Visualization 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>

Exploring the dataset

Using command line: .. code:

python run.py --data --dump_dir tests/dump  # build the database
python run.py --explore_lightcurves --dump_dir tests/dump

Outputs: .png files in the tests/dump/explore folder. You should obtain something that looks like this:

Predictions as a function of time

Assuming you have a trained model stored under tests/dump/models/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean and that you have already created the database as above:

Using command line: .. code:

python run.py --plot_lcs --dump_dir tests/dump --model_files tests/dump/models/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean.pt

Using Yaml: .. code:

python run_yaml.py <yaml_file_with_config> --mode plot_lcs

an example <yaml_file_with_config> is at configs_yml.

Outputs: a figure folder under tests/dump/lightcurves/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean.

This folder contains the plot of several random lightcurves and the predictions made by the neural network referred to by the model_files argument.

If you want to plot a selection of lightcurves you can add --plot_file <filename.csv> which contains a column SNID with the ids requested to be plotted.

Below is a sample plot:

Predictions + uncertainty for bayesian models

Assuming you have a variational RNN model stored under tests/dump/models/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07 and that you have already created the database as above:

python run.py --plot_lcs --dump_dir tests/dump --model_files tests/dump/models/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07.pt

Outputs: a figure folder under tests/dump/lightcurves/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07.

This folder contains the plot of several lightcurves and the predictions made by the neural network referred to by the model_files argument. Several predictions are sampled at each timestep and the prediction contours at 68% and 94% are shown.

Below is a sample plot:

Predictions from multiple models

To compare the predictions from multiple models, simply call the above, while providing multiple model_files

python run.py --plot_lcs --dump_dir tests/dump --model_files tests/dump/models/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07.pt tests/dump/models/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean.pt

Outputs: a figure folder under tests/dump/figures/multimodel_early_prediction.

This folder contains the plot of several lightcurves and the predictions made by the neural networks referred to by the model_files argument.

Below is a sample plot:

Science plots

The plots of the paper can be reproduced by running in the paper branch:

python run_paper.py