easyvvuq.sampling.replica_sampler
Replica Sampler
Summary
Primarily intended for sampling the same paramater values but with different random seed. Other uses may be possible. It takes a finite sampler and produces an infinite sampler from it. This infinite sampler loops through the parameters produced by the finite sampler and at each cycle adds a unique id number for that cycle to the parameter dictionary.
1"""Replica Sampler 2 3Summary 4------- 5 6Primarily intended for sampling the same paramater values but with 7different random seed. Other uses may be possible. It takes a finite 8sampler and produces an infinite sampler from it. This infinite 9sampler loops through the parameters produced by the finite sampler 10and at each cycle adds a unique id number for that cycle to the 11parameter dictionary. 12""" 13 14from easyvvuq.sampling import BaseSamplingElement 15from itertools import cycle 16 17 18class ReplicaSampler(BaseSamplingElement, sampler_name='replica_sampler'): 19 """Replica Sampler 20 21 Parameters 22 ---------- 23 sampler : an instance of a class derived from BaseSamplingElement 24 a finite sampler to loop over 25 26 replica_col : string 27 a parameter name for the replica id 28 seed_col : string 29 a parameter name for the input parameter that specifies the RNG seed 30 replicas : int 31 number of replicas, if zero will result in an infinite sampler 32 """ 33 34 def __init__(self, sampler, replica_col='ensemble_id', seed_col=None, replicas=0): 35 if not sampler.is_finite(): 36 raise RuntimeError("Replica sampler only works with finite samplers") 37 self.sampler = sampler 38 self.replica_col = replica_col 39 self.replicas = replicas 40 self.sampler.n_replicas = replicas 41 self.reset() 42 43 def reset(self): 44 self.history = [] 45 for sample in self.sampler: 46 self.history.append(sample) 47 self.size = len(self.history) 48 self.cycle = cycle(self.history) 49 self.counter = 0 50 if isinstance(self.sampler.n_samples, int): 51 self.total_counter = self.replicas * self.sampler.n_samples 52 else: 53 self.total_counter = self.replicas * self.sampler.n_samples() 54 55 def is_finite(self): 56 if self.replicas == 0: 57 return False 58 else: 59 return True 60 61 def n_samples(self): 62 if self.replicas == 0: 63 raise RuntimeError("You can't get the number of samples in an infinite sampler") 64 else: 65 return self.replicas * self.sampler.n_samples() 66 67 def __next__(self): 68 if self.replicas != 0: 69 self.total_counter -= 1 70 if self.total_counter < 0: 71 raise StopIteration 72 params = dict(next(self.cycle)) 73 params[self.replica_col] = self.counter 74 self.counter = (self.counter + 1) % self.size 75 return params 76 77 def update(self, result, invalid): 78 self.reset() 79 return self.sampler.update(result, invalid) 80 81 @property 82 def iteration(self): 83 return self.sampler.iteration 84 85 @property 86 def analysis_class(self): 87 return self.sampler.analysis_class 88 89 @property 90 def inputs(self): 91 return self.sampler.inputs 92 93 @property 94 def qoi(self): 95 return self.sampler.qoi
19class ReplicaSampler(BaseSamplingElement, sampler_name='replica_sampler'): 20 """Replica Sampler 21 22 Parameters 23 ---------- 24 sampler : an instance of a class derived from BaseSamplingElement 25 a finite sampler to loop over 26 27 replica_col : string 28 a parameter name for the replica id 29 seed_col : string 30 a parameter name for the input parameter that specifies the RNG seed 31 replicas : int 32 number of replicas, if zero will result in an infinite sampler 33 """ 34 35 def __init__(self, sampler, replica_col='ensemble_id', seed_col=None, replicas=0): 36 if not sampler.is_finite(): 37 raise RuntimeError("Replica sampler only works with finite samplers") 38 self.sampler = sampler 39 self.replica_col = replica_col 40 self.replicas = replicas 41 self.sampler.n_replicas = replicas 42 self.reset() 43 44 def reset(self): 45 self.history = [] 46 for sample in self.sampler: 47 self.history.append(sample) 48 self.size = len(self.history) 49 self.cycle = cycle(self.history) 50 self.counter = 0 51 if isinstance(self.sampler.n_samples, int): 52 self.total_counter = self.replicas * self.sampler.n_samples 53 else: 54 self.total_counter = self.replicas * self.sampler.n_samples() 55 56 def is_finite(self): 57 if self.replicas == 0: 58 return False 59 else: 60 return True 61 62 def n_samples(self): 63 if self.replicas == 0: 64 raise RuntimeError("You can't get the number of samples in an infinite sampler") 65 else: 66 return self.replicas * self.sampler.n_samples() 67 68 def __next__(self): 69 if self.replicas != 0: 70 self.total_counter -= 1 71 if self.total_counter < 0: 72 raise StopIteration 73 params = dict(next(self.cycle)) 74 params[self.replica_col] = self.counter 75 self.counter = (self.counter + 1) % self.size 76 return params 77 78 def update(self, result, invalid): 79 self.reset() 80 return self.sampler.update(result, invalid) 81 82 @property 83 def iteration(self): 84 return self.sampler.iteration 85 86 @property 87 def analysis_class(self): 88 return self.sampler.analysis_class 89 90 @property 91 def inputs(self): 92 return self.sampler.inputs 93 94 @property 95 def qoi(self): 96 return self.sampler.qoi
Replica Sampler
Parameters
- sampler (an instance of a class derived from BaseSamplingElement): a finite sampler to loop over
- replica_col (string): a parameter name for the replica id
- seed_col (string): a parameter name for the input parameter that specifies the RNG seed
- replicas (int): number of replicas, if zero will result in an infinite sampler
ReplicaSampler(sampler, replica_col='ensemble_id', seed_col=None, replicas=0)
35 def __init__(self, sampler, replica_col='ensemble_id', seed_col=None, replicas=0): 36 if not sampler.is_finite(): 37 raise RuntimeError("Replica sampler only works with finite samplers") 38 self.sampler = sampler 39 self.replica_col = replica_col 40 self.replicas = replicas 41 self.sampler.n_replicas = replicas 42 self.reset()
def
reset(self):
44 def reset(self): 45 self.history = [] 46 for sample in self.sampler: 47 self.history.append(sample) 48 self.size = len(self.history) 49 self.cycle = cycle(self.history) 50 self.counter = 0 51 if isinstance(self.sampler.n_samples, int): 52 self.total_counter = self.replicas * self.sampler.n_samples 53 else: 54 self.total_counter = self.replicas * self.sampler.n_samples()
iteration
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.__int__(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.
>>> int('0b100', base=0)
4