0.8.17

bartzbeielstein · bartzbeielstein · commit 280fa7bcb146 · 2024-01-01T11:43:52.000+01:00
diff --git a/notebooks/testKriging.ipynb b/notebooks/testKriging.ipynb
@@ -1451,55 +1451,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Seed set to 123\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "S.X: [[ 0.          1.        ]\n",
-      " [ 1.          0.        ]\n",
-      " [ 1.          1.        ]\n",
-      " [ 1.          1.        ]\n",
-      " [ 0.54509876 -0.36921401]\n",
-      " [ 0.54509876 -0.36921401]\n",
-      " [ 0.18642675  0.87708546]\n",
-      " [ 0.18642675  0.87708546]\n",
-      " [-0.45060393 -0.208063  ]\n",
-      " [-0.45060393 -0.208063  ]]\n",
-      "S.y: [0.98021757 0.99264427 2.02575851 2.00387949 0.45185626 0.44499372\n",
-      " 0.79130456 0.81487288 0.24000221 0.23988634]\n",
-      "X_shape_before: (10, 2)\n",
-      "y_size_before: 10\n",
-      "S.X: [[ 0.          1.        ]\n",
-      " [ 1.          0.        ]\n",
-      " [ 1.          1.        ]\n",
-      " [ 1.          1.        ]\n",
-      " [ 0.54509876 -0.36921401]\n",
-      " [ 0.54509876 -0.36921401]\n",
-      " [ 0.18642675  0.87708546]\n",
-      " [ 0.18642675  0.87708546]\n",
-      " [-0.45060393 -0.208063  ]\n",
-      " [-0.45060393 -0.208063  ]\n",
-      " [-0.39841465 -0.21105872]\n",
-      " [-0.39841465 -0.21105872]]\n",
-      "S.y: [0.98021757 0.99264427 2.02575851 2.00387949 0.45185626 0.44499372\n",
-      " 0.79130456 0.81487288 0.24000221 0.23988634 0.18349759 0.19592429]\n",
-      "S.n_points: 1\n",
-      "S.ocba_delta: 1\n",
-      "X_shape_after: (12, 2)\n",
-      "y_size_after: 12\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from spotPython.fun.objectivefunctions import analytical\n",
@@ -1548,6 +1502,91 @@
     "print(f\"y_size_after: {S.y.size}\")\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spotPython.budget.ocba import get_ocba, get_ocba_X\n",
+    "from spotPython.utils.aggregate import aggregate_mean_var\n",
+    "import numpy as np\n",
+    "X = np.array([[1,2,3],\n",
+    "              [1,2,3],\n",
+    "              [4,5,6],\n",
+    "              [4,5,6],\n",
+    "              [4,5,6],\n",
+    "              [7,8,9],\n",
+    "              [7,8,9],])\n",
+    "y = np.array([1,2,30,40, 40, 500, 600  ])\n",
+    "Z = aggregate_mean_var(X=X, y=y)\n",
+    "mean_X = Z[0]\n",
+    "mean_y = Z[1]\n",
+    "var_y = Z[2]\n",
+    "print(f\"X: {X}\")\n",
+    "print(f\"y: {y}\")\n",
+    "print(f\"mean_X: {mean_X}\")\n",
+    "print(f\"mean_y: {mean_y}\")\n",
+    "print(f\"var_y: {var_y}\")\n",
+    "delta = 5\n",
+    "# get_ocba(means, vars, delta,verbose=True)\n",
+    "X_new = get_ocba_X(X=mean_X, means=mean_y, vars=var_y, delta=delta,verbose=True)\n",
+    "X_new\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X: [[1 2 3]\n",
+      " [1 2 3]\n",
+      " [4 5 6]\n",
+      " [4 5 6]\n",
+      " [4 5 6]\n",
+      " [4 5 6]\n",
+      " [4 5 6]]\n",
+      "mean_X.shape: (2, 3)\n",
+      "y: [  1   2  30  40  40 500 600]\n",
+      "mean_X: [[1. 2. 3.]\n",
+      " [4. 5. 6.]]\n",
+      "mean_y: [  1.5 242. ]\n",
+      "var_y: [5.000e-01 8.032e+04]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from spotPython.budget.ocba import get_ocba, get_ocba_X\n",
+    "from spotPython.utils.aggregate import aggregate_mean_var\n",
+    "import numpy as np\n",
+    "X = np.array([[1,2,3],\n",
+    "              [1,2,3],\n",
+    "              [4,5,6],\n",
+    "              [4,5,6],\n",
+    "              [4,5,6],\n",
+    "              [4, 5, 6],\n",
+    "              [4, 5 ,6],])\n",
+    "y = np.array([1,2,30,40, 40, 500, 600  ])\n",
+    "Z = aggregate_mean_var(X=X, y=y)\n",
+    "mean_X = Z[0]\n",
+    "mean_y = Z[1]\n",
+    "var_y = Z[2]\n",
+    "print(f\"X: {X}\")\n",
+    "print((f\"mean_X.shape: {mean_X.shape}\"))\n",
+    "print(f\"y: {y}\")\n",
+    "print(f\"mean_X: {mean_X}\")\n",
+    "print(f\"mean_y: {mean_y}\")\n",
+    "print(f\"var_y: {var_y}\")\n",
+    "delta = 5\n",
+    "# get_ocba(means, vars, delta,verbose=True)\n",
+    "X_new = get_ocba_X(X=mean_X, means=mean_y, vars=var_y, delta=delta,verbose=True)\n",
+    "assert X_new is None\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/pyproject.toml b/pyproject.toml
@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.8.16"
+version = "0.8.17"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]
diff --git a/src/spotPython/budget/ocba.py b/src/spotPython/budget/ocba.py
@@ -6,6 +6,7 @@
 from numpy import int32, float64
 from numpy import argpartition, repeat
 from numpy import zeros, square, sqrt, full, around, array
+import numpy as np
 
