0.20.4

Author: bartzbeielstein

notebooks/00_spotPython_tests.ipynb

@@ -6709,56 +6709,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Seed set to 123\n",
-      "Seed set to 123\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In update_writer().\n",
-      "No spot_writer available.\n",
-      "spotpython tuning: 0.0 [########--] 80.00% \n",
-      "In update_writer().\n",
-      "No spot_writer available.\n",
-      "spotpython tuning: 0.0 [#########-] 86.67% \n",
-      "In update_writer().\n",
-      "No spot_writer available.\n",
-      "spotpython tuning: 0.0 [#########-] 93.33% \n",
-      "In update_writer().\n",
-      "No spot_writer available.\n",
-      "spotpython tuning: 0.0 [##########] 100.00% Done...\n",
-      "\n",
-      "S.X: [[ 0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  1.00000000e+00]\n",
-      " [ 1.00000000e+00  0.00000000e+00]\n",
-      " [ 1.00000000e+00  1.00000000e+00]\n",
-      " [-9.09243389e-01 -1.58234577e-01]\n",
-      " [-2.05817107e-01 -4.81249089e-01]\n",
-      " [ 9.49741171e-01 -9.46312716e-01]\n",
-      " [-1.20955714e-01  6.38358863e-02]\n",
-      " [-6.62787018e-01  1.74316373e-01]\n",
-      " [ 2.82008441e-01  9.30010114e-01]\n",
-      " [ 4.78788115e-01  6.53210582e-01]\n",
-      " [ 2.80654143e-04  4.06814022e-03]\n",
-      " [-2.09901507e-04  4.88466998e-03]\n",
-      " [-4.56773307e-04  5.27725863e-03]\n",
-      " [ 1.93158942e-03  1.63023818e-02]]\n",
-      "S.y: [0.00000000e+00 1.00000000e+00 1.00000000e+00 2.00000000e+00\n",
-      " 8.51761723e-01 2.73961367e-01 1.79751605e+00 1.87053051e-02\n",
-      " 4.69672829e-01 9.44447573e-01 6.55922124e-01 1.66285316e-05\n",
-      " 2.39040594e-05 2.80581006e-05 2.69498690e-04]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from spotpython.fun.objectivefunctions import Analytical\n",
@@ -6843,161 +6796,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'act_fn' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['act_fn'])`.\n",
-      "GPU available: True (mps), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/trainer/configuration_validator.py:70: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n",
-      "\n",
-      "  | Name   | Type       | Params | Mode  | In sizes | Out sizes\n",
-      "---------------------------------------------------------------------\n",
-      "0 | layers | Sequential | 20.2 K | train | [64, 10] | [64, 1]  \n",
-      "---------------------------------------------------------------------\n",
-      "20.2 K    Trainable params\n",
-      "0         Non-trainable params\n",
-      "20.2 K    Total params\n",
-      "0.081     Total estimated model params size (MB)\n",
-      "33        Modules in train mode\n",
-      "0         Modules in eval mode\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([64, 10])\n",
-      "torch.Size([64, 1])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n",
-      "/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (16) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
-     ]
-    },
-    {
-     "data": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=2` reached.\n",
-      "/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8576e16320cb450ca1a0c679b8595d01",
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "┃<span style=\"font-weight: bold\">      Validate metric      </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         hp_metric         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     75.71305847167969     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     75.71305847167969     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
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-       "┃\u001b[1m \u001b[0m\u001b[1m     Validate metric     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        hp_metric        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    75.71305847167969    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    75.71305847167969    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
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-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n"
-     ]
-    },
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-     "data": {
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "Testing: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "data": {
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         hp_metric         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     75.71305847167969     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     75.71305847167969     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
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-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        hp_metric        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    75.71305847167969    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    75.71305847167969    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'val_loss': 75.71305847167969, 'hp_metric': 75.71305847167969}]"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from torch.utils.data import DataLoader\n",
     "from spotpython.light.regression import NNLinearRegressor\n",
@@ -7049,20 +6850,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 36., 405.,  -3.])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from spotpython.fun.objectivefunctions import Analytical\n",
     "import numpy as np\n",
@@ -7073,26 +6863,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spotpython.fun.objectivefunctions import Analytical\n",
+    "import numpy as np\n",
+    "X = np.array([np.zeros(10), np.ones(10)])\n",
+    "fun = analytical()\n",
+    "fun.fun_wingwt(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test architecture"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spotpython.hyperparameters.architecture import generate_div2_list\n",
+    "# call the function with all integer values between 5 and 10\n",
+    "for n in range(5, 21):\n",
+    "    print(generate_div2_list(n, n_min=5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spotpython.hyperparameters.architecture import get_hidden_sizes\n",
+    "_L_in = 10\n",
+    "max_n = 10\n",
+    "for l1 in range(5, 20):    \n",
+    "    print(get_hidden_sizes(_L_in, l1, max_n))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import ceil\n",
+    "def get_three_layers(_L_in, l1) -> list:\n",
+    "    \"\"\"\n",
+    "    Calculate three layers based on input values.\n",
+    "\n",
+    "    Args:\n",
+    "        _L_in (float): The input value to be multiplied.\n",
+    "        l1 (float): The multiplier for the layers.\n",
+    "\n",
+    "    Returns:\n",
+    "        list: A list containing three calculated layers [a, b, c] where:\n",
+    "            - a = 3 * l1 * _L_in\n",
+    "            - b = 2 * l1 * _L_in\n",
+    "            - c = l1 * _L_in\n",
+    "\n",
+    "    Examples:\n",
+    "        >>> from spotpython.hyperparameters.architecture import get_three_layers\n",
+    "            _L_in = 10\n",
+    "            l1 = 20\n",
+    "            get_three_layers(_L_in, l1)\n",
+    "            [600, 400, 200]\n",
+    "    \"\"\"\n",
+    "    a = 3 * l1 * _L_in\n",
+    "    b = 2 * l1 * _L_in\n",
+    "    c = ceil(l1/2) * _L_in\n",
+    "    return [a, b, a, b, b, c, c] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([158.28245046, 409.33182691])"
+       "[240, 160, 240, 160, 160, 40, 40]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
+      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
+      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
     }
    ],
    "source": [
-    "from spotpython.fun.objectivefunctions import Analytical\n",
-    "import numpy as np\n",
-    "X = np.array([np.zeros(10), np.ones(10)])\n",
-    "fun = analytical()\n",
-    "fun.fun_wingwt(X)"
+    "_L_in = 20\n",
+    "l1 = 4\n",
+    "get_three_layers(_L_in, l1)"
    ]
   },
   {

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.20.3"
+version = "0.20.4"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]