Hyperparameters ============================= General parameters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ====================== ============ ========================================= Argument Type Help ====================== ============ ========================================= --seed int random seed to be used --use_cuda bool Use GPU ====================== ============ ========================================= Data parameters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ====================== ============ ================================================================== Argument Type Help ====================== ============ ================================================================== --dump_dir str path where data and models are dumped --norm str Feature normalization used in training/validation: None, perfilter, global, cosmo, cosmo_quantile --redshift str Host redshift used in training/validation: zpho, zspe or None --source_data str Data source: photometry or salt --no_overwrite bool If True, overwrite preprocessed dir when creating database --data_fraction float Fraction of data to use --override_source_data str Change the source data (use saltfit or photometry) --sntypes dict SN type mapping (e.g. '{"101":"Ia","120":"IIP"}'). Types in data not listed here are auto-assigned to a ``contaminant`` class. --target_sntype str Class value in --sntypes to use as target 0 for binary classification (default: Ia) --sntype_var str Column name for event types (default: SNTYPE) --nb_classes int Number of classification targets (default: 2) ====================== ============ ================================================================== Training parameters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ====================== ============ ================================================================== Argument Type Help ====================== ============ ================================================================== --train_rnn bool Train RNN model --monitor_interval int Validate every monitor_interval epochs--metrics ====================== ============ ================================================================== Validation Parameters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ====================== ============ ===================================================== Argument Type Help ====================== ============ ===================================================== --speed bool Run RNN speed classification benchmark --calibration bool Evaluate model calibration --performance bool Get performance metrics + plots --metrics bool Compute performance metrics --model_files bool Path to model files --prediction_files bool Path to prediction files --metric_files bool Path to metric files ====================== ============ ===================================================== Visualization Parameters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ =============================== ============ ========================================================== Argument Type Help =============================== ============ ========================================================== --explore_lightcurves bool Plot a random selection of lightcurves --plot_lcs bool Plot a random selection of lightcurves predictions --plot_prediction_distribution bool Plot lcs and the histogram of probability for each class =============================== ============ ========================================================== RNN parameters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ======================== ============ ================================================================== Argument Type Help ======================== ============ ================================================================== --cyclic bool Use cyclic learning rate --cyclic_phases list Cyclic phases --random_length bool Use random length sequences for training --random_redshift bool If True, randomly set the spectroscopic redshift --weight_decay float L2 decay on weights (for variational RNN) --layer_type str Recurrent layer type. Choose lstm,gru,rnn --model str Recurrent model type. Choose vanilla,variational,bayesian --learning_rate float Learning rate --nb_classes int Number of classification targets --nb_epoch int Number of epoch --batch_size int Batch size --hidden_dim int Hidden layer dimension --num_layers int Number of recurrent layers --dropout float Dropout value --bidirectional bool Use bidirectional models --rnn_output_option str RNN output options. standard or mean --pi float mixing coefficient for Bayes prior --log_sigma1 float Initialization parameter for BayesRNN layers --log_sigma2 float Initialization parameter for BayesRNN layers --rho_scale_lower float Initialization parameter for BayesRNN layers --rho_scale_upper float Initialization parameter for BayesRNN layers --log_sigma1_output float Initialization parameter for BayesLinear output layers --log_sigma2_output float Initialization parameter for BayesLinear output layers --rho_scale_lower_output float Initialization parameter for BayesLinear output layers --rho_scale_upper_output float Initialization parameter for BayesLinear output layers --num_inference_samples int Number of samples to use for Bayesian inference --mean_field_inference bool Use mean field inference for bayesian models ======================== ============ ==================================================================