 
 def get_ocba(means, vars, delta, verbose=False) -> array:
@@ -47,63 +48,66 @@ def get_ocba(means, vars, delta, verbose=False) -> array:
             get_ocba(mean_y, var_y, 50)
             array([11,  9, 19,  9,  2])
     """
-    n_designs = means.shape[0]
-    allocations = zeros(n_designs, int32)
-    ratios = zeros(n_designs, float64)
-    budget = delta
-    ranks = get_ranks(means)
-    best, second_best = argpartition(ranks, 2)[:2]
-    ratios[second_best] = 1.0
-    select = [i for i in range(n_designs) if i not in [best, second_best]]
-    temp = (means[best] - means[second_best]) / (means[best] - means[select])
-    ratios[select] = square(temp) * (vars[select] / vars[second_best])
-    select = [i for i in range(n_designs) if i not in [best]]
-    temp = (square(ratios[select]) / vars[select]).sum()
-    ratios[best] = sqrt(vars[best] * temp)
-    more_runs = full(n_designs, True, dtype=bool)
-    add_budget = zeros(n_designs, dtype=float)
-    more_alloc = True
-    if verbose:
-        print("\nIn get_ocba():")
-        print(f"means: {means}")
-        print(f"vars: {vars}")
-        print(f"delta: {delta}")
-        print(f"n_designs: {n_designs}")
-        print(f"Allocations: {allocations}")
-        print(f"Ratios: {ratios}")
-        print(f"Budget: {budget}")
-        print(f"Ranks: {ranks}")
-        print(f"Best: {best}")
-        print(f"Second best: {second_best}")
-        print(f"Select: {select}")
-        print(f"Temp: {temp}")
-        print(f"More runs: {more_runs}")
-        print(f"Add budget: {add_budget}")
-        print(f"More allocations: {more_alloc}")
-    while more_alloc:
-        more_alloc = False
-        ratio_s = (more_runs * ratios).sum()
-        add_budget[more_runs] = (budget / ratio_s) * ratios[more_runs]
-        add_budget = around(add_budget).astype(int)
-        mask = add_budget < allocations
-        add_budget[mask] = allocations[mask]
-        more_runs[mask] = 0
+    if np.all(vars > 0) and (means.shape[0] > 2):
+        n_designs = means.shape[0]
+        allocations = zeros(n_designs, int32)
+        ratios = zeros(n_designs, float64)
+        budget = delta
+        ranks = get_ranks(means)
+        best, second_best = argpartition(ranks, 2)[:2]
+        ratios[second_best] = 1.0
+        select = [i for i in range(n_designs) if i not in [best, second_best]]
+        temp = (means[best] - means[second_best]) / (means[best] - means[select])
+        ratios[select] = square(temp) * (vars[select] / vars[second_best])
+        select = [i for i in range(n_designs) if i not in [best]]
+        temp = (square(ratios[select]) / vars[select]).sum()
+        ratios[best] = sqrt(vars[best] * temp)
+        more_runs = full(n_designs, True, dtype=bool)
+        add_budget = zeros(n_designs, dtype=float)
+        more_alloc = True
         if verbose:
-            print("\nIn more_alloc:")
-            print(f"ratio_s: {ratio_s}")
-            print(f"more_runs: {more_runs}")
-            print(f"add_budget: {add_budget}")
-        if mask.sum() > 0:
-            more_alloc = True
-        if more_alloc:
-            budget = allocations.sum() + delta
-            budget -= (add_budget * ~more_runs).sum()
-    t_budget = add_budget.sum()
-    add_budget[best] += allocations.sum() + delta - t_budget
-    return add_budget - allocations
+            print("\nIn get_ocba():")
+            print(f"means: {means}")
+            print(f"vars: {vars}")
+            print(f"delta: {delta}")
+            print(f"n_designs: {n_designs}")
+            print(f"Allocations: {allocations}")
+            print(f"Ratios: {ratios}")
+            print(f"Budget: {budget}")
+            print(f"Ranks: {ranks}")
+            print(f"Best: {best}")
+            print(f"Second best: {second_best}")
+            print(f"Select: {select}")
+            print(f"Temp: {temp}")
+            print(f"More runs: {more_runs}")
+            print(f"Add budget: {add_budget}")
+            print(f"More allocations: {more_alloc}")
+        while more_alloc:
+            more_alloc = False
+            ratio_s = (more_runs * ratios).sum()
+            add_budget[more_runs] = (budget / ratio_s) * ratios[more_runs]
+            add_budget = around(add_budget).astype(int)
+            mask = add_budget < allocations
+            add_budget[mask] = allocations[mask]
+            more_runs[mask] = 0
+            if verbose:
+                print("\nIn more_alloc:")
+                print(f"ratio_s: {ratio_s}")
+                print(f"more_runs: {more_runs}")
+                print(f"add_budget: {add_budget}")
+            if mask.sum() > 0:
+                more_alloc = True
+            if more_alloc:
+                budget = allocations.sum() + delta
+                budget -= (add_budget * ~more_runs).sum()
+        t_budget = add_budget.sum()
+        add_budget[best] += allocations.sum() + delta - t_budget
+        return add_budget - allocations
+    else:
+        return None
 
 
-def get_ocba_X(X, means, vars, delta) -> float64:
+def get_ocba_X(X, means, vars, delta, verbose=False) -> float64:
     """
     This function calculates the OCBA allocation and repeats the input array X along the specified axis.
 
@@ -112,6 +116,7 @@ def get_ocba_X(X, means, vars, delta) -> float64:
         means (list): List of means for each alternative.
         vars (list): List of variances for each alternative.
         delta (float): Indifference zone parameter.
+        verbose (bool): If True, print the results.
 
     Returns:
         (numpy.ndarray): Repeated array of X along the specified axis based on the OCBA allocation.
@@ -130,5 +135,8 @@ def get_ocba_X(X, means, vars, delta) -> float64:
                         [4, 5, 6]])
 
     """
-    o = get_ocba(means=means, vars=vars, delta=delta)
-    return repeat(X, o, axis=0)
+    if np.all(vars > 0) and (means.shape[0] > 2):
+        o = get_ocba(means=means, vars=vars, delta=delta, verbose=verbose)
+        return repeat(X, o, axis=0)
+    else:
+        return None
diff --git a/src/spotPython/spot/spot.py b/src/spotPython/spot/spot.py
@@ -749,12 +749,12 @@ def update_design(self) -> None:
         """
         # OCBA (only if noise). Determination of the OCBA points depends on the
         # old X and y values.
-        if self.noise and self.ocba_delta > 0 and not np.all(self.var_y > 0):
-            logger.warning(
-                "self.var_y <= 0. OCBA points are not generated, because there are points with no variance information."
-            )
+        if self.noise and self.ocba_delta > 0 and not np.all(self.var_y > 0) and (self.mean_X.shape[0] <= 2):
+            logger.warning("self.var_y <= 0. OCBA points are not generated:")
+            logger.warning("There are less than 3 points or points with no variance information.")
+            logger.debug("In update_design(): self.mean_X: %s", self.mean_X)
             logger.debug("In update_design(): self.var_y: %s", self.var_y)
-        if self.noise and self.ocba_delta > 0 and np.all(self.var_y > 0):
+        if self.noise and self.ocba_delta > 0 and np.all(self.var_y > 0) and (self.mean_X.shape[0] > 2):
             X_ocba = get_ocba_X(self.mean_X, self.mean_y, self.var_y, self.ocba_delta)
         else:
             X_ocba = None
diff --git a/test/test_ocba.py b/test/test_ocba.py
@@ -1,15 +1,15 @@
-def test_ocba():
-    """
-    Test OCBA.
-
-    """
-
 import copy
 import numpy as np
 from spotPython.fun.objectivefunctions import analytical
 from spotPython.spot import spot
-from spotPython.budget.ocba import get_ocba
+from spotPython.budget.ocba import get_ocba, get_ocba_X
+from spotPython.utils.aggregate import aggregate_mean_var
 
+def test_ocba():
+    """
+    Test OCBA.
+
+    """
 # Test based on the example from the book:
 # Chun-Hung Chen and Loo Hay Lee:
 #     Stochastic Simulation Optimization: An Optimal Computer Budget Allocation,
@@ -43,4 +43,32 @@ def test_ocba():
 n = 50
 o = get_ocba(spot_2.mean_y, spot_2.var_y, n)
 assert sum(o) == 50
-assert (o == np.array([[11, 9, 19, 9, 2]])).all()
+assert (o == np.array([[11, 9, 19, 9, 2]])).all()
+
+def test_ocba_none_result():
+    """
+    Test OCBA with None result.
+
+    """
+    X = np.array([[1,2,3],
+                [1,2,3],
+                [4,5,6],
+                [4,5,6],
+                [4,5,6],
+                [4, 5, 6],
+                [4, 5 ,6],])
+    y = np.array([1,2,30,40, 40, 500, 600  ])
+    Z = aggregate_mean_var(X=X, y=y)
+    mean_X = Z[0]
+    mean_y = Z[1]
+    var_y = Z[2]
+    print(f"X: {X}")
+    print((f"mean_X.shape: {mean_X.shape}"))
+    print(f"y: {y}")
+    print(f"mean_X: {mean_X}")
+    print(f"mean_y: {mean_y}")
+    print(f"var_y: {var_y}")
+    delta = 5
+    # get_ocba(means, vars, delta,verbose=True)
+    X_new = get_ocba_X(X=mean_X, means=mean_y, vars=var_y, delta=delta,verbose=True)
+    assert X_new is None

Original file line number	Diff line number	Diff line change
`@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"`
`7`	`7`
`8`	`8`	`[project]`
`9`	`9`	`name = "spotPython"`
`10`		`-version = "0.8.16"`
	`10`	`+version = "0.8.17"`
`11`	`11`	`authors = [`
`12`	`12`	`{ name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }`
`13`	`13`	`]`