DeepBayes

An estimator for parameter estimation in stochastic nonlinear dynamical models by leveraging deep recurrent neural networks

Dependencies

PSEM, CME-MH, CPF-SAEM (MATLAB Code)

MATLAB 2020a with plug-ins such as:

• System identification toolbox
• Control systems toolbox

DeepBayes Code (in Python)

python==3.6.8
pytorch==1.6.0
numpy==1.21.5
scipy==1.7.3
control==0.9.0
matplotlib==3.4.3

Repository organization

• /data/ : Folder that should contains the training data, viz. simulated trajectories for a chosen value of P, M (P - number of parameter realizations, M - number of simulated trajectories per realization).

• /src/: Folder that contains essential scripts containing model descriptions such as

  • rnn_models_NLSS_modified.py : RNN model defined for the NLSS models.
  • rnn_models_ce_drive.py : RNN model defined for the real-world coupled electric drive model.

• /utils/: This folder contains help scripts necessary to train the RNNs, help in generate training datasets, etc.

• /config/: This folder contains configurations for training RNN models in .json format.

• /log/: This folder contains the logfiles related to the training of the RNN models.

• /models/: This folder contains the trained model checkpoints saved as *.pt files.

• /MATLAB_Code/: This folder contains relevant code to simulate the MATLAB models. Note that for the inference comparison, both MATLAB and Python based methods should use the same kind of trajectories (which can be created in Python as .mat file, usually done in the analysis notebooks and then the relevant path is provided to the MATLAB scripts. The datafile by default provided in the scripts may need to be changed depending on the filename of the generated dataset. Users are used to check the information in the subfolders provided:

  • /cpf_saem/: This folder contains code to execute the conditional CPF-SAEM method for estimating the two variance parameters, you should execute the file compute_ML_performance.m.
  • /SimplerNLSS_Model/: This folder contains code to execute the PSEM method for the simpler NLSS model for estimating 4 unknown parameters, you should execute the file run_ML_based_fixed_theta_random_init.m
  • /Linear_Toy_model/ : This folder contains code for executing the linear estimator based on the approach of DeepBayes for the linear Toy model, you should run run_multiple_simulations_fixed_theta_w_bias.m to execute the method for a multiple values of P, M in order to replicate the table of varying MSE versus P, M.

• /analysis_ipynbs/: This folder contains IPython notebooks used for analysing the results.

• /evaluation_data/: Trajectories generated using Python for performing inference

• /matlab_results/: Results of ML-based methods on the evaluation datasets

Dataset creation

• create_dataset_ce_drive.py : This script generates the training data for the coupled-electric drive model
• create_dataset_revised.py: This script generates the training data for NLSS models - depending on whether we are estimating variance parameters or some subset of the total number of parameters
• create_dataset_simpler_model.py: The same kind of script as create_dataset_revised.py but for the simpler version of the NLSS model used in the experimental setup.

Running scripts

The scripts with the name main... are used for starting the model training. Depending on the type of model required to be executed, the specific mainfile needs to be called. Options need to be provided while executing the scripts as command line arguments. To see list of available options prior to execution, use the option --help as a command line argument. E.g. to find the list of available options for the running script main.py, you can use:

python3 main.py --help

• main.py : Running the program for NLSS models for estimation of a subset of the unknown parameters
• main_with_grid_search.py: Running the program to conduct grid search for the RNN architecture (GRU / LSTM) for estimation of a subset of the unknown parameters
• main_for_ce_drive.py : Running the program for the coupled electric drive model
• main_for_ce_drive_with_grid_search.py : Running the program to conduct grid search for the RNN architecture (GRU / LSTM) for the coupled electric drive model
• main_for_var_estimate.py : Running the program for NLSS models for estimation of the variance parameters
• main_for_var_estimate_grid_search.py : Running the program to conduct grid search for the RNN architecture (GRU / LSTM) for estimation of the variance parameters

Analysis of results

We can see the analysed results in the notebooks under /analysis_ipynbs/ where evaluation / inference is done using test data and loaded model checkpoints.

How to run the code for DeepBayes ...

1. Generate the appropriate dataset by calling one of the dataset creation files. List of available options that can be accessed are found by using python3 create_dataset_[revised/ce_drive/simpler_model].py --help E.g. create_dataset_revised.py can be used to create the dataset for estimating variance as:

   python3 create_dataset_revised.py --num_realizations P --num_trajs M --sequence_length N --mode vars --use_norm [0/1 (Usually kept 0)] --output_path [output path to the .pkl file containing the dataset] 
   

2. The configuration to run the DeepBayes method is available for edit (if necessary) in /config/.
3. Run the training algorithm by calling the appropriate main function, e.g. for simpler_model (estimating all parameters of the simpler model shown in the paper) use main.py, for variance estimation use main_for_var_estimate.py and for coupled drive use main_for_ce_drive.py. Before running the file check the values of the variables logfile_path, modelfile_path, main_exp_name and set your own path if required (for now these values are hardcoded). E.g. to run main.py for DeepBayes GRU for estimating all 4 parameters of the simpler NLSS model, one may need to use:

   python3 main.py --mode train --model_type gru --dataset_mode pfixed --config [path to config file] --datafile [path to the dataset (.pkl file)] --use_norm 0 --splits [path to the splits file (splitting data into train, val, ...), for the first time this will be created]
   

4. The log files will be stored under appropriate folders under /log/ and model checkpoints under /models/.
5. Check one of the analysis IPython notebooks where you can simulate or load an evaluation dataset, load the saved model and perform inference. In some cases, it may be required to run the MATLAB code for the comparing methods (like PSEM (NLSS Simpler Model), CPF_SAEM and CME-MH (Metropolis hastings) and then put the location to the results in the notebook.

License

The code is published under the MIT license