0.24.7

theoretical variance

Author: bartzbeielstein

notebooks/00_spotPython_tests.ipynb

@@ -8454,6 +8454,162 @@
     "print(f\"X0: {X0}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## aggregate_mean_var()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2]\n",
+      " [3 4]]\n",
+      "[2. 2.]\n",
+      "[1. 0.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from spotpython.utils.aggregate import aggregate_mean_var\n",
+    "import numpy as np\n",
+    "X = np.array([[1, 2], [3, 4], [1, 2]])\n",
+    "y = np.array([1, 2, 3])\n",
+    "X_agg, y_mean, y_var = aggregate_mean_var(X, y)\n",
+    "print(X_agg)\n",
+    "print(y_mean)\n",
+    "print(y_var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2]\n",
+      " [3 4]]\n",
+      "[3. 3.]\n",
+      "[2.66666667 1.        ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1,2]])\n",
+    "y = np.array([1, 2, 3, 4, 5])\n",
+    "X_agg, y_mean, y_var = aggregate_mean_var(X, y)\n",
+    "print(X_agg)\n",
+    "print(y_mean)\n",
+    "print(y_var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 1. 1.]\n",
+      " [1. 1. 1.]\n",
+      " [2. 2. 2.]\n",
+      " [2. 2. 2.]\n",
+      " [1. 1. 1.]\n",
+      " [1. 1. 1.]]\n",
+      "[3. 3. 6. 6. 6. 6.]\n",
+      "(array([[1., 1., 1.],\n",
+      "       [2., 2., 2.]]), array([4.5, 6. ]), array([3., 0.]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_1 = np.ones((2, 3))\n",
+    "y_1 = np.sum(X_1, axis=1)\n",
+    "y_2 = 2 * y_1\n",
+    "X_2 = np.append(X_1, 2 * X_1, axis=0)\n",
+    "X = np.append(X_2, X_1, axis=0)\n",
+    "y = np.append(y_1, y_2, axis=0)\n",
+    "y = np.append(y, y_2, axis=0)\n",
+    "print(X)\n",
+    "print(y)\n",
+    "Z = aggregate_mean_var(X, y, var_empirical=True)\n",
+    "print(Z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 1. 1.]\n",
+      " [1. 1. 1.]\n",
+      " [2. 2. 2.]\n",
+      " [2. 2. 2.]\n",
+      " [1. 1. 1.]\n",
+      " [1. 1. 1.]]\n",
+      "[3. 3. 6. 6. 6. 6.]\n",
+      "(array([[1., 1., 1.],\n",
+      "       [2., 2., 2.]]), array([4.5, 6. ]), array([2.25, 0.  ]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_1 = np.ones((2, 3))\n",
+    "y_1 = np.sum(X_1, axis=1)\n",
+    "y_2 = 2 * y_1\n",
+    "X_2 = np.append(X_1, 2 * X_1, axis=0)\n",
+    "X = np.append(X_2, X_1, axis=0)\n",
+    "y = np.append(y_1, y_2, axis=0)\n",
+    "y = np.append(y, y_2, axis=0)\n",
+    "print(X)\n",
+    "print(y)\n",
+    "Z = aggregate_mean_var(X, y, var_empirical=False)\n",
+    "print(Z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2]\n",
+      " [3 4]]\n",
+      "[2. 2.]\n",
+      "[ 2. nan]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from spotpython.utils.aggregate import aggregate_mean_var\n",
+    "import numpy as np\n",
+    "X = np.array([[1, 2], [3, 4], [1, 2]])\n",
+    "y = np.array([1, 2, 3])\n",
+    "X_agg, y_mean, y_var = aggregate_mean_var(X, y, var_empirical=True)\n",
+    "print(X_agg)\n",
+    "print(y_mean)\n",
+    "print(y_var)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.24.6"
+version = "0.24.7"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotpython/spot/spot.py

@@ -235,7 +235,7 @@ def __init__(
         # because the writer is passed to the surrogate model:
         self.init_spot_writer()
 
-        self._surrogate_update(surrogate)
+        self._surrogate_setup(surrogate)
 
         if self.fun_control.get("save_experiment"):
             self.save_experiment(verbosity=self.verbosity)
@@ -392,7 +392,7 @@ def _surrogate_control_setup(self) -> None:
             if self.surrogate_control["n_theta"] > 1:
                 self.surrogate_control.update({"n_theta": self.k})
 
-    def _surrogate_update(self, surrogate) -> None:
+    def _surrogate_setup(self, surrogate) -> None:
         # Surrogate related information:
         self.surrogate = surrogate
         # If no surrogate model is specified, use the internal
@@ -950,7 +950,7 @@ def initialize_design(self, X_start=None) -> None:
 
         self.evaluate_initial_design()
 
-        self.write_tensorboard_log()
+        self.write_initial_tensorboard_log()
 
     def initialize_design_matrix(self, X_start=None) -> None:
         """
@@ -1098,7 +1098,7 @@ def evaluate_initial_design(self) -> None:
         logger.debug("In Spot() evaluate_initial_design(), final X val, after remove nan: self.X: %s", self.X)
         logger.debug("In Spot() evaluate_initial_design(), final y val, after remove nan: self.y: %s", self.y)
 
-    def write_tensorboard_log(self) -> None:
+    def write_initial_tensorboard_log(self) -> None:
         """Writes initial design data using the spot_writer. The spot_writer
         is a tensorboard writer that writes the data to a tensorboard file.
 
