Working on usage test fix

Author: Julian Blank

doc/source/operators/repair.ipynb

@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -61,18 +61,22 @@
     }
    ],
    "source": [
-    "from pymoo.factory import get_algorithm, get_crossover, get_mutation, get_sampling\n",
+    "from pymoo.factory import get_crossover, get_mutation, get_sampling\n",
     "from pymoo.optimize import minimize\n",
+    "from pymoo.algorithms.so_genetic_algorithm import GA\n",
+    "\n",
+    "\n",
     "from pymoo.problems.single.knapsack import create_random_knapsack_problem\n",
     "\n",
     "problem = create_random_knapsack_problem(30)\n",
     "\n",
-    "algorithm = get_algorithm(\"ga\",\n",
-    "                       pop_size=200,\n",
-    "                       sampling=get_sampling(\"bin_random\"),\n",
-    "                       crossover=get_crossover(\"bin_hux\"),\n",
-    "                       mutation=get_mutation(\"bin_bitflip\"),\n",
-    "                       eliminate_duplicates=True)\n",
+    "\n",
+    "algorithm = GA(pop_size=200,\n",
+    "               sampling=get_sampling(\"bin_random\"),\n",
+    "               crossover=get_crossover(\"bin_hux\"),\n",
+    "               mutation=get_mutation(\"bin_bitflip\"),\n",
+    "               eliminate_duplicates=True)\n",
+    "\n",
     "\n",
     "res = minimize(problem,\n",
     "               algorithm,\n",
@@ -137,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -147,33 +151,28 @@
       "===========================================================================\n",
       "n_gen |  n_eval |   cv (min)   |   cv (avg)   |     fopt     |     favg    \n",
       "===========================================================================\n",
-      "    1 |     200 |  1.28000E+02 |  5.00570E+02 |            - |            -\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    2 |     400 |  9.80000E+01 |  3.56070E+02 |            - |            -\n",
-      "    3 |     600 |  0.00000E+00 |  2.59780E+02 |         -291 | -2.91000E+02\n",
-      "    4 |     800 |  0.00000E+00 |  1.82020E+02 |         -425 | -3.36400E+02\n",
-      "    5 |    1000 |  0.00000E+00 |  1.19465E+02 |         -454 | -3.35556E+02\n",
-      "    6 |    1200 |  0.00000E+00 |  6.68000E+01 |         -454 | -2.95400E+02\n",
-      "    7 |    1400 |  0.00000E+00 |  2.88800E+01 |         -454 | -2.90133E+02\n",
-      "    8 |    1600 |  0.00000E+00 |  4.270000000 |         -522 | -2.80194E+02\n",
-      "    9 |    1800 |  0.00000E+00 |  0.00000E+00 |         -553 | -3.10615E+02\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   10 |    2000 |  0.00000E+00 |  0.00000E+00 |         -553 | -3.59055E+02\n"
+      "    1 |     171 |  0.00000E+00 |  0.00000E+00 |         -437 | -1.47743E+02\n",
+      "    2 |     371 |  0.00000E+00 |  0.00000E+00 |         -437 | -2.21790E+02\n",
+      "    3 |     571 |  0.00000E+00 |  0.00000E+00 |         -474 | -2.66900E+02\n",
+      "    4 |     771 |  0.00000E+00 |  0.00000E+00 |         -524 | -3.20120E+02\n",
+      "    5 |     971 |  0.00000E+00 |  0.00000E+00 |         -544 | -3.71800E+02\n",
+      "    6 |    1171 |  0.00000E+00 |  0.00000E+00 |         -640 | -4.14070E+02\n",
+      "    7 |    1371 |  0.00000E+00 |  0.00000E+00 |         -640 | -4.54460E+02\n",
+      "    8 |    1571 |  0.00000E+00 |  0.00000E+00 |         -657 | -4.84725E+02\n",
+      "    9 |    1771 |  0.00000E+00 |  0.00000E+00 |         -657 | -5.16825E+02\n",
+      "   10 |    1971 |  0.00000E+00 |  0.00000E+00 |         -657 | -5.34625E+02\n"
      ]
     }
    ],
    "source": [
-    "algorithm.repair = ConsiderMaximumWeightRepair()\n",
+    "algorithm = GA(pop_size=200,\n",
+    "               sampling=get_sampling(\"bin_random\"),\n",
+    "               crossover=get_crossover(\"bin_hux\"),\n",
+    "               mutation=get_mutation(\"bin_bitflip\"),\n",
+    "               repair=ConsiderMaximumWeightRepair(),\n",
+    "               eliminate_duplicates=True)\n",
+    "\n",
+    "\n",
     "\n",
     "res = minimize(problem,\n",
     "               algorithm,\n",
@@ -205,7 +204,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,

pymoo/algorithms/deriv/__init__.py pymoo/algorithms/convex/__init__.pypymoo/algorithms/deriv/__init__.py renamed to pymoo/algorithms/convex/__init__.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from pymoo.algorithms.so_gradient_descent import GradientBasedAlgorithm
+from pymoo.algorithms.convex.base import GradientBasedAlgorithm
 
 
 class Adam(GradientBasedAlgorithm):

pymoo/algorithms/deriv/adam.py pymoo/algorithms/convex/adam.pypymoo/algorithms/deriv/adam.py renamed to pymoo/algorithms/convex/adam.py

@@ -79,16 +79,3 @@ def _next(self):
 
         # set the gradient to none to be ready for the next iteration
         self.dX = None
-
-
-class GradientDescent(GradientBasedAlgorithm):
-
-    def __init__(self, X, learning_rate=0.005, **kwargs) -> None:
-        super().__init__(X, **kwargs)
-        self.learning_rate = learning_rate
-
-    def restart(self):
-        self.learning_rate /= 2
-
-    def apply(self):
-        self.X = self.X - self.learning_rate * self.dX

…moo/algorithms/deriv/gradient_descent.py pymoo/algorithms/convex/base.pypymoo/algorithms/deriv/gradient_descent.py renamed to pymoo/algorithms/convex/base.py pymoo/algorithms/deriv/gradient_descent.py renamed to pymoo/algorithms/convex/base.py

@@ -0,0 +1,14 @@
+from pymoo.algorithms.convex.base import GradientBasedAlgorithm
+
+
+class GradientDescent(GradientBasedAlgorithm):
+
+    def __init__(self, X, learning_rate=0.005, **kwargs) -> None:
+        super().__init__(X, **kwargs)
+        self.learning_rate = learning_rate
+
+    def restart(self):
+        self.learning_rate /= 2
+
+    def apply(self):
+        self.X = self.X - self.learning_rate * self.dX

pymoo/algorithms/convex/gradient_descent.py

@@ -6,7 +6,6 @@
 from pymoo.algorithms.genetic_algorithm import GeneticAlgorithm
 from pymoo.docs import parse_doc_string
 from pymoo.factory import get_decomposition
-from pymoo.model.individual import Individual
 from pymoo.model.population import Population
 from pymoo.operators.crossover.simulated_binary_crossover import SimulatedBinaryCrossover
 from pymoo.operators.mutation.polynomial_mutation import PolynomialMutation

pymoo/algorithms/ctaea.py

@@ -88,6 +88,8 @@ def setup(self, problem, **kwargs):
         if not isinstance(self.termination, MaximumGenerationTermination):
             raise Exception("Please use the n_gen or n_eval as a termination criterion to execute RVEA!")
 
+        return self
+
     def _next(self):
         super()._next()