tests

Author: bartzbeielstein

notebooks/testKriging.ipynb

@@ -133,31 +133,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1 2]\n",
-      "[3]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from spotPython.build.kriging import Kriging\n",
     "n=2\n",
-    "p=4\n",
+    "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",
-    "print(S.theta)\n",
-    "print(S.p)\n",
+    "assert len(S.theta) == n\n",
+    "\n",
     "\n",
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## optimize_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spotPython.build.kriging import Kriging\n",
+    "import numpy as np\n",
+    "nat_X = np.array([[1, 2], [3, 4]])\n",
+    "nat_y = np.array([1, 2])\n",
+    "n=2\n",
+    "p=2\n",
+    "S=Kriging(name='kriging', seed=124, n_theta=n, n_p=p, optim_p=True, noise=True)\n",
+    "S.initialize_variables(nat_X, nat_y)\n",
+    "S.set_variable_types()\n",
+    "S.nat_to_cod_init()\n",
+    "S.set_theta_values()\n",
+    "S.initialize_matrices()\n",
+    "S.set_de_bounds()\n",
+    "new_theta_p_Lambda = S.optimize_model()\n",
+    "assert len(new_theta_p_Lambda) == n+p+1\n",
+    "# no noise, so Lambda is not considered\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",
+    "S.set_theta_values()\n",
+    "S.initialize_matrices()\n",
+    "S.set_de_bounds()\n",
+    "new_theta_p_Lambda = S.optimize_model()\n",
+    "assert len(new_theta_p_Lambda) == n+p"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.7.7"
+version = "0.7.8"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/build/kriging.py

@@ -294,17 +294,16 @@ def extract_from_bounds(self, new_theta_p_Lambda: np.ndarray) -> None:
                 1d-array with theta, p, and Lambda values. Order is important.
 
         Examples:
-
-            >>> from spotPython.build.kriging import Kriging
-            >>> MyClass = Kriging(name='kriging', seed=124)
-            >>> obj = MyClass()
-            >>> obj.extract_from_bounds(np.array([1, 2, 3]))
-            >>> print(obj.theta)
-            [1]
-            >>> print(obj.p)
-            [2]
-            >>> print(obj.Lambda)
-            3
+            >>> import numpy as np
+                from spotPython.build.kriging import Kriging
+                n=2
+                p=4
+                S = Kriging(name='kriging', seed=124, n_theta=n, n_p=p, optim_p=True, noise=False)
+                S.extract_from_bounds(np.array([1, 2, 3]))
+                print(S.theta)
+                print(S.p)
+                [1 2]
+                [3]             
 
         Returns:
             None
@@ -332,13 +331,21 @@ def optimize_model(self) -> Union[List[float], Tuple[float]]:
             self (object): The Kriging object.
 
         Examples:
-
             >>> from spotPython.build.kriging import Kriging
-            >>> MyClass = Kriging(name='kriging', seed=124)
-            >>> obj = MyClass()
-            >>> result = obj.optimize_model()
-            >>> print(result)
-            [optimized_theta, optimized_p, optimized_Lambda]
+                import numpy as np
+                nat_X = np.array([[1, 2], [3, 4]])
+                nat_y = np.array([1, 2])
+                n=2
+                p=2
+                S=Kriging(name='kriging', seed=124, n_theta=n, n_p=p, optim_p=True, noise=True)
+                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()
+                print(new_theta_p_Lambda)
 
         Returns:
             result["x"] (Union[List[float], Tuple[float]]):

test/test_kriging.py

@@ -58,3 +58,35 @@ def test_de_bounds():
     S = Kriging(name='kriging', seed=124, n_theta=n, n_p=p, optim_p=False)
     S.set_de_bounds()
     assert len(S.de_bounds) == n
+
+def test_extract_from_bounds():
+    n=2
+    p=2
+    S = Kriging(name='kriging', seed=124, n_theta=n, n_p=p, optim_p=True, noise=False)
+    S.extract_from_bounds(np.array([1, 2, 3]))
+    assert len(S.theta) == n
+
+def test_optimize_model():
+    nat_X = np.array([[1, 2], [3, 4]])
+    nat_y = np.array([1, 2])
+    n=2
+    p=2
+    S=Kriging(name='kriging', seed=124, n_theta=n, n_p=p, optim_p=True, noise=True)
+    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()
+    assert len(new_theta_p_Lambda) == n+p+1
+    # no noise, so Lambda is not considered
+    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()
+    assert len(new_theta_p_Lambda) == n+p