133 | history = ku.train_and_log(X[train], X_raw[train], run, model, **vars(args)) 134 | else: 135 | sample_weight = (X[train, :, -1] != -1) 136 | history = ku.train_and_log({'main_input': X[train], 'aux_input': X[train, :, 0:1]}, 137 | X_raw[train, :, 1:2], run, model, 138 | sample_weight=sample_weight, **vars(args)) 139 | return X, Y, X_raw, model, args 140 | 141 | 142 | if __name__ == '__main__': 143 | X, Y, X_raw, model, args = main() 144 | -------------------------------------------------------------------------------- /light_curve.py: -------------------------------------------------------------------------------- 1 | import glob 2 | import os 3 | from io import StringIO 4 | 5 | import numpy as np 6 | import pandas as pd 7 | import joblib 8 | 9 | 10 | class LightCurve(): 11 | def __init__(self, times, measurements, errors, survey=None, name=None, 12 | best_period=None, best_score=None, label=None, p=None, 13 | p_signif=None, p_class=None, ss_resid=None): 14 | self.times = times 15 | self.measurements = measurements 16 | self.errors = errors 17 | self.survey = survey 18 | self.name = name 19 | self.best_period = best_period 20 | self.best_score = best_score 21 | self.label = label 22 | self.p = p 23 | self.p_signif = p_signif 24 | self.p_class = p_class 25 | self.ss_resid = ss_resid 26 | 27 | def __repr__(self): 28 | return "LightCurve(" + ', '.join("{}={}".format(k, v) 29 | for k, v in self.__dict__.items()) + ")" 30 | 31 | def __len__(self): 32 | return len(self.times) 33 | 34 | def split(self, n_min=0, n_max=np.inf): 35 | inds = np.arange(len(self.times)) 36 | splits = [np.array(x) 37 | for x in np.array_split(inds, np.arange(n_max, len(inds), step=n_max)) 38 | if len(x) >= n_min] 39 | return [LightCurve(survey=self.survey, name=self.name, 40 | times=self.times[s], 41 | measurements=self.measurements[s], 42 | errors=self.errors[s], best_period=self.best_period, 43 | best_score=self.best_score, label=self.label, 44 | p=self.p, p_signif=self.p_signif, p_class=self.p_class, 45 | ss_resid=self.ss_resid) 46 | for s in splits] 47 | 48 | def fit_lomb_scargle(self): 49 | from gatspy.periodic import LombScargleFast 50 | period_range = (0.005 * (max(self.times) - min(self.times)), 51 | 0.95 * (max(self.times) - min(self.times))) 52 | model_gat = LombScargleFast(fit_period=True, silence_warnings=True, 53 | optimizer_kwds={'period_range': period_range, 'quiet': True}) 54 | model_gat.fit(self.times, self.measurements, self.errors) 55 | self.best_period = model_gat.best_period 56 | self.best_score = model_gat.score(model_gat.best_period).item() 57 | 58 | def fit_supersmoother(self, periodic=True, scale=True): 59 | from supersmoother import SuperSmoother 60 | model = SuperSmoother(period=self.p if periodic else None) 61 | try: 62 | model.fit(self.times, self.measurements, self.errors) 63 | self.ss_resid = np.sqrt(np.mean((model.predict(self.times) - self.measurements) ** 2)) 64 | if scale: 65 | self.ss_resid /= np.std(self.measurements) 66 | except ValueError: 67 | self.ss_resid = np.inf 68 | 69 | def period_fold(self, p=None): 70 | if p is None: 71 | p = self.p 72 | self.times = self.times % p 73 | inds = np.argsort(self.times) 74 | self.times = self.times[inds] 75 | self.measurements = self.measurements[inds] 76 | self.errors = self.errors[inds] 77 | 78 | def load_asas(): 79 | light_curves = [] 80 | bigmacc = pd.read_csv('data/asas/asas_class_catalog_v3_0.csv', index_col='ASAS_ID') 81 | # thousands=',') 82 | for fname in glob.glob('./data/asas/*/*'): 83 | with open(fname) as f: 84 | dfs = [pd.read_csv(StringIO(chunk), comment='#', delim_whitespace=True) 85 | for chunk in f.read().split('# ')[1:]] 86 | if len(dfs) > 0: 87 | df = pd.concat(dfs)[['HJD', 'MAG_0', 'MER_0', 'GRADE']].sort_values(by='HJD') 88 | df = df[df.GRADE <= 'B'] 89 | df.drop_duplicates(subset=['HJD'], keep='first', inplace=True) 90 | lc = LightCurve(name=os.path.basename(fname), survey='ASAS', 91 | times=df.HJD.values, measurements=df.MAG_0.values, 92 | errors=df.MER_0.values) 93 | entry = bigmacc.loc[lc.name] 94 | lc.p = entry.P 95 | lc.p_signif = entry.P_signif 96 | if not pd.isnull(entry.Train_Class): 97 | lc.label = entry.Train_Class 98 | lc.p_class = 1.0 99 | elif entry.P_Class > 0.95: 100 | lc.label = entry.Class 101 | lc.p_class = entry.P_Class 102 | else: 103 | lc.label = None 104 | lc.p_class = None 105 | # lc.fit_lomb_scargle() 106 | lc.fit_supersmoother() 107 | light_curves.append(lc) 108 | return light_curves 109 | 110 | def load_linear(): 111 | header_fname = 'data/linear/LINEARattributesFinalApr2013.dat' 112 | light_curves = [] 113 | header = pd.read_table(header_fname, comment='#', header=None, 114 | delim_whitespace=True) 115 | colnames = [l for l in open(header_fname) if 116 | l[0] == '#'][-1].lstrip('#').split() 117 | header.columns = colnames 118 | header.set_index('LINEARobjectID', inplace=True) 119 | LC_types = ['RR_Lyrae_FM', 'RR_Lyrae_FO', '???', 'Beta_Persei', 120 | 'W_Ursae_Maj', 'Delta_Scuti'] 121 | 122 | for fname in glob.glob('./data/linear/lc/*'): 123 | df = pd.read_csv(fname, header=0) 124 | df.drop_duplicates(subset=['mjd'], keep='first', inplace=True) 125 | lc = LightCurve(name=os.path.splitext(os.path.basename(fname))[0], 126 | survey='LINEAR', times=df.mjd.values, 127 | measurements=df.m.values, errors=df.merr.values) 128 | lc.label = LC_types[header.LCtype.loc[int(lc.name)] - 1] 129 | # lc.fit_lomb_scargle() 130 | lc.p = 10 ** header.logP.loc[int(lc.name)] 131 | light_curves.append(lc) 132 | return light_curves 133 | 134 | 135 | def load_macho(): 136 | header_fname = 'data/macho/machovar.dat' 137 | light_curves = [] 138 | header = pd.read_table(header_fname, header=None, delim_whitespace=True) 139 | colnames = ['Field', 'Tile', 'Seqn', 'RA_DEC', 'rPer', 'bPer', 'Vmag', 140 | 'Rmag', 'rAmp', 'bAmp', 'cAmp', 'rSupRSA', 'bSupRSA', 'rchi2', 141 | 'bchi2', 'rsig', 'bsig', 'Var', 'Class', 'Points', 'cPoints', 142 | 'rPoints', 'bPoints'] 143 | header.columns = colnames 144 | header.index = ['.'.join(str(el) for el in row) 145 | for row in header.values[:, :3]] 146 | LC_types = { 147 | 1: 'RRL AB', 148 | 2: 'RRL C', 149 | 3: 'RRL E', 150 | 4: 'Ceph Fund', 151 | 5: 'Ceph 1st', 152 | 6: 'LPV WoodA', 153 | 7: 'LPV WoodB', 154 | 8: 'LPV WoodC', 155 | 9: 'LPV WoodD', 156 | 10: 'EB', 157 | 11: 'RRL + GB', 158 | } 159 | 160 | import datetime 161 | for i, fname in enumerate(glob.glob('/fastdisks/bnaul/*.txt')): 162 | if i % 100 == 0: 163 | print(f"{i:5d}/{header.shape[0]}", datetime.datetime.now()) 164 | df = pd.read_csv(fname, sep=';', header=None) 165 | df.columns = ['t', 'mr', 'er', 'mb', 'eb'] 166 | df.drop_duplicates(subset=['t'], keep='first', inplace=True) 167 | df.values[(df.values[:, 1] < -50) | (df.values[:, 2] > 9), 1:3] = np.nan 168 | df.values[(df.values[:, 3] < -50) | (df.values[:, 4] > 9), 3:5] = np.nan 169 | if np.isnan(df.values[:, 1]).all(): 170 | continue 171 | df = df[~np.isnan(df['mr'])] 172 | name = '.'.join(os.path.splitext(os.path.basename(fname))[0].split('_')[1:]) 173 | inds = np.argsort(df['t']) 174 | lc = LightCurve(name=name, survey='MACHO', times=df['t'].values[inds], 175 | measurements=df['mr'].values[inds], 176 | errors=df['er'].values[inds]) 177 | lc.label = LC_types[header.Class.loc[lc.name]] 178 | # lc.fit_lomb_scargle() 179 | lc.p = header.rPer.loc[lc.name] 180 | lc.fit_supersmoother() 181 | light_curves.append(lc) 182 | return light_curves 183 | 184 | 185 | 186 | 187 | if __name__ == "__main__": 188 | print("Adding light curve data") 189 | # light_curves = LightCurve.load_asas() 190 | # joblib.dump(light_curves, 'asas.pkl', compress=3) 191 | # light_curves = LightCurve.load_linear() 192 | # joblib.dump(light_curves, 'linear.pkl', compress=3) 193 | light_curves = LightCurve.load_macho() 194 | joblib.dump(light_curves, 'macho.pkl', compress=3) 195 | -------------------------------------------------------------------------------- /keras_logs/period/uneven/noise0.5/lstm_064_x2_5m04_drop25_bidir/training.csv: -------------------------------------------------------------------------------- 1 | epoch,time,loss,val_loss 2 | 0,2017-02-14 04:26:50.699522,0.541327880695,0.292365770042 3 | 1,2017-02-14 04:32:47.239887,0.27902555447099997,0.196471493691 4 | 2,2017-02-14 04:38:44.122629,0.21947710216,0.17062022239 5 | 3,2017-02-14 04:44:41.399412,0.19506820067800001,0.15057942643799999 6 | 4,2017-02-14 04:50:37.725239,0.177039841376,0.137015307322 7 | 5,2017-02-14 04:56:34.349473,0.163401638158,0.12689203470899998 8 | 6,2017-02-14 05:02:30.952005,0.149575078115,0.108899353072 9 | 7,2017-02-14 05:08:27.897220,0.14041425082800002,0.10793054401900001 10 | 8,2017-02-14 05:14:24.698633,0.129526096676,0.0996438503265 11 | 9,2017-02-14 05:20:21.668440,0.119142320286,0.09130348786710002 12 | 10,2017-02-14 05:26:17.942776,0.113737865165,0.09753482975069999 13 | 11,2017-02-14 05:32:14.833946,0.11019831625700001,0.07999872602519999 14 | 12,2017-02-14 05:38:12.022280,0.10247568450899999,0.08389692567289998 15 | 13,2017-02-14 05:44:08.440215,0.0977893965319,0.0718984719366 16 | 14,2017-02-14 05:50:05.129863,0.0943124109879,0.07024389393630001 17 | 15,2017-02-14 05:56:02.053944,0.0913079871796,0.06947034839539999 18 | 16,2017-02-14 06:01:58.587454,0.0888610805385,0.06795895826069999 19 | 17,2017-02-14 06:07:55.202496,0.08526941314339999,0.0648919474334 20 | 18,2017-02-14 06:13:52.133480,0.0842153757811,0.0643196577206 21 | 19,2017-02-14 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11,2017-02-13 19:45:02.798755,0.10664098169700001,0.07228254564110001 14 | 12,2017-02-13 19:52:30.365834,0.0995893344283,0.0676035741344 15 | 13,2017-02-13 19:59:58.227148,0.0960102614015,0.0652590580285 16 | 14,2017-02-13 20:07:26.827155,0.09058804837989999,0.064537332952 17 | 15,2017-02-13 20:14:54.938385,0.0870985477231,0.0628026979044 18 | 16,2017-02-13 20:22:22.766201,0.0842865674756,0.0608418211341 19 | 17,2017-02-13 20:29:51.016403,0.0811192551628,0.055802907422199996 20 | 18,2017-02-13 20:37:18.740643,0.078604847379,0.052999310754199995 21 | 19,2017-02-13 20:44:47.017999,0.0756947984919,0.0499538106844 22 | 20,2017-02-13 20:52:14.658627,0.073409684794,0.0517353022471 23 | 21,2017-02-13 20:59:42.833553,0.07184756705539999,0.051765879057300006 24 | 22,2017-02-13 21:07:11.251499,0.070204595523,0.049293588660699994 25 | 23,2017-02-13 21:14:39.252805,0.06869382522999999,0.045504096150400006 26 | 24,2017-02-13 21:22:07.243894,0.0669486471452,0.04868698194619999 27 | 25,2017-02-13 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39,2017-02-13 23:14:08.580795,0.0551208310761,0.0376302964054 42 | 40,2017-02-13 23:21:37.288144,0.0541091301478,0.0395188974217 43 | 41,2017-02-13 23:29:05.104377,0.053305990295500004,0.042296713218099996 44 | 42,2017-02-13 23:36:33.111965,0.0528676167596,0.0443179098889 45 | 43,2017-02-13 23:44:01.597442,0.052594992518400004,0.0404520697892 46 | 44,2017-02-13 23:51:28.910801,0.05173254939730001,0.040890511684099996 47 | 45,2017-02-13 23:58:57.220050,0.051256214734199994,0.0388916013762 48 | 46,2017-02-14 00:06:25.384282,0.051205717120300004,0.0396220177412 49 | 47,2017-02-14 00:13:53.181385,0.050881188455999996,0.0402602996677 50 | 48,2017-02-14 00:21:21.066300,0.0516473267693,0.038153744675199996 51 | 49,2017-02-14 00:28:49.183482,0.0499435721897,0.036841275543 52 | 50,2017-02-14 00:36:17.457090,0.05009715007619999,0.0361972371116 53 | 51,2017-02-14 00:43:45.878299,0.049550530035,0.0379993025213 54 | 52,2017-02-14 00:51:13.863472,0.048749132547500004,0.0359116160311 55 | 53,2017-02-14 00:58:41.978436,0.048871144047,0.038887802697699994 56 | 54,2017-02-14 01:06:10.477795,0.048163775168399994,0.037041263189199994 57 | 55,2017-02-14 01:13:38.331578,0.0480236121919,0.03577044578269999 58 | 56,2017-02-14 01:21:06.425554,0.047860055370300005,0.038006531447199995 59 | 57,2017-02-14 01:28:35.200768,0.046912293182699995,0.0350476929918 60 | 58,2017-02-14 01:36:03.562711,0.04725667824969999,0.03598760114980001 61 | 59,2017-02-14 01:43:31.464610,0.04750057524069999,0.0385045647621 62 | 60,2017-02-14 01:50:59.995367,0.046627510851199996,0.035007882025099996 63 | 61,2017-02-14 01:58:26.225862,0.046137613011499996,0.0365028956905 64 | 62,2017-02-14 02:05:53.278936,0.04551102975380001,0.0347105777822 65 | 63,2017-02-14 02:13:22.985127,0.0457148431335,0.034866790194099995 66 | 64,2017-02-14 02:20:49.546726,0.045274914708000005,0.0356768167578 67 | 65,2017-02-14 02:28:21.111453,0.045261721825199996,0.037395968008800004 68 | 66,2017-02-14 02:35:52.651728,0.0443481027149,0.0353433399461 69 | 67,2017-02-14 02:43:24.018291,0.0451981978491,0.0347426419146 70 | 68,2017-02-14 02:50:55.593775,0.0440054323524,0.033195442706299996 71 | 69,2017-02-14 02:58:26.890988,0.0441639899742,0.0339385722764 72 | 70,2017-02-14 03:05:58.750546,0.044393327413099995,0.0332672011107 73 | 71,2017-02-14 03:13:30.757149,0.0438094677869,0.034213030990200004 74 | 72,2017-02-14 03:21:02.184835,0.0438032366801,0.032108333986299996 75 | 73,2017-02-14 03:28:32.962165,0.04649753873240001,0.0330922964029 76 | 74,2017-02-14 03:36:04.994415,0.043669211166000005,0.03724933331830001 77 | 75,2017-02-14 03:43:36.215665,0.043287667585500005,0.034542981535199996 78 | 76,2017-02-14 03:51:07.466449,0.043241286743400005,0.031119994912299997 79 | 77,2017-02-14 03:58:39.480100,0.0427040802781,0.034682823903900005 80 | 78,2017-02-14 04:06:11.167620,0.042659962084099994,0.0328018900007 81 | 79,2017-02-14 04:13:42.288800,0.0427168665454,0.03513517249380001 82 | 80,2017-02-14 04:21:13.577262,0.0418714312371,0.035171260405300005 83 | 81,2017-02-14 04:28:45.712074,0.0426384454593,0.034167340025299996 84 | 82,2017-02-14 04:36:18.052289,0.041698052640999995,0.036900030169599994 85 | 83,2017-02-14 04:43:50.236774,0.0426585320383,0.0341808932833 86 | 84,2017-02-14 04:51:22.298509,0.04177108169530001,0.0354433952831 87 | 85,2017-02-14 04:58:54.109187,0.0429766298272,0.0317334814928 88 | 86,2017-02-14 05:06:26.519633,0.04172509855130001,0.0335265573114 89 | 87,2017-02-14 05:13:58.326906,0.0417773559224,0.0334882360883 90 | 88,2017-02-14 05:21:30.044745,0.0408435728401,0.035765342228099996 91 | 89,2017-02-14 05:29:02.293041,0.0408775714692,0.0335025919601 92 | 90,2017-02-14 05:36:34.160594,0.040925564291,0.032927262038 93 | 91,2017-02-14 05:44:06.251409,0.040627343300699996,0.0324350341223 94 | 92,2017-02-14 05:51:38.000647,0.0406798936892,0.0315934904851 95 | 93,2017-02-14 05:59:09.421900,0.0405592032708,0.032555994112 96 | 94,2017-02-14 06:06:42.021950,0.0406114705373,0.030359720625 97 | 95,2017-02-14 06:13:06.286003,0.0401877303142,0.0313870783895 98 | 96,2017-02-14 06:18:43.405948,0.0403440466151,0.0313767598942 99 | 97,2017-02-14 06:24:20.628823,0.0404814504087,0.033557495567900004 100 | 98,2017-02-14 06:29:57.831959,0.040393466222999996,0.0339529236779 101 | 99,2017-02-14 06:35:35.304230,0.0401987726334,0.030079911835500003 102 | 100,2017-02-14 06:41:12.611255,0.0401404233649,0.0320114320144 103 | 101,2017-02-14 06:46:49.462616,0.040423430036800004,0.0332462111488 104 | 102,2017-02-14 06:52:26.639452,0.039935967838400005,0.0308323170058 105 | 103,2017-02-14 06:58:04.479229,0.0398206908721,0.0342084900476 106 | 104,2017-02-14 07:03:41.649123,0.0397894864902,0.033424775581799994 107 | 105,2017-02-14 07:09:18.473806,0.0392375428695,0.0341085068882 108 | 106,2017-02-14 07:14:55.581270,0.039148196391800005,0.0301334528252 109 | 107,2017-02-14 07:20:33.289695,0.0392980932491,0.0308421138674 110 | 108,2017-02-14 07:26:10.460820,0.039063777402,0.0313659694977 111 | 109,2017-02-14 07:31:47.679589,0.039644565759199994,0.0318998112343 112 | 110,2017-02-14 07:37:24.893023,0.0386023074854,0.0314854202792 113 | 111,2017-02-14 07:43:01.892743,0.0394447550178,0.030836247839000002 114 | 112,2017-02-14 07:48:39.427583,0.0390908840112,0.0294073797762 115 | 113,2017-02-14 07:54:16.773090,0.0383553564548,0.0321359386668 116 | 114,2017-02-14 07:59:53.801045,0.038760960544500005,0.0308331251144 117 | 115,2017-02-14 08:05:30.620375,0.0385051824385,0.0302944178693 118 | 116,2017-02-14 08:11:08.143129,0.038358438247800004,0.0302119993605 119 | 117,2017-02-14 08:16:45.203187,0.038726052665199996,0.0304433614016 120 | 118,2017-02-14 08:22:22.401909,0.0382268395042,0.029438855312799998 121 | 119,2017-02-14 08:27:59.761051,0.0386313718976,0.030384140368599998 122 | 120,2017-02-14 08:33:37.763350,0.038394302479,0.034105891641199995 123 | 121,2017-02-14 08:39:15.398660,0.0388222778914,0.0300747323781 124 | 122,2017-02-14 08:44:53.245313,0.038469739235,0.0307949620299 125 | 123,2017-02-14 08:50:31.043135,0.037537118676100004,0.0298192140646 126 | 124,2017-02-14 08:56:08.504866,0.0382721583592,0.0307988691144 127 | 125,2017-02-14 09:01:46.244442,0.0378828122513,0.0296920114197 128 | 126,2017-02-14 09:07:23.823505,0.0375293012476,0.030433822516400005 129 | 127,2017-02-14 09:13:01.526932,0.0385290351696,0.031653496157400006 130 | 128,2017-02-14 09:18:39.040308,0.03773337409369999,0.0296847599559 131 | 129,2017-02-14 09:24:17.221512,0.037134345248300005,0.030658564996 132 | 130,2017-02-14 09:29:55.077202,0.037567331921300004,0.0293322645128 133 | 131,2017-02-14 09:35:32.411155,0.037101365020499995,0.0288519688882 134 | 132,2017-02-14 09:41:09.883900,0.0375040799845,0.0288348844275 135 | 133,2017-02-14 09:46:48.040627,0.0370915846899,0.0309719725512 136 | 134,2017-02-14 09:52:25.583613,0.0370857111411,0.0311645328999 137 | 135,2017-02-14 09:58:03.101206,0.0376166067552,0.0305635473691 138 | 136,2017-02-14 10:03:40.636491,0.0370541644748,0.0313630717807 139 | 137,2017-02-14 10:09:18.340091,0.036773867090199995,0.0296472582035 140 | 138,2017-02-14 10:14:56.194959,0.0367896076292,0.0311948337592 141 | 139,2017-02-14 10:20:33.706491,0.0367719417438,0.0313266503625 142 | 140,2017-02-14 10:26:11.374227,0.0373872775352,0.0319551124237 143 | 141,2017-02-14 10:31:48.806461,0.036981308669800005,0.030219871457700002 144 | 142,2017-02-14 10:39:12.001973,0.036493425350599996,0.0321467335336 145 | 143,2017-02-14 10:47:35.438889,0.0369161429815,0.029659092705700003 146 | 144,2017-02-14 10:54:31.011583,0.0364335643128,0.0312364584766 147 | 145,2017-02-14 11:00:24.920494,0.0367149964906,0.029544961825 148 | 146,2017-02-14 11:06:57.452162,0.036357377166900004,0.0301212769002 149 | 147,2017-02-14 11:13:13.403051,0.0364659782499,0.029082425869999998 150 | 148,2017-02-14 11:20:02.108215,0.036610374273699994,0.030358786787799998 151 | 149,2017-02-14 11:24:22.541561,0.0365000856109,0.030088663287499998 152 | 150,2017-02-14 11:28:11.389619,0.03575408787,0.029766261298199998 153 | 151,2017-02-14 11:32:03.197806,0.0364688562462,0.029303618520499997 154 | 152,2017-02-14 11:35:56.813282,0.0361514670309,0.028546958137300002 155 | 153,2017-02-14 11:39:48.752515,0.0363781695487,0.0288145015948 156 | 154,2017-02-14 11:43:35.194781,0.0361186367925,0.0330467071384 157 | 155,2017-02-14 11:47:16.559576,0.0357589083957,0.029386315029099996 158 | 156,2017-02-14 11:51:00.918485,0.035574702546,0.029278333950799998 159 | 157,2017-02-14 11:54:44.803265,0.0361950327642,0.0298030333593 160 | 158,2017-02-14 11:58:39.365139,0.0361813266296,0.0291523312218 161 | 159,2017-02-14 12:02:34.144192,0.036059581721199996,0.029980199690900003 162 | 160,2017-02-14 12:06:29.021565,0.035586556955199994,0.0292820280418 163 | 161,2017-02-14 12:10:21.123555,0.0376361294882,0.029547607433099997 164 | 162,2017-02-14 12:14:01.462591,0.035899782064400004,0.0296788491309 165 | 163,2017-02-14 12:17:41.795473,0.0356919019716,0.0299458347261 166 | 164,2017-02-14 12:21:22.434835,0.0360191816464,0.028990390058599998 167 | 165,2017-02-14 12:25:02.922200,0.0350755550899,0.029039763473 168 | 166,2017-02-14 12:28:43.066172,0.035616124025500004,0.0350909590721 169 | 167,2017-02-14 12:32:23.740569,0.0357511477079,0.028396055847400004 170 | 168,2017-02-14 12:36:04.230472,0.0353184798732,0.030005933903200003 171 | 169,2017-02-14 12:39:44.632512,0.035710272938,0.029561072588 172 | 170,2017-02-14 12:43:25.599266,0.035015597590199996,0.0283764143474 173 | 171,2017-02-14 12:47:06.432094,0.0350999536924,0.030705067981 174 | 172,2017-02-14 12:50:47.033178,0.0356131126871,0.030668348632799998 175 | 173,2017-02-14 12:54:27.997135,0.0352926179767,0.028897368721699998 176 | 174,2017-02-14 12:58:17.729023,0.0347912213765,0.0298498205841 177 | 175,2017-02-14 13:02:11.445605,0.034796743048400004,0.0287530713715 178 | 176,2017-02-14 13:06:05.229551,0.0348452206003,0.028814663738000002 179 | 177,2017-02-14 13:10:00.330834,0.03743975632819999,0.0311321000569 180 | -------------------------------------------------------------------------------- /keras_util.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import csv 3 | from functools import wraps 4 | from itertools import cycle, islice 5 | from math import ceil 6 | import datetime 7 | import json 8 | import os 9 | import shutil 10 | import types 11 | import numpy as np 12 | import tensorflow as tf 13 | import keras.backend as K 14 | from collections import Iterable, OrderedDict 15 | from keras.optimizers import Adam 16 | from keras.callbacks import (Callback, TensorBoard, EarlyStopping, 17 | ModelCheckpoint, CSVLogger, ProgbarLogger) 18 | 19 | if 'get_ipython' in vars() and get_ipython().__class__.__name__ == 'ZMQInteractiveShell': 20 | from keras_tqdm import TQDMNotebookCallback as Progbar 21 | else: 22 | from keras_tqdm import TQDMCallback 23 | import sys 24 | class Progbar(TQDMCallback): # redirect TQDMCallback to stdout 25 | def __init__(self): 26 | TQDMCallback.__init__(self) 27 | self.output_file = sys.stdout 28 | 29 | 30 | class LogDirLogger(Callback): 31 | def __init__(self, log_dir): 32 | self.log_dir = log_dir 33 | 34 | def on_epoch_begin(self, epoch, logs=None): 35 | print('\n' + self.log_dir + '\n') 36 | 37 | 38 | class TimedCSVLogger(CSVLogger): 39 | def on_epoch_end(self, epoch, logs=None): 40 | logs = logs or {} 41 | 42 | def handle_value(k): 43 | is_zero_dim_ndarray = isinstance(k, np.ndarray) and k.ndim == 0 44 | if isinstance(k, Iterable) and not is_zero_dim_ndarray: 45 | return '"[%s]"' % (', '.join(map(str, k))) 46 | else: 47 | return k 48 | 49 | if not self.writer: 50 | self.keys = sorted(logs.keys()) 51 | 52 | class CustomDialect(csv.excel): 53 | delimiter = self.sep 54 | 55 | self.writer = csv.DictWriter(self.csv_file, 56 | fieldnames=['epoch', 'time'] + self.keys, 57 | dialect=CustomDialect) 58 | if self.append_header: 59 | self.writer.writeheader() 60 | 61 | row_dict = OrderedDict({'epoch': epoch, 'time': str(datetime.datetime.now())}) 62 | row_dict.update((key, handle_value(logs[key])) for key in self.keys) 63 | self.writer.writerow(row_dict) 64 | self.csv_file.flush() 65 | 66 | 67 | def times_to_lags(T): 68 | """(N x n_step) matrix of times -> (N x n_step) matrix of lags. 69 | First time is assumed to be zero. 70 | """ 71 | assert T.ndim == 2, "T must be an (N x n_step) matrix" 72 | return np.c_[np.diff(T, axis=1), np.zeros(T.shape[0])] 73 | 74 | 75 | def lags_to_times(dT): 76 | """(N x n_step) matrix of lags -> (N x n_step) matrix of times 77 | First time is assumed to be zero. 78 | """ 79 | assert dT.ndim == 2, "dT must be an (N x n_step) matrix" 80 | return np.c_[np.zeros(dT.shape[0]), np.cumsum(dT[:,:-1], axis=1)] 81 | 82 | 83 | def noisify_samples(inputs, outputs, errors, batch_size=500, sample_weight=None): 84 | """ 85 | inputs: {'main_input': X, 'aux_input': X[:, :, [1]]} 86 | outputs: X[:, :, [1]] 87 | errors: X_raw[:, :, 2] 88 | """ 89 | if sample_weight is None: 90 | sample_weight = np.ones(errors.shape) 91 | X = inputs['main_input'] 92 | X_aux = inputs['aux_input'] 93 | shuffle_inds = np.arange(len(X)) 94 | while True: 95 | # New epoch 96 | np.random.shuffle(shuffle_inds) 97 | noise = errors * np.random.normal(size=errors.shape) 98 | X_noisy = X.copy() 99 | X_noisy[:, :, 1] += noise 100 | # Re-scale to have mean 0 and std dev 1; TODO make this optional 101 | X_noisy[:, :, 1] -= np.atleast_2d(np.nanmean(X_noisy[:, :, 1], axis=1)).T 102 | X_noisy[:, :, 1] /= np.atleast_2d(np.std(X[:, :, 1], axis=1)).T 103 | 104 | for i in range(ceil(len(X) / batch_size)): 105 | inds = shuffle_inds[(i * batch_size):((i + 1) * batch_size)] 106 | yield ([X_noisy[inds], X_aux[inds]], X_noisy[inds, :, 1:2], sample_weight[inds]) 107 | 108 | 109 | def parse_model_args(arg_dict=None): 110 | """Parse command line arguments and optionally combine with values in `arg_dict`.""" 111 | parser = argparse.ArgumentParser() 112 | parser.add_argument("--size", type=int) 113 | parser.add_argument("--num_layers", type=int) 114 | parser.add_argument("--drop_frac", type=float) 115 | parser.add_argument("--batch_size", type=int, default=500) 116 | parser.add_argument("--nb_epoch", type=int, default=250) 117 | parser.add_argument("--lr", type=float) 118 | parser.add_argument("--loss", type=str, default='mse') 119 | parser.add_argument("--loss_weights", type=float, nargs='*') 120 | parser.add_argument("--model_type", type=str) 121 | parser.add_argument("--decode_type", type=str, default=None) 122 | parser.add_argument("--decode_layers", type=int, default=None) 123 | parser.add_argument("--gpu_frac", type=float, default=None) 124 | parser.add_argument("--sigma", type=float, default=2e-9) 125 | parser.add_argument("--sim_type", type=str) 126 | parser.add_argument("--data_type", type=str, default='sinusoid') 127 | parser.add_argument("--N_train", type=int, default=50000) 128 | parser.add_argument("--N_test", type=int, default=1000) 129 | parser.add_argument("--n_min", type=int, default=200) 130 | parser.add_argument("--n_max", type=int, default=200) 131 | parser.add_argument('--even', dest='even', action='store_true') 132 | parser.add_argument('--uneven', dest='even', action='store_false') 133 | parser.add_argument('--no_train', dest='no_train', action='store_true') 134 | parser.add_argument('--embedding', type=int, default=None) 135 | parser.add_argument("--patience", type=int, default=20) 136 | parser.add_argument('--pool', type=int, default=None) 137 | parser.add_argument("--first_N", type=int, default=None) 138 | parser.add_argument("--m_max", type=float, default=20.) 139 | parser.add_argument("--lomb_score", type=float, default=None) 140 | parser.add_argument("--ss_resid", type=float, default=None) 141 | parser.add_argument('--pretrain', type=str, default=None) 142 | parser.add_argument('--finetune_rate', type=float, default=None) 143 | parser.add_argument('--bidirectional', dest='bidirectional', action='store_true') 144 | parser.add_argument("--survey_files", type=str, nargs='*') 145 | parser.add_argument('--noisify', dest='noisify', action='store_true') 146 | parser.add_argument('--period_fold', dest='period_fold', action='store_true') 147 | parser.set_defaults(even=False, bidirectional=False, noisify=False, 148 | period_fold=False) 149 | # Don't read argv if arg_dict present 150 | args = parser.parse_args(None if arg_dict is None else []) 151 | 152 | if arg_dict: # merge additional arguments w/ defaults 153 | args = argparse.Namespace(**{**args.__dict__, **arg_dict}) 154 | 155 | required_args = ['size', 'num_layers', 'drop_frac', 'lr', 'model_type', 'sim_type', 156 | 'n_min', 'n_max'] 157 | for key in required_args: 158 | if getattr(args, key) is None: 159 | parser.error("Missing argument {}".format(key)) 160 | 161 | return args 162 | 163 | 164 | def get_run_id(model_type, size, num_layers, lr, drop_frac=0.0, embedding=None, 165 | decode_type=None, decode_layers=None, bidirectional=False, **kwargs): 166 | """Generate unique ID from model params.""" 167 | run = "{}_{:03d}_x{}_{:1.0e}_drop{}".format(model_type, size, num_layers, lr, 168 | int(100 * drop_frac)).replace('e-', 'm') 169 | if embedding: 170 | run += '_emb{}'.format(embedding) 171 | if decode_type: 172 | run += '_decode{}'.format(decode_type) 173 | if decode_layers: 174 | run += '_x{}'.format(decode_layers) 175 | if bidirectional: 176 | run += '_bidir' 177 | 178 | return run 179 | 180 | 181 | def limited_memory_session(gpu_frac): 182 | if gpu_frac <= 0.0: 183 | K.set_session(tf.Session()) 184 | else: 185 | gpu_opts = tf.ConfigProto(gpu_options=tf.GPUOptions( 186 | per_process_gpu_memory_fraction=gpu_frac)) 187 | K.set_session(tf.Session(config=gpu_opts)) 188 | 189 | 190 | def train_and_log(X, Y, run, model, nb_epoch, batch_size, lr, loss, sim_type, metrics=[], 191 | sample_weight=None, no_train=False, patience=20, finetune_rate=None, 192 | validation_split=0.2, validation_data=None, gpu_frac=None, 193 | noisify=False, errors=None, pretrain_weights=None, **kwargs): 194 | """Train model and write logs/weights to `keras_logs/{run_id}/`. 195 | 196 | If weights already existed, they will be loaded and training will be skipped. 197 | """ 198 | optimizer = Adam(lr=lr if not finetune_rate else finetune_rate) 199 | if gpu_frac is not None: 200 | limited_memory_session(gpu_frac) 201 | model.compile(optimizer=optimizer, loss=loss, metrics=metrics, 202 | sample_weight_mode='temporal' if sample_weight is not None else None) 203 | 204 | log_dir = os.path.join(os.getcwd(), 'keras_logs', sim_type, run) 205 | weights_path = os.path.join(log_dir, 'weights.h5') 206 | loaded = False 207 | if os.path.exists(weights_path): 208 | print("Loading {}...".format(weights_path)) 209 | history = [] 210 | model.load_weights(weights_path) 211 | loaded = True 212 | elif no_train or finetune_rate: 213 | raise FileNotFoundError("No weights found in {}.".format(log_dir)) 214 | 215 | if finetune_rate: # write logs to new directory 216 | log_dir += "_ft{:1.0e}".format(finetune_rate).replace('e-', 'm') 217 | 218 | if (not loaded or finetune_rate) and not no_train: 219 | shutil.rmtree(log_dir, ignore_errors=True) 220 | os.makedirs(log_dir) 221 | param_log = {key: value for key, value in locals().items()} 222 | param_log.update(kwargs) 223 | param_log = {k: v for k, v in param_log.items() 224 | if k not in ['X', 'Y', 'model', 'optimizer', 'sample_weight', 225 | 'kwargs', 'validation_data', 'errors'] 226 | and not isinstance(v, types.FunctionType)} 227 | json.dump(param_log, open(os.path.join(log_dir, 'param_log.json'), 'w'), 228 | sort_keys=True, indent=2) 229 | if pretrain_weights: 230 | model.load_weights(pretrain_weights, by_name=True) 231 | if not noisify: 232 | history = model.fit(X, Y, nb_epoch=nb_epoch, batch_size=batch_size, 233 | sample_weight=sample_weight, 234 | callbacks=[Progbar(), 235 | TensorBoard(log_dir=log_dir, write_graph=False), 236 | TimedCSVLogger(os.path.join(log_dir, 'training.csv'), append=True), 237 | # EarlyStopping(patience=patience), 238 | ModelCheckpoint(weights_path, save_weights_only=True), 239 | LogDirLogger(log_dir)], verbose=False, 240 | validation_split=validation_split, 241 | validation_data=validation_data) 242 | else: 243 | history = model.fit_generator(noisify_samples(X, Y, errors, batch_size, 244 | sample_weight), 245 | samples_per_epoch=len(Y), nb_epoch=nb_epoch, 246 | callbacks=[Progbar(), 247 | TensorBoard(log_dir=log_dir, 248 | write_graph=False), 249 | TimedCSVLogger(os.path.join(log_dir, 250 | 'training.csv'), 251 | append=True), 252 | ModelCheckpoint(weights_path, 253 | save_weights_only=True), 254 | LogDirLogger(log_dir)], 255 | verbose=True, 256 | validation_data=validation_data) 257 | return history 258 | -------------------------------------------------------------------------------- /keras_logs/asas_linear_fold/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir/training.csv: -------------------------------------------------------------------------------- 1 | epoch,time,loss,val_loss 2 | 0,2017-04-27 18:42:06.363080,5.65074887736,6.31446290832 3 | 1,2017-04-27 18:50:11.458914,3.32287706707,6.86038691356 4 | 2,2017-04-27 18:58:15.756642,2.26089686925,3.8519778538 5 | 3,2017-04-27 19:06:21.091708,1.83091924988,3.55948371307 6 | 4,2017-04-27 19:14:27.228908,1.60999559567,2.7452918136 7 | 5,2017-04-27 19:22:33.206726,1.5084514691,2.51159923365 8 | 6,2017-04-27 19:30:38.493038,1.43726390067,2.50111639753 9 | 7,2017-04-27 19:38:45.043029,1.37427397891,2.3237190237 10 | 8,2017-04-27 19:46:52.762794,1.3393395069,2.27273364983 11 | 9,2017-04-27 19:54:59.597158,1.30121344675,2.17954077073 12 | 10,2017-04-27 20:03:06.151652,1.26895261499,2.07708541515 13 | 11,2017-04-27 20:11:12.440393,1.24802559668,2.11229845292 14 | 12,2017-04-27 20:19:17.301088,1.22493529841,2.06712839708 15 | 13,2017-04-27 20:27:22.271271,1.20094420239,2.00767589877 16 | 14,2017-04-27 20:35:27.031552,1.18608175092,1.96517879591 17 | 15,2017-04-27 20:43:30.941208,1.17045719252,1.94339921607 18 | 16,2017-04-27 20:51:35.036881,1.15748422793,1.90680902883 19 | 17,2017-04-27 20:59:38.825169,1.13886283633,1.89141887129 20 | 18,2017-04-27 21:07:43.308197,1.12456766405,1.84895181836 21 | 19,2017-04-27 21:15:46.808375,1.11294547037,1.83869347594 22 | 20,2017-04-27 21:23:50.639120,1.10417782512,1.82045716726 23 | 21,2017-04-27 21:31:55.389947,1.09406827145,1.83350685074 24 | 22,2017-04-27 21:39:59.314907,1.07872150512,1.79010815342 25 | 23,2017-04-27 21:48:02.869040,1.07230993345,1.78321184218 26 | 24,2017-04-27 21:56:08.329285,1.06510677161,1.76599044894 27 | 25,2017-04-27 22:04:12.484868,1.05850108079,1.78838049521 28 | 26,2017-04-27 22:12:15.926756,1.04985601966,1.77489579327 29 | 27,2017-04-27 22:20:19.134470,1.04521878621,1.71491412548 30 | 28,2017-04-27 22:28:24.095782,1.03572182611,1.7514079325 31 | 29,2017-04-27 22:36:27.459066,1.03434347238,1.72597756837 32 | 30,2017-04-27 22:44:32.053752,1.02360972763,1.68995841671 33 | 31,2017-04-27 22:52:35.323856,1.02013884221,1.6951087431 34 | 32,2017-04-27 23:00:40.258125,1.01372098857,1.69944494685 35 | 33,2017-04-27 23:08:43.599839,1.01353855298,1.69299790431 36 | 34,2017-04-27 23:16:48.330307,1.00489878189,1.69655902351 37 | 35,2017-04-27 23:24:52.157811,1.00471513567,1.67694690807 38 | 36,2017-04-27 23:32:56.492468,0.997378173279,1.66083821119 39 | 37,2017-04-27 23:40:59.545615,0.99371096609,1.66706066908 40 | 38,2017-04-27 23:49:04.461369,0.993376814951,1.65574432139 41 | 39,2017-04-27 23:57:07.882277,0.988545658395,1.66127778259 42 | 40,2017-04-28 00:05:12.641991,0.988644457858,1.64981995671 43 | 41,2017-04-28 00:13:16.275150,0.979589746707,1.66430964083 44 | 42,2017-04-28 00:21:19.566184,0.977593711556,1.62766718986 45 | 43,2017-04-28 00:29:22.571879,0.974526490335,1.65015637676 46 | 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