Validation walkthrough

Activate the environment

Either use docker

cd env && python launch_docker.py (--use_cuda optional)

Or activate your conda environment

source activate <conda_env_name>

Validation

Assuming a database has been created and models have been trained, a model can be validated as follows:

Using command line: .. code:

python run.py --validate_rnn --dump_dir /path/to/dump_dir
python run.py --validate_rnn --dump_dir /path/to/dump_dir

Using Yaml: .. code:

python run_yaml.py <yaml_file_with_config> --mode validate_rnn

an example <yaml_file_with_config> is at configs_yml.

In that case, the model corresponding to the command line arguments will be loaded and validated. Output will be written in dump_dir/models/yourmodelname/.

Alternatively, one or more model files can be specified

python run.py --validate_rnn --dump_dir /path/to/dump_dir --model_files /path/to/model/file(s)
python run.py --validate_rnn --dump_dir /path/to/dump_dir --model_files /path/to/model/file(s)

In that case, validation will be carried out for each of the models specified by the model files. This will use the database in dump_dir/processed directory.

This will:

  • Make predictions on a test set (saved to a file with the PRED_ prefix)

  • Compute metrics on the test (saved to a file with the METRICS_ prefix)

  • All results are dumped in the same folder as the folder where the trained model was dumped

To make predictions on an independent database than the one used to train a given model

python run.py --dump_dir  /path/to/dump_dir --validate_rnn --model_files path/to/modelfile/modelfile.pt

In this case it will run the model provided in model_files with the normalization of the model on the database available in dump_dir/processed. Predictions will be saved in dump_dir/models/modelname/.

Predictions format

For a binary classification task, predictions files contain the following columns:

all_class0            float32  - probability of classifying complete light-curves as --sntype [0] (usually Ia)
all_class1            float32  - probability of classifying complete light-curves as --sntype [1:] (usually nonIas)
PEAKMJD-2_class0      float32  - probability of classifying light-curves up to 2 days before maximum as --sntype [0] (usually Ia)
PEAKMJD-2_class1      float32  - probability of classifying light-curves up to 2 days before maximum as  --sntype [1:] (usually nonIas)
PEAKMJD-1_class0      float32  - up to one day before maximum light
PEAKMJD-1_class1      float32
PEAKMJD_class0        float32  - up to maximum light lightcurves
PEAKMJD_class1        float32
PEAKMJD+1_class0      float32  - one day post maximum lightcurves
PEAKMJD+1_class1      float32
PEAKMJD+2_class0      float32  - two days post maximum lightcurves
PEAKMJD+2_class1      float32
all_random_class0     float32  - Out-of-distribution: probability of classifying randomly generated complete lightcurves as --sntype [0]
all_random_class1     float32
all_reverse_class0    float32  - Out-of-distribution: probability of classifying time reversed complete lightcurves as --sntype [0]
all_reverse_class1    float32
all_shuffle_class0    float32  - Out-of-distribution: probability of classifying shuffled complete lightcurves (permutations of time-series) as --sntype [0]
all_shuffle_class1    float32
all_sin_class0        float32  - Out-of-distribution: probability of classifying sinusoidal complete lightcurves (permutations of time-series) as --sntype [0]
all_sin_class1        float32
target                  int64  - Type of the supernova, simulated class.
SNID                    int64  - ID number of the light-curve

these columns rely on maximum light information and target (original type) from simulations. Out-of-distribution classifications are done on the fly. Bayesian Networks (variational and Bayes by Backprop) have an entry for each probability distribution sampling, to get the mean and std of the classification read the _aggregated.pickle file.

RNN speed

Run RNN classification speed benchmark as follows

python run.py --data --dump_dir /path/to/dump_dir  # create database
python run.py --speed --dump_dir /path/to/dump_dir

This will create tests/dump/stats/rnn_speed.csv showing the classification throughput of RNN models.

Calibration

Assuming a database has been created and models have been trained, evaluate classifier calibration as follows:

python run.py --calibration --dump_dir /path/to/dump_dir --metric_files /path/to/metric_file

This will output a figure in path/to/dump_dir/figures showing how well a given model is calibrated. A metric file looks like this: METRICS_{model_name}.pickle. For instance: METRICS_DES_vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean.pickle Multiple metric files can be specified, the results will be charted on the same graph.

Science plots

Assuming a database has been created and models have been trained, how some graphs of scientific interest:

python run.py --science_plots --dump_dir /path/to/dump_dir --prediction_files /path/to/prediction_file

This will output figures in path/to/dump_dir/figures showing various plots of interest: Hubble residuals, purity vs redshift etc. A prediction file looks like this: PRED_{model_name}.pickle. For instance: PRED_DES_vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean.pickle

Performance metrics

Assuming a database has been created and models have been trained, compute performance metrics

python run.py --performance --dump_dir /path/to/dump_dir

This will output a csv file in path/to/dump_dir/stats, which aggregates various performance metrics for each model that has been trained and for which a METRICS file has been created.