kriging tests

Author: bartzbeielstein

notebooks/testKriging.ipynb

@@ -143,10 +143,7 @@
     "p=2\n",
     "S = Kriging(name='kriging', seed=124, n_theta=n, n_p=p, optim_p=True, noise=False)\n",
     "S.extract_from_bounds(np.array([1, 2, 3]))\n",
-    "assert len(S.theta) == n\n",
-    "\n",
-    "\n",
-    "\n"
+    "assert len(S.theta) == n"
    ]
   },
   {
@@ -402,6 +399,73 @@
     "assert S.Psi.shape == (n, n)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## fun_likelihood"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0]\n",
+      " [1]]\n",
+      "[0 1]\n",
+      "S.theta: [0.]\n",
+      "S.theta: [1.72284258]\n",
+      "S.Psi: [[1.00000001e+00 1.14348852e-23]\n",
+      " [1.14348852e-23 1.00000001e+00]]\n",
+      "S.U:[[1.00000001e+00 1.14348851e-23]\n",
+      " [0.00000000e+00 1.00000001e+00]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "-1.3862943611198906"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from spotPython.build.kriging import Kriging\n",
+    "import numpy as np\n",
+    "nat_X = np.array([[0], [1]])\n",
+    "nat_y = np.array([0, 1])\n",
+    "n=1\n",
+    "p=1\n",
+    "S=Kriging(name='kriging', seed=124, n_theta=n, n_p=p, optim_p=True, noise=False)\n",
+    "S.initialize_variables(nat_X, nat_y)\n",
+    "S.set_variable_types()\n",
+    "S.nat_to_cod_init()\n",
+    "print(S.cod_X)\n",
+    "print(S.cod_y)\n",
+    "S.set_theta_values()\n",
+    "print(f\"S.theta: {S.theta}\")\n",
+    "S.initialize_matrices()\n",
+    "S.set_de_bounds()\n",
+    "new_theta_p_Lambda = S.optimize_model()\n",
+    "S.extract_from_bounds(new_theta_p_Lambda)\n",
+    "print(f\"S.theta: {S.theta}\")\n",
+    "S.build_Psi()\n",
+    "print(f\"S.Psi: {S.Psi}\")\n",
+    "S.build_U()\n",
+    "print(f\"S.U:{S.U}\")\n",
+    "S.likelihood()\n",
+    "# assert S.negLnLike < 0\n",
+    "S.negLnLike"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.7.10"
+version = "0.7.12"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/build/kriging.py

@@ -701,19 +701,31 @@ def fun_likelihood(self, new_theta_p_Lambda: np.ndarray) -> float:
                 The negative log likelihood of the surface at the specified hyperparameters.
 
         Examples:
-
             >>> from spotPython.build.kriging import Kriging
-            >>> class MyClass(Kriging):
-            >>>     def __init__(self):
-            >>>         super().__init__()
-            >>>         self.n_p = 2
-            >>>         self.n = 3
-            >>>         self.nat_y = np.array([1, 2, 3])
-            >>>         self.k = 2
-            >>>         self.seed = 1
-            >>> instance = MyClass()
-            >>> negLnLike = instance.fun_likelihood(new_theta_p_Lambda)
-            >>> print(negLnLike)
+                import numpy as np
+                nat_X = np.array([[0], [1]])
+                nat_y = np.array([0, 1])
+                n=1
+                p=1
+                S=Kriging(name='kriging', seed=124, n_theta=n, n_p=p, optim_p=True, noise=False)
+                S.initialize_variables(nat_X, nat_y)
+                S.set_variable_types()
+                S.nat_to_cod_init()
+                print(S.cod_X)
+                print(S.cod_y)
+                S.set_theta_values()
+                print(f"S.theta: {S.theta}")
+                S.initialize_matrices()
+                S.set_de_bounds()
+                new_theta_p_Lambda = S.optimize_model()
+                S.extract_from_bounds(new_theta_p_Lambda)
+                print(f"S.theta: {S.theta}")
+                S.build_Psi()
+                print(f"S.Psi: {S.Psi}")
+                S.build_U()
+                print(f"S.U:{S.U}")
+                S.likelihood()
+                S.negLnLike
 
         """
         self.extract_from_bounds(new_theta_p_Lambda)

test/test_kriging.py

@@ -209,4 +209,25 @@ def test_initialize_matrices():
     # if var(self.nat_y) is > 0, then self.pen_val = self.n * log(var(self.nat_y)) + 1e4
     # else self.pen_val = self.n * var(self.nat_y) + 1e4
     assert S.pen_val == nat_X.shape[0] * (var(S.nat_y)) + 1e4
-    assert S.Psi.shape == (n, n)
+    assert S.Psi.shape == (n, n)
+
+def test_fun_likelihood():
+    from spotPython.build.kriging import Kriging
+    import numpy as np
+    nat_X = np.array([[0], [1]])
+    nat_y = np.array([0, 1])
+    n=1
+    p=1
+    S=Kriging(name='kriging', seed=124, n_theta=n, n_p=p, optim_p=True, noise=False)
+    S.initialize_variables(nat_X, nat_y)
+    S.set_variable_types()
+    S.nat_to_cod_init()
+    S.set_theta_values()
+    S.initialize_matrices()
+    S.set_de_bounds()
+    new_theta_p_Lambda = S.optimize_model()
+    S.extract_from_bounds(new_theta_p_Lambda)
+    S.build_Psi()
+    S.build_U()
+    assert S.negLnLike < 0
+