deploy: 1714504

Author: till-m

master/_images/acquisition_functions_10_1.png

@@ -141,7 +141,15 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "| \u001b[39m2        \u001b[39m | \u001b[39m0.7862   \u001b[39m | \u001b[39m-0.331911\u001b[39m | \u001b[39m1.3219469\u001b[39m |\n"
+     ]
+    }
+   ],
    "source": [
     "optimizer.register(\n",
     "    params=next_point_to_probe,\n",
@@ -160,18 +168,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-18.707136686093495 {'x': np.float64(1.9261486197444082), 'y': np.float64(-2.9996360060323246)}\n",
-      "0.750594563473972 {'x': np.float64(-0.3763326769822668), 'y': np.float64(1.328297354179696)}\n",
-      "-6.559031075654336 {'x': np.float64(1.979183535803597), 'y': np.float64(2.9083667381450318)}\n",
-      "-6.915481333972961 {'x': np.float64(-1.9686133847781613), 'y': np.float64(-1.009985740060171)}\n",
-      "-6.8600832617014085 {'x': np.float64(-1.9763198875239296), 'y': np.float64(2.9885278383464513)}\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-18.41   \u001b[39m | \u001b[39m1.9506186\u001b[39m | \u001b[39m-2.950721\u001b[39m |\n",
+      "-18.413111112960056 {'x': np.float64(1.9506186451101901), 'y': np.float64(-2.9507212017944955)}\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m0.7603   \u001b[39m | \u001b[39m-0.379805\u001b[39m | \u001b[39m1.3089202\u001b[39m |\n",
+      "0.7603162209132889 {'x': np.float64(-0.37980530851809036), 'y': np.float64(1.3089202270946163)}\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-6.841   \u001b[39m | \u001b[39m-1.990473\u001b[39m | \u001b[39m2.9694974\u001b[39m |\n",
+      "-6.840906127161674 {'x': np.float64(-1.9904737772920469), 'y': np.float64(2.9694974661254085)}\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-6.879   \u001b[39m | \u001b[39m1.9740210\u001b[39m | \u001b[39m2.9954409\u001b[39m |\n",
+      "-6.8785435274794136 {'x': np.float64(1.9740210595375953), 'y': np.float64(2.995440899646362)}\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m-7.124   \u001b[39m | \u001b[39m-1.985509\u001b[39m | \u001b[39m-1.044851\u001b[39m |\n",
+      "-7.123667302755344 {'x': np.float64(-1.9855094780813816), 'y': np.float64(-1.0448519298972099)}\n",
       "{'target': np.float64(0.7861845912690544), 'params': {'x': np.float64(-0.331911981189704), 'y': np.float64(1.3219469606529486)}}\n"
      ]
     }
@@ -181,7 +194,7 @@
     "    next_point = optimizer.suggest()\n",
     "    target = black_box_function(**next_point)\n",
     "    optimizer.register(params=next_point, target=target)\n",
-    "    \n",
+    "\n",
     "    print(target, next_point)\n",
     "print(optimizer.max)"
    ]
@@ -215,12 +228,12 @@
      "text": [
       "|   iter    |  target   |     x     |     y     |\n",
       "-------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m0.7862   \u001b[39m | \u001b[39m-0.331911\u001b[39m | \u001b[39m1.3219469\u001b[39m |\n",
-      "| \u001b[39m2        \u001b[39m | \u001b[39m-18.34   \u001b[39m | \u001b[39m1.9021640\u001b[39m | \u001b[39m-2.965222\u001b[39m |\n",
-      "| \u001b[35m3        \u001b[39m | \u001b[35m0.8731   \u001b[39m | \u001b[35m-0.298167\u001b[39m | \u001b[35m1.1948749\u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m-6.497   \u001b[39m | \u001b[39m1.9876938\u001b[39m | \u001b[39m2.8830942\u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m-4.286   \u001b[39m | \u001b[39m-1.995643\u001b[39m | \u001b[39m-0.141769\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m-6.781   \u001b[39m | \u001b[39m-1.953302\u001b[39m | \u001b[39m2.9913127\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m0.7862   \u001b[39m | \u001b[39m-0.331911\u001b[39m | \u001b[39m1.3219469\u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-18.34   \u001b[39m | \u001b[39m1.9021640\u001b[39m | \u001b[39m-2.965222\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m0.8731   \u001b[39m | \u001b[35m-0.298167\u001b[39m | \u001b[35m1.1948749\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-6.497   \u001b[39m | \u001b[39m1.9876938\u001b[39m | \u001b[39m2.8830942\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-4.286   \u001b[39m | \u001b[39m-1.995643\u001b[39m | \u001b[39m-0.141769\u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m-6.781   \u001b[39m | \u001b[39m-1.953302\u001b[39m | \u001b[39m2.9913127\u001b[39m |\n",
       "=================================================\n"
      ]
     }
@@ -257,137 +270,6 @@
     "\n",
     "By default this package uses the Matern 2.5 kernel. Depending on your use case you may find that tuning the GP kernel could be beneficial. You're on your own here since these are very specific solutions to very specific problems. You should start with the [scikit learn docs](https://scikit-learn.org/stable/modules/gaussian_process.html#kernels-for-gaussian-processes)."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Observers Continued\n",
-    "\n",
-    "Observers are objects that subscribe and listen to particular events fired by the `BayesianOptimization` object. \n",
-    "\n",
-    "When an event gets fired a callback function is called with the event and the `BayesianOptimization` instance passed as parameters. The callback can be specified at the time of subscription. If none is given it will look for an `update` method from the observer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bayes_opt.event import DEFAULT_EVENTS, Events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer = BayesianOptimization(\n",
-    "    f=black_box_function,\n",
-    "    pbounds={'x': (-2, 2), 'y': (-3, 3)},\n",
-    "    verbose=2,\n",
-    "    random_state=1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class BasicObserver:\n",
-    "    def update(self, event, instance):\n",
-    "        \"\"\"Does whatever you want with the event and `BayesianOptimization` instance.\"\"\"\n",
-    "        print(\"Event `{}` was observed\".format(event))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_observer = BasicObserver()\n",
-    "\n",
-    "optimizer.subscribe(\n",
-    "    event=Events.OPTIMIZATION_STEP,\n",
-    "    subscriber=my_observer,\n",
-    "    callback=None, # Will use the `update` method as callback\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alternatively you have the option to pass a completely different callback."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_callback(event, instance):\n",
-    "    print(\"Go nuts here!\")\n",
-    "\n",
-    "optimizer.subscribe(\n",
-    "    event=Events.OPTIMIZATION_START,\n",
-    "    subscriber=\"Any hashable object\",\n",
-    "    callback=my_callback,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Go nuts here!\n",
-      "Event `optimization:step` was observed\n",
-      "Event `optimization:step` was observed\n",
-      "Event `optimization:step` was observed\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer.maximize(init_points=1, n_iter=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For a list of all default events you can checkout `DEFAULT_EVENTS`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['optimization:start', 'optimization:step', 'optimization:end']"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "DEFAULT_EVENTS"
-   ]
   }
  ],
  "metadata": {
@@ -406,7 +288,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.13.1"
   },
   "nbdime-conflicts": {
    "local_diff": [

master/_images/acquisition_functions_12_1.png

@@ -3,9 +3,3 @@ Other
 
 .. autoclass:: bayes_opt.ScreenLogger
     :members:
-
-.. autoclass:: bayes_opt.JSONLogger
-    :members:
-
-.. autoclass:: bayes_opt.Events
-    :members: