text: from collections import OrderedDict
  
  from experiments.experiment import Experiment
  from models.hmc_samplers import HMCSampler, SGHMCSampler
  from models.ld_samplers import LDSampler, SGLDSampler, pSGLDSampler
  from experiments.energy.energy_env import EnergyEnv
  
  
  class EnergyExp(Experiment):
      def __init__(self):
          super().__init__()
  
      def _setup_sampler_defaults(self, sampler_params):
          sampler_params['noise_precision'] = 10.
          sampler_params['weights_precision'] = 1.
  
      def run_baseline_hmc(self):
          env = EnergyEnv()
          self.setup_env_defaults(env)
  
          env.model_name = 'hmc'
          env.test_case_name = 'baseline'
  
          env.chains_num = 1
          env.n_samples = 100
          env.thinning = 4
  
          sampler_params = dict()
          sampler_params['step_sizes'] = .0005
          sampler_params['hmc_steps'] = 10
          sampler_params['mh_correction'] = True
  
          sampler_params['batch_size'] = None
          sampler_params['seek_step_sizes'] = False
          sampler_params['fade_in_velocities'] = True
          env.setup_data_dir()
          self.configure_env_mcmc(env, HMCSampler, sampler_params)
          env.run()
  
      def run_sgld(self):
          env = EnergyEnv()
          self.setup_env_defaults(env)
  
          env.model_name = 'sgld'
  
          env.n_samples = 100
          env.thinning = 29
  
          sampler_params = dict()
          sampler_params['step_sizes'] = .001
  
          sampler_params['fade_in_velocities'] = True
  
          env.setup_data_dir()
          self.configure_env_mcmc(env, SGLDSampler, sampler_params)
          env.run()
  
      def run_sghmc(self):
          env = EnergyEnv()
          self.setup_env_defaults(env)
  
          env.model_name = 'sghmc'
  
          env.chains_num = 1
          env.n_samples = 100
          env.thinning = 1
  
          sampler_params = dict()
          sampler_params['step_sizes'] = .0005
          sampler_params['hmc_steps'] = 10
          sampler_params['friction'] = 1.
  
          sampler_params['fade_in_velocities'] = True
  
          env.setup_data_dir()
          self.configure_env_mcmc(env, SGHMCSampler, sampler_params)
          env.run()
  
      def run_psgld(self):
          env = EnergyEnv()
          self.setup_env_defaults(env)
  
          env.model_name = 'psgld'
  
          env.n_samples = 100
          env.thinning = 19
  
          sampler_params = dict()
          sampler_params['step_sizes'] = .001
          sampler_params['preconditioned_alpha'] = .999
          sampler_params['preconditioned_lambda'] = .01
  
          sampler_params['fade_in_velocities'] = True
  
          env.setup_data_dir()
          self.configure_env_mcmc(env, pSGLDSampler, sampler_params)
          env.run()
  
      def run_dropout(self):
          env = EnergyEnv()
          self.setup_env_defaults(env)
  
          env.model_name = 'dropout'
  
          env.n_samples = 100
  
          sampler_params = dict()
          sampler_params['n_epochs'] = 10
  
          dropout = 0.01
          tau = 0.427
  
          env.setup_data_dir()
          self.configure_env_dropout(env, sampler_params=sampler_params, dropout=dropout, tau=tau)
          env.run()
  
      def run_bbb(self):
          env = EnergyEnv()
          self.setup_env_defaults(env)
  
          env.model_name = 'bbb'
          n_epochs = 15
  
          env.setup_data_dir()
          self.configure_env_bbb(env, n_epochs=n_epochs)
          env.run()
  
      def run_pbp(self):
          env = EnergyEnv()
          self.setup_env_defaults(env)
  
          env.model_name = 'pbp'
          env.n_samples = 100
          env.n_chunks = 20
  
          env.setup_data_dir()
          self.configure_env_pbp(env, n_epochs=5)
          env.run()
  
  
  def main():
      experiment = EnergyExp()
  
      queue = OrderedDict()
      queue['HMC'] = experiment.run_baseline_hmc
      queue['SGLD'] = experiment.run_sgld
      queue['SGHMC'] = experiment.run_sghmc
      queue['pSGLD'] = experiment.run_psgld
      queue['BBB'] = experiment.run_bbb
      # queue["PBP"] = experiment.run_pbp
      queue['Dropout'] = experiment.run_dropout
  
      experiment.run_queue(queue, cpu=True)
      experiment.report_metrics_table(queue)
  
      del queue['HMC']
  
      max_time = 15
      experiment.plot_multiple_metrics('HMC', queue.keys(), ['KS'], max_time=max_time, title_name='KS distance')
      experiment.plot_multiple_metrics('HMC', queue.keys(), ['Precision'], max_time=max_time, title_name='Precision')
      experiment.plot_multiple_metrics('HMC', queue.keys(), ['Recall'], max_time=max_time, title_name='Recall')
      # experiment.plot_multiple_metrics("HMC", queue.keys(), ["KL"])
      # experiment.plot_multiple_metrics("HMC", queue.keys(), ["F1"], max_time=max_time, title_name="F1 score")
      # experiment.plot_multiple_metrics("HMC", queue.keys(), ["IoU"], max_time=max_time)
  
  
  if __name__ == '__main__':
      main()
  