7709518362

Committed 30 Jan 2024 10:11AM UTC coverage: 77.332% (-0.2%) from 77.518%

Build # 7709518362

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push

github

Committed by

thouska

Commit Message

Merge branch 'master' of https://github.com/thouska/spotpy

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94.74

/src/spotpy/algorithms/list_sampler.py

# -*- coding: utf-8 -*-
"""
Copyright (c) 2018 by Tobias Houska
This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY).
:author: Tobias Houska
"""
from .. import analyser
from . import _algorithm


class list_sampler(_algorithm):
    """
    This class holds the List sampler, which samples from a given spotpy database
    """

    _excluded_parameter_classes = ()

    def __init__(self, *args, **kwargs):
        """
        Input
        ----------
        spot_setup: class
            model: function
                Should be callable with a parameter combination of the parameter-function
                and return an list of simulation results (as long as evaluation list)
            parameter: function
                When called, it should return a random parameter combination. Which can
                be e.g. uniform or Gaussian
            objectivefunction: function
                Should return the objectivefunction for a given list of a model simulation and
                observation.
            evaluation: function
                Should return the true values as return by the model.

        dbname: str
            * Name of the database where parameter, objectivefunction value and simulation results will be saved.

        dbformat: str
            * ram: fast suited for short sampling time. no file will be created and results are saved in an array.
            * csv: A csv file will be created, which you can import afterwards.

        parallel: str
            * seq: Sequentiel sampling (default): Normal iterations on one core of your cpu.
            * mpi: Message Passing Interface: Parallel computing on cluster pcs (recommended for unix os).

        save_sim: boolean
            * True:  Simulation results will be saved
            * False: Simulation results will not be saved
        """
        kwargs["algorithm_name"] = "List Sampler"
        super(list_sampler, self).__init__(*args, **kwargs)

    def sample(self, repetitions=None):
        """

        Parameters
        ----------
        Optional:
        repetitions: int
            maximum number of function evaluations allowed during sampling
            If not given number if iterations will be determined based on given list
        """

        parameters = analyser.load_csv_parameter_results(self.dbname)
        self.dbname = self.dbname + "list"
        if not repetitions:
            repetitions = len(parameters)
        self.set_repetiton(repetitions)

        # Initialization
        print("Starting the List sampler with " + str(repetitions) + " repetitions...")
        param_generator = (
            (rep, list(parameters[rep])) for rep in range(int(repetitions))
        )
        for rep, randompar, simulations in self.repeat(param_generator):
            # A function that calculates the fitness of the run and the manages the database
            self.postprocessing(rep, list(randompar), simulations)
        self.final_call()

1	# -- coding: utf-8 --
2	"""	6✔
3	Copyright (c) 2018 by Tobias Houska
4	This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY).
5	:author: Tobias Houska
6	"""
7	from .. import analyser	6✔
8	from . import _algorithm	6✔
9
10
11	class list_sampler(_algorithm):	6✔
12	"""
13	This class holds the List sampler, which samples from a given spotpy database
14	"""
15
16	_excluded_parameter_classes = ()	6✔
17
18	def __init__(self, args, *kwargs):	6✔
19	"""
20	Input
21	----------
22	spot_setup: class
23	model: function
24	Should be callable with a parameter combination of the parameter-function
25	and return an list of simulation results (as long as evaluation list)
26	parameter: function
27	When called, it should return a random parameter combination. Which can
28	be e.g. uniform or Gaussian
29	objectivefunction: function
30	Should return the objectivefunction for a given list of a model simulation and
31	observation.
32	evaluation: function
33	Should return the true values as return by the model.
34
35	dbname: str
36	* Name of the database where parameter, objectivefunction value and simulation results will be saved.
37
38	dbformat: str
39	* ram: fast suited for short sampling time. no file will be created and results are saved in an array.
40	* csv: A csv file will be created, which you can import afterwards.
41
42	parallel: str
43	* seq: Sequentiel sampling (default): Normal iterations on one core of your cpu.
44	* mpi: Message Passing Interface: Parallel computing on cluster pcs (recommended for unix os).
45
46	save_sim: boolean
47	* True: Simulation results will be saved
48	* False: Simulation results will not be saved
49	"""
50	kwargs["algorithm_name"] = "List Sampler"	4✔
51	super(list_sampler, self).__init__(args, *kwargs)	4✔
52
53	def sample(self, repetitions=None):	6✔
54	"""
55
56	Parameters
57	----------
58	Optional:
59	repetitions: int
60	maximum number of function evaluations allowed during sampling
61	If not given number if iterations will be determined based on given list
62	"""
63
64	parameters = analyser.load_csv_parameter_results(self.dbname)	4✔
65	self.dbname = self.dbname + "list"	4✔
66	if not repetitions:	4✔
67	repetitions = len(parameters)	×
68	self.set_repetiton(repetitions)	4✔
69
70	# Initialization
71	print("Starting the List sampler with " + str(repetitions) + " repetitions...")	4✔
72	param_generator = (	4✔
73	(rep, list(parameters[rep])) for rep in range(int(repetitions))
74	)
75	for rep, randompar, simulations in self.repeat(param_generator):	4✔
76	# A function that calculates the fitness of the run and the manages the database
77	self.postprocessing(rep, list(randompar), simulations)	4✔
78	self.final_call()	4✔

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