Source code for openmdao.components.meta_model_structured_comp

"""Define the MetaModelStructured class."""
from __future__ import division, print_function, absolute_import

from six import raise_from, iteritems, itervalues
from six.moves import range

import numpy as np

from openmdao.components.interp_util.outofbounds_error import OutOfBoundsError
from openmdao.components.interp_util.interp import InterpND, TABLE_METHODS
from openmdao.core.analysis_error import AnalysisError
from openmdao.core.explicitcomponent import ExplicitComponent
from openmdao.utils.general_utils import warn_deprecation


[docs]class MetaModelStructuredComp(ExplicitComponent): """ Interpolation Component generated from data on a regular grid. Produces smooth fits through provided training data using polynomial splines of various orders. Analytic derivatives are automatically computed. For multi-dimensional data, fits are computed on a separable per-axis basis. If a particular dimension does not have enough training data points to support a selected spline method (e.g. 3 sample points, but an fifth order quintic spline is specified) the order of the fitted spline with be automatically reduced for that dimension alone. Extrapolation is supported, but disabled by default. It can be enabled via initialization option. Attributes ---------- grad_shape : tuple Cached shape of the gradient of the outputs wrt the training inputs. interps : dict Dictionary of interpolations for each output. params : list List containing training data for each input. pnames : list Cached list of input names. training_outputs : dict Dictionary of training data each output. """
[docs] def __init__(self, **kwargs): """ Initialize all attributes. Parameters ---------- **kwargs : dict of keyword arguments Keyword arguments that will be mapped into the Component options. """ super(MetaModelStructuredComp, self).__init__(**kwargs) self.pnames = [] self.params = [] self.training_outputs = {} self.interps = {} self.grad_shape = ()
[docs] def initialize(self): """ Initialize the component. """ self.options.declare('extrapolate', types=bool, default=False, desc='Sets whether extrapolation should be performed ' 'when an input is out of bounds.') self.options.declare('training_data_gradients', types=bool, default=False, desc='Sets whether gradients with respect to output ' 'training data should be computed.') self.options.declare('vec_size', types=int, default=1, desc='Number of points to evaluate at once.') self.options.declare('method', values=TABLE_METHODS, default='scipy_cubic', desc='Spline interpolation method to use for all outputs.')
[docs] def add_input(self, name, val=1.0, training_data=None, **kwargs): """ Add an input to this component and a corresponding training input. Parameters ---------- name : string Name of the input. val : float or ndarray Initial value for the input. training_data : ndarray training data sample points for this input variable. **kwargs : dict Additional agruments for add_input. """ n = self.options['vec_size'] # Currently no support for vector inputs, apart from vec_size if not np.isscalar(val): if len(val) not in [1, n] or len(val.shape) > 1: msg = "{}: Input {} must either be scalar, or of length equal to vec_size." raise ValueError(msg.format(self.msginfo, name)) super(MetaModelStructuredComp, self).add_input(name, val * np.ones(n), **kwargs) self.pnames.append(name) self.params.append(np.asarray(training_data))
[docs] def add_output(self, name, val=1.0, training_data=None, **kwargs): """ Add an output to this component and a corresponding training output. Parameters ---------- name : string Name of the output. val : float or ndarray Initial value for the output. training_data : ndarray training data sample points for this output variable. **kwargs : dict Additional agruments for add_output. """ n = self.options['vec_size'] # Currently no support for vector outputs, apart from vec_size if not np.isscalar(val): if len(val) not in [1, n] or len(val.shape) > 1: msg = "{}: Output {} must either be scalar, or of length equal to vec_size." raise ValueError(msg.format(self.msginfo, name)) super(MetaModelStructuredComp, self).add_output(name, val * np.ones(n), **kwargs) self.training_outputs[name] = training_data if self.options['training_data_gradients']: super(MetaModelStructuredComp, self).add_input("%s_train" % name, val=training_data, **kwargs)
def _setup_var_data(self, recurse=True): """ Instantiate surrogates for the output variables that use the default surrogate. Parameters ---------- recurse : bool Whether to call this method in subsystems. """ interp_method = self.options['method'] opts = {} if 'interp_options' in self.options: opts = self.options['interp_options'] for name, train_data in iteritems(self.training_outputs): self.interps[name] = InterpND(method=interp_method, points=self.params, values=train_data, extrapolate=self.options['extrapolate']) if self.options['training_data_gradients']: self.grad_shape = tuple([self.options['vec_size']] + [i.size for i in self.params]) super(MetaModelStructuredComp, self)._setup_var_data(recurse=recurse) def _setup_partials(self, recurse=True): """ Process all partials and approximations that the user declared. Metamodel needs to declare its partials after inputs and outputs are known. Parameters ---------- recurse : bool Whether to call this method in subsystems. """ super(MetaModelStructuredComp, self)._setup_partials() arange = np.arange(self.options['vec_size']) pnames = tuple(self.pnames) dct = { 'rows': arange, 'cols': arange, 'dependent': True, } for name in self._outputs: self._declare_partials(of=name, wrt=pnames, dct=dct) if self.options['training_data_gradients']: self._declare_partials(of=name, wrt="%s_train" % name, dct={'dependent': True}) # The scipy methods do not support complex step. if self.options['method'].startswith('scipy'): self.set_check_partial_options('*', method='fd')
[docs] def compute(self, inputs, outputs): """ Perform the interpolation at run time. Parameters ---------- inputs : Vector unscaled, dimensional input variables read via inputs[key] outputs : Vector unscaled, dimensional output variables read via outputs[key] """ pt = np.array([inputs[pname].flatten() for pname in self.pnames]).T for out_name, interp in iteritems(self.interps): if self.options['training_data_gradients']: # Training point values may have changed every time we compute. interp.values = inputs["%s_train" % out_name] interp._compute_d_dvalues = True try: val = interp._interpolate(pt) except OutOfBoundsError as err: varname_causing_error = '.'.join((self.pathname, self.pnames[err.idx])) errmsg = "{}: Error interpolating output '{}' because input '{}' " \ "was out of bounds ('{}', '{}') with " \ "value '{}'".format(self.msginfo, out_name, varname_causing_error, err.lower, err.upper, err.value) raise_from(AnalysisError(errmsg), None) except ValueError as err: raise ValueError("{}: Error interpolating output '{}':\n{}".format(self.msginfo, out_name, str(err))) outputs[out_name] = val
[docs] def compute_partials(self, inputs, partials): """ Collect computed partial derivatives and return them. Checks if the needed derivatives are cached already based on the inputs vector. Refreshes the cache by re-computing the current point if necessary. Parameters ---------- inputs : Vector unscaled, dimensional input variables read via inputs[key] partials : Jacobian sub-jac components written to partials[output_name, input_name] """ pt = np.array([inputs[pname].flatten() for pname in self.pnames]).T for out_name, interp in iteritems(self.interps): dval = interp.gradient(pt).T for i, p in enumerate(self.pnames): partials[out_name, p] = dval[i, :] if self.options['training_data_gradients']: dy_ddata = np.zeros(self.grad_shape) if interp._d_dvalues is not None: # Akima must be handled individually. dy_ddata[:] = interp._d_dvalues else: # This way works for most of the interpolation methods. for j in range(self.options['vec_size']): val = interp.training_gradients(pt[j, :]) dy_ddata[j] = val.reshape(self.grad_shape[1:]) partials[out_name, "%s_train" % out_name] = dy_ddata
[docs]class MetaModelStructured(MetaModelStructuredComp): """ Deprecated. """
[docs] def __init__(self, *args, **kwargs): """ Capture Initialize to throw warning. Parameters ---------- *args : list Deprecated arguments. **kwargs : dict Deprecated arguments. """ warn_deprecation("'MetaModelStructured' has been deprecated. Use " "'MetaModelStructuredComp' instead.") super(MetaModelStructured, self).__init__(*args, **kwargs)