Source code for

# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry,
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# Lesser General Public License for more details.
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

# Contributors:
#    INITIAL AUTHORS - API and implementation and/or documentation
#        :author: Francois Gallard
#        :author: Damien Guenot
"""A scatter plot matrix to display optimization history."""
from __future__ import division, unicode_literals

import logging
from typing import Sequence

from matplotlib import pyplot
from pandas.core.frame import DataFrame

from import OptPostProcessor

    from import scatter_matrix
except ImportError:
    from pandas.plotting import scatter_matrix

LOGGER = logging.getLogger(__name__)

[docs]class ScatterPlotMatrix(OptPostProcessor): """Scatter plot matrix among design variables, output functions and constraints. The list of variable names has to be passed as arguments of the plot method. x- and y- figure sizes can be changed in option. """ def _plot( self, variables_list, # type: Sequence[str] figsize_x=10, # type: int figsize_y=10, # type: int ): # type: (...) -> None """ Args: variables_list: The functions names or design variables to plot. If the list is empty, plot all design variables. figsize_x: The size of the figure in horizontal direction (inches). figsize_y: The size of the figure in vertical direction (inches). """ add_dv = False all_funcs = self.opt_problem.get_all_functions_names() all_dv_names = self.opt_problem.design_space.variables_names variables_list.sort() if not variables_list: # In this case, plot all design variables, no functions. vals = self.database.get_x_history() # This section creates readable labels for design variables # i.e. toto_0, toto_1 if toto is a variable with 2 components x_labels = self.__get_design_var_labels(all_dv_names) else: design_variables = [] for func in list(variables_list): if func not in all_funcs and func not in all_dv_names: min_f = "-{}".format(func) == if min_f and not self.opt_problem.minimize_objective: variables_list[variables_list.index(func)] = "-{}".format(func) variables_list.sort() else: msg = ( "Cannot build scatter plot matrix, " "Function {} is neither among" " optimization problem functions : {}" " nor design variables : {}".format( func, all_funcs, all_dv_names ) ) raise ValueError(msg) if func in self.opt_problem.design_space.variables_names: # if given function is a design variable, then remove it add_dv = True variables_list.remove(func) design_variables.append(func) if not design_variables: design_variables = None if add_dv: # Sort the design variables to be consistent with GEMSEO. design_variables = sorted( set(all_dv_names) & set(design_variables), key=all_dv_names.index, ) # This section creates readable labels for design variables # and functions i.e. toto_0, toto_1 if toto is a variable # with 2 components dv_labels = self.__get_design_var_labels(design_variables) if variables_list: _, func_labels, _ = self.database.get_history_array( functions=variables_list, design_variables_names=None, add_dv=False, ) else: func_labels = [] # vname contains function names + condensed variable names # i.e. "toto" even if toto has 2 components or more vname = variables_list + design_variables # x_labels contains function names + readable variable names x_labels = func_labels + dv_labels else: # In this case we are only plotting functions. # Functions have unique names, so x_labels and # vname are equal. vname = variables_list _, x_labels, _ = self.database.get_history_array( functions=variables_list, design_variables_names=None, add_dv=False, ) x_labels.sort() dataset = self.opt_problem.export_to_dataset("OptimizationProblem") vals = dataset.get_data_by_names(vname, False) # Next line is a trick for a bug workaround in numpy/matplotlib # # pandas-dataframe-valueerror-num-must-be-1-num-0-not-1 vals = (list(x) for x in vals) frame = DataFrame(vals, columns=x_labels) scatter_matrix(frame, alpha=1.0, figsize=(figsize_x, figsize_y), diagonal="kde") fig = pyplot.gcf() fig.tight_layout() self._add_figure(fig) def __get_design_var_labels(self, des_vars): """Create labels for design variables. Args: des_vars (list(str)): The design variables to get its labels. Returns: list(str): The labels for the design variables. """ dv_names = [] for d_v in des_vars: dv_size = self.opt_problem.design_space.variables_sizes[d_v] if dv_size == 1: dv_names.append(d_v) else: for k in range(dv_size): dv_names.append("{}_{}".format(d_v, k)) return dv_names