Improved Documentation

Author: Julian Blank

pymoo/algorithms/moead.py

@@ -2,13 +2,12 @@
 from scipy.spatial.distance import cdist
 
 from pymoo.algorithms.genetic_algorithm import GeneticAlgorithm
-from pymoo.docs import parse_doc_string
 from pymoo.factory import get_decomposition
 from pymoo.operators.crossover.simulated_binary_crossover import SimulatedBinaryCrossover
-from pymoo.util.display import MultiObjectiveDisplay
-from pymoo.util.misc import set_if_none
 from pymoo.operators.mutation.polynomial_mutation import PolynomialMutation
 from pymoo.operators.sampling.random_sampling import FloatRandomSampling
+from pymoo.util.display import MultiObjectiveDisplay
+from pymoo.util.misc import set_if_none
 
 
 # =========================================================================================================
@@ -28,19 +27,12 @@ def __init__(self,
 
         Parameters
         ----------
-        ref_dirs : {ref_dirs}
-
-        decomposition : {{ 'auto', 'tchebi', 'pbi' }}
-            The decomposition approach that should be used. If set to `auto` for two objectives `tchebi` and for more than
-            two `pbi` will be used.
-
-        n_neighbors : int
-            Number of neighboring reference lines to be used for selection.
-
-        prob_neighbor_mating : float
-            Probability of selecting the parents in the neighborhood.
-
-
+        ref_dirs
+        n_neighbors
+        decomposition
+        prob_neighbor_mating
+        display
+        kwargs
         """
 
         self.n_neighbors = n_neighbors
@@ -130,4 +122,4 @@ def _next(self):
             pop[N[I]] = off
 
 
-parse_doc_string(MOEAD.__init__)
+# parse_doc_string(MOEAD.__init__)

pymoo/algorithms/so_cmaes.py

@@ -50,6 +50,7 @@ def _do(self, problem, evaluator, algorithm):
 class CMAES(LocalSearch):
 
     def __init__(self,
+                 x0=None,
                  sigma=0.5,
                  parallelize=True,
                  maxfevals=np.inf,
@@ -333,7 +334,7 @@ def __init__(self,
               for a list of available options.
 
         """
-        super().__init__(display=display, **kwargs)
+        super().__init__(x0=x0, display=display, **kwargs)
 
         self.es = None
         self.cma = None

pymoo/algorithms/so_direct.py

@@ -0,0 +1,175 @@
+import numpy as np
+
+from pymoo.algorithms.so_local_search import LocalSearch
+from pymoo.model.individual import Individual
+from pymoo.model.population import Population
+from pymoo.model.problem import Problem
+from pymoo.optimize import minimize
+from pymoo.problems.single import Himmelblau, Sphere, Rastrigin
+from pymoo.util.display import SingleObjectiveDisplay
+from pymoo.util.nds.non_dominated_sorting import NonDominatedSorting
+from pymoo.util.normalization import normalize, denormalize
+
+
+def norm_bounds(pop, problem):
+    nxl = normalize(pop.get("xl"), problem.xl, problem.xu)
+    nxu = normalize(pop.get("xu"), problem.xl, problem.xu)
+    return nxl, nxu
+
+
+def update_bounds(ind, xl, xu, k, delta):
+    _xl = np.copy(xl)
+    _xl[k] = ind.X[k] - delta
+    ind.set("xl", _xl)
+
+    _xu = np.copy(xu)
+    _xu[k] = ind.X[k] + delta
+    ind.set("xu", _xu)
+
+
+class DIRECT(LocalSearch):
+
+    def __init__(self,
+                 eps=1e-2,
+                 penalty=0.1,
+                 display=SingleObjectiveDisplay(),
+                 **kwargs):
+        super().__init__(display=display, **kwargs)
+        self.eps = eps
+        self.penalty = penalty
+
+    def initialize(self, problem, **kwargs):
+        super().initialize(problem, **kwargs)
+
+        xl, xu = problem.bounds()
+        X = denormalize(0.5 * np.ones(problem.n_var), xl, xu)
+        x0 = Individual(X=X)
+        x0.set("xl", xl)
+        x0.set("xu", xu)
+        x0.set("depth", 0)
+        self.x0 = x0
+
+    def _initialize(self, **kwargs):
+        super()._initialize(**kwargs)
+
+    def _potential_optimal(self):
+        pop = self.pop
+
+        if len(pop) == 1:
+            return pop
+
+        # get the intervals of each individual
+        _F, _CV, xl, xu = pop.get("F", "CV", "xl", "xu")
+        nF = normalize(_F)
+        F = nF + self.penalty * _CV
+
+        # get the length of the interval of each solution
+        nxl, nxu = norm_bounds(pop, problem)
+        length = (nxu - nxl) / 2
+
+        val = length.max(axis=1)
+
+        # (a) non-dominated with respect to interval
+        obj = np.column_stack([-val, F])
+        I = NonDominatedSorting().do(obj, only_non_dominated_front=True)
+        candidates, F, xl, xu, val = pop[I], F[I], xl[I], xu[I], val[I]
+
+        # import matplotlib.pyplot as plt
+        # plt.scatter(obj[:, 0], obj[:, 1])
+        # plt.scatter(obj[I, 0], obj[I, 1], color="red")
+        # plt.show()
+
+        if len(candidates) == 1:
+            return candidates
+
+        else:
+            # TODO: The second condition needs to be implemented here. Exact implementation still unclear.
+
+            n_max_candidates = 10
+
+            if len(candidates) > n_max_candidates:
+                I = list(np.random.choice(np.arange(len(candidates)), n_max_candidates - 1))
+                k = np.argmin(F[:, 0])
+                if k not in I:
+                    I.append(k)
+                candidates = candidates[I]
+
+            return candidates
+
+    def _next(self):
+        # the offspring population to finally evaluate and attach to the population
+        off = Population()
+
+        # find the potential optimal solution in the current population
+        potential_optimal = self._potential_optimal()
+
+        # for each of those solutions execute the division move
+        for current in potential_optimal:
+
+            # find the largest dimension the solution has not been evaluated yet
+            nxl, nxu = norm_bounds(current, problem)
+            k = np.argmax(nxu - nxl)
+
+            # the delta value to be used to get left and right - this is one sixth of the range
+            xl, xu = current.get("xl"), current.get("xu")
+
+            delta = (xu[k] - xl[k]) / 6
+
+            # print(current.X, delta, k, xl, xu)
+
+            # create the left individual
+            left_x = np.copy(current.X)
+            left_x[k] = xl[k] + delta
+            left = Individual(X=left_x)
+
+            # create the right individual
+            right_x = np.copy(current.X)
+            right_x[k] = xu[k] - delta
+            right = Individual(X=right_x)
+
+            # update the boundaries for all the points accordingly
+            for ind in [current, left, right]:
+                update_bounds(ind, xl, xu, k, delta)
+
+            # create the offspring population, evaluate and attach to current population
+            _off = Population.create(left, right)
+            _off.set("depth", current.get("depth") + 1)
+
+            off = Population.merge(off, _off)
+
+        # evaluate the offsprings
+        self.evaluator.eval(self.problem, off, algorithm=self)
+
+        # print(off.get("X"))
+
+        # add the offsprings to the population
+        self.pop = Population.merge(self.pop, off)
+
+
+class ExSwarm(Problem):
+
+    def __init__(self):
+        super().__init__(n_var=2, n_obj=1, n_constr=0, xl=np.array([-1.0, -1.0]), xu=np.array([1.0, 1.0]))
+
+    def _evaluate(self, x, out, *args, **kwargs):
+        v1 = 20 * x[:, 0]
+        v2 = 20 * x[:, 1]
+        out["F"] = -np.sin(v1 / np.pi) ** 2. * np.sin(v2 / np.pi) ** 2. * (abs(v1) + abs(v2) + 0.1 * (v1 + v2)) + (
+                v1 * v1 + v2 * v2) / 30
+
+
+if __name__ == '__main__':
+    problem = ExSwarm()
+    problem = Himmelblau()
+    # problem = Sphere(n_var=100, opt=0.2 * np.ones(100))
+    problem = Rastrigin(n_var=10)
+    problem.xl *= 1.5
+
+    # problem = get_problem("g02")
+    algorithm = DIRECT()
+    # algorithm = GA()
+
+    ret = minimize(problem,
+                   algorithm,
+                   ("n_iter", 1000),
+                   verbose=True)

pymoo/algorithms/so_pattern_search.py

@@ -43,20 +43,30 @@ def do_continue(self, algorithm):
 
 
 class PatternSearch(LocalSearch):
-
     def __init__(self,
                  explr_delta=0.25,
                  explr_rho=0.5,
                  pattern_step=2,
                  eps=1e-5,
                  display=PatternSearchDisplay(),
                  **kwargs):
+        """
+
+        Parameters
+        ----------
+        explr_delta
+        explr_rho
+        pattern_step
+        eps
+        display
+        kwargs
+        """
 
         super().__init__(display=display, **kwargs)
         self.explr_rho = explr_rho
         self.pattern_step = pattern_step
         self.explr_delta = explr_delta
-        self.default_termination = PatternSearchTermination(eps=eps, x_tol=1e-6, f_tol=1e-6, nth_gen=1, n_last=2)
+        self.default_termination = PatternSearchTermination(eps=eps, x_tol=1e-6, f_tol=1e-6, nth_gen=1, n_last=30)
 
     def _initialize(self, **kwargs):
         super()._initialize(**kwargs)
@@ -80,9 +90,15 @@ def _next(self):
             # perform an exploration move around the trial vector - the best known solution is always stored in _current
             explr = self._exploration_move(trial, opt=self._current)
 
+            # we can break if we did not improve
             if not is_better(explr, self._current):
                 break
 
+            # else also check if we are terminating - otherwise this loop might run far too long
+            self._set_optimum()
+            if self.termination.has_terminated(self):
+                break
+
             self._previous, self._current = self._current, explr
 
         self.explr_delta *= self.explr_rho

pymoo/docs.py

@@ -100,40 +100,40 @@
 
     "tight_layout": """bool
                         Whether tight layout should be used.
-                        """,
+                    """,
 
     "bounds": """tuple
                 If plot requires normalization, it might be necessary to supply the boundaries. (Otherwise they might be
                 approximate by the minimum and maximum of the provided data). The boundaries should be provided as a list/tuple or
                 2D numpy array, where the first element represents the minimum, second the second the maximum values.
                 If only an integer or float is supplied, the boundaries apply for each variable.
-                """,
+            """,
 
     "reverse": """bool
                     If plot requires normalization, then the reverse values can be plotted (1 - Input). For some plots
                     it can be useful to interpret a larger area as better regarding a value. If minimization applies, a smaller
                     area means better, which can be misleading.
-                    """,
+                """,
 
     "axis_style": """dict
                         Most of the plots consists of an axis. The style of the axis, e.g. color, alpha, ..., can be changed to
                         further modify the plot appealing.
-                        """,
+                    """,
 
     "cmap": """colormap
                     For some plots different kind of colors are used. The colormap can be changed to modify the color sequence
                     for the plots.
-                    """,
+            """,
 
     "labels": """str or list
                     The labels to be used for each variable provided in the plot. If a string is used, then they will
                     be enumerated. Otherwise, a list equal to the number of variables can be provided directly.
-                    """,
+            """,
 
     "func_number_to_text": """func
                                 A function which defines how numerical values should be represented if present in the plot 
                                 for instance scientific notation, rounding and so on.
-                                """,
+                            """,
 
 }
 

pymoo/factory.py

@@ -8,7 +8,6 @@
 import re
 
 from pymoo.configuration import Configuration
-
 from pymoo.problems.many import *
 from pymoo.problems.multi import *
 from pymoo.problems.single import *
@@ -19,6 +18,7 @@
 # =========================================================================================================
 
 
+
 def get_from_list(l, name, args, kwargs):
     i = None
 
@@ -67,12 +67,14 @@ def get_algorithm_options():
     from pymoo.algorithms.so_nelder_mead import NelderMead
     from pymoo.algorithms.so_cmaes import CMAES
     from pymoo.algorithms.so_brkga import BRKGA
+    from pymoo.algorithms.so_pattern_search import PatternSearch
 
     ALGORITHMS = [
         ("ga", GA),
         ("brkga", BRKGA),
         ("de", DE),
         ("nelder-mead", NelderMead),
+        ("pattern-search", PatternSearch),
         ("cmaes", CMAES),
         ("nsga2", NSGA2),
         ("rnsga2", RNSGA2),
@@ -317,11 +319,13 @@ def get_reference_direction_options():
     from pymoo.util.reference_direction import MultiLayerReferenceDirectionFactory
     from pymoo.util.ref_dirs.reduction import ReductionBasedReferenceDirectionFactory
     from pymoo.util.ref_dirs.energy import RieszEnergyReferenceDirectionFactory
+    from pymoo.util.ref_dirs.energy_layer import LayerwiseRieszEnergyReferenceDirectionFactory
 
     REFERENCE_DIRECTIONS = [
         ("(das-dennis|uniform)", UniformReferenceDirectionFactory),
         ("multi-layer", MultiLayerReferenceDirectionFactory),
         ("(energy|riesz)", RieszEnergyReferenceDirectionFactory),
+        ("(layer-energy|layer-riesz)", LayerwiseRieszEnergyReferenceDirectionFactory),
         ("red", ReductionBasedReferenceDirectionFactory)
     ]