adapted template script to experiment class

alperyeg · Sep 17, 2020 · 53a286e · 53a286e
1 parent 2aad0f4
commit 53a286e
Showing 1 changed file with 29 additions and 68 deletions.
diff --git a/bin/l2l-template.py b/bin/l2l-template.py
@@ -7,9 +7,10 @@
 import os
 
 from l2l.utils.environment import Environment
+from l2l.utils.experiment import Experiment
 
 from l2l.logging_tools import create_shared_logger_data, configure_loggers
-from l2l.optimizees.optimizee import Optimizee
+from l2l.optimizees.optimizee import Optimizee, OptimizeeParameters
 from l2l.optimizers.optimizer import Optimizer, OptimizerParameters
 from l2l.paths import Paths
 
@@ -21,80 +22,40 @@
 
 
 def main():
-    # TODO when using the template: Give some *meaningful* name here
-    name = 'L2L'
-
-    # TODO when using the template: make a path.conf file and write the root path there
-    try:
-        with open('bin/path.conf') as f:
-            root_dir_path = f.read().strip()
-    except FileNotFoundError:
-        raise FileNotFoundError(
-            "You have not set the root path to store your results."
-            " Write the path to a path.conf text file in the bin directory"
-            " before running the simulation"
-        )
-    paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
-
-    # Load the logging config which tells us where and what to log (loglevel, destination)
-
-    print("All output logs can be found in directory ", paths.logs_path)
-
-    # Create an environment that handles running our simulation
-    # This initializes an environment. This environment is based on the Pypet implementation.
-    # Uncomment 'freeze_input', 'multipproc', 'use_scoop' and 'wrap_mode' lines to disable running the experiment
-    # across cores and nodes.
-    env = Environment(trajectory=name, filename=paths.output_dir_path, file_title='{} data'.format(name),
-                      comment='{} data'.format(name),
-                      add_time=True,
-                      freeze_input=False,
-                      multiproc=True,
-                      automatic_storing=True,
-                      log_stdout=False,  # Sends stdout to logs
-                      )
-    create_shared_logger_data(logger_names=['bin', 'optimizers'],
-                              log_levels=['INFO', 'INFO'],
-                              log_to_consoles=[True, True],
-                              sim_name=name,
-                              log_directory=paths.logs_path)
-    configure_loggers()
-
-    # Get the trajectory from the environment.
-    traj = env.trajectory
-
-    # Set JUBE params
-    traj.f_add_parameter_group("JUBE_params", "Contains JUBE parameters")
-    # Execution command
-    traj.f_add_parameter_to_group("JUBE_params", "exec", "python " +
-                                  os.path.join(paths.simulation_path, "run_files/run_optimizee.py"))
-    # Paths
-    traj.f_add_parameter_to_group("JUBE_params", "paths", paths)
+    # TODO: use  the experiment module to prepare and run later the simulation
+    # define a directory to store the results
+    experiment = Experiment(root_dir_path='~/home/user/L2L/results')
+    # TODO when using the template: use keywords to prepare the experiment and
+    #  create a dictionary for jube parameters
+    # prepare_experiment returns the trajectory and all jube parameters
+    jube_params = {"nodes": "2",
+                   "walltime": "10:00:00",
+                   "ppn": "1",
+                   "cpu_pp": "1"}
+    traj, all_jube_params = experiment.prepare_experiment(name='L2L',
+                                                          log_stdout=True,
+                                                          **jube_params)
 
     ## Innerloop simulator
-    # TODO when using the template: Change the optimizee to the appropriate Optimizee class
+    # TODO when using the template: Change the optimizee to the appropriate
+    #  Optimizee class
     optimizee = Optimizee(traj)
-
-    # Prepare optimizee for jube runs
-    jube.prepare_optimizee(optimizee, paths.simulation_path)
+    # TODO Create optimizee parameters
+    optimizee_parameters = OptimizeeParameters()
 
     ## Outerloop optimizer initialization
-    # TODO when using the template: Change the optimizer to the appropriate Optimizer class
-    # and use the right value for optimizee_fitness_weights. Length is the number of dimensions of fitness, and
-    # negative value implies minimization and vice versa
+    # TODO when using the template: Change the optimizer to the appropriate
+    #  Optimizer class and use the right value for optimizee_fitness_weights.
+    #  Length is the number of dimensions of fitness, and negative value
+    #  implies minimization and vice versa
     optimizer_parameters = OptimizerParameters()
-    optimizer = Optimizer(traj, optimizee.create_individual, (1.0,), optimizer_parameters)
-
-    # Add post processing
-    env.add_postprocessing(optimizer.post_process)
-
-    # Run the simulation with all parameter combinations
-    env.run(optimizee.simulate)
-
-    ## Outerloop optimizer end
-    optimizer.end(traj)
+    optimizer = Optimizer(traj, optimizee.create_individual, (1.0,),
+                          optimizer_parameters)
 
-    # Finally disable logging and close all log-files
-    env.disable_logging()
+    experiment.run_experiment(optimizee=optimizee,
+                              optimizee_parameters=optimizee_parameters,
+                              optimizer=optimizer,
+                              optimizer_parameters=optimizer_parameters)
 
 
 if __name__ == '__main__':