tests for kriging

Author: bartzbeielstein

notebooks/testKriging.ipynb

@@ -189,6 +189,89 @@
     "assert len(new_theta_p_Lambda) == n+p"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## update_log"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'negLnLike': array([-1.38629436]), 'theta': array([-1.14525993,  1.6123372 ]), 'p': array([1.84444406, 1.74590865]), 'Lambda': array([0.44268472])}\n",
+      "{'negLnLike': array([-1.38629436, -1.38629436]), 'theta': array([-1.14525993,  1.6123372 , -1.14525993,  1.6123372 ]), 'p': array([1.84444406, 1.74590865, 1.84444406, 1.74590865]), 'Lambda': array([0.44268472, 0.44268472])}\n"
+     ]
+    }
+   ],
+   "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",
+    "S.update_log()\n",
+    "print(S.log)\n",
+    "assert len(S.log[\"negLnLike\"]) == 1\n",
+    "assert len(S.log[\"theta\"]) == n\n",
+    "assert len(S.log[\"p\"]) == p\n",
+    "assert len(S.log[\"Lambda\"]) == 1\n",
+    "S.update_log()\n",
+    "print(S.log)\n",
+    "assert len(S.log[\"negLnLike\"]) == 2\n",
+    "assert len(S.log[\"theta\"]) == 2*n\n",
+    "assert len(S.log[\"p\"]) == 2*p\n",
+    "assert len(S.log[\"Lambda\"]) == 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.00000001 1.        ]\n",
+      " [1.         1.00000001]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from spotPython.build.kriging import Kriging\n",
+    "import numpy as np\n",
+    "nat_X = np.array([[1, 0], [1, 0]])\n",
+    "nat_y = np.array([1, 2])\n",
+    "S = Kriging()\n",
+    "S.fit(nat_X, nat_y)\n",
+    "assert S.Psi.shape == (2, 2)\n",
+    "assert len(S.log[\"negLnLike\"]) == 1\n",
+    "print(S.Psi)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.7.8"
+version = "0.7.9"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/build/kriging.py

@@ -303,7 +303,7 @@ def extract_from_bounds(self, new_theta_p_Lambda: np.ndarray) -> None:
                 print(S.theta)
                 print(S.p)
                 [1 2]
-                [3]             
+                [3]
 
         Returns:
             None
@@ -395,13 +395,27 @@ def update_log(self) -> None:
             None
 
         Examples:
-
             >>> from spotPython.build.kriging import Kriging
-            >>> MyClass = Kriging(name='kriging', seed=124)
-            >>> obj = MyClass()
-            >>> obj.update_log()
-            >>> print(obj.log)
-            {'negLnLike': [0.5], 'theta': [0.1], 'p': [0.2], 'Lambda': [0.3]}
+                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()
+                S.update_log()
+                print(S.log)
+                {'negLnLike': array([-1.38629436]),
+                 'theta': array([-1.14525993,  1.6123372 ]),
+                  'p': array([1.84444406, 1.74590865]),
+                  'Lambda': array([0.44268472])}
+
         """
         self.log["negLnLike"] = append(self.log["negLnLike"], self.negLnLike)
         self.log["theta"] = append(self.log["theta"], self.theta)
@@ -457,12 +471,16 @@ def fit(self, nat_X: np.ndarray, nat_y: np.ndarray) -> object:
             Lambda (float): lambda noise value.
 
         Examples:
-
             >>> from spotPython.build.kriging import Kriging
-            >>> nat_X = np.array([[1, 2], [3, 4]])
-            >>> nat_y = np.array([1, 2])
-            >>> surrogate = Kriging()
-            >>> surrogate.fit(nat_X, nat_y)
+                import numpy as np
+                nat_X = np.array([[1, 0], [1, 0]])
+                nat_y = np.array([1, 2])
+                S = Kriging()
+                S.fit(nat_X, nat_y)
+                print(S.Psi)
+                [[1.00000001 1.        ]
+                [1.         1.00000001]]
+                
         """
         self.initialize_variables(nat_X, nat_y)
         self.set_variable_types()

test/test_kriging.py

@@ -90,3 +90,40 @@ def test_optimize_model():
     S.set_de_bounds()
     new_theta_p_Lambda = S.optimize_model()
     assert len(new_theta_p_Lambda) == n+p
+
+def test_update_log():
+    from spotPython.build.kriging import Kriging
+    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()
+    S.update_log()
+    assert len(S.log["negLnLike"]) == 1
+    assert len(S.log["theta"]) == n
+    assert len(S.log["p"]) == p
+    assert len(S.log["Lambda"]) == 1
+    S.update_log()
+    # now that we have log iterations two, there should be two entries in the log
+    assert len(S.log["negLnLike"]) == 2
+    assert len(S.log["theta"]) == 2*n
+    assert len(S.log["p"]) == 2*p
+    assert len(S.log["Lambda"]) == 2
+
+def test_fit():
+    from spotPython.build.kriging import Kriging
+    import numpy as np
+    nat_X = np.array([[1, 0], [1, 0]])
+    nat_y = np.array([1, 2])
+    S = Kriging()
+    S.fit(nat_X, nat_y)
+    assert S.Psi.shape == (2, 2)
+    assert len(S.log["negLnLike"]) == 1