Update scripts/NNDy_Interface.py
complete commenting added handling of uncertainities
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#imports for runmanager - labscript
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from runmanager_remote import run_experiment
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import time
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import numpy as np
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#Imports for M-LOOP
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import mloop.interfaces as mli
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#importlib allows to import the costfunction defined in cost_model.py
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import importlib
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_module_cache = {} #avoid multiple calls of cost function for same routine
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#Declare your custom class that inherits from the Interface class
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class NNDy_Interface(mli.Interface):
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def __init__(self, routine_name, cost_model, hyperpars):
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#You must include the super command to call the parent class, Interface, constructor
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super(NNDy_Interface,self).__init__()
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#Attributes of the interface can be added here
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self.exp_global_par = hyperpars['globalPar']
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self.input_names = hyperpars['inputPar_names']
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self.routine = routine_name
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self.cost_model = cost_model
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def cost(self, parameters):
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module_name = self.cost_model
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if module_name not in _module_cache:
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try:
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module = importlib.import_module(module_name)
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cost_func = getattr(module, 'cost')
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#analysis_func = getattr(self.module, 'analysis')
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_module_cache[module_name] = {
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"cost_func": cost_func ,
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# "analysis_func": analysis_func
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}
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except (ModuleNotFoundError, AttributeError) as e:
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raise ImportError(f'Failed to load cost function from "{module_name}.py": {e}')
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cost_model = _module_cache[module_name]["cost_func"]
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return cost_model(parameters)
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#the method that runs the experiment given a set of parameters and returns a cost
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def get_next_cost_dict(self,In_params_raw):
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#The parameters come in a dictionary and are provided in a numpy array
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In_params = In_params_raw['params']
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#print(In_params)
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#optimization parameters to be send back to labscript are converted back into dictionaries
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if len(In_params) != len(self.input_names):
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raise Exception('number of optimized parameters and names do not match')
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In_params_dict = {}
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for par,name in zip(In_params, self.input_names) :
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In_params_dict.update({name: par})
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#merge with fixed global variables
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global_group = In_params_dict | self.exp_global_par
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#Here you can include the code to run your experiment given a particular set of parameters
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#run_experiment runs the routine specified by routine name with global variables equal to the new set of parameters given by the optimizer
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#this means that the experiment parameters - In_params - are chosen among the global variablesof this labscript routine and are passed as a dictionary
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#print('running the experiment')
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results = {
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'cost': np.inf,
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'bad': False
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}
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try:
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hdf_output_file = run_experiment(self.routine, global_var = global_group)
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results = self.cost(hdf_output_file)
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except Exception as e:
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print(f"Exception type '{e}', considered as bad run!")
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results['bad'] = True
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#self.analysis(hdf_output_file)
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#print('cost is computed')
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uncer = 0
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time.sleep(0.001)
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#The cost, uncertainty and bad boolean must all be returned as a dictionary
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cost_dict = {'cost':results['cost'], 'uncer':uncer, 'bad':results['bad']}
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import time
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import numpy as np
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#imports for runmanager - labscript
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from runmanager_remote import run_experiment
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#Imports for M-LOOP
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import mloop.interfaces as mli
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# THIS FILE SHOULDN'T BE MODIFIED
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# if you're trying to improve the design of the interface define it in a new script
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# possible improvements of this version
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# - have the interface run the experiment many times to gather statistics
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# - run two or more optimizers in parallel with different settings (like trust regions, update of parameters) for efficiency
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#only exception
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# if during debugging you want to know where the errors are generated in the experiment look for this mark $%$%$
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#importlib allows to import the costfunction defined in cost_model.py
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import importlib
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_module_cache = {} #avoid multiple calls of cost function for same routine
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#Declare your custom class that inherits from the Interface class
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class NNDy_Interface(mli.Interface):
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def __init__(self, routine_name, cost_model, hyperpars):
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#You must include the super command to call the parent class, Interface, constructor
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super(NNDy_Interface,self).__init__()
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#Attributes of the interface can be added here
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#here the interface is passed global variables eventually set from the main and the names of the input parameters that the controller controller will optimize
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self.exp_global_par = hyperpars['globalPar']
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self.input_names = hyperpars['inputPar_names']
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self.routine = routine_name
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self.cost_model = cost_model
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#the cost function is retrieved from /{cost_model}.py
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#in such file there should be a function called cost that takes as input the hdf5_file of the run and returns a dictionary with arguments 'bad', 'cost', 'uncer'
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def cost(self, hdf5_file):
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module_name = self.cost_model
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#looks in the cache if the file was accessed already, otherwise imports the cost function
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if module_name not in _module_cache:
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try:
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module = importlib.import_module(module_name)
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cost_func = getattr(module, 'cost')
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_module_cache[module_name] = {
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"cost_func": cost_func ,
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}
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except (ModuleNotFoundError, AttributeError) as e:
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raise ImportError(f'Failed to load cost function from "{module_name}.py": {e}')
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cost_model = _module_cache[module_name]["cost_func"]
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return cost_model(hdf5_file)
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#the method that runs the experiment given a set of parameters and returns a cost
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def get_next_cost_dict(self,In_params_raw):
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#The parameters come in a dictionary and are provided in a numpy array
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In_params = In_params_raw['params']
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#print(In_params)
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#optimization parameters to be send back to labscript are converted back into dictionaries
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if len(In_params) != len(self.input_names):
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raise Exception('number of optimized parameters and names do not match')
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In_params_dict = {}
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for par,name in zip(In_params, self.input_names) :
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In_params_dict.update({name: par})
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#merge with fixed global variables
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global_group = In_params_dict | self.exp_global_par
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#Here you can include the code to run your experiment given a particular set of parameters
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# default values
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results = {
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'bad': False,
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'cost': float('inf'), #may cause some problem, in case try with an absurdly high but finite value, or with 0 if cost is always negative
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'uncer': 0
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}
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#run_experiment runs the routine specified by self.routine with global variables equal to the new set of parameters given by the optimizer
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#print('running the experiment')
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# $%$%$ disable this expection catch if the experiment is always giving 'bad' results
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try:
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hdf_output_file = run_experiment(self.routine, global_var = global_group)
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#results should be a dictionary that contains at least a 'cost' item
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results = self.cost(hdf_output_file)
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except Exception as e:
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print(f"Exception type '{e}', considered as bad run!")
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results['bad'] = True
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#print('cost is computed')
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#sets uncer to 0 if it's not returned
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try:
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uncer = results['uncer']
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except Exception as e:
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uncer = 0
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#print(f"Exception type '{e}', no uncertainity set")
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time.sleep(0.001)
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#The cost, uncertainty and bad boolean must all be returned as a dictionary
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cost_dict = {'cost':results['cost'], 'uncer':uncer, 'bad':results['bad']}
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return cost_dict
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