@@ -1116,7 +1116,7 @@ def write_tensorboard_log(self) -> None:
                             fun_control=fun_control,
                             )
                 S.initialize_design()
-                S.write_tensorboard_log()
+                S.write_initial_tensorboard_log()
                     Moving TENSORBOARD_PATH: runs/ to TENSORBOARD_PATH_OLD: runs_OLD/runs_2025_01_12_09_24_15
                     Created spot_tensorboard_path: runs/spot_logs/00_p040025_2025-01-12_09-24-15 for SummaryWriter()
         """
@@ -1135,6 +1135,46 @@ def write_tensorboard_log(self) -> None:
                 self.spot_writer.add_hparams(config, {"hp_metric": y_j})
                 self.spot_writer.flush()
 
+    def update_stats(self) -> None:
+        """
+        Update the following stats: 1. `min_y` 2. `min_X` 3. `counter`
+        If `noise` is `True`, additionally the following stats are computed: 1. `mean_X`
+        2. `mean_y` 3. `min_mean_y` 4. `min_mean_X`.
+
+        Args:
+            self (object): Spot object
+
+        Returns:
+            (NoneType): None
+
+        Attributes:
+            self.min_y (float): minimum y value
+            self.min_X (numpy.ndarray): X value of the minimum y value
+            self.counter (int): number of function evaluations
+            self.mean_X (numpy.ndarray): mean X values
+            self.mean_y (numpy.ndarray): mean y values
+            self.var_y (numpy.ndarray): variance of y values
+            self.min_mean_y (float): minimum mean y value
+            self.min_mean_X (numpy.ndarray): X value of the minimum mean y value
+
+        """
+        self.min_y = min(self.y)
+        self.min_X = self.X[argmin(self.y)]
+        self.counter = self.y.size
+        self.fun_control.update({"counter": self.counter})
+        # Update aggregated x and y values (if noise):
+        if self.noise:
+            Z = aggregate_mean_var(X=self.X, y=self.y)
+            self.mean_X = Z[0]
+            self.mean_y = Z[1]
+            self.var_y = Z[2]
+            # X value of the best mean y value so far:
+            self.min_mean_X = self.mean_X[argmin(self.mean_y)]
+            # variance of the best mean y value so far:
+            self.min_var_y = self.var_y[argmin(self.mean_y)]
+            # best mean y value so far:
+            self.min_mean_y = self.mean_y[argmin(self.mean_y)]
+
     def save_result(self, filename=None, path=None, overwrite=True, verbosity=0) -> None:
         """
         Save the results to a file.
@@ -1614,46 +1654,6 @@ def generate_design(self, size, repeats, lower, upper) -> np.array:
         """
         return self.design.scipy_lhd(n=size, repeats=repeats, lower=lower, upper=upper)
 
-    def update_stats(self) -> None:
-        """
-        Update the following stats: 1. `min_y` 2. `min_X` 3. `counter`
-        If `noise` is `True`, additionally the following stats are computed: 1. `mean_X`
-        2. `mean_y` 3. `min_mean_y` 4. `min_mean_X`.
-
-        Args:
-            self (object): Spot object
-
-        Returns:
-            (NoneType): None
-
-        Attributes:
-            self.min_y (float): minimum y value
-            self.min_X (numpy.ndarray): X value of the minimum y value
-            self.counter (int): number of function evaluations
-            self.mean_X (numpy.ndarray): mean X values
-            self.mean_y (numpy.ndarray): mean y values
-            self.var_y (numpy.ndarray): variance of y values
-            self.min_mean_y (float): minimum mean y value
-            self.min_mean_X (numpy.ndarray): X value of the minimum mean y value
-
-        """
-        self.min_y = min(self.y)
-        self.min_X = self.X[argmin(self.y)]
-        self.counter = self.y.size
-        self.fun_control.update({"counter": self.counter})
-        # Update aggregated x and y values (if noise):
-        if self.noise:
-            Z = aggregate_mean_var(X=self.X, y=self.y)
-            self.mean_X = Z[0]
-            self.mean_y = Z[1]
-            self.var_y = Z[2]
-            # X value of the best mean y value so far:
-            self.min_mean_X = self.mean_X[argmin(self.mean_y)]
-            # variance of the best mean y value so far:
-            self.min_var_y = self.var_y[argmin(self.mean_y)]
-            # best mean y value so far:
-            self.min_mean_y = self.mean_y[argmin(self.mean_y)]
-
     def update_writer(self) -> None:
         if hasattr(self, "spot_writer") and self.spot_writer is not None:
             # get the last y value: