Source code for openmdao.drivers.doe_generators

"""
Case generators for Design-of-Experiments Driver.
"""
import csv
import os.path
import re
from collections import OrderedDict

import numpy as np
import pyDOE2

from openmdao.utils.name_maps import prom_name2abs_name

_LEVELS = 2  # default number of levels for pyDOE generators


[docs]class DOEGenerator(object): """ Base class for a callable object that generates cases for a DOEDriver. """ def __call__(self, design_vars, model=None): """ Generate case. Parameters ---------- design_vars : OrderedDict Dictionary of design variables for which to generate values. model : Group The model containing the design variables (used by some subclasses). Returns ------- list list of name, value tuples for the design variables. """ return []
[docs]class ListGenerator(DOEGenerator): """ DOE case generator that reads cases from a provided list of DOE cases. This DOE case generator will accept an existing data set in the form of a list of DOE cases, each of which consists of a collection of name/value pairs specifying values for design variables. Attributes ---------- _data : list List of collections of name, value pairs for the design variables. """
[docs] def __init__(self, data=[]): """ Initialize the ListGenerator. Parameters ---------- data : list list of collections of name, value pairs for the design variables """ super().__init__() if not isinstance(data, list): msg = "Invalid DOE case data, expected a list but got a {}." raise RuntimeError(msg.format(data.__class__.__name__)) self._data = data
def __call__(self, design_vars, model=None): """ Generate case. Parameters ---------- design_vars : OrderedDict Dictionary of design variables for which to generate values. model : Group The model containing the design variables. Yields ------ list list of name, value tuples for the design variables. """ for case in self._data: if not isinstance(case, list): msg = "Invalid DOE case found, expecting a list of name/value pairs:\n{}" raise RuntimeError(msg.format(case)) name_map = {} for tup in case: if not isinstance(tup, (tuple, list)) or len(tup) != 2: msg = "Invalid DOE case found, expecting a list of name/value pairs:\n{}" raise RuntimeError(msg.format(case)) name = tup[0] if name in design_vars: name_map[name] = name elif model: abs_name = prom_name2abs_name(model, name, 'output') if abs_name in design_vars: name_map[name] = abs_name # any names not found in name_map are invalid design vars invalid_desvars = [name for name, _ in case if name not in name_map] if invalid_desvars: if len(invalid_desvars) > 1: msg = "Invalid DOE case found, {} are not valid design variables:\n{}" raise RuntimeError(msg.format(invalid_desvars, case)) else: msg = "Invalid DOE case found, '{}' is not a valid design variable:\n{}" raise RuntimeError(msg.format(invalid_desvars[0], case)) yield [(name_map[name], val) for name, val in case]
[docs]class CSVGenerator(DOEGenerator): """ DOE case generator that reads cases from a CSV file. This DOE case generator will accept an existing data set in the form of a CSV file containing DOE cases. The CSV file should have one column per design variable and the header row should have the names of the design variables. Attributes ---------- _filename : str the name of the file from which to read cases """
[docs] def __init__(self, filename): """ Initialize the CSVGenerator. Parameters ---------- filename : str the name of the file from which to read cases """ super().__init__() if not isinstance(filename, str): raise RuntimeError("'{}' is not a valid file name.".format(filename)) if not os.path.isfile(filename): raise RuntimeError("File not found: {}".format(filename)) self._filename = filename
def __call__(self, design_vars, model=None): """ Generate case. Parameters ---------- design_vars : OrderedDict Dictionary of design variables for which to generate values. model : Group The model containing the design variables. Yields ------ list list of name, value tuples for the design variables. """ name_map = {} with open(self._filename, 'r') as f: # map header names to absolute names if necessary names = re.sub(' ', '', f.readline()).strip().split(',') for name in names: if name in design_vars: name_map[name] = name elif model: abs_name = prom_name2abs_name(model, name, 'output') if abs_name in design_vars: name_map[name] = abs_name # any names not found in name_map are invalid design vars invalid_desvars = [name for name in names if name not in name_map] if invalid_desvars: if len(invalid_desvars) > 1: msg = "Invalid DOE case file, {} are not valid design variables." raise RuntimeError(msg.format(invalid_desvars)) else: msg = "Invalid DOE case file, '{}' is not a valid design variable." raise RuntimeError(msg.format(invalid_desvars[0])) # read cases from file, parse values into numpy arrays with open(self._filename, 'r') as f: reader = csv.DictReader(f) for row in reader: case = [(name_map[name.strip()], np.fromstring(re.sub(r'[\[\]]', '', row[name]), sep=' ')) for name in reader.fieldnames] yield case
[docs]class UniformGenerator(DOEGenerator): """ DOE case generator implementing the Uniform method. Attributes ---------- _num_samples : int The number of samples in the DOE. _seed : int or None Random seed. """
[docs] def __init__(self, num_samples=1, seed=None): """ Initialize the UniformGenerator. Parameters ---------- num_samples : int, optional The number of samples to run. Defaults to 1. seed : int or None, optional Seed for random number generator. """ super().__init__() self._num_samples = num_samples self._seed = seed
def __call__(self, design_vars, model=None): """ Generate case. Parameters ---------- design_vars : OrderedDict Dictionary of design variables for which to generate values. model : Group The model containing the design variables (not used). Yields ------ list list of name, value tuples for the design variables. """ if self._seed is not None: np.random.seed(self._seed) for _ in range(self._num_samples): sample = [] for name, meta in design_vars.items(): size = meta['size'] lower = meta['lower'] if not isinstance(lower, np.ndarray): lower = lower * np.ones(size) upper = meta['upper'] if not isinstance(upper, np.ndarray): upper = upper * np.ones(size) sample.append((name, np.random.uniform(lower, upper))) yield sample
class _pyDOE_Generator(DOEGenerator): """ Base class for DOE case generators implementing methods from pyDOE2. Attributes ---------- _levels : int or dict(str, int) The number of evenly spaced levels between each design variable lower and upper bound. Dictionary input is supported by Full Factorial or Generalized Subset Design. """ def __init__(self, levels=_LEVELS): """ Initialize the _pyDOE_Generator. Parameters ---------- levels : int or dict, optional The number of evenly spaced levels between each design variable lower and upper bound. Dictionary input is supported by Full Factorial or Generalized Subset Design. Defaults to 2. """ super().__init__() self._levels = levels self._sizes = None def _get_dv_levels(self, name): """ Get the number of levels of a design variable. If the name is not given, it looks for a "default" key in the dictionary. If this is also missing, it uses the default number of levels (2). Parameters ---------- name : str Design variable name Returns ------- int """ levels = self._levels if isinstance(levels, int): return levels else: return levels.get(name, levels.get("default", _LEVELS)) def _get_all_levels(self): """Return the levels of all factors.""" sizes = self._sizes if isinstance(self._levels, int): # All have the same number of levels return [self._levels] * sum(self._sizes.values()) elif isinstance(self._levels, dict): # Different DVs have different number of levels return sum([v * [self._get_dv_levels(k)] for k, v in sizes.items()], []) else: raise ValueError(f"Levels should be an int or dictionary, not '{type(self._levels)}'") def __call__(self, design_vars, model=None): """ Generate case. Parameters ---------- design_vars : OrderedDict Dictionary of design variables for which to generate values. model : Group The model containing the design variables (not used). Yields ------ list list of name, value tuples for the design variables. """ self._sizes = OrderedDict([(name, _get_size(meta)) for name, meta in design_vars.items()]) size = sum(self._sizes.values()) doe = self._generate_design(size).astype('int') # Maximum number of levels, or the default if the maximum is smaller than the default. # This is to ensure that the array will be big enough even if some keys are missing # from levels (defaulted). levels_max = self._levels if isinstance(self._levels, int) else \ max(max(self._levels.values()), _LEVELS) # Generate values for each level for each design variable # over the range of that variable's lower to upper bound # rows = vars (# rows/var = var size), cols = levels values = np.empty((size, levels_max)) # Initialize array for the largest number of levels values[:] = np.nan # and fill with NaNs. row = 0 for name, meta in design_vars.items(): size = _get_size(meta) for k in range(size): lower = meta['lower'] if isinstance(lower, np.ndarray): lower = lower[k] upper = meta['upper'] if isinstance(upper, np.ndarray): upper = upper[k] levels = self._get_dv_levels(name) values[row, 0:levels] = np.linspace(lower, upper, num=levels) row += 1 # yield values for doe generated indices for idxs in doe: retval = [] row = 0 for name, meta in design_vars.items(): size_i = _get_size(meta) val = np.empty(size_i) for k in range(size_i): idx = idxs[row + k] val[k] = values[row + k][idx] retval.append((name, val)) row += size_i yield retval def _generate_design(self, size): """ Generate DOE design. Parameters ---------- size : int The number of factors for the design. Returns ------- ndarray The design matrix as a size x levels array of indices. """ pass
[docs]class FullFactorialGenerator(_pyDOE_Generator): """ DOE case generator implementing the Full Factorial method. """ def _generate_design(self, size): """ Generate a full factorial DOE design. Parameters ---------- size : int The number of factors for the design. Returns ------- ndarray The design matrix as a size x levels array of indices. """ return pyDOE2.fullfact(self._get_all_levels())
[docs]class GeneralizedSubsetGenerator(_pyDOE_Generator): """ DOE case generator implementing the General Subset Design Factorial method. Attributes ---------- _reduction : int Reduction factor (bigger than 1). Larger `reduction` means fewer experiments in the design and more possible complementary designs. _n : int, optional Number of complementary GSD-designs. The complementary designs are balanced analogous to fold-over in two-level fractional factorial designs. Defaults to 1. """
[docs] def __init__(self, levels, reduction, n=1): """ Initialize the GeneralizedSubsetGenerator. Parameters ---------- levels : int or dict The number of evenly spaced levels between each design variable lower and upper bound. Defaults to 2. reduction : int Reduction factor (bigger than 1). Larger `reduction` means fewer experiments in the design and more possible complementary designs. n : int, optional Number of complementary GSD-designs. The complementary designs are balanced analogous to fold-over in two-level fractional factorial designs. Defaults to 1. """ super().__init__(levels=levels) self._reduction = reduction self._n = n
def _generate_design(self, size): """ Generate a general subset DOE design. Parameters ---------- size : int The number of factors for the design. Returns ------- ndarray The design matrix as a size x levels array of indices. """ return pyDOE2.gsd(levels=self._get_all_levels(), reduction=self._reduction, n=self._n)
[docs]class PlackettBurmanGenerator(_pyDOE_Generator): """ DOE case generator implementing the Plackett-Burman method. """
[docs] def __init__(self): """ Initialize the PlackettBurmanGenerator. """ super().__init__(levels=2)
def _generate_design(self, size): """ Generate a Plackett-Burman DOE design. Parameters ---------- size : int The number of factors for the design. Returns ------- ndarray The design matrix as a size x levels array of indices. """ doe = pyDOE2.pbdesign(size) doe[doe < 0] = 0 # replace -1 with zero return doe
[docs]class BoxBehnkenGenerator(_pyDOE_Generator): """ DOE case generator implementing the Box-Behnken method. Attributes ---------- _center : int The number of center points to include. """
[docs] def __init__(self, center=None): """ Initialize the BoxBehnkenGenerator. Parameters ---------- center : int, optional The number of center points to include (default = None). """ super().__init__(levels=3) self._center = center
def _generate_design(self, size): """ Generate a Box-Behnken DOE design. Parameters ---------- size : int The number of factors for the design. Returns ------- ndarray The design matrix as a size x levels array of indices. """ if size < 3: raise RuntimeError("Total size of design variables is %d," "but must be at least 3 when using %s. " % (size, self.__class__.__name__)) doe = pyDOE2.bbdesign(size, center=self._center) return doe + 1 # replace [-1, 0, 1] with [0, 1, 2]
[docs]class LatinHypercubeGenerator(DOEGenerator): """ DOE case generator implementing Latin hypercube method via pyDOE2. Attributes ---------- _samples : int The number of evenly spaced levels between each design variable lower and upper bound. _criterion : string the pyDOE criterion to use. _iterations : int The number of iterations to use for maximin and correlations algorithms. _seed : int or None Random seed. """ # supported pyDOE criterion names. _supported_criterion = [ "center", "c", "maximin", "m", "centermaximin", "cm", "correlation", "corr", None ]
[docs] def __init__(self, samples=None, criterion=None, iterations=5, seed=None): """ Initialize the LatinHypercubeGenerator. See : https://pythonhosted.org/pyDOE/randomized.html Parameters ---------- samples : int, optional The number of samples to generate for each factor (Defaults to n) criterion : str, optional Allowable values are "center" or "c", "maximin" or "m", "centermaximin" or "cm", and "correlation" or "corr". If no value given, the design is simply randomized. iterations : int, optional The number of iterations in the maximin and correlations algorithms (Defaults to 5). seed : int, optional Random seed to use if design is randomized. Defaults to None. """ super().__init__() if criterion not in self._supported_criterion: raise ValueError("Invalid criterion '%s' specified for %s. " "Must be one of %s." % (criterion, self.__class__.__name__, self._supported_criterion)) self._samples = samples self._criterion = criterion self._iterations = iterations self._seed = seed
def __call__(self, design_vars, model=None): """ Generate case. Parameters ---------- design_vars : OrderedDict Dictionary of design variables for which to generate values. model : Group The model containing the design variables (not used). Yields ------ list list of name, value tuples for the design variables. """ if self._seed is not None: np.random.seed(self._seed) size = sum([meta['size'] for meta in design_vars.values()]) if self._samples is None: self._samples = size # generate design doe = pyDOE2.lhs(size, samples=self._samples, criterion=self._criterion, iterations=self._iterations, random_state=self._seed) # yield desvar values for doe samples for row in doe: retval = [] col = 0 for name, meta in design_vars.items(): size = meta['size'] sample = row[col:col + size] lower = meta['lower'] if not isinstance(lower, np.ndarray): lower = lower * np.ones(size) upper = meta['upper'] if not isinstance(upper, np.ndarray): upper = upper * np.ones(size) val = lower + sample * (upper - lower) retval.append((name, val)) col += size yield retval
def _get_size(dct): # Returns global size of the variable if it is distributed, size otherwise. return dct['global_size'] if dct['distributed'] else dct['